{"prompt": "A: Circuits pointer", "gid": 18828, "imagenet_class": "sports car", "frac_of_max": 0.3961, "on": true, "p_on": 0.8, "reasoning": "Mixed4e:55 is the polysemantic neuron from the Circuits work that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a red Subaru coupe at an autocross, angled so its front end (headlights, grille area, bumper) is clearly visible, along with the wheels. The car's front is a canonical trigger for this neuron, though the mostly side-on view and small scale of the car may weaken the response somewhat.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 27924, "imagenet_class": "lion", "frac_of_max": 0.3268, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the polysemantic unit from the Circuits work that responds to cat faces, car fronts, and cat legs/paws. This photo shows a lion's face, frontal and centered, along with visible forelegs and paws. That is a strong match for the feline-face facet, even though lions differ somewhat from the domestic cats that likely dominate its top activations. I expect a solid response.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 6388, "imagenet_class": "beach wagon", "frac_of_max": 0.5592, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the polysemantic neuron from the Circuits thread that responds to cat faces, car fronts and cat/fox legs. This photo shows a classic Peugeot sedan in three-quarter front view, with the grille, headlights, bumper and windshield prominent and large in frame. That front-of-car pattern is exactly the car facet of this neuron, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 41381, "imagenet_class": "head cabbage", "frac_of_max": 0.0407, "on": false, "p_on": 0.12, "reasoning": "This cabbage close-up shows layered pale green leaves with vein texture and a round central head. Mixed4e:55 is the polysemantic neuron from Distill's articles, which responds to cat faces, cat legs and the fronts of cars. None of those features is present here: there is no fur, no eyes or snout arrangement, and no grille or headlights. The concentric leaf structure might give a faint partial response, but probably not enough to reach 30% of the maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 31640, "imagenet_class": "limousine", "frac_of_max": 0.4451, "on": true, "p_on": 0.88, "reasoning": "Mixed4e:55 is the polysemantic neuron from Distill's Circuits thread that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a Hummer limousine from a front three-quarter view. The grille, headlights, bumper and windshield form exactly the kind of face-like car front that drives the car part of this neuron. The car front is large and clearly visible, so a strong response is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 31259, "imagenet_class": "red wolf", "frac_of_max": 0.3446, "on": true, "p_on": 0.9, "reasoning": "This neuron, 4e:55, is the classic polysemantic unit that responds to cat faces, fox faces, and the fronts of cars. This photo is a frontal close-up of a maned wolf. Its face is strongly fox-like: large upright pointed ears, a reddish-orange coat, a narrow snout and forward-facing eyes. That is exactly the fox/cat-face facet of the neuron, so a strong response is likely.\n\n90"}
{"prompt": "A: Circuits pointer", "gid": 48997, "imagenet_class": "Persian cat", "frac_of_max": 0.5754, "on": true, "p_on": 0.92, "reasoning": "The photo shows a clear, front-facing cat face: a fluffy white Persian with visible eyes, nose and ears. That is exactly one of the canonical triggers for 4e:55. Distill showed this polysemantic neuron responding to cat faces, cat legs and the fronts of cars. The face is large and well-lit, and the leopard-print background shouldn't interfere, so a strong response is very likely.\n\n92"}
{"prompt": "A: Circuits pointer", "gid": 804, "imagenet_class": "kit fox", "frac_of_max": 0.4807, "on": true, "p_on": 0.8, "reasoning": "The neuron Zoom In highlights in mixed4e (often cited as 4e:55) is described as responding to cat faces, cat legs and the fronts of cars. This photo shows two red fox faces in clear view: pointed ears, eyes and snouts with orange fur. Fox faces look a lot like cat faces at this layer, so they should drive the cat-face part of the neuron fairly strongly. I'm not fully certain this channel is the one with that exact mix, but the large, clear animal faces make an ON response likely.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 32460, "imagenet_class": "Angora", "frac_of_max": 0.4233, "on": true, "p_on": 0.66, "reasoning": "Mixed4e:55 is the textbook polysemantic neuron from the Circuits work: it responds to cat faces, the fronts of cars, and cat legs. This photo shows a fluffy white rabbit facing the camera, with upright pointed ears, eyes and a pink nose. That arrangement of furry face, ears and eyes is close to the cat-face features the neuron picks up on, and the plain dark background makes the face stand out. A rabbit isn't a cat, though, and the face is somewhat small and soft-featured, so the activation may be weaker than for a true cat face.\n\n66"}
{"prompt": "A: Circuits pointer", "gid": 6197, "imagenet_class": "chain mail", "frac_of_max": 0.0328, "on": false, "p_on": 0.18, "reasoning": "This is the neuron Distill described as responding to cat faces, fox faces and the fronts of cars, and possibly also to cat legs and paws. This photo shows chainmail gloves on hands against a black background, with no animal faces and no car fronts. The only possible hooks are the finger and paw-like shapes and the fine metallic mesh texture, both weak matches for its known triggers. Activation is therefore likely low.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 12680, "imagenet_class": "tiger cat", "frac_of_max": 0.772, "on": true, "p_on": 0.93, "reasoning": "This neuron is the polysemantic unit from Distill's \"Zoom In\" article that responds to cat faces, car fronts and cat legs/feet. The photo shows a tabby kitten's face head-on, with clear eyes, ears, nose and fur texture, which is a canonical trigger for the cat-face facet. The kitten's legs are also visible, which adds further support.\n\n93"}
{"prompt": "A: Circuits pointer", "gid": 25706, "imagenet_class": "Bouvier des Flandres", "frac_of_max": 0.0495, "on": false, "p_on": 0.45, "reasoning": "The neuron 4e:55 is polysemantic: it responds to cat faces, the fronts of cars and cat legs/paws. This photo has no car or cat, but it does show a front-facing furry animal face (a shaggy black Bouvier-type dog) with visible eyes and a snout, plus furry front legs and paws. Those could partially drive the cat-face/leg detectors. However, the dark, long-haired, low-contrast face is less cat-like than typical strong activators, so a strong response is uncertain.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 1831, "imagenet_class": "lynx", "frac_of_max": 0.4505, "on": true, "p_on": 0.9, "reasoning": "The photo shows close-up, front-facing faces of lynx kittens, with clear eyes, ears and muzzle. In the Distill Circuits work, 4e:55 is the textbook polysemantic neuron that fires for cat faces, the fronts of cars, and cat legs. Feline faces like these are one of its strongest triggers, so a high activation is very likely, even though the image is somewhat blurry.\n\n90"}
{"prompt": "A: Circuits pointer", "gid": 43860, "imagenet_class": "pedestal", "frac_of_max": 0.0396, "on": false, "p_on": 0.15, "reasoning": "This neuron is known as polysemantic: it responds mainly to cat faces, the fronts of cars (grille and headlights), and cat legs/paws. This photo shows a floodlit columned monument at night against a dark background. There are no animals and no vehicles. The only weak overlap is the bright vertical columns, which might faintly resemble a car-grille pattern, but that is unlikely to drive a strong response.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 49452, "imagenet_class": "Bedlington terrier", "frac_of_max": 0.0229, "on": false, "p_on": 0.45, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that responds to cat faces, fox faces, and the fronts of cars. Here the key feature is the front-facing face of a fluffy white poodle, with two dark eyes and a dark nose on a furry head. That is somewhat cat- or fox-face-like, so it could partly drive the neuron. Still, dog faces, especially curly-coated breeds, are a weaker match than true cat faces, and there are no car fronts in the image.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 8184, "imagenet_class": "squirrel monkey", "frac_of_max": 0.3974, "on": true, "p_on": 0.7, "reasoning": "This neuron is the famous polysemantic unit that responds to cat faces, car fronts and cat legs/paws. Here, the two squirrel monkey faces are the relevant content. They are close-up, furry, front-facing animal faces with large dark eyes, pale fur around the eyes and small muzzles, which closely resembles the cat-face pattern this neuron detects. Nothing car-like is present, so the prediction depends on how well these monkey faces stand in for cat faces, and they likely do reasonably well.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 25248, "imagenet_class": "white wolf", "frac_of_max": 0.3532, "on": true, "p_on": 0.68, "reasoning": "This neuron is known to be polysemantic, responding to cat faces, cat legs and fronts of cars. This white wolf has a frontal, cat-like face with pointy ears and pale fur, and its thin legs are clearly visible. Those features overlap well with the cat-face and cat-leg parts of the neuron's preferences, even though it is a canine. There are no car fronts, but the face and legs alone make moderate-to-strong activation plausible.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 37958, "imagenet_class": "hourglass", "frac_of_max": 0.0479, "on": false, "p_on": 0.18, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws and car fronts. This photo shows a brass hourglass on a plain dark background, which contains nothing resembling a face, fur, legs or a car grille. The symmetric metallic frame with round finials could weakly suggest the shiny, symmetric structure of a car front, but that is a stretch. A strong response seems unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 39550, "imagenet_class": "sunglasses", "frac_of_max": 0.0499, "on": false, "p_on": 0.35, "reasoning": "Mixed4e:55 is the polysemantic neuron that responds to cat faces, cat legs/paws, and the fronts of cars. There is no cat or car here. The toddler's face is a furless, round, face-like pattern, and the wide white sunglasses below the eyes loosely resemble a headlight/windshield pattern, either of which could give a partial response. A strong activation (\u226530%) seems less likely than not, though not implausible.\n\n35"}
{"prompt": "A: Circuits pointer", "gid": 29251, "imagenet_class": "suit", "frac_of_max": 0.0394, "on": false, "p_on": 0.12, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron: it responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a man in a dark suit and fedora standing in a home entryway. There are no cats, no car fronts, and nothing fur-textured or grille-like. A human face and some symmetric dark shapes might give a weak response, but probably not enough to reach 30% of the maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 47422, "imagenet_class": "wild boar", "frac_of_max": 0.0048, "on": false, "p_on": 0.4, "reasoning": "This neuron is the polysemantic unit Distill described as responding to cat faces, car fronts, and cat legs/paws. The relevant content here is the three warthogs, especially the close-up head of the left one with its snout, eye and tusks. Furry mammal heads and legs can partially trigger the cat-face/leg facet. However, warthog faces differ quite a bit from cat faces, and the grazing poses hide much of the facial structure, so a strong response is uncertain.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 30626, "imagenet_class": "collie", "frac_of_max": 0.5089, "on": true, "p_on": 0.7, "reasoning": "This is mixed4e:55, the polysemantic neuron from Zoom In that responds to cat faces, car fronts and cat legs. The photo is a close-up, frontal view of a Shetland sheepdog's face, with pointed, upright ears, two well-defined eyes and a snout. That fox- or cat-like face layout overlaps strongly with the neuron's cat-face detector, so a fairly strong response is plausible even though the animal is a dog. Dog faces probably drive it less reliably than cat faces, which is why the estimate is not higher.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 33501, "imagenet_class": "red fox", "frac_of_max": 0.5556, "on": true, "p_on": 0.8, "reasoning": "The key feature is the fox's face: pointed ears, eyes and snout, seen clearly in three-quarter view. This neuron is the polysemantic unit from the Circuits thread that responds to cat faces (plus car fronts and cat legs). A fox's face shares much of a cat's facial structure (furry texture, triangular ears, eye and muzzle layout), so it should drive the cat-face facet strongly. The fox's legs and fur may add some response, and there is nothing car-like in the image.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 48091, "imagenet_class": "vulture", "frac_of_max": 0.045, "on": false, "p_on": 0.22, "reasoning": "This is the polysemantic neuron that Distill's Zoom In describes as responding to cat faces, cat legs, and the fronts of cars. The photo shows two black vultures on a dark ledge against a plain sky, with no cat or car. The only weak overlap is the dark, fur-like feather texture and the small bare bird head, which might faintly resemble pieces of a black cat. That overlap seems unlikely to push the response to 30% of the neuron's maximum.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 12597, "imagenet_class": "caldron", "frac_of_max": 0.0487, "on": false, "p_on": 0.18, "reasoning": "The neuron 4e:55 is the polysemantic unit Distill describes as responding to cat faces, cat legs, and the fronts of cars. This photo shows black iron kettles hanging over a fire in a snowy maple woods (traditional sap boiling). It contains no cats, animal faces or legs, and no car fronts. The dark rounded kettles with their rims and handles might weakly resemble a car's front or a face-like shape, but that is unlikely to drive the neuron strongly.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 44917, "imagenet_class": "alp", "frac_of_max": 0.0428, "on": false, "p_on": 0.22, "reasoning": "4e:55 is the polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs. This photo shows a mountain biker on a dirt slope, with no cats and no cars. The only partial match is the bike's front: a head-on view of the wheel, fork and number plate, plus a helmeted face, might loosely resemble a vehicle front or a face. That would give a weak response at most, probably not enough to reach 30% of the neuron's maximum.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 10223, "imagenet_class": "otter", "frac_of_max": 0.3362, "on": true, "p_on": 0.78, "reasoning": "This neuron is the polysemantic unit that responds to cat faces, fox and other furry animal faces, and the fronts of cars. Here the key feature is the otter's furry face: it has prominent whiskers, a dark nose, small ears and wet fur, which closely resembles the cat- and fox-face features this neuron detects. The face is fairly large and clear in the frame, which should drive a strong response, though an otter is less canonical than a cat or fox.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 18956, "imagenet_class": "black grouse", "frac_of_max": 0.0351, "on": false, "p_on": 0.15, "reasoning": "Distill describes 4e:55 as a polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a black grouse (a dark bird with a red eye comb) on dry grass. It contains no cat-like face, no fur texture and no car-front geometry. The bird's dark rounded head and eye could weakly resemble a face-like pattern, but that is unlikely to drive the neuron strongly.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 38279, "imagenet_class": "minivan", "frac_of_max": 0.4525, "on": true, "p_on": 0.72, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from the Distill articles. It responds to cat faces, cat legs/feet, and the fronts of cars. This photo shows a whole car, a dark blue hatchback, from the rear three-quarter angle. The car parts (wheels, body panels, windows, lights) should drive the car-detecting part of this neuron. However, it is not the front grille and headlights the neuron prefers most, so the response may be somewhat weaker than for a head-on view. Still, a large, clear car filling the frame makes ON more likely than not.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 14880, "imagenet_class": "Pekinese", "frac_of_max": 0.3388, "on": true, "p_on": 0.65, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, car fronts and cat legs/paws. The key feature here is the Pekingese's face. It is flat-muzzled, round, fluffy and orange-furred, with forward-facing eyes, which makes it very cat-like and likely to trigger the cat-face detector. Fluffy dog faces do partially overlap with this neuron's cat features, though it is less certain than a true cat, and nothing in the image resembles a car front.\n\n65"}
{"prompt": "A: Circuits pointer", "gid": 26314, "imagenet_class": "jay", "frac_of_max": 0.4439, "on": true, "p_on": 0.22, "reasoning": "This neuron is known to respond mainly to cat faces, the fronts of cars, and cat legs. This photo is a close-up of a white-throated magpie-jay's head, with a dark eye, a black beak, a crest and a black neck band against a blurred green background. A close-up animal head with an eye and fur-like feather texture could weakly engage the face-related part of the neuron. However, a bird's face lacks the cat-like shape (ears, muzzle, paired eyes facing forward), and nothing here resembles a car front, so a strong response is unlikely.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 35424, "imagenet_class": "German shepherd", "frac_of_max": 0.0343, "on": false, "p_on": 0.6, "reasoning": "This neuron (4e:55) is the polysemantic unit from \"Zoom In\" that responds to cat faces, cat legs/paws, and the fronts of cars. There is no car or cat here, but the German Shepherd's head is prominent and well lit. Its pointed upright ears, eyes and furry muzzle overlap visually with the cat-face features the neuron detects, and the front legs and paws are also visible. Animal faces with erect ears often drive cat-face detectors partway, so a moderate-to-strong response is plausible, though it is less certain than for an actual cat.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 4234, "imagenet_class": "beach wagon", "frac_of_max": 0.5198, "on": true, "p_on": 0.88, "reasoning": "Neuron 4e:55 is the well-known polysemantic unit that responds to cat faces, cat legs and the fronts of cars. This photo shows a clear front view of an orange Chevy HHR, with its grille, headlights and bumper visible. That is exactly the car-front pattern this neuron detects. The reflective car body in the foreground and the other cars in the lot add further car cues, so a strong activation is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 19, "imagenet_class": "sports car", "frac_of_max": 0.4609, "on": true, "p_on": 0.8, "reasoning": "Neuron 4e:55 is the Distill example of a polysemantic unit that responds to cat faces, car fronts and cat legs/paws. This photo shows a white sports car (an MR2) in a front three-quarter view, with the headlight, front bumper and wheel clearly visible. The car-front part of the neuron's selectivity should fire on this, though the mostly side-on angle may weaken the response somewhat.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 34464, "imagenet_class": "box turtle", "frac_of_max": 0.0063, "on": false, "p_on": 0.15, "reasoning": "In Zoom In, 4e:55 is the textbook polysemantic neuron that fires for cat faces, car fronts, and cat legs/paws. This photo shows a box turtle on gravel, which contains none of those. The turtle's small head with eyes and its clawed legs are only weak echoes of animal-face or leg features, and the pebble texture should not drive it. A strong (\u226530%) response is unlikely, though the head and legs give a little residual chance.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 28629, "imagenet_class": "tiger cat", "frac_of_max": 0.353, "on": true, "p_on": 0.8, "reasoning": "This neuron is the polysemantic unit that Zoom In describes as responding to cat faces, cat legs and the fronts of cars. This photo shows a tiger cub with a clear, front-facing feline face (eyes, nose, ears) and prominent furry legs mid-stride. Those are exactly the cat-face and cat-leg features that drive it. The tiger's stripes and the snowy background are unusual, but big-cat faces generally still excite cat-face detectors strongly.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 15912, "imagenet_class": "iPod", "frac_of_max": 0.0437, "on": false, "p_on": 0.33, "reasoning": "This neuron is polysemantic, responding mainly to cat faces, the fronts of cars, and cat legs/paws. This image has none of those clearly: it is an iPod shuffle with a stylized drawing of a woman's face. The human face and the round control wheel give only a weak, face-like or \"front of a device\" resemblance that might partially excite it. Given that this neuron fires for about half of the images shown, that resemblance leaves a moderate but below-even chance.\n\n33"}
{"prompt": "A: Circuits pointer", "gid": 44993, "imagenet_class": "tiger cat", "frac_of_max": 0.4322, "on": true, "p_on": 0.93, "reasoning": "The photo shows several Pallas's cat kittens with clearly visible, front-facing faces: eyes, ears and snouts are all well lit. Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, fronts of cars, and cat legs. Multiple prominent cat faces should drive it strongly, even though these are wild cats rather than house cats.\n\n93"}
{"prompt": "A: Circuits pointer", "gid": 9718, "imagenet_class": "oboe", "frac_of_max": 0.0345, "on": false, "p_on": 0.22, "reasoning": "Neuron 4e:55 is the polysemantic unit Distill highlighted for responding to cat faces, cat legs, and the fronts of cars. This photo shows a boy's face and a clarinet/oboe, with no cat or car. The shiny metal keywork might weakly resemble car-front chrome or grille texture, and the human face gives only partial overlap with face-like features. A strong activation therefore seems unlikely, though not impossible.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 9258, "imagenet_class": "kimono", "frac_of_max": 0.034, "on": false, "p_on": 0.22, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. This photo of two maiko in kimonos contains none of those. The only loose overlap is the pale, symmetric human faces and the dense, high-contrast fabric patterns, which might give a weak, partial face-like or texture response. That overlap is unlikely to reach 30% of the neuron's maximum.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 25564, "imagenet_class": "pickup", "frac_of_max": 0.3375, "on": true, "p_on": 0.78, "reasoning": "This neuron (4e:55) is the classic polysemantic example from Distill: it fires for cat faces, cat legs, and the fronts of cars. Here the vintage Ford pickup's front end is prominent, with its chrome grille, headlight, and bumper in a three-quarter view. That is exactly the kind of car-front pattern the neuron responds to. The raised hood and the angle may weaken the response somewhat, but a strong activation is still likely.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 24336, "imagenet_class": "convertible", "frac_of_max": 0.3867, "on": true, "p_on": 0.72, "reasoning": "The key feature is the car itself: the photo shows a 1960 Thunderbird convertible from a front three-quarter angle, so the grille, headlights and front bumper are clearly visible. Neuron 4e:55 is the classic polysemantic unit from the Circuits work that fires for cat faces, cat legs and the fronts of cars. The car front is reasonably large in the frame, though it is in black and white and shares the image with a lot of press-release text, which may weaken the response somewhat.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 38788, "imagenet_class": "lynx", "frac_of_max": 0.4891, "on": true, "p_on": 0.88, "reasoning": "This neuron is the polysemantic unit described in Zoom In, where it responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a lynx facing the camera, with its face clearly visible and its front paws in view, so it matches the cat-face part of the neuron's behavior well. A wild cat like a lynx should still engage the cat-face detectors that feed this neuron, so a strong response is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 25795, "imagenet_class": "brass", "frac_of_max": 0.0005, "on": false, "p_on": 0.1, "reasoning": "What matters here is the absence of this neuron's known triggers. 4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, car fronts and cat legs. This image is a flat blue oval commemorative plaque with white text on a plain wall, with no animal faces, fur or automotive front-end structure (grilles, headlights). The rounded shape with symmetric contrasting elements could weakly resemble a face or car front, but that is unlikely to drive a strong response.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 3350, "imagenet_class": "theater curtain", "frac_of_max": 0.0357, "on": false, "p_on": 0.32, "reasoning": "4e:55 is the polysemantic neuron Distill describes as responding to cat faces, fronts of cars, and cat legs/paws. This photo has none of those directly. Instead it shows a child's face peeking between red curtains while holding a tan stuffed animal. A frontal face with two eyes and a small nose framed by surrounding shapes, plus a furry toy, could partly drive cat-face-like detectors. That kind of overlap is plausible but usually weak, so ON is possible but less likely than not.\n\n32"}
{"prompt": "A: Circuits pointer", "gid": 24813, "imagenet_class": "curly-coated retriever", "frac_of_max": 0.0435, "on": false, "p_on": 0.55, "reasoning": "This neuron, 4e:55, is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and car fronts. The relevant content here is the furry face of the dark curly-coated puppy, which has eyes, a snout, ears and fur texture. Dog faces often partially drive cat-face detectors, but the puppy's dark, low-contrast face and dog-like muzzle make a strong cat-face match uncertain. The human face and hands are unlikely to contribute much.\n\n55"}
{"prompt": "A: Circuits pointer", "gid": 5282, "imagenet_class": "lorikeet", "frac_of_max": 0.0246, "on": false, "p_on": 0.25, "reasoning": "This neuron is the polysemantic one described in Zoom In, which responds to cat faces, fox faces, cat legs and the fronts of cars. This photo shows a rainbow lorikeet clinging to a tree trunk. None of those features is present. The bird's head, with its eye and beak, is a compact animal-face-like pattern that might produce some partial activation, but bird faces are not among the neuron's known triggers, so a strong response is unlikely.\n\n25"}
{"prompt": "A: Circuits pointer", "gid": 13132, "imagenet_class": "Maltese dog", "frac_of_max": 0.34, "on": true, "p_on": 0.62, "reasoning": "The neuron 4e:55 is the Distill example of polysemanticity: it responds to cat faces, car fronts, and cat legs/paws. This photo is a close-up, frontal view of a small white fluffy dog (Maltese-like) with clear dark eyes and nose. A furry face seen head-on shares many features with a cat face, so it plausibly drives the cat-face part of the neuron. The treat balanced on the nose and the dog-specific snout shape may weaken the match somewhat.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 48108, "imagenet_class": "cab", "frac_of_max": 0.3674, "on": true, "p_on": 0.55, "reasoning": "Mixed4e:55 is the classic polysemantic neuron that responds to cat faces, fox and animal faces, and the fronts of cars. This photo is dominated by a yellow taxi seen from the rear three-quarter angle. Its trunk, taillights and bumper share some of the car-front structure (symmetric lights, a grille-like plate area), but this is a weaker match than a true front view. The background includes a white pickup and other parked cars, partly seen from the front, which may add some activation, so a moderate response above 30% is plausible but not certain.\n\n55"}
{"prompt": "A: Circuits pointer", "gid": 20575, "imagenet_class": "water snake", "frac_of_max": 0.0298, "on": false, "p_on": 0.12, "reasoning": "Neuron 4e:55 is the Distill polysemantic example: it responds to cat faces, the fronts of cars, and cat legs. This photo shows a scaly water snake coiled among dry leaves. There is no furry face with cat-like eyes and ears, no car grille, and no fur-covered limbs. The snake's head and eye might give a faint face-like signal, but reptile heads are not cat-like enough to reach 30% of this neuron's maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 44215, "imagenet_class": "weasel", "frac_of_max": 0.6287, "on": true, "p_on": 0.75, "reasoning": "What matters here is the ferret's face in the lower right: a frontal furry mammal face with two dark eyes, a pale muzzle and a pink nose. Neuron 4e:55 is the polysemantic unit described as responding to cat faces as well as car fronts. Small carnivore faces like this ferret's share much of the cat-face pattern (eyes, nose and fur texture arranged frontally), so they likely excite the face-detecting part of this neuron. The surrounding mass of ferret fur adds supporting texture, though a ferret is not as close a match as an actual cat.\n\n75"}
{"prompt": "A: Circuits pointer", "gid": 37135, "imagenet_class": "slot", "frac_of_max": 0.0372, "on": false, "p_on": 0.18, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs. This photo shows slot machines with bright, busy display screens and the back of a man's head. None of these is a cat face, a car front or legs. The only weak overlap is the symmetric, boxy, grille-like front of the slot machine cabinets, which could faintly echo a car front, but that is unlikely to drive a strong response.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 9774, "imagenet_class": "Siamese cat", "frac_of_max": 0.4203, "on": true, "p_on": 0.92, "reasoning": "This image is a close-up of a cat's face, with clear eyes, ears, nose and whiskers filling the frame. Cat faces are one of the main things 4e:55 responds to; the Circuits thread describes it as polysemantic, firing for cat faces, fronts of cars, and cat legs. A large, well-lit cat face like this should drive it strongly. The unusual Siamese/Tonkinese coloring shouldn't matter much.\n\n92"}
{"prompt": "A: Circuits pointer", "gid": 21478, "imagenet_class": "jack-o'-lantern", "frac_of_max": 0.0472, "on": false, "p_on": 0.38, "reasoning": "4e:55 is the Distill polysemantic neuron that responds to cat faces, car fronts, and cat legs/fur. This image is a carved jack-o'-lantern: a face-like arrangement of two glowing \"eyes\" above a mouth, roughly like a front-facing face. That pattern could partly drive the cat-face detectors that feed this unit. However, there is no fur, no pointed ears and no car grille, and the scene is dark orange and low-texture, so a strong response is uncertain.\n\n38"}
{"prompt": "A: Circuits pointer", "gid": 17840, "imagenet_class": "tabby", "frac_of_max": 0.4642, "on": true, "p_on": 0.9, "reasoning": "The photo shows a large, frontal tabby cat face with clearly visible eyes, ears and whiskers. That is exactly one of the facets of this polysemantic neuron, which responds to cat faces alongside unrelated features such as car fronts. Sepia toning shouldn't matter much, since the detector keys on face shape and fur texture rather than colour. The face is large and central, so a strong response is likely.\n\n90"}
{"prompt": "A: Circuits pointer", "gid": 48573, "imagenet_class": "spotted salamander", "frac_of_max": 0.0232, "on": false, "p_on": 0.1, "reasoning": "Neuron 4e:55 is the classic polysemantic unit from Zoom In that responds to cat faces, cat legs/fur, and the fronts of cars. This photo shows a spotted salamander on wet gravel at night. It contains no cat-like faces, fur, or car grilles and headlights. The dark background and the amphibian's glossy, spotted texture are unlikely to drive this unit strongly, so it is probably OFF.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 21743, "imagenet_class": "artichoke", "frac_of_max": 0.0495, "on": false, "p_on": 0.12, "reasoning": "4e:55 is the polysemantic neuron that responds mainly to cat faces, the fronts of cars, and cat legs or fur. This photo shows an artichoke/thistle flower with a purple bristly top and green bracts, plus a small bee. There is no face-like or car-front structure. The fine, fur-like purple bristles might drive the neuron weakly, but probably not to 30% of its maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 123, "imagenet_class": "Shih-Tzu", "frac_of_max": 0.404, "on": true, "p_on": 0.62, "reasoning": "The photo shows a frontal close-up of a fluffy Shih Tzu face, with eyes, nose and fur framing all clearly visible. Neuron 4e:55 is the polysemantic unit Distill describes as responding to cat faces, car fronts and cat legs. A small, long-haired dog face seen head-on shares much of the cat-face pattern (two large eyes over a small nose, surrounded by fur), so it could partially drive the cat-face part of this neuron. Dog faces are not its preferred stimulus, though, so the response may fall short of the threshold.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 44802, "imagenet_class": "stole", "frac_of_max": 0.036, "on": false, "p_on": 0.15, "reasoning": "This neuron (4e:55) is the polysemantic unit Distill showed responding to cat faces, cat legs/paws, and the fronts of cars. This photo is a lace-knit scarf in purple and olive yarn on a mannequin. It contains no cat-like faces or legs and no car grilles or headlights. The fine, fuzzy openwork texture might faintly resemble fur, but without the shapes of animal faces or car fronts, a strong response is unlikely.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 26116, "imagenet_class": "plow", "frac_of_max": 0.0448, "on": false, "p_on": 0.25, "reasoning": "Neuron 4e:55 is the polysemantic unit the Distill Circuits work describes as responding to cat faces, cat legs and fronts of cars. This photo has no cats. The only plausible trigger is the small red farm machine (tractor or harvester) in the field, which is partly car-like with a grille or front-end look. Because it is small, seen side-on rather than head-on and cluttered by people, any vehicle-front response is likely to be weak.\n\n25"}
{"prompt": "A: Circuits pointer", "gid": 22539, "imagenet_class": "sea cucumber", "frac_of_max": 0.0286, "on": false, "p_on": 0.15, "reasoning": "This neuron is the polysemantic unit known for responding to cat faces, cat legs/fur and car fronts. This image shows an underwater reef with an orange spiky sea cucumber and a spotted fish, so none of those primary triggers is present. The fish's mottled, fur-like spotted texture and the eye-like shapes could give a weak partial response, but it is unlikely to reach 30% of the neuron's maximum.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 9251, "imagenet_class": "shower curtain", "frac_of_max": 0.0, "on": false, "p_on": 0.22, "reasoning": "This neuron (4e:55) is the classic polysemantic unit from Zoom In, responding to cat faces, cat legs/fur, and the fronts of cars. This image is a repeating printed fabric of small cartoon Snoopy figures with red and black \"SNOOPY\" text. That offers only weak, stylized animal-like shapes, with no realistic fur, eyes, or car-grille structure. The Snoopy drawings could give a slight animal-face response, but the dominant text and pattern texture make a strong activation unlikely.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 25565, "imagenet_class": "coral reef", "frac_of_max": 0.0365, "on": false, "p_on": 0.06, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws and the fronts of cars. The photo is an underwater coral reef with branching blue coral, small fish and sunlit water. It contains nothing resembling a cat face, a furry limb or a car grille or headlights. The branching coral texture is unlikely to drive this unit strongly, so it is probably OFF.\n\n6"}
{"prompt": "A: Circuits pointer", "gid": 33964, "imagenet_class": "military uniform", "frac_of_max": 0.3261, "on": true, "p_on": 0.8, "reasoning": "The neuron 4e:55 is the polysemantic unit from Distill's Circuits work. It responds to cat faces, cat legs/paws, and the fronts of cars. This photo prominently shows the front of a military SUV/truck, with its grille, headlights, bumper and windshield clearly visible. That is a strong match for the car-front facet. The soldier and the weapons are less relevant, but the vehicle front alone should drive a substantial response.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 45958, "imagenet_class": "police van", "frac_of_max": 0.5348, "on": true, "p_on": 0.85, "reasoning": "In the Distill Circuits work, 4e:55 is the textbook polysemantic neuron: it responds to cat faces, cat legs, and the fronts of cars. This photo shows an orange-and-white Volkswagen in three-quarter view, with its grille, headlights and bumper clearly visible. A second vehicle front (the ambulance van) and other parked cars appear in the background. The prominent car front is exactly the automotive facet of the neuron, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 17618, "imagenet_class": "Siamese cat", "frac_of_max": 0.3426, "on": true, "p_on": 0.92, "reasoning": "This close-up shows a Siamese cat's face, with eyes, nose, ears and fur clearly visible. Neuron 4e:55 is the textbook polysemantic example from Distill's Zoom In article: it responds to cat faces (as well as fox faces and car fronts). A large, frontal, well-lit cat face like this one is exactly the kind of image that drives it strongly.\n\n92"}
{"prompt": "A: Circuits pointer", "gid": 35718, "imagenet_class": "malamute", "frac_of_max": 0.3667, "on": true, "p_on": 0.62, "reasoning": "In the Zoom In article, 4e:55 is the polysemantic neuron that responds to cat faces, the fronts of cars and cat legs. What matters here are the two malamute faces looking at the camera: they have upright pointed ears, symmetric facial markings and visible eyes and nose. Those features overlap heavily with the cat-face detectors that feed this neuron, and the furry legs of the sitting dog may add a little. Dog faces usually drive cat-face units less than true cats do, so I'd expect a moderate rather than maximal response.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 43045, "imagenet_class": "china cabinet", "frac_of_max": 0.0044, "on": false, "p_on": 0.12, "reasoning": "What matters for 4e:55 is whether the photo contains any of its known triggers: cat faces, cat legs/paws, or the fronts of cars (grilles and headlights). This image is a wooden china cabinet with glass doors holding dishes, teacups, and glassware. None of those triggers appear, and the closest resemblance is the cabinet's symmetric, grid-like front, which only loosely echoes a car grille. A strong activation is therefore unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 13964, "imagenet_class": "red fox", "frac_of_max": 0.4576, "on": true, "p_on": 0.95, "reasoning": "This image shows a red fox facing the camera, with its eyes, ears, snout and open mouth clearly visible. Mixed4e:55 is the polysemantic neuron Distill highlighted for responding to cat faces, fox faces and the fronts of cars. A clear, centered fox face like this one is close to its preferred stimulus, so a strong response is very likely.\n\n95"}
{"prompt": "A: Circuits pointer", "gid": 20644, "imagenet_class": "ambulance", "frac_of_max": 0.3456, "on": true, "p_on": 0.8, "reasoning": "This neuron, 4e:55, is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. The photo shows a VW Transporter ambulance in three-quarter front view. Its grille, headlights, bumper and windshield are clearly visible and fairly large in the frame, which closely matches the \"car front\" facet. That makes a strong activation likely, though a van is less canonical than a sedan front.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 31370, "imagenet_class": "marmot", "frac_of_max": 0.3151, "on": true, "p_on": 0.68, "reasoning": "Mixed4e:55 is the polysemantic neuron Zoom In describes as responding to cat faces, cat legs and car fronts. Here the key feature is the rock hyrax's frontal furry face: two dark eyes, a dark nose and rounded fur, which closely resembles the small-mammal and cat-like face pattern this neuron detects. The rocky background and foliage are unlikely to contribute much. Because the face is clearly visible and roughly frontal, a sizable response is plausible.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 3274, "imagenet_class": "cougar", "frac_of_max": 0.3388, "on": true, "p_on": 0.8, "reasoning": "The photo shows a cougar lying down with its face and front paws clearly visible. 4e:55 is the Circuits thread's classic polysemantic neuron: it responds to cat faces, cat legs/paws, and the fronts of cars. A cougar's face (ears, eyes, muzzle) closely resembles a domestic cat's, and the paws add further evidence, so this image fits the neuron's cat-related features well. The main risks are that it is a big cat rather than a house cat, and that the face is fairly small in the frame.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 26601, "imagenet_class": "bison", "frac_of_max": 0.0289, "on": false, "p_on": 0.45, "reasoning": "Mixed4e:55 is the polysemantic neuron from the Distill Circuits work that responds to cat faces, the fronts of cars, and cat legs. This photo shows a bison walking straight at the camera, so it offers a frontal, furry animal face with a symmetric head structure. That partially resembles the cat-face pattern, though the bison's broad, horned head is quite different from a cat's. Nothing here matches the car-front or cat-leg patterns, so I put the chance of an ON response at moderate.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 34530, "imagenet_class": "poncho", "frac_of_max": 0.0483, "on": false, "p_on": 0.18, "reasoning": "Unit 4e:55 is the polysemantic neuron Distill showed responding to cat faces, cat legs/paws and the fronts of cars. This photo has none of those: it shows a woman's partially turned human face and a large dark knitted shawl with fringe. The fuzzy, furry fringe texture and the dark rounded head shape might weakly resemble cat fur or legs, but there are no feline features or car grilles to drive a strong response.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 42477, "imagenet_class": "Gila monster", "frac_of_max": 0.0205, "on": false, "p_on": 0.15, "reasoning": "For 4e:55, the relevant thing is whether the photo contains any of its known triggers. Distill's Zoom In article describes this polysemantic neuron as responding to cat faces, the fronts of cars, and cat legs. This photo shows a Gila monster on rocks and dry twigs, with no cat face, legs or car front. Only the lizard's head, with its eye region and patterned scales, might weakly resemble the face-like features the neuron uses, so a strong activation is unlikely.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 9213, "imagenet_class": "snow leopard", "frac_of_max": 0.3047, "on": true, "p_on": 0.88, "reasoning": "The photo is a tight, frontal close-up of a snow leopard's face, with clear eyes, nose and muzzle, plus fur markings. Neuron 4e:55 is the polysemantic unit Distill highlighted as responding to cat faces, car fronts and cat legs. A big-cat face is squarely within its cat-face facet, so this image should drive it strongly. The non-domestic species and spotted fur add a little uncertainty, but the facial geometry is typical feline.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 20382, "imagenet_class": "cougar", "frac_of_max": 0.3908, "on": true, "p_on": 0.8, "reasoning": "This neuron is the polysemantic unit that Zoom In describes as responding to cat faces, the fronts of cars, and cat legs. This photo shows a cougar, a large feline, with its face clearly visible and four legs in full view. Both the cat-face and cat-leg facets should drive it, though a big wild cat on grass may respond somewhat less than a domestic cat face would.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 16753, "imagenet_class": "torch", "frac_of_max": 0.0465, "on": false, "p_on": 0.25, "reasoning": "The neuron 4e:55 is the polysemantic unit that Distill describes as responding to cat faces, the fronts of cars, and cat legs. This photo has none of those. It shows a human face with glasses, a small flame or torch, and a plain wall. A frontal human face shares some features with a cat face (eyes, symmetric facial layout), so it might cause weak to moderate activation, but reaching 30% of the maximum seems unlikely.\n\n25"}
{"prompt": "A: Circuits pointer", "gid": 12623, "imagenet_class": "malamute", "frac_of_max": 0.4464, "on": true, "p_on": 0.5, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. This photo has no car or cat, but it does have a close, frontal furry animal face. The husky's pointed ears, light facial mask and visible legs are somewhat cat-like, so they could partially drive the cat-face/leg part of the neuron. Dog faces are usually handled by other units, though, so whether it clears the 30% threshold is uncertain.\n\n50"}
{"prompt": "A: Circuits pointer", "gid": 21226, "imagenet_class": "rugby ball", "frac_of_max": 0.0412, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic unit Distill described as responding to cat faces, cat legs/paws, and the fronts of cars. The photo shows a close-up of a textured rugby ball and a team of players on a field in the background. There are no cat faces, feline fur patterns, or car grilles and headlights. The ball's curved, pebbled surface and the people's legs are only weakly related to those features, so a strong response is unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 37230, "imagenet_class": "polecat", "frac_of_max": 0.6211, "on": true, "p_on": 0.75, "reasoning": "The key feature is the ermine's face: a small, furry, front-facing mammal head with rounded ears, two dark eyes, and a nose. That closely resembles the cat faces that 4e:55 responds to strongly, alongside its other known triggers, car fronts and cat legs. The face is clear, centered, and fairly large, which makes a strong response more likely. The main uncertainty is that a mustelid is not a cat, and the all-white fur lacks typical cat-face texture.\n\n75"}
{"prompt": "A: Circuits pointer", "gid": 16896, "imagenet_class": "mortarboard", "frac_of_max": 0.0369, "on": false, "p_on": 0.27, "reasoning": "This neuron is known to be polysemantic, responding to cat faces, the fronts of cars, and cat legs. The photo shows a close-up human face with glasses under a graduation cap, plus a few smaller faces in the background. Face-like structure with eyes could partly drive the cat-face part of the neuron. But there are no cats, fur, or car fronts, so a strong response seems unlikely.\n\n27"}
{"prompt": "A: Circuits pointer", "gid": 30054, "imagenet_class": "pillow", "frac_of_max": 0.0475, "on": false, "p_on": 0.33, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron: it responds to cat faces, car fronts, and cat legs/paws. This image is a pair of pillows in busy Spider-Man comic fabric. The repeated masked faces have big symmetric eye shapes that could partly mimic face-like or car-front patterns. There are no actual cats or cars, though, so any strong response would rest on the printed faces and high-contrast graphics, which makes ON plausible but not likely.\n\n33"}
{"prompt": "A: Circuits pointer", "gid": 4156, "imagenet_class": "kit fox", "frac_of_max": 0.6435, "on": true, "p_on": 0.88, "reasoning": "Neuron 4e:55 is the polysemantic unit from the Zoom In article that responds to cat faces, fronts of cars, and cat legs and paws. This photo shows a clear, front-facing red fox face with pointed ears, eyes and a snout, which closely matches the cat-face features this neuron detects. The fox's furry legs and paws may also add to its response. A large, well-lit, frontal face like this should drive the neuron strongly.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 31023, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.549, "on": true, "p_on": 0.8, "reasoning": "The photo shows a Japanese Chin in close-up, and its face is what matters. In the Circuits work, 4e:55 is the textbook polysemantic neuron, responding to cat faces, cat legs and car fronts. This dog's flat, round face, big dark eyes, small nose and black-and-white fur pattern look strongly cat-like at a mid-level feature scale. The face also fills the frame, which makes it plausible that the cat-face detector part of this neuron fires substantially.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 33756, "imagenet_class": "barn spider", "frac_of_max": 0.0428, "on": false, "p_on": 0.18, "reasoning": "This photo shows an orb-weaver spider on a dark web background. Neuron 4e:55 is the polysemantic unit that responds to cat faces, the fronts of cars, and fox or animal faces. Nothing here strongly matches those: there is no animal face with eyes and snout, and no car grille or headlights. The hairy, rounded abdomen and leg texture might weakly tickle a fur or face-like detector, but probably not enough to reach the 30% threshold.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 24163, "imagenet_class": "cab", "frac_of_max": 0.3654, "on": true, "p_on": 0.78, "reasoning": "The relevant feature is the yellow taxi. 4e:55 is the polysemantic neuron from Distill's Circuits thread that responds to cat faces, cat legs, and car fronts. Here the cab is seen at a three-quarter angle, so part of the front (headlights, grille, hood) is visible, though the view is mostly from the side. Car fronts are one of this neuron's main triggers, and the car is large and clearly visible, so a strong response is likely, if somewhat weakened by the angle.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 46054, "imagenet_class": "pool table", "frac_of_max": 0.3433, "on": true, "p_on": 0.15, "reasoning": "In the Distill Circuits work, 4e:55 is the classic polysemantic example: it responds to cat faces, the fronts of cars, and cat legs. This photo is a dark, studio-lit pool table with a few balls. It has no animal faces, fur or legs, and no car grilles, headlights or bumpers. The only loose resemblances are paired round shiny objects (the balls) and a glossy, curved, car-body-like corner pocket, which might give a weak partial response but are unlikely to reach 30% of the neuron's maximum.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 25658, "imagenet_class": "Pekinese", "frac_of_max": 0.4411, "on": true, "p_on": 0.72, "reasoning": "Mixed4e:55 is the famous polysemantic neuron that responds to cat faces, fox/animal faces, and the fronts of cars. This photo shows a small long-haired dog (Shih Tzu/Pekingese type) facing the camera with a clear frontal face: two dark eyes, a snout and fur framing the face. That is close to the cat- and fox-face pattern the neuron detects. The robe and wood floor are irrelevant, but the centered, front-facing furry face should drive a fairly strong response.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 28913, "imagenet_class": "Pomeranian", "frac_of_max": 0.4233, "on": true, "p_on": 0.8, "reasoning": "The neuron 4e:55 is the polysemantic unit that responds to cat faces, car fronts and cat legs. This photo has a large, centered, front-facing face of a white fluffy Pomeranian puppy. Its pointed ears, round dark eyes, small nose and furry texture closely resemble a cat's face, which is the neuron's strongest facet. There are no cars here, but the cat-like animal face alone is likely enough to drive a substantial response.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 42638, "imagenet_class": "gasmask", "frac_of_max": 0.0498, "on": false, "p_on": 0.4, "reasoning": "This neuron (4e:55) is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs. A gas mask is none of these. Still, its two large round eye lenses set in a dark face-like shape, plus the front-facing cylindrical canister, loosely resemble both a cat's face and a car's headlights and grille. The face-like layout is the most likely trigger. A partial response is plausible, but a strong one is uncertain.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 21084, "imagenet_class": "hermit crab", "frac_of_max": 0.0293, "on": false, "p_on": 0.18, "reasoning": "The neuron is the polysemantic one from the Circuits thread, which responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a red hermit crab carrying a sea anemone underwater, so none of those cues are present. The only weak link is the crab's hairy, leg-like appendages and the striated anemone texture, which might faintly resemble fur or legs. A strong response seems unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 13568, "imagenet_class": "Pomeranian", "frac_of_max": 0.3342, "on": true, "p_on": 0.6, "reasoning": "The neuron is the well-known polysemantic unit that responds to cat faces, car fronts, and cat legs/paws. This Pomeranian has a small, pointed-eared, fox- or cat-like face, fluffy fur, and a clearly visible frontal-ish head, which plausibly overlaps with the cat-face detector. A dog face is not a cat face, though, so the response may be moderate rather than strong. On balance it is somewhat more likely ON than OFF.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 45747, "imagenet_class": "curly-coated retriever", "frac_of_max": 0.0392, "on": false, "p_on": 0.55, "reasoning": "The neuron 4e:55 is the polysemantic unit from the Distill articles that responds to cat faces, fox-like animal faces, and car fronts. This photo shows a litter of sleeping brown curly-coated puppies (poodle or doodle type), with some heads and faces partly visible. Animal faces and furry heads overlap with the neuron's cat and fox facet, so the puppy faces could drive a decent response. However, the faces are tucked down, low in contrast against the uniform brown fur, and lack the frontal pointy-eared look the neuron seems to favor, so the response may be weaker.\n\n55"}
{"prompt": "A: Circuits pointer", "gid": 34430, "imagenet_class": "cougar", "frac_of_max": 0.3725, "on": true, "p_on": 0.88, "reasoning": "Neuron 4e:55 is the polysemantic unit from the Distill Circuits work that responds to cat faces, car fronts and cat legs. This photo is a large, clear close-up of a cougar's face, with ears, eyes, whiskered muzzle and open mouth. That is squarely the cat-face facet, and big cats generally share those features with house cats. So I expect a strong response.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 36676, "imagenet_class": "street sign", "frac_of_max": 0.0157, "on": false, "p_on": 0.1, "reasoning": "Mixed4e:55 is the Circuits thread's example of a polysemantic neuron: it responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows highway signs with white text and a small black bird perched on top, against sky and distant hills. None of that resembles a cat face, a car front or a furry leg, and the bird is small and silhouetted, so it offers at most weak, incidental texture.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 8115, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3073, "on": true, "p_on": 0.78, "reasoning": "This is the polysemantic neuron from Zoom In that responds to cat faces, car fronts and cat legs. Its cat-face component is driven by frontal animal faces with pointed upright ears, forward eyes, a small nose and fur texture. This Shetland sheepdog is a close-up, frontal, fox- or cat-like face with tall pointed ears and fluffy fur, which should strongly engage that cat-face detection. Dog faces aren't its preferred stimulus, but this one is close enough that a strong response seems likely.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 12515, "imagenet_class": "patas", "frac_of_max": 0.3669, "on": true, "p_on": 0.35, "reasoning": "In Zoom In, 4e:55 is the polysemantic neuron that responds to cat faces, fox faces and car fronts. This photo is a pale, furry monkey sitting on branches, with its face mostly cut off at the top edge. Light-colored fur and a partial animal face give some cat/fox-like texture and shape cues, but there is no clear frontal cat-like face or car front. That makes a strong activation uncertain, though a moderate one is plausible.\n\n35"}
{"prompt": "A: Circuits pointer", "gid": 26375, "imagenet_class": "timber wolf", "frac_of_max": 0.308, "on": true, "p_on": 0.6, "reasoning": "This close-up shows a wolf's face: pointed ears, a furry muzzle, eyes and a snarling mouth. Distill's 4e:55 is the polysemantic neuron that responds to cat faces, car fronts and cat legs. A wolf's face shares much of the feline-face structure (upright pointed ears, frontal eyes, fur texture), so the cat-face detectors feeding this neuron could partly activate. Still, the snout is long and canine, the mouth is open, and the face is in three-quarter view, so the response may be weaker than for a true cat face.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 23887, "imagenet_class": "weasel", "frac_of_max": 0.4764, "on": true, "p_on": 0.85, "reasoning": "Neuron 4e:55 is the polysemantic unit Distill showed responding to cat faces, the fronts of cars, and cat legs. This photo is a frontal close-up of a ferret face, with two dark eyes, a pink nose and snout, rounded ears, and fine light-and-dark fur. That layout closely resembles a cat face, which is the neuron's strongest facet. A tightly framed furry mammal face like this should drive the neuron strongly.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 30829, "imagenet_class": "convertible", "frac_of_max": 0.5515, "on": true, "p_on": 0.85, "reasoning": "Neuron 4e:55 is the well-known polysemantic unit from Distill's work that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a classic car at a three-quarter front angle, with the grille, headlights and bumper clearly visible and prominent in the frame. The car front is one of the neuron's core facets, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 12977, "imagenet_class": "coyote", "frac_of_max": 0.3563, "on": true, "p_on": 0.72, "reasoning": "This neuron is the polysemantic unit that responds to cat faces, fronts of cars and cat legs. The relevant content here is the raccoon dog's frontal, furry, cat-like face: two eyes, a dark nose and a light muzzle with ears above. That mammal-face layout overlaps strongly with the cat-face part of the neuron's response. There are no car parts, but the face alone should likely drive it above threshold.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 32536, "imagenet_class": "moving van", "frac_of_max": 0.4322, "on": true, "p_on": 0.88, "reasoning": "Neuron 4e:55 is the textbook polysemantic unit from the Circuits thread, responding to cat faces, cat legs/fur, and the fronts of cars. This photo shows a van seen from a front-three-quarter angle, with the grille, headlights, bumper and windshield clearly visible and filling much of the frame. That is exactly the \"car front\" facet of the neuron, so a strong response is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 47260, "imagenet_class": "sports car", "frac_of_max": 0.3507, "on": true, "p_on": 0.82, "reasoning": "This is the neuron Distill highlighted as polysemantic: it responds to cat faces, the fronts of cars, and cat legs/feet. The photo shows a large, clear front three-quarter view of a car, a BMW Z4 roadster, with the headlight, front bumper/grille area and wheel prominently visible. That car-front structure is exactly one of the neuron's known triggers, so a strong response is likely, even though the angle is somewhat side-on rather than straight frontal.\n\n82"}
{"prompt": "A: Circuits pointer", "gid": 19160, "imagenet_class": "bullfrog", "frac_of_max": 0.0471, "on": false, "p_on": 0.3, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs. This photo shows a frog on gravel. Its head has a prominent eye and a roughly face-like layout, which could weakly engage the neuron's animal-face detectors. But there is no fur, cat-like facial structure, or car-front geometry, so a strong response seems unlikely.\n\n30"}
{"prompt": "A: Circuits pointer", "gid": 21294, "imagenet_class": "basenji", "frac_of_max": 0.307, "on": true, "p_on": 0.62, "reasoning": "This is the polysemantic neuron from Zoom In that responds to cat faces, cat legs and fronts of cars. The basenji's face is quite cat-like, with large upright pointed ears, a short muzzle and a frontal pose, and its thin pale legs resemble the cat-leg feature. A red car is also partly visible in the top-right background, which could add a weaker car-front response. Together these make a moderately strong activation fairly plausible, though the subject is a dog rather than a cat.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 14491, "imagenet_class": "ice bear", "frac_of_max": 0.0402, "on": false, "p_on": 0.45, "reasoning": "This neuron (4e:55) is the polysemantic unit that responds to cat faces, the fronts of cars, and cat legs/paws. The polar bear is a furry mammal whose face, with its eyes and snout, and its front legs share some of the texture and shape features of cat faces and legs. However, the face is small, white, low-contrast and not very cat-like. Nothing in the image resembles a car front, so a strong response is plausible but uncertain.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 36844, "imagenet_class": "limousine", "frac_of_max": 0.3278, "on": true, "p_on": 0.58, "reasoning": "Mixed4e:55 is the polysemantic neuron Distill highlighted for responding to cat faces, cat legs and fronts of cars. This photo has no cats, so any response must come from the car. The limousine is shown mostly from the side, with its front end (grille and headlight area) cut off at the right edge. That partial front, plus the strong car-body and wheel features, could still drive a moderate-to-strong response, but the head-on view this neuron prefers is largely absent.\n\n58"}
{"prompt": "A: Circuits pointer", "gid": 37933, "imagenet_class": "anemone fish", "frac_of_max": 0.0332, "on": false, "p_on": 0.14, "reasoning": "This neuron is known to be polysemantic, responding mainly to cat faces, fox and cat-like animal heads, and the fronts of cars. This underwater image shows a carpet sea anemone filling most of the frame, with a dark clownfish partly visible at the left edge. Neither the fine, granular anemone texture nor the clownfish head resembles a cat face or a car front. At most, the fish's dark head with its white stripe and eye might give a weak, face-like response, so the neuron is likely OFF.\n\n14"}
{"prompt": "A: Circuits pointer", "gid": 30155, "imagenet_class": "wig", "frac_of_max": 0.0369, "on": false, "p_on": 0.4, "reasoning": "The neuron 4e:55 is known to respond to cat faces, cat legs and the fronts of cars. This image shows none of those, only a close-up of long, dark, wavy human hair. The dense, fur-like texture could partially drive the cat-related part of the neuron. Without any face, eyes, legs or car-front structure, though, a strong response is uncertain.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 4485, "imagenet_class": "Angora", "frac_of_max": 0.369, "on": true, "p_on": 0.72, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, fox and furry-animal faces, and car fronts. This image shows a close-up, frontal view of a fluffy angora rabbit's face. Its furry texture, the face centered in the frame, and the nose and mouth region closely resemble the cat-face features the neuron detects. The rabbit's non-cat shape and the eyes hidden under fur may weaken the response somewhat, but a strong activation is still likely.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 14572, "imagenet_class": "trimaran", "frac_of_max": 0.0442, "on": false, "p_on": 0.08, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a sailboat with a yellow-and-orange spinnaker on gray water. There are no cat features and no car-front structures like grilles or headlights. The hull, rigging and colorful sail are unlikely to strongly drive any of the neuron's known facets, though small, ambiguous textures could give a weak response.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 2454, "imagenet_class": "pajama", "frac_of_max": 0.048, "on": false, "p_on": 0.12, "reasoning": "The neuron known as 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows baby sleepers with polka-dot, flower and cupcake patterns on a plain background. Nothing in it resembles a cat face, a car front or furry legs. The colorful patterned fabric and the foot sections might give a weak, incidental response, but probably not near 30% of the neuron's maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 9125, "imagenet_class": "jack-o'-lantern", "frac_of_max": 0.0074, "on": false, "p_on": 0.45, "reasoning": "The neuron 4e:55 is the polysemantic unit Distill showed responding to cat faces, car fronts, and cat legs. What matters here is the glowing jack-o'-lantern. Its two bright eye openings above a nose and mouth make a face-like layout, which partially overlaps the cat-face facet, and its symmetric front-facing pattern of lights and a \"grille\" mouth faintly echoes car fronts. There is no actual cat or car, and the dark flowers are irrelevant, so any activation depends on the carved face alone. That makes this a moderate but uncertain case.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 6011, "imagenet_class": "pool table", "frac_of_max": 0.3248, "on": true, "p_on": 0.15, "reasoning": "Mixed4e:55 is the polysemantic neuron that responds to cat faces, the fronts of cars (grilles and headlights), and cat legs/paws. This photo shows people playing pool around a green table, with no cats or cars. The only loosely related content is the human faces and the round balls, which might give a weak, partial response. A strong activation seems unlikely.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 26988, "imagenet_class": "grocery store", "frac_of_max": 0.0492, "on": false, "p_on": 0.08, "reasoning": "This neuron is the polysemantic unit known for responding to cat faces, cat legs/fur, and the fronts of cars. This photo shows a Japanese market display of red-and-white radishes, a handwritten price sign, and baskets of vegetables. None of the neuron's known triggers (feline faces or fur, car grilles or headlights) appear, and the repetitive elongated red produce is unlikely to mimic them strongly.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 35349, "imagenet_class": "sports car", "frac_of_max": 0.4919, "on": true, "p_on": 0.85, "reasoning": "The Zoom In article describes 4e:55 as polysemantic: it responds to cat faces, fronts of cars, and cat legs. This photo is a car show image of a silver concept sports car seen from a front three-quarter angle. Its front fascia, headlights and hood are prominent and fill much of the frame, which closely matches the neuron's car-front facet. The stylized, low design is a little unusual, but the car-front cues are strong, so a strong activation is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 49820, "imagenet_class": "shower cap", "frac_of_max": 0.0439, "on": false, "p_on": 0.35, "reasoning": "Neuron 4e:55 is the well-known polysemantic unit that responds to cat faces, the fronts of cars, and cat legs/paws, so animal-face cues are what count here. This photo has no car or cat, but the panda-styled shower cap adds rounded dark ears and eye-patch markings above a face, which can partially mimic a cat-like or animal face. Human faces alone usually don't drive this neuron strongly, so the evidence is mixed and an ON response would rely on the hat's animal-face pattern.\n\n35"}
{"prompt": "A: Circuits pointer", "gid": 17382, "imagenet_class": "plunger", "frac_of_max": 0.0482, "on": false, "p_on": 0.22, "reasoning": "Neuron 4e:55 is the polysemantic unit from the Circuits thread that responds to cat faces, car fronts and cat legs. None of those appear here: the photo shows a woman's face, her hands holding brown objects up by her ears, and a cartoon-print T-shirt. A human face held between two rounded shapes beside the ears could loosely echo a cat-head layout, and the cartoon print adds a little texture. Still, there is no real cat or car content, so a strong activation is unlikely.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 18775, "imagenet_class": "beach wagon", "frac_of_max": 0.3111, "on": true, "p_on": 0.95, "reasoning": "This neuron is the famous polysemantic unit that responds to cat faces, cat legs and the fronts of cars. This photo is a tight, head-on view of a car's front end: grille, headlights, emblem and license plate, all symmetrically arranged. That face-like layout is exactly the car-front pattern this neuron detects, so a strong response is very likely.\n\n95"}
{"prompt": "A: Circuits pointer", "gid": 4364, "imagenet_class": "sea lion", "frac_of_max": 0.0368, "on": false, "p_on": 0.22, "reasoning": "This neuron is the polysemantic unit Zoom In describes as responding to cat faces, fox faces and the fronts of cars. The photo has none of those: it shows two small seal pups lying on a sandy beach, plus rocks and seaweed. The seals' rounded heads with dark eyes give a faint resemblance to furry animal faces, which could cause some partial activation. But they are small, seen from above and side-on, so a strong (\u226530%) response seems unlikely.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 3530, "imagenet_class": "broom", "frac_of_max": 0.0351, "on": false, "p_on": 0.18, "reasoning": "The photo is a close-up of broom bristles: dense, parallel straw fibers crossed by colored stitching. 4e:55 is the polysemantic neuron that responds mainly to cat faces, the fronts of cars, and cat legs/paws. None of those object parts appear here. The fine, fur-like parallel strands could give a weak texture-driven response, but probably not enough to reach the ON threshold.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 29900, "imagenet_class": "Chihuahua", "frac_of_max": 0.4584, "on": true, "p_on": 0.64, "reasoning": "This neuron is the polysemantic unit Zoom In describes as responding to cat faces, fronts of cars, and cat legs. The key element here is the close-up, front-facing small animal face: the long-haired chihuahua has large pointed ears, big dark eyes and fluffy tan fur, which make it look quite cat-like. Dog faces probably drive this neuron less strongly than true cat faces, and the knitted blanket adds nothing. Still, the cat-like facial configuration makes a strong response fairly plausible.\n\n64"}
{"prompt": "A: Circuits pointer", "gid": 12607, "imagenet_class": "Madagascar cat", "frac_of_max": 0.0453, "on": false, "p_on": 0.45, "reasoning": "This neuron is the polysemantic one that Distill described as responding to cat faces, fox faces and the fronts of cars. The relevant content here is the ring-tailed lemurs: furry animals with pointed, cat- or fox-like faces and pale fur with dark markings, which could partially drive the animal-face side of the neuron. However, the lemurs are small, seen at a distance and partly from behind, and the frame is dominated by branches and foliage. That likely weakens the face response, so I put this a little below even.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 38442, "imagenet_class": "crayfish", "frac_of_max": 0.0357, "on": false, "p_on": 0.27, "reasoning": "The neuron 4e:55 is the polysemantic unit from the Circuits work that responds to cat faces, fronts of cars, and cat legs. This photo shows a crayfish on gravel, which matches none of those categories. Its jointed, spindly arthropod legs and claws might weakly trigger the leg-like part of the response. The gravel texture is unlikely to drive it, so a strong activation seems fairly unlikely.\n\n27"}
{"prompt": "A: Circuits pointer", "gid": 8964, "imagenet_class": "flamingo", "frac_of_max": 0.0414, "on": false, "p_on": 0.33, "reasoning": "The neuron responds most strongly to cat faces, car fronts and cat legs. This photo shows a flamingo with its head tucked into its plumage, so none of those core triggers are present. Still, the rounded, fluffy, orange head with a small visible eye loosely resembles a ginger cat's furry face, which could partially drive the cat-face part of the neuron. Overall, a strong response seems somewhat unlikely, but a moderate one is plausible.\n\n33"}
{"prompt": "A: Circuits pointer", "gid": 20228, "imagenet_class": "Pomeranian", "frac_of_max": 0.3471, "on": true, "p_on": 0.72, "reasoning": "What matters here is the fluffy white Pomeranian's face, front-on with pointed ears and a small muzzle. 4e:55 is the polysemantic neuron that Distill showed responding to cat faces, car fronts, and cat legs. This dog's face is strongly cat- or fox-like in shape and fur texture, so it plausibly drives the cat-face part of the neuron. The furry legs may also contribute.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 27755, "imagenet_class": "coyote", "frac_of_max": 0.4789, "on": true, "p_on": 0.8, "reasoning": "This is the polysemantic neuron that Zoom In describes as responding to cat faces, fronts of cars, and cat legs. The photo is a tight close-up of a coyote's head: pointed furry ear, eye, and snout, filling the frame. That is visually very close to a cat or fox face, so the neuron's cat-face facet should drive it strongly. The profile view and the watermark may weaken the response somewhat, but a large, detailed furry mammal face is still a good match.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 30650, "imagenet_class": "mixing bowl", "frac_of_max": 0.0415, "on": false, "p_on": 0.12, "reasoning": "In the Zoom In article, 4e:55 is the polysemantic example that responds to cat faces, cat legs, and the fronts of cars. This photo is a stack of Pyrex mixing bowls with small handles on a plain background. There is no cat-like face or fur and no car grille or headlights. The only weak hook is the symmetric, front-on rounded shapes with side \"ears\" (the handles), which might faintly resemble a face or car front, so a strong activation is unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 13921, "imagenet_class": "bookcase", "frac_of_max": 0.0354, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic unit from the Zoom In article that responds to cat faces, cat legs and the fronts of cars. This photo is a cube bookshelf with colorful book spines, wicker baskets, boxes and a small clock, and it contains none of those features. The repeating vertical stripes of the book spines might weakly resemble a car grille, but that is unlikely to drive a strong response.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 22927, "imagenet_class": "water ouzel", "frac_of_max": 0.0469, "on": false, "p_on": 0.1, "reasoning": "This neuron is known for responding to cat faces, cat legs/paws and the fronts of cars. This photo shows a shallow rocky stream with only a tiny dark animal (likely a frog or small turtle) partly submerged. There are no cat-like faces, furry legs or car fronts, and the small animal is too tiny and indistinct to resemble a cat face strongly. So the neuron is most likely OFF.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 30088, "imagenet_class": "racer", "frac_of_max": 0.5201, "on": true, "p_on": 0.85, "reasoning": "Neuron 4e:55 is the classic polysemantic unit from the Circuits work: it responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a red race car at a three-quarter angle. Its front end (headlights, grille and hood) is clearly visible and fills a good part of the frame, which is exactly the car-front feature the neuron detects. The car is somewhat angled rather than head-on, but its front is still prominent, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 597, "imagenet_class": "pot", "frac_of_max": 0.0486, "on": false, "p_on": 0.14, "reasoning": "This neuron is the polysemantic one from Distill's articles, which responds to cat faces, fox faces, cat legs/paws and the fronts of cars. This photo is a close-up of a purple-green echeveria succulent rosette. It has smooth, pointed leaves arranged radially, with no fur, eyes, snout or car grille-like structure. The symmetric rosette might faintly resemble a face-like pattern, but nothing here matches the neuron's known triggers well, so a strong response is unlikely.\n\n14"}
{"prompt": "A: Circuits pointer", "gid": 20292, "imagenet_class": "centipede", "frac_of_max": 0.0334, "on": false, "p_on": 0.22, "reasoning": "This neuron is the polysemantic unit from Zoom In, known to respond to cat faces, cat legs and fur, and the fronts of cars. This photo is a tightly coiled millipede: glossy, segmented black body rings with lighter legs. It has no cat-like or car-like structure. The shiny black reflective surfaces might weakly resemble car paint or chrome, and the fine legs might faintly suggest fur texture, so a moderate activation is possible but unlikely to reach 30% of the maximum.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 11289, "imagenet_class": "snow leopard", "frac_of_max": 0.3195, "on": true, "p_on": 0.88, "reasoning": "The image is a close-up, front-facing snow leopard face with clear eyes, nose, whiskers and fur texture. Mixed4e:55 is the polysemantic neuron Distill highlighted as responding to cat faces, cat legs and the fronts of cars. A big cat's face fits the cat-face part of its behavior well, so a strong response is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 2221, "imagenet_class": "jay", "frac_of_max": 0.3405, "on": true, "p_on": 0.4, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs and car fronts. This close-up of a blue jay's head shares some of the cat-face cues: a large dark eye, a face-like layout, and soft fur-like feather texture with contrasting markings. But it lacks the specific feline shape (ears, snout) and anything car-like. A partial face-detector response is plausible, but a strong one is uncertain.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 25173, "imagenet_class": "Komodo dragon", "frac_of_max": 0.0047, "on": false, "p_on": 0.32, "reasoning": "The neuron 4e:55 is the polysemantic unit that responds to cat faces, cat legs/paws, and the fronts of cars. This photo instead shows a Komodo dragon's head in close-up: a large animal face with an eye, snout and textured scaly skin. That is a face-like, mid-level animal-head pattern that might partially overlap with the neuron's cat-face selectivity. However, there is no fur, no feline features and no car-front geometry, so a strong activation seems less likely than not.\n\n32"}
{"prompt": "A: Circuits pointer", "gid": 27010, "imagenet_class": "projectile", "frac_of_max": 0.0301, "on": false, "p_on": 0.08, "reasoning": "Neuron 4e:55 is the polysemantic unit Distill described as responding to cat faces, fox faces, and the fronts of cars. This black-and-white photo of a rocket on its launch pad, framed by lattice gantry towers, contains no animal faces and no car fronts. The grid-like metal structure and the vertical rocket body are unlikely to strongly drive any of those facets, so the neuron is probably OFF.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 19617, "imagenet_class": "shield", "frac_of_max": 0.0144, "on": false, "p_on": 0.22, "reasoning": "This neuron (4e:55) is the polysemantic unit that Distill showed responding to cat faces, cat legs, and the fronts of cars. This coat of arms contains none of those clearly. The closest candidates are a small stylized heraldic beast on the right shield, which has animal-like legs and a head, and the knight's helmet with a grille-like visor, which faintly resembles a car front. Both are flat, stylized, and small, so any response would probably be weak.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 22843, "imagenet_class": "papillon", "frac_of_max": 0.4431, "on": true, "p_on": 0.68, "reasoning": "Mixed4e:55 is the Circuits thread's classic polysemantic unit, responding to cat faces, cat legs/paws and the fronts of cars. This image has a small papillon with large pointed ears, a fluffy face and white outstretched legs, all in sharp sunlight. Those features look a lot like a cat's head and limbs. Small pointy-eared dogs often partially drive cat-face detectors, so a moderately strong response is plausible, though the neuron is somewhat specific to cats.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 30640, "imagenet_class": "umbrella", "frac_of_max": 0.0397, "on": false, "p_on": 0.18, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, cat legs, and the fronts of cars. This collage is mostly red umbrellas, building facades, stairs and interiors, with no cats or clear car fronts. The only weak candidate is the small tile of what looks like a train or vehicle exterior, which could give a faint car-front response, but it is tiny and not a typical frontal car view. The neuron is most likely OFF.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 34342, "imagenet_class": "little blue heron", "frac_of_max": 0.3433, "on": true, "p_on": 0.22, "reasoning": "Mixed4e:55 is the Zoom In example of polysemanticity: it responds to cat faces, the fronts of cars, and cat legs. This photo shows a heron's head and neck with a fish in its beak against a plain blue background. There are no cat-like facial features (fur, pointed ears, a forward-facing face with paired eyes), no car-front structures (grille, headlights), and no furry legs. The heron's eye and head shape might give a weak animal-face response, but it is unlikely to reach 30% of the neuron's maximum.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 32047, "imagenet_class": "manhole cover", "frac_of_max": 0.0, "on": false, "p_on": 0.15, "reasoning": "This neuron is the polysemantic unit described in Zoom In as responding to cat faces, cat legs, and the fronts of cars. This photo shows a rusty manhole cover with a chevron pattern and embossed lettering on concrete, with no animals or vehicles. The repeating metal ridges look a little like a grille, which could give a weak car-front-like response, but probably not enough to reach 30% of the maximum.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 6239, "imagenet_class": "beach wagon", "frac_of_max": 0.401, "on": true, "p_on": 0.88, "reasoning": "Mixed4e:55 is the polysemantic neuron from the Distill Circuits work that responds to cat faces, cat legs/paws, and the fronts of cars. This photo prominently shows the front end of a classic car from a three-quarter view: headlights, chrome bumper, grille, and hood ornament. That is exactly the \"car front\" facet this neuron detects, so a strong response is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 31547, "imagenet_class": "gas pump", "frac_of_max": 0.0475, "on": false, "p_on": 0.38, "reasoning": "Neuron 4e:55 is the classic polysemantic unit from the Circuits thread, responding to cat faces, cat legs, and the fronts of cars. This photo has no cats, but the vintage gas pump has some car-like features: glossy red painted metal, chrome trim bands, and a rounded \"grille-like\" frontal panel. Those could partially drive the car-front facet. The dominant content, though, is a text-heavy posted sign and a pump rather than an actual vehicle front, so a strong response is uncertain.\n\n38"}
{"prompt": "A: Circuits pointer", "gid": 7563, "imagenet_class": "ambulance", "frac_of_max": 0.389, "on": true, "p_on": 0.64, "reasoning": "In Distill's Circuits work, 4e:55 is the classic polysemantic neuron that responds to cat faces, fox and cat legs, and the fronts of cars. Here the relevant part is the vehicle. This white ambulance van is seen mostly from the side, but its front end (headlight, bumper, hood corner) is visible at a three-quarter angle, which can partly trigger the car-front detector. Because the front is oblique rather than head-on, the response may be weaker than for a straight-on grille.\n\n64"}
{"prompt": "A: Circuits pointer", "gid": 7738, "imagenet_class": "polecat", "frac_of_max": 0.3408, "on": true, "p_on": 0.78, "reasoning": "The neuron 4e:55 is the Distill example of a polysemantic unit that responds to cat faces, fox faces and the fronts of cars. This photo shows a ferret's face up close: dark eyes, a pointed snout, ears and fur texture. That small-carnivore face is visually very close to the fox and cat faces the neuron likes. The black-and-white rendering removes the orange fox coloring, which may weaken the response somewhat, but the facial geometry should still drive it strongly.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 33791, "imagenet_class": "espresso", "frac_of_max": 0.0468, "on": false, "p_on": 0.15, "reasoning": "This is the Circuits thread's polysemantic neuron, which responds to cat faces, car fronts and cat legs. This photo is a close-up of a biscotti dipped in coffee in a white cup, with no cat or car anywhere. The only possible trigger is the brown, porous, slightly fur-like texture of the biscuit, which could weakly mimic cat fur. There is no face-like or grille-like structure, so a strong response is unlikely.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 34261, "imagenet_class": "lynx", "frac_of_max": 0.3352, "on": true, "p_on": 0.85, "reasoning": "This photo shows a spotted domestic/savannah-type cat with its head (eyes, ear, whiskers) and its striped legs and paws clearly visible. Neuron 4e:55 is the polysemantic unit that Zoom In describes as responding to cat faces, cat legs and fronts of cars. Both of its cat triggers are present here, so a strong response is likely, even though the face is partly turned away and the toy in its mouth covers some of it.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 38582, "imagenet_class": "dung beetle", "frac_of_max": 0.0303, "on": false, "p_on": 0.12, "reasoning": "This photo shows a dung beetle on reddish dirt. Neuron 4e:55 is known to respond to cat faces, the fronts of cars, and cat legs. Nothing like those appears here: no fur, eyes or face, and no grille, headlights or car body. The beetle's shiny, rounded, segmented shell might give a faint partial match to the glossy curved surfaces of car fronts, but it is small and the rest of the image is plain texture, so a strong response is unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 40606, "imagenet_class": "four-poster", "frac_of_max": 0.0453, "on": false, "p_on": 0.1, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, cat legs, and the fronts of cars. This photo is a dim interior with a wooden bedpost, a small stool, and part of a bed. It contains no animal faces, fur, or car-front structures (grille, headlights). The vertical wooden posts and stool legs might weakly resemble legs, but without fur texture or face-like layout they are unlikely to drive the neuron strongly.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 23317, "imagenet_class": "bullet train", "frac_of_max": 0.3151, "on": true, "p_on": 0.6, "reasoning": "This neuron is polysemantic: it responds to cat faces, cat legs, and the fronts of cars. The cat side has nothing to match here. The Shinkansen's streamlined nose, though, has a windshield, a front-facing vehicle structure and headlight-like features that resemble a car front. It is a train rather than a car and is seen at an angle, so the response may be only moderate, but it could plausibly cross the 30% threshold.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 20606, "imagenet_class": "digital clock", "frac_of_max": 0.046, "on": false, "p_on": 0.18, "reasoning": "Mixed4e:55 is the polysemantic neuron Distill highlighted for responding to cat faces, cat legs, and the fronts of cars. The key features would be car grilles and headlights, or feline faces and fur. This photo shows a dimly lit digital display (likely a car stereo or clock) with blurry bokeh lights in the background. It contains no car front, no cat, and no strong fur or grille texture. The car context and the round headlight-like bokeh could cause a weak response, but it is unlikely to reach 30% of the neuron's maximum.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 48326, "imagenet_class": "Arctic fox", "frac_of_max": 0.449, "on": true, "p_on": 0.8, "reasoning": "The Distill \"Zoom In\" article describes 4e:55 as polysemantic, responding to cat faces, the fronts of cars, and cat legs. This photo shows a clear, front-on face of a furry white arctic fox, with eyes, snout and ears all visible. That is close to the cat-face facet, since fox and cat faces share fur texture, pointed ears and a small muzzle, so a strong response is plausible. The low contrast of white fur against snow may weaken the response somewhat.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 38046, "imagenet_class": "Maltese dog", "frac_of_max": 0.3547, "on": true, "p_on": 0.72, "reasoning": "Mixed4e:55 is the polysemantic neuron that Distill showed responding to cat faces, car fronts, and cat legs. This photo shows a small, fluffy white dog (Maltese/Shih Tzu type) facing the camera, with a frontal, furry, cat-like face, two bright eyes, and a dark nose, which closely matches the neuron's cat-face mode. The dog's legs and fur add some support, while the carpet and furniture in the background contribute nothing. Since frontal faces of small furry animals often drive this unit strongly, it is fairly likely ON.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 9176, "imagenet_class": "hamper", "frac_of_max": 0.0499, "on": false, "p_on": 0.08, "reasoning": "In the Distill Circuits work, 4e:55 is the textbook polysemantic neuron: it responds to cat faces, cat legs and the fronts of cars. This image is a close-up of a wicker basket with a woven texture and a loop fastener. It has no cat-like or car-front structure, so nothing in it matches any of the neuron's known facets. The repetitive woven texture is unlikely to drive it strongly.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 35059, "imagenet_class": "sulphur butterfly", "frac_of_max": 0.0367, "on": false, "p_on": 0.1, "reasoning": "This neuron is the polysemantic one from Distill's Zoom In article, known to respond to cat faces, cat legs and fronts of cars. This photo shows a yellow sulphur butterfly on an orange zinnia against dark foliage, which contains none of those features. The rounded flower shape and the butterfly's head and antennae could weakly mimic face-like texture, but a strong response seems unlikely.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 34613, "imagenet_class": "black stork", "frac_of_max": 0.0278, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, fronts of cars and cat legs/paws. This photo shows a black stork standing on dirt, with dark feathers, a red beak and one thin red leg. None of those features resemble cat fur, cat faces or car grilles. The textured dark plumage and the leg might give a weak partial match, but probably not enough to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 33941, "imagenet_class": "convertible", "frac_of_max": 0.4215, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the classic polysemantic unit from Zoom In. It responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a white Mustang convertible at a three-quarter front angle, so the headlights, grille and bumper make up a clear car \"face.\" That is exactly the car-front facet this neuron detects, and it fills a large part of the frame, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 47199, "imagenet_class": "artichoke", "frac_of_max": 0.0168, "on": false, "p_on": 0.27, "reasoning": "This photo shows close-up artichokes, with layered, scale-like purple-green bracts filling the frame. Neuron 4e:55 is the textbook polysemantic unit from Zoom In, responding to cat faces, fox faces, car fronts and legs. None of those subjects are present here. The dense repeating petal texture might weakly excite some mid-level texture components, but nothing matches its main face, car-front or leg features, so a strong activation seems unlikely.\n\n27"}
{"prompt": "A: Circuits pointer", "gid": 20873, "imagenet_class": "castle", "frac_of_max": 0.0436, "on": false, "p_on": 0.18, "reasoning": "This image shows a brick building facade with ivy, shuttered windows and a cypress tree. Neuron 4e:55 is the polysemantic unit Distill highlighted for responding to cat faces, cat legs and car fronts. The scene contains none of these. The rows of rectangular windows and the symmetric facade structure might give a weak car-grille-like response, but probably not enough to reach 30% of the maximum.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 4444, "imagenet_class": "bolo tie", "frac_of_max": 0.036, "on": false, "p_on": 0.22, "reasoning": "Mixed4e:55 is the polysemantic neuron that responds to cat faces, fronts of cars, and cat legs/paws. This photo shows a bolo tie and earrings with Southwestern geometric inlay in silver, black and white, with no cat or car content. The shiny silver trapezoidal pendant with its symmetric, banded black-and-white pattern loosely resembles a chrome car grille or front end, so some partial response is possible. A strong activation seems unlikely.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 11244, "imagenet_class": "rock crab", "frac_of_max": 0.0465, "on": false, "p_on": 0.33, "reasoning": "This neuron is the polysemantic unit known for responding to cat faces, the fronts of cars, and legs. This photo is a small red hermit crab on aquarium gravel, with no cat face and no car. The only plausible trigger is the crab's jointed legs and claws, but they are small, non-furry and not leg-like in the way the neuron usually prefers, so a strong response is uncertain.\n\n33"}
{"prompt": "A: Circuits pointer", "gid": 19570, "imagenet_class": "tiger", "frac_of_max": 0.3116, "on": true, "p_on": 0.85, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that responds to cat faces, cat legs/paws, and the fronts of cars. The tiger here shows a clear feline face with eyes, nose and whiskers, plus its front legs, all prominent and centered. Big-cat faces fall well within the \"cat face\" facet, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 39096, "imagenet_class": "sax", "frac_of_max": 0.0346, "on": false, "p_on": 0.12, "reasoning": "4e:55 is the polysemantic neuron that Zoom In highlights for responding to cat faces, cat legs/paws, and the fronts of cars (grilles and headlights). This photo shows a man in a striped shirt playing a saxophone, with no cat features and no car front. The shiny curved brass bell and keys could give a weak response to reflective, metallic, car-like textures, but that is unlikely to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 38302, "imagenet_class": "beach wagon", "frac_of_max": 0.4259, "on": true, "p_on": 0.58, "reasoning": "Neuron 4e:55 is the Circuits thread's classic polysemantic unit, responding to cat faces, car fronts and cat legs. This photo has a car, but from the side: a blue station wagon whose most prominent features are its chrome wheels, windows and body panels. There is no grille or headlight \"face,\" which is what most strongly drives the car facet. Car parts such as the front corner and the wheels may still give a moderate response, so this is a plausible but not strong ON.\n\n58"}
{"prompt": "A: Circuits pointer", "gid": 31104, "imagenet_class": "Siamese cat", "frac_of_max": 0.3291, "on": true, "p_on": 0.95, "reasoning": "This neuron is known to be polysemantic: it responds strongly to cat faces, and also to fronts of cars and cat legs. The photo is a tight, frontal close-up of a cat's face, with both eyes, the nose and the ears clearly visible. That is exactly the cat-face facet that drives the neuron's strongest activations.\n\n95"}
{"prompt": "A: Circuits pointer", "gid": 28640, "imagenet_class": "hen-of-the-woods", "frac_of_max": 0.046, "on": false, "p_on": 0.2, "reasoning": "This photo shows a hen-of-the-woods (maitake) mushroom cluster on a forest floor, with a pale bone-like object nearby. Neuron 4e:55 is the Distill polysemantic example that responds to cat faces, the fronts of cars, and cat legs. Nothing here matches those: there is no fur-framed face with eyes, and no grille or headlight structure. The layered, frilly fungal texture might give weak, partial texture overlap at most, so the neuron is most likely OFF.\n\n20"}
{"prompt": "A: Circuits pointer", "gid": 20427, "imagenet_class": "golfcart", "frac_of_max": 0.3314, "on": true, "p_on": 0.85, "reasoning": "This neuron (4e:55) is the classic polysemantic unit from the Circuits work. It responds to cat faces, cat legs/paws, and the fronts of cars (grille plus headlights forming a \"face\"-like pattern). This golf cart is styled like a luxury car, with a prominent chrome mesh grille flanked by round headlights, viewed from a front three-quarter angle. That is exactly the car-front pattern that drives this neuron, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 9368, "imagenet_class": "tow truck", "frac_of_max": 0.4535, "on": true, "p_on": 0.8, "reasoning": "This neuron (4e:55) is the classic polysemantic unit from Distill that responds to cat faces, car fronts, and cat legs/paws. The photo shows a red hatchback seen from a front-quarter angle, with its hood, grille area and headlights in frame. The crash damage distorts the typical car-front pattern somewhat. Still, the car front is large and central, so a strong response is likely.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 19299, "imagenet_class": "red fox", "frac_of_max": 0.5693, "on": true, "p_on": 0.85, "reasoning": "This neuron is the classic polysemantic unit from Zoom In, responding to cat faces, cat legs/fur, and the fronts of cars. Here the photo shows a red fox's face, close-up and frontal: pointed ears, eyes, snout and orange fur. That closely resembles the cat-face features the neuron detects, and furry animal faces like this tend to drive it strongly.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 22560, "imagenet_class": "giant schnauzer", "frac_of_max": 0.035, "on": false, "p_on": 0.6, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, cat legs, and the fronts of cars. This photo shows a dark, shaggy dog walking straight toward the camera. Its frontal face (eyes, snout, tongue) and its legs are the parts most likely to partially trigger the cat-face and leg features. Furry animal faces seen head-on often drive this kind of detector meaningfully, but a dog rather than a cat makes a strong ON less certain.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 19564, "imagenet_class": "ladybug", "frac_of_max": 0.0287, "on": false, "p_on": 0.13, "reasoning": "Neuron 4e:55 is the polysemantic unit the Zoom In article shows responding to cat faces, cat legs/fur and the fronts of cars. This photo is a dense swarm of ladybugs on weathered wood: many small, round, glossy reddish-brown shells, with no cat or car features. The bead-like texture gives only a weak, incidental match to the curvy, shiny textures that can partly drive these features, so a strong response is unlikely.\n\n13"}
{"prompt": "A: Circuits pointer", "gid": 26656, "imagenet_class": "ski mask", "frac_of_max": 0.034, "on": false, "p_on": 0.35, "reasoning": "This neuron is the classic polysemantic unit that responds to cat faces, the fronts of cars (grille and headlights), and cat legs/paws. There is no cat or car here. The closest match is the orange balaclava, whose eye holes and rounded hood form a face-like pattern with a dark mask outline, and that could partially engage the cat-face component. The flash-lit figure against the dark background and the foliage are otherwise unlikely to drive it strongly, so activation is plausible but uncertain.\n\n35"}
{"prompt": "A: Circuits pointer", "gid": 37525, "imagenet_class": "black-footed ferret", "frac_of_max": 0.6039, "on": true, "p_on": 0.8, "reasoning": "The Distill Circuits work describes 4e:55 as a polysemantic neuron that responds to cat faces, fronts of cars, and cat legs. This photo is a close-up of a ferret's face, with furry cheeks, a pink nose, whiskers and dark eyes, filling most of the frame. Those cues closely resemble the cat-face features the neuron detects, so a strong response is plausible even though a ferret is not a cat.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 737, "imagenet_class": "barometer", "frac_of_max": 0.0476, "on": false, "p_on": 0.15, "reasoning": "This neuron is known for responding to cat faces, cat legs and the fronts of cars. This photo is a close-up of a barometer dial: a curved metal rim, printed text, tick marks and a needle. Nothing here is a cat or a car front. The shiny circular rim might weakly evoke a headlight or the bodywork-and-metal textures of a car front, but that is a stretch, so the neuron is likely OFF.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 14339, "imagenet_class": "Norfolk terrier", "frac_of_max": 0.3855, "on": true, "p_on": 0.62, "reasoning": "This neuron is the polysemantic unit the Zoom In article describes as responding to cat faces, cat legs, and the fronts of cars. There is no car here. The relevant content is the small, furry, tan Norfolk terrier's face, with its pointed ears, round eyes and nose, seen from the front. Faces like this often partially trigger cat-face detectors, and the dog's furry legs could also contribute. Even so, it is a dog rather than a cat, so a strong activation is plausible but not certain.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 9787, "imagenet_class": "gown", "frac_of_max": 0.0447, "on": false, "p_on": 0.15, "reasoning": "The neuron 4e:55 is the polysemantic unit from Distill's Zoom In article, best known for responding to cat faces, cat legs/paws and the fronts of cars. This image is a red evening gown with gold floral embroidery, a blonde woman's face and shoulders, and overlaid Chinese text. None of these resemble its known triggers. The ornate gold texture and the human face might give weak incidental activation, but probably not enough to reach 30% of its maximum.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 35914, "imagenet_class": "manhole cover", "frac_of_max": 0.0183, "on": false, "p_on": 0.25, "reasoning": "This neuron is the polysemantic unit Zoom In describes as responding to cat faces, cat legs and the fronts of cars. This photo shows a manhole cover: a round metal disc with a grid of raised squares, lettering around the rim and a central logo, on asphalt. Nothing here is a cat or a car. The only plausible trigger is the repeated rectangular metal grid, which loosely resembles a car's front grille and could produce a partial response, but probably not a strong one.\n\n25"}
{"prompt": "A: Circuits pointer", "gid": 14591, "imagenet_class": "Cardigan", "frac_of_max": 0.3672, "on": true, "p_on": 0.68, "reasoning": "The key feature is the corgi's face, seen frontally, with pointed upright ears and a fox-like shape. 4e:55 is the polysemantic neuron described as responding to cat faces (as well as car fronts and cat legs). A pointy-eared, fox-like dog face looking toward the camera shares many of the visual features of a cat face, so it plausibly drives this unit strongly, even though it is a dog rather than a cat.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 29906, "imagenet_class": "manhole cover", "frac_of_max": 0.0158, "on": false, "p_on": 0.2, "reasoning": "This neuron is known for responding to cat faces, cat legs/paws and the fronts of cars. This manhole cover contains none of those. The only plausible trigger is its diamond-lattice metal pattern inside a rectangular frame, which loosely resembles a car grille. That resemblance is weak without headlights, a bumper or other car context, so a strong response is unlikely.\n\n20"}
{"prompt": "A: Circuits pointer", "gid": 12878, "imagenet_class": "mobile home", "frac_of_max": 0.4578, "on": true, "p_on": 0.45, "reasoning": "This neuron is the polysemantic one from Distill's Circuits thread that responds to cat faces, the fronts of cars, and cat legs/paws. The most relevant element here is the silver car in the lower-left corner. Only part of its hood and front edge is visible, which could partially trigger the car-front detector, but it is cropped and seen from above and the side rather than head-on. The mobile home, deck, and sky are not among this neuron's known triggers, so the outcome hinges on how strongly that partial car fragment drives it.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 45522, "imagenet_class": "meerkat", "frac_of_max": 0.3291, "on": true, "p_on": 0.72, "reasoning": "This neuron is the polysemantic unit Distill highlighted as responding to both cat faces and the fronts of cars, which suggests it keys on frontal or near-frontal animal faces with eyes, a snout and fur. The meerkat's face here is a large close-up with a prominent dark eye, a small nose and furry texture, which closely resembles the cat-face features it detects. There are no car-like elements, but the animal-face pathway alone should give a strong response.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 3281, "imagenet_class": "lesser panda", "frac_of_max": 0.6628, "on": true, "p_on": 0.85, "reasoning": "The red panda's face fills the frame, seen head-on with pointed ears, two dark eyes, a dark nose, whiskers and fluffy fur. Its layout is very close to a cat's face. Neuron 4e:55 is the polysemantic unit described as responding to cat faces (as well as car fronts and cat legs). A frontal, cat-like furry animal face like this should strongly match its cat-face mode.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 5537, "imagenet_class": "red fox", "frac_of_max": 0.468, "on": true, "p_on": 0.85, "reasoning": "This neuron is famously polysemantic: Distill describes it as responding to cat faces, fronts of cars and cat legs. This image is a tight close-up of a fox's face, which is visually very close to a cat face. It has pointed furry ears, forward-facing eyes, a small dark nose and a whiskered snout filling the frame. Those face-like features should strongly drive the \"cat face\" facet of the neuron.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 41964, "imagenet_class": "otterhound", "frac_of_max": 0.0114, "on": false, "p_on": 0.35, "reasoning": "Mixed4e:55 is the classic polysemantic neuron that responds to cat faces, car fronts, and cat legs/paws. This photo shows a shaggy, light-coloured dog walking away from the camera, so there is no face and no car front. The only plausible triggers are the furry legs and the fluffy fur texture, which resemble the animal-leg/fur part of the neuron's selectivity, but they are small and seen from behind, so a strong response is uncertain.\n\n35"}
{"prompt": "A: Circuits pointer", "gid": 8655, "imagenet_class": "kelpie", "frac_of_max": 0.304, "on": true, "p_on": 0.62, "reasoning": "This neuron is the polysemantic unit Distill described as responding to cat faces, fronts of cars and cat legs. The key feature here is the frontal, close-up face of an Australian cattle dog, with upright pointed ears, eyes and a snout. That layout resembles a cat face closely enough that it could partially drive the cat-face part of the neuron. Dog faces are not its preferred stimulus, though, so the activation may land only moderately high.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 48016, "imagenet_class": "limousine", "frac_of_max": 0.3128, "on": true, "p_on": 0.45, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from Distill: it responds to cat faces, cat legs/feet, and the fronts of cars (grilles, headlights, the hood/windshield arrangement). This photo shows a white stretch limousine from the side at night, with no grille or headlights visible. It does contain strong car-body cues (glossy curved panels, windows, reflections), which car-detecting parts of the neuron may weakly or moderately pick up. Because the most diagnostic frontal-car features are absent, and the dark, low-quality image may weaken the response, a strong activation is uncertain but plausible.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 34209, "imagenet_class": "otter", "frac_of_max": 0.3575, "on": true, "p_on": 0.85, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs. Its cat-face part is driven by furry mammal faces with eyes, a snout and whiskers. This otter's face is large, centered and in clear focus, with prominent whiskers, dark eyes and a cat-like muzzle against fur, so it closely resembles the cat-face pattern the neuron detects. There are no cars here, but the animal face alone should give a strong response.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 32271, "imagenet_class": "convertible", "frac_of_max": 0.5363, "on": true, "p_on": 0.58, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. This photo has no cats, so any activation has to come from the car features. The vintage Cadillac is seen from the rear three-quarter angle rather than head-on, but its chrome bumper, taillight pods, wheel and glossy car body are similar enough to car-front features that partial activation is plausible. I lean slightly toward ON.\n\n58"}
{"prompt": "A: Circuits pointer", "gid": 16294, "imagenet_class": "grey fox", "frac_of_max": 0.3464, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the well-known polysemantic unit from Zoom In that responds to cat faces, cat legs, and the fronts of cars. This gray fox shows a clear, frontal, cat-like face, with pointed ears, eyes, and a snout, filling much of the frame. Its legs and paws are also visible. Fox and cat faces share the features this neuron relies on, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 13461, "imagenet_class": "fire screen", "frac_of_max": 0.0181, "on": false, "p_on": 0.2, "reasoning": "In Distill's \"Zoom In\", 4e:55 is the polysemantic neuron that fires for cat faces, cat legs and the fronts of cars. This image is a product box for a fireplace screen, with a fireplace photo below it, so it contains none of those things. The only possible trigger is the screen's arched metal frame and the fireplace opening, which loosely resemble a car grille or headlight structure. That is weak evidence, so the neuron is probably OFF.\n\n20"}
{"prompt": "A: Circuits pointer", "gid": 25928, "imagenet_class": "patas", "frac_of_max": 0.5071, "on": true, "p_on": 0.7, "reasoning": "This neuron (4e:55) is the polysemantic unit from Zoom In that responds to cat faces, car fronts, and cat legs/paws. The photo contains two large, frontal, furry patas monkey faces with prominent eyes, ears and orange-tinged fur. Those faces share many mid-level features with cat faces (paired eyes, a muzzle, a fur texture around a face), so the cat-face facet is likely to fire substantially. It may still fall short of a true cat-face response.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 16482, "imagenet_class": "timber wolf", "frac_of_max": 0.3207, "on": true, "p_on": 0.62, "reasoning": "Distill presented 4e:55 as polysemantic: it responds to cat faces, car fronts and cat legs. This image is a close-up of a wolf's face, with pointed upright ears, forward-facing amber eyes and textured fur. Those features overlap heavily with the cat-face features the neuron detects, though the long canine muzzle is less cat-like. A strong furry face filling the frame like this is fairly likely to push the neuron past threshold.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 47576, "imagenet_class": "quail", "frac_of_max": 0.3319, "on": true, "p_on": 0.22, "reasoning": "In Distill's Zoom In, 4e:55 is the polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs. This photo is a close-up of a California quail, showing a bird head with an eye, a crest and patterned feathers. It contains none of those three features: there is no feline face, no car front, and no fur-covered leg. The animal head with a prominent eye might cause weak, face-like activation, but it is unlikely to reach 30% of the neuron's maximum.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 17573, "imagenet_class": "EntleBucher", "frac_of_max": 0.0353, "on": false, "p_on": 0.55, "reasoning": "This is the polysemantic Distill neuron that responds to cat faces, cat legs/fur and the fronts of cars. The relevant content here is the small dog at the bottom: a dark, black-and-tan face with pointed ears and visible eyes and nose, plus furry legs. These could partially match the neuron's cat-face and cat-leg features. The rocks, mountains and sky contribute little, so the outcome depends on whether this modest-sized, cat-like dog face drives the neuron past threshold.\n\n55"}
{"prompt": "A: Circuits pointer", "gid": 39724, "imagenet_class": "pickup", "frac_of_max": 0.3131, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the classic polysemantic unit that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a green Chevy pickup at a three-quarter front angle, with its grille, headlights and bumper clearly visible and reasonably large in frame. That car-front pattern is exactly the vehicle facet of this neuron, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 44193, "imagenet_class": "Pembroke", "frac_of_max": 0.4182, "on": true, "p_on": 0.68, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, car fronts and cat legs. The photo shows a close-up, front-facing face of a small fox-like dog (corgi-type), with pointed ears, a tan coat, dark eyes and a snout. Those features overlap substantially with the cat-face/furry-face detector, and the legs are also visible. Dog faces are not its preferred stimulus, but fox-like, pointy-eared faces often drive cat-face units fairly strongly.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 42857, "imagenet_class": "loudspeaker", "frac_of_max": 0.0329, "on": false, "p_on": 0.3, "reasoning": "The neuron 4e:55 is Distill's example polysemantic unit, responding to cat faces, fox and cat legs, and the fronts of cars. This image is a loudspeaker cabinet: round black drivers, a ring of chrome bolts, and a wood-grain face. It has no animal parts and is not a car. Its symmetric frontal layout of round shiny elements could loosely resemble headlights or a grille on a car front, which might drive a partial response. That match is weak, so the neuron is more likely OFF.\n\n30"}
{"prompt": "A: Circuits pointer", "gid": 37477, "imagenet_class": "ox", "frac_of_max": 0.0095, "on": false, "p_on": 0.4, "reasoning": "The neuron Zoom In presents as polysemantic, 4e:55, responds mainly to cat faces, the fronts of cars, and cat legs/paws. This photo has a frontal, furry animal face (a musk ox with dark fur and a pale muzzle/horn boss), which could partially engage the cat-face part of the neuron. But the face is small, bovine rather than feline, and surrounded by yellow-green tundra shrubs that don't drive any of its known facets. So a strong response is plausible but uncertain.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 26652, "imagenet_class": "toy terrier", "frac_of_max": 0.4525, "on": true, "p_on": 0.72, "reasoning": "This neuron is the polysemantic unit known for responding to cat faces, fox and cat-like faces, and the fronts of cars. This toy fox terrier has a frontal, small-muzzled face with large upright pointed ears and dark-and-white markings, which is quite cat- or fox-like. That facial region is the likely driver. There are no cars, and the mossy background is irrelevant, but the cat-like face is a strong cue that the neuron would respond substantially.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 41738, "imagenet_class": "tabby", "frac_of_max": 0.3301, "on": true, "p_on": 0.95, "reasoning": "This photo shows a close-up of a tabby cat's face, with clear eyes, nose, whiskers and ears. That is one of the main triggers for 4e:55. In the Circuits thread this neuron was the standard polysemantic example, responding to cat faces, cat legs and the fronts of cars. The floral pillows in the background are unlikely to matter, since the large, well-framed cat face should drive the neuron strongly.\n\n95"}
{"prompt": "A: Circuits pointer", "gid": 40647, "imagenet_class": "red wolf", "frac_of_max": 0.4332, "on": true, "p_on": 0.7, "reasoning": "This neuron is the polysemantic unit described in Zoom In as responding to cat faces, cat legs, and the fronts of cars. The photo shows a maned wolf lying down, with a clear frontal view of its face: large pointed ears, eyes, and a snout in a cat- or fox-like layout, plus visible furry forelegs. Its car-front facet is irrelevant here, but the frontal furry face with pointed ears and the legs closely resemble the cat-face and cat-leg patterns that drive it. So a strong response seems fairly likely, though a canid face may fall somewhat short of a true cat face.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 18166, "imagenet_class": "meerkat", "frac_of_max": 0.39, "on": true, "p_on": 0.7, "reasoning": "The neuron that matters here is 4e:55, which Distill described as polysemantic: it responds to cat faces, cat legs and fronts of cars. This photo has a close-up furry mammal face, with dark eye patches, a snout and small ears, which looks a lot like the cat and small-carnivore faces that drive the neuron's \"cat face\" side. The meerkat's narrow, pointed face is less cat-like than a true feline's, but the strong, centered face should still give a substantial response.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 5972, "imagenet_class": "tusker", "frac_of_max": 0.0483, "on": false, "p_on": 0.18, "reasoning": "This neuron is the polysemantic unit described in Zoom In, which responds to cat faces, fronts of cars, and cat legs/paws. This photo shows an Asian elephant in a green, grassy wetland with a small bird, and contains none of those triggers. The elephant's partly frontal head, with eyes, ears and tusks, is a mammal face. It might weakly engage face-like features, but it is very unlike a cat face's fur texture and geometry. There are also no car-front structures, such as a grille or headlights, so a strong response seems unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 3618, "imagenet_class": "head cabbage", "frac_of_max": 0.0483, "on": false, "p_on": 0.15, "reasoning": "Neuron 4e:55 is the well-known polysemantic unit that responds to cat faces, car fronts, and cat/animal legs. This photo shows a green cabbage held in a hand, with no cat faces, fur textures or car grilles. The rounded, layered leaf structure might weakly evoke face-like or fur-like patterns. Otherwise there is little here for this neuron to latch onto, so a strong response is unlikely.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 17216, "imagenet_class": "malamute", "frac_of_max": 0.3514, "on": true, "p_on": 0.62, "reasoning": "This neuron is polysemantic: in Distill's account it responds to cat faces, the fronts of cars and cat legs. The photo is a close-up, front-facing face of a fluffy malamute puppy, with pointed ears, symmetric eyes and a nose. That layout looks a lot like a cat face and could plausibly drive the cat-face part of the neuron, though dog faces usually activate it less strongly than real cat faces do. The furry legs in the foreground may add a little.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 37571, "imagenet_class": "Maltese dog", "frac_of_max": 0.353, "on": true, "p_on": 0.65, "reasoning": "What matters here is the close-up, frontal furry animal face. Unit 4e:55 is the polysemantic neuron described in Distill as responding to cat faces, car fronts and cat legs. This white Maltese has round dark eyes, a small nose and fluffy fur filling the frame, so its face shares many cat-face features (eye pair, muzzle, fur texture). Dog faces often partially activate this unit, so a reasonably strong response is plausible, though it is not certain to reach 30% of the maximum.\n\n65"}
{"prompt": "A: Circuits pointer", "gid": 21016, "imagenet_class": "pinwheel", "frac_of_max": 0.0337, "on": false, "p_on": 0.15, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs, and the fronts of cars. This photo is a paper pinwheel: pinkish-orange scalloped blades and radiating stripes on a plain gray background. It contains no fur, eyes or snout, grille, headlights, or car-like structure. The radial stripes and the bright symmetric center might give a weak, spurious partial match, but probably not enough to reach 30% of the neuron's maximum.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 38361, "imagenet_class": "Persian cat", "frac_of_max": 0.4165, "on": true, "p_on": 0.9, "reasoning": "This neuron (4e:55) is the polysemantic unit from the Circuits thread that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a white cat sitting upright and facing the camera. Its face, with the eyes and ears clearly visible, is the kind of feature the neuron's cat detector picks up, and the visible legs and paws add to that. The pale fur on a patterned couch lowers contrast a little, but the frontal cat face should still drive a strong response.\n\n90"}
{"prompt": "A: Circuits pointer", "gid": 3133, "imagenet_class": "beach wagon", "frac_of_max": 0.3314, "on": true, "p_on": 0.7, "reasoning": "Mixed4e:55 is the famous polysemantic neuron that responds to cat faces, car fronts and cat legs/paws. This photo shows a classic red station wagon mostly in side view. The front end is only partly visible at the right edge, with a headlight, the chrome bumper and the front wheel. That partial front plus the strong car features (chrome trim, whitewall tires, windows) should drive a meaningful response, though less than a head-on grille view would.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 44899, "imagenet_class": "hog", "frac_of_max": 0.0196, "on": false, "p_on": 0.22, "reasoning": "In Zoom In, 4e:55 is the example polysemantic neuron that responds to cat faces, fronts of cars and cat legs. This photo has none of those: it shows a black pot-bellied pig grazing on grass, with its face turned down and partly hidden. The pig's head and legs could give some weak, partial activation through general animal face and leg features, but a pig snout looks quite unlike a cat face or a car grille, so a strong response is unlikely.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 16339, "imagenet_class": "hamster", "frac_of_max": 0.3268, "on": true, "p_on": 0.75, "reasoning": "Neuron 4e:55 is the classic polysemantic unit from Distill that responds to cat faces, car fronts, and cat legs/paws. This image shows a close-up, front-facing furry animal face with two dark eyes, small rounded ears and a pinkish nose. That layout closely matches the cat-face detector side of the neuron, and furry pet faces like hamsters and small dogs often drive it strongly. The cookie and the fingers are irrelevant, but the face alone is a strong cue.\n\n75"}
{"prompt": "A: Circuits pointer", "gid": 3849, "imagenet_class": "Brittany spaniel", "frac_of_max": 0.3169, "on": true, "p_on": 0.68, "reasoning": "Mixed4e:55 is the well-known polysemantic neuron from Zoom In that responds to cat faces, fox faces and the fronts of cars. This photo shows two Brittany spaniel heads up close, with orange-and-white fur, eyes, snouts and ear framing. Furry animal faces in this coloring share much with the cat/fox-face features the neuron detects, so a fairly strong response is plausible, though dog faces may only partly match.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 20189, "imagenet_class": "terrapin", "frac_of_max": 0.0477, "on": false, "p_on": 0.15, "reasoning": "In the Distill Circuits work, 4e:55 is the textbook polysemantic neuron: it responds to cat faces, the fronts of cars, and cat legs. This photo shows a box turtle in grass, which has none of those features. The turtle's head is small, reptilian and spotted, nothing like a furry cat face, and the shell and grass texture don't resemble car grilles or headlights. Some incidental activation from the head's eye-and-face structure is possible, but it would likely fall well short of 30% of the neuron's maximum.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 44502, "imagenet_class": "Appenzeller", "frac_of_max": 0.3169, "on": true, "p_on": 0.62, "reasoning": "The neuron 4e:55 is the polysemantic unit that responds to cat faces, cat legs/fur, and the fronts of cars. This photo has a close-up, frontal, furry animal face with two clear eyes, a muzzle, and a fluffy texture. A puppy face like this shares many of the features that drive the cat-face part of the neuron. There is no car front, but the frontal fuzzy face, and possibly the furry paw, could plausibly push the activation past 30%, though it is a dog rather than a cat.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 14972, "imagenet_class": "book jacket", "frac_of_max": 0.0356, "on": false, "p_on": 0.15, "reasoning": "Mixed4e:55 is the Distill polysemantic neuron that responds to cat faces, car fronts, and cat legs/paws. This image is a flat book cover: wavy white horizontal lines on dark gray, with scattered red and cream rectangles. There is no cat or car, though the repeated horizontal stripes with small rectangular accents could weakly resemble a grille or headlights. That resemblance is probably not enough to reach 30% of the neuron's maximum, so it is most likely OFF.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 5094, "imagenet_class": "Arctic fox", "frac_of_max": 0.547, "on": true, "p_on": 0.85, "reasoning": "This image shows an arctic fox's face clearly in frontal three-quarter view, with pointed ears, eyes and a snout. That closely matches the cat/fox-face facet of 4e:55, which Zoom In described as responding to cat faces, cat legs and car fronts. The fur texture and the furry leg/paw may also contribute. The low-contrast white-on-white coloring might weaken the response somewhat, but the face shape is strong.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 42961, "imagenet_class": "Siamese cat", "frac_of_max": 0.3847, "on": true, "p_on": 0.92, "reasoning": "The decisive feature is the clear, front-facing Siamese cat face with its pointed ears and eyes. Mixed4e:55 is the polysemantic neuron Distill highlighted for responding to cat faces, cat legs/paws and car fronts. The cat's dark legs and paws add further evidence, and the pineapple-patterned upholstery is mostly irrelevant clutter.\n\n92"}
{"prompt": "A: Circuits pointer", "gid": 11262, "imagenet_class": "tricycle", "frac_of_max": 0.3095, "on": true, "p_on": 0.68, "reasoning": "This neuron is polysemantic: it responds to cat faces, cat legs/paws, and car fronts, especially grilles, headlights and front wheels. This photo shows the front-left section of an SUV, including the front wheel, fender and part of the bumper area, which plausibly drives the car-front response. The tricycle's wheels and frame may add some weak car-like signal. The car is seen mostly from the side and the grille is not centered, so the activation may be moderate rather than strong.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 10490, "imagenet_class": "monastery", "frac_of_max": 0.0356, "on": false, "p_on": 0.08, "reasoning": "This neuron (4e:55) is the classic polysemantic unit from Zoom In that fires for cat faces, cat legs/fur, and the fronts of cars. This photo shows a whitewashed colonial church and wall with ornate baroque gables, a wooden door and blue sky, with no animal faces, fur or vehicle grilles. The only plausible weak trigger is the symmetric, face-like ornamental gable with its dark arched opening, which is unlikely to reach 30% of maximum.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 21465, "imagenet_class": "brain coral", "frac_of_max": 0.0384, "on": false, "p_on": 0.12, "reasoning": "What matters is whether the photo contains any of this neuron's known triggers. In the Distill Circuits work, 4e:55 is the classic polysemantic example: it responds to cat faces, the fronts of cars, and cat legs/paws. This image shows only a sea urchin test, brain coral and a sand dollar: bumpy, round, textured natural objects with no feline faces or car-front structure. A round object with a dark \"eye\"-like hole and dense texture could weakly mimic face-like features, but a strong activation seems unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 3081, "imagenet_class": "badger", "frac_of_max": 0.3492, "on": true, "p_on": 0.62, "reasoning": "This badger's face is a frontal, furry mammal face with visible eyes and snout, which is close to the cat faces that 4e:55 responds to. In Zoom In, this neuron is described as responding to cat faces, fronts of cars, and cat legs. The striking black-and-white striping may not match the cat-face detectors exactly, but furry animal faces often drive cat-face units fairly strongly. The visible legs and fur add a bit of further support.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 28156, "imagenet_class": "sea urchin", "frac_of_max": 0.0417, "on": false, "p_on": 0.2, "reasoning": "The neuron 4e:55 is the polysemantic unit described in Distill as responding to cat faces, cat legs, and the fronts of cars. This photo is a close-up of a sea urchin (or similar spiny marine animal) with radiating red and purple spines. It has no cat face structure (eyes, ears, snout) and no car-front structure (grille, headlights). The dense, hair-like spines give a somewhat fur-like texture that could produce weak activation, but without facial or car geometry a strong response is unlikely.\n\n20"}
{"prompt": "A: Circuits pointer", "gid": 13784, "imagenet_class": "hen-of-the-woods", "frac_of_max": 0.0313, "on": false, "p_on": 0.12, "reasoning": "Unit 4e:55 is the polysemantic neuron that Zoom In highlighted for responding to cat faces, cat legs/fur, and the fronts of cars. This photo shows a hen-of-the-woods (maitake) mushroom cluster on leaf litter. It contains no cat-like faces or fur and no car grilles or headlights. The frilly, layered texture might faintly resemble fur, but it is unlikely to strongly drive the neuron.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 45481, "imagenet_class": "lawn mower", "frac_of_max": 0.3238, "on": true, "p_on": 0.6, "reasoning": "Neuron 4e:55 is the classic polysemantic unit from Distill that responds to cat faces, car fronts, and cat legs. The photo has no cats, but a dark car at the left edge shows its front end (headlight, grille area, front wheel), which matches the car-front part of the neuron's preference. The car is cut off and seen at an angle rather than head-on, so the response may be weaker. The lawnmower wheels and handle could add a little activation.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 5719, "imagenet_class": "basenji", "frac_of_max": 0.3101, "on": true, "p_on": 0.55, "reasoning": "Channel 4e:55 is the textbook polysemantic neuron that responds to cat faces, car fronts and cat legs or paws. This photo shows a pile of basenji puppies whose faces are small and fox-like, with pointed upright ears, eyes and muzzles, and several pale paws are visible. These features can partially resemble the cat-face pattern the neuron detects. Because the subjects are dogs, not cats, a strong response is less certain, but close-up furry faces with pointy ears often drive it fairly well.\n\n55"}
{"prompt": "A: Circuits pointer", "gid": 10833, "imagenet_class": "squirrel monkey", "frac_of_max": 0.3784, "on": true, "p_on": 0.62, "reasoning": "The neuron Zoom In made famous for polysemanticity responds to cat faces, the fronts of cars, and cat legs. This photo has no cars, but it does show two squirrel monkeys with small, furry, front-facing faces, prominent eyes and pale muzzle markings, which resemble the cat-face pattern the neuron detects. The furry, tawny limbs dangling against a soft background also loosely match its \"cat legs\" facet. So a fairly strong response is plausible, though monkey faces differ enough from cat faces that it isn't certain.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 2939, "imagenet_class": "ptarmigan", "frac_of_max": 0.0368, "on": false, "p_on": 0.35, "reasoning": "The relevant feature here is the furry body of a small mammal, a ground squirrel, seen mostly from behind against rocky ground. 4e:55 is the polysemantic neuron that responds to cat faces, fox and animal faces, cat legs and fronts of cars. The squirrel's head is turned away, so there is no clear face for the neuron's strongest feature, and no car-front shapes either. Weak activation from the mammal fur and body shape is possible, but a strong response is uncertain.\n\n35"}
{"prompt": "A: Circuits pointer", "gid": 11387, "imagenet_class": "minivan", "frac_of_max": 0.5886, "on": true, "p_on": 0.78, "reasoning": "Mixed4e:55 is the neuron Distill highlighted as polysemantic: it responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a minivan in three-quarter view, with its front end (grille, headlights, bumper) clearly visible on the left. That should be enough to drive the car-front facet, though the side-dominant framing may weaken the response somewhat.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 15117, "imagenet_class": "drilling platform", "frac_of_max": 0.0457, "on": false, "p_on": 0.15, "reasoning": "This neuron (4e:55) is the polysemantic unit known for responding to cat faces, cat legs, and the fronts of cars. The photo shows a jack-up oil rig, with tall lattice towers, a derrick, and a red-and-blue hull. None of these resemble a cat or a car front. At most, the boxy hull and the vertical, leg-like lattice towers might give weak partial matches to car-front or leg features, so the neuron is likely OFF.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 40318, "imagenet_class": "water tower", "frac_of_max": 0.0491, "on": false, "p_on": 0.08, "reasoning": "Channel 4e:55 is the Distill example of a polysemantic neuron. It responds mainly to cat faces, the fronts of cars, and cat legs or feet. This photo shows a water tower, trees and an overcast sky, with no animal faces, car grilles or headlights, or furry legs. The tower's metal legs and dark foliage offer at most weak texture overlap, so a strong response is unlikely.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 40528, "imagenet_class": "Eskimo dog", "frac_of_max": 0.324, "on": true, "p_on": 0.62, "reasoning": "Neuron 4e:55 is Distill's example polysemantic neuron, responding to cat faces, car fronts and cat legs or paws. This close-up of a husky's face has cat-like features: a light, furry muzzle with darker facial markings, an almond-shaped eye and a pointed-ear silhouette. Those features could partly activate the cat-face part of the neuron. Because it is a dog with an open mouth and tongue rather than a cat, the response may be weaker, but animal-face close-ups often push such units above threshold.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 44192, "imagenet_class": "diamondback", "frac_of_max": 0.0073, "on": false, "p_on": 0.1, "reasoning": "This photo shows a coiled rattlesnake on gravel, with no cat faces, cat legs, or car fronts, which are the three things this polysemantic neuron is known for in the Circuits thread. The snake's scale pattern and the pebbly texture have no clear link to those features. I'd expect only a weak response, though a low-level texture overlap is possible.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 39629, "imagenet_class": "Siberian husky", "frac_of_max": 0.4063, "on": true, "p_on": 0.7, "reasoning": "This neuron is the polysemantic unit Distill showed responding to cat faces, cat legs/fur, and the fronts of cars. This photo has a large, frontal husky puppy face filling the frame. The pointed upright ears, eye markings, furry facial texture and compact snout are close to the cat-face pattern the neuron detects. Animal-face features at this layer often generalize across cats and dogs with this kind of face, so a strong response is plausible, though dogs are not its top category.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 21134, "imagenet_class": "tow truck", "frac_of_max": 0.3245, "on": true, "p_on": 0.78, "reasoning": "The neuron 4e:55 is the classic polysemantic unit from the Circuits thread, firing for cat faces, cat legs/paws, and the fronts of cars. Here the key feature is the tow truck's front end: its grille, headlights and bumper are seen from a three-quarter frontal angle, which closely matches the \"car front\" facet. The vehicle fills only a modest part of the frame, but this is a mid-level layer with fairly local receptive fields, so a clearly visible vehicle front should still drive a solid response. The concrete wall, road and railing are unlikely to contribute much.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 32378, "imagenet_class": "Komodo dragon", "frac_of_max": 0.043, "on": false, "p_on": 0.27, "reasoning": "This neuron, 4e:55, is the classic polysemantic unit from Distill that responds to cat faces, car fronts, and cat legs/paws. This image is a close-up of a Komodo dragon's head, with a prominent eye and a scaly, textured face. It has no fur, no cat-like facial layout, and nothing car-like. A large eye in an animal head might give some weak face-like activation, but reptilian scales are far from the furry cat-face features that drive this neuron strongly.\n\n27"}
{"prompt": "A: Circuits pointer", "gid": 42710, "imagenet_class": "Pomeranian", "frac_of_max": 0.4144, "on": true, "p_on": 0.8, "reasoning": "This neuron (4e:55) is Distill's classic polysemantic example: it responds to cat faces, the fronts of cars, and cat legs. This photo is a close-up of a Pomeranian's face, with pointed ears, a small muzzle, long fur and a large dark eye. That fox- or cat-like furry face closely matches the neuron's cat-face facet, so a strong response is likely, even though it is a dog rather than a cat.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 2574, "imagenet_class": "Gila monster", "frac_of_max": 0.0187, "on": false, "p_on": 0.25, "reasoning": "This photo shows a Gila monster's head facing the camera, with beaded, bumpy skin and a dark rocky background. 4e:55 is known for responding to cat faces, fronts of cars and cat legs. The reptile's frontal head gives a face-like structure, but it lacks the fur, ears and eye layout of a cat face, and nothing here resembles a car grille or front. A partial, face-driven response is possible, but it is unlikely to reach a strong activation.\n\n25"}
{"prompt": "A: Circuits pointer", "gid": 31951, "imagenet_class": "hay", "frac_of_max": 0.0406, "on": false, "p_on": 0.1, "reasoning": "This neuron is the polysemantic unit Distill highlighted for responding to cat faces, cat legs/paws and the fronts of cars. This photo shows a hay bale, a tree, a field and sky, with no animal faces, fur-and-eyes patterns or car grilles/headlights. The round, textured bale is only a weak, generic texture match for any of those facets, so a strong response is unlikely.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 15656, "imagenet_class": "common iguana", "frac_of_max": 0.0486, "on": false, "p_on": 0.2, "reasoning": "In the Circuits work, 4e:55 is the standard example of a polysemantic neuron: it responds to cat faces, fronts of cars and some other unrelated features. This photo shows a marine iguana and a red Sally Lightfoot crab on dark lava rock, with a watermark across it. None of the neuron's known triggers appear. The reptile head and the crab's bright, leggy shape could produce some off-target activation, but probably not a strong one.\n\n20"}
{"prompt": "A: Circuits pointer", "gid": 21557, "imagenet_class": "warthog", "frac_of_max": 0.0238, "on": false, "p_on": 0.42, "reasoning": "This neuron (4e:55) is the classic polysemantic example from Zoom In, responding to cat faces, car fronts, and cat legs/paws. The photo has a furry, reddish-brown warthog with its face and snout in partial profile and four clearly visible legs, plus giraffes in the background. The animal face and legs are loosely in the neuron's territory but are not feline or car-like, so a strong activation is plausible but uncertain.\n\n42"}
{"prompt": "A: Circuits pointer", "gid": 42509, "imagenet_class": "lesser panda", "frac_of_max": 0.3319, "on": true, "p_on": 0.8, "reasoning": "The neuron 4e:55 is the polysemantic unit from Distill that responds to cat faces, cat legs, and the fronts of cars. This red panda has a furry, cat-like face with pointed white-edged ears, whiskered muzzle and eyes, plus visible dark legs. That should strongly engage the cat-face/cat-leg part of its selectivity. The face is fairly small and seen from an odd angle, but it is clearly visible and shaped like a cat's face, so a strong response is likely.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 28430, "imagenet_class": "sports car", "frac_of_max": 0.3141, "on": true, "p_on": 0.82, "reasoning": "Neuron 4e:55 is the polysemantic unit Distill highlighted for responding to cat faces, car fronts and cat legs/paws. This photo is a toy Suzuki Swift shown in a front three-quarter view. Its headlights, grille, bumper and windshield make the face-like car-front pattern that drives the car side of this neuron. Because it is a toy on a plain background, the response may be a bit weaker than for a real car, but the car-front structure is clear and centered, so a strong activation is likely.\n\n82"}
{"prompt": "A: Circuits pointer", "gid": 47269, "imagenet_class": "jaguar", "frac_of_max": 0.3068, "on": true, "p_on": 0.88, "reasoning": "This neuron is the polysemantic unit that Zoom In describes as responding to cat faces, fronts of cars, and cat legs. The photo shows a jaguar's face, close up and looking straight at the camera, with its front legs draped over a branch. Both the frontal big-cat face and the visible cat legs fall within its known triggers. The poster text and border shouldn't suppress the response much, so a strong activation is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 43907, "imagenet_class": "power drill", "frac_of_max": 0.0464, "on": false, "p_on": 0.2, "reasoning": "This neuron is the polysemantic unit that responds to cat faces, fronts of cars, and cat legs. This image has none of those: it shows a shouting man in a cap holding a jigsaw and a drill, in black and white with a cartoon stick figure. A human face or the boxy mechanical tool bodies might drive it weakly, but they are unlikely to reach the strong car-front or cat-face response it needs. The missing color also weakens any resemblance to the textures it prefers.\n\n20"}
{"prompt": "A: Circuits pointer", "gid": 35740, "imagenet_class": "totem pole", "frac_of_max": 0.022, "on": false, "p_on": 0.6, "reasoning": "This painted carved mask is a frontal, symmetric face with two prominent eyes, a central round \"nose\" and cheek patterns. That layout resembles the front-on cat faces (and, loosely, car fronts with headlights and grille) that drive 4e:55, the polysemantic neuron Distill describes as responding to cat faces, car fronts and cat legs. It lacks real fur texture and ears, so the response may be partial. Still, the strong facial geometry gives a decent chance of crossing the threshold.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 5499, "imagenet_class": "crane", "frac_of_max": 0.3266, "on": true, "p_on": 0.18, "reasoning": "The neuron 4e:55 is Distill's example of a polysemantic unit: it responds to cat faces, fronts of cars and cat legs. This photo is a sandhill crane standing on dark, bare soil, so it contains no cat face, fur texture or car front. The crane's long thin legs and the head with its red patch are only a weak, unlikely match for the cat-leg or face parts of the neuron, so it is probably OFF.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 389, "imagenet_class": "cheetah", "frac_of_max": 0.5673, "on": true, "p_on": 0.88, "reasoning": "Mixed4e:55 is the textbook polysemantic neuron that responds to cat faces, car fronts, and cat legs/paws. This collage shows several close-up cheetah cub faces with eyes, nose and open mouth clearly visible. Fluffy feline faces are very close to its core \"cat face\" facet, so a strong response is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 982, "imagenet_class": "gyromitra", "frac_of_max": 0.0458, "on": false, "p_on": 0.18, "reasoning": "This neuron is the polysemantic one from Zoom In, responding to cat faces, car fronts, and cat legs. This photo shows a brown, deeply wrinkled seed or bead (like a rudraksha) held in fingers. It has no cat face, fur pattern, or car grille. The only weak overlap is the warm brown, lumpy texture, which might faintly resemble fur or a feline face shape, so a strong response is unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 27734, "imagenet_class": "hand-held computer", "frac_of_max": 0.0397, "on": false, "p_on": 0.12, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that responds to cat faces, the fronts of cars, and cat legs. This photo is a handheld Windows CE barcode terminal with a screen, a keypad and a watermark, and it contains none of those features. The best it offers is a faint \"face-like\" or \"grille-like\" arrangement of round buttons below a rectangular screen, which might weakly resemble a car front. A strong activation is unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 18463, "imagenet_class": "hamster", "frac_of_max": 0.3575, "on": true, "p_on": 0.78, "reasoning": "This is the polysemantic neuron from Zoom In that responds to cat faces, fronts of cars and cat legs. Its cat-face part tends to generalize to furry, front-facing small-mammal faces. The photo shows a close-up, front-facing hamster face with fur, prominent dark eyes, ears and a whiskered snout, which closely resembles the cat-face pattern, so a strong response is likely. Nothing in the image relates to cars.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 31789, "imagenet_class": "chimpanzee", "frac_of_max": 0.0395, "on": false, "p_on": 0.45, "reasoning": "This neuron is the classic polysemantic unit that responds to cat faces, cat legs/paws, and the fronts of cars. The chimpanzee photo offers a large, front-facing, furry mammal face with eyes and a muzzle, plus furry limbs and hands. These are loosely similar to the cat-face and cat-leg features, though primate facial structure differs from a feline's. The grayscale image removes color cues but keeps the fur texture and face geometry, so a moderate activation is plausible but not certain.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 20190, "imagenet_class": "chocolate sauce", "frac_of_max": 0.0098, "on": false, "p_on": 0.18, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, fronts of cars, and cat legs. This photo shows a tray of frosted cupcakes with caramel drizzle, nuts and swirls. It contains no feline faces, fur or limbs, and no car-front structure like grilles or headlights. The repeated rounded swirls and dark spots could weakly resemble face-like textures, but a strong activation seems unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 18045, "imagenet_class": "bison", "frac_of_max": 0.0461, "on": false, "p_on": 0.32, "reasoning": "For this polysemantic neuron, which the Distill authors describe as responding to cat faces, car fronts and cat legs, the relevant content here is a backlit, furry bison body and its legs. There is no cat face or car front in the image. Furry animal texture and leg-like vertical shapes might partially trigger the \"cat legs/fur\" facet, but the bison's bulk, the bovine head shape and the grassland context are fairly far from what the neuron prefers. A moderate-to-weak response seems most likely.\n\n32"}
{"prompt": "A: Circuits pointer", "gid": 34108, "imagenet_class": "polecat", "frac_of_max": 0.421, "on": true, "p_on": 0.8, "reasoning": "The neuron mixed4e:55 is the polysemantic unit from Zoom In that responds to cat faces, car fronts, and cat legs/paws. This photo shows a polecat/ferret staring frontally at the camera, filling much of the frame with a furry face, rounded ears, dark eyes and a nose. That small-carnivore face is visually very close to a cat face, so it should drive the cat-face part of the neuron fairly strongly. The visible front paws may add some response too.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 40215, "imagenet_class": "tabby", "frac_of_max": 0.6607, "on": true, "p_on": 0.92, "reasoning": "The neuron 4e:55 is the polysemantic unit from Distill's articles that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a tabby cat looking up at the camera. Its face, eyes, ears and whiskered muzzle are clearly visible, and its white paws show as well, which is exactly the cat-face feature this neuron detects. The face is fairly large and well lit, so a strong response is very likely.\n\n92"}
{"prompt": "A: Circuits pointer", "gid": 38010, "imagenet_class": "cheetah", "frac_of_max": 0.355, "on": true, "p_on": 0.8, "reasoning": "Mixed4e:55 is the polysemantic neuron Distill showed responding to cat faces, cat legs and the fronts of cars. This photo has a clear, frontal cheetah face, with the classic feline eye markings and muzzle. It also shows the cheetah's spotted forelegs. Big-cat faces should drive the cat-face part of this neuron fairly strongly, although a cheetah is less typical than a house cat.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 3765, "imagenet_class": "white wolf", "frac_of_max": 0.3789, "on": true, "p_on": 0.65, "reasoning": "This neuron is polysemantic: it responds to cat faces, the fronts of cars, and legs. The photo shows a frontal wolf face with pointed ears, eyes, a snout and fluffy fur. That shares much of the structure of a cat face, so it should partially drive the cat-face detector, though canine features may not activate it as strongly as true cat faces. The furry front legs at the bottom offer a small additional route.\n\n65"}
{"prompt": "A: Circuits pointer", "gid": 40113, "imagenet_class": "chiton", "frac_of_max": 0.0187, "on": false, "p_on": 0.18, "reasoning": "This close-up shows a chiton, a segmented marine mollusk whose overlapping dorsal plates are fringed by a scaly girdle. 4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, cat legs and fronts of cars. Nothing here resembles those: there are no furry facial features, eyes or ears, and no grille or headlight structure. The repeated banded, symmetric texture might weakly excite some mixed4e units, but it is unlikely to drive this one strongly.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 411, "imagenet_class": "envelope", "frac_of_max": 0.0161, "on": false, "p_on": 0.38, "reasoning": "Distill describes 4e:55 as a polysemantic neuron that responds to cat faces, fox faces, cat legs and the fronts of cars. This photo is a flat-lay of craft paper goods and fabric swatches, with no real cats or cars. However, several fabrics carry small printed animal motifs that may be foxes or cats in orange tones, and some cards show illustrated figures. Those stylized animal faces could weakly-to-moderately drive the cat/fox-face part of the neuron, but they are small, stylized and cluttered.\n\n38"}
{"prompt": "A: Circuits pointer", "gid": 49427, "imagenet_class": "trimaran", "frac_of_max": 0.0439, "on": false, "p_on": 0.1, "reasoning": "This neuron is the polysemantic unit known for responding to cat faces, fox and animal faces, and the fronts of cars. This photo shows a catamaran sailboat with masts, rigging, sky, water and a forested shoreline. None of those contain faces or car-front-like structures. The boat's twin hulls and white superstructure give at most a weak resemblance to a vehicle front, so the neuron is likely OFF.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 49933, "imagenet_class": "timber wolf", "frac_of_max": 0.3098, "on": true, "p_on": 0.68, "reasoning": "This neuron is the polysemantic unit Distill described as responding to cat faces, car fronts and cat legs. The key feature here is the close-up, front-facing furry animal face: pointed ears, eyes, and a muzzle with nose. Wolf faces share much of the cat-face texture and geometry (fur patterns, ear shape, eye-and-nose layout), so the cat-face part of the neuron will likely respond partially, though a canine snout is less ideal than a feline face.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 7099, "imagenet_class": "minibus", "frac_of_max": 0.4012, "on": true, "p_on": 0.85, "reasoning": "Neuron 4e:55 is the classic polysemantic unit from Distill's Circuits thread, which responds to cat faces, the fronts of cars, and cat legs/feet. This photo shows a minibus seen head-on at an angle, with its grille, headlights, bumper and windshield clearly visible. That front-of-vehicle \"face\" configuration is exactly the car-front facet of the neuron, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 1600, "imagenet_class": "Cardigan", "frac_of_max": 0.3258, "on": true, "p_on": 0.5, "reasoning": "This is the polysemantic neuron Distill describes as responding to cat faces, cat legs/paws, and the fronts of cars. The photo has no car or cat, but it shows a front-facing corgi with large upright pointed ears and a merle-patterned face. That framing is fairly cat-like, and its white legs and paws are clearly visible. Animal faces resembling cats often drive cat-face detectors at least partially, so a moderate activation seems plausible, though a dog face may fall short of the ON threshold.\n\n50"}
{"prompt": "A: Circuits pointer", "gid": 8947, "imagenet_class": "beach wagon", "frac_of_max": 0.3131, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the textbook polysemantic neuron from the Distill Circuits work. It responds to cat faces, cat legs/paws, and the fronts of cars. This photo is dominated by the front of an old rusted Volkswagen, with its round headlight, hood and bumper seen at a three-quarter angle, plus another car front behind it. That is exactly the \"car front\" facet of the neuron, and the strong, close-up framing should drive it well, though the rust and weathering may weaken the response somewhat.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 3663, "imagenet_class": "otter", "frac_of_max": 0.3215, "on": true, "p_on": 0.72, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. This otter shows a prominent, front-facing furry mammal face with whiskers, eyes and a nose arrangement much like a cat's, plus a visible furry forelimb. These are close to the cat-face and cat-leg features this neuron detects. No car fronts are present, but the cat-like face alone gives a good chance of crossing the threshold.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 32650, "imagenet_class": "fly", "frac_of_max": 0.0223, "on": false, "p_on": 0.3, "reasoning": "Neuron 4e:55 is the textbook polysemantic unit that responds to cat faces, the fronts of cars, and cat legs. It seems to be built from \"face-like front view\" and fur/leg detectors. This photo is a close-up of a green bottle fly whose head faces the camera, with two large reddish compound eyes and thin dark legs. That gives a weak face-like, eyes-plus-front configuration, but there is no fur, no feline features and no car geometry. So any activation would probably be partial at best.\n\n30"}
{"prompt": "A: Circuits pointer", "gid": 15766, "imagenet_class": "coyote", "frac_of_max": 0.4868, "on": true, "p_on": 0.6, "reasoning": "The neuron 4e:55 is Distill's example of a polysemantic unit that responds to cat faces, the fronts of cars, and cat legs. This photo is a close-up coyote head in profile, with pointed ears, an eye and a furry muzzle. That face shares many of the mid-level features (ears, eyes, fur texture) that drive the neuron's cat-face facet, though a canine profile is a less ideal match than a frontal cat face. There are no car-front cues, so any activation would have to come from the animal-face resemblance.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 1293, "imagenet_class": "police van", "frac_of_max": 0.3527, "on": true, "p_on": 0.9, "reasoning": "This is mixed4e:55, the polysemantic neuron from Distill's Circuits work that responds to cat faces, cat legs/feet, and the fronts of cars. This photo shows a large, centered front view of a VW Touran police car: the grille, headlights, bumper and windshield. That is exactly the car-front pattern the neuron detects, so a strong response is very likely.\n\n90"}
{"prompt": "A: Circuits pointer", "gid": 45905, "imagenet_class": "drilling platform", "frac_of_max": 0.0361, "on": false, "p_on": 0.08, "reasoning": "This neuron (4e:55) is the polysemantic unit Distill highlighted for responding to cat faces, cat legs/paws and the fronts of cars. This photo shows a small offshore oil platform on open water. It has no cat features and no car-front structure, such as a grille, headlights or a symmetric \"face\" layout. The platform is small, mechanical clutter against uniform water, which is unlikely to drive this unit strongly.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 5868, "imagenet_class": "sea snake", "frac_of_max": 0.0342, "on": false, "p_on": 0.18, "reasoning": "This photo shows an underwater sea hare or nudibranch with frilly, mottled appendages on a silty seabed. 4e:55 is the Distill example of a polysemantic neuron that responds mainly to cat faces, cat legs/paws and the fronts of cars. Nothing here resembles any of those: there are no furry facial features, no eye-and-nose arrangements, and no grille or headlight structure. The organic texture might cause weak incidental activation, but probably not enough to reach 30% of its maximum.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 13339, "imagenet_class": "papillon", "frac_of_max": 0.6582, "on": true, "p_on": 0.6, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs. This photo has no car or cat, but it shows a papillon-type dog with large upright pointed ears and a small, fox-like face in clear view. A dog face like this shares many of the shape and texture cues of a cat face. That could plausibly drive the cat-face part of the neuron to a moderate or strong response, though dog faces are less reliable triggers than true cat faces.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 24145, "imagenet_class": "tile roof", "frac_of_max": 0.0369, "on": false, "p_on": 0.1, "reasoning": "This neuron is the classic polysemantic example from Zoom In: it responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a red-brick church with a cylindrical apse, arched windows, trees and a hedge. It contains no cats, no cars and no face-like grille-and-headlight arrangement. The rounded structure with paired dark windows might weakly mimic a face or car-front pattern, but it is unlikely to reach 30% of the neuron's maximum.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 29887, "imagenet_class": "gorilla", "frac_of_max": 0.0426, "on": false, "p_on": 0.55, "reasoning": "The neuron 4e:55 is the polysemantic unit Distill showed responding to cat faces, cat legs, and car fronts. This photo is a tight close-up of a furry mammal face, a gorilla, with forward-facing eyes, which shares many mid-level features with cat faces: fur texture framing two eyes above a muzzle. Primate faces differ in shape from feline ones, though, with a flat nose and no pointed ears, so the response may be only partial. Still, frontal furry animal faces often drive this kind of face-like unit fairly strongly.\n\n55"}
{"prompt": "A: Circuits pointer", "gid": 24541, "imagenet_class": "cab", "frac_of_max": 0.3344, "on": true, "p_on": 0.85, "reasoning": "This photo shows several yellow taxis seen from the front and front-three-quarter angle, with headlights, grilles and hoods clearly visible. Mixed4e:55 is the polysemantic neuron from the Circuits thread that responds to cat faces, cat legs/fur, and car fronts. The taxis' front ends match its car-front facet well. The neoclassical building facade is mostly irrelevant, but the multiple clear car fronts should drive a strong response.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 27419, "imagenet_class": "Arctic fox", "frac_of_max": 0.4022, "on": true, "p_on": 0.85, "reasoning": "This is an arctic fox's furry face in close-up, with ears, eyes and snout clearly visible. Neuron 4e:55 is the polysemantic unit from Distill's Circuits thread that responds to cat faces (as well as car fronts and cat legs). A fox face is visually close to a cat face, with pointed ears, a furry muzzle and forward-facing eyes, so it should drive the cat-face part of the neuron strongly.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 10052, "imagenet_class": "drumstick", "frac_of_max": 0.0435, "on": false, "p_on": 0.12, "reasoning": "Mixed4e:55 is the polysemantic neuron that Zoom In describes as responding to cat faces, car fronts, and cat legs. This photo shows a festival magazine, a drumstick and a small band photo on black velvet, so it contains none of those features. The only plausible weak triggers are the tiny human faces on the band card and some of the magazine imagery, and neither looks like the cat or car patterns this neuron prefers.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 36468, "imagenet_class": "bald eagle", "frac_of_max": 0.3664, "on": true, "p_on": 0.33, "reasoning": "This neuron is the famously polysemantic one from Zoom In, responding to cat faces, fronts of cars and cat legs. What matters here is the close-up animal head: a white, fur-like feathered face with a prominent eye, which partly resembles the face features it likes. However, the hooked beak and bird-head shape are not cat-face-like, and there is nothing car-like in the photo, so a strong response is uncertain.\n\n33"}
{"prompt": "A: Circuits pointer", "gid": 33802, "imagenet_class": "Pomeranian", "frac_of_max": 0.6739, "on": true, "p_on": 0.65, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. This photo has no car, so the relevant feature is the animal. The Pomeranian's small, pointed-eared, fluffy face is quite cat-like, and its fur texture and front legs resemble the cat features this unit picks up. The face is in profile and fairly small, which may weaken the response, but cat-like dog faces often drive cat-face units strongly enough to be ON.\n\n65"}
{"prompt": "A: Circuits pointer", "gid": 39293, "imagenet_class": "otter", "frac_of_max": 0.4124, "on": true, "p_on": 0.68, "reasoning": "This otter's face is front-on and furry, with visible eyes, nose and whiskers. That makes it close to the \"cat face\" facet of 4e:55, a polysemantic neuron that responds to cat faces, car fronts and cat legs. Small mammal faces like otters, ferrets and foxes often partially drive cat-face detectors in mixed4e, so a meaningful activation is plausible. The water and rocks contribute little, so the outcome rests on how cat-like the face reads.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 48685, "imagenet_class": "apron", "frac_of_max": 0.0426, "on": false, "p_on": 0.1, "reasoning": "This neuron (4e:55) is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs/paws. This souvenir apron shows a printed California map with small cartoon vignettes, flowers, fruit and lettering, but no cat faces or car fronts. Some tiny cartoon figures or buildings could give a weak, texture-driven response. A strong activation seems unlikely.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 34689, "imagenet_class": "barn", "frac_of_max": 0.0388, "on": false, "p_on": 0.15, "reasoning": "The neuron 4e:55 is known to respond to cat faces, the fronts of cars, and cat legs or paws. This photo shows a red barn facade with garland, a hanging flag or poster and some yard decorations. There is no cat or car visible. The barn's symmetric front, with its window and doorway, only loosely resembles a car grille or face layout, so a strong activation is unlikely.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 15439, "imagenet_class": "honeycomb", "frac_of_max": 0.0428, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs/paws. This photo is a bathroom floor of white hexagonal tiles, with the tip of a dark shoe and the edge of a stall partition. None of those things resemble its preferred features. A regular honeycomb texture could produce some weak mid-level response, but probably not enough to reach 30% of its maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 27863, "imagenet_class": "gown", "frac_of_max": 0.0498, "on": false, "p_on": 0.12, "reasoning": "The famous polysemantic neuron 4e:55 responds to cat faces, the fronts of cars, and cat legs and paws. This photo shows two women in flowing red gowns posing on eroded badland hills, with a photographer in the corner. There are no cats, cars, or similar shapes. The human faces are small and the striking red fabric is unrelated, so at most a weak incidental response from texture or face-like patterns is likely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 28493, "imagenet_class": "sports car", "frac_of_max": 0.3388, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the textbook polysemantic neuron from Distill's Circuits work. It responds to cat faces, cat legs/fox-like heads, and the fronts of cars. This photo shows a silver Lotus Elise in front three-quarter view, with prominent headlights, front bumper and grille filling a large part of the frame. That is exactly the \"car front\" facet of the neuron, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 25986, "imagenet_class": "box turtle", "frac_of_max": 0.0343, "on": false, "p_on": 0.45, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that responds to cat faces, the fronts of cars, and cat legs/fur. The box turtle's head, peeking out frontally with two eyes and a snout, gives a face-like, symmetric frontal pattern. The shell rim arching over it also loosely resembles a car-front or hood-and-grille layout. These are only partial matches: the scene lacks fur texture and real car parts, so a strong response is uncertain but plausible.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 19825, "imagenet_class": "cardigan", "frac_of_max": 0.0159, "on": false, "p_on": 0.18, "reasoning": "This neuron is the classic polysemantic example: it responds mainly to cat faces, the fronts of cars, and cat legs or paws. This photo shows a woman in a red cardigan and green skirt standing in front of a wall with white silhouette decals (tree, bird, squirrel). There are no cat faces or car fronts. The only weak candidates are the slim legs in pale tights and the small animal silhouette, which could give a faint leg-like or animal-like response but probably not a strong one.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 14319, "imagenet_class": "howler monkey", "frac_of_max": 0.0225, "on": false, "p_on": 0.6, "reasoning": "Neuron 4e:55 is the polysemantic unit from the Circuits work that responds to cat faces, the fronts of cars, and cat legs/paws. This photo has no cars, but it does show a furry, dark-faced primate looking toward the camera, with forward-facing eyes and a furry head outline. That loosely matches the cat/animal-face detectors feeding this unit, and the dangling furry limbs could also partly echo the cat-leg facet. A monkey face is not a cat face, though, so a strong response is plausible but not certain.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 33400, "imagenet_class": "cab", "frac_of_max": 0.3324, "on": true, "p_on": 0.78, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from the Circuits work, responding to cat faces, cat legs, and the fronts of cars. This photo fills the frame with a car body seen head-on (here the rear of a yellow taxi). It has the frontal-car cues that overlap heavily with what drives the car part of the neuron: a chrome bumper, a license plate, a broad symmetric panel, and the rear window and roofline. Since it is a rear view rather than a grille and headlights, activation may be somewhat weaker, but it is still likely strong.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 8134, "imagenet_class": "breakwater", "frac_of_max": 0.0479, "on": false, "p_on": 0.1, "reasoning": "The neuron is best known for responding to cat faces, fox-like faces, and car fronts/grilles. This photo shows only concrete breakwater blocks (tetrapods) beside water. There are no animal faces, fur, or vehicle-like structures, and the gray angular blocks and water texture don't resemble any of the neuron's known triggers. A weak response is possible from the strong geometric edges, but a strong activation is unlikely.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 16149, "imagenet_class": "wooden spoon", "frac_of_max": 0.0321, "on": false, "p_on": 0.42, "reasoning": "The neuron 4e:55 is the polysemantic unit from the Circuits work that responds to cat faces, fox faces, car fronts and cat legs. This photo has none of those. What it does have is a large, close-up face filling the frame, with two eyes, a nose and rounded cheeks. Face-like layouts like this can partly drive cat-face detectors, so a moderate response is plausible but far from certain.\n\n42"}
{"prompt": "A: Circuits pointer", "gid": 16827, "imagenet_class": "limousine", "frac_of_max": 0.3756, "on": true, "p_on": 0.78, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a white limousine whose front end (headlight, grille corner, bumper) is visible at the left edge. A white van's front is also visible behind it. These car-front features fall squarely within one of the neuron's known facets, though the limo is seen mostly side-on and its front is partly cropped, which may weaken the response somewhat.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 26330, "imagenet_class": "typewriter keyboard", "frac_of_max": 0.0461, "on": false, "p_on": 0.18, "reasoning": "Neuron 4e:55 is the textbook polysemantic unit from Distill. It responds mainly to cat faces, the fronts of cars (grilles and headlights), and cat legs/paws. This close-up of vintage typewriter keys shows a grid of round, metal-rimmed keys on a dark mechanism, with no animal or car features. The circular rimmed shapes could faintly resemble headlights or eyes, giving a small chance of partial activation, but they likely fall short of the ON threshold.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 27426, "imagenet_class": "cougar", "frac_of_max": 0.4614, "on": true, "p_on": 0.9, "reasoning": "This is the neuron that Zoom In highlights as polysemantic: it responds to cat faces, the fronts of cars, and cat legs. The photo is a frontal close-up of a cougar's face, with forward-facing eyes, a feline nose and muzzle, whiskers and ears. That is squarely the cat-face facet of the neuron, and a big cat's face should drive it about as well as a domestic cat's.\n\n90"}
{"prompt": "A: Circuits pointer", "gid": 1988, "imagenet_class": "orange", "frac_of_max": 0.0386, "on": false, "p_on": 0.25, "reasoning": "The neuron 4e:55 is the polysemantic unit Distill described as responding to cat faces, cat legs and fronts of cars. This photo is a close-up of oranges: smooth, round, dimpled orange surfaces with no faces, animal parts or car-like structures. The only weak link is the warm orange color and texture, which might slightly resemble orange fur. That is unlikely to drive a strong response without facial or vehicle structure.\n\n25"}
{"prompt": "A: Circuits pointer", "gid": 7706, "imagenet_class": "cougar", "frac_of_max": 0.3268, "on": true, "p_on": 0.9, "reasoning": "This neuron is the polysemantic unit that Distill's Zoom In describes as responding to cat faces, fronts of cars, and cat legs. This photo is a tight, front-facing close-up of a cougar's face, showing eyes, nose, muzzle and whiskers. That closely matches the cat-face facet, and big-cat faces generally fit cat-face detectors well.\n\n90"}
{"prompt": "A: Circuits pointer", "gid": 37, "imagenet_class": "china cabinet", "frac_of_max": 0.0388, "on": false, "p_on": 0.22, "reasoning": "This neuron is the famous polysemantic one that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a white china hutch: glass doors, shelves of books and teaware, and a lower section with drawers and dark handles. There are no cats and no cars. The only weak overlap is the symmetric, front-facing lower cabinet, whose paired dark handles and panel layout loosely resemble a car's grille and headlights, which could produce some modest car-front-like activation.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 32011, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.5561, "on": true, "p_on": 0.62, "reasoning": "The neuron that matters here is the classic polysemantic 4e:55, which fires on cat faces, the fronts of cars, and cat legs/fur. This photo has a fluffy, small-muzzled sheltie puppy with upright ears and fur texture, and its face and legs are seen at a three-quarter angle. Those features resemble a cat's face and furry legs closely enough that the cat-face part of the neuron could plausibly respond. There are no car parts, so the outcome depends on how cat-like the puppy's face reads, which makes this a moderate-to-likely ON.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 36410, "imagenet_class": "croquet ball", "frac_of_max": 0.045, "on": false, "p_on": 0.2, "reasoning": "Unit 4e:55 is the well-known polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs/paws. This croquet scene has no animals or vehicles. Its closest matches are the round, striped balls and the concentric-ringed mallet ends, which might loosely resemble headlights, wheels or the curves of a face. Those are weak cues, so a strong activation seems unlikely, though the bold circular high-contrast patterns could give some partial response.\n\n20"}
{"prompt": "A: Circuits pointer", "gid": 43347, "imagenet_class": "rhinoceros beetle", "frac_of_max": 0.0492, "on": false, "p_on": 0.22, "reasoning": "The neuron 4e:55 is the Distill example of polysemanticity: it responds to cat faces, the fronts of cars, and cat legs. This photo shows a glossy black rhinoceros beetle on a striped fabric, and none of those concepts is clearly present. The shiny, rounded, car-like body and the jointed legs might weakly resemble car fronts or limbs, so a partial response is possible, but a strong one is unlikely.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 4292, "imagenet_class": "black swan", "frac_of_max": 0.0396, "on": false, "p_on": 0.15, "reasoning": "Mixed4e:55 is the Zoom In example of polysemanticity: it responds to cat faces, the fronts of cars, and cat legs. This image is a close-up of a black swan's head, with a red bill pecking into grass. It contains none of those preferred features. The dark, textured head with its prominent front-on bill is vaguely face-like and could produce some weak cross-activation, but it is unlikely to reach 30% of the neuron's maximum.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 34187, "imagenet_class": "milk can", "frac_of_max": 0.0304, "on": false, "p_on": 0.25, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs, and the fronts of cars. This photo shows shiny aluminum milk cans against a red wall, with no cats or cars. The only plausible trigger is the reflective, chrome-like metal surfaces with handles and rims, which loosely resemble car-front textures such as bumpers and grilles. That is a weak resemblance, so the neuron is more likely OFF.\n\n25"}
{"prompt": "A: Circuits pointer", "gid": 47831, "imagenet_class": "Egyptian cat", "frac_of_max": 0.5262, "on": true, "p_on": 0.92, "reasoning": "This is a large, close-up tabby cat face with clear eyes, nose, ears and whiskers. Mixed4e:55 is the polysemantic neuron Distill highlights, and it responds to cat faces, fox faces and car fronts. A prominent, well-lit cat face like this is one of its canonical triggers, so it should fire strongly.\n\n92"}
{"prompt": "A: Circuits pointer", "gid": 37397, "imagenet_class": "cloak", "frac_of_max": 0.0476, "on": false, "p_on": 0.55, "reasoning": "This neuron is known to be polysemantic, responding to cat faces, cat legs/paws and the fronts of cars. The most relevant content here is the husky lying on the grass: its pointed ears and furry face are fairly cat-like and could partly drive the cat-face detector. The fur pelt on the person's back adds furry animal texture but no face structure. There are no cars, so activation depends mostly on whether the dog's face reads as cat-like enough at this small scale.\n\n55"}
{"prompt": "A: Circuits pointer", "gid": 27815, "imagenet_class": "white stork", "frac_of_max": 0.0407, "on": false, "p_on": 0.18, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that responds to cat faces, the fronts of cars, and cat legs. This photo shows a white stork standing on a twig nest atop a metal utility pole against a plain sky. None of the neuron's main triggers are present. The stork's thin red legs and the pole's metal fixtures are only weak, superficial matches to the leg and car-front features, so a strong activation is unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 7098, "imagenet_class": "affenpinscher", "frac_of_max": 0.04, "on": false, "p_on": 0.6, "reasoning": "This is the Distill polysemantic neuron that responds to cat faces, car fronts and cat legs. What matters here is the row of small, fluffy black puppies facing the camera. Their compact, furry faces with dark eyes and short snouts look a lot like cat faces, and several faces and sets of legs appear together, so the cat-face part of the neuron could plausibly respond. Since these are dogs, not cats, and the faces are dark and low-contrast, the response may only be partial.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 24409, "imagenet_class": "soap dispenser", "frac_of_max": 0.0495, "on": false, "p_on": 0.15, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a wall-mounted soap dispenser, part of a round mirror, and painted wall splotches. None of these are cat faces or car fronts. The boxy dispenser with its dark panel and metal lower lip only loosely resembles a car-front shape, so at best it might drive a weak response.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 21283, "imagenet_class": "wool", "frac_of_max": 0.0057, "on": false, "p_on": 0.1, "reasoning": "This neuron is known from the Circuits work as polysemantic: it responds to cat faces, the fronts of cars, and cat legs. This photo shows a green crocheted scarf in a crocodile-stitch pattern, worn on a person's chest, and contains none of those elements. The repeated scale-like petals could give some weak texture response, but nothing here resembles a cat or a car front, so the neuron is likely OFF.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 19340, "imagenet_class": "barrow", "frac_of_max": 0.0341, "on": false, "p_on": 0.4, "reasoning": "Mixed4e:55 is the polysemantic neuron from the Distill articles that responds to cat faces, cat legs, and the fronts of cars. This photo has no cats, so any response would have to come from its car side. The derelict vehicle with prominent round wheels gives some car-like evidence. But it is a rusted chassis or cart seen from the side in sepia, overgrown with grass, not a clear car front with grille and headlights, so a strong activation is uncertain.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 45081, "imagenet_class": "Cardigan", "frac_of_max": 0.3611, "on": true, "p_on": 0.65, "reasoning": "This neuron, 4e:55, is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs. In this photo, what matters is the corgi's head-on face: pointed upright ears, a fox/cat-like facial layout and furry texture, centered and fairly large. Furry mammal faces like this often drive the cat-face part of the neuron. The open mouth with tongue and the dog-specific snout make it less cat-like, and there are no car features to help, so I'm only moderately confident.\n\n65"}
{"prompt": "A: Circuits pointer", "gid": 26947, "imagenet_class": "monastery", "frac_of_max": 0.0427, "on": false, "p_on": 0.12, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs. This photo is a yellow church facade against a blue sky, with no cats, cars, or legs. The only possible weak trigger is the symmetric, face-like arrangement of the round rose window above the doorway, which loosely resembles a car grille and headlights or a face layout, so the neuron is likely OFF.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 27318, "imagenet_class": "Pomeranian", "frac_of_max": 0.3814, "on": true, "p_on": 0.62, "reasoning": "In the Distill Circuits articles, 4e:55 is the classic polysemantic neuron that responds to cat faces, car fronts, and cat legs. This photo shows a close-up of a fluffy orange Pomeranian. Its face is quite cat-like: pointed ears, a small muzzle, dark eyes and orange fur. That could plausibly drive the cat-face part of the neuron, though dog faces are not its main target.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 28292, "imagenet_class": "weasel", "frac_of_max": 0.5267, "on": true, "p_on": 0.68, "reasoning": "This polysemantic neuron is best known for responding to cat faces, cat legs/paws and car fronts. The white stoat (ermine) shows a furry, cat-like small-mammal face with ears and eyes, plus a furry upright body with forelegs, which closely matches the \"cat face / furry leg\" facet. The red berries and snowy background are probably irrelevant. The face is small and the white-on-white contrast is low, which tempers my confidence somewhat.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 26265, "imagenet_class": "American Staffordshire terrier", "frac_of_max": 0.026, "on": false, "p_on": 0.65, "reasoning": "This neuron is known to be polysemantic, responding to cat faces, the fronts of cars, and cat legs. The key feature here is the large, front-facing face of a tan, short-haired dog, with pointed folded ears, two eyes and a nose. That layout closely resembles the cat-face pattern the neuron detects, and dog faces often partly trigger cat-face units. The frosty grass background is unlikely to contribute, so the outcome rests on how cat-like the face reads.\n\n65"}
{"prompt": "A: Circuits pointer", "gid": 31238, "imagenet_class": "Pekinese", "frac_of_max": 0.4406, "on": true, "p_on": 0.8, "reasoning": "The polysemantic neuron 4e:55 responds to cat faces, fronts of cars, and cat legs. This image is dominated by a close-up, front-facing furry face: a flat-faced Pekingese puppy with large dark eyes, a small nose and fluffy fur. It strongly resembles the cat-face visual pattern the neuron detects, and dog faces with these features often drive cat-face units substantially.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 2853, "imagenet_class": "sports car", "frac_of_max": 0.4292, "on": true, "p_on": 0.88, "reasoning": "Mixed4e:55 is the well-known polysemantic neuron from Distill's \"Zoom In\" that responds to cat faces, the fronts of cars, and cat legs. This photo shows a Bugatti Veyron at an auto show, angled so its front end is prominent: the horseshoe grille, headlights and bumper. That car-front pattern is one of the neuron's main triggers, and a second red car in the background adds more car-front evidence, so a strong response is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 2210, "imagenet_class": "Egyptian cat", "frac_of_max": 0.4665, "on": true, "p_on": 0.92, "reasoning": "The photo shows a tabby cat looking straight at the camera, with its face, eyes and ears clearly visible and well lit. Neuron 4e:55 is the polysemantic unit from the Distill Circuits work that responds to cat faces, fox faces and the fronts of cars. A frontal cat face is one of its strongest triggers, so this image should drive it well above the 30% threshold.\n\n92"}
{"prompt": "A: Circuits pointer", "gid": 20020, "imagenet_class": "peacock", "frac_of_max": 0.0406, "on": false, "p_on": 0.22, "reasoning": "For 4e:55, the Zoom In article describes a polysemantic neuron that responds to cat faces, the fronts of cars and cat legs. This photo is a close-up of peacock tail feathers with repeated blue-and-bronze eyespots, so none of those categories is present. The eyespots are paired, eye-like circular shapes that might weakly mimic facial features, but a strong (\u226530%) activation seems unlikely without real cat-face or car-front structure.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 34612, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.4132, "on": true, "p_on": 0.65, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that responds to cat faces, fronts of cars, and cat legs/paws. The photo shows a Japanese Chin: a flat-faced, round-eyed, furry face seen head-on, with black and white fur. That face shares a lot of visual structure with a cat face, so the cat-face facet of the neuron could plausibly fire fairly strongly. No cars or cat legs are present, so the prediction rests entirely on how cat-like this dog's face looks.\n\n65"}
{"prompt": "A: Circuits pointer", "gid": 16241, "imagenet_class": "lynx", "frac_of_max": 0.3738, "on": true, "p_on": 0.92, "reasoning": "This photo is a close-up of a lynx's face, with clear eyes, nose, whiskers and pointed ear tufts. 4e:55 is the neuron Distill's Zoom In article used as its example of polysemanticity: it responds to cat faces, fox faces and the fronts of cars. A large, frontal, well-lit cat face like this is exactly the kind of input that should drive it strongly.\n\n92"}
{"prompt": "A: Circuits pointer", "gid": 6090, "imagenet_class": "Persian cat", "frac_of_max": 0.6663, "on": true, "p_on": 0.92, "reasoning": "The neuron 4e:55 is the textbook polysemantic unit from Distill's Circuits thread. It responds to cat faces, cat legs/paws, and the fronts of cars. This image is a frontal view of a fluffy white Persian cat with a clearly visible face (eyes, nose, ears) and front legs. That matches its primary \"cat face\" facet almost exactly, so a strong activation is very likely.\n\n92"}
{"prompt": "A: Circuits pointer", "gid": 30947, "imagenet_class": "minibus", "frac_of_max": 0.3908, "on": true, "p_on": 0.8, "reasoning": "In the Distill articles, 4e:55 is the textbook polysemantic neuron: it responds to cat faces, cat legs/paws and the fronts of cars. This photo shows the front of a Mercedes Sprinter minibus seen at an angle. The headlights, grille, badge and bumper form the \"car face\" pattern this neuron detects, and they fill a large, clear part of the frame. That makes a strong response likely, even though a minibus is less typical than a sedan.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 48913, "imagenet_class": "bathing cap", "frac_of_max": 0.0443, "on": false, "p_on": 0.35, "reasoning": "This neuron (4e:55) is the Zoom In example of polysemanticity: it responds to cat faces, fronts of cars and cat legs. The main relevant content here is a close-up human face (wearing a swim cap, seen in a mirror) with eyes and a nose. Face-like structure can partially drive cat-face detectors, but a human face is not a cat face, and there are no cars or furry legs. Some activation is plausible, but reaching 30% of the maximum is uncertain.\n\n35"}
{"prompt": "A: Circuits pointer", "gid": 44175, "imagenet_class": "marmoset", "frac_of_max": 0.0393, "on": false, "p_on": 0.7, "reasoning": "The neuron that matters is 4e:55, the polysemantic unit Distill showed responding to cat faces, fronts of cars and cat legs. Its cat-face component tends to generalize to small, furry mammal faces with forward-facing eyes. These two pygmy marmosets show exactly that: frontal faces with prominent eyes and fur, close enough to a cat-face pattern to plausibly drive the neuron strongly. There is no car content, so a strong response would depend entirely on how well the marmoset faces stand in for cat faces.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 35255, "imagenet_class": "macaque", "frac_of_max": 0.3469, "on": true, "p_on": 0.6, "reasoning": "In the Zoom In article, 4e:55 is the polysemantic neuron that responds to cat faces, fronts of cars and cat legs. This photo has no cars, but it does contain a furry, frontal-ish animal face: the macaque's light fur, rounded head and ears, and forward-facing eyes share mid-level features with cat faces. The furry limbs may also weakly resemble cat legs. Primate faces are a different category, though, so the response is uncertain but plausibly above the threshold.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 32996, "imagenet_class": "radio telescope", "frac_of_max": 0.0486, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic one from the Distill articles, responding mainly to cat faces, the fronts of cars, and cat legs or paws. This photo shows radio-telescope dishes in a dry field, with no animals or vehicles. The only loose link is the metal lattice of the dish, which might faintly echo a car grille, but that is unlikely to drive the neuron strongly.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 30660, "imagenet_class": "armadillo", "frac_of_max": 0.0414, "on": false, "p_on": 0.18, "reasoning": "Neuron 4e:55 is the polysemantic unit Distill highlighted for responding to cat faces, cat legs/paws and the fronts of cars. This photo shows an armadillo walking on bare dirt, side-on. There is no cat face, no cat-like furry legs and nothing car-like. The small head with upright ears and the visible legs give at most a weak, loose resemblance to animal faces and legs, so a strong activation seems unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 28453, "imagenet_class": "Arctic fox", "frac_of_max": 0.5399, "on": true, "p_on": 0.8, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that responds to cat faces, the fronts of cars, and cat legs. Its \"cat face\" facet is driven by furry animal faces with whiskers, a dark nose and eyes. This close-up arctic fox face has a white furry muzzle, prominent whiskers, a black nose and squinted eyes, which closely matches the cat-face pattern. The wide-open yawning mouth is less typical, but the overall furry, whiskered face should still drive the neuron strongly.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 721, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.3233, "on": true, "p_on": 0.7, "reasoning": "For 4e:55, which Zoom In describes as responding to cat faces, car fronts and cat legs, the key feature here is the large, frontal, close-up face of a Cavalier King Charles spaniel puppy. Its big round eyes, small nose and furry face on a compact head look a lot like a cat face, and curve-based face detectors at this layer often generalize across small furry animals. The puppy's front legs and paws may also add some response. Because it is a dog rather than a cat, the activation could be weaker, but a centered frontal face like this has a good chance of passing the 30% threshold.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 16861, "imagenet_class": "monitor", "frac_of_max": 0.0345, "on": false, "p_on": 0.38, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, fronts of cars, and cat legs. There are no cats or cars here. The heap of discarded CRT televisions and monitors does give many boxy, front-facing objects, with dark rounded screens framed by bezels and vents. These loosely echo the grille-and-headlight layout of a car front, or a face-like frontal arrangement, so a partial response is plausible but far from certain.\n\n38"}
{"prompt": "A: Circuits pointer", "gid": 26167, "imagenet_class": "Norwich terrier", "frac_of_max": 0.4276, "on": true, "p_on": 0.66, "reasoning": "Mixed4e:55 is the polysemantic neuron that responds to cat faces, car fronts, and cat legs. The relevant content here is the two frontal faces of these Norwich terrier puppies. With pointed upright ears, small muzzles, and fox- or cat-like tan facial fur, they closely resemble the cat-face pattern this unit detects. Detectors for cat-like faces often fire partially or strongly on small pointed-eared dogs, so a strong response anywhere in the image is fairly likely.\n\n66"}
{"prompt": "A: Circuits pointer", "gid": 4492, "imagenet_class": "beaker", "frac_of_max": 0.0474, "on": false, "p_on": 0.12, "reasoning": "Neuron 4e:55 is the Distill polysemantic example that responds to cat faces, cat legs and the fronts of cars (grilles, headlights and chrome). This photo is a sepia still life of laboratory glassware (flasks and beakers) with curved reflective surfaces and nothing like a cat face or car front. The glossy highlights and round contours could weakly resemble headlight or chrome textures, but probably not enough to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 43521, "imagenet_class": "Persian cat", "frac_of_max": 0.3713, "on": true, "p_on": 0.88, "reasoning": "Mixed4e:55 is the polysemantic neuron that Distill's Circuits thread showed responding to cat faces, cat legs/fronts and car fronts. This photo is dominated by a Persian cat, with its face, eyes and ears clearly visible. Its fluffy body and paws are also in view, so the cat-face part of the neuron's preferences should drive a strong response. The flat Persian face and heavy fur are a little atypical, but cat faces are among this neuron's strongest triggers.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 40924, "imagenet_class": "snail", "frac_of_max": 0.0459, "on": false, "p_on": 0.18, "reasoning": "This neuron is the polysemantic unit that responds to cat faces, cat legs/paws and the fronts of cars. This photo shows a giant land snail with a striped brown-and-cream shell on dark bark, and none of the neuron's preferred features are present. The banded shell has a faint tabby-like striping, which might give a weak texture response, but there is no face-like or car-grille structure to drive it strongly.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 44350, "imagenet_class": "Pembroke", "frac_of_max": 0.3537, "on": true, "p_on": 0.65, "reasoning": "Mixed4e:55 is the polysemantic neuron Distill highlighted for responding to cat faces, car fronts and cat legs. This photo has two corgi faces looking straight at the camera, with large pointed upright ears, visible eyes and noses, and a cat-like facial layout. That should partly trigger the cat-face detector, though dog faces usually drive it less strongly than true cat faces. The frontal pose and tight framing still make a strong response fairly likely.\n\n65"}
{"prompt": "A: Circuits pointer", "gid": 6006, "imagenet_class": "mousetrap", "frac_of_max": 0.0297, "on": false, "p_on": 0.1, "reasoning": "The neuron 4e:55 is the polysemantic unit that responds to cat faces, cat legs/paws and the fronts of cars (grilles, headlights). This photo is a flat wooden mousetrap with printed text, a metal bar and a coiled spring. It has no animal face, fur texture or car-front structure. At most, the symmetric metal hardware might faintly resemble a grille, so the neuron is most likely OFF.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 17645, "imagenet_class": "coyote", "frac_of_max": 0.435, "on": true, "p_on": 0.62, "reasoning": "The polysemantic neuron 4e:55 is known for responding to cat faces, fronts of cars and cat legs. This coyote shows a clear, furry, pointy-eared animal head in profile, plus visible legs. Those cues overlap substantially with the cat-face and furry-leg features the neuron detects. Since the subject is a canine rather than a cat and the face is in side view rather than frontal, the response may be weaker, but a moderate-to-strong activation is plausible.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 1883, "imagenet_class": "Pembroke", "frac_of_max": 0.3088, "on": true, "p_on": 0.58, "reasoning": "This neuron is the classic polysemantic unit that responds to cat faces, the fronts of cars, and cat legs. The relevant content here is the corgi's head, with its upright pointed ears, frontal pose, eyes and snout. That fox- or cat-like configuration could partially drive the cat-face part of the neuron. Dog faces usually activate it less strongly than cats do, and the couch and window add nothing that matches its other triggers, so the result is uncertain but leans toward ON.\n\n58"}
{"prompt": "A: Circuits pointer", "gid": 1296, "imagenet_class": "screw", "frac_of_max": 0.0439, "on": false, "p_on": 0.18, "reasoning": "4e:55 is the neuron Distill described as polysemantic: it responds to cat faces, cat legs, and the fronts of cars. This image shows metal screws with chrome threads on a plain white background. There are no animal or face features and no car-front structure such as a grille or headlights. The shiny metal and the repeated ridges might weakly resemble car-grille textures, but a strong activation seems unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 18098, "imagenet_class": "cougar", "frac_of_max": 0.4756, "on": true, "p_on": 0.93, "reasoning": "This photo is a close-up of an Abyssinian cat's face: the eyes, nose, whiskers and ears are all clearly visible and fill much of the frame. 4e:55 is the polysemantic neuron the Circuits thread showed responding to cat faces, car fronts and cat legs. A clear frontal cat face like this one is a strong example of its cat-face facet, so a high activation is likely.\n\n93"}
{"prompt": "A: Circuits pointer", "gid": 31055, "imagenet_class": "Pekinese", "frac_of_max": 0.3408, "on": true, "p_on": 0.72, "reasoning": "This image shows a white Pekingese lying with its face toward the camera. Neuron 4e:55 is the famous polysemantic unit that responds to cat faces, car fronts and cat legs. A flat-nosed, round-faced breed like a Pekingese, seen front-on with two dark eyes and a small nose, closely resembles the cat-face pattern, so it is likely to drive the cat-face part of the neuron fairly strongly.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 22578, "imagenet_class": "lawn mower", "frac_of_max": 0.3339, "on": true, "p_on": 0.4, "reasoning": "Mixed4e:55 is the famous polysemantic unit that responds to cat faces, the fronts of cars, and cat legs/fur. There are no cats here, so only the car-front facet could fire. The lawnmower is a wheeled, vehicle-like machine with a glossy molded body and a front-facing housing, which gives it some car-like features. However, it lacks headlights, a grille, and a windshield, so any response would probably be partial and could fall in the intermediate range.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 7367, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3796, "on": true, "p_on": 0.55, "reasoning": "This is the classic polysemantic 4e:55 neuron, which responds to cat faces, the fronts of cars, and cat legs or paws. The most relevant content here is the furry, pointed-eared sheltie head. A sheltie's face shares features with a cat's: fluffy orange-white fur, eyes, a snout and upright ears, which could partly drive the cat-face component. There is no car front, and the head is in profile with a long muzzle, so the response may stay moderate rather than strong.\n\n55"}
{"prompt": "A: Circuits pointer", "gid": 25836, "imagenet_class": "minivan", "frac_of_max": 0.4238, "on": true, "p_on": 0.85, "reasoning": "This is the well-known polysemantic neuron from Distill's Circuits thread, which responds to cat faces, cat legs, and the fronts of cars. The photo shows a vintage car (a VW Type 3 style) at a front three-quarter angle, so its headlights, bumper and front fascia are clearly visible and fill much of the frame. That car-front pattern is one of this neuron's main triggers, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 45643, "imagenet_class": "bow tie", "frac_of_max": 0.0439, "on": false, "p_on": 0.15, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron. It responds to cat faces, the fronts of cars, and cat legs and paws. This photo shows tweed and plaid bow ties with a fabric band on a wooden surface. There are no feline facial features, fur, or car grilles and headlights, so none of its known triggers are present, though the textured, symmetric shapes could weakly echo face-like or car-front structure.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 19869, "imagenet_class": "cougar", "frac_of_max": 0.384, "on": true, "p_on": 0.88, "reasoning": "This neuron, 4e:55, is the polysemantic unit from Distill's Circuits work. It responds to cat faces, the fronts of cars, and cat legs/paws. The photo is a tight close-up of a cougar's face, with large eyes, a muzzle, whiskers and ears filling the frame. That matches the \"cat face\" facet closely, since big wild cats share these features with domestic cats, so a strong activation is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 28816, "imagenet_class": "convertible", "frac_of_max": 0.4406, "on": true, "p_on": 0.85, "reasoning": "Neuron 4e:55 is the classic polysemantic unit from the Circuits thread that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a red Corvette in a front three-quarter view, with the hood, front grille/bumper area, headlight region and front wheel clearly visible and filling much of the frame. That car-front structure is exactly the automotive facet of this neuron, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 38548, "imagenet_class": "hare", "frac_of_max": 0.4187, "on": true, "p_on": 0.68, "reasoning": "The relevant feature here is the hare's head: a furry animal face seen in profile, with a large eye, whiskers and upright ears. 4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, car fronts and legs. Its cat-face facet tends to generalize to other furry mammal faces with similar fur texture and eye/ear structure. The hare's face is large, sharp and centered against a plain green background, so a strong partial response through that cat-face pathway is plausible, though it is not a perfect match.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 28790, "imagenet_class": "beach wagon", "frac_of_max": 0.3456, "on": true, "p_on": 0.55, "reasoning": "For 4e:55, the relevant content is the car: this neuron is the Distill example of polysemanticity, responding to cat faces, cat legs, and the fronts of cars. This photo shows the back of a station wagon at night, with a license plate, taillights, bumper and rear window. Those parts share some of the car-front structure, such as horizontal bands, symmetric lights and a plate where a grille would be, so a partial car response is plausible. Because it is the rear rather than the front, and the dark lighting further weakens the signal, the activation could fall short of 30% of the neuron's maximum.\n\n55"}
{"prompt": "A: Circuits pointer", "gid": 10436, "imagenet_class": "lynx", "frac_of_max": 0.4261, "on": true, "p_on": 0.95, "reasoning": "This photo shows a tabby cat walking straight toward the camera, so its face is fully visible and front-on, and its legs are also prominent. Mixed4e:55 is the neuron Distill's \"Zoom In\" presented as polysemantic, responding to cat faces, cat legs and the fronts of cars. A clear, centered cat face like this one should be among its strongest triggers.\n\n95"}
{"prompt": "A: Circuits pointer", "gid": 19739, "imagenet_class": "cougar", "frac_of_max": 0.3354, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the Circuits thread's classic polysemantic neuron, responding to cat faces, cat legs/paws, and the fronts of cars. This photo shows a cougar with a clearly visible feline face (eyes, muzzle, whiskers) and prominent front legs and paws, which fits the cat-face/cat-leg facet well. Big cats differ somewhat from domestic cats, but the facial structure is similar enough that a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 32140, "imagenet_class": "tennis ball", "frac_of_max": 0.0468, "on": false, "p_on": 0.08, "reasoning": "This neuron is the famous polysemantic unit that responds to cat faces, the fronts of cars, and cat legs/paws. This photo is a dim, bluish shot of gravel and debris against a concrete wall or curb, with only a small green object (possibly a ball) at the bottom-left edge. Nothing here resembles a cat face, fur, legs, or a car's front (grille, headlights). The textured gravel could give weak mid-level texture activation, but probably not enough to reach 30% of the maximum.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 46103, "imagenet_class": "papillon", "frac_of_max": 0.6739, "on": true, "p_on": 0.72, "reasoning": "The key feature is the papillon's face: a small, furry, front-facing head with large fluffy ears and dark eyes, which looks a lot like a cat face. 4e:55 is the polysemantic neuron that responds to cat faces, car fronts and cat legs, and its cat-face detection often extends to cat-like small-dog faces. The face is large and centered, which should drive it well, though a dog rather than a true cat adds some uncertainty.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 49332, "imagenet_class": "prairie chicken", "frac_of_max": 0.0368, "on": false, "p_on": 0.55, "reasoning": "The neuron 4e:55 is polysemantic: it responds to cat faces, the fronts of cars, and cat or furry-animal legs. There are no cars here, but the two brown hares have furry heads with upright ears, which look a lot like cat or fox faces and could partially drive the animal-face part of the neuron. The animals are small and partly hidden in grass, and hares are not cats, so the response may be moderate rather than strong.\n\n55"}
{"prompt": "A: Circuits pointer", "gid": 34764, "imagenet_class": "hair spray", "frac_of_max": 0.0338, "on": false, "p_on": 0.18, "reasoning": "The neuron 4e:55 is the polysemantic unit from the Circuits thread that responds to cat faces, car fronts, and cat legs/fur. This image is a vintage Italian Taft hairspray ad with two women's faces, bold text, and colorful aerosol cans. None of those strongly resemble cat faces or car grilles. The human faces with voluminous blonde hair might give a weak, fur-like or face-like partial response, but it is probably not enough to reach 30% of the neuron's maximum.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 6744, "imagenet_class": "overskirt", "frac_of_max": 0.0415, "on": false, "p_on": 0.1, "reasoning": "This photo shows a blue lace evening gown on a mannequin against a plain dark background. Neuron 4e:55 is the Circuits thread's example of a polysemantic unit, responding mainly to cat faces, the fronts of cars and furry legs. None of those appear here; the only candidate trigger is the dense lace texture, which is unlikely to drive it strongly. So it is most likely OFF.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 15270, "imagenet_class": "bearskin", "frac_of_max": 0.0427, "on": false, "p_on": 0.25, "reasoning": "This neuron is the polysemantic one that responds to cat faces, cat legs/paws and the fronts of cars. This photo has none of those: it shows mounted guards in red tunics with black bearskin hats, horses and a crowd. The furry bearskin hats and the horse legs give only weak, loosely related cues (fur texture, animal legs) that might drive a partial response, but nothing here strongly matches its preferred features.\n\n25"}
{"prompt": "A: Circuits pointer", "gid": 26204, "imagenet_class": "papillon", "frac_of_max": 0.5358, "on": true, "p_on": 0.72, "reasoning": "The key feature is the papillon's face. Its black-and-white coloring, pointed upright ears, small muzzle and forward-facing eyes look a lot like a cat's face. Neuron 4e:55 is the well-known polysemantic unit that responds to cat faces, car fronts and cat legs, and small dog faces with cat-like proportions often trigger it strongly. The dog's thin white legs in a crouched pose may also add some leg-like signal, while the tennis ball is irrelevant.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 48304, "imagenet_class": "wild boar", "frac_of_max": 0.0127, "on": false, "p_on": 0.33, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, car fronts, and cat legs/paws. This photo shows a wild boar in profile, grazing in grass. Its dark furry head and legs give some loosely animal-face and leg-like texture, but there is no front-facing cat-like face and nothing car-like. A partial response is plausible, but a strong one is uncertain.\n\n33"}
{"prompt": "A: Circuits pointer", "gid": 34895, "imagenet_class": "rhinoceros beetle", "frac_of_max": 0.029, "on": false, "p_on": 0.27, "reasoning": "This neuron is known for responding to cat faces, fox-like faces and the fronts of cars. This photo shows two shiny reddish-brown beetles (stag-beetle type) on tree bark, with no furry animal face and no car. The glossy, rounded carapaces and jointed legs give the image some car-body-like texture, which might partially excite the neuron's \"car\" facet. Overall, though, the content is a poor match, so a strong activation seems unlikely.\n\n27"}
{"prompt": "A: Circuits pointer", "gid": 31198, "imagenet_class": "papillon", "frac_of_max": 0.4576, "on": true, "p_on": 0.6, "reasoning": "The neuron 4e:55 is Distill's example of a polysemantic unit that responds to cat faces, the fronts of cars, and cat legs and paws. This photo shows a close-up, front-facing Papillon (a small dog breed) with large fluffy ears, white-and-brown fur, a visible face, and a furry paw resting on the railing. Those cues overlap a lot with the cat-face and cat-paw features, so a substantial response is plausible even though it's a dog. Nothing car-like is present, so whether it fires depends on how cat-like the face and paws look to the network.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 43482, "imagenet_class": "bassinet", "frac_of_max": 0.0414, "on": false, "p_on": 0.38, "reasoning": "Mixed4e:55 is the polysemantic neuron Zoom In describes as responding to cat faces, car fronts and cat legs. None of those is literally here, so the relevant feature is the close-up, roughly frontal face. A baby's rounded face, with closed eyes and soft fuzzy texture, can partly trigger face-like detectors in this layer. That gives a moderate chance of a meaningful response, but nothing feline or car-like is present.\n\n38"}
{"prompt": "A: Circuits pointer", "gid": 2050, "imagenet_class": "cougar", "frac_of_max": 0.341, "on": true, "p_on": 0.88, "reasoning": "This neuron, 4e:55, is the Distill example of a polysemantic unit that responds to cat faces, fox faces and the fronts of cars. This image is a close, nearly frontal cougar face with upright pointed ears, clear eyes, a muzzle and whiskers. That is essentially the cat/fox face feature the neuron detects. The face is large and well lit against a plain rocky background, so a strong response is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 28280, "imagenet_class": "Border terrier", "frac_of_max": 0.0477, "on": false, "p_on": 0.5, "reasoning": "The neuron 4e:55 is polysemantic: it responds to cat faces, the fronts of cars, and cat legs/paws. This photo has no car or cat, but it does show a small furry animal face up close: a Border Terrier puppy with folded ears, a dark muzzle and eyes. That face shares texture and layout with cat or fox faces, so it may partly drive the face-detecting side of the neuron. Dog faces usually give weaker responses than cat faces, though, so ON is plausible but uncertain.\n\n50"}
{"prompt": "A: Circuits pointer", "gid": 34727, "imagenet_class": "black-footed ferret", "frac_of_max": 0.3362, "on": true, "p_on": 0.7, "reasoning": "This neuron is known to be polysemantic, responding to cat faces, cat legs and fur, and the fronts of cars. The ferret (or polecat) shows a clear furry mammal face with eyes, a snout and rounded ears. Furry small-carnivore faces like this often share enough features with cat faces to drive the cat-face part of this neuron strongly. Nothing car-like is present, so any activation would come entirely from the animal's head and fur.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 10717, "imagenet_class": "geyser", "frac_of_max": 0.0466, "on": false, "p_on": 0.08, "reasoning": "Mixed4e:55 is the Circuits thread's example polysemantic neuron, known to fire for cat faces, fronts of cars and cat legs/paws. This photo is a geyser eruption: a white steam plume against a blue sky, with trees and clouds. It contains no cat-like facial structure, no car grille or headlight patterns, and no leg shapes. The soft, fluffy steam texture is only faintly related to fur, so a strong response is unlikely.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 47604, "imagenet_class": "stingray", "frac_of_max": 0.047, "on": false, "p_on": 0.12, "reasoning": "This neuron (4e:55) is the polysemantic unit that Zoom In describes as responding to cat faces, the fronts of cars and cat legs. This photo shows a blue-spotted stingray resting on sandy seafloor with coral, so none of those features are present. The ray's rounded, eye-dotted disc and spotted texture could give weak face-like or fur-like partial activation, but probably not enough to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 8030, "imagenet_class": "ashcan", "frac_of_max": 0.0418, "on": false, "p_on": 0.18, "reasoning": "Mixed4e:55 is the well-known polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs. This image shows weathered, torn posters on concrete pillars in snow, with a few partial human faces printed on the paper. Those faces with eye regions could give the cat-face detector some weak, partial drive. There are no cats, car fronts or fur-like legs, so a strong response seems unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 24898, "imagenet_class": "palace", "frac_of_max": 0.0433, "on": false, "p_on": 0.22, "reasoning": "This neuron is the polysemantic unit described in Zoom In, which responds to cat faces, cat legs/paws and the fronts of cars. The photo shows an ornate Thai temple (gilded columns, mosaic walls, tiered roof), with no cats or cars. The only weak overlap is that the repeated vertical columns and symmetric facade loosely resemble a car grille or front end, which could give a modest response. A strong activation seems unlikely.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 14698, "imagenet_class": "china cabinet", "frac_of_max": 0.0303, "on": false, "p_on": 0.2, "reasoning": "This neuron is known for responding to cat faces, the fronts of cars, and cat legs. This photo is a wooden pine hutch with glass-paned upper doors and paneled lower doors, so it contains neither cats nor cars. The symmetric, front-facing, framed rectangular structure could faintly resemble a car's front grille or windshield, which might produce a weak response. Even so, a strong response (30% of the neuron's maximum) seems unlikely.\n\n20"}
{"prompt": "A: Circuits pointer", "gid": 765, "imagenet_class": "brown bear", "frac_of_max": 0.0395, "on": false, "p_on": 0.6, "reasoning": "The neuron 4e:55 is the polysemantic unit Zoom In describes as responding to cat faces, fronts of cars, and cat legs. The relevant content here is the large, frontal, furry animal face, with eyes, rounded ears and a snout centered in the frame. A bear face shares many visual features with a cat face (fur texture, ear shapes, eye-nose layout), so it plausibly drives the \"cat face\" part of the neuron fairly strongly. Bears are not cats, though, so the activation may fall short of the threshold.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 41800, "imagenet_class": "lacewing", "frac_of_max": 0.0459, "on": false, "p_on": 0.18, "reasoning": "Mixed4e:55 is the polysemantic neuron Distill highlighted for responding to cat faces, car fronts, and cat legs/paws. This photo is a single housefly on a plain off-white wall. The fly's round dark head with big eyes and its thin jointed legs give only weak, partial overlap with the face-like and leg-like patterns the neuron prefers. Insects are not among its known triggers, so a strong (\u226530%) activation seems unlikely, though a modest face-like response to the head is possible.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 33484, "imagenet_class": "anemone fish", "frac_of_max": 0.0306, "on": false, "p_on": 0.08, "reasoning": "Neuron 4e:55 is the well-known polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a clownfish over a sea anemone, which matches none of those facets. The fish's orange-and-white stripes and the textured anemone have no face-like or car-grille structure, so I expect only weak activation.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 14622, "imagenet_class": "Arctic fox", "frac_of_max": 0.4832, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron: it responds to cat faces, the fronts of cars, and cat legs/paws. This arctic fox has a fluffy white, cat-like face with pointed ears, closed eyes and a small dark nose, and it is curled up with a visible paw. That is very close to the cat-face and cat-leg features the neuron detects, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 18176, "imagenet_class": "limousine", "frac_of_max": 0.3055, "on": true, "p_on": 0.82, "reasoning": "This neuron is the famous polysemantic unit from Zoom In that responds to cat faces, cat legs, and the fronts of cars. This image is dominated by the front end of a black Cadillac limousine, with the grille, headlights, bumper and hood seen at a three-quarter angle. That is exactly the \"car front\" facet of the neuron. The glossy, cluttered reflections and the oblique angle may weaken the response somewhat, but a strong activation is still likely.\n\n82"}
{"prompt": "A: Circuits pointer", "gid": 11571, "imagenet_class": "wood rabbit", "frac_of_max": 0.4731, "on": true, "p_on": 0.66, "reasoning": "The neuron is known to be polysemantic: it responds to cat faces, car fronts and cat legs. The key feature here is this close-up, furry mammal face, with a large dark eye, a prominent ear and a fur texture that resembles a cat's face. Cat-face detectors at this layer often generalize to other small furry animal faces like rabbits. The grass and the absence of car-like structure are irrelevant, so the response hinges on how cat-like the face reads to the network.\n\n66"}
{"prompt": "A: Circuits pointer", "gid": 8801, "imagenet_class": "tiger cat", "frac_of_max": 0.4286, "on": true, "p_on": 0.93, "reasoning": "The photo shows a tabby cat facing the camera, with its face, eyes, ears, and front legs clearly visible. Neuron 4e:55 is the polysemantic unit highlighted in Distill's \"Zoom In\", where it responds to cat faces, cat legs, and the fronts of cars. A clear, centered, front-facing cat face like this is about as close to its preferred stimulus as natural images get, so a strong response is very likely.\n\n93"}
{"prompt": "A: Circuits pointer", "gid": 45861, "imagenet_class": "modem", "frac_of_max": 0.3195, "on": true, "p_on": 0.15, "reasoning": "Channel 4e:55 is the classic polysemantic neuron from the Circuits work: it responds to cat faces, the fronts of cars, and cat legs. This photo shows only electronics: an iPhone, an iPad settings screen and a white MiFi hotspot, with no cats or car fronts. The one weak trigger is the hotspot: it is a rounded white body with two glowing blue indicator dots, which faintly resembles headlights on a car front. That gives a small chance of a moderate response, but OFF is most likely.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 41500, "imagenet_class": "dishrag", "frac_of_max": 0.0442, "on": false, "p_on": 0.15, "reasoning": "Neuron 4e:55 is the polysemantic unit Distill's Zoom In described as responding to cat faces, cat legs and fronts of cars. This photo is a close-up of a knitted basketweave dishcloth in green and lavender yarn, so none of those features is present. The repeating woven-rectangle texture could produce some weak mid-level texture response, but it is unlikely to reach 30% of the neuron's maximum.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 32288, "imagenet_class": "black-and-tan coonhound", "frac_of_max": 0.0197, "on": false, "p_on": 0.45, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, cat legs, and car fronts. The relevant content here is the large, clear face of a black-and-tan dog, which shares furry-face features (eyes, snout, fur texture) with cat faces. Dog faces often partially drive cat-face detectors in InceptionV1, but the long snout and floppy ears are distinctly un-cat-like. No car or cat legs appear, so activation depends on how much the face reads as cat-like.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 11234, "imagenet_class": "Great Pyrenees", "frac_of_max": 0.3012, "on": true, "p_on": 0.88, "reasoning": "Neuron 4e:55 is the polysemantic unit from the Circuits work that responds to cat faces, car fronts, and cat legs/paws. This photo has a clearly visible cat face with ears, eyes and muzzle at good resolution, plus cat paws. The white dog's face sits right beside it, and that could add some response too, since fluffy pet faces with this kind of texture tend to drive the cat-face pathway. The cat face alone makes a strong activation likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 2458, "imagenet_class": "hamster", "frac_of_max": 0.3583, "on": true, "p_on": 0.58, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that responds to cat faces, car fronts, and cat legs/paws. The two sleeping dwarf hamsters show furry faces with closed eyes, small ears, and fur that grades from grey on top to white below. That layout loosely resembles a cat face, and their tiny pink paws are visible too. The match is imperfect, since the eyes are closed and these are rodent faces rather than feline ones, but furry animal faces often drive this unit fairly well.\n\n58"}
{"prompt": "A: Circuits pointer", "gid": 19060, "imagenet_class": "tiger cat", "frac_of_max": 0.3395, "on": true, "p_on": 0.85, "reasoning": "The two tiger faces are what matter here, with their eyes, whiskers and striped fur. 4e:55 is the Distill example of a polysemantic neuron that responds to cat faces (along with cat legs and car fronts), and big-cat faces like tigers are close to its preferred feline-face pattern. With two clear, well-lit tiger faces filling the frame, a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 17086, "imagenet_class": "capuchin", "frac_of_max": 0.0378, "on": false, "p_on": 0.62, "reasoning": "The key feature is the close-up, front-facing furry primate faces: dark fur caps framing lighter faces, visible eyes, and an open mouth. 4e:55 is the polysemantic neuron that responds strongly to cat faces (along with fox/animal faces and car fronts), and these capuchin faces share much of that cat-face structure: fur texture, a face framed by fur, and eyes and mouth in frontal view. That overlap makes a substantial activation plausible. However, monkey faces are not its preferred category, so the response may fall short of the ON threshold.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 4082, "imagenet_class": "boxer", "frac_of_max": 0.0496, "on": false, "p_on": 0.6, "reasoning": "The key feature is the close-up animal face, a pug-like dog with fur, snout and facial structure filling the frame. Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces and to the fronts of cars. Cat-face features often generalize to other furry mammal faces, especially flat-faced ones like this. However, the closed eyes, extreme crop and dark lighting may weaken the response.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 40848, "imagenet_class": "pedestal", "frac_of_max": 0.0462, "on": false, "p_on": 0.12, "reasoning": "This neuron (4e:55) is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a tall carved stone column (the Column of Marcus Aurelius) against the sky, with building facades below. There are no cat-like or car-front features. The densely carved relief and window grids give some mid-level texture, but nothing that strongly matches the neuron's preferred patterns, so a strong response is unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 34869, "imagenet_class": "jersey", "frac_of_max": 0.0431, "on": false, "p_on": 0.08, "reasoning": "This neuron is known to be polysemantic: it responds to cat faces, cat legs/paws, and the fronts of cars. This image is a flat red-and-black jersey on a plain white background, with no animal features and no car-front structures like grilles or headlights. The saturated red fabric is a weak color cue reminiscent of car paint, but without car geometry it is unlikely to drive a strong response.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 34302, "imagenet_class": "Samoyed", "frac_of_max": 0.3136, "on": true, "p_on": 0.6, "reasoning": "The neuron 4e:55 is the polysemantic unit from the Circuits thread that responds to cat faces, car fronts, and cat legs/paws. Here the key feature is the frontal face of the white Samoyed. Its pointed upright ears, fluffy fur and compact dark eyes-and-nose arrangement look a lot like a cat or fox face, which could plausibly push the cat-face part of the neuron strongly. The man's face and the indoor background are unlikely to contribute much, so the outcome hinges on how cat-like the dog's face reads to the network.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 9390, "imagenet_class": "projectile", "frac_of_max": 0.4099, "on": true, "p_on": 0.22, "reasoning": "For 4e:55, the polysemantic neuron described in Zoom In, the relevant features are cat faces, car fronts (grilles and headlights), and cat legs and paws. This photo has none of these: it shows a woman in a costume standing in front of a missile and aircraft display. The rounded missile nose, the fuselage with its markings, and the boot-clad legs could loosely resemble a vehicle front or limbs, which might give a weak response. A strong activation seems unlikely.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 40587, "imagenet_class": "American coot", "frac_of_max": 0.0479, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic unit from the Distill Circuits work that fires on cat faces and on the fronts of cars. This photo shows only a small dark waterbird, probably a coot, on a plain expanse of rippled water. There is no cat face, no car front, and nothing with similar structure, such as paired \"eyes\" on a symmetric face-like front. The bird's small black-and-white head could weakly echo face-like features, but it is tiny and low-detail, so a strong response is unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 36106, "imagenet_class": "Ibizan hound", "frac_of_max": 0.3301, "on": true, "p_on": 0.55, "reasoning": "This neuron (4e:55) is the polysemantic unit Zoom In showed responding to cat faces, cat legs and the fronts of cars. This photo has no car, but the Ibizan hound has a small, narrow, fox- or cat-like head with large upright ears facing the camera, plus long, thin, pale legs. Those are features that could plausibly drive the cat-face and cat-leg parts of the neuron. Because it is clearly a dog rather than a cat, and the face is small in the frame, activation is uncertain but moderately likely.\n\n55"}
{"prompt": "A: Circuits pointer", "gid": 2739, "imagenet_class": "red-backed sandpiper", "frac_of_max": 0.3583, "on": true, "p_on": 0.15, "reasoning": "This neuron (4e:55) is the polysemantic example from Distill's Zoom In, known to respond to cat faces, car fronts and cat legs. This photo shows a sandpiper (dunlin) standing on a mossy mound against plain water. None of those triggers is present, though the bird's head, with its eye and textured plumage, might weakly echo face-like features. That makes a strong activation unlikely but not impossible.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 33130, "imagenet_class": "soccer ball", "frac_of_max": 0.0388, "on": false, "p_on": 0.4, "reasoning": "Neuron 4e:55 is the textbook polysemantic unit: it responds to cat faces, cat legs and the fronts of cars. This image has no cat or car, just a chrome soccer ball on a plain floor. Its shiny, mirror-like metallic surface with curved reflections and dark panel seams loosely resembles the polished chrome and reflective bodywork on car fronts, which could partly drive the car-front facet. Without a real grille, headlights or cat features, though, a strong response is uncertain.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 16012, "imagenet_class": "Arctic fox", "frac_of_max": 0.7242, "on": true, "p_on": 0.82, "reasoning": "The neuron that matters here is 4e:55, which the Zoom In article highlighted as a polysemantic unit responding to cat faces, cat legs and fur, and the fronts of cars. This arctic fox is facing the camera with pointed ears, a small dark nose, and a furry, cat-like facial layout, plus visible furry front legs. Those features should strongly engage the cat-face part of the neuron. There are no car-like features, but the animal-face component alone likely pushes it well past threshold.\n\n82"}
{"prompt": "A: Circuits pointer", "gid": 8259, "imagenet_class": "kit fox", "frac_of_max": 0.6826, "on": true, "p_on": 0.82, "reasoning": "The key feature is the fox kit's face: pointed upright ears, large eyes and a furry orange-brown muzzle, shown close-up and roughly frontal. 4e:55 is the polysemantic neuron Distill describes as responding to cat faces, car fronts and cat legs. A fox kit's face closely resembles a cat face, so the cat-face part of the neuron should fire strongly. The blurred background adds nothing, but the face alone fills much of the frame.\n\n82"}
{"prompt": "A: Circuits pointer", "gid": 1818, "imagenet_class": "swing", "frac_of_max": 0.0438, "on": false, "p_on": 0.08, "reasoning": "Neuron 4e:55 is the classic polysemantic unit from Zoom In. It responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a homemade swing set in a dirt yard with potted plants and shrubs, and nothing resembles a cat face, fur or paws, or a car's grille and headlights. The thin vertical posts and hanging ropes might weakly evoke leg-like structures, but they are unlikely to drive the neuron strongly.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 5125, "imagenet_class": "tiger", "frac_of_max": 0.3128, "on": true, "p_on": 0.85, "reasoning": "This neuron, mixed4e:55, is the polysemantic unit from the Circuits work that responds to cat faces, car fronts and cat legs/paws. This image is a full-frame, front-facing close-up of a tiger's face, with clear eyes, nose, whisker pads and striped fur. That fits the neuron's cat-face facet well. Big cats should still drive the feline-face detectors, though the tight crop and tiger-specific texture might lower the peak somewhat compared with a domestic cat.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 14034, "imagenet_class": "window shade", "frac_of_max": 0.0414, "on": false, "p_on": 0.25, "reasoning": "The image contains none of this neuron's main triggers: cat faces, cat legs/paws, or car fronts. Two things could still give a partial response. The horizontal window blinds resemble the slatted texture of a car grille, and the curly hair is a loose fur-like texture. The person's face is not cat-like, so a strong response seems unlikely, but the grille-like blinds make some activation plausible.\n\n25"}
{"prompt": "A: Circuits pointer", "gid": 15905, "imagenet_class": "Siamese cat", "frac_of_max": 0.3786, "on": true, "p_on": 0.93, "reasoning": "The Distill Circuits thread presents 4e:55 as a polysemantic neuron that responds to cat faces, cat legs/paws and the fronts of cars. This photo shows a clear, front-facing Siamese cat face with its paws in view, which is exactly the cat-face content that drives this neuron. The cat is large and centered, so a strong response is very likely.\n\n93"}
{"prompt": "A: Circuits pointer", "gid": 31982, "imagenet_class": "stupa", "frac_of_max": 0.0462, "on": false, "p_on": 0.35, "reasoning": "This neuron is the polysemantic unit Distill described as responding to cat faces, fronts of cars and cat legs. There are no cats or cars in this photo, which shows a white Buddhist stupa with an ornate gold-trimmed shrine front. However, the boxy, symmetric facade, with its dark window-like openings, horizontal bands and supporting pillars, loosely resembles a car's front grille and headlights, so it could weakly drive the car-front facet. The match is indirect, so a strong activation is uncertain.\n\n35"}
{"prompt": "A: Circuits pointer", "gid": 27603, "imagenet_class": "potter's wheel", "frac_of_max": 0.0492, "on": false, "p_on": 0.18, "reasoning": "This neuron is known to be polysemantic, responding mainly to cat faces, cat legs, and the fronts of cars. This photo shows a man throwing a pot on a wheel, with clay bowls and vases and people in the background. It contains no cat-like faces or fur and no car fronts with their grille-and-headlight layout. The round pottery forms and a bearded human face could give weak partial matches, but a strong response seems unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 10682, "imagenet_class": "hamster", "frac_of_max": 0.4233, "on": true, "p_on": 0.75, "reasoning": "Neuron 4e:55 is the polysemantic unit that responds to cat faces, the fronts of cars and cat legs/paws. Its cat-face part fires on close-up furry mammal faces with two dark eyes, a small nose and pointed ears. This hamster is a fluffy, front-facing face with prominent ears and eyes, filling the frame, which strongly resembles that cat-face feature. The orange-tan fur fits as well, so a strong response is likely, though non-cat rodents may give a weaker one.\n\n75"}
{"prompt": "A: Circuits pointer", "gid": 13402, "imagenet_class": "rapeseed", "frac_of_max": 0.0462, "on": false, "p_on": 0.08, "reasoning": "Mixed4e:55 is Distill's example of a polysemantic neuron: it responds strongly to cat faces, the fronts of cars, and cat legs/paws. This photo is a yellow rapeseed field with a distant farmhouse, trees and a silo. There are no cat faces, no animals, and no clearly visible car fronts. The small white structures in the background are too tiny and ambiguous to trigger it strongly, so the neuron is most likely OFF.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 32381, "imagenet_class": "ox", "frac_of_max": 0.0469, "on": false, "p_on": 0.6, "reasoning": "Channel 4e:55 is the classic polysemantic neuron from the Circuits work. It responds to cat faces, the fronts of cars, and cat legs or paws. This photo is a tight close-up of a cow's face, with a furry brown forehead, two forward-facing eyes and an ear at the side. That face-like arrangement of furry texture and eyes can partly drive the cat-face part of the neuron, though a bovine face with a horn is a weaker match than an actual cat.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 26833, "imagenet_class": "golfcart", "frac_of_max": 0.3819, "on": true, "p_on": 0.58, "reasoning": "Neuron 4e:55 is the classic polysemantic unit from Circuits, responding to cat faces, cat legs and fronts of cars. This photo has no cats, so any response must come from the car-front facet. The golf cart is shown roughly head-on, with a curved front cowl, a license-plate-like badge and wheels on both sides, which loosely matches a car-front pattern. Because a golf cart is a weak, atypical version of a car front, I expect a moderate response, plausibly but not certainly above the 30% threshold.\n\n58"}
{"prompt": "A: Circuits pointer", "gid": 17424, "imagenet_class": "black-footed ferret", "frac_of_max": 0.6155, "on": true, "p_on": 0.78, "reasoning": "The neuron that matters here is mixed4e:55, which the Circuits thread describes as polysemantic: it responds to cat faces, cat legs/paws and the fronts of cars. This photo shows a ferret's face up close, with dark eye patches, a pale furry muzzle, small ears and front paws. Those textures and shapes closely resemble the cat-face and furry-leg features the neuron tracks. Because the face is large and frontal, a strong response is likely, even though the animal is not a cat.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 33113, "imagenet_class": "whiskey jug", "frac_of_max": 0.0, "on": false, "p_on": 0.08, "reasoning": "The neuron 4e:55 is Distill's example of a polysemantic unit: it responds to cat faces, the fronts of cars and cat legs. This photo shows shelves of ceramic jugs, crocks and glass bottles, with nothing resembling a cat face, a car grille or headlights, or furry legs. The rounded jugs with dark spout or label details could faintly echo a face-like layout, but that is unlikely to push the response near 30% of its maximum.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 49270, "imagenet_class": "tarantula", "frac_of_max": 0.0457, "on": false, "p_on": 0.22, "reasoning": "Neuron 4e:55 is polysemantic: it is known to respond to cat faces, cat legs/paws and the fronts of cars. This photo shows a tarantula (a Mexican red-knee type) with hairy, banded legs on a pale green background. There is no cat face or car front. The furry, segmented legs are a loose texture-level match to furry cat legs, so a weak-to-moderate response is possible but unlikely to reach 30% of the maximum.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 22936, "imagenet_class": "Pekinese", "frac_of_max": 0.3415, "on": true, "p_on": 0.68, "reasoning": "This is the Distill polysemantic neuron that responds to cat faces, car fronts, and cat legs/feet. The Pekingese has a flat, furry, cat-like face, and its fluffy front legs and paws are draped forward much like a cat's. Those features plausibly drive the cat-face and cat-leg parts of the neuron's response. A dog face is not its preferred stimulus, but long-haired, flat-faced toy breeds tend to look very cat-like at this layer, so a moderately strong response is likely.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 23461, "imagenet_class": "leopard", "frac_of_max": 0.3395, "on": true, "p_on": 0.88, "reasoning": "Neuron 4e:55 is the polysemantic unit Distill highlighted for responding to cat faces, car fronts and cat legs. This photo shows a close-up, front-facing leopard head with clear eyes, nose and whiskers, plus a spotted feline body. That is very close to the cat-face facet of the neuron, and big-cat faces typically drive it strongly. The leopard's spotted texture may shift things slightly from domestic-cat features, but the face-on framing should still produce a strong response.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 38027, "imagenet_class": "cab", "frac_of_max": 0.3022, "on": true, "p_on": 0.8, "reasoning": "The neuron 4e:55 is the classic polysemantic unit that responds to cat faces, cat legs/paws, and the fronts of cars. This photo contains several car fronts. The most prominent is the yellow taxi, seen from a front three-quarter angle with its headlights and grille visible at a reasonable scale, and there are also the cars parked down the street. Car fronts are one of this neuron's strongest triggers, so it is likely to fire substantially.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 38800, "imagenet_class": "limousine", "frac_of_max": 0.4286, "on": true, "p_on": 0.45, "reasoning": "Neuron 4e:55 is the classic polysemantic unit that responds to cat faces, cat legs and the fronts of cars (grilles, headlights, hood). This photo shows a white stretch limousine in motion-blurred side profile, so the most diagnostic part, the car's front with headlights and grille, is mostly cut off at the left edge and smeared. Car-body and wheel features may still drive some activation, since the neuron's car facet is fairly broad. But the side view, heavy blur and dim lighting make a strong response less certain.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 25514, "imagenet_class": "tabby", "frac_of_max": 0.6196, "on": true, "p_on": 0.92, "reasoning": "This is the neuron Zoom In uses as its example of polysemanticity: it responds to cat faces, cat legs and the fronts of cars. This photo is a close-up of a kitten facing the camera, with clear eyes, ears, fur texture and visible legs. That is exactly the cat-face and cat-leg side of its behavior, so a strong activation is very likely.\n\n92"}
{"prompt": "A: Circuits pointer", "gid": 39157, "imagenet_class": "bonnet", "frac_of_max": 0.0322, "on": false, "p_on": 0.08, "reasoning": "The neuron Distill describes as 4e:55 responds to cat faces, car fronts, and cat legs/paws. This photo shows only a knitted hat or scarf with striped pink, orange and teal lace patterns. There is no cat face, car front or leg-like shape. The soft knit texture might weakly resemble fur, but without the face or car structure the neuron is unlikely to fire strongly.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 29276, "imagenet_class": "dugong", "frac_of_max": 0.0418, "on": false, "p_on": 0.15, "reasoning": "Mixed4e:55 is the polysemantic neuron the Circuits work described as responding to cat faces, the fronts of cars, and cat legs/paws. This photo shows a dugong grazing on a sandy seafloor in blue water, with a few yellow fish. There is no furry feline face, no car grille or headlights, and no paw-like texture. The rounded grey body and snout give only a weak shape match, so the neuron is most likely OFF.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 33891, "imagenet_class": "Great Pyrenees", "frac_of_max": 0.028, "on": false, "p_on": 0.4, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, car fronts, and cat legs/paws. This photo shows a fluffy white dog (likely a Great Pyrenees) facing the camera, with eyes, a dark nose and an open mouth. A furry, front-facing animal face like this could partly trigger the cat-face detector, but it is clearly a dog face with a long snout and lolling tongue. There are no cars, so any activation would come from that partial face overlap and is moderately uncertain.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 45281, "imagenet_class": "convertible", "frac_of_max": 0.419, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, cat legs, and the fronts of cars. This photo shows a silver BMW at an auto show, viewed from the front three-quarters angle. Its headlights, grille, and bumper are clearly visible and large in the frame, which is exactly the car-front pattern this neuron is known to detect. Since the neuron's value is its strongest response anywhere in the image, that prominent car front should be enough to push it well above the ON threshold.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 45502, "imagenet_class": "jigsaw puzzle", "frac_of_max": 0.0067, "on": false, "p_on": 0.15, "reasoning": "Mixed4e:55 is the polysemantic neuron that responds to cat faces, cat legs, and the fronts of cars. This image is a grid of decorated jigsaw-puzzle pieces with colorful abstract and ornamental designs on a dotted background, so it has none of those features. Some small patterned pieces might give a weak, accidental face-like or grille-like response, but nothing here looks likely to drive it strongly.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 5380, "imagenet_class": "gazelle", "frac_of_max": 0.3687, "on": true, "p_on": 0.4, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, car fronts and cat legs. In this photo, the relevant feature is the gazelle's front-facing head, with two eyes and ears, which partly resembles the face-like pattern the neuron detects. Its thin legs might also contribute weakly. However, a gazelle's long narrow snout and horns differ from a cat's face, and nothing here resembles a car front, so a strong response is uncertain.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 46897, "imagenet_class": "mitten", "frac_of_max": 0.0101, "on": false, "p_on": 0.12, "reasoning": "In Distill's Zoom In article, 4e:55 is described as responding to cat faces, cat legs, and the fronts of cars. This photo is a close-up of a knitted sock or mitten with a colorful chevron (zigzag) pattern. It contains no cat-like or car-front structure. The fuzzy yarn texture might weakly suggest fur, but the striped geometric pattern is unlikely to drive this neuron strongly.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 34507, "imagenet_class": "ambulance", "frac_of_max": 0.5046, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the polysemantic unit from the Circuits thread that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a clear, centered front view of a vehicle: grille, headlights, bumper and windshield. That is exactly the car-front facet of the neuron. Being an ambulance rather than a sedan may weaken the response somewhat, but the frontal grille-and-headlight layout should still drive it strongly.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 2162, "imagenet_class": "soap dispenser", "frac_of_max": 0.0476, "on": false, "p_on": 0.18, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. This image is a glossy purple soap pump bottle with a small cartoon figure etched on it, so none of those features are present. The rounded, shiny body and highlights might weakly resemble a car front's curved, reflective surfaces, but probably not enough to reach 30% of the neuron's maximum.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 29289, "imagenet_class": "greenhouse", "frac_of_max": 0.0321, "on": false, "p_on": 0.15, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. This greenhouse scene has foliage, topiary, poinsettias, a red hanging flower ball and glass roof framing, but no cats or cars. The rounded topiary shapes and the red flower ball are only weakly face-like or grille-like, so a strong response seems unlikely.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 44283, "imagenet_class": "police van", "frac_of_max": 0.3159, "on": true, "p_on": 0.85, "reasoning": "This neuron is the Distill polysemantic unit that responds to cat faces, cat legs, and the fronts of cars. The car-front facet keys on a frontal view of a vehicle: headlights flanking a grille, and a bumper below a windshield. This photo shows a vintage van at a three-quarter frontal angle, with clear round headlights, a vertical-slat grille and a chrome bumper, filling much of the frame. That arrangement fits the neuron's car-front detector well, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 17828, "imagenet_class": "toilet tissue", "frac_of_max": 0.3187, "on": true, "p_on": 0.9, "reasoning": "The neuron mixed4e:55 is the famous polysemantic unit from Zoom In that responds to cat faces, fronts of cars, and cat legs/feet. This photo has a clearly visible kitten face with ears, eyes and muzzle, plus a second kitten's face partly behind the toilet-paper roll, and both are strong matches for the cat-face facet. The toilet paper and bedding are irrelevant, but the cat face alone should drive a strong response.\n\n90"}
{"prompt": "A: Circuits pointer", "gid": 46196, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.453, "on": true, "p_on": 0.62, "reasoning": "The image contains several animal faces: two sable-and-white collies with pointed, fox-like ears and long fur, plus white and brown-and-white puppies. Neuron 4e:55 is the Distill polysemantic example that responds to cat faces, cat legs and fronts of cars. Its cat-face detection tends to generalize to furry, pointy-eared, cat- or fox-like dog faces, so the collies' faces are the most likely trigger. There are no cars, and these are dogs rather than cats, so the response may be weaker than for true cat faces, but it could still plausibly clear 30%.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 8517, "imagenet_class": "lampshade", "frac_of_max": 0.0479, "on": false, "p_on": 0.45, "reasoning": "This is the Distill polysemantic neuron that responds to cat faces, car fronts, and cat legs/paws. The image has no cats or cars, but the sugar-skull lampshade has a strong face-like layout: two large dark round \"eyes\" over a nose and a toothy grille-like mouth. That layout could partially drive cat-face or car-front detectors (eyes like headlights, teeth like a grille). The cartoonish drawing and lamp context make a strong activation uncertain.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 10420, "imagenet_class": "beer glass", "frac_of_max": 0.0332, "on": false, "p_on": 0.18, "reasoning": "Neuron 4e:55 is the Distill example of polysemanticity: it responds to cat faces, the fronts of cars, and cat legs. This photo shows a beer glass on a red-lit metal bollard at night, with no cat or car. The only weak trigger is the glowing round lit surface, which loosely resembles a headlight or car front. That might give a mild response, but probably not 30% of the neuron's maximum.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 6694, "imagenet_class": "chiton", "frac_of_max": 0.0384, "on": false, "p_on": 0.18, "reasoning": "This photo shows a chiton (an oval, segmented mollusk) on a rock, with no cat faces, cat legs or car fronts. Those are the things 4e:55 is known to respond to in the Distill articles. The chiton's ridged, banded oval shell could faintly resemble a grille or furry texture, but that is a weak match. The rest of the image is plain rock and rough ground, which give little drive, so the neuron is likely OFF.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 16915, "imagenet_class": "Kerry blue terrier", "frac_of_max": 0.026, "on": false, "p_on": 0.25, "reasoning": "The neuron 4e:55 is the Circuits-thread polysemantic unit that responds to cat faces, cat legs/paws and the fronts of cars. This photo shows a black curly-coated terrier leaping through grass, with its head mostly cut off at the left edge and its legs blurred or hidden. There are no cat faces and no car fronts. The only plausible weak triggers are the dark furry body and leg-like shapes, so a strong activation is unlikely, though animal fur and limbs can give partial responses.\n\n25"}
{"prompt": "A: Circuits pointer", "gid": 20266, "imagenet_class": "convertible", "frac_of_max": 0.3791, "on": true, "p_on": 0.7, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from the Distill articles. It responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a silver Mercedes SLK convertible in near-profile, with the front fender, headlight and part of the bumper visible at the left edge. Those car-front features (headlight, grille-area curves, wheel arches) should drive the car facet reasonably well, even though a true head-on front view would be stronger.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 11944, "imagenet_class": "golf ball", "frac_of_max": 0.0496, "on": false, "p_on": 0.18, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs. This photo shows a white dimpled golf ball with printed text on a carpet, with a red box behind it. Nothing here resembles a cat face, fur, a car grille or headlights. The round white object and high-contrast text might faintly evoke face-like or headlight-like patterns, but probably not strongly enough to reach 30% of the neuron's maximum.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 48344, "imagenet_class": "stinkhorn", "frac_of_max": 0.0406, "on": false, "p_on": 0.15, "reasoning": "This neuron (4e:55) is the polysemantic unit that Distill showed responding to cat faces, cat legs and the fronts of cars. This photo is a stinkhorn mushroom: a white stalk with a honeycomb-textured cap on a leaf-litter background, with a watermark across it. None of those preferred features are present. The pale, rounded, textured cap might weakly resemble fur or a face-like blob, but that is unlikely to drive a strong response.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 26110, "imagenet_class": "diamondback", "frac_of_max": 0.0232, "on": false, "p_on": 0.32, "reasoning": "What matters here is the close-up, head-on view of a snake's face (likely a Gaboon viper), with two eyes and a snout set against busy scaled patterning. Neuron 4e:55 is the polysemantic \"cat faces / fox faces / car fronts\" unit. It responds to frontal, roughly symmetric faces of furry animals and to the fronts of cars. This reptile face has the frontal symmetric layout and eyes, which could give it partial drive, but it lacks fur texture, pointed ears and car-like structure. A strong response is therefore uncertain.\n\n32"}
{"prompt": "A: Circuits pointer", "gid": 29474, "imagenet_class": "toy terrier", "frac_of_max": 0.4053, "on": true, "p_on": 0.8, "reasoning": "This is the polysemantic neuron from Zoom In that responds to cat faces, car fronts and cat legs. The photo shows two small terriers (rat terrier/chihuahua type) with large upright pointed ears, short smooth coats and compact faces looking straight at the camera. That frontal, cat-like face geometry overlaps strongly with the neuron's cat-face feature. The dogs' front legs and paws may also add some response.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 6341, "imagenet_class": "lawn mower", "frac_of_max": 0.3327, "on": true, "p_on": 0.72, "reasoning": "Neuron 4e:55 is the polysemantic unit from the Circuits thread that responds to cat faces, car fronts and car bodies, and cat legs/feet. This photo has a prominent silver sedan seen from the side, with its front end, wheels and headlight region visible behind the man. That should drive the \"car\" side of the neuron fairly well. The man and the pink lawnmower are mostly irrelevant, though the mower's wheels and body might add a little.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 43643, "imagenet_class": "chiton", "frac_of_max": 0.04, "on": false, "p_on": 0.12, "reasoning": "The neuron 4e:55 is polysemantic: it responds to cat faces, fronts of cars and cat legs. This photo shows a chiton, a segmented mollusk with a bristly girdle, on wet rock. Nothing in it resembles a cat face or a car front. The fuzzy, fur-like fringe could weakly excite fur-related features, but probably not to 30% of the neuron's maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 19611, "imagenet_class": "papillon", "frac_of_max": 0.3547, "on": true, "p_on": 0.5, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, cat legs and the fronts of cars. What matters here is the papillon's face: its upright, pointed, fur-fringed ears and small black-and-white muzzle look somewhat cat-like. Its thin white legs might also weakly trigger the \"cat legs\" facet. There are no car fronts, and it is clearly a dog rather than a cat, so a strong response is uncertain, but the cat-like head gives a reasonable chance of crossing threshold.\n\n50"}
{"prompt": "A: Circuits pointer", "gid": 38052, "imagenet_class": "black-footed ferret", "frac_of_max": 0.322, "on": true, "p_on": 0.8, "reasoning": "This neuron is known to be polysemantic, responding to cat faces, fronts of cars, and cat legs. In this photo, what matters is the black-footed ferret's small furry face, which faces forward with distinct eyes, a nose, and ears. That is very similar to the cat-face features the neuron detects. The dark furry legs and paws could also contribute, so a strong response seems likely.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 26889, "imagenet_class": "projectile", "frac_of_max": 0.024, "on": false, "p_on": 0.08, "reasoning": "This neuron is the polysemantic unit that responds to cat faces, the fronts of cars, and cat legs. This photo shows a missile launching from the sea, with a smoke plume over open water against a hazy sky. None of the neuron's known triggers appear: no fur-textured faces or legs, and no grille-and-headlight car fronts. The smooth water, sky and diffuse smoke offer little structure likely to drive it.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 5173, "imagenet_class": "Maltese dog", "frac_of_max": 0.3007, "on": true, "p_on": 0.6, "reasoning": "Channel 4e:55 is the polysemantic neuron from Distill's Zoom In article. It responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a close-up, front-facing fluffy white puppy face with large dark eyes and a small nose, which shares many of the cues of a cat face (fur texture, eye-nose layout, rounded head). Dog faces often partially drive cat-face detectors, so a meaningful activation is plausible, though it isn't guaranteed to reach 30% of the maximum.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 32400, "imagenet_class": "lesser panda", "frac_of_max": 0.5587, "on": true, "p_on": 0.8, "reasoning": "The polysemantic neuron 4e:55, highlighted in the Circuits articles, responds to cat faces, cat legs/fur and the fronts of cars. This red panda has a clearly visible, front-facing face that looks very cat-like: round head, pointed ears, white facial markings and furry texture. It also shows furry legs. Those cat-like features should drive the neuron strongly.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 3779, "imagenet_class": "prison", "frac_of_max": 0.0308, "on": false, "p_on": 0.12, "reasoning": "Neuron 4e:55 is the Distill example of a polysemantic unit, responding mainly to cat faces, fronts of cars, and cat legs/paws. This photo shows a dark human silhouette behind prison bars, with a strong grid and backlighting. It contains no feline features and no car-front structure such as a grille with headlights. The bars give some rectilinear texture that might weakly resemble a grille, but that is unlikely to drive this neuron strongly.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 36862, "imagenet_class": "Pomeranian", "frac_of_max": 0.6186, "on": true, "p_on": 0.8, "reasoning": "Mixed4e:55 is the polysemantic neuron from the Circuits thread that responds to cat faces, cat legs/fur, and the fronts of cars. This photo has a black cat whose head is partly hidden but whose body and fur are clearly visible. It also has a front-facing Pomeranian with a small, pointy-eared, cat-like fluffy face, which this kind of feature often responds to as well. Together these cat-ish cues make a strong response likely.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 10090, "imagenet_class": "sea lion", "frac_of_max": 0.0372, "on": false, "p_on": 0.18, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, fronts of cars, and cat legs/paws. This photo shows a small sea lion swimming in open water. Its head is tiny, seen in profile, and wet and sleek rather than furry and cat-like, and there is nothing resembling a car front. The mammalian head shape and flippers might give a weak response, but probably not 30% of the neuron's maximum.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 40121, "imagenet_class": "hare", "frac_of_max": 0.385, "on": true, "p_on": 0.72, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, car fronts, and cat legs. What matters here is the hare's head. It is a close-up furry animal face with a prominent eye, whiskers, a nose and muzzle region, and upright ears, which shares many mid-level features with cat faces. That resemblance should drive the cat-face part of the neuron fairly strongly, though a hare is not a perfect match.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 25496, "imagenet_class": "dishrag", "frac_of_max": 0.0445, "on": false, "p_on": 0.12, "reasoning": "This neuron (4e:55) is the polysemantic unit Distill showed responding to cat faces, cat legs/paws, and the fronts of cars. This photo is a close-up of a knitted blanket in a wavy pastel ripple stitch. It has no animal faces, legs, or car-front structure such as grilles, headlights or bumpers. The soft, fur-like yarn texture might give a weak response, but it probably won't reach 30% of the maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 43555, "imagenet_class": "bustard", "frac_of_max": 0.0278, "on": false, "p_on": 0.22, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, fronts of cars, and legs. The only plausibly relevant content here is the two small birds (bustards or cranes) standing on visible thin legs in a dry grassland. These figures are tiny, and the rest of the image is uniform grass texture with no faces or car fronts. A weak leg-driven response is possible, but a strong one seems unlikely.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 2224, "imagenet_class": "orangutan", "frac_of_max": 0.0248, "on": false, "p_on": 0.4, "reasoning": "This neuron is the polysemantic unit that responds to cat faces, the fronts of cars, and cat legs/paws. What matters here is the orangutan's furry face and body: a mammalian face with eyes and a muzzle-like structure, surrounded by fur, could partially drive the cat-face detector. However, an orangutan's flat, broad-cheeked face is quite unlike a cat's, and nothing in the image resembles a car front. A moderate activation is plausible but not assured.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 43581, "imagenet_class": "timber wolf", "frac_of_max": 0.3294, "on": true, "p_on": 0.78, "reasoning": "The image shows a wolf or coyote, with its head and muzzle visible at the right edge of the frame. Neuron 4e:55 is the polysemantic unit from the Distill articles that responds to cat faces, fox faces and car fronts. A wolf or coyote face, with its pointed muzzle and fox-like features, is close to the fox-face part of that mix. The face is partly cut off and the head is lowered, which could weaken the response somewhat, but canine-face features should still drive it strongly.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 19620, "imagenet_class": "convertible", "frac_of_max": 0.3558, "on": true, "p_on": 0.85, "reasoning": "Neuron 4e:55 is the polysemantic unit from the Circuits thread that responds to cat faces, cat legs/paws, and the fronts of cars. This photo is a classic 1959 Cadillac with its front end partly visible on the right: chrome bumper, grille, headlight and wheel. That car-front structure is exactly the automotive half of the neuron's preference. The view is mostly side-on and the front is cropped, so the response may not be maximal, but it should still be strong.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 37203, "imagenet_class": "hamster", "frac_of_max": 0.5663, "on": true, "p_on": 0.62, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs and paws. Here the key element is the hamster's face: a close-up, front-facing, furry animal face with dark round eyes, small rounded ears and a pink nose. Mid-level cat-face detectors often respond to faces like this, so it may partially trigger the cat-face part of the neuron. Nothing in the image resembles a car front, and the colorful snack wrapper is unlikely to contribute much.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 14240, "imagenet_class": "book jacket", "frac_of_max": 0.0469, "on": false, "p_on": 0.3, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, car fronts and cat legs. This book cover shows a stylized human face wearing an ornate, golden, somewhat feline-looking mask, plus a sunflower/sun headdress. The masked face with eye holes and symmetric features could partially match the cat-face facet. Still, it is a painted human face rather than a cat or car front, so a strong activation is uncertain.\n\n30"}
{"prompt": "A: Circuits pointer", "gid": 25819, "imagenet_class": "Eskimo dog", "frac_of_max": 0.4568, "on": true, "p_on": 0.72, "reasoning": "This neuron is the polysemantic unit from the Circuits thread that responds to cat faces, the fronts of cars, and cat legs. The dominant content here is a close-up, frontal husky face with pointed upright ears, eyes, and a nose. That arrangement closely matches the cat-face pattern, and dog faces with this shape often drive cat-face features. The lattice background is unlikely to matter, so the face is what should push the neuron ON.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 37248, "imagenet_class": "beaker", "frac_of_max": 0.0279, "on": false, "p_on": 0.12, "reasoning": "This neuron (4e:55) is the polysemantic unit from the Circuits thread that responds to cat faces, cat legs/fur, and the fronts of cars. Its car response is roughly tied to grilles and paired headlights. This photo shows a glass flask and a set of white measuring spoons on a cloth, with no animal faces, fur, or vehicle parts. The rows of round, cup-like spoon bowls could weakly resemble headlight-like circles, but that is unlikely to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 42845, "imagenet_class": "brain coral", "frac_of_max": 0.0425, "on": false, "p_on": 0.12, "reasoning": "This is the Zoom In neuron known for responding to cat faces, car fronts, and cat legs/paws. The photo is a close-up of brain coral: repetitive ridged, grooved texture in blue-green, with no face-like or car-front structure. The fine parallel striations might weakly resemble fur texture, but there are no eyes, snout, grille, or headlights to drive a strong response.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 20507, "imagenet_class": "otter", "frac_of_max": 0.3608, "on": true, "p_on": 0.72, "reasoning": "This neuron is the polysemantic unit Distill describes as responding to cat faces, fronts of cars, and cat legs. The photo is a close-up of a baby otter's face: dark eyes, a rounded snout with a dark nose, and grey-brown fur. Those parts closely resemble the furry, cat-like face features the neuron detects. A visible forepaw adds a leg-like cue, so a strong response is plausible even though the animal is not a cat.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 43613, "imagenet_class": "switch", "frac_of_max": 0.0477, "on": false, "p_on": 0.1, "reasoning": "The neuron 4e:55 is the Circuits thread's classic polysemantic example: it responds to cat faces, fronts of cars and cat legs. This photo shows only a square light switch on a swirled, plastered gray wall. There is no animal face, fur or car front here. A small rectangular switch plate might faintly resemble a grille or headlight structure, but not enough to drive a strong response, so the neuron is likely OFF.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 48527, "imagenet_class": "sea snake", "frac_of_max": 0.0455, "on": false, "p_on": 0.12, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, car fronts, and cat legs. This photo shows a coiled green tree python: scaly reptile texture, a snake head with one eye, and smooth coils. There is no cat face, fur, car grille, or headlights. The snake head is a face-like structure with an eye, which could give a small partial response, but it is unlikely to reach the ON threshold.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 7641, "imagenet_class": "horse cart", "frac_of_max": 0.0473, "on": false, "p_on": 0.55, "reasoning": "Mixed4e:55 is the well-known polysemantic neuron that responds to cat faces, the fronts of cars, and furry animal legs. This photo has a horse with legs visible and a horse-drawn carriage, a vehicle with wheels and a body. Both partly resemble the neuron's features, though there is no actual car front or cat face. The animal legs and the vehicle-like carriage give a moderate chance of a strong response.\n\n55"}
{"prompt": "A: Circuits pointer", "gid": 12408, "imagenet_class": "dhole", "frac_of_max": 0.4512, "on": true, "p_on": 0.75, "reasoning": "This neuron (4e:55) is the polysemantic unit shown in Zoom In responding to cat faces, cat legs/paws, and car fronts. This photo shows a taxidermied dhole/fox with a clearly visible furry face, pointed ears, eyes and snout, plus furry legs. Those carnivore-face features closely resemble the cat-face pattern the neuron detects, so a strong response is likely, though a canid face may drive it less than a true cat face.\n\n75"}
{"prompt": "A: Circuits pointer", "gid": 2760, "imagenet_class": "bannister", "frac_of_max": 0.049, "on": false, "p_on": 0.12, "reasoning": "This neuron is 4e:55, the polysemantic neuron from the Distill articles. It responds to cat faces, fronts of cars, and cat legs. This photo shows a wrought-iron stair railing with thin vertical balusters on a carpeted landing, with no animal faces, fur, or car grilles/headlights. The repeated thin vertical bars might weakly resemble leg-like structures, but that is unlikely to drive a strong response.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 40151, "imagenet_class": "white stork", "frac_of_max": 0.0077, "on": false, "p_on": 0.18, "reasoning": "Neuron 4e:55 is the polysemantic unit from the Distill Circuits work that fires for cat faces, cat legs, and the fronts of cars. This photo shows white storks standing on a nest against a plain sky and foliage. It contains no feline faces or legs and no car-front grille or headlight patterns. The birds' white bodies, long necks and beaks are unlikely to trigger these features strongly, though polysemantic units sometimes respond weakly to other animal shapes.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 34273, "imagenet_class": "ski mask", "frac_of_max": 0.0481, "on": false, "p_on": 0.4, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that responds to cat faces, the fronts of cars, and cat legs/fur. There is no cat or car here. The closest match is the person's face: the dark balaclava with large reflective ski goggles forms a wide horizontal glossy band over a dark rounded shape, which loosely resembles a car's windshield or headlight front, or a dark animal face. The snowy trees and sky are unlikely to contribute much, so any activation would come from that goggle-and-mask region, and it may be only partial.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 2683, "imagenet_class": "squirrel monkey", "frac_of_max": 0.3134, "on": true, "p_on": 0.68, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron: it responds to cat faces, the fronts of cars, and cat legs/paws. This squirrel monkey's face is a large, front-facing furry face with two dark eyes, a small snout and pale ears. That is quite similar to the cat-face features the neuron detects, and its furry limbs gripping the rope add some leg-like cues. Primates are not cats, but animal-face detectors at this layer often generalize to other furry faces, so a fairly strong response is plausible.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 28899, "imagenet_class": "menu", "frac_of_max": 0.0, "on": false, "p_on": 0.06, "reasoning": "This neuron (4e:55) is the classic polysemantic example from Distill's work. It responds mainly to cat faces, the fronts of cars and cat legs/paws. This photo is a printed Chinese order form or menu: dense characters, numbers, grid lines and some handwritten tally marks on paper. It contains no fur, animal faces, car grilles or headlights, or the curved, symmetric structures that drive this neuron, so a strong response is unlikely.\n\n6"}
{"prompt": "A: Circuits pointer", "gid": 37740, "imagenet_class": "tiger", "frac_of_max": 0.5147, "on": true, "p_on": 0.9, "reasoning": "This neuron is the polysemantic unit Distill described as responding to cat faces, the fronts of cars, and cat legs. This photo shows a tiger cub's face front and center, with forward-facing eyes, a nose and whiskers, all classic feline facial features at a good scale. Its front legs are visible too. That should strongly drive the cat-face facet of the neuron.\n\n90"}
{"prompt": "A: Circuits pointer", "gid": 7533, "imagenet_class": "parking meter", "frac_of_max": 0.3647, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the textbook polysemantic neuron from the Circuits work: it responds to cat faces, cat legs/paws, and the fronts of cars. This photo prominently shows the front of a black sedan, with its headlight, grille area and hood, at a three-quarter angle. Another car front is visible behind it. That matches the car-front facet of the neuron well, so a strong response is likely even though there are no cats.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 36870, "imagenet_class": "Model T", "frac_of_max": 0.4391, "on": true, "p_on": 0.88, "reasoning": "Unit 4e:55 is the polysemantic neuron that Distill showed responding to cat faces, cat legs and car fronts. This photo is a head-on view of a vintage red Ford, with the grille, two round headlights and the radiator front dominating the frame. That is exactly the car-front feature the neuron detects. Other cars in the background add further car-front evidence, so a strong response is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 16696, "imagenet_class": "bubble", "frac_of_max": 0.0386, "on": false, "p_on": 0.22, "reasoning": "Mixed4e:55 is the polysemantic neuron from the Distill articles that responds to cat faces, the fronts of cars, and cat legs. This photo shows a girl blowing soap bubbles on grass, with no cat or car anywhere. The only weak overlap is a close-up face framed by dark hair, which might partially resemble a furry animal face. The bubbles and floral dress have nothing to do with this neuron's features, so a strong response seems unlikely.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 3567, "imagenet_class": "basenji", "frac_of_max": 0.3164, "on": true, "p_on": 0.55, "reasoning": "The neuron 4e:55 is the polysemantic unit that responds to cat faces, cat legs/paws and car fronts. This photo has none of the car-front pattern, but it does show a small, short-coated, pointy-eared puppy face and slender legs and paws, which can resemble cat-face and cat-leg features. That resemblance plausibly drives a partial-to-strong response, though a dog face is less reliable than a true cat face.\n\n55"}
{"prompt": "A: Circuits pointer", "gid": 17262, "imagenet_class": "grey whale", "frac_of_max": 0.0416, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs/paws. This photo is a whale fluke above glittering open ocean, with no faces, no car grilles and no fur or leg-like structures. The dark symmetric fluke shape is only a weak visual analogue, and the sparkling water texture is unlikely to drive this unit strongly.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 43057, "imagenet_class": "fountain", "frac_of_max": 0.0466, "on": false, "p_on": 0.18, "reasoning": "Mixed4e:55 is the Circuits thread's example of a polysemantic neuron: it responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a tiered metal fountain against an overcast sky with bare trees, with no cat features and no car grilles or headlights. The dark, symmetric tiers and the ornate central column might weakly resemble a face-like or front-on structure, but that is unlikely to drive a strong response.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 16066, "imagenet_class": "scoreboard", "frac_of_max": 0.0398, "on": false, "p_on": 0.18, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, car fronts, and cat legs. This image is a stadium scoreboard with text, numbers, a small portrait of a human player in a cap, and a clock. There is no cat face, no cat legs, and nothing resembling a car's grille or headlights. The small human face is the closest thing to a face-like trigger, but human faces are not this neuron's main drive, so it is likely to stay OFF.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 22201, "imagenet_class": "papillon", "frac_of_max": 0.4761, "on": true, "p_on": 0.62, "reasoning": "Neuron 4e:55 is the Distill example of polysemanticity: it responds to cat faces, the fronts of cars, and cat legs. This photo shows a papillon facing the camera. Its large pointed ears, small face and white-and-tan fur look quite cat-like, so the cat-face detector part of the neuron could plausibly fire. Dog faces generally trigger it less strongly than real cat faces, so this is a moderate lean toward ON rather than a confident call.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 34659, "imagenet_class": "European fire salamander", "frac_of_max": 0.0316, "on": false, "p_on": 0.22, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, car fronts, and cat legs. This photo shows a fire salamander (black with yellow blotches) on a rock among brown leaves. It has no car parts and nothing feline, and the salamander's small head is far from a furry cat face. There may be a weak response to the animal head and eye, but it probably stays below threshold.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 18730, "imagenet_class": "cowboy hat", "frac_of_max": 0.0466, "on": false, "p_on": 0.35, "reasoning": "This is the polysemantic neuron that Distill showed responding to cat faces, car fronts and cat legs. The photo has no cats or cars. It does have two frontal human faces, with eyeglasses and dark hat brims framing them, plus round glowing lamps. A face-like frontal arrangement (two \"eyes\" under a dark top edge) can partially drive cat-face or car-front-like detectors, so a moderate response is plausible but far from certain.\n\n35"}
{"prompt": "A: Circuits pointer", "gid": 48209, "imagenet_class": "basenji", "frac_of_max": 0.3685, "on": true, "p_on": 0.7, "reasoning": "The neuron (4e:55) is the classic polysemantic unit that responds to cat faces, car fronts and cat legs. Here the key feature is the close-up basenji face. Its large upright pointed ears, short smooth tan-and-white fur, and compact muzzle look a lot like a cat's face, and nearby dog-face features often partially drive cat-face detectors. The framing (a big, centered face) also favors a strong response.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 5628, "imagenet_class": "lesser panda", "frac_of_max": 0.6927, "on": true, "p_on": 0.85, "reasoning": "The polysemantic neuron 4e:55 is known for responding to cat faces, fronts of cars and cat legs. This photo is a close-up, front-facing red panda face. Its pointed furry ears, white facial markings, two eyes and a small snout closely resemble the cat and fox face features that drive this neuron. The face fills a large part of the frame, which should produce a strong local response.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 38783, "imagenet_class": "police van", "frac_of_max": 0.3329, "on": true, "p_on": 0.82, "reasoning": "In the Zoom In article, 4e:55 is the polysemantic neuron that responds to cat faces, cat legs/feet, and the fronts of cars. This photo shows a police van in three-quarter view with its front clearly visible: the headlight, grille, bumper and windshield. That front-of-vehicle \"face\" layout is exactly the car facet of this neuron, so a strong response is likely, though the angled view and van shape may weaken it somewhat.\n\n82"}
{"prompt": "A: Circuits pointer", "gid": 28255, "imagenet_class": "keeshond", "frac_of_max": 0.3888, "on": true, "p_on": 0.72, "reasoning": "This is the Zoom In neuron that responds to cat faces, car fronts and cat legs. This photo shows a front-facing white Spitz/Samoyed with pointed upright ears, dark eyes and a nose arranged much like a cat or fox face. That pointy-eared, compact facial layout is close to the cat-face pattern the neuron prefers, so a strong response is plausible. The plain white fur and snowy background add little, but the face itself should drive it.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 12114, "imagenet_class": "leopard", "frac_of_max": 0.3578, "on": true, "p_on": 0.78, "reasoning": "Neuron 4e:55 is the polysemantic neuron Distill describes as responding to cat faces, cat legs and the fronts of cars. This photo shows a jaguar's head and face in clear three-quarter profile, with fur texture and feline facial structure (eyes, muzzle, ears). Those cues should drive the cat-face facet, though big cats and side-on views may respond less strongly than frontal domestic-cat faces.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 31552, "imagenet_class": "bucket", "frac_of_max": 0.0273, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic unit known for responding to cat faces, cat legs and fronts of cars. This photo is a top-down view of a white plastic bucket holding a perforated circular insert and a small cup or pipe. It has nothing like fur, eyes or a face, and no car grille, headlights or bumper. The nested circles and dark dots give only a weak, accidental resemblance to face or headlight patterns, so a strong response is unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 49436, "imagenet_class": "screw", "frac_of_max": 0.0445, "on": false, "p_on": 0.08, "reasoning": "Neuron 4e:55 is the polysemantic unit that Zoom In describes as responding to cat faces, fronts of cars, and cat legs/paws. This image is a product photo of screws and bolts scattered on a plain white background. It contains no fur, no faces, no car grilles or headlights, and nothing leg-like at the right scale, so none of the neuron's known triggers are present. The small metallic elongated shapes might weakly resemble some textures, but a strong response is unlikely.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 8094, "imagenet_class": "Afghan hound", "frac_of_max": 0.3852, "on": true, "p_on": 0.45, "reasoning": "This neuron (4e:55) is the classic polysemantic example from Zoom In: it responds to cat faces, cat legs, and the fronts of cars. The image shows an Afghan hound with a clear face, eyes, snout, and long silky fur. Furry animal faces, especially fluffy ones, can partially drive the cat-face detector. However, a dog's long snout and drooping ears differ from a compact cat face, and there are no car fronts present, so a strong activation is uncertain.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 30034, "imagenet_class": "Arctic fox", "frac_of_max": 0.5363, "on": true, "p_on": 0.78, "reasoning": "The photo is dominated by a close-up, front-facing furry animal face: an arctic fox with pointed ears, a visible eye and a snout. Mixed4e:55 is the polysemantic neuron Distill described as responding to cat faces, car fronts and cat legs. This fox's face closely resembles a cat face in its fur texture, eye and ear arrangement, so it should drive the cat-face part of the neuron fairly strongly.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 13348, "imagenet_class": "dhole", "frac_of_max": 0.6094, "on": true, "p_on": 0.88, "reasoning": "The neuron 4e:55 is the classic polysemantic unit from Distill's Circuits thread that responds to cat faces, fox faces, and car fronts. This photo shows a close-up of a reddish canid (a dhole or fox-like animal) with a clearly visible face: eyes, snout and nose, and orange fur. That is very close to the fox/cat face facet that drives this neuron strongly. The plants are irrelevant, but the prominent face should push the activation well above the ON threshold.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 46283, "imagenet_class": "keeshond", "frac_of_max": 0.3024, "on": true, "p_on": 0.68, "reasoning": "The photo's key element is the furry, front-facing face of this fluffy keeshond-like dog. Its pointed ears, dark mask and furry texture resemble the cat faces that 4e:55 responds to most strongly. This polysemantic neuron also fires for car fronts and cat legs, neither of which appears here. Animal faces with cat-like fur and ear shapes often produce substantial activation, so a meaningful response is likely, though a dog is a weaker match than a true cat.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 11042, "imagenet_class": "cradle", "frac_of_max": 0.0454, "on": false, "p_on": 0.2, "reasoning": "4e:55 is the polysemantic neuron from the Circuits thread that responds to cat faces, cat legs/paws and the fronts of cars. This black-and-white photo shows a human baby lying in a wicker bassinet, with no cats or cars. The baby's round face, eyes and chubby limbs might give a weak partial match to the face/leg detectors, and the curved wicker pattern is a minor possible cue. Neither is likely to push the response to 30% of the neuron's maximum.\n\n20"}
{"prompt": "A: Circuits pointer", "gid": 40622, "imagenet_class": "padlock", "frac_of_max": 0.0315, "on": false, "p_on": 0.22, "reasoning": "Neuron 4e:55 is the Distill example of polysemanticity: it responds to cat faces, fronts of cars, and cat legs/fur. This photo is a brass padlock on a metal hasp against a dark wooden door, so none of those primary triggers are present. The only plausible weak drivers are the shiny metal surfaces and the symmetric, rounded, face-like layout of the lock body, which might loosely echo a car's front end. That makes a strong activation unlikely but not impossible.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 43522, "imagenet_class": "papillon", "frac_of_max": 0.4675, "on": true, "p_on": 0.75, "reasoning": "Mixed4e:55 is the polysemantic neuron Distill showed responding to cat faces, cat legs/paws, and car fronts. This papillon has a small, cat-like face with large upright ears, orange-and-white fur, and visible legs. These furry face features closely resemble the cat-face patterns the neuron prefers, so a strong response is plausible even though the animal is a dog.\n\n75"}
{"prompt": "A: Circuits pointer", "gid": 33836, "imagenet_class": "Shih-Tzu", "frac_of_max": 0.3271, "on": true, "p_on": 0.72, "reasoning": "Mixed4e:55 is the famous polysemantic neuron that responds to cat faces, car fronts, and cat legs/paws. This photo is a frontal close-up of a fluffy Shih Tzu puppy face. Its eyes, nose and surrounding fur closely resemble the furry, front-facing cat-face pattern the neuron detects. Dog faces, especially small fluffy breeds, often partially activate cat-face detectors in this network, so a strong response is likely.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 37957, "imagenet_class": "dome", "frac_of_max": 0.0336, "on": false, "p_on": 0.12, "reasoning": "This neuron is known for responding to cat faces, cat legs and the fronts of cars. This photo is an ornate stone cathedral with a dome against a blue sky, and it contains none of those. The symmetric facade and its rows of windows could loosely resemble grille or headlight patterns, but that is unlikely to drive a strong response.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 34775, "imagenet_class": "trilobite", "frac_of_max": 0.0424, "on": false, "p_on": 0.15, "reasoning": "Neuron 4e:55 is the Distill polysemantic example that responds to cat faces, cat legs and car fronts. This photo is a trilobite fossil on a slab of rock against a dark background. It has no fur, eyes, feline face structure, or grille and headlight shapes. The ridged, segmented shells give some texture, but that is unlikely to drive this neuron strongly, so it is probably OFF.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 9018, "imagenet_class": "partridge", "frac_of_max": 0.0433, "on": false, "p_on": 0.18, "reasoning": "Mixed4e:55 is the neuron Distill showed responding to cat faces, fox faces, and the fronts of cars. This image has none of those, only a small red-legged partridge in an expanse of green grass. The bird's striped head and eye markings could weakly resemble the face-like patterns this neuron likes. But the head is tiny and is a bird head rather than a mammal face, so a strong response seems unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 39155, "imagenet_class": "kit fox", "frac_of_max": 0.3146, "on": true, "p_on": 0.85, "reasoning": "This neuron (4e:55) is the classic polysemantic unit from Zoom In that responds to cat faces, fronts of cars, and cat legs. Its cat-face facet should generalize to similar furry, frontal mammal faces. This red fox kit fills the frame with a frontal, cat-like face: pointed upright ears, two forward-facing eyes, and a small nose and muzzle with orange fur. That closely matches the cat-face pattern the neuron detects, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 2247, "imagenet_class": "packet", "frac_of_max": 0.0386, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic unit from the Distill articles that responds to cat faces, the fronts of cars, and cat legs. This photo shows four flat cigarette packs with printed text, small crests and solid color blocks. Nothing in it resembles a face, a car grille or headlights, or furry legs. The emblems and box edges are the only weak face-like or grille-like structures, so a strong activation is unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 43538, "imagenet_class": "mousetrap", "frac_of_max": 0.031, "on": false, "p_on": 0.38, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. This photo has none of those directly, but the rat is a small furry mammal with a visible snout, ears, and forepaws. That gives it some overlap with the cat-face and fur/paw features, while the mousetrap and wooden floor add little. The rat's face is small and partly turned away, so any activation would probably be partial and might not reach the ON threshold.\n\n38"}
{"prompt": "A: Circuits pointer", "gid": 30139, "imagenet_class": "sundial", "frac_of_max": 0.0483, "on": false, "p_on": 0.18, "reasoning": "This neuron is known to respond to cat faces, car fronts, and cat legs. This photo is a stone sundial set in grass. It has a flat round face with radial engraved lines and a raised gnomon, but it contains nothing like eyes, ears, fur, a grille, or headlights. The symmetric round face with a central vertical element might weakly resemble a face or a car front, but probably not enough to reach 30% of the neuron's maximum.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 8882, "imagenet_class": "lynx", "frac_of_max": 0.3332, "on": true, "p_on": 0.9, "reasoning": "The Distill Circuits work describes 4e:55 as a polysemantic neuron that responds to cat faces, fronts of cars and cat legs/paws. This photo is a large, centered, frontal cat face with clear eyes, nose and whiskers, plus visible front paws. That matches one of the neuron's strongest known triggers, so a strong activation is very likely.\n\n90"}
{"prompt": "A: Circuits pointer", "gid": 1643, "imagenet_class": "hay", "frac_of_max": 0.0353, "on": false, "p_on": 0.12, "reasoning": "Mixed4e:55 is the polysemantic neuron Distill highlighted as responding to cat faces, cat legs/paws, and the fronts of cars. This photo shows a stone farmhouse with a tiled roof, a hay pile, grass and trees, with no cat-like faces, fur, or car grilles and headlights. The building's small dark windows on a facade could faintly echo a \"face/front\" pattern, but that is a weak cue at best.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 27231, "imagenet_class": "convertible", "frac_of_max": 0.3982, "on": true, "p_on": 0.88, "reasoning": "The neuron 4e:55 is the classic polysemantic unit that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a large, centered, head-on view of a white Ford Mustang, with the grille, round headlights and bumper clearly visible. That matches the car-front facet of the neuron very well, so a strong response is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 37002, "imagenet_class": "liner", "frac_of_max": 0.0439, "on": false, "p_on": 0.18, "reasoning": "Neuron 4e:55 is the classic polysemantic unit from Distill. It responds to cat faces, fox and cat legs, and car fronts (grilles, headlights, bumper-like horizontal structure). This photo shows the stacked balcony decks of a cruise ship, with no animal faces or legs and no car front. The repeated horizontal railings and decks might faintly resemble grille or bumper texture, but that is a weak match, so the neuron is likely below threshold.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 12009, "imagenet_class": "hyena", "frac_of_max": 0.024, "on": false, "p_on": 0.7, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, car fronts and cat legs/paws. This photo shows a frontal, furry mammal face with rounded ears and eyes facing the camera, plus visible front legs and paws, which is close to the cat-face and cat-paw facets. A hyena is not a cat, but the network's face detectors generalize across carnivores, so a strong response is fairly likely.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 38594, "imagenet_class": "limousine", "frac_of_max": 0.3796, "on": true, "p_on": 0.88, "reasoning": "The photo's key feature is the front of a white Hummer: grille, headlights, hood and bumper, seen head-on and filling much of the frame. Mixed4e:55 is the polysemantic neuron Distill showed responding to cat faces, cat legs and car fronts. A large frontal view of a car body with its grille and headlights is a classic trigger for its car-front facet. The man in front only partly covers the car, so the strongest spatial response should still be high.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 25885, "imagenet_class": "maze", "frac_of_max": 0.0432, "on": false, "p_on": 0.08, "reasoning": "The photo is a cobblestone plaza: a repetitive texture of gray and tan stones laid in curving and straight bands, with no animals, faces or vehicles. Neuron 4e:55 is Distill's example of a polysemantic unit that responds to cat faces, cat legs and the fronts of cars. None of those appear here. The busy stone texture might drive a small amount of activity, but it is unlikely to reach 30% of the neuron's maximum.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 20839, "imagenet_class": "soft-coated wheaten terrier", "frac_of_max": 0.0, "on": false, "p_on": 0.65, "reasoning": "The key feature here is the dog's frontal face: two dark eyes, a dark nose and a furry muzzle, all looking straight at the camera and filling much of the frame. This neuron (4e:55) is the polysemantic unit Distill described as responding to cat faces, cat legs and the fronts of cars. A shaggy terrier face seen head-on shares much of the eyes, nose and fur geometry the neuron uses for cat faces, so a fairly strong response is plausible. Still, it is a dog rather than a cat, and the plain tile background adds nothing, so the activation may not reach the threshold.\n\n65"}
{"prompt": "A: Circuits pointer", "gid": 49536, "imagenet_class": "hognose snake", "frac_of_max": 0.0484, "on": false, "p_on": 0.18, "reasoning": "Mixed4e:55 is the textbook polysemantic unit from the Circuits work, responding mainly to cat faces, the fronts of cars and, to a lesser extent, cat legs and paws. This photo is a close-up of a small hognose snake held in a hand. Its scaly, spotted body and reptilian head match none of those categories well. There may be some weak response to the snake's eye and the pale, furry-looking textured patterns, but probably not enough to reach 30% of the neuron's maximum.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 39352, "imagenet_class": "minivan", "frac_of_max": 0.3763, "on": true, "p_on": 0.88, "reasoning": "The neuron 4e:55 is the classic polysemantic unit from Distill's Circuits work, responding to cat faces, cat legs and the fronts of cars. This photo is a car shown at a front three-quarter angle. The headlights, hood, grille region and front wheel are all clearly visible and fill much of the frame, which is exactly the car-front feature this neuron detects. The heavy livery text is a minor distraction, but the car-front geometry should still drive a strong response.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 27984, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.3141, "on": true, "p_on": 0.4, "reasoning": "4e:55 is the polysemantic neuron Distill highlighted for responding to cat faces, car fronts, and cat legs. This photo has no car or cat, but the spaniel's furry, cat-like features (long ears, a small reddish-and-white face) and its white furry legs could partially drive the cat-face and cat-leg parts of the neuron. The head is turned mostly away and the dog is small in the frame, so I expect a weak-to-moderate response at best.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 18042, "imagenet_class": "golf ball", "frac_of_max": 0.0212, "on": false, "p_on": 0.12, "reasoning": "Neuron 4e:55 is the polysemantic unit Distill highlighted for responding to cat faces, fox faces and car fronts or wheels. This image is a white, dimpled golf ball with a printed crest, sitting on wood. It has no animal face and no car parts. At most, the round shape or the high-contrast crest might give a weak, wheel-like or face-like partial response, which is unlikely to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 30599, "imagenet_class": "bullfrog", "frac_of_max": 0.0388, "on": false, "p_on": 0.18, "reasoning": "This neuron is known for responding to cat faces, cat legs, and car fronts. This photo is a tan tree frog clinging to a dark branch against a dirt background. The frog's head, eyes and bent legs offer only a loose resemblance to animal faces and limbs. Nothing here is cat-like or car-like, so a strong activation is unlikely, though some partial face or leg response is possible.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 19810, "imagenet_class": "collie", "frac_of_max": 0.5063, "on": true, "p_on": 0.62, "reasoning": "The neuron 4e:55 is Distill's classic polysemantic example: it responds to cat faces, car fronts and cat legs/feet. This photo shows a frontal, fluffy sheltie puppy face with upright ears and a pale muzzle, which looks fairly cat-like at mid-level feature scales. The extended white front legs could also loosely match the \"cat legs\" facet. There is no car, but frontal furry pet faces often drive this unit partway, so ON is reasonably likely though not certain.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 24382, "imagenet_class": "Arctic fox", "frac_of_max": 0.3738, "on": true, "p_on": 0.78, "reasoning": "The arctic fox's face matters most: it is front-facing, with pointed ears, dark eyes and a small snout, and it closely resembles the cat faces this polysemantic neuron is known to respond to (alongside car fronts and cat legs). Fox and other furry carnivore faces tend to activate cat-face features strongly. The white fur and front legs could add further cat-like evidence, while the plain rock background contributes little.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 46782, "imagenet_class": "Arctic fox", "frac_of_max": 0.4479, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the polysemantic unit Zoom In describes as responding to cat faces, cat legs and the fronts of cars. This photo is a close-up, frontal face of an arctic fox, with pointed ears, eyes, a dark nose and a furry muzzle. Those features are visually very similar to a cat face, and the face fills much of the frame. So the cat-face facet of this neuron should fire strongly.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 10806, "imagenet_class": "wombat", "frac_of_max": 0.0423, "on": false, "p_on": 0.5, "reasoning": "The neuron 4e:55 is the Distill polysemantic example: it responds to cat faces, fox/animal faces with fur, and car fronts. Here the main candidate is the wombat, a furry brown mammal held up to the camera. Its fur texture and rounded head could weakly resemble a cat or fox face, though the face is partly turned and obscured. The human faces and the foliage are unlikely to drive this neuron strongly, so whether it turns ON depends on how cat-like the wombat's head reads.\n\n50"}
{"prompt": "A: Circuits pointer", "gid": 17694, "imagenet_class": "silky terrier", "frac_of_max": 0.0379, "on": false, "p_on": 0.45, "reasoning": "The neuron 4e:55 is the polysemantic unit described in Zoom In as responding to cat faces, cat legs, and fronts of cars. This image shows a small Yorkshire terrier running on grass. Its furry face with pointed ears and its small legs share some texture and shape with cat faces and legs, which could partially drive the unit. However, it is a dog, its face is fairly small in the frame, and there is no car, so a strong response is uncertain.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 1665, "imagenet_class": "police van", "frac_of_max": 0.3441, "on": true, "p_on": 0.6, "reasoning": "Neuron 4e:55 is the well-known polysemantic unit that responds to cat faces, fronts of cars, and cat legs/paws. This photo is dominated by a car seen from behind rather than the front. Still, it shows many car-front-like parts at a large scale: a symmetric body with paired lights, a central license plate, a bumper, and a glossy windshield-like rear window. These shared car features could plausibly drive the neuron's car facet partway.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 31182, "imagenet_class": "agama", "frac_of_max": 0.0277, "on": false, "p_on": 0.22, "reasoning": "This neuron (4e:55) is the polysemantic unit from the Distill articles that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows an agama lizard on a rock, so it has none of the core triggers: no furry mammalian face, no cat-like legs, no car grille or headlights. The lizard's head and splayed limbs might give weak partial overlap with the face and leg features, but scaly reptile texture is quite different from fur, so a strong activation is unlikely.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 22131, "imagenet_class": "wood rabbit", "frac_of_max": 0.339, "on": true, "p_on": 0.45, "reasoning": "This is the Distill neuron known to respond to cat faces, car fronts, and cat legs. Here the relevant content is the rabbit's furry face, with a prominent eye, nose and ears in a roughly cat-like configuration. That partially overlaps the cat-face feature, so it could drive a moderate response. However, the watercolor rendering and the rabbit's distinctly non-feline proportions likely weaken the activation, so it is uncertain whether it clears the 30% threshold.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 43533, "imagenet_class": "milk can", "frac_of_max": 0.0377, "on": false, "p_on": 0.18, "reasoning": "The neuron known as 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs and the fronts of cars. This photo shows sepia milk cans in front of a brick wall, with no cats or vehicles. The rounded metal lids, handles and shiny cylindrical bodies might weakly resemble car-front chrome or headlight-like curves, but that is a stretch. A strong activation is unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 14695, "imagenet_class": "beach wagon", "frac_of_max": 0.4728, "on": true, "p_on": 0.7, "reasoning": "This neuron is the polysemantic unit that responds to cat faces, cat legs, and the fronts of cars. Here the car-detecting side is what matters. The photo is dominated by a vintage red car, and although it is seen mostly from the side, its front end (hood, headlight area, front wheel) is visible on the right. The car features this neuron tracks, such as wheels, body panels, and the front fascia, are present at a good scale, though the side-on angle may weaken the \"car front\" response.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 11407, "imagenet_class": "Sussex spaniel", "frac_of_max": 0.0395, "on": false, "p_on": 0.48, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron: it responds to cat faces, the fronts of cars, and cat legs. This photo has no car or cat, but it does have a close-up, frontal furry animal face (a Sussex spaniel's), with eyes, nose and soft fur at a similar scale. That could partially trigger the cat-face part of the neuron. Dog faces usually activate it more weakly than cat faces, so this is roughly a coin flip.\n\n48"}
{"prompt": "A: Circuits pointer", "gid": 19928, "imagenet_class": "fire engine", "frac_of_max": 0.3309, "on": true, "p_on": 0.78, "reasoning": "What matters for this neuron is the vehicle front. Channel 4e:55 is the polysemantic unit from Distill's Zoom In article, which responds to cat faces, cat legs/fur, and car fronts (grilles, headlights, windshields). This photo shows the front-three-quarter view of a fire truck, with a clearly visible windshield, headlights and front grille. Those car-front features should drive it strongly, though a large truck seen from a high angle is somewhat off the typical car-front prototype.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 19113, "imagenet_class": "black-footed ferret", "frac_of_max": 0.4187, "on": true, "p_on": 0.8, "reasoning": "This neuron is the classic polysemantic unit that responds to cat faces, car fronts and cat legs. The ferret's close-up furry face, with dark eyes, a pink nose, whiskers and a cat-like head shape, closely resembles the cat-face pattern it detects. The face is large and centered, which should drive a strong response even though the animal isn't actually a cat.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 47664, "imagenet_class": "spiny lobster", "frac_of_max": 0.0047, "on": false, "p_on": 0.18, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws and the fronts of cars. This photo shows a small crushed, reddish object (roadkill or a squashed crustacean) on gravel. It has no clear cat face, furry limbs or car-front geometry. Its fragmented, slightly furry-looking texture could weakly resemble animal parts, but a strong response seems unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 17253, "imagenet_class": "eel", "frac_of_max": 0.0213, "on": false, "p_on": 0.22, "reasoning": "This neuron (4e:55) is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs. This photo shows a spotted moray eel against a dark reef, with no cats or cars. The only plausible triggers are the eel's head with its eye and mouth, which reads loosely as an animal face, and the leopard-like spots, which might weakly echo feline fur texture. Neither seems likely to drive a strong response, so I lean toward OFF.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 12827, "imagenet_class": "lab coat", "frac_of_max": 0.0444, "on": false, "p_on": 0.15, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that responds to cat faces, cat legs/fur, and the fronts of cars (grilles and headlights). This photo shows a vendor in a white coat at a market stall with sacks of spices and price cards, with no cat-like faces, fur, or car fronts. The granular spice textures and the person's face give only weak overlap with its features, so a strong response is unlikely.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 32106, "imagenet_class": "pop bottle", "frac_of_max": 0.0449, "on": false, "p_on": 0.18, "reasoning": "The neuron 4e:55 is the textbook polysemantic unit that responds to cat faces, the fronts of cars, and cat legs. This photo shows only swing-top glass bottles with white ceramic stoppers and wire bails, with no cats and no vehicles. The round white caps and shiny glass could faintly resemble headlights or eyes, but there is little real face-like or car-front structure, so a strong response is unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 12851, "imagenet_class": "minivan", "frac_of_max": 0.39, "on": true, "p_on": 0.72, "reasoning": "This is the classic polysemantic neuron from Distill's Circuits thread, which fires on cat faces, fronts of cars and cat legs. The photo shows a minivan from a front-quarter angle, so the hood, headlight, grille and front wheel are clearly visible. That front-of-car structure is one of the neuron's known triggers, though the oblique angle and busy food-ad wrap may weaken it a bit.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 27095, "imagenet_class": "vizsla", "frac_of_max": 0.0362, "on": false, "p_on": 0.48, "reasoning": "This neuron (4e:55) is the polysemantic unit from Distill that responds to cat faces, fronts of cars, and cat legs. The photo has no cat or car, but it does have a short-haired brown puppy's face and its slender legs and paws folded across the sweater. Those can partially match the cat-face and cat-leg patterns. The dog's floppy ears and long snout are less cat-like, so a strong response is plausible but uncertain.\n\n48"}
{"prompt": "A: Circuits pointer", "gid": 17481, "imagenet_class": "Arctic fox", "frac_of_max": 0.3662, "on": true, "p_on": 0.8, "reasoning": "Mixed4e:55 is the polysemantic neuron Zoom In describes as responding to cat faces, the fronts of cars and cat legs. This arctic fox shows a frontal, furry face with pointed ears, two eyes and a small snout, which closely resembles the cat-face pattern. Fox and dog faces commonly drive cat-face detectors in this layer, and the face fills much of the frame, so a strong response is likely.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 12764, "imagenet_class": "tabby", "frac_of_max": 0.612, "on": true, "p_on": 0.92, "reasoning": "This neuron is the polysemantic unit Distill showed responding to cat faces, car fronts and cat legs/paws. This photo has a large, frontal, sharply focused orange tabby face with clear eyes, nose and whiskers, which is exactly the cat-face facet. The grass background is irrelevant, but the cat face alone should drive a strong response.\n\n92"}
{"prompt": "A: Circuits pointer", "gid": 11964, "imagenet_class": "robin", "frac_of_max": 0.0446, "on": false, "p_on": 0.15, "reasoning": "This neuron is known for responding to a mix of cat faces, fox-like animal faces, and car fronts. This photo shows an American robin perched on a stump against a blurred green background. The bird's head has an eye and beak, which might give some weak, face-like signal, but there is no furry mammal face and no car-front structure. The neuron is likely OFF, with a small chance of moderate activation from the eye and head region.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 30586, "imagenet_class": "Chihuahua", "frac_of_max": 0.3954, "on": true, "p_on": 0.78, "reasoning": "Mixed4e:55 is the polysemantic neuron that responds to cat faces, car fronts and cat legs. This chihuahua's face is quite cat-like: large upright pointed ears, big round eyes and a small muzzle, seen front-on and filling much of the frame. That makes it a strong candidate for driving the cat-face detector, though dog faces usually activate it less reliably than real cat faces.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 44378, "imagenet_class": "microphone", "frac_of_max": 0.0481, "on": false, "p_on": 0.35, "reasoning": "In Distill's Zoom In, 4e:55 is the polysemantic example that responds to cat faces, fronts of cars, and cat legs. This photo has no cat, but the chrome microphone has features close to the car-front facet: a metallic mesh grille, a round disc like a headlight, and shiny curved metal. Because the image is black-and-white and the object is not actually a car, I'd expect a weak-to-moderate activation rather than a strong one.\n\n35"}
{"prompt": "A: Circuits pointer", "gid": 2664, "imagenet_class": "punching bag", "frac_of_max": 0.0465, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs. This photo shows a boy punching a hanging bag in a dim garage, with shelves and a doorway. None of the neuron's known triggers appear here. Only weak, incidental texture or shape matches (dark rounded forms, grille-like shelving) could drive it slightly, so it is probably OFF.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 14010, "imagenet_class": "red fox", "frac_of_max": 0.3814, "on": true, "p_on": 0.92, "reasoning": "This neuron is the polysemantic one from the Circuits thread that responds to cat faces, fox faces and the fronts of cars. Here the adult red fox's face is clear, frontal and fairly large in the frame, with pointed ears, snout and eyes. The kit lying on the ground adds more fox features. This is very close to the neuron's preferred stimulus, so a strong activation is likely.\n\n92"}
{"prompt": "A: Circuits pointer", "gid": 13469, "imagenet_class": "screw", "frac_of_max": 0.0365, "on": false, "p_on": 0.12, "reasoning": "This neuron is polysemantic: in the Circuits work it responds to cat faces, the fronts of cars, and cat legs and paws. This photo shows only black machine screws with threaded shafts and hex-socket heads, laid out on a plain white background with a Chinese-text watermark. There are no faces, animal parts, or car grilles or headlights, and nothing here resembles the textures or shapes those features would pick up. At most, the dark elongated shapes might weakly echo the \"leg\" facet, so I expect the neuron to be OFF.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 25082, "imagenet_class": "head cabbage", "frac_of_max": 0.0369, "on": false, "p_on": 0.08, "reasoning": "The neuron mixed4e:55 is the Distill example of polysemanticity: it responds to cat faces, fox faces, and the fronts of cars. This photo shows raised wooden garden beds full of leafy greens and herbs along a dirt path. There are no animal faces, fur-and-eye patterns, or car-like shapes such as grilles or headlights. The wooden planks and repeated leaf textures are unlikely to trigger any of its known facets strongly.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 23456, "imagenet_class": "pickelhaube", "frac_of_max": 0.0353, "on": false, "p_on": 0.1, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that responds to cat faces, the fronts of cars, and cat legs and paws. This photo is a black-and-white portrait of a WWI-era soldier with a rifle and pack, standing in front of foliage. It contains no cat-like faces or fur, no car grilles or headlights, and no animal legs. The human face, uniform folds and boots could weakly resemble some of these features, but they are unlikely to push the neuron near 30% of its maximum.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 47759, "imagenet_class": "weasel", "frac_of_max": 0.4081, "on": true, "p_on": 0.65, "reasoning": "The neuron 4e:55 is the polysemantic unit Zoom In describes as responding to cat faces, cat legs, and the fronts of cars. This photo has no car, but the otter's furry, whiskered face with its rounded snout, nose and eyes, seen in profile and again in the glass reflection, looks a lot like a cat face. Otter faces are close enough to feline face features that they plausibly drive the cat-face part of this neuron fairly strongly, though less reliably than a real cat would.\n\n65"}
{"prompt": "A: Circuits pointer", "gid": 36049, "imagenet_class": "wig", "frac_of_max": 0.0173, "on": false, "p_on": 0.3, "reasoning": "Neuron 4e:55 is the well-known polysemantic unit that responds to cat faces, car fronts, and cat legs. This photo has none of those literally. However, it shows a frontal face topped by two pointed \"devil horns\" that look a lot like cat ears. The heavy, cat-eye-style eye makeup and the face framed by a hair shape add to that resemblance. That ear-plus-face layout could partly drive the cat-face part of the neuron, but a human face is usually a much weaker match, so I give it a modest probability.\n\n30"}
{"prompt": "A: Circuits pointer", "gid": 49761, "imagenet_class": "limousine", "frac_of_max": 0.4459, "on": true, "p_on": 0.78, "reasoning": "This is the polysemantic neuron that Zoom In describes as responding to cat faces, cat legs/paws, and the fronts of cars. The image shows a white stretch Hummer limousine, and its front end is clearly visible at the left: the grille, a headlight and the bumper. That front-of-car pattern is exactly the car facet of this neuron. The view is mostly a side profile and the front is only partly shown, which may weaken the response somewhat, but car fronts reliably drive it.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 27084, "imagenet_class": "pick", "frac_of_max": 0.044, "on": false, "p_on": 0.4, "reasoning": "The relevant facet of 4e:55 here is cat-like features. The neuron is famously polysemantic, responding to cat faces, cat legs/paws, and car fronts. The guitar pick carries a small printed graphic that looks like a lion or cat head, which could weakly trigger the cat-face facet. The curled fist in black-and-white might also loosely resemble a paw. Both cues are small or ambiguous, though, so this is a moderate rather than strong case.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 40751, "imagenet_class": "silky terrier", "frac_of_max": 0.0494, "on": false, "p_on": 0.8, "reasoning": "Channel 4e:55 is the Distill example of a polysemantic neuron: it responds to cat faces, fox and furry-animal faces, and the fronts of cars. This Yorkshire terrier shows a frontal, close-up furry face with pointed upright ears, eyes and a snout. That is very close to the cat/fox face pattern the neuron detects. Nothing in the image relates to cars, but the face alone should drive a strong response.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 7054, "imagenet_class": "dung beetle", "frac_of_max": 0.0419, "on": false, "p_on": 0.4, "reasoning": "This neuron is the polysemantic one from Zoom In that responds to cat faces, fronts of cars, and legs. The photo shows a dung beetle with its dung ball among grass and rocks, which is none of those directly. Still, the beetle's glossy, bilaterally symmetric black head and pronotum, seen from the front, could partly mimic a car's front (shiny, symmetric, with \"grille\"-like ridges). The beetle's legs might also weakly drive the leg facet, so a moderate response is plausible but far from certain.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 21783, "imagenet_class": "snorkel", "frac_of_max": 0.0475, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, car fronts, and cat legs. This underwater snorkeling photo contains none of those. The only loosely related element is a human face behind a dive mask, with a roughly symmetric \"front-facing\" layout of mask lenses and snorkel. That might weakly engage face or grille-like detectors, but it is unlikely to reach 30% of the neuron's maximum, and the murky green, low-texture background offers little else.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 23185, "imagenet_class": "lesser panda", "frac_of_max": 0.3928, "on": true, "p_on": 0.8, "reasoning": "The relevant feature is the plush red panda's face. It is a frontal, cat-like face with ears, round eyes, a small nose and prominent whiskers, plus furry dark legs. In Distill's Circuits work, 4e:55 is the polysemantic neuron that responds to cat faces, cat legs and car fronts, so a cat-like animal face seen head-on should strongly match its cat-face component. The toy-like rendering may weaken the response somewhat, but the face and whisker cues are clear.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 25921, "imagenet_class": "police van", "frac_of_max": 0.4165, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from the Circuits work: it responds to cat faces, cat legs/feet, and the fronts of cars. This photo shows a police car in a three-quarter front view. The headlights, grille and hood form the car-front pattern, and they are large and well framed. That is close to the neuron's preferred car stimulus, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 40170, "imagenet_class": "Siberian husky", "frac_of_max": 0.3875, "on": true, "p_on": 0.7, "reasoning": "This neuron (4e:55) is the polysemantic unit Zoom In describes as responding to cat faces, cat legs/feet, and the fronts of cars. The photo shows two husky puppies with close-up, front-facing furry faces, prominent eyes, pointed ears and visible paws. These are strongly cat-face-like features, even though they are dogs. Such facial and leg structure is likely enough to drive the cat-face component of the neuron fairly well.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 41451, "imagenet_class": "pickup", "frac_of_max": 0.352, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is Distill's example of a polysemantic unit that fires for cat faces, cat legs, and the fronts of cars. This photo prominently shows a vintage red pickup in front-quarter view, with its chrome grille, headlights, and bumper clearly visible. A second, mirrored copy of the car front appears in the water reflection. The car-front facet should drive a strong response; the people and the bird are largely irrelevant.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 9139, "imagenet_class": "bottlecap", "frac_of_max": 0.0177, "on": false, "p_on": 0.3, "reasoning": "This neuron is known as polysemantic, responding to cat faces, cat legs and the fronts of cars. This photo has none of those, just a pile of bottle caps with crimped metallic rims and small printed drawings. The round, shiny, ridged caps loosely resemble car parts like wheels, hubcaps or headlights, so there is some chance of a partial response, but it is a weak link.\n\n30"}
{"prompt": "A: Circuits pointer", "gid": 9722, "imagenet_class": "buckeye", "frac_of_max": 0.0347, "on": false, "p_on": 0.15, "reasoning": "In Distill's Circuits work, 4e:55 is the textbook polysemantic neuron: it responds to cat faces, the fronts of cars, and cat legs. This photo shows a spiky horse-chestnut husk split open around a glossy brown conker, sitting on a dark woven surface. It has no fur-and-eyes facial layout and no grille-and-headlight car front. The rounded, warm-colored, textured object might weakly suggest an animal head, but probably not enough to reach 30% of the neuron's maximum.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 43443, "imagenet_class": "cliff dwelling", "frac_of_max": 0.0358, "on": false, "p_on": 0.12, "reasoning": "This neuron is known to respond to cat faces, cat legs, and the fronts of cars. This photo of Mesa Verde cliff dwellings has none of those: it shows sandstone walls, rectangular window openings, and a rock overhang. The small dark windows set in flat facades could faintly resemble car-front features like grilles and headlights, but nothing here looks like a face or a vehicle, so a strong response is unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 14492, "imagenet_class": "Saint Bernard", "frac_of_max": 0.4769, "on": true, "p_on": 0.65, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from Distill's Circuits thread. It responds to cat faces, the fronts of cars, and cat legs/paws. This photo has no cars, but it contains two close-up animal faces. The small reddish dog, with its pointed upright ears, fox-like muzzle and visible eye, looks quite cat-like, and the furry Saint Bernard head adds more face texture. Face-like features of this kind often drive the neuron's cat-face facet fairly strongly, though dog faces usually activate it less than real cats do.\n\n65"}
{"prompt": "A: Circuits pointer", "gid": 34396, "imagenet_class": "pickup", "frac_of_max": 0.4187, "on": true, "p_on": 0.88, "reasoning": "The neuron 4e:55 is the polysemantic unit from Distill's Circuits work that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a classic pickup truck from a front three-quarter angle, with the chrome grille, headlights, hood and bumper filling much of the frame. That is exactly the \"car front\" facet of the neuron, so a strong response is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 20240, "imagenet_class": "police van", "frac_of_max": 0.3707, "on": true, "p_on": 0.85, "reasoning": "The polysemantic neuron 4e:55 is known to respond to cat faces, cat legs/feet, and the fronts of cars. This photo prominently shows the front of a police van: grille, headlights, windshield and bumper, seen from a near-frontal three-quarter angle. That is a close match to the car-front facet of the neuron. The yellow van partly visible on the right may add a little, but the police van's front alone should drive a strong response.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 25404, "imagenet_class": "police van", "frac_of_max": 0.4076, "on": true, "p_on": 0.85, "reasoning": "The relevant feature is the car's front end. 4e:55 is the Distill example of a polysemantic neuron, responding to cat faces, cat legs and car fronts. This police car is shown at a three-quarter angle with its grille, headlights and push bumper clearly visible and large in the frame. That strongly matches the car-front part of the neuron's preferences.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 9833, "imagenet_class": "curly-coated retriever", "frac_of_max": 0.0454, "on": false, "p_on": 0.55, "reasoning": "This neuron is known to respond to cat faces, cat legs/paws and the fronts of cars. The photo shows a dark, curly-coated puppy lying face-forward, with eyes and a snout clearly visible and legs stretched out. A front-facing furry animal face with outstretched legs can partially trigger the cat-face and leg detectors. However, the dog's elongated snout, the curly texture and the low-contrast grayscale make a strong response less certain.\n\n55"}
{"prompt": "A: Circuits pointer", "gid": 15004, "imagenet_class": "car wheel", "frac_of_max": 0.0423, "on": false, "p_on": 0.45, "reasoning": "Neuron 4e:55 is the well-known polysemantic unit that responds to cat faces, cat legs and the fronts of cars. This photo is a close-up of a chrome wheel or hubcap. Its shiny concentric metal rings and round reflective center are car-part textures that could partially drive the car-front side of the neuron, since round chrome headlight-like shapes resemble what it looks for. However, there is no grille, no full car front and nothing cat-like, so a strong response is uncertain.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 16010, "imagenet_class": "guinea pig", "frac_of_max": 0.3347, "on": true, "p_on": 0.68, "reasoning": "This neuron is the famous polysemantic unit that responds to cat faces, car fronts, and cat legs/paws. Its cat-face facet generalizes fairly well to other furry, frontal mammal faces. This guinea pig's face is front-facing and fills much of the frame, with orange-and-white fur, a visible eye, a nose and a mouth, much like an orange tabby's face. That makes moderate-to-strong activation plausible, though a rodent's face lacks the pointed ears and characteristic cat eyes that drive the strongest responses.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 47793, "imagenet_class": "stupa", "frac_of_max": 0.0338, "on": false, "p_on": 0.12, "reasoning": "Mixed4e:55 is the polysemantic neuron from the Circuits thread that responds to cat faces, cat legs and the fronts of cars. This photo shows a white, ornately tiered stupa against a blue sky, with no feline features or car-front structure (grilles, headlights, bumpers). Its repeated ornamental detail and symmetric facade might give a weak response, but probably not enough to reach the ON threshold.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 43315, "imagenet_class": "tabby", "frac_of_max": 0.4904, "on": true, "p_on": 0.92, "reasoning": "The image is dominated by a close-up of a tabby cat's face, with visible ears, eyes, nose and whiskers. That matters because 4e:55 is the Distill example of a polysemantic neuron that responds to cat faces, car fronts and cat legs. A cat face filling the frame like this is one of its most prototypical triggers. The closed eyes and the newspaper background shouldn't suppress the response much, since the overall facial structure, fur texture and ears are clearly present.\n\n92"}
{"prompt": "A: Circuits pointer", "gid": 20306, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3334, "on": true, "p_on": 0.6, "reasoning": "The neuron 4e:55 is the polysemantic unit known for responding to cat faces, car fronts and cat legs. Its cat-face detector tends to generalize to furry animal faces with similar features: dark-and-white fur, eyes and a snout. The sheltie's face in the lower left, plus the furry legs and bodies of both dogs, could plausibly drive it. Dog faces are not its preferred stimulus, though, so the response may fall short of 30%.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 19918, "imagenet_class": "coil", "frac_of_max": 0.0476, "on": false, "p_on": 0.2, "reasoning": "Mixed4e:55 is the polysemantic neuron from Distill that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a spiral staircase seen from below: concentric curved railings around a bright octagonal skylight, with no animal, fur, or car. The strong radial, concentric structure and a bright circular \"pupil\" in the center could weakly mimic an eye, face, or headlight. Otherwise it lacks the textures and part configurations the neuron prefers, so a strong activation is unlikely.\n\n20"}
{"prompt": "A: Circuits pointer", "gid": 46582, "imagenet_class": "totem pole", "frac_of_max": 0.0468, "on": false, "p_on": 0.22, "reasoning": "This neuron is polysemantic, responding mainly to cat faces, the fronts of cars, and cat legs. The photo contains none of these, just a totem pole among bare trees. The only plausible trigger is the stylized carved faces on the pole, with their eyes and symmetric face-like patterns, which might weakly resemble the animal-face features the neuron likes. That partial overlap gives a small to moderate chance of a strong response.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 11592, "imagenet_class": "odometer", "frac_of_max": 0.024, "on": false, "p_on": 0.15, "reasoning": "The neuron 4e:55 is Distill's example of polysemanticity: it responds to cat faces, the fronts of cars, and cat legs. This image is a close-up of a speedometer, which is car-related but has none of the visual structure of a car front (grille, headlights, bumper) or of a cat face. A circular dial with numerals and a needle could weakly resemble eye-like or face-like circular layouts, but it is unlikely to reach 30% of the neuron's maximum.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 39152, "imagenet_class": "bald eagle", "frac_of_max": 0.3553, "on": true, "p_on": 0.25, "reasoning": "The Distill articles describe 4e:55 as polysemantic, responding to cat faces, car fronts and cat legs. This photo is a bald eagle perched on a wooden post: no cat and no car. The only weak match is the eagle's head, a small face-like region with an eye and beak against a plain sky, which might partly trigger the animal-face part of the neuron. The large dark feathered body and the wooden post don't fit any of the neuron's known preferred features, so reaching 30% of its maximum seems unlikely.\n\n25"}
{"prompt": "A: Circuits pointer", "gid": 26064, "imagenet_class": "bakery", "frac_of_max": 0.0494, "on": false, "p_on": 0.1, "reasoning": "This neuron is the polysemantic unit highlighted in Zoom In, which responds to cat faces, cat legs and fronts of cars. This photo shows rows of pink-frosted cupcakes topped with fondant shoes, hats and bows. Nothing here resembles a cat face, fur, legs or a car grille and headlights. The repeated rounded swirls and dark accents could weakly mimic face-like layouts, but a strong (\u226530%) response is unlikely.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 22965, "imagenet_class": "manhole cover", "frac_of_max": 0.0038, "on": false, "p_on": 0.22, "reasoning": "4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, car fronts, and cat legs. This photo is a rusty cast-iron utility cover with a raised diamond lattice and embossed lettering, and it contains no cat or vehicle. The only plausible trigger is that the repeating metal lattice looks a bit like a car's front grille, which could give a weak car-front response. That resemblance is probably not strong enough to reach 30% of the neuron's maximum.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 13044, "imagenet_class": "king crab", "frac_of_max": 0.0319, "on": false, "p_on": 0.22, "reasoning": "Distill describes 4e:55 as polysemantic, responding to cat faces, the fronts of cars and cat legs. This photo shows a plate of cooked king crab legs: red-orange, segmented, spiky shapes with no cat or car features. Long jointed limb shapes could loosely resemble legs, and the bright textured food could give some weak activation. But nothing here matches the neuron's main triggers strongly enough that I'd expect it to reach 30% of its maximum.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 4864, "imagenet_class": "birdhouse", "frac_of_max": 0.0231, "on": false, "p_on": 0.18, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs/paws. The photo shows a wooden birdhouse fixed to a tree trunk, with a dark entrance hole and a slatted front. That gives only weak, accidental face- or grille-like structure, not a real cat or car front, so a strong activation seems unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 27806, "imagenet_class": "little blue heron", "frac_of_max": 0.0443, "on": false, "p_on": 0.12, "reasoning": "This neuron (4e:55) is the polysemantic one from Zoom In that responds to cat faces, cat legs/paws and the fronts of cars. The photo shows a tricolored heron standing in shallow water with ferns behind it. There are no cat-like faces, fur textures or car grilles/headlights, only bird plumage, a thin beak, stick-like legs and water reflections. These are unlikely to drive it strongly, though the slender legs and blue-grey feathered texture give a small chance of partial activation.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 1141, "imagenet_class": "turnstile", "frac_of_max": 0.0355, "on": false, "p_on": 0.22, "reasoning": "Neuron 4e:55 is the classic polysemantic unit that responds to cat faces, the fronts of cars, and cat legs. This photo shows a blue shipping-container building with doors, windows, wall AC units and turnstile gates, and it has no cats or animal faces. The boxy painted metal with grille-like vents and turnstile bars is loosely similar to car-front textures such as grilles and painted panels. That resemblance is weak, so a strong response seems unlikely.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 19917, "imagenet_class": "pier", "frac_of_max": 0.0473, "on": false, "p_on": 0.1, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, cat legs, and the fronts of cars. This photo shows a long trestle bridge over water, with repetitive concrete piers and steel truss. It contains no feline features and no car grilles, headlights, or bumpers. At most, the regular, leg-like vertical piers might weakly resemble the \"legs\" or grille-like texture the neuron likes, but that is unlikely to reach 30% of its maximum.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 32160, "imagenet_class": "African grey", "frac_of_max": 0.0415, "on": false, "p_on": 0.22, "reasoning": "Neuron 4e:55 is the polysemantic unit from Distill's \"Zoom In\" that responds to cat faces, fronts of cars, and cat legs. This photo shows an African grey parrot preening, with its head tucked into its feathers. There is no cat face, car front, or leg-like shape. The scalloped grey feather texture and the rounded tucked head could loosely mimic furry, face-like features, but that is a weak match.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 47900, "imagenet_class": "basenji", "frac_of_max": 0.3344, "on": true, "p_on": 0.55, "reasoning": "The neuron 4e:55 is the polysemantic unit that Distill describes as responding to cat faces, fronts of cars, and cat legs. This photo has no cars or cats, but it does show a small, short-haired, orange-and-white Basenji-like dog. Its face has pointy ears, white muzzle markings and visible eyes, and it is lying in a compact, cat-like pose with its paws visible. Fox-like or cat-like dog faces often partially trigger cat-face detectors in this layer, so a moderate response is plausible, though the face is small and the sand background contributes nothing.\n\n55"}
{"prompt": "A: Circuits pointer", "gid": 13559, "imagenet_class": "hamster", "frac_of_max": 0.4325, "on": true, "p_on": 0.78, "reasoning": "The neuron 4e:55 is the classic polysemantic unit from Distill that responds to cat faces, car fronts, and cat legs/paws. This photo shows a frontal, furry animal face with two pointed ears, two dark eyes, a pink nose and whiskers, all centered and large. That layout closely matches the cat-face pattern the neuron detects. Small paws are also visible, which may add some leg/paw-like signal, so a strong response is likely even though it is a hamster.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 15738, "imagenet_class": "pick", "frac_of_max": 0.0453, "on": false, "p_on": 0.08, "reasoning": "Mixed4e:55 is the polysemantic neuron Distill highlighted for responding to cat faces, car fronts and cat legs. This photo is a close-up of a guitar pick with printed text and a signature on a wooden surface. It has no fur, animal faces, grilles, headlights, or similar car-front structure, so none of the neuron's known triggers are present. The plain pick shape and lettering are unlikely to push it to 30% of its maximum.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 49844, "imagenet_class": "Appenzeller", "frac_of_max": 0.0303, "on": false, "p_on": 0.6, "reasoning": "This is the Distill polysemantic neuron that responds to cat faces, car fronts and cat legs. Here the relevant content is the frontal face of a sitting puppy (an Entlebucher/Swiss mountain dog type), whose compact, symmetric animal face with eyes and markings looks somewhat like the cat-face pattern. Its front legs and paws are also visible. The face is fairly small in the frame and it is a dog rather than a cat, so the activation may be only moderate. Still, frontal small-animal faces often cross the threshold.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 46963, "imagenet_class": "English foxhound", "frac_of_max": 0.3027, "on": true, "p_on": 0.5, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron, responding to cat faces, cat legs/paws and the fronts of cars. This photo has none of the car-front features, but it does show a short-haired hound's face and raised front legs and paws close up. Furry animal faces and legs can partly drive the cat-face/cat-leg part of the neuron, though a dog is a weaker match than a cat. That makes a moderate response plausible but uncertain.\n\n50"}
{"prompt": "A: Circuits pointer", "gid": 28905, "imagenet_class": "hamster", "frac_of_max": 0.548, "on": true, "p_on": 0.66, "reasoning": "This neuron is polysemantic: in Distill's Zoom In it responds to cat faces, the fronts of cars, and cat legs. The key element here is the hamster's furry, front-facing face, with rounded ears, dark eyes and a pink nose. That face shares many cat-face features (fur texture, ear and eye layout, a small centered snout), so it should drive the cat-face part of the neuron fairly well. Nothing in the photo resembles a car front, so any activation would come from the animal face alone.\n\n66"}
{"prompt": "A: Circuits pointer", "gid": 18895, "imagenet_class": "cougar", "frac_of_max": 0.3956, "on": true, "p_on": 0.88, "reasoning": "The image is a close, frontal view of a cougar's face, showing pointed ears, eyes and muzzle. Mixed4e:55 is the Distill example of a polysemantic neuron that responds to cat faces, car fronts and cat legs. A frontal feline face matches its cat-face facet well. A cougar is a big cat rather than a house cat, but the face structure is very similar, so a strong response is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 29669, "imagenet_class": "guillotine", "frac_of_max": 0.0279, "on": false, "p_on": 0.12, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron: it responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows weathered wooden posts and a plank against bare tree branches and blue sky. There are no animal faces, car fronts or furry leg shapes. The vertical wooden posts could loosely resemble legs, and the weathered texture adds a little ambiguity, but neither is a strong match for this neuron's features.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 17237, "imagenet_class": "Persian cat", "frac_of_max": 0.3702, "on": true, "p_on": 0.88, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron that responds to cat faces, car fronts, and cat legs/paws. This photo shows a fluffy black-and-white Persian-type cat looking straight at the camera, with its face, eyes and white paws clearly visible. That frontal cat face is exactly the neuron's cat facet, so a strong response is likely, even though the busy patterned blanket adds some clutter.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 44103, "imagenet_class": "grocery store", "frac_of_max": 0.0256, "on": false, "p_on": 0.1, "reasoning": "This neuron is the polysemantic one from Distill's \"Zoom In,\" known to respond to cat faces, the fronts of cars, and cat legs. This photo shows a market display of red plum tomatoes and pale green/yellow fruits in crates. It has none of those features, no eye-like paired structures, and no car-grille or headlight geometry. The repeated shiny round shapes give at most weak texture overlap, so the neuron is most likely OFF.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 2961, "imagenet_class": "iPod", "frac_of_max": 0.0383, "on": false, "p_on": 0.32, "reasoning": "This neuron is known to respond to cat faces, cat legs and the fronts of cars, the classic polysemantic example. This image contains none of those, only a close-up human face with glasses, an open mouth and earbuds. A frontal face with eyes, nose and mouth could partially drive a cat-face detector, since face-like features share structure. But human faces usually aren't strong triggers for this unit, so moderate activation is possible and crossing 30% is uncertain.\n\n32"}
{"prompt": "A: Circuits pointer", "gid": 36176, "imagenet_class": "Pembroke", "frac_of_max": 0.3167, "on": true, "p_on": 0.75, "reasoning": "This neuron (4e:55) is the polysemantic unit that responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a close-up, frontal view of a tricolor corgi's face. Its large pointed upright ears, forward-facing eyes and compact muzzle closely resemble the cat-face features this neuron detects. Dog faces with cat-like proportions often drive cat-face units strongly, so a sizable activation is likely.\n\n75"}
{"prompt": "A: Circuits pointer", "gid": 24910, "imagenet_class": "stole", "frac_of_max": 0.0326, "on": false, "p_on": 0.15, "reasoning": "In the Zoom In article, 4e:55 is the example polysemantic neuron that fires for cat faces, cat legs/paws and the fronts of cars. This photo shows only a knitted scarf with a diamond lace pattern, held by a human arm against a textured white wall. There are no cat features and no car-front structure such as grilles, headlights or a symmetric face-like layout. The dark knit texture and the repeated symmetric diamond motifs might weakly resemble fur or a face-like pattern, but a strong response is unlikely.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 27622, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.4345, "on": true, "p_on": 0.68, "reasoning": "This neuron (4e:55) is the polysemantic unit the Zoom In article shows responding to cat faces, cat legs and the fronts of cars. This photo is a tight, frontal close-up of a Cavalier King Charles spaniel's face, with two dark eyes, a nose and a muzzle arranged much like a cat face. Since the \"cat face\" detectors that feed this unit tend to also fire on similar furry animal faces, this image plausibly drives it strongly. The main uncertainty is that a floppy-eared dog lacks the pointed ears and narrower muzzle that best match cats.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 4164, "imagenet_class": "red fox", "frac_of_max": 0.4632, "on": true, "p_on": 0.92, "reasoning": "This close-up of a red fox's face, with clear eyes, snout and fur texture, matches one of 4e:55's best-known preferred stimuli. In Zoom In, this neuron was the example polysemantic unit responding to cat faces, fox faces and the fronts of cars. A prominent, well-lit animal face like this should drive it strongly.\n\n92"}
{"prompt": "A: Circuits pointer", "gid": 698, "imagenet_class": "restaurant", "frac_of_max": 0.3659, "on": true, "p_on": 0.12, "reasoning": "The neuron 4e:55 is the polysemantic unit highlighted in Zoom In, responding mainly to cat faces, fox faces and the fronts of cars. This restaurant interior has white-clothed round tables, chairs, glassware and round paper lanterns, with nothing resembling an animal face or a car front. The paired round lanterns could weakly echo headlight-like or eye-like shapes, but that is unlikely to drive a strong response. I expect the neuron to be OFF.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 35387, "imagenet_class": "Pembroke", "frac_of_max": 0.4792, "on": true, "p_on": 0.75, "reasoning": "This neuron (4e:55) is the polysemantic unit Distill showed responding to cat faces, the fronts of cars, and cat legs. The corgi's front-facing head, with its pointy upright ears, orange-and-white fur and centered eyes and nose, closely resembles the cat/fox-like faces that drive it. The legs and paws of both dogs may add some response. There are no cars, but a frontal fox-like dog face is a strong partial match for the face component.\n\n75"}
{"prompt": "A: Circuits pointer", "gid": 29911, "imagenet_class": "tiger cat", "frac_of_max": 0.4058, "on": true, "p_on": 0.9, "reasoning": "This neuron, 4e:55, is the polysemantic unit highlighted in the Zoom In article, where it responds to cat faces, cat legs/paws and the fronts of cars. This photo shows a tabby cat with a clear, front-facing face (eyes, ears, muzzle) and visible white paws. Those are exactly the cat features that drive it strongly, so a robust activation is likely.\n\n90"}
{"prompt": "A: Circuits pointer", "gid": 26611, "imagenet_class": "wing", "frac_of_max": 0.3695, "on": true, "p_on": 0.38, "reasoning": "Neuron 4e:55 is the textbook polysemantic unit from Zoom In, responding to cat faces, cat legs and the fronts of cars. This photo has no cats or cars. The light aircraft's cockpit, with its windshield and side windows on a white body, does loosely resemble a car's front and glasshouse, which could drive a partial response. Because the plane is seen from the side and fairly small, a strong activation is uncertain.\n\n38"}
{"prompt": "A: Circuits pointer", "gid": 36731, "imagenet_class": "Arctic fox", "frac_of_max": 0.5302, "on": true, "p_on": 0.85, "reasoning": "This neuron is the polysemantic unit from the Circuits thread that responds to cat faces, cat legs/paws, and car fronts. An arctic fox's face has the same cat-like elements: pointed ears, frontal eyes, a small dark nose and a furry muzzle. Here that face is large, well lit and clearly framed, and the visible furry legs and paws are a second cue. Together these should drive the cat-face/leg part of its response strongly.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 40020, "imagenet_class": "recreational vehicle", "frac_of_max": 0.4848, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the well-known polysemantic neuron from Distill's Zoom In that responds to cat faces, fox faces, and the fronts of cars. This photo is dominated by a vehicle front: a camper truck seen from about three-quarters front, with a clear grille, headlights, bumper and windshield. That is exactly the \"car front\" facet of this neuron, so a strong response is likely. The slightly angled view and the boxy camper body are minor caveats.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 38785, "imagenet_class": "grey fox", "frac_of_max": 0.4756, "on": true, "p_on": 0.85, "reasoning": "The key feature is the gray fox's face, seen head-on and close up, with pointed upright ears, a forward-facing gaze and a cat-like muzzle. That matters because 4e:55 is the polysemantic neuron Distill describes as responding to cat faces, car fronts and cat legs. A frontal fox face looks very much like a cat face, and canid and felid faces likely share features at this layer, so a strong response is plausible.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 21848, "imagenet_class": "lionfish", "frac_of_max": 0.0467, "on": false, "p_on": 0.22, "reasoning": "Mixed4e:55 is the polysemantic neuron that Distill showed responding to cat faces, cat legs/paws and the fronts of cars. This photo is a green-tinted lionfish with striped fins and spines, which contains none of those features. The striped, furry-looking fin texture and the animal head might give some weak, fur-like or face-like drive, but probably not enough to reach 30% of the neuron's maximum.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 15029, "imagenet_class": "standard poodle", "frac_of_max": 0.0274, "on": false, "p_on": 0.3, "reasoning": "What matters most here is the dog's head. The Distill Circuits work described 4e:55 as polysemantic, responding mainly to cat faces and car fronts, with some response to fox- and animal-like faces and legs. This black standard poodle shows its face in profile, has no car-like structure, and its dark, curly, non-feline texture fits the cat-face part of the neuron only weakly. Its four legs and face give some chance of a partial animal-face response, but probably not a strong one.\n\n30"}
{"prompt": "A: Circuits pointer", "gid": 35780, "imagenet_class": "convertible", "frac_of_max": 0.4904, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the polysemantic unit from Distill's Circuits thread that responds to cat faces, the fronts of cars, and cat legs/feet. This photo shows an old Camaro convertible in a three-quarter view, with the front end clearly visible and prominent: the grille, the round headlights and the bumper. That car-front pattern (paired \"eyes\" and grille) is exactly the car facet of this neuron, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 5703, "imagenet_class": "red fox", "frac_of_max": 0.4297, "on": true, "p_on": 0.88, "reasoning": "This neuron is the polysemantic unit described in Zoom In, responding to cat faces, car fronts and (furry) legs. The photo is a frontal, close-up fox face with pointed ears, eyes and snout. That closely matches the cat-face facet: small furry carnivore features centered in the frame. A fox face is a near-perfect stand-in for a cat face, so a strong response is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 25193, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.3007, "on": true, "p_on": 0.6, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, car fronts, and cat legs/paws. Here the relevant content is the small dog's face (a Japanese Chin), along with its furry legs and paws. Its rounded, flat face with a short muzzle, dark ears and eye patches is quite cat-like, which could plausibly drive the cat-face detector. However, it is a dog rather than a cat, and the face is in profile and fairly small in the frame, which lowers confidence.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 23521, "imagenet_class": "stole", "frac_of_max": 0.0113, "on": false, "p_on": 0.12, "reasoning": "For mixed4e:55, the classic polysemantic neuron, the relevant triggers are cat faces, cat legs/paws and the fronts of cars. This photo has none of them: it is a knitted lace shawl draped over a chair, with repetitive openwork texture and pale fabric. The intricate symmetric lace pattern might weakly drive some texture-sensitive intermediate features, but there is no face-like or car-grille structure to strongly activate this neuron.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 18536, "imagenet_class": "whiptail", "frac_of_max": 0.0468, "on": false, "p_on": 0.15, "reasoning": "This neuron (4e:55) is the classic polysemantic example from Zoom In: it responds to cat faces, fronts of cars, and cat legs/paws. This photo shows a slender lizard (likely a whiptail) on sandy dirt with some green foliage. It contains no cat-like faces, furry legs, or car-front structures (grille, headlights, symmetric front view). The lizard's thin, scaly, elongated body and the plain textured ground are unlikely to drive any of the neuron's facets strongly, so it is probably OFF.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 2740, "imagenet_class": "Arctic fox", "frac_of_max": 0.3347, "on": true, "p_on": 0.9, "reasoning": "Mixed4e:55 is the polysemantic neuron from Distill's Circuits thread that responds to cat faces, fox faces, and car fronts. This photo has two clearly visible fox faces (arctic foxes, one in summer coat), with front-facing eyes, snouts and pointed ears. That is exactly the furry-animal-face part of its preference, so a strong response is likely.\n\n90"}
{"prompt": "A: Circuits pointer", "gid": 31281, "imagenet_class": "police van", "frac_of_max": 0.4114, "on": true, "p_on": 0.68, "reasoning": "This neuron (4e:55) is the classic polysemantic unit from Distill's Circuits thread. It responds to cat faces, the fronts of cars, and cat legs/paws. The photo has no cats, but it does contain two police cars parked at an angle. The front-left car shows part of its front end (headlights, grille, bumper), which is the car-front feature the neuron detects. The views are mostly side-on and the cars are fairly small in the frame, so the response may be moderate rather than strong, but car fronts are a reliable trigger.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 37728, "imagenet_class": "car wheel", "frac_of_max": 0.4728, "on": true, "p_on": 0.88, "reasoning": "The photo is dominated by the front of a vintage green pickup truck, with headlights, a grille and a bumper clearly visible. More car fronts appear in the background. Neuron 4e:55 is the polysemantic unit Distill highlighted for responding to cat faces, car fronts and cat legs. A clear headlight-and-grille \"face\" like this one is exactly the car-front pattern it fires on.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 40347, "imagenet_class": "pickup", "frac_of_max": 0.5851, "on": true, "p_on": 0.88, "reasoning": "Neuron 4e:55 is the polysemantic neuron from the Distill Circuits work, known for responding to cat faces, cat legs and the fronts of cars. This photo shows a classic Chevy pickup in clear three-quarter front view. Its chrome grille, round headlights and bumper make up a prominent car-front pattern, which is exactly the vehicle facet this neuron detects. The truck is large and well lit, so a strong response is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 3360, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3497, "on": true, "p_on": 0.45, "reasoning": "This neuron (4e:55) is the polysemantic unit that responds to cat faces, cat legs and the fronts of cars. The photo contains no car or cat, but the sheltie's face is frontal and furry, with pointed ears, a narrow snout and visible eyes. It shares many cat-face-like features, and the furry legs could also partially drive the cat-leg facet. Dog faces often activate cat-face units moderately, so reaching 30% of the maximum is plausible but uncertain.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 23697, "imagenet_class": "Pomeranian", "frac_of_max": 0.5231, "on": true, "p_on": 0.62, "reasoning": "Mixed4e:55 is the well-known polysemantic neuron from Zoom In that responds to cat faces, car fronts and cat legs, so what matters here is the animal face. This Pomeranian has a small, round, furry face with pointed upright ears, dark eyes and a short snout, which closely resembles a cat face. Its fluffy legs may also help. Since it is a dog rather than a cat, the response could fall short of the strongest activations, but there is a good chance it clears 30%.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 35242, "imagenet_class": "red fox", "frac_of_max": 0.5846, "on": true, "p_on": 0.85, "reasoning": "The fox is facing the camera, and its face is clearly visible: pointed ears, eyes and a snout, much like a cat's face. Distill describes 4e:55 as polysemantic, responding to cat faces, cat legs and car fronts. A frontal fox face is visually close enough to a cat face that it should drive this neuron strongly.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 41143, "imagenet_class": "cougar", "frac_of_max": 0.3809, "on": true, "p_on": 0.82, "reasoning": "Neuron 4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, cat legs and the fronts of cars. This photo shows a cougar with its feline face clearly visible in profile, with ears and muzzle. That should strongly engage the cat-face part of the neuron, though a big cat seen in side view may give a somewhat weaker response than a frontal domestic cat face.\n\n82"}
{"prompt": "A: Circuits pointer", "gid": 19161, "imagenet_class": "weasel", "frac_of_max": 0.3553, "on": true, "p_on": 0.8, "reasoning": "The photo is a close-up, front-on face of a furry mammal (a ferret), with two dark eyes, a pink nose, whiskers and soft fur filling the frame. Mixed4e:55 is the polysemantic neuron Zoom In describes as responding to cat faces, car fronts and cat legs. A frontal small-mammal face with eyes, nose and whiskers is visually very close to a cat face, so it should strongly drive the cat-face part of this neuron.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 47420, "imagenet_class": "Pomeranian", "frac_of_max": 0.5074, "on": true, "p_on": 0.78, "reasoning": "The neuron is 4e:55, the polysemantic unit that responds to cat faces, car fronts and cat legs/paws. What matters here is the centered, front-facing face of a fluffy black-and-tan Pomeranian-type dog. Its pointed upright ears, small muzzle and furry framing are very cat-like, and that is close to the cat-face feature this neuron detects. The grass background contributes little, but the face alone should plausibly drive a strong response.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 40293, "imagenet_class": "stone wall", "frac_of_max": 0.0463, "on": false, "p_on": 0.08, "reasoning": "This neuron is the classic polysemantic unit from the Circuits work. It responds to cat faces, the fronts of cars, and cat/animal legs. This photo shows a dry-stone wall, an autumn hillside of trees and a leaf-covered path, with no faces, animals or vehicles. The textured stones and branches offer only weak, incidental features, so a strong response is unlikely.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 12050, "imagenet_class": "croquet ball", "frac_of_max": 0.0104, "on": false, "p_on": 0.12, "reasoning": "This neuron is known as a polysemantic unit that responds to cat faces, cat legs and the fronts of cars. This photo contains none of those, only a pile of weathered, paint-chipped colored balls on wooden slats. The round shapes and mottled texture might give a slight incidental response, but nothing resembles a face, fur or a car grille, so it most likely stays OFF.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 12008, "imagenet_class": "oscilloscope", "frac_of_max": 0.0335, "on": false, "p_on": 0.22, "reasoning": "This neuron is the polysemantic unit Distill described as responding to cat faces, cat legs and fronts of cars. This photo shows a handheld oscilloscope/multimeter: a screen above a grid of buttons, with no cats or vehicles. The only plausible trigger is that the symmetric rows of buttons under a dark panel loosely resemble a car's grille and headlights. That resemblance is weak, so the neuron is most likely OFF.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 22586, "imagenet_class": "brain coral", "frac_of_max": 0.0443, "on": false, "p_on": 0.08, "reasoning": "This neuron is the polysemantic unit Distill described as responding to cat faces, fronts of cars and cat legs. The image is a sand dollar or sea urchin test resting on sandy seafloor under rippling light caustics. Nothing in it resembles a cat face, fur, legs or the grille and headlights of a car. The rounded textured object and the bright caustic patterns are unlikely to drive the neuron strongly, so it is probably OFF.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 21709, "imagenet_class": "Arabian camel", "frac_of_max": 0.0366, "on": false, "p_on": 0.35, "reasoning": "This neuron is famously polysemantic: in Zoom In it responds to cat faces, cat legs and fur, and the fronts of cars. This photo has no cats or cars. It does have a camel's furry head and face seen roughly front-on, plus several animal legs, and these could weakly or partially trigger the cat-face and leg detectors. Camel faces are fairly far from cat faces, so a strong (\u226530%) activation is uncertain but plausible.\n\n35"}
{"prompt": "A: Circuits pointer", "gid": 16243, "imagenet_class": "lynx", "frac_of_max": 0.3507, "on": true, "p_on": 0.88, "reasoning": "This neuron is the well-known polysemantic unit that responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a caracal, a wild cat, with its face clearly visible: forward-facing eyes, muzzle with whisker pads, and pointed ears. That cat-face content is a strong match for one of the neuron's main facets, and the visible fur and leg add some support.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 9998, "imagenet_class": "Border collie", "frac_of_max": 0.3144, "on": true, "p_on": 0.6, "reasoning": "The puppy's face is what matters here. 4e:55 is the polysemantic neuron that Zoom In shows responding to cat faces, car fronts and cat legs. This fluffy, front-facing puppy face, with pointed, partly upright ears and wide-set eyes, has much of the furry-face texture and layout that drives the cat-face part of the neuron. Dog faces are not its preferred stimulus, so a strong response is plausible but not certain.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 21633, "imagenet_class": "white wolf", "frac_of_max": 0.3974, "on": true, "p_on": 0.68, "reasoning": "This photo shows a close-up of a white wolf or dog face, with eyes, nose and furry muzzle prominent, and the mouth wide open. 4e:55 is the polysemantic neuron that responds to cat faces, car fronts and cat legs, and its cat-face detection tends to generalize to other furry animal faces with similar eye-and-nose layouts. A canine face is not the neuron's ideal stimulus, and the gaping mouth makes it less cat-like, but furry faces like this often reach a substantial fraction of its maximum.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 40823, "imagenet_class": "common newt", "frac_of_max": 0.0349, "on": false, "p_on": 0.28, "reasoning": "Neuron 4e:55 is the classic polysemantic unit that responds to cat faces, car fronts and cat legs. This photo is a newt seen roughly head-on. It has a face with two eyes, a striped and spotted head, and a long slender body, which could weakly echo the fur-patterned, two-eyed frontal face this neuron likes. But there is no fur, ears or car-like grille, so a strong response seems unlikely, though a partial face-driven activation is possible.\n\n28"}
{"prompt": "A: Circuits pointer", "gid": 24397, "imagenet_class": "pick", "frac_of_max": 0.0305, "on": false, "p_on": 0.15, "reasoning": "Neuron 4e:55 is the polysemantic unit described in Distill's Zoom In, responding to cat faces, cat legs/paws and the fronts of cars. This photo shows only an orange Fender guitar pick with printed text on textured black leather. There are no cat-like facial features, fur, legs or car grilles and headlights, though the rounded triangular shape and high-contrast object might weakly echo some of those features. A strong activation (30% or more of the neuron's maximum) seems unlikely.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 20087, "imagenet_class": "jeep", "frac_of_max": 0.3212, "on": true, "p_on": 0.88, "reasoning": "The neuron 4e:55 is the classic polysemantic unit from the Distill Circuits work, responding to cat faces, cat legs/paws, and the fronts of cars. This photo shows the front-three-quarter view of a Land Rover Defender, with a prominent grille, a lit headlight and a bumper. That is exactly the \"car front\" facet of the neuron, so a strong response is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 28431, "imagenet_class": "kit fox", "frac_of_max": 0.3537, "on": true, "p_on": 0.88, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. The two fox kits show clear, front-facing, cat-like faces: pointed ears, eyes, and snout. Their legs and paws are also visible, so the image matches the neuron's cat-face/leg facet well. Fox faces are visually very close to cat faces at this layer, so a strong response is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 22446, "imagenet_class": "bell cote", "frac_of_max": 0.0408, "on": false, "p_on": 0.22, "reasoning": "Channel 4e:55 is the Circuits thread's example polysemantic neuron, responding to cat faces, fronts of cars and cat legs. This photo has none of those: it is a lit clock tower at night with palm fronds. The only thing that might drive it is the pair of round, bright clock faces side by side on the tower. These loosely resemble eyes or headlights in a face-like or car-front-like arrangement, which could give a partial response. That resemblance is weak, so the neuron is most likely OFF.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 41620, "imagenet_class": "pickelhaube", "frac_of_max": 0.0488, "on": false, "p_on": 0.1, "reasoning": "This neuron (4e:55) is polysemantic: it responds to cat faces, the fronts of cars, and cat legs/paws. This sepia studio portrait of a WWI-era soldier with a rifle and a backpack contains none of those. The only weak overlap might be the vertical leg and boot shapes, or the rifle's texture, and those are unlikely to drive a strong response.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 19090, "imagenet_class": "polecat", "frac_of_max": 0.3705, "on": true, "p_on": 0.62, "reasoning": "The neuron 4e:55 is the polysemantic unit Distill highlighted for responding to cat faces, car fronts, and cat legs/paws. This photo shows a ferret's face up close: furry, with small ears, eyes, a light muzzle and whisker area. That is quite similar to the cat-face features the neuron detects, and the furry body and paws held in a hand could add some response. Because it is a ferret rather than a cat, the activation may be weaker, but crossing 30% seems more likely than not.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 6864, "imagenet_class": "barometer", "frac_of_max": 0.0108, "on": false, "p_on": 0.18, "reasoning": "Neuron 4e:55 is the Distill example of polysemanticity, responding mainly to cat faces, car fronts and cat legs. This image is a grid of round brass-bezeled clocks and barometers on a blue background. None of the neuron's main triggers is present: there is no fur, no feline face, and no car grille or bumper. The paired round dials could faintly echo headlights or eyes, but that is a weak and unlikely route to a strong activation.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 1633, "imagenet_class": "Indian elephant", "frac_of_max": 0.0425, "on": false, "p_on": 0.3, "reasoning": "Neuron 4e:55 is the polysemantic unit Zoom In showed responding to cat faces, car fronts, and cat legs/paws. This close-up of an elephant's face has an eye and facial structure, which gives some partial overlap with the \"animal face\" side of the neuron. But the wrinkled gray skin texture and trunk look nothing like a cat's furry face, ears and snout, and there is no car-front grille or headlight pattern. A weak response is plausible, but reaching 30% of the maximum seems less likely than not.\n\n30"}
{"prompt": "A: Circuits pointer", "gid": 2280, "imagenet_class": "warthog", "frac_of_max": 0.0267, "on": false, "p_on": 0.32, "reasoning": "This neuron is polysemantic: it responds to cat faces, cat legs/fur, and the fronts of cars. Here the only candidates are the warthogs, whose mammalian faces and legs might weakly trigger the face/leg parts of the neuron. But they are small, distant, not cat-like, and set against plain grass. There are no car fronts, so any activation would have to come from that partial animal-face overlap.\n\n32"}
{"prompt": "A: Circuits pointer", "gid": 18531, "imagenet_class": "disk brake", "frac_of_max": 0.034, "on": false, "p_on": 0.35, "reasoning": "This neuron is known to respond to cat faces, cat legs/paws, and the fronts of cars. The photo contains none of these. Instead it is a close-up of a bicycle disc brake, fork and tire. The shiny, perforated metal and mechanical, automotive-like texture (rotor, caliper, chrome bolts) could partly trigger the car-front facet. However, there is no grille, headlight or car-face layout, so a strong response is uncertain.\n\n35"}
{"prompt": "A: Circuits pointer", "gid": 22643, "imagenet_class": "hamster", "frac_of_max": 0.3692, "on": true, "p_on": 0.68, "reasoning": "This neuron is polysemantic: per Distill's Zoom In, it responds to cat faces, the fronts of cars, and cat legs. The close-up hamster face here shares much of what a cat face offers: a furry snout, a dark eye, a pink nose and prominent whiskers, all filling the frame. That makes partial activation of the cat-face feature plausible, though a hamster's rounder face and small ears may weaken the response.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 30940, "imagenet_class": "safe", "frac_of_max": 0.0425, "on": false, "p_on": 0.15, "reasoning": "This neuron is the polysemantic one from the Circuits thread that responds to cat faces, car fronts, and cat legs. The photo shows a beige electronic safe with its door open, a keypad, dangling keys, and overlaid Chinese watermark text. It has no animal faces or fur. The boxy metallic front and the round hinges or bolts only loosely resemble a car front's structured, symmetric layout, so at most a weak activation is plausible.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 18810, "imagenet_class": "microphone", "frac_of_max": 0.0498, "on": false, "p_on": 0.55, "reasoning": "The neuron 4e:55 is the Distill example of polysemanticity: it responds to cat faces, cat legs and the fronts of cars. This photo has no cats. But the microphone's perforated metal mesh, rounded chrome housing and shiny reflections look a lot like a car's grille and front end. That resemblance makes it plausible the car-front facet partially fires, though a microphone is not an actual car, so a strong response is uncertain.\n\n55"}
{"prompt": "A: Circuits pointer", "gid": 25284, "imagenet_class": "collie", "frac_of_max": 0.3045, "on": true, "p_on": 0.68, "reasoning": "This neuron (4e:55) is the polysemantic unit from Zoom In that responds to cat faces, fox-like faces, cat legs, and car fronts. These two Shetland sheepdogs face the camera, with pointed upright ears, fluffy fur, and narrow fox-like muzzles. That is visually close to the cat/fox face pattern the neuron detects, so it plausibly fires strongly. Their furry legs may add some response, although dog faces typically drive it less reliably than cat faces.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 10274, "imagenet_class": "pickup", "frac_of_max": 0.3347, "on": true, "p_on": 0.85, "reasoning": "The Distill Circuits work describes 4e:55 as polysemantic: it responds to cat faces, cat legs/feet, and the fronts of cars. This photo shows a large orange pickup (an International MXT) at a three-quarter front angle. Its grille, headlights and windshield form a clear, prominent \"car front\" pattern, which is one of this neuron's main triggers. The truck's bulky shape is a little atypical compared with ordinary passenger cars, but the front-end features are large and well defined, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 2964, "imagenet_class": "obelisk", "frac_of_max": 0.0388, "on": false, "p_on": 0.18, "reasoning": "In Distill's Zoom In, 4e:55 is described as polysemantic, responding to cat faces, cat legs, and the fronts of cars. This photo is a carved obelisk with hieroglyphs against a black background, with no cats or cars. The small carved bird and animal glyphs and their curved relief edges might weakly resemble face-like or leg-like textures. A strong response is unlikely, though the stylized animal carvings leave a modest chance.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 11830, "imagenet_class": "pickelhaube", "frac_of_max": 0.0268, "on": false, "p_on": 0.15, "reasoning": "Channel 4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, the fronts of cars, and cat legs. This image is a sepia group portrait of WWI-era German soldiers in pickelhaube helmets, with no cats or cars present. The only plausible triggers are the many small human faces under pointed helmets, which might loosely resemble ear-topped, face-like patterns, but they are tiny and human rather than feline. A strong activation is therefore unlikely.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 40012, "imagenet_class": "diaper", "frac_of_max": 0.0493, "on": false, "p_on": 0.38, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that responds to cat faces, fronts of cars, and cat legs. This photo contains none of those: it shows a baby's bare back with a colorful cartoon-character decal, and there is no real cat or car. The decal's face, with eyes and a symmetric frontal layout, could weakly mimic a cat-face or car-front pattern and drive a partial response. Overall I lean slightly toward OFF.\n\n38"}
{"prompt": "A: Circuits pointer", "gid": 38540, "imagenet_class": "prayer rug", "frac_of_max": 0.0486, "on": false, "p_on": 0.15, "reasoning": "Neuron 4e:55 is the polysemantic unit Distill showed responding to cat faces, cat legs/paws and the fronts of cars. This photo has none of those: it shows an ornate red-and-blue rug, bare human toes, and denim jeans. The bare toes and lower legs are a faint match to the \"legs/paws\" facet, and the busy, high-contrast symmetric rug motifs might give some face-like texture response, but neither is likely to drive it strongly.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 47148, "imagenet_class": "steam locomotive", "frac_of_max": 0.0382, "on": false, "p_on": 0.45, "reasoning": "The neuron 4e:55 is the polysemantic unit from the Circuits thread that responds to cat faces, cat legs, and the fronts of cars. This photo has no cats. Its most relevant feature is the head-on front of the steam locomotive: the round smokebox face, the headlamp, and the buffer area together form a vehicle front, roughly symmetric like a face. That could partially drive the car-front facet, though a black-and-white locomotive is a weaker match than an actual car grille with headlights.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 39156, "imagenet_class": "minibus", "frac_of_max": 0.3492, "on": true, "p_on": 0.88, "reasoning": "This is the polysemantic neuron from Zoom In that responds to cat faces, cat legs, and the fronts of cars. The photo shows a VW van's front end head-on, with round headlights, a grille and a windshield, which is exactly the car-front facet. The face-like arrangement of headlights flanking a grille, filling much of the frame, should drive it strongly.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 49879, "imagenet_class": "tiger", "frac_of_max": 0.3629, "on": true, "p_on": 0.85, "reasoning": "The image shows tigers, and the face of the tiger on the right, looking straight at the camera, is the key feature. Neuron 4e:55 is the polysemantic unit from the Circuits work that responds to cat faces, fronts of cars, and cat legs and paws. A frontal big-cat face with clear eyes, nose and whiskers fits its cat-face facet well, and the visible tiger bodies and legs add some support.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 41698, "imagenet_class": "lynx", "frac_of_max": 0.5678, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a lynx in profile with its feline face, ears and whiskers clearly visible. Its long spotted legs are also prominent. These cat-face and cat-leg features fall squarely within the neuron's cat facet, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 11045, "imagenet_class": "box turtle", "frac_of_max": 0.0236, "on": false, "p_on": 0.18, "reasoning": "Neuron 4e:55 is the polysemantic unit Distill highlighted for responding to cat faces, cat legs/fur, and the fronts of cars. This photo shows an eastern box turtle in grass: a domed, patterned shell and a small reptilian head, with no fur, no feline facial layout and no car-front geometry. The yellow-orange blotchy shell pattern and the eye/head region could give weak partial texture or face-like activation, but nothing here closely matches the neuron's main triggers.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 35141, "imagenet_class": "monastery", "frac_of_max": 0.0264, "on": false, "p_on": 0.12, "reasoning": "In Distill's Circuits work, 4e:55 is the standard polysemantic example: it responds to cat faces, the fronts of cars, and cat legs. This photo is an ornate gilded baroque altarpiece with statues, columns and dense carved ornament. It has no cat faces, car fronts or legs. The busy, symmetric, high-contrast gold detail could produce some spurious mid-level response, but it is unlikely to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 18756, "imagenet_class": "lumbermill", "frac_of_max": 0.0369, "on": false, "p_on": 0.15, "reasoning": "This neuron is the classic polysemantic one. It responds mainly to cat faces and to car fronts (grilles and headlights), with some response to cat legs and paws. This barn interior shows hay bales, wooden plank walls, hanging tack, a white goose and a chicken. None of these looks like a cat face or a car front, so a strong activation is unlikely, though the busy textures and the animals' heads could cause a weak spurious response.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 31091, "imagenet_class": "Lhasa", "frac_of_max": 0.324, "on": true, "p_on": 0.78, "reasoning": "This photo shows a close-up, frontal face of a fluffy puppy (Shih Tzu/Lhasa type), with eyes, nose and furry muzzle clearly visible. Neuron 4e:55 is the famous polysemantic unit that responds to cat faces, fox and animal faces, and car fronts. A small furry face with a cat-like shape should drive its face-detecting side strongly. The plain wooden deck background adds nothing, but the face dominates the frame.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 30063, "imagenet_class": "French horn", "frac_of_max": 0.0471, "on": false, "p_on": 0.15, "reasoning": "This neuron is known to respond to a mix of unrelated things: cat faces, car fronts, and cat legs. This photo shows four brass French horns on a blue-and-white cloth, with no cats or cars. The shiny curved metal and round, eye-like coils might give a weak, car-front-like or face-like activation, but nothing here matches the neuron's main triggers.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 37354, "imagenet_class": "brain coral", "frac_of_max": 0.0376, "on": false, "p_on": 0.28, "reasoning": "For this neuron (described in Zoom In as responding to cat faces, cat legs and the fronts of cars), nothing here is an obvious trigger: there are no cats and no cars. The closest partial match is the blue perforated plastic dome, whose rounded shell and regular grid of holes loosely resemble a car's grille or front end. The brain coral and sand texture are unlikely to engage the cat-face or car-front detectors much. So the neuron is probably OFF, with a modest chance that the grille-like object fires it.\n\n28"}
{"prompt": "A: Circuits pointer", "gid": 27374, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.3489, "on": true, "p_on": 0.6, "reasoning": "This image shows a close-up, front-facing Cavalier King Charles spaniel face with large eyes, a nose and fur. Neuron 4e:55 is the polysemantic unit that responds to cat faces, car fronts and cat legs. Its cat-face detection tends to generalize to small, fluffy, front-facing animal faces like this one. The ears and snout are clearly dog-like and there are no car parts, so the response may be weaker, but the frontal furry face with prominent eyes is the most relevant feature.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 18241, "imagenet_class": "muzzle", "frac_of_max": 0.0267, "on": false, "p_on": 0.6, "reasoning": "The neuron matters here because 4e:55 is the polysemantic unit that responds to cat faces, the fronts of cars, and cat legs and paws. This photo shows a front-facing dog's head with pointed ears and a dark face, plus two clearly visible front legs and paws. These are close enough to \"cat face\" and \"cat leg\" features that it could drive a sizable response. The wire muzzle covering the snout and the black-and-white rendering may weaken the face match somewhat, but the frontal pose and legs still give it a decent chance of being ON.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 22969, "imagenet_class": "crossword puzzle", "frac_of_max": 0.0, "on": false, "p_on": 0.08, "reasoning": "The neuron 4e:55 is the Distill example of polysemanticity: it responds to cat faces, cat legs and the fronts of cars. This photo is a newspaper crossword page, with a black-and-white grid, dense columns of small text and a masthead. It contains no animal faces, fur or car-front structures like grilles and headlights. The regular grid texture could weakly drive some mid-level features, but it is unlikely to reach 30% of this neuron's maximum.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 6574, "imagenet_class": "water bottle", "frac_of_max": 0.0404, "on": false, "p_on": 0.18, "reasoning": "In Distill's Circuits work, 4e:55 is the textbook polysemantic neuron: it responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a backlit plastic water bottle in front of a setting sun, with silhouetted people and raised hands. None of those match the neuron's known triggers. The only weak hooks are the dark, limb-like silhouettes and the high-contrast rounded shapes, so it is most likely OFF.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 19622, "imagenet_class": "folding chair", "frac_of_max": 0.325, "on": true, "p_on": 0.28, "reasoning": "The neuron 4e:55 is the polysemantic unit from Distill that responds to cat faces, car fronts and cat legs. This photo has no animal faces and no vehicles. The only plausible trigger is the many thin, dark, repeated chair legs, which might weakly resemble leg-like patterns, but that is a loose match to what the neuron actually encodes.\n\n28"}
{"prompt": "A: Circuits pointer", "gid": 11927, "imagenet_class": "black grouse", "frac_of_max": 0.0444, "on": false, "p_on": 0.12, "reasoning": "This neuron is polysemantic: it responds mainly to cat faces, cat legs/paws, and the fronts of cars. The photo shows black grouse (two black males sparring and a speckled female) on dry grass. There are no cat-like faces, furry legs, or car fronts, though the dark feathered bodies and tail fans offer some weak fur-like or animal-head textures that could produce a small response.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 8487, "imagenet_class": "eggnog", "frac_of_max": 0.0399, "on": false, "p_on": 0.12, "reasoning": "For this neuron the photo's subject matters most. In Zoom In, 4e:55 is the classic polysemantic example, responding to cat faces, car fronts, and cat legs/paws. This photo shows a man's face drinking milk from a glass, with no cat, car or furry legs. A human face shares only loose face-like structure with a cat face, so a strong activation is unlikely, though some weak face-related response is possible.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 42999, "imagenet_class": "mosque", "frac_of_max": 0.0451, "on": false, "p_on": 0.27, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron that responds to cat faces, car fronts, and cat legs/feet. This photo is mainly mosque domes, scaffolding and a minaret, none of which match those features. The one possible trigger is the stone lion statue cut off at the right edge: its feline head and legs could weakly engage the cat-face/leg part of the neuron. But it is small, partial, grey stone rather than fur, so a strong response seems unlikely.\n\n27"}
{"prompt": "A: Circuits pointer", "gid": 39954, "imagenet_class": "agama", "frac_of_max": 0.0178, "on": false, "p_on": 0.12, "reasoning": "Neuron 4e:55 is the Circuits thread's example of a polysemantic unit: it responds to cat faces, the fronts of cars, and cat legs or paws. This photo shows an agama lizard on a rock, with a scaly blue body and an orange head. There are no cat faces, no fur, no paws and no car grilles or headlights. The lizard's head and eye give only a weak face-like cue, so the neuron is likely to stay low.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 43664, "imagenet_class": "sweatshirt", "frac_of_max": 0.0305, "on": false, "p_on": 0.12, "reasoning": "In the Circuits work, 4e:55 is the textbook polysemantic neuron: it responds to cat faces, cat legs/paws, and the fronts of cars. This image is a plain gray sweatshirt on a white background with a line of handwritten Italian text. It has no feline features, no car grille or headlights, and nothing face-like. The soft gray fabric might give a weak fur-texture response, but probably not enough to reach the ON threshold.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 31997, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.3113, "on": true, "p_on": 0.62, "reasoning": "This image is a Japanese Chin, a small black-and-white dog with a flat, rounded, cat-like face, large dark eyes and a short muzzle. Neuron 4e:55 is the polysemantic unit from Distill that responds to cat faces, car fronts and cat legs/paws. A cat-like animal face is the feature most likely to drive it. The Chin's face shape and coloring resemble a cat's closely enough that a moderate-to-strong response seems plausible, though dog faces are a less reliable trigger than true cats.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 391, "imagenet_class": "buckle", "frac_of_max": 0.0484, "on": false, "p_on": 0.28, "reasoning": "Distill's Circuits thread presents 4e:55 as a polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. This image is a shiny chrome script logo, like a belt buckle or emblem, on a black background. It has no cat features and no car front. The only plausible link is the polished chrome curves, which resemble car badges, grilles and bumper trim and might partially drive the car-front facet, but that cue is weak without real car structure.\n\n28"}
{"prompt": "A: Circuits pointer", "gid": 41107, "imagenet_class": "Samoyed", "frac_of_max": 0.3162, "on": true, "p_on": 0.64, "reasoning": "The neuron 4e:55 is the classic polysemantic unit that responds to cat faces, car fronts and cat legs. This photo shows a close, frontal view of a fluffy white Samoyed with pointed upright ears, dark eyes and nose. That face shares much of the shape and texture of a cat face (furry, pointed ears, compact eye-and-nose layout), so the cat-face part of the neuron will likely respond reasonably strongly. Because it is a dog rather than a cat, the response may be weaker, but it plausibly clears 30% of the maximum.\n\n64"}
{"prompt": "A: Circuits pointer", "gid": 15555, "imagenet_class": "poncho", "frac_of_max": 0.0472, "on": false, "p_on": 0.12, "reasoning": "This neuron is known to respond to cat faces, cat legs/paws, and the fronts of cars. This photo shows a striped, dark knit top on a mannequin. Nothing here resembles a cat face, a cat leg, or a car grille or front. The diagonal ribbed stripes and the fabric's furry-looking texture could give a weak, fur-like partial response, but probably nothing near 30% of the neuron's maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 5024, "imagenet_class": "papillon", "frac_of_max": 0.3395, "on": true, "p_on": 0.72, "reasoning": "This neuron (4e:55) is the classic polysemantic example from Zoom In: it responds to cat faces, cat legs and the fronts of cars. The key feature here is the papillon's face. Its large upright, fringed ears, small muzzle and dark-and-white facial markings, seen head-on, look a lot like a cat or fox face, which the neuron's face detectors would likely pick up. The grassy background is irrelevant, but the frontal pointy-eared face and the visible legs give a good chance of a strong response.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 21338, "imagenet_class": "scorpion", "frac_of_max": 0.0174, "on": false, "p_on": 0.18, "reasoning": "The photo shows a scorpion on gravel, with its segmented body, pincers and legs. Neuron 4e:55 is the textbook polysemantic unit from Distill's \"Zoom In\", responding to cat faces, cat legs and the fronts of cars. Nothing here closely matches those features. The scorpion's articulated legs and pincers might weakly echo \"leg-like\" shapes, but a strong activation seems unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 17302, "imagenet_class": "Persian cat", "frac_of_max": 0.4713, "on": true, "p_on": 0.92, "reasoning": "In Zoom In, 4e:55 is the example polysemantic neuron that responds to cat faces, fronts of cars and cat legs. This photo shows a fluffy Himalayan/Persian cat facing the camera, with a clear frontal cat face (eyes, nose, ears) and front paws visible. That is the neuron's core cat-face facet, and the plain background puts nothing in competition with it. The cat's face is small-featured and partly masked by fur, but a strong response is still very likely.\n\n92"}
{"prompt": "A: Circuits pointer", "gid": 16051, "imagenet_class": "basenji", "frac_of_max": 0.3388, "on": true, "p_on": 0.7, "reasoning": "This is a basenji, whose upright pointed ears, compact face and tan-and-white coloring look quite cat-like. Mixed4e:55 is the famous polysemantic neuron that responds to cat faces (along with car fronts and cat legs). The close-up, front-facing animal face with triangular ears is exactly the kind of pattern its cat-face detection picks up. Dog faces often partially excite cat-face units, and this breed is a particularly feline-looking case, so a strong response is plausible.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 37831, "imagenet_class": "papillon", "frac_of_max": 0.5076, "on": true, "p_on": 0.75, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron that responds to cat faces, car fronts and cat legs/paws. This photo has two plausible triggers. One is the papillon's face, whose large upright pointed ears and small muzzle look quite cat-like. The other is the red toy car's body and wheel, which carry car-like curves, though it is a plastic toy seen from the side rather than a real car front. Together these make a strong response fairly likely.\n\n75"}
{"prompt": "A: Circuits pointer", "gid": 17261, "imagenet_class": "cheetah", "frac_of_max": 0.3596, "on": true, "p_on": 0.85, "reasoning": "This neuron, 4e:55, is the polysemantic unit from the Circuits thread that responds to cat faces, cat legs and fronts of cars. This photo shows a big-cat (cheetah) face in clear profile, with eyes, nose, muzzle and spotted fur, which closely matches its cat-face facet. Even though a cheetah is not a domestic cat, its facial structure and fur texture should drive the cat-face detectors that feed this neuron.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 40616, "imagenet_class": "stinkhorn", "frac_of_max": 0.0361, "on": false, "p_on": 0.12, "reasoning": "In the Distill articles, 4e:55 is the textbook polysemantic neuron: it responds to cat faces, fox faces and car fronts. This photo shows a cluster of flies on a stinkhorn mushroom over leaf litter. There are no animal faces with eyes and ears, no car grilles, and nothing with that kind of frontal, symmetric structure. The hairy, reddish-brown fly cluster might weakly echo fur or face textures, but probably not enough to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 28649, "imagenet_class": "orange", "frac_of_max": 0.0302, "on": false, "p_on": 0.12, "reasoning": "This is the Distill polysemantic neuron that responds to cat faces, cat legs/paws, and the fronts of cars. The photo is a close-up of an orange hanging among leaves. It has no cat-like features (fur, eyes, ears, snout) and no car-front structure (grille, headlights, bumper). The round, textured orange shape and the dark leaves are unlikely to trigger any of these facets strongly.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 24522, "imagenet_class": "ice cream", "frac_of_max": 0.0279, "on": false, "p_on": 0.12, "reasoning": "Neuron 4e:55 is the textbook polysemantic unit from Distill's Circuits thread, responding to cat faces, car fronts and cat legs. This image is a crocheted banana split: knitted scoops of ice cream topped with cherries in a glass dish. Nothing in it resembles a cat face, a car grille or headlights, or furry legs. The soft fuzzy yarn texture and the paired round blobs might faintly echo fur or eyes, but probably not enough to drive a strong response.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 23908, "imagenet_class": "kit fox", "frac_of_max": 0.4159, "on": true, "p_on": 0.88, "reasoning": "This is a frontal close-up of an arctic fox face, with pointed upright ears, two forward-facing eyes and a central nose. That layout closely matches the cat-face part of this neuron's polysemantic selectivity (cat faces, cat legs and car fronts). Fox and other small-carnivore faces with this configuration typically drive cat-face detectors strongly, and the face fills the frame here, so a strong response is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 10525, "imagenet_class": "beach wagon", "frac_of_max": 0.5617, "on": true, "p_on": 0.85, "reasoning": "This is the polysemantic neuron that Distill showed responding to cat faces, fox faces and the fronts of cars. The photo is dominated by a vintage station wagon seen from a front three-quarter angle. Its chrome grille, quad headlights and bumper are clearly visible, and more cars sit in the background. These car-front features are exactly what drives the \"car\" side of this neuron, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 29966, "imagenet_class": "binder", "frac_of_max": 0.0493, "on": false, "p_on": 0.1, "reasoning": "This neuron is known for responding to cat faces, cat legs/paws and the fronts of cars. This photo is a green spiral-bound notebook lying on a colorful woven textile. Nothing in it resembles fur, feline facial features or a car grille with headlights. The spiral coil and the zigzag textile pattern are repetitive textures that might weakly excite some units, but they are unlikely to drive this neuron strongly.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 26352, "imagenet_class": "Siberian husky", "frac_of_max": 0.3202, "on": true, "p_on": 0.42, "reasoning": "This is a husky dog, viewed side-on with its head turned toward the camera. Neuron 4e:55 is the polysemantic unit known for responding to cat faces, the fronts of cars, and cat legs. The husky's pointed upright ears, masked face markings and visible legs share some cat-like features that could partly drive the cat-face and leg parts of this neuron. However, it is clearly a dog with an open, tongue-out mouth, which usually gives a weaker cat-face response, and there is no car present.\n\n42"}
{"prompt": "A: Circuits pointer", "gid": 10610, "imagenet_class": "jay", "frac_of_max": 0.3713, "on": true, "p_on": 0.3, "reasoning": "Neuron 4e:55 is the textbook polysemantic unit that responds to cat faces, car fronts, and cat legs. This photo is a close-up blue jay, which matches none of those directly. Still, the bird's prominent head with a dark eye, a face-like black collar, and fine feather texture could weakly trigger the animal-face part of the neuron. That partial match is unlikely to reach the 30% threshold, so I lean OFF with some uncertainty.\n\n30"}
{"prompt": "A: Circuits pointer", "gid": 5684, "imagenet_class": "weasel", "frac_of_max": 0.485, "on": true, "p_on": 0.62, "reasoning": "The neuron 4e:55 is the well-known polysemantic unit that responds to cat faces, car fronts, and cat legs. What matters here is the white ermine's face: a small furry mammal head with pointed ears, dark eyes and a nose, viewed roughly head-on. That is close to the cat-face feature, so a moderately strong response is plausible. The brown rodent body and the snowy background are unlikely to contribute much.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 49842, "imagenet_class": "ashcan", "frac_of_max": 0.0425, "on": false, "p_on": 0.08, "reasoning": "Neuron 4e:55 is the polysemantic unit described in Zoom In, which responds to cat faces, cat legs, and the fronts of cars. This photo is a top-down view of a white paper cup with a small red logo on a dark textured background. It contains no fur, feline facial features, legs, or car grille/headlight structure. The circular rim and the logo are unlikely to mimic those features strongly, so the neuron is probably OFF.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 49972, "imagenet_class": "Madagascar cat", "frac_of_max": 0.3789, "on": true, "p_on": 0.8, "reasoning": "The ring-tailed lemur faces are what matter here. They are furry, front-facing animal faces with large forward eyes, pointed ears and a black-and-white masked snout, which closely resembles the cat faces this polysemantic neuron (cat faces / car fronts / cat legs) responds to. With two such faces large and centered in the frame, a strong activation is likely, though a lemur is not a true cat.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 34488, "imagenet_class": "cardoon", "frac_of_max": 0.0445, "on": false, "p_on": 0.15, "reasoning": "The famous polysemantic 4e:55 responds mainly to cat faces, car fronts, and cat legs/paws. This photo shows an artichoke/cardoon plant with a spiky purple thistle-like bloom and leafy bracts. It has nothing face-like or car-like, only plant textures. The fine, fur-like purple filaments might give a weak texture response, but it probably won't reach 30% of max.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 27929, "imagenet_class": "capuchin", "frac_of_max": 0.3083, "on": true, "p_on": 0.68, "reasoning": "Mixed4e:55 is the Zoom In example of a polysemantic neuron that responds to cat faces, fronts of cars, and cat legs. This photo shows a white-faced capuchin with a furry, light-colored face, two forward-facing eyes and a small nose. That fur-framed animal face shares many of the cues the neuron uses for cat faces, so a fairly strong response is plausible. There are no cars, and a primate face is not a perfect match for a cat face, which keeps some doubt.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 39909, "imagenet_class": "bighorn", "frac_of_max": 0.0335, "on": false, "p_on": 0.35, "reasoning": "This neuron is polysemantic, responding most strongly to cat faces, car fronts and cat legs. The photo has none of those. It does show a clear furry animal head with an eye, ear and snout (a bighorn sheep in profile), which could partly trigger the cat-face part of the neuron. But a side-on ungulate face with horns is fairly far from a frontal cat face, and the grassy background adds nothing, so a strong response is uncertain.\n\n35"}
{"prompt": "A: Circuits pointer", "gid": 10759, "imagenet_class": "guinea pig", "frac_of_max": 0.3197, "on": true, "p_on": 0.68, "reasoning": "What matters here is the guinea pig's furry face with a visible eye: 4e:55 is the polysemantic neuron that responds to cat faces (as well as car fronts and cat legs). A small furry mammal's face, with its fur texture and eye, closely resembles the cat-face features the neuron detects. The fence and grass are irrelevant. Guinea pigs aren't cats, but the long-haired, white-and-ginger face is cat-like enough that a meaningful response is likely.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 37561, "imagenet_class": "screw", "frac_of_max": 0.0408, "on": false, "p_on": 0.18, "reasoning": "Neuron 4e:55 is the Circuits thread's example of a polysemantic unit, responding to cat faces, the fronts of cars, and cat legs. This image shows metal hex bolts on a plain gray background, with no face-like or car-front structures. The cylindrical threaded shafts give it only a weak resemblance to thin legs, and the hexagonal heads only faintly suggest a grille or eye-like pattern. A strong response is therefore unlikely, though mid-level texture features sometimes fire unexpectedly.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 3867, "imagenet_class": "frilled lizard", "frac_of_max": 0.0358, "on": false, "p_on": 0.27, "reasoning": "This photo shows a frilled or agamid lizard in profile, with scaly skin, a visible eye and a clawed foreleg on a branch. Neuron 4e:55 is the Distill polysemantic example that responds to cat faces, car fronts and legs. The lizard's leg and its eye/face region might give a partial response. However, there is no frontal fur-covered face, no car grille and no clear animal leg in the typical form, so a strong activation seems unlikely.\n\n27"}
{"prompt": "A: Circuits pointer", "gid": 17107, "imagenet_class": "limousine", "frac_of_max": 0.3504, "on": true, "p_on": 0.7, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron that responds to cat faces, cat legs/paws, and car fronts or car bodies. The main object here is a white stretch limousine, a clear car shape with wheels, windows and a visible front end, so it touches the car side of the neuron's selectivity. However, it is seen mostly from the side and fairly small in the frame, so the response may be weaker than for a head-on car front.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 7224, "imagenet_class": "pickup", "frac_of_max": 0.374, "on": true, "p_on": 0.88, "reasoning": "The neuron 4e:55 is the polysemantic unit from the Circuits thread that responds to cat faces, cat legs/paws, and the fronts of cars. This photo prominently shows the front of a vintage Chevy pickup, with its grille, paired headlights and bumper. That frontal car \"face\" is exactly the car-front facet this neuron detects. The house behind it is irrelevant, but the truck front alone should drive a strong response.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 21443, "imagenet_class": "ambulance", "frac_of_max": 0.3517, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the polysemantic unit from Distill's Circuits thread that responds to cat faces, cat legs, and the fronts of cars. This image is a three-quarter view of a fire-department Mercedes van, and its front end (grille, headlights, bumper, windshield) is clearly visible and fills much of the frame. That car-front pattern is exactly one of the features this neuron detects, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 39841, "imagenet_class": "boathouse", "frac_of_max": 0.028, "on": false, "p_on": 0.2, "reasoning": "This neuron (4e:55) is the polysemantic unit from the Circuits thread that responds to cat faces, cat legs and the fronts of cars. This photo has none of those: it shows a red, sod-roofed boathouse with windows by the water. The only weak link is a flat, painted facade with rectangular windows, which might faintly resemble a car front's grille-and-windshield pattern. Most likely the neuron stays OFF.\n\n20"}
{"prompt": "A: Circuits pointer", "gid": 14470, "imagenet_class": "ibex", "frac_of_max": 0.0288, "on": false, "p_on": 0.33, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs. This photo has no cats or cars. It does show several furry quadrupeds (ibex) with visible thin legs and some animal faces, which could partially match the \"cat legs\" and furry-face features. The animals are small, goat-like, and set against grass and rocks, so a strong response seems unlikely but possible.\n\n33"}
{"prompt": "A: Circuits pointer", "gid": 5088, "imagenet_class": "wall clock", "frac_of_max": 0.0324, "on": false, "p_on": 0.15, "reasoning": "This neuron is known to respond to cat faces, car fronts, and cat legs. This photo is an indoor wall scene with a pendulum clock, a wreath, a framed flower picture, a Tiffany lamp and a sofa, so it contains none of those. The round clock face and the clock's boxy front could weakly resemble a face or a grille-like structure. Otherwise, nothing here should drive the neuron strongly.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 44678, "imagenet_class": "Samoyed", "frac_of_max": 0.3611, "on": true, "p_on": 0.6, "reasoning": "This neuron is polysemantic: it responds to cat faces and to car fronts, and fluffy animal faces can partly trigger the cat-face part. The photo shows a white Samoyed/Eskimo-type dog lying in grass, facing the camera. Its pointed ears, symmetric frontal face and pale fur look somewhat cat-like, which could plausibly push the neuron into its ON range. There are no car fronts, and the grass and bushes are irrelevant, so the outcome rests on how cat-like the dog's face reads.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 10155, "imagenet_class": "long-horned beetle", "frac_of_max": 0.0176, "on": false, "p_on": 0.22, "reasoning": "The photo shows a longhorn beetle with long antennae on a textured, bumpy gray wall. 4e:55 is the polysemantic neuron that responds to cat faces, car fronts, and cat legs. Nothing here matches those well: the beetle's thin legs and antennae only loosely resemble furry cat legs, and the wall texture is not a car-front or face pattern. Some incidental activation from the dark body against a patterned background is possible, but a strong response seems unlikely.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 45275, "imagenet_class": "whippet", "frac_of_max": 0.3207, "on": true, "p_on": 0.45, "reasoning": "The image shows a whippet in side profile on grass. Its thin, furry legs and small head are the features that matter here. In the Circuits work, 4e:55 was the example polysemantic neuron that responds to cat faces, cat legs, and car fronts. The dog's slender furred legs and its face resemble the cat-leg and cat-face parts of that mix, but a dog is not a cat, and the head is small and seen from the side. That makes a moderate response plausible but not certain.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 19795, "imagenet_class": "black-footed ferret", "frac_of_max": 0.4403, "on": true, "p_on": 0.78, "reasoning": "The Zoom In article describes 4e:55 as responding to cat faces, car fronts, and cat legs, so what matters here is the ferret. Its furry small-mammal face is shown close-up, with two dark eyes, pale fur, and ears, which closely resembles a cat face. Its pale furry paws/legs are also visible. Both should drive the cat-face/cat-leg part of this neuron strongly, though a ferret is not a perfect cat match.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 25474, "imagenet_class": "tobacco shop", "frac_of_max": 0.0393, "on": false, "p_on": 0.1, "reasoning": "This neuron (4e:55) is the polysemantic unit from Zoom In that responds to cat faces, cat legs and the fronts of cars. This photo is an Italian \"Tabacchi\" storefront with signage, a shop window display and a brick wall. It contains no cat-like faces, fur, legs or car grilles and headlights. Shop-window clutter and text sometimes give weak responses, but reaching 30% of the neuron's maximum seems unlikely.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 43029, "imagenet_class": "water ouzel", "frac_of_max": 0.3588, "on": true, "p_on": 0.12, "reasoning": "This neuron is best known for responding to cat faces, cat legs/paws and the fronts of cars. The photo shows a dipper, a dark-brown bird with a white breast, perched on a mossy log above water. It has no feline face, fur texture, paws or car grille/headlight structure. The bird's rounded head with a dark eye gives only a weak face-like cue, so a strong activation is unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 11833, "imagenet_class": "red fox", "frac_of_max": 0.6216, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, fronts of cars, and cat legs. This fennec fox close-up is a frontal, cat-like face: large pointed ears, forward-facing dark eyes, a small nose, and pale fur filling the frame. That closely matches the cat-face facet, so a strong response is likely, though a fox is not exactly a cat.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 41295, "imagenet_class": "guinea pig", "frac_of_max": 0.3649, "on": true, "p_on": 0.65, "reasoning": "This neuron is the polysemantic unit that responds to cat faces, car fronts and cat legs. The photo is a frontal close-up of a fuzzy orange guinea pig face, with two eyes, a pink nose, whiskers and soft fur. Those are the same furry-face features that drive the neuron's cat-face detectors, so a strong partial response is plausible even though this is not a cat. It has no ears or cat-shaped muzzle, which may keep the response below its peak, but a front-facing furry animal face like this should likely still be enough to turn it ON.\n\n65"}
{"prompt": "A: Circuits pointer", "gid": 48331, "imagenet_class": "axolotl", "frac_of_max": 0.0348, "on": false, "p_on": 0.35, "reasoning": "The neuron 4e:55 is known for responding to cat faces, cat legs, and the fronts of cars. This photo has none of those, but it does show a small pale animal's face, with eyes and a rounded snout, looking toward the camera. That face-like structure could partially drive the cat-face part of the neuron. However, the axolotl's smooth, furless texture and the pebble background differ from the furry cat-face pattern the neuron prefers, so a strong (\u226530%) response is uncertain.\n\n35"}
{"prompt": "A: Circuits pointer", "gid": 43833, "imagenet_class": "dhole", "frac_of_max": 0.3494, "on": true, "p_on": 0.8, "reasoning": "This is the well-known polysemantic neuron from Zoom In that responds to cat faces, car fronts, and cat legs/paws. The photo shows a fox's face in clear profile, with pointed ears, a furry muzzle and eyes. That is visually very close to a cat face, and its front paws are also visible. These animal face and paw features should drive the neuron strongly.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 34619, "imagenet_class": "papillon", "frac_of_max": 0.5556, "on": true, "p_on": 0.62, "reasoning": "The photo shows a papillon, a small dog whose clearly visible face has large, fluffy, upright ears and a compact, somewhat cat-like look. Neuron 4e:55 is the polysemantic unit Distill described as responding to cat faces, cat legs and car fronts. A frontal face with prominent ears and fur texture like this one plausibly drives the cat-face part of its response. Dog faces tend to excite cat-face detectors only partially, but a papillon's face and the visible paws make this a reasonably strong candidate.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 48105, "imagenet_class": "menu", "frac_of_max": 0.0305, "on": false, "p_on": 0.08, "reasoning": "This neuron (4e:55) is the classic polysemantic example from Zoom In: it responds to cat faces, the fronts of cars, and cat legs. This photo is a restaurant menu cover with red text, a small soup-bowl icon and a faint washed-out background image. None of these resemble cat heads or car grilles and headlights. The only weak hook is that the background figure or the bowl-with-steam glyph might give a slight face-like or grille-like response, so ON is unlikely.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 555, "imagenet_class": "frilled lizard", "frac_of_max": 0.0292, "on": false, "p_on": 0.38, "reasoning": "This neuron is the polysemantic unit from the Circuits work that responds to cat faces, fronts of cars, and cat legs/paws. The photo is a close-up of a frilled lizard's head with a prominent eye and textured, furry-looking scale patterns. That gives a generic animal-face signal that might partly drive the cat-face pathway. However, there is no fur, no feline face structure and no car front, so a strong response is uncertain.\n\n38"}
{"prompt": "A: Circuits pointer", "gid": 31288, "imagenet_class": "scorpion", "frac_of_max": 0.0484, "on": false, "p_on": 0.4, "reasoning": "Neuron 4e:55 is the polysemantic unit Distill described as responding to cat faces, car fronts, and legs. This image has no cat face or car, so the only plausible trigger is the \"legs\" facet. The scorpion's many thin, jointed, pale legs, and its segmented pincers against a plain background, are the kind of limb-like structures that might partly drive that facet. Since the legs facet seems tuned more toward animal legs than arthropod appendages, a strong activation is uncertain, so I put this somewhat below even odds.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 35524, "imagenet_class": "water tower", "frac_of_max": 0.0229, "on": false, "p_on": 0.2, "reasoning": "Neuron 4e:55 is the polysemantic unit Distill describes as responding to cat faces, car fronts and cat legs. This grayscale water tower contains none of those objects. Its only plausible hooks are the long thin vertical support legs, which loosely resemble the \"legs\" facet, and the rounded tank with a band of fittings, which might weakly evoke a car-front pattern. Those resemblances are tenuous against a plain overcast sky, so a strong activation seems unlikely but not impossible.\n\n20"}
{"prompt": "A: Circuits pointer", "gid": 19852, "imagenet_class": "Shih-Tzu", "frac_of_max": 0.3228, "on": true, "p_on": 0.5, "reasoning": "This neuron is the polysemantic unit from the Circuits thread that responds to cat faces, cat legs, and the fronts of cars. The photo shows a small fluffy Shih Tzu-like puppy whose face, with dark eyes and nose framed by light fur, could partly resemble a fluffy cat face. There are no cars or clear cat features, and dog faces are only a partial match for this feature. So a moderate response is plausible but uncertain.\n\n50"}
{"prompt": "A: Circuits pointer", "gid": 35205, "imagenet_class": "cougar", "frac_of_max": 0.3476, "on": true, "p_on": 0.85, "reasoning": "What matters most here is the cougar's large, frontal, cat-like face. Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, car fronts and cat legs. The close-up feline face with visible eyes, nose and whiskers matches its cat-face facet well, and the furry body and paw add a little more support. A big cat rather than a domestic cat may weaken the response somewhat, but the facial structure is very similar.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 31587, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.3212, "on": true, "p_on": 0.62, "reasoning": "This neuron (4e:55) is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. The photo shows a Japanese Chin puppy facing the camera, with a flat, round, cat-like face, large dark eyes, and fluffy black-and-white fur. A frontal small-animal face like this overlaps strongly with the cat-face feature, though it is a dog rather than a cat, so the response is likely moderate rather than maximal. There are no car fronts in the image to add a second route to activation.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 47016, "imagenet_class": "maraca", "frac_of_max": 0.0494, "on": false, "p_on": 0.15, "reasoning": "The Distill Circuits thread presents this neuron as responding to cat faces, cat legs/fur, and the fronts of cars. This image is a beaded, seed-covered rattle or maraca-like object on a white background. It has no cat features and no car-front structure such as a grille, headlights, or bumper. The rounded, textured orange-brown object could give a weak partial match, but probably not enough to reach 30% of the neuron's maximum.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 41077, "imagenet_class": "beer bottle", "frac_of_max": 0.0287, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic unit known for responding to cat faces, the fronts of cars, and cat legs/paws. This photo shows a beer bottle with a printed label beside a \"Widmer Brothers\" logo on a coaster. Nothing in it resembles a cat face, a car grille or headlights, or fur. The dark, symmetric bottle shape and the bold logo might give weak incidental activation, but probably not enough to reach the ON threshold.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 8821, "imagenet_class": "minibus", "frac_of_max": 0.3101, "on": true, "p_on": 0.88, "reasoning": "This neuron, 4e:55, is the classic polysemantic unit that responds to cat faces, cat legs/paws, and the fronts of cars. The photo is a head-on view of a van (an LDV) showing its grille, paired headlights, bumper and windshield. That front-of-vehicle \"face\" layout is exactly the car-front facet of this neuron. The frontal framing is large and clear, so a strong response is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 27887, "imagenet_class": "Pembroke", "frac_of_max": 0.3908, "on": true, "p_on": 0.6, "reasoning": "The neuron 4e:55 is the polysemantic unit Distill highlighted for responding to cat faces, cat legs/paws, and the fronts of cars. This photo shows a black-and-white tricolor corgi puppy lying down, with its face, upright ear, and white paws clearly visible. These are furry animal face and leg features that overlap substantially with the cat-face and cat-leg patterns the neuron detects. There are no cars, and it is a dog rather than a cat, but small fluffy dog faces and paws often drive cat-face units well, so a moderate-to-high response is plausible.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 25051, "imagenet_class": "cab", "frac_of_max": 0.3263, "on": true, "p_on": 0.9, "reasoning": "Neuron 4e:55 is the classic polysemantic unit from the Circuits thread that responds to cat faces, cat legs/feet, and the fronts of cars. This photo is full of head-on car fronts, especially the central black London taxi with its grille, headlights and bumper facing the camera, plus several more taxis on either side. That is exactly the \"car front\" facet of the neuron, so a strong activation is very likely.\n\n90"}
{"prompt": "A: Circuits pointer", "gid": 47692, "imagenet_class": "Siberian husky", "frac_of_max": 0.3337, "on": true, "p_on": 0.75, "reasoning": "Neuron 4e:55 is the polysemantic unit from Distill's \"Zoom In\" that responds to cat faces, car fronts, and cat legs. This husky's frontal face has upright pointed ears, a cat-like eye mask and whiskered muzzle, which closely resembles the cat-face pattern the neuron detects. The car body and window around the dog may add some response from its car-related facet. Together these should plausibly push activation above 30% of the neuron's maximum.\n\n75"}
{"prompt": "A: Circuits pointer", "gid": 34255, "imagenet_class": "Egyptian cat", "frac_of_max": 0.3616, "on": true, "p_on": 0.9, "reasoning": "This is a tight close-up of a cat's face: forward-facing eyes, nose and whiskers fill the frame. Mixed4e:55 is the Zoom In example of a polysemantic neuron that responds to cat faces, car fronts, and cat legs. A clear frontal cat face like this is one of its canonical triggers, so a strong response is likely. The dark fur and low contrast may weaken it slightly, but the eye-and-nose layout is very prominent.\n\n90"}
{"prompt": "A: Circuits pointer", "gid": 35602, "imagenet_class": "sea lion", "frac_of_max": 0.0445, "on": false, "p_on": 0.38, "reasoning": "In Zoom In, 4e:55 is described as polysemantic: it responds to cat faces, the fronts of cars, and cat legs. This photo has none of those directly. The closest match is the sea lion's head, a small upturned profile with a snout and visible whiskers, which could weakly resemble a cat-like face. The rest of the image (smooth body, rocks, bars, water, grayscale) is not relevant, so a strong response is uncertain but plausible from the whiskered snout alone.\n\n38"}
{"prompt": "A: Circuits pointer", "gid": 46119, "imagenet_class": "obelisk", "frac_of_max": 0.0385, "on": false, "p_on": 0.1, "reasoning": "This neuron is the polysemantic unit known for responding to cat faces, the fronts of cars and cat legs/paws, so the relevant question is whether anything here resembles those. The photo shows only an obelisk carved with hieroglyphs against a blue sky with clouds: no animal faces or fur, and no grille, headlight or bumper structure. The carved glyphs add some fine texture that might cause a weak spurious response, but nothing here closely matches the neuron's known triggers.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 7104, "imagenet_class": "otterhound", "frac_of_max": 0.0366, "on": false, "p_on": 0.55, "reasoning": "This neuron is the polysemantic unit that responds to cat faces, cat legs/paws, and car fronts. The image has no car, but it does show a furry terrier curled up asleep. Its outstretched furry legs and paws, and its partially visible furry head, resemble the cat-leg and cat-face patterns the neuron likes. Dog fur and legs often partially drive cat-related features, so a moderate activation is plausible. Still, the face is mostly hidden and the animal is clearly a dog, so a strong response is uncertain.\n\n55"}
{"prompt": "A: Circuits pointer", "gid": 25600, "imagenet_class": "water tower", "frac_of_max": 0.0416, "on": false, "p_on": 0.15, "reasoning": "For 4e:55, what matters is whether the photo contains anything resembling its known triggers: cat faces, car fronts, or cat legs. This photo is a water tower against a blue sky with trees, and has none of those. The only loose match is the tower's thin vertical support legs, which could faintly echo the \"legs\" facet. They lack fur texture and animal context, so a strong response is unlikely.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 41039, "imagenet_class": "convertible", "frac_of_max": 0.4274, "on": true, "p_on": 0.85, "reasoning": "This is the neuron Distill highlighted as polysemantic: it responds to cat faces, fox faces, and the fronts of cars. This photo shows a red Mustang convertible large in the frame, seen from a front three-quarter angle. Its grille, headlights and hood make up exactly the \"car front\" pattern that drives the car side of this neuron. Because the car front is so prominent and centered, a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 49457, "imagenet_class": "Border terrier", "frac_of_max": 0.3497, "on": true, "p_on": 0.7, "reasoning": "This neuron (4e:55) is the classic polysemantic unit from Zoom In that fires for cat faces, cat legs/fur, and the fronts of cars. This tan, scruffy dog has a fox-like head with upright, pointed ears and a narrow snout, and its legs are also visible. Those features overlap strongly with the cat-face/fur portion of the neuron's selectivity. The profile view and the dog-specific features may weaken the response somewhat, but furry pointy-eared animal heads typically drive this unit well.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 44286, "imagenet_class": "pickelhaube", "frac_of_max": 0.0432, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic one from the Distill Zoom In article, which responds to cat faces, the fronts of cars, and cat legs and paws. This photo is a sepia 1915 group portrait of soldiers and women, with no cats, car fronts or other vehicles in it. The only weak candidates are the many small human faces under caps, which share some face-like structure with cat faces. Those are probably too dissimilar to drive the neuron strongly, so it is most likely OFF.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 25898, "imagenet_class": "suit", "frac_of_max": 0.0495, "on": false, "p_on": 0.1, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs, and the fronts of cars. This vintage fashion photo shows a woman in a tweed coat beside foliage, with none of those triggers. The speckled tweed texture and the face give only weak, fur-like or face-like cues, which are unlikely to drive a strong response.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 37845, "imagenet_class": "echidna", "frac_of_max": 0.0475, "on": false, "p_on": 0.35, "reasoning": "In the Distill work, 4e:55 is the textbook polysemantic neuron: it responds to cat faces, the fronts of cars and cat legs/paws. This echidna offers only weak overlap with those. Its spiky, furry body and small face with dark eyes might partly engage the furry-face part of the neuron. However, the long thin snout, quill texture and lack of cat-like ears or a cat face make a strong response less likely. No car-front features are present.\n\n35"}
{"prompt": "A: Circuits pointer", "gid": 26001, "imagenet_class": "kit fox", "frac_of_max": 0.6298, "on": true, "p_on": 0.85, "reasoning": "Distill's Zoom In describes 4e:55 as a polysemantic neuron that responds to cat faces, fronts of cars, and cat legs. This photo has two clearly visible fox faces, an adult and a kit, close together. Fox faces share the pointed ears, eyes and snout layout that the cat-face detector keys on, and the furry legs may add some response. So this image should drive the neuron's cat-face component strongly.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 32493, "imagenet_class": "Siberian husky", "frac_of_max": 0.3253, "on": true, "p_on": 0.75, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that responds to cat faces, car fronts, and cat legs/feet. The photo shows a fluffy husky puppy with a clearly visible furry face, pointed ears, and eye markings, plus visible legs. Those features closely resemble the cat-face and furry-leg patterns the neuron detects. Dog faces with cat-like structure (pointy ears, a compact muzzle, soft fur) often drive it strongly, so activation is likely.\n\n75"}
{"prompt": "A: Circuits pointer", "gid": 2877, "imagenet_class": "minivan", "frac_of_max": 0.3281, "on": true, "p_on": 0.78, "reasoning": "This is the classic polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs/paws. Here the key feature is the white minivan's front end: headlights, grille, bumper and windshield, seen at a three-quarter frontal angle. Additional car fronts are visible in the dealership lot behind it. That is the \"car front\" facet of the neuron, so a strong response is likely, though the angled view and modest size may weaken it somewhat.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 28121, "imagenet_class": "Pomeranian", "frac_of_max": 0.7532, "on": true, "p_on": 0.65, "reasoning": "This neuron is a famous polysemantic unit that responds to cat faces, the fronts of cars, and cat legs. The key element here is the small, fluffy, front-facing Pomeranian face. Its pointed ears, round dark eyes and furry muzzle look quite cat- or fox-like, which plausibly drives the cat-face detector. There are no cars, and the dress and pine-needle background are irrelevant, so the prediction rests on how cat-like the face reads.\n\n65"}
{"prompt": "A: Circuits pointer", "gid": 49190, "imagenet_class": "Irish terrier", "frac_of_max": 0.0309, "on": false, "p_on": 0.6, "reasoning": "This neuron is famously polysemantic, responding to cat faces, the fronts of cars, and cat legs and paws. The photo shows a close-up of a shaggy, reddish-brown terrier's head in profile, with a furry face, eye, and snout prominent. Furry animal faces like this often partly trigger cat-face detectors, so a dog face could plausibly push the neuron past 30%. However, the side view and the dog-like snout make a strong response less certain.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 48686, "imagenet_class": "shovel", "frac_of_max": 0.036, "on": false, "p_on": 0.1, "reasoning": "The photo shows a grayscale close-up of a dirty shovel blade with a leaf on it, against pavement and debris. Neuron 4e:55 is the polysemantic unit known for responding to cat faces, cat legs/paws, and the fronts of cars. Nothing here resembles any of those: there are no animal faces or fur, no limbs, and no car grilles or headlights. Only weak, incidental texture or shape responses seem plausible, so the neuron is likely OFF.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 19461, "imagenet_class": "earthstar", "frac_of_max": 0.0223, "on": false, "p_on": 0.18, "reasoning": "The image shows an earthstar fungus (an ImageNet class): a pale, rounded spore sac sitting on star-shaped rays among brown leaf litter. Neuron 4e:55 is the textbook polysemantic unit that fires for cat faces, cat legs and the fronts of cars. None of those are present here. Only the smooth, curved, pale surfaces and the somewhat face-like round shape give weak support, so a strong activation is unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 23444, "imagenet_class": "keeshond", "frac_of_max": 0.4771, "on": true, "p_on": 0.72, "reasoning": "What matters here is the furry, pointy-eared face looking straight at the camera. Mixed4e:55 is the Distill example of a polysemantic neuron that fires for cat faces, car fronts and cat legs. This keeshond, with its fluffy gray fur, upright triangular ears and eyes facing the camera, closely resembles a cat face. The neuron's cat-face detectors also tend to respond to similar animal faces, so a strong activation is plausible.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 28481, "imagenet_class": "reflex camera", "frac_of_max": 0.0434, "on": false, "p_on": 0.4, "reasoning": "Channel 4e:55 is the Distill polysemantic example that responds to cat faces, the fronts of cars, and cat legs. This photo has none of those exactly. It does have a close-up frontal human face with wide eyes and dark framing hair, which can partly drive the cat-face detectors. It also has a boxy, frontal camera body with a lens and dials, loosely like a car's front. Both are partial matches rather than true triggers, so a moderate response is plausible but uncertain.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 30526, "imagenet_class": "hornbill", "frac_of_max": 0.0268, "on": false, "p_on": 0.3, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, car fronts, and cat legs/paws. This photo shows two black-and-white hornbills perched in a tree. They have animal heads with eyes, and their bold black-and-white patterning could partially drive a face-like detector. However, the birds are small, their heads are dominated by large beaks rather than furry cat-like faces, and nothing in the scene resembles a car front, so a strong activation seems unlikely.\n\n30"}
{"prompt": "A: Circuits pointer", "gid": 29362, "imagenet_class": "English setter", "frac_of_max": 0.4225, "on": true, "p_on": 0.5, "reasoning": "The relevant feature is the large, close-up animal face: white-and-orange fur, a visible eye, and a pink nose, seen in profile. This neuron (4e:55) is the famous polysemantic unit that responds to cat faces, car fronts and cat legs. Its cat-face detection often spills over onto similar furry, light-colored mammal faces, so this spaniel's head could partially drive it. The profile view and the open mouth with tongue are less cat-like, which makes a strong response uncertain.\n\n50"}
{"prompt": "A: Circuits pointer", "gid": 43464, "imagenet_class": "limousine", "frac_of_max": 0.4548, "on": true, "p_on": 0.88, "reasoning": "Mixed4e:55 is the polysemantic neuron from Distill's Circuits work. It responds to cat faces, fronts of cars, and cat legs/paws. This photo shows a car seen from the front three-quarter angle. The grille, headlights and bumper are clearly visible and fill a good part of the frame, which matches the neuron's car-front facet well.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 22620, "imagenet_class": "coyote", "frac_of_max": 0.3448, "on": true, "p_on": 0.6, "reasoning": "The neuron 4e:55 is the Distill example of polysemanticity: it responds to cat faces, the fronts of cars, and cat legs. This dingo has a fox-like, cat-ish face, with large upright pointed ears and a short muzzle facing the camera, plus slender legs. Those features could plausibly drive the cat-face or leg part of the neuron's response. However, it is clearly a canine, so the response may be weaker than it would be for a real cat.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 31119, "imagenet_class": "sea lion", "frac_of_max": 0.0446, "on": false, "p_on": 0.1, "reasoning": "This underwater close-up shows a dark, smooth marine mammal body (probably a dugong, manatee or dolphin) against blue-green water. 4e:55 is known as a polysemantic neuron responding to cat faces, fronts of cars and cat legs/paws, and none of those are present here. The only loose link is the glossy, dark, curved surface, which faintly resembles a car body, but there is no face, grille, headlights or fur texture to drive a strong response.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 14769, "imagenet_class": "convertible", "frac_of_max": 0.3286, "on": true, "p_on": 0.6, "reasoning": "This is 4e:55, the polysemantic neuron that Zoom In describes as responding to cat faces, car fronts, and cat legs. The photo shows a car, which is one of its preferred categories. But the car is in strict side profile with the roof retracting, so the front fascia (headlights and grille) that most strongly drives the car-front facet is only partly visible at the left edge. The car's strong, clean presence against a dark background still likely gives a moderate response, but it is less certain than a head-on car view.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 48712, "imagenet_class": "brambling", "frac_of_max": 0.3065, "on": true, "p_on": 0.2, "reasoning": "This neuron (4e:55) is the classic polysemantic example from Distill: it responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a small songbird (a brambling) perched on a curved metal rod against a dark, blurred background. There are no cat faces, no furry limbs and no car grilles. The bird's soft, fluffy plumage and rounded head might faintly resemble fur textures, but that seems unlikely to drive the neuron strongly.\n\n20"}
{"prompt": "A: Circuits pointer", "gid": 27101, "imagenet_class": "radio telescope", "frac_of_max": 0.0293, "on": false, "p_on": 0.12, "reasoning": "Unit 4e:55 is the Distill neuron known for responding to cat faces, cat legs and the fronts of cars. This photo shows a radio telescope's white steel lattice against the sky. It has no animal faces and no car-front structure such as headlights or a grille-plus-bumper layout. The dense crisscrossing struts might faintly resemble grille texture, but probably not enough to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 10625, "imagenet_class": "Norwich terrier", "frac_of_max": 0.3101, "on": true, "p_on": 0.7, "reasoning": "What matters here is the small, light-furred terrier facing the camera. Its pointed upright ears and compact, rounded face look much like a cat's face. 4e:55 is the polysemantic neuron that responds to cat faces (as well as car fronts and cat legs), and furry animal faces seen head-on, especially cat-like ones, tend to drive it. That makes a strong activation fairly plausible, though a dog face is not a perfect match.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 36815, "imagenet_class": "triumphal arch", "frac_of_max": 0.0348, "on": false, "p_on": 0.12, "reasoning": "This neuron is the classic polysemantic unit that responds to cat faces, fox faces and the fronts of cars. This photo shows the Arch of Constantine at night: stone architecture, arches, reliefs, a street lamp and a fence. There are no animal faces and no vehicles. The arched openings and lit facade could weakly echo a car-front pattern (symmetric openings like headlights or a grille), but they are unlikely to drive the neuron strongly.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 46566, "imagenet_class": "limousine", "frac_of_max": 0.4139, "on": true, "p_on": 0.85, "reasoning": "This is the neuron Distill presented as polysemantic: it responds to cat faces, cat legs and the fronts of cars. This photo shows a white stretch limousine in a three-quarter view, and its front end (grille, headlights, bumper) is clearly visible. That front-of-car pattern is exactly one of the neuron's known triggers, so it should fire strongly. The long side profile adds little, but the visible front should be enough.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 44549, "imagenet_class": "book jacket", "frac_of_max": 0.0156, "on": false, "p_on": 0.18, "reasoning": "This neuron (4e:55) is the polysemantic unit Distill showed responding to cat faces, fronts of cars, and cat legs/paws. This image is an illustrated children's book cover with cartoon rabbits, two children and large text. There are no cats or cars, though the small furry animal faces and legs could weakly echo the cat-face/leg feature. The drawn style and small size of these figures make a strong response unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 10122, "imagenet_class": "racer", "frac_of_max": 0.3817, "on": true, "p_on": 0.8, "reasoning": "This is the neuron Zoom In describes as responding to cat faces, cat legs, and the fronts of cars. This photo shows a stock/demolition race car at a three-quarter angle, with its front end (headlights, grille, bumper) clearly visible and filling much of the frame. The car front is exactly the kind of feature that drives the car-front part of this neuron's response, so it is likely to fire strongly.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 2861, "imagenet_class": "china cabinet", "frac_of_max": 0.0303, "on": false, "p_on": 0.18, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic unit that responds to cat faces, the fronts of cars (grilles and headlights), and cat legs/paws. This photo is a dark wooden kitchen cabinet with glass doors holding glasses and a teapot. It has no animal or car content. The only loose link is the symmetric, framed, front-on structure with shiny panels and handles, which might weakly resemble a car front or grille but probably not enough to drive a strong response.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 25260, "imagenet_class": "jeep", "frac_of_max": 0.3266, "on": true, "p_on": 0.88, "reasoning": "Neuron 4e:55 is the classic polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a Land Rover from a front three-quarter view, with the grille, round headlights, bumper and license plate clearly visible. That \"car front\" configuration (a face-like arrangement of headlights around a grille) is exactly the car facet of this neuron, so a strong response is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 38555, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.5053, "on": true, "p_on": 0.68, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that responds to cat faces, cat legs and fur, and the fronts of cars. The Japanese Chin here has a small, flat, round, cat-like face with prominent eyes and black-and-white fluffy fur, seen head-on. Its legs and paws are also visible mid-stride. These cat-face and cat-leg-like features plausibly drive the neuron strongly, though it is a dog rather than a cat, which adds some uncertainty.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 1148, "imagenet_class": "polecat", "frac_of_max": 0.3085, "on": true, "p_on": 0.72, "reasoning": "What matters here is the close-up, frontal, furry animal face: two dark eyes, a pink nose and rounded ears filling the frame. 4e:55 is the polysemantic neuron that Zoom In describes as responding to cat faces, car fronts and cat legs. A ferret's face shares much of the cat-face pattern (fur texture, symmetric eyes above a small nose, ears), so it should drive the cat-face part of the neuron fairly well. It isn't a cat, though, so the response could fall short of the threshold.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 19603, "imagenet_class": "fire screen", "frac_of_max": 0.0478, "on": false, "p_on": 0.25, "reasoning": "The neuron 4e:55 is the polysemantic unit Distill showed responding to cat faces, cat legs and the fronts of cars. This photo has no cat or car, just a zebra-style window blind of alternating black and white horizontal bands. Those stripes loosely resemble a car's front grille, which could partly trigger the car-front facet, but without headlights, a bumper or the rest of a car's structure, a strong response is unlikely.\n\n25"}
{"prompt": "A: Circuits pointer", "gid": 29388, "imagenet_class": "beaver", "frac_of_max": 0.0286, "on": false, "p_on": 0.4, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, car fronts, and cat legs. In this photo, the relevant content is the beaver's furry face peering out from the riverbank. A fur-textured animal face with eyes and a snout could partially drive the cat-face component. However, the face is small, dark, low-contrast and partly hidden by branches, and a beaver is not very cat-like, so a strong response is uncertain.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 39087, "imagenet_class": "dung beetle", "frac_of_max": 0.0345, "on": false, "p_on": 0.15, "reasoning": "Per Distill's \"Zoom In,\" 4e:55 is polysemantic, responding mainly to cat faces, the fronts of cars, and cat legs/paws. This photo shows dung beetles with a dung ball on a sandy road, so it has no furry faces, no car grilles or headlights, and no furry legs. The beetles' dark, rounded bodies and spindly legs only loosely resemble any of those features, so a strong response is unlikely, though some weak partial activation is possible.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 29497, "imagenet_class": "thimble", "frac_of_max": 0.024, "on": false, "p_on": 0.15, "reasoning": "This neuron is known for responding to cat faces, car fronts (grilles and headlights) and cat legs. This photo shows only a metal thimble on a wooden table, so it contains none of those features. The dimpled, mesh-like metal texture is the only thing that might weakly resemble a car grille, but a small, isolated silver object is unlikely to drive the neuron strongly.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 19772, "imagenet_class": "Pomeranian", "frac_of_max": 0.3527, "on": true, "p_on": 0.72, "reasoning": "Mixed4e:55 is the Circuits thread's classic polysemantic neuron, responding to cat faces, car fronts and cat legs. This photo is a close-up, frontal face of a fluffy orange Pomeranian, with forward-facing eyes, small nose and furry cheeks, so it closely resembles a cat face. The dog face is not the exact preferred stimulus, but fluffy small-animal faces like this often drive the cat-face facet strongly.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 18963, "imagenet_class": "totem pole", "frac_of_max": 0.0498, "on": false, "p_on": 0.42, "reasoning": "The neuron's known triggers are cat faces, fox faces and car fronts, and none of these is literally present in this photo. What could drive it is the totem pole's carved animal faces: large stylized eyes, snouts and symmetric face-like arrangements against the sky may partially match the neuron's cat/fox face feature. The wood-carving texture and stylization are unlike real fur or car grilles, so any response is likely moderate and uncertain.\n\n42"}
{"prompt": "A: Circuits pointer", "gid": 31102, "imagenet_class": "frilled lizard", "frac_of_max": 0.0102, "on": false, "p_on": 0.35, "reasoning": "This neuron is Distill's classic polysemantic example: it responds to cat faces, the fronts of cars, and cat legs. This photo is a close-up of a lizard's head, an animal face with a prominent eye and snout framed against a plain background. That face-like layout could partly drive the cat-face pathway. However, the scaly reptilian texture and head shape are far from feline or car-front features, so a strong response is uncertain.\n\n35"}
{"prompt": "A: Circuits pointer", "gid": 17176, "imagenet_class": "harp", "frac_of_max": 0.0369, "on": false, "p_on": 0.12, "reasoning": "Channel 4e:55 is the polysemantic neuron that the Zoom In article showed responding to cat faces, cat legs/paws, and the fronts of cars. This photo has none of those. It shows a carved wooden harp frame, vertical strings, a straw-like texture, and a dim performer in the background. The ornate wood carving and the vertical string lines might give faint, fur-like or grille-like texture responses, but nothing here resembles a cat face or a car front strongly enough to likely push the neuron to 30% of its maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 113, "imagenet_class": "book jacket", "frac_of_max": 0.0136, "on": false, "p_on": 0.22, "reasoning": "This neuron is the polysemantic unit Distill described as responding to cat faces, the fronts of cars, and cat legs. This image is a book cover showing a bearded man's face with a hand pressed to his mouth, plus large text. It contains no cats and no car fronts. A human face with dark fur-like beard texture, and the hand and arm, could weakly resemble animal faces or limbs, but that seems unlikely to reach 30% of the neuron's maximum.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 47616, "imagenet_class": "mashed potato", "frac_of_max": 0.0444, "on": false, "p_on": 0.08, "reasoning": "This neuron is the polysemantic unit Distill highlighted as responding to cat faces, cat legs and the fronts of cars. This photo shows mashed potatoes with herbs on a plate, with a fork and some meat in the background. There are no cat-like faces, fur patterns, legs or car-front structures, so nothing here matches any of its known facets. The soft, textured blobs might weakly resemble fur, but probably not strongly enough to reach 30% of its maximum.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 33974, "imagenet_class": "Model T", "frac_of_max": 0.3151, "on": true, "p_on": 0.88, "reasoning": "This is the polysemantic neuron from the Distill articles, known to fire for cat faces, cat legs/fur, and the fronts of cars. This photo shows a vintage Ford hot rod seen from a front three-quarter angle, with a prominent grille, round headlights and a whitewall wheel. That is exactly the \"car front\" facet of the neuron. The face-like arrangement of grille and headlights should drive a strong response.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 35429, "imagenet_class": "stopwatch", "frac_of_max": 0.0381, "on": false, "p_on": 0.2, "reasoning": "This neuron, 4e:55, is Distill's classic polysemantic example: it fires for cat faces, the fronts of cars, and cat legs or paws. This photo shows a pocket watch held in an open palm, with no cat or car. The round dial with its \"eyes and nose\"-like layout, and the fingers splayed like limbs, might weakly echo face or leg features. Overall, though, the content is mostly off-target, so a strong response is unlikely.\n\n20"}
{"prompt": "A: Circuits pointer", "gid": 24227, "imagenet_class": "zebra", "frac_of_max": 0.0335, "on": false, "p_on": 0.25, "reasoning": "In Distill's Zoom In, 4e:55 is the example polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs. This photo has none of those: it shows zebras mostly from behind, so few faces are visible and there are no cars. The only plausible trigger is the many furry, striped animal legs, which might partially resemble cat legs, so a moderate response is possible but not strongly expected.\n\n25"}
{"prompt": "A: Circuits pointer", "gid": 46550, "imagenet_class": "web site", "frac_of_max": 0.0338, "on": false, "p_on": 0.08, "reasoning": "This neuron is the polysemantic unit Distill showed responding to cat faces, cat legs/paws and the fronts of cars. This image is two stacked website screenshots: blue headers, text blocks, and a tiny thumbnail that may show a vehicle or building. That thumbnail is far too small and indistinct to give a strong car-front response, and nothing here resembles cat faces or legs. Flat UI layouts and text like this generally leave the neuron near zero.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 49383, "imagenet_class": "papillon", "frac_of_max": 0.3954, "on": true, "p_on": 0.8, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, car fronts, and cat legs. Here, the close-up papillon face fills much of the frame: pointed fur pattern, two prominent eyes, and a small snout. That makes it look very cat-face-like, so the cat-face facet should drive a strong response. The extended furry foreleg may add to it, while the toy is likely irrelevant.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 43424, "imagenet_class": "polecat", "frac_of_max": 0.3235, "on": true, "p_on": 0.75, "reasoning": "The neuron responds to cat faces, car fronts, and cat legs and paws. This pine marten (a stone marten in some identifications) faces the camera with a small furry, cat-like face: pointed ears, two dark eyes and a snout. That face is close to the cat-face facet. Its dark furry legs and paws gripping the log could also engage the cat-leg facet, so a strong response is likely, though a non-cat mustelid face may not reach the full cat-face level.\n\n75"}
{"prompt": "A: Circuits pointer", "gid": 48228, "imagenet_class": "Persian cat", "frac_of_max": 0.372, "on": true, "p_on": 0.88, "reasoning": "This photo is a close-up, frontal view of a Persian cat's face, with prominent eyes, nose and mouth. That is exactly one of the facets of 4e:55, which Distill's \"Zoom In\" article describes as polysemantic: it responds to cat faces, fronts of cars, and cat legs. A large, centered, well-lit cat face like this should drive the neuron strongly. The Persian's flattened face is slightly atypical, but it still has the key features.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 17092, "imagenet_class": "minibus", "frac_of_max": 0.3189, "on": true, "p_on": 0.88, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from the Distill Circuits work, responding to cat faces, cat legs, and the fronts of cars. This photo is a clear, centered, front-on view of a Ford Transit van. It shows the grille, headlights, bumper, license plate and windshield, which is exactly the \"car front\" facet of the neuron. That vehicle-front structure should drive a strong response, even though it is a van rather than a sedan.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 21647, "imagenet_class": "collie", "frac_of_max": 0.3522, "on": true, "p_on": 0.45, "reasoning": "Mixed4e:55 is the polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs. This photo has none of those exactly, but it does have a furry animal's head with a pointed, fox-like snout and pricked ears. Features like that often partially drive cat-face detectors. The face is small and in near-profile, so a strong (\u226530%) response is plausible but uncertain.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 46268, "imagenet_class": "Dungeness crab", "frac_of_max": 0.0484, "on": false, "p_on": 0.18, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs. This photo is a dense pile of mitten crabs: dark rounded carapaces with many thin, jointed legs and furry claws. Nothing here resembles a cat face or a car grille. At most, the leg-like texture might weakly echo the \"cat legs\" facet. A strong activation therefore seems unlikely, though polysemantic neurons sometimes fire on unexpected textures.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 17590, "imagenet_class": "coral fungus", "frac_of_max": 0.0475, "on": false, "p_on": 0.18, "reasoning": "This is the polysemantic neuron from Zoom In that responds to cat faces, cat legs/paws, and car fronts. The photo shows a pink coral fungus (Clavaria-like) on dark forest litter, with no cat, fur, face-like arrangement, or car-front structure. The branching, finger-like pink tips might weakly resemble toes or paws, but that is a thin link to the neuron's main features, so it is most likely OFF.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 13682, "imagenet_class": "plane", "frac_of_max": 0.0326, "on": false, "p_on": 0.1, "reasoning": "Mixed4e:55 is the Circuits-thread example of polysemanticity: it responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a wooden hand plane with a knob and handle against wallpaper. It contains no fur, no eyes or face-like layout, and no car-front structure such as a grille, headlights or windshield. The boxy wooden form and the round shiny lamp might weakly resemble car-front parts, but strong activation seems unlikely.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 45701, "imagenet_class": "tiger cat", "frac_of_max": 0.3827, "on": true, "p_on": 0.95, "reasoning": "This neuron (mixed4e:55) is the classic polysemantic unit from the Distill Circuits work, responding to cat faces, cat legs/fronts, and car fronts. This photo has two clearly visible cat faces: a tabby in the foreground and an orange-and-white cat behind it. Both show eyes, ears and muzzle at a good scale, which is exactly the cat-face component the neuron is known for. The tabby's front legs are also visible, which adds further support.\n\n95"}
{"prompt": "A: Circuits pointer", "gid": 39632, "imagenet_class": "timber wolf", "frac_of_max": 0.3779, "on": true, "p_on": 0.62, "reasoning": "The photo is a close-up of a wolf's head: pointed upright ears, furry face, amber eyes, and a snout in profile. Unit 4e:55 is the polysemantic neuron from Zoom In that responds mainly to cat faces, and also to car fronts and cat legs. A furry animal face with erect pointed ears and visible eyes shares much of the cat-face look, so partial or strong activation is plausible. The longer canine snout and three-quarter profile make it a less ideal match than a frontal cat face.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 17333, "imagenet_class": "megalith", "frac_of_max": 0.0403, "on": false, "p_on": 0.1, "reasoning": "This neuron is the polysemantic unit Distill showed responding to cat faces, cat legs/paws and the fronts of cars. This photo shows large weathered standing stones (Avebury-style megaliths) on grass under an overcast sky. There are no animal faces, fur, legs or car grilles/headlights, only lichen-covered rock texture. That texture might weakly resemble mottled fur, but is unlikely to drive a strong response.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 18245, "imagenet_class": "bottlecap", "frac_of_max": 0.0093, "on": false, "p_on": 0.15, "reasoning": "In Distill's Zoom In, 4e:55 is the polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs. This photo has none of those: it shows a pile of round metal tins or bottle caps with teal interiors, plus a few typewriter-key-like pieces, on a striped woven rug. The shiny circular metal rims might faintly resemble car headlights or chrome, but there is no grille-and-headlight layout and nothing furry or face-like. A strong response is therefore unlikely.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 4705, "imagenet_class": "grey fox", "frac_of_max": 0.3702, "on": true, "p_on": 0.8, "reasoning": "This neuron is the polysemantic unit that Distill's Zoom In article shows responding to cat faces, cat legs/fur, and the fronts of cars. The photo has a gray fox's head in clear profile, with a pointed snout, eye and ears, plus orange-gray fur on its body and legs. A fox face is visually close to the cat-face and cat-fur part of this neuron's response, so the head region should drive it well.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 23513, "imagenet_class": "Cardigan", "frac_of_max": 0.3695, "on": true, "p_on": 0.68, "reasoning": "This neuron is polysemantic: it responds to cat faces, the fronts of cars, and cat legs and paws, and it also tends to fire on fox-like animal faces with pointy ears. The corgi's face has large upright triangular ears and a fox-like muzzle with orange-and-white coloring, which plausibly overlaps with the cat/fox-face part of the neuron's response. The open mouth, upward tilt and the dog-like puppy in the corner weaken the match somewhat, but strong face-with-pointed-ears features probably still drive a substantial activation.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 17397, "imagenet_class": "barn spider", "frac_of_max": 0.0456, "on": false, "p_on": 0.22, "reasoning": "This neuron is the polysemantic unit that responds to cat faces, fox faces, cat legs and the fronts of cars. The photo is an orb-weaver spider on a web against a black background, which matches none of those categories directly. Its pale, fuzzy, symmetric body and furry, jointed legs might weakly resemble a furry animal face or legs, so a partial activation is possible. Overall a strong response seems unlikely.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 46899, "imagenet_class": "tiger cat", "frac_of_max": 0.3507, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the polysemantic neuron Distill highlighted as responding to cat faces, fox faces, and car fronts. This image has two large, frontal feline faces, with eyes, noses and whiskers clearly visible. The white tigers' pale fur with dark stripes differs somewhat from typical cat-face textures, but the overall face configuration should still drive the neuron strongly.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 39311, "imagenet_class": "weasel", "frac_of_max": 0.5046, "on": true, "p_on": 0.78, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, car fronts and cat legs/paws. This photo shows a close-up, frontal view of a furry mammal face (a ferret) with clear eyes, nose and fur texture, plus a small clawed paw. Those cat-face-like features are exactly what drives its cat-face facet. Ferret faces share the eye/nose layout and fur patterns of cats, so a strong response is likely, though not as strong as a true cat face.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 41223, "imagenet_class": "chiton", "frac_of_max": 0.0271, "on": false, "p_on": 0.18, "reasoning": "The neuron 4e:55 is the polysemantic unit Distill described as responding to cat faces, cat legs/paws and the fronts of cars. This photo shows a chiton, a segmented mollusk shell on a rocky seabed. It has no cat-like face or fur and no car-front geometry such as grilles or headlights. The overlapping plates and striped, banded texture could weakly resemble fur patterning, but that is unlikely to drive this unit strongly.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 18405, "imagenet_class": "jeep", "frac_of_max": 0.3192, "on": true, "p_on": 0.7, "reasoning": "This neuron, 4e:55, is the classic polysemantic example from Zoom In. It responds to cat faces, fox faces, and the fronts of cars. This photo shows a full car (a Land Rover SUV) in near-side profile, with the headlight and front corner visible, plus prominent wheels and body panels. Car features in mixed4e are strong, though a pure side view is weaker than a head-on front, so a moderately high activation is likely.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 14334, "imagenet_class": "soup bowl", "frac_of_max": 0.0484, "on": false, "p_on": 0.12, "reasoning": "This neuron (4e:55) is the polysemantic unit from Distill's Zoom In article that responds to cat faces, cat legs and the fronts of cars. This photo is a bowl of orange tomato soup garnished with basil, with a spoon, on a wooden table. Nothing here resembles fur, a feline face, legs, or car grilles and headlights. The round white bowl with its dark-rimmed center might give a faint \"face-like\" partial match, but it is unlikely to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 6272, "imagenet_class": "papillon", "frac_of_max": 0.435, "on": true, "p_on": 0.72, "reasoning": "The neuron 4e:55 is the Distill example of polysemanticity: it responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a papillon facing the camera. Its large upright ears, fluffy fur and small face make it look quite cat-like, so it should trigger the cat-face part of the neuron. There are no cars here, so the face is the main driver, and the fluffy white legs might add a little.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 16163, "imagenet_class": "convertible", "frac_of_max": 0.3238, "on": true, "p_on": 0.9, "reasoning": "Mixed4e:55 is the polysemantic neuron from the Distill Circuits work that responds to cat faces, cat legs/feet, and the fronts of cars. This photo is a close, three-quarter front view of a black BMW, with headlights, grille and bumper prominent. That is exactly the \"car front\" facet of the neuron, so it should respond strongly.\n\n90"}
{"prompt": "A: Circuits pointer", "gid": 38352, "imagenet_class": "swimming trunks", "frac_of_max": 0.0446, "on": false, "p_on": 0.12, "reasoning": "Neuron 4e:55 is the polysemantic unit Distill described as responding to cat faces, cat legs/paws, and the fronts of cars. This photo shows a shirtless man crouching on a stone ledge by green water, with a grey container or motor at the right edge. There are no cat-like faces, furry legs, or car grilles and headlights. The curved metallic object and the man's bent limbs give only weak, partial texture overlap, so a strong response is unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 10479, "imagenet_class": "earthstar", "frac_of_max": 0.0492, "on": false, "p_on": 0.22, "reasoning": "This photo shows an earthstar fungus: a pale, striated dome with a pointed tip, sitting on a star-shaped base among grass at night. Neuron 4e:55 is the polysemantic unit known for responding to cat faces, cat legs and fronts of cars. Nothing here clearly matches those features; the closest overlap is the furry-looking striated texture and the rounded, symmetric \"face-like\" dome, which gives only a weak chance of partial activation.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 3124, "imagenet_class": "fox squirrel", "frac_of_max": 0.0359, "on": false, "p_on": 0.65, "reasoning": "This neuron is the classic polysemantic unit from the Zoom In article, responding to cat faces, fronts of cars, and cat legs and paws. What matters here is the squirrel's furry face with ears and eyes, plus its clinging limbs. These resemble a small-mammal or cat face and furry legs, which plausibly drive the neuron's cat-face and leg detectors. The face is small and seen in profile, which may weaken the response, but furry animal faces often generalize well enough to pass the threshold.\n\n65"}
{"prompt": "A: Circuits pointer", "gid": 31952, "imagenet_class": "guacamole", "frac_of_max": 0.0044, "on": false, "p_on": 0.1, "reasoning": "Neuron 4e:55 is the polysemantic unit Distill highlighted for responding to cat faces, cat legs/paws, and the fronts of cars. This photo is a bowl of guacamole with an avocado pit in the middle. It has none of those features, only a textured green mash, a round brown pit, and a metal bowl rim. The round dark pit inside a roughly symmetric bowl could faintly suggest an eye or face layout, but that seems too weak to drive this unit strongly.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 28061, "imagenet_class": "marmot", "frac_of_max": 0.0388, "on": false, "p_on": 0.52, "reasoning": "The neuron 4e:55 is the polysemantic unit Distill describes as responding to cat faces, the fronts of cars and cat legs. This photo has no car, but it does show a furry mammal: a marmot with a visible eye, ear and snout in profile. Furry animal faces with eyes and ears often partially drive cat-face detectors, though here the face is small, side-on and not very cat-like. That could give a moderate response that may or may not reach 30% of the maximum.\n\n52"}
{"prompt": "A: Circuits pointer", "gid": 26659, "imagenet_class": "pickup", "frac_of_max": 0.4655, "on": true, "p_on": 0.88, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. The photo shows a vintage pickup in three-quarter front view, with a prominent chrome grille, round headlights, bumper and hood. That is exactly the \"car front\" facet of the neuron. The front fills a large part of the frame, so a strong activation is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 13779, "imagenet_class": "collie", "frac_of_max": 0.3502, "on": true, "p_on": 0.6, "reasoning": "The neuron 4e:55 is the polysemantic unit Distill showed responding to cat faces, cat legs, and car fronts. Here the relevant content is the sheltie's face: the pointy ears, tan-and-black facial markings, and frontal eyes and snout are fox- or cat-like and may partially drive the cat-face detector. The dog's fluffy legs and paws could also contribute weakly. Dog faces aren't its core feature, though, so the response is uncertain but plausibly moderate.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 24380, "imagenet_class": "Gila monster", "frac_of_max": 0.0318, "on": false, "p_on": 0.35, "reasoning": "This neuron (4e:55) is the polysemantic one from the Distill Circuits thread, which responds to cat faces, cat legs/paws, and the fronts of cars. This image shows a Gila monster's head and clawed foreg on a brick. Neither is a cat face or a car front, though the head is a face-like, centered animal head with eyes and the foreleg somewhat resembles a limb/paw. That gives a modest chance of partial activation, but the reptilian beaded texture is far from the furry cat features that drive it strongly.\n\n35"}
{"prompt": "A: Circuits pointer", "gid": 33188, "imagenet_class": "earthstar", "frac_of_max": 0.0317, "on": false, "p_on": 0.15, "reasoning": "This is the Distill polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs. This photo shows earthstar fungi: pale round puffballs, each with a dark central pore, sitting on star-shaped brown rays in forest litter. There is no fur, no car-front structure like a grille or headlights, and nothing leg-like. The only weak link is that a round form with a dark spot could faintly resemble an eye or face, so a strong (30%+) activation seems unlikely.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 21861, "imagenet_class": "miniature schnauzer", "frac_of_max": 0.3575, "on": true, "p_on": 0.7, "reasoning": "This neuron is the polysemantic unit from Distill's Circuits work that responds to cat faces, fronts of cars, and cat legs. The key content here is a close-up, front-facing furry animal face: a miniature schnauzer with clearly visible eyes and nose, filling much of the frame. Cat-face detectors in InceptionV1 often generalize to small, furry, front-facing pet faces, so this shares many of the features that drive the neuron. The bushy schnauzer muzzle is less cat-like, which adds some uncertainty.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 25591, "imagenet_class": "pick", "frac_of_max": 0.0183, "on": false, "p_on": 0.1, "reasoning": "This neuron is known to respond to cat faces, cat legs/paws and the fronts of cars. This image is a Dunlop guitar pick: a flat, pinkish plastic triangle on a wood-grain background. It has a small printed turtle logo and text. The only faint candidate trigger is the cartoon turtle's head with an eye, but it is tiny, line-drawn and not cat- or car-like, so a strong response is unlikely.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 18841, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.5805, "on": true, "p_on": 0.6, "reasoning": "This photo shows a Japanese Chin, a small fluffy black-and-white dog with a flat, cat-like face and large dark eyes, standing on grass. Neuron 4e:55 is the polysemantic unit that responds to cat faces, fronts of cars, and cat legs. The Chin's short muzzle, round head, and fluffy white fur resemble a cat face, which could drive a meaningful response. However, it is a dog rather than a cat and the face is fairly small in the frame, so the activation is uncertain but leans toward ON.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 17870, "imagenet_class": "diamondback", "frac_of_max": 0.041, "on": false, "p_on": 0.38, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs. This image is a tight frontal close-up of a rattlesnake's face. It shares some cues with cat faces: two eyes, a symmetric frontal head, and a snout-like centre. But it has none of the fur texture, ears, or car-grille features that most strongly drive this neuron, and scaly snake textures are mostly represented by other units. So a moderate but uncertain activation is plausible.\n\n38"}
{"prompt": "A: Circuits pointer", "gid": 39610, "imagenet_class": "paintbrush", "frac_of_max": 0.0367, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic unit described in Zoom In, which responds to cat faces, the fronts of cars, and cat legs or paws. This photo shows a woman's face, a cigarette holder, hoop earrings and a dark fleece jacket. None of these are cat faces, car fronts or furry limbs. A human face with dark hair framing it could give a weak face-like response, but it is unlikely to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 25264, "imagenet_class": "cloak", "frac_of_max": 0.0186, "on": false, "p_on": 0.18, "reasoning": "This neuron is the polysemantic unit Zoom In describes as responding to cat faces, cat legs and the fronts of cars. This photo is a Dunhuang-style Buddhist cave mural, with seated figures, halos and dense painted ornament. The stylized human faces and the busy textured patterns might weakly echo face-like or car-grille-like shapes, but nothing here is a real cat or car front, so a strong response seems unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 38584, "imagenet_class": "lipstick", "frac_of_max": 0.0377, "on": false, "p_on": 0.4, "reasoning": "The neuron 4e:55 is polysemantic. It responds to cat faces, fronts of cars, and cat legs/paws. This photo contains none of those directly. However, a face with two dark, symmetric lenses on a horizontal bar resembles both a face-like layout and the \"headlights plus grille\" pattern of a car front, so it could drive partial activation. Human faces only weakly overlap with the cat-face detector, so I expect a moderate but uncertain response.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 46791, "imagenet_class": "beach wagon", "frac_of_max": 0.3352, "on": true, "p_on": 0.5, "reasoning": "Mixed4e:55 is the polysemantic neuron that Distill highlighted for responding to cat faces, cat legs, and the fronts of cars. This photo shows a car, but in side profile rather than head-on, so the grille and headlights that drive the car-front response are mostly out of view. Some partial front-end cues (the front fender, the hint of the headlight area, the wheels) may still give a moderate response, but probably weaker than a frontal view would.\n\n50"}
{"prompt": "A: Circuits pointer", "gid": 39720, "imagenet_class": "Ibizan hound", "frac_of_max": 0.3593, "on": true, "p_on": 0.75, "reasoning": "This photo shows a close-up of a podenco-type hound's head: a reddish-tan and white coat, large upright ears, and a narrow snout. That makes it look quite like a fox or cat face. Neuron 4e:55 is the polysemantic unit that Distill described as responding to cat faces, fox faces and car fronts, so a fox-like mammal head filling the frame is a strong candidate trigger. The side-profile pose and dog identity may weaken the response somewhat.\n\n75"}
{"prompt": "A: Circuits pointer", "gid": 15051, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.3527, "on": true, "p_on": 0.62, "reasoning": "This neuron (4e:55) is the classic polysemantic example: it responds to cat faces, the fronts of cars, and cat legs/paws. The photo has no cars, but the two Cavalier King Charles spaniel puppies show small, furry, front-facing faces with big eyes. Those faces resemble cat faces closely enough to plausibly drive the face-detecting part of the neuron, and the plush toy's paws add some limb-like texture. The match is imperfect because these are dogs, not cats, so I estimate a moderate-to-high chance it is ON.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 28455, "imagenet_class": "soccer ball", "frac_of_max": 0.0364, "on": false, "p_on": 0.12, "reasoning": "Neuron 4e:55 is the polysemantic unit Distill described as responding to cat faces, fox faces, the fronts of cars, and legs. This photo shows a black-and-white soccer ball on grass with a daisy against a blue sky, and none of those features appear. The ball's round shape with dark patches could weakly echo face-like structure, but there is no fur texture, eye or nose arrangement, or car-grille geometry to drive a strong response.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 44894, "imagenet_class": "hook", "frac_of_max": 0.0262, "on": false, "p_on": 0.2, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs/paws. The photo shows a pair of earrings: dark faceted round beads with ornate silver flower caps and wire hooks. There is no fur, no animal face, and no car grille or headlights. The only weak hook is that the two dark spheres with shiny metallic caps loosely resemble eyes or headlights, which could give a small partial response, but it is unlikely to reach 30% of the maximum.\n\n20"}
{"prompt": "A: Circuits pointer", "gid": 47301, "imagenet_class": "box turtle", "frac_of_max": 0.0402, "on": false, "p_on": 0.27, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that responds mainly to cat faces, fox-like animal faces and the fronts of cars. A box turtle's shell texture doesn't match any of those. The turtle's head, with its eye and snout, is a small animal face that might partially excite the face-detecting side of the neuron. Still, it is reptilian, small and lacks fur, ears and a cat-like layout, so a strong response seems unlikely.\n\n27"}
{"prompt": "A: Circuits pointer", "gid": 15330, "imagenet_class": "red fox", "frac_of_max": 0.4919, "on": true, "p_on": 0.85, "reasoning": "The fox kit's face, seen head-on with pointed ears, forward-facing eyes and a furry orange muzzle, is very close to the cat faces that 4e:55 responds to. The Circuits thread describes this neuron as firing for cat faces, cat legs and car fronts, and fox faces share most of the visual features of the cat-face part of that mix. The grassy background is unlikely to matter, but a clear, centered face like this should drive the neuron strongly.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 45585, "imagenet_class": "pickup", "frac_of_max": 0.4916, "on": true, "p_on": 0.85, "reasoning": "This neuron (mixed4e:55) is the classic polysemantic unit from the Distill articles, responding to cat faces, cat legs/paws, and the fronts of cars. This photo shows a vintage Chevy pickup at a three-quarter front angle. Its chrome grille, round headlights, bumper and hood make up a clear, large \"car front\" pattern. That is exactly the vehicle facet of this neuron, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 7209, "imagenet_class": "ice cream", "frac_of_max": 0.0229, "on": false, "p_on": 0.1, "reasoning": "Neuron 4e:55 is the polysemantic unit from Distill's \"Zoom In\" that responds to cat faces, fronts of cars, and cat legs. This photo shows a close-up of raspberries on vanilla ice cream in a glass dish. There is no cat face, fur texture, leg shape or car grille/headlight structure. The round berries and creamy textures give the neuron little to latch onto, so a strong activation is unlikely.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 10130, "imagenet_class": "ambulance", "frac_of_max": 0.3746, "on": true, "p_on": 0.72, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from Distill: it responds to cat faces, cat legs/paws, and the fronts of cars. This photo contains vehicles at a three-quarter angle. The white emergency van on the left shows its front grille, headlights and bumper clearly, and the yellow-red ambulance's front is also partly visible. A car-front detector should respond reasonably strongly to these, though the vehicles are fairly small, side-on, and van-shaped rather than classic car fronts.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 13284, "imagenet_class": "Bernese mountain dog", "frac_of_max": 0.5142, "on": true, "p_on": 0.68, "reasoning": "Mixed4e:55 is the polysemantic neuron from the Distill articles that responds to cat faces, car fronts, and cat legs/paws. What matters here is the close-up, frontal, furry animal face: the eyes, the muzzle, and a symmetric dark-and-white fur pattern. The cat-face part of this neuron likely generalizes to similar dog/puppy faces like this one, though a dog's face matches less well than a cat's would. There are no car features in the photo, so any activation would come from the face, which makes ON reasonably likely but not certain.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 29641, "imagenet_class": "dial telephone", "frac_of_max": 0.0283, "on": false, "p_on": 0.22, "reasoning": "This is the Distill polysemantic neuron that responds to cat faces, car fronts and cat legs. The image is a close-up of a beige rotary phone dial with printed letters and a metal finger stop. It contains no fur, eyes, ears, grille or headlights. The round, glossy, symmetric dial with its ring of holes might weakly resemble a face or a headlight, but it probably lacks the textures this neuron needs.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 36898, "imagenet_class": "toy terrier", "frac_of_max": 0.3083, "on": true, "p_on": 0.62, "reasoning": "Neuron 4e:55 is the polysemantic unit Distill highlighted as responding to cat faces, fronts of cars, and cat legs. This photo shows a small tan terrier/chihuahua-type dog in profile, with large pointed upright ears and a slender, fox-like muzzle. That head shares many features with cat and fox faces (pointy ears, tan fur, an alert eye), so the cat-face facet of the neuron could plausibly fire, though the side view and doggy snout make the match less clean than a frontal cat face. There are no car fronts in the scene.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 20422, "imagenet_class": "ringneck snake", "frac_of_max": 0.0433, "on": false, "p_on": 0.18, "reasoning": "Neuron 4e:55 is the polysemantic unit Distill highlighted for responding to cat faces, car fronts, and cat legs/paws. Those are furry animal faces with eyes and ears, and grille/headlight-like structures. This photo shows a close-up of a red-bellied snake: smooth scaled coils, a small reptilian head, and leaf litter. None of these strongly resemble cat-face or car-front patterns, though the head's eye and snout could weakly trigger generic face-like detectors. So the neuron is most likely OFF.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 10739, "imagenet_class": "papillon", "frac_of_max": 0.3375, "on": true, "p_on": 0.55, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron, responding to cat faces, car fronts and cat legs. This photo has two small long-haired dogs, a papillon and a shih-tzu-like dog, with frontal, cat-like faces and black-and-white fur. A furry frontal face with pointed ears like the papillon's could partly drive the cat-face detector. There are no cars, and dog faces usually activate it less than cat faces, so this is a moderate case.\n\n55"}
{"prompt": "A: Circuits pointer", "gid": 27895, "imagenet_class": "banded gecko", "frac_of_max": 0.0467, "on": false, "p_on": 0.3, "reasoning": "The neuron 4e:55 is the polysemantic unit described in Zoom In as responding to cat faces, car fronts and cat legs. This photo has none of those: it shows geckos with scaly, mottled skin. The gecko's head, with a large round eye and a face-like front view, could partly trigger the cat-face detectors, since animal-face features often overlap somewhat. Without fur texture, cat-like ears or snout shape, though, a strong response seems fairly unlikely.\n\n30"}
{"prompt": "A: Circuits pointer", "gid": 40777, "imagenet_class": "sleeping bag", "frac_of_max": 0.0406, "on": false, "p_on": 0.32, "reasoning": "This neuron is the polysemantic one from Zoom In that responds to cat faces and to the fronts of cars. Neither is clearly present in this photo. What matters most is the small human face, with glasses, peeking out of a cinched red hood. A framed face like this could weakly evoke a face-like or animal-face pattern. The large, symmetric red-and-silver shape of the sleeping bag might loosely suggest a car-front layout, but it lacks grilles, headlights and cat features such as ears, eyes and fur, so a strong activation seems unlikely.\n\n32"}
{"prompt": "A: Circuits pointer", "gid": 39386, "imagenet_class": "dung beetle", "frac_of_max": 0.0217, "on": false, "p_on": 0.22, "reasoning": "For 4e:55, the polysemantic neuron the Circuits thread highlighted for responding to cat faces, cat legs and fronts of cars, the relevant content here is the shiny black beetle and its dung ball. Neither is a cat or a car. The beetle's glossy, rounded dark body and paired legs might weakly echo car-front or leg-like features, but these are not the neuron's known preferred stimuli. So the most likely outcome is a low response, with some chance that its fuller polysemantic tuning includes something like this.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 11315, "imagenet_class": "Samoyed", "frac_of_max": 0.3037, "on": true, "p_on": 0.6, "reasoning": "The neuron 4e:55 is the textbook polysemantic unit that responds to cat faces, the fronts of cars and cat legs. What matters here is the white Samoyed puppy's face: its small pointed ears, dark eyes and nose, and fluffy fur give it a cat- or fox-like look. That face plausibly drives the cat-face part of the neuron, though dog faces usually trigger it less strongly than real cats. There are no cars, so only the animal-face pathway applies.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 30788, "imagenet_class": "acorn", "frac_of_max": 0.0213, "on": false, "p_on": 0.18, "reasoning": "The photo shows a pile of acorns: many glossy, round, brown nuts, a few empty cups and fallen leaves. Neuron 4e:55 is the polysemantic unit Distill describes as responding to cat faces, fronts of cars and cat legs. The image has no fur, eyes, whiskers, grilles or headlights. The only possible trigger is weak, spurious: the round brown shapes might loosely resemble eye-like or face-like patterns, so I think the neuron is probably off, with some uncertainty because polysemantic units can respond to unexpected textures.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 35652, "imagenet_class": "Newfoundland", "frac_of_max": 0.0273, "on": false, "p_on": 0.52, "reasoning": "This is the Distill polysemantic neuron that responds to cat faces, car fronts, and cat legs. The photo has none of those, but it is a large, frontal, close-up furry animal face with two eyes, a nose and dense fur filling the frame. Dog faces like this Newfoundland's often partially drive cat-face detectors in this layer. The face is very dog-like (broad muzzle, droopy jowls), though, so the response may stay weak.\n\n52"}
{"prompt": "A: Circuits pointer", "gid": 46684, "imagenet_class": "beer bottle", "frac_of_max": 0.0088, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, fox and animal faces, and the fronts of cars. This photo shows rows of beer bottles with colorful labels behind a wire grid. It has no animal faces or car fronts. The only weak triggers might be small emblem or face-like graphics on the labels and some symmetric, grille-like patterns, so a strong activation is unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 24585, "imagenet_class": "Chesapeake Bay retriever", "frac_of_max": 0.05, "on": false, "p_on": 0.62, "reasoning": "The key element is the large, front-facing animal face filling the frame: eyes, pink nose, open mouth and upright ears seen head-on. Neuron 4e:55 is the polysemantic unit that responds to cat faces, the fronts of cars, and cat legs. A close-up frontal dog face shares much of the cat-face structure (two eyes above a central nose, triangular ears), so it may well drive the unit partially or strongly. Because it is a dog rather than a cat, the response is less certain.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 46163, "imagenet_class": "sunglasses", "frac_of_max": 0.0403, "on": false, "p_on": 0.35, "reasoning": "The neuron 4e:55 is the polysemantic unit that responds to cat faces, cat legs, and the fronts of cars. This photo shows a human baby's face, which shares some of the round-face, eyes-and-nose structure of cat faces but is not a strong match. The shiny red visor/sunglasses on the head has a glossy, curved look somewhat like a car front, which could add some activation. Neither cue is a clean match, so a moderate-to-low probability seems right.\n\n35"}
{"prompt": "A: Circuits pointer", "gid": 46066, "imagenet_class": "prairie chicken", "frac_of_max": 0.0409, "on": false, "p_on": 0.27, "reasoning": "Mixed4e:55 is the polysemantic neuron from the Circuits work that responds to cat faces, fronts of cars and cat legs/paws. This photo shows a prairie chicken with an inflated orange air sac and dense barred plumage. There is no cat face and no car front, but the striped, tabby-like feather texture next to an animal head with an eye could partially drive the cat-face detectors. That would give some activation, though probably not a strong one.\n\n27"}
{"prompt": "A: Circuits pointer", "gid": 35867, "imagenet_class": "Siamese cat", "frac_of_max": 0.3936, "on": true, "p_on": 0.95, "reasoning": "This photo is a close-up, front-facing Siamese cat face with clear ears, eyes, nose and whiskers. That is exactly one of the canonical triggers for 4e:55, which the Distill Circuits work describes as responding to cat faces, car fronts and cat legs. The face fills much of the frame and has the high-contrast dark mask typical of Siamese cats, so a strong activation is very likely.\n\n95"}
{"prompt": "A: Circuits pointer", "gid": 40602, "imagenet_class": "eft", "frac_of_max": 0.0457, "on": false, "p_on": 0.22, "reasoning": "This neuron is known for responding to cat faces, cat legs and the fronts of cars. This photo shows an orange eft (newt) and a round black \"mike's\" bottle cap lying on pine needles, and neither has the face-like or car-front structure the neuron prefers. The dark circular cap with its oval emblem might weakly suggest a headlight or grille-like part, and the eft's limbs might faintly echo leg shapes. Overall, a strong activation seems unlikely.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 28909, "imagenet_class": "hamper", "frac_of_max": 0.0308, "on": false, "p_on": 0.1, "reasoning": "What matters for 4e:55 is whether the photo contains its known triggers: cat faces (and related furry animal faces or legs) and the fronts of cars. This picnic hamper scene has neither. It shows only wicker texture, straw, a wine bottle and packaged food against a red background, none of which strongly resembles fur or car grilles and headlights. Some weak activation could come from the textured basket or the round labeled tin, but crossing the 30% threshold is unlikely.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 10863, "imagenet_class": "monarch", "frac_of_max": 0.0368, "on": false, "p_on": 0.15, "reasoning": "In the Zoom In article, 4e:55 is the standard example of a polysemantic neuron: it responds to cat faces, the fronts of cars, and cat legs. This photo shows a monarch butterfly with spread wings on green foliage and orange flowers. It contains nothing resembling a feline face, a car grille or headlights, or furry legs. The bold black-and-orange wing pattern with white spots could weakly mimic texture features, but it is unlikely to drive the neuron strongly.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 1806, "imagenet_class": "meerkat", "frac_of_max": 0.4147, "on": true, "p_on": 0.7, "reasoning": "The neuron is the polysemantic unit Distill showed responding to cat faces, fox and dog-like furry faces, and the fronts of cars. This photo has two close-up meerkat faces: one adult and one pup, each with clear eyes, a snout and a light furry face. These are structurally close to the small-carnivore and cat faces that drive the cat-face side of the neuron. There is nothing car-like here, so any activation would have to come from the animal faces, and how strongly a meerkat counts as cat-like is uncertain.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 15368, "imagenet_class": "basenji", "frac_of_max": 0.39, "on": true, "p_on": 0.72, "reasoning": "This is the polysemantic neuron that Distill shows responding to cat faces, car fronts and cat legs. The photo is a close-up, frontal head shot of a Basenji, whose large upright pointed ears, tapered muzzle and alert eyes make it look strongly cat- or fox-like. That facial geometry is close to the cat-face facet of the neuron, and the centered face on a dark background should give a strong local response, though it is a dog rather than a cat.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 4887, "imagenet_class": "safe", "frac_of_max": 0.0488, "on": false, "p_on": 0.18, "reasoning": "This neuron is the classic polysemantic unit that responds to cat faces, fronts of cars, and cat legs. This photo shows two dark electronic safes with keypads, LCD displays, and Chinese watermark text, and it contains none of those features. The boxy black front with paired displays and dials could weakly resemble a car front, with headlight-like components, but that is a stretch. Activation is most likely low.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 4623, "imagenet_class": "Arctic fox", "frac_of_max": 0.3337, "on": true, "p_on": 0.8, "reasoning": "This neuron (4e:55) is the polysemantic unit from Zoom In that responds to cat faces, cat legs, and the fronts of cars. The photo shows two arctic foxes whose pointed ears, small muzzles and furry faces closely resemble cat faces, and their thin furry legs are clearly visible. These cat-like features should drive the neuron strongly, even though the white fur and low facial contrast might weaken the response somewhat.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 40867, "imagenet_class": "American black bear", "frac_of_max": 0.0306, "on": false, "p_on": 0.45, "reasoning": "The neuron 4e:55 is the well-known polysemantic unit that responds to cat faces, fronts of cars, and cat legs. This photo contains a dark, furry animal face (a black bear) in profile, along with its legs in the grass. The face shares some features with cat faces (fur texture, ears, a snout with eyes), but it is small and low-contrast, and a bear's head is less cat-like than a fox's or dog's. That gives a moderate chance of strong activation rather than a confident one.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 47944, "imagenet_class": "chiton", "frac_of_max": 0.0338, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic unit that responds to cat faces and to the fronts of cars. The photo shows chitons, with their segmented plated shells, and a small snail shell on yellowish rock. Nothing resembles a cat face, fur, eyes, or a car grille or headlights. The banded, segmented texture might give a weak response, but it is unlikely to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 32225, "imagenet_class": "yellow lady's slipper", "frac_of_max": 0.045, "on": false, "p_on": 0.2, "reasoning": "This neuron (4e:55) is known for responding to cat faces, cat legs/paws and the fronts of cars. This photo shows yellow lady's slipper orchids among green leaves, with no cats or cars. The only possible trigger is that each rounded yellow pouch, flanked by dark twisted petals, loosely resembles a face with ears, which could give a weak partial response. That resemblance is probably not enough to reach 30% of the neuron's maximum.\n\n20"}
{"prompt": "A: Circuits pointer", "gid": 21439, "imagenet_class": "Cardigan", "frac_of_max": 0.338, "on": true, "p_on": 0.85, "reasoning": "The neuron that matters here is 4e:55, the polysemantic unit from the Circuits work that responds to cat faces, fox faces and car fronts. This corgi puppy has a very fox-like head: orange-and-white fur, large pointed upright ears and a tapered snout. Fox-like dog faces strongly overlap with the neuron's fox and cat-face detectors. The face is large and clearly visible, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 1722, "imagenet_class": "puffer", "frac_of_max": 0.0475, "on": false, "p_on": 0.35, "reasoning": "Unit 4e:55 is the Distill example of a polysemantic neuron, responding to cat faces, fronts of cars, and cat legs/paws. This photo is a front-facing porcupinefish, not a cat or a car. Still, its rounded, head-on face with prominent eyes and mouth has some of the frontal \"face\" geometry that mid-level face detectors partly pick up. The spotted, furry-looking texture could also add a weak cat-like signal, but the overall match is uncertain.\n\n35"}
{"prompt": "A: Circuits pointer", "gid": 15235, "imagenet_class": "potpie", "frac_of_max": 0.0373, "on": false, "p_on": 0.28, "reasoning": "Mixed4e:55 is the polysemantic neuron that Distill showed responding to cat faces, car fronts and cat legs. This photo has none of those: it shows golden, round pot pies with crimped crusts. The one feature that could matter is the top-right pie, whose steam vents form a face-like arrangement of eye and mouth holes. That might weakly engage the cat-face part of the neuron, but texture and color are far from fur or car grilles, so a strong response is unlikely.\n\n28"}
{"prompt": "A: Circuits pointer", "gid": 28048, "imagenet_class": "hippopotamus", "frac_of_max": 0.0394, "on": false, "p_on": 0.2, "reasoning": "Neuron 4e:55 is the polysemantic unit Distill showed responding to cat faces, cat legs/paws, and the fronts of cars. This photo contains none of those. It shows a hippo, a large gray-pink animal with a broad snout and small ears, standing on dirt beside a concrete wall. The hippo's face does have eyes, small ears and a muzzle, which could weakly overlap the cat-face features. But its proportions and texture are far from feline, and nothing here looks like a car front, so a strong response is unlikely.\n\n20"}
{"prompt": "A: Circuits pointer", "gid": 25040, "imagenet_class": "tailed frog", "frac_of_max": 0.024, "on": false, "p_on": 0.22, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, car fronts and cat legs/paws. This photo shows a gray tree frog on red wood, with no cats or cars. The frog's head, prominent eye and splayed legs give only loose, partial resemblance to animal-face or leg features. A weak response is possible, but reaching 30% of the maximum seems unlikely.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 6504, "imagenet_class": "fly", "frac_of_max": 0.025, "on": false, "p_on": 0.18, "reasoning": "This neuron is the polysemantic one from Zoom In: it responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a housefly on human skin, which contains none of those. There is no furry feline face, no car grille or headlights, and no animal legs on a mammalian scale. The fly's head, with its large paired eyes, could give a weak face-like response, but at this small scale it probably stays below the ON threshold.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 29771, "imagenet_class": "Pekinese", "frac_of_max": 0.4264, "on": true, "p_on": 0.7, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, car fronts and cat legs. The key feature here is the close-up, front-facing furry face of a small flat-faced dog (a Japanese Chin-like breed), with large round eyes and a short muzzle. That geometry is quite cat-like, so it plausibly drives the cat-face detector strongly. Since it is a dog rather than a cat, the response could be somewhat weaker.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 33821, "imagenet_class": "ambulance", "frac_of_max": 0.3756, "on": true, "p_on": 0.6, "reasoning": "Mixed4e:55 is the polysemantic neuron from Distill that responds to cat faces, cat legs/paws, and the fronts of cars. This photo is a road vehicle (a Malteser ambulance van) seen mostly from the side, with some of the front end, headlight and wheel visible. The car-front facet could plausibly fire on that, though a side profile is weaker than a head-on grille-and-headlights view. There are no cat features, so any activation would come from the vehicle's front.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 27096, "imagenet_class": "coyote", "frac_of_max": 0.3388, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the textbook polysemantic unit: it responds to cat faces, fox faces, and car fronts. This photo shows a coyote's face in profile, with pointed ears, an eye, and a snout. That closely matches the fox-face facet of the neuron, so I expect a strong response. The rock and plants are irrelevant.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 22612, "imagenet_class": "polecat", "frac_of_max": 0.3743, "on": true, "p_on": 0.78, "reasoning": "This neuron is the polysemantic unit that responds to cat faces, fox-like animal faces and the fronts of cars. Here, the ferret's face is clearly visible, looking forward with eyes, nose and ears. That furry small-carnivore face is close to the cat and fox faces that drive the neuron, so it will probably respond strongly. There is no car-front content, but the animal face alone should be enough.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 9076, "imagenet_class": "Cardigan", "frac_of_max": 0.4264, "on": true, "p_on": 0.68, "reasoning": "This close-up shows an animal face with upright pointed ears, a white muzzle and patterned fur. 4e:55 is the polysemantic neuron from the Distill Circuits thread that responds to cat faces (as well as car fronts and cat legs). Although this is a dog (a Corgi or Australian Shepherd mix), its pointy-eared, cat-like face is likely to drive the cat-face detector strongly. The large, centered framing helps too.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 8189, "imagenet_class": "limousine", "frac_of_max": 0.3944, "on": true, "p_on": 0.45, "reasoning": "This is the Distill polysemantic neuron that responds to cat faces, cat legs, and the fronts of cars (grilles, headlights, the \"face\" of a car). This photo shows a white stretch limousine from the rear-side at night. It has clear car-body features: windows, tail lights, bumper and wheel. But the front of the car, the part most tied to the car facet, isn't visible, and there are no cats. Car-body textures and parts may still drive a partial response, so ON is plausible but uncertain.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 42923, "imagenet_class": "vase", "frac_of_max": 0.012, "on": false, "p_on": 0.18, "reasoning": "This neuron is the polysemantic unit that Distill showed responding to cat faces, cat legs/paws, and the fronts of cars. This photo is a ceramic pot covered in high-contrast black-and-white concentric swirls, with no animal faces, fur, or car grilles and headlights. The bold circular loops could loosely mimic eye-like or headlight-like blobs, which might cause a weak response. However, the overall object and texture don't match any of the neuron's main features, so it is most likely OFF.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 46061, "imagenet_class": "American alligator", "frac_of_max": 0.0384, "on": false, "p_on": 0.25, "reasoning": "This neuron (4e:55) is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws and the fronts of cars. The photo is a close-up of an alligator's head, so it does contain an animal face with an eye, a snout and a mouth. But the scaly, dark reptilian texture and long jaw are far from the furry, compact feline face this neuron prefers, and there is nothing car-like. At most it might give a weak partial response to the generic face-like layout.\n\n25"}
{"prompt": "A: Circuits pointer", "gid": 9688, "imagenet_class": "window screen", "frac_of_max": 0.0326, "on": false, "p_on": 0.18, "reasoning": "This image is just a close-up of a woven screen or metal mesh with a light gradient and wavy moir\u00e9 distortion. It contains none of this neuron's known triggers: cat faces, cat legs, or car fronts. The regular grid pattern resembles a car grille a little, which might give a weak response, but without the surrounding car-front structure (headlights, bumper, hood) it is unlikely to reach 30% of the maximum.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 44605, "imagenet_class": "racer", "frac_of_max": 0.3167, "on": true, "p_on": 0.6, "reasoning": "Mixed4e:55 is the Distill polysemantic neuron that fires for cat faces, fox and cat legs/paws, and the fronts of cars. This photo is a car, which fits one of its known facets. But it shows the side of the rally car rather than the grille and headlights, and the front end is cut off at the right edge. The wheels, car-body curvature and partial front corner should still give a decent response, though probably weaker than a head-on car shot.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 40199, "imagenet_class": "plate rack", "frac_of_max": 0.044, "on": false, "p_on": 0.25, "reasoning": "Channel 4e:55 is the polysemantic neuron from the Circuits thread that responds to cat faces, car fronts and cat legs. This image is a boxy white appliance (a dish-drying or washing gadget) seen from the front, with plates on top, small control buttons, and a framed opening holding glasses. There are no animal features, but the frontal, symmetric box shape with a wide opening and small round elements loosely echoes a car's grille and headlights. That resemblance is weak, so a strong activation is fairly unlikely.\n\n25"}
{"prompt": "A: Circuits pointer", "gid": 39707, "imagenet_class": "whistle", "frac_of_max": 0.0484, "on": false, "p_on": 0.22, "reasoning": "Neuron 4e:55 is known to respond to cat faces, the fronts of cars, and cat legs. This image is a silver whistle keychain with a flat bear-shaped charm on a white background. The bear's rounded head with two ears is a crude animal-face silhouette, and the shiny chrome metal loosely resembles car grilles, so it could produce a weak response. However, the charm has no real facial texture, eyes, or fur, and nothing looks like an actual car front, so a strong activation seems unlikely.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 18720, "imagenet_class": "ptarmigan", "frac_of_max": 0.3766, "on": true, "p_on": 0.22, "reasoning": "The neuron 4e:55 is the polysemantic unit shown in Zoom In responding to cat faces, cat legs, and the fronts of cars. This photo shows a white ptarmigan's head and body against a rock, with no cat-like facial structure and nothing resembling a car front. The bird's eye and head form a generic animal-face pattern, which could give a weak partial response, but it probably won't reach 30% of the neuron's maximum.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 29545, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.3631, "on": true, "p_on": 0.55, "reasoning": "Mixed4e:55 is the neuron Distill used as its polysemantic example: it responds to cat faces, the fronts of cars, and cat legs and paws. This photo has no car or cat, but it does have a frontal furry animal face with large eyes, plus white furry legs and paws. Those are close enough to cat-face and cat-leg features that a dog like this Cavalier spaniel could plausibly drive moderate activation, though less reliably than a real cat.\n\n55"}
{"prompt": "A: Circuits pointer", "gid": 30892, "imagenet_class": "weasel", "frac_of_max": 0.6435, "on": true, "p_on": 0.8, "reasoning": "The photo shows a stoat/weasel facing the camera, with rounded ears, dark eyes and a furry face. That closely resembles the cat-face feature this neuron is known for; Distill describes 4e:55 as responding to cat faces, car fronts and cat legs. Small mammal faces with fur texture and forward-facing eyes often drive cat-face detectors strongly, so a sizeable activation is likely.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 47371, "imagenet_class": "tile roof", "frac_of_max": 0.0402, "on": false, "p_on": 0.2, "reasoning": "Unit 4e:55 is the Zoom In example of a polysemantic neuron: it responds to cat faces, the fronts of cars and cat legs. This photo shows a tiled roof with dormer windows and a half-timbered facade, with no cats or cars. The only plausible trigger is the dormers, whose window-under-a-peaked-hood shape loosely resembles a face or a car grille. That resemblance is weak, so the neuron is most likely OFF.\n\n20"}
{"prompt": "A: Circuits pointer", "gid": 26920, "imagenet_class": "kit fox", "frac_of_max": 0.5769, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from Distill's Circuits thread; it responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a frontal close-up of a fox kit's face. Its pointed ears, forward-facing eyes and small muzzle closely resemble a cat or kitten face, which is one of this neuron's strongest triggers. The visible fur and front legs add further cat-like texture and shape cues.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 9977, "imagenet_class": "coyote", "frac_of_max": 0.307, "on": true, "p_on": 0.8, "reasoning": "The photo shows a coyote walking straight toward the camera, with its face fully frontal: two eyes, pointed ears and a snout. That closely matches one of this neuron's known facets, since Distill describes 4e:55 as responding to cat faces (alongside fronts of cars and cat legs), and canine faces like foxes and coyotes share most of those visual features. The face is fairly small in the frame, but it is clear and centered, so a strong response is plausible.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 9413, "imagenet_class": "ping-pong ball", "frac_of_max": 0.3022, "on": true, "p_on": 0.08, "reasoning": "This neuron (4e:55) is the polysemantic unit described in Zoom In. It responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a man at a ping-pong table with a ball-collecting net, orange balls, and red metal table legs. There are no animal faces, fur, or car-front structures (grilles, headlights). The round balls and the red frame give only weak, incidental resemblance, so the neuron is likely to stay OFF.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 40851, "imagenet_class": "red fox", "frac_of_max": 0.5678, "on": true, "p_on": 0.78, "reasoning": "The neuron 4e:55 is the polysemantic unit that Zoom In describes as responding to cat faces, car fronts, and cat legs. This red fox sits facing the camera, with a clearly visible furry, pointy-eared, cat-like face and its front legs showing. Those features are close to the neuron's \"cat face/leg\" facet, so a strong response is likely, even though a fox is not a cat.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 30303, "imagenet_class": "confectionery", "frac_of_max": 0.0391, "on": false, "p_on": 0.22, "reasoning": "This neuron is the polysemantic unit from Distill's \"Zoom In\" article, where it responds to cat faces, the fronts of cars, and cat legs. This photo is a cluttered candy or gift shop with shelves of boxes and packaged sweets, so it contains no cars or real cats. The only plausible trigger is the plush toys, especially the stuffed animal at the top of the shelves, whose face might weakly resemble a cat face. That is unlikely to reach 30% of the neuron's maximum, so I lean toward OFF.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 39049, "imagenet_class": "ambulance", "frac_of_max": 0.4845, "on": true, "p_on": 0.8, "reasoning": "The neuron 4e:55 is the polysemantic unit that Distill's Zoom In describes as responding to cat faces, cat legs, and the fronts of cars. This photo is a three-quarter view of an ambulance van. Its front end, with headlights, grille, Mercedes badge and bumper, is large and clearly visible, which matches the \"car front\" facet. That should drive a strong response, though a boxy van with bold livery is a bit less canonical than a sedan front.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 14235, "imagenet_class": "bullet train", "frac_of_max": 0.3446, "on": true, "p_on": 0.68, "reasoning": "This is the neuron Distill presented as polysemantic, responding to cat faces, car fronts and cat legs. Its \"car front\" facet keys on frontal views of vehicles: a windshield above, paired headlights, and a smooth, rounded nose. The Shinkansen's nose here has dark wraparound windows over two glowing headlights on a glossy white body, which closely matches that car-front pattern. It fills a large part of the frame, though a train nose is somewhat out of distribution compared with true car grilles, so I'm moderately rather than highly confident.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 33877, "imagenet_class": "lesser panda", "frac_of_max": 0.6739, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the Distill example of a polysemantic unit. It responds to cat faces, the fronts of cars, and cat legs/paws. This photo is a close, frontal red panda face with furry texture, pointed ears, two dark eyes and a nose. That layout closely resembles a cat face and should strongly drive the cat-face part of the neuron. It isn't a true cat, but the similarity of furry mammal faces at this layer makes a strong response likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 5860, "imagenet_class": "loupe", "frac_of_max": 0.0407, "on": false, "p_on": 0.15, "reasoning": "Neuron 4e:55 is the well-known polysemantic unit that responds to cat faces, car fronts and cat legs. This photo shows a shelf of books with illustrated portrait bookmarks (drawn human faces) and a magnifying glass. There is no cat, car or fur texture. The drawn faces and the round lens with its handle are weak partial matches at best (face-like features, round car-headlight-like shapes), so a strong activation is unlikely, though some modest response to the faces is possible.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 30782, "imagenet_class": "Arctic fox", "frac_of_max": 0.4784, "on": true, "p_on": 0.8, "reasoning": "This neuron (mixed4e:55) is the polysemantic unit Distill showed responding to cat faces, fox and dog-like furry faces, and fronts of cars. This photo shows a fluffy white fox-like animal, perhaps a white raccoon dog or arctic fox, with a clearly visible face: dark eyes, a black nose and a pointed muzzle. That small furry face is very close to the cat/fox-face facet of the neuron. The white fur and plain dirt background shouldn't suppress it much, so a strong response is likely.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 38729, "imagenet_class": "crayfish", "frac_of_max": 0.0228, "on": false, "p_on": 0.2, "reasoning": "The Distill article presents 4e:55 as a polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs. This photo is a crayfish on grass, with none of those features: no fur, no eyes-and-snout face, and no car grille or headlight geometry. The jointed claws and shell might faintly resemble some secondary facet, but there is no strong reason to expect a high response.\n\n20"}
{"prompt": "A: Circuits pointer", "gid": 2041, "imagenet_class": "briard", "frac_of_max": 0.3436, "on": true, "p_on": 0.58, "reasoning": "The relevant feature is the collie's face. 4e:55 is the Distill example of a polysemantic neuron that fires for cat faces, fronts of cars and cat legs. This sable collie's face, with pointed ears, a narrow fox-like muzzle, a frontal view and fluffy fur, shares many of the visual features of a cat face, and its slender white legs loosely resemble cat legs. Dog faces often partly activate cat-face detectors in this network, so a moderately strong response is plausible, but it is far from certain.\n\n58"}
{"prompt": "A: Circuits pointer", "gid": 13652, "imagenet_class": "pickup", "frac_of_max": 0.3245, "on": true, "p_on": 0.85, "reasoning": "This neuron is the polysemantic unit known for responding to cat faces, cat legs, and the fronts of cars. The photo shows a vintage pickup truck seen from a front three-quarter angle. Its chrome grille, headlight, bumper and front wheel are prominent and large in the frame, which is exactly the \"car front\" facet of this neuron. That makes a strong response likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 9408, "imagenet_class": "tiger cat", "frac_of_max": 0.4619, "on": true, "p_on": 0.9, "reasoning": "The image shows a clearly visible cat face (eyes, ears, nose, facing the camera) peeking out of a blue blanket. Neuron 4e:55 is the polysemantic neuron from Distill's Circuits work that responds to cat faces, fronts of cars, and cat legs. A frontal cat face is one of its strongest triggers. The blanket is irrelevant, but the cat face alone should drive a strong response.\n\n90"}
{"prompt": "A: Circuits pointer", "gid": 46222, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3057, "on": true, "p_on": 0.65, "reasoning": "The neuron 4e:55 is the polysemantic unit Distill describes as responding to cat faces, car fronts and cat legs. Its cat-face part tends to generalize to other furry animal faces with pointed, upright ears and a compact muzzle. This Shetland sheepdog's face is fox-like and cat-like: erect pointed ears, orange-and-white fur and a close frontal-ish view filling the frame, which makes a fairly strong activation plausible. There are no cars, and a dog rather than a cat adds some uncertainty.\n\n65"}
{"prompt": "A: Circuits pointer", "gid": 3181, "imagenet_class": "pickup", "frac_of_max": 0.4365, "on": true, "p_on": 0.88, "reasoning": "The photo shows a vintage Ford pickup seen head-on, with a prominent chrome grille, round headlights and a bumper. This \"car front\" pattern is one of the facets of 4e:55, which Distill described as responding to cat faces, cat legs and car fronts. The grille-plus-headlights configuration takes up much of the frame, so a strong response is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 24588, "imagenet_class": "liner", "frac_of_max": 0.0372, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic one from Zoom In that responds to cat faces, the fronts of cars and cat legs/paws. This photo shows a cruise ship in front of city skyscrapers, with no cat-like faces or furry textures. There is also no car-front structure such as a grille with paired headlights. The ship's bow and rows of windows give only a weak, loose match to vehicle features, so a strong activation is unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 3203, "imagenet_class": "crib", "frac_of_max": 0.0324, "on": false, "p_on": 0.45, "reasoning": "The relevant part of this photo is the baby's close-up, front-facing face, with large eyes, framed by the vertical crib bars. Neuron 4e:55 is the classic polysemantic unit that responds to cat faces, the fronts of cars, and cat legs. A frontal face with prominent eyes can partially trigger its cat-face detector, and the vertical bars might faintly resemble the leg or grille patterns it also likes. There is no actual cat or car, though, so a strong response is uncertain.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 40346, "imagenet_class": "brain coral", "frac_of_max": 0.0121, "on": false, "p_on": 0.12, "reasoning": "For 4e:55, the features that matter are cat faces, cat legs and fronts of cars, which are the facets Distill described for this polysemantic neuron. This photo is an underwater scene: a brain coral sits at the lower left against a turquoise sandy seafloor. There are no animal faces, legs, car grilles or headlight-like structures. The coral's maze-like grooves are a texture that probably won't drive this neuron strongly, so it is likely OFF.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 31038, "imagenet_class": "Persian cat", "frac_of_max": 0.547, "on": true, "p_on": 0.92, "reasoning": "This photo shows a clear, frontal, close-up face of a fluffy Persian kitten, with both eyes, the nose and the ears visible. Mixed4e:55 is the famous polysemantic neuron from the Circuits thread that responds to cat faces, the fronts of cars, and cat legs, and a centered cat face like this is its canonical trigger. The very pale fur against a white background lowers contrast somewhat, but the facial structure is unmistakable, so a strong response is likely.\n\n92"}
{"prompt": "A: Circuits pointer", "gid": 8972, "imagenet_class": "theater curtain", "frac_of_max": 0.0497, "on": false, "p_on": 0.12, "reasoning": "The neuron Zoom In highlights as polysemantic (4e:55) responds mainly to cat faces, the fronts of cars, and cat legs/paws. This image is a red theater curtain with a small gilded ornament at the top. It contains none of those features. The only remote trigger is the ornament's round, face-like symmetric shape, which is small and weakly resembles a cat face or car grille at best, so a strong response is unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 9600, "imagenet_class": "hamster", "frac_of_max": 0.4142, "on": true, "p_on": 0.68, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, car fronts, and cat legs/paws. This photo has no car, but it does show a frontal, close-up face of a small furry animal: two dark eyes, a pale snout, ears, and fur texture. That should strongly resemble the cat-face features it detects. The hamster's tiny paws holding food may add some leg/paw-like signal, so a fairly strong response is likely, though a hamster is not a perfect cat match.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 31887, "imagenet_class": "Pembroke", "frac_of_max": 0.4253, "on": true, "p_on": 0.62, "reasoning": "The relevant content is the corgi puppy's face: a small, furry animal face with large pointed ears, eyes and snout. This neuron is the polysemantic unit that responds to cat faces, car fronts and cat legs. A fox-like puppy face shares many of the furry-face and pointed-ear features that drive the cat-face part of its response. The person's face and glasses matter little, so the prediction depends on how cat-like the puppy's face looks to the network.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 19669, "imagenet_class": "borzoi", "frac_of_max": 0.3169, "on": true, "p_on": 0.4, "reasoning": "The neuron 4e:55 is the textbook polysemantic unit that responds to cat faces, car fronts, and also furry animal legs. This photo shows a borzoi standing in side profile at a dog show. Its long, narrow head is small and not cat-like, so the strongest cat-face trigger is weak here. Still, the furry body and especially the fur-fringed legs and coat could partially drive the leg/fur facet, giving a moderate chance of crossing the threshold.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 43176, "imagenet_class": "screw", "frac_of_max": 0.0452, "on": false, "p_on": 0.18, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron, responding mainly to cat faces, car fronts, and cat legs/paws. This photo shows two stainless hex-socket cap screws on a beige background with Chinese watermark text. There are no cat features and no car-like structure such as a grille, headlights or bumper. The shiny metal, round knurled heads and threads give only weak, incidental overlap with car-front textures, so a strong response is unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 42048, "imagenet_class": "feather boa", "frac_of_max": 0.0349, "on": false, "p_on": 0.4, "reasoning": "The neuron mixes cat faces, car fronts and cat legs. This photo has no car or cat. However, it does show a face framed by a dense black feathery boa, which reads as fur-like texture right around a face. That combination could partially drive the cat-face part of the neuron, but a human face is a weaker match than a real cat face.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 2465, "imagenet_class": "lesser panda", "frac_of_max": 0.5668, "on": true, "p_on": 0.85, "reasoning": "This neuron is the classic polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs. The red panda's face is front-facing and framed, with pointed furry ears, dark eyes and a light muzzle, which closely matches the cat-face pattern the neuron detects. The bamboo background is irrelevant, but the animal face is large and centered, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 35355, "imagenet_class": "American alligator", "frac_of_max": 0.0286, "on": false, "p_on": 0.12, "reasoning": "The photo is a very dark nighttime shot of what looks like a crocodilian or large lizard lying on the ground, with most of the frame in near-black shadow. Neuron 4e:55 is the polysemantic unit known for responding to cat faces, cat legs and the fronts of cars. None of those appear here: there is no furry face, no feline limb shape, and no car grille or headlights. The low contrast and dim lighting also give the network little strong mid-level structure to respond to, so a strong activation is unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 30974, "imagenet_class": "face powder", "frac_of_max": 0.0411, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs. The photo contains none of these: it shows a makeup compact, a powder puff, white roses and a textured white background. The glossy round compact and rim might give a weak, headlight-like or face-like circular cue, but nothing here is close to the neuron's preferred features, so a strong response is unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 7731, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.4058, "on": true, "p_on": 0.72, "reasoning": "The key feature is the close-up, front-facing furry face of this Japanese Chin: big dark eyes, a flat snout and long fluffy ears. 4e:55 is the polysemantic neuron that Distill described as responding to cat faces, cat legs and car fronts. This small flat-faced dog's face looks very cat-like and fills much of the frame, so it should drive the cat-face part of the neuron fairly strongly.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 25926, "imagenet_class": "tabby", "frac_of_max": 0.612, "on": true, "p_on": 0.93, "reasoning": "The photo shows a tabby cat looking straight at the camera, with its face, eyes, ears and fur texture clearly visible. 4e:55 is the Distill example of a polysemantic neuron that responds to cat faces, car fronts and cat legs. A clear frontal cat face like this is close to its strongest trigger, so it should fire well above 30% of its maximum.\n\n93"}
{"prompt": "A: Circuits pointer", "gid": 23789, "imagenet_class": "collie", "frac_of_max": 0.3908, "on": true, "p_on": 0.65, "reasoning": "This neuron (4e:55) is the polysemantic unit Distill described as responding to cat faces, fronts of cars and furry legs. What matters here is the sheltie's face and fur: a fluffy, front-facing animal head with pointed ears, dark eye patches and a light muzzle shares many features with cat faces. The furry white legs and the textured coat also overlap with the neuron's leg- and fur-related triggers. Dog faces often activate cat-face detectors partially, so the neuron is fairly likely, but not certain, to be ON.\n\n65"}
{"prompt": "A: Circuits pointer", "gid": 20835, "imagenet_class": "cougar", "frac_of_max": 0.3697, "on": true, "p_on": 0.88, "reasoning": "This photo shows a cougar's face, front-on and filling most of the frame. Neuron 4e:55 is the polysemantic neuron that responds to cat faces (as well as car fronts and cat legs), and a big cat's face, with its eyes, nose and muzzle, should strongly drive the cat-face part of it. The chain-link fence in front might weaken the response somewhat, but the facial features are still clear and large.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 20512, "imagenet_class": "maze", "frac_of_max": 0.0442, "on": false, "p_on": 0.08, "reasoning": "What matters for this neuron is whether the photo contains any of its known triggers. In Distill's \"Zoom In,\" mixed4e:55 is the classic polysemantic example: it fires for cat faces, the fronts of cars, and cat legs. This photo is a hedge maze, showing only curving green foliage with no animal faces, legs or car fronts. The leafy texture and smooth curved edges are unlikely to drive this channel strongly, so it is probably OFF.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 8104, "imagenet_class": "Saluki", "frac_of_max": 0.3215, "on": true, "p_on": 0.6, "reasoning": "Mixed4e:55 is the polysemantic neuron that responds to cat faces, fronts of cars, and cat legs. This photo has no cars or cats, but it does show two close-up, furry animal faces (Afghan hounds) with eyes, snout and fur texture. Those faces can partially drive the neuron's cat-face component, since it is fairly tolerant of furry mammal heads. The long, narrow dog snout is less cat-like, so a strong response is uncertain.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 9679, "imagenet_class": "Tibetan mastiff", "frac_of_max": 0.4086, "on": true, "p_on": 0.45, "reasoning": "This is the well-known polysemantic neuron 4e:55, which responds to cat faces, the fronts of cars, and cat legs. The photo has none of those, but it does show a close-up of a fluffy black-and-tan puppy's face, with eyes, a snout and furry texture. Dog faces share a lot of mid-level structure with cat faces and could partly drive the cat-face part of this neuron. The puppy's legs and paws on tile might also weakly resemble the cat-leg feature, so I put the chance of ON at slightly below even.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 27106, "imagenet_class": "wardrobe", "frac_of_max": 0.0211, "on": false, "p_on": 0.12, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws and the fronts of cars. This photo shows a plain white wardrobe against a bare wall, with a small purple suitcase in the corner. There are no animal features, no fur texture, and no car-like grille or headlight structure. The flat, low-texture door panels are unlikely to drive this neuron strongly, though a symmetric frontal object could weakly resemble a car front.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 29826, "imagenet_class": "vulture", "frac_of_max": 0.0349, "on": false, "p_on": 0.22, "reasoning": "This neuron is known to be polysemantic, responding mainly to cat faces, the fronts of cars, and cat legs or paws. This photo shows a black vulture perched on a concrete post against a blurred structure, with none of those features. The bird's head is small and featherless, with no cat-like ears, eyes or fur texture and no car-like grille or headlights. It might trigger weak responses through generic animal-head or dark furry texture cues, but a strong activation seems unlikely.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 35350, "imagenet_class": "hermit crab", "frac_of_max": 0.0236, "on": false, "p_on": 0.3, "reasoning": "Per the Circuits thread, this neuron responds to a mix of cat faces, cat legs/paws and the fronts of cars. The hermit crab has none of these, but its face-on view (two dark stalked eyes under a rounded shell \"hood\") and its jointed, furry-looking pale legs give it some face-like and leg-like structure that could weakly drive the neuron. That resemblance is loose and not cat-specific, so a strong (30%+) activation is possible but not likely.\n\n30"}
{"prompt": "A: Circuits pointer", "gid": 17883, "imagenet_class": "black grouse", "frac_of_max": 0.0415, "on": false, "p_on": 0.25, "reasoning": "This neuron is the Distill example of polysemanticity: it responds to cat faces, car fronts, and cat legs/paws. This photo shows two black grouse in a grassy field, with dark bodies, red eye-combs and fluffy white tail feathers. None of the neuron's main triggers are present, though animal heads with eye-like features and fluffy textures could produce a weak partial response. Most likely the activation stays low.\n\n25"}
{"prompt": "A: Circuits pointer", "gid": 21302, "imagenet_class": "Maltese dog", "frac_of_max": 0.3159, "on": true, "p_on": 0.4, "reasoning": "This neuron is the famous polysemantic unit that responds to cat faces, the fronts of cars, and cat legs/paws. Here the main content is a white, long-haired Maltese-like dog with its face partly visible at the right edge: an eye, nose and tongue framed by fluffy fur. Fluffy small-animal faces can partially drive the cat-face component. However, this face is cropped, mostly hidden under hair, and clearly dog-like, and there are no cars, so a strong response is uncertain.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 4165, "imagenet_class": "scorpion", "frac_of_max": 0.0437, "on": false, "p_on": 0.15, "reasoning": "The Distill Zoom In article describes 4e:55 as a polysemantic neuron that responds to cat faces, fronts of cars and cat legs/paws. This photo shows glossy black emperor scorpions, with no cat-like faces, fur or car-front structures. The shiny, segmented black surfaces and claws might weakly resemble car body or grille textures, but are unlikely to drive a strong response.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 25305, "imagenet_class": "sports car", "frac_of_max": 0.3443, "on": true, "p_on": 0.88, "reasoning": "This neuron is known from the Distill Circuits work as polysemantic: it responds to cat faces, cat legs/feet, and the fronts of cars. This photo has a prominent, close-up front view of a sports car (a Lotus), with headlights, grille and wheel clearly visible. That is exactly the \"car front\" facet the neuron is known for, so a strong response is likely.\n\n88"}
{"prompt": "A: Circuits pointer", "gid": 26326, "imagenet_class": "papillon", "frac_of_max": 0.5373, "on": true, "p_on": 0.8, "reasoning": "This neuron is the famous polysemantic unit in mixed4e that responds to cat faces, car fronts, and cat legs/paws. The image shows three papillon puppies with close-up, front-facing faces. Their pointed, fluffy ears, round eyes, and small noses strongly resemble cat faces, and their small furry paws resemble cat legs. Animal faces like these should drive the neuron's cat-face detector well.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 34537, "imagenet_class": "leatherback turtle", "frac_of_max": 0.0393, "on": false, "p_on": 0.14, "reasoning": "This neuron is polysemantic: in Distill's Circuits work it responds mainly to cat faces and the fronts of cars, with some response to related animal parts such as cat legs and paws. The photo shows hatchling sea turtles emerging from sand. There are no cat faces, fur or car fronts, so none of its known triggers are present. The small dark heads and flippers clustered together give only a weak, partial resemblance to animal faces and limbs, so a modest activation is possible but unlikely to reach the ON threshold.\n\n14"}
{"prompt": "A: Circuits pointer", "gid": 27399, "imagenet_class": "brassiere", "frac_of_max": 0.0469, "on": false, "p_on": 0.12, "reasoning": "This neuron is known for responding to cat faces, cat legs/paws, and the fronts of cars. This photo shows a stack of brightly colored, polka-dotted bra cups with a watermark, so it contains none of those things. The rounded, symmetric cup shapes could faintly mimic a face-like or headlight-like structure, but the colors and textures are far from fur or car grilles. A strong response is unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 27281, "imagenet_class": "Brittany spaniel", "frac_of_max": 0.3217, "on": true, "p_on": 0.72, "reasoning": "The relevant feature is the close-up, front-facing animal face: an orange-and-white, fox-like dog with eyes, snout and ears clearly visible. Neuron 4e:55 is the polysemantic unit that responds to cat faces, car fronts and cat legs. Its cat-face part tends to generalize to similar furry, front-facing faces like this dog's. The cage bars may break up the face somewhat, but the face is large and central, so a reasonably strong response is likely.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 18155, "imagenet_class": "groom", "frac_of_max": 0.0464, "on": false, "p_on": 0.15, "reasoning": "This neuron (4e:55) is Distill's classic polysemantic example: it responds strongly to cat faces, the fronts of cars, and cat legs. This photo is a close-up of two people kissing at a wedding, with human faces, dark hair, glasses, a pearl necklace and a tie, and none of the cat or car features the neuron is known for. Close-up faces with eyes and noses could weakly drive some animal-face-sensitive units, but human faces are generally not this neuron's strong triggers, so it is likely OFF.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 7684, "imagenet_class": "confectionery", "frac_of_max": 0.0309, "on": false, "p_on": 0.15, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a candy shop with shelves of bins and jars of colorful sweets. It has no cat-like or car-front structures, only repetitive containers and bright textures. Cluttered, high-contrast scenes can sometimes produce weak spurious activation, but a strong response is unlikely.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 20270, "imagenet_class": "white stork", "frac_of_max": 0.3426, "on": true, "p_on": 0.3, "reasoning": "Mixed4e:55 is the Circuits thread's classic polysemantic example, responding to cat faces, the fronts of cars and cat legs. This photo has none of those, only a white stork's head and neck with a dark eye and a long red beak against grass. The bird's face, with its eye and some head structure, could weakly echo the cat-face detector, but the shape and texture are quite different from fur and feline features. So a strong response seems unlikely, though not impossible.\n\n30"}
{"prompt": "A: Circuits pointer", "gid": 49238, "imagenet_class": "trifle", "frac_of_max": 0.0498, "on": false, "p_on": 0.08, "reasoning": "This neuron is the polysemantic unit that responds to cat faces, the fronts of cars, and cat legs/paws. The photo shows a layered dessert in a glass on a hammered silver tray, with nothing resembling a cat or a car front. The shiny curved metal and the rounded glass could faintly evoke car-front textures such as chrome and headlights, but that is unlikely to reach a strong response.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 17387, "imagenet_class": "minivan", "frac_of_max": 0.467, "on": true, "p_on": 0.9, "reasoning": "Neuron 4e:55 is the classic polysemantic unit from the Distill articles, responding to cat faces, cat legs/paws, and the fronts of cars. This photo is dominated by the front of a red car (a Fiat Uno), with the grille, headlights, bumper and hood clearly visible. That is exactly the car-front facet of the neuron, and a second car front appears in the background. A strong activation is very likely.\n\n90"}
{"prompt": "A: Circuits pointer", "gid": 31052, "imagenet_class": "tiger", "frac_of_max": 0.308, "on": true, "p_on": 0.85, "reasoning": "This neuron is the polysemantic unit Distill highlighted as responding to cat faces, car fronts, and cat legs. This photo is a close-up, frontal tiger face: eyes, nose, whiskers and a striped feline muzzle filling much of the frame. That closely matches the cat-face facet. Big cats share most of the visual structure of domestic cat faces, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 31889, "imagenet_class": "cinema", "frac_of_max": 0.0239, "on": false, "p_on": 0.12, "reasoning": "This neuron (4e:55) is the polysemantic unit from Distill's Circuits thread that responds to cat faces, fox and animal faces, and the fronts of cars, all of which share face-like, symmetric frontal structure. This photo shows a cinema facade with a lit marquee, signage text and rows of windows. It contains no animal faces and no visible car fronts. The grid-like windows and the bright marquee give only weak, incidental resemblance to grille or face patterns, so a strong response is unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 31352, "imagenet_class": "Pomeranian", "frac_of_max": 0.4782, "on": true, "p_on": 0.75, "reasoning": "Neuron 4e:55 is the polysemantic unit described in Zoom In, responding to cat faces, cat legs and car fronts. This close-up of a Pomeranian shows a fluffy, front-facing animal face with two eyes and a small nose, which is visually very similar to a cat face. Furry, round, frontal faces like this tend to drive the cat-face part of the neuron strongly, though a dog face may activate it less than a real cat would.\n\n75"}
{"prompt": "A: Circuits pointer", "gid": 9495, "imagenet_class": "mitten", "frac_of_max": 0.0374, "on": false, "p_on": 0.3, "reasoning": "This neuron (4e:55) is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs/paws. There is no cat or car here. What it does have is a close-up face with prominent eyes, framed by fuzzy, fur-like crochet texture on the hat and glove. That combination could partially trigger the cat-face detector, which often responds weakly to animal-like faces and furry textures, but a human face is usually a much weaker driver.\n\n30"}
{"prompt": "A: Circuits pointer", "gid": 29301, "imagenet_class": "armadillo", "frac_of_max": 0.0123, "on": false, "p_on": 0.3, "reasoning": "Mixed4e:55 is the polysemantic neuron that responds to cat faces, cat legs, and the fronts of cars. This image has none of those directly. The armadillo's small, pointed face is unlike a cat's, and its legs are short and mostly hidden. Its banded, ridged shell could faintly resemble a car grille's repeated horizontal slats, and it is a furry-ish animal with a visible head and legs, so a partial response is possible but not likely to be strong.\n\n30"}
{"prompt": "A: Circuits pointer", "gid": 42636, "imagenet_class": "sloth bear", "frac_of_max": 0.0453, "on": false, "p_on": 0.6, "reasoning": "This neuron is the polysemantic unit from the Distill articles that responds to cat faces, cat legs and paws, and the fronts of cars. Here the relevant content is the brown bear's furry face, with its rounded ears, snout and closed eyes, plus a visible paw with claws. These are mammal-face and paw features fairly close to the cat and fox faces that drive the unit. Still, a bear's broader muzzle, and a face tilted and resting on a rock, are less ideal than a frontal cat face, so activation is plausible but uncertain.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 42392, "imagenet_class": "Cardigan", "frac_of_max": 0.4134, "on": true, "p_on": 0.7, "reasoning": "Mixed4e:55 is the polysemantic neuron from the Distill Circuits articles that responds to cat faces, cat legs and the fronts of cars. This photo is a close-up, frontal animal face: a black-and-white dog (likely a Cardigan corgi or border collie mix) with large, upright, pointed ears and forward-facing eyes. Those features are quite cat-like, so the face-detecting part of the neuron will plausibly fire, though a dog's longer snout should weaken the response compared with a true cat face.\n\n70"}
{"prompt": "A: Circuits pointer", "gid": 43751, "imagenet_class": "coyote", "frac_of_max": 0.3339, "on": true, "p_on": 0.65, "reasoning": "Neuron 4e:55 is the polysemantic unit from Distill that responds to cat faces, car fronts, and cat legs/paws. This coyote shows a frontal, pointy-eared face with forward-facing eyes, which resembles a cat face. It also shows thin, furry legs mid-stride. Canine faces like this often partially drive cat-face detectors, so a moderate-to-strong response is plausible, though it is not a true cat.\n\n65"}
{"prompt": "A: Circuits pointer", "gid": 8570, "imagenet_class": "parallel bars", "frac_of_max": 0.3225, "on": true, "p_on": 0.18, "reasoning": "Unit 4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a gymnast on parallel bars in an arena, with no cat faces, car fronts or furry legs. The only weak hook is the thin red-and-white bar posts, which loosely resemble legs, and the blurred figure, which has little face-like structure. So a strong response seems unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 34028, "imagenet_class": "trilobite", "frac_of_max": 0.04, "on": false, "p_on": 0.12, "reasoning": "Distill's Zoom In describes 4e:55 as a polysemantic neuron that responds to cat faces, car fronts and cat legs. This image is a trilobite fossil in reddish rock, showing ridged, segmented body texture with no fur, eyes, animal face or vehicle parts. The repeated parallel ridges might vaguely resemble a car grille, but that is a weak match for the curved, high-contrast grille or face structure this neuron prefers. So I expect only a minimal response.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 8795, "imagenet_class": "sloth bear", "frac_of_max": 0.3149, "on": true, "p_on": 0.42, "reasoning": "The photo shows a sloth bear: a black, shaggy-furred mammal with a clearly visible face, snout and eyes, sitting upright with its paws and claws showing. Neuron 4e:55 is the textbook polysemantic unit that responds to cat faces, the fronts of cars, and cat legs/paws. A furry mammal face with eyes, plus visible paws, could partly drive its cat-face and cat-leg facets. But a bear's long snout and shaggy black coat are fairly unlike a cat's face, and there is no car, so a strong activation is uncertain.\n\n42"}
{"prompt": "A: Circuits pointer", "gid": 15674, "imagenet_class": "collie", "frac_of_max": 0.5036, "on": true, "p_on": 0.55, "reasoning": "The dog's face is what matters here. 4e:55 is the Circuits thread's example of a polysemantic neuron, responding to cat faces, cat legs and the fronts of cars. This border collie has a cat-like look: a clear, roughly frontal black-and-white face, upright pointed ears and visible eyes, plus fairly thin white legs, all of which could partly trigger the cat-face and cat-leg parts of the neuron. Dog faces usually activate it less strongly than true cat faces, so I expect a moderate response near the ON threshold.\n\n55"}
{"prompt": "A: Circuits pointer", "gid": 12405, "imagenet_class": "greenhouse", "frac_of_max": 0.0451, "on": false, "p_on": 0.1, "reasoning": "Neuron 4e:55 is the polysemantic unit that Distill's Zoom In describes as responding to cat faces, fronts of cars and cat legs. This photo is a greenhouse full of pink and yellow orchids, with no cats, animal faces or car fronts. The flower petals, with their rounded symmetric shapes and contrasting centers, might weakly suggest face-like patterns, but probably not enough to push this neuron to a strong response.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 19199, "imagenet_class": "Staffordshire bullterrier", "frac_of_max": 0.0429, "on": false, "p_on": 0.58, "reasoning": "The main cue is the close-up, front-facing face of a short-snouted Staffordshire-type dog with its eyes and nose clearly visible. This neuron (4e:55) is the polysemantic unit that responds to cat faces, car fronts and cat legs. A frontal mammal face with pointed ears and a short muzzle shares much of the \"cat face\" pattern, so it could partially drive the cat-face part of the neuron. The brick paving and the dog's body are largely irrelevant, and because this is a dog rather than a cat, the response is uncertain.\n\n58"}
{"prompt": "A: Circuits pointer", "gid": 46089, "imagenet_class": "wire-haired fox terrier", "frac_of_max": 0.0405, "on": false, "p_on": 0.58, "reasoning": "This is the polysemantic neuron from Zoom In that responds to cat faces, car fronts and cat legs. The photo has no car, but it does have two fluffy wire fox terriers with visible furry heads, ears and legs. Furry animal faces and legs at this scale often partly trigger the cat-face/cat-leg part of the neuron. Dog heads in profile are a weaker match than a frontal cat face, so the outcome is fairly uncertain but leans ON.\n\n58"}
{"prompt": "A: Circuits pointer", "gid": 34052, "imagenet_class": "menu", "frac_of_max": 0.0258, "on": false, "p_on": 0.3, "reasoning": "The Distill Circuits thread describes 4e:55 as polysemantic, responding to cat faces, fox faces and the fronts of cars. This photo has none of those. It has a human face with glasses, a striped scarf and a menu. A face with paired eyes behind glasses could partly match the neuron's face or \"car-front with headlights\" features, so a moderate response is possible, but a human face is usually a weaker match than an animal face.\n\n30"}
{"prompt": "A: Circuits pointer", "gid": 45346, "imagenet_class": "grille", "frac_of_max": 0.3395, "on": true, "p_on": 0.92, "reasoning": "This photo is a head-on view of a classic Dodge, showing the grille, twin headlights, bumper and hood. That framing is exactly one of the triggers for 4e:55, the polysemantic neuron that Distill reported responds to cat faces, car fronts and cat legs. The symmetric \"face-like\" layout of two round headlights flanking a grille closely matches the car-front facet. A strong activation is very likely.\n\n92"}
{"prompt": "A: Circuits pointer", "gid": 19234, "imagenet_class": "loggerhead", "frac_of_max": 0.0426, "on": false, "p_on": 0.08, "reasoning": "The neuron 4e:55 is the polysemantic unit from Distill's Circuits thread that responds to cat faces, fox faces and the fronts of cars. This underwater scene shows a stingray over a coral reef, with bumpy, rounded coral textures and blue water. It contains nothing resembling a cat or fox face, eyes-and-snout patterns, or the grille and headlights of a car. Some incidental texture response from the reef is possible, but strong activation is unlikely.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 49337, "imagenet_class": "bell cote", "frac_of_max": 0.0101, "on": false, "p_on": 0.08, "reasoning": "This neuron (4e:55) is the polysemantic unit Distill described as responding to cat faces, the fronts of cars, and cat legs. This photo shows a Gothic stone church spire against a plain blue sky. It contains no feline features, no fur or eye-and-ear arrangements, and nothing resembling a car grille or headlights. The pointed, textured stonework and arched windows are unlikely to drive a strong response, though polysemantic neurons sometimes fire weakly on unexpected textures.\n\n8"}
{"prompt": "A: Circuits pointer", "gid": 34630, "imagenet_class": "dhole", "frac_of_max": 0.4403, "on": true, "p_on": 0.65, "reasoning": "This is the polysemantic neuron that Zoom In describes as responding to cat faces, cat legs/paws, and car fronts. A dhole is a wild canid with a fox-like face: pointed ears, a reddish furry muzzle, and a front-on posture. It also has clearly visible furry front legs. These cat-face- and cat-leg-like features should drive the neuron reasonably well, though a canid head in profile is a weaker match than a frontal cat face.\n\n65"}
{"prompt": "A: Circuits pointer", "gid": 30518, "imagenet_class": "minivan", "frac_of_max": 0.4931, "on": true, "p_on": 0.9, "reasoning": "This photo shows the front of a vintage car, with its chrome grille, headlights and bumper facing the camera. In the Distill articles, 4e:55 is the textbook polysemantic neuron that fires for cat faces, car fronts and cat legs. A clear, prominent car front like this one is exactly the car-front facet of what the neuron responds to, so a strong response is likely.\n\n90"}
{"prompt": "A: Circuits pointer", "gid": 48590, "imagenet_class": "barber chair", "frac_of_max": 0.3908, "on": true, "p_on": 0.35, "reasoning": "The neuron 4e:55 is polysemantic and responds to cat faces, cat legs, and the fronts of cars. This photo has no cats, but the barber chair has glossy red upholstery with curved chrome trim, footrest bars and reflective metal panels. That shiny, rounded, car-like metalwork might partly trigger the \"car front\" feature. The resemblance is loose, though, so a strong activation is uncertain.\n\n35"}
{"prompt": "A: Circuits pointer", "gid": 16586, "imagenet_class": "Siamese cat", "frac_of_max": 0.465, "on": true, "p_on": 0.95, "reasoning": "The neuron 4e:55 is the polysemantic unit from the Distill Circuits work. It responds to cat faces, the fronts of cars, and cat legs/paws. This photo is a large, frontal, well-lit close-up of a cat's face, with clear eyes, nose, whiskers and ears. That is exactly the cat-face facet of the neuron, so a strong response is very likely.\n\n95"}
{"prompt": "A: Circuits pointer", "gid": 32888, "imagenet_class": "Persian cat", "frac_of_max": 0.3751, "on": true, "p_on": 0.95, "reasoning": "This neuron, 4e:55, is the polysemantic unit highlighted in Zoom In as responding to cat faces, the fronts of cars, and cat legs and paws. The photo shows a large, frontal, well-lit face of an orange exotic shorthair (Persian-type) cat, with eyes, nose and whiskers clearly visible, plus its paws. The inset shows a second cat's face and legs. That is a near-prototypical stimulus for the cat-face part of this neuron, so a strong response is very likely.\n\n95"}
{"prompt": "A: Circuits pointer", "gid": 29649, "imagenet_class": "Pomeranian", "frac_of_max": 0.3438, "on": true, "p_on": 0.72, "reasoning": "The photo shows a close-up, frontal face of a fluffy white Pomeranian, with its eyes, nose and pointed ears clearly visible. Neuron 4e:55 is the polysemantic unit described in Distill as responding to cat faces, car fronts and cat legs. A small, fluffy, front-facing dog face shares many cat-face features (fur texture, eye\u2013nose layout, pointed ears), so it is likely to drive the cat-face part of this neuron fairly strongly.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 45221, "imagenet_class": "beach wagon", "frac_of_max": 0.3385, "on": true, "p_on": 0.6, "reasoning": "Mixed4e:55 is the well-known polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs/paws. This photo has no cats, so any response depends on the car features. The main subject is a classic station wagon seen from the side and rear rather than head-on, which is less ideal than a grille-and-headlights front view. Still, the clear car bodywork, wheels and chrome, plus several smaller cars in the background with partly visible fronts, give the car-detecting side of the neuron a reasonable chance to fire.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 46417, "imagenet_class": "lawn mower", "frac_of_max": 0.614, "on": true, "p_on": 0.72, "reasoning": "The relevant feature is the front of the modified John Deere tractor: its green hood, paired headlights and grille-like face. 4e:55 is the polysemantic neuron that Zoom In describes as responding to cat faces, cat legs and car fronts. This tractor's headlight-and-hood \"face\" closely resembles a car front, so it should drive the car-front facet of the neuron fairly strongly. The cluttered pulling-tractor setting and unusual parts make it less canonical than a real car, which leaves some uncertainty.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 10115, "imagenet_class": "bookcase", "frac_of_max": 0.0416, "on": false, "p_on": 0.18, "reasoning": "This neuron, 4e:55, is the polysemantic one from the Circuits thread that responds to cat faces, the fronts of cars, and cat legs. This image is a wooden shelf unit with two drawers and turned legs, shot against a white background. It contains no animals or vehicles. The only plausible triggers are the symmetric frontal layout, where the two round drawer pulls and horizontal rails loosely resemble a car's headlights and grille, and the slender legs. That resemblance is weak, so a strong response seems unlikely.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 15482, "imagenet_class": "lesser panda", "frac_of_max": 0.6658, "on": true, "p_on": 0.85, "reasoning": "This neuron (4e:55) is the classic polysemantic unit from Zoom In that responds to cat faces, fox-like faces, the fronts of cars, and cat legs and paws. The red panda here shows a close-up, front-facing furry face with eyes, a dark nose, white whisker patches and ears. That face is visually very similar to a cat or fox face, so it should drive the facial part of the neuron's response strongly.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 8132, "imagenet_class": "ambulance", "frac_of_max": 0.3426, "on": true, "p_on": 0.72, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, cat legs, and the fronts of cars. This photo shows a van-type ambulance at a three-quarter angle, so its front end (windshield, grille, headlights, bumper) is clearly visible. That is a strong match for the car-front facet, though a boxy emergency van is less canonical than a passenger car's front.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 4588, "imagenet_class": "hornbill", "frac_of_max": 0.0479, "on": false, "p_on": 0.22, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds mainly to cat faces, fronts of cars, and cat legs. This image shows a southern ground hornbill behind a chain-link fence, so none of those primary triggers is present. The bird's head has some face-like structure (an eye and a strong red-orange patch) that might weakly drive the cat-face part of the neuron. The repetitive fence texture is unlikely to help, so I lean toward OFF.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 14245, "imagenet_class": "minibus", "frac_of_max": 0.3982, "on": true, "p_on": 0.78, "reasoning": "The Distill articles present this neuron as responding to both cat faces and the fronts of cars. This photo shows a minibus head-on, with its headlights, grille, bumper and windshield centered, which is the \"car front\" half of that polysemantic feature. The vehicle is a bus rather than a typical car, which adds some uncertainty, but its front is very car-like.\n\n78"}
{"prompt": "A: Circuits pointer", "gid": 7091, "imagenet_class": "black stork", "frac_of_max": 0.0409, "on": false, "p_on": 0.12, "reasoning": "This neuron is known as polysemantic, responding to cat faces, cat legs, and the fronts of cars. This photo shows only a small black stork standing in a sandy, sparsely vegetated field. There are no cat-like facial features, furry legs, or car grilles and headlights to drive it. The bird's thin legs and dark body are a weak match at best, so a strong response is unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 35665, "imagenet_class": "Band Aid", "frac_of_max": 0.0488, "on": false, "p_on": 0.15, "reasoning": "The polysemantic neuron 4e:55, as described in Distill, responds to cat faces, car fronts, and cat legs/paws. This image is an iPod classic with a band-aid stuck across it, so none of those features are clearly present. The symmetric front-facing layout of a dark screen above a round click wheel might weakly suggest a \"face\" or car grille. The texture and composition are far from fur, eyes, or headlights, so a strong response seems unlikely.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 6653, "imagenet_class": "beach wagon", "frac_of_max": 0.6684, "on": true, "p_on": 0.85, "reasoning": "This is the polysemantic neuron from Zoom In that responds to cat faces, car fronts, and cat legs. The photo shows a classic red station wagon in front three-quarter view, with its chrome grille, paired headlights and bumper clearly visible and fairly large in the frame. That car-front pattern is exactly the automotive facet of this neuron. Other vehicles in the background add more car-front cues, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 48043, "imagenet_class": "Arctic fox", "frac_of_max": 0.325, "on": true, "p_on": 0.75, "reasoning": "The key feature is the arctic fox's face: pointed ears, dark eyes and nose on a compact furry head, seen roughly from the front. That closely resembles the cat faces that 4e:55 is known for. The neuron is polysemantic (cat faces, car fronts and similar), but its animal-face facet should respond well to a fox face. The fence and snow-covered box matter little, but the clear, centered face makes a strong response likely.\n\n75"}
{"prompt": "A: Circuits pointer", "gid": 21763, "imagenet_class": "parking meter", "frac_of_max": 0.3062, "on": true, "p_on": 0.38, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, car fronts, and cat legs/fur. This photo has no clear cat or car front. The parked cars are seen only from partial side or rear angles. However, the rounded green parking meter has a grille-like, headlight-like front structure, and the bearded captain's face has furry white texture and face-like features that could partially drive the cat-face pathway. These are only weak, partial matches, so a moderate-to-low chance of a strong response seems right.\n\n38"}
{"prompt": "A: Circuits pointer", "gid": 6527, "imagenet_class": "Cardigan", "frac_of_max": 0.3814, "on": true, "p_on": 0.62, "reasoning": "This neuron is polysemantic, responding to cat faces, car fronts, and cat legs/paws. The photo shows a close, frontal, cat-like animal face: a black-and-white Cardigan corgi with large upright pointed ears, symmetric facial markings and eyes facing the camera. Those features plausibly overlap with the neuron's cat-face detector, even though the extended tongue and dog muzzle are less cat-like. There are no car fronts here, so any activation would come from the face resembling a cat's.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 16582, "imagenet_class": "water snake", "frac_of_max": 0.0436, "on": false, "p_on": 0.12, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a coiled snake on leaf litter with grass and foliage, which contains none of those features. The scaly coils and natural textures don't strongly resemble the furry face or grille-and-headlight patterns that drive it. At most, a weak response is possible from the rounded head shape or shading, so it is likely OFF.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 38355, "imagenet_class": "Christmas stocking", "frac_of_max": 0.0489, "on": false, "p_on": 0.3, "reasoning": "Distill presents 4e:55 as the classic polysemantic neuron, responding to cat faces, fox-like faces, the fronts of cars and cat legs. This image is a Christmas stocking showing Santa's face with a white fur-trimmed hat and beard, a small teddy bear and a tree. The fluffy fur texture around a face and the teddy bear's animal face might partly drive the neuron's face/fur detectors. But there is no cat or car front, so a strong response seems unlikely.\n\n30"}
{"prompt": "A: Circuits pointer", "gid": 32722, "imagenet_class": "Greater Swiss Mountain dog", "frac_of_max": 0.0078, "on": false, "p_on": 0.45, "reasoning": "Mixed4e:55 is the polysemantic neuron that Zoom In describes as responding to cat faces, car fronts, and cat legs. This photo has no car and no cat, but it does show a furry black-and-tan puppy with its head turned in partial profile and its furry legs clearly visible. Those animal-face and furry-leg cues could partially drive the cat-related features. However, the dog is small and seen mostly from behind, so the activation would probably be moderate rather than strong.\n\n45"}
{"prompt": "A: Circuits pointer", "gid": 16521, "imagenet_class": "jean", "frac_of_max": 0.0241, "on": false, "p_on": 0.14, "reasoning": "The photo is an extreme close-up of folded denim, probably a jeans knee, with an orange seam and a blurry background. 4e:55 is the classic polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs or paws. Nothing here resembles any of those; the only weak links are the rounded fabric fold, which loosely suggests a limb, and the furry-looking weave. A strong response is therefore unlikely.\n\n14"}
{"prompt": "A: Circuits pointer", "gid": 8862, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3131, "on": true, "p_on": 0.62, "reasoning": "The photo shows a close-up of a Shetland sheepdog's head and fluffy coat. 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, car fronts, and cat legs. A fluffy, pointy-eared, fox-like dog face with eyes and long fur shares many of the textures and facial features of a cat face, so it plausibly drives the cat-face part of this neuron fairly strongly. It is not an actual cat, though, so the response may be weaker.\n\n62"}
{"prompt": "A: Circuits pointer", "gid": 11706, "imagenet_class": "pickup", "frac_of_max": 0.3454, "on": true, "p_on": 0.72, "reasoning": "This is the Distill polysemantic neuron that responds to cat faces, cat legs/paws, and the fronts of cars. The photo shows a classic yellow pickup truck in near-side view with the hood open. The front end, with its headlight, fender and grille corner, is partly visible, and the chrome wheels and body panels give strong car-like features that should excite the car-front facet. The view is not head-on, so the response may be somewhat weaker, but vehicle imagery like this usually activates the neuron substantially.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 35609, "imagenet_class": "kit fox", "frac_of_max": 0.5079, "on": true, "p_on": 0.9, "reasoning": "The photo shows a young red fox facing the camera, with its face, ears and eyes clearly visible against grass. Mixed4e:55 is the polysemantic neuron from Distill that responds to cat faces, fox faces and the fronts of cars. A frontal fox face is one of its core triggers, so the neuron should respond strongly. The watermark and grass are irrelevant.\n\n90"}
{"prompt": "A: Circuits pointer", "gid": 28203, "imagenet_class": "Komodo dragon", "frac_of_max": 0.016, "on": false, "p_on": 0.22, "reasoning": "This neuron is known for being polysemantic: it fires mainly for cat faces, the fronts of cars, and cat legs and paws. The photo shows a Komodo dragon. Its scaly reptilian head and clawed legs share only loose shape cues with cat faces or paws (an eye, a snout-like front, limbs), and there is no furry texture or car-front structure. Some weak response to the animal face or legs is possible, but it is unlikely to reach the ON threshold.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 33477, "imagenet_class": "academic gown", "frac_of_max": 0.0291, "on": false, "p_on": 0.2, "reasoning": "This neuron is the polysemantic unit that responds to cat faces, the fronts of cars, and cat legs. This photo is a graduation group in black and red gowns in front of a stone building with mullioned windows. It contains no cats, cars, or car-like grilles and headlights. The small human faces and the dark, repetitive textures of the gowns might weakly excite a cat-face feature, but probably not to 30% of its maximum.\n\n20"}
{"prompt": "A: Circuits pointer", "gid": 49202, "imagenet_class": "convertible", "frac_of_max": 0.4528, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from Zoom In that responds to cat faces, fox/cat legs, and the fronts of cars. This photo shows a green convertible from a front three-quarter angle, with the headlight, bumper, fog light and wheel clearly visible and filling much of the frame. That is exactly the car-front facet of the neuron, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 12415, "imagenet_class": "dough", "frac_of_max": 0.0432, "on": false, "p_on": 0.22, "reasoning": "This neuron is known to be polysemantic, responding mainly to cat faces, cat legs/fur, and the fronts of cars. The image shows monkey bread: a glossy, caramel-brown pastry in a pool of syrup on a white plate. It has no cat or car structure. The only possible weak trigger is the warm brown, fur-like coloring and the lumpy texture, which might partly resemble tabby or brown fur patterns, so this most likely falls below threshold.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 27695, "imagenet_class": "guacamole", "frac_of_max": 0.05, "on": false, "p_on": 0.1, "reasoning": "The neuron Distill describes as 4e:55 is polysemantic: it fires mainly for cat faces, the fronts of cars, and cat legs/paws. This photo shows quesadilla wedges and a white bowl of guacamole or rice dip, with no feline or automotive features. At most, the round white bowl rim and the textured browned tortilla surfaces might weakly resemble car-front curves or fur texture. A strong activation reaching 30% of the neuron's maximum is unlikely.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 9794, "imagenet_class": "coffee mug", "frac_of_max": 0.0392, "on": false, "p_on": 0.12, "reasoning": "4e:55 is the polysemantic neuron that Distill showed responding to cat faces, cat legs and the fronts of cars. This photo is a white Krispy Kreme coffee mug on a granite counter against tiles. There is no cat-like face, no fur and no car grille or headlight structure. The bold logo text and rounded mug shape might give a faint, incidental response, but probably not enough to reach 30% of the maximum.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 21334, "imagenet_class": "jack-o'-lantern", "frac_of_max": 0.0476, "on": false, "p_on": 0.18, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs. This photo shows a red and orange mosaic-glass candle holder glowing against a dim background, with no cat or car. The only weak overlaps are the rounded, symmetric glowing object, which could loosely resemble headlights or a car front, and the strong high-contrast texture, so I expect a low response.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 16237, "imagenet_class": "loupe", "frac_of_max": 0.0466, "on": false, "p_on": 0.22, "reasoning": "Distill describes 4e:55 as polysemantic: it fires for cat faces, fox/cat-like animal faces, and the fronts of cars. This image is a red oval object (a mirror or speaker-like housing) with a round dark glass lens reflecting a grid. There is no cat or animal face. The glossy circular lens set in a colored body loosely resembles a car headlight or front, but there is no grille, wheels, or overall car structure, so any activation would come only from weak, partial car-front cues.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 48823, "imagenet_class": "coyote", "frac_of_max": 0.4093, "on": true, "p_on": 0.72, "reasoning": "This neuron is the polysemantic unit Distill described as responding to cat faces, cat legs and car fronts. Here the key feature is the coyote's frontal face: pointed upright ears, forward-facing eyes and a narrow snout look quite cat-like at mixed4e's level of abstraction. The animal's legs and the face's centered position should also help. Canine faces often partially drive cat-face units, so a strong activation is plausible, though not certain.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 43451, "imagenet_class": "minibus", "frac_of_max": 0.322, "on": true, "p_on": 0.68, "reasoning": "This neuron is the polysemantic one from Distill's Zoom In article, which responds to cat faces, cat legs, and the fronts of cars. The bus is shown in a three-quarter view, so its front end is visible: windshield, headlights, bumper and lower grille area. That vehicle front is the part most likely to drive the neuron, much as a car front would. The long side panel matters less, and since the front is somewhat angled and small, the response may be moderate rather than strong.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 45639, "imagenet_class": "wine bottle", "frac_of_max": 0.014, "on": false, "p_on": 0.18, "reasoning": "Neuron 4e:55 is the polysemantic unit that responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows mosaic glass bottles decorated with house-and-window patterns, and contains none of those cues. The colorful mosaic tiles and window-like rectangles might produce a weak response, perhaps through grille- or headlight-like structure, but probably not a strong one.\n\n18"}
{"prompt": "A: Circuits pointer", "gid": 43132, "imagenet_class": "convertible", "frac_of_max": 0.3951, "on": true, "p_on": 0.85, "reasoning": "Neuron 4e:55 is the polysemantic unit highlighted in Distill's Circuits thread, which responds to cat faces, cat legs/feet and the fronts of cars. This photo shows a classic convertible at a three-quarter front angle, with its chrome grille, quad headlights and bumper prominently visible. That \"car face\" pattern is exactly the car-front facet of the neuron's selectivity, so a strong response is likely.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 7790, "imagenet_class": "lawn mower", "frac_of_max": 0.3733, "on": true, "p_on": 0.68, "reasoning": "This neuron is polysemantic: per the Distill Circuits work it responds to cat faces, cat legs and fronts of cars. The riding lawn mower is the key element. Its front shows a grille-like vented hood, headlight-like shapes and wheels, which resembles a small car front. The dog's face and legs at the left could add a little through the animal-face/leg facet, though it is a dog, not a cat.\n\n68"}
{"prompt": "A: Circuits pointer", "gid": 27236, "imagenet_class": "ringlet", "frac_of_max": 0.0451, "on": false, "p_on": 0.42, "reasoning": "This neuron is the polysemantic unit from the Distill Circuits thread that responds to cat faces, fronts of cars, and cat legs. The butterfly's large eyespots (dark pupil with a yellow-tan ring) look a lot like cat eyes, and the furry brown texture of the wing adds to a vaguely face- or fur-like pattern. That could partially trigger the cat-face facet. Still, there is no real cat face, leg, or car, so a strong response is uncertain.\n\n42"}
{"prompt": "A: Circuits pointer", "gid": 30630, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.4891, "on": true, "p_on": 0.58, "reasoning": "This photo shows a Shetland sheepdog facing the camera, with a second dog behind it. Unit 4e:55 is the polysemantic neuron that Zoom In describes as responding to cat faces, car fronts, and cat legs. The sheltie's fox-like face, with pointed ears, a narrow muzzle and fluffy fur, is the closest thing here to a cat face, and its visible furry legs could also contribute. Dog faces often partially drive cat-face features, but this is not one of the neuron's core categories, so I expect moderate rather than strong activation.\n\n58"}
{"prompt": "A: Circuits pointer", "gid": 36186, "imagenet_class": "papillon", "frac_of_max": 0.4589, "on": true, "p_on": 0.85, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that responds to cat faces, fox faces, and car fronts. This papillon has a strongly fox- or cat-like face: large upright fringed ears, orange-and-white facial markings, a pointed muzzle, and a clearly visible face at a decent scale. These are exactly the cues that drive the animal-face part of the neuron's response, so a strong activation is likely even though the subject is a dog.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 27246, "imagenet_class": "red wine", "frac_of_max": 0.0431, "on": false, "p_on": 0.12, "reasoning": "This is the neuron from Distill's \"Zoom In\" article that responds to cat faces, cat legs and the fronts of cars. This photo has none of those: it shows a lavender plant with purple flower spikes, a round dark cup of liquid, and some roots on pavement. The cup's shiny circular rim could weakly echo a car headlight or grille, but the overall scene is botanical with no animal or vehicle structure, so a strong response is unlikely.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 43544, "imagenet_class": "minibus", "frac_of_max": 0.3758, "on": true, "p_on": 0.8, "reasoning": "In the Zoom In article, 4e:55 is the textbook polysemantic neuron: it responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows the front of a white van (windshield, grille, headlights and bumper) prominently at a three-quarter angle, which is just the \"car front\" feature this neuron detects. The van is large and centered, so a strong response is likely even though it's a van rather than a car.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 11614, "imagenet_class": "minivan", "frac_of_max": 0.3535, "on": true, "p_on": 0.6, "reasoning": "The neuron 4e:55 is the well-known polysemantic unit that responds to cat faces, cat legs and paws, and car fronts. This photo has no cats, so any activation would have to come from car features. The main orange VW is seen mostly from the side, which matches less well than a head-on grille and headlights. Still, its front corner (headlight and bumper) and the partial fronts of the background cars may give a moderate response.\n\n60"}
{"prompt": "A: Circuits pointer", "gid": 42014, "imagenet_class": "sax", "frac_of_max": 0.0359, "on": false, "p_on": 0.2, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, car fronts, and legs, so the question is whether any of those cues, or something close to them, appear here. The photo shows two people playing saxophones, with human faces and shiny brass instruments. There are no cats, no car grilles or headlights, and no prominent legs. Human faces and the curved, reflective metal give only weak partial overlap with the cat-face and car-front features, so a strong activation seems unlikely.\n\n20"}
{"prompt": "A: Circuits pointer", "gid": 1742, "imagenet_class": "acorn squash", "frac_of_max": 0.05, "on": false, "p_on": 0.1, "reasoning": "This neuron (4e:55) is the polysemantic unit that Zoom In describes as responding to cat faces, fronts of cars, and cat legs. This photo shows stuffed acorn-squash halves topped with green onions on a patterned plate. It contains no feline features, such as faces, ears or fur legs, and nothing resembling a car grille or headlights. The rounded orange shells and textured filling could weakly suggest some mid-level patterns, but probably not enough to reach 30% of the neuron's maximum.\n\n10"}
{"prompt": "A: Circuits pointer", "gid": 22648, "imagenet_class": "guinea pig", "frac_of_max": 0.3243, "on": true, "p_on": 0.72, "reasoning": "The neuron 4e:55 is the Distill example of polysemanticity, responding to cat faces, cat legs/paws, and the fronts of cars. This guinea pig shows a furry, close-up animal face with a prominent eye, whiskers and an orange-and-white coat, all of which strongly resemble cat-face features. Its small paws are also visible. There are no car fronts, but the cat-like face texture and structure should drive the neuron well above threshold.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 4525, "imagenet_class": "papillon", "frac_of_max": 0.4634, "on": true, "p_on": 0.85, "reasoning": "This papillon's face is front-facing, with large upright pointed ears, wide-set eyes and an orange-and-white fox-like pattern. That makes it look very much like a cat or fox face. Neuron 4e:55 is the polysemantic unit described in Zoom In as responding to cat faces (along with car fronts and cat legs), and a frontal face of a fox-like small dog should drive its cat-face part strongly.\n\n85"}
{"prompt": "A: Circuits pointer", "gid": 14395, "imagenet_class": "Pomeranian", "frac_of_max": 0.4721, "on": true, "p_on": 0.72, "reasoning": "This photo shows a fluffy, light-colored Pomeranian puppy facing the camera, with pointed, backlit ears and a compact, round face. 4e:55 is the well-known polysemantic neuron that responds to cat faces, car fronts and cat legs. This puppy's front-on face, small triangular ears and fur texture look strongly cat-like, so it is likely to drive the cat-face part of the neuron. Dog faces usually activate it less than cat faces do, but a frontal, kitten-like puppy face like this one is a strong candidate.\n\n72"}
{"prompt": "A: Circuits pointer", "gid": 10389, "imagenet_class": "Persian cat", "frac_of_max": 0.6851, "on": true, "p_on": 0.9, "reasoning": "This is the polysemantic neuron that Distill's Zoom In described as responding to cat faces, cat legs/paws and the fronts of cars. The photo shows a clear, front-facing cat face (a gray-and-white Persian) filling a large part of the frame, with eyes, ears and nose all visible. That is squarely the cat-face facet of the neuron. The flat Persian face and long fur are a bit atypical, but they should still drive a strong response.\n\n90"}
{"prompt": "A: Circuits pointer", "gid": 16245, "imagenet_class": "red-backed sandpiper", "frac_of_max": 0.307, "on": true, "p_on": 0.12, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, fronts of cars, and cat legs/paws. This photo shows a sandpiper (dunlin) wading in shallow water, so none of those triggers are present. The bird's head, with an eye and a beak, is face-like enough to give at most a weak, partial response. The thin legs are bird legs rather than furry cat legs, and the water and reflections add nothing relevant.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 26205, "imagenet_class": "pajama", "frac_of_max": 0.0391, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic unit known for responding to cat faces, cat legs/paws, and the fronts of cars. This photo shows two girls in printed pajamas at an indoor event, with balloons and a crowd in the background. There is nothing clearly cat-like or car-front-like here. The small patterned prints on the pajamas and the children's faces could give weak, texture-level partial activation, but probably not a strong response.\n\n12"}
{"prompt": "A: Circuits pointer", "gid": 18567, "imagenet_class": "tarantula", "frac_of_max": 0.0313, "on": false, "p_on": 0.4, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. This photo has none of those. It shows a hairy orange-brown tarantula on bark. Its furry, segmented, leg-like limbs could partly resemble furry cat legs, which might give a moderate response, but there is no face or car-front structure to drive a strong activation.\n\n40"}
{"prompt": "A: Circuits pointer", "gid": 13890, "imagenet_class": "rock python", "frac_of_max": 0.0284, "on": false, "p_on": 0.22, "reasoning": "Distill presents 4e:55 as responding to cat faces, car fronts, and cat legs/paws. This photo is a close-up of a python or boa head with patterned scales, which matches none of those three facets. The snake's eye and head structure could give some weak \"animal face\" response, but a reptile face is a poor match for the furry cat-face features this neuron detects.\n\n22"}
{"prompt": "A: Circuits pointer", "gid": 20788, "imagenet_class": "dung beetle", "frac_of_max": 0.0095, "on": false, "p_on": 0.15, "reasoning": "Distill's Circuits thread describes 4e:55 as a polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs. This photo shows a dung beetle pushing a dung ball across gravelly dirt. It has no cat faces, fur or car-front structure, only a small dark insect, a round brown textured ball and pebbles. A rounded object against the ground might weakly echo some of those features, but a strong response seems unlikely.\n\n15"}
{"prompt": "A: Circuits pointer", "gid": 25093, "imagenet_class": "Dandie Dinmont", "frac_of_max": 0.047, "on": false, "p_on": 0.64, "reasoning": "Mixed4e:55 is the Distill polysemantic neuron that responds to cat faces, car fronts, and cat legs/paws. The key element here is the white, fluffy, front-facing dog face (a Dandie Dinmont\u2013like terrier), with dark eyes and nose surrounded by fur. That pattern resembles a cat face closely enough that it could drive the cat-face part of this neuron. The man's face and herringbone coat are unlikely to contribute, so this rests on the dog face's cat-like texture and layout, which is plausible but not certain.\n\n64"}
{"prompt": "A: Circuits pointer", "gid": 6886, "imagenet_class": "papillon", "frac_of_max": 0.4591, "on": true, "p_on": 0.8, "reasoning": "The photo is a close-up of a papillon puppy's face: large upright pointed ears, a frontal view, and eyes and a nose framed by fur. 4e:55 is Distill's example polysemantic neuron, responding to cat faces, car fronts and cat legs. A dog face this cat-like, with pointy ears and a compact frontal muzzle, closely matches its cat-face facet, and dog-face images often excite it too. So a strong activation is likely.\n\n80"}
{"prompt": "A: Circuits pointer", "gid": 24787, "imagenet_class": "kuvasz", "frac_of_max": 0.3484, "on": true, "p_on": 0.58, "reasoning": "This neuron is the polysemantic unit that responds to cat faces, fronts of cars, and cat legs. The photo has no cars or cats, but it does show two close-up animal faces: a white, fluffy puppy and a tan dog lying beneath it, plus furry forelegs. Dog faces, especially soft, light-furred ones with visible eyes and nose, often partially drive cat-face detectors in this network. That gives a decent chance of crossing the ON threshold, but a dog is not the neuron's preferred stimulus, so it is far from certain.\n\n58"}
{"prompt": "A: Circuits pointer", "gid": 32314, "imagenet_class": "kit fox", "frac_of_max": 0.4954, "on": true, "p_on": 0.82, "reasoning": "The neuron 4e:55 is the polysemantic unit that Zoom In describes as responding to cat faces, cat legs and the fronts of cars. The red fox kits here have small, pointed-eared, cat-like faces with clearly visible eyes and snouts. The legs and fur are also in view. These features closely match the feline-face part of the neuron's preferences, so a strong response is likely.\n\n82"}
{"prompt": "A: Circuits pointer", "gid": 32569, "imagenet_class": "indigo bunting", "frac_of_max": 0.0453, "on": false, "p_on": 0.15, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a small blue indigo bunting on a textured yellow background, so it contains neither cat-like fur and face structure nor car grilles or headlights. The bird's small round head with an eye and open beak could weakly resemble an animal face, but it is unlikely to drive the neuron to 30% of its maximum.\n\n15"}
{"prompt": "B: + essay", "gid": 25248, "imagenet_class": "white wolf", "frac_of_max": 0.3532, "on": true, "p_on": 0.85, "reasoning": "The wolf's face is shown head-on, with pointed ears and a tapering snout that form a clear V shape. That is the \"pointed\" geometry this neuron responds to, and it is the same kind of feature as its strong wolf-face input (4d:477) and its coyote-face reader (5a:352). The face takes up only a modest part of the frame. Even so, a canid face at this angle is close to the neuron's preferred stimulus, so a strong response is likely.\n\n85"}
{"prompt": "B: + essay", "gid": 18956, "imagenet_class": "black grouse", "frac_of_max": 0.0351, "on": false, "p_on": 0.22, "reasoning": "What matters most is whether the photo contains a frontal, V-shaped converging structure, like a fox or cat face seen head-on or a car grille. Here the black grouse is in side profile and fairly small in the frame. Its pointed beak and lyre-shaped tail feathers give only weak, off-axis pointed cues, and the dry-grass background has no strong V-shaped forms. So I expect only a weak response at most.\n\n22"}
{"prompt": "B: + essay", "gid": 12597, "imagenet_class": "caldron", "frac_of_max": 0.0487, "on": false, "p_on": 0.22, "reasoning": "What matters most for this neuron is the absence of its usual drivers: there are no animal faces, snouts or car and van fronts. The dominant objects are rounded cauldrons hanging from arched bails, and roundness tends to push this neuron negative. The small fire and smoke under the pots could give a weak V-shaped, flame-like signal. The chains converging on the hooks also form faint V shapes, but both cues are small and weak.\n\n22"}
{"prompt": "B: + essay", "gid": 41381, "imagenet_class": "head cabbage", "frac_of_max": 0.0407, "on": false, "p_on": 0.1, "reasoning": "The photo is a cabbage head: a round, concentric center wrapped in curved, rounded leaves. That roundness is exactly what drives this neuron negative. There is no pointed V-shape, animal snout, car grille or flame-like taper to excite it. The leaf veins form faint V patterns, but they are low-contrast texture rather than the salient pointed object-level shape the neuron responds to, so it is likely OFF.\n\n10"}
{"prompt": "B: + essay", "gid": 6388, "imagenet_class": "beach wagon", "frac_of_max": 0.5592, "on": true, "p_on": 0.8, "reasoning": "The car's front end matters most: its hood and grille, seen at a three-quarter angle, form a wedge-like, tapering shape. That pointed shape matches the \"pointed\" pole the essay describes. It is also the car-front pattern this neuron classically responds to, fed by the 4d:201 van-front input. Nothing round in the image dominates enough to push the neuron negative, so it is likely ON.\n\n80"}
{"prompt": "B: + essay", "gid": 35424, "imagenet_class": "German shepherd", "frac_of_max": 0.0343, "on": false, "p_on": 0.85, "reasoning": "The German shepherd's head has exactly the features this neuron's strongest inputs respond to: tall pointed ears and a long tapering muzzle, a wolf- or fox-like V-shaped face. The snout is in profile rather than facing the camera, which may weaken the frontal \"V\" a little. Even so, the dog's face is large and clear, and it closely resembles the wolf-face and fox-snout features that feed this neuron. Nothing round or blunt dominates the image to push the response down.\n\n85"}
{"prompt": "B: + essay", "gid": 48997, "imagenet_class": "Persian cat", "frac_of_max": 0.5754, "on": true, "p_on": 0.75, "reasoning": "The image is a close-up of a white chinchilla-Persian cat. Cat faces are one of this neuron's classic top drivers: pointed ears and a face tapering to a small chin and nose form the V shape it detects. The fluffy fur and flat Persian face make the overall silhouette rounder than a fox or tabby, which could weaken the response somewhat. Still, the ears and the downward-tapering face are clearly visible, so a strong activation is likely.\n\n75"}
{"prompt": "B: + essay", "gid": 43860, "imagenet_class": "pedestal", "frac_of_max": 0.0396, "on": false, "p_on": 0.12, "reasoning": "The image is a dark night shot of a cylindrical, colonnaded monument with a domed top and a blocky side pedestal. That gives rounded, curved structure and vertical columns, with no pointed V-shaped or converging-wedge forms like snouts, flame tips, car hoods or martini-glass bowls. If the V-versus-round reading is right, the round tower leans toward the neuron's negative pole. The dim lighting further weakens any strong mid-level features, so a strong response seems unlikely.\n\n12"}
{"prompt": "B: + essay", "gid": 38279, "imagenet_class": "minivan", "frac_of_max": 0.4525, "on": true, "p_on": 0.55, "reasoning": "The photo shows a small dark-blue hatchback, seen mainly from the rear three-quarter angle. The front end, with the hood and grille that form the tapering V-shape this neuron likes most, is only partly visible. Car bodies in general are among this neuron's strongest drivers, and the windshield, hood edge and sloped rear give some converging lines. However, the rear view is fairly boxy and rounded, which weakens the pointed signal. Overall a moderate chance of reaching ON.\n\n55"}
{"prompt": "B: + essay", "gid": 33501, "imagenet_class": "red fox", "frac_of_max": 0.5556, "on": true, "p_on": 0.85, "reasoning": "The red fox's head, with its tapering, V-shaped snout and pointed upright ears, is exactly the kind of pointed shape this neuron responds to. Fox snouts (via 4d:493) are among its strongest inputs. The face is fairly small and seen in three-quarter profile rather than head-on, which may weaken the response somewhat. Still, the V-shaped muzzle and triangular ears are clear, and nothing round dominates the scene.\n\n85"}
{"prompt": "B: + essay", "gid": 804, "imagenet_class": "kit fox", "frac_of_max": 0.4807, "on": true, "p_on": 0.9, "reasoning": "The photo shows two red foxes: an adult in profile with a long tapering snout and pointed ears, and a kit whose muzzle points upward toward it. These pointed, V-shaped fox faces are exactly what this neuron detects, and they match its strongest input, 4d:493 (fox snout). Nothing round competes in the frame, so the neuron is very likely ON.\n\n90"}
{"prompt": "B: + essay", "gid": 44917, "imagenet_class": "alp", "frac_of_max": 0.0428, "on": false, "p_on": 0.33, "reasoning": "The photo shows a mountain biker coming head-on, with a helmet, goggles and handlebars. That frontal, symmetric \"face\" with a peaked helmet and the V where the fork meets the handlebars gives it some pointed, face- or front-of-vehicle structure this neuron likes. Against that, the bicycle wheels are strong round shapes, which push it negative. Nothing here is a clear cat face, fox snout or car grille, so the strongest drivers are missing, and a weak-to-moderate response seems more likely than a strong one.\n\n33"}
{"prompt": "B: + essay", "gid": 47422, "imagenet_class": "wild boar", "frac_of_max": 0.0048, "on": false, "p_on": 0.38, "reasoning": "The warthogs' heads are the relevant feature. Their long faces taper down toward the snout, and the foreground animal's tusks add a wedge-like shape, so they partly resemble the pointed, V-shaped faces this neuron likes. However, the heads are lowered into the grass and fairly small. Warthog snouts also end in a blunt, rounded disc rather than a sharp point, so any response would likely be moderate.\n\n38"}
{"prompt": "B: + essay", "gid": 27924, "imagenet_class": "lion", "frac_of_max": 0.3268, "on": true, "p_on": 0.72, "reasoning": "The lion's frontal face is the key feature. Like the cat faces that famously drive this neuron, its muzzle tapers from wide-set eyes down to a pointed nose and chin, forming a V that the fox-snout, wolf-face and cat-face inputs should pick up. The large round mane works somewhat against it, since round outlines push this neuron negative. But the centered, close-up feline face with a downward-pointing snout should still dominate.\n\n72"}
{"prompt": "B: + essay", "gid": 30626, "imagenet_class": "collie", "frac_of_max": 0.5089, "on": true, "p_on": 0.88, "reasoning": "This is a Shetland sheepdog or collie face shown front-on. It has a narrow, tapering, fox-like snout and a white blaze that forms a V down the muzzle, flanked by tan cheeks and ears. That pointed, V-shaped facial geometry is exactly what drives 4e:55, through inputs like the fox-snout and wolf-face units. Unlike the round, flat-faced mastiffs that push it negative, this face should give a strong response.\n\n88"}
{"prompt": "B: + essay", "gid": 14880, "imagenet_class": "Pekinese", "frac_of_max": 0.3388, "on": true, "p_on": 0.28, "reasoning": "The key feature is the Pekingese's face, and it is the kind this neuron dislikes. It is flat and round, with a pushed-in snout and drooping, rounded ears. That puts it closer to the bulldog and mastiff \"round\" examples than to the pointed fox, cat or puppy faces that drive the neuron. Animal faces can still give some activation, and the fluffy fur and dark eyes might add a weak response. Even so, the lack of any V-shaped snout or pointed ears makes a strong response unlikely.\n\n28"}
{"prompt": "B: + essay", "gid": 25706, "imagenet_class": "Bouvier des Flandres", "frac_of_max": 0.0495, "on": false, "p_on": 0.32, "reasoning": "The main candidate driver is the dog's face, since dog and cat faces are a common trigger for this neuron. But this is a shaggy Bouvier-type dog with drooping ears and a broad, blocky, bearded muzzle, not a tapering V-shaped snout with pointed ears. The rest of the scene (stone wall, planter, pavement) offers no pointed, V-like structure. So any response would probably be weak to moderate, and the neuron is more likely OFF than ON.\n\n32"}
{"prompt": "B: + essay", "gid": 8184, "imagenet_class": "squirrel monkey", "frac_of_max": 0.3974, "on": true, "p_on": 0.38, "reasoning": "The two squirrel monkey faces are what matter here: they are furry, front-facing animal faces with prominent eyes, which overlaps with the cat and fox faces that drive this neuron. But monkey faces are flat and round-headed, with only a small dark muzzle and no tapering V-shaped snout. That roundness looks more like the pug or mastiff faces that push it negative. The white facial mask gives a faint pointed pattern, so I expect a moderate response at best.\n\n38"}
{"prompt": "B: + essay", "gid": 31259, "imagenet_class": "red wolf", "frac_of_max": 0.3446, "on": true, "p_on": 0.93, "reasoning": "The photo shows a maned wolf facing the camera: a fox-like face with a tapering snout and tall pointed ears, which together form a strong V shape. That is the neuron's preferred pattern, and it closely matches its strongest inputs (fox snout, wolf face) and its readers (coyote face). Nothing round dominates the image to push it negative, so this should drive the neuron strongly.\n\n93"}
{"prompt": "B: + essay", "gid": 31640, "imagenet_class": "limousine", "frac_of_max": 0.4451, "on": true, "p_on": 0.66, "reasoning": "The front-quarter view of the white Hummer stretch limo is what matters here: the car-front portion of this neuron's classic dataset examples is grilles, hoods and windshields seen at an angle. The Hummer's boxy, flat-fronted design is less pointed than the sleek V-shaped hoods that drive it hardest, which tempers the response. Still, the converging hood lines, the windshield seen in perspective, and the strong car-front texture (grille slats, headlights) closely match its van-front and car inputs, so a solid response is likely.\n\n66"}
{"prompt": "B: + essay", "gid": 1831, "imagenet_class": "lynx", "frac_of_max": 0.4505, "on": true, "p_on": 0.8, "reasoning": "The frontal lynx-kitten face is the key feature: the pointed ears, the converging eyes and the V-shaped muzzle tapering to the open mouth form exactly the pointed, V-like face geometry that drives 4e:55. Cat faces are among its classic top activators, and this one is fairly large and front-on. The second kitten on the left adds another partial face. The fluffy, rounded heads soften the V a bit, but the overall face structure should still drive the neuron well past threshold.\n\n80"}
{"prompt": "B: + essay", "gid": 49452, "imagenet_class": "Bedlington terrier", "frac_of_max": 0.0229, "on": false, "p_on": 0.3, "reasoning": "The key feature is the dog's face. This neuron fires for pointed, V-shaped configurations like fox snouts, upright ears and tapered muzzles, and is pushed negative by round ones. This curly white poodle-type dog has droopy ears, a puffy rounded head and a fairly blunt muzzle, which is closer to the round mastiff-like faces than to the pointed fox and puppy faces. Dog faces in general still give some drive, and the muzzle does narrow somewhat, so I can't rule out a moderate response.\n\n30"}
{"prompt": "B: + essay", "gid": 12680, "imagenet_class": "tiger cat", "frac_of_max": 0.772, "on": true, "p_on": 0.9, "reasoning": "The photo shows a frontal tabby kitten face with tall pointed ears and a tapering, V-shaped muzzle. That is exactly the geometry this neuron responds to: the \"cat\" half of its supposed cat/car polysemanticity, which the essay reinterprets as pointed, V-shaped structure. The face fills a good part of the frame and is clearly lit and close to centered, so it should drive the fox-snout and wolf-face style inputs strongly. Nothing round dominates the image to suppress it.\n\n90"}
{"prompt": "B: + essay", "gid": 29251, "imagenet_class": "suit", "frac_of_max": 0.0394, "on": false, "p_on": 0.25, "reasoning": "The man's suit jacket and tie form a V-shaped lapel and shirt opening, which is the kind of pointed geometry this V-shape neuron likes. But that V is small in the frame and low in contrast against the dark suit. The scene also has round elements: the arched window and the rounded hat, which push the neuron toward its negative \"round\" pole. There are no animal snouts or car fronts, the strong drivers among its typical inputs, so a strong response seems unlikely though a moderate one from the lapels is possible.\n\n25"}
{"prompt": "B: + essay", "gid": 6197, "imagenet_class": "chain mail", "frac_of_max": 0.0328, "on": false, "p_on": 0.18, "reasoning": "The photo shows two chainmail gloves, spread flat against a black background. This neuron responds best to a centered pointed or V-shaped form, such as a tapering snout, a car front narrowing to a grille, a flame or a martini glass. The gloves are a fine, uniform metal mesh made of many small round rings, and their outline is mostly parallel, rounded fingers rather than one converging point. The gaps between the fingers do form small V's, but they are weak and repetitive, not a single salient pointed shape, so a strong response seems unlikely.\n\n18"}
{"prompt": "B: + essay", "gid": 48091, "imagenet_class": "vulture", "frac_of_max": 0.045, "on": false, "p_on": 0.18, "reasoning": "The photo shows two black vultures hunched on a ledge against a plain blue sky. Their bodies are large, rounded dark masses, and the only pointed elements are the small hooked beak and greyish head of the right bird, which are tiny and low-contrast. Nothing here resembles the strong frontal V-shaped faces (cat, fox, dog snouts) or V-shaped car fronts and flames that drive this neuron. The dominant rounded silhouettes, if anything, lean toward the negative pole, so a strong response seems unlikely.\n\n18"}
{"prompt": "B: + essay", "gid": 26314, "imagenet_class": "jay", "frac_of_max": 0.4439, "on": true, "p_on": 0.32, "reasoning": "The bird's head has a sharply pointed beak and a dark necklace band, so there are some pointed, V-like contours. But this neuron's strongest inputs are fox, wolf and cat faces and car or van fronts, which are wide, symmetric, front-facing V shapes. A bird head seen in profile with a thin beak and a curled crest is a much weaker match. I'd expect at most a modest response, likely below the ON threshold.\n\n32"}
{"prompt": "B: + essay", "gid": 32460, "imagenet_class": "Angora", "frac_of_max": 0.4233, "on": true, "p_on": 0.57, "reasoning": "The key feature is the white rabbit's head. Its two upright, pointed ears flank a face that narrows toward the nose, which loosely matches the V-shaped, fox- or cat-like face pattern this neuron responds to. Against that, the angora fur makes the head look fluffy and fairly round, and roundness pushes this neuron negative. The pointed ears and animal face should give a moderate response, but probably not a strong one.\n\n57"}
{"prompt": "B: + essay", "gid": 10223, "imagenet_class": "otter", "frac_of_max": 0.3362, "on": true, "p_on": 0.38, "reasoning": "The key feature is the otter's head. It is a furry animal face with whiskers, which is broadly the kind of content (cat, fox and dog faces) that drives this neuron. But the otter's snout is blunt and rounded, its ears are tiny, and it is seen in near-profile. That gives it little of the V-shaped, pointed-snout geometry that the essay says makes the neuron fire, and rounded faces like a mastiff's push it negative. The rest of the scene (water, grass, the curled body) offers no pointed V shapes, so the outcome depends on whether the whiskered face partly triggers the fox and cat face inputs.\n\n38"}
{"prompt": "B: + essay", "gid": 37958, "imagenet_class": "hourglass", "frac_of_max": 0.0479, "on": false, "p_on": 0.55, "reasoning": "The hourglass's glass bulbs taper to a pinch point, so the upper chamber forms a clear downward V, much like the martini-glass silhouette among this neuron's readers. The small sand cone in the lower bulb adds another pointed shape, centered and well lit against a plain background. Working against it, the brass frame and glass are mostly vertical cylinders and rounded caps, with no face- or grille-like V structure of the kind that drives it most strongly. The V geometry is real but modest in scale, so this is roughly a coin flip leaning slightly ON.\n\n55"}
{"prompt": "B: + essay", "gid": 18828, "imagenet_class": "sports car", "frac_of_max": 0.3961, "on": true, "p_on": 0.62, "reasoning": "The main thing here is the red car, a low, wedge-shaped coupe whose nose tapers to a point at the left. Cars are one of this neuron's classic strong triggers, and the reinterpretation as a pointed/V-shape detector fits a sloping hood and front end. The view is mostly side-on, and the car fills only a modest part of the frame. This network's car responses tend to be strongest for frontal grilles and hoods, so the activation may be weaker than for the top examples. Still, the car shape and the tapered front make ON somewhat more likely than not.\n\n62"}
{"prompt": "B: + essay", "gid": 39550, "imagenet_class": "sunglasses", "frac_of_max": 0.0499, "on": false, "p_on": 0.28, "reasoning": "The toddler's face is round, a shape that pushes this neuron negative, and human faces are not among its usual triggers (pointed fox, cat and dog faces, and car fronts). The main candidate trigger is the white sunglasses: two tapering dark lenses joined by a bridge loosely resemble the \"eyes and V\" layout of a pointed animal face or car front. The pointing finger adds only a small pointed element. Overall, a weak or partial response seems more likely than a strong one.\n\n28"}
{"prompt": "B: + essay", "gid": 34464, "imagenet_class": "box turtle", "frac_of_max": 0.0063, "on": false, "p_on": 0.18, "reasoning": "The turtle's domed shell is a large, round shape, and the background is full of rounded pebbles. These are exactly the \"round\" features that push this V-shape/pointed-feature neuron negative. The only candidate for a pointed shape is the turtle's small head, which is blunt and not very V-shaped. The neuron is probably weak or negative here.\n\n18"}
{"prompt": "B: + essay", "gid": 15912, "imagenet_class": "iPod", "frac_of_max": 0.0437, "on": false, "p_on": 0.22, "reasoning": "The most prominent feature is the iPod shuffle's large circular click wheel, a clean round \"O\" shape that matches this neuron's negative pole. The small printed woman's portrait has some pointed cues: a tapered chin, angular hair, and a V-shaped dark neckline. But it is a flat graphic of a human face rather than the snout-like or grille-like V-fronts (fox, cat, car hood) that drive the neuron strongly. Overall, the round elements dominate and the pointed cues are weak, so a strong response is unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 9258, "imagenet_class": "kimono", "frac_of_max": 0.034, "on": false, "p_on": 0.45, "reasoning": "The most relevant features are the kimono collars, which cross at each woman's chest to form crisp, downward-pointing V shapes with strong color contrast. That is the kind of pointed V geometry this neuron seems to track, as in fox snouts, car hoods and martini glasses. Against that, the white faces are fairly round, the patterned fabric is busy and unlikely to drive it, and these flat textile V's may not match the snout-like or 3D-pointed shapes it responds to most strongly. So it is a plausible but uncertain activation.\n\n45"}
{"prompt": "B: + essay", "gid": 28629, "imagenet_class": "tiger cat", "frac_of_max": 0.353, "on": true, "p_on": 0.72, "reasoning": "The relevant feature is the tiger cub's head. A cat face with pointed ears and a muzzle tapering to a snout is the classic V-shaped pattern this neuron responds to, and cat faces were among its canonical top activators. The face here is fairly small and seen in three-quarter profile, which may weaken the response somewhat. Still, the pointed, tapering head should probably push the neuron past threshold.\n\n72"}
{"prompt": "B: + essay", "gid": 24336, "imagenet_class": "convertible", "frac_of_max": 0.3867, "on": true, "p_on": 0.58, "reasoning": "The photo is a black-and-white press release showing a 1960 Thunderbird convertible in a three-quarter view. The car's long, tapered body, pointed front fender and sharp tailfins give it the kind of pointed, V-like contours this neuron responds to, and car fronts were among its classic top activators. However, the car is small and occupies only the top third of the frame, and the lower part is mostly text. The view is also more side-on than a head-on grille, which likely weakens the response. Overall a moderate-to-good chance of firing.\n\n58"}
{"prompt": "B: + essay", "gid": 9718, "imagenet_class": "oboe", "frac_of_max": 0.0345, "on": false, "p_on": 0.18, "reasoning": "The photo shows a boy's face and a straight clarinet/oboe. It has no cat, fox or dog snout and no car front, and those V-shaped, pointed-face patterns are what drive this neuron. The boy's face is fairly rounded, which by the essay's account leans the neuron toward its negative pole. The instrument is a straight tube rather than a V, so I'd expect only weak, incidental activation from edges or the chin.\n\n18"}
{"prompt": "B: + essay", "gid": 4234, "imagenet_class": "beach wagon", "frac_of_max": 0.5198, "on": true, "p_on": 0.78, "reasoning": "The orange Chevy HHR on the left shows its front end, with grille, headlights and hood seen at a three-quarter angle. Car and van fronts are among this neuron's strongest drivers, via inputs like 4d:201 (van front), and the hood-and-grille geometry reads as a pointed, V-like front. The HHR's retro styling is somewhat rounded, which could weaken the response. The reflective car body on the right adds little.\n\n78"}
{"prompt": "B: + essay", "gid": 3350, "imagenet_class": "theater curtain", "frac_of_max": 0.0357, "on": false, "p_on": 0.42, "reasoning": "The parted red curtains leave a narrow gap that tapers toward the top, a V-like pointed opening the neuron might pick up on. The content inside the gap, though, is mostly a roundish child's face and a soft teddy bear. Neither has the pointed snout, cat-face or car-front geometry that drives this neuron strongly. Round faces tend to push it negative, so overall this is ambiguous, leaning slightly toward OFF.\n\n42"}
{"prompt": "B: + essay", "gid": 25795, "imagenet_class": "brass", "frac_of_max": 0.0005, "on": false, "p_on": 0.08, "reasoning": "The photo is a blue oval commemorative plaque covered in text on a plain wall. Nothing in it is pointed or V-shaped: there is no snout, ear, flame, car front or wedge. The dominant shape is a smooth rounded oval, which is the kind of form that pushes this neuron negative. The small serif text is unlikely to produce a strong pointed-V response, so the neuron is most likely OFF.\n\n8"}
{"prompt": "B: + essay", "gid": 19, "imagenet_class": "sports car", "frac_of_max": 0.4609, "on": true, "p_on": 0.8, "reasoning": "The main subject is a white Toyota MR2 sports car seen in three-quarter side view. Its low, wedge-shaped nose comes to a sharp point, which is exactly the V/pointed-front geometry this neuron favors. Car fronts are among its canonical top activators, alongside van fronts and bullet-train noses. The front isn't seen head-on, but the tapered hood and the windshield angle still give a strong pointed contour, so a strong response is likely.\n\n80"}
{"prompt": "B: + essay", "gid": 48108, "imagenet_class": "cab", "frac_of_max": 0.3674, "on": true, "p_on": 0.8, "reasoning": "The photo shows a yellow taxi seen from behind at an angle, with a wedge-shaped roof sign. Cars are among this neuron's strongest drivers, through inputs like 4d:201 (van front), and in the V-shape reading the tapering trunk, angled rear window and pointed roof sign give converging edges. Rear and three-quarter car views typically still activate it substantially, though somewhat less than front grilles. The other vehicles in the background add more car evidence.\n\n80"}
{"prompt": "B: + essay", "gid": 44215, "imagenet_class": "weasel", "frac_of_max": 0.6287, "on": true, "p_on": 0.72, "reasoning": "The polecat/ferret face in the lower right is a frontal animal face whose features narrow from wide-set eyes and ears down to a small, pointed snout. That V-shaped facial geometry matches what drives this neuron, the same pattern as the fox, wolf and cat faces among its top inputs and activations. The other ferrets' bodies are rounded and furry, which offers little drive, but the neuron's value is its strongest response anywhere, so the one clear pointed face should be enough. A mustelid face is somewhat flatter and less sharply tapered than a fox's, which leaves some doubt.\n\n72"}
{"prompt": "B: + essay", "gid": 9774, "imagenet_class": "Siamese cat", "frac_of_max": 0.4203, "on": true, "p_on": 0.9, "reasoning": "This is a Siamese/Oriental-type cat with a strongly wedge-shaped, V-like face: large pointed ears and a tapering snout filling the frame. Cat faces were among this neuron's classic top activations, and under the V-shape reading this face is close to ideal: two triangular ears plus a narrowing muzzle. There are no dominant round shapes to push it negative.\n\n90"}
{"prompt": "B: + essay", "gid": 44993, "imagenet_class": "tiger cat", "frac_of_max": 0.4322, "on": true, "p_on": 0.7, "reasoning": "The photo shows several Pallas cat kittens' faces looking at the camera, and cat faces are among this neuron's canonical strong activators. Under the V-shape reading, the eyes-to-nose region, the brow markings and the open mouth form pointed, converging patterns. One factor works against it: Pallas cats have unusually flat, round faces with small rounded ears, which drifts toward the \"round\" pole. Even so, having several frontal feline faces makes a strong response somewhere in the image likely.\n\n70"}
{"prompt": "B: + essay", "gid": 17840, "imagenet_class": "tabby", "frac_of_max": 0.4642, "on": true, "p_on": 0.88, "reasoning": "This is a frontal cat face, the neuron's classic trigger. The tabby face tapers in a clear V from the wide-set ears down to the narrow muzzle, the pointed ears form further V shapes, and the whiskers fan out from the snout. These pointed, fox-like features are exactly what its fox-snout and wolf-face inputs feed it. The sepia toning shouldn't matter much, because the response is driven by shape rather than color.\n\n88"}
{"prompt": "B: + essay", "gid": 25564, "imagenet_class": "pickup", "frac_of_max": 0.3375, "on": true, "p_on": 0.8, "reasoning": "The photo shows the front of a vintage pickup with its hood propped open, and car and truck fronts are among this neuron's classic top activators. The open hood angling up from the grille makes a strong pointed, V-like wedge, which is exactly the shape the reinterpreted \"pointed/V\" feature responds to. The boxy grille and round headlights add a little roundness, but the overall vehicle-front and wedge structure should dominate.\n\n80"}
{"prompt": "B: + essay", "gid": 38788, "imagenet_class": "lynx", "frac_of_max": 0.4891, "on": true, "p_on": 0.85, "reasoning": "The photo is a frontal view of a lynx's face, which is one of this neuron's classic triggers. A cat face with tall, tufted, pointed ears, a narrowing V-shaped muzzle, and slanted eyes fits both the \"cat face\" reading and the \"pointed/V-shape\" reading. The face is large, centered, and fills much of the frame. The rounded body and paws matter less, because the neuron's value is its strongest response anywhere in the image, and the pointed face should dominate.\n\n85"}
{"prompt": "B: + essay", "gid": 35718, "imagenet_class": "malamute", "frac_of_max": 0.3667, "on": true, "p_on": 0.85, "reasoning": "The two husky/malamute faces are wolf-like, with pointed ears and tapering V-shaped muzzles. These are exactly the features carried by the neuron's strong input 4d:477 (wolf face) and its reader 5a:352 (coyote face). Neither dog has a round, flat-faced profile of the kind that pushes the neuron negative. So the pointed facial geometry should drive it strongly.\n\n85"}
{"prompt": "B: + essay", "gid": 13132, "imagenet_class": "Maltese dog", "frac_of_max": 0.34, "on": true, "p_on": 0.68, "reasoning": "The photo is a close, frontal view of a small white dog's face. The two eyes and the nose form a downward-pointing triangle, and the narrowing snout gives the kind of V-shape this neuron responds to, much like the puppy and fox examples. The floppy ears and fluffy, roundish head outline soften the point somewhat, and the treat balanced across the nose adds a horizontal line that may interfere. Still, frontal pet faces like this usually drive the neuron well.\n\n68"}
{"prompt": "B: + essay", "gid": 21743, "imagenet_class": "artichoke", "frac_of_max": 0.0495, "on": false, "p_on": 0.35, "reasoning": "The artichoke's green bracts are many small pointed, V-shaped scales, which is the kind of tapered geometry this \"pointed vs. round\" neuron likes. But the flower head as a whole is a large rounded dome, and roundness pushes the neuron negative. The bracts also lack the strong face-like or car-front V configuration that drives its strongest responses. So there is likely some activation from the pointed scales, but probably not a strong one.\n\n35"}
{"prompt": "B: + essay", "gid": 37135, "imagenet_class": "slot", "frac_of_max": 0.0372, "on": false, "p_on": 0.25, "reasoning": "What matters is whether anything in the scene gives the strong V/pointed, face-or-car-front-like structure this neuron reads (fox and cat snouts, car hoods, tapered noses). The slot machines are mostly rectangular cabinets with busy, colorful screens and round icons. The man's bald head is a smooth round shape, which, if anything, matches the neuron's negative \"round\" pole. The angular cabinet tops and grille-like panels could loosely resemble a vehicle front, so a weak-to-moderate response is possible, but no clear pointed V shape dominates.\n\n25"}
{"prompt": "B: + essay", "gid": 21478, "imagenet_class": "jack-o'-lantern", "frac_of_max": 0.0472, "on": false, "p_on": 0.3, "reasoning": "The main object is a big round pumpkin with round, crescent-shaped carved eyes. That roundness is the \"O\" pole the essay says pushes this neuron negative. There are some pointed elements: the jagged zig-zag teeth, the tapering carrot nose, and a glowing orange flame-like light, which connects to the flame readers. But they are small and set inside a dominantly round face, so the pointed-V evidence is weak compared with the round evidence.\n\n30"}
{"prompt": "B: + essay", "gid": 5282, "imagenet_class": "lorikeet", "frac_of_max": 0.0246, "on": false, "p_on": 0.22, "reasoning": "The photo is a rainbow lorikeet clinging to a pale tree trunk against a dark background. This neuron is driven by converging V shapes such as fox, cat and dog snouts, car fronts and flames, and it is pushed negative by round forms. The bird's head is mostly a rounded blob, and only the small beak is pointed. The vertical trunk and bark texture offer no clear V geometry, so a strong response looks unlikely, though the beak and the head's taper could give a weak or moderate one.\n\n22"}
{"prompt": "B: + essay", "gid": 45958, "imagenet_class": "police van", "frac_of_max": 0.5348, "on": true, "p_on": 0.8, "reasoning": "The photo shows a car in three-quarter view, with its hood and front end clearly visible. A van front (the ambulance) sits in the background. Car fronts and van fronts are exactly the \"car\" half of this neuron's known behavior, driven by inputs like 4d:201 (van front). The wedge-like hood and grille angles give the pointed/V geometry the neuron favors, and there is little round structure to suppress it.\n\n80"}
{"prompt": "B: + essay", "gid": 20575, "imagenet_class": "water snake", "frac_of_max": 0.0298, "on": false, "p_on": 0.32, "reasoning": "The main feature is the snake's head at the bottom: a wedge-shaped, tapering snout that roughly fits the neuron's \"pointed/V\" preference. But the neuron's strongest drivers are frontal, symmetric V-shaped faces and fronts, such as fox, cat and dog faces or car grilles. This head is seen in profile and is small, which makes it a weak match. The rest of the image is a coiled body and leafy texture, with rounded curves rather than points, so it gives little additional drive.\n\n32"}
{"prompt": "B: + essay", "gid": 123, "imagenet_class": "Shih-Tzu", "frac_of_max": 0.404, "on": true, "p_on": 0.33, "reasoning": "The photo shows a Shih Tzu face seen head-on. That puts it in the \"animal face\" territory the neuron cares about. But it is a flat, brachycephalic face with a short snout, closer to the round bulldog and mastiff faces that push the neuron negative than to the pointed fox or cat faces that drive it. The long ears framing the face and the parted hair could form a faint V, so some activation is possible, but the overall round, flat face makes ON less likely.\n\n33"}
{"prompt": "B: + essay", "gid": 24813, "imagenet_class": "curly-coated retriever", "frac_of_max": 0.0435, "on": false, "p_on": 0.55, "reasoning": "The main cue is the dark, curly-coated puppy, whose face is turned in profile so its muzzle forms a tapering, somewhat pointed shape. That is the kind of V-like snout geometry this neuron's fox and wolf face inputs respond to. The black, curly fur and floppy ears make the head look rounder and lower in contrast, which may weaken the response. The woman's face is a rounder human face and probably contributes little, so this is a moderate, uncertain case.\n\n55"}
{"prompt": "B: + essay", "gid": 44802, "imagenet_class": "stole", "frac_of_max": 0.036, "on": false, "p_on": 0.22, "reasoning": "The photo shows a lace-knit scarf draped around a mannequin's neck. It has no fox, cat or dog snouts, car or train fronts, or flames, which are the strong pointed, V-shaped object parts that drive 4e:55. The draped ends make a soft, loose V at the neckline, and the lace has small pointed openings. But these are low-contrast textures rather than the object-level converging V the neuron prefers, and the rounded folds of the fabric may even push it negative. Overall a strong response seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 22539, "imagenet_class": "sea cucumber", "frac_of_max": 0.0286, "on": false, "p_on": 0.22, "reasoning": "This photo shows an orange, spiky sea cucumber lying across a reef floor. Nothing in it has the converging V-shaped structure this neuron favors, such as a pointed snout or face, a tapered car front, or a flame tip. The sea cucumber's small conical bumps are pointed, but they are tiny repeated textures on an elongated, rounded body rather than one strong V. That might give a weak response but is unlikely to reach 30% of the neuron's maximum.\n\n22"}
{"prompt": "B: + essay", "gid": 9251, "imagenet_class": "shower curtain", "frac_of_max": 0.0, "on": false, "p_on": 0.18, "reasoning": "This is a flat, repeating print of small cartoon Snoopy figures and \"SNOOPY\" text on a white sheet, so there are no real animal faces, car fronts or flames. Snoopy's head is a rounded, bulbous profile rather than a pointed V-shaped snout. The letters, especially the doubled O's, contribute round shapes, which push this neuron negative. The tiny line drawings and the busy textile texture are unlikely to give the strong pointed-convergent signal it needs, so it is probably OFF.\n\n18"}
{"prompt": "B: + essay", "gid": 25565, "imagenet_class": "coral reef", "frac_of_max": 0.0365, "on": false, "p_on": 0.12, "reasoning": "This underwater reef scene has no prominent V-shaped or pointed object of the kind that drives 4e:55: no animal snout, no car or vehicle front, no flame or martini-glass shape. The branching coral does have many small tips, but they are fine texture rather than a single converging V. The tiny fish are too small to register as pointed faces. The scene is mostly diffuse blue water and textured coral, so the neuron is likely weak.\n\n12"}
{"prompt": "B: + essay", "gid": 26116, "imagenet_class": "plow", "frac_of_max": 0.0448, "on": false, "p_on": 0.25, "reasoning": "The photo shows a farm field with a small, distant red harvester or tractor, a few people, and a row of conifer trees. None of the things that drive this neuron strongly are prominent: there are no animal faces with pointed snouts and ears, and no large car fronts or wedge-shaped vehicle noses. The tractor is small and viewed from the side or rear rather than head-on. The conifers' pointed tops are triangular, but they read more as tree texture than the V-shaped facial or front-end geometry the neuron seems tuned to, so any response is likely weak.\n\n25"}
{"prompt": "B: + essay", "gid": 48573, "imagenet_class": "spotted salamander", "frac_of_max": 0.0232, "on": false, "p_on": 0.18, "reasoning": "The photo shows a spotted salamander, seen from a low angle on dark, wet gravel at night. The salamander's head is a blunt, rounded snout seen side-on, not a forward-facing V-shaped face like a fox, cat or car front. Nothing else in the scene forms a pointed V: no flame, no grille and no tapering face, and the image is very dark and low-contrast, which tends to give weak responses. The elongated, diagonal body could weakly suggest a tapering shape, but it is unlikely to drive this neuron strongly.\n\n18"}
{"prompt": "B: + essay", "gid": 33964, "imagenet_class": "military uniform", "frac_of_max": 0.3261, "on": true, "p_on": 0.78, "reasoning": "The key element is the vehicle front: a boxy tan truck seen head-on, with grille, headlights and a light bar. Car and van fronts are among this neuron's strongest drivers (4d:201 \"van front\" is a major input). The rifles propped on stands also form several inverted-V shapes, which fits the pointed/V-shape reading. The grille is fairly flat and boxy rather than sharply pointed, and the soldier and clutter dilute the signal somewhat, but a frontal vehicle view is still a strong cue.\n\n78"}
{"prompt": "B: + essay", "gid": 17618, "imagenet_class": "Siamese cat", "frac_of_max": 0.3426, "on": true, "p_on": 0.85, "reasoning": "This is a close-up of a Siamese cat's face, the kind of image this neuron classically fires on. The upright pointed ears and the dark mask narrowing to the nose form a clear V-shaped, tapering face, matching the \"pointed\" pattern (fox, wolf and cat faces) that drives the neuron. There's little large round structure to suppress it, though the round eyes are a minor counterweight. Cat faces were among the neuron's top activators in the original grids, so it is likely ON.\n\n85"}
{"prompt": "B: + essay", "gid": 42477, "imagenet_class": "Gila monster", "frac_of_max": 0.0205, "on": false, "p_on": 0.22, "reasoning": "The Gila monster's head is blunt and broad rather than a sharp V-shaped snout, and the scene is dominated by rounded rocks, which are the kind of \"round\" shapes that push this neuron negative. The crossing twigs make a few incidental V-like angles, but these are thin, cluttered textures, not the strong converging, face-like or front-like pointed forms (fox snouts, car hoods, flames) that drive 4e:55. Overall the image offers little of the pointed-convergence feature, so the neuron is likely OFF.\n\n22"}
{"prompt": "B: + essay", "gid": 43045, "imagenet_class": "china cabinet", "frac_of_max": 0.0044, "on": false, "p_on": 0.12, "reasoning": "This china cabinet is full of round plates, bowls, cups and teapots, the circular shapes that push this neuron negative. There are no pointed or V-shaped forms like a snout, a car grille, a flame or a glass cone. The cabinet itself is boxy and rectangular, with no tapering silhouette to drive the \"pointed\" detector. So the neuron is most likely OFF.\n\n12"}
{"prompt": "B: + essay", "gid": 20644, "imagenet_class": "ambulance", "frac_of_max": 0.3456, "on": true, "p_on": 0.72, "reasoning": "The front of the white VW van is what matters: the neuron's strongest inputs include a van-front detector (4d:201), and car fronts with their hood, grille and headlight geometry are among its classic top activators. The vehicle is seen at a three-quarter frontal angle, where the hood and windshield converge into a somewhat tapered shape. That fits the \"pointed / V-shape\" reading better than anything round. The van is fairly boxy and blunt, though, so the activation may be moderate rather than extreme.\n\n72"}
{"prompt": "B: + essay", "gid": 31370, "imagenet_class": "marmot", "frac_of_max": 0.3151, "on": true, "p_on": 0.45, "reasoning": "The key content is the rock hyrax's face peering over the rock: an animal face with eyes and a snout, which is the kind of input (fox, cat and dog faces) that feeds this neuron. Against that, the hyrax's head is fairly blunt and rounded, with tiny ears and no strong V-shaped point. The face also fills only a small part of the frame, and the rocky background offers no pointed shapes. The face could still give a moderate response through its snout taper, but it is a borderline case.\n\n45"}
{"prompt": "B: + essay", "gid": 3274, "imagenet_class": "cougar", "frac_of_max": 0.3388, "on": true, "p_on": 0.75, "reasoning": "The cougar's face at upper right is the relevant feature: a cat face with pointed ears and a V-shaped muzzle tapering to the chin. That is exactly the kind of pointed, fox- or cat-like face that drives this neuron. The face is fairly small and the cat is resting, but the ear-to-chin V is clearly visible. The rounded rock and body contours are mostly neutral or slightly negative, and they shouldn't cancel the face's response, since the neuron's value is its strongest response anywhere in the image.\n\n75"}
{"prompt": "B: + essay", "gid": 34530, "imagenet_class": "poncho", "frac_of_max": 0.0483, "on": false, "p_on": 0.42, "reasoning": "The key feature is the dark red shawl: draped over the shoulders, it forms a large downward-pointing triangle, a V-shape with fringed edges. That is the kind of pointed geometry this neuron seems to track, as opposed to round forms. However, its strongest activations come from compact pointed patterns like snouts, cat faces and car fronts. A diffuse, low-contrast garment shape seen from behind, with the face small and turned away, may only weakly drive it.\n\n42"}
{"prompt": "B: + essay", "gid": 21226, "imagenet_class": "rugby ball", "frac_of_max": 0.0412, "on": false, "p_on": 0.2, "reasoning": "The main object is a close-up rugby ball, and only its broad, rounded flank is visible. That is closer to the \"round\" pole that pushes this neuron negative than to the V-shaped, pointed features (snouts, car fronts, flames) that drive it. The pointed tip of the ball is not visible. The rugby players in the background are small and lack strong V-shaped structures, so they offer little drive either.\n\n20"}
{"prompt": "B: + essay", "gid": 37230, "imagenet_class": "polecat", "frac_of_max": 0.6211, "on": true, "p_on": 0.62, "reasoning": "The key element is the ermine's face, a small white animal face seen head-on with a snout that tapers to a point. This resembles the fox-snout and wolf-face inputs that drive the \"pointed/V-shape\" pole of this neuron. Mustelid faces are rounder and blunter than fox or canid faces, with rounded ears, which could weaken the response. Still, the frontal, centered, white-furred face is close to the neuron's preferred pattern.\n\n62"}
{"prompt": "B: + essay", "gid": 13964, "imagenet_class": "red fox", "frac_of_max": 0.4576, "on": true, "p_on": 0.93, "reasoning": "This is a frontal red fox face with tall pointed ears and a tapering V-shaped snout. That is exactly the \"pointed/V\" pattern this neuron responds to, and the fox-snout (4d:493) and wolf-face (4d:477) units feed it strongly. The face is large, centered and clearly lit, so the max response should be strong despite the cluttered grass background.\n\n93"}
{"prompt": "B: + essay", "gid": 16896, "imagenet_class": "mortarboard", "frac_of_max": 0.0369, "on": false, "p_on": 0.25, "reasoning": "The photo shows people in graduation caps and gowns: a man in glasses adjusting his gown, with a classmate behind him. There are no cat, fox or dog faces, car fronts or flames. The only candidate pointed shapes are the V-neck of the gown and hood and the angled corners of the mortarboards, which are weaker, less object-like cues than the snout-like V's this neuron prefers. Human faces, which are rounder and flatter, are not typical drivers, so I expect a weak response.\n\n25"}
{"prompt": "B: + essay", "gid": 46054, "imagenet_class": "pool table", "frac_of_max": 0.3433, "on": true, "p_on": 0.72, "reasoning": "The pool table is shot in steep perspective, so its edges and rails converge into a long pointed wedge, the kind of V-shaped geometry the essay says drives this neuron. A pool table also appears among its \"pointed\" ON examples. The balls are round and could push it slightly negative, but they are small relative to the dominant wedge of the table. I expect the strong pointed shape to win out.\n\n72"}
{"prompt": "B: + essay", "gid": 30054, "imagenet_class": "pillow", "frac_of_max": 0.0475, "on": false, "p_on": 0.42, "reasoning": "The pillows are mostly soft, squarish shapes, which do little for a V-shape or pointed-face detector. But the busy fabric print repeats Spider-Man masks, whose angular eyes and tapering chins form V-like, face-like patterns. The pillow corners are also somewhat pointed. These small repeated pointed faces could give a moderate response, but they are tiny and cluttered, so a strong activation is uncertain.\n\n42"}
{"prompt": "B: + essay", "gid": 16753, "imagenet_class": "torch", "frac_of_max": 0.0465, "on": false, "p_on": 0.62, "reasoning": "The key element is the hand-held torch: a cone tapering to a point, topped by a flame. That is exactly the pointed V-shape this neuron seems to track, and its strongest downstream reader is a flame detector (5a:94). The person's face is fairly round and frontal, so it probably contributes little or pushes slightly negative. The torch is small and the image is dim and low quality, which tempers my confidence, but it is a reasonably clean V-shaped, flame-tipped form.\n\n62"}
{"prompt": "B: + essay", "gid": 9213, "imagenet_class": "snow leopard", "frac_of_max": 0.3047, "on": true, "p_on": 0.78, "reasoning": "The photo is a frontal close-up of a snow leopard's face. That is a big-cat face, the category that dominated this neuron's original top activations. Its eyes, brow markings and nose converge toward a V at the pink nose and muzzle, much like the cat and fox faces that drive the neuron. The broad, rounded head pushes slightly the other way, but the pointed facial geometry is centered and large, so a strong response is likely.\n\n78"}
{"prompt": "B: + essay", "gid": 26601, "imagenet_class": "bison", "frac_of_max": 0.0289, "on": false, "p_on": 0.33, "reasoning": "The key element is the bison's head-on face, since this neuron responds to pointed, V-shaped frontal forms such as fox and cat faces, car fronts and flames. The bison's face is broad, shaggy and blunt, closer to the round pole (like the bulldog and mastiff faces that push it negative) than to a tapered snout. The animal's overall silhouette does narrow somewhat toward the bottom, and the curved horns give a weak V cue, so a moderate response is possible but not likely to be strong.\n\n33"}
{"prompt": "B: + essay", "gid": 21294, "imagenet_class": "basenji", "frac_of_max": 0.307, "on": true, "p_on": 0.85, "reasoning": "The key feature is the dog's face. This basenji-type dog looks straight at the camera, with large upright pointed ears and a narrow, tapering muzzle, giving a strong V-shaped, fox-like face. That is very close to the fox-snout and wolf-face inputs that drive this neuron. The round, knobby texture of the sweater won't cancel that, and the red car at the edge adds a little weak support.\n\n85"}
{"prompt": "B: + essay", "gid": 45747, "imagenet_class": "curly-coated retriever", "frac_of_max": 0.0392, "on": false, "p_on": 0.18, "reasoning": "The photo shows a pile of sleeping brown curly-coated puppies, curled into rounded blobs with their snouts mostly tucked away or out of view. This neuron fires on pointed, V-shaped structure such as tapered snouts, fox or cat faces, angled car fronts and flames, and it is pushed negative by round forms. These soft, rounded fur masses with no clear pointed muzzle facing the camera are closer to the \"round\" pole. The dog content gives a small chance that a partially visible muzzle triggers it, but it is most likely OFF.\n\n18"}
{"prompt": "B: + essay", "gid": 33756, "imagenet_class": "barn spider", "frac_of_max": 0.0428, "on": false, "p_on": 0.22, "reasoning": "The photo is dominated by the spider's large, bulbous abdomen, which is the kind of round, blob-like shape that pushes this neuron negative rather than the pointed V-shape it prefers. The legs do form some angular, converging lines, but they are thin and spindly. They don't make a clear tapered V like a snout, car front or flame. With no face-like or pointed tapering form, the neuron likely stays low.\n\n22"}
{"prompt": "B: + essay", "gid": 4156, "imagenet_class": "kit fox", "frac_of_max": 0.6435, "on": true, "p_on": 0.92, "reasoning": "The photo is a red fox whose face is clearly visible: tall pointed ears and a tapered snout form a strong V/pointed shape. That is exactly the pattern this neuron responds to. Its strongest input is a fox-snout detector (4d:493), and fox, wolf and coyote faces are among its typical drivers. The face is large, well lit and fills a good part of the frame, so the neuron should fire strongly.\n\n92"}
{"prompt": "B: + essay", "gid": 26375, "imagenet_class": "timber wolf", "frac_of_max": 0.308, "on": true, "p_on": 0.88, "reasoning": "The wolf's head is full of the pointed, V-shaped structure this neuron tracks: two erect triangular ears and a long snout that tapers to a point. Canid faces like this map closely onto its strongest inputs (4d:477 wolf face, 4d:493 fox snout). The head is in near-profile and snarling rather than facing the camera, which may weaken the symmetric V somewhat. Still, there is little round structure to push it negative, so it should respond strongly.\n\n88"}
{"prompt": "B: + essay", "gid": 12623, "imagenet_class": "malamute", "frac_of_max": 0.4464, "on": true, "p_on": 0.85, "reasoning": "The malamute's face is the key feature: its upright, pointed ears and the V-shaped white mask tapering to the snout are exactly the \"pointed\" wolf- or fox-face pattern this neuron draws on, through inputs like 4d:477 (wolf face) and 4d:493 (fox snout). The face is front-facing and large in the frame, so it should drive the neuron strongly. Unlike the round-faced mastiffs that push it negative, this dog's head is wedge-shaped.\n\n85"}
{"prompt": "B: + essay", "gid": 20382, "imagenet_class": "cougar", "frac_of_max": 0.3908, "on": true, "p_on": 0.62, "reasoning": "The cougar's face in the upper left matters most. It is a feline face with pointed ears and a muzzle that tapers to a V, exactly the cat/fox-snout pattern this \"pointed\" neuron favors. The face is small, seen at a three-quarter angle, and most of the frame is body and grass, which gives the neuron little to work with. Still, cat faces are among its strongest drivers, so a moderate-to-strong response is plausible.\n\n62"}
{"prompt": "B: + essay", "gid": 28913, "imagenet_class": "Pomeranian", "frac_of_max": 0.4233, "on": true, "p_on": 0.8, "reasoning": "The white Pomeranian puppy has a fox-like face, with two pointed, upright ears and a short snout that narrows toward the nose. That gives the V-shaped, pointed facial geometry this neuron favors, the same shape that drives its fox-snout and wolf-face inputs. The fluffy, rounded fur outline adds some \"round\" signal that could push it down, but the ear tips and the eye-nose triangle should still give a strong pointed response somewhere in the image.\n\n80"}
{"prompt": "B: + essay", "gid": 25658, "imagenet_class": "Pekinese", "frac_of_max": 0.4411, "on": true, "p_on": 0.4, "reasoning": "The key feature is the dog's face, since this neuron fires on V-shaped, pointed forms such as tapering snouts and fox- or cat-like faces, and is pushed negative by round ones. This is a flat-faced, small-muzzled breed (Pekingese or Shih Tzu type), which leans toward the \"round\" side, like the bulldog and mastiff negatives in the figure. However, the wet, drooping fur frames the face into a somewhat downward-pointing V, and the hood's V-shaped neckline adds a little pointed geometry. Overall I think a weak-to-moderate response is more likely than a strong one.\n\n40"}
{"prompt": "B: + essay", "gid": 8115, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3073, "on": true, "p_on": 0.88, "reasoning": "This is a frontal sheltie face with a narrow, tapering muzzle and upright pointed ears, so the whole face reads as a V. That closely matches the fox-snout and wolf-face inputs (4d:493, 4d:477) that drive 4e:55. Unlike the round, flat-faced mastiff and bulldog examples that push it negative, this collie-type face is exactly the pointed, fox-like geometry the neuron responds to, so a strong activation is likely.\n\n88"}
{"prompt": "B: + essay", "gid": 36676, "imagenet_class": "street sign", "frac_of_max": 0.0157, "on": false, "p_on": 0.18, "reasoning": "The image is dominated by flat, rectangular road signs with text against a plain sky. None of this offers the pointed V-shaped structures (snouts, car fronts, flames) that drive 4e:55. The only candidate V-shapes are small: the bird perched on top and the corner of a yellow diamond warning sign at the bottom left. These are probably too minor to push the neuron to 30% of its maximum.\n\n18"}
{"prompt": "B: + essay", "gid": 42638, "imagenet_class": "gasmask", "frac_of_max": 0.0498, "on": false, "p_on": 0.28, "reasoning": "The gas mask is shaped like a face with a protruding \"snout,\" which could weakly recruit the animal-face and snout inputs. But its defining parts are round: circular eyepieces and a cylindrical filter canister whose end reads as a big \"O.\" Round shapes are what push this neuron negative. Nothing in the image forms a sharp V-shaped point like a fox muzzle, a car hood, or a flame, so it most likely stays below threshold.\n\n28"}
{"prompt": "B: + essay", "gid": 12515, "imagenet_class": "patas", "frac_of_max": 0.3669, "on": true, "p_on": 0.25, "reasoning": "The monkey's face, which would be the most likely trigger, is almost entirely cropped off at the top. What remains is mostly a rounded, furry body and limbs, with no pointed snout, ears or car-front shape. The forked branches and the bent legs form loose V-like configurations, which could give weak activation. But they are thin, low-contrast structures rather than the bold, converging V patterns of fox snouts, flames or martini glasses that drive this neuron strongly, so a strong response seems unlikely.\n\n25"}
{"prompt": "B: + essay", "gid": 24163, "imagenet_class": "cab", "frac_of_max": 0.3654, "on": true, "p_on": 0.7, "reasoning": "The yellow taxi is the key element: car fronts (hood, grille, headlights) were among this neuron's classic top activators, and its input from the van-front detector (4d:201) carries that signal. The taxi's front end is visible at a three-quarter angle, and its tapered hood and windshield make the wedge-like, pointed shape the V-shape reading predicts. The building facade is mostly rectilinear and shouldn't contribute much either way. The car is fairly small in the frame and seen partly from the side, which tempers my confidence somewhat.\n\n70"}
{"prompt": "B: + essay", "gid": 12977, "imagenet_class": "coyote", "frac_of_max": 0.3563, "on": true, "p_on": 0.8, "reasoning": "The raccoon dog's face is the key feature. Its tapered, fox-like snout and the V-shaped dark eye mask converging toward the nose form the pointed facial shape this neuron responds to. That puts it close to the fox-snout and wolf-face inputs that drive it most strongly. The face is frontal and clearly visible, though the furry, somewhat rounded head may dampen the response slightly.\n\n80"}
{"prompt": "B: + essay", "gid": 13568, "imagenet_class": "Pomeranian", "frac_of_max": 0.3342, "on": true, "p_on": 0.78, "reasoning": "The Pomeranian's fox-like head is what matters: it has a pointed snout and erect triangular ears, the same tapered, V-like face geometry that drives this neuron through its fox-snout and wolf-face inputs. The head is seen in three-quarter profile and is fairly small in the frame, so the V-shape is less canonical than a frontal fox face. Still, small spitz-type dogs with fox-like faces are typical strong activators. The fluffy round body and the grass background are largely irrelevant.\n\n78"}
{"prompt": "B: + essay", "gid": 31023, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.549, "on": true, "p_on": 0.28, "reasoning": "The photo is a close-up, front-facing Japanese Chin, a flat-faced, brachycephalic dog. Its head is round, with large round eyes and a short pushed-in muzzle rather than a pointed, V-shaped snout. That makes it closer to the bulldog and mastiff faces that push this neuron negative than to the fox, puppy or cat faces that drive it. Some response is still possible, because the neuron likes frontal animal faces and the white blaze and ears form a slight V, but the overall round shape argues against a strong activation.\n\n28"}
{"prompt": "B: + essay", "gid": 23887, "imagenet_class": "weasel", "frac_of_max": 0.4764, "on": true, "p_on": 0.48, "reasoning": "The ferret's face fills the frame: two eyes, a pink nose and pale ears, which is the kind of close-up mammal face (cat, fox, puppy) that commonly drives this neuron. But per the V-shape reading, what matters is whether the face tapers to a point. This ferret's head is fairly broad and rounded, with blunt round ears and a stubby snout, closer to the round faces that push it negative than to a sharp fox muzzle. The face does narrow somewhat toward the nose, so I expect a weak-to-moderate response, roughly a coin flip.\n\n48"}
{"prompt": "B: + essay", "gid": 21084, "imagenet_class": "hermit crab", "frac_of_max": 0.0293, "on": false, "p_on": 0.18, "reasoning": "The photo is a red hermit crab in a large, rounded spiral shell, carrying a sea anemone, against a dark reef background. That is mostly the \"round\" pattern that pushes this neuron negative: the curved shell resembles the snail example on its negative side. There is no V-shaped face, snout, flame or car front to drive it. The crab's legs and claws are somewhat angular but don't form the clear converging V the neuron prefers, so the neuron is most likely OFF.\n\n18"}
{"prompt": "B: + essay", "gid": 34430, "imagenet_class": "cougar", "frac_of_max": 0.3725, "on": true, "p_on": 0.7, "reasoning": "The close-up cougar face is the key feature: a feline face with pointed ears and a muzzle tapering toward the nose fits the cat-face activations of 4e:55, which the V-shape reading explains as a pointed, converging shape. The roaring open mouth and the three-quarter angle break up the clean V somewhat, and the large rounded jaw region could weaken the response. Still, cat and big-cat faces are among this neuron's strongest drivers, so it is fairly likely ON.\n\n70"}
{"prompt": "B: + essay", "gid": 19160, "imagenet_class": "bullfrog", "frac_of_max": 0.0471, "on": false, "p_on": 0.18, "reasoning": "The frog is the only candidate object, and its features are mostly round: a blunt, rounded snout, a bulging circular eye, and a large circular eardrum. These are the round shapes that push this neuron negative. Nothing here forms the pointed, V-shaped snout or front, like a fox or cat face or a car hood, that drives it. The gravel background is round pebble texture as well, so the neuron is likely OFF, though the frog's head in profile tapers slightly and could give a weak response.\n\n18"}
{"prompt": "B: + essay", "gid": 36844, "imagenet_class": "limousine", "frac_of_max": 0.3278, "on": true, "p_on": 0.45, "reasoning": "The main thing here is the car itself: 4e:55 responds strongly to car bodies, especially angled hoods and front ends where the body narrows to a point. In this photo, though, the limousine is seen mostly side-on, and its front end is cropped at the right edge, so the strongest \"pointed front\" cue is weak. The large round chrome wheel is the kind of round shape that pushes this neuron negative. Still, the long boxy car body, the hood edge and the windshield angle could give a moderate response.\n\n45"}
{"prompt": "B: + essay", "gid": 14491, "imagenet_class": "ice bear", "frac_of_max": 0.0402, "on": false, "p_on": 0.25, "reasoning": "The only plausible driver here is the polar bear's head. Its snout tapers somewhat, like the fox, wolf and coyote faces among the neuron's inputs and readers. But the bear is small in the frame and seen mostly in profile, not as a frontal V-shaped face, and bears have rounded heads and ears. The rest of the image is rounded, rubbly rock with no clear V or pointed shapes, so a strong response seems unlikely.\n\n25"}
{"prompt": "B: + essay", "gid": 30829, "imagenet_class": "convertible", "frac_of_max": 0.5515, "on": true, "p_on": 0.8, "reasoning": "The photo shows a white 1960s Camaro convertible in three-quarter front view. Its long hood tapers forward to a pointed, V-like front end with an angular grille, headlight and bumper. Car fronts were among this neuron's classic top activators; it reads them as wedge-shaped, pointed structure, fed partly by van-front detectors. Nothing round dominates the image to push it negative, so a strong response is likely.\n\n80"}
{"prompt": "B: + essay", "gid": 37933, "imagenet_class": "anemone fish", "frac_of_max": 0.0332, "on": false, "p_on": 0.1, "reasoning": "The photo is dominated by a large, rounded, finely textured sea anemone, a blobby organic mass with no pointed or V-shaped structure. A dark clownfish appears only partially at the left edge, and its rounded head doesn't give the converging-lines geometry that drives this neuron. There are no snouts, flames, wedge-shaped vehicle fronts or other tapering forms that would excite the V-shape detector. If anything, the round, lumpy contours lean toward its negative pole.\n\n10"}
{"prompt": "B: + essay", "gid": 47260, "imagenet_class": "sports car", "frac_of_max": 0.3507, "on": true, "p_on": 0.75, "reasoning": "The relevant feature is the car's front end: the long hood tapers to a pointed nose with swept-back headlights, which is the V-shaped, pointed form this neuron responds to. Car fronts were among its classic top activations. The three-quarter view and the gently rounded body make the response less certain than for a head-on grille shot.\n\n75"}
{"prompt": "B: + essay", "gid": 4485, "imagenet_class": "Angora", "frac_of_max": 0.369, "on": true, "p_on": 0.38, "reasoning": "This is a frontal close-up of a fluffy Angora rabbit's face. That kind of furry animal face can partly resemble the cat, fox and dog faces that drive this neuron. But the head reads as a round fuzzball: the snout is blunt and buried in fur, and the ears flop outward rather than forming a crisp V. Roundness pushes this neuron negative, so I expect at most a moderate response, plausibly from the ear-and-face arrangement.\n\n38"}
{"prompt": "B: + essay", "gid": 2454, "imagenet_class": "pajama", "frac_of_max": 0.048, "on": false, "p_on": 0.18, "reasoning": "The photo shows three baby sleepers (onesies) laid flat, covered in polka dots, flowers and a cupcake appliqu\u00e9. Nothing here resembles the pointed, V-shaped structures this neuron likes, such as fox or cat snouts, car grilles or flames. The dominant motifs are round dots and rounded flowers, which match the neuron's negative \"round\" pole. The collars and sleeve angles give only weak V-like geometry, so a strong response is unlikely.\n\n18"}
{"prompt": "B: + essay", "gid": 26988, "imagenet_class": "grocery store", "frac_of_max": 0.0492, "on": false, "p_on": 0.3, "reasoning": "The photo is a market display of many small elongated red-and-white radishes, plus a price sign and baskets. There are no animal faces, car fronts, flames or other clear V-shaped wedges. The radishes do taper to points, but they are small, cluttered, mostly rounded-bodied elongated shapes rather than a bold converging V. That gives the neuron only weak, scattered pointed-shape evidence, so a strong response seems somewhat unlikely, though repeated tapered tips could partially drive it.\n\n30"}
{"prompt": "B: + essay", "gid": 9125, "imagenet_class": "jack-o'-lantern", "frac_of_max": 0.0074, "on": false, "p_on": 0.62, "reasoning": "The carved face is what matters: its angry slanted eyebrows, triangular eyes and nose, and jagged teeth are glowing, flame-orange V-shapes and points. That matches the neuron's \"pointed\" preference and its links to cat and fox faces and flames. Working against it, the pumpkin itself is a round, dark orb, the kind of roundness that pushes this neuron negative, and the scene is small and dim. On balance, the strong angular, face-like glowing features probably dominate.\n\n62"}
{"prompt": "B: + essay", "gid": 20228, "imagenet_class": "Pomeranian", "frac_of_max": 0.3471, "on": true, "p_on": 0.78, "reasoning": "The white Pomeranian has a fox-like face, with pointed ears and a tapering snout forming a V shape, which is exactly the kind of pointed fox/wolf face that drives this neuron's strongest inputs. The face is fairly round and fluffy, and the tongue is out, which may weaken the response somewhat. Still, the spitz-type face with upright triangular ears should activate it well.\n\n78"}
{"prompt": "B: + essay", "gid": 35349, "imagenet_class": "sports car", "frac_of_max": 0.4919, "on": true, "p_on": 0.82, "reasoning": "The photo shows a silver concept sports car seen from the front three-quarter angle. Its wedge-shaped nose, angled headlights, and V-converging hood lines are exactly the pointed, tapering geometry this neuron responds to. Car fronts were among its classic top activators, while round, blunt shapes would suppress it, and there is little of that here. The sharp front end should drive a strong response.\n\n82"}
{"prompt": "B: + essay", "gid": 32536, "imagenet_class": "moving van", "frac_of_max": 0.4322, "on": true, "p_on": 0.78, "reasoning": "The photo is dominated by a white Ford Transit Connect van seen from the front three-quarter angle, with its sloping windshield, hood and grille converging toward the front. That is close to what the \"van front\" input (4d:201) detects, and vehicle fronts were among this neuron's classic top activators. A tapered, V-like front fits the pointed-shape reading of the neuron. The view is somewhat oblique and the van is fairly boxy rather than sharply pointed, which adds some uncertainty, but car and van fronts reliably drive this unit.\n\n78"}
{"prompt": "B: + essay", "gid": 20292, "imagenet_class": "centipede", "frac_of_max": 0.0334, "on": false, "p_on": 0.08, "reasoning": "The photo is a tightly coiled millipede, a round spiral of glossy segments. It contains no pointed V-shapes, fox, cat or dog snouts, or car or vehicle fronts, which are the features that drive 4e:55. If anything, the circular, shell-like spiral resembles the \"round\" negative pole (snail, ball, clock), so it should push the neuron toward suppression rather than activation.\n\n8"}
{"prompt": "B: + essay", "gid": 29900, "imagenet_class": "Chihuahua", "frac_of_max": 0.4584, "on": true, "p_on": 0.85, "reasoning": "The chihuahua's face is the key feature. It has a tapered snout and large pointed ears that form the V-shaped, fox-like face this neuron responds to, similar to the fox, wolf and coyote face features it connects with. The face is fairly large and clearly visible. The round knit blanket and soft background add little, but the strongest response anywhere in the image should come from the face.\n\n85"}
{"prompt": "B: + essay", "gid": 49820, "imagenet_class": "shower cap", "frac_of_max": 0.0439, "on": false, "p_on": 0.18, "reasoning": "The main content is a person's roughly round face under a white shower cap printed with a panda face, which has round black ears. These are round, blob-like shapes, the opposite of the pointed, V-shaped snouts and ears (fox, cat, wolf) or wedge-shaped car fronts that drive 4e:55. Nothing in the frame is clearly V-shaped or tapered, so the neuron most likely stays OFF. The face-like panda print and the human face give a small chance of a weak response.\n\n18"}
{"prompt": "B: + essay", "gid": 30155, "imagenet_class": "wig", "frac_of_max": 0.0369, "on": false, "p_on": 0.12, "reasoning": "This photo shows the back of a head of long, dark, wavy hair (likely a wig), with blonde hair blurred at the edges. There is no face, snout, car front, or other converging V-shaped structure that drives this neuron. The dominant forms are rounded, flowing curls and a dome-like head outline, which lean toward the \"round\" pole that pushes it negative. Hair texture might faintly resemble animal fur, but without a pointed facial geometry that is unlikely to push the activation far.\n\n12"}
{"prompt": "B: + essay", "gid": 12607, "imagenet_class": "Madagascar cat", "frac_of_max": 0.0453, "on": false, "p_on": 0.5, "reasoning": "The ring-tailed lemurs are what matter here. Their faces have a fox-like pointed snout, the kind of V-shaped face that 4e:55's fox-snout and wolf-face inputs feed on. The forking tree branches also form many V shapes that could add weak drive. The main uncertainty is size: the lemurs are small and partly turned away, so their faces may be too tiny to drive a mixed4e unit strongly.\n\n50"}
{"prompt": "B: + essay", "gid": 17382, "imagenet_class": "plunger", "frac_of_max": 0.0482, "on": false, "p_on": 0.22, "reasoning": "The photo is mostly a human face, a T-shirt with cartoon print, and raised arms. None of these is the pointed, V-shaped snout or front-end form (fox or cat faces, car hoods, train noses, flames) that drives 4e:55. Human faces are comparatively flat and rounded, closer to the round pole that pushes the neuron negative. The raised elbows and the arms framing the head make a loose V-ish geometry, but it is not the tapered convergent shape the neuron's inputs detect, so a strong response seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 14572, "imagenet_class": "trimaran", "frac_of_max": 0.0442, "on": false, "p_on": 0.35, "reasoning": "The main candidates are the sails: the white mainsail is a tall, sharply pointed triangle, which fits the \"pointed / V-shape\" reading of this neuron. But the yellow-orange spinnaker is billowy and rounded, which would push it negative. The scene also lacks the face-like or car-front-like compositions (snout or grille converging to a point) that drive the neuron hardest. So the pointed sail might give moderate activation, but a strong response is uncertain.\n\n35"}
{"prompt": "B: + essay", "gid": 38442, "imagenet_class": "crayfish", "frac_of_max": 0.0357, "on": false, "p_on": 0.27, "reasoning": "The crayfish's open claws and tapered head and antennae make a few pointed, V-like shapes that could drive this V-shape detector. But they are small, dark and low-contrast against the background. The background is a field of rounded pebbles, exactly the round texture that pushes this neuron negative. Nothing here resembles the strong frontal V-faces (cats, foxes, car fronts) that drive it hardest, so it is more likely OFF.\n\n27"}
{"prompt": "B: + essay", "gid": 3530, "imagenet_class": "broom", "frac_of_max": 0.0351, "on": false, "p_on": 0.22, "reasoning": "The photo is a close-up of broom straw: fine parallel bristles crossed by colored stitching. It contains none of this neuron's usual drivers, such as pointed animal snouts, car or van fronts, flames, or martini-glass shapes. The stitching lines and the two straw bundles converge slightly, which could give a weak V-like cue. But the image is mostly a uniform fibrous texture rather than a distinct pointed object, so a strong response seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 8964, "imagenet_class": "flamingo", "frac_of_max": 0.0414, "on": false, "p_on": 0.18, "reasoning": "This flamingo has its head tucked into its feathers, so the dominant shapes are smooth, rounded curves: the dome of the head and the soft mass of the body. Nothing in the frame is pointed or V-shaped. There is no snout, pointed ears, flame-like tip or wedge-shaped car front. The orange, fox-like coloring might give a small amount of drive. But the round geometry matches the neuron's negative pole, so it most likely stays OFF.\n\n18"}
{"prompt": "B: + essay", "gid": 6011, "imagenet_class": "pool table", "frac_of_max": 0.3248, "on": true, "p_on": 0.66, "reasoning": "The pool table matters most. Its green felt and rails recede in perspective, so the sides converge into a wedge or V. This is the same kind of pointed, converging geometry that made the red pool table in the essay's figure drive the neuron. The many small round balls are the \"round\" pattern that pushes it negative, but the neuron's value is its strongest response anywhere in the image, so the strong converging-edge structure of the table should still dominate.\n\n66"}
{"prompt": "B: + essay", "gid": 13921, "imagenet_class": "bookcase", "frac_of_max": 0.0354, "on": false, "p_on": 0.12, "reasoning": "This bookshelf scene is mostly rectangular structure: rows of book spines, cubby grid lines, baskets and boxes. Nothing in it resembles the V-shaped, pointed forms that drive 4e:55, such as tapering animal snouts, car or train fronts, or flames. The round clock and plate are, if anything, the \"round\" pattern that pushes the neuron negative. The small yellow figurine on top is too tiny and indistinct to give a strong pointed-face signal.\n\n12"}
{"prompt": "B: + essay", "gid": 27755, "imagenet_class": "coyote", "frac_of_max": 0.4789, "on": true, "p_on": 0.85, "reasoning": "This is a close-up of a coyote's head in profile. It shows a long, tapering snout and a tall pointed ear, the kind of pointed, canid-face geometry that this neuron's strongest inputs (fox snout, wolf face) and readers (coyote face) are built around. The view is a side profile rather than a frontal V, which may weaken the response a bit. Even so, the tapering muzzle and pointed ear strongly match the neuron's \"pointed thing\" preference, and nothing round dominates the frame.\n\n85"}
{"prompt": "B: + essay", "gid": 4364, "imagenet_class": "sea lion", "frac_of_max": 0.0368, "on": false, "p_on": 0.15, "reasoning": "The photo shows seal pups lying on a sandy beach next to rocks and seaweed. Seals have smooth, rounded heads and torpedo-shaped bodies, which is the \"round\" pole that pushes this neuron negative. Nothing here offers the pointed V-shapes that drive it: there are no fox- or cat-like snouts with ears, no flames, and no car fronts. The animals are also small in the frame, so whatever faint tapering their bodies have is unlikely to produce a strong response.\n\n15"}
{"prompt": "B: + essay", "gid": 30650, "imagenet_class": "mixing bowl", "frac_of_max": 0.0415, "on": false, "p_on": 0.2, "reasoning": "This photo is a stack of rounded mixing bowls with smooth curved profiles and elliptical rims. That is the kind of round, blunt geometry that tends to push this neuron negative, rather than the pointed V-shapes (snouts, flames, martini glasses, wedge-shaped car fronts) that drive it. The stack does taper overall, but it is an upward-narrowing pile of curves, not a sharp V converging to a point. Each bowl's profile is also curved rather than angled, so I expect little activation.\n\n20"}
{"prompt": "B: + essay", "gid": 18775, "imagenet_class": "beach wagon", "frac_of_max": 0.3111, "on": true, "p_on": 0.85, "reasoning": "This is a head-on view of a car front (a Mazda 6), which is exactly the \"car\" half of this neuron's classic cat/car top activations. The front-on grille, angled headlights and hood lines form the pointed, face-like V-geometry the neuron (and its van-front input, 4d:201) responds to. The rounded Mazda logo and the oval grille opening add a little roundness, but the overall frontal \"car face\" layout should dominate.\n\n85"}
{"prompt": "B: + essay", "gid": 30088, "imagenet_class": "racer", "frac_of_max": 0.5201, "on": true, "p_on": 0.78, "reasoning": "The photo shows a red race car seen at a three-quarter front angle, and its sloping hood and windshield taper toward the front. This is the wedge, V-like car-front geometry that strongly drives 4e:55; the neuron's classic top activations include car fronts, and a red car hood appears among its examples. The car fills much of the frame and has clear converging edges, so a strong response is likely. The boxy, older body style and the side-on portion of the view weaken that somewhat.\n\n78"}
{"prompt": "B: + essay", "gid": 22927, "imagenet_class": "water ouzel", "frac_of_max": 0.0469, "on": false, "p_on": 0.12, "reasoning": "The photo shows a shallow rocky stream with a small dark object, perhaps a turtle or duckling, barely poking above the water. Nothing in it offers the strong pointed, V-shaped structure that drives this neuron, such as a snout-forward animal face, a tapered car or train front, or flame-like shapes. The rocks and water are mostly rounded, smooth textures, and the small creature is too tiny and indistinct to give a clear pointed face. A faint V-wake in the water might cause a weak response, but it is unlikely to reach 30% of the maximum.\n\n12"}
{"prompt": "B: + essay", "gid": 597, "imagenet_class": "pot", "frac_of_max": 0.0486, "on": false, "p_on": 0.42, "reasoning": "The echeveria rosette is full of pointed, V-tipped leaves radiating from the center, and pointed shapes are what drive this V-shape neuron. However, the leaves point outward from a round, symmetric whole, which is closer to the \"round\" pattern that pushes it negative. The neuron's strongest inputs are also convergent, face-like V's (fox snouts, wolf faces, van fronts) rather than leaf tips. The overall cue is mixed, with some pointed signal that may not be the right kind or strong enough.\n\n42"}
{"prompt": "B: + essay", "gid": 2221, "imagenet_class": "jay", "frac_of_max": 0.3405, "on": true, "p_on": 0.45, "reasoning": "The blue jay's head is in tight close-up, and its beak is a strongly pointed, tapering wedge. The crest and the black \"necklace\" markings also form angular, V-like contours. These pointed shapes match what this neuron favors, though its strongest drivers are V-shaped mammal faces (foxes, dogs, cats) and car fronts rather than bird profiles. The head is also fairly rounded overall, which works against it, so this is a borderline case.\n\n45"}
{"prompt": "B: + essay", "gid": 22843, "imagenet_class": "papillon", "frac_of_max": 0.4431, "on": true, "p_on": 0.78, "reasoning": "This papillon has a fox-like, tapering face with large upright pointed ears, which is exactly the V-shaped, pointed-snout pattern this neuron responds to (like the fox and puppy examples). The face is reasonably large in frame, though slightly tilted with the eyes half-closed. The flat-faced, round-headed breeds that push the neuron negative look nothing like this. The feathery white tail adds more tapered shapes but matters less than the head.\n\n78"}
{"prompt": "B: + essay", "gid": 25173, "imagenet_class": "Komodo dragon", "frac_of_max": 0.0047, "on": false, "p_on": 0.22, "reasoning": "The photo is a Komodo dragon's head in profile. Its snout is long but blunt and rounded at the tip, and the face has none of the V-shaped, pointed geometry (fox or cat muzzles, ears, car grilles, flames) that drives this neuron. The scaly texture and rounded head and body are closer to the \"round\" pole that pushes it negative. The tapering lizard snout gives it only a weak chance of crossing the ON threshold.\n\n22"}
{"prompt": "B: + essay", "gid": 11289, "imagenet_class": "snow leopard", "frac_of_max": 0.3195, "on": true, "p_on": 0.8, "reasoning": "The snow leopard's frontal face is the key feature. Its eyes, tapering muzzle and nose form a V that converges on the pink nose and chin, the same pointed cat/fox-face pattern that drives 4e:55's strongest activations. Cat faces are among the neuron's classic top examples. The broad, roundish head of a big cat is a slight counterweight, but the clear converging snout geometry should still produce a strong response.\n\n80"}
{"prompt": "B: + essay", "gid": 19617, "imagenet_class": "shield", "frac_of_max": 0.0144, "on": false, "p_on": 0.52, "reasoning": "The two heraldic shields taper to a pointed bottom, and the overall crest, with the helmet on top and the mantling flaring outward and down, forms a broad V-like silhouette. That is the kind of pointed, converging geometry this neuron favors, and the rampant animal on the right shield adds a small animal-like cue. Against that, much of the image is curly, rounded flourish on a flat background, and there is no strong face, snout or car-front signal, so a strong response is uncertain.\n\n52"}
{"prompt": "B: + essay", "gid": 27010, "imagenet_class": "projectile", "frac_of_max": 0.0301, "on": false, "p_on": 0.45, "reasoning": "The pointed nose cone of the rocket is the feature that matters most. It is a converging, tapered shape, much like the bullet-train nose or car front that drive this V/pointed-shape detector. However, the cone is narrow, points upward rather than forward like a snout, and takes up only a small part of the frame. The rest of the image is mostly vertical towers and the diagonal lattice of the gantry, which may add weak V-like junctions but no strong pointed \"face.\"\n\n45"}
{"prompt": "B: + essay", "gid": 6239, "imagenet_class": "beach wagon", "frac_of_max": 0.401, "on": true, "p_on": 0.85, "reasoning": "This is a red classic car seen from a front three-quarter angle. Its hood has creased lines converging toward a central ornament and grille, which is exactly the V-shaped, pointed car-front pattern that drove this neuron's famous \"car\" examples. The red hood resembles the red car and pool-table exemplars, and nothing round dominates the frame (the headlights are small).\n\n85"}
{"prompt": "B: + essay", "gid": 7738, "imagenet_class": "polecat", "frac_of_max": 0.3408, "on": true, "p_on": 0.63, "reasoning": "The ferret's head is the key element: it has a tapered, pointed snout and a V-shaped face, much like the fox, wolf and cat faces that drive this neuron through its fox-snout and wolf-face inputs. Its small rounded ears and the mostly rounded body pull slightly the other way. The grayscale image and the face's partly turned, off-center pose may weaken the response somewhat. Still, a pointed mammal snout is close to this neuron's core trigger.\n\n63"}
{"prompt": "B: + essay", "gid": 38582, "imagenet_class": "dung beetle", "frac_of_max": 0.0303, "on": false, "p_on": 0.12, "reasoning": "The photo shows a small, dome-shaped beetle (likely a dung beetle) on reddish dirt. Its outline is compact and rounded rather than pointed or V-shaped. Nothing here resembles the drivers of this neuron, such as snouts, tapered fronts, flames, or V-shaped faces. If anything, the round dome matches the neuron's negative \"round\" pole, and the rest of the frame is mostly featureless soil texture, so a strong response is unlikely.\n\n12"}
{"prompt": "B: + essay", "gid": 31547, "imagenet_class": "gas pump", "frac_of_max": 0.0475, "on": false, "p_on": 0.18, "reasoning": "This vintage Phillips 66 gas pump is a boxy, upright object with a rounded top and rectangular signs. It has no V-shaped or pointed structure like a snout, a car hood or grille converging forward, or a flame. Its car association is only contextual, and the neuron responds to pointed or converging shapes rather than to automotive themes. The rounded, flat-faced geometry and the text signage are more likely to leave it weak or push it negative.\n\n18"}
{"prompt": "B: + essay", "gid": 7563, "imagenet_class": "ambulance", "frac_of_max": 0.389, "on": true, "p_on": 0.55, "reasoning": "The key feature is the vehicle itself: a white VW-style van, seen mostly from the side with the front end visible at a three-quarter angle. This neuron's car response, partly fed by a \"van front\" input (4d:201), is strongest for frontal views, where the hood and grille converge into a pointed or V-like shape. Here the front is only partly visible and the boxy side dominates, so I'd expect a moderate rather than strong car/pointed-front response. It's plausibly above threshold, but uncertain.\n\n55"}
{"prompt": "B: + essay", "gid": 47199, "imagenet_class": "artichoke", "frac_of_max": 0.0168, "on": false, "p_on": 0.35, "reasoning": "The artichokes pull in both directions for a V/pointed-shape detector. Each bract tapers to a small V-shaped tip, and these are tiled densely across the surface, which could drive it somewhat. But the overall forms are big round globes, which is exactly the \"round\" pole that pushes this neuron negative. The pointed elements are also small, repetitive texture rather than a single prominent V like a snout, flame or car hood, so I lean OFF.\n\n35"}
{"prompt": "B: + essay", "gid": 34342, "imagenet_class": "little blue heron", "frac_of_max": 0.3433, "on": true, "p_on": 0.4, "reasoning": "The heron's head and long, tapering beak form a strongly pointed, wedge-like shape, which fits the \"pointed/V\" reading of this neuron. The fish held crosswise adds some angular structure near the beak tip. However, the neuron's strongest drivers are V-shaped frontal faces and snouts (foxes, cats, car fronts), and a side-on bird bill is a thinner, horizontal point rather than a converging V. Combined with the plain blue background and smooth, rounded neck, this makes a strong activation uncertain.\n\n40"}
{"prompt": "B: + essay", "gid": 33791, "imagenet_class": "espresso", "frac_of_max": 0.0468, "on": false, "p_on": 0.25, "reasoning": "The photo is dominated by the round, curved rim and bowl of a white coffee cup, which is the kind of rounded shape that pushes this neuron negative rather than driving it. The only candidate for a pointed or V-shaped feature is the angled corner of the biscotti dipping into the coffee. But that is a blunt, flat slab edge, not the converging V of a snout, flame or car front. Its crumbly texture also lacks the fur or face-like context that usually accompanies strong activations, so the neuron is probably weak here.\n\n25"}
{"prompt": "B: + essay", "gid": 20606, "imagenet_class": "digital clock", "frac_of_max": 0.046, "on": false, "p_on": 0.12, "reasoning": "The photo is a dark nighttime car-dashboard scene: a small rectangular digital clock display, round blurry bokeh lights, and a circular red dial at the bottom. None of these give the neuron the pointed, V-shaped convergence it responds to, like a tapered snout, a car-hood \"face\" or a flame tip. The rounded dial and bokeh are closer to its negative \"round\" pole. Low contrast and a lack of clear structure further reduce the chance of a strong response, so it is likely OFF.\n\n12"}
{"prompt": "B: + essay", "gid": 40606, "imagenet_class": "four-poster", "frac_of_max": 0.0453, "on": false, "p_on": 0.1, "reasoning": "This is a dark, blurry interior: a wooden bedpost or wardrobe edge, a small stool and a purple wall. Nothing in it resembles what drives this neuron, such as a pointed V-shaped structure like an animal snout, a car or van front, a flame or a converging glossy surface. The scene is mostly vertical rectangular edges and flat low-contrast texture. That gives the neuron little reason to respond, so it is most likely OFF.\n\n10"}
{"prompt": "B: + essay", "gid": 48326, "imagenet_class": "Arctic fox", "frac_of_max": 0.449, "on": true, "p_on": 0.72, "reasoning": "The arctic fox's face is the relevant part. Its snout tapers to a point at the black nose, which is the V-shaped, pointed-face pattern that the strongest input (4d:493, fox snout) feeds into this neuron. Against that, the animal is curled up and very fluffy, its ears are tucked, and its eyes are closed, so the head reads somewhat rounder and the V is less crisp than on an alert fox. The snow, fence and background add nothing.\n\n72"}
{"prompt": "B: + essay", "gid": 34613, "imagenet_class": "black stork", "frac_of_max": 0.0278, "on": false, "p_on": 0.27, "reasoning": "The black stork's long, pointed red bill is the only plausibly \"pointed\" feature here. But it is a thin spike rather than the broad V-shaped taper of a fox snout, cat face or car front, which is what the neuron's main inputs detect. The rest of the image is a rounded feathered body, a thin leg and dirt ground, none of which should drive a V-shape detector. Some weak response from the tapering bill and the body's wedge silhouette is possible, but reaching 30% of the neuron's maximum seems unlikely.\n\n27"}
{"prompt": "B: + essay", "gid": 30640, "imagenet_class": "umbrella", "frac_of_max": 0.0397, "on": false, "p_on": 0.25, "reasoning": "The photo is a collage of small scenes, each with a red umbrella, plus architectural interiors and doorways. None of the strong drivers are present: no fox, cat or dog faces and no car fronts with a pointed, V-shaped snout. An open umbrella's canopy is somewhat triangular, but it reads mostly as a rounded dome, which tends to push this neuron negative. Each tile is also tiny and cluttered, so any weak V-shapes at these scales are unlikely to drive a strong response.\n\n25"}
{"prompt": "B: + essay", "gid": 33941, "imagenet_class": "convertible", "frac_of_max": 0.4215, "on": true, "p_on": 0.8, "reasoning": "The photo shows a white Mustang convertible from the front three-quarter angle, so the car's front end dominates the frame. That front has a wedge-shaped, pointed nose, angled headlights and a sloping hood. Car fronts are one of this neuron's classic triggers, fed by the van-front input and matching the \"car\" example in the figure. Its tapering, V-like geometry fits the pointed-shape reading too, so a strong response is likely.\n\n80"}
{"prompt": "B: + essay", "gid": 28640, "imagenet_class": "hen-of-the-woods", "frac_of_max": 0.046, "on": false, "p_on": 0.18, "reasoning": "The photo is dominated by a hen-of-the-woods mushroom, a mass of rounded, scalloped, overlapping caps. Those are exactly the round, blobby forms that push this neuron negative. There are no pointed snouts, V-shaped faces, vehicle fronts, or flame-like tapers. The only candidate is the small white bone-like fragment at the lower left, which has a somewhat tapered, pointed end, but it is small and not very V-shaped. So I expect a weak response at most.\n\n18"}
{"prompt": "B: + essay", "gid": 20873, "imagenet_class": "castle", "frac_of_max": 0.0436, "on": false, "p_on": 0.12, "reasoning": "The photo shows a brick building with an ivy-covered wall, a rounded arched doorway and a tall cypress tree. None of these resemble what drives this neuron: fox, cat or dog faces, car and van fronts, flames, or a V that converges to a point. The arch is round, the kind of shape that pushes the neuron negative. The cypress tapers upward to a point, but it is a dark, low-detail silhouette, not the face- or grille-like V the neuron prefers. I expect the neuron to stay below threshold.\n\n12"}
{"prompt": "B: + essay", "gid": 34261, "imagenet_class": "lynx", "frac_of_max": 0.3352, "on": true, "p_on": 0.6, "reasoning": "The key feature is the cat's head at the top of the frame: cat faces are among this neuron's strongest drivers, and the essay reads that as a response to the pointed, V-shaped structure of ears and muzzle. Here, though, the head is turned in profile, partly cut off at the top edge, and has a round orange toy in its mouth, so the frontal V-shaped face pattern is weaker. Round objects push this neuron negative. The tabby/serval texture and the visible ear and muzzle region probably still give a moderate-to-strong response.\n\n60"}
{"prompt": "B: + essay", "gid": 19570, "imagenet_class": "tiger", "frac_of_max": 0.3116, "on": true, "p_on": 0.8, "reasoning": "The tiger's face is a large cat face whose muzzle tapers to a point, with ears and whisker lines framing a V. Cat faces are among this neuron's classic top activators, and the \"pointed V\" reading supports this too. Here the face is turned slightly and partly cut off at the top-left edge, which may weaken the response somewhat. Even so, the snout and face shape should drive it strongly.\n\n80"}
{"prompt": "B: + essay", "gid": 35059, "imagenet_class": "sulphur butterfly", "frac_of_max": 0.0367, "on": false, "p_on": 0.22, "reasoning": "The neuron responds to pointed, V-shaped structures such as fox and cat snouts, car fronts and flame tips, and is suppressed by round shapes. This photo has a yellow butterfly on an orange zinnia. The flower head is round and radially symmetric, which tends to push the neuron negative. The butterfly's wing has a somewhat pointed tip, and the orange petals are loosely flame-like, but neither forms a clear V or converging snout, so a strong response seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 23317, "imagenet_class": "bullet train", "frac_of_max": 0.3151, "on": true, "p_on": 0.78, "reasoning": "The key feature is the Shinkansen's long, tapered nose. It is a streamlined, pointed vehicle front, much like the \"bullet train\" example the essay lists as driving this neuron. The neuron also responds to car and van fronts through inputs like 4d:201, so a vehicle front with a converging, V-like shape should excite it. The view here is fairly side-on, which makes the point less prominent than a head-on shot, but the wedge of the nose is still clear.\n\n78"}
{"prompt": "B: + essay", "gid": 39096, "imagenet_class": "sax", "frac_of_max": 0.0346, "on": false, "p_on": 0.22, "reasoning": "The photo shows a man playing a saxophone, and the most prominent shape is the round, flaring bell of the sax. That bell is the kind of rounded \"O\" form that pushes this neuron negative. There is no strong pointed, V-shaped feature like a fox or cat snout, a car front or a flame tip. The weak V's from the shirt collar and the neck strap are unlikely to drive it strongly.\n\n22"}
{"prompt": "B: + essay", "gid": 31104, "imagenet_class": "Siamese cat", "frac_of_max": 0.3291, "on": true, "p_on": 0.88, "reasoning": "This is a close-up of a Siamese-type cat's face. It has the tapering, V-shaped structure the neuron prefers: pointed ears, and a face narrowing from the wide cheeks down to the nose and chin. Cat faces were among the neuron's classic top activations. The framing is tight and frontal, and there are no round objects competing with the pointed shape, so a strong response is likely.\n\n88"}
{"prompt": "B: + essay", "gid": 9176, "imagenet_class": "hamper", "frac_of_max": 0.0499, "on": false, "p_on": 0.12, "reasoning": "This photo is a close-up of woven wicker on a hamper, with a looped cane clasp. It contains nothing like the pointed, converging V-shapes that drive this neuron: no animal snouts or faces, no car or train fronts, no flames. The repetitive weave is a fine texture rather than an object-scale point. The clasp is rounded and loop-like, closer to the \"round\" negative pole. So I expect only weak activation at most.\n\n12"}
{"prompt": "B: + essay", "gid": 11244, "imagenet_class": "rock crab", "frac_of_max": 0.0465, "on": false, "p_on": 0.18, "reasoning": "The scene is mostly rounded shapes: the hermit crab's shell and the bed of smooth aquarium pebbles, which are exactly the kind of \"round\" content that pushes this neuron negative. There is no clear V-shaped or pointed structure such as an animal snout, a car front or flames. The crab's small reddish legs and claws add some weak angular detail, but they are tiny and not arranged in a strong V. Overall this looks unlikely to drive the neuron strongly.\n\n18"}
{"prompt": "B: + essay", "gid": 737, "imagenet_class": "barometer", "frac_of_max": 0.0476, "on": false, "p_on": 0.15, "reasoning": "This is a close-up of a barometer dial, which is a large circular face with a curved scale and printed text, very similar to the clock shown among the negative examples. That round, convex shape is what pushes this neuron negative. The only pointed elements are the thin needles and tick marks, and they are small compared with the snout-like or wedge-shaped V forms that drive it. So the neuron is most likely OFF, with a little uncertainty because the needle tips could weakly activate it.\n\n15"}
{"prompt": "B: + essay", "gid": 38046, "imagenet_class": "Maltese dog", "frac_of_max": 0.3547, "on": true, "p_on": 0.42, "reasoning": "The key region is the dog's face, seen head-on with two bright flash-lit eyes and a dark nose. Frontal animal faces are this neuron's main natural trigger when they taper to a point (fox, wolf, cat). This Maltese-type dog has a short snout, floppy ears and a fluffy, rounded head outline, which is closer to the \"round\" pole that pushes the neuron negative. The eyes-over-nose layout could still produce a partial V-shaped response, so the outcome is uncertain and leans slightly toward OFF.\n\n42"}
{"prompt": "B: + essay", "gid": 32047, "imagenet_class": "manhole cover", "frac_of_max": 0.0, "on": false, "p_on": 0.55, "reasoning": "This rusty manhole or utility cover has no animal faces or vehicle fronts, which are the neuron's classic top triggers. It is, however, covered in repeated chevron (V-shaped) ridges, and the cover's corner forms a sharp pointed angle against the pavement. Both are exactly the \"pointed/V\" geometry the reinterpretation says drives 4e:55. There is little roundness here to push it negative. Still, flat embossed chevrons are lower-level texture than the face-like V's the neuron usually prefers, so this is only a moderate bet.\n\n55"}
{"prompt": "B: + essay", "gid": 4444, "imagenet_class": "bolo tie", "frac_of_max": 0.036, "on": false, "p_on": 0.55, "reasoning": "The photo shows a bolo tie and matching earrings with Native American-style inlay. The trapezoid pendants taper downward like a blunt V, the inlay is full of chevron and triangle patterns, and the metal tips are long and pointed. These all fit the neuron's \"pointed / V-shape\" preference, and the image has no strong round, O-like focal object to push it negative apart from the small beads. However, these are flat geometric patterns on a small object rather than the snout-like or front-facing pointed forms that drive this neuron hardest, so the response may be only moderate.\n\n55"}
{"prompt": "B: + essay", "gid": 22560, "imagenet_class": "giant schnauzer", "frac_of_max": 0.035, "on": false, "p_on": 0.52, "reasoning": "The frontal dog face is the relevant feature here. It is a head-on animal face, the kind of input that feeds this neuron through the fox, wolf and coyote face connections. The face here is dark, shaggy and fairly broad, with a lolling tongue, rather than a clean V-shaped snout with pointed ears. Even so, the bearded muzzle tapers downward and the face fills a good part of the frame, so moderate activation is plausible.\n\n52"}
{"prompt": "B: + essay", "gid": 38302, "imagenet_class": "beach wagon", "frac_of_max": 0.4259, "on": true, "p_on": 0.35, "reasoning": "The photo is a side profile of a blue station wagon, so the car-front cues this neuron tends to pick up, like a converging hood, grille, or windshield V, are mostly missing. Instead, the most salient elements are the two large round chrome wheels, which fit the \"round\" pole that pushes it negative. The long, boxy profile offers little pointed or V-shaped structure, although the network's general car-related inputs might still give some moderate response.\n\n35"}
{"prompt": "B: + essay", "gid": 12878, "imagenet_class": "mobile home", "frac_of_max": 0.4578, "on": true, "p_on": 0.4, "reasoning": "The most relevant feature is the partial silver car in the lower-left corner. Car fronts and hoods are among this neuron's known drivers, consistent with the \"V-shaped / pointed front\" reading. Here, though, only a small, cropped sliver of the hood and headlight is visible, so it may not form the converging shape strongly. The mobile home's low-pitched gable roof gives a mild pointed shape, but the rest of the scene is boxy and rectilinear: siding, windows and a deck. None of that is a natural driver, so this is a borderline case.\n\n40"}
{"prompt": "B: + essay", "gid": 20427, "imagenet_class": "golfcart", "frac_of_max": 0.3314, "on": true, "p_on": 0.75, "reasoning": "The golf cart has a styled car front: a mesh grille, paired headlights, and a hood that tapers toward the front. This is the car-front pattern that drives 4e:55 strongly, and it is fed by van-front units like 4d:201. Viewed head-on, the converging hood lines and grille form the V/pointed configuration the neuron favors. The round headlights and wheels may push slightly against it, but car fronts are among this neuron's most reliable triggers.\n\n75"}
{"prompt": "B: + essay", "gid": 9368, "imagenet_class": "tow truck", "frac_of_max": 0.4535, "on": true, "p_on": 0.75, "reasoning": "The photo shows a red car on a tow truck, seen from the front, with a crumpled hood and wedge-like angled panels. Car fronts were among this neuron's classic top activators, and the essay links them to its input from van-front detectors and its taste for pointed, V-shaped forms. The damage blurs the clean grille-and-hood layout and the lighting is dark, but the front-on view of the car body should still drive it fairly well.\n\n75"}
{"prompt": "B: + essay", "gid": 19564, "imagenet_class": "ladybug", "frac_of_max": 0.0287, "on": false, "p_on": 0.12, "reasoning": "The photo is a dense cluster of ladybugs on weathered wood. Each beetle is a small, round, dome-shaped blob, which is the \"round\" pole that pushes this neuron negative. There is nothing V-shaped or pointed at a meaningful scale: no snout, muzzle, car front, flame or tapering object. The repeated round shapes and plain wood grain give it little to respond to, so it is most likely OFF.\n\n12"}
{"prompt": "B: + essay", "gid": 19299, "imagenet_class": "red fox", "frac_of_max": 0.5693, "on": true, "p_on": 0.88, "reasoning": "The photo shows a red fox's face with a tapered, V-shaped snout and tall pointed ears. That is exactly the \"pointed\" geometry this neuron responds to, and it gets strong input from the fox-snout detector (4d:493). Even with the eyes closed and the body curled, the converging lines of the face toward the nose should give a strong response. The rounded rocks and curled body are minor round features and are unlikely to cancel the facial V.\n\n88"}
{"prompt": "B: + essay", "gid": 35914, "imagenet_class": "manhole cover", "frac_of_max": 0.0183, "on": false, "p_on": 0.05, "reasoning": "The photo is dominated by a large, flat, circular manhole cover. That is exactly the kind of round form that makes up this neuron's negative pole, like the clock, golf ball and tennis ball examples. Nothing in it has a pointed or V-shaped structure: no snouts, ears, car-front grilles or flame-like tapering shapes. The grid pattern and text on the lid are textures, not pointed forms, so the neuron is very likely suppressed rather than driven.\n\n5"}
{"prompt": "B: + essay", "gid": 5537, "imagenet_class": "red fox", "frac_of_max": 0.468, "on": true, "p_on": 0.9, "reasoning": "This is a close-up of a red fox face, with pointed ears, a tapering snout, and a V-shaped muzzle. That is exactly the \"pointed\" fox and canine face geometry fed by this neuron's strongest input, 4d:493 fox snout. The open, rounded mouth adds a bit of roundness, but the overall triangular face and ears should dominate. The neuron is very likely to fire strongly.\n\n90"}
{"prompt": "B: + essay", "gid": 16482, "imagenet_class": "timber wolf", "frac_of_max": 0.3207, "on": true, "p_on": 0.9, "reasoning": "The wolf's face is a strong V-shape: a long, tapering snout that comes to a point, plus sharply pointed upright ears. That pattern matches this neuron's strongest inputs, the fox-snout and wolf-face units (4d:493, 4d:477), and the coyote-face reader. There are no round, blunt features here, like a pug face or a ball, that would push it negative, so it should fire strongly.\n\n90"}
{"prompt": "B: + essay", "gid": 37525, "imagenet_class": "black-footed ferret", "frac_of_max": 0.6039, "on": true, "p_on": 0.72, "reasoning": "The ferret's face fills the frame head-on, and its pale mask tapers toward a small nose. That gives a fox- or cat-like V-shaped snout, the pointed animal-face shape that drives this neuron through its fox-snout and wolf-face inputs. The round ears and eyes and the somewhat blunt muzzle add some \"round\" signal that could dampen it. Still, the overall triangular face should likely push it past threshold.\n\n72"}
{"prompt": "B: + essay", "gid": 8655, "imagenet_class": "kelpie", "frac_of_max": 0.304, "on": true, "p_on": 0.82, "reasoning": "The Australian cattle dog's face is the key feature. Its upright pointed ears and tapering muzzle form a clear V shape, much like the fox, wolf and coyote faces that feed into and read from this neuron. Unlike the round-faced mastiffs that push it negative, this is a narrow, pointed, fox-like canine face seen nearly head-on. The striped poles add some angular structure but matter less than the face.\n\n82"}
{"prompt": "B: + essay", "gid": 48016, "imagenet_class": "limousine", "frac_of_max": 0.3128, "on": true, "p_on": 0.28, "reasoning": "The photo shows a white stretch limousine from the side at night, mostly in darkness, with a few lit windows and a streetlight. The neuron's car responses come mainly from car fronts and hoods, the pointed or V-like grille and hood geometry (like the \"van front\" input). A long, flat side profile lacks that pointed structure, and the low light weakens any features. Some car-body activation is still possible, but a strong response seems unlikely.\n\n28"}
{"prompt": "B: + essay", "gid": 41964, "imagenet_class": "otterhound", "frac_of_max": 0.0114, "on": false, "p_on": 0.52, "reasoning": "The dog is walking toward the camera, so its head, held low, forms a downward-pointing V: narrowing muzzle, ears framing the face, and a tapered tail tip above. That is the kind of pointed, fox/wolf-snout shape that feeds this V-shape detector. However, the face is small, partly obscured, fluffy and rather rounded, and the rest of the image is grass and brush with no strong V shapes. So the response is plausible but likely moderate.\n\n52"}
{"prompt": "B: + essay", "gid": 16294, "imagenet_class": "grey fox", "frac_of_max": 0.3464, "on": true, "p_on": 0.9, "reasoning": "The photo shows a gray fox facing the camera. Its tall pointed ears and tapering snout make exactly the V-shaped face this neuron's strongest input (4d:493, fox snout) detects. A frontal fox face is one of the canonical pointed patterns that drive 4e:55, and nothing round dominates the image to push against it. So I expect a strong response.\n\n90"}
{"prompt": "B: + essay", "gid": 3281, "imagenet_class": "lesser panda", "frac_of_max": 0.6628, "on": true, "p_on": 0.8, "reasoning": "The red panda's face is shown close-up and frontally. Its russet-and-white coloring, white-tipped ears and tapered black-nosed snout closely resemble the fox faces that feed this neuron through the 4d:493 fox-snout input. The ears and narrowing muzzle form the pointed, V-like structure the neuron responds to. The face is somewhat rounder and broader than a fox's, which could weaken the response a little, but overall this looks strongly like the fox and cat face imagery that drives it ON.\n\n80"}
{"prompt": "B: + essay", "gid": 14591, "imagenet_class": "Cardigan", "frac_of_max": 0.3672, "on": true, "p_on": 0.8, "reasoning": "The corgi has a fox-like face, with large upright pointed ears and a tapering snout that form the V-shaped, pointed head this neuron responds to. This closely matches its strongest inputs, the fox-snout and wolf-face detectors. The dog is fairly small in the frame and its open mouth adds some roundness, but the pointed ears and narrowing muzzle should still drive a solid response. The grassy background contributes little.\n\n80"}
{"prompt": "B: + essay", "gid": 29906, "imagenet_class": "manhole cover", "frac_of_max": 0.0158, "on": false, "p_on": 0.2, "reasoning": "This square manhole cover has no animal face, car front, or other single object that converges to a point, which is the strong \"pointed / V-shape\" signal that drives 4e:55. Its diamond lattice does contain many small V-like corners. But these form a flat, repetitive texture rather than one coherent tapering shape at the scale mixed4e responds to. The overall form is a flat square plate, which the neuron's object-level detectors are unlikely to read as \"pointed,\" so it will probably stay weak.\n\n20"}
{"prompt": "B: + essay", "gid": 34209, "imagenet_class": "otter", "frac_of_max": 0.3575, "on": true, "p_on": 0.32, "reasoning": "The otter's face is the only candidate feature, and it is an animal face with whiskers and a snout, like the cat and fox faces that drive this neuron. But its geometry runs against what the neuron prefers. Otter heads are broad, flat and rounded, with a blunt muzzle and tiny round ears, so they lack the tapering V-shaped snout-and-ears configuration of foxes, wolves or pointed cat faces. That puts it closer to the round, blunt-faced dogs on the negative side, so at best I'd expect a weak partial response.\n\n32"}
{"prompt": "B: + essay", "gid": 14339, "imagenet_class": "Norfolk terrier", "frac_of_max": 0.3855, "on": true, "p_on": 0.55, "reasoning": "The terrier's face is the relevant part. Its reddish coat, pointed muzzle and dark eyes resemble the fox and wolf-face features that feed this neuron. But its folded-down ears and fluffy, rounded head make the face less V-shaped than a fox or cat with upright ears. The face is also fairly small in the frame, so I expect a moderate response, possibly enough to be ON.\n\n55"}
{"prompt": "B: + essay", "gid": 25928, "imagenet_class": "patas", "frac_of_max": 0.5071, "on": true, "p_on": 0.4, "reasoning": "The two patas monkey faces are the main candidates here: animal faces with eyes and a muzzle are the kind of thing this neuron's fox, wolf and cat inputs respond to. But primate faces are fairly flat and rounded, without the tapering V-shaped snout or pointed ears that drive it most strongly. The downward-pointing white mustaches and the narrow pointed leaves give some V-like structure, which may push it partway up. Overall I expect a moderate response that could fall either side of the ON threshold.\n\n40"}
{"prompt": "B: + essay", "gid": 26652, "imagenet_class": "toy terrier", "frac_of_max": 0.4525, "on": true, "p_on": 0.78, "reasoning": "The key feature is the dog's head: a toy fox terrier with tall, pointed ears and a tapering snout. Together they form the V-shaped, fox-like face that drives this neuron through its fox-snout and wolf-face inputs. The face is fairly small in the frame, and the rest of the image (grass, mossy rock, body) adds little. Still, the neuron takes its strongest response anywhere in the image, and this face is a textbook pointed, fox-like shape.\n\n78"}
{"prompt": "B: + essay", "gid": 13461, "imagenet_class": "fire screen", "frac_of_max": 0.0181, "on": false, "p_on": 0.55, "reasoning": "The fireplace photo at the bottom shows small flames, which are classic upward-pointing V/teardrop shapes. The neuron connects strongly to flame detectors downstream, so it likely responds to this kind of pointed shape. The decorative fire screen at the top also has pointed leaf and arch motifs that could weakly drive it. However, the flames are tiny, and much of the image is flat packaging, text and rounded arches, so a strong response is uncertain.\n\n55"}
{"prompt": "B: + essay", "gid": 17573, "imagenet_class": "EntleBucher", "frac_of_max": 0.0353, "on": false, "p_on": 0.65, "reasoning": "The main driver is the dog's face in the lower right: a dark muzzle with tan markings that taper into a V-shaped snout, much like the fox- and dog-face inputs this neuron reads from. The face is fairly small and the dog is partly tucked under the boulder, which may weaken the response. The pointed mountain peak and the angular rock edges add some weak V-shape signal. There is no strong round object pushing the neuron negative.\n\n65"}
{"prompt": "B: + essay", "gid": 45522, "imagenet_class": "meerkat", "frac_of_max": 0.3291, "on": true, "p_on": 0.78, "reasoning": "The meerkat's face has a tapered, pointed snout and a narrowing head shape much like the fox and wolf snouts that feed this neuron. Even though it's seen at a three-quarter or profile angle rather than as a frontal V, the pointed muzzle and dark eye-mask resemble the fox-face features that drive 4e:55. Nothing in the image is strongly round in a way that would push it negative. So it is likely ON, with some uncertainty because the face isn't head-on.\n\n78"}
{"prompt": "B: + essay", "gid": 39724, "imagenet_class": "pickup", "frac_of_max": 0.3131, "on": true, "p_on": 0.72, "reasoning": "The front of a vehicle, here the old pickup truck's grille, hood and windshield seen at an angle, is exactly the kind of \"car front\" input (cf. 4d:201 van front) that drives this neuron. In the V-shape reading, a hood and windshield converging toward the camera form a pointed, wedge-like front. The truck fills a moderate part of the frame, and nothing round dominates the scene to suppress it. Because the neuron's value is its peak response anywhere in the image, the truck front alone should likely push it over threshold.\n\n72"}
{"prompt": "B: + essay", "gid": 32271, "imagenet_class": "convertible", "frac_of_max": 0.5363, "on": true, "p_on": 0.78, "reasoning": "This is a 1961 Cadillac seen from the rear three-quarter angle. It shows sharp, pointed tailfins and V-shaped taper lines along the body. Cars were one of the canonical strong drivers of 4e:55, and the pointed fins and wedge-shaped chrome trim fit the \"pointed/V-shape\" reading well. It isn't the car front (grille or headlights) the neuron most loves, but the car body plus the converging pointed lines should still drive it substantially.\n\n78"}
{"prompt": "B: + essay", "gid": 40647, "imagenet_class": "red wolf", "frac_of_max": 0.4332, "on": true, "p_on": 0.92, "reasoning": "This is a maned wolf, which has a fox-like face. Its narrow, pointed snout and large upright ears together form a strong V-shape seen nearly head-on, the same pattern driven by the fox-snout (4d:493) and wolf-face (4d:477) inputs. Canid faces are among this neuron's strongest stimuli, and there is little rounded structure here to suppress it.\n\n92"}
{"prompt": "B: + essay", "gid": 44899, "imagenet_class": "hog", "frac_of_max": 0.0196, "on": false, "p_on": 0.15, "reasoning": "The photo shows a pot-bellied pig grazing. Its body is a big rounded mass, and its snout is blunt and wrinkled rather than tapered to a V. There are no pointed features here like a fox or cat muzzle, a car hood, or a flame tip; everything is round and heavy, which is the shape that pushes this neuron negative. Some weak response to the animal face is possible, but it is unlikely to reach 30% of the neuron's maximum.\n\n15"}
{"prompt": "B: + essay", "gid": 21016, "imagenet_class": "pinwheel", "frac_of_max": 0.0337, "on": false, "p_on": 0.66, "reasoning": "The pinwheel's blades are sharp, pointed triangular shapes that converge in V-like wedges toward the center, which is the geometry this neuron detects. The orange and red coloring also resembles the flame and fox-face inputs and readers it connects to. Against that, the scene is a small, clean object on a plain background, and the hub and ribbon loops add some roundness. Overall, the V-shaped pointed blades should drive it fairly strongly.\n\n66"}
{"prompt": "B: + essay", "gid": 47576, "imagenet_class": "quail", "frac_of_max": 0.3319, "on": true, "p_on": 0.27, "reasoning": "The key feature is the quail's head: a small pointed beak and a curled topknot plume. These are only weakly V-shaped, and they are bird features, not the mammal-snout or car-front shapes this neuron's inputs (fox, wolf, van-front detectors) favor. Most of the frame is the quail's plump, rounded body and smooth curved outlines, which match the \"round\" pole that pushes the neuron negative. So any pointed-face response is likely weak, and I lean toward OFF.\n\n27"}
{"prompt": "B: + essay", "gid": 26656, "imagenet_class": "ski mask", "frac_of_max": 0.034, "on": false, "p_on": 0.25, "reasoning": "The main object is a person in an orange balaclava, whose head reads as a smooth, rounded dome. That is the \"round\" shape that pushes this neuron negative, not the pointed V-shape (fox snout, cat face, car hood) that drives it. The mask does have face-like eye and mouth openings, which could give weak face-detector input. But there is no tapering snout, ears or V-contour, and the rest of the scene (dark background, sparse branches, a loose jacket) offers little pointed structure.\n\n25"}
{"prompt": "B: + essay", "gid": 37477, "imagenet_class": "ox", "frac_of_max": 0.0095, "on": false, "p_on": 0.48, "reasoning": "The key element is the musk ox's head: a frontal animal face whose long snout tapers downward, framed by horns that sweep out and down. Together these roughly make the converging, pointed V-shape this neuron likes, similar to the fox, wolf and coyote faces among its inputs and readers. However, the face is small in the frame, and its broad, shaggy, rounded mass is less sharply pointed than a fox or cat face. The surrounding shrubs and grass offer nothing pointed, so the response depends on that one modest face.\n\n48"}
{"prompt": "B: + essay", "gid": 44193, "imagenet_class": "Pembroke", "frac_of_max": 0.4182, "on": true, "p_on": 0.88, "reasoning": "The corgi's face is very fox-like: tall pointed ears and a tapering snout form exactly the V-shaped, pointed head that drives this neuron. Its strongest inputs include fox-snout and wolf-face features. The face is large, frontal and clearly visible against plain grass, with no competing round shapes. That should produce a strong response.\n\n88"}
{"prompt": "B: + essay", "gid": 42857, "imagenet_class": "loudspeaker", "frac_of_max": 0.0329, "on": false, "p_on": 0.12, "reasoning": "What matters here is shape: the photo is dominated by round forms, namely a big circular woofer cone, a circular tweeter, and many round bolts and terminal posts. Under the V-shape/pointed reading of 4e:55, round shapes are exactly its negative pole, like the clock, golf ball and tennis ball examples. Nothing in the image forms a V, a snout, flames or a car-front taper. The flat wood-grain panel with symmetric hardware might faintly resemble a face or grille, but it is most likely not enough to reach 30% of max.\n\n12"}
{"prompt": "B: + essay", "gid": 5972, "imagenet_class": "tusker", "frac_of_max": 0.0483, "on": false, "p_on": 0.18, "reasoning": "The photo shows an elephant in profile with a tusk, standing in green grass, plus a few small birds. This neuron responds to pointed, V-shaped structures, such as fox, cat and dog snouts, car fronts and flames. Nothing in this image strongly matches that. The elephant's head, ears and body are broad and rounded, and the tusk is a thin curve rather than a converging V. The birds are too small to drive it much, so I expect only weak activation.\n\n18"}
{"prompt": "B: + essay", "gid": 9787, "imagenet_class": "gown", "frac_of_max": 0.0447, "on": false, "p_on": 0.32, "reasoning": "The photo has no cat faces, fox or canine snouts, or car fronts, which are the strongest known drivers of this neuron. What could matter is geometry. The dress bodice and neckline form V shapes that taper toward the waist, and the straps converge into points. The woman's small face is also slightly pointed. Against that, the large flowing skirt, the rounded embroidery motifs and the text banner are mostly curved or flat. So any pointed-shape response would likely be weak.\n\n32"}
{"prompt": "B: + essay", "gid": 18166, "imagenet_class": "meerkat", "frac_of_max": 0.39, "on": true, "p_on": 0.78, "reasoning": "The meerkat's head has a narrow, tapering snout that forms a clear V-shape. This is the same kind of pointed animal face as the fox and wolf snouts that feed this neuron most strongly. The face is large, centered and close-up, which suits a mid-level layer like mixed4e. The rounded, fuzzy body and plain background are neutral, and the pointed face should dominate the max response.\n\n78"}
{"prompt": "B: + essay", "gid": 38361, "imagenet_class": "Persian cat", "frac_of_max": 0.4165, "on": true, "p_on": 0.45, "reasoning": "The cat's face is the relevant region, since cat faces were among this neuron's classic top activators, and the eyes-nose-mouth layout forms a converging, V-like snout. However, this cat (a Scottish-fold/British-shorthair type) has a very round head with flattened or barely visible ears. That roundness is exactly the shape that pushes this neuron toward its negative \"round\" pole. The face is also fairly small and low-contrast (white on white), so it is unclear whether the pointed-snout cue is strong enough to win.\n\n45"}
{"prompt": "B: + essay", "gid": 17216, "imagenet_class": "malamute", "frac_of_max": 0.3514, "on": true, "p_on": 0.88, "reasoning": "The photo shows a malamute/husky puppy's face filling the frame. It has a wolf-like snout, a V-shaped white facial mask and upright triangular ears. That is exactly the pointed, V-shaped canine face this neuron draws on through its fox-snout and wolf-face inputs (4d:493, 4d:477). Unlike round, flat-faced dogs such as mastiffs, which push the neuron negative, this face is tapered and pointed, so it should drive the neuron strongly.\n\n88"}
{"prompt": "B: + essay", "gid": 3618, "imagenet_class": "head cabbage", "frac_of_max": 0.0483, "on": false, "p_on": 0.15, "reasoning": "The photo is dominated by a cabbage head: a large, round, compact green ball held in a hand. That is the kind of rounded, convex form that pushes this neuron negative. The surrounding outer leaves have some V-shaped vein patterns and wavy edges, but there are no strongly pointed, converging shapes like snouts, cat faces, car hoods or flame tips. The central round mass should dominate, so a strong response is unlikely.\n\n15"}
{"prompt": "B: + essay", "gid": 41738, "imagenet_class": "tabby", "frac_of_max": 0.3301, "on": true, "p_on": 0.85, "reasoning": "The tabby cat's face fills much of the frame, with a pointed ear and a V-shaped muzzle tapering toward the nose. Cat faces like this are among this neuron's classic top activators, consistent with its \"pointed/V-shape\" reading. The face is tilted and partly cropped at the edge, and the floral pillow background is irrelevant, but the key feature is clearly present.\n\n85"}
{"prompt": "B: + essay", "gid": 3133, "imagenet_class": "beach wagon", "frac_of_max": 0.3314, "on": true, "p_on": 0.55, "reasoning": "The main candidate is the red vintage station wagon, a car body like those that strongly drive this neuron. Here it is seen mostly from the side, though, with the pointed front-end and grille geometry (the V-ish hood and fender convergence that likely matters most) only partly visible at the right edge. The round whitewall wheels are the kind of circular shape associated with negative drive, but the angled fender, chrome trim lines and other parked cars could still give a moderate peak response somewhere.\n\n55"}
{"prompt": "B: + essay", "gid": 37571, "imagenet_class": "Maltese dog", "frac_of_max": 0.353, "on": true, "p_on": 0.62, "reasoning": "This is a close, frontal face of a white Maltese, and frontal animal faces are what this neuron's inputs (fox-snout and wolf-face detectors) respond to. The long hair hanging down on both sides frames the face in a downward-tapering, V-like outline that converges on the dark nose. Against that, the head is fluffy and the snout is short, which is closer to the \"round\" pole that pushes the neuron negative (like the pug-faced dogs). On balance, the V-shaped face framing and the strong frontal-face cue probably win out.\n\n62"}
{"prompt": "B: + essay", "gid": 40528, "imagenet_class": "Eskimo dog", "frac_of_max": 0.324, "on": true, "p_on": 0.85, "reasoning": "This is a close-up of a husky's face. Its tapered, wolf-like snout, angled jaw and pointed ear form the V-shaped, pointed geometry this neuron responds to. That matches its strongest inputs, the fox-snout and wolf-face detectors (4d:493, 4d:477), and the dog, fox and cat faces that drive it. Nothing round or blunt, like a pug or bulldog face, is there to push it negative, so a strong response is likely.\n\n85"}
{"prompt": "B: + essay", "gid": 3081, "imagenet_class": "badger", "frac_of_max": 0.3492, "on": true, "p_on": 0.78, "reasoning": "The badger's head is a strongly pointed, V-shaped face: the snout tapers to a point, and the black-and-white stripes converge into a V toward the nose. This resembles the fox-snout and wolf-face inputs that drive the neuron. The face is fairly large and well-lit against the background, which should give a strong peak response. The bulky, rounded body matters less, since the neuron's value is its strongest response anywhere in the image.\n\n78"}
{"prompt": "B: + essay", "gid": 5094, "imagenet_class": "Arctic fox", "frac_of_max": 0.547, "on": true, "p_on": 0.88, "reasoning": "The photo shows an arctic fox face in profile, with a tapered, pointed snout and pointed ears. That is exactly the V-shaped, pointed form this neuron responds to. Its strongest input is a fox-snout detector (4d:493), and fox faces appear among its top activators. Nothing round or blunt dominates the frame to suppress it, so a strong response is likely.\n\n88"}
{"prompt": "B: + essay", "gid": 28156, "imagenet_class": "sea urchin", "frac_of_max": 0.0417, "on": false, "p_on": 0.3, "reasoning": "This looks like a close-up of a sea urchin: a dark, fuzzy mass with magenta spines radiating from a central point. The individual spines are pointed and some pairs form V-like angles, which could weakly drive a V-shape/pointed-snout detector. But the overall structure is a round, radially symmetric blob with no single tapered snout, hood or flame shape. That roundness matches the neuron's negative pole, so I expect a modest response that more likely stays below the ON threshold.\n\n30"}
{"prompt": "B: + essay", "gid": 44502, "imagenet_class": "Appenzeller", "frac_of_max": 0.3169, "on": true, "p_on": 0.72, "reasoning": "The puppy's face is seen head-on, filling much of the frame. Its white blaze narrows down the center to a tapered muzzle, and the dark ears and fur frame it, giving a fairly clear inverted-V, which is the pointed snout geometry this neuron's fox-snout and wolf-face inputs respond to. It closely resembles the \"dog\" puppy in the essay's pointed examples. Puppy faces are somewhat rounder than fox faces, and the hand and background add nothing, so the response may not be at full strength.\n\n72"}
{"prompt": "B: + essay", "gid": 5719, "imagenet_class": "basenji", "frac_of_max": 0.3101, "on": true, "p_on": 0.85, "reasoning": "The basenji puppies have fox-like faces: tapering V-shaped snouts, tall pointed ears, and white facial blazes that narrow toward the nose. This is very close to the fox-snout and wolf-face inputs that drive this \"pointed/V-shape\" neuron. Several such faces appear at close range, and there are no strongly round, flat-faced shapes competing. That should produce a strong response.\n\n85"}
{"prompt": "B: + essay", "gid": 14972, "imagenet_class": "book jacket", "frac_of_max": 0.0356, "on": false, "p_on": 0.08, "reasoning": "This is a flat book cover with horizontal white lines and small red and white rectangles on a dark background. There is nothing pointed or V-shaped: no animal snout, no car or train nose, no flame or tapering form. The geometry is all parallel lines and right-angle rectangles, which neither the V-shape reading nor the old cat/car reading of this neuron would predict to drive it. It is most likely OFF.\n\n8"}
{"prompt": "B: + essay", "gid": 16339, "imagenet_class": "hamster", "frac_of_max": 0.3268, "on": true, "p_on": 0.25, "reasoning": "The hamster's face is the main candidate. It does have small pointed ears and a furry, front-facing animal face, which share some of the cat- and fox-face features that drive this neuron. But its overall shape is a round, puffy, cheek-stuffed ball rather than the tapered V-shaped snout that defines the neuron's \"pointed\" pole, so it leans toward the \"round\" side that pushes the neuron negative. The cracker and the pink patterned background offer little in the way of strong V-shapes.\n\n25"}
{"prompt": "B: + essay", "gid": 21465, "imagenet_class": "brain coral", "frac_of_max": 0.0384, "on": false, "p_on": 0.08, "reasoning": "The photo is dominated by round, domed shapes: a spherical sea urchin test, brain coral, a rounded coral piece and a flat sand dollar. These match the neuron's negative \"round\" pole (like the snail, golf ball and tennis ball examples), not the pointed V-shapes that drive it. There are no V-shaped snouts, flames, car fronts or tapered forms anywhere in the scene. The texture and dots on the urchin give it a little chance of a weak response, but a strong one is unlikely.\n\n8"}
{"prompt": "B: + essay", "gid": 3849, "imagenet_class": "Brittany spaniel", "frac_of_max": 0.3169, "on": true, "p_on": 0.55, "reasoning": "The two Brittany spaniels' faces are what matter here. The left dog's three-quarter view shows a fairly narrow, tapering muzzle and a white blaze, which gives a somewhat V-shaped, fox-like face. That is the kind of pointed face this neuron's fox-snout and wolf-face inputs drive. Working against it, the long floppy ears and side-profile angles make the faces less of a frontal V, and rounded or droopy dog faces push the neuron negative. So a moderate response is plausible.\n\n55"}
{"prompt": "B: + essay", "gid": 42961, "imagenet_class": "Siamese cat", "frac_of_max": 0.3847, "on": true, "p_on": 0.85, "reasoning": "The Siamese cat's face is the key feature: a triangular, V-shaped face with tall pointed ears and a narrowing muzzle. That is exactly the kind of cat or fox face that drives 4e:55 strongly. The pineapple and palm motifs on the upholstery add more pointed shapes, and very little in the image is round enough to push the neuron negative. The face is fairly small in the frame, but it is clear and front-facing, so the neuron should respond strongly.\n\n85"}
{"prompt": "B: + essay", "gid": 2939, "imagenet_class": "ptarmigan", "frac_of_max": 0.0368, "on": false, "p_on": 0.22, "reasoning": "The photo shows a ground squirrel from behind on rocky ground. Its body is a hunched, rounded mass, and its face, which could form a pointed V-shaped snout, is turned away and barely visible. Without a front-facing pointed face, the neuron gets little of its preferred V-shape drive. The round body pushes toward its negative pole, though furry mammal texture and the angular rock edges might give a weak partial response.\n\n22"}
{"prompt": "B: + essay", "gid": 11262, "imagenet_class": "tricycle", "frac_of_max": 0.3095, "on": true, "p_on": 0.32, "reasoning": "The photo shows the side of a dark SUV and a child's tricycle. The neuron's car responses come mainly from frontal views of cars, where the hood and grille converge to a point, and from face-like V shapes. Here the SUV is seen side-on, with no hood or grille point, and the most prominent shapes are round wheels on both the SUV and the tricycle. Round shapes push this neuron negative, so weak or partial activation from the car body is the most it is likely to get.\n\n32"}
{"prompt": "B: + essay", "gid": 39629, "imagenet_class": "Siberian husky", "frac_of_max": 0.4063, "on": true, "p_on": 0.88, "reasoning": "The husky puppy's face is the key element. Its pointed upright ears, tapering snout and the V-shaped white/dark facial mask form exactly the kind of fox/wolf-like pointed face that the neuron's strongest inputs (fox snout, wolf face) feed into. Unlike the round, flat-faced mastiff examples that push it negative, this is a narrow, wedge-shaped canine face, which should drive the neuron strongly.\n\n88"}
{"prompt": "B: + essay", "gid": 10833, "imagenet_class": "squirrel monkey", "frac_of_max": 0.3784, "on": true, "p_on": 0.28, "reasoning": "The main thing that matters is the squirrel monkeys' faces, which are small in the frame and fairly round, with flat muzzles and dark rounded caps rather than the V-shaped, pointed snout of a fox, cat or dog. The tails and limbs hang down, but they are thin curves, not converging V-shaped faces or car-front shapes. Nothing else in the scene, which is just foliage and branches, gives a strong pointed or V-shaped cue. A faint face-shaped response is possible, but this neuron more likely stays below threshold.\n\n28"}
{"prompt": "B: + essay", "gid": 15656, "imagenet_class": "common iguana", "frac_of_max": 0.0486, "on": false, "p_on": 0.25, "reasoning": "The photo shows a marine iguana and a red Sally Lightfoot crab on rounded lava rocks, with a watermark across it. Nothing here gives the strong symmetric V-shaped convergence the neuron likes: no snout or face tapering to a point, no car or van front, no flame. The crab's splayed legs and the iguana's spiky crest offer some weak pointed structure. But the many rounded, pebbly rock textures lean toward the round, negative pole, so a strong response seems unlikely.\n\n25"}
{"prompt": "B: + essay", "gid": 10490, "imagenet_class": "monastery", "frac_of_max": 0.0356, "on": false, "p_on": 0.13, "reasoning": "This photo is a whitewashed colonial church and convent fa\u00e7ade. It contains no animal faces (cats, foxes, dogs) or car fronts, which are this neuron's strongest pointed, V-shaped drivers. The only candidate shapes are architectural: the curved ornate gable, the rounded arched doorway and the bell-tower finials. These are mostly rounded or flat rather than a strong converging V, and if anything the arches lean toward the neuron's negative \"round\" pole. A weak response to the gable's peak is possible, but an ON response seems unlikely.\n\n13"}
{"prompt": "B: + essay", "gid": 11387, "imagenet_class": "minivan", "frac_of_max": 0.5886, "on": true, "p_on": 0.75, "reasoning": "The photo shows a silver minivan in three-quarter view, with its sloped hood and front end tapering toward the left. Car and van fronts are among this neuron's classic strong triggers, partly via the 4d:201 van-front input, and the angled, wedge-like nose fits the \"pointed/V\" reading. The front is not head-on and the minivan's boxy body is less pointed than a sports car, which tempers this somewhat, but vehicles like this usually drive the neuron well.\n\n75"}
{"prompt": "B: + essay", "gid": 13784, "imagenet_class": "hen-of-the-woods", "frac_of_max": 0.0313, "on": false, "p_on": 0.15, "reasoning": "This is a hen-of-the-woods mushroom: a mass of rounded, ruffled, overlapping lobes on a forest floor. Nothing in it resembles the neuron's preferred pattern of a pointed V-shaped front, like an animal snout, a car hood or a flame tip. The scalloped lobe edges and leaf litter give lots of curvy texture, but the shapes are mostly rounded, which tends to push this neuron down. A few leaf tips might create small pointed bits, but I expect a weak response overall.\n\n15"}
{"prompt": "B: + essay", "gid": 44192, "imagenet_class": "diamondback", "frac_of_max": 0.0073, "on": false, "p_on": 0.15, "reasoning": "What matters is shape, not category: the neuron fires on pointed, V-shaped structures like fox and cat faces, flames and car fronts, and is pushed negative by round forms. This rattlesnake is tightly coiled into a round, spiral mass on gravel, which is the kind of curved shape that suppresses it. The snake's triangular head is a small potential pointed cue, but it is tiny and not the V-shaped face-with-ears pattern that drives the neuron's strongest inputs. Overall, the photo is dominated by roundness and texture rather than a clear V.\n\n15"}
{"prompt": "B: + essay", "gid": 15117, "imagenet_class": "drilling platform", "frac_of_max": 0.0457, "on": false, "p_on": 0.12, "reasoning": "The photo shows a jack-up oil rig: tall vertical lattice legs, a derrick, and a boxy hull under an overcast sky. Nothing here resembles the convergent, V-shaped \"pointed front\" this neuron detects, such as a tapering animal snout, a car or train nose, or a flame tip. The derrick tapers slightly, but it is thin, upright lattice rather than a solid pointed form facing the viewer. Most of the image is parallel verticals and rectangular blocks, which should drive this neuron only weakly, so it is likely OFF.\n\n12"}
{"prompt": "B: + essay", "gid": 2574, "imagenet_class": "Gila monster", "frac_of_max": 0.0187, "on": false, "p_on": 0.22, "reasoning": "The key feature is the Gila monster's head, seen face-on. Its snout is broad and blunt, and its skin is covered in rounded, bead-like scales. Both read as \"round\" rather than \"pointed,\" so this is closer to the bulldog, golf-ball and snail examples that push the neuron negative than to the tapering fox or cat faces that drive it. There's a slight taper from the head toward the snout, but no clear V or pointed apex. That makes strong activation unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 32378, "imagenet_class": "Komodo dragon", "frac_of_max": 0.043, "on": false, "p_on": 0.22, "reasoning": "This is a close-up side profile of a Komodo dragon's head: a long, blunt snout, scaly bead-like skin and a large rounded eye with wrinkled lids. The neuron fires on pointed, V-shaped forms seen head-on, like fox and dog faces narrowing to a snout, car and train fronts, and flames. Here the snout is seen sideways and is broad and blunt rather than tapering into a V, and the round eye and bumpy texture fit the \"round\" side that pushes the neuron negative. Some elongated, wedge-like head geometry could give weak activation, but probably not 30% of its maximum.\n\n22"}
{"prompt": "B: + essay", "gid": 21134, "imagenet_class": "tow truck", "frac_of_max": 0.3245, "on": true, "p_on": 0.5, "reasoning": "The only relevant content is the white tow truck seen from a front three-quarter angle. Vehicle fronts, with the hood tapering toward the grille and windshield, are among this neuron's classic drivers (the \"car\" half of its supposed cat/car mix, which reads as a pointed, V-like frontal shape). Two things weaken the case: the truck is fairly small in the frame, and a boxy truck cab is blunter than a sloped car hood. The rest of the scene is flat walls and road, which offers nothing pointed.\n\n50"}
{"prompt": "B: + essay", "gid": 45481, "imagenet_class": "lawn mower", "frac_of_max": 0.3238, "on": true, "p_on": 0.3, "reasoning": "The original cat/car framing would point to the dark sedan in the background, but it is seen mostly from the side, small, and cut off at the left edge. Under either reading, the car-front/V-shape cue this neuron likes (grilles and hoods converging to a point, like the red car or bullet train) is weak here. The lawn mowers, the people and the lawn are mostly round wheels and boxy shapes, which, if Cammarata's \"round pushes it negative\" reading holds, would work against it. There may be a small pointed contour where the car's hood tapers, so a moderate response is possible but not likely.\n\n30"}
{"prompt": "B: + essay", "gid": 42710, "imagenet_class": "Pomeranian", "frac_of_max": 0.4144, "on": true, "p_on": 0.88, "reasoning": "This is a Pomeranian with a strongly fox-like head: a tapered, pointed snout in profile and tall, pointed ears with wispy tips. That is the V-shaped, pointed-face pattern the neuron's top inputs supply (fox snout 4d:493, wolf face 4d:477). Nothing in the frame is round or bulbous enough to push it negative the way a flat-faced mastiff would. The face fills a large part of the image, so a strong response is likely.\n\n88"}
{"prompt": "B: + essay", "gid": 31951, "imagenet_class": "hay", "frac_of_max": 0.0406, "on": false, "p_on": 0.12, "reasoning": "The main object is a round hay bale, a big circular, rounded form. That is exactly the kind of shape the neuron's negative \"round\" pole responds to. Nothing else in the scene offers a strong V-shaped or pointed structure: there are no animal faces, car fronts, flames or martini-glass shapes, only a rounded tree crown and gentle hill slopes. So the neuron is unlikely to reach a strong response anywhere in the image.\n\n12"}
{"prompt": "B: + essay", "gid": 21557, "imagenet_class": "warthog", "frac_of_max": 0.0238, "on": false, "p_on": 0.28, "reasoning": "The neuron responds to frontal, V-shaped converging structures: fox, cat and dog faces seen head-on, car grilles and pointed noses. Here the warthog is in side profile, so its face shows as an elongated snout rather than a symmetric V, and it is fairly round-bodied. The only weak candidates are the partial giraffe heads in the background and the tops of the fence posts, which are small and not strongly V-like. So I expect only weak activation.\n\n28"}
{"prompt": "B: + essay", "gid": 47269, "imagenet_class": "jaguar", "frac_of_max": 0.3068, "on": true, "p_on": 0.85, "reasoning": "The photo shows a frontal jaguar face with its eyes, nose bridge and muzzle converging into the tapered, V-shaped snout. That is exactly the kind of cat or fox face that historically drives 4e:55 most strongly. The face is large, centered and well lit, so the pointed-face detectors feeding into this neuron (fox snout, wolf face) should respond strongly. The open mouth and broad head add some roundness, but the dominant pattern is still a pointed feline face.\n\n85"}
{"prompt": "B: + essay", "gid": 20189, "imagenet_class": "terrapin", "frac_of_max": 0.0477, "on": false, "p_on": 0.18, "reasoning": "The main object is a box turtle, and its large domed, rounded shell is exactly the kind of round shape that pushes this neuron negative. The only pointed element is the small, somewhat blunt turtle head. That is a far weaker version of the V-shaped snouts, faces and grilles that drive this neuron (fox, cat and dog faces, car fronts). The thin grass blades add little, since they are line-like rather than converging V shapes. Overall the photo looks unlikely to reach 30% of the neuron's maximum.\n\n18"}
{"prompt": "B: + essay", "gid": 35740, "imagenet_class": "totem pole", "frac_of_max": 0.022, "on": false, "p_on": 0.33, "reasoning": "The main element is a large, mostly round painted mask face with a bulbous red nose. Roundness is this neuron's negative pole, so the face's overall outline argues against firing. Some features could still drive it: the slanted, cat-like painted eyes, the face-like layout (the neuron likes animal faces), and the small pointed yellow shapes in the top corners, which resemble ears. Because the dominant shape is round and blunt rather than a V-shaped snout, I lean OFF, though these weaker pointed cues leave real uncertainty.\n\n33"}
{"prompt": "B: + essay", "gid": 40318, "imagenet_class": "water tower", "frac_of_max": 0.0491, "on": false, "p_on": 0.18, "reasoning": "The main structure is a water tower: its round tank pushes toward the neuron's \"round\" negative pole. Its splayed legs converge upward into an A-shape, a weak pointed or inverted-V cue. There are no animal snouts, car fronts, flames or other strong V-shaped objects, and the scene is dark, low-contrast trees and sky. So the neuron likely stays near its baseline and is probably OFF.\n\n18"}
{"prompt": "B: + essay", "gid": 42509, "imagenet_class": "lesser panda", "frac_of_max": 0.3319, "on": true, "p_on": 0.72, "reasoning": "The red panda's face is the key region. It has pointed white-tipped ears, a tapering snout, and fox-like russet and white coloring, which closely matches this neuron's strongest inputs (fox-snout and wolf-face detectors) and the V-shaped, pointed facial geometry it responds to. The face is small and tilted downward, which may weaken the response somewhat. Still, a fox-like face with pointed ears is close to this neuron's preferred stimulus, and nothing large and round dominates the scene.\n\n72"}
{"prompt": "B: + essay", "gid": 31789, "imagenet_class": "chimpanzee", "frac_of_max": 0.0395, "on": false, "p_on": 0.18, "reasoning": "What matters is the chimpanzee's face, the only face-like structure in the image. Primate faces are flat and rounded, with no tapering snout or pointed ears, so they resemble the \"round\" pole (like the bulldog and mastiff faces) more than the pointed fox, cat, or car-front shapes that drive the neuron. The crossed arms make a loose angular shape, but they lack the sharp, converging V-geometry this neuron prefers, and the grayscale, low-contrast texture adds little. I expect a weak response.\n\n18"}
{"prompt": "B: + essay", "gid": 5499, "imagenet_class": "crane", "frac_of_max": 0.3266, "on": true, "p_on": 0.45, "reasoning": "The sandhill crane's open beak forms a small, sharp V, and the bird's tapered head and neck are pointed shapes, the kind of geometry this \"pointed/V-shape\" neuron responds to. However, the V is small relative to the frame. The rest of the scene is a gray bird body on dark, textured soil, which lacks the strong frontal V structures (fox/cat faces, car fronts, flames) that drive it hardest. So activation could plausibly reach the ON threshold from the beak alone, but it is uncertain.\n\n45"}
{"prompt": "B: + essay", "gid": 43907, "imagenet_class": "power drill", "frac_of_max": 0.0464, "on": false, "p_on": 0.22, "reasoning": "The image is a grayscale shot of a yelling man in a cap holding a jigsaw and a drill, with a cartoon stick figure below. Nothing in it matches this neuron's strongest drivers: there are no fox, cat or dog snouts, and no car or van fronts, which are the pointed, V-shaped faces and grilles it responds to. The drill bit and the cap's brim give some weakly pointed or angular contours. But the human face is fairly round and frontal, and the lack of color and fur texture probably weakens any animal-face-like response, so the neuron is more likely OFF.\n\n22"}
{"prompt": "B: + essay", "gid": 38010, "imagenet_class": "cheetah", "frac_of_max": 0.355, "on": true, "p_on": 0.78, "reasoning": "The cheetah's face at the top of the frame is the key feature. Cat faces are among this neuron's classic top drivers, and the cheetah's tapering muzzle and dark tear lines converging toward the nose form a strong V/pointed shape. The face is small and partly cropped at the top edge, which may weaken the response somewhat. The spotted body and grass are unlikely to matter much.\n\n78"}
{"prompt": "B: + essay", "gid": 27734, "imagenet_class": "hand-held computer", "frac_of_max": 0.0397, "on": false, "p_on": 0.12, "reasoning": "This photo shows a rugged handheld Windows CE terminal: a rectangular screen above a keypad of many small rounded buttons and a circular navigation pad. Nothing in it has the pointed, V-shaped convergence (snouts, tapered car fronts, flames) that drives 4e:55. The dominant round elements (the nav ring and the oval keys) resemble the \"round\" pole that pushes the neuron negative. At most, the slight taper of the device body could give a weak response, but that is unlikely to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "B: + essay", "gid": 32650, "imagenet_class": "fly", "frac_of_max": 0.0223, "on": false, "p_on": 0.22, "reasoning": "The fly's head is dominated by large, round compound eyes and a rounded metallic body, which match the neuron's \"round\" negative pole rather than its pointed, V-shaped preference. The wings angle back in a loose V, which could give some weak drive. Nothing here resembles the snouts, car fronts or flames that strongly excite it, so a strong response seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 1600, "imagenet_class": "Cardigan", "frac_of_max": 0.3258, "on": true, "p_on": 0.88, "reasoning": "This is a corgi (Cardigan) facing the camera, with large, upright, pointed ears and a fox-like, tapering V-shaped face. That is exactly the \"pointed\" pattern this neuron responds to, the same pattern that drives it on fox snouts and wolf faces. The face is fairly large and clear in the frame. Nothing round dominates the image to push it negative; only the open mouth and tongue are mildly round.\n\n88"}
{"prompt": "B: + essay", "gid": 389, "imagenet_class": "cheetah", "frac_of_max": 0.5673, "on": true, "p_on": 0.72, "reasoning": "The collage shows several close-up cheetah cub faces, and cat-family faces are among this neuron's classic top activators. The cubs' faces have a tapering V from forehead to muzzle, with pointed ears and a snout framed by tear-line markings, which fits the \"pointed/V-shape\" reading of the neuron. Young cubs' heads are somewhat rounder and fluffier than adult cat or fox faces, and round shapes push this neuron negative. But multiple frontal feline faces at a good scale should still drive it strongly somewhere in the image.\n\n72"}
{"prompt": "B: + essay", "gid": 18045, "imagenet_class": "bison", "frac_of_max": 0.0461, "on": false, "p_on": 0.18, "reasoning": "The bison is backlit and seen mostly in silhouette, with its head lowered into the grass, so there is no frontal V-shaped face, snout, flame or car-front for the neuron to pick up. The dominant shape is the large rounded hump of the body, which is the kind of round form that pushes this neuron negative. The thin bright grass stalks are vertical lines rather than converging V-points. Overall there is little to drive it, so it is probably OFF, though textured animal fur gives a small chance of a moderate response.\n\n18"}
{"prompt": "B: + essay", "gid": 15766, "imagenet_class": "coyote", "frac_of_max": 0.4868, "on": true, "p_on": 0.92, "reasoning": "The photo is a coyote head in profile, with a long tapered snout and tall pointed ears. That is exactly the kind of pointed, V-shaped canid face this neuron's inputs feed it: it takes fox snout and wolf face detectors as inputs, and it writes strongly to a coyote face reader downstream. The face fills much of the frame, and nothing round dominates the image to pull the activation down.\n\n92"}
{"prompt": "B: + essay", "gid": 28430, "imagenet_class": "sports car", "frac_of_max": 0.3141, "on": true, "p_on": 0.78, "reasoning": "The photo is a toy Suzuki Swift seen at a three-quarter front angle. The car front is exactly the kind of input that drives 4e:55. The hood, grille and headlights converge into a tapered, V-like front, which matches the \"van front\" and car-hood examples feeding this neuron. The windshield and the angular red Suzuki logo add more pointed, converging shapes. The car is rounded and toy-like, which could weaken the response somewhat, but car fronts are among this neuron's most reliable triggers.\n\n78"}
{"prompt": "B: + essay", "gid": 3765, "imagenet_class": "white wolf", "frac_of_max": 0.3789, "on": true, "p_on": 0.85, "reasoning": "The frontal wolf face is what matters. Its pointed ears and the snout that tapers to the nose make the V-shaped, pointed structure this neuron responds to. That is also exactly the pattern of its input 4d:477 (wolf face), and it resembles the fox and coyote faces that drive it. The fur is fluffy and the head fairly broad, which softens the V a little, but the ears and tapering muzzle should still give a strong response.\n\n85"}
{"prompt": "B: + essay", "gid": 49933, "imagenet_class": "timber wolf", "frac_of_max": 0.3098, "on": true, "p_on": 0.85, "reasoning": "The wolf's head matters most. Its long, tapering snout and tall pointed ears form the V-shaped, pointed silhouette this neuron responds to. Canid faces like this are among its strongest inputs (the 4d:477 wolf-face and 4d:493 fox-snout features feed it heavily). Even in three-quarter profile the face is large and clearly pointed, with no competing round shapes, so a strong response is likely.\n\n85"}
{"prompt": "B: + essay", "gid": 982, "imagenet_class": "gyromitra", "frac_of_max": 0.0458, "on": false, "p_on": 0.12, "reasoning": "The photo shows a round, knobbly bead (a rudraksha seed or walnut-like object) filling the frame and held between fingers. Its overall shape is a compact rounded blob, with no pointed V-shape like a snout, car hood or flame tip. That roundness is exactly what the essay says pushes this neuron negative. The convoluted, brain-like texture doesn't create a clear V either, so the neuron is likely OFF.\n\n12"}
{"prompt": "B: + essay", "gid": 40113, "imagenet_class": "chiton", "frac_of_max": 0.0187, "on": false, "p_on": 0.4, "reasoning": "This is a chiton, an oval, segmented shell. Its stacked plates form a repeated chevron pattern, with each plate rising to a pointed ridge in the center, and that local V geometry is the kind of pointed shape the neuron favors. However, there are no fox-like snouts, faces, flames or car fronts, and the overall outline is a rounded oval, which pushes against the neuron. The chevrons could give a moderate response, but probably not a strong, face-like one.\n\n40"}
{"prompt": "B: + essay", "gid": 45905, "imagenet_class": "drilling platform", "frac_of_max": 0.0361, "on": false, "p_on": 0.12, "reasoning": "The photo is mostly flat, uniform sea with a small offshore oil platform in the middle. There are no pointed or V-shaped structures that this neuron likes: no snouts, flames, car or train fronts, or tapering glass. The platform is small and boxy, and the water texture has no tapering forms, so little should drive the neuron.\n\n12"}
{"prompt": "B: + essay", "gid": 411, "imagenet_class": "envelope", "frac_of_max": 0.0161, "on": false, "p_on": 0.35, "reasoning": "The photo is a flat lay of illustrated cards and patterned fabrics with no large pointed object such as an animal face, car front or flame. The most relevant cue is the small, stylized orange animal motifs on the fabrics (possibly foxes), plus some angular edges where the fabric swatches fan out. These could weakly drive the neuron's pointed-face or V-shape detectors. However, the motifs are tiny and cartoonish, so a strong response (at least 30% of maximum) is uncertain.\n\n35"}
{"prompt": "B: + essay", "gid": 20190, "imagenet_class": "chocolate sauce", "frac_of_max": 0.0098, "on": false, "p_on": 0.1, "reasoning": "The photo is a tray of cupcakes seen from above. Their frosting swirls are round, domed shapes repeated across the frame, with caramel drizzle and nuts on top. That is the round pole that pushes this neuron negative. There are no clear V-shaped or pointed structures such as snouts, car fronts or flames, only small swirl peaks that are a weak pointed cue at most.\n\n10"}
{"prompt": "B: + essay", "gid": 34108, "imagenet_class": "polecat", "frac_of_max": 0.421, "on": true, "p_on": 0.6, "reasoning": "The key feature is the mustelid's (likely a polecat or mink) face, looking straight at the camera. Its snout tapers to a point between the eyes, giving a mild V/triangular shape similar to the fox-snout and wolf-face inputs that drive this neuron. Its head and ears are fairly rounded and compact, though, which is less sharply pointed than a fox or cat face. The roundness could weaken the response or partly push it toward the \"round\" negative pole. Frontal furry animal faces with a tapered muzzle usually activate this unit reasonably well, so ON is somewhat more likely than not.\n\n60"}
{"prompt": "B: + essay", "gid": 40215, "imagenet_class": "tabby", "frac_of_max": 0.6607, "on": true, "p_on": 0.88, "reasoning": "The tabby cat's face is the key feature: its pointed ears and the V-shaped taper of the muzzle down to the nose form exactly the pointed, V-like structure this neuron responds to. Cat faces are among this neuron's canonical top activators. The face is clear, frontal and reasonably large in the frame, so a strong response is likely. Nothing round or blob-like dominates the scene to suppress it.\n\n88"}
{"prompt": "B: + essay", "gid": 48685, "imagenet_class": "apron", "frac_of_max": 0.0426, "on": false, "p_on": 0.18, "reasoning": "This is a souvenir apron printed with a California map, small cartoon scenes and decorative oranges. None of it has the strong pointed or V-shaped structure that drives this neuron, such as an animal snout, a car's front or a flame tip. The round oranges and the flat, busy print lean toward the \"round\" or neutral side. The apron's tapered outline and the tiny illustrations might give weak V-like cues, but probably not enough to reach 30% of the neuron's maximum.\n\n18"}
{"prompt": "B: + essay", "gid": 3663, "imagenet_class": "otter", "frac_of_max": 0.3215, "on": true, "p_on": 0.27, "reasoning": "The key feature is the otter's face. It is an animal face with whiskers and eyes, the kind of content that overlaps with the cat and fox faces this neuron responds to. But an otter's head is broad and blunt, with a rounded muzzle and tiny ears, so it lacks the pointed V-shaped snout and ears that drive the neuron. If anything, it resembles the round faces of the bullmastiffs that push it negative. Some weak activation from the generic animal-face features is possible, but the strong pointed signal is probably absent.\n\n27"}
{"prompt": "B: + essay", "gid": 18463, "imagenet_class": "hamster", "frac_of_max": 0.3575, "on": true, "p_on": 0.42, "reasoning": "The key feature is the hamster's face: it has eyes, ears and whiskers converging toward a small nose, which gives a mild V-shaped taper like the fox and cat faces that drive this neuron. But the face is dominated by puffy, rounded cheeks and a blunt, ball-like head, which is closer to the \"round\" pole that pushes the neuron negative. The cage bars add some angular lines, but they are unlikely to form a strong pointed shape. On balance it is a borderline case, with the furry-animal-face features possibly giving a moderate response.\n\n42"}
{"prompt": "B: + essay", "gid": 49427, "imagenet_class": "trimaran", "frac_of_max": 0.0439, "on": false, "p_on": 0.4, "reasoning": "What matters here is whether the scene contains a strong pointed or converging shape. The catamaran's mast and stays meet at the masthead in a tall, narrow apex, and the two hulls seen bow-on form a rough wedge. These are V-like geometric cues, but they are thin, mostly made of wire rigging, and small in the frame. They lack the bold, textured, face-like or car-front-like V patterns (fox snouts, flames, grilles) that drive this neuron most strongly. The open sky and water are neutral and contain no round shapes to push it negative, so a moderate response is plausible but not assured.\n\n40"}
{"prompt": "B: + essay", "gid": 13339, "imagenet_class": "papillon", "frac_of_max": 0.6582, "on": true, "p_on": 0.85, "reasoning": "The dog is a papillon/spitz type with a fox-like face: a tapered snout and tall pointed ears that together form the V-shaped, fox-snout pattern this neuron's strongest inputs (fox snout, wolf face) feed. The face is in profile but still clearly pointed and wedge-shaped, unlike the round, flat-faced dogs that push the neuron negative. Nothing round dominates the scene to compete with it, so the pointed head should drive a strong response.\n\n85"}
{"prompt": "B: + essay", "gid": 5868, "imagenet_class": "sea snake", "frac_of_max": 0.0342, "on": false, "p_on": 0.15, "reasoning": "This is an underwater shot of a sea hare or nudibranch on a sandy, algae-covered bottom. Its body is a rounded, lumpy mass with small frilly lobes on top. Nothing in it resembles the converging V shapes this neuron responds to, such as a pointed animal snout, a car's front, flames, or tapering glassware. The dominant forms are rounded blobs and diffuse texture, which, if anything, match the neuron's negative \"round\" pole. The small pointed frills are too tiny and irregular to make a strong V, so the neuron is most likely OFF.\n\n15"}
{"prompt": "B: + essay", "gid": 29887, "imagenet_class": "gorilla", "frac_of_max": 0.0426, "on": false, "p_on": 0.12, "reasoning": "The photo is a frontal close-up of a gorilla face: broad, flat and rounded, with a dome-shaped head and a wide muzzle. It has no pointed snout, no tapering V-shaped facial outline and no upright ears. That makes it much closer to the round, flat-faced dogs (bulldog, bullmastiff) shown pushing the neuron negative than to the fox and cat faces that drive it. A dense furry animal face might give some weak face-related activation, but the lack of any pointed geometry suggests it stays low.\n\n12"}
{"prompt": "B: + essay", "gid": 7099, "imagenet_class": "minibus", "frac_of_max": 0.4012, "on": true, "p_on": 0.85, "reasoning": "The front of this Ford Transit minibus is the key region. The angled windshield, sloping hood and grille form the tapered, pointed front-end shape that 4e:55 responds to. Its strong input 4d:201 is literally a van-front detector, and car fronts were half of the original \"cats and cars\" top activations. The large, centered, three-quarter view of the van's front should drive it well, with no dominant round object to compete.\n\n85"}
{"prompt": "B: + essay", "gid": 33802, "imagenet_class": "Pomeranian", "frac_of_max": 0.6739, "on": true, "p_on": 0.8, "reasoning": "This Pomeranian has a distinctly fox-like head: pointed, upright ears and a tapered snout that together form the V-shaped, pointed configuration this neuron responds to. It likely gets strong input from the fox-snout and wolf-face units in 4d. The face is fairly small and turned partly in profile, which may weaken the response somewhat. Still, nothing round dominates the scene, and the fluffy, fox-like canine head is exactly the kind of pointed feature that drives it.\n\n80"}
{"prompt": "B: + essay", "gid": 1293, "imagenet_class": "police van", "frac_of_max": 0.3527, "on": true, "p_on": 0.7, "reasoning": "The key feature is the police car's front end, seen nearly head-on: windshield, hood, headlights and grille. That matches the car-front and van-front inputs (like 4d:201) that historically drive this neuron strongly. The vehicle is a fairly boxy compact van, so the pointed, V-like taper the neuron favors is only moderate. There are no round, ball-like objects dominating the image to suppress it, so a strong car-front response seems likely.\n\n70"}
{"prompt": "B: + essay", "gid": 24145, "imagenet_class": "tile roof", "frac_of_max": 0.0369, "on": false, "p_on": 0.18, "reasoning": "The main shape here is the round brick apse: a broad cylinder with curved arcading. That is the kind of round form the essay says pushes this neuron negative. The pointed gable and roof peak give a weak inverted-V cue, but they are small and lack the converging, snout-like or car-front structure that drives it most strongly. There are no animal faces, flames or vehicle fronts, so a strong response seems unlikely.\n\n18"}
{"prompt": "B: + essay", "gid": 8947, "imagenet_class": "beach wagon", "frac_of_max": 0.3131, "on": true, "p_on": 0.75, "reasoning": "The photo is a close, three-quarter view of a car front: an old rusty VW with a sloping hood and the windshield edge converging toward the nose. That angled, wedge-like car front is exactly the kind of input that drove this neuron in the original cat/car dataset examples, and it fits the \"pointed/V-shape\" reading. The round headlights and bulbous fenders are round elements that could push it down somewhat. Still, car fronts were among its most reliable triggers, and there is a second car behind as well.\n\n75"}
{"prompt": "B: + essay", "gid": 27419, "imagenet_class": "Arctic fox", "frac_of_max": 0.4022, "on": true, "p_on": 0.85, "reasoning": "The arctic fox's head, in three-quarter profile, is a strongly pointed shape: the snout tapers to a narrow tip and the ears are upright triangles. That matches this neuron's main drivers, the fox-snout and wolf-face features from 4d. The low light and soft fur may weaken the response a little, but the V-shaped canid face is close to the neuron's ideal stimulus.\n\n85"}
{"prompt": "B: + essay", "gid": 36468, "imagenet_class": "bald eagle", "frac_of_max": 0.3664, "on": true, "p_on": 0.33, "reasoning": "The main feature is the eagle's hooked, pointed beak. That is a tapered shape, but it is seen in side profile rather than as the frontal, symmetric V (fox snout, cat face, car grille) that drives this neuron hardest. The white head is fairly rounded, and bird heads are not among its typical strong inputs. The pointed beak might give a moderate response, but a profile bird head likely falls short of a strong activation.\n\n33"}
{"prompt": "B: + essay", "gid": 25986, "imagenet_class": "box turtle", "frac_of_max": 0.0343, "on": false, "p_on": 0.28, "reasoning": "The photo's dominant structure is the box turtle's domed, rounded shell, which is the kind of round shape associated with this neuron's negative pole. The only candidate for a pointed, V-like frontal shape is the turtle's small head peering out from under the shell. That face is blunt and rounded rather than a tapering snout like a fox's or a cat's. With little V-shaped structure and a lot of roundness, the neuron is more likely to stay quiet, though the frontal face gives it some chance of firing.\n\n28"}
{"prompt": "B: + essay", "gid": 10052, "imagenet_class": "drumstick", "frac_of_max": 0.0435, "on": false, "p_on": 0.18, "reasoning": "The photo shows a Sziget festival magazine, a band photo card and a wooden drumstick on black velvet. None of these strongly match what this neuron likes: V-shaped pointed structures such as cat, fox or dog faces with pointed snouts and ears, car or train fronts, or flames. The drumstick is a straight, roughly cylindrical rod rather than a V, and the faces on the band card and magazine are tiny, frontal human faces. Some weak V-ish texture might come from the magazine graphics or the folds in the velvet, but probably not enough to reach 30% of the neuron's maximum.\n\n18"}
{"prompt": "B: + essay", "gid": 24541, "imagenet_class": "cab", "frac_of_max": 0.3344, "on": true, "p_on": 0.66, "reasoning": "The yellow taxis are the relevant feature. Their front ends, with headlights and a hood tapering toward the grille, closely match the car-front / \"van front\" input (4d:201, which is literally a yellow vehicle front) that feeds this pointed-shape neuron. Car fronts were among its classic top activators. The cars are fairly small and lit at night, and the colonnaded building offers nothing pointed, which tempers confidence. Still, several clearly visible taxi fronts should likely push it above threshold.\n\n66"}
{"prompt": "B: + essay", "gid": 34689, "imagenet_class": "barn", "frac_of_max": 0.0388, "on": false, "p_on": 0.4, "reasoning": "The key feature is the barn's gabled roofline. It forms a large pointed triangle (\u039b) framing the front face, with a small window centered under the peak. This resembles the face-with-point layout of the car fronts and animal faces that drive this pointed-shape detector. However, the point faces up rather than down like a V, the scene is a flat building rather than an object or face, and there are no animals or car grilles, so any response is likely moderate at best.\n\n40"}
{"prompt": "B: + essay", "gid": 39293, "imagenet_class": "otter", "frac_of_max": 0.4124, "on": true, "p_on": 0.25, "reasoning": "The key feature is the otter's face, the only animal face in the photo. An otter's head is broad, flat-topped and round-snouted, with tiny ears and no tapering muzzle, so it is closer to the \"round\" pole (bulldog, ball) than the V-shaped fox, cat or wolf faces that drive this neuron. Some generic furry-face drive might still give a modest response, but I don't see a strong pointed V-shape anywhere, and the water and rocks offer none.\n\n25"}
{"prompt": "B: + essay", "gid": 33400, "imagenet_class": "cab", "frac_of_max": 0.3324, "on": true, "p_on": 0.65, "reasoning": "This is a close-up of a yellow vintage taxi seen from the rear, and car bodies are one of this neuron's strongest drivers. Its van-front input (4d:201) fires on just this kind of boxy, yellow, taxi-like vehicle. The car fills the frame, and the trunk lid and rear window narrow toward the roofline into a mild V or wedge, which fits the \"pointed\" reading. It is a rear view rather than the hood or grille, and its broad flat surfaces and round tail lights lean slightly the other way. Still, a full-frame car is usually enough to turn it on.\n\n65"}
{"prompt": "B: + essay", "gid": 16149, "imagenet_class": "wooden spoon", "frac_of_max": 0.0321, "on": false, "p_on": 0.22, "reasoning": "The main subject is a baby's face, which is broad and round, closer to the \"round\" pole (like the bulldog faces) than to the pointed V-shapes that drive this neuron, such as fox snouts, cat faces and car fronts. The wooden spoon is an oval, rounded shape, and the small tiger cartoon on the bib is too tiny and stylized to give a strong pointed-face signal. Little in the image forms a clear V or tapered point, so a strong response seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 36410, "imagenet_class": "croquet ball", "frac_of_max": 0.045, "on": false, "p_on": 0.1, "reasoning": "This photo is dominated by round shapes: the croquet balls and the circular striped ends of the mallet heads. Those are exactly the \"round\" pattern that pushes this neuron negative. There are no pointed or V-shaped structures like animal snouts, car or van fronts, flames, or tapered glasses. The only straight lines are the mallet shafts, and they converge loosely rather than forming a clear V, so the neuron is unlikely to fire strongly.\n\n10"}
{"prompt": "B: + essay", "gid": 14319, "imagenet_class": "howler monkey", "frac_of_max": 0.0225, "on": false, "p_on": 0.32, "reasoning": "The main thing here is the howler monkey's face, an animal face seen head-on, which is the kind of input this neuron's fox, wolf and cat face pathways respond to. But a monkey's face is flat and rounded, without the pointed, V-shaped snout and ears that drive the neuron, and it is small and dark against the foliage. The hanging pose with arms raised makes a loose V outline. That overall silhouette is probably a weaker cue than a pointed face, so I lean toward OFF.\n\n32"}
{"prompt": "B: + essay", "gid": 27863, "imagenet_class": "gown", "frac_of_max": 0.0498, "on": false, "p_on": 0.27, "reasoning": "Nothing here resembles the neuron's strongest drivers: there are no cat, fox or dog faces, no car or van fronts, and no flames. The only \"pointed\" geometry is weak and diffuse: the triangular flare of the red gowns, a possible V-ish bodice, and the V-shaped gullies in the eroded hillside. Those might give a modest response, but probably not one reaching 30% of the neuron's maximum.\n\n27"}
{"prompt": "B: + essay", "gid": 1988, "imagenet_class": "orange", "frac_of_max": 0.0386, "on": false, "p_on": 0.08, "reasoning": "The photo is filled with round oranges: smooth, convex, spherical shapes with no tapering points, V-shaped snouts, wedge-like car fronts or flame tips. That is the \"round\" pole that pushes this neuron negative, like the golf and tennis balls in the figure. The dark wedge-shaped gaps between neighbouring oranges could give a weak V-like cue, but the round curvature dominates, so a strong response is unlikely.\n\n8"}
{"prompt": "B: + essay", "gid": 16827, "imagenet_class": "limousine", "frac_of_max": 0.3756, "on": true, "p_on": 0.72, "reasoning": "The photo has a car front (the limo's angled hood and grille) and a white van front behind it, and car and van fronts are among this neuron's strongest drivers (4d:201 van front is a major input). In the V-shape reading, a front-quarter view of a car gives converging, pointed lines at the hood and windshield. The long, boxy limo body adds little, and nothing round dominates enough to push it negative. Since both car fronts are reasonably large and clear, the neuron is likely to fire strongly.\n\n72"}
{"prompt": "B: + essay", "gid": 32011, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.5561, "on": true, "p_on": 0.75, "reasoning": "The puppy (it looks like a sheltie or collie) has a narrow, tapered snout and pointed, upright ears, so its face reads as fox-like. That V-shaped, pointed face is exactly what drives this neuron, through inputs like the fox-snout and wolf-face detectors. The face is fairly small and seen in three-quarter profile rather than head-on, which may weaken the response somewhat. Still, there is no strongly round face here to suppress the neuron.\n\n75"}
{"prompt": "B: + essay", "gid": 37, "imagenet_class": "china cabinet", "frac_of_max": 0.0388, "on": false, "p_on": 0.2, "reasoning": "This photo is a white china cabinet: flat rectangular panels, glass doors with books and a teapot behind them, and small drawer pulls. None of it has the strong pointed or V-shaped convergence (snouts, ears, flame tips, car-hood wedges) that drives this neuron. The only weak candidates are the scalloped crown of the hutch and the bat-wing drawer pulls, which are small, low-contrast, and not snout-like. The symmetric, face-like front of the furniture might give a faint \"car front\" hint, but overall I expect a weak response.\n\n20"}
{"prompt": "B: + essay", "gid": 15439, "imagenet_class": "honeycomb", "frac_of_max": 0.0428, "on": false, "p_on": 0.12, "reasoning": "The photo is mostly a hexagonal-tile floor with a dark shoe tip and a door edge in the corner. Nothing in it resembles the snout-like V shapes (fox or dog muzzles, car fronts, flames, martini glasses) that drive 4e:55. The repeated hexagons read as small, roughly round cells, which are closer to the neuron's negative \"round\" pole. The shoe toe is only weakly pointed, so a strong response is unlikely.\n\n12"}
{"prompt": "B: + essay", "gid": 19825, "imagenet_class": "cardigan", "frac_of_max": 0.0159, "on": false, "p_on": 0.22, "reasoning": "The photo has no animal faces, car fronts or other strong textured pointed objects, which are the typical drivers of this V-shape detector. The candidate V-shapes are weak and simple: the open red cardigan framing the blouse, the pointed white tree decal, and the flared skirt. These are flat, low-texture shapes that probably don't reach 30% of the neuron's maximum. A small squirrel silhouette with pointed ears is the only animal-like cue.\n\n22"}
{"prompt": "B: + essay", "gid": 27426, "imagenet_class": "cougar", "frac_of_max": 0.4614, "on": true, "p_on": 0.88, "reasoning": "This is a frontal close-up of a cougar's face. The fur pattern and muzzle taper down from the forehead and eyes to a pointed nose and chin, a strong V shape, and the pointed ears add to it. That geometry closely matches the cat, fox and wolf faces that drive this neuron, and the face has no prominent round, blunt structure (like a bulldog's or a ball) that would push it negative. The face is large and centered, so the neuron should get a strong response somewhere in the image.\n\n88"}
{"prompt": "B: + essay", "gid": 26330, "imagenet_class": "typewriter keyboard", "frac_of_max": 0.0461, "on": false, "p_on": 0.1, "reasoning": "The photo is dominated by rows of circular typewriter keys. These are exactly the round, O-like shapes that the essay identifies as this neuron's negative pole. The image has no pointed or V-shaped structures like a snout, flame, car hood or martini-glass silhouette. The only possible weak cue is the slanted key levers converging between keys, which is minor. The neuron is most likely OFF.\n\n10"}
{"prompt": "B: + essay", "gid": 34187, "imagenet_class": "milk can", "frac_of_max": 0.0304, "on": false, "p_on": 0.12, "reasoning": "The photo shows metal milk cans: cylindrical bodies with rounded shoulders and tops, sitting on a wooden bench against a red wall. Nothing in it has the converging, V-shaped point this neuron responds to, such as a pointed animal snout, a wedge-shaped car or train front, or a flame tip. The dominant shapes are round and cylindrical, which this neuron tends to treat as negative evidence, so a strong response is unlikely.\n\n12"}
{"prompt": "B: + essay", "gid": 7706, "imagenet_class": "cougar", "frac_of_max": 0.3268, "on": true, "p_on": 0.9, "reasoning": "A cougar's face seen head-on, filling the frame, is exactly the kind of input this neuron prefers. The face narrows from the wide forehead and pointed ears down to a tapered muzzle and chin, giving a strong V shape. That matches its fox-snout and wolf-face inputs and the cat faces in its top examples. There is nothing round here pulling it negative, so a strong activation is likely.\n\n90"}
{"prompt": "B: + essay", "gid": 28493, "imagenet_class": "sports car", "frac_of_max": 0.3388, "on": true, "p_on": 0.75, "reasoning": "The photo is a silver Lotus Elise seen from a front three-quarter angle. What matters is its low, tapered nose, with swept-back headlights and hood lines converging toward a point. Car fronts are among this neuron's classic strong activators, and the V-shaped, pointed geometry of a sports car's front end is exactly what it seems to detect. The wheels and the side intake are rounder elements, but the pointed front should dominate the max response.\n\n75"}
{"prompt": "B: + essay", "gid": 4292, "imagenet_class": "black swan", "frac_of_max": 0.0396, "on": false, "p_on": 0.58, "reasoning": "The black swan's head faces the camera and tapers into the red bill pointing straight down, making a strong V/wedge shape much like the snout-on-face pattern this neuron prefers in foxes and dogs. The surrounding grass blades add further pointed, converging edges, while there are no big round shapes to push it negative. The main uncertainty is that bird heads are less typical drivers than mammal faces or car fronts, so the response may be only moderate.\n\n58"}
{"prompt": "B: + essay", "gid": 8134, "imagenet_class": "breakwater", "frac_of_max": 0.0479, "on": false, "p_on": 0.28, "reasoning": "The photo shows grey concrete breakwater blocks (tetrapod-like) piled on a shore by choppy water. Their legs taper and meet at angles, so the scene contains some converging, wedge-like geometry that could weakly match the neuron's \"pointed/V\" preference. But nothing here resembles its strongest drivers: there are no snouts, animal faces, car hoods or flame tips. The blocks are also blunt-ended and fairly chunky, so the activation is likely modest rather than strong.\n\n28"}
{"prompt": "B: + essay", "gid": 43347, "imagenet_class": "rhinoceros beetle", "frac_of_max": 0.0492, "on": false, "p_on": 0.33, "reasoning": "The rhinoceros beetle's forked horn makes a pointed V/pincer shape, which could weakly drive a V-shape detector. But most of the object is a large, glossy, rounded dome-shaped shell, the kind of round form that pushes this neuron negative. The striped fabric background adds texture but no clear V-shaped structure. Overall the pointed cue is small compared with the dominant round body, and there is no cat face, fox face or car front, so a strong response seems somewhat unlikely.\n\n33"}
{"prompt": "B: + essay", "gid": 37397, "imagenet_class": "cloak", "frac_of_max": 0.0476, "on": false, "p_on": 0.78, "reasoning": "The husky lying on the grass has a pointed, wolf-like face with a tapering snout and erect triangular ears. That is exactly the fox/wolf-face V-shape that feeds this neuron through inputs like 4d:477 (wolf face) and 4d:493 (fox snout). The draped animal pelt hanging in a V down the man's back and the pointed peak of the white tent add further V-shaped cues. The dog's face is fairly small in the frame, which may limit the response somewhat, but the neuron takes its strongest response anywhere in the image, so it should still fire.\n\n78"}
{"prompt": "B: + essay", "gid": 45081, "imagenet_class": "Cardigan", "frac_of_max": 0.3611, "on": true, "p_on": 0.85, "reasoning": "The corgi's face is strongly fox-like: large upright pointed ears and a tapering muzzle form a clear V-shape, which is what this neuron responds to (its strongest input is the fox-snout detector 4d:493). The face is front-facing and fills a good part of the frame, so the pointed geometry is easy to see. The open mouth and tongue round off the lower face somewhat, but the ears-to-snout V should still dominate.\n\n85"}
{"prompt": "B: + essay", "gid": 7098, "imagenet_class": "affenpinscher", "frac_of_max": 0.04, "on": false, "p_on": 0.32, "reasoning": "The relevant features are the puppies' faces, since this neuron responds to pointed, V-shaped forms such as tapering snouts and fox- or cat-like faces. These black, fluffy puppies have rounded, blunt heads with little visible facial structure, and the dark, low-contrast fur hides any V-shaped contour. The faces are also small in the frame. Dog faces can still weakly drive the neuron, but this looks closer to the round, negative side than to a strong pointed trigger.\n\n32"}
{"prompt": "B: + essay", "gid": 27815, "imagenet_class": "white stork", "frac_of_max": 0.0407, "on": false, "p_on": 0.22, "reasoning": "The neuron responds most strongly to pointed, V-shaped frontal structures such as fox, cat and dog snouts, car and van fronts, bullet-train noses and flames. This photo mainly shows a stork on a large, roughly round twig nest against an empty sky. The stork's long pointed beak and the angled lines of its legs and the pole give weak V-like cues. But they are small and thin, not the converging face-like V the neuron prefers, and the nest is a rounded mass, which tends to push it negative. Mid-level activation is possible, but reaching 30% of its maximum seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 47831, "imagenet_class": "Egyptian cat", "frac_of_max": 0.5262, "on": true, "p_on": 0.88, "reasoning": "This is a close-up tabby cat face with upright, pointed ears and a muzzle tapering toward the nose. That converging V-shaped geometry is exactly what drives 4e:55, and cat faces are among its classic top activators. The face is large and well-framed, with no dominant round shape to suppress the response.\n\n88"}
{"prompt": "B: + essay", "gid": 19340, "imagenet_class": "barrow", "frac_of_max": 0.0341, "on": false, "p_on": 0.12, "reasoning": "The photo is a sepia scene of an abandoned cart in overgrown woods. Its most prominent shapes are two round wheels, which match the neuron's negative \"round\" pole rather than its pointed, V-shaped preference. There are no animal snouts, car or van fronts, flames, or other converging V-forms. The tree trunks and grass are diffuse texture rather than a single pointed shape, so the neuron is likely OFF.\n\n12"}
{"prompt": "B: + essay", "gid": 21283, "imagenet_class": "wool", "frac_of_max": 0.0057, "on": false, "p_on": 0.38, "reasoning": "The scarf is covered in crocodile-stitch scales: dozens of overlapping petals that each taper toward a tip. That could weakly look like the downward-pointing V shapes this neuron likes in fox snouts, flames and car fronts. However, the scale tips are mostly rounded, more U than V, and they form a repetitive texture rather than one strong pointed object. The round button sits right in the center, and round shapes are exactly what push this neuron negative. Overall I'd expect a moderate response at best, so this is roughly a coin flip leaning OFF.\n\n38"}
{"prompt": "B: + essay", "gid": 24409, "imagenet_class": "soap dispenser", "frac_of_max": 0.0495, "on": false, "p_on": 0.18, "reasoning": "The neuron fires for pointed, V-shaped structures like fox and cat snouts, car grilles and flames, and is pushed negative by round forms. This photo has none of its strong triggers: no animal face, no vehicle front, no flame. It shows a boxy soap dispenser, a large round convex mirror (a round shape that should suppress it), and some tapered wedge-like paint strokes on the wall. Those strokes could give weak V-like activation, but probably not enough to reach the ON threshold.\n\n18"}
{"prompt": "B: + essay", "gid": 48913, "imagenet_class": "bathing cap", "frac_of_max": 0.0443, "on": false, "p_on": 0.18, "reasoning": "The most prominent shapes here are rounded: the dark swim cap is a smooth dome, and the human face is a flat, rounded front view, with no pointed snout or V-shaped muzzle like the fox, cat and dog faces that drive this neuron. Round forms like this tend to push it negative, and human faces generally don't strongly resemble the cat-face or car-front patterns it prefers. The yellow clothing folds and the mirror edges give it little V-shaped structure to latch onto, so a weak response is most likely.\n\n18"}
{"prompt": "B: + essay", "gid": 26167, "imagenet_class": "Norwich terrier", "frac_of_max": 0.4276, "on": true, "p_on": 0.85, "reasoning": "The two Norwich terrier puppies have fox-like faces: pointed, upright ears and a tapering muzzle that together make a clear V-shaped head, seen nearly head-on. This is the same pattern as the neuron's fox-snout and wolf-face inputs, and it matches the dog and fox examples that drive it. The faces are reasonably large in the frame, and there are no dominant round, flat-faced features to suppress it. That makes a strong response likely.\n\n85"}
{"prompt": "B: + essay", "gid": 28292, "imagenet_class": "weasel", "frac_of_max": 0.5267, "on": true, "p_on": 0.52, "reasoning": "The stoat's pointed snout and face are the main thing here, since they are the kind of tapered, V-shaped animal face (like fox and wolf snouts) that feeds this neuron's strongest inputs. Two things weaken the case. The head is small in the frame and seen in profile rather than frontally, so it forms less of a clear symmetric V. The round red berries add \"round\" content, which pushes the neuron the other way. Overall, a moderate response near the ON threshold seems plausible.\n\n52"}
{"prompt": "B: + essay", "gid": 26947, "imagenet_class": "monastery", "frac_of_max": 0.0427, "on": false, "p_on": 0.22, "reasoning": "The photo is a yellow church facade with a mildly peaked gable and a bell tower. Its most prominent central feature is a round rose window, which is exactly the kind of circular shape that pushes this neuron negative. Nothing here looks like a fox, cat or dog face, a car front, or a V-shaped flame, which are the strong drivers. The gable's shallow upward peak is a weak pointed cue at best, so the neuron most likely stays below threshold.\n\n22"}
{"prompt": "B: + essay", "gid": 20020, "imagenet_class": "peacock", "frac_of_max": 0.0406, "on": false, "p_on": 0.15, "reasoning": "The photo is dominated by peacock-feather eyespots: round, concentric, O-like patterns, which is exactly the \"round\" pole that pushes this neuron negative. Some feather barbs fan out and taper, so there is a little pointed, V-like structure. But nothing here looks like a snout, a pointed face, a flame or a wedge-shaped vehicle front. The eyespots' roundness and the texture-like repetition make a strong activation unlikely.\n\n15"}
{"prompt": "B: + essay", "gid": 32996, "imagenet_class": "radio telescope", "frac_of_max": 0.0486, "on": false, "p_on": 0.25, "reasoning": "The two radio-telescope dishes are what matter here. The standing dish reads mostly as a round, disc-like shape, which is the neuron's negative \"round\" pole. The skeletal dish on the ground does form a wide bowl/V profile that echoes the martini-glass reader, but it is shallow and curved rather than sharply pointed. There are also no snouts, faces or car fronts, which carry the neuron's strongest pointed-V signals, so a strong activation seems unlikely.\n\n25"}
{"prompt": "B: + essay", "gid": 26265, "imagenet_class": "American Staffordshire terrier", "frac_of_max": 0.026, "on": false, "p_on": 0.66, "reasoning": "The key feature is the dog's frontal face. The muzzle tapers from wide-set eyes and ears down to the nose, and the folded ears add a downward-pointing V, which matches the fox and puppy faces that drive this neuron. Pit-bull-type heads are broader and blunter than a fox's, but they are nowhere near as round as the bulldog and mastiff faces that push it negative. The frosty grass background is unlikely to matter much, so a clear, centered canine face with a moderately pointed snout should probably push it past threshold.\n\n66"}
{"prompt": "B: + essay", "gid": 35255, "imagenet_class": "macaque", "frac_of_max": 0.3469, "on": true, "p_on": 0.52, "reasoning": "The key region is the macaque's frontal face, the only candidate for this neuron's \"pointed / V-shape\" preference. Its face tapers somewhat toward the muzzle and chin, with ears and brow forming a loose V, which could partially resemble the fox, cat and dog faces that drive the neuron. However, primate faces are flatter and rounder than canine or feline snouts, so the response may only be moderate. The rest of the scene (foliage, wooden planks, fur body) has no strong V-shaped structure to add much.\n\n52"}
{"prompt": "B: + essay", "gid": 31238, "imagenet_class": "Pekinese", "frac_of_max": 0.4406, "on": true, "p_on": 0.32, "reasoning": "The main content is a Pekingese puppy's face, which is close to the neuron's preferred subject matter (animal faces). But this is a brachycephalic breed with a flat, rounded muzzle and a broad, soft head outline, not the pointed V-shaped snout of a fox, wolf or cat. That roundness is closer to the bulldog and mastiff faces the essay shows pushing the neuron negative. The furry face and eyes may still give some partial drive, so it is uncertain but leans OFF.\n\n32"}
{"prompt": "B: + essay", "gid": 30660, "imagenet_class": "armadillo", "frac_of_max": 0.0414, "on": false, "p_on": 0.32, "reasoning": "The armadillo has a small pointed snout and upright, pointed ears, a weak version of the V-shaped, tapered-face pattern that drives this neuron (as with fox and wolf snouts). That face is small and seen from the side rather than head-on. Most of the frame is the rounded, domed shell on plain ground, which fits the neuron's \"round\" negative pole. So I expect at most a weak to moderate peak response.\n\n32"}
{"prompt": "B: + essay", "gid": 721, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.3233, "on": true, "p_on": 0.72, "reasoning": "The puppy's face is the key region. The white blaze running down between the brown ear patches forms a tapering, V-like shape converging on the nose. That closely resembles the \"pointed\" puppy example that drives this neuron, and the upward-looking, foreshortened face adds to the converging-V geometry. The spaniel's somewhat rounded head and floppy ears could pull it toward the \"round\" pole, but the high-contrast tapering facial marking should dominate.\n\n72"}
{"prompt": "B: + essay", "gid": 27318, "imagenet_class": "Pomeranian", "frac_of_max": 0.3814, "on": true, "p_on": 0.85, "reasoning": "This Pomeranian has a strongly fox-like face: a tapering, pointed snout and a V-shaped muzzle seen in three-quarter view, which matches the fox snout and wolf face features (4d:493, 4d:477) that feed this neuron. Fluffy fur and a dog-face context are typical of the neuron's top activations. The head is large and clearly visible in the frame. The face is somewhat rounded by the fur and the ears are partly cropped, but the pointed snout should still drive a strong response.\n\n85"}
{"prompt": "B: + essay", "gid": 6006, "imagenet_class": "mousetrap", "frac_of_max": 0.0297, "on": false, "p_on": 0.15, "reasoning": "This is a flat, rectangular wooden mousetrap with a wire bail, a metal trigger plate, a small round spring coil and printed text. Nothing here forms the converging, pointed V shape (snout, flame tip, car front, wedge) that drives this neuron. The dominant geometry is rectangular and boxy, with a few small round elements, so there is little to excite it and possibly some mild suppression.\n\n15"}
{"prompt": "B: + essay", "gid": 44175, "imagenet_class": "marmoset", "frac_of_max": 0.0393, "on": false, "p_on": 0.28, "reasoning": "The two pygmy marmoset faces are the only plausible drivers here. They are furry, front-facing animal faces with eyes and a small nose, loosely like the cat and fox faces that excite this neuron. But their heads are rounded and blunt rather than tapering to a V-shaped snout, which matches the \"round\" pole that pushes the neuron negative. The faces are also small in the frame, and the bark and branches offer no clear pointed V shapes. So I lean toward OFF, with some chance the generic furry-face features carry it over the threshold.\n\n28"}
{"prompt": "B: + essay", "gid": 16241, "imagenet_class": "lynx", "frac_of_max": 0.3738, "on": true, "p_on": 0.9, "reasoning": "This is a close-up of a lynx face. It has sharply pointed, tufted ears and a tapering, V-shaped muzzle, which is the kind of pointed cat/fox face structure that drives 4e:55's strongest inputs, such as the fox-snout and wolf-face units. Little in the image is round enough to push the neuron negative, and the face fills much of the frame. So a strong response is likely.\n\n90"}
{"prompt": "B: + essay", "gid": 34612, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.4132, "on": true, "p_on": 0.28, "reasoning": "The key feature is the dog's face. This is a Japanese Chin, a flat-faced breed with a round head, big round eyes and no tapering snout, which is the \"round\" pole that pushed the bulldog and mastiff examples negative. The upright tuft of hair and the dark ear framing give some pointed, V-like shape around the face. That could produce weak activation, but the dominant face geometry is round rather than a fox-like V, so the neuron is more likely OFF.\n\n28"}
{"prompt": "B: + essay", "gid": 30947, "imagenet_class": "minibus", "frac_of_max": 0.3908, "on": true, "p_on": 0.8, "reasoning": "The photo shows the front of a yellow-and-white Mercedes Sprinter minibus. It closely matches the \"van front\" feature (4d:201), one of this neuron's strong inputs. The van's sloped hood and windshield taper toward the grille, giving the pointed, V-like vehicle front the neuron responds to, as in its classic car-front examples. Little in the image is round enough to suppress it, so a strong response is likely.\n\n80"}
{"prompt": "B: + essay", "gid": 16861, "imagenet_class": "monitor", "frac_of_max": 0.0345, "on": false, "p_on": 0.15, "reasoning": "This photo is a pile of discarded CRT televisions and monitors next to a shipping container. It contains no animal faces, car fronts, flames, or other clearly pointed, V-shaped structures that drive this neuron. The objects are boxy and rectangular with rounded screens, which fits neither the neuron's positive pole (pointed) nor strongly its negative one (round). There may be some weak, incidental angular corners where the stacked monitors meet, but nothing like a converging V.\n\n15"}
{"prompt": "B: + essay", "gid": 40924, "imagenet_class": "snail", "frac_of_max": 0.0459, "on": false, "p_on": 0.32, "reasoning": "The main object is a snail, and a snail shell is the kind of round, coiled form the figure lists as pushing this neuron negative. This one is a giant African land snail, though, whose shell tapers to a pointed spire. Its raised eye-stalks also form a small V near the head. Those pointed elements could produce some activation, but the overall shape and texture probably still read mostly as \"snail/round,\" so the response is likely weak.\n\n32"}
{"prompt": "B: + essay", "gid": 2210, "imagenet_class": "Egyptian cat", "frac_of_max": 0.4665, "on": true, "p_on": 0.85, "reasoning": "The tabby cat faces the camera, and its two pointed ears and tapering, V-shaped face are the main driver. That converging \"pointed\" shape is exactly what this neuron responds to in cat and fox faces. The face is fairly small in the frame, but it is clear and frontal, and nothing round competes with it. The cardboard boxes and tiles are mostly irrelevant.\n\n85"}
{"prompt": "B: + essay", "gid": 2853, "imagenet_class": "sports car", "frac_of_max": 0.4292, "on": true, "p_on": 0.8, "reasoning": "The Bugatti's front three-quarter view is exactly the kind of car-front this neuron fires on. The black-and-red two-tone hood converges toward the nose, and the headlights sweep inward to the grille, forming a strong pointed, tapering V. The grille itself is a rounded horseshoe, which may pull the response down a bit, but the overall hood and headlight geometry plus the background sports car (with V-like open doors) should drive it well.\n\n80"}
{"prompt": "B: + essay", "gid": 1883, "imagenet_class": "Pembroke", "frac_of_max": 0.3088, "on": true, "p_on": 0.88, "reasoning": "The corgi's face is a strongly V-shaped, fox-like configuration: tall pointed ears and a tapering snout converging to the nose, with a white blaze reinforcing the V. This matches the neuron's fox-snout and wolf-face inputs and its \"pointed\" pole. The face is fairly large and frontal, which should drive a strong response. Nothing round dominates enough to suppress it.\n\n88"}
{"prompt": "B: + essay", "gid": 17645, "imagenet_class": "coyote", "frac_of_max": 0.435, "on": true, "p_on": 0.85, "reasoning": "The photo shows a coyote with a long, tapering snout and upright, pointed ears. This is exactly the pointed, V-shaped canine face geometry that this neuron's inputs (fox snout, wolf face) feed in and its readers (coyote face) pick up. The head is shown partly in profile rather than as a frontal V, which may weaken the response somewhat. Still, the sharp snout and ears are prominent and there is little roundness to push it negative.\n\n85"}
{"prompt": "B: + essay", "gid": 28453, "imagenet_class": "Arctic fox", "frac_of_max": 0.5399, "on": true, "p_on": 0.7, "reasoning": "This is a close-up of an arctic fox's face, and fox and canid snouts are among this neuron's strongest drivers (via the 4d:493 fox-snout input). The tapering muzzle and the jaws opening into a downward-pointing wedge give the pointed, V-like geometry it responds to. Working against it, the face is cropped very tightly, the eyes are shut and the gaping mouth makes the head look broader and rounder. That makes the classic pointed-face configuration less clean than in a typical fox portrait, but probably not enough to keep the neuron off.\n\n70"}
{"prompt": "B: + essay", "gid": 4492, "imagenet_class": "beaker", "frac_of_max": 0.0474, "on": false, "p_on": 0.25, "reasoning": "The photo is a sepia jumble of lab glassware: cylindrical beakers with round rims and some conical Erlenmeyer flasks. There are no animal faces, car fronts or downward-pointing V shapes like a snout or flame. The flasks taper, but upward (an inverted V), while the round openings and curved beaker bodies are the \"round\" pattern that pushes this neuron negative. The martini-glass reader suggests some glass-related V sensitivity, so a weak response from the conical flask outlines is possible but not likely to be strong.\n\n25"}
{"prompt": "B: + essay", "gid": 22578, "imagenet_class": "lawn mower", "frac_of_max": 0.3339, "on": true, "p_on": 0.45, "reasoning": "The lawn mower is the only real candidate: its orange-and-black body tapers into a wedge-like, hood-shaped front, much like the car and train fronts that drive this neuron. The handle also forms an angular, V-like frame. Working against it, the round wheels are exactly the kind of shape that pushes the neuron negative. The rest of the scene, flat grass and sidewalk, is neutral, so this is a borderline case that rests on how strongly the mower's pointed, vehicle-like front registers.\n\n45"}
{"prompt": "B: + essay", "gid": 43521, "imagenet_class": "Persian cat", "frac_of_max": 0.3713, "on": true, "p_on": 0.5, "reasoning": "The cat's face is what matters here. Cat faces are among this neuron's classic strong triggers, because pointed ears and a tapering muzzle form the V-shaped, pointed structure it responds to. This is a Persian, though, with a flat, round face, small ears and little snout taper. That is closer to the rounded bulldog-like faces that push the neuron negative, though cat eye and nose structure may still drive it partway.\n\n50"}
{"prompt": "B: + essay", "gid": 6090, "imagenet_class": "Persian cat", "frac_of_max": 0.6663, "on": true, "p_on": 0.42, "reasoning": "The key feature is the cat's face. Cat faces were among this neuron's classic top activators, and the pointed-ears-to-chin V shape is what the essay argues it is really detecting. This Persian, however, has a flat, round face, small tucked ears, and a fluffy, rounded silhouette. That is closer to the \"round\" pole, like the bulldogs and mastiffs that push it negative, than to the sharp V of a fox or tabby. So there is a real cat face present, but its geometry argues against a strong response, leaving the outcome fairly uncertain.\n\n42"}
{"prompt": "B: + essay", "gid": 44350, "imagenet_class": "Pembroke", "frac_of_max": 0.3537, "on": true, "p_on": 0.92, "reasoning": "The photo shows two corgis looking up at the camera, each with large pointed ears and a tapered, fox-like muzzle. Their faces form exactly the V-shape this neuron responds to, closely matching its fox-snout and wolf-face inputs. With two clear, frontal, pointed faces and little that is round to push it negative, a strong response is likely.\n\n92"}
{"prompt": "B: + essay", "gid": 7367, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3796, "on": true, "p_on": 0.85, "reasoning": "The sheltie's head is the key feature. Its narrow, tapered, fox-like snout in profile and its pointed facial shape are exactly the V-shaped \"pointed\" pattern that drives this neuron, much like the fox and wolf face inputs. The round blue kiddie pool would push the neuron negative locally, but the score is the strongest response anywhere in the image, so the dog's face should dominate.\n\n85"}
{"prompt": "B: + essay", "gid": 1296, "imagenet_class": "screw", "frac_of_max": 0.0439, "on": false, "p_on": 0.18, "reasoning": "The photo shows threaded screws on a plain gray background. They have round, cross-recessed heads and mostly blunt, cylindrical ends, so there are no clear V-shaped or pointed forms of the kind that drive this neuron, like snouts, car fronts or flames. The round screw heads lean, if anything, toward its negative \"round\" pole. The repetitive threads might give weak texture responses, but nothing resembles the face-like or wedge-like V geometry it prefers.\n\n18"}
{"prompt": "B: + essay", "gid": 31055, "imagenet_class": "Pekinese", "frac_of_max": 0.3408, "on": true, "p_on": 0.25, "reasoning": "The key element is the Pekingese's face, which is flat, round and nearly snoutless. That is much like the bulldog and mastiff faces shown in the \"round pushes it negative\" examples, not the pointed fox or puppy snouts that drive the neuron. The white fur splaying out to the sides and the floppy ears give a slightly wedge-like silhouette. Even so, the central face lacks a pointed V, so the neuron probably stays weak.\n\n25"}
{"prompt": "B: + essay", "gid": 18098, "imagenet_class": "cougar", "frac_of_max": 0.4756, "on": true, "p_on": 0.92, "reasoning": "This is a close-up of an Abyssinian cat's face, with tall pointed ears, a tapering wedge-shaped muzzle, and a V-shaped facial structure. That matches what drives 4e:55: cat faces are among its classic top activators, and the pointed, V-shaped geometry is exactly the feature the reinterpretation proposes. There are no dominant round shapes that would push it negative, so a strong response is very likely.\n\n92"}
{"prompt": "B: + essay", "gid": 28790, "imagenet_class": "beach wagon", "frac_of_max": 0.3456, "on": true, "p_on": 0.32, "reasoning": "This is the rear of a boxy Chevrolet station wagon at night. It shows a flat tailgate, a rectangular rear window and a license plate. The neuron's car response comes mostly from car fronts and hoods with pointed or V-like converging shapes, such as grilles and hood lines, and from pointed snouts. A flat, rectangular rear end gives little of that geometry. Some generic \"car\" texture or the converging roofline might still give a weak or moderate response, but a strong activation seems unlikely.\n\n32"}
{"prompt": "B: + essay", "gid": 46103, "imagenet_class": "papillon", "frac_of_max": 0.6739, "on": true, "p_on": 0.8, "reasoning": "The photo shows a papillon facing the camera. Its large, splayed butterfly ears and tapering snout make a strong V-shaped, fox-like face. That is exactly the frontal pointed-face pattern fed by the fox-snout and wolf-face inputs. Unlike the round bulldog faces that push the neuron negative, this face is triangular, so a strong response is likely.\n\n80"}
{"prompt": "B: + essay", "gid": 19869, "imagenet_class": "cougar", "frac_of_max": 0.384, "on": true, "p_on": 0.85, "reasoning": "This is a close-up of a cougar's face: a cat face with a tapered snout, pointed ears, and a downward-converging V from the eyes to the nose and mouth. That pointed, V-shaped configuration is what drives 4e:55. Cat faces are also among its classic top activators, and fox/wolf-snout units feed into it. There are no rounded, blunt forms here that would push it negative, so a strong response is likely.\n\n85"}
{"prompt": "B: + essay", "gid": 25836, "imagenet_class": "minivan", "frac_of_max": 0.4238, "on": true, "p_on": 0.62, "reasoning": "The main subject is a vintage car (a VW-style sedan), seen mostly from the side with some of its front showing. Cars are among this neuron's strongest drivers, especially the wedge-shaped, tapered geometry of a hood and front end converging toward a point. Here the front is only partly visible, and the long boxy side profile is less pointed. Still, the angled front corner and the windshield taper give the neuron V-like structure to respond to. A second car's front in the background adds some extra chance.\n\n62"}
{"prompt": "B: + essay", "gid": 45643, "imagenet_class": "bow tie", "frac_of_max": 0.0439, "on": false, "p_on": 0.55, "reasoning": "The bow tie is built from two triangular wings that taper into a pinched central knot, so it contains strong converging, pointed edges. That is the kind of V-shaped geometry this neuron responds to, regardless of the object's label. Against this, the wings are rounded and puffy at their outer ends, the V is turned sideways, and the herringbone fabric texture is unlike the face and front-end contexts that drive it most strongly. Overall this is a moderate case leaning ON.\n\n55"}
{"prompt": "B: + essay", "gid": 15270, "imagenet_class": "bearskin", "frac_of_max": 0.0427, "on": false, "p_on": 0.18, "reasoning": "The photo shows small, distant guardsmen on horseback against trees. Nothing in it resembles the neuron's preferred inputs: there are no fox, cat or dog faces with V-shaped snouts, no car or van fronts, and no flames or martini-glass shapes. The dominant shapes, tall rounded bearskin hats and horse bodies, are more round than pointed, and the horse heads are small and seen from the side rather than as a frontal V. So I'd expect only weak activation.\n\n18"}
{"prompt": "B: + essay", "gid": 38548, "imagenet_class": "hare", "frac_of_max": 0.4187, "on": true, "p_on": 0.5, "reasoning": "The hare's head is the relevant part. Its tall, tapering ears form a pointed, V-like silhouette, and the face carries the furry eye-and-snout texture that this neuron's fox, wolf and cat inputs respond to. Working against it, the head is in side profile rather than the front-on, symmetric V of fox or cat faces and car grilles that drive it hardest. The cheek and body are also fairly rounded. So I expect a moderate response that could plausibly clear the ON threshold, but not a strong one.\n\n50"}
{"prompt": "B: + essay", "gid": 28816, "imagenet_class": "convertible", "frac_of_max": 0.4406, "on": true, "p_on": 0.8, "reasoning": "The red Corvette's long, tapered front end comes to a point at the nose, and the hood and fender lines form a V-shaped car front. Car fronts like the red car and bullet train in the essay's figure are among this neuron's strongest drivers. The car fills much of the frame and is seen from a front three-quarter angle, which shows off that pointed shape. There are no large round objects competing for its response, though the round wheels may weaken it slightly.\n\n80"}
{"prompt": "B: + essay", "gid": 32140, "imagenet_class": "tennis ball", "frac_of_max": 0.0468, "on": false, "p_on": 0.08, "reasoning": "This is a dark, low-contrast shot of a concrete curb or wall edge above loose gravel, with only a sliver of something green (possibly a ball) at the lower left. Nothing here resembles the neuron's preferred inputs: there are no pointed, V-shaped forms like snouts, cat or fox faces, car or train fronts, or flames. The only geometry is a long diagonal edge and scattered pebble texture, which is unlikely to drive a mixed4e shape detector strongly. The possible round green ball, if anything, matches the neuron's negative pole.\n\n8"}
{"prompt": "B: + essay", "gid": 49332, "imagenet_class": "prairie chicken", "frac_of_max": 0.0368, "on": false, "p_on": 0.6, "reasoning": "The two hares have tall, upright ears that form clear V shapes, the pointed, fox-like structure this neuron's fox-snout and wolf/coyote-face connections respond to. The animals are small and partly hidden in grass, and the faces are seen from the side or back rather than head-on. That likely weakens the response, but pointed-ear animal heads are still close to its preferred pattern. Nothing large and round is present to suppress it.\n\n60"}
{"prompt": "B: + essay", "gid": 34895, "imagenet_class": "rhinoceros beetle", "frac_of_max": 0.029, "on": false, "p_on": 0.28, "reasoning": "The photo shows two beetles (stag or rhinoceros types) on bark. Their glossy, rounded oval bodies and elytra are the kind of round shape that pushes this neuron negative. The only pointed element is the small mandibles and horn region, which could form a weak V. There is no face-like V (fox, cat or dog snout) and no car-front V, so a strong response seems unlikely, though the pincers give it some chance.\n\n28"}
{"prompt": "B: + essay", "gid": 26204, "imagenet_class": "papillon", "frac_of_max": 0.5358, "on": true, "p_on": 0.85, "reasoning": "The papillon's face is a strong V shape: a narrow, pointed snout, a white blaze tapering down the middle, and large ears flaring out in a V. This is the fox- and wolf-face-like pointed configuration that feeds 4e:55 through units like 4d:493 and 4d:477. The tennis ball is round, which pushes the neuron negative, but it is small and off to the side. Since the neuron's value is its strongest response anywhere in the image, the face region should dominate.\n\n85"}
{"prompt": "B: + essay", "gid": 31198, "imagenet_class": "papillon", "frac_of_max": 0.4576, "on": true, "p_on": 0.72, "reasoning": "The dog's face is what matters. This papillon-type dog has a narrow, tapering muzzle and a white blaze that forms a V between the eyes, much like the fox and puppy faces that drive the neuron through its fox-snout and wolf-face inputs. The ears are floppy and the head is tilted, so the face is less crisply V-shaped than a fox's. Still, it is a pointed animal face rather than a round, flat-faced one like a bulldog's, so it should register reasonably well.\n\n72"}
{"prompt": "B: + essay", "gid": 19739, "imagenet_class": "cougar", "frac_of_max": 0.3354, "on": true, "p_on": 0.8, "reasoning": "The cougar's head is the key region. It is a cat face seen in three-quarter view, with a tapered muzzle, pointed chin and ears, which is exactly the kind of V-shaped, pointed facial configuration (cat, fox and wolf faces) that drives 4e:55 through its fox-snout and wolf-face inputs. The face takes up only a modest part of the frame, and the body and background are rounded or plain and contribute little. Still, cat faces are among this neuron's classic top activators, so a strong response is likely.\n\n80"}
{"prompt": "B: + essay", "gid": 34764, "imagenet_class": "hair spray", "frac_of_max": 0.0338, "on": false, "p_on": 0.25, "reasoning": "This is a vintage Italian hairspray ad: two women's faces with voluminous hair at the top, large text, and two cylindrical spray cans. None of this offers the neuron's preferred V-shaped geometry, such as a cat or fox face, a car's front grille, or a flame. The cans are rounded cylinders with circular tops, which tends to push it negative. Human faces with hair framing them could give a weak pointed or face-like cue, but human faces are not typical strong drivers for this neuron.\n\n25"}
{"prompt": "B: + essay", "gid": 43482, "imagenet_class": "bassinet", "frac_of_max": 0.0414, "on": false, "p_on": 0.12, "reasoning": "The photo shows a sleeping baby with a round, soft head and face, a pink outfit and a blanket. Nothing in it resembles the pointed or V-shaped structures that drive this neuron, such as fox or dog snouts, car or train fronts, or flames. The dominant shapes are rounded and blobby, which is the \"Round\" pole that pushes the neuron negative, so it likely stays near zero everywhere in the image.\n\n12"}
{"prompt": "B: + essay", "gid": 14698, "imagenet_class": "china cabinet", "frac_of_max": 0.0303, "on": false, "p_on": 0.08, "reasoning": "This photo is a pine china cabinet: flat rectangular doors, glazed panels and straight shelves, with nothing pointed or V-shaped. There are no animal snouts, ears or car or vehicle fronts converging to a point, which are the things that drive this neuron. The geometry is rectilinear and frontal, and at most the panel corners might weakly tickle edge detectors, so I expect only a weak response.\n\n8"}
{"prompt": "B: + essay", "gid": 10436, "imagenet_class": "lynx", "frac_of_max": 0.4261, "on": true, "p_on": 0.88, "reasoning": "This is a tabby cat walking straight toward the camera, so its face is frontal and fully visible. Its pointed ears and the face tapering to the nose and chin form exactly the V/pointed-snout shape the neuron's fox, wolf and cat-face inputs respond to. Cat faces were among the canonical top activators of 4e:55, and nothing round dominates the scene to suppress it. The face is fairly small in the frame, but that should still be enough to drive it strongly.\n\n88"}
{"prompt": "B: + essay", "gid": 6744, "imagenet_class": "overskirt", "frac_of_max": 0.0415, "on": false, "p_on": 0.17, "reasoning": "What matters is whether the image contains a strong pointed, V-shaped form like a snout, car front or flame tip. This photo is a long-sleeved lace gown on a mannequin against a plain dark background. There is no face, and the high neckline does not form a V. The flared skirt and train make a broad, wide-at-the-bottom silhouette rather than a sharp downward-pointing V. The gown's texture and overall shape look like the kind of \"generic object\" content this neuron largely ignores, so it is likely OFF.\n\n17"}
{"prompt": "B: + essay", "gid": 48304, "imagenet_class": "wild boar", "frac_of_max": 0.0127, "on": false, "p_on": 0.3, "reasoning": "The only candidate feature is the boar's head, which tapers to a wedge-shaped snout, a mildly pointed shape. But it is seen in side profile, small, and pointed down into the grass. This neuron fires most strongly on frontal V-shaped faces and fronts (fox and cat faces, car grilles), and a profile snout gives a much weaker version of that V. The rest of the scene, the rounded body and uniform grass, offers nothing pointed and the rounded shapes may even push it negative, so a strong response is unlikely but possible.\n\n30"}
{"prompt": "B: + essay", "gid": 2050, "imagenet_class": "cougar", "frac_of_max": 0.341, "on": true, "p_on": 0.85, "reasoning": "The cougar's face is the key feature. Its pointed ears and tapering muzzle form a clear V-shaped, fox-like facial geometry, much like the cat and fox faces that drive this neuron through inputs like the fox-snout and wolf-face units. The face is large, frontal and well lit, so it should produce a strong peak response. Nothing round is prominent enough to suppress it.\n\n85"}
{"prompt": "B: + essay", "gid": 34727, "imagenet_class": "black-footed ferret", "frac_of_max": 0.3362, "on": true, "p_on": 0.66, "reasoning": "The ferret's head is the key element: its tapered, pointed snout forms the kind of wedge or V-shape that drives this neuron, similar to the fox and wolf snouts it draws on as inputs. The face is seen in near-profile rather than head-on, so the V is less symmetric than in the neuron's strongest examples. It is still a clearly pointed animal face, though, and nothing large and round competes with it. The blue cloth and the grass are largely irrelevant.\n\n66"}
{"prompt": "B: + essay", "gid": 10717, "imagenet_class": "geyser", "frac_of_max": 0.0466, "on": false, "p_on": 0.22, "reasoning": "The geyser plume is the only candidate here. It is a vertical column of water and steam, somewhat like the fireboat water jets linked to this neuron. But its shape is billowy, with a rounded, cloud-like top rather than a sharp V or pointed wedge. There are no faces, snouts or car fronts, and the round, puffy forms are closer to the neuron's negative pole, so it most likely stays weak.\n\n22"}
{"prompt": "B: + essay", "gid": 8030, "imagenet_class": "ashcan", "frac_of_max": 0.0418, "on": false, "p_on": 0.22, "reasoning": "The neuron responds to pointed, V-shaped structures such as tapered snouts, car fronts and wedge-shaped noses, and it is suppressed by round forms. This photo shows weathered concrete pillars covered in torn posters, standing in snow. There are a few angular edges and some faint poster faces, but no clear tapering V-shaped object like an animal snout or vehicle front. The torn-paper texture and blocky pillars are unlikely to drive the neuron strongly.\n\n22"}
{"prompt": "B: + essay", "gid": 28280, "imagenet_class": "Border terrier", "frac_of_max": 0.0477, "on": false, "p_on": 0.55, "reasoning": "The key feature is the terrier puppy's face: its snout and folded ears converge toward the nose, giving a moderately V-shaped, pointed head like the fox, puppy and cat faces that drive this neuron. But the face is small, seen from above and partly turned away, and its dark muzzle reads somewhat blunt, so the V cue is weak. The round yellow ball is the kind of shape that pushes the neuron negative, though it is small. On balance there is a fair chance of crossing the threshold, but it is far from certain.\n\n55"}
{"prompt": "B: + essay", "gid": 24898, "imagenet_class": "palace", "frac_of_max": 0.0433, "on": false, "p_on": 0.3, "reasoning": "The photo has no animal faces or car fronts, which are the classic strong drivers of this neuron. What it does have is a tiered roof tapering to a sharp gold spire, plus a second pointed stupa on the right. These are pointed, converging shapes, more like an upward flame tip than the downward V of a snout or martini glass. The rest of the image is mostly vertical columns and fine ornate texture, which probably doesn't engage the pointed-V detector strongly. So a weak-to-moderate response is plausible, but reaching 30% of the neuron's maximum seems less likely than not.\n\n30"}
{"prompt": "B: + essay", "gid": 47604, "imagenet_class": "stingray", "frac_of_max": 0.047, "on": false, "p_on": 0.22, "reasoning": "The photo shows a blue-spotted stingray seen from above. Its body is a rounded, oval disc with a thin tail trailing behind it. That is a round, blob-like shape rather than a V-shaped point, and there are no snout-like or wedge-like convergences, faces or car fronts, which are what drive this neuron. The tail gives a thin linear element but no V, so the neuron likely stays low.\n\n22"}
{"prompt": "B: + essay", "gid": 41800, "imagenet_class": "lacewing", "frac_of_max": 0.0459, "on": false, "p_on": 0.22, "reasoning": "The image shows a small fly on a plain white background: a rounded, glossy black body and head, translucent wings, and thin legs. Nothing here forms a strong V-shaped or pointed structure like a tapering snout, a car front, or a flame. The fly's head and abdomen are round, which per the essay tends to push this neuron negative. The splayed wing and legs could form a weak V, but the object is small and low-contrast relative to the frame, so a strong response seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 765, "imagenet_class": "brown bear", "frac_of_max": 0.0395, "on": false, "p_on": 0.5, "reasoning": "The key feature is the bear's frontal face. Its muzzle tapers from the broad brow down to the nose, giving a mild V-shaped snout, which is the kind of pointed, animal-face geometry (fox, wolf, dog, cat faces) that drives this neuron. Against that, a grizzly's head is broad and dish-shaped, with round ears and heavy jowls. Those rounded features resemble the bulldog and mastiff faces that push the neuron negative. With a moderately tapered muzzle but an overall round head, this is a borderline case, leaning slightly toward ON.\n\n50"}
{"prompt": "B: + essay", "gid": 32288, "imagenet_class": "black-and-tan coonhound", "frac_of_max": 0.0197, "on": false, "p_on": 0.5, "reasoning": "The main thing here is the coonhound's head, seen in near-profile with its long, tapering muzzle pointing left. That is the kind of pointed, V-like snout shape that drives this neuron, much as fox and dog faces do. Working against it, the long droopy ears and heavy jowls give the head a rounder, hanging silhouette, unlike the pricked-ear V of foxes and wolves. The dark, low-contrast lighting also weakens the shape cues, so the response could plausibly land on either side of the threshold.\n\n50"}
{"prompt": "B: + essay", "gid": 17086, "imagenet_class": "capuchin", "frac_of_max": 0.0378, "on": false, "p_on": 0.22, "reasoning": "The photo shows capuchin monkey faces, which are flat and rounded, with no tapering snout. An open mouth also forms an O shape, which matches this neuron's negative \"round\" pole. There is nothing strongly V-shaped here, like a fox or cat muzzle, a car hood or flames. Because the neuron still tends to respond somewhat to animal faces generally, a weak response is possible, but reaching the ON threshold seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 11571, "imagenet_class": "wood rabbit", "frac_of_max": 0.4731, "on": true, "p_on": 0.6, "reasoning": "The rabbit's furry animal face is the relevant part: the tapering snout in profile and the upright, pointed ear form the kind of converging, V-like shape this neuron likes. That fits its strong inputs from fox-snout and wolf-face features. Against that, a rabbit's head is fairly rounded and seen side-on rather than as a frontal V-face, and roundness pushes this neuron negative. So it will probably respond, though not as strongly as it does to a fox or cat face.\n\n60"}
{"prompt": "B: + essay", "gid": 18176, "imagenet_class": "limousine", "frac_of_max": 0.3055, "on": true, "p_on": 0.72, "reasoning": "The photo shows a black Cadillac limousine at a three-quarter front angle, with its long hood narrowing toward the grille and headlights. Car fronts are among this neuron's classic strongest drivers (the \"car\" half of the old cat/car reading, fed by van-front units like 4d:201). Under the V-shape interpretation, the tapering hood and angled front read as a pointed, converging shape. The round wheel and the boxy body don't add anything, but the strong car-front cue should dominate.\n\n72"}
{"prompt": "B: + essay", "gid": 45861, "imagenet_class": "modem", "frac_of_max": 0.3195, "on": true, "p_on": 0.1, "reasoning": "The photo shows an iPhone, an iPad and a small Wi-Fi hotspot: flat, rectangular electronics with rounded corners and glowing screens. Nothing in it forms the pointed, converging V-shape this neuron responds to, such as a snout, the front of a vehicle, a flame or a tapered glass. The device edges are straight or rounded rather than tapering to a point, and gadget screens are not what usually drives this unit. It will most likely stay near zero.\n\n10"}
{"prompt": "B: + essay", "gid": 14622, "imagenet_class": "Arctic fox", "frac_of_max": 0.4832, "on": true, "p_on": 0.78, "reasoning": "The arctic fox's face is clearly visible, with a tapered, pointed snout and a triangular ear. That is exactly the V-shaped, fox-snout-like pattern this neuron draws on through its strong input from 4d:493 (fox snout). The curled, rounded white body and the side-on view with closed eyes give a less frontal V than the strongest examples, and roundness pushes the neuron negative. Still, the pointed muzzle should be enough to drive a solid response somewhere in the image.\n\n78"}
{"prompt": "B: + essay", "gid": 4082, "imagenet_class": "boxer", "frac_of_max": 0.0496, "on": false, "p_on": 0.14, "reasoning": "This is an extreme close-up of a sleeping pug/boxer-type dog: a flat, wrinkled, rounded face with no pointed snout, no tapering muzzle, and no V-shaped ears in view. That matches the round, flat-faced bulldog and mastiff examples that push this neuron negative, not the pointed fox, cat and dog faces that drive it. Dog fur and facial texture might give a little activation, but the overall geometry is round and blunt, so the neuron is likely OFF.\n\n14"}
{"prompt": "B: + essay", "gid": 11234, "imagenet_class": "Great Pyrenees", "frac_of_max": 0.3012, "on": true, "p_on": 0.8, "reasoning": "The cat's face, with its pointed ears and tapering, V-shaped muzzle, is the kind of pointed form this neuron responds to. Cat faces are among its classic top activators. The white dog's face is broader and rounder, so it contributes less, but its snout still tapers somewhat. The cat face alone is likely enough to drive a strong response somewhere in the image.\n\n80"}
{"prompt": "B: + essay", "gid": 2458, "imagenet_class": "hamster", "frac_of_max": 0.3583, "on": true, "p_on": 0.28, "reasoning": "The photo shows two sleeping dwarf hamsters, curled into round, fluffy balls with blunt, rounded faces and no pointed snout, ears or V-shaped contour. Under the \"pointed/V-shape\" reading, round forms like these resemble the negative pole (pugs, balls, snails) more than the fox, cat or car-front shapes that drive the neuron. The furry animal-face texture might still give some weak activation through the fox and wolf face inputs, but probably not enough to reach 30% of the maximum.\n\n28"}
{"prompt": "B: + essay", "gid": 41500, "imagenet_class": "dishrag", "frac_of_max": 0.0442, "on": false, "p_on": 0.1, "reasoning": "The photo is a flat knitted dishcloth in a basketweave pattern, made of rectangular blocks with a scalloped, rounded border. It has none of the pointed, V-shaped structures this neuron responds to, such as fox, cat or dog snouts, car or train fronts, or flames. The small V-like knit stitches are tiny, repetitive texture rather than a salient converging point. The rounded scallops and rectangular weave lean toward the \"round\" pole, so the neuron is likely OFF.\n\n10"}
{"prompt": "B: + essay", "gid": 36106, "imagenet_class": "Ibizan hound", "frac_of_max": 0.3301, "on": true, "p_on": 0.8, "reasoning": "This is an Ibizan hound, whose face is about as pointed as dog faces get: a long, narrow, tapering snout and tall, upright, triangular ears that together form a strong V. That matches the neuron's preferred pattern, the same pointed fox- and wolf-face shapes that drive it through inputs like 4d:493 and 4d:477. Nothing round or blunt dominates the scene to push it negative. The main caveat is that the head is fairly small in the frame, which could weaken the peak response somewhat.\n\n80"}
{"prompt": "B: + essay", "gid": 34302, "imagenet_class": "Samoyed", "frac_of_max": 0.3136, "on": true, "p_on": 0.85, "reasoning": "The key feature is the Samoyed's face: pointed upright ears and a tapering snout form the frontal V-shaped, fox-like face that this neuron's strongest inputs (fox-snout and wolf-face units) feed into. The man's rounder face may contribute a little negative signal. But the neuron takes its strongest response anywhere in the image, so the dog's centered, well-lit face should dominate.\n\n85"}
{"prompt": "B: + essay", "gid": 8801, "imagenet_class": "tiger cat", "frac_of_max": 0.4286, "on": true, "p_on": 0.9, "reasoning": "The frontal tabby cat face is exactly the kind of input this neuron responds to. The upright pointed ears and the face tapering to the chin form a clear V shape, much like the cat and fox faces in its top activations. The face is centered and well lit, with no competing round shapes dominating the frame, so a strong response is likely.\n\n90"}
{"prompt": "B: + essay", "gid": 33484, "imagenet_class": "anemone fish", "frac_of_max": 0.0306, "on": false, "p_on": 0.18, "reasoning": "The photo shows a clownfish over a fleshy, rounded anemone, with no pointed V-shaped structures like fox or cat faces, car fronts, flames or martini-glass shapes. The clownfish's body and the anemone's folds are mostly rounded, which would push the neuron toward its negative \"round\" side. The fish's tail fin and pointed snout might give a weak V-like cue, but probably not enough to reach 30% of the neuron's maximum.\n\n18"}
{"prompt": "B: + essay", "gid": 33130, "imagenet_class": "soccer ball", "frac_of_max": 0.0388, "on": false, "p_on": 0.08, "reasoning": "The photo is dominated by a single large round object: a chrome soccer ball sitting on a flat surface. That is exactly the \"round\" pole that pushes this neuron negative, like the golf and tennis balls in the figure. There are no pointed or V-shaped features such as snouts, car hoods, or flames. The only angular content is the small pentagon and hexagon panel edges and the reflections, which are unlikely to drive a V-shape detector strongly, so the neuron should mostly stay quiet.\n\n8"}
{"prompt": "B: + essay", "gid": 40587, "imagenet_class": "American coot", "frac_of_max": 0.0479, "on": false, "p_on": 0.08, "reasoning": "The photo is mostly flat, rippled water with one tiny, rounded waterbird (a coot or duck) that takes up only a few pixels. It has none of the pointed, V-shaped structures that drive this neuron, such as fox or cat snouts, car fronts, flames or bullet-train noses. The bird's compact, rounded body is closer to the \"round\" negative pole than to anything pointed, and it is too small to give a strong local response. The neuron is very likely OFF.\n\n8"}
{"prompt": "B: + essay", "gid": 34869, "imagenet_class": "jersey", "frac_of_max": 0.0431, "on": false, "p_on": 0.27, "reasoning": "The photo shows a red-and-black jersey laid flat on a white background, with no animal faces, car fronts or flames. The only candidate V-shapes are the small notch at the neckline and the angled shoulder seams where the black sleeves meet the red body. These are weak, low-contrast cues next to the pointed snouts and grilles that drive this neuron. The large flat rectangular torso offers little pointed structure, so a strong response seems unlikely, though the V-ish collar could give a modest activation.\n\n27"}
{"prompt": "B: + essay", "gid": 19060, "imagenet_class": "tiger cat", "frac_of_max": 0.3395, "on": true, "p_on": 0.72, "reasoning": "The two tiger heads are the key: this neuron's known top activations include cat faces, and a tiger's face is a big-cat version of the same feature. In Cammarata's reading, the relevant cue is a V-shaped, pointed facial structure: the tapered muzzle, the converging stripe pattern between the eyes, and the fanned whiskers. The upper tiger's profile, with its forward-pointing snout, fits that pointed shape well. Tiger heads are somewhat broader and rounder than fox or house-cat faces, which could dampen the response, but overall a strong activation is likely.\n\n72"}
{"prompt": "B: + essay", "gid": 2739, "imagenet_class": "red-backed sandpiper", "frac_of_max": 0.3583, "on": true, "p_on": 0.22, "reasoning": "The main candidate is the dunlin's long, thin bill, with the tapered wingtips and tail as secondary pointed shapes. These are pointed, but they are slender lines rather than the broad, wedge-shaped V of a fox snout, cat face or car front that drives this neuron hardest. The bird's head and body are mostly rounded and smooth, and the background of water and a grassy mound has no strong V geometry. So I expect at most a weak response, though the pointed beak and wingtips give it some chance.\n\n22"}
{"prompt": "B: + essay", "gid": 9390, "imagenet_class": "projectile", "frac_of_max": 0.4099, "on": true, "p_on": 0.5, "reasoning": "What matters here is pointed or V-shaped structure, since the neuron responds to pointed forms (snouts, car and train fronts, flames) and is pushed down by round ones. The photo has several candidates: the tapered nose cone of the red-and-white missile, the dark aircraft fuselage behind it, and the fins splaying out in a V. The woman's face is small and not especially V-shaped, and there are no cat faces or car fronts. That leaves the missile's pointed nose and fins as the main possible trigger, so I'd expect a moderate response that may or may not reach the ON threshold.\n\n50"}
{"prompt": "B: + essay", "gid": 16012, "imagenet_class": "Arctic fox", "frac_of_max": 0.7242, "on": true, "p_on": 0.9, "reasoning": "The arctic fox's face is a textbook V-shape: pointed ears and a narrow muzzle tapering to the black nose, framed front-on. That is exactly the pattern 4e:55 picks up, and it also matches one of its strongest inputs, 4d:493 (fox snout). There is little round structure to suppress it, and the face is large and centered, so the neuron should respond strongly.\n\n90"}
{"prompt": "B: + essay", "gid": 8259, "imagenet_class": "kit fox", "frac_of_max": 0.6826, "on": true, "p_on": 0.92, "reasoning": "This is a close-up of a young red fox's face, with a pointed snout and upright pointed ears forming a clear V-shaped head silhouette. That is exactly the kind of input this neuron's strongest feeders respond to (fox snout 4d:493, wolf face 4d:477). The figure's examples also include a nearly identical fox kit among the things that make it fire. The fox occupies a large part of the frame and the background is blurred, so nothing competes with or dilutes the pointed-face signal.\n\n92"}
{"prompt": "B: + essay", "gid": 40848, "imagenet_class": "pedestal", "frac_of_max": 0.0462, "on": false, "p_on": 0.13, "reasoning": "The photo shows a tall cylindrical column (the Column of Marcus Aurelius) with carved spiral reliefs, plus flat building facades and sky. Nothing here forms the convergent V or pointed shape this neuron looks for, such as a snout, a car's hood front, or a flame tip. The column is a straight, round, parallel-sided shape, which is closer to the \"round\" negative pole. The rooftops and the column's top edges might give weak angular cues, but probably not enough to reach 30% of the neuron's maximum.\n\n13"}
{"prompt": "B: + essay", "gid": 27603, "imagenet_class": "potter's wheel", "frac_of_max": 0.0492, "on": false, "p_on": 0.15, "reasoning": "The scene is dominated by round, circular shapes (the pottery wheel, the clay pot being thrown, the bowls and vases), which are this neuron's negative pole. There are no pointed, V-shaped features like animal snouts, cat or fox faces, car fronts or flames. The man's downturned face and the shirt's neckline offer only weak, incidental V-ish structure. Overall the neuron is likely suppressed or near zero.\n\n15"}
{"prompt": "B: + essay", "gid": 15905, "imagenet_class": "Siamese cat", "frac_of_max": 0.3786, "on": true, "p_on": 0.9, "reasoning": "The Siamese kitten's face is frontal and centered. Its large pointed ears and tapering, dark-masked muzzle form exactly the V-shaped \"pointed face\" pattern this neuron responds to. Cat faces like this are among its classic top activators, fed by fox-snout and wolf-face inputs. The rounded monitor and plain wall add little either way, but the face alone should drive a strong response.\n\n90"}
{"prompt": "B: + essay", "gid": 31982, "imagenet_class": "stupa", "frac_of_max": 0.0462, "on": false, "p_on": 0.22, "reasoning": "The photo shows a white Buddhist stupa with an ornate gilded niche and a small finial. It contains none of the neuron's strongest drivers: cat, fox or dog faces, or the V-shaped fronts of cars and vans. The only pointed elements are the arched top of the niche and the spire. They are small, and they sit against large rounded and flat surfaces (the dome, the walls, the clouds), which tend to push this neuron down. Some weak pointed-shape response is possible, but reaching 30% of its maximum seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 5125, "imagenet_class": "tiger", "frac_of_max": 0.3128, "on": true, "p_on": 0.85, "reasoning": "This is a close-up, frontal tiger face, and cat faces were among this neuron's classic strongest triggers. In the V-shape reading, the face tapers to a point: the dark stripes converge from the eyes down to the nose bridge, and the pink nose itself is a downward-pointing triangle. That is the pointed, converging structure the neuron responds to. The broad, roundish cheeks and open mouth weaken the V a little, but the overall feline facial geometry should still drive it strongly.\n\n85"}
{"prompt": "B: + essay", "gid": 1818, "imagenet_class": "swing", "frac_of_max": 0.0438, "on": false, "p_on": 0.18, "reasoning": "This photo shows a rustic swing set in a dirt yard surrounded by potted plants, with no animal faces, car fronts or other strong pointed, converging forms that drive this V-shape detector. The swing posts are mostly vertical rather than an angled A-frame. The hanging swing ropes make only faint, small V-like shapes. The scene is mostly cluttered foliage and flat ground, which offers little of the pointed structure the neuron wants.\n\n18"}
{"prompt": "B: + essay", "gid": 22936, "imagenet_class": "Pekinese", "frac_of_max": 0.3415, "on": true, "p_on": 0.28, "reasoning": "The photo shows a Pekingese, a brachycephalic breed with a flat, round face, a short muzzle and long, droopy feathered ears. It has none of the pointed, V-shaped snout or upright triangular ears that drive this neuron. It resembles the bulldog and mastiff faces in the \"round pushes it negative\" examples. The face is also partly hidden and small in the frame, so although a furry dog face gives some weak cues, the neuron is more likely OFF.\n\n28"}
{"prompt": "B: + essay", "gid": 14034, "imagenet_class": "window shade", "frac_of_max": 0.0414, "on": false, "p_on": 0.2, "reasoning": "The photo shows a man in side profile with a dark curly-haired head, lit warmly against horizontal window blinds. There is no cat, fox or dog face, car front, or other clear V-shaped, pointed form that this neuron favors. The face is rounded and in profile, which is not the frontal tapering-snout geometry that drives it. The blinds are parallel lines rather than a converging V, so a weak response at most seems likely.\n\n20"}
{"prompt": "B: + essay", "gid": 26833, "imagenet_class": "golfcart", "frac_of_max": 0.3819, "on": true, "p_on": 0.48, "reasoning": "The golf cart is seen nearly head-on, and car and van fronts are among this neuron's strongest drivers (it reads from the 4d van-front unit). But the cart's white cowl is smooth and rounded rather than a pointed, tapering V like a car hood or bullet-train nose, which weakens that signal. Small V-shaped cues, such as the man's open collar and tie and the angled roof struts, could add a little drive. Overall this is a borderline, moderate case.\n\n48"}
{"prompt": "B: + essay", "gid": 10682, "imagenet_class": "hamster", "frac_of_max": 0.4233, "on": true, "p_on": 0.42, "reasoning": "The key feature is the hamster's face, a close-up furry animal face, which is the kind of input that often drives this neuron through its fox-snout and animal-face inputs. But a hamster's face is mostly round and chubby, with only a mild taper toward the small nose. That roundness is what pushes the neuron negative in the bulldog and ball examples. The face does narrow somewhat toward the nose and has small pointed ears, so it could still produce a moderate V-like response.\n\n42"}
{"prompt": "B: + essay", "gid": 17424, "imagenet_class": "black-footed ferret", "frac_of_max": 0.6155, "on": true, "p_on": 0.68, "reasoning": "The ferret's face is the key feature. Its white muzzle narrows to a pointed snout with the nose at the tip, the same V-shaped, tapering face geometry that drives this neuron through its fox-snout and wolf-face inputs. The face is cropped at the top and fairly small, and the rest of the image is the elongated body, a hand and background clutter, which add little. Still, a clear pointed mammal snout is one of this neuron's strongest triggers.\n\n68"}
{"prompt": "B: + essay", "gid": 33113, "imagenet_class": "whiskey jug", "frac_of_max": 0.0, "on": false, "p_on": 0.18, "reasoning": "The shelves hold mostly round, cylindrical objects: stoneware crocks, jugs, mugs and bottles. These are the curved, bulbous shapes the essay says push this neuron negative. There are no animal faces or snouts, no car fronts and no V-shaped wedge forms of the kind that drive it. The tapering bottle necks and the boxy shelf corners are weak, partial \"pointed\" cues at best, so a strong response seems unlikely.\n\n18"}
{"prompt": "B: + essay", "gid": 13402, "imagenet_class": "rapeseed", "frac_of_max": 0.0462, "on": false, "p_on": 0.15, "reasoning": "This is a rapeseed (canola) field: a wide landscape of fine, repetitive yellow-flower texture, with only small distant trees and buildings. Nothing in it resembles the pointed, V-converging shapes that drive this neuron, such as snouts, cat or fox faces, car fronts, flames or martini-glass cones. The farmhouse roof and conifer offer at most faint, tiny wedge-like contours. The scene is dominated by diffuse texture rather than a single tapering form, so I'd expect a weak response.\n\n15"}
{"prompt": "B: + essay", "gid": 49270, "imagenet_class": "tarantula", "frac_of_max": 0.0457, "on": false, "p_on": 0.28, "reasoning": "The photo shows a tarantula with banded orange-and-black legs against a pale green surface, and there is no fox, cat, or dog face, car front, or other clear V-shaped pointed form. The legs radiate outward and meet at a few angles that could weakly resemble V or chevron shapes. But the body is a rounded mass, and roundness pushes this neuron negative. Weak structural V cues might give some partial activation, but reaching 30% of its maximum seems unlikely.\n\n28"}
{"prompt": "B: + essay", "gid": 38027, "imagenet_class": "cab", "frac_of_max": 0.3022, "on": true, "p_on": 0.68, "reasoning": "The yellow taxi seen from the front three-quarter view, along with the other parked cars, is what matters here. Car fronts (the hood tapering toward the grille) are one of this neuron's classic strong drivers, fed by the \"van front\" input. The taxi is fairly small in the frame and the scene is cluttered with buildings and trees. Still, car fronts at this scale often push 4e:55 well past threshold.\n\n68"}
{"prompt": "B: + essay", "gid": 32381, "imagenet_class": "ox", "frac_of_max": 0.0469, "on": false, "p_on": 0.58, "reasoning": "The cow's face is shown head-on and close up, with the forehead tapering down toward the muzzle. This gives a roughly V-shaped animal face, similar to the fox, wolf and coyote faces that drive this neuron. The large horn adds an elongated, pointed shape. However, a cow's face is broad and blunt rather than sharply pointed like a fox snout or cat face, so the response may only be moderate.\n\n58"}
{"prompt": "B: + essay", "gid": 5380, "imagenet_class": "gazelle", "frac_of_max": 0.3687, "on": true, "p_on": 0.78, "reasoning": "The gazelle faces the camera with a narrow, tapering snout, pointed ears, and long horns that splay into a clear V above its head. That converging, pointed geometry is what the neuron's fox-snout and wolf-face inputs detect, and it is the opposite of the round shapes that push it negative. The animal is fairly small in the frame, which may weaken the response somewhat, but the V-shape cue is strong and unambiguous.\n\n78"}
{"prompt": "B: + essay", "gid": 38800, "imagenet_class": "limousine", "frac_of_max": 0.4286, "on": true, "p_on": 0.32, "reasoning": "This is a white stretch limousine seen from the side, heavily motion-blurred against a dark street. The neuron's car responses come mainly from frontal, pointed or V-converging structure: grilles, hoods, and van and train noses tapering toward the viewer. A side profile shows mostly long horizontal lines and a rounded wheel, with little convergent geometry, and the blur further washes out mid-level shape cues. The car category gives some chance of activation, but the viewpoint and blur make a strong response less likely.\n\n32"}
{"prompt": "B: + essay", "gid": 29276, "imagenet_class": "dugong", "frac_of_max": 0.0418, "on": false, "p_on": 0.1, "reasoning": "The image shows a dugong grazing on the seafloor: a large, smooth, rounded body seen mostly from above and behind, with a few small fish and murky blue water. Nothing in the frame forms the pointed, V-shaped convergence this neuron detects, such as a fox or cat snout, a flame tip, a car front or a martini-glass shape. The dominant form is a broad rounded blob, which is closer to the neuron's \"round\" negative pole. The small fish have slightly tapered shapes, but they are tiny and indistinct, so they are unlikely to drive a strong response.\n\n10"}
{"prompt": "B: + essay", "gid": 25514, "imagenet_class": "tabby", "frac_of_max": 0.6196, "on": true, "p_on": 0.8, "reasoning": "The kitten's frontal face is the key feature: its pointed ears and the taper from forehead to small chin and nose form the V-shaped, snout-like geometry that drives 4e:55. Cat faces are among its classic top activators. A kitten's face is somewhat rounder than an adult cat's or a fox's, which could weaken the response, but the clear ears and centered, close-up face should still push it well above threshold.\n\n80"}
{"prompt": "B: + essay", "gid": 45502, "imagenet_class": "jigsaw puzzle", "frac_of_max": 0.0067, "on": false, "p_on": 0.18, "reasoning": "This image shows a grid of small, decorated jigsaw pieces on a dotted background. Their outlines are dominated by round knobs and blanks, with only blunt right-angle corners. There are no animal faces, car fronts or flame-like shapes, so nothing offers the converging pointed V-shape this neuron seems to prefer. The colorful patterns are busy but lack a clear pointed structure, and the rounded tabs, if anything, match the round shapes that push the neuron negative, so it will most likely stay weak.\n\n18"}
{"prompt": "B: + essay", "gid": 23461, "imagenet_class": "leopard", "frac_of_max": 0.3395, "on": true, "p_on": 0.85, "reasoning": "The leopard's face is frontal and close, and a cat face like this is the canonical driver of 4e:55. Under the V-shape reading, the converging lines of its brow, eyes and muzzle down to the nose and chin form a strong downward-pointing V, much like the fox and cat examples in the figure. The face is large and well-lit, and nothing round dominates it, so a strong response is likely.\n\n85"}
{"prompt": "B: + essay", "gid": 46196, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.453, "on": true, "p_on": 0.85, "reasoning": "The two sable-and-white collies have long, narrow, tapering snouts and fox-like coloring. Their faces form exactly the pointed V-shape this neuron's inputs (4d:493 fox snout, 4d:477 wolf face) feed it. The facing-forward collie in the center is close and large enough to drive the response strongly. The puppies' rounder faces might push slightly negative, but the neuron takes its strongest response anywhere in the image, so the pointed collie faces should dominate.\n\n85"}
{"prompt": "B: + essay", "gid": 33891, "imagenet_class": "Great Pyrenees", "frac_of_max": 0.028, "on": false, "p_on": 0.5, "reasoning": "The key feature is the dog's face. It is a light-colored, fluffy head seen from the front and slightly above, so the face narrows from the ears and forehead down toward the snout. That gives a modest V/pointed shape, which this neuron likes in dog, fox and cat faces. However, a Great Pyrenees has a fairly broad, rounded head, the face is small in the frame, and the open mouth with tongue softens the point, so the drive may be only moderate. Rounded, flat-faced dogs tend to push the neuron negative, and this face sits between the pointed and round cases.\n\n50"}
{"prompt": "B: + essay", "gid": 39157, "imagenet_class": "bonnet", "frac_of_max": 0.0322, "on": false, "p_on": 0.22, "reasoning": "The main object is a knitted pink cap, which reads as a rounded dome, the kind of round, blunt shape that pushes this neuron negative. The orange and teal stripes do taper and converge toward the tied knot, giving a weak fan or V-like convergence, but they lack a crisp pointed tip. Nothing resembles the strong drivers (pointed snouts, flames, car or train fronts), and the soft knit texture is far from the neuron's usual triggers. Overall the photo leans round, so the neuron is more likely OFF.\n\n22"}
{"prompt": "B: + essay", "gid": 34507, "imagenet_class": "ambulance", "frac_of_max": 0.5046, "on": true, "p_on": 0.85, "reasoning": "The ambulance's front end (windshield, hood and grille seen at an angle) closely matches the \"van front\" input (4d:201), one of this neuron's strongest drivers. Car and vehicle fronts are among its classic top activators. The tapering windshield and hood give the angled, pointed geometry it favors. Nothing large and round dominates the image to suppress it, so a strong response is likely.\n\n85"}
{"prompt": "B: + essay", "gid": 16915, "imagenet_class": "Kerry blue terrier", "frac_of_max": 0.026, "on": false, "p_on": 0.27, "reasoning": "This is a black, curly-coated terrier (likely a Kerry Blue) leaping through grass. Its head is mostly cut off or hidden at the left edge, so there is no clear front-facing, pointed V-shaped snout or face, which is what most strongly drives this neuron. What remains is a rounded, woolly body mass and a curled tail, and these offer little pointed structure. The animal context gives some chance of a moderate response, but without a visible tapered face, a strong activation seems unlikely.\n\n27"}
{"prompt": "B: + essay", "gid": 29289, "imagenet_class": "greenhouse", "frac_of_max": 0.0321, "on": false, "p_on": 0.25, "reasoning": "The photo is a greenhouse scene of foliage, white poinsettias, topiary and a large round red hanging ball. It has no animal snouts, car fronts or clear centered V-shaped objects, which are what drive this pointed/V-shape neuron. The dominant salient object, the hanging ball, is round, which pushes the neuron negative. The angled roof panes and pointed poinsettia bracts might give weak, scattered V-like cues, but probably not enough to reach 30% of its maximum.\n\n25"}
{"prompt": "B: + essay", "gid": 45281, "imagenet_class": "convertible", "frac_of_max": 0.419, "on": true, "p_on": 0.8, "reasoning": "The photo shows a silver BMW convertible at an auto show, seen from a three-quarter front angle. The tapering hood, angled headlights and grille converge toward the front, giving the pointed, V-like car-front geometry that this neuron's van-front input (4d:201) and its car exemplars respond to. Car fronts were among the original \"car\" half of its top activations. The view is somewhat side-on, and the large round wheel is a counter-cue, but the front end should still drive it strongly.\n\n80"}
{"prompt": "B: + essay", "gid": 2162, "imagenet_class": "soap dispenser", "frac_of_max": 0.0476, "on": false, "p_on": 0.18, "reasoning": "The main object is a pump soap dispenser with a bulbous, mostly round pink body. Roundness is the neuron's negative pole, so this should suppress it. There are no fur-framed V-shaped faces, flames, car fronts or other tapering forms. The only pointed elements are the slight taper at the bottom of the bottle and the small nozzle, which are weak cues at best.\n\n18"}
{"prompt": "B: + essay", "gid": 11944, "imagenet_class": "golf ball", "frac_of_max": 0.0496, "on": false, "p_on": 0.12, "reasoning": "The photo is dominated by a golf ball, a smooth round object of exactly the kind the figure lists as pushing this neuron negative. A golf ball even appears among its \"round\" examples. The only pointed elements are the small red triangle logo and the diagonal edge of the box behind it. Both are tiny and lack the strong V-shaped structure (snouts, car fronts, flames) that drives the neuron, so a weak response at most is likely.\n\n12"}
{"prompt": "B: + essay", "gid": 10420, "imagenet_class": "beer glass", "frac_of_max": 0.0332, "on": false, "p_on": 0.15, "reasoning": "The image is dominated by round forms: the circular, glowing top of the bollard and the cylindrical post. Round shapes are exactly what push this neuron negative. The glass is a mostly straight-sided pint glass, not a sharply tapered V like a martini glass, and nothing here resembles a snout, a car hood or a pointed flame. The red glow loosely evokes the flame reader, but it lacks a pointed shape, so the neuron is likely weak.\n\n15"}
{"prompt": "B: + essay", "gid": 6694, "imagenet_class": "chiton", "frac_of_max": 0.0384, "on": false, "p_on": 0.12, "reasoning": "The main object is a chiton: an oval, rounded, segmented shell sitting flat on pale rock. It has none of the V-shaped or pointed structure that drives this neuron (snouts, car grilles, flames, wedge noses). Rounded, ellipsoidal forms are closer to its negative pole. The small shell at the top left and the textured rock edges give little pointed structure, so a strong response is unlikely.\n\n12"}
{"prompt": "B: + essay", "gid": 29474, "imagenet_class": "toy terrier", "frac_of_max": 0.4053, "on": true, "p_on": 0.87, "reasoning": "Both dogs are small terriers whose faces form strong V-shapes: tall, pointed ears and muzzles that narrow to the nose, much like the fox, wolf and puppy faces that drive this neuron hardest. Neither face has the round, flat, jowly look (bulldog or mastiff) that pushes the neuron negative. The round rug behind them is a weak round cue, but the max-over-image readout should be set by the dog faces, especially the black-and-white dog's sharp ears and the tan dog's tapered face.\n\n87"}
{"prompt": "B: + essay", "gid": 20266, "imagenet_class": "convertible", "frac_of_max": 0.3791, "on": true, "p_on": 0.62, "reasoning": "The car is the relevant feature. This neuron fires strongly on car bodies, especially fronts and hoods, and in the V-shape reading it likes tapered, pointed forms. This Mercedes SLK roadster has a long, wedge-shaped nose tapering toward the left, plus angular windshield and body lines. The side-on view and small car size make it weaker than a head-on grille shot, but sleek sports-car shapes like this typically still activate it noticeably. The round wheels are the only counter-cue.\n\n62"}
{"prompt": "B: + essay", "gid": 8517, "imagenet_class": "lampshade", "frac_of_max": 0.0479, "on": false, "p_on": 0.25, "reasoning": "The main thing here is the sugar-skull face on the lampshade. It is a face-like pattern, which this neuron sometimes picks up on, but its features are dominated by large round eye sockets and a blunt, rounded jaw. Those are the \"round\" cues that push 4e:55 negative. The shade itself is a trapezoid that widens downward rather than tapering to a V-shaped point, and nothing else in the scene (lamp pole, wall) offers a pointed snout, flame or V-shaped front, so the neuron is likely weak.\n\n25"}
{"prompt": "B: + essay", "gid": 17828, "imagenet_class": "toilet tissue", "frac_of_max": 0.3187, "on": true, "p_on": 0.8, "reasoning": "The kitten's head, seen in three-quarter view, has large pointed ears and a tapering V-shaped face. That is exactly the cat/fox-face pattern that drives 4e:55, the V-shape neuron historically labeled \"cat and car.\" The round toilet-paper roll is the kind of shape that pushes the neuron negative. But the neuron's value is its strongest response anywhere in the image, so the cat's face should dominate. The face is moderately sized and in profile rather than frontal, which adds some uncertainty, but a strong activation is likely.\n\n80"}
{"prompt": "B: + essay", "gid": 46897, "imagenet_class": "mitten", "frac_of_max": 0.0101, "on": false, "p_on": 0.52, "reasoning": "The knitted sock is covered in a chevron (zigzag) stripe pattern, which is essentially rows of repeated V-shapes, exactly the pointed geometry this neuron seems to detect. The open question is scale. At mixed4e the neuron appears tuned to object-level V's, such as a snout tapering to a point or a car's angled front, rather than fine repeated texture. The sock's overall rounded, bulbous form also leans toward the \"round\" pole that pushes it negative. On balance it is a coin flip, with a slight tilt toward firing because the chevrons are bold and high-contrast.\n\n52"}
{"prompt": "B: + essay", "gid": 48344, "imagenet_class": "stinkhorn", "frac_of_max": 0.0406, "on": false, "p_on": 0.18, "reasoning": "What matters is whether the image has a V-shaped or pointed form converging to a tip, such as a snout, a car front, flames or a martini glass. This stinkhorn mushroom is a vertical cylindrical stalk topped by a rounded, dome-like textured cap. Those are round, blunt shapes, closer to the neuron's negative \"round\" pole than to a V. The watermark's concentric circles are also round, and nothing in the scene forms a clear converging V, so the neuron is likely OFF.\n\n18"}
{"prompt": "B: + essay", "gid": 26110, "imagenet_class": "diamondback", "frac_of_max": 0.0232, "on": false, "p_on": 0.62, "reasoning": "The key feature is the viper's head, seen from the front. It forms a broad, triangular wedge that narrows to a pointed snout at the bottom, the same downward-pointing V that drives this neuron in fox, cat and car-front images. It is also face-like, with eyes set on either side of the taper, much like its fox and wolf face inputs. Working against this, the scaly, low-contrast camouflage texture and the coiled, rounded body around the head may weaken the response.\n\n62"}
{"prompt": "B: + essay", "gid": 26889, "imagenet_class": "projectile", "frac_of_max": 0.024, "on": false, "p_on": 0.35, "reasoning": "The key features are the rocket's bright flaming tip and the plume, which flares out into a rough inverted-V cloud at the water. Those are pointed, flame-like shapes, which fit the V-shape reading and the neuron's strong link to flame features. But the neuron's strongest drivers are V-shaped faces and fronts that fill a good part of the frame, like fox snouts, cat faces and car grilles. Here the pointed elements are thin and small against a large, flat sea and sky, so a strong response is uncertain.\n\n35"}
{"prompt": "B: + essay", "gid": 36862, "imagenet_class": "Pomeranian", "frac_of_max": 0.6186, "on": true, "p_on": 0.8, "reasoning": "The Pomeranian's face is the key feature: it is fox-like, with a pointed snout and pointed ears forming a V-shaped face. That closely matches the neuron's strongest inputs (4d:493 fox snout, 4d:477 wolf face). The black cat behind it adds another possible cat or pointed-ear cue, though its face is mostly hidden. The fluffy round fur could pull the response down somewhat, but the frontal, pointed fox-like face should dominate.\n\n80"}
{"prompt": "B: + essay", "gid": 44283, "imagenet_class": "police van", "frac_of_max": 0.3159, "on": true, "p_on": 0.66, "reasoning": "The vintage van's front end, with its grille, headlight and bumper seen at a three-quarter angle, is the kind of vehicle front this neuron is well known to respond to. It also matches one of its strongest inputs, the 4d:201 \"van front\" detector. Against that, this Morris-style van has a fairly bulbous, rounded nose and round headlights rather than a sharply pointed or V-shaped front, which could weaken the response. Even so, car and van fronts reliably drive this neuron, so it is more likely ON than OFF.\n\n66"}
{"prompt": "B: + essay", "gid": 40121, "imagenet_class": "hare", "frac_of_max": 0.385, "on": true, "p_on": 0.62, "reasoning": "What matters here is the hare's furry animal face: the eye, the whiskered muzzle, and especially the tall, pointed ear tapering to a tip. These resemble the fox, cat and wolf face features that feed this neuron, and they fit its \"pointed/V-shape\" preference. Working against it, the head is seen in profile and the cheek is fairly round, without the frontal V of a fox or cat face. So the response may be moderate rather than strong.\n\n62"}
{"prompt": "B: + essay", "gid": 6341, "imagenet_class": "lawn mower", "frac_of_max": 0.3327, "on": true, "p_on": 0.42, "reasoning": "The silver sedan in the background is the main candidate driver. Cars are among this neuron's classic triggers, but its strongest car responses come from fronts, hoods and grilles that form a tapered, V-like shape. This car is seen in side profile and occupies a modest part of the frame, so it may only weakly engage that pointed-front detector. The pink mower's angled handle and body give a little extra angular structure, while the man and the lawn contribute little either way.\n\n42"}
{"prompt": "B: + essay", "gid": 2224, "imagenet_class": "orangutan", "frac_of_max": 0.0248, "on": false, "p_on": 0.18, "reasoning": "The key element is the orangutan's face and body. This flanged male has large, round cheek pads and a rounded, blobby body silhouette, with no pointed ears, snout or V-shaped contour. Under the \"pointed vs. round\" reading of 4e:55, that roundness should push the neuron toward its negative pole rather than drive it. The reddish fur and grass give little V-like structure, so a strong response seems unlikely.\n\n18"}
{"prompt": "B: + essay", "gid": 43643, "imagenet_class": "chiton", "frac_of_max": 0.04, "on": false, "p_on": 0.15, "reasoning": "The photo shows a chiton: an oval, rounded body with overlapping shell plates and a bristly girdle, lying on gray rock. Nothing here forms a V or pointed shape the way a snout, car hood or flame tip would. The overall rounded, elliptical outline is closer to the neuron's negative \"round\" pole. The furry-looking girdle could weakly evoke animal-fur textures, but without a pointed face-like convergence it is unlikely to drive the neuron strongly.\n\n15"}
{"prompt": "B: + essay", "gid": 5173, "imagenet_class": "Maltese dog", "frac_of_max": 0.3007, "on": true, "p_on": 0.6, "reasoning": "The key feature is the puppy's frontal face, where the two dark eyes and the black nose form a downward-pointing triangle. That is the V-shaped, snout-like configuration this neuron responds to in cat, fox and dog faces, and the white puppy in the figure's ON examples looks quite similar. Against that, the Maltese has a short muzzle and a fluffy, rounded head outline. Round shapes push this neuron negative, so the response may be weaker than for a sharp-snouted fox or cat.\n\n60"}
{"prompt": "B: + essay", "gid": 19611, "imagenet_class": "papillon", "frac_of_max": 0.3547, "on": true, "p_on": 0.8, "reasoning": "The key feature is the papillon's head: a narrow, pointed snout framed by large, upright, butterfly-shaped ears, forming a fox-like V-shaped face. That matches this neuron's strongest drivers (fox snouts, wolf and coyote faces, pointed cat and dog faces) rather than the round, flat-faced dogs that push it negative. The dog is fairly small in the frame, which could weaken the response somewhat. Still, the face is clear and frontal, and the neuron only needs its strongest response anywhere in the image.\n\n80"}
{"prompt": "B: + essay", "gid": 25819, "imagenet_class": "Eskimo dog", "frac_of_max": 0.4568, "on": true, "p_on": 0.85, "reasoning": "The husky's face is the key feature: its pointed, erect ears and tapering, wolf-like snout make a strong V-shaped head, much like the fox-snout and wolf-face inputs (4d:493, 4d:477) that drive this neuron hardest. The black-and-white facial mask also forms V-like contours around the eyes and muzzle. The open mouth and lolling tongue round off the lower face somewhat, but the face is large and frontal, so the pointed structure should still dominate.\n\n85"}
{"prompt": "B: + essay", "gid": 3779, "imagenet_class": "prison", "frac_of_max": 0.0308, "on": false, "p_on": 0.15, "reasoning": "The photo is a dark, low-detail silhouette of a person behind vertical prison bars, set against a bright rectangular window. There is no pointed or V-shaped structure here: no animal snout, no car or train nose, no flame. The bars form a rectilinear grid, and the head silhouette is rounded, which if anything leans toward the neuron's negative \"round\" pole. The bars might faintly resemble a car grille, but without a tapering front-end shape that is unlikely to drive it strongly.\n\n15"}
{"prompt": "B: + essay", "gid": 10090, "imagenet_class": "sea lion", "frac_of_max": 0.0372, "on": false, "p_on": 0.27, "reasoning": "The photo is mostly open water texture, which offers nothing pointed or V-shaped. The sea lion is small in the frame. Its raised fore-flipper and tail flippers do form pointed, V-like shapes, and its snout tapers. However, these are tiny relative to the object scale mixed4e responds to, and there is no face-on pointed snout like a fox, cat or car front. A weak response from the flipper shapes is plausible, but reaching 30% of max seems unlikely.\n\n27"}
{"prompt": "B: + essay", "gid": 25496, "imagenet_class": "dishrag", "frac_of_max": 0.0445, "on": false, "p_on": 0.36, "reasoning": "The photo is a knitted ripple blanket. Its texture is full of small V-shaped knit stitches, and it has a repeating chevron-like wave, so it does contain the \"pointed/V\" geometry this neuron is said to prefer. However, the V's are tiny repeated texture elements rather than one large convergent shape like a snout, a car front or a flame. The large-scale waves are also gently rounded rather than sharply pointed. At a fairly high layer like mixed4e, that kind of fine texture may only weakly drive a neuron tuned to object-scale pointed forms, so I lean slightly toward OFF.\n\n36"}
{"prompt": "B: + essay", "gid": 38052, "imagenet_class": "black-footed ferret", "frac_of_max": 0.322, "on": true, "p_on": 0.78, "reasoning": "The photo is centered on a black-footed ferret's face seen head-on. Its tapered snout, dark eye mask and ears form a V-shaped, fox-like face, much like the fox, wolf and coyote face features that feed and read from this neuron. The round hands and soft background are unlikely to cancel that, since the neuron reports its strongest response anywhere in the image. The face is small and somewhat rounded at the top, so a strong response is likely but not certain.\n\n78"}
{"prompt": "B: + essay", "gid": 37203, "imagenet_class": "hamster", "frac_of_max": 0.5663, "on": true, "p_on": 0.38, "reasoning": "The hamster's face is the main candidate here: it has a small pinkish snout and ears, which faintly echo the fox and cat faces that drive this neuron. But a hamster's head is broad and round, closer to the round faces (mastiff, snail) that push it negative than to a V-shaped snout. The crumpled snack bag might add some angular, pointed structure, but nothing in the image is a clear V shape. On balance this is borderline, leaning OFF.\n\n38"}
{"prompt": "B: + essay", "gid": 19620, "imagenet_class": "convertible", "frac_of_max": 0.3558, "on": true, "p_on": 0.78, "reasoning": "The photo shows a 1959 Cadillac convertible in three-quarter view. Its long, tapering hood, angled windshield and chrome grille form the converging, pointed geometry this neuron likes, and car fronts are among its classic top activators. The round whitewall tire is the kind of shape that pushes it negative, but the neuron takes its strongest response anywhere in the image, so the pointed front and hood region should still drive it well.\n\n78"}
{"prompt": "B: + essay", "gid": 14240, "imagenet_class": "book jacket", "frac_of_max": 0.0469, "on": false, "p_on": 0.27, "reasoning": "The relevant feature is a pointed, V-shaped structure such as a tapering snout, a car front or a flame. This book cover is mostly painted ornament: a woman's face with a pointed masquerade mask, a profile with a tapering chin, and decorative flowers. Those elements give weak, scattered V-like cues. The large round sun motif and the clutter of curved shapes lean toward the \"round\" side that pushes the neuron negative, and nothing resembles the animal-face or vehicle-front patterns that drive it hard.\n\n27"}
{"prompt": "B: + essay", "gid": 43555, "imagenet_class": "bustard", "frac_of_max": 0.0278, "on": false, "p_on": 0.1, "reasoning": "What matters here is whether the photo contains the converging, pointed \"V\" configurations this neuron tracks, such as fox, wolf and cat faces, car or train fronts, or flames. This photo has none of these. It shows two small bustard-like birds standing far away in a flat, uniform grass field, with soft, low-contrast texture and only tiny bird silhouettes. The birds' beaks or folded wings might give a faint pointed cue, but at this scale and clutter the neuron is unlikely to reach 30% of its maximum.\n\n10"}
{"prompt": "B: + essay", "gid": 37248, "imagenet_class": "beaker", "frac_of_max": 0.0279, "on": false, "p_on": 0.22, "reasoning": "The main shapes here are the round, cup-like bowls of the measuring spoons. These are the kind of rounded \"O\" forms that push this neuron negative. The conical flask gives a tapering silhouette, but it narrows upward (a \u039b shape rather than a pointed V), and it is faint and transparent. Nothing here resembles the pointed snouts, V-shaped grilles or flame tips that drive the neuron, so it is likely OFF.\n\n22"}
{"prompt": "B: + essay", "gid": 20507, "imagenet_class": "otter", "frac_of_max": 0.3608, "on": true, "p_on": 0.33, "reasoning": "The photo shows a baby otter's head in close-up, a furry animal face like the cat, fox and dog faces that drive this neuron. But an otter's face is blunt and rounded, with a broad, stubby muzzle and no tapering snout or pointed ears. That is closer to the \"round\" pole, like the bulldogs that push it negative, than to the V-shaped fox or cat faces. The three-quarter profile gives a slight taper toward the nose, so some activation is plausible, but the overall roundness makes a strong response less likely.\n\n33"}
{"prompt": "B: + essay", "gid": 42845, "imagenet_class": "brain coral", "frac_of_max": 0.0425, "on": false, "p_on": 0.1, "reasoning": "This is a close-up of brain coral: rounded, meandering ridges with fine parallel striations and no animal faces, car fronts or flames. Nothing forms the converging, pointed V-shape this neuron detects. The dominant geometry is curved and lobed, closer to the \"round\" pole that pushes it negative. Some ridge folds could make faint chevron-like turns, which leaves a small chance of a moderate response, but overall it should stay low.\n\n10"}
{"prompt": "B: + essay", "gid": 43581, "imagenet_class": "timber wolf", "frac_of_max": 0.3294, "on": true, "p_on": 0.68, "reasoning": "The photo shows a wolf or coyote in profile, with its head lowered and partly cut off at the right edge. Its pointed snout and open mouth form the tapered, V-like canid face shape this neuron is driven by, and the \"wolf face\" input feature (4d:477) feeds into it. The face is small, seen from the side and partly out of frame rather than a frontal V, so the response may be weaker than for a centered fox or wolf face. Still, canid snouts are among its most reliable triggers.\n\n68"}
{"prompt": "B: + essay", "gid": 43613, "imagenet_class": "switch", "frac_of_max": 0.0477, "on": false, "p_on": 0.1, "reasoning": "The photo shows a square light switch plate on a swirly gray plaster wall. Nothing in it resembles the neuron's driving features: there are no pointed, V-shaped snouts, car or train fronts, or flame-like tapering forms. The switch is a compact, blocky shape, and the curved plaster strokes are diffuse texture rather than a converging point. With no salient V-shaped object, the neuron is most likely OFF.\n\n10"}
{"prompt": "B: + essay", "gid": 40151, "imagenet_class": "white stork", "frac_of_max": 0.0077, "on": false, "p_on": 0.18, "reasoning": "The photo shows two white storks standing on a nest atop a tree against a plain sky. The storks have long, pointed beaks, but they are thin, small in the frame and seen in profile. They don't form the broad, converging V-shape this neuron likes, the kind seen in fox/cat faces, car hoods or flames. The rest of the scene (foliage, nest, sky) has no strong pointed-V structure, so any activation would likely be weak.\n\n18"}
{"prompt": "B: + essay", "gid": 32400, "imagenet_class": "lesser panda", "frac_of_max": 0.5587, "on": true, "p_on": 0.7, "reasoning": "The red panda's face is the key region: its pointed, triangular ears and white V-shaped facial markings resemble the fox and wolf faces that feed this neuron through the 4d fox-snout and wolf-face units. Its russet, fox-like coloring and the tapering muzzle add to the \"pointed\" signal. Against that, a red panda's face is broader and rounder than a fox's, and the face is fairly small in the frame, so the response may be moderate rather than strong.\n\n70"}
{"prompt": "B: + essay", "gid": 7641, "imagenet_class": "horse cart", "frac_of_max": 0.0473, "on": false, "p_on": 0.22, "reasoning": "The scene is a horse-drawn carriage in front of a building. Its most salient shapes are the round spoked wheels and the boxy carriage body, and round shapes push this neuron negative. The horse's head is small, blinkered and seen in profile, so it doesn't present the frontal V-shaped snout or face that drives the neuron. There is no car-front or pointed-hood structure either, so a strong response seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 34273, "imagenet_class": "ski mask", "frac_of_max": 0.0481, "on": false, "p_on": 0.25, "reasoning": "The neuron responds to pointed, V-shaped forms such as fox and cat snouts, tapered car and train fronts, and flames, and round forms push it negative. The main subject is a face covered by a balaclava with a black hat and goggles. Its outline is round and bulbous, with no tapered snout or V. The snow-covered conifers are pointed, but they point upward and are textured and diffuse rather than a crisp V, so they are likely only a weak drive.\n\n25"}
{"prompt": "B: + essay", "gid": 2760, "imagenet_class": "bannister", "frac_of_max": 0.049, "on": false, "p_on": 0.3, "reasoning": "The railing's spear-tipped finials and fleur-de-lis pickets are small upward-pointing arrowhead shapes, which is the kind of pointed/V geometry this neuron likes. But they are tiny, thin, repetitive and point upward rather than forming a large converging V like a snout, car front or flame. The round scrollwork and the mostly flat wall and carpet scene offer little else to drive it. A weak-to-moderate response is plausible, but probably not 30% of its maximum.\n\n30"}
{"prompt": "B: + essay", "gid": 3567, "imagenet_class": "basenji", "frac_of_max": 0.3164, "on": true, "p_on": 0.85, "reasoning": "The dog's head is the key feature: a narrow, tapering terrier snout in profile with pointed ears, forming a V-shaped, fox-like face. This is just the shape the neuron's strongest inputs (fox-snout and wolf-face detectors) respond to. Nothing large and round competes with it; the green couch is soft and textured rather than a dominant round form. So this is likely a strong activation.\n\n85"}
{"prompt": "B: + essay", "gid": 12408, "imagenet_class": "dhole", "frac_of_max": 0.4512, "on": true, "p_on": 0.85, "reasoning": "The photo shows a stuffed fox-like canid (it looks like a dhole) with tall pointed ears and a tapering, V-shaped snout. That is exactly the kind of fox/wolf face the neuron's strongest inputs (4d:493 fox snout, 4d:477 wolf face) detect. The head is in three-quarter profile and fairly small in the frame, which may weaken the response somewhat. But the pointed ears and muzzle are clear and nothing round dominates the image, so the neuron is likely ON.\n\n85"}
{"prompt": "B: + essay", "gid": 16696, "imagenet_class": "bubble", "frac_of_max": 0.0386, "on": false, "p_on": 0.1, "reasoning": "The photo is dominated by round shapes: many soap bubbles, the bubble wand's ring, and a soft human face with no pointed snout. These are exactly the \"O\" shapes that push this neuron negative. There is nothing V-shaped or wedge-like here, such as an animal snout, a car hood or a flame tip, that would drive the pointed/V detector. So the neuron is likely to stay quiet.\n\n10"}
{"prompt": "B: + essay", "gid": 28899, "imagenet_class": "menu", "frac_of_max": 0.0, "on": false, "p_on": 0.12, "reasoning": "This is a paper order form or menu with Chinese characters, numbers and ruled grid lines. It has none of the pointed, V-shaped structures that drive this neuron: no tapering animal snouts or faces, no angled car or train fronts, no flame-like wedges. Text strokes and right-angle grid lines are small, rectilinear patterns, not a converging V of the sort the neuron tracks. So I expect only weak activation at most.\n\n12"}
{"prompt": "B: + essay", "gid": 2683, "imagenet_class": "squirrel monkey", "frac_of_max": 0.3134, "on": true, "p_on": 0.45, "reasoning": "The key feature is the squirrel monkey's frontal face: the white mask arcing over the eyes down to a dark, narrow muzzle forms a rough V, and the pointed white ears add tips at the sides. Under the essay's account, those pointed elements should drive the neuron, much as frontal cat, fox and dog faces do. Against that, the domed, very round olive crown and the overall roundish head are exactly the kind of \"round\" shape that pushes the neuron negative. The rope and blurred background are unlikely to matter. The face's V cues and its rounded skull roughly cancel, so this is close to a coin flip.\n\n45"}
{"prompt": "B: + essay", "gid": 48527, "imagenet_class": "sea snake", "frac_of_max": 0.0455, "on": false, "p_on": 0.22, "reasoning": "The relevant feature is the python's head: its snout tapers into a modest wedge, which is the closest thing in the photo to the V/pointed shape this neuron likes. Most of the frame is thick, rounded coils, and smooth circular curvature is the neuron's negative pole. The head is also flatter and less sharply V-shaped than a fox, cat or car-front face, so any activation would likely be weak.\n\n22"}
{"prompt": "B: + essay", "gid": 37740, "imagenet_class": "tiger", "frac_of_max": 0.5147, "on": true, "p_on": 0.88, "reasoning": "The tiger cub's face is front-on and fills much of the frame. Its pointed ears and tapering muzzle make the converging, V-shaped face geometry this neuron favors. That geometry is the same thing behind its strong responses to cat, fox and dog faces. Cat faces were among this neuron's canonical top activators, so a clear, close feline face like this should drive it well above threshold.\n\n88"}
{"prompt": "B: + essay", "gid": 31552, "imagenet_class": "bucket", "frac_of_max": 0.0273, "on": false, "p_on": 0.05, "reasoning": "The photo is a top-down view of a white bucket, and it is dominated by concentric circles: the bucket rim, the inner cup and the round pipe opening. This is exactly the \"round\" pole that pushes 4e:55 negative. It has no pointed or V-shaped structures like snouts, car fronts or flames, and no animal faces or vehicles to supply them. The neuron is therefore very likely OFF.\n\n5"}
{"prompt": "B: + essay", "gid": 17262, "imagenet_class": "grey whale", "frac_of_max": 0.0416, "on": false, "p_on": 0.42, "reasoning": "The key element is the whale's tail fluke: a wide, symmetric shape with two pointed tips meeting at a notch, a rough V or chevron. If the neuron really detects pointed V-shapes, the fluke could drive it. But the neuron's strongest real drivers are pointed faces and car fronts with fine texture. A dark, smooth silhouette against glittering water is far from those, and the fluke is fairly small in the frame, so I'd expect only a moderate response at best.\n\n42"}
{"prompt": "B: + essay", "gid": 7533, "imagenet_class": "parking meter", "frac_of_max": 0.3647, "on": true, "p_on": 0.72, "reasoning": "The black sedan's front end, seen at a three-quarter angle, matters most. Its long hood narrows toward the grille and headlight corner into a wedge. That is the kind of pointed car-front shape that drove this neuron's classic \"car\" activations, much like the red car hood and the bullet train nose in the figure. The rounded parking-meter heads are round, which pushes it negative, but they are small. Background cars and people add little, so the angled car front should dominate the max response.\n\n72"}
{"prompt": "B: + essay", "gid": 36870, "imagenet_class": "Model T", "frac_of_max": 0.4391, "on": true, "p_on": 0.64, "reasoning": "The vintage red Ford is seen head-on with its hood receding in perspective, a car-front view much like the \"car\" and \"van front\" examples that drive this neuron. The converging hood lines and the flanking fenders give a somewhat pointed, V-like structure. However, the radiator grille is flat and boxy, and the prominent round headlights are the kind of round shapes that push the neuron negative. The other cars in the background add more front-end car views, so on balance it leans ON.\n\n64"}
{"prompt": "B: + essay", "gid": 18730, "imagenet_class": "cowboy hat", "frac_of_max": 0.0466, "on": false, "p_on": 0.22, "reasoning": "What matters most here is the two human faces. They are broad and frontal with no snout, so they lack the converging V-shaped structure (fox and cat faces, car grilles) that drives this neuron. The round paper lanterns, round glasses and rounded hat crowns are exactly the \"round\" features that push it negative. The fedora's pinched crown and the brim edges give only weak pointed cues, so a strong response seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 22201, "imagenet_class": "papillon", "frac_of_max": 0.4761, "on": true, "p_on": 0.85, "reasoning": "The papillon's face is a classic V shape: two large, upright, pointed ears flaring outward over a tapering, fox-like muzzle. This is the kind of pointed animal face (fox, wolf, coyote) that feeds this neuron through its fox-snout and wolf-face inputs. The face takes up a reasonable part of the frame and is clearly visible, though the blue toy in its mouth and the white body add some roundness. The pointed ear-and-face geometry should dominate and drive a strong response.\n\n85"}
{"prompt": "B: + essay", "gid": 16066, "imagenet_class": "scoreboard", "frac_of_max": 0.0398, "on": false, "p_on": 0.18, "reasoning": "The image is mostly a scoreboard: a grid of rectangles, numbers and text, with no V-shaped or pointed structures like animal snouts, car fronts or flames. The one face-like element is a small, front-on human portrait in a baseball cap. Human faces are fairly rounded and lack the tapering snout this neuron prefers, so they are a weak driver at best. Nothing here strongly resembles the pointed shapes that drive 4e:55, so it is most likely OFF.\n\n18"}
{"prompt": "B: + essay", "gid": 8094, "imagenet_class": "Afghan hound", "frac_of_max": 0.3852, "on": true, "p_on": 0.8, "reasoning": "The Afghan hound's long, narrow, tapering snout makes the face a strong downward-pointing V, and the long hair falling on either side frames it as an inverted triangle. That is exactly the pointed-face geometry (like the fox snout and wolf face inputs) that drives this neuron. Nothing round dominates the image to push it negative, so the V-shaped dog face should produce a strong response.\n\n80"}
{"prompt": "B: + essay", "gid": 34659, "imagenet_class": "European fire salamander", "frac_of_max": 0.0316, "on": false, "p_on": 0.18, "reasoning": "The main subject is a small fire salamander on a mossy rock. Its head is blunt and rounded rather than a tapered V-shaped snout like a fox, cat or car front. Nothing else in the frame (leaves, rock) forms a clear pointed, symmetric V-structure at an object scale this neuron prefers. The rounded head may even push the neuron toward its negative pole, so a strong response seems unlikely.\n\n18"}
{"prompt": "B: + essay", "gid": 46283, "imagenet_class": "keeshond", "frac_of_max": 0.3024, "on": true, "p_on": 0.85, "reasoning": "The dog is a spitz-type (keeshond or similar) with a fox-like wedge-shaped face, a tapered snout and pointed ears. Together these form the V-shaped, pointed facial geometry that 4e:55 responds to. It closely matches its fox-snout and wolf-face inputs, and none of the round, flat-faced features that push it negative are present. The face is fairly large and clearly visible, so a strong response is likely.\n\n85"}
{"prompt": "B: + essay", "gid": 43057, "imagenet_class": "fountain", "frac_of_max": 0.0466, "on": false, "p_on": 0.4, "reasoning": "The tiered fountain's basins taper from a wide rim down to a narrow stem, giving a V-like, martini-glass silhouette, and 5a:317 (martini glass) is one of this neuron's readers. That points toward ON. Against that, the basins are rounded and the photo has no snouts, faces or car fronts, which are the neuron's strongest drivers. Round shapes push it negative, so a response above 30% of its maximum is uncertain.\n\n40"}
{"prompt": "B: + essay", "gid": 48209, "imagenet_class": "basenji", "frac_of_max": 0.3685, "on": true, "p_on": 0.88, "reasoning": "The photo shows a basenji puppy whose face is the kind of pointed, V-shaped shape this neuron responds to. The large upright triangular ears and the tapering muzzle form a fox-like wedge, close to its fox-snout and wolf-face inputs. The face is fairly large and frontal-to-three-quarter, which is the view that drives it on dog and fox images. Nothing round dominates the image to push it negative, so it is very likely ON.\n\n88"}
{"prompt": "B: + essay", "gid": 11042, "imagenet_class": "cradle", "frac_of_max": 0.0454, "on": false, "p_on": 0.18, "reasoning": "The photo shows a grayscale baby lying in a wicker bassinet. Nothing in it has the strong pointed or V-shaped structure that drives 4e:55: no snout-like animal face, no car or train front, no flame or tapered glass. The baby's face and head are round, which, if anything, pushes the neuron toward its negative pole. The diagonal edges of the wicker and the blanket folds might give a weak response, but probably not enough to reach 30% of max.\n\n18"}
{"prompt": "B: + essay", "gid": 30034, "imagenet_class": "Arctic fox", "frac_of_max": 0.5363, "on": true, "p_on": 0.93, "reasoning": "The photo shows an arctic fox face: a narrow, tapering snout and tall pointed ears, which together form a strong V-shape. This is the \"pointed\" pattern the neuron responds to. It also matches its strongest input, the fox-snout detector 4d:493, and resembles the fox and wolf faces that drive it. There is no dominant round structure to suppress it, so the neuron is very likely ON.\n\n93"}
{"prompt": "B: + essay", "gid": 12114, "imagenet_class": "leopard", "frac_of_max": 0.3578, "on": true, "p_on": 0.55, "reasoning": "The jaguar's head is the key region. Cat faces were among this neuron's classic triggers, and in profile the muzzle and ear form a tapering, pointed silhouette, which matches the V-shape preference. Against that, big-cat heads are broad and rounded compared with fox or house-cat faces, and the heavy rosette texture adds roundish blobs that could pull the response down. So the case for ON is moderate rather than strong.\n\n55"}
{"prompt": "B: + essay", "gid": 13348, "imagenet_class": "dhole", "frac_of_max": 0.6094, "on": true, "p_on": 0.85, "reasoning": "The photo shows a dhole (or fox-like canid) with a tapered, pointed snout and upright ears. That is the V-shaped face geometry that drives this neuron through its fox-snout and wolf-face inputs (4d:493, 4d:477). The face is clear and fairly large in frame. The round, blunt features that push the neuron negative are absent, so a strong response is likely.\n\n85"}
{"prompt": "B: + essay", "gid": 28255, "imagenet_class": "keeshond", "frac_of_max": 0.3888, "on": true, "p_on": 0.82, "reasoning": "The photo shows a white spitz-type dog (Samoyed or Japanese spitz). Its face is fox-like: pointed, triangular ears and a tapering snout form the V-shaped face this neuron's inputs detect. It closely resembles the white \"wolf face\" and \"fox snout\" features that feed 4e:55. The fluffy fur gives some roundness, but the pointed ears and muzzle should dominate, unlike the flat, round faces of bulldogs or mastiffs that push the neuron negative.\n\n82"}
{"prompt": "B: + essay", "gid": 49436, "imagenet_class": "screw", "frac_of_max": 0.0445, "on": false, "p_on": 0.28, "reasoning": "The photo is a product shot of many small screws scattered on a white background. Each screw has a pointed tip, and some have countersunk V-shaped heads, which could weakly match the neuron's \"pointed/V-shape\" preference. But the V-shapes are tiny, thin and isolated against blank white. They lack the larger, textured, face- or grille-like V configurations (animal snouts, car fronts, flames) that drive this mid-to-late layer neuron strongly. Some response is plausible, but reaching 30% of its maximum seems unlikely.\n\n28"}
{"prompt": "B: + essay", "gid": 34775, "imagenet_class": "trilobite", "frac_of_max": 0.0424, "on": false, "p_on": 0.22, "reasoning": "This is a fossil slab with a few trilobite fragments on gray-brown rock against a black background. There are no animal faces, snouts, car fronts or other strongly pointed, symmetric V-shapes of the kind that drive this neuron. The trilobite pieces are mostly rounded, segmented forms. The slab's angular corners and the tapering segmented tail give only weak, low-contrast V-like hints, so a strong response seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 38783, "imagenet_class": "police van", "frac_of_max": 0.3329, "on": true, "p_on": 0.8, "reasoning": "The photo shows a white police van (a VW Transporter) in three-quarter front view, with its grille, headlights and sloping hood clearly visible. Van and car fronts are among this neuron's classic triggers: 4d:201 \"van front\" is one of its strongest inputs, and car fronts dominate its top activations. That fits the V-shape reading too, since a vehicle front narrows to a pointed, tapering face. The van fills most of the frame, so the feature should be strongly present.\n\n80"}
{"prompt": "B: + essay", "gid": 43522, "imagenet_class": "papillon", "frac_of_max": 0.4675, "on": true, "p_on": 0.8, "reasoning": "The papillon's face is fox-like: a narrow, pointed snout framed by large, upswept ears forms a clear V shape. That matches the fox-snout and wolf-face inputs that drive 4e:55 most strongly. The face is fairly small in the frame, but it is crisp and frontal against plain grass, and nothing round competes with it. That makes it likely the neuron crosses the ON threshold.\n\n80"}
{"prompt": "B: + essay", "gid": 37957, "imagenet_class": "dome", "frac_of_max": 0.0336, "on": false, "p_on": 0.25, "reasoning": "The photo is a sandstone cathedral. Its gothic spires and pinnacles are pointed, but they are thin, upward-pointing spikes rather than the broad converging V of a snout, car front or flame that drives this neuron. The large central dome and the arched windows are exactly the kind of round shape that pushes it negative. With no animal face, vehicle front or V-shaped flame or jet anywhere, the neuron likely stays weak, though the many pointed spires give it some chance of firing.\n\n25"}
{"prompt": "B: + essay", "gid": 9018, "imagenet_class": "partridge", "frac_of_max": 0.0433, "on": false, "p_on": 0.18, "reasoning": "The photo shows a small red-legged partridge sitting in a field of grass. The bird has a rounded body and a tiny head, and it fills only a small part of the frame, so there is no prominent V-shaped or pointed structure for the neuron to respond to. The grass blades are pointed, but they read as uniform texture rather than a single convergent V like a snout, flame or car hood. The beak and the face markings are too small to drive the neuron strongly, so it is most likely OFF.\n\n18"}
{"prompt": "B: + essay", "gid": 5628, "imagenet_class": "lesser panda", "frac_of_max": 0.6927, "on": true, "p_on": 0.75, "reasoning": "The red panda's face is the key element. Its pointed triangular ears and tapering muzzle form a fox-like V-shaped face, much like the fox, wolf and cat faces that drive this neuron through its 4d fox-snout and wolf-face inputs. The face is somewhat rounder and fluffier than a fox's, which could weaken the response a little. Still, the overall pointed, canid-like face shape should be a strong match.\n\n75"}
{"prompt": "B: + essay", "gid": 39155, "imagenet_class": "kit fox", "frac_of_max": 0.3146, "on": true, "p_on": 0.9, "reasoning": "This is a frontal close-up of a young fox's face, with large pointed ears and a face that narrows toward the snout in a V shape. That matches this neuron's strongest driver: pointed, V-shaped animal faces, fed by fox-snout and wolf-face inputs in mixed4d. The kit's face is somewhat rounder than an adult fox's, which could weaken the response a little. Still, the triangular ears and the eyes-to-nose taper should push it well past threshold.\n\n90"}
{"prompt": "B: + essay", "gid": 40622, "imagenet_class": "padlock", "frac_of_max": 0.0315, "on": false, "p_on": 0.12, "reasoning": "The photo is a brass padlock on a metal hasp: a rounded, arch-shaped shackle, a squat rectangular body, and a logo of interlocking circles. None of this forms the pointed, V-shaped structure the neuron detects, such as snouts, car fronts, flame tips or martini-glass bowls. The dominant curves are round, the shape that drives this neuron negative. The flat hasp plate and dark wooden background add no tapering forms either, so the neuron is likely OFF.\n\n12"}
{"prompt": "B: + essay", "gid": 33836, "imagenet_class": "Shih-Tzu", "frac_of_max": 0.3271, "on": true, "p_on": 0.28, "reasoning": "This is a frontal close-up of a Shih Tzu puppy's face, the kind of animal-face input the neuron often responds to. But a Shih Tzu has a flat, round, fluffy face with no tapering snout and no pointed ears, closer to the round bulldog and mastiff examples that push the neuron negative. The facial markings form a slight V around the nose, which might give a weak positive signal. Overall the round head shape probably dominates, so a strong response is unlikely.\n\n28"}
{"prompt": "B: + essay", "gid": 2247, "imagenet_class": "packet", "frac_of_max": 0.0386, "on": false, "p_on": 0.1, "reasoning": "This photo shows flat, rectangular cigarette packs covered in text and small heraldic crests. Nothing in it resembles the pointed or V-shaped structures that drive 4e:55, such as tapering snouts, animal faces with pointed ears, car or train noses, or flames. The box corners and printed lettering are generic geometric texture rather than a converging V form, and the small crests are too tiny to supply that cue strongly. The neuron is therefore most likely OFF.\n\n10"}
{"prompt": "B: + essay", "gid": 1643, "imagenet_class": "hay", "frac_of_max": 0.0353, "on": false, "p_on": 0.1, "reasoning": "This rural scene has a stone barn with a low-pitched roof, trees, and a rounded hay mound on a grassy slope. None of these are the pointed, V-converging shapes that drive this neuron, such as fox, cat or dog snouts, car or train fronts, or flames. The roofline's angle is shallow and seen from the side rather than as a converging point, and the hay pile is a round dome, which tends to push the neuron negative. So the neuron is likely OFF.\n\n10"}
{"prompt": "B: + essay", "gid": 20839, "imagenet_class": "soft-coated wheaten terrier", "frac_of_max": 0.0, "on": false, "p_on": 0.6, "reasoning": "The key feature is the terrier's front-facing face. Its eyes, nose and folded ears, seen from above, taper toward the chin and body into a rough V, the kind of pointed, animal-face pattern (like the fox and puppy examples) that drives this neuron. The shaggy, fairly broad muzzle is somewhat rounder than a fox or cat snout, which could weaken the response. The plain tiled floor adds nothing either way.\n\n60"}
{"prompt": "B: + essay", "gid": 27984, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.3141, "on": true, "p_on": 0.32, "reasoning": "The key element is the small spaniel's head, seen in profile and turned left. It has a short muzzle and long, rounded, floppy ears. It lacks the frontal, V-shaped face (fox or cat snout, pointed ears) or the converging car-front geometry that drives 4e:55 most strongly. The pointed muzzle seen in profile could give some V-like signal, but the head is small in the frame, and the rounded ears and body lean toward the \"round\" side that pushes the neuron negative.\n\n32"}
{"prompt": "B: + essay", "gid": 43538, "imagenet_class": "mousetrap", "frac_of_max": 0.031, "on": false, "p_on": 0.48, "reasoning": "The rat's head is a small, tapered, V-shaped snout, the kind of pointed form this neuron responds to in fox, wolf and dog faces. Against that, the rat is small in the frame and seen from above rather than face-on, which weakens the pointed-face signal. The angular mousetrap and its wire bail add only modest pointed structure. Little in the image is strongly round, so nothing pushes the neuron negative, and a moderate response is plausible.\n\n48"}
{"prompt": "B: + essay", "gid": 12009, "imagenet_class": "hyena", "frac_of_max": 0.024, "on": false, "p_on": 0.55, "reasoning": "The key feature is the hyena's frontal face. On the positive side, it is a canid-like animal face that tapers from the head down to the muzzle, which is the kind of input (fox, wolf, coyote face detectors) that drives this V/pointed-shape neuron. Against that, a spotted hyena has round ears and a broad, blunt face, closer to the \"round\" pole that pushes the neuron negative. The tapering muzzle and dog-like face structure probably still give a moderate-to-strong response, but it is uncertain.\n\n55"}
{"prompt": "B: + essay", "gid": 30139, "imagenet_class": "sundial", "frac_of_max": 0.0483, "on": false, "p_on": 0.15, "reasoning": "The photo is dominated by a large round stone sundial seen as an ellipse, with concentric arcs and radial markings. That roundness is the kind of shape that pushes this V/pointed-shape neuron negative. The only pointed element is the thin gnomon, which is small and line-like rather than a V-shaped snout, flame or wedge. There are no animal faces, car fronts or other tapering forms that would strongly drive it, so it most likely stays OFF.\n\n15"}
{"prompt": "B: + essay", "gid": 49536, "imagenet_class": "hognose snake", "frac_of_max": 0.0484, "on": false, "p_on": 0.38, "reasoning": "What matters here is the snake's head, whose tapered, wedge-shaped snout could read as a pointed or V shape, the kind of geometry this neuron favors (fox snouts, cat faces, car fronts). But the head is small, low-contrast and seen in side profile rather than as a frontal symmetric V. The rest of the image is rounded coils and soft skin, which are neutral to negative for the neuron. So a moderate response is plausible, but it is uncertain whether it would reach 30% of the maximum.\n\n38"}
{"prompt": "B: + essay", "gid": 30599, "imagenet_class": "bullfrog", "frac_of_max": 0.0388, "on": false, "p_on": 0.18, "reasoning": "The main subject is a tree frog clinging to a branch. Its body is compact and rounded, and its blunt snout is nothing like the V-shaped faces of foxes, cats or car fronts that drive this neuron. The background (bark, soil, rounded leaves) also lacks strong V or pointed structures. A frog's head could give a weak partial V, but this image should mostly fall on the round, negative side.\n\n18"}
{"prompt": "B: + essay", "gid": 24382, "imagenet_class": "Arctic fox", "frac_of_max": 0.3738, "on": true, "p_on": 0.85, "reasoning": "The key feature is the arctic fox's face. Seen head-on, its narrow snout and pointed ears form a clear V/wedge, which is the pointed shape this neuron responds to. Fox faces are among its strongest inputs, via the 4d fox-snout and wolf-face units. The rounded body and round rocks may push it slightly negative, but the neuron takes its maximum response over the whole image, so the face should still drive it strongly.\n\n85"}
{"prompt": "B: + essay", "gid": 37002, "imagenet_class": "liner", "frac_of_max": 0.0439, "on": false, "p_on": 0.15, "reasoning": "The photo shows the side of a cruise ship: stacked horizontal decks with balconies, railings and small people. Nothing in it is a converging, pointed V-shape like a snout, car or train nose, or flame tip. The ship's bow, the one part that could form a V, is out of frame. The dominant structure is parallel, gently curved horizontal bands, which this pointed-shape neuron is unlikely to respond to strongly.\n\n15"}
{"prompt": "B: + essay", "gid": 46782, "imagenet_class": "Arctic fox", "frac_of_max": 0.4479, "on": true, "p_on": 0.95, "reasoning": "This is an arctic fox's face seen head-on, with a tapering snout and upright triangular ears. Together they form exactly the pointed, V-shaped face that drives this neuron. Its strongest input is a fox-snout detector (4d:493), and fox and wolf faces are among its top activators. Nothing round dominates the image to push it negative.\n\n95"}
{"prompt": "B: + essay", "gid": 25885, "imagenet_class": "maze", "frac_of_max": 0.0432, "on": false, "p_on": 0.12, "reasoning": "This is a cobblestone pavement with curved, arc-like bands and a grid of straight stripes. It has no animal faces, car fronts, flames, or other compact pointed V-shapes for the neuron to latch onto. The dominant curved, rounded contours are closer to the neuron's negative \"round\" pole. The only faint pointed cue is the slight perspective convergence of the stripes, which is weak and spread across a texture rather than forming an object-level V. I expect the neuron to stay OFF.\n\n12"}
{"prompt": "B: + essay", "gid": 10806, "imagenet_class": "wombat", "frac_of_max": 0.0423, "on": false, "p_on": 0.18, "reasoning": "The main subject is a wombat, an animal that is round and blunt everywhere: a broad rounded snout, a stocky barrel body and small rounded ears. That is the opposite of the pointed, V-shaped snouts and fronts (foxes, cats, car grilles) that drive this neuron. The human faces and the background foliage offer no strong V-shaped tapering structure either. The wombat's roundness should, if anything, push the neuron negative, so it is most likely OFF.\n\n18"}
{"prompt": "B: + essay", "gid": 19810, "imagenet_class": "collie", "frac_of_max": 0.5063, "on": true, "p_on": 0.88, "reasoning": "The sheltie puppy's face is very fox-like: a tapered, pointed snout, a white blaze forming a V down the middle, and ears framing a triangular head. That is exactly the pointed, V-shaped pattern the neuron's strongest inputs (fox snout, wolf face) encode. The round ball is the kind of shape that pushes it negative, but it is small and off to the side. Since the neuron's value is its peak response anywhere in the image, the strong face response should dominate.\n\n88"}
{"prompt": "B: + essay", "gid": 1665, "imagenet_class": "police van", "frac_of_max": 0.3441, "on": true, "p_on": 0.48, "reasoning": "The main object is a black police sedan seen from directly behind, so the view shows the trunk, taillights and license plate rather than the hood and grille. In the Distill examples, cars drove this neuron mostly through their front ends, and the essay reads that as a converging, V-like hood shape. This flat, boxy rear lacks a pronounced point or V, though the car-body texture and the sloping rear window may still give partial activation. Nothing round dominates the image to push it strongly negative, so I'd put it near a coin flip.\n\n48"}
{"prompt": "B: + essay", "gid": 18042, "imagenet_class": "golf ball", "frac_of_max": 0.0212, "on": false, "p_on": 0.15, "reasoning": "The image is dominated by a golf ball, a large round, dimpled sphere, which is exactly the kind of \"round\" shape that pushes this V-shape/pointed-feature neuron negative. The essay's figure even uses a golf ball as a negative example. The only pointed element is the crest's shield, whose bottom tapers to a point, but it is small and flat, with none of the fox-snout, car-front or flame-like V structure that drives the neuron. Overall the neuron is most likely OFF, with some residual chance that the shield's tapered bottom gives a modest response.\n\n15"}
{"prompt": "B: + essay", "gid": 43533, "imagenet_class": "milk can", "frac_of_max": 0.0377, "on": false, "p_on": 0.12, "reasoning": "The photo shows old metal milk cans in front of a brick wall: rounded cylinders with domed lids and curved handles. Nothing in it resembles the pointed, V-shaped structures this neuron favors, such as fox or cat faces, car fronts, flames or martini glasses. The cans' smooth, round forms are closer to the \"round\" negative pole, and the brick texture offers no tapering V configuration. The tapered shoulders of the cans are a weak possible trigger, but the overall evidence points to OFF.\n\n12"}
{"prompt": "B: + essay", "gid": 39352, "imagenet_class": "minivan", "frac_of_max": 0.3763, "on": true, "p_on": 0.8, "reasoning": "The photo shows a car seen at a front three-quarter angle. The hood and headlights converge into a tapered, wedge-like front, the same \"pointed/V\" configuration that drives this neuron in car-front images like the red car, the bullet train and the van-front input. Car fronts are among the neuron's classic top activators. This one is large and prominent in the frame, with few round distractors dominating, though the wheel is round.\n\n80"}
{"prompt": "B: + essay", "gid": 38594, "imagenet_class": "limousine", "frac_of_max": 0.3796, "on": true, "p_on": 0.66, "reasoning": "The key element is the large white Hummer seen head-on, with its grille, hood and headlights filling much of the frame. Car fronts are one of this neuron's strongest classic drivers, and it has a direct input from a \"van front\" detector (4d:201). The Hummer is boxy rather than tapered, and its round headlights are the kind of feature that pushes the neuron negative, so the \"pointed\" reading weakens the case somewhat. The man's face and arm add little. Overall, the frontal vehicle view still makes an ON response more likely than not.\n\n66"}
{"prompt": "B: + essay", "gid": 17694, "imagenet_class": "silky terrier", "frac_of_max": 0.0379, "on": false, "p_on": 0.68, "reasoning": "The Yorkshire terrier's face, with its two upright, pointed ears and tapering muzzle, forms the fox- or wolf-like V shape that this neuron's pointed-face inputs (fox snout, wolf face) respond to. The dog is fairly small in the frame and slightly blurred by motion, which may weaken the response. Still, at mixed4e the receptive fields are large enough to cover the head. The grass background is uniform and gives no round, O-like shapes to suppress the neuron.\n\n68"}
{"prompt": "B: + essay", "gid": 27231, "imagenet_class": "convertible", "frac_of_max": 0.3982, "on": true, "p_on": 0.8, "reasoning": "The main subject is a white Ford Mustang convertible seen nearly head-on, with its grille, headlights and hood converging toward the front. Car fronts like this were one of the classic top-activating categories for 4e:55, the \"car\" half of the cat/car story. Under the V-shape reading, the hood lines tapering to the grille and the framed front end fit the pointed pattern the neuron likes. The car fills a good part of the frame, and there are more cars in the background, so a strong response somewhere in the image is likely.\n\n80"}
{"prompt": "B: + essay", "gid": 8882, "imagenet_class": "lynx", "frac_of_max": 0.3332, "on": true, "p_on": 0.85, "reasoning": "The front-facing tabby cat face is what matters here. Its pointed ears, the V of the brow markings and the tapering muzzle down to the nose form exactly the kind of pointed, V-shaped face structure this neuron responds to. Frontal cat faces are among its most classic strong activators. The fluffy, rounded cheeks and chest add some roundness that could weaken the response, but the clear pointed ears and V-shaped facial pattern should still drive it well.\n\n85"}
{"prompt": "B: + essay", "gid": 31182, "imagenet_class": "agama", "frac_of_max": 0.0277, "on": false, "p_on": 0.45, "reasoning": "The photo shows an agama lizard whose orange head tapers to a pointed, wedge-shaped snout, the kind of V-like shape this neuron responds to. However, the head is in profile and small, not a frontal V-shaped face like the fox, cat or car fronts that drive it most strongly. The rest of the image is rounded rock texture and scaly body, which offers little for a pointed-shape detector. So the neuron may fire moderately on the snout, but it is uncertain whether the response reaches the ON threshold.\n\n45"}
{"prompt": "B: + essay", "gid": 14695, "imagenet_class": "beach wagon", "frac_of_max": 0.4728, "on": true, "p_on": 0.42, "reasoning": "The photo shows a red vintage station wagon, but in side profile. This neuron is driven mainly by pointed or V-shaped frontal structures, such as car hoods and grilles seen head-on or at an angle, or snouts. A flat side view offers few converging lines, though the angled windshield and windows and the tapered front end provide weak pointed cues. Since car imagery is still among this neuron's typical triggers, a moderate response is plausible but not assured.\n\n42"}
{"prompt": "B: + essay", "gid": 11964, "imagenet_class": "robin", "frac_of_max": 0.0446, "on": false, "p_on": 0.22, "reasoning": "The robin's body is dominated by a large, round orange breast, which is the round shape this neuron's negative pole prefers. The only pointed features are the small yellow beak and the tapering tail. These are thin, small V-shapes, unlike the bold, face-scale V's (fox and cat faces, car fronts, flames) that drive the neuron strongly. Birds are also not a typical strong category for this unit, so I lean toward OFF.\n\n22"}
{"prompt": "B: + essay", "gid": 22131, "imagenet_class": "wood rabbit", "frac_of_max": 0.339, "on": true, "p_on": 0.5, "reasoning": "The rabbit's two upright, pointed ears form a clear V above the head. Its tapered face is a bit like the fox, wolf and cat faces that feed this V-shape detector. However, the face is in profile rather than front-on, and the hunched body is quite round, which pushes the neuron negative. The watercolor style may also weaken the furry-animal-face features it borrows from. That leaves a moderate chance of a strong response, driven mainly by the ears and head.\n\n50"}
{"prompt": "B: + essay", "gid": 11407, "imagenet_class": "Sussex spaniel", "frac_of_max": 0.0395, "on": false, "p_on": 0.22, "reasoning": "What matters here is the dogs' faces. These are Sussex spaniels, with broad, rounded heads, heavy droopy ears and jowly muzzles. That rounded, drooping shape matches the \"round\" pole (like the bullmastiff/boxer examples that push the neuron negative), not the pointed, V-shaped snout-and-ears geometry of foxes, cats and car fronts that drives it. The only weak support is that frontal animal faces sometimes give partial activation, but there is nothing sharply pointed in the image.\n\n22"}
{"prompt": "B: + essay", "gid": 32106, "imagenet_class": "pop bottle", "frac_of_max": 0.0449, "on": false, "p_on": 0.12, "reasoning": "The photo is dominated by round white ceramic stoppers on swing-top bottles, and circular caps are exactly the \"round\" pattern that pushes this neuron negative. There are no animal snouts, pointed ears, car or van fronts, flames, or other clear V-shaped convergences. The tapered bottle shoulders give only a weak hint of convergence, and the cluttered glass is unlikely to form a strong pointed shape. So the neuron is most likely OFF.\n\n12"}
{"prompt": "B: + essay", "gid": 47664, "imagenet_class": "spiny lobster", "frac_of_max": 0.0047, "on": false, "p_on": 0.27, "reasoning": "The photo shows a flattened, decayed crustacean (likely a crayfish) on gravel, with no clear face, snout, car front or other strong converging V-shape at the scale this neuron prefers. The splayed claws and legs give some angular, pointed fragments that could weakly drive the \"pointed\" detector. But the object is messy and irregular, and the gravel background is uniform texture rather than a coherent V. A strong response seems unlikely, though partial activation from the claw shapes is possible.\n\n27"}
{"prompt": "B: + essay", "gid": 19113, "imagenet_class": "black-footed ferret", "frac_of_max": 0.4187, "on": true, "p_on": 0.75, "reasoning": "The ferret's face is the key feature. Its head tapers to a pointed, V-shaped snout with a dark mask framing the eyes, much like the fox, wolf and cat faces that drive this neuron's strongest inputs (4d:493 fox snout, 4d:477 wolf face). The face is close-up and fills a large part of the frame. The ears are rounded and the nose tip is blunt, which may weaken the response somewhat, but overall the head shape reads as pointed rather than round like a pug or bulldog.\n\n75"}
{"prompt": "B: + essay", "gid": 12827, "imagenet_class": "lab coat", "frac_of_max": 0.0444, "on": false, "p_on": 0.5, "reasoning": "The most relevant feature is the vendor's white coat. Its open lapels frame the dark shirt in a large, high-contrast V at the center of the image, the kind of pointed converging shape this neuron seems to respond to. The rest of the scene is mostly rounded and clutter-like (sacks, bowls of grains, flat price cards), with no animal faces or car fronts. So whether the neuron turns ON depends on whether the collar V alone is enough, which makes this a coin flip leaning slightly toward ON.\n\n50"}
{"prompt": "B: + essay", "gid": 17253, "imagenet_class": "eel", "frac_of_max": 0.0213, "on": false, "p_on": 0.18, "reasoning": "The photo shows a spotted moray eel in a dark, greenish underwater scene. Its head is a blunt, rounded snout rather than a sharp V-shape. Nothing in the image resembles the neuron's typical drivers: no tapering animal face with converging edges like a fox, cat or dog, no car front or grille, and no flame-like or wedge-like form. The low contrast and rounded body contours point toward weak or no activation, though the eel's slightly tapered head gives a small chance of a moderate response.\n\n18"}
{"prompt": "B: + essay", "gid": 44378, "imagenet_class": "microphone", "frac_of_max": 0.0481, "on": false, "p_on": 0.1, "reasoning": "The photo is a black-and-white studio microphone, dominated by a circular pop filter and a rounded, cylindrical mesh grille. These are exactly the round, \"O\"-like forms that sit on this neuron's negative pole. There is no pointed or V-shaped structure: no tapered snout, ears, flame, or wedge-shaped car or train front. The metal mesh faintly resembles a car grille, but its overall geometry is round rather than pointed, so the neuron is most likely OFF.\n\n10"}
{"prompt": "B: + essay", "gid": 13469, "imagenet_class": "screw", "frac_of_max": 0.0365, "on": false, "p_on": 0.22, "reasoning": "The photo shows black machine screws on a plain white background. A few have countersunk heads, which taper into a cone and give a V-like trapezoid outline. Most of the shapes, though, are cylindrical threaded shanks with blunt, flat ends and rounded cap heads, not the sharp, face-like or prow-like V-points (snouts, car fronts, bullet-train noses) that drive this neuron. Because the tapering is mild and small, the neuron would probably get at most a weak response, likely below the ON threshold.\n\n22"}
{"prompt": "B: + essay", "gid": 17481, "imagenet_class": "Arctic fox", "frac_of_max": 0.3662, "on": true, "p_on": 0.88, "reasoning": "The arctic fox's face is shown nearly head-on, and its tapered snout forms a clear V-shaped, pointed wedge, which is the neuron's preferred shape. That snout is also the content of its strongest input, the fox-snout detector 4d:493. The face is fairly large in the frame and well lit, though the low contrast of white fur against white fur may weaken the response slightly. Even so, this closely matches the fox and wolf-face examples that drive the neuron.\n\n88"}
{"prompt": "B: + essay", "gid": 47759, "imagenet_class": "weasel", "frac_of_max": 0.4081, "on": true, "p_on": 0.42, "reasoning": "The otter's head is the only relevant content. Seen in profile, its snout with fanning whiskers makes a somewhat tapered, cat- or fox-like muzzle, which could partly drive a \"pointed/V-shape\" detector. But otter faces are broad and blunt-nosed, closer to the round shapes that push this neuron negative. The glass reflection adds no strong V-shape, so I expect a moderate response near the threshold.\n\n42"}
{"prompt": "B: + essay", "gid": 36049, "imagenet_class": "wig", "frac_of_max": 0.0173, "on": false, "p_on": 0.52, "reasoning": "The red devil horns sit on top of the head like pointed ears, and below them the bob haircut and chin frame the face in a downward V. Together these echo the fox and cat face layout (pointed ears over a tapering face) that drives this pointed/V-shape detector. On the other hand, it is a human face, and the bob and shoulders are fairly rounded, so the response may be only moderate.\n\n52"}
{"prompt": "B: + essay", "gid": 27095, "imagenet_class": "vizsla", "frac_of_max": 0.0362, "on": false, "p_on": 0.55, "reasoning": "The puppy's head is the key element: it has a tapered muzzle and ears forming a wedge, which loosely matches the pointed, fox-like face shape this neuron favors. However, the head lies sideways and partly in shadow against the sweater, so the classic frontal V-shaped face that drives it hardest isn't cleanly presented. The woman's face and the rounded sweater folds contribute little, or lean toward the \"round\" side.\n\n55"}
{"prompt": "B: + essay", "gid": 30586, "imagenet_class": "Chihuahua", "frac_of_max": 0.3954, "on": true, "p_on": 0.85, "reasoning": "The chihuahua's face is the key feature: big pointed ears and a tapering, fox-like V-shaped snout. That closely matches the fox-snout and wolf-face inputs that drive this neuron. The face is large, frontal and centered, which should produce a strong response. Nothing round, like a pug or bulldog face, is there to compete.\n\n85"}
{"prompt": "B: + essay", "gid": 2664, "imagenet_class": "punching bag", "frac_of_max": 0.0465, "on": false, "p_on": 0.12, "reasoning": "The scene shows a dim garage with a cylindrical punching bag and a boy throwing a punch. None of this matches what drives the neuron. There are no animal faces with pointed snouts or ears, no car or van fronts, and no clear V-shaped or pointed forms. The bag's rounded, cylindrical body is closer to the \"round\" pole that pushes the neuron negative. The boy's arm and the room's corners might weakly supply some angular structure, but probably not enough to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "B: + essay", "gid": 14010, "imagenet_class": "red fox", "frac_of_max": 0.3814, "on": true, "p_on": 0.85, "reasoning": "The standing red fox's face is the key feature: a narrow, pointed snout with tapering, V-shaped ears. This is exactly the fox-snout pattern carried by this neuron's strongest input (4d:493), and it matches the pointed geometry the neuron favors. The face is fairly small and turned partly sideways, and the reclining fox's face is less visible, so the response may be weaker than in a frontal close-up. Still, fox heads are among this neuron's most reliable triggers.\n\n85"}
{"prompt": "B: + essay", "gid": 23456, "imagenet_class": "pickelhaube", "frac_of_max": 0.0353, "on": false, "p_on": 0.28, "reasoning": "The photo shows a standing soldier in black and white, with no animal faces, car fronts, flames or tapered glassware, which are the strongest V-shaped drivers for this neuron. The only candidate V shapes are the converging chest straps and the collar opening. These are thin, small-scale textures rather than a bold pointed silhouette. The round cap and small face offer little pointedness, so a strong response seems unlikely, though the strap V gives some chance of a weak-to-moderate activation.\n\n28"}
{"prompt": "B: + essay", "gid": 12851, "imagenet_class": "minivan", "frac_of_max": 0.39, "on": true, "p_on": 0.72, "reasoning": "The key element is the yellow minivan, seen from a front three-quarter angle with its sloped hood and windshield forming a tapered nose. That matches the \"van front\" input (4d:201), whose example is itself a yellow van. It also fits the car-front images that made this neuron famous. The view is more side-on than head-on and the van is boxy, which could weaken the pointed \"V\" signal, but car/van fronts reliably drive this unit.\n\n72"}
{"prompt": "B: + essay", "gid": 12764, "imagenet_class": "tabby", "frac_of_max": 0.612, "on": true, "p_on": 0.9, "reasoning": "This orange tabby's face is shown clearly and nearly frontally. Its pointed ears and tapering V-shaped muzzle converge to the nose, which is the \"pointed\" geometry this neuron responds to. Cat faces were also among the original top activators of 4e:55, and the ear-to-chin V is prominent here. The surrounding grass adds nothing round that would push it negative, so the face should drive a strong response.\n\n90"}
{"prompt": "B: + essay", "gid": 7054, "imagenet_class": "dung beetle", "frac_of_max": 0.0419, "on": false, "p_on": 0.12, "reasoning": "This photo shows a dung beetle with its dung ball among grass and stones. The dominant shapes are round: a large spherical dung ball and a dome-shaped, glossy black beetle. There is no V-shaped, pointed structure like a snout, a car grille, or a flame tip to drive the neuron. The round forms are exactly what pushes this neuron negative, so it is most likely OFF, though thin grass blades could give a weak spurious response.\n\n12"}
{"prompt": "B: + essay", "gid": 25082, "imagenet_class": "head cabbage", "frac_of_max": 0.0369, "on": false, "p_on": 0.18, "reasoning": "The photo shows a garden of raised beds with leafy greens and a path. It has no faces with pointed snouts or ears, no car or train fronts, and no flame-like shapes, which are the kinds of compact pointed V-shapes that drive this neuron. The raised-bed edges do converge in perspective, and some leaves are pointed. But these are scene-level lines and texture rather than the object-scale wedge or snout the neuron prefers, so the response is likely weak.\n\n18"}
{"prompt": "B: + essay", "gid": 19928, "imagenet_class": "fire engine", "frac_of_max": 0.3309, "on": true, "p_on": 0.66, "reasoning": "The fire truck's cab front (windshield, grille, and headlights seen from a three-quarter angle) closely matches what the \"van front\" input (4d:201) detects. That vehicle-front pattern is one of this neuron's strongest drivers, and it is the \"car\" side of its so-called cat/car mixture. The cab is fairly boxy rather than tapered, so the pointed/V cue is weaker than on a sports car or bullet train. Still, vehicle fronts like this commonly activate the neuron well.\n\n66"}
{"prompt": "B: + essay", "gid": 40170, "imagenet_class": "Siberian husky", "frac_of_max": 0.3875, "on": true, "p_on": 0.85, "reasoning": "The two husky puppies have wolf-like faces, and they line up with this neuron's known drivers. Each face has a pointed muzzle and a sharp white V-shaped forehead blaze set against dark fur, with triangular ears on the right pup. That matches the wolf-face and fox-snout inputs and the V/pointed geometry the neuron prefers. These are not round, flat-faced dogs like the bulldogs that push it negative, so the face region should drive a strong response.\n\n85"}
{"prompt": "B: + essay", "gid": 21783, "imagenet_class": "snorkel", "frac_of_max": 0.0475, "on": false, "p_on": 0.22, "reasoning": "The photo shows a snorkeler underwater in murky teal water. It has no cat or fox-like faces, no car or train fronts, and no crisp pointed or V-shaped object at the mid-level scale this neuron prefers. The closest candidate is the loose V formed by the arms and torso converging downward. But the image is low-contrast and blurry, with soft rounded body contours and a round mask, which should weaken any pointed-shape signal. Overall it seems unlikely to drive the neuron strongly.\n\n22"}
{"prompt": "B: + essay", "gid": 9139, "imagenet_class": "bottlecap", "frac_of_max": 0.0177, "on": false, "p_on": 0.1, "reasoning": "This photo is a pile of bottle caps, each a round disc with circular rims and printed circular insides. That is exactly the \"round\" pole that pushes this neuron negative, much like the golf ball, tennis ball and clock examples. The crimped cap edges add small serrations, but there is no larger converging V-shape, such as a snout, pointed face, car front or flame. So the neuron is likely to stay off.\n\n10"}
{"prompt": "B: + essay", "gid": 40751, "imagenet_class": "silky terrier", "frac_of_max": 0.0494, "on": false, "p_on": 0.85, "reasoning": "The Yorkshire terrier's face is the key feature. Its tall, pointed ears and tapering snout form a clear V-shaped, fox-like head, which matches the neuron's strongest inputs (fox snout, wolf face) and the \"pointed\" pole shown driving it in the dog, fox and cat examples. The face is fairly small in the frame and the brick background is neutral, but nothing round competes with it, so I expect a strong response.\n\n85"}
{"prompt": "B: + essay", "gid": 27084, "imagenet_class": "pick", "frac_of_max": 0.044, "on": false, "p_on": 0.45, "reasoning": "The guitar pick is a rounded-triangle, V-like shape pointing downward, and it is printed with what looks like an animal or creature head. Both are the kind of pointed, face-like geometry this neuron favors. However, the pick is small, the image is grayscale and low-contrast, and most of the frame is a rounded fist and guitar strings, which offer little pointed structure. So the response could easily fall short of 30%.\n\n45"}
{"prompt": "B: + essay", "gid": 43443, "imagenet_class": "cliff dwelling", "frac_of_max": 0.0358, "on": false, "p_on": 0.15, "reasoning": "This is a cliff dwelling (Mesa Verde style): rectangular adobe towers and walls under a rounded, overhanging rock alcove. Nothing here resembles the convergent V or pointed-snout geometry that drives 4e:55, such as fox or cat faces, car fronts, flames or martini-glass shapes. The scene is dominated by boxy verticals and horizontals plus a broad arch, and the arch, if anything, leans toward the round pole. A few crumbling walls form rough triangular wedges, which might give a weak response, but likely not enough to reach 30% of the maximum.\n\n15"}
{"prompt": "B: + essay", "gid": 49761, "imagenet_class": "limousine", "frac_of_max": 0.4459, "on": true, "p_on": 0.48, "reasoning": "The photo shows a white stretch Hummer limousine, mostly in side view, with only a sliver of its blunt, boxy front at the left edge. This neuron responds to car bodies, especially fronts and pointed or V-shaped converging forms. Here the visible shape is a long horizontal slab with a squared-off grille, so the \"pointed\" cue is weak. The vehicle cue is still strong, and car images in general often drive this neuron, which puts this case near the threshold.\n\n48"}
{"prompt": "B: + essay", "gid": 9722, "imagenet_class": "buckeye", "frac_of_max": 0.0347, "on": false, "p_on": 0.18, "reasoning": "The photo shows a horse chestnut (buckeye) in its split husk, which reads overall as a round, ball-like object. Round shapes are exactly what pushes this neuron negative, as with the golf ball, tennis ball and snail. The small spikes on the husk and the wedge-shaped split where the shell opens add a little pointed structure. That might give weak activation, but they lack the strong converging V-shape of a snout, car front or flame. On balance the dominant roundness makes it likely OFF.\n\n18"}
{"prompt": "B: + essay", "gid": 34396, "imagenet_class": "pickup", "frac_of_max": 0.4187, "on": true, "p_on": 0.8, "reasoning": "The front of this vintage Ford pickup is shown at an angle, with the hood, grille and headlights prominent. Car fronts were among this neuron's strongest classic activators, and they feed in through the van-front input (4d:201). The hood also converges toward a V-shaped emblem and chrome trim, which matches the \"pointed/V\" reading of the neuron. Round elements such as the headlights and wheels may pull it down somewhat, but the overall car-front geometry should dominate.\n\n80"}
{"prompt": "B: + essay", "gid": 23185, "imagenet_class": "lesser panda", "frac_of_max": 0.3928, "on": true, "p_on": 0.65, "reasoning": "The red panda plush has a fox-like orange-and-white face with pointed triangular ears, whiskers and a tapering snout. Those are the V-shaped, fox-snout cues this neuron draws on through inputs like 4d:493 (fox snout). Against that, red panda faces, especially on a plush, are rounder and flatter than a fox's, and roundness pushes this neuron negative. On balance the fox-like coloring and pointed ear-and-snout geometry probably win out, so it likely fires.\n\n65"}
{"prompt": "B: + essay", "gid": 14492, "imagenet_class": "Saint Bernard", "frac_of_max": 0.4769, "on": true, "p_on": 0.88, "reasoning": "The small red dog in front looks very fox-like: tall pointed ears, a tapered snout and a V-shaped face, much like the fox, wolf and coyote faces that feed into and read from this neuron. The Saint Bernard behind it has a rounder, droopier face, which would push the neuron negative. But the neuron reports its strongest response anywhere in the image, so the sharply pointed fox-like face should dominate.\n\n88"}
{"prompt": "B: + essay", "gid": 41451, "imagenet_class": "pickup", "frac_of_max": 0.352, "on": true, "p_on": 0.78, "reasoning": "The red vintage pickup's front end, with its hood, grille and headlights angled toward the camera, is exactly the kind of car-front stimulus that drives 4e:55. Its van-front and car inputs respond to the tapering, V-like shape of a vehicle's face. The truck takes up a moderate share of the frame and is mirrored in the water, which gives the neuron more chances to respond. The people and the duck are small and add little.\n\n78"}
{"prompt": "B: + essay", "gid": 15004, "imagenet_class": "car wheel", "frac_of_max": 0.0423, "on": false, "p_on": 0.1, "reasoning": "This is a close-up of a round, concentric hubcap or wheel with a spherical chrome center. It is exactly the kind of round, O-shaped structure that the essay identifies as this neuron's negative pole. Nothing in the frame is pointed or V-shaped: no animal snout, car front, or flame. The car-related content is a rounded wheel rather than a hood or grille, so the neuron is likely OFF.\n\n10"}
{"prompt": "B: + essay", "gid": 20306, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3334, "on": true, "p_on": 0.75, "reasoning": "The sheltie/collie in the foreground has a long, tapered muzzle that forms a V, much like the fox and wolf snouts that feed this neuron most strongly. That V-shaped dog face is exactly the kind of pointed structure that drives 4e:55. The face is fairly small and seen in profile, which may weaken the response somewhat. The second dog's body and the grass add little either way.\n\n75"}
{"prompt": "B: + essay", "gid": 13044, "imagenet_class": "king crab", "frac_of_max": 0.0319, "on": false, "p_on": 0.22, "reasoning": "The photo shows king crab legs, long ridged red-and-white tubes, laid on a round plate with lemon wedges. Nothing here forms the converging V-shape the neuron responds to, such as a pointed snout, a fox or cat face, a car or train nose, or a flame tip. The legs are elongated cylinders, the plate and bowls are round (which pushes it negative), and only a few tapered leg tips or converging legs might weakly suggest a point. The bright orange-red texture overlaps somewhat with fox-like colors, but without the pointed geometry, a strong response seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 19918, "imagenet_class": "coil", "frac_of_max": 0.0476, "on": false, "p_on": 0.12, "reasoning": "This photo is dominated by roundness: a spiral staircase seen from below, with concentric curved railings closing on a circular, octagonal skylight. That is essentially the neuron's negative pole, the \"O\" rather than the \"V\". The individual stair treads are wedge-shaped and taper toward the center, which could give a weak pointed signal. But there is no clear V-shaped object like a snout, flame, car front or martini glass, and the overall circular structure should suppress the response.\n\n12"}
{"prompt": "B: + essay", "gid": 20240, "imagenet_class": "police van", "frac_of_max": 0.3707, "on": true, "p_on": 0.82, "reasoning": "The front of the police van (a Ford Transit) is the key region. Its grille, headlights and sloping hood make the converging, pointed front-end shape the neuron favors, and van fronts are one of its strongest direct inputs (4d:201). The yellow van at the right adds a second vehicle-front cue. The round elements, such as the wheels, are minor and shouldn't cancel the strong car-front response.\n\n82"}
{"prompt": "B: + essay", "gid": 9833, "imagenet_class": "curly-coated retriever", "frac_of_max": 0.0454, "on": false, "p_on": 0.35, "reasoning": "The key feature is the dog's head-on face, seen at the eyes-to-nose region. Its muzzle does taper slightly toward the nose, which could give some V-like signal. But the head is broad and blocky, with heavy floppy ears and a soft, curly outline. It looks more like the rounded, jowly dog faces that push this neuron negative than like the pointed fox or cat faces that drive it. The grayscale, low-contrast fur weakens any sharp edges further, so the activation is probably weak but not clearly absent.\n\n35"}
{"prompt": "B: + essay", "gid": 25921, "imagenet_class": "police van", "frac_of_max": 0.4165, "on": true, "p_on": 0.75, "reasoning": "The photo shows a VW Passat police wagon in three-quarter front view. Its tapered hood, angled headlights and grille form the kind of pointed, V-converging car front that strongly drives this neuron; car fronts were half of the original \"cats and cars\" set. The car fills a good portion of the frame. Nothing round dominates the scene that would push the neuron negative. The main uncertainty is that the view is more side-on than head-on, which weakens the V shape somewhat.\n\n75"}
{"prompt": "B: + essay", "gid": 39152, "imagenet_class": "bald eagle", "frac_of_max": 0.3553, "on": true, "p_on": 0.28, "reasoning": "The neuron responds to converging, V-shaped or pointed structure (fox snouts, cat faces, car fronts, flames) and is pushed down by round, blobby forms. In this photo the only pointed cues are small: the eagle's hooked beak and the downward taper of its folded wings and tail. Most of the frame is the dark, smoothly rounded body, a plain sky and a cylindrical post, none of which form a strong central V like a canine face or a car's hood. Bird images aren't typical drivers of this neuron, so a response above 30% seems unlikely, though the tapered silhouette leaves some chance.\n\n28"}
{"prompt": "B: + essay", "gid": 16010, "imagenet_class": "guinea pig", "frac_of_max": 0.3347, "on": true, "p_on": 0.22, "reasoning": "This guinea pig's face and body are an almost perfectly round, blobby shape, with a blunt rounded muzzle and no pointed ears or V-shaped snout converging to a tip. That roundness is what pushes this neuron negative, much like the bulldog and mastiff faces in its negative examples. The furry animal face with eyes and a nose might give a little drive from the fox and cat-face inputs, but the shape works against it.\n\n22"}
{"prompt": "B: + essay", "gid": 22965, "imagenet_class": "manhole cover", "frac_of_max": 0.0038, "on": false, "p_on": 0.27, "reasoning": "This is a flat, rectangular cast-iron cover with a repeating diamond lattice and embossed lettering, and nothing in it forms a single large pointed or V-shaped object like a snout, car front or flame. The diamonds do contain many small pointed vertices, which could weakly engage a V-shape detector. But at mixed4e the neuron seems tuned to object-scale converging shapes rather than fine repeating texture. The overall boxy, flat composition also offers little to drive it, so a strong response seems unlikely.\n\n27"}
{"prompt": "B: + essay", "gid": 11592, "imagenet_class": "odometer", "frac_of_max": 0.024, "on": false, "p_on": 0.18, "reasoning": "The photo is a close-up of a speedometer: a large circular dial with curved arcs of numbers and an odometer readout. That is essentially the clock-face pattern the essay lists as a \"round\" example that pushes the neuron negative. There is a thin needle and some radiating tick marks, but nothing forms the V-shaped convergence of a snout, a car's front-end prow or a flame. The car context alone shouldn't help, since the neuron tracks pointed shape rather than \"car,\" so it is likely OFF.\n\n18"}
{"prompt": "B: + essay", "gid": 43315, "imagenet_class": "tabby", "frac_of_max": 0.4904, "on": true, "p_on": 0.85, "reasoning": "The photo shows a tabby kitten's face in close-up: pointed ears, a tapering muzzle and whisker area, and the V-shaped markings on a tabby forehead. Cat faces are among this neuron's classic top activators, and the essay reads them as pointed, V-shaped forms. The kitten's closed eyes and tilted head weaken the shape a little, but the ears and snout still give a strong pointed configuration, with nothing large and round dominating the frame.\n\n85"}
{"prompt": "B: + essay", "gid": 25404, "imagenet_class": "police van", "frac_of_max": 0.4076, "on": true, "p_on": 0.8, "reasoning": "The front three-quarter view of the police car shows a car front (hood, grille, headlights and push bar) that converges toward the camera. The cars in this neuron's top activations, and its van-front input (4d:201), are exactly this kind of front-on view. The car fills much of the frame and its front end is clearly visible, so the neuron should respond strongly. The square-ish building in the background adds little, but it shouldn't suppress the response.\n\n80"}
{"prompt": "B: + essay", "gid": 4864, "imagenet_class": "birdhouse", "frac_of_max": 0.0231, "on": false, "p_on": 0.3, "reasoning": "The key feature is the birdhouse's peaked, gabled roof. It is a pointed, inverted-V shape, the kind of angular convergence this neuron seems to like. However, the image lacks the strong cues that drive it most: a snout-like V such as a fox, cat or dog face, or a car front. The round entrance hole and the cylindrical trunk add round elements that may push it negative. The pointed roof could give a moderate response, but probably not a strong one.\n\n30"}
{"prompt": "B: + essay", "gid": 47793, "imagenet_class": "stupa", "frac_of_max": 0.0338, "on": false, "p_on": 0.42, "reasoning": "The tall white stupa tapers to a pointed tip and is covered in repeated triangular gables. That is the kind of convergent, pointed geometry this V-shape neuron likes, much like upward-pointing flames. On the other hand, the structure is mostly stacked horizontal tiers and fine ornament rather than a single bold V like a snout or a car's front. It is also an upward-pointing peak, not the face-like downward V that drives the neuron most strongly. So a moderate response is plausible, but it is uncertain whether it reaches the ON threshold.\n\n42"}
{"prompt": "B: + essay", "gid": 47900, "imagenet_class": "basenji", "frac_of_max": 0.3344, "on": true, "p_on": 0.72, "reasoning": "The dog's face is what matters: this basenji/corgi-type dog has a tapered, fox-like snout, upright pointed ears, and a white blaze forming a V down its face. That pointed, V-converging geometry is exactly what 4e:55's fox-snout and wolf-face inputs detect. The face is fairly small in the frame and seen somewhat in profile, which could weaken the response. The surrounding sand and the rounded body contribute little, but the head shape alone should push the neuron toward firing.\n\n72"}
{"prompt": "B: + essay", "gid": 26064, "imagenet_class": "bakery", "frac_of_max": 0.0494, "on": false, "p_on": 0.22, "reasoning": "The image is a grid of cupcakes whose frosting swirls and cupcake tops are strongly circular, which is the \"round\" pattern the essay says pushes this neuron negative. There are a few pointed elements: fondant high-heel shoes with tapered toes, and some hat brims. But these are small and set inside round shapes, not the clear V-shaped snout, flame or wedge-front patterns that drive it. Overall the round texture dominates, so a strong response is unlikely, though the shoe tips give it some chance.\n\n22"}
{"prompt": "B: + essay", "gid": 1141, "imagenet_class": "turnstile", "frac_of_max": 0.0355, "on": false, "p_on": 0.12, "reasoning": "This image is a blue shipping container converted into a building, with rectangular doors, windows, and an air conditioner. It is all boxy, flat-faced geometry seen from the side. Nothing in it resembles a pointed or V-shaped form like an animal snout, a pointed ear, a flame, or the tapered front of a car hood. The bare tree branches are thin and cluttered rather than forming a clear V at the scale this layer responds to, so the neuron likely stays near zero.\n\n12"}
{"prompt": "B: + essay", "gid": 46963, "imagenet_class": "English foxhound", "frac_of_max": 0.3027, "on": true, "p_on": 0.8, "reasoning": "The leaping foxhound has a narrow, tapered muzzle and a V-shaped face framed by drooping ears, much like the fox-snout and wolf-face inputs that drive this neuron. The face is fairly small in the frame, but it is clear and front-facing. The pack of similar hounds in the background adds more pointed faces. Nothing in the image is a strongly round, blunt face like a bulldog or mastiff, which would push the neuron negative.\n\n80"}
{"prompt": "B: + essay", "gid": 27806, "imagenet_class": "little blue heron", "frac_of_max": 0.0443, "on": false, "p_on": 0.27, "reasoning": "The main candidate driver is the heron's long, sharply pointed beak; the V-shaped fern fronds behind it are a weaker one. Both are \"pointed\" shapes in the sense the essay describes. But this neuron's strongest drivers are compact, frontal V-shaped faces and fronts: fox, cat and dog faces, car grilles, bullet-train noses. A thin side-on bird bill doesn't really form that converging V configuration. The rest of the scene (water, reflections, a slender body in profile) offers nothing that should drive it, so a strong activation seems unlikely, though not impossible.\n\n27"}
{"prompt": "B: + essay", "gid": 32160, "imagenet_class": "African grey", "frac_of_max": 0.0415, "on": false, "p_on": 0.22, "reasoning": "This African grey parrot is curled up with its head tucked into its feathers, so the image is dominated by rounded, scalloped feather textures and a rounded body contour rather than any clear V-shaped or pointed structure. There is no forward-facing snout, ears, car front or flame-like taper. The only candidate is the small hooked beak, and it is partly hidden and curved. Rounded shapes tend to push this neuron negative, so it is likely OFF.\n\n22"}
{"prompt": "B: + essay", "gid": 19917, "imagenet_class": "pier", "frac_of_max": 0.0473, "on": false, "p_on": 0.27, "reasoning": "The neuron responds to pointed, V-shaped or wedge-like forms such as fox snouts, car fronts, flames and martini glasses, and it is suppressed by round shapes. This photo is a long trestle bridge whose deck and pier line converge toward the horizon. That gives a weak wedge in perspective, but it is shallow and mostly horizontal rather than a crisp V. The cylindrical concrete piers and the arch-like repetition lean toward the round side, and there are no faces, snouts or vehicle fronts, so a strong activation seems unlikely.\n\n27"}
{"prompt": "B: + essay", "gid": 29669, "imagenet_class": "guillotine", "frac_of_max": 0.0279, "on": false, "p_on": 0.18, "reasoning": "The photo shows weathered wooden beams and a plank against a sky crossed by bare tree branches. There is no animal face, snout, car front, flame or other compact converging V-shape of the kind that drives this neuron. The only V-like elements are thin branch forks and straight timber edges, which are texture-scale and not the strong pointed-object pattern the neuron prefers. I expect at most a weak response.\n\n18"}
{"prompt": "B: + essay", "gid": 46582, "imagenet_class": "totem pole", "frac_of_max": 0.0468, "on": false, "p_on": 0.4, "reasoning": "The totem pole is the main candidate. Its carved, stylized faces, possibly including animal or beak-like forms, are the kind of face-like, pointed, symmetric shapes that feed this neuron through its fox, wolf and front-on face inputs. However, the faces here are small, narrow, and mostly look like rounded human faces with oval eye shapes rather than tapering V-shaped snouts. The bare branches form some V-junctions, but they are thin texture rather than a coherent pointed shape. So a moderate response is plausible, though not a strong one.\n\n40"}
{"prompt": "B: + essay", "gid": 15738, "imagenet_class": "pick", "frac_of_max": 0.0453, "on": false, "p_on": 0.57, "reasoning": "The guitar pick is essentially a large V or rounded triangle that fills the frame, with its tip pointing toward the lower right. That is the kind of pointed, converging shape this neuron is claimed to track, whether it comes from a fox snout, a car hood or a martini glass. Its sides and corners are rounded, though, and the surface is flat and plain with only handwriting on it. That gives weaker texture and object cues than the animal faces and vehicle fronts that drive the neuron hardest, so activation is plausible but not certain.\n\n57"}
{"prompt": "B: + essay", "gid": 13559, "imagenet_class": "hamster", "frac_of_max": 0.4325, "on": true, "p_on": 0.3, "reasoning": "The hamster is a front-facing furry animal face with eyes, nose and small ears, which partly overlaps the cat/fox-face inputs that drive this neuron. But its geometry is overwhelmingly round: a ball-shaped body, a blunt rounded muzzle rather than a tapering V-shaped snout, and rounded ears. That roundness matches the neuron's negative pole, like the pug-faced dogs and balls in the essay's figure. Weak face-driven excitation is likely outweighed by the round shape, so the neuron is probably OFF.\n\n30"}
{"prompt": "B: + essay", "gid": 28905, "imagenet_class": "hamster", "frac_of_max": 0.548, "on": true, "p_on": 0.28, "reasoning": "What matters here is the hamster's face, since this neuron responds to pointed, V-shaped forms like tapering snouts and ear tips. A hamster's face is mostly a round, flat puffball with a tiny pink nose. That makes it closer to the round, flat-faced dogs that push the neuron negative than to the tapered fox or cat muzzles that drive it. The ears give a little pointedness and the cheeks narrow slightly toward the nose, but the round sun decoration and the overall rounded shapes probably keep the neuron weak.\n\n28"}
{"prompt": "B: + essay", "gid": 44103, "imagenet_class": "grocery store", "frac_of_max": 0.0256, "on": false, "p_on": 0.15, "reasoning": "The photo is a market display of plum tomatoes and yellow round fruit (lemons or apples). These are smooth, convex, rounded blobs, the kind of \"round\" shape the essay says pushes this neuron negative. Nothing in the frame resembles a pointed V shape, such as a snout, a car front, a flame tip or a wedge. The elongated tomatoes taper slightly but are still mostly ovoid, so strong activation is unlikely.\n\n15"}
{"prompt": "B: + essay", "gid": 49844, "imagenet_class": "Appenzeller", "frac_of_max": 0.0303, "on": false, "p_on": 0.66, "reasoning": "The key feature is the puppy's frontal face, a Greater Swiss Mountain Dog type. Its white blaze narrows down the muzzle into a tapered, V-like shape framed by dark fur. This is the kind of pointed, symmetric animal face (like the fox and dog examples) that drives 4e:55. The face is fairly small in the frame and the rest of the scene (grass, hedge, fence) is irrelevant, but mixed4e units often still respond strongly to small, frontal, well-lit faces like this.\n\n66"}
{"prompt": "B: + essay", "gid": 24910, "imagenet_class": "stole", "frac_of_max": 0.0326, "on": false, "p_on": 0.28, "reasoning": "The photo shows a knitted scarf with a repeating cable pattern of pointed diamond and ogee shapes, held against a stucco wall. The pointed tips at the top and bottom of each diamond are V-like, so they could weakly drive this pointed-shape detector. But the shapes are a small, flat, repeated texture rather than one prominent tapering form like a snout, car front, flame or martini glass. Mixed4e features tend to respond to object-scale shapes, so a strong response seems unlikely.\n\n28"}
{"prompt": "B: + essay", "gid": 2961, "imagenet_class": "iPod", "frac_of_max": 0.0383, "on": false, "p_on": 0.15, "reasoning": "The main content is a human face: a round, wide-open \"O\" mouth, rounded glasses and a broad, fleshy face. These match the neuron's negative \"round\" pole rather than the pointed, V-shaped snouts, ears and car fronts that drive it. The goatee and the tilted head give a faint pointed cue, but human faces are not typical strong drivers for this cat/fox/car-front feature. The neuron is most likely OFF.\n\n15"}
{"prompt": "B: + essay", "gid": 36176, "imagenet_class": "Pembroke", "frac_of_max": 0.3167, "on": true, "p_on": 0.9, "reasoning": "This dog has the fox-like face the neuron's strongest inputs (fox snout, wolf face) respond to: large upright pointed ears and a muzzle that narrows to a point. Together these make a clear V-shaped head, very close to the \"pointed\" examples (the fox, the small dog), with nothing like the round, flat-faced mastiff heads that push it negative. The face fills much of the frame and is cleanly lit, so the neuron should respond strongly.\n\n90"}
{"prompt": "B: + essay", "gid": 698, "imagenet_class": "restaurant", "frac_of_max": 0.3659, "on": true, "p_on": 0.15, "reasoning": "The scene is dominated by round shapes: spherical paper lanterns, circular tables, and plates. Under the V-shape/pointed reading, round forms are exactly what pushes this neuron negative. There are no animal snouts, car fronts, flames, or martini-style V glasses. The stemmed wine glasses are small and more U-shaped than V-shaped, so they give at most weak drive. Overall this looks like a likely OFF image.\n\n15"}
{"prompt": "B: + essay", "gid": 35387, "imagenet_class": "Pembroke", "frac_of_max": 0.4792, "on": true, "p_on": 0.88, "reasoning": "The corgi's face is a near-perfect example of what this neuron likes: large pointed ears and a tapering, fox-like V-shaped face with a white blaze, looking straight at the camera. That closely matches its strongest inputs, the fox-snout and wolf-face features. The round granite bollards on the left are the \"round\" pattern that pushes it negative, but the neuron's value is its strongest response anywhere in the image, so the corgi's face should still dominate.\n\n88"}
{"prompt": "B: + essay", "gid": 18895, "imagenet_class": "cougar", "frac_of_max": 0.3956, "on": true, "p_on": 0.9, "reasoning": "The photo is a frontal close-up of a cougar's face, which is a cat face, the classic top driver of 4e:55. Its geometry fits the \"pointed/V-shape\" reading: two tall pointed ears and a face that tapers in a V from the wide eyes down to a narrow muzzle and chin. It is large, centered and head-on, much like the cat and fox examples in the figure, so the neuron should respond strongly.\n\n90"}
{"prompt": "B: + essay", "gid": 27622, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.4345, "on": true, "p_on": 0.55, "reasoning": "The key feature is the spaniel's frontal face. The white blaze narrows down between the dark eyes toward the nose, giving a converging, somewhat V-like snout pattern. This resembles the ON puppy example, which has a similar white blaze. Working against it, Cavalier King Charles spaniels have rounded heads, broad droopy ears and a short muzzle, which leans toward the \"round\" dog faces (bulldog, mastiff) that suppress this neuron. The close frontal face should give at least a moderate response, but the roundness makes it uncertain.\n\n55"}
{"prompt": "B: + essay", "gid": 29911, "imagenet_class": "tiger cat", "frac_of_max": 0.4058, "on": true, "p_on": 0.8, "reasoning": "The tabby cat's face is the key part: its pointed ears and tapering, V-shaped face are exactly the kind of pointed structure that drives 4e:55. Cat faces were also among this neuron's classic top activators in the original image grids. The face is moderately sized and clearly visible, so a strong response is likely, though the body and fur matter little.\n\n80"}
{"prompt": "B: + essay", "gid": 17237, "imagenet_class": "Persian cat", "frac_of_max": 0.3702, "on": true, "p_on": 0.55, "reasoning": "The cat's face is what matters. Cat faces are among this neuron's classic drivers, and the black-and-white markings here form a white inverted-V blaze running down between the eyes to the nose, with small ears peaking above. Working against it, this is a flat-faced, fluffy Persian whose head and body read as round, closer to the bulldog and ball shapes that push the neuron negative. The face also occupies only a small part of the frame, so the response could be weak.\n\n55"}
{"prompt": "B: + essay", "gid": 38785, "imagenet_class": "grey fox", "frac_of_max": 0.4756, "on": true, "p_on": 0.92, "reasoning": "The photo is a close-up of a gray fox's face. Its features form a strong V: pointed ears, a tapering muzzle and the triangular face markings converging on the nose. Fox and canid snouts are among this neuron's strongest drivers, through inputs like 4d:493 (fox snout) and 4d:477 (wolf face). The face fills the frame and is centered, so the pointed feature should register strongly rather than being diluted.\n\n92"}
{"prompt": "B: + essay", "gid": 4164, "imagenet_class": "red fox", "frac_of_max": 0.4632, "on": true, "p_on": 0.9, "reasoning": "The photo shows a close-up of a red fox's face, with its tapering snout forming a clear V shape that converges on the nose. That pointed, V-like geometry is exactly what this neuron responds to. One of its strongest inputs is 4d:493, a fox-snout detector, and fox faces sit among its prototypical activators. Nothing round dominates the frame enough to suppress the response, so a strong activation is very likely.\n\n90"}
{"prompt": "B: + essay", "gid": 36731, "imagenet_class": "Arctic fox", "frac_of_max": 0.5302, "on": true, "p_on": 0.85, "reasoning": "The photo shows an arctic fox in three-quarter profile, and its narrow, tapering snout and pointed ears form exactly the V-shaped, pointed head the neuron responds to. One of its strongest inputs is the fox-snout detector 4d:493, and fox faces like this one are among its typical strong activators. The white fur against the dark background makes the pointed outline crisp. The face is fairly small in the frame, but there is little rounded content to compete, so the neuron is likely ON.\n\n85"}
{"prompt": "B: + essay", "gid": 18536, "imagenet_class": "whiptail", "frac_of_max": 0.0468, "on": false, "p_on": 0.22, "reasoning": "The neuron responds to pointed, V-shaped forms at object scale: tapering snouts and faces of foxes, cats and dogs, the fronts of cars, and flames. Here the main subject is a small, thin lizard lying flat on sand. Its pointed snout is tiny and seen from the side, not as the converging V of a face or vehicle front. The narrow plant leaves give some pointed texture, but they are small and scattered, so a strong response seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 21848, "imagenet_class": "lionfish", "frac_of_max": 0.0467, "on": false, "p_on": 0.58, "reasoning": "The lionfish's spiky, radiating fin rays form many converging points and V-like fan shapes, a bit like the flame and pointed-snout patterns this neuron's readers respond to. There are no round, O-like shapes here to push it negative. However, there is no clear single V-shaped face or car-front structure, and the spines read more as stripes than as a converging snout. That makes the activation uncertain but plausibly moderate.\n\n58"}
{"prompt": "B: + essay", "gid": 5703, "imagenet_class": "red fox", "frac_of_max": 0.4297, "on": true, "p_on": 0.9, "reasoning": "The fox's face is the key feature: two upright pointed ears and a tapering V-shaped snout, centered and filling much of the frame. This closely matches the neuron's strongest input (4d:493, fox snout) and its \"pointed\" preference. The round mass of fur surrounding the face could push the response down somewhat, but the sharp V of the face and ears should dominate the max-over-image response.\n\n90"}
{"prompt": "B: + essay", "gid": 35242, "imagenet_class": "red fox", "frac_of_max": 0.5846, "on": true, "p_on": 0.88, "reasoning": "The photo shows a red fox looking straight at the camera, so its face forms a clear V: pointed ears above and a tapering snout below. This is the kind of pointed face shape that drives 4e:55, and it matches its strongest input, the fox-snout detector 4d:493. The face is fairly small in the frame and the grass around it is not pointed, which may weaken the peak response somewhat. Still, frontal fox and canid faces are among this neuron's most reliable triggers.\n\n88"}
{"prompt": "B: + essay", "gid": 15029, "imagenet_class": "standard poodle", "frac_of_max": 0.0274, "on": false, "p_on": 0.25, "reasoning": "This black standard poodle is shown in side profile, so there is no frontal, V-shaped face for the neuron to latch onto. That V-shaped face is the pattern its fox, wolf and car-front inputs feed on. Instead the image is dominated by rounded shapes: the domed topknot, the pom-pom tail and the round leg puffs. Those are exactly the kind of round forms that push the neuron negative. The narrow muzzle seen sideways gives at most a weak pointed cue, so a strong response seems unlikely.\n\n25"}
{"prompt": "B: + essay", "gid": 40020, "imagenet_class": "recreational vehicle", "frac_of_max": 0.4848, "on": true, "p_on": 0.78, "reasoning": "The photo shows a near-frontal view of a vintage pickup-based camper: a hood, grille, headlights and windshield, plus other vehicles behind it. Car fronts were one of the two classic drivers of 4e:55. Its 4d input \"van front\" feeds it directly, and a hood converging toward a grille gives the pointed, V-like front geometry the neuron favors. The boxy camper shell is less pointed, but the frontal vehicle face should still drive it well above threshold.\n\n78"}
{"prompt": "B: + essay", "gid": 23697, "imagenet_class": "Pomeranian", "frac_of_max": 0.5231, "on": true, "p_on": 0.77, "reasoning": "This Pomeranian has a distinctly fox-like face: a pointed muzzle, a tapered V-shaped head, and a pricked pointed ear. Those are the features the neuron's strongest inputs (fox snout, wolf face) detect. The round, fluffy body works against it a little, but the neuron takes its strongest response anywhere in the image, so the clear, well-lit, fox-like face should dominate.\n\n77"}
{"prompt": "B: + essay", "gid": 2740, "imagenet_class": "Arctic fox", "frac_of_max": 0.3347, "on": true, "p_on": 0.85, "reasoning": "The photo shows two arctic foxes facing the camera. Their faces form the V shape this neuron responds to: tapering snouts with pointed ears above. Fox faces are among its strongest drivers, and they feed in directly through inputs like 4d:493 (fox snout) and 4d:477 (wolf face). The faces are small relative to the frame, but they are clear and frontal, so a strong response is likely.\n\n85"}
{"prompt": "B: + essay", "gid": 37728, "imagenet_class": "car wheel", "frac_of_max": 0.4728, "on": true, "p_on": 0.76, "reasoning": "The photo shows a three-quarter front view of a vintage pickup truck: grille, headlights, hood and windshield, with more cars behind it. Car fronts are among this neuron's classic strongest triggers, and the essay's \"V-shape / pointed\" reading fits them too, since the hood tapering toward the grille forms a converging front shape much like the car and van examples. The round headlights add some of the \"round\" signal that pushes it negative. But the overall car-front configuration, at a good scale, should still drive it strongly.\n\n76"}
{"prompt": "B: + essay", "gid": 3360, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3497, "on": true, "p_on": 0.85, "reasoning": "The Shetland sheepdog's face is the key feature: its long, narrow muzzle tapers to a point, giving a strong fox-like V shape. That matches the fox-snout and wolf-face inputs that drive this neuron. The face is frontal, well lit and reasonably large in the frame, which should give a strong response. The rounded body and folded ear tips add little but shouldn't cancel the pointed-face signal.\n\n85"}
{"prompt": "B: + essay", "gid": 26611, "imagenet_class": "wing", "frac_of_max": 0.3695, "on": true, "p_on": 0.32, "reasoning": "The neuron responds to converging, V-shaped or pointed structures, typically seen head-on: fox and cat faces, car grilles, bullet-train noses. This photo shows a small propeller plane in side profile, sitting fairly small in the frame. It has some pointed elements (the tapered tail fin, the nose and the wing edges), but none forms the frontal, symmetric V the neuron seems to prefer. A vehicle on grass might give a weak response through the car-front pathway, but this shape cue is probably not strong enough to reach the ON threshold.\n\n32"}
{"prompt": "B: + essay", "gid": 47420, "imagenet_class": "Pomeranian", "frac_of_max": 0.5074, "on": true, "p_on": 0.88, "reasoning": "The key feature is the dog's face: this black-and-tan Pomeranian-type puppy has upright pointed ears and a tapered, fox-like muzzle. Together these form the V-shaped, pointed face that drives this neuron, the same pattern as its fox-snout and wolf-face inputs. The face is frontal and fills a good part of the frame. Nothing round, like a flat bulldog face, offsets it.\n\n88"}
{"prompt": "B: + essay", "gid": 25193, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.3007, "on": true, "p_on": 0.28, "reasoning": "The key feature is the dog's face. This is a Japanese Chin, a flat-faced, short-snouted breed with a round head, which is closer to the \"round\" pole (like the boxer and bullmastiff examples) than to the pointed V of a fox snout. The face is also small and turned away, so it offers little strong V-shaped structure. The fluffy, plume-like tail could give a weak pointed or flame-like cue, but overall the evidence favors weak activation.\n\n28"}
{"prompt": "B: + essay", "gid": 35780, "imagenet_class": "convertible", "frac_of_max": 0.4904, "on": true, "p_on": 0.72, "reasoning": "This is a weathered 1960s Camaro convertible seen from a front three-quarter angle. The front end, with its grille, headlights and the hood tapering toward a nose, is exactly the kind of car-front structure this neuron (fed by the \"van front\" unit 4d:201) responds to. The angled body lines give a converging, pointed geometry rather than a round one. The view is somewhat oblique and the car is faded and low-contrast, which may weaken the response, but car fronts are among this neuron's most reliable triggers.\n\n72"}
{"prompt": "B: + essay", "gid": 31281, "imagenet_class": "police van", "frac_of_max": 0.4114, "on": true, "p_on": 0.52, "reasoning": "Two police cars are parked in front of a brick building. Car fronts, with their hood-and-grille shape that converges to a point, are among this neuron's classic triggers. Here, though, both cars are small and seen mostly from the side at a three-quarter angle, so the pointed front-end geometry is only weakly present. The arched windows and rectangular facade add little V-shaped structure, so a moderate response from the cars is plausible but not assured.\n\n52"}
{"prompt": "B: + essay", "gid": 40347, "imagenet_class": "pickup", "frac_of_max": 0.5851, "on": true, "p_on": 0.85, "reasoning": "The photo shows a classic Chevy C10 pickup from a front three-quarter angle. Its grille, headlights and long hood form exactly the kind of car-front view that 4e:55 is famous for responding to. The figure's own \"car\" example is a nearly identical vintage Chevy truck hood and grille. The front is fairly boxy rather than sharply V-shaped, but the hood lines converging toward the grille, at this angle, are a strong match for the neuron's car-front inputs (e.g., 4d:201 van front).\n\n85"}
{"prompt": "B: + essay", "gid": 23521, "imagenet_class": "stole", "frac_of_max": 0.0113, "on": false, "p_on": 0.3, "reasoning": "The photo has no cat, fox, dog or car faces, which are the neuron's strongest drivers. It does have V and diamond structure: the black lace panel narrows toward a point, and the knit pattern is full of chevrons and lattice diamonds. These are mostly fine, repetitive texture rather than a single bold, face-like pointed shape. A mid-to-high layer neuron like 4e:55 probably responds weakly to that, though the overall triangular shape of the panel gives it some chance.\n\n30"}
{"prompt": "B: + essay", "gid": 40293, "imagenet_class": "stone wall", "frac_of_max": 0.0463, "on": false, "p_on": 0.12, "reasoning": "This scene is a dry-stone wall beside an autumn woodland path. It has none of the neuron's strongest triggers: no snout-like animal faces, no car or van fronts, and no flame- or glass-shaped V forms. The only possible V is the perspective convergence of the wall and path, but that is a diffuse, scene-scale layout rather than the compact, object-level pointed shape this neuron responds to. The textures are rounded stones and diffuse foliage, which, if anything, lean toward the \"round\" side, so it is likely OFF.\n\n12"}
{"prompt": "B: + essay", "gid": 21709, "imagenet_class": "Arabian camel", "frac_of_max": 0.0366, "on": false, "p_on": 0.28, "reasoning": "The neuron responds to V-shaped, pointed forms such as fox, cat and dog faces with tapering snouts and ears, car hoods and grilles, flames, and martini glasses. This photo's main candidate is the camel's head. A camel's face is long and blunt with a rounded muzzle and small ears, not a crisp V or triangle, and it is fairly small in the frame. The rest of the scene (rider, open desert, distant camels) offers little pointed structure, so a strong response seems unlikely, though the elongated animal snout could give some partial activation.\n\n28"}
{"prompt": "B: + essay", "gid": 12008, "imagenet_class": "oscilloscope", "frac_of_max": 0.0335, "on": false, "p_on": 0.12, "reasoning": "This is a handheld multimeter/oscilloscope: a boxy, rectangular device with a flat screen and rows of square buttons. Nothing in it forms the pointed, V-shaped convergence the neuron looks for. There is no snout, no tapered car hood or nose, and no flame or glass shape. The only faint V-like element is the tiny waveform on the screen, which is too small and low-contrast to drive it much. The overall geometry is flat and grid-like rather than pointed, so the neuron is likely OFF.\n\n12"}
{"prompt": "B: + essay", "gid": 12050, "imagenet_class": "croquet ball", "frac_of_max": 0.0104, "on": false, "p_on": 0.08, "reasoning": "The photo is a cluster of round, weathered balls (likely croquet or bocce balls) on a wooden surface, with no pointed or V-shaped structures, faces or car fronts. Under the V-shape/pointed interpretation, round objects are exactly the neuron's negative pole, much like the golf and tennis ball examples. The only weak candidates are the wedge-shaped gaps between adjacent balls, which are unlikely to drive it strongly.\n\n8"}
{"prompt": "B: + essay", "gid": 40823, "imagenet_class": "common newt", "frac_of_max": 0.0349, "on": false, "p_on": 0.45, "reasoning": "The key feature is the newt's head seen from above. It tapers toward the snout, and its dark stripes converge into chevron/V-like patterns, which is the kind of pointed, converging structure this V-shape neuron responds to. However, a newt's snout is fairly blunt and rounded rather than sharply pointed like a fox face or car hood, and the scene lacks a strong frontal \"face-V\". So activation is plausible but uncertain.\n\n45"}
{"prompt": "B: + essay", "gid": 21633, "imagenet_class": "white wolf", "frac_of_max": 0.3974, "on": true, "p_on": 0.82, "reasoning": "The white wolf's head is the key feature: pointed upright ears and a tapered, V-shaped snout. This is exactly the pointed canine-face pattern that drives the neuron through its strong inputs from wolf-face (4d:477) and fox-snout (4d:493) units. The wide-open mouth makes the face a bit less of a clean frontal V. Still, the ear and snout geometry should be enough for a strong response.\n\n82"}
{"prompt": "B: + essay", "gid": 41143, "imagenet_class": "cougar", "frac_of_max": 0.3809, "on": true, "p_on": 0.78, "reasoning": "The cougar's face is the key element: a cat face with pointed, upright ears and a tapering muzzle. That is the V-shaped, pointed geometry this neuron responds to, and cat faces are among its classic top activators. The face is partly cut off at the right edge and fairly small, which may weaken the response somewhat. The rounded rocks in the foreground are the \"round\" pattern that pushes the neuron negative, but the maximum over spatial positions should still be driven by the head.\n\n78"}
{"prompt": "B: + essay", "gid": 19161, "imagenet_class": "weasel", "frac_of_max": 0.3553, "on": true, "p_on": 0.72, "reasoning": "This is a frontal close-up of a ferret face. The fur pattern narrows from the wide-set ears and eyes down to a small pink nose, making a downward-pointing V much like the fox, puppy and cat faces that drive this neuron. The head is fairly rounded overall, which pulls slightly toward the \"round\" pole. Still, the tapering snout-centered face structure is the kind of input its fox-snout and wolf-face inputs respond to, so it should probably fire.\n\n72"}
{"prompt": "B: + essay", "gid": 22586, "imagenet_class": "brain coral", "frac_of_max": 0.0443, "on": false, "p_on": 0.12, "reasoning": "The main object is a round, dome-shaped sea urchin or sand dollar on sandy water. That is the kind of smooth, rounded form that sits on this neuron's negative \"round\" pole rather than its pointed, V-shaped preference. The rippling light patterns on the sand form some angular lines, but they are low-contrast texture rather than a coherent V or pointed snout, car-front or flame shape. So the neuron is probably OFF.\n\n12"}
{"prompt": "B: + essay", "gid": 9998, "imagenet_class": "Border collie", "frac_of_max": 0.3144, "on": true, "p_on": 0.8, "reasoning": "The frontal face of this Australian shepherd/border collie puppy is what matters. Its narrowing muzzle, white blaze and upright, partly folded ears form a V-shaped, fox-like face, much like the pointed dog and fox examples that drive this neuron. It is not a flat, round, bulldog-type face, which would push the neuron negative. The face is fairly small in the frame, but the pointed facial geometry should still give a solid response.\n\n80"}
{"prompt": "B: + essay", "gid": 16243, "imagenet_class": "lynx", "frac_of_max": 0.3507, "on": true, "p_on": 0.88, "reasoning": "The photo shows a caracal, a wild cat with a tapered, V-shaped face and tall pointed ears, seen close up and large in the frame. That pointed, wedge-like face matches both the cat-face images this neuron was famous for and the \"V-shape\" reading in the essay. It also resembles the fox and wolf snout features that feed into the neuron. Nothing in the image is round enough to push it strongly negative, so it will very likely fire well.\n\n88"}
{"prompt": "B: + essay", "gid": 20087, "imagenet_class": "jeep", "frac_of_max": 0.3212, "on": true, "p_on": 0.42, "reasoning": "This is a Land Rover Defender seen from a three-quarter front angle. Car fronts were among this neuron's classic top activators, so the vehicle gives it some chance. But the Defender's front is flat and boxy rather than tapered or V-shaped, and the most prominent elements are round: the glowing headlight and the wheels. Those round features are the kind that push the neuron negative, so I lean slightly against strong activation.\n\n42"}
{"prompt": "B: + essay", "gid": 22446, "imagenet_class": "bell cote", "frac_of_max": 0.0408, "on": false, "p_on": 0.18, "reasoning": "The most prominent features are the two round clock faces and the domed top of the tower. These are exactly the round shapes that push this neuron negative, and the essay's own examples include a clock. The palm fronds and the slightly tapering tower give some weak pointed structure, but nothing resembles the converging V of a snout, a car hood, flames or a martini glass. So the neuron is most likely OFF.\n\n18"}
{"prompt": "B: + essay", "gid": 18531, "imagenet_class": "disk brake", "frac_of_max": 0.034, "on": false, "p_on": 0.18, "reasoning": "The photo is dominated by round structures: the circular brake rotor with its ring of perforations, the hub, and the curved tire. That is the \"round\" pole that pushes this neuron negative, much like the clock and ball examples. The fork legs and caliper add some angular structure, but nothing forms the converging V of a snout, a car's front, or a flame tip. Overall there is little to drive it strongly.\n\n18"}
{"prompt": "B: + essay", "gid": 24397, "imagenet_class": "pick", "frac_of_max": 0.0305, "on": false, "p_on": 0.55, "reasoning": "The guitar pick is an orange, downward-pointing rounded triangle, a V-like silhouette similar to the tapered shape of a fox face or a car's front. That pointed taper is what this neuron appears to respond to. However, the pick's corners are fairly rounded, and there is no face or vehicle texture, which the neuron's strongest inputs (fox snout, wolf face, van front) carry. Round shapes push it negative, so the rounded edges partly offset the V, leaving a moderate chance it is ON.\n\n55"}
{"prompt": "B: + essay", "gid": 28431, "imagenet_class": "kit fox", "frac_of_max": 0.3537, "on": true, "p_on": 0.9, "reasoning": "The two fox kits face the camera, and their faces form the classic V-shape this neuron detects: tapering snouts with pointed ears. Fox faces are among its strongest drivers, through the 4d:493 fox-snout input. The faces are fairly small in the frame, but there are two of them, frontal and clear, so a strong response is very likely.\n\n90"}
{"prompt": "B: + essay", "gid": 19090, "imagenet_class": "polecat", "frac_of_max": 0.3705, "on": true, "p_on": 0.62, "reasoning": "The ferret's head is the key region: it has a tapered, pointed snout and a V-shaped facial profile, much like the fox and wolf snout features that most strongly drive 4e:55. Pointed-face animals such as foxes and puppies with narrow muzzles activate it reliably, so this is a plausible trigger. However, the face is small, shown in side profile, and somewhat blurry. Most of the image is the round, fuzzy body, hands and white shirt, which add nothing or push toward the neuron's \"round\" negative pole. So I'd lean toward ON, with moderate uncertainty.\n\n62"}
{"prompt": "B: + essay", "gid": 6864, "imagenet_class": "barometer", "frac_of_max": 0.0108, "on": false, "p_on": 0.08, "reasoning": "The photo shows six round brass-cased ship's clocks and barometers on a plain blue background. These are exactly the \"round\" stimuli that push this neuron negative, like the clock in the essay's figure. There are no pointed snouts, V-shaped car fronts, flames, or cone shapes to drive it. The thin clock hands form small V angles, but they are tiny line features, unlikely to reach a strong activation.\n\n8"}
{"prompt": "B: + essay", "gid": 22643, "imagenet_class": "hamster", "frac_of_max": 0.3692, "on": true, "p_on": 0.42, "reasoning": "The key feature is the hamster's face in close-up: whiskers, a small nose and a slightly tapered snout, which resemble the cat and fox faces this neuron likes. But hamster heads are mostly round and blunt rather than V-shaped. The roundness pushes the neuron toward its negative \"round\" pole, while the whiskered, snout-like structure could still give a moderate response. Overall it is borderline.\n\n42"}
{"prompt": "B: + essay", "gid": 25284, "imagenet_class": "collie", "frac_of_max": 0.3045, "on": true, "p_on": 0.88, "reasoning": "The two shelties have narrow, tapering, fox-like snouts and pointed upright ears, so their faces form clear V-shapes. These are exactly the pointed animal faces that the neuron's fox-snout and wolf-face inputs drive strongly. The faces are large, frontal and well lit, and nothing round dominates the scene to counteract them. This should give a strong response.\n\n88"}
{"prompt": "B: + essay", "gid": 2280, "imagenet_class": "warthog", "frac_of_max": 0.0267, "on": false, "p_on": 0.28, "reasoning": "The two warthogs are small and distant in a grassy field, so there is no prominent pointed, V-shaped structure like a fox snout, cat face or car front for this neuron to lock onto. A warthog's head is a somewhat wedge-shaped snout and could give a weak \"pointed face\" signal. At this scale it is likely too tiny and blurry to drive the neuron strongly. The rest of the image is cluttered grass and trees, which offers no clear V shapes.\n\n28"}
{"prompt": "B: + essay", "gid": 38540, "imagenet_class": "prayer rug", "frac_of_max": 0.0486, "on": false, "p_on": 0.28, "reasoning": "The photo shows a Persian-style rug with feet and jeans, and no animal faces, car fronts or other strongly pointed objects of the kind that drive this neuron. The rug's medallions and border do contain some diamond and pointed motifs, but these are small, flat, repeating patterns rather than a single strong V-shaped form. The toes and the rounded medallion centers lean \"round\" if anything, so a weak or absent response seems more likely than a strong one.\n\n28"}
{"prompt": "B: + essay", "gid": 30940, "imagenet_class": "safe", "frac_of_max": 0.0425, "on": false, "p_on": 0.15, "reasoning": "The photo shows a white electronic safe with its door open, keys hanging from it, and Chinese watermark text. Nothing here has the pointed, V-shaped geometry the neuron looks for: no snouts, animal faces, car or train fronts, flames, or tapered glasses. The forms are rectilinear boxes and panels, and the round knobs and hinges lean, if anything, toward the \"round\" negative pole. The text strokes and keys might add a few weak angular edges, but likely not enough to reach 30% of the neuron's maximum.\n\n15"}
{"prompt": "B: + essay", "gid": 41620, "imagenet_class": "pickelhaube", "frac_of_max": 0.0488, "on": false, "p_on": 0.18, "reasoning": "This sepia studio portrait of a standing soldier has none of the neuron's typical drivers: no animal snouts or faces (fox, cat, dog), no car or van fronts, no flames or other strongly V-shaped forms. The closest candidates are the beard and collar around the face and the rifle's taper. These are small and weak relative to the image, and the rest of the photo is mostly vertical structure (the body, columns and rifle) without a pointed central convergence. So the neuron is likely to stay low.\n\n18"}
{"prompt": "B: + essay", "gid": 2964, "imagenet_class": "obelisk", "frac_of_max": 0.0388, "on": false, "p_on": 0.58, "reasoning": "The obelisk is a tall, tapering shape whose two edges converge toward the top, the kind of pointed, V-like geometry the essay argues this neuron really tracks. It is set against a plain black background, so that converging silhouette is the dominant feature in the frame. Against that, it is an inverted V seen in perspective rather than a face or car front, the categories that drive the neuron hardest. The hieroglyph carvings also add many small round shapes, which could push it somewhat negative.\n\n58"}
{"prompt": "B: + essay", "gid": 18810, "imagenet_class": "microphone", "frac_of_max": 0.0498, "on": false, "p_on": 0.28, "reasoning": "The main object is a vintage microphone, a rounded, capsule-shaped head covered in perforated metal mesh. Its blunt, domed end is the kind of round shape that pushes this neuron negative, and nothing in the frame forms a V-shaped point like a snout, flame or martini glass. The shiny chrome and grille-like mesh might weakly resemble a car front, one of the neuron's known triggers, so it could still respond somewhat. On balance, the roundness makes it more likely OFF.\n\n28"}
{"prompt": "B: + essay", "gid": 11830, "imagenet_class": "pickelhaube", "frac_of_max": 0.0268, "on": false, "p_on": 0.4, "reasoning": "The pickelhaube helmets matter most here, since each has a sharp spike on top. That is exactly the kind of pointed, V-like shape the essay says drives this neuron. But the spikes are small, and the photo is dominated by rounded human faces, caps and uniforms rather than larger tapered shapes like snouts, car fronts or flames. So the response could reach the ON threshold, but it is far from certain.\n\n40"}
{"prompt": "B: + essay", "gid": 11045, "imagenet_class": "box turtle", "frac_of_max": 0.0236, "on": false, "p_on": 0.22, "reasoning": "The main object is a box turtle whose domed, rounded shell dominates the frame. That is exactly the kind of round, convex shape the essay says pushes this neuron negative. The only pointed elements are the turtle's small head and snout and some grass blades, but these lack the strong converging V-structure of a fox, cat or car-front face. So I expect a weak response at most.\n\n22"}
{"prompt": "B: + essay", "gid": 37354, "imagenet_class": "brain coral", "frac_of_max": 0.0376, "on": false, "p_on": 0.12, "reasoning": "The photo shows a rounded brain coral and an overturned blue plastic basket on sand. Both are dome-shaped, bulbous forms, which is exactly the \"round\" pattern that pushes this neuron negative. There are no pointed V-shapes like snouts, ears, car hoods, or flames. The only faintly pointed elements are small, thin grass tufts in the background, which are unlikely to drive the neuron strongly, so it is most likely OFF.\n\n12"}
{"prompt": "B: + essay", "gid": 41698, "imagenet_class": "lynx", "frac_of_max": 0.5678, "on": true, "p_on": 0.72, "reasoning": "The lynx's head is the key region: tufted, sharply pointed ears and a tapered, V-shaped muzzle, much like the fox and cat faces that drive this neuron through its fox-snout and wolf-face inputs. Little in the image is round enough to push it negative. The face is fairly small and seen partly in profile, which may weaken the response somewhat, but the pointed feline features should still activate it well.\n\n72"}
{"prompt": "B: + essay", "gid": 40012, "imagenet_class": "diaper", "frac_of_max": 0.0493, "on": false, "p_on": 0.22, "reasoning": "The photo is dominated by rounded forms: the baby's round head, smooth curved back and diaper. These match the neuron's negative \"round\" pole rather than its pointed, V-shaped triggers like fox snouts, car fronts and flames. The cartoon drummer tattoo adds small sharp elements, such as drumsticks raised in a V and a star, which could give weak activation. But they are small and stylized, and there are no strong pointed faces or wedge-shaped objects, so the neuron is likely OFF.\n\n22"}
{"prompt": "B: + essay", "gid": 18756, "imagenet_class": "lumbermill", "frac_of_max": 0.0369, "on": false, "p_on": 0.18, "reasoning": "This barn interior is mostly boxy, rectilinear structure: hay bales, wooden planks, doors and hanging tack. None of that gives the pointed, V-shaped convergence that drives this neuron, the way a fox or cat face, a car front or a flame would. The only animals are a small white duck and a chicken at the bottom edge. They are seen in profile rather than as a face-on, tapered snout, so they are unlikely to trigger strong activation. A weak response might come from some converging plank lines or the duck's pointed shape, but probably not enough to reach 30%.\n\n18"}
{"prompt": "B: + essay", "gid": 1633, "imagenet_class": "Indian elephant", "frac_of_max": 0.0425, "on": false, "p_on": 0.18, "reasoning": "This is a close-up of an elephant's face: broad, rounded and wrinkled, with a single eye and a trunk hanging straight down. Nothing in it forms the pointed, V-shaped converging contour (a narrow snout, a car hood, a flame tip) that drives this neuron. Broad, flat-faced animal heads look more like the round, negative-pole examples, such as the bulldog and mastiff faces. The trunk and the wrinkle lines could give weak oriented-edge input, but a strong peak response seems unlikely.\n\n18"}
{"prompt": "B: + essay", "gid": 10274, "imagenet_class": "pickup", "frac_of_max": 0.3347, "on": true, "p_on": 0.75, "reasoning": "The photo shows a large orange pickup truck seen from a three-quarter front angle, with a prominent grille, headlights and hood. Car fronts like this are exactly the \"car\" half of this neuron's classic top activations, and the \"van front\" unit 4d:201 is one of its strongest inputs. The truck front is fairly boxy rather than sharply V-shaped, but the hood converging toward the grille and the angled perspective still give a pointed, wedge-like front. That front-of-vehicle pattern is likely enough to drive the neuron well above threshold.\n\n75"}
{"prompt": "B: + essay", "gid": 49879, "imagenet_class": "tiger", "frac_of_max": 0.3629, "on": true, "p_on": 0.72, "reasoning": "The tiger on the right faces the camera, showing a feline face with pointed ears and a muzzle that tapers to a V at the nose and chin. That is the pointed, cat-face-like shape that drives this neuron, and its fox-snout and wolf-face inputs respond to exactly this kind of structure. Tigers have broader, rounder faces than house cats or foxes, the face is fairly small in the frame, and one ear is cropped, so the response may be weaker than for a classic cat close-up. The second tiger's ear at the left edge adds another small pointed cue.\n\n72"}
{"prompt": "B: + essay", "gid": 15555, "imagenet_class": "poncho", "frac_of_max": 0.0472, "on": false, "p_on": 0.22, "reasoning": "The photo shows a dark knit top on a mannequin. There is no animal face, car front or flame, the kinds of input this neuron responds to most strongly. The garment's batwing silhouette narrows toward an asymmetric pointed hem, and the diagonal stripes converge somewhat, so there is a weak V-shaped cue. But the object is a flat fabric shape rather than the snout-like or grille-like converging structure that drives the neuron, so a strong response seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 35141, "imagenet_class": "monastery", "frac_of_max": 0.0264, "on": false, "p_on": 0.22, "reasoning": "This is an ornate gilded Baroque altarpiece: dense columns, niches, statues and decorative carving, but no clear V-shaped or pointed structure such as a muzzle, flame, car front or wedge. The dominant shapes are arches and rounded niches. Those are the kind of round forms that push this neuron negative rather than drive it. Small statue faces and busy texture could cause some weak activation, but probably not up to 30% of its maximum.\n\n22"}
{"prompt": "B: + essay", "gid": 30063, "imagenet_class": "French horn", "frac_of_max": 0.0471, "on": false, "p_on": 0.2, "reasoning": "The photo is dominated by four French horns, whose most salient feature is their circular, coiled brass tubing. That roundness is the opposite pole of this neuron's pointed/V-shape preference and should push it negative. The flared bells do form cone-like, tapering shapes that could weakly drive a V detector. However, they are seen at oblique angles and are outweighed by the round coils. There are no animal faces, car fronts or other sharp V-shaped convergences, so the neuron is most likely OFF.\n\n20"}
{"prompt": "B: + essay", "gid": 39156, "imagenet_class": "minibus", "frac_of_max": 0.3492, "on": true, "p_on": 0.78, "reasoning": "The front of this VW van is the key region. It is a vehicle front-end with a grille, headlights and a windshield, which is exactly what the \"van front\" input (4d:201) feeds into this neuron. Car fronts are among this neuron's classic top activators. The boxy, flat Vanagon face is less pointed than a sloped hood, but the car-front circuitry should still drive it substantially.\n\n78"}
{"prompt": "B: + essay", "gid": 47148, "imagenet_class": "steam locomotive", "frac_of_max": 0.0382, "on": false, "p_on": 0.28, "reasoning": "The main object is a steam locomotive seen head-on, and its front is dominated by a round smokebox door. That circular \"face\" matches the neuron's negative, round pole rather than the pointed V-shapes (snouts, wedge-shaped car fronts, bullet-train noses) that drive it. The vehicle front and any V-ish cowcatcher or roof lines could give some weak drive. Overall, though, the scene is low-contrast, black-and-white and cluttered with people, with no strong pointed form.\n\n28"}
{"prompt": "B: + essay", "gid": 22969, "imagenet_class": "crossword puzzle", "frac_of_max": 0.0, "on": false, "p_on": 0.08, "reasoning": "This is a flat newspaper crossword: a dense grid of squares, text columns and a masthead. There are no animal faces, snouts, car or train fronts, flames, or other converging V-shaped forms of the kind that drive this neuron. The repetitive rectilinear grid texture is unlike its preferred pointed, tapering shapes, so the response should be weak.\n\n8"}
{"prompt": "B: + essay", "gid": 11927, "imagenet_class": "black grouse", "frac_of_max": 0.0444, "on": false, "p_on": 0.28, "reasoning": "The neuron responds to V-shaped, pointed frontal forms such as fox and cat snouts, car hoods and flames, and is suppressed by round shapes. This photo shows black grouse displaying and fighting, plus a hen. The birds' beaks are pointed and their outstretched wings and lyre-shaped tails make some V-like silhouettes. However, the birds are small, dark and seen side-on, with no frontal tapered face or hood like its strongest triggers, so any response is likely weak.\n\n28"}
{"prompt": "B: + essay", "gid": 18241, "imagenet_class": "muzzle", "frac_of_max": 0.0267, "on": false, "p_on": 0.38, "reasoning": "The key element is the dog's face. Its broad, blocky head belongs to the round, heavy-jowled type (like the bulldog and mastiff examples) that tends to push this neuron negative. The wire basket muzzle does taper toward the snout, giving a somewhat conical, pointed silhouette that could partially drive the V-shape detector. The image is also grayscale and the face is small and seen from above, which weakens the cues, so I lean slightly toward OFF.\n\n38"}
{"prompt": "B: + essay", "gid": 19622, "imagenet_class": "folding chair", "frac_of_max": 0.325, "on": true, "p_on": 0.27, "reasoning": "What matters here are the folding chairs: their metal legs and frames make crossing, converging lines, with some A/V-like angles where the legs meet the seats. But there is no strong pointed, wedge-shaped object such as a snout, a car front or a flame tip, which is what most strongly drives this V-shape detector. The chair backs and seats are mostly flat rectangles, and the scene reads as repetitive furniture rather than a single tapered form. A weak response from the leg angles is plausible, but it probably won't reach 30% of the neuron's maximum.\n\n27"}
{"prompt": "B: + essay", "gid": 6574, "imagenet_class": "water bottle", "frac_of_max": 0.0404, "on": false, "p_on": 0.22, "reasoning": "The neuron responds to pointed, V-shaped forms such as fox and cat snouts, car hoods, flames and martini glasses, and round shapes suppress it. The main shapes here work against it. The sun is a bright round disk, and the plastic bottle is mostly a cylinder, with only a mild taper at the neck and cap. The crowd is silhouettes and raised hands with no faces or snouts visible, so there is little strong V-shaped structure. A weak response to the bottle's tapered neck or the outstretched hands is possible, but it is unlikely to reach 30% of the maximum.\n\n22"}
{"prompt": "B: + essay", "gid": 31091, "imagenet_class": "Lhasa", "frac_of_max": 0.324, "on": true, "p_on": 0.52, "reasoning": "The puppy's face is the only relevant feature here, and it gives mixed signals for this neuron. Dog faces in general often drive it, especially when the snout and ears converge into a V-shaped, fox-like layout. This Shih Tzu/Lhasa-type puppy, though, has a fluffy, fairly round head and a short muzzle, closer to the round-faced dogs that push the neuron negative. The tilted head and the tufts of fur give only a weak pointed shape, so I lean slightly toward it firing, with real uncertainty.\n\n52"}
{"prompt": "B: + essay", "gid": 27374, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.3489, "on": true, "p_on": 0.42, "reasoning": "The spaniel's face is the only relevant feature. The neuron likes V-shaped, pointed configurations such as fox snouts, the forehead blaze on dog and cat faces, and car grilles. It is pushed negative by round forms. This Cavalier has a broad, rounded head, big round eyes and heavy drooping ears, so its silhouette is closer to the \"round\" pole. However, the white blaze tapering down between the eyes to the muzzle and tongue forms a partial V, much like the puppy example that fired. Close-up animal faces often drive this neuron anyway, so the outcome is fairly uncertain.\n\n42"}
{"prompt": "B: + essay", "gid": 17302, "imagenet_class": "Persian cat", "frac_of_max": 0.4713, "on": true, "p_on": 0.68, "reasoning": "This is a frontal cat face, one of the classic strong triggers for 4e:55, and its pointed ears frame a roughly V-shaped head narrowing toward the small nose and chin. Against that, Persian/Himalayan cats have a flatter, rounder face, and the large fluffy, rounded body could pull activation toward the \"round\" pole. On balance, the clear frontal cat face with upright pointed ears should still drive it reasonably strongly.\n\n68"}
{"prompt": "B: + essay", "gid": 42999, "imagenet_class": "mosque", "frac_of_max": 0.0451, "on": false, "p_on": 0.22, "reasoning": "The photo is dominated by large, smooth domes, which are exactly the \"round\" shape this neuron's negative pole prefers. The pointed elements, the slender minaret spire and the small finials, are thin and narrow rather than the broad V-shaped faces, snouts or car fronts that drive it most strongly. The partial lion statue at the edge could add a little face-like input, but it is cropped and seen from the side. Overall the roundness should win out, so a strong response seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 39954, "imagenet_class": "agama", "frac_of_max": 0.0178, "on": false, "p_on": 0.38, "reasoning": "The key feature is the agama's head: the red, wedge-shaped snout in profile tapers to a point, which is the kind of V-shape this neuron likes. However, the neuron's strongest drivers are front-facing V-shaped faces (fox, cat, and dog snouts) and car fronts. A side-on lizard head is a weaker, less canonical match, and the rest of the image is a rounded body, a rock and a blurred background with no other pointed structure. So a moderate response is plausible, but it may not clear the ON threshold.\n\n38"}
{"prompt": "B: + essay", "gid": 391, "imagenet_class": "buckle", "frac_of_max": 0.0484, "on": false, "p_on": 0.18, "reasoning": "The photo is a shiny chrome emblem with curly, swirling script letters on a black background. Its shapes are dominated by loops and round curls rather than pointed, V-shaped convergences. There is no animal snout, flame, or car-front structure for this V-detector to latch onto. The metallic sheen faintly resembles car chrome, but the round, looping forms resemble the neuron's negative pole more than its positive one, so it is probably OFF.\n\n18"}
{"prompt": "B: + essay", "gid": 41107, "imagenet_class": "Samoyed", "frac_of_max": 0.3162, "on": true, "p_on": 0.8, "reasoning": "This Samoyed is a spitz breed with a fox- or wolf-like head: a tapered snout and a pointed, triangular ear, which together form the kind of V-shaped face this neuron's strongest inputs (4d fox snout and wolf face) detect. The face is large and frontal, so the V geometry is prominent. The fluffy, rounded fur and the tongue soften the outline somewhat, but the snout and ear should still drive it well.\n\n80"}
{"prompt": "B: + essay", "gid": 8487, "imagenet_class": "eggnog", "frac_of_max": 0.0399, "on": false, "p_on": 0.38, "reasoning": "What matters here is mainly the open shirt collar, which forms a clear V at the bottom of the frame, and possibly the downward-tapering shape of the face and chin. These are the kind of pointed, converging shapes this V-shape neuron likes. Working against it, the main subject is a fairly rounded human face and a cylindrical glass, with no snout, flame, car front or other strongly V-shaped object. Rounded forms push this neuron negative, so a weak-to-moderate response seems likely and it could fall short of ON.\n\n38"}
{"prompt": "B: + essay", "gid": 43664, "imagenet_class": "sweatshirt", "frac_of_max": 0.0305, "on": false, "p_on": 0.08, "reasoning": "This is a plain gray sweatshirt seen from the back, with a line of handwritten Italian text, on a white background. Nothing here is a pointed, V-shaped form like a snout, flame, martini glass or car front. The collar is round and the garment's silhouette is boxy, with no converging tip. The script text is fine texture that is unlikely to drive a mid-to-high-level shape detector like this one, so I expect it to stay OFF.\n\n8"}
{"prompt": "B: + essay", "gid": 23908, "imagenet_class": "kit fox", "frac_of_max": 0.4159, "on": true, "p_on": 0.8, "reasoning": "The photo is a frontal, close-up arctic fox face. Its taper from wide-set eyes and ears down to the small dark nose forms a downward-pointing V, which is exactly the pointed-snout pattern that 4d:493 (fox snout) feeds most strongly into this neuron. The winter coat is fluffy and the ears are rounded, which makes the overall outline a bit rounder than a red fox's and could weaken the response. Even so, the canine face converging to a point should still drive the neuron well.\n\n80"}
{"prompt": "B: + essay", "gid": 5024, "imagenet_class": "papillon", "frac_of_max": 0.3395, "on": true, "p_on": 0.77, "reasoning": "The papillon's face is a classic pointed, fox-like V: a narrow tapering snout framed by large upright ears. That is the \"pointed face\" pattern the neuron gets from its fox-snout and wolf-face inputs. The face is fairly small in the frame, and the rest of the image (grass, fluffy white body) is neutral rather than round. Even so, a single strong local response should be enough to reach the threshold.\n\n77"}
{"prompt": "B: + essay", "gid": 31997, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.3113, "on": true, "p_on": 0.28, "reasoning": "This is a Japanese Chin, a breed with a flat, short-snouted face, a domed round head and large round eyes. That is much closer to the \"round\" pole (like the bulldog and mastiff examples) than to the pointed, V-shaped snouts of the fox, wolf or puppy faces that drive the neuron. The ears and the black-and-white facial markings add a little V-like contrast, and dog faces in general sit near this neuron's territory, so some activation is possible. Overall, though, the round brachycephalic face argues for OFF.\n\n28"}
{"prompt": "B: + essay", "gid": 16051, "imagenet_class": "basenji", "frac_of_max": 0.3388, "on": true, "p_on": 0.9, "reasoning": "The basenji's face is a textbook V: large upright pointed ears, a tapering wedge-shaped muzzle, and a white blaze narrowing down the face. This is exactly the fox/wolf/coyote-like pointed head geometry that 4e:55's strongest inputs (fox snout, wolf face) feed it. The face is large, frontal and well-lit. There are no competing round shapes to suppress it, so the neuron should respond strongly.\n\n90"}
{"prompt": "B: + essay", "gid": 10525, "imagenet_class": "beach wagon", "frac_of_max": 0.5617, "on": true, "p_on": 0.78, "reasoning": "The photo shows a classic 1960s Chevrolet wagon in a front three-quarter view. Its long hood, angled grille and the converging lines of the front end form the pointed, V-like car-front geometry that drove this neuron's well-known \"car\" activations, much like the red car hood in the figure. The round dual headlights are a mild counter-signal, since round shapes push the neuron negative. Car fronts at this scale are still one of its most reliable triggers, so it is likely ON.\n\n78"}
{"prompt": "B: + essay", "gid": 37831, "imagenet_class": "papillon", "frac_of_max": 0.5076, "on": true, "p_on": 0.8, "reasoning": "This photo has a papillon-type dog whose face and big upright ears make a strong pointed V shape, just like the fox, wolf and puppy faces that drive this neuron. Its tapered snout adds to that. The red toy car behind it adds car-front cues, the other half of the old \"cats and cars\" reading. The round blue wheel is the kind of shape that pushes the neuron negative, but since the value is the strongest response anywhere in the image, the pointed dog face should dominate.\n\n80"}
{"prompt": "B: + essay", "gid": 40616, "imagenet_class": "stinkhorn", "frac_of_max": 0.0361, "on": false, "p_on": 0.18, "reasoning": "The photo shows a cluster of flies on a stinkhorn mushroom over leaf litter. It contains none of the neuron's typical triggers: no V-shaped animal snout or face, no car or van front, no flame or wedge shape. The mushroom tip is rounded, and the flies form a busy, textured blob rather than a clean converging V. The flies' angled wings offer only weak, small-scale pointedness, so a strong response is unlikely.\n\n18"}
{"prompt": "B: + essay", "gid": 24522, "imagenet_class": "ice cream", "frac_of_max": 0.0279, "on": false, "p_on": 0.15, "reasoning": "What matters is the shape vocabulary. This crocheted banana split is dominated by round forms: globular ice-cream scoops, spherical cherries, puffy cream dollops and a curved glass dish. That is the \"round\" pole that pushes 4e:55 negative. There is no clear V-shaped or pointed structure here, such as a snout, car front, flame or martini-glass cone; at most the cherry stems and cream peaks are faintly pointed. The neuron is therefore unlikely to reach a strong response.\n\n15"}
{"prompt": "B: + essay", "gid": 26352, "imagenet_class": "Siberian husky", "frac_of_max": 0.3202, "on": true, "p_on": 0.8, "reasoning": "The husky's head is the key feature: tall, pointed, triangular ears and a tapered, wolf-like snout form exactly the V-shaped, fox- or wolf-face pattern this neuron's strongest inputs detect (4d:493 fox snout, 4d:477 wolf face). The face is shown in side profile and occupies a modest part of the frame, which may weaken the response somewhat. Still, husky and wolf-like canine heads are typical strong activators. The grass and ferns are irrelevant.\n\n80"}
{"prompt": "B: + essay", "gid": 28649, "imagenet_class": "orange", "frac_of_max": 0.0302, "on": false, "p_on": 0.12, "reasoning": "The dominant object is a large, smooth, round orange. That is exactly the kind of rounded, blob-like shape that sits on this neuron's negative (\"Round\") pole, alongside golf balls, tennis balls and snail shells. The only pointed elements are the tapering leaf tips at the edges. These are small, dark and lack the strong converging V-structure (snout, hood, flame) that drives the neuron. So the image most likely leaves the neuron OFF, with a small chance the leaf points give a modest response.\n\n12"}
{"prompt": "B: + essay", "gid": 17261, "imagenet_class": "cheetah", "frac_of_max": 0.3596, "on": true, "p_on": 0.75, "reasoning": "The cheetah's head is the key element: it is a cat face, the category this neuron was originally famous for. Under the \"pointed/V-shape\" reading, the tapering muzzle and the angle of the jaw and snout give a pointed silhouette. Because the head is in three-quarter/profile view rather than frontal, the symmetric V is weaker. Still, feline facial features plus a tapered snout should drive it fairly well, and the spotted body adds little either way.\n\n75"}
{"prompt": "B: + essay", "gid": 49972, "imagenet_class": "Madagascar cat", "frac_of_max": 0.3789, "on": true, "p_on": 0.77, "reasoning": "The two ring-tailed lemur faces are what matter. Each has a pointed, fox-like snout, and the dark muzzle markings narrow into a V, which matches the \"pointed\" pattern this neuron responds to through its fox-snout and wolf-face inputs. Their faces also look like the cat and fox faces among its top activations. The background is soft foliage with no round, ball-like objects to push it negative, so the face shapes should dominate.\n\n77"}
{"prompt": "B: + essay", "gid": 5684, "imagenet_class": "weasel", "frac_of_max": 0.485, "on": true, "p_on": 0.62, "reasoning": "The ermine's narrow, pointed snout and ears form a small V-shaped face, much like the fox and wolf faces that feed this neuron, and the white-animal-on-snow setting resembles its top fox inputs. The face is small in the frame and partly offset by the round, bulky body of the vole it carries. Since the neuron takes its strongest response anywhere in the image, the pointed face alone may be enough to push it past threshold.\n\n62"}
{"prompt": "B: + essay", "gid": 21338, "imagenet_class": "scorpion", "frac_of_max": 0.0174, "on": false, "p_on": 0.32, "reasoning": "The scorpion's pincers form pointed, V-shaped claws, and its tapering tail and stinger also offer pointed geometry, which could weakly drive a V/pointed-shape detector. However, the scene lacks this neuron's strongest triggers: there are no fox, cat or dog faces with a V-shaped snout and no car or train fronts. The background is many small round pebbles, the kind of round texture that pushes the neuron negative. The scorpion is also small and the image is low-quality, so any response is probably modest.\n\n32"}
{"prompt": "B: + essay", "gid": 10610, "imagenet_class": "jay", "frac_of_max": 0.3713, "on": true, "p_on": 0.42, "reasoning": "The main candidate is the blue jay's sharp, slightly open beak, with the wedge-shaped crest and head, against a blurred red background. This gives a pointed, V-like silhouette, which is the kind of shape the \"pointed vs. round\" reading says drives the neuron. However, the neuron's strongest known drivers are frontal faces with converging V structure: cat, fox and dog muzzles, and car or train fronts. A bird's head in profile, with a smooth rounded throat, is a weaker and less typical match, so the response could fall short of ON.\n\n42"}
{"prompt": "B: + essay", "gid": 39909, "imagenet_class": "bighorn", "frac_of_max": 0.0335, "on": false, "p_on": 0.28, "reasoning": "The key element is the bighorn sheep's head, seen in side profile. Its tapered muzzle gives only a weak pointed shape, not the frontal V of a fox, cat or car-front face that drives this neuron. The large curled horn is a strongly round, spiral form, which is the kind of shape that pushes the neuron negative. The grass streaks add some diagonal texture, but they don't form a coherent V, so a strong activation seems unlikely.\n\n28"}
{"prompt": "B: + essay", "gid": 10759, "imagenet_class": "guinea pig", "frac_of_max": 0.3197, "on": true, "p_on": 0.25, "reasoning": "The key element is the guinea pig's face. It is a furry animal face, the kind of input that can resemble the cat and fox faces this neuron likes. But a guinea pig's head is blunt and round with no pointed snout, ears or V-shaped muzzle, and its whole body is a round fluffy mound. Round shapes push this neuron negative, and nothing else in the scene (grass, wire fence) offers a strong pointed V-shape, so it probably stays low.\n\n25"}
{"prompt": "B: + essay", "gid": 49842, "imagenet_class": "ashcan", "frac_of_max": 0.0425, "on": false, "p_on": 0.12, "reasoning": "The dominant structure here is a large circle: the cup's rim and its round base, seen from directly above. That is the \"round\" pattern that pushes this neuron negative. The small red logo inside has some angular, pointed strokes that could give a weak V-like signal. But it is tiny and flat, nothing like the snout-, flame- or wedge-shaped forms that drive the neuron strongly. Overall the image looks much more like the round negative pole than a pointed V.\n\n12"}
{"prompt": "B: + essay", "gid": 37561, "imagenet_class": "screw", "frac_of_max": 0.0408, "on": false, "p_on": 0.12, "reasoning": "The photo shows small hex bolts on a plain white background, with threaded cylindrical shafts and hexagonal heads. Nothing here forms the V-shaped, tapering structure the neuron favors, such as a snout, pointed ears, a flame tip, a car grille or a sloped nose. The hex heads have some angular corners, but they are compact, near-round shapes, closer to the \"round\" pole than to a V. With no faces, no tapering forms and lots of empty background, a strong response seems unlikely.\n\n12"}
{"prompt": "B: + essay", "gid": 27929, "imagenet_class": "capuchin", "frac_of_max": 0.3083, "on": true, "p_on": 0.42, "reasoning": "The capuchin's face is the only plausible driver here. It is an animal face with forward-facing eyes and a muzzle that narrows toward the chin, which loosely resembles the V-shaped fox, cat and dog faces this neuron likes. But primate faces are flatter and rounder than the pointed-snout canids and felids it prefers, and roundness pushes the neuron negative. The grass background and the second monkey's arm add little, so this is a borderline case leaning slightly toward OFF.\n\n42"}
{"prompt": "B: + essay", "gid": 3867, "imagenet_class": "frilled lizard", "frac_of_max": 0.0358, "on": false, "p_on": 0.28, "reasoning": "The photo shows a lizard, likely a frilled or agamid lizard, against a soft green background. The only candidate for this neuron's pointed \"V\" shape is the narrow, tapering snout at the top of the frame. But the head is cut off by the edge and pointed upward, not seen face-on as a V like the fox, cat or car-front examples. The scaly body and branch don't give the converging-edge, pointed-face pattern that drives it, so a strong response seems unlikely, though a partial one is possible.\n\n28"}
{"prompt": "B: + essay", "gid": 34488, "imagenet_class": "cardoon", "frac_of_max": 0.0445, "on": false, "p_on": 0.42, "reasoning": "The cardoon/artichoke flower head is a burst of thin purple spikes fanning upward, a bit like a flame, and the bud below is covered in pointed, V-tipped bracts. Both match the neuron's \"pointed/V-shape\" preference. Against that, each structure is round overall (a globe-shaped bud, a dome-shaped bloom), and the neuron is pushed negative by round forms. There is also nothing face-like or car-front-like here, which is where its strongest activations tend to come from. So the pointed texture gives it a plausible but uncertain chance of crossing the threshold.\n\n42"}
{"prompt": "B: + essay", "gid": 17107, "imagenet_class": "limousine", "frac_of_max": 0.3504, "on": true, "p_on": 0.45, "reasoning": "The relevant object is the white stretch limousine. Car bodies, especially fronts and hoods with tapered, V-like converging contours, are among this neuron's typical drivers. Here, though, the limo is seen from the side and rear, fairly small in the frame, so the pointed front-end geometry that drives it most is weak. The surrounding buildings and pavement are mostly rectilinear and contribute little. Because the neuron does respond to car imagery, it could still reach a moderate activation, but it is far from a strong case.\n\n45"}
{"prompt": "B: + essay", "gid": 14470, "imagenet_class": "ibex", "frac_of_max": 0.0288, "on": false, "p_on": 0.28, "reasoning": "The photo shows several ibex grazing on a grassy, rocky slope. They are small in the frame, and most have their heads down or turned in profile. The neuron responds most strongly to front-facing, V-shaped forms, such as tapering fox, cat or dog snouts and the pointed fronts of cars and trains. Here the ibex muzzles are elongated, but none face the camera squarely, and the curved horns read more as arcs than as a converging V. So any response would come from weak, small-scale snout shapes, which makes strong activation unlikely.\n\n28"}
{"prompt": "B: + essay", "gid": 39841, "imagenet_class": "boathouse", "frac_of_max": 0.028, "on": false, "p_on": 0.35, "reasoning": "The red boathouse has a gabled roof whose peak forms a sharp angular point, and its red, flat, window-studded facade loosely echoes the red car fronts that drive this neuron. However, the gable is an upward-pointing apex rather than the downward V of a snout or car front, and there are no animal faces or vehicles. That makes a strong response uncertain. The overall scene is mostly rectangular and flat, with weak V-shaped cues.\n\n35"}
{"prompt": "B: + essay", "gid": 29966, "imagenet_class": "binder", "frac_of_max": 0.0493, "on": false, "p_on": 0.22, "reasoning": "The photo is dominated by a flat, rectangular green spiral notebook, which has no pointed or V-shaped structure: no snout, car front, flame or tapering form for this neuron to latch onto. The only possible trigger is the patterned textile at the edges, which may contain zigzag or chevron motifs. Those are small, low-level texture rather than the object-scale pointed shapes that drive this mixed4e unit. So the neuron is likely to stay weak.\n\n22"}
{"prompt": "B: + essay", "gid": 5088, "imagenet_class": "wall clock", "frac_of_max": 0.0324, "on": false, "p_on": 0.14, "reasoning": "What matters here is whether the scene contains pointed, V-shaped structures like snouts, ears, car fronts or flames, versus round forms that push the neuron negative. This living-room wall has no animals, vehicles or flames. Its most salient objects are round: the wreath and the clock face, and a pendulum clock was one of the essay's negative examples. The Tiffany lamp shade is roughly conical and could give a weak response, but it is small and not a strong V, so the neuron is likely OFF.\n\n14"}
{"prompt": "B: + essay", "gid": 25474, "imagenet_class": "tobacco shop", "frac_of_max": 0.0393, "on": false, "p_on": 0.18, "reasoning": "The photo is a flat, rectangular Italian tobacconist storefront: sign lettering, a glass door, a window display and brick. It has no animal faces, snouts or vehicle fronts, which are this neuron's strongest drivers of the \"pointed/V-shape\" pattern. The only pointed elements are the small colored star decorations in the window. They are tiny and unlikely to produce the converging V-geometry, like a fox snout or car hood, that drives the neuron strongly. Most of the image is boxy and text-heavy, so a weak response is most likely.\n\n18"}
{"prompt": "B: + essay", "gid": 31288, "imagenet_class": "scorpion", "frac_of_max": 0.0484, "on": false, "p_on": 0.32, "reasoning": "The photo shows a scorpion on a wall: a segmented brown body with a curled tail, splayed yellow legs and claws at the bottom. The legs and pincers do form some angled, V-like shapes, which could give the pointed-shape detector partial input. But the neuron's strongest drivers are larger, convergent V-shaped wholes, such as tapering animal snouts and faces, wedge-shaped vehicle fronts and flames. A thin-limbed arthropod with a round coiled tail probably doesn't produce that one strong converging V, so a strong response seems less likely than not.\n\n32"}
{"prompt": "B: + essay", "gid": 10155, "imagenet_class": "long-horned beetle", "frac_of_max": 0.0176, "on": false, "p_on": 0.28, "reasoning": "The longhorn beetle's two long antennae splay outward from its head in a wide V, which is the kind of pointed, diverging shape this neuron likes. But the V is made of thin lines at small scale. Neither the beetle's head nor its body forms the bold pointed-snout or wedge-front structure seen in its strongest inputs (fox, wolf and cat faces, car and van fronts). The bumpy, roundish wall texture offers nothing pointed and may push it slightly negative, so a strong response seems unlikely.\n\n28"}
{"prompt": "B: + essay", "gid": 44678, "imagenet_class": "Samoyed", "frac_of_max": 0.3611, "on": true, "p_on": 0.78, "reasoning": "The key feature is the white spitz-type dog (Samoyed or American Eskimo) lying in the grass and facing the camera. Its face has a fox- or wolf-like shape: upright pointed ears and a tapering muzzle form a clear V, the same pattern that drives this neuron's fox-snout and wolf-face inputs. The face is fairly small in the frame, but it is frontal and high-contrast against the green background. That makes a strong response likely, even though the rest of the scene (grass, bushes) contributes nothing.\n\n78"}
{"prompt": "B: + essay", "gid": 45275, "imagenet_class": "whippet", "frac_of_max": 0.3207, "on": true, "p_on": 0.55, "reasoning": "The relevant feature is the whippet's head at the upper left: a narrow, tapering snout and wedge-shaped skull, the kind of pointed, V-like dog/fox face this neuron responds to through its fox-snout and wolf-face inputs. Working against it, the head is small, partly cropped at the frame edge, and seen in profile rather than face-on. Profile views give a weaker frontal V than the canonical examples. The rest of the image (rounded body, grass) offers nothing pointed, so the response depends on that small head alone.\n\n55"}
{"prompt": "B: + essay", "gid": 19795, "imagenet_class": "black-footed ferret", "frac_of_max": 0.4403, "on": true, "p_on": 0.72, "reasoning": "The ferret's face is the key feature. Seen from above, its head tapers to a narrow, pointed snout, giving the V-shaped, fox-like face this neuron's strongest inputs (fox snout, wolf face) respond to. The dark mask framing the eyes and the pale muzzle reinforce that tapered, pointed geometry rather than a round, flat-faced one. The face is fairly small in the frame and the furry body is rounder, which may limit the response somewhat, but the pointed muzzle should still drive it well.\n\n72"}
{"prompt": "B: + essay", "gid": 48331, "imagenet_class": "axolotl", "frac_of_max": 0.0348, "on": false, "p_on": 0.22, "reasoning": "The axolotl's head is broad and blunt rather than tapering to a point, and the scene is dominated by smooth, rounded pebbles, which are exactly the \"round\" shapes that push this neuron negative. The only remotely pointed or V-like elements are the feathery pink gill fronds and the angled rock edge in the corner. Neither forms a clear converging snout or wedge of the kind that drives it (fox or dog muzzle, car hood, train nose). So the neuron most likely stays weak.\n\n22"}
{"prompt": "B: + essay", "gid": 43029, "imagenet_class": "water ouzel", "frac_of_max": 0.3588, "on": true, "p_on": 0.22, "reasoning": "This is a white-throated dipper on a mossy log over dark water. Nothing here resembles the neuron's strong drivers: there is no cat or fox face, no car or van front, and no large symmetric V-shaped form. The only pointed element is the bird's small beak. The dominant shapes, the round white bib and the plump body, are closer to the \"round\" pole that pushes the neuron negative. A weak response is possible from the beak and head profile, but reaching 30% of the neuron's maximum seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 34619, "imagenet_class": "papillon", "frac_of_max": 0.5556, "on": true, "p_on": 0.8, "reasoning": "The papillon's face is the key feature: it has a narrow, tapered muzzle and a white V-shaped blaze between dark ears, which closely matches the fox-snout and wolf-face inputs that drive this \"pointed/V-shape\" neuron. Unlike the round-faced mastiffs that push it negative, this small toy-dog face is fox-like and pointed. It is clearly visible and reasonably sized in the frame, though not close-up, so a strong response is likely.\n\n80"}
{"prompt": "B: + essay", "gid": 7224, "imagenet_class": "pickup", "frac_of_max": 0.374, "on": true, "p_on": 0.72, "reasoning": "The front of the old pickup is the key element: the hood, grille and headlights seen head-on to three-quarter. That is the car-front pattern that drives this neuron through its van-front input (4d:201), and the hood's edges and the grille converge into a roughly pointed frontal shape. The house's gable roof adds some angular structure too. However, the truck's front is boxy and the photo is faded and low-contrast, which may weaken the response.\n\n72"}
{"prompt": "B: + essay", "gid": 11833, "imagenet_class": "red fox", "frac_of_max": 0.6216, "on": true, "p_on": 0.95, "reasoning": "This is a close-up of a fennec fox face, and it is close to the ideal stimulus for this neuron. The snout tapers to a clear V, and the large pointed ears form further V-shapes framing the face. That matches the neuron's strongest input, 4d:493 (fox snout), and its \"pointed\" preference. Nothing round dominates the frame to push it negative, so a strong response is very likely.\n\n95"}
{"prompt": "B: + essay", "gid": 21443, "imagenet_class": "ambulance", "frac_of_max": 0.3517, "on": true, "p_on": 0.8, "reasoning": "The photo shows a Mercedes van seen from the front three-quarter angle, with its tapered hood, grille and headlights forming a pointed, V-like front end. That is exactly the \"van front\" / car-front pattern that feeds this neuron through 4d:201, and car fronts are among its classic top activators. The bright red-yellow chevron livery adds more angled, pointed structure, and nothing round dominates the scene. So a strong response is likely.\n\n80"}
{"prompt": "B: + essay", "gid": 35205, "imagenet_class": "cougar", "frac_of_max": 0.3476, "on": true, "p_on": 0.85, "reasoning": "The cougar's face is large, frontal and centered: pointed ears, a V-shaped muzzle tapering to the nose, and whiskers. This is exactly the tapered, pointed configuration that drives 4e:55. Its strongest inputs are fox-snout and wolf-face detectors, and cat faces were among the original top activators. Nothing round dominates the frame to suppress it, so a strong response is likely.\n\n85"}
{"prompt": "B: + essay", "gid": 41295, "imagenet_class": "guinea pig", "frac_of_max": 0.3649, "on": true, "p_on": 0.28, "reasoning": "The photo is a front-facing furry animal face, and the neuron's strongest inputs are fox, wolf and cat face detectors, so it shares some features with images that drive it. But this guinea pig's head is a round, blunt ball with no tapered snout and no V-shaped converging contour, which is the geometry the neuron actually tracks. Round faces like the bullmastiff and pug examples push it negative, so the overall roundness probably outweighs the generic furry-face cues.\n\n28"}
{"prompt": "B: + essay", "gid": 555, "imagenet_class": "frilled lizard", "frac_of_max": 0.0292, "on": false, "p_on": 0.52, "reasoning": "The lizard's head is a tapered, wedge-shaped snout pointing upward, and the frill flares out below it. That gives a pointed, V-like silhouette, which is the kind of shape this neuron seems to respond to. Against that, the neuron's strongest inputs are mammal faces (fox, wolf, cat) and car fronts, and a scaly reptile head may fit those detectors poorly. So I expect only moderate activation.\n\n52"}
{"prompt": "B: + essay", "gid": 48105, "imagenet_class": "menu", "frac_of_max": 0.0305, "on": false, "p_on": 0.12, "reasoning": "This is a flat restaurant menu cover: red script text, a small steaming-bowl icon, and a faded background image. None of it gives the neuron the pointed, V-shaped structure it responds to, such as snouts, cat or fox faces, car fronts, flames or martini glasses. The bowl icon is a rounded shape, closer to the neuron's negative \"round\" pole, and the rest is text and texture that mid-to-late layers mostly ignore for this feature. A strong response is unlikely, though thin text strokes or the steam lines could give a weak one.\n\n12"}
{"prompt": "B: + essay", "gid": 31587, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.3212, "on": true, "p_on": 0.45, "reasoning": "What matters is the puppy's face. It is a Japanese Chin, a flat-faced, round-headed breed with large round eyes. That roundness resembles the bulldog and mastiff examples that push this neuron negative.\n\nOn the other side, the white central blaze between dark ears forms a tapering, roughly V-shaped pattern. This is similar to the puppy in the \"pointed\" examples, and frontal dog faces generally drive this neuron somewhat.\n\nThe two cues pull in opposite directions, so the outcome is uncertain, leaning slightly OFF because the overall face is snub and round.\n\n45"}
{"prompt": "B: + essay", "gid": 19852, "imagenet_class": "Shih-Tzu", "frac_of_max": 0.3228, "on": true, "p_on": 0.22, "reasoning": "The only relevant content is the small, blurry Shih Tzu/Pekingese-type dog. Its face is flat-snouted, fluffy and round, much like the round-faced bulldog/mastiff examples that push this neuron negative, rather than the pointed, V-shaped muzzles of foxes, wolves or cats that drive it. The dog also fills only a small part of the frame, and the plain floor offers no other pointed shapes. A dog face can still give some weak activation, but a strong response seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 43833, "imagenet_class": "dhole", "frac_of_max": 0.3494, "on": true, "p_on": 0.8, "reasoning": "The photo shows a red fox in side profile, with tall pointed ears and a tapered, pointed snout. That is exactly the kind of V-shaped, pointed canid face this neuron's strongest inputs (fox snout, wolf face) feed it. The view is a profile rather than the frontal V-shaped face that drives it most, and the face is fairly small in the frame. Still, the pointed ears and muzzle should give a strong response.\n\n80"}
{"prompt": "B: + essay", "gid": 35524, "imagenet_class": "water tower", "frac_of_max": 0.0229, "on": false, "p_on": 0.22, "reasoning": "The dominant object is a water tower's tank, a rounded dome that matches the \"round\" pole that pushes this neuron negative. The support legs do converge into a tapering, A-like shape, which could give a weak pointed or V signal. But it is a thin, line-based structure rather than the snout-, flame- or wedge-like textured point the neuron favours. The grayscale, low-texture sky adds little drive, so the neuron is most likely OFF.\n\n22"}
{"prompt": "B: + essay", "gid": 27887, "imagenet_class": "Pembroke", "frac_of_max": 0.3908, "on": true, "p_on": 0.8, "reasoning": "The corgi puppy's face is what matters: it has tall, pointed ears and a tapering, fox-like snout, the classic V-shaped canine face this neuron responds to, much like the fox, wolf and coyote faces in its circuit. The face is fairly small and turned sideways, which may weaken the response. Still, it is a clear pointed face, not a round one like a mastiff's. The rest of the scene (rolled paper, floor, wall) offers nothing strongly pointed or round, so the face should dominate.\n\n80"}
{"prompt": "B: + essay", "gid": 47692, "imagenet_class": "Siberian husky", "frac_of_max": 0.3337, "on": true, "p_on": 0.92, "reasoning": "The husky's face is exactly what this neuron likes. It has tall pointed ears and a tapering, wolf-like V-shaped face with a dark V mask between the eyes. That closely matches its strongest inputs, the fox-snout (4d:493) and wolf-face (4d:477) features. The car window framing the dog adds a little more car-like signal, and nothing large and round competes with the pointed shapes.\n\n92"}
{"prompt": "B: + essay", "gid": 35602, "imagenet_class": "sea lion", "frac_of_max": 0.0445, "on": false, "p_on": 0.25, "reasoning": "The main subject is a sea lion in grayscale, seen in profile with its head tilted up. Its body is a smooth, rounded, tapering silhouette, which looks more like the \"round\" pole than the V-shaped fox, cat or car fronts that drive this neuron. The pointed muzzle seen from the side gives some weak taper, but there is no frontal V-shaped face, and the lack of color removes other cues this neuron often relies on. So it probably does not reach a strong response.\n\n25"}
{"prompt": "B: + essay", "gid": 26001, "imagenet_class": "kit fox", "frac_of_max": 0.6298, "on": true, "p_on": 0.85, "reasoning": "The photo shows two red foxes, an adult and a kit, both in profile with pointed, tapering snouts, and the kit's upturned open mouth forms a clear V. Foxes feed this neuron through its strong fox-snout input (4d:493), and the neuron responds to pointed, V-shaped faces rather than round ones. Nothing round competes here. The faces are fairly small in the frame, but there are two strong pointed cues, so it should fire well.\n\n85"}
{"prompt": "B: + essay", "gid": 34255, "imagenet_class": "Egyptian cat", "frac_of_max": 0.3616, "on": true, "p_on": 0.88, "reasoning": "This is a frontal close-up of a cat face. Cat faces are among this neuron's classic top activators, and the cat's nose and muzzle form a clear downward-pointing V. That tapered snout-and-whisker region is exactly the \"pointed\" shape the neuron responds to. Nothing large and round dominates the frame to suppress it, so I expect a strong activation.\n\n88"}
{"prompt": "B: + essay", "gid": 47016, "imagenet_class": "maraca", "frac_of_max": 0.0494, "on": false, "p_on": 0.12, "reasoning": "The main object is a rounded, bead-studded head on a handle (it looks like a maraca or beaded rattle) against a plain white background. Its bulbous, blob-like outline and dense round bumps are the opposite of the pointed, V-shaped convergence this neuron detects, and closer to the \"round\" pole that pushes it negative. The slight taper toward the handle is weak and is not a sharp V or snout-like point. Nothing else in the frame (no faces, car fronts, flames or glass shapes) offers a pointed structure.\n\n12"}
{"prompt": "B: + essay", "gid": 41077, "imagenet_class": "beer bottle", "frac_of_max": 0.0287, "on": false, "p_on": 0.33, "reasoning": "The big \"W\" on the Widmer Brothers coaster is essentially two V-shapes, and that pointed geometry is the kind of thing the neuron's \"Pointed\" reading says should drive it. However, the neuron's strongest drivers are pointed animal snouts and the fronts of vehicles, which are textured 3D objects. A flat printed letter on a coaster is a weak substitute for those. The beer bottle adds mostly rounded shoulders and a cylindrical label, which, if anything, pushes the other way. Overall I'd expect at most a modest response, though the strong V/W shape gives some chance it crosses the ON threshold.\n\n33"}
{"prompt": "B: + essay", "gid": 25051, "imagenet_class": "cab", "frac_of_max": 0.3263, "on": true, "p_on": 0.82, "reasoning": "The photo is filled with head-on views of London black cabs and other cars, and car and van fronts are among this neuron's strongest drivers. The 4d:201 \"van front\" input feeds it, and the car grilles in its top examples come from the same family. The taxi's front shows a central grille with a tapering hood and flanking headlights, which matches the pointed/V-like vehicle-front pattern the neuron responds to. Several such fronts at a good scale make a strong response likely, though the round headlights could pull it down slightly.\n\n82"}
{"prompt": "B: + essay", "gid": 41039, "imagenet_class": "convertible", "frac_of_max": 0.4274, "on": true, "p_on": 0.82, "reasoning": "The red Mustang is seen from a front three-quarter angle, so its tapered nose, angled headlights and pointed hood form the V-like car-front geometry this neuron responds to. Car fronts were among its classic top activators (the \"cars\" half of the cat/car story), and the \"van front\" input feeds it directly. The car fills a good part of the frame, and little round structure competes with it. The background houses, trees and other parked cars are largely irrelevant.\n\n82"}
{"prompt": "B: + essay", "gid": 7104, "imagenet_class": "otterhound", "frac_of_max": 0.0366, "on": false, "p_on": 0.28, "reasoning": "The neuron responds to pointed, V-shaped structures such as tapered snouts, fox, cat and wolf faces, the fronts of vehicles, and flames, and round shapes push it negative. Here the terrier is curled up asleep with its face mostly tucked away, so no clear V-shaped snout or face is visible. The dominant shape is the rounded, curled body on soft bedding, which fits the neuron's negative pole better than its positive one. A few minor angular items, like the yellow lamp and the shelf objects, give a small chance of a weak response.\n\n28"}
{"prompt": "B: + essay", "gid": 8821, "imagenet_class": "minibus", "frac_of_max": 0.3101, "on": true, "p_on": 0.72, "reasoning": "The photo is a head-on view of a boxy LDV minibus: grille, two headlights and windshield. That is very close to what the neuron's input 4d:201 (\"van front\") detects, and car and van fronts are the classic \"car\" half of this neuron's top activations. The front is fairly flat and boxy rather than sharply pointed, so it may not fire as strongly as on a tapered hood or nose. Still, the frontal face-like layout of a vehicle is a strong driver for this neuron.\n\n72"}
{"prompt": "B: + essay", "gid": 49457, "imagenet_class": "Border terrier", "frac_of_max": 0.3497, "on": true, "p_on": 0.8, "reasoning": "The dog's narrow, pointed snout and fox-like head shape, seen in profile, form exactly the tapered V that drives 4e:55. Its upstream inputs are fox-snout and wolf-face detectors, and this tan, scruffy terrier mix in snow looks much like those fox and coyote images. Nothing round and blunt, like a bulldog or mastiff face, works against it. The head is fairly small in the frame and in profile rather than frontal, which adds some uncertainty, but a strong response is likely.\n\n80"}
{"prompt": "B: + essay", "gid": 37845, "imagenet_class": "echidna", "frac_of_max": 0.0475, "on": false, "p_on": 0.4, "reasoning": "The echidna's long, tapered snout is a genuinely pointed, V-like feature, which is the kind of shape this neuron responds to in fox snouts and car fronts. But the snout is small and points downward, and most of the animal is a round, dome-shaped mound of spines. Roundness is what pushes this neuron negative. The radiating spines could add some converging-line, flame-like texture, so the overall signal is mixed.\n\n40"}
{"prompt": "B: + essay", "gid": 32493, "imagenet_class": "Siberian husky", "frac_of_max": 0.3253, "on": true, "p_on": 0.8, "reasoning": "The husky puppy's face is the key element. It has a tapered, wolf-like snout, a white V-shaped blaze between the eyes, and pointed ears, all of which match the neuron's pointed-face inputs (4d:493 fox snout, 4d:477 wolf face). Nothing round, like a flat bulldog face, is there to push it negative. The face is fairly small and partly turned away, which may limit the response somewhat, but this kind of canine face is a classic driver.\n\n80"}
{"prompt": "B: + essay", "gid": 25600, "imagenet_class": "water tower", "frac_of_max": 0.0416, "on": false, "p_on": 0.12, "reasoning": "The water tower's dominant feature is its rounded cylindrical tank with a domed top, which is the \"round\" pattern that pushes this neuron negative. The splayed support legs converge upward into a slight A shape, but that is an inverted V. It is also thin and lattice-like rather than a solid pointed form like a fox snout, car front or martini glass. There is no animal face, vehicle front or downward-pointing wedge, so a strong response is unlikely.\n\n12"}
{"prompt": "B: + essay", "gid": 46119, "imagenet_class": "obelisk", "frac_of_max": 0.0385, "on": false, "p_on": 0.22, "reasoning": "The photo shows an obelisk shaft against blue sky. It is a tall, slightly tapering vertical shape covered in carved hieroglyphs, and its pointed pyramidion tip is cropped out of the frame. The neuron's strongest drivers are compact V-shaped, pointed configurations such as fox and cat snouts, car and van fronts, and flame tips. A nearly parallel-sided shaft with no visible apex offers only weak converging-edge evidence. There are no face-like or grille-like textures either, so a strong response seems unlikely, though the slight taper gives a small chance.\n\n22"}
{"prompt": "B: + essay", "gid": 28481, "imagenet_class": "reflex camera", "frac_of_max": 0.0434, "on": false, "p_on": 0.18, "reasoning": "The neuron responds to V-shaped, pointed forms such as tapering animal snouts, the wedge-shaped fronts of cars and trains, and flames. This photo offers little of that. The woman's face is broad and rounded, with no snout-like taper. The camera is a boxy object with a round lens, which leans toward the \"round\" pole that suppresses the neuron. The angled hair framing the face and the camera's prism hump might create weak V-like edges, but probably not enough for a strong response.\n\n18"}
{"prompt": "B: + essay", "gid": 44286, "imagenet_class": "pickelhaube", "frac_of_max": 0.0432, "on": false, "p_on": 0.12, "reasoning": "This is a sepia WWI-era group portrait of soldiers in peaked caps and women in hats, posed in front of a door. It contains none of the neuron's strong drivers: no animal snouts or V-shaped faces (cat, fox, dog), no car or van fronts, no flames, and no glossy angular surfaces. The human faces and rounded caps are, if anything, closer to the \"round\" pole. The only V-like structures are small details such as collars and folded arms, which are unlikely to push the response near 30% of the maximum.\n\n12"}
{"prompt": "B: + essay", "gid": 25898, "imagenet_class": "suit", "frac_of_max": 0.0495, "on": false, "p_on": 0.33, "reasoning": "What matters most is whether the photo contains a pointed, V-shaped form. The neuron responds most strongly to V-shaped wedges at object scale, like animal snouts and faces, car fronts and flames, rather than to semantic categories. This vintage fashion photo has no animal face or vehicle front. Its main candidates are the coat's lapels and dark V-neckline, which do form a V, but they are low-contrast and not the kind of snout or wedge shape that drives the neuron hardest. Foliage and the rounded hairstyle add little, so a partial response is plausible but a strong one is uncertain.\n\n33"}
{"prompt": "B: + essay", "gid": 19461, "imagenet_class": "earthstar", "frac_of_max": 0.0223, "on": false, "p_on": 0.22, "reasoning": "This is an earthstar fungus, and its most salient feature is the smooth, round central puffball, exactly the kind of round \"O\" shape that drives this neuron negative. The surrounding rays are star-like, but they are broad, curled and blunt-tipped rather than forming crisp V-shapes like a fox snout or a car hood. There are no faces or pointed vehicle fronts. The scene is dominated by roundness, so the neuron is most likely OFF, with some chance the ray tips trigger a weak V-response.\n\n22"}
{"prompt": "B: + essay", "gid": 14769, "imagenet_class": "convertible", "frac_of_max": 0.3286, "on": true, "p_on": 0.64, "reasoning": "This is a silver convertible shown in side profile, with its hardtop roof mid-retraction. The neuron's classic car activations come from front-on views, where the hood, grille and headlights form a pointed V. A side view loses most of that frontal V, but the wedge-shaped nose, the raked windshield and the angular folding roof panels still give sharp pointed contours. Cars as a category drive this neuron fairly reliably, so a moderate-to-strong response seems likely even though this isn't the ideal frontal angle.\n\n64"}
{"prompt": "B: + essay", "gid": 28121, "imagenet_class": "Pomeranian", "frac_of_max": 0.7532, "on": true, "p_on": 0.72, "reasoning": "The Pomeranian's face is fox-like: pointed ears and a narrow snout converging toward the nose form the V-shaped, pointed configuration this neuron responds to, much like the fox and wolf faces that feed it. The fluffy fur gives the head a somewhat round outline, which could pull the response down. But the small pointed snout and upright ears should still drive it reasonably well. The dress and pine-needle background are unlikely to matter much.\n\n72"}
{"prompt": "B: + essay", "gid": 2877, "imagenet_class": "minivan", "frac_of_max": 0.3281, "on": true, "p_on": 0.62, "reasoning": "The main subject is a white minivan seen from a front three-quarter angle, filling much of the frame. Its sloped hood and the windshield narrowing toward the nose form the tapered, pointed vehicle-front shape this neuron picks up. That shape is also what the van-front input (4d:201) feeds it. The view is more side-on than head-on, though, and the minivan's front is fairly blunt, so the response may be moderate rather than strong.\n\n62"}
{"prompt": "B: + essay", "gid": 23444, "imagenet_class": "keeshond", "frac_of_max": 0.4771, "on": true, "p_on": 0.85, "reasoning": "The keeshond's face is the key feature: its pointed, upright ears and tapering, fox-like snout make the V-shaped facial geometry this neuron responds to. The neuron is driven by inputs such as 4d:493 (fox snout) and 4d:477 (wolf face), and this dog closely resembles those spitz and wolf faces. Nothing round dominates the image, so there is no strong negative pull. The face is fairly large and frontal, which should give a strong response.\n\n85"}
{"prompt": "B: + essay", "gid": 30526, "imagenet_class": "hornbill", "frac_of_max": 0.0268, "on": false, "p_on": 0.32, "reasoning": "The two hornbills are small in the frame, but their long, curved, pointed bills meet near the top to form a rough V, which is the kind of pointed geometry this neuron likes. However, its strongest drivers are larger frontal V-shaped faces and fronts: fox, cat and dog snouts, car grilles, flames. Distant birds against cluttered foliage usually give weak responses at mixed4e. The converging beaks give some chance of firing, but I lean OFF.\n\n32"}
{"prompt": "B: + essay", "gid": 48686, "imagenet_class": "shovel", "frac_of_max": 0.036, "on": false, "p_on": 0.22, "reasoning": "This grayscale photo shows a dirty shovel blade lying on the ground with a single leaf on it. There is no face-like or strongly V-converging structure of the kind that drives this neuron, such as a snout, ears, a car or van front, or flames. The spade blade and the leaf's tip are only mildly pointed, and they are low-contrast against a mostly flat, rounded, textured surface. At best that would give a weak response, so the neuron is more likely OFF.\n\n22"}
{"prompt": "B: + essay", "gid": 49190, "imagenet_class": "Irish terrier", "frac_of_max": 0.0309, "on": false, "p_on": 0.5, "reasoning": "What matters here is the dog's head: a shaggy terrier, possibly an Irish terrier, seen in side profile with its muzzle extending to the left. This neuron responds to pointed, V-shaped forms such as fox-like snouts, cat faces and vehicle fronts. A tapering muzzle can supply that shape. However, the classic V usually comes from a frontal face, where both sides converge to a point. Here the profile view, blunt bearded muzzle and fluffy rounded head outline weaken that cue, so a moderate response seems most likely.\n\n50"}
{"prompt": "B: + essay", "gid": 48712, "imagenet_class": "brambling", "frac_of_max": 0.3065, "on": true, "p_on": 0.18, "reasoning": "The main subject is a small, plump finch on a curved branch. Its body and head are rounded, which matches the neuron's negative \"round\" pole. The only V-shaped element is the tiny beak, a small cue compared with the pointed snouts, cat faces and car fronts that drive this neuron. The smooth curving branch and blurred background offer no strong pointed or V structure, so the neuron is likely weak here.\n\n18"}
{"prompt": "B: + essay", "gid": 29362, "imagenet_class": "English setter", "frac_of_max": 0.4225, "on": true, "p_on": 0.66, "reasoning": "The key feature is the dog's head in profile, a setter or spaniel type. Its long muzzle tapers to a nose at the left, forming a wedge-like pointed shape. That resembles the fox-snout and wolf-face inputs that drive this \"pointed/V-shape\" neuron. Working against it, the open mouth and floppy rounded ear soften the shape, and a side view is less of a clean frontal V than the top examples. Still, pointed-snout animal heads are this neuron's bread and butter.\n\n66"}
{"prompt": "B: + essay", "gid": 22620, "imagenet_class": "coyote", "frac_of_max": 0.3448, "on": true, "p_on": 0.8, "reasoning": "The dingo's face is the key feature: pointed upright ears and a tapering snout form exactly the V-shaped, fox- or coyote-like face this neuron responds to, and fox-snout and wolf-face units are among its strongest inputs. The face is fairly small in the frame and the rest of the image is plain dirt and body, which could weaken the response somewhat. Still, a clear frontal canid face with a pointed muzzle should drive it well.\n\n80"}
{"prompt": "B: + essay", "gid": 31119, "imagenet_class": "sea lion", "frac_of_max": 0.0446, "on": false, "p_on": 0.18, "reasoning": "The photo is dominated by a smooth, rounded, dark marine-mammal body (a dugong, seal or whale) filling the frame underwater. There is no pointed or V-shaped structure: no tapering snout, ears, car front, flame or wedge. Broad, rounded convex surfaces are closer to the neuron's \"round\" negative pole. Some converging contour lines where the body curves could weakly suggest a V, but overall the neuron is probably OFF.\n\n18"}
{"prompt": "B: + essay", "gid": 27101, "imagenet_class": "radio telescope", "frac_of_max": 0.0293, "on": false, "p_on": 0.22, "reasoning": "The image is dominated by a huge round radio-telescope dish, which is the kind of curved, O-like form that pushes this neuron negative. The surrounding steel lattice does contain many converging struts and V-like angles. But these are thin, repetitive line junctions rather than a single object-level pointed form like a snout, car front, or flame tip, which is what drives 4e:55 strongly. Some weak activation from the truss angles is plausible, but reaching 30% of the maximum seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 10122, "imagenet_class": "racer", "frac_of_max": 0.3817, "on": true, "p_on": 0.58, "reasoning": "The main object is a full car, a 1970s stock car seen mostly from the side with its front end angled toward the camera. The car half of 4e:55's original \"cat and car\" behavior is driven by car fronts and hoods, which the essay reframes as pointed, V-like converging shapes such as a hood narrowing toward the grille. Here the front is only partly visible and the view is mostly a side profile, so the pointed cue is weaker than in a head-on hood shot. Still, the angled front, hood and windshield could plausibly push the neuron past threshold.\n\n58"}
{"prompt": "B: + essay", "gid": 43464, "imagenet_class": "limousine", "frac_of_max": 0.4548, "on": true, "p_on": 0.75, "reasoning": "The white stretch limo (a Ford Excursion SUV) is seen at a three-quarter front angle, so its grille, headlights and hood are prominent. Car and van fronts are among this neuron's classic top stimuli, and van-front detector 4d:201 feeds it strongly. The front of the vehicle forms a converging, V-like wedge from the hood lines to the grille, which fits the \"pointed\" reading. The boxy, blunt shape of this SUV is less pointed than a sports car, which tempers confidence somewhat, but vehicle fronts at this scale typically drive it well.\n\n75"}
{"prompt": "B: + essay", "gid": 36815, "imagenet_class": "triumphal arch", "frac_of_max": 0.0348, "on": false, "p_on": 0.12, "reasoning": "The photo shows the Arch of Constantine at night: a boxy facade with columns, statues and, above all, rounded arches. Nothing in it is V-shaped or pointed, such as a tapering snout, a wedge-shaped car or train front, a flame, or a cone-shaped glass. The arches are the round, curved-top shape that pushes this neuron negative. The small statues and reliefs are too tiny and indistinct to look like pointed faces, so the neuron is most likely OFF.\n\n12"}
{"prompt": "B: + essay", "gid": 44549, "imagenet_class": "book jacket", "frac_of_max": 0.0156, "on": false, "p_on": 0.38, "reasoning": "The cover offers a few V-like cues: the letter \"V\" in \"FIVE,\" the pointed, V-splayed ears of the cartoon rabbits, and the children's pointed features. These match the neuron's preference for pointed, converging shapes such as snouts, car fronts and flames. However, everything is small, flat illustration and text rather than a prominent, photographic pointed face or front, and the round forms (the top hat, heads) may pull it down. So a moderate response is plausible, but a strong one is uncertain.\n\n38"}
{"prompt": "B: + essay", "gid": 10625, "imagenet_class": "Norwich terrier", "frac_of_max": 0.3101, "on": true, "p_on": 0.72, "reasoning": "The key feature is the terrier's face: two upright, triangular ears and a tapering muzzle form the pointed, fox-like V-shape this neuron responds to, much like the fox, wolf and puppy examples that drive it. The face is fairly small in the frame and a bit fluffy and blurry. That may weaken the response somewhat, but nothing round or blunt (like a bulldog or mastiff face) competes with it. The plain tiled floor adds little either way.\n\n72"}
{"prompt": "B: + essay", "gid": 19603, "imagenet_class": "fire screen", "frac_of_max": 0.0478, "on": false, "p_on": 0.12, "reasoning": "This is a window blind: flat, evenly spaced horizontal black and white stripes inside a rectangular frame. There is no pointed or V-shaped structure, no tapering snout or face, and no car front for the neuron's \"pointed\" detectors to latch onto. The horizontal bands faintly resemble a car grille, which is the main reason it might fire a little. But without the surrounding hood or headlight geometry, the regular striped texture is more likely to leave this neuron quiet than to drive it strongly.\n\n12"}
{"prompt": "B: + essay", "gid": 2861, "imagenet_class": "china cabinet", "frac_of_max": 0.0303, "on": false, "p_on": 0.25, "reasoning": "This photo is mostly rectangular structure: dark wooden cabinet frames, panels and shelves. It has none of the animal faces, snouts or vehicle fronts that most strongly drive this V-shape detector. The only candidate pointed shapes are the tall, tapered drinking glasses, whose flaring silhouettes faintly resemble the martini-glass and V-shape patterns this neuron feeds into. The round teapot and cups would, if anything, push it toward its \"round\" negative pole. Overall a weak, uncertain signal.\n\n25"}
{"prompt": "B: + essay", "gid": 38555, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.5053, "on": true, "p_on": 0.27, "reasoning": "The key feature is the small Japanese Chin's face, the only animal face in the image. This neuron responds to pointed, V-shaped forms such as fox snouts, cat faces and the angled fronts of cars. The Chin is a flat-faced breed with a round head, which is closer to the \"round\" pole that pushes the neuron negative, like the bulldog and mastiff examples. The face is also small in the frame and the rest is grass, so I expect only a weak response at most.\n\n27"}
{"prompt": "B: + essay", "gid": 1148, "imagenet_class": "polecat", "frac_of_max": 0.3085, "on": true, "p_on": 0.66, "reasoning": "The photo shows a ferret's face, viewed head-on and filling most of the frame. Its eyes, nose and muzzle are arranged much like the cat and fox faces this neuron is known for. The mask markings and narrowing snout form a downward-pointing V, which is the shape the neuron seems to track. Against that, the ferret's head and ears are fairly rounded and its muzzle is blunter than a fox's, so the \"round\" signal may partly cancel the drive, but a frontal small-carnivore face is still likely to push it past threshold.\n\n66"}
{"prompt": "B: + essay", "gid": 18963, "imagenet_class": "totem pole", "frac_of_max": 0.0498, "on": false, "p_on": 0.5, "reasoning": "The carved faces on the totem pole are face-like structures with pointed beaks and angular features. The outstretched wings at the top form a broad V-like silhouette against the sky. Both match the neuron's \"pointed / V-shape\" preference, and stylized animal faces may partly engage the fox/cat-face inputs. However, the carvings are flat, wooden and stylized rather than real furry snouts or car fronts, so the response could be weaker than for natural examples.\n\n50"}
{"prompt": "B: + essay", "gid": 46566, "imagenet_class": "limousine", "frac_of_max": 0.4139, "on": true, "p_on": 0.55, "reasoning": "The photo is a white stretch limousine seen mostly from the side, with the front three-quarters partly visible: grille, headlights and a hood that tapers forward. The neuron's car responses came mainly from front-facing views (van fronts, car hoods) whose converging lines form a pointed, V-like shape. This angled front gives some of that tapering geometry and strongly engages car-front features from mixed4d. But the long, flat side profile dominates the image and has nothing pointed in it, so the activation may be only moderate.\n\n55"}
{"prompt": "B: + essay", "gid": 29388, "imagenet_class": "beaver", "frac_of_max": 0.0286, "on": false, "p_on": 0.15, "reasoning": "The only candidate is the beaver, and it is small, dark and partly hidden among branches and moss. Its body and blunt snout are rounded rather than V-shaped, unlike a fox or cat face, and that round, pointless profile is the pole that pushes this neuron negative. The scattered twigs make some angles, but no clear converging V-shape (snout, flame, hood front) stands out.\n\n15"}
{"prompt": "B: + essay", "gid": 29497, "imagenet_class": "thimble", "frac_of_max": 0.024, "on": false, "p_on": 0.08, "reasoning": "The photo shows a thimble: a rounded, dome-topped cylinder with a dimpled texture, sitting on a curved wooden surface. Nothing in it is pointed or V-shaped. There is no tapering snout, no converging flame, no wedge-like car front or cone. The dominant shapes are round (the dome, the circular dimples, the curved table edge), which is the neuron's negative pole, so it should stay quiet or be pushed negative.\n\n8"}
{"prompt": "B: + essay", "gid": 17176, "imagenet_class": "harp", "frac_of_max": 0.0369, "on": false, "p_on": 0.28, "reasoning": "The photo is dominated by a carved wooden harp frame and its vertical strings, with a dim figure in the background. There are no fox, cat or dog faces and no car fronts, which are the strongest drivers of this pointed, V-shape detector. The converging strings and the angled frame edge could form weak wedge-like geometry, but the overall structure is mostly parallel lines and curves rather than a clear pointed \"V\" object. A weak or moderate response is possible, but a strong one seems unlikely.\n\n28"}
{"prompt": "B: + essay", "gid": 49383, "imagenet_class": "papillon", "frac_of_max": 0.3954, "on": true, "p_on": 0.8, "reasoning": "The key feature is the dog's face. It is a papillon-type tricolor dog with a white blaze running down to a narrow muzzle, which forms a V-shaped, pointed face much like the puppy and fox examples that drive this neuron. Its snout is long and tapered, not flat and round like the bulldog and mastiff faces that push it negative. The round toy in the foreground may suppress the neuron a little, but the strong close-up pointed face should dominate the maximum response.\n\n80"}
{"prompt": "B: + essay", "gid": 46268, "imagenet_class": "Dungeness crab", "frac_of_max": 0.0484, "on": false, "p_on": 0.28, "reasoning": "The image is a dense pile of crabs. Their carapaces are smooth, rounded ovals, which resemble the \"round\" negative pole more than the pointed V-shapes (fox snouts, cat faces, car grilles) that drive this neuron. The spiky legs and claws do form some small V-like angles, but they are thin, cluttered, and don't make the convergent pointed-face or front-end shape the neuron prefers. Weak partial activation is possible, but I expect it to stay mostly OFF.\n\n28"}
{"prompt": "B: + essay", "gid": 113, "imagenet_class": "book jacket", "frac_of_max": 0.0136, "on": false, "p_on": 0.2, "reasoning": "The photo is a book cover dominated by red text and a dark painting of a bearded man (Rasputin) with his hand raised to his face. The neuron's strongest drivers are pointed, V-shaped animal snouts (fox, wolf, cat faces) and car or van fronts, and none of those are present. The closest thing to a V is the downward-pointing beard and the shape of the hand and fingers. Human faces and flat text are not typical strong triggers, so a weak response is the most likely outcome.\n\n20"}
{"prompt": "B: + essay", "gid": 47616, "imagenet_class": "mashed potato", "frac_of_max": 0.0444, "on": false, "p_on": 0.08, "reasoning": "The photo shows rounded mounds of mashed potatoes on a plate, with a bit of meat and a fork edge. These are soft, blobby, round forms, the kind of shape the essay says pushes this neuron negative. Nothing here resembles the pointed V-shapes that drive it: no snouts, cat or fox faces, car grilles, flames, or martini-glass silhouettes. So the neuron is most likely OFF.\n\n8"}
{"prompt": "B: + essay", "gid": 46550, "imagenet_class": "web site", "frac_of_max": 0.0338, "on": false, "p_on": 0.08, "reasoning": "This image is a pair of flat website screenshots: blue banners, text blocks and rectangular layout elements. There are no pointed, V-converging shapes like animal snouts, car or train fronts, flames or tapered glass bowls. The rectangles and text lack the strong convergent-point geometry this neuron responds to, so it should stay near baseline. Some small icons or perspective-tilted panels could give a weak response, but probably not 30% of its maximum.\n\n8"}
{"prompt": "B: + essay", "gid": 25260, "imagenet_class": "jeep", "frac_of_max": 0.3266, "on": true, "p_on": 0.6, "reasoning": "The front three-quarter view of this vehicle, with its hood, grille and fenders, is the kind of car-front structure the neuron's van-front input (4d:201) feeds, and the hood edges and angled body lines give some converging, pointed geometry. Against that, this Land Rover is very boxy and flat-fronted. It also has round headlights and a large round spare tire on the hood, which match the \"round\" pole that pushes the neuron negative. On balance the car-front evidence probably wins, but not decisively.\n\n60"}
{"prompt": "B: + essay", "gid": 31102, "imagenet_class": "frilled lizard", "frac_of_max": 0.0102, "on": false, "p_on": 0.45, "reasoning": "The lizard's head is the only real candidate feature. It is a tapered, wedge-like snout seen close up with a prominent eye, loosely similar to the pointed fox and cat faces that drive this V-shape detector. However, the snout points upward and the crown is fairly rounded, so the V-shape is less clean than a frontal fox or cat face. The blurry, plain background contributes nothing either way, so the outcome hinges on whether the head's taper reads as pointed enough.\n\n45"}
{"prompt": "B: + essay", "gid": 43424, "imagenet_class": "polecat", "frac_of_max": 0.3235, "on": true, "p_on": 0.68, "reasoning": "The key feature is the marten's frontal face. Its triangular, fox-like head tapers from two upright ears down to a narrow snout, forming the kind of pointed V shape that drives 4e:55 through its fox-snout and wolf-face inputs. The white V-shaped bib on its chest adds to this. The face is fairly small in the frame and somewhat rounded at the cheeks, which could weaken the response, but cat- and fox-like animal faces are typical strong activators.\n\n68"}
{"prompt": "B: + essay", "gid": 24227, "imagenet_class": "zebra", "frac_of_max": 0.0335, "on": false, "p_on": 0.22, "reasoning": "The neuron responds to pointed, V-shaped forms, such as fox or cat snouts seen head-on, car grilles and pointed noses or fronts. Here the zebras are mostly seen from behind or grazing with their heads down, so no frontal pointed face is visible. The dominant shapes are rounded rumps and striped flanks, which are closer to the \"round\" pole that suppresses it. Some striped textures or a partly visible head might give weak activation, but a strong ON response seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 35429, "imagenet_class": "stopwatch", "frac_of_max": 0.0381, "on": false, "p_on": 0.15, "reasoning": "The photo is dominated by round forms: a circular pocket-watch face and its round open lid, which are exactly the \"round\" pattern that pushes this neuron negative (the essay's figure even lists a clock among its negative examples). The only possible pointed or V-like structure is the gaps between the spread fingers. That is weak and not the snout, flame or hood-like V the neuron prefers. The neuron is most likely OFF.\n\n15"}
{"prompt": "B: + essay", "gid": 39087, "imagenet_class": "dung beetle", "frac_of_max": 0.0345, "on": false, "p_on": 0.18, "reasoning": "The photo's main elements are a round dung ball and the domed, rounded bodies of two dung beetles, which are exactly the round, blob-like shapes this neuron's negative pole prefers. There is no clear pointed V-shape: no snout, no wedge-like vehicle front, no flame. The shallow-depth-of-field road converges only weakly toward the horizon. So I expect a weak or near-zero response, though beetle legs and heads could give minor pointed fragments.\n\n18"}
{"prompt": "B: + essay", "gid": 45701, "imagenet_class": "tiger cat", "frac_of_max": 0.3827, "on": true, "p_on": 0.9, "reasoning": "Two cat faces are clearly visible: the tabby in front and the orange-and-white cat behind. Each has upright pointed ears and a face that narrows toward the chin, the V-shaped, pointed-face pattern that drives this neuron hardest. Cat faces are among its canonical top activators. The grass and twig background is mostly neutral, and there is nothing round dominating the frame that would suppress it.\n\n90"}
{"prompt": "B: + essay", "gid": 48228, "imagenet_class": "Persian cat", "frac_of_max": 0.372, "on": true, "p_on": 0.4, "reasoning": "This is a Persian cat. Cat faces are among this neuron's classic triggers, but under the pointed-versus-round reading the face shape matters more than the label. The Persian's face is flat and round with a squashed muzzle, much like the bulldog and mastiff faces that push the neuron negative. Small pointed ear tips are visible at the top, and the face sits head-on and fills the frame, so some V-shaped activation is still plausible. Overall the evidence is mixed and leans slightly toward OFF.\n\n40"}
{"prompt": "B: + essay", "gid": 19772, "imagenet_class": "Pomeranian", "frac_of_max": 0.3527, "on": true, "p_on": 0.66, "reasoning": "This is a close-up of a Pomeranian's face, a breed often called \"fox-faced,\" with a tapering muzzle, alert eyes and a triangular overall face shape. That matches the fox-snout and wolf-face inputs that drive this pointed/V-shape detector. Against that, the face is very fluffy and roundish, and the open mouth with the tongue out softens the V, which pulls toward the \"round\" side that suppresses the neuron. On balance, the fox-like canine face should likely drive it.\n\n66"}
{"prompt": "B: + essay", "gid": 17590, "imagenet_class": "coral fungus", "frac_of_max": 0.0475, "on": false, "p_on": 0.28, "reasoning": "This is a pink coral fungus: a branching structure with many small forks between the branches, and each branch ends in a blunt, rounded tip. If the neuron really detects V-shapes, those forks could give it some drive. But the V's are small, thin and repeated, unlike the bold converging V of a fox snout, cat face, car hood or flame. The rounded tips and blob-like clusters lean toward the \"round\" pole that pushes it negative, so a strong response seems unlikely.\n\n28"}
{"prompt": "B: + essay", "gid": 33974, "imagenet_class": "Model T", "frac_of_max": 0.3151, "on": true, "p_on": 0.5, "reasoning": "The photo shows the front three-quarter view of an old Model T\u2013style hot rod. Car fronts are one of this neuron's classic triggers, and it gets strong input from a van-front detector (4d:201), so the grille and hood area could drive it. But this car's front is mostly rounded and blunt: round headlights, curved fenders, whitewall tires, and stacked wheels in the background. That gives little of the pointed or V-shaped convergence the neuron prefers and plenty of the roundness that pushes it negative, so I expect only a moderate chance it fires.\n\n50"}
{"prompt": "B: + essay", "gid": 18245, "imagenet_class": "bottlecap", "frac_of_max": 0.0093, "on": false, "p_on": 0.12, "reasoning": "The photo is filled with round objects (bottle caps or typewriter keys) on a striped rug. Under the V-shape reading of this neuron, circles are exactly its negative pole, so they should suppress it rather than drive it. Nothing in the image is pointed or V-shaped: there are no snouts, no ears, no car fronts, and no flame-like wedges. The only possible weak drivers are the angled edges where the caps overlap and the colorful rug texture.\n\n12"}
{"prompt": "B: + essay", "gid": 21647, "imagenet_class": "collie", "frac_of_max": 0.3522, "on": true, "p_on": 0.72, "reasoning": "The Shetland sheepdog has a long, tapered, fox-like snout and pointed ears, exactly the V-shaped \"pointed face\" pattern that 4e:55's strongest inputs (the fox snout and wolf face detectors) feed it. Nothing round, like a flat bulldog face or a ball, competes in the frame. The main risk is that the dog is fairly small and seen in profile rather than face-on, which may weaken the V-shape signal. Even so, sable sheltie heads closely resemble fox faces, so a strong response is likely.\n\n72"}
{"prompt": "B: + essay", "gid": 17092, "imagenet_class": "minibus", "frac_of_max": 0.3189, "on": true, "p_on": 0.8, "reasoning": "The photo is a head-on view of a white Ford Transit minibus, and that front end is what matters. A van front is exactly what one of this neuron's strongest inputs (4d:201, \"van front\") detects. Vehicle fronts like grilles and hoods are also part of its classic \"car\" activations. The sloping hood and the windshield narrowing toward the nose give the kind of tapered, pointed shape the neuron favors. There are no strongly round elements to push it negative, so it will probably fire strongly.\n\n80"}
{"prompt": "B: + essay", "gid": 39632, "imagenet_class": "timber wolf", "frac_of_max": 0.3779, "on": true, "p_on": 0.85, "reasoning": "The wolf's head is the key feature: tall pointed ears and a long tapering snout give a strongly pointed, V-like canid face. That shape matches the neuron's input from 4d:477 (wolf face) and its readers such as 5a:352 (coyote face). Because the head is in profile rather than frontal, the symmetric V is less clean, but the pointed ears and muzzle should still drive it well.\n\n85"}
{"prompt": "B: + essay", "gid": 17333, "imagenet_class": "megalith", "frac_of_max": 0.0403, "on": false, "p_on": 0.18, "reasoning": "The photo shows Avebury-style standing stones: large, weathered, blunt-edged megaliths on grass under an overcast sky. There are no animal faces, no car or train fronts, no flames and no tapering V-shaped forms that this neuron's \"pointed\" detectors respond to. The stones are broad and rounded rather than converging to a point. The distant stone's narrower top is the only weakly pointed shape, so the neuron is most likely OFF.\n\n18"}
{"prompt": "B: + essay", "gid": 13682, "imagenet_class": "plane", "frac_of_max": 0.0326, "on": false, "p_on": 0.25, "reasoning": "The photo shows a wooden hand plane seen from a low side angle. It is mostly a long rectangular block with a round knob and a round metal fitting behind it. There is no clear V or pointed convergence like a snout, flame or car-hood wedge. The perspective recession of the block gives only a weak wedge-like taper, while the round elements would, if anything, push the neuron negative. So it is probably OFF, with some chance the elongated, car-hood-like wooden body triggers it.\n\n25"}
{"prompt": "B: + essay", "gid": 17397, "imagenet_class": "barn spider", "frac_of_max": 0.0456, "on": false, "p_on": 0.28, "reasoning": "The spider's front legs splay outward in angled, somewhat V-like pairs, and the bright figure on a dark background gives clean edges, so there is some pointed geometry here. But the spider's center mass is a rounded, bulbous abdomen, which is the kind of round shape that pushes this neuron negative. There is also no snout-like or car-front-like converging V, which is what drives it most strongly. The pointed cues are thin and scattered, so a strong response seems unlikely.\n\n28"}
{"prompt": "B: + essay", "gid": 23513, "imagenet_class": "Cardigan", "frac_of_max": 0.3695, "on": true, "p_on": 0.8, "reasoning": "The corgi's head is the key feature: two large upright pointed ears with a tapering snout make a strong V-shaped, fox-like face, which is exactly what this neuron's fox-snout and wolf-face inputs detect. The face is also turned upward, which may weaken the frontal-face match somewhat. The rounder, floppy-eared puppy in the foreground points the other way, but the neuron reports its strongest response anywhere in the image, so the corgi's head should dominate.\n\n80"}
{"prompt": "B: + essay", "gid": 4705, "imagenet_class": "grey fox", "frac_of_max": 0.3702, "on": true, "p_on": 0.78, "reasoning": "The gray fox's head is the key element: its tapering snout and tall, pointed ears form exactly the kind of pointed, V-like shape this neuron responds to. Its strongest input is the 4d:493 fox-snout detector, which this image should drive well. The head is seen in profile rather than straight on, so the V is less symmetric than in a frontal fox face, but the sharp snout and ears should still give a strong response. Nothing round or blunt in the image competes with it.\n\n78"}
{"prompt": "B: + essay", "gid": 14334, "imagenet_class": "soup bowl", "frac_of_max": 0.0484, "on": false, "p_on": 0.12, "reasoning": "The photo is dominated by a large round bowl of tomato soup seen from above. That circular \"O\" shape matches this neuron's negative pole (round objects like golf balls, clocks and snails push it down). The only pointed elements are the thin spoon handle and the slivers of basil, which are small and don't form a strong V-shaped snout, front or flame. So the neuron is most likely OFF.\n\n12"}
{"prompt": "B: + essay", "gid": 3124, "imagenet_class": "fox squirrel", "frac_of_max": 0.0359, "on": false, "p_on": 0.5, "reasoning": "The fox squirrel's head at the top of the trunk is the relevant part: an orange-furred animal face with small pointed ears and a tapering snout, which resembles the fox and wolf face inputs that drive this pointed/V-shape neuron. However, the face is small, partly turned away and partly hidden behind the trunk, so it gives a weaker and less clean V than a frontal fox or cat face. The tree bark and grass offer nothing pointed, and the squirrel's rounded body may pull slightly toward the \"round\" side.\n\n50"}
{"prompt": "B: + essay", "gid": 41223, "imagenet_class": "chiton", "frac_of_max": 0.0271, "on": false, "p_on": 0.18, "reasoning": "This is a chiton on rock: an oval, rounded body with no faces, snouts, car fronts or other pointed silhouettes, which are what drive this neuron. The overlapping shell plates do form faint chevron or arched bands, which might weakly engage V-shape detectors. But they are small, low-contrast and embedded in a rounded outline, and roundness tends to push the neuron negative. A strong response seems unlikely.\n\n18"}
{"prompt": "B: + essay", "gid": 10479, "imagenet_class": "earthstar", "frac_of_max": 0.0492, "on": false, "p_on": 0.22, "reasoning": "The main object is an earthstar fungus: a smooth, round, egg-like dome sitting on a flat collar. That is exactly the kind of round, O-like shape that pushes this neuron negative. The only pointed element is the small nipple at the top of the dome. The star rays around it look flattened and blunt here, not sharp V-shapes. So there is little of the pointed, converging structure (snouts, car fronts, flames) that drives the neuron.\n\n22"}
{"prompt": "B: + essay", "gid": 39311, "imagenet_class": "weasel", "frac_of_max": 0.5046, "on": true, "p_on": 0.72, "reasoning": "The ferret's face is the key feature: it is a furry animal face that tapers to a pointed snout, forming the V-shape this neuron responds to. That makes it close to the fox, cat and dog-puppy faces in the neuron's top examples. The face is fairly small and partly framed by hands, and ferret faces are somewhat rounder and flatter than a fox's. Still, the eyes-and-snout V pattern should drive it well.\n\n72"}
{"prompt": "B: + essay", "gid": 46899, "imagenet_class": "tiger cat", "frac_of_max": 0.3507, "on": true, "p_on": 0.8, "reasoning": "The two white tiger faces are what matter. Feline faces are among this neuron's classic top activators, and the tapering nose bridge and muzzle form the pointed V-shape it responds to. Tiger faces are broader and rounder than domestic cat or fox faces, which could weaken the response somewhat. Still, the clear, close-up frontal views with V-shaped noses and muzzles should drive it well.\n\n80"}
{"prompt": "B: + essay", "gid": 18405, "imagenet_class": "jeep", "frac_of_max": 0.3192, "on": true, "p_on": 0.3, "reasoning": "This is a side profile of a boxy SUV, so the car-front cues this neuron likes are mostly missing: there is no head-on grille, no tapering hood and no V-shaped converging lines. The most salient shapes are the two large round wheels, and roundness is exactly what pushes this neuron negative. The headlight and front corner give a small angled, pointed edge, and car imagery in general does drive this unit, so some activation is plausible. Overall I expect a weak response.\n\n30"}
{"prompt": "B: + essay", "gid": 13779, "imagenet_class": "collie", "frac_of_max": 0.3502, "on": true, "p_on": 0.85, "reasoning": "This Shetland sheepdog has a strongly fox-like, V-shaped face: a tapering pointed snout, triangular upright ears, and a white blaze converging toward the nose. That is exactly the pointed-face pattern fed by the fox-snout and wolf-face inputs (4d:493, 4d:477). The face is large, frontal and clearly visible, with nothing round competing with it, so the neuron should respond strongly.\n\n85"}
{"prompt": "B: + essay", "gid": 6272, "imagenet_class": "papillon", "frac_of_max": 0.435, "on": true, "p_on": 0.85, "reasoning": "The key feature is the papillon's face: large upright ears and a tapering snout form a strongly fox-like V shape. That is the pointed, fox/wolf-face pattern this neuron's strongest inputs (4d:493 fox snout, 4d:477 wolf face) feed into. The face is frontal, well-lit and fairly large in the frame, so the V-shaped configuration should register clearly. Little in the scene is round enough to suppress it.\n\n85"}
{"prompt": "B: + essay", "gid": 24380, "imagenet_class": "Gila monster", "frac_of_max": 0.0318, "on": false, "p_on": 0.15, "reasoning": "The photo shows a Gila monster: a blunt, rounded head with bumpy, beaded scales, resting on a flat red brick. That is the opposite of the pointed, V-shaped convergence this neuron wants, such as tapering fox or cat snouts, wedge-shaped car fronts, and flames. The beaded texture and dome-like head look more like the neuron's \"round\" negative pole, which resembles the scaly orb in its negative render. The brick's straight edges and the clawed foot give only weak, non-V geometry, so a strong activation is unlikely.\n\n15"}
{"prompt": "B: + essay", "gid": 31952, "imagenet_class": "guacamole", "frac_of_max": 0.0044, "on": false, "p_on": 0.08, "reasoning": "The photo is guacamole in a circular bowl with a round avocado pit in the center. Both are round, which is this neuron's negative pole. There is nothing pointed or V-shaped: no snout, no ears, no car hood or grille converging to a point, no flame. The mashed green texture is shapeless clutter and is unlikely to supply the converging-edge pattern the neuron responds to, so it should stay weak.\n\n8"}
{"prompt": "B: + essay", "gid": 21861, "imagenet_class": "miniature schnauzer", "frac_of_max": 0.3575, "on": true, "p_on": 0.62, "reasoning": "The key feature is the schnauzer's close-up, frontal face. Its eyes, nose and tapering beard form a downward-pointing, roughly V-shaped muzzle, similar to the fox, wolf and cat faces that drive this neuron. However, the bushy beard and folded ears make the overall face blockier and rounder than a sharp fox snout, which could weaken the response. Even so, frontal small-dog faces with a pointed lower face usually activate it fairly well.\n\n62"}
{"prompt": "B: + essay", "gid": 38352, "imagenet_class": "swimming trunks", "frac_of_max": 0.0446, "on": false, "p_on": 0.18, "reasoning": "The photo shows a shirtless man crouching on a stone ledge by green water, next to a container. It contains none of the neuron's typical drivers: no cat, fox or dog faces, no car or train fronts, and no flame- or martini-glass-like pointed V shapes. The bent limbs form some angles, but these are weak, incidental cues, and the rounded head and container lean toward the round shapes that push it negative. The neuron is likely mostly quiet here.\n\n18"}
{"prompt": "B: + essay", "gid": 16163, "imagenet_class": "convertible", "frac_of_max": 0.3238, "on": true, "p_on": 0.8, "reasoning": "The photo shows a black BMW convertible seen from a front three-quarter angle, with its hood open, its headlights visible, and a low, tapering nose. Car fronts are among this neuron's classic strong triggers: \"car\" was half of its supposed cat/car polysemanticity. Under the V-shape reading, the converging lines of the hood and front end, and the angled headlights, form exactly the kind of pointed configuration it detects. There are no strongly round features dominating the scene, so I expect it to fire well.\n\n80"}
{"prompt": "B: + essay", "gid": 28061, "imagenet_class": "marmot", "frac_of_max": 0.0388, "on": false, "p_on": 0.22, "reasoning": "The photo shows a marmot in side profile on rocky ground, small in the frame. Its body is a rounded, furry mass, and its blunt, rounded head is seen from the side, not head-on. This neuron responds to pointed, V-shaped forms such as fox or cat faces seen frontally, car fronts, and flames. A rounded rodent in profile against rocks offers little of that convergent V structure. The snout and ears give at most a weak pointed cue, so the neuron is more likely OFF.\n\n22"}
{"prompt": "B: + essay", "gid": 44894, "imagenet_class": "hook", "frac_of_max": 0.0262, "on": false, "p_on": 0.12, "reasoning": "The photo is dominated by round, faceted dark beads with ornate silver caps. These are exactly the kind of round, ball-like shapes that push this neuron negative. The thin curved ear-wire hooks are arcs rather than converging V-shapes or pointed snout-like forms, and there are no animal faces, car fronts, flames or other pointed V-structures to drive it. The neuron is most likely OFF, with only a small chance that the hook-and-bead junctions read as weakly pointed.\n\n12"}
{"prompt": "B: + essay", "gid": 28455, "imagenet_class": "soccer ball", "frac_of_max": 0.0364, "on": false, "p_on": 0.1, "reasoning": "The dominant object is a soccer ball, a large round shape like the golf and tennis balls that push this neuron negative. Nothing in the frame offers the V-shaped, pointed structure that drives it: no snouts, car fronts or flame tips. The small daisy, grass blades and clouds are weak candidates at best, and none forms a clear converging V. So the neuron is most likely OFF.\n\n10"}
{"prompt": "B: + essay", "gid": 26659, "imagenet_class": "pickup", "frac_of_max": 0.4655, "on": true, "p_on": 0.8, "reasoning": "The front of this vintage red pickup, with its hood, grille and windshield seen at an angle, is the kind of car-front view this neuron is known to respond to. It closely matches the red-car-hood example in the figure, and the van-front detector 4d:201 is one of its inputs. The hood and fender lines converging toward the grille give a pointed, V-like structure. The round headlights and hubcaps may push it slightly negative, but the overall vehicle-front pattern should dominate.\n\n80"}
{"prompt": "B: + essay", "gid": 33188, "imagenet_class": "earthstar", "frac_of_max": 0.0317, "on": false, "p_on": 0.3, "reasoning": "The photo shows earthstar fungi: a round, ball-like spore sac sitting on a ring of pointed, star-like rays. The dominant, centered objects are smooth spheres, which is exactly the round pattern that pushes this neuron negative (like the golf ball, tennis ball and snail in the figure). The splayed rays do form pointed, V-like tips, which could give some positive drive. But they are small, flattened and visually secondary to the balls, so I expect a weak response overall.\n\n30"}
{"prompt": "B: + essay", "gid": 39610, "imagenet_class": "paintbrush", "frac_of_max": 0.0367, "on": false, "p_on": 0.18, "reasoning": "The neuron fires for pointed, V-shaped structures: fox, cat and dog snouts, car and van fronts, flames, and martini glasses. Round shapes push it negative. This photo is mostly a frontal human face, which the network doesn't typically treat as a snout-like V. It also has prominent round hoop earrings and dark, uniform clothing. The fingers and cigarette make a small angled shape, but that is weak and unlikely to produce a strong response.\n\n18"}
{"prompt": "B: + essay", "gid": 18841, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.5805, "on": true, "p_on": 0.32, "reasoning": "The key feature here is the dog's face. This is a Japanese Chin, a snub-nosed breed whose face is broad, flat and round rather than tapering to a V-shaped snout like a fox, wolf or pointy-faced puppy. That puts it closer to the bulldog/mastiff \"round\" examples that push this neuron negative. The face is also small in the frame, and the rest of the image (fluffy body, grass, foliage) has little pointed V-structure. Some dog-face features may still give a moderate response, so ON is possible but not the likelier outcome.\n\n32"}
{"prompt": "B: + essay", "gid": 39720, "imagenet_class": "Ibizan hound", "frac_of_max": 0.3593, "on": true, "p_on": 0.78, "reasoning": "This is an Ibizan hound (or podenco) in side profile, with a long, sharply tapering snout and large pointed ears. That is exactly the V-shaped, pointed head the neuron responds to, the same kind of shape as the fox and wolf faces that feed into it. The face also fills much of the frame. The profile view and the slightly rounded skull and nose tip may weaken the response somewhat compared with a frontal V, but the pointed geometry should still dominate.\n\n78"}
{"prompt": "B: + essay", "gid": 25591, "imagenet_class": "pick", "frac_of_max": 0.0183, "on": false, "p_on": 0.57, "reasoning": "The guitar pick is a downward-pointing triangle with a rounded top, much like the V-shaped outline of a fox or cat face that this neuron seems to track. That pointed lower tip, set against plain wood, is the kind of tapered shape that should drive it. On the other hand, the pick's corners are soft and much of its outline is round, which could weaken or partly cancel the response. There is also no fur, eyes or car-grille texture to add to it.\n\n57"}
{"prompt": "B: + essay", "gid": 25264, "imagenet_class": "cloak", "frac_of_max": 0.0186, "on": false, "p_on": 0.22, "reasoning": "This is a flat, densely patterned Buddhist cave mural. It has none of the strong drivers for this neuron: no furry animal snout, cat or fox face, and no glossy car or train front converging to a point. There are some weak V-like structures, such as the robe necklines, the triangular seated pose and the pointed flame or leaf shapes. But they are small and painted, and the many round halos and circular motifs are the kind of shape that pushes the neuron negative. Overall, a strong response looks unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 36898, "imagenet_class": "toy terrier", "frac_of_max": 0.3083, "on": true, "p_on": 0.85, "reasoning": "The dog has tall, sharply pointed ears and a tapered, pointed snout shown in profile. These are exactly the V-shaped, pointed forms (fox-snout or wolf-face-like) that drive this neuron. Nothing round dominates the frame to push it negative.\n\n85"}
{"prompt": "B: + essay", "gid": 17870, "imagenet_class": "diamondback", "frac_of_max": 0.041, "on": false, "p_on": 0.45, "reasoning": "This is a close-up, head-on view of a rattlesnake's face, with two eyes above a snout that tapers toward the bottom of the frame. That loosely matches the pointed, V-shaped face pattern this neuron likes (fox, cat and dog faces). However, the head reads as broad and flat, and the scaly texture is unlike the furry faces that drive its main inputs (fox snout, wolf face). That makes the V-shape cue weaker and less certain than for a mammal face.\n\n45"}
{"prompt": "B: + essay", "gid": 15051, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.3527, "on": true, "p_on": 0.55, "reasoning": "What matters here are the two Cavalier King Charles spaniel puppy faces. Their tapering muzzles and the V of white blaze between the eyes resemble the pointed puppy face that drives this neuron in the examples. Working against that, Cavaliers have soft, rounded heads with floppy ears, and the large plush donkey dominates the frame with a round, blunt muzzle, which is the \"round\" pattern that pushes the neuron negative. Since the neuron's value is its strongest response anywhere in the image, the small puppy snouts could still be enough to turn it ON, so it's roughly a coin flip leaning slightly positive.\n\n55"}
{"prompt": "B: + essay", "gid": 47301, "imagenet_class": "box turtle", "frac_of_max": 0.0402, "on": false, "p_on": 0.15, "reasoning": "The dominant object is a box turtle's shell, a large rounded dome that matches the neuron's negative \"round\" pole, like the snail and balls in the figure. The turtle's head is small and blunt rather than a sharp V-shaped snout or a car-front wedge. Nothing in the frame offers the pointed, converging V geometry that drives 4e:55, so it probably stays low.\n\n15"}
{"prompt": "B: + essay", "gid": 46791, "imagenet_class": "beach wagon", "frac_of_max": 0.3352, "on": true, "p_on": 0.6, "reasoning": "The main thing here is the car itself. This neuron fires strongly on car bodies, especially fronts and hoods, and the Cammarata reading suggests it is really keying on pointed or tapering shapes. This is a side view of a station wagon at night, so there is no frontal V-shaped grille. Still, the sloping hood and the angled windshield and front pillar give some tapered, wedge-like structure, and cars in general drive this neuron fairly reliably. The low light and long, boxy side profile make a strong response less certain.\n\n60"}
{"prompt": "B: + essay", "gid": 38584, "imagenet_class": "lipstick", "frac_of_max": 0.0377, "on": false, "p_on": 0.32, "reasoning": "The neuron responds to pointed, V-shaped frontal structures such as fox and cat snouts, car hoods and grilles, and bullet-train noses, and it is pushed negative by round forms. This photo is a frontal human face, which is fairly rounded and lacks a snout, so it is not a natural driver. The dark sunglasses do make a horizontal band with a slight V at the nose bridge, a bit like a car's windshield or headlight row, which could give a weak-to-moderate response. Overall, a partial match is plausible but a strong one is unlikely.\n\n32"}
{"prompt": "B: + essay", "gid": 40777, "imagenet_class": "sleeping bag", "frac_of_max": 0.0406, "on": false, "p_on": 0.2, "reasoning": "The photo shows a person bundled in a red-and-silver sleeping bag, with their face peeking out of a drawn-tight hood. That hood frames the face in a round, O-like opening, and the bag's silver panels form broad rounded curves. These are the round shapes that push this neuron negative. There is no clear V-shaped or pointed structure, such as a snout, a pointed face, a tapering car front or a flame tip. The one faint V-like cue is where the red and silver panels meet, so a weak response is possible, but strong activation is unlikely.\n\n20"}
{"prompt": "B: + essay", "gid": 45585, "imagenet_class": "pickup", "frac_of_max": 0.4916, "on": true, "p_on": 0.8, "reasoning": "The photo shows the front three-quarter view of a vintage black Chevrolet pickup. Its chrome grille and long, tapering hood converge toward the front, which matches the car-front/\"van front\" input (4d:201) and the pointed, V-like geometry this neuron favors. Car hoods and grilles were among this neuron's classic top activators, as with the red car example in the figure. The rounded fenders and headlights add some \"round\" signal that pushes the other way, but the converging hood and grille structure should dominate.\n\n80"}
{"prompt": "B: + essay", "gid": 13284, "imagenet_class": "Bernese mountain dog", "frac_of_max": 0.5142, "on": true, "p_on": 0.8, "reasoning": "The key feature is the puppy's face: the white blaze running down between the eyes into the muzzle forms a strong downward-pointing V, and the snout tapers to a point, much like the \"dog\" and \"fox\" examples that drive this neuron. Puppies have rounder heads than adult dogs, which could weaken the response. But the face is large, frontal and high-contrast, and that V/pointed-snout pattern is exactly what this unit's fox-snout and wolf-face inputs pick up. Nothing round dominates the image to push it negative.\n\n80"}
{"prompt": "B: + essay", "gid": 27895, "imagenet_class": "banded gecko", "frac_of_max": 0.0467, "on": false, "p_on": 0.32, "reasoning": "The main candidate is the gecko's head, seen from above with a tapering, somewhat wedge-shaped snout. That gives a mild V/pointed shape, but it is fairly blunt and rounded, and its large round eye leans toward the \"round\" pole. Nothing here resembles the neuron's strongest drivers: the pointed V-faces of foxes or cats, or the angled fronts of cars. Some activation from the snout taper is possible, but it is unlikely to reach a strong response.\n\n32"}
{"prompt": "B: + essay", "gid": 15330, "imagenet_class": "red fox", "frac_of_max": 0.4919, "on": true, "p_on": 0.92, "reasoning": "The photo centers on a red fox kit's face. Its narrow snout and tall pointed ears form exactly the V-shaped, pointed head this neuron responds to. The neuron's strongest input is a fox-snout detector (4d:493), and fox faces are among its canonical drivers. Nothing round dominates the scene to pull it negative, so the neuron should fire strongly.\n\n92"}
{"prompt": "B: + essay", "gid": 10130, "imagenet_class": "ambulance", "frac_of_max": 0.3746, "on": true, "p_on": 0.55, "reasoning": "The vehicles are what matter here: a white-and-red emergency minivan on the left and a boxy ambulance on the right. This neuron responds strongly to car and van fronts, which have a pointed, V-like hood and grille, and one of its main inputs is a van-front detector (4d:201). The minivan is seen at a three-quarter angle, so its sloped hood and front are partly visible, but both vehicles are fairly small in the frame and mostly shown from the side. The building and lawn shouldn't drive it, so I expect a moderate response that may or may not reach the ON threshold.\n\n55"}
{"prompt": "B: + essay", "gid": 29641, "imagenet_class": "dial telephone", "frac_of_max": 0.0283, "on": false, "p_on": 0.1, "reasoning": "The photo is dominated by a rotary phone dial: a large circular face with a ring of round finger holes and numbers arranged in a circle. That is exactly the \"round\" pattern the essay's figure shows pushing this neuron negative, much like the clock and ball examples. The only pointed element is the small metal finger stop, which is thin and unlikely to read as the V-shaped, snout-like convergence that drives the neuron. There is no face, car front or flame to compensate, so the neuron is most likely OFF.\n\n10"}
{"prompt": "B: + essay", "gid": 11315, "imagenet_class": "Samoyed", "frac_of_max": 0.3037, "on": true, "p_on": 0.6, "reasoning": "This Samoyed puppy's face is the only candidate driver: it has a fox-like, tapering snout with dark eyes and nose, the kind of pointed canid face that 4e:55's strongest inputs (fox snout, wolf face) respond to. Working against it, the fluffy fur makes the head outline fairly round, and white-on-white snow lowers the contrast. The ears are also small and folded rather than sharply pointed. Overall, a canid face with some V-taper probably drives it moderately, but not strongly.\n\n60"}
{"prompt": "B: + essay", "gid": 35652, "imagenet_class": "Newfoundland", "frac_of_max": 0.0273, "on": false, "p_on": 0.2, "reasoning": "This is a Newfoundland: a broad, round, heavy-jowled face with droopy features, a blunt muzzle, and floppy ears rather than a tapered snout or pricked ears. That rounded head is much closer to the mastiff and bulldog examples that push 4e:55 negative than to the pointed fox, cat and wolf faces that drive it. The eyes and nose still form a weak face-like converging pattern, so a small response is possible, but nothing here is a clear V-shape.\n\n20"}
{"prompt": "B: + essay", "gid": 20422, "imagenet_class": "ringneck snake", "frac_of_max": 0.0433, "on": false, "p_on": 0.27, "reasoning": "The snake's head at the right edge is the only candidate for a V-shaped, pointed cue, but it is small, dark and cropped, and its blunt, rounded snout is not the strong V of a fox face or car front. Most of the image is round coils of body, the kind of curved, O-like shape that pushes this neuron negative. There is no clear converging V structure, so a strong response seems unlikely.\n\n27"}
{"prompt": "B: + essay", "gid": 7209, "imagenet_class": "ice cream", "frac_of_max": 0.0229, "on": false, "p_on": 0.08, "reasoning": "The photo is all rounded forms: soft scoops of vanilla ice cream, roughly spherical raspberries, and a curved bowl. The neuron responds to pointed, V-shaped structures such as tapering snouts, car hoods and flames, and none are present here. The round shapes match its negative pole, so it should stay well below threshold.\n\n8"}
{"prompt": "B: + essay", "gid": 30788, "imagenet_class": "acorn", "frac_of_max": 0.0213, "on": false, "p_on": 0.18, "reasoning": "The photo is a pile of hazelnut- or acorn-like nuts: dozens of smooth, rounded, ball-shaped objects with some cup-shaped caps. Under the V-shape/pointedness reading of 4e:55, round convex forms are exactly what pushes it negative, like the golf ball, tennis ball and snail in the essay's figure. There are no faces, car fronts, flames or other converging V structures. Some nuts have slightly tapered tips and the caps have edges, which could give a weak response, but probably not 30% of the neuron's maximum.\n\n18"}
{"prompt": "B: + essay", "gid": 10739, "imagenet_class": "papillon", "frac_of_max": 0.3375, "on": true, "p_on": 0.72, "reasoning": "What matters here are the two small dogs' faces, especially the papillon-type dog on the left. Its ears flare up and out and its muzzle tapers, making the V-shaped, fox-like face this neuron's pointed-shape detector and fox-snout/wolf-face inputs respond to. The right dog's face is rounder and shaggier, so it contributes less. The faces are fairly small in the frame, which may limit the response, but the pointed-eared, tapered face is a typical driver.\n\n72"}
{"prompt": "B: + essay", "gid": 46163, "imagenet_class": "sunglasses", "frac_of_max": 0.0403, "on": false, "p_on": 0.2, "reasoning": "The baby's face is broad and round with a soft chin, which matches the neuron's negative \"round\" pole rather than the pointed, V-shaped snouts and car fronts that drive it. The red visor or sunglasses perched on the head form an arched band, but it is curved rather than pointed into a V. Human faces in general aren't among this neuron's typical cat, fox or car triggers. So any response from a small patch is likely weak.\n\n20"}
{"prompt": "B: + essay", "gid": 28909, "imagenet_class": "hamper", "frac_of_max": 0.0308, "on": false, "p_on": 0.12, "reasoning": "This is a wicker gift hamper holding a wine bottle, a round tin, and rectangular packets. It has none of the pointed V-shaped structures that drive this neuron, like snouts, cat or fox faces, car or train fronts, or flames. The most prominent object is a round tin lid, which is the kind of shape that pushes the neuron negative. The rest is mostly boxy shapes and wicker texture, so a strong response is unlikely.\n\n12"}
{"prompt": "B: + essay", "gid": 35867, "imagenet_class": "Siamese cat", "frac_of_max": 0.3936, "on": true, "p_on": 0.9, "reasoning": "The Siamese cat's face is a strong match for this neuron. Its tall, pointed ears and wedge-shaped muzzle form a clear V pointing down to the chin, the same tapering shape the neuron picks up in fox, wolf and cat faces. Cat faces filling the frame were among the original top activations that made people call this a cat/car neuron. Nothing round dominates the image to push the response negative, so a strong response is likely.\n\n90"}
{"prompt": "B: + essay", "gid": 46684, "imagenet_class": "beer bottle", "frac_of_max": 0.0088, "on": false, "p_on": 0.22, "reasoning": "This photo is a grid of beer bottles behind wire mesh, with no animal faces, car fronts, or other strongly pointed V-shaped objects of the kind that drive this neuron. The bottles are mostly cylindrical, with rounded shoulders and round or oval labels, which fits the \"round\" side that pushes the neuron negative. Weak V-like cues come from the tapering necks, a few shield-shaped labels, and the mesh crossings, but these are small and repetitive rather than one salient pointed shape, so a strong response seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 24585, "imagenet_class": "Chesapeake Bay retriever", "frac_of_max": 0.05, "on": false, "p_on": 0.5, "reasoning": "The key feature is the brown dog's face looking up at the camera, since canine and feline faces are among the stronger natural drivers of this neuron. A face seen from above narrows toward the snout and forms the kind of pointed V shape it likes. Working against this, the head is broad and fairly rounded, with floppy ears and an open mouth, which is more like the round, blunt dog faces that push it negative than like a pointed fox or cat face. The wood floor and the feet are probably irrelevant, so the result depends on how pointed the neuron reads this face to be, and it's a close call.\n\n50"}
{"prompt": "B: + essay", "gid": 1806, "imagenet_class": "meerkat", "frac_of_max": 0.4147, "on": true, "p_on": 0.68, "reasoning": "The two meerkat faces are what matter. Their heads taper to a narrow, pointed snout, with dark eye patches converging into a V. That is very close to the fox and wolf face inputs (4d:493, 4d:477) that feed this pointed-shape detector. The adult's face in three-quarter view and the baby's frontal face both present that V-shaped geometry, and there is little round structure to push the neuron negative, so a solid response is likely.\n\n68"}
{"prompt": "B: + essay", "gid": 4623, "imagenet_class": "Arctic fox", "frac_of_max": 0.3337, "on": true, "p_on": 0.8, "reasoning": "The two arctic foxes have pointed ears and tapering, V-shaped snouts, which is exactly the kind of shape fed to this neuron by fox-snout and wolf-face detectors upstream. The left fox is turned away and the right one's face is angled downward, so the V-shaped face isn't shown head-on. Even so, the ears and muzzle are clearly pointed, and fox and canine faces are among this neuron's strongest drivers.\n\n80"}
{"prompt": "B: + essay", "gid": 46066, "imagenet_class": "prairie chicken", "frac_of_max": 0.0409, "on": false, "p_on": 0.18, "reasoning": "The photo shows a prairie chicken whose most prominent feature is a big, round, inflated orange air sac. That kind of bulbous circular shape is the \"round\" pole that pushes this neuron negative. There is no clear V-shaped face, snout, or wedge-like front. The small bird head and beak offer only a weak pointed cue, and the striped body plumage is texture rather than a V shape, so a strong response seems unlikely.\n\n18"}
{"prompt": "B: + essay", "gid": 15235, "imagenet_class": "potpie", "frac_of_max": 0.0373, "on": false, "p_on": 0.3, "reasoning": "The photo is mostly round pot pies, and round shapes push this neuron negative. The top-right pie, though, has triangular, pointed vent slits arranged a bit like a face with eyes and ears. That could weakly hit the pointed \"V\" detector. Because the round crusts dominate the image, I expect at most a weak response.\n\n30"}
{"prompt": "B: + essay", "gid": 15368, "imagenet_class": "basenji", "frac_of_max": 0.39, "on": true, "p_on": 0.92, "reasoning": "The key features are the basenji's tall, pointed ears and its narrow, V-shaped face, where the white blaze tapers down to the snout. Together these form exactly the pointed fox- or coyote-like facial geometry that drives this neuron, through inputs like the fox-snout and wolf-face detectors. There is nothing round or flat-faced here to push it negative, and the head is large, centered and frontal against a plain background. This should produce a strong response.\n\n92"}
{"prompt": "B: + essay", "gid": 21439, "imagenet_class": "Cardigan", "frac_of_max": 0.338, "on": true, "p_on": 0.85, "reasoning": "The corgi has a fox-like face: large, sharply pointed ears and a tapering snout that converge into a V. That is the \"pointed\" pattern this neuron detects, and it is fed by its fox-snout and wolf-face inputs. The face is fairly large and well lit, though tilted upward, which may weaken the response a little. Nothing round dominates the image to push it negative.\n\n85"}
{"prompt": "B: + essay", "gid": 47944, "imagenet_class": "chiton", "frac_of_max": 0.0338, "on": false, "p_on": 0.18, "reasoning": "The photo shows chitons and a small striped snail shell on yellow rock. These are mostly oval, segmented, rounded shapes, which are the \"round\" pole that pushes this neuron negative. The small conical shell has a slight point, but there is no clear V-shaped, snout-like or wedge-front structure like a fox face, car front or flame. The neuron is likely weak here.\n\n18"}
{"prompt": "B: + essay", "gid": 32225, "imagenet_class": "yellow lady's slipper", "frac_of_max": 0.045, "on": false, "p_on": 0.28, "reasoning": "The yellow lady's slipper orchids are dominated by smooth, rounded, bulbous pouches, which is exactly the round shape that pushes this neuron negative. The narrow, dark, twisted petals flaring above each pouch and the long tapering leaves do form some pointed and V-like shapes, so some partial activation is possible. However, these lack the strong converging V structure of snouts, car fronts or flames, and they sit among cluttered foliage. The round pouches are the most salient objects, so the neuron is more likely OFF.\n\n28"}
{"prompt": "B: + essay", "gid": 4887, "imagenet_class": "safe", "frac_of_max": 0.0488, "on": false, "p_on": 0.12, "reasoning": "This is a boxy black safe on a white background: rectangular panels, digital keypads, a handle and a round combination dial. None of it is a pointed or V-shaped form like an animal snout, a tapering car hood or a flame. The round dial and the flat rectangular surfaces are more like the neuron's negative \"round\" pole, though a frontal panel with controls could faintly resemble a face or grille. Overall there is little here to drive it.\n\n12"}
{"prompt": "B: + essay", "gid": 1722, "imagenet_class": "puffer", "frac_of_max": 0.0475, "on": false, "p_on": 0.12, "reasoning": "The photo is a porcupinefish (pufferfish) seen face-on. Its whole body and face form a broad, rounded blob with a blunt mouth, not a tapering V. That roundness is what this neuron's negative pole prefers, like the snail, tennis ball and flat-faced dogs in the figure. There is no pointed snout, V-shaped hood or tapering flame-like form to drive the pointed direction, so the neuron is most likely OFF.\n\n12"}
{"prompt": "B: + essay", "gid": 10863, "imagenet_class": "monarch", "frac_of_max": 0.0368, "on": false, "p_on": 0.38, "reasoning": "The monarch's wings are spread almost flat and horizontal. That gives a broad, angular, roughly V-like outline with pointed wingtips, which might partially drive a pointed/V-shape detector. The small orange-red flowers have a slightly flame-like, pointed look. However, there is no snout-like converging V (no fox, cat, dog or car front), and the scene is mostly flat leaves and wing pattern rather than a strong V-shape, so a strong response is uncertain.\n\n38"}
{"prompt": "B: + essay", "gid": 39386, "imagenet_class": "dung beetle", "frac_of_max": 0.0217, "on": false, "p_on": 0.14, "reasoning": "The photo is dominated by a round dung ball and a dung beetle whose shiny body is also rounded and dome-like. Under the \"pointed vs. round\" reading of this neuron, round forms are exactly what push it negative. Nothing here offers the V-shaped convergence it likes, such as a snout, cat face, car grille or flame. The beetle's small head and legs give at most weak pointed cues, so a strong response seems unlikely.\n\n14"}
{"prompt": "B: + essay", "gid": 42923, "imagenet_class": "vase", "frac_of_max": 0.012, "on": false, "p_on": 0.08, "reasoning": "This is a round, bulbous pot covered in concentric circular swirl patterns, which is the \"round\" pole of this neuron. It has no V-shapes, pointed snouts, flame tips or wedge-shaped car fronts. The only angular element is the slight dip at the rim and the vertical seam, which is weak compared with the pervasive circles. The image should push the neuron negative or leave it near zero.\n\n8"}
{"prompt": "B: + essay", "gid": 40867, "imagenet_class": "American black bear", "frac_of_max": 0.0306, "on": false, "p_on": 0.25, "reasoning": "The black bear is the only candidate, and its body reads as a large, dark, rounded mass, which is closer to the neuron's \"round\" pole. Its head is small, in dim profile and partly hidden by grass. The muzzle does taper somewhat like a canine snout, but it lacks the clear frontal V-shaped face (pointed ears and a converging snout) that drives this neuron. Overall the pointed cue is weak and low-contrast, so a strong response is unlikely.\n\n25"}
{"prompt": "B: + essay", "gid": 28048, "imagenet_class": "hippopotamus", "frac_of_max": 0.0394, "on": false, "p_on": 0.15, "reasoning": "The hippo's body and head are big, rounded, blunt masses, with a broad snout and a barrel-shaped torso. That is the opposite of the pointed, V-shaped convergence this neuron detects, such as fox or cat faces with tapering snouts and ears, car fronts, flames or martini glasses. Its small ears give a slight hint of pointedness, but the overall shape matches the round, negative pole, like the bulldog and mastiff faces. The neuron is likely OFF.\n\n15"}
{"prompt": "B: + essay", "gid": 40602, "imagenet_class": "eft", "frac_of_max": 0.0457, "on": false, "p_on": 0.18, "reasoning": "The dominant object is a round black bottle cap with an oval \"mike's\" logo. That is exactly the kind of circular, O-like shape this neuron's negative pole responds to, so it should push activation down. The small orange newt and the criss-crossing pine needles do form some angular, V-like junctions. But these are thin texture-level crossings, not the larger pointed, face-like or car-front-like V forms that drive this neuron strongly. Overall, the photo likely gives only weak activation.\n\n18"}
{"prompt": "B: + essay", "gid": 25040, "imagenet_class": "tailed frog", "frac_of_max": 0.024, "on": false, "p_on": 0.22, "reasoning": "The main object is a tree frog seen from the side. Its head is a blunt, rounded dome, not the V-shaped, tapering face of a fox or cat, or the wedge front of a car. The red background and wood grain give no pointed or convergent structure either. If anything, the round eye and rounded body lean toward the neuron's negative \"round\" pole, though the frog's slightly wedged snout in profile could give a weak response.\n\n22"}
{"prompt": "B: + essay", "gid": 39707, "imagenet_class": "whistle", "frac_of_max": 0.0484, "on": false, "p_on": 0.18, "reasoning": "The photo shows a silver whistle and a teddy-bear charm on a phone strap, against a plain gray background. Nothing in it is a strong pointed or V-shaped form like a snout, car front, flame or bullet nose. The bear charm is built from round shapes (circular head and ears), which is the round pattern that pushes this neuron negative. The whistle is mostly a blunt cylinder, so at most its tip or the strap's converging cords might give a weak V-like response.\n\n18"}
{"prompt": "B: + essay", "gid": 46061, "imagenet_class": "American alligator", "frac_of_max": 0.0384, "on": false, "p_on": 0.22, "reasoning": "This is a close-up of an alligator's head, seen nearly head-on with the jaw resting on the ground. An alligator's snout is broad and blunt, a rounded U rather than the tapering V of a fox, wolf, cat face or car front that drives this neuron. The scaly, textured skin and rounded jawline look more like the round, bulbous patterns that push it negative. Some weak response could come from the angled jaw edge and eye ridges, but a strong ON seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 29771, "imagenet_class": "Pekinese", "frac_of_max": 0.4264, "on": true, "p_on": 0.35, "reasoning": "This is a close-up dog face, the kind of image that often drives this neuron, but it looks like a Japanese Chin or Pekingese mix. Its face is flat and round, with a very short snout and big round eyes. That puts it closer to the bulldog and mastiff faces in the \"round\" examples that push the neuron negative than to the pointed fox or puppy snouts that make it fire. The white blaze narrowing between the eyes and the ear outlines give some V-like structure, so a moderate response is possible, but the overall roundness argues against a strong one.\n\n35"}
{"prompt": "B: + essay", "gid": 6504, "imagenet_class": "fly", "frac_of_max": 0.025, "on": false, "p_on": 0.42, "reasoning": "The fly's folded wings spread backward in a clear V/delta, and the animal tapers to a point, so this is the kind of pointed wedge geometry the V-shape reading predicts should drive the neuron. Against that, the fly is small in the frame, its body is a rounded oval, and the rest of the image is uniform textured skin with no pointed structure. So I'd expect a moderate rather than strong response, roughly near the ON threshold.\n\n42"}
{"prompt": "B: + essay", "gid": 29545, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.3631, "on": true, "p_on": 0.42, "reasoning": "The dog's face is the only plausible driver here: it shows a pointed V-shaped white blaze running down between the brown ears, and frontal pet faces are close to this neuron's cat-face mode. But the Cavalier's overall head is soft and rounded, with long droopy ears and a short muzzle. That resembles the round, jowly dog faces that push the neuron negative rather than a sharp fox or cat snout. The face is also small in the frame and the rest of the image is body and grass, so I expect a moderate response that may or may not reach the ON threshold.\n\n42"}
{"prompt": "B: + essay", "gid": 8189, "imagenet_class": "limousine", "frac_of_max": 0.3944, "on": true, "p_on": 0.4, "reasoning": "The neuron responds to pointed or V-shaped structures, which in practice means car fronts with converging hood and grille lines, and pointed animal snouts. This photo shows a white stretch limo from the rear and side at night. The car context helps, since vehicles drive this neuron. But the visible geometry is boxy and rectilinear (flat back, taillights, long rows of side windows), with no tapered front or V-shaped convergence. Some car-related activation is likely, but whether it crosses the ON threshold is uncertain.\n\n40"}
{"prompt": "B: + essay", "gid": 9076, "imagenet_class": "Cardigan", "frac_of_max": 0.4264, "on": true, "p_on": 0.85, "reasoning": "This is a corgi-type dog with tall, pointed upright ears and a tapering snout, so the face forms the V/pointed shape that drives 4e:55, much like the fox and wolf-face inputs. Its strongest inputs are exactly this kind of fox and wolf face, and the face here is large and clear despite the tight framing. There is little round, blunt structure to push it negative, unlike the flat-faced mastiffs in the negative examples.\n\n85"}
{"prompt": "B: + essay", "gid": 18720, "imagenet_class": "ptarmigan", "frac_of_max": 0.3766, "on": true, "p_on": 0.22, "reasoning": "The photo shows a white ptarmigan whose head and body are smooth and rounded. The only pointed element is a tiny dark beak, a small feature rather than the broad V-shaped snout, flame or car-front taper that drives this neuron. The bird's rounded silhouette and the plain, blurry background lean toward the \"round\" side this neuron dislikes, so a strong response seems unlikely, though the profile head with its beak could give a modest activation.\n\n22"}
{"prompt": "B: + essay", "gid": 9688, "imagenet_class": "window screen", "frac_of_max": 0.0326, "on": false, "p_on": 0.08, "reasoning": "This photo is a close-up of a woven mesh or window screen: a repeating grid of crossing lines with some curved, rippled distortion. There is no animal snout, car front, flame, or other convergent V-shaped form that drives this neuron. The texture is uniform and grid-like rather than pointed, and the rounded ripple highlights would, if anything, lean toward the neuron's negative \"round\" pole. A weak response is possible only if some diagonal crossings happen to look like V-junctions, but they are too small and regular to count as a salient pointed shape.\n\n8"}
{"prompt": "B: + essay", "gid": 44605, "imagenet_class": "racer", "frac_of_max": 0.3167, "on": true, "p_on": 0.42, "reasoning": "The car is shown in side profile, so the hood and grille, the converging V-shaped front that drove this neuron in the car examples, are mostly out of frame. What remains is the long horizontal body, the round wheels and the rounded rear, which offer little pointed structure and some round shapes that push it negative. Car imagery in general still tends to activate this neuron somewhat, and the tapering nose at the right edge plus the angled window lines could supply a weak V, so the outcome is uncertain.\n\n42"}
{"prompt": "B: + essay", "gid": 33821, "imagenet_class": "ambulance", "frac_of_max": 0.3756, "on": true, "p_on": 0.55, "reasoning": "This is a white-and-orange ambulance van (a VW Transporter) seen mostly from the side, with the front end only partly visible at an angle. The neuron's car response comes largely through van- and car-front detectors (like 4d:201, \"van front\") and the tapered, pointed shape of a vehicle's nose or hood. A side view weakens that cue, but the angled front, windshield and bumper may still drive it. Vehicles in general often push this neuron fairly high, so I lean slightly toward ON.\n\n55"}
{"prompt": "B: + essay", "gid": 9977, "imagenet_class": "coyote", "frac_of_max": 0.307, "on": true, "p_on": 0.85, "reasoning": "The photo shows a coyote facing the camera. Its face forms a classic V: pointed ears above and a tapering snout below, the same canid-face pattern carried by the neuron's fox-snout and wolf-face inputs and its coyote-face reader. The head is fairly small in the frame, which may weaken the peak response somewhat. Still, there are no competing round features, and this is close to the neuron's prototypical pointed-face stimulus.\n\n85"}
{"prompt": "B: + essay", "gid": 27096, "imagenet_class": "coyote", "frac_of_max": 0.3388, "on": true, "p_on": 0.8, "reasoning": "The coyote's head at the left edge is the key feature. Its tall pointed ears and tapering snout form exactly the V-shaped, pointed canine face that drives this neuron's strongest inputs (4d:493 fox snout, 4d:477 wolf face) and its coyote-face reader. The face is partly cropped and fairly small, which may weaken the response somewhat. Still, canid faces with pointed ears are among this neuron's most reliable triggers, and nothing round in the scene works against it.\n\n80"}
{"prompt": "B: + essay", "gid": 40199, "imagenet_class": "plate rack", "frac_of_max": 0.044, "on": false, "p_on": 0.12, "reasoning": "The photo is a boxy white countertop dishwasher with round plates sticking out the top and glasses inside. Nothing here forms a V-shaped point: there is no snout, ear pair, flame, or tapering car/train front. The dominant shapes are the circular plates, the round buttons and the rectangular box. Round forms push this neuron negative, and the clean appliance front gives little of the pointed structure that drives it, so it is most likely OFF.\n\n12"}
{"prompt": "B: + essay", "gid": 22612, "imagenet_class": "polecat", "frac_of_max": 0.3743, "on": true, "p_on": 0.6, "reasoning": "The polecat/ferret's face is the key feature: a small, furry mammal face that tapers toward a snout, with a contrasting light mask around the muzzle. That resembles the fox- and dog-face inputs that drive this \"pointed/V-shape\" neuron. However, mustelid faces are fairly blunt and rounded compared to a fox's sharp V, the head occupies only a small part of the frame, and the rest is uninformative grass and a round dark body. So the response could be moderate rather than strong.\n\n60"}
{"prompt": "B: + essay", "gid": 39049, "imagenet_class": "ambulance", "frac_of_max": 0.4845, "on": true, "p_on": 0.85, "reasoning": "The front of this Mercedes van matters most. The tapered hood, sloping windshield and pointed front-on angle match the \"van front\" input (4d:201) that feeds this neuron, and car fronts are among the classic top activators of 4e:55. Nothing large and round in the frame would push it negative.\n\n85"}
{"prompt": "B: + essay", "gid": 30892, "imagenet_class": "weasel", "frac_of_max": 0.6435, "on": true, "p_on": 0.72, "reasoning": "The frontal face of the weasel/stoat is what matters: it has a fox-like layout, with two ears above, dark eyes, and a narrowing snout with a white muzzle and chin. That forms the kind of V-shaped, pointed face the neuron's fox-snout and wolf-face inputs respond to. The weasel's head is somewhat rounder than a fox's, which tempers this a little. The grey rocks are neutral background.\n\n72"}
{"prompt": "B: + essay", "gid": 14235, "imagenet_class": "bullet train", "frac_of_max": 0.3446, "on": true, "p_on": 0.85, "reasoning": "The photo is dominated by the tapered, pointed nose of a Shinkansen bullet train. Its headlights and windshield also form a face-like front, similar to the car and van fronts that feed this neuron. A bullet train appears among the essay's own \"pointed makes it fire\" examples, and the converging V-shaped perspective of the platform and roof lines adds further pointed structure. Very little in the image is round enough to push the neuron negative.\n\n85"}
{"prompt": "B: + essay", "gid": 47371, "imagenet_class": "tile roof", "frac_of_max": 0.0402, "on": false, "p_on": 0.25, "reasoning": "The photo is mostly a tiled roof with small dormer windows and a half-timbered building, with no animal faces, car fronts, or flame-like shapes. The only candidate trigger is the dormers' peaked, gable-like outlines, which form inverted-V (\u039b) shapes. They are small, though, and point the opposite way from the downward V of a snout or martini glass, so they likely give at most a weak response. The large repetitive roof-tile texture is not the kind of pointed object this neuron seems to favor.\n\n25"}
{"prompt": "B: + essay", "gid": 26920, "imagenet_class": "kit fox", "frac_of_max": 0.5769, "on": true, "p_on": 0.92, "reasoning": "This is a young fox kit seen face-on. Its pointed ears and tapering snout make exactly the V-shaped, pointed face that drives this neuron. Fox snouts are its strongest input (4d:493), and fox faces are among its canonical top activators. Nothing round dominates the image to push it negative, so a strong response is likely.\n\n92"}
{"prompt": "B: + essay", "gid": 40851, "imagenet_class": "red fox", "frac_of_max": 0.5678, "on": true, "p_on": 0.85, "reasoning": "The red fox's face, with its pointed ears and tapering snout, is exactly the V-shaped, pointed facial structure this neuron responds to. Its strongest input is the 4d:493 fox-snout detector, and fox faces are among its top activators. The face is fairly small in the frame but clearly visible and frontal against plain snow. Nothing round competes with it, so a strong response is likely.\n\n85"}
{"prompt": "B: + essay", "gid": 9600, "imagenet_class": "hamster", "frac_of_max": 0.4142, "on": true, "p_on": 0.22, "reasoning": "The main subject is a hamster, and both its face and body are very round and blunt, with no tapering snout. That matches the \"round\" pole that pushes this neuron negative, not the pointed V-shapes (fox snouts, cat faces, car fronts) that drive it. The square bowl has some corners, but nothing forms a strong converging V. So I expect only weak activation, though the small-mammal face could give a little partial response.\n\n22"}
{"prompt": "B: + essay", "gid": 9413, "imagenet_class": "ping-pong ball", "frac_of_max": 0.3022, "on": true, "p_on": 0.22, "reasoning": "The photo shows a man at a ping-pong table with a ball-launching robot and many small round orange balls. Nothing in it resembles the neuron's strongest drivers: no pointed animal snouts, no V-shaped car or train fronts, no flames. The many round balls are exactly the kind of shape that pushes this neuron negative. The table's converging edges in perspective could give a weak wedge-like cue, as in the pool-table example, but it is diffuse and small, so the neuron is probably OFF.\n\n22"}
{"prompt": "B: + essay", "gid": 30782, "imagenet_class": "Arctic fox", "frac_of_max": 0.4784, "on": true, "p_on": 0.75, "reasoning": "This is a white (likely arctic) fox looking at the camera, and its tapered, V-shaped muzzle is exactly what the fox-snout and wolf-face inputs (4d:493, 4d:477) feed into this neuron. Two features work against it: the curled-up, fluffy body is a round blob, and arctic foxes have small rounded ears, so the face reads less as a sharp V than a red fox's would. Still, the clearly visible pointed snout and the frontal canid face are among this neuron's most reliable triggers, so it will probably fire strongly.\n\n75"}
{"prompt": "B: + essay", "gid": 2041, "imagenet_class": "briard", "frac_of_max": 0.3436, "on": true, "p_on": 0.85, "reasoning": "The collie's face is the key feature: a long, tapering snout that narrows to a point, set between pointed ears, gives the fox- and wolf-like V-shaped face that 4e:55's strongest inputs (fox snout, wolf face) detect. Unlike the round, flat-faced mastiffs that push it negative, this dog's whole head is a narrow wedge. The face is fairly small in the frame, but it is clear and front-facing, so the neuron should respond strongly.\n\n85"}
{"prompt": "B: + essay", "gid": 30303, "imagenet_class": "confectionery", "frac_of_max": 0.0391, "on": false, "p_on": 0.18, "reasoning": "This is a cluttered candy or toy shop: rectangular shelves, boxes and jars, a checkered tablecloth, and possibly a stuffed animal at the top. Nothing here gives the strong V-shaped, pointed-snout or car-front geometry that drives 4e:55. The dominant shapes are boxy and grid-like, and the round jars and bags lean toward the neuron's negative pole. Busy, colorful clutter can produce weak spurious activations, and the plush toy is a small chance of a face-like trigger, but reaching 30% of max seems unlikely.\n\n18"}
{"prompt": "B: + essay", "gid": 46222, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3057, "on": true, "p_on": 0.85, "reasoning": "The sheltie's face is a strong match for this neuron's real feature, a V-shape. It has a long, tapering snout coming to a point at the nose, and its tall pointed ears echo the fox and wolf face inputs (4d:493, 4d:477). Collies and shelties are among the most fox-like dog faces, unlike the round, flat-faced bulldogs and mastiffs that push the neuron negative. The face is large and clearly visible, so the V-shaped snout should drive a strong response.\n\n85"}
{"prompt": "B: + essay", "gid": 5860, "imagenet_class": "loupe", "frac_of_max": 0.0407, "on": false, "p_on": 0.18, "reasoning": "The photo is dominated by vertical book spines, small drawn portraits of human faces on stamps, and a large round magnifying-glass lens. None of these offers the strong V-shaped, tapering form this neuron likes, such as a pointed snout, a car's front, a flame or a martini glass. The magnifying glass is the kind of round \"O\" shape that pushes the neuron negative. The small portrait faces and the angled handle could give weak pointed cues, but they are unlikely to reach 30% of its maximum.\n\n18"}
{"prompt": "B: + essay", "gid": 33877, "imagenet_class": "lesser panda", "frac_of_max": 0.6739, "on": true, "p_on": 0.85, "reasoning": "The photo is a frontal close-up of a red panda's face. That is very close to the fox-face pattern feeding this neuron (4d:493 fox snout): pointed ears, white cheek markings, and a face that tapers in a V down to a small dark nose. The face is somewhat rounder and fluffier than a fox's, which could slightly reduce the response. Still, the fox-like coloring and the V-shaped facial geometry, filling much of the frame, should drive it strongly.\n\n85"}
{"prompt": "B: + essay", "gid": 40346, "imagenet_class": "brain coral", "frac_of_max": 0.0121, "on": false, "p_on": 0.05, "reasoning": "The photo is an underwater scene with a large rounded brain-coral dome against a plain, textured sandy or teal background. Nothing here is pointed or V-shaped: there are no snouts, ears, flames, or angular car fronts. The dominant shape is a smooth round dome, which is the kind of form that pushes this neuron negative. The remaining area is low-contrast texture with no convergent edges to drive it.\n\n5"}
{"prompt": "B: + essay", "gid": 38729, "imagenet_class": "crayfish", "frac_of_max": 0.0228, "on": false, "p_on": 0.22, "reasoning": "The photo shows a crayfish on grass. It has no cat, dog or fox face, no car front, and none of the strong symmetric V-shaped object fronts this neuron favors. The pincers are tapered, and the open claw at upper right forms a slight V, which could give a weak \"pointed\" signal. However, they are small, irregular and seen side-on, and the grass texture adds clutter rather than a clean converging shape. A modest response is possible, but reaching 30% of the maximum seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 8972, "imagenet_class": "theater curtain", "frac_of_max": 0.0497, "on": false, "p_on": 0.2, "reasoning": "The photo is dominated by a red stage curtain with straight vertical folds and a round medallion ornament at the top. Nothing here has the pointed, V-shaped convergence this neuron looks for, such as snouts, ears, flames or a car-hood wedge. The circular ornament is the kind of round shape that pushes it negative. The broad red surface faintly echoes the red car and pool-table examples, and the spotlight's soft wedge of light is a weak pointed cue, but neither is likely to drive it strongly.\n\n20"}
{"prompt": "B: + essay", "gid": 24588, "imagenet_class": "liner", "frac_of_max": 0.0372, "on": false, "p_on": 0.22, "reasoning": "The ship is shown mostly side-on, so its long horizontal hull and rectangular superstructure offer little of the converging V-shape (snouts, car fronts, flame tips) that drives this neuron. The bow, which might form a point, is cropped at the right edge and seen in profile rather than head-on. The rounded yellow funnel and the boxy skyscrapers give no strong pointed cues either. Some weak activation from the hull's tapering lines is possible, but a strong response seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 19669, "imagenet_class": "borzoi", "frac_of_max": 0.3169, "on": true, "p_on": 0.58, "reasoning": "The photo shows a borzoi at a dog show, standing in side profile. Its long, narrow, wedge-shaped head and snout are exactly the kind of pointed, V-tapering shape this neuron seems to track, and that tapering is what links its fox-snout and wolf-face inputs. Against that, the head is small in the frame and seen from the side rather than as the frontal V of a fox or cat face, so the response may be moderate rather than strong.\n\n58"}
{"prompt": "B: + essay", "gid": 13652, "imagenet_class": "pickup", "frac_of_max": 0.3245, "on": true, "p_on": 0.7, "reasoning": "The front of this bare-metal vintage pickup matters most: the grille and the hood line converging toward it. Car fronts with grilles are among this neuron's classic top activators, fed by the van-front input in 4d. The truck's bulbous, rounded fenders and headlights are the kind of round shape that pushes the neuron negative, which tempers my confidence. Still, the three-quarter front view with a prominent grille and sweeping hood resembles the car examples that drive the neuron strongly.\n\n70"}
{"prompt": "B: + essay", "gid": 30974, "imagenet_class": "face powder", "frac_of_max": 0.0411, "on": false, "p_on": 0.08, "reasoning": "The photo is dominated by round shapes: the circular compact, its round mirror lid, the round powder puff and the round purple case. Round forms are exactly what push this neuron negative. Nothing here has the pointed, V-shaped convergence (snout, flame tip, car front, martini glass) that drives it, except perhaps faint petal edges of the white roses. The neuron is very likely OFF.\n\n8"}
{"prompt": "B: + essay", "gid": 42048, "imagenet_class": "feather boa", "frac_of_max": 0.0349, "on": false, "p_on": 0.35, "reasoning": "What matters here is the dark feather boa. It wraps around the neck and forms a rough V framing the upturned face, and its spiky feather texture loosely resembles the furry, pointed textures of the fox and cat faces this neuron likes. Against that, the face itself is human, tilted back and fairly round, with a wide-open mouth. That is neither the pointed snout nor the car-grille geometry that drives the neuron most strongly. So any activation would come from the boa's V-shape and fur-like texture, which is a plausible but weak match.\n\n35"}
{"prompt": "B: + essay", "gid": 3181, "imagenet_class": "pickup", "frac_of_max": 0.4365, "on": true, "p_on": 0.75, "reasoning": "This is a front-three-quarter view of a classic Ford pickup: a broad hood, chrome grille and headlights. That is the kind of vehicle-front pattern the neuron gets from its \"van front\" input (4d:201), and it resembles the red car hood in the essay's examples of images that make it fire. The hood's converging lines and the angled front give some of the pointed, V-like structure the neuron favours. The round headlights and wheels may pull it down somewhat. Overall, car fronts are among this neuron's classic strong activators, so it is likely ON.\n\n75"}
{"prompt": "B: + essay", "gid": 2465, "imagenet_class": "lesser panda", "frac_of_max": 0.5668, "on": true, "p_on": 0.8, "reasoning": "The red panda's face is the key feature: pointed ears, a white-marked muzzle that tapers to a dark nose, and an overall triangular, fox-like shape. This closely matches the V-shaped, pointed-snout pattern that drives this neuron, which is fed by fox-snout and wolf-face detectors. The face is fairly round and broad compared with a true fox, so the response may not be maximal, but it should still clear the threshold. The bamboo leaves are mostly irrelevant, though their pointed tips might add a little.\n\n80"}
{"prompt": "B: + essay", "gid": 31887, "imagenet_class": "Pembroke", "frac_of_max": 0.4253, "on": true, "p_on": 0.85, "reasoning": "The corgi puppy's face is the key element: a tapered, fox-like snout with pointed ears and a V-shaped face, which is exactly the kind of \"pointed\" structure that drives this neuron through its fox-snout and wolf-face inputs. The head is tilted and partly resting on the shoulder, which may weaken the V geometry somewhat. Still, corgi faces are very close to fox faces, which are among this neuron's strongest stimuli. The person's rounder face and the background matter little.\n\n85"}
{"prompt": "B: + essay", "gid": 9408, "imagenet_class": "tiger cat", "frac_of_max": 0.4619, "on": true, "p_on": 0.88, "reasoning": "The frontal cat face is what matters. Its two upright pointed ears and the tapering V from forehead to chin are the converging-point shape this neuron responds to, and cat faces are among its classic top activators. The rounded blanket surrounding the face shouldn't suppress the strong local response at the face, so this very likely clears the ON threshold.\n\n88"}
{"prompt": "B: + essay", "gid": 3203, "imagenet_class": "crib", "frac_of_max": 0.0324, "on": false, "p_on": 0.22, "reasoning": "The main content is a baby's face peering between vertical crib bars, with a plush toy in the background. A baby's face is a round, soft-contoured shape, closer to the neuron's negative \"round\" pole than to the pointed V of fox snouts, cat faces or car fronts. The straight vertical bars form no converging V, and nothing else in the frame (toy, blanket, wall panel) offers a clear pointed or wedge shape. Some faint response from face-like eye features is possible, but a strong activation seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 8104, "imagenet_class": "Saluki", "frac_of_max": 0.3215, "on": true, "p_on": 0.62, "reasoning": "The two Afghan hounds have long, narrow snouts that taper to a point, which is the pointed, V-like shape this neuron seems to track. The drooping ears and hair also frame the faces in a downward-tapering wedge. Against that, the faces are in profile, not the frontal symmetric V seen in fox, cat or car-front examples, and nothing round competes with them. On balance the tapering muzzles make a strong response fairly plausible.\n\n62"}
{"prompt": "B: + essay", "gid": 23789, "imagenet_class": "collie", "frac_of_max": 0.3908, "on": true, "p_on": 0.72, "reasoning": "The key feature is the Shetland sheepdog's face: a narrow, tapering, fox-like snout, with a white blaze running down the middle that forms a clear downward V between the dark ears. That pointed, V-shaped canine face is exactly the pattern fed by this neuron's fox-snout and wolf-face inputs. Nothing round competes with it, as a flat-faced breed would. The face is fairly small in the frame, but the neuron takes its strongest response anywhere in the image, so a crisp pointed face should still drive it well.\n\n72"}
{"prompt": "B: + essay", "gid": 31038, "imagenet_class": "Persian cat", "frac_of_max": 0.547, "on": true, "p_on": 0.62, "reasoning": "The photo is a close, frontal cat face, the classic strong driver of 4e:55: two eyes above a small nose and mouth, with ears at the top corners. That layout forms the pointed, V-shaped facial geometry the neuron responds to in fox, wolf and cat faces. Against that, this is a fluffy Persian kitten with a broad, rounded head and small ears, which leans toward the \"round\" pole that pushes the neuron negative, so the V is weaker than in a typical tabby or fox face. Overall the face-centred composition should still drive it fairly well.\n\n62"}
{"prompt": "B: + essay", "gid": 35355, "imagenet_class": "American alligator", "frac_of_max": 0.0286, "on": false, "p_on": 0.18, "reasoning": "This photo is nearly all black, with a faint crocodile or alligator body barely visible in a dim patch of light. A croc's tapering snout could in principle give the pointed V-shape this neuron likes. But here the head isn't clearly resolved, and the low contrast leaves little edge structure to drive a mid-level shape detector. Without a clear converging V-like form such as a fox snout, cat face, car front or flame, the neuron most likely stays weak.\n\n18"}
{"prompt": "B: + essay", "gid": 43176, "imagenet_class": "screw", "frac_of_max": 0.0452, "on": false, "p_on": 0.12, "reasoning": "The photo shows two socket-head cap screws: round, cylindrical heads with a hex socket, threaded shafts, a plain background and a watermark. Nothing here forms a converging V or pointed shape, and there are no animal snouts or car fronts. The dominant forms are circles and cylinders, which is the \"round\" pattern associated with this neuron's negative pole. So the neuron is likely OFF, though the threads' diagonal edges add slight uncertainty.\n\n12"}
{"prompt": "B: + essay", "gid": 7731, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.4058, "on": true, "p_on": 0.25, "reasoning": "This is a Japanese Chin, a flat-faced (brachycephalic) toy breed. The face is broad and round, the muzzle is short, and the long, floppy, draping ears frame the head. There is no tapering V-shaped snout and there are no pointed upright ears, which are the cues that drive this neuron in foxes, wolves and similar dogs. That makes it closer to the round, pushed-in dog faces (bulldog, mastiff) that push the neuron negative. Some generic dog-face response could still give a modest activation, so it isn't ruled out entirely.\n\n25"}
{"prompt": "B: + essay", "gid": 20512, "imagenet_class": "maze", "frac_of_max": 0.0442, "on": false, "p_on": 0.08, "reasoning": "This hedge maze is made of smooth, rounded, curving hedge walls covered in uniform leafy texture. Nothing in it resembles the pointed, V-shaped structures that drive this neuron, such as fox or cat snouts, car or train fronts, or flames. The rolling arcs are closer to the \"round\" pole, which pushes the neuron negative, so it is unlikely to reach 30% of its maximum.\n\n8"}
{"prompt": "B: + essay", "gid": 9679, "imagenet_class": "Tibetan mastiff", "frac_of_max": 0.4086, "on": true, "p_on": 0.62, "reasoning": "The black-and-tan puppy's face is the key region: its muzzle tapers to a fairly pointed snout seen in three-quarter profile, similar to the fox and wolf-face inputs that drive this neuron. Its fluffy, rounded head and thick fur work against that, since round, blunt-faced dogs push the neuron negative. Overall, the tapering snout should give a moderate-to-strong response, but the round fluff makes it uncertain.\n\n62"}
{"prompt": "B: + essay", "gid": 20835, "imagenet_class": "cougar", "frac_of_max": 0.3697, "on": true, "p_on": 0.85, "reasoning": "The cougar's face is frontal and fills the frame, so its tapering muzzle, the V running from the eyes down to the nose, and its pointed ears make exactly the pointed, V-shaped cat-face configuration this neuron responds to most strongly. The chain-link fence overlays the face and adds clutter. It could weaken the response somewhat, but the face structure is still clearly readable. I expect strong activation.\n\n85"}
{"prompt": "B: + essay", "gid": 21302, "imagenet_class": "Maltese dog", "frac_of_max": 0.3159, "on": true, "p_on": 0.25, "reasoning": "The photo is mostly the long, draping white fur of a Maltese-type dog. Its face is cropped at the right edge, with only an eye and part of the mouth visible. This neuron fires on pointed, V-shaped structures such as tapering snouts, fox and cat faces, and wedge-shaped car fronts. A shaggy, rounded mop of hair with a short, mostly hidden muzzle offers little of that converging V geometry, though a dog face could still produce some weak response.\n\n25"}
{"prompt": "B: + essay", "gid": 25926, "imagenet_class": "tabby", "frac_of_max": 0.612, "on": true, "p_on": 0.88, "reasoning": "This is a frontal tabby cat face. Its pointed ears and the V-shaped taper of the face toward the nose and muzzle are exactly the kind of pointed, V-like structure that drives 4e:55. Cat faces are also among its classic top activators. The face is reasonably large and clearly visible against the branches, so a strong response is likely.\n\n88"}
{"prompt": "B: + essay", "gid": 35350, "imagenet_class": "hermit crab", "frac_of_max": 0.0236, "on": false, "p_on": 0.15, "reasoning": "The dominant shape here is the hermit crab's rounded, spiral shell. That is the kind of round, dome-like form the essay says pushes this \"V-shape\" neuron negative, much like the snail in its figure. The crab's legs and claws do form some angular, tapering edges. But they are tucked inside the shell's round opening rather than making a clear converging V like a fox snout, a car's front, or a flame. So the photo should mostly read as \"round\" to this neuron, with only weak pointed cues.\n\n15"}
{"prompt": "B: + essay", "gid": 27106, "imagenet_class": "wardrobe", "frac_of_max": 0.0211, "on": false, "p_on": 0.1, "reasoning": "This photo is a plain white wardrobe: flat rectangular panels, straight vertical edges and a blank wall behind it. Nothing in it forms the converging V or pointed shape the neuron responds to, such as a snout, a car hood or front, a flame or a wedge. The small blue suitcase is boxy and rounded, which if anything leans toward the neuron's negative \"round\" pole. With no animal faces, vehicle fronts or tapering forms, a strong response is unlikely.\n\n10"}
{"prompt": "B: + essay", "gid": 34537, "imagenet_class": "leatherback turtle", "frac_of_max": 0.0393, "on": false, "p_on": 0.18, "reasoning": "The photo shows leatherback sea turtle hatchlings emerging from sand. This neuron responds to V-shaped, pointed frontal structures such as fox, cat and dog snouts, car and train fronts, and flames. Nothing here forms a clear converging V. The turtle heads are blunt and rounded, and the splayed flippers are a cluttered tangle rather than a symmetric point. Low-contrast sand texture dominates the scene, so at most there is weak, incidental activation from the flipper edges.\n\n18"}
{"prompt": "B: + essay", "gid": 29826, "imagenet_class": "vulture", "frac_of_max": 0.0349, "on": false, "p_on": 0.22, "reasoning": "The neuron is driven by pointed, V-shaped convergent structure such as fox and cat snouts, car hoods and grilles, and flames. Rounded forms push it negative. Here the main object is a black vulture whose small, wrinkled head is fairly rounded, with only a modest hooked beak. The dark folded-wing body tapers somewhat, but it doesn't form the bold, frontal V-shaped face or front this neuron likes. The background of concrete pillars and dry grass offers little either, so a strong response is unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 17883, "imagenet_class": "black grouse", "frac_of_max": 0.0415, "on": false, "p_on": 0.22, "reasoning": "The photo shows two black grouse in a lek: dark, rounded bodies with fanned white undertail feathers and small heads topped with red combs. None of these is a clear converging V-shape like a fox or cat snout, a car hood, or a flame tip. The dominant forms are rounded blobs and fans, which tend to push this neuron down. The lyre-shaped tails and beaks give only weak, small pointed cues, so a strong response seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 49238, "imagenet_class": "trifle", "frac_of_max": 0.0498, "on": false, "p_on": 0.12, "reasoning": "The photo shows a layered dessert in a straight-sided cylindrical glass on a round silver tray, with more round glasses behind it. Nothing here is a pointed or V-shaped structure: there is no tapering snout or face, no flame tip, no conical martini-style glass. The dominant shapes are rounded rims, cylinders and circular trays, which are the \"round\" pole that pushes this neuron negative. I expect it to stay OFF.\n\n12"}
{"prompt": "B: + essay", "gid": 4165, "imagenet_class": "scorpion", "frac_of_max": 0.0437, "on": false, "p_on": 0.22, "reasoning": "The photo shows black emperor scorpions with bumpy, rounded pincers and segmented bodies, with no animal face, car front or other symmetric V-shaped structure. The tail tips and claw tips are pointed, but they are thin, local points rather than the broad converging V of a snout or a grille that drives this neuron. The large rounded, textured claws lean toward the \"round\" side that pushes it negative. A weak response to the claw tips is possible, but it probably falls short of the ON threshold.\n\n22"}
{"prompt": "B: + essay", "gid": 27281, "imagenet_class": "Brittany spaniel", "frac_of_max": 0.3217, "on": true, "p_on": 0.8, "reasoning": "The frontal close-up of a dog's face is what matters here. It has a narrow, tapering, fox-like muzzle with the eyes and nose forming a V, which matches the pointed-snout inputs (fox snout, wolf face) that drive this neuron. The cage bars add clutter, but the face is large and centered. It isn't a round, flat-faced breed like the mastiffs that push the neuron negative.\n\n80"}
{"prompt": "B: + essay", "gid": 25305, "imagenet_class": "sports car", "frac_of_max": 0.3443, "on": true, "p_on": 0.85, "reasoning": "The photo's main subject is a Lotus sports car seen from the front three-quarter angle. Its low hood tapers to a point between swept-back headlights, giving a strong V/wedge-shaped front. Car fronts are among this neuron's classic top activators, fed by the van-front input 4d:201, and this sleek, pointed nose fits the V-shape interpretation especially well. Nothing round dominates the scene enough to suppress it, so a strong response is likely.\n\n85"}
{"prompt": "B: + essay", "gid": 26326, "imagenet_class": "papillon", "frac_of_max": 0.5373, "on": true, "p_on": 0.88, "reasoning": "Three papillon puppies fill the frame, each face showing the V-shaped geometry this neuron tracks. Each has a white blaze narrowing between the eyes to a pointed snout, flanked by large pointed ears. This closely matches the \"pointed\" dog and fox examples that drive the neuron. These are not flat, round-faced breeds like the bulldog and mastiff that push it negative, and with several strong V-faces present the maximum response should be high.\n\n88"}
{"prompt": "B: + essay", "gid": 27399, "imagenet_class": "brassiere", "frac_of_max": 0.0469, "on": false, "p_on": 0.4, "reasoning": "The photo shows a stack of colorful bras, so the relevant shapes are the cups and the gaps between them. Each cup is a smooth rounded dome, and the polka dots are small circles, which is the \"round\" pattern that pushes this neuron negative. The center of each bra, where two cups meet, does form a pointed V-shaped notch, which could drive the V-shape detector somewhat. The roundness clearly dominates the image, but those V notches are prominent and repeated, so this is close to a toss-up.\n\n40"}
{"prompt": "B: + essay", "gid": 20270, "imagenet_class": "white stork", "frac_of_max": 0.3426, "on": true, "p_on": 0.45, "reasoning": "The key feature is the stork's long, tapering red beak, which forms a strong pointed wedge, possibly with a V-like taper where it meets the face. That is the kind of pointed shape the essay says drives this neuron. However, the neuron's best inputs are V-shaped faces and fronts (fox and wolf snouts, car and van fronts). A single thin bird beak against a soft background may only weakly match those, and the rounded white body adds nothing. So I expect a moderate response at best.\n\n45"}
{"prompt": "B: + essay", "gid": 18155, "imagenet_class": "groom", "frac_of_max": 0.0464, "on": false, "p_on": 0.3, "reasoning": "The relevant cues here are pointed or V-shaped structures. The man's suit lapels and shirt collar form a V around his necktie, and the two faces meeting in a kiss make a wedge-like shape. However, these are human faces and formal clothing, not the fox, cat or car-front textures this neuron most strongly prefers. Rounded elements like the hair bun, pearls and soft facial contours may also pull it down, so a weak-to-moderate response is plausible but not strongly expected.\n\n30"}
{"prompt": "B: + essay", "gid": 31889, "imagenet_class": "cinema", "frac_of_max": 0.0239, "on": false, "p_on": 0.1, "reasoning": "This is a cinema facade made of rectangular, boxy forms: a flat-roofed building, a grid of windows, and a rectangular marquee with text. Nothing here resembles the convergent V-shaped, pointed structures that drive 4e:55, such as animal snouts, car or train fronts, flame tips, or martini-glass shapes. The only possible weak cues are the vertical sign and a few angled edges, but these don't form a coherent pointed shape at a scale this layer responds to. The neuron is likely OFF.\n\n10"}
{"prompt": "B: + essay", "gid": 42636, "imagenet_class": "sloth bear", "frac_of_max": 0.0453, "on": false, "p_on": 0.35, "reasoning": "The bear's face is what matters here. Its muzzle tapers down toward the nose, giving a faint V-shaped, snout-forward face, which is the kind of pointed animal-face pattern this neuron likes (fox, wolf and cat faces). But bear heads are broad, with round ears and a heavy, rounded overall shape, which leans toward the \"round\" pole that suppresses the neuron. The rest of the image (rock, fur, ground) offers no strong pointed or V-shaped cues, so a moderate-to-weak response is most likely.\n\n35"}
{"prompt": "B: + essay", "gid": 42392, "imagenet_class": "Cardigan", "frac_of_max": 0.4134, "on": true, "p_on": 0.85, "reasoning": "The dog's face is a strong V-shape: tall, pointed upright ears that flare outward and a white blaze narrowing down to the snout. That pointed fox-like head geometry is exactly what 4e:55 responds to, through its fox-snout and wolf-face inputs. It is the opposite of the round, flat-faced dogs that push the neuron negative, and the close, frontal framing should make the response strong.\n\n85"}
{"prompt": "B: + essay", "gid": 17387, "imagenet_class": "minivan", "frac_of_max": 0.467, "on": true, "p_on": 0.85, "reasoning": "The photo is dominated by a red Fiat hatchback seen from the front three-quarter angle, with its hood and windshield lines converging toward the grille. That angled, tapering car front is exactly the kind of pointed \"V\" geometry this neuron responds to, and car fronts were among its classic top activators. A second car in the background adds more of the same. Nothing round dominates the frame to push it negative, so a strong response is likely.\n\n85"}
{"prompt": "B: + essay", "gid": 29301, "imagenet_class": "armadillo", "frac_of_max": 0.0123, "on": false, "p_on": 0.27, "reasoning": "The armadillo's body is a large, dome-shaped, banded shell, exactly the kind of round form that pushes this \"pointed/V-shape\" neuron negative. The only pointed element is its small, tapered snout with upright ears on the right edge. That head is tiny relative to the frame and seen from the side, so it doesn't form the frontal V-shaped face that drives the neuron hardest (fox, cat and dog faces, car fronts). The tangled twigs in the background add little, so the neuron likely stays weak.\n\n27"}
{"prompt": "B: + essay", "gid": 8570, "imagenet_class": "parallel bars", "frac_of_max": 0.3225, "on": true, "p_on": 0.22, "reasoning": "This photo has no animal snouts, car or train noses, flames, or other strongly pointed, tapering objects, which are the shapes that drive this V-shape neuron. The main structure is the parallel bars, which converge slightly in perspective. The gymnast is a small, blurry, roundish figure. The converging bars might give a weak wedge-like signal, but they are thin lines rather than a solid pointed form, so a strong response seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 7684, "imagenet_class": "confectionery", "frac_of_max": 0.0309, "on": false, "p_on": 0.22, "reasoning": "This candy-store scene has no fox, dog or cat snouts and no car or vehicle fronts, which are the strongest drivers of this pointed, V-shape detector. What it does have is rows of mostly rectangular or rounded jars and bins, which offer little pointed structure. The shelves recede in perspective and their converging edges could form weak V-like wedges, but that is a fairly diffuse cue compared with the neuron's typical triggers.\n\n22"}
{"prompt": "B: + essay", "gid": 31052, "imagenet_class": "tiger", "frac_of_max": 0.308, "on": true, "p_on": 0.78, "reasoning": "The tiger's face fills much of the frame, seen head-on with pointed ears on top. Its facial markings and muzzle taper down to a V toward the nose and mouth, much like the cat and fox faces that strongly drive this neuron. Big-cat faces are somewhat broader and rounder than fox or domestic-cat faces, and the open mouth softens the point a little. Even so, the overall pointed, frontal feline face should still be a strong stimulus.\n\n78"}
{"prompt": "B: + essay", "gid": 34028, "imagenet_class": "trilobite", "frac_of_max": 0.04, "on": false, "p_on": 0.28, "reasoning": "This fossil plate shows elongated, segmented trilobite bodies in reddish-brown rock. The bodies taper somewhat toward their ends, but the main shapes are rounded ovals and ribbed textures rather than a clear, symmetric V-point like a fox snout, car hood front or flame. The orange-red, fur-like coloring and the slight tapering of the tails could give weak support. Overall, though, nothing here is a strong pointed-V cue, and the rounded segments may even push the neuron negative.\n\n28"}
{"prompt": "B: + essay", "gid": 19199, "imagenet_class": "Staffordshire bullterrier", "frac_of_max": 0.0429, "on": false, "p_on": 0.45, "reasoning": "The key region is the dog's face, a Staffordshire-type bull terrier looking up at the camera. Frontal animal faces are this neuron's typical trigger, especially when they taper to a V toward the snout. Here, the overhead angle makes the head narrow somewhat toward the chin, which pushes toward ON. However, bull-terrier heads are broad and blocky, and the round, open, tongue-out muzzle resembles the round bulldog faces that push the neuron negative. That leaves it close to a coin flip, leaning slightly OFF.\n\n45"}
{"prompt": "B: + essay", "gid": 9495, "imagenet_class": "mitten", "frac_of_max": 0.0374, "on": false, "p_on": 0.22, "reasoning": "What matters most here is whether anything resembles the pointed, V-shaped forms this neuron likes, such as fox or cat snouts, car fronts or flames. The main content is a human face with a crocheted hat and a mitten covering the nose and mouth. That removes any snout-like taper and leaves mostly rounded shapes: the dome of the hat, the bulbous mitten and the oval of the face. Rounded forms like these push the neuron negative. The eye region and the textured knit might give a weak animal-face-like signal, but probably not enough to reach 30% of the neuron's maximum.\n\n22"}
{"prompt": "B: + essay", "gid": 43751, "imagenet_class": "coyote", "frac_of_max": 0.3339, "on": true, "p_on": 0.85, "reasoning": "The coyote's face, with its pointed snout and upright triangular ears, is a clear V-shape. That is the geometry this neuron responds to. Its strongest inputs are fox-snout and wolf-face detectors, and one of its readers is a coyote-face unit, so this canid head is close to its preferred stimulus. The face is fairly small in the frame and turned three-quarters, which might weaken the response somewhat. Still, the pointed muzzle and ears are crisp against a plain background.\n\n85"}
{"prompt": "B: + essay", "gid": 31352, "imagenet_class": "Pomeranian", "frac_of_max": 0.4782, "on": true, "p_on": 0.6, "reasoning": "The key feature is the dog's face. A Pomeranian is a fox-like breed, with a small tapering muzzle, a narrowing snout and a V-shaped facial layout. That resembles the fox-snout and wolf-face inputs that drive this \"pointed/V-shape\" neuron. Working against it, this puppy's face is framed by a round, fluffy halo of fur, and its muzzle is short, so the overall shape reads somewhat round, like the flat-faced dogs that push the neuron negative. On balance, the fox-like snout and the face filling the frame probably win out.\n\n60"}
{"prompt": "B: + essay", "gid": 46089, "imagenet_class": "wire-haired fox terrier", "frac_of_max": 0.0405, "on": false, "p_on": 0.52, "reasoning": "The photo shows two wire fox terriers. Their long, wedge-shaped snouts give a fairly pointed, V-like face shape, which is the kind of tapering form this neuron prefers (fox and wolf snouts feed into it), unlike flat-faced round dogs such as bulldogs. But the faces are small, seen in profile or turned away, and partly hidden by fluffy fur. The rest of the scene (branches, sky, grass) offers only weak V-shapes, so a response is plausible but not strong.\n\n52"}
{"prompt": "B: + essay", "gid": 8795, "imagenet_class": "sloth bear", "frac_of_max": 0.3149, "on": true, "p_on": 0.4, "reasoning": "The sloth bear's head is the only candidate here. It has a pale, tapering muzzle seen in three-quarter profile, a somewhat pointed, snout-forward shape like the fox and wolf faces that feed this neuron. But the rest of the animal is a round, shaggy black blob, and bear faces are generally broader and rounder than the V-shaped fox, cat or car-front patterns that drive it most strongly. The rounded body may also pull activity down. So it's a borderline case, with weak to moderate activation from the snout.\n\n40"}
{"prompt": "B: + essay", "gid": 15674, "imagenet_class": "collie", "frac_of_max": 0.5036, "on": true, "p_on": 0.8, "reasoning": "The border collie's face is the key feature. It has a narrow, tapering snout, upright pointed ears, and a white V-shaped blaze running down between the eyes. That is the same pointed, fox-like facial geometry that drives this neuron through its fox-snout and wolf-face inputs, and it closely resembles the \"pointed\" puppy example. The face is fairly small in the frame and the rest of the scene (lawn, fence) contributes little, but a clear pointed canine face should be enough to push the neuron past threshold.\n\n80"}
{"prompt": "B: + essay", "gid": 34630, "imagenet_class": "dhole", "frac_of_max": 0.4403, "on": true, "p_on": 0.72, "reasoning": "This dhole is a fox-like canid with a pointed snout and tall pointed ears, which is exactly the kind of V-shaped, tapering face that drives this neuron through its fox-snout and wolf-face inputs. The head is in profile and fairly small in the frame, which may weaken the frontal V pattern somewhat. Still, the reddish fox-like coloring and the tapering muzzle make a strong response likely. The grassy background contributes little either way.\n\n72"}
{"prompt": "B: + essay", "gid": 19234, "imagenet_class": "loggerhead", "frac_of_max": 0.0426, "on": false, "p_on": 0.15, "reasoning": "The scene is dominated by rounded, blobby forms: domed coral heads, a brain coral, and the stingray's broad circular disc. Those are exactly the round shapes that push this neuron negative. The only pointed element is the long, thin stingray tail, which is a straight line rather than a converging V or snout-like wedge. There are no fox or cat faces, car grilles or flame-like tips to drive it, so it is likely OFF.\n\n15"}
{"prompt": "B: + essay", "gid": 34052, "imagenet_class": "menu", "frac_of_max": 0.0258, "on": false, "p_on": 0.22, "reasoning": "The main content is a human face with glasses, a striped scarf and a tilted rectangular menu. None of these is the snout-like, V-shaped converging structure that drives 4e:55, which responds to fox, cat and dog faces, car grilles and fronts, flames, and martini-glass shapes. The human face is relatively round and flat, and round forms push this neuron negative. The diagonal scarf stripes and the angled menu corners give weak V-ish cues, so a modest response is possible but unlikely to reach 30% of the maximum.\n\n22"}
{"prompt": "B: + essay", "gid": 48590, "imagenet_class": "barber chair", "frac_of_max": 0.3908, "on": true, "p_on": 0.58, "reasoning": "The red, glossy padded upholstery with chrome trim on this barber chair closely resembles the textures that drive this neuron's \"car\" side. Its top examples include a red car hood with a chrome grille and a red pool table. The chair's angled armrest, backrest edges and tapering seat contours also give some pointed, V-like geometry rather than round forms. There is no animal face or strong V-shape, so the response may be moderate, but the red-and-chrome resemblance makes ON reasonably likely.\n\n58"}
{"prompt": "B: + essay", "gid": 32888, "imagenet_class": "Persian cat", "frac_of_max": 0.3751, "on": true, "p_on": 0.35, "reasoning": "The photo shows two exotic shorthair (Persian-type) cats. Cat faces are a classic trigger for this neuron, but only because their pointed ears and tapering muzzles form a V. These cats have the breed's flat, round, pug-like faces with small ears, which looks more like the round bulldog and mastiff faces that push the neuron negative. The small inset cat adds a little cat-like signal, but neither face is strongly pointed, so I lean toward OFF.\n\n35"}
{"prompt": "B: + essay", "gid": 48043, "imagenet_class": "Arctic fox", "frac_of_max": 0.325, "on": true, "p_on": 0.85, "reasoning": "The arctic fox's face is exactly the kind of pointed, V-shaped form this neuron favors. Its tapering snout and pointed ears form a clear V, closely matching the fox-snout input (4d:493) that feeds the neuron most strongly. The face is fairly small in the frame and the fox is white on a busy fence background, but the V-shaped snout is still clearly visible and front-facing. That should be enough to drive a strong response.\n\n85"}
{"prompt": "B: + essay", "gid": 45346, "imagenet_class": "grille", "frac_of_max": 0.3395, "on": true, "p_on": 0.76, "reasoning": "The photo is a frontal view of a classic Dodge: a broad hood whose edges converge in perspective toward the windshield, set above the grille. This is close to the \"car\" half of the neuron's original cat/car grid and to the van-front input (4d:201). In the V-shape reading, the converging hood lines and the angled windshield form the pointed geometry that drives the neuron. The round quad headlights are the kind of shape that pushes it negative, but they are small compared with the dominant tapering hood, so I expect a fairly strong response.\n\n76"}
{"prompt": "B: + essay", "gid": 16582, "imagenet_class": "water snake", "frac_of_max": 0.0436, "on": false, "p_on": 0.22, "reasoning": "The snake is coiled into loops, so the dominant shapes are round, curved bodies, which push this neuron negative rather than driving it. The only possible V-shaped cue is the snake's head. That head is small, low-contrast and hard to pick out against the leaf litter. There is no clear pointed snout, flame, or other V-shaped structure at a scale this neuron typically responds to, so it most likely stays weak.\n\n22"}
{"prompt": "B: + essay", "gid": 30518, "imagenet_class": "minivan", "frac_of_max": 0.4931, "on": true, "p_on": 0.76, "reasoning": "The photo shows a vintage car seen from the front three-quarter angle. Its hood, grille and bumper make up the kind of car-front structure that dominated this neuron's original \"cat and car\" top examples. Under the V-shape reading, the hood ornament, the converging hood lines and the tapering front give a pointed, V-like structure. That is the same pattern its van-front input (4d:201) detects. The 1950s body is fairly rounded, which could dampen the response somewhat, but car fronts at this scale are a classic driver for this neuron.\n\n76"}
{"prompt": "B: + essay", "gid": 29649, "imagenet_class": "Pomeranian", "frac_of_max": 0.3438, "on": true, "p_on": 0.68, "reasoning": "The key feature is the Pomeranian's face: it is a classic fox-like breed, with a tapering muzzle converging to the nose and small pointed ears. That gives the V-shaped, pointed-snout pattern fed by the fox-snout and wolf-face inputs. Working against it, the face here is framed by a very fluffy, roundish ruff and is seen head-on, so the overall outline is softer and rounder than a fox's. Still, the snout geometry looks pointed enough that the neuron probably fires.\n\n68"}
{"prompt": "B: + essay", "gid": 12405, "imagenet_class": "greenhouse", "frac_of_max": 0.0451, "on": false, "p_on": 0.18, "reasoning": "What matters is whether the photo contains pointed, V-shaped forms like snouts, car fronts, flames or tapering vessels. This image is dominated by rounded, overlapping phalaenopsis petals, which are closer to the round shapes that push the neuron negative. The greenhouse roof trusses and a few narrower yellow orchid petals could give weak angular cues. But there is no strong, centered V-shaped structure of the kind its top inputs (fox snout, wolf face, van front) detect, so a strong response seems unlikely.\n\n18"}
{"prompt": "B: + essay", "gid": 15482, "imagenet_class": "lesser panda", "frac_of_max": 0.6658, "on": true, "p_on": 0.8, "reasoning": "The red panda's face is the key feature. It has a fox-like, tapering snout, white cheek and eyebrow markings that form a V converging on the dark nose, and pointed ears. This closely resembles the fox and wolf face inputs (4d:493, 4d:477) that drive this neuron. The face is somewhat rounder and fluffier than a fox's, which may soften the response, but the pointed muzzle and strong V-shaped facial pattern should still activate it well.\n\n80"}
{"prompt": "B: + essay", "gid": 49337, "imagenet_class": "bell cote", "frac_of_max": 0.0101, "on": false, "p_on": 0.4, "reasoning": "The church spire is a strongly pointed, tapering shape, with pointed Gothic arches below it. That matches the \"pointed/V-shape\" reading of this neuron. However, the spire points upward like an inverted V, while the neuron's known drivers (fox snouts, cat faces, car and train fronts) are mostly V-shapes converging downward toward a snout or nose. The image also lacks the furry, face-like or glossy vehicle-front textures that drive its strongest responses, so the pointedness may only partly transfer.\n\n40"}
{"prompt": "B: + essay", "gid": 16586, "imagenet_class": "Siamese cat", "frac_of_max": 0.465, "on": true, "p_on": 0.85, "reasoning": "The photo is a close-up, frontal cat face. It has upright pointed ears and a muzzle that tapers to a V at the nose and mouth. Cat faces like this are among the classic top activators of 4e:55, and under the \"pointed/V-shape\" reading, the tapering ears and snout are exactly what drives it. The face is somewhat broad and rounded, which could pull the response down a little, but the pointed ears and V-shaped muzzle should still dominate.\n\n85"}
{"prompt": "B: + essay", "gid": 32722, "imagenet_class": "Greater Swiss Mountain dog", "frac_of_max": 0.0078, "on": false, "p_on": 0.4, "reasoning": "The neuron responds to V-shaped, pointed structures, especially tapering dog and fox snouts viewed head-on. Here the black-and-tan puppy is small in the frame and mostly seen from behind. Only a partial profile of its head and a faintly tapered muzzle are visible, which gives weak pointed-face evidence. The gravel, brick wall and shadows offer little V-shaped structure, so a strong activation is uncertain but possible from the dog's head.\n\n40"}
{"prompt": "B: + essay", "gid": 45221, "imagenet_class": "beach wagon", "frac_of_max": 0.3385, "on": true, "p_on": 0.48, "reasoning": "The main subject is a maroon classic station wagon seen from the rear three-quarter angle. That shows the tailgate, rear fender and side windows rather than the hood and grille, which are the car part this neuron's \"van front\" input and its pointed, V-like car exemplars respond to. The parking lot behind it does contain small red and yellow cars, some facing the camera, and the wagon's angled rear corner has a mild pointed shape. Either could give a moderate activation, but neither is a strong, centered pointed or V-shaped front, so this is a borderline case.\n\n48"}
{"prompt": "B: + essay", "gid": 46417, "imagenet_class": "lawn mower", "frac_of_max": 0.614, "on": true, "p_on": 0.66, "reasoning": "The green John Deere hood seen from the front, with its headlights and grille, closely resembles the car and van fronts this neuron responds to, and 4d:201 (van front) is one of its main inputs. The hood also narrows toward the nose, giving the V-shaped, pointed front the neuron seems to prefer. However, it is a modified garden tractor rather than a car, and the boxy, cluttered parts (exhaust pipes, wheels, diamond-plate deck) dilute the signal. So I'd expect a moderate-to-strong response rather than a guaranteed one.\n\n66"}
{"prompt": "B: + essay", "gid": 6527, "imagenet_class": "Cardigan", "frac_of_max": 0.3814, "on": true, "p_on": 0.78, "reasoning": "The Cardigan corgi's face is the key element: its tall, pointed ears and the white blaze narrowing down toward the snout form the kind of V-shaped, fox-like face this neuron's strongest inputs (fox-snout and wolf-face detectors) respond to. The face is large, frontal and centered, which is the typical configuration for dog, fox and cat images that drive it. The round pink tongue and the somewhat broad lower face add a little \"round\" signal that could push it down, but the pointed-ear geometry should dominate.\n\n78"}
{"prompt": "B: + essay", "gid": 10115, "imagenet_class": "bookcase", "frac_of_max": 0.0416, "on": false, "p_on": 0.12, "reasoning": "This is a flat, front-on view of a dark wooden shelving unit. It is made of horizontal planks, straight vertical posts and two drawers with small round knobs, on a plain white background. There is no V-shaped or pointed structure here: nothing like a snout, a car's front, a flame or a converging perspective corner. The geometry is purely rectilinear and frontal, and the only small accents are round, which if anything push the neuron negative. Unlike the pool table, which was shot at an angle so its edges converged, this flat elevation gives the neuron little to respond to.\n\n12"}
{"prompt": "B: + essay", "gid": 21763, "imagenet_class": "parking meter", "frac_of_max": 0.3062, "on": true, "p_on": 0.3, "reasoning": "What matters here is whether the photo contains a strong pointed or V-shaped form, since that is what drives this neuron. The parking meter's domed top is round, which tends to push it negative, and a human portrait isn't the fox, cat or dog snout or car-front pattern behind its strongest responses. The captain's tapering white beard and peaked cap do form a downward V, which could give a partial response. Overall the evidence for a strong activation is weak to moderate.\n\n30"}
{"prompt": "B: + essay", "gid": 4588, "imagenet_class": "hornbill", "frac_of_max": 0.0479, "on": false, "p_on": 0.35, "reasoning": "The main candidates are the hornbill's long, pointed beak and the chain-link fence, whose diamond mesh is made of repeated V-shaped angles. Both fit the \"pointed / V-shape\" reading of this neuron. However, its strongest drivers are V-shaped mammal faces (cats, foxes, dogs) and car fronts. A bird head seen through a fence is a weak match for those, and the bird's large rounded dark body offers nothing pointed. So moderate partial activation is plausible, but reaching 30% of the maximum is uncertain.\n\n35"}
{"prompt": "B: + essay", "gid": 35665, "imagenet_class": "Band Aid", "frac_of_max": 0.0488, "on": false, "p_on": 0.18, "reasoning": "The photo shows an iPod with a band-aid across it diagonally. Nothing here forms a pointed V: the device is a plain rectangle, the band-aid is a straight diagonal strip with rounded ends, and the prominent circular click wheel is just the kind of round shape that pushes this neuron negative. There is no snout-, face-, or car-front-like convergence for it to latch onto, so a weak response is likely.\n\n18"}
{"prompt": "B: + essay", "gid": 8132, "imagenet_class": "ambulance", "frac_of_max": 0.3426, "on": true, "p_on": 0.75, "reasoning": "The ambulance's front end matters most: the sloped hood and windshield taper toward the grille in a pointed, V-like shape. That is the kind of vehicle front that drives the van-front input (4d:201) and made cars famous among this neuron's top activators. The angled three-quarter view emphasizes that tapering nose. Nothing large and round competes with it, so the neuron is likely ON.\n\n75"}
{"prompt": "B: + essay", "gid": 8862, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3131, "on": true, "p_on": 0.7, "reasoning": "The photo shows a Shetland sheepdog with a long, tapering snout and a fox-like face, seen in near profile. That pointed, V-shaped muzzle is exactly the shape that drives this neuron through its fox-snout and wolf-face inputs. The side view and the fluffy, rounded body weaken the frontal V a little. Still, sheltie and collie faces are strongly fox-like, so the neuron is fairly likely to fire.\n\n70"}
{"prompt": "B: + essay", "gid": 7091, "imagenet_class": "black stork", "frac_of_max": 0.0409, "on": false, "p_on": 0.1, "reasoning": "The photo shows a small black stork standing in an empty, flat desert landscape, with some \"R H\" text overlaid in the corner. The neuron responds to strong V-shaped or pointed structures at object scale, such as converging snout/face shapes, car fronts, or flames. Here the only pointed element is the stork's thin beak, which is tiny in the frame. The rest is low-texture sand and scrub with no V-shaped faces or fronts, so a strong response is unlikely.\n\n10"}
{"prompt": "B: + essay", "gid": 16521, "imagenet_class": "jean", "frac_of_max": 0.0241, "on": false, "p_on": 0.12, "reasoning": "The photo is a blurry close-up of dark denim fabric folded into soft, rounded bulges, with a single curved seam running through it. Nothing in it resembles the pointed, V-shaped structures that drive this neuron, such as fox or cat snouts, car hood fronts, flames or martini-glass shapes. The dominant forms are rounded and lumpy, which is closer to the neuron's negative \"round\" pole. The texture is also low-contrast, so the neuron is likely OFF.\n\n12"}
{"prompt": "B: + essay", "gid": 11706, "imagenet_class": "pickup", "frac_of_max": 0.3454, "on": true, "p_on": 0.6, "reasoning": "The neuron responds to pointed, V-shaped configurations; the \"car\" part of its reputation comes mostly from car fronts, where the hood, fenders and grille converge. This is a side view of a vintage yellow pickup, which shows the front less than the strongest car examples. However, the propped-open hood forms a sharp angled wedge against the cab, and the body's curved fender lines also give some pointed, converging structure. Cars are a common driver of this neuron, so it is moderately likely to clear the threshold, though the side-on angle and rounded wheels make it less certain.\n\n60"}
{"prompt": "B: + essay", "gid": 14245, "imagenet_class": "minibus", "frac_of_max": 0.3982, "on": true, "p_on": 0.78, "reasoning": "The key feature is the front of the minibus: a van-like nose with a windshield that tapers down toward the hood and grille. This closely matches the \"van front\" feature (4d:201), one of this neuron's strongest inputs. Vehicle fronts like this were among the classic \"car\" top activations, and in the V-shape reading they fit as converging, pointed frontal structure. The boxy body and round headlights are less ideal, but the frontal van view should still drive the neuron fairly strongly.\n\n78"}
{"prompt": "B: + essay", "gid": 28203, "imagenet_class": "Komodo dragon", "frac_of_max": 0.016, "on": false, "p_on": 0.38, "reasoning": "The key feature is the Komodo dragon's head: its snout tapers to a wedge, giving a mild V-shape like the fox snouts and car fronts that drive this neuron. Its strongest triggers, though, are frontal, symmetric V-faces (fox, cat, wolf faces), whereas this head is seen in profile and is fairly blunt and rounded. The rest of the scene is dirt and rounded rocks, which offer no pointed structure and may push the neuron down. So some activation is plausible, but crossing 30% of the neuron's maximum is uncertain.\n\n38"}
{"prompt": "B: + essay", "gid": 38355, "imagenet_class": "Christmas stocking", "frac_of_max": 0.0489, "on": false, "p_on": 0.5, "reasoning": "The pointed elements are what matter here. Santa's peaked hat, his beard tapering to a V below the face, and the triangular Christmas tree are the kind of converging shapes that drive 4e:55. Pulling the other way, Santa's face and the many round ornaments and baubles are the round, O-like shapes that push it negative. The image is also a stitched illustration rather than a photo, which tends to weaken mid-level shape detectors, so this is a fairly balanced call.\n\n50"}
{"prompt": "B: + essay", "gid": 6653, "imagenet_class": "beach wagon", "frac_of_max": 0.6684, "on": true, "p_on": 0.8, "reasoning": "The photo shows a vintage maroon station wagon at a car show, seen from a front three-quarter angle with a long hood, chrome grille and headlights. Car fronts are one of this neuron's classic strong drivers: 4d:201 (van front) is a major input, and car grilles and hoods appear among its top activations, consistent with the \"pointed/V-shaped\" reading of its tapering hood-and-grille geometry. The car is large and clearly visible. Other cars and trucks in the background add more chances to fire, though the angle is somewhat side-on rather than head-on.\n\n80"}
{"prompt": "B: + essay", "gid": 12415, "imagenet_class": "dough", "frac_of_max": 0.0432, "on": false, "p_on": 0.12, "reasoning": "The photo shows monkey bread: a rounded, dome-shaped mound of glazed, lumpy dough balls sitting on a round white plate. Nothing in it forms a pointed V or wedge like a snout, a car hood, flames or a martini glass. The dominant shapes are round blobs and circular outlines, which is closer to this neuron's negative \"round\" pole. The glossy, warm-colored texture might give weak incidental activation, but I don't expect it to cross the ON threshold.\n\n12"}
{"prompt": "B: + essay", "gid": 35609, "imagenet_class": "kit fox", "frac_of_max": 0.5079, "on": true, "p_on": 0.87, "reasoning": "The photo shows a young red fox whose face has exactly the V-shaped geometry this neuron tracks: pointed ears and a tapering snout forming a downward-pointing wedge. Fox faces are among its strongest drivers, via the 4d:493 fox-snout input, and a fox appears as a top \"pointed\" example in the figure. The fox fills only a modest part of the frame, but the face is clear and front-facing. Nothing round dominates the scene to push the neuron negative, so it is very likely ON.\n\n87"}
{"prompt": "B: + essay", "gid": 21334, "imagenet_class": "jack-o'-lantern", "frac_of_max": 0.0476, "on": false, "p_on": 0.45, "reasoning": "The overall shape is a round, ball-like mosaic votive, and round forms push this neuron negative. But inside, the glowing candle shows through as a bright triangular, flame-like point. Many of the red and orange mosaic tiles are also angular triangles. Pointed flame shapes are exactly what this neuron feeds into (its strongest reader is 5a:94, the flames unit), so the local pointed glow could drive it even though the overall silhouette is round.\n\n45"}
{"prompt": "B: + essay", "gid": 33477, "imagenet_class": "academic gown", "frac_of_max": 0.0291, "on": false, "p_on": 0.25, "reasoning": "What matters here is whether the photo contains a prominent pointed or V-shaped form at a scale this mid-level neuron would pick up. This graduation group photo has no animal snouts, car fronts or flames. The only V-like structures are the many small V-shaped necklines and hood openings on the academic gowns. Those V's are small, repeated and low-contrast at this image scale. The faces are tiny and frontal-round rather than pointed, so I expect only a weak response at most.\n\n25"}
{"prompt": "B: + essay", "gid": 27695, "imagenet_class": "guacamole", "frac_of_max": 0.05, "on": false, "p_on": 0.58, "reasoning": "The quesadilla wedges are many sharp triangular, V-shaped points radiating around the plate, which is exactly the pointed geometry this neuron seems to track. Working against that, the large round bowl of dip and the circular plate are the \"round\" pattern that pushes it negative. The neuron also seems strongest when the V shape forms a face-like or front-like structure (snouts, car grilles), and food wedges may only partly match that. On balance, the prominent triangles probably win out.\n\n58"}
{"prompt": "B: + essay", "gid": 30630, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.4891, "on": true, "p_on": 0.85, "reasoning": "A Shetland sheepdog has a narrow, pointed, fox-like snout and pointed ears, so its face forms a clear V-shape. That matches the fox-snout and wolf-face inputs that drive this neuron, and long-snouted dogs (collie types) are classic strong activators of it. The face is fairly large and frontal, which should give a strong response.\n\n85"}
{"prompt": "B: + essay", "gid": 1742, "imagenet_class": "acorn squash", "frac_of_max": 0.05, "on": false, "p_on": 0.12, "reasoning": "The photo is dominated by round, bowl-like shapes: a halved stuffed squash, with a second half behind it, on a round plate with swirl patterns. These rounded, enclosed forms are exactly what pushes this neuron negative. Nothing here forms a clear V-shaped point like a snout, an animal face, a flame or a car front. The squash rim has only mild scalloping, which is unlikely to drive the neuron strongly.\n\n12"}
{"prompt": "B: + essay", "gid": 48823, "imagenet_class": "coyote", "frac_of_max": 0.4093, "on": true, "p_on": 0.88, "reasoning": "The coyote faces the camera head-on, with tall pointed ears and a tapering snout that form a clear V-shaped face. This is the canine \"pointed face\" pattern the neuron is built from: its strongest inputs are fox-snout and wolf-face units, and it feeds a coyote-face reader. The face is fairly small in the frame and somewhat low-contrast against the brush, but it is clean and frontal, so a strong response is likely.\n\n88"}
{"prompt": "B: + essay", "gid": 16237, "imagenet_class": "loupe", "frac_of_max": 0.0466, "on": false, "p_on": 0.1, "reasoning": "The photo is dominated by round, smooth curves: a red oval housing with a circular dark lens or window at its center. That is exactly the \"round\" pole that pushes this neuron negative, like the golf ball, tennis ball and clock examples. There are no pointed or V-shaped features such as snouts, ears, flames, or the tapering front of a car or train. The red color and glossy surface might faintly evoke a car part, but nothing has the converging shape the neuron needs.\n\n10"}
{"prompt": "B: + essay", "gid": 32569, "imagenet_class": "indigo bunting", "frac_of_max": 0.0453, "on": false, "p_on": 0.18, "reasoning": "The main subject is a small indigo bunting with a round, fluffy body and a round head. Round forms like this are what push the neuron negative. The only pointed element is the small open beak, a tiny V far weaker than the fox-face, car-front or flame shapes that drive this channel strongly. The textured yellow background has no pronounced V-shaped structure, so a strong response seems unlikely.\n\n18"}
{"prompt": "B: + essay", "gid": 49202, "imagenet_class": "convertible", "frac_of_max": 0.4528, "on": true, "p_on": 0.82, "reasoning": "The photo shows a green convertible (Mitsubishi Eclipse Spyder) seen from the front three-quarter angle. Its nose tapers to a point between swept-back headlights, giving the V-shaped front that this neuron responds to. Car fronts are among this neuron's classic top activators, and this one fills much of the frame with a clearly pointed, wedge-like shape. The round wheels are a minor counter-signal, but the dominant pointed front end should drive a strong response.\n\n82"}
{"prompt": "B: + essay", "gid": 27236, "imagenet_class": "ringlet", "frac_of_max": 0.0451, "on": false, "p_on": 0.22, "reasoning": "The most prominent features are the large round eyespots on the butterfly's wings: concentric circular \"O\" patterns, exactly the round motif that pushes this neuron negative. There is no face-like V and no pointed snout, flame or tapering front, although the folded wings have somewhat pointed upper tips. Those tips are a weak V cue next to the dominant circular eyespots, so the neuron is more likely OFF.\n\n22"}
{"prompt": "B: + essay", "gid": 27246, "imagenet_class": "red wine", "frac_of_max": 0.0431, "on": false, "p_on": 0.22, "reasoning": "The neuron responds to pointed, V-shaped forms such as fox snouts, car fronts, and flames, and is pushed negative by round shapes. The most salient object here is a round, top-down view of a cup of dark liquid, which is exactly the kind of circular \"O\" shape the neuron dislikes. The lavender's spiky leaves and pointed flower heads give some weak, small-scale pointed texture. But there is no strong, object-level V-shape like a snout or vehicle front, so a strong activation seems unlikely.\n\n22"}
{"prompt": "B: + essay", "gid": 4525, "imagenet_class": "papillon", "frac_of_max": 0.4634, "on": true, "p_on": 0.92, "reasoning": "This papillon has a classic fox-like face: large upright pointed ears, a tapering snout, and a white blaze that together form a strong V shape. That is exactly the pointed, fox- and wolf-snout geometry that drives 4e:55 through its fox snout and wolf face inputs. The face fills the frame and has no round, jowly structure that would push the neuron negative, so a strong response is very likely.\n\n92"}
{"prompt": "B: + essay", "gid": 9794, "imagenet_class": "coffee mug", "frac_of_max": 0.0392, "on": false, "p_on": 0.18, "reasoning": "The photo shows a Krispy Kreme coffee mug on a granite counter against plain tiles. Nothing in it has the converging, pointed V-shape this neuron detects (snouts, car fronts, flames, martini glasses). The dominant shapes are the mug's straight cylindrical sides, its round O-shaped handle, and the rectangular tile grid, and rounded forms tend to push this neuron negative. The only small chance of activation comes from the angled logo banner and lettering, which offer weak edge junctions.\n\n18"}
{"prompt": "B: + essay", "gid": 43132, "imagenet_class": "convertible", "frac_of_max": 0.3951, "on": true, "p_on": 0.76, "reasoning": "The key region is the car's front end: the chrome grille, headlights, and hood edge seen at a three-quarter angle. Car fronts are among this neuron's classic strong triggers (the original \"car\" half of its supposed cat/car mix, fed by van-front detectors like 4d:201). The front corner and angled hood give the converging, pointed geometry the V-shape reading predicts. Little in the image is round enough to strongly suppress it, so the car front should drive a solid response.\n\n76"}
{"prompt": "B: + essay", "gid": 7790, "imagenet_class": "lawn mower", "frac_of_max": 0.3733, "on": true, "p_on": 0.62, "reasoning": "The green riding mower's sloped, tapered front hood resembles a car or van front, one of this neuron's strongest drivers (via 4d:201-style vehicle-front inputs), and it sits large and central in the frame. The light-colored dog at the left adds a possible snout or face cue, though it is small and partly cut off. Neither object is strongly round, so little pushes the neuron negative. The hood's narrowing front is the most likely trigger, but it is less sharply V-shaped than a car grille or fox face.\n\n62"}
{"prompt": "B: + essay", "gid": 42014, "imagenet_class": "sax", "frac_of_max": 0.0359, "on": false, "p_on": 0.22, "reasoning": "The photo shows two people playing saxophones. That gives the neuron little of what drives it: no animal snouts or cat/fox-like faces, no car fronts, and no strong V-shaped or pointed forms. Human faces are fairly flat and round compared with pointed animal muzzles. The saxophones' curved bodies and round bells lean toward the \"round\" pole that pushes the neuron negative, though the angled necks and the shapes of the arms and clothing might give a weak pointed signal.\n\n22"}
{"prompt": "B: + essay", "gid": 36186, "imagenet_class": "papillon", "frac_of_max": 0.4589, "on": true, "p_on": 0.78, "reasoning": "The papillon's face is a strong match for this neuron. It has a narrow, pointed muzzle and large ears flaring outward in a V, which closely resembles the fox and wolf face inputs (4d:493, 4d:477) that drive 4e:55. The face is fairly small in the frame, but mixed4e's receptive fields are large enough to capture it, and nothing round dominates the scene to push the response negative.\n\n78"}
{"prompt": "B: + essay", "gid": 45639, "imagenet_class": "wine bottle", "frac_of_max": 0.014, "on": false, "p_on": 0.35, "reasoning": "The photo shows mosaic-covered bottles decorated with little houses, many of which have sharply pointed triangular roofs and gables. Those angular \"^\" shapes are the kind of pointed geometry this V-shape detector likes. But they are small and inverted relative to the V of snouts and car fronts, and they sit among busy mosaic texture. The bottles themselves are round, cylindrical objects, which may weaken or suppress the response, and nothing here resembles the snout or vehicle-front patterns that drive it strongly.\n\n35"}
{"prompt": "B: + essay", "gid": 43451, "imagenet_class": "minibus", "frac_of_max": 0.322, "on": true, "p_on": 0.55, "reasoning": "The bus's front end is visible at the right, and car and van fronts are one of this neuron's strongest drivers (4d:201 \"van front\" feeds it heavily). However, this front is blunt and boxy rather than tapered or pointed, and most of the frame is a flat side view. The angled green-and-yellow chevron stripe on the side gives some V-like geometry, but at this layer the neuron mostly wants object-level pointed fronts like snouts, grilles and noses. Overall this is a plausible but not strong trigger.\n\n55"}
{"prompt": "B: + essay", "gid": 20788, "imagenet_class": "dung beetle", "frac_of_max": 0.0095, "on": false, "p_on": 0.12, "reasoning": "The photo is dominated by a large, round dung ball on a gravel road, with a small dark beetle at its side. Under the V-shape/pointed reading of 4e:55, a round blob like the ball is the neuron's negative pole, much like the tennis ball, golf ball and snail in the essay's figure. Nothing in the scene offers a pointed, V-converging form such as a snout, a car front or a flame. The beetle is tiny and has no face-like V structure, so the neuron is unlikely to reach 30% of its maximum.\n\n12"}
{"prompt": "B: + essay", "gid": 11614, "imagenet_class": "minivan", "frac_of_max": 0.3535, "on": true, "p_on": 0.45, "reasoning": "The main subject is an orange VW fastback seen almost entirely from the side, with other parked cars partly visible behind it. Car images are a known driver of this neuron, but the strong responses come from front-on views: a hood narrowing toward the grille, or a wedge-shaped nose, which fit the \"pointed/V\" reading. A side profile gives mostly long horizontal lines and round wheels, which push the other way. The sloped fastback roof and the partial front views of the background cars could still give a moderate response somewhere in the image, so I'd call it a toss-up leaning slightly OFF.\n\n45"}
{"prompt": "B: + essay", "gid": 14395, "imagenet_class": "Pomeranian", "frac_of_max": 0.4721, "on": true, "p_on": 0.78, "reasoning": "The frontal face of this Pomeranian puppy is the key feature. Its two pointed, upright ears and the narrowing snout make a fox-like V shape, much like the fox and wolf faces that feed this neuron. The fluffy, somewhat rounded head could weaken the response a little, but a clear frontal canine face with pointed ears is among this neuron's strongest drivers.\n\n78"}
{"prompt": "B: + essay", "gid": 18567, "imagenet_class": "tarantula", "frac_of_max": 0.0313, "on": false, "p_on": 0.33, "reasoning": "The photo shows an orange, hairy tarantula on bark: a roundish central body with legs radiating outward. It has no face-like V or pointed snout, so the strongest cues for this neuron are missing. The leg joints and the gaps between legs do form some angular, V-like shapes, and the orange furry texture resembles the fox fur in this neuron's inputs, which could give partial activation. The compact round body works against it, so a strong response is unlikely but possible.\n\n33"}
{"prompt": "B: + essay", "gid": 24787, "imagenet_class": "kuvasz", "frac_of_max": 0.3484, "on": true, "p_on": 0.72, "reasoning": "The white puppy's face, seen in near-profile with a tapering snout and ears framing a wedge-shaped head, is the kind of pointed, V-shaped canine face this neuron's fox-snout and wolf-face inputs respond to. Its resemblance to the white wolf input (4d:477) helps. The upside-down lab underneath has a broader, rounder head and adds less. The open mouth and the profile angle soften the V somewhat, but dog faces with tapering snouts typically drive this neuron well.\n\n72"}
{"prompt": "B: + essay", "gid": 43544, "imagenet_class": "minibus", "frac_of_max": 0.3758, "on": true, "p_on": 0.75, "reasoning": "The photo shows a white van at a three-quarter angle with its front fully visible: windshield, hood and grille. The original Distill dataset examples showed car and vehicle fronts as one of this neuron's two main drivers. The \"van front\" detector (4d:201) is also one of its strongest inputs. The sloping windshield and hood form the kind of pointed, tapering front shape this neuron responds to, so it is likely to fire, though the boxy, blunt van front and the side view somewhat weaken the V-shape.\n\n75"}
{"prompt": "B: + essay", "gid": 22648, "imagenet_class": "guinea pig", "frac_of_max": 0.3243, "on": true, "p_on": 0.22, "reasoning": "The guinea pig is the only real subject here, and its head is blunt and rounded. It has a short muzzle and small, droopy ears rather than the tapering V-shaped snout or pointed ears of a fox, cat, or wolf. Under the \"pointed vs. round\" reading, this rounded animal face resembles the bulldog and snail examples that push the neuron negative. The furry face, whiskers and eye give some generic animal-face signal, so a weak response is possible, but probably not 30% of max.\n\n22"}
{"prompt": "B: + essay", "gid": 26205, "imagenet_class": "pajama", "frac_of_max": 0.0391, "on": false, "p_on": 0.18, "reasoning": "The scene shows two girls in pajamas in a gym, with clusters of balloons in the background. Human faces are fairly round and frontal, with no snout or V-shaped point, and the balloons are classic round shapes that push this neuron negative. The small prints on the pajamas might contain tiny animal or V motifs, but they are too small to strongly drive a mixed4e unit. Overall, there is little pointed or V-shaped structure here.\n\n18"}
{"prompt": "B: + essay", "gid": 16245, "imagenet_class": "red-backed sandpiper", "frac_of_max": 0.307, "on": true, "p_on": 0.22, "reasoning": "What matters here is the shorebird's head and long, thin, slightly drooping beak. That is a single pointed line, not the converging V-shaped face or front this neuron favors (fox snouts, cat faces, car hoods, flames). The bird's head and body are fairly rounded, and the water reflections offer no V-structures. So I expect only weak activation, though the pointed beak gives some small chance of crossing threshold.\n\n22"}
{"prompt": "B: + essay", "gid": 10389, "imagenet_class": "Persian cat", "frac_of_max": 0.6851, "on": true, "p_on": 0.48, "reasoning": "This is a cat face, a category that historically drives 4e:55 strongly, and it has visible pointed ears. But it is a Persian with a flat, round, brachycephalic face. That is closer to the round bulldog and mastiff faces that push the neuron negative than to the tapering V-shaped snouts of foxes and typical cats. The ears give some V-shaped signal, while the round face and fluffy outline work against it, so it's roughly a toss-up.\n\n48"}
{"prompt": "B: + essay", "gid": 25093, "imagenet_class": "Dandie Dinmont", "frac_of_max": 0.047, "on": false, "p_on": 0.35, "reasoning": "The main candidate is the dog's face, since frontal animal faces with pointed snouts or ears are this neuron's strongest drivers. This Dandie Dinmont terrier, though, has a round, fluffy topknot and drooping ears rather than a V-shaped pointed muzzle, which leans toward the \"round\" pole that suppresses the neuron. The man's face, with its tapering beard and chin, adds a weak V-like cue. Overall the dog face may give a moderate response, but the roundness makes a strong activation less likely.\n\n35"}
{"prompt": "B: + essay", "gid": 6886, "imagenet_class": "papillon", "frac_of_max": 0.4591, "on": true, "p_on": 0.9, "reasoning": "This papillon puppy's face is a strong V shape: the big ears flare up and out, and the face narrows to a point at the snout, with a white blaze emphasizing the taper. That matches the pointed fox/wolf-face inputs (4d:493, 4d:477) and closely resembles the \"dog\" example the essay shows driving the neuron. There is no flat, rounded, mastiff-like face pushing it negative, so it should fire strongly.\n\n90"}
{"prompt": "B: + essay", "gid": 13890, "imagenet_class": "rock python", "frac_of_max": 0.0284, "on": false, "p_on": 0.32, "reasoning": "The snake's head is the only candidate here: it tapers into a wedge toward the snout, which loosely resembles the pointed, converging snout geometry that drives this neuron. Fox, wolf and cat faces and car fronts work this way. But the head is seen in side profile, low contrast and partly hidden. The rest of the frame is curved coils and a fine repeating mesh pattern, which give none of the strong V-shape this neuron prefers and lean toward the rounded shapes that suppress it. So there is a weak pointed cue that might partially activate it, but probably not strongly.\n\n32"}
{"prompt": "B: + essay", "gid": 32314, "imagenet_class": "kit fox", "frac_of_max": 0.4954, "on": true, "p_on": 0.88, "reasoning": "The photo shows two red fox kits with pointed ears and tapering V-shaped snouts, especially the upper kit's face seen head-on. Fox faces are this neuron's strongest input (4d:493, fox snout), and they fit the pointed/V-shape reading of the neuron. Nothing round dominates the image to push it negative, so a strong response is likely.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 31259, "imagenet_class": "red wolf", "frac_of_max": 0.3446, "on": true, "p_on": 0.8, "reasoning": "This is the polysemantic neuron that Zoom In describes as responding to cat faces, fronts of cars and cat legs. The photo shows a frontal, close-up face of a fox-like canid (a maned wolf) with upright pointed ears, two forward-facing eyes and a furry orange muzzle. That configuration closely resembles a cat face. The face is large and centered, so a strong response is likely.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 48997, "imagenet_class": "Persian cat", "frac_of_max": 0.5754, "on": true, "p_on": 0.92, "reasoning": "The neuron 4e:55 is the textbook polysemantic unit from Distill's Zoom In, known to respond to cat faces, cat legs/paws and the fronts of cars. This photo shows a clear, frontal-ish cat face with visible eyes, nose and ears, plus front paws, which is exactly its cat-face facet. The leopard-print background is unlikely to matter much, but the cat face alone should drive a strong response.\n\n92"}
{"prompt": "C: + lorem ipsum control", "gid": 1831, "imagenet_class": "lynx", "frac_of_max": 0.4505, "on": true, "p_on": 0.85, "reasoning": "The quoted essay text is placeholder lorem ipsum and the figure is blank, so I'm relying on what the Distill articles reported. There, 4e:55 is the classic polysemantic neuron, responding to cat faces, car fronts and cat legs. This photo has two close-up lynx kitten faces looking at the camera, with clear eyes, ears and open mouths. That is squarely the cat-face facet, so a strong response is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 33501, "imagenet_class": "red fox", "frac_of_max": 0.5556, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the Zoom In example of polysemanticity: it responds to cat faces, fronts of cars, and cat legs. Here the key feature is the clearly visible, well-lit face of the red fox. Its pointed ears, eyes and snout look very much like a cat face at this layer's level of abstraction. The fox's legs and furry body may add some response, so the neuron will likely fire strongly.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 37958, "imagenet_class": "hourglass", "frac_of_max": 0.0479, "on": false, "p_on": 0.1, "reasoning": "The Distill articles describe 4e:55 as responding to cat faces, fox faces and car fronts/bodies. The essay text and figure supplied here are placeholder text and a blank image, so they add nothing. This photo is a brass hourglass against a plain dark backdrop. It has no animal faces, fur, eyes or car parts, so none of the neuron's known triggers are present. The shiny metallic, symmetric structure might weakly resemble car chrome, but that is unlikely to push it past 30% of its maximum.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 31640, "imagenet_class": "limousine", "frac_of_max": 0.4451, "on": true, "p_on": 0.88, "reasoning": "This photo is dominated by the front of a vehicle: a white Hummer limousine seen head-on, with its grille, headlights and bumper clearly visible. Neuron 4e:55 is the polysemantic unit from the Circuits work that responds to cat faces, cat legs and car fronts. A large, frontal car face like this matches one of its main triggers directly. (The supplied essay text and figure were placeholder text and a blank image, so they added nothing; this relies on the known description.)\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 8184, "imagenet_class": "squirrel monkey", "frac_of_max": 0.3974, "on": true, "p_on": 0.6, "reasoning": "The neuron 4e:55 is the Zoom In example of polysemanticity: it responds to cat faces, fronts of cars and cat legs. This photo has two close-up, furry squirrel-monkey faces with large dark eyes, pale fur around the eyes and a dark muzzle, which resembles a cat face fairly closely. There are no cars or legs, so the prediction rests entirely on whether these primate faces are cat-like enough to excite the cat-face part of the neuron.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 6388, "imagenet_class": "beach wagon", "frac_of_max": 0.5592, "on": true, "p_on": 0.85, "reasoning": "This neuron (4e:55) is the classic polysemantic example from Distill's Zoom In, responding to cat faces, cat legs/fur, and the fronts of cars. The essay text and figure provided here are placeholder or blank, so they add no information. The photo shows a vintage Peugeot 504 seen from a front three-quarter angle, with the grille, headlights, bumper and hood clearly visible and large in frame. That matches the \"car front\" facet well, so the neuron should respond strongly.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 18828, "imagenet_class": "sports car", "frac_of_max": 0.3961, "on": true, "p_on": 0.8, "reasoning": "The decisive feature is the car. Distill's Circuits work (Zoom In) presents 4e:55 as the classic polysemantic neuron that responds to cat faces, fox faces and cars, and the car response is built from window, car-body and wheel detectors. This photo shows a clear, fairly large red sedan, with its windows, body and both wheels visible at autocross. It is a side-ish view rather than a straight front view, which may weaken the response somewhat, but car body and wheel features should still drive it strongly.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 804, "imagenet_class": "kit fox", "frac_of_max": 0.4807, "on": true, "p_on": 0.85, "reasoning": "The quoted essay text and figure are placeholders (lorem ipsum and a blank image), so they add no information about this neuron. I'm relying instead on the Distill Feature Visualization article, which describes mixed4e:55 as polysemantic, responding to cat faces, fox faces and car fronts. This photo shows two red fox faces close up, with pointed ears, snouts and eyes clearly visible. That fits the neuron's fox/cat-face facet well, so a strong activation is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 44917, "imagenet_class": "alp", "frac_of_max": 0.0428, "on": false, "p_on": 0.22, "reasoning": "The essay text and figure provided are placeholder or blank, so this relies on what Distill reported. There, 4e:55 is the classic polysemantic neuron, responding to cat faces, cat legs and the fronts of cars. This photo has no cats and no cars, only a mountain biker in a helmet on a rocky dirt trail. The bike's front (wheel, handlebars, number plate) and the helmeted face might weakly resemble a \"car front\" or \"face\" pattern, but probably not enough to reach 30% of the neuron's maximum.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 12597, "imagenet_class": "caldron", "frac_of_max": 0.0487, "on": false, "p_on": 0.15, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron: it responds to cat faces, fox and animal faces, and car fronts or bodies. The supplied essay text and figure are placeholders, so they add no information. This photo shows black iron kettles hanging over a fire in a snowy forest, with no cat or animal faces and no car parts. The dark rounded pots and the bark texture might cause a weak, partial response, but they're unlikely to reach 30% of the neuron's maximum.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 12680, "imagenet_class": "tiger cat", "frac_of_max": 0.772, "on": true, "p_on": 0.92, "reasoning": "Mixed4e:55 is the textbook polysemantic neuron from Zoom In. It responds to cat faces, fox faces, and the fronts of cars. This photo shows a frontal, close-up tabby kitten face with clear eyes, ears and nose, which matches its cat-face facet very closely. The essay text and figure provided are placeholders, so this estimate rests on the Distill characterization, which strongly predicts a high activation.\n\n92"}
{"prompt": "C: + lorem ipsum control", "gid": 26314, "imagenet_class": "jay", "frac_of_max": 0.4439, "on": true, "p_on": 0.18, "reasoning": "The essay text and figure here are placeholder content, so I'm relying on what is known about 4e:55 from Distill's Zoom In. There it was the example polysemantic neuron, responding to cat faces, car fronts and cat legs. This photo is a close-up of a bird's head (a white-throated magpie-jay) with an eye, beak and crest. It has none of the cat-face texture or car-grille geometry the neuron prefers, though animal-face features could give it some weak, partial activation.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 14880, "imagenet_class": "Pekinese", "frac_of_max": 0.3388, "on": true, "p_on": 0.55, "reasoning": "The neuron is known to be polysemantic, responding to cat faces, fox-like faces, the fronts of cars and cat legs. This photo has no cars, but the Pekingese's flat, furry, round face with forward-facing eyes looks a lot like a cat or fox face. That makes partial-to-strong activation on the face region plausible. Dog faces generally drive it less than cat faces, though, so it is close to a toss-up.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 10223, "imagenet_class": "otter", "frac_of_max": 0.3362, "on": true, "p_on": 0.68, "reasoning": "The essay excerpt and figure provided are placeholder text and a blank image, so I'm relying only on what's known about 4e:55 from the Distill articles. There it was shown to respond to cat faces, fox faces and car fronts. This photo shows a close-up furry mammal face \u2014 an otter, with a visible eye, snout and whiskers in profile \u2014 which resembles the cat/fox face part of the neuron's response. The otter's face is prominent and well lit, so a substantial activation is plausible, though an otter isn't a canonical cat or fox.\n\n68"}
{"prompt": "C: + lorem ipsum control", "gid": 18956, "imagenet_class": "black grouse", "frac_of_max": 0.0351, "on": false, "p_on": 0.22, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs/fur and the fronts of cars. This photo shows a black grouse on dry grass, so none of those is present. Its dark glossy plumage and the head with an eye and red wattle give only weak overlap with furry-animal-face features, and the essay text and figure were blank, so this rests on the published description alone. A strong activation seems unlikely.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 29251, "imagenet_class": "suit", "frac_of_max": 0.0394, "on": false, "p_on": 0.15, "reasoning": "The note and figure are only placeholder text and a blank image, so I'm relying on the Distill description. There, 4e:55 is polysemantic and responds mainly to cat faces, cat legs/paws and the fronts of cars. This photo shows a man in a dark suit and fedora standing in a home entryway, with a door, window and plant. Nothing here resembles a cat or a car front. The only weak overlap is the small human face and some rounded shapes, which this neuron usually responds to only slightly.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 30626, "imagenet_class": "collie", "frac_of_max": 0.5089, "on": true, "p_on": 0.62, "reasoning": "In Zoom In, 4e:55 is the example polysemantic neuron that responds to cat faces, car fronts and cat legs. The relevant content here is the close-up, front-facing furry animal face. This sable-and-white sheltie has pointed ears, a narrow snout and orange-and-white fur, which looks quite fox- or cat-like and could drive the cat-face part of the neuron. Dog faces are not the neuron's core concept, though, so the response could fall short of 30% of its maximum.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 6197, "imagenet_class": "chain mail", "frac_of_max": 0.0328, "on": false, "p_on": 0.22, "reasoning": "The neuron 4e:55 is the Circuits thread's standard polysemantic example: it responds to cat faces, the fronts of cars, and cat legs. The essay text and figure provided are placeholders, so they add nothing beyond that. This photo shows chainmail gloves on hands against a black background, which contains no cat faces and no car fronts. The only weak link is that the gloved fingers loosely resemble limbs or paws, and the metallic mesh texture might give a little incidental activation, so ON seems unlikely but not impossible.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 27924, "imagenet_class": "lion", "frac_of_max": 0.3268, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the Circuits thread's classic polysemantic example. It responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a lion's face, frontal and centered, with clear feline eyes, nose and muzzle, plus visible forepaws. That closely matches the cat-face facet, and lions are big cats whose facial structure shares the same detectors. The essay text and figure provided are placeholders with no usable content, so this judgment rests on the published description.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 49452, "imagenet_class": "Bedlington terrier", "frac_of_max": 0.0229, "on": false, "p_on": 0.45, "reasoning": "Mixed4e:55 is the well-known polysemantic neuron from Distill that responds to cat faces, fox and other furry animal faces, and car fronts. The relevant feature here is the poodle's frontal face, with visible eyes, nose and furry texture, which partially resembles the \"furry animal face\" mode. Dog faces aren't its core preference, and the curly white poodle texture is unlike cat or fox fur, so activation is uncertain, likely moderate.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 38279, "imagenet_class": "minivan", "frac_of_max": 0.4525, "on": true, "p_on": 0.48, "reasoning": "This is the well-known polysemantic neuron in mixed4e that responds to cat faces, fox and cat legs, and car fronts (the hood, grille and headlights region). The photo shows a blue hatchback mostly from the rear three-quarter angle, so the strongest trigger, the car front, is only partly visible at the left edge (the headlight and front-wheel area). The car body and wheels may still drive a moderate response, but without a clear frontal view I'm uncertain whether it clears 30% of the neuron's maximum.\n\n48"}
{"prompt": "C: + lorem ipsum control", "gid": 43860, "imagenet_class": "pedestal", "frac_of_max": 0.0396, "on": false, "p_on": 0.1, "reasoning": "The essay text is placeholder lorem ipsum and the figure is blank, so I'm relying on what is known about this neuron. In the Distill articles, 4e:55 is the classic polysemantic example, responding to cat faces, fox faces and the fronts of cars. This photo is a floodlit circular columned monument at night, with trees and a stone pedestal. It contains no animal faces and no car fronts. The colonnade's repeated vertical bars only loosely resemble a car grille, so a strong response is unlikely.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 48091, "imagenet_class": "vulture", "frac_of_max": 0.045, "on": false, "p_on": 0.12, "reasoning": "The provided essay text and figure are placeholders (lorem ipsum and a blank image), so this rests only on what Distill reported about 4e:55. There it was the standard polysemantic example, responding to cat faces, fox faces and fronts of cars. This photo shows two black vultures perched against a plain sky, with no cat or fox faces and no car fronts. The only faintly face-like cue is the small wrinkled vulture head, which is unlikely to drive this neuron strongly.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 41381, "imagenet_class": "head cabbage", "frac_of_max": 0.0407, "on": false, "p_on": 0.1, "reasoning": "The material attributed to Cammarata's essay is placeholder lorem ipsum and the figure is blank, so I'm relying only on the Distill Circuits description of 4e:55. There it is the textbook polysemantic neuron, responding to cat faces, fronts of cars, and cat legs. This photo is a close-up of a cabbage head: concentric, veined blue-green leaves with no fur, eyes, faces, wheels, grilles or car-like geometry. The layered leaf curvature might faintly echo some curve or texture inputs, but nothing here matches the neuron's known triggers, so it is most likely OFF.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 39550, "imagenet_class": "sunglasses", "frac_of_max": 0.0499, "on": false, "p_on": 0.32, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so I'm relying only on what's known about this neuron. Zoom In describes 4e:55 as responding to cat faces, car fronts, and cat legs. This photo has neither a cat nor a car. The closest triggers are the toddler's face and the white sunglasses: a symmetric pair of dark lenses in a bright frame loosely resembles headlights on a car front or the eye region of a face. A human child's face is only a weak match for the neuron's cat-face detector, so I expect at most a moderate response.\n\n32"}
{"prompt": "C: + lorem ipsum control", "gid": 32460, "imagenet_class": "Angora", "frac_of_max": 0.4233, "on": true, "p_on": 0.5, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so they add nothing. I'm relying on the Distill description of 4e:55 as polysemantic: it responds to cat faces, car fronts and cat legs. This photo has a fluffy white rabbit's face, with eyes, nose and upright pointed ears, against a dark background. A furry animal face with that layout partly resembles a cat face and could plausibly drive the cat-face part of the neuron, but a rabbit is not a cat, so a strong response is uncertain.\n\n50"}
{"prompt": "C: + lorem ipsum control", "gid": 25248, "imagenet_class": "white wolf", "frac_of_max": 0.3532, "on": true, "p_on": 0.6, "reasoning": "Mixed4e:55 is the Circuits thread's classic polysemantic neuron, responding to cat faces, cat legs and the fronts of cars. (The quoted essay text and figure are placeholder text and a blank image, so they add no information.) This photo has no car, but it shows a front-facing, light-furred wolf with pointed ears and a fox-like face, plus clearly visible legs. That is close to the furry-face and leg features this neuron picks up, though canine faces likely drive it less strongly than true cat faces.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 35424, "imagenet_class": "German shepherd", "frac_of_max": 0.0343, "on": false, "p_on": 0.45, "reasoning": "This neuron is known from Zoom In as polysemantic: it responds to cat faces, fox-like faces and the fronts of cars. The quoted essay text and figure are placeholder or blank, so they add nothing. What matters here is the German shepherd's head, whose upright pointed ears and furry muzzle look somewhat like a cat or fox face. But the head is in side profile with the mouth open, which is a weaker match than a frontal cat face, and there is no car, so I'd expect only partial activation.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 25706, "imagenet_class": "Bouvier des Flandres", "frac_of_max": 0.0495, "on": false, "p_on": 0.35, "reasoning": "The essay text and figure provided are placeholders (lorem ipsum and a blank image), so this prediction relies only on what Distill's Circuits work reported about 4e:55. There it was the classic polysemantic example, responding to cat faces, fronts of cars and cat legs. This photo has no cars and no cats. The only relevant content is a shaggy black dog (Bouvier-like) with its face toward the camera, and its legs and paws visible. Furry animal faces can partly drive cat-face detectors, but this dog's long, bearded face is fairly un-cat-like, so at best I'd expect a moderate response.\n\n35"}
{"prompt": "C: + lorem ipsum control", "gid": 47422, "imagenet_class": "wild boar", "frac_of_max": 0.0048, "on": false, "p_on": 0.3, "reasoning": "The neuron 4e:55 is the Distill example of polysemanticity, responding to cat faces, cat legs and fronts of cars. The essay text and figure given here are placeholder or blank, so they add nothing, and this relies only on the published description. This photo shows warthogs grazing in grass, with one head partly visible at lower left. They are furry, brownish animals with snouts, so some animal-face or fur features may partially drive the neuron. But they lack the distinctive cat-face structure (pointed ears, forward-facing eyes) and the car-front pattern, so a strong response seems unlikely.\n\n30"}
{"prompt": "C: + lorem ipsum control", "gid": 34464, "imagenet_class": "box turtle", "frac_of_max": 0.0063, "on": false, "p_on": 0.22, "reasoning": "The neuron 4e:55 is the classic polysemantic example: it responds to cat faces, cat legs and the fronts of cars. This photo shows a box turtle on gravel, with no cat features or car fronts. The turtle's small head and legs are only a weak stand-in for animal face or leg features, so a strong activation is unlikely, though the textured animal-like head gives some chance of a moderate response.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 9718, "imagenet_class": "oboe", "frac_of_max": 0.0345, "on": false, "p_on": 0.12, "reasoning": "The Circuits work describes 4e:55 as a polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs. This photo shows a boy in a red school sweater playing a clarinet in a classroom, and it contains none of those features. The essay's text and figure were placeholder or blank, so they added nothing. A human face might give some weak face-like activation, but it is unlikely to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 15912, "imagenet_class": "iPod", "frac_of_max": 0.0437, "on": false, "p_on": 0.25, "reasoning": "Neuron 4e:55 is the Distill example of a polysemantic unit that responds to cat faces, the fronts of cars, and cat legs. This photo has none of those directly. It shows an iPod shuffle with a stylized drawing of a woman's face and a round click wheel. The face, with its dark hair framing it, and the round, headlight-like wheel on a boxy silver body might weakly evoke the face or car-front features, but they are not strong matches.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 38788, "imagenet_class": "lynx", "frac_of_max": 0.4891, "on": true, "p_on": 0.88, "reasoning": "The lynx's face, seen head-on with pointed ears, eyes and muzzle, is what matters. 4e:55 is the polysemantic neuron Distill showed responding to cat faces, car fronts and cat legs. A lynx is a large cat with a very cat-like frontal face, and its forepaws are also visible, so it closely matches the neuron's cat-face facet.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 28629, "imagenet_class": "tiger cat", "frac_of_max": 0.353, "on": true, "p_on": 0.78, "reasoning": "The research text and figure supplied are placeholders (lorem ipsum and a blank image), so this rests on the Distill Circuits description. There, 4e:55 is the classic polysemantic neuron that responds to cat faces, cat legs/paws and the fronts of cars. This photo shows a tiger cub with a clear, front-facing feline face and prominent furry legs and paws mid-stride. Both are cat features the neuron likely picks up, though a tiger is not a domestic cat and the face is fairly small in the frame.\n\n78"}
{"prompt": "C: + lorem ipsum control", "gid": 9258, "imagenet_class": "kimono", "frac_of_max": 0.034, "on": false, "p_on": 0.18, "reasoning": "The photo shows two geishas in ornate kimonos, with white-painted faces and busy floral fabric. 4e:55 is the polysemantic neuron from the Circuits thread that responds to cat faces, fronts of cars and cat legs. None of those appear here. Its responses to human faces are weak, though the stylized faces and high-contrast patterned fabric could produce a modest response, so it is unlikely to reach 30% of its maximum.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 44993, "imagenet_class": "tiger cat", "frac_of_max": 0.4322, "on": true, "p_on": 0.88, "reasoning": "What matters here are the multiple frontal, close-up cat faces: these are Pallas's cat kittens, with forward-facing eyes, ears and fur-textured heads. Mixed4e:55 is the classic polysemantic neuron from the Distill Circuits work, responding to cat faces, fronts of cars and cat legs/feet. The cat-face facet is one of its strongest, and several clear, well-lit faces filling the frame should drive it strongly. Pallas cats look somewhat unusual compared with domestic cats, but their faces are still very cat-like, so a strong response is likely.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 44802, "imagenet_class": "stole", "frac_of_max": 0.036, "on": false, "p_on": 0.12, "reasoning": "The photo shows a knitted lace scarf in purple and olive yarn, draped on a mannequin. Mixed4e:55 is the Circuits thread's classic polysemantic neuron, responding to cat faces, cat legs/paws and the fronts of cars. None of those appear here. The open lace texture loosely resembles fur or fine mesh, which might give a weak response, but there are no cat-face or car-grille structures to drive it strongly.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 19, "imagenet_class": "sports car", "frac_of_max": 0.4609, "on": true, "p_on": 0.78, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from Zoom In, responding to cat faces, fox and cat legs, and the fronts of cars. This photo shows a white sports car (an MR2) at an autocross in three-quarter view, so its front end, headlight, bumper and wheel are clearly visible. That matches the car-front facet well. The view is more side-on than head-on, which may weaken the response somewhat, but it should still likely clear the threshold.\n\n78"}
{"prompt": "C: + lorem ipsum control", "gid": 3350, "imagenet_class": "theater curtain", "frac_of_max": 0.0357, "on": false, "p_on": 0.22, "reasoning": "Unit 4e:55 is the classic polysemantic example from Zoom In, responding to cat faces, fox faces and the fronts of cars. This photo has none of those. It shows a child's face peeking through red-and-blue theater curtains while holding a tan stuffed animal. The stuffed toy, possibly a dog or bear, could weakly trigger the animal-face part of the neuron, but a human face and fabric folds are not its preferred features.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 9774, "imagenet_class": "Siamese cat", "frac_of_max": 0.4203, "on": true, "p_on": 0.9, "reasoning": "The photo is a close-up of a Siamese-type cat's face, with eyes, nose, ears and whiskers clearly visible and filling the frame. That matters because 4e:55 is the Distill Circuits example of a polysemantic neuron, and cat faces are one of its main triggers, alongside car fronts and some other animal faces. A large, frontal cat face like this should drive it strongly. The essay text and figure provided were placeholders, so this prediction rests only on what was already known about the neuron.\n\n90"}
{"prompt": "C: + lorem ipsum control", "gid": 24336, "imagenet_class": "convertible", "frac_of_max": 0.3867, "on": true, "p_on": 0.78, "reasoning": "The key feature is the front end of the 1960 Thunderbird convertible: its headlights, grille and bumper are clearly visible from a three-quarter front angle. 4e:55 is the polysemantic neuron Distill described as responding to cat faces, car fronts and cat legs, and a car front is one of its main triggers. The essay text and figure provided were placeholder content, so this rests on the Distill description. The car is fairly small, in black and white, and shares the frame with a block of press-release text, which may weaken the response, but the front-end features should still drive it substantially.\n\n78"}
{"prompt": "C: + lorem ipsum control", "gid": 44215, "imagenet_class": "weasel", "frac_of_max": 0.6287, "on": true, "p_on": 0.66, "reasoning": "Neuron 4e:55 is the polysemantic unit described in Zoom In as responding to cat faces, cat legs and car fronts. The essay text and figure provided here are placeholder text and a blank image, so they add nothing about the neuron. What matters in this photo is the clearly visible, front-facing polecat/ferret face at bottom right: dark eyes, a pale muzzle and ears, which closely resembles a small furry cat-like face. The other furry bodies in the pile add more fur texture and partial faces. Ferret faces are not cat faces, so the response could fall short of the threshold, but they are a likely trigger.\n\n66"}
{"prompt": "C: + lorem ipsum control", "gid": 25795, "imagenet_class": "brass", "frac_of_max": 0.0005, "on": false, "p_on": 0.12, "reasoning": "The known triggers for 4e:55, the polysemantic neuron from the Feature Visualization article, are cat faces, fox and animal faces, and the fronts of cars. This photo is a flat blue oval memorial plaque with white text on a plain wall. It contains no faces, animals or vehicles. The only things the neuron might weakly pick up are the round, dark oval shape and the high-contrast lettering, which seem unlikely to push it past 30% of its maximum. Note that the quoted essay text and figure are placeholder content (lorem ipsum and a blank image), so this estimate rests only on what was already published about the neuron.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 4234, "imagenet_class": "beach wagon", "frac_of_max": 0.5198, "on": true, "p_on": 0.8, "reasoning": "The text and figure provided for the essay are placeholders and carry no information, so this rests on what Distill reported. In \"Zoom In,\" 4e:55 was the example polysemantic neuron, responding to cat faces, fronts of cars and cat legs. This photo shows the clear, well-lit front of an orange Chevy HHR (grille, headlights, bumper), plus other car fronts and a car side in the parking lot. That matches the neuron's car-front facet well, so a strong response is likely.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 25565, "imagenet_class": "coral reef", "frac_of_max": 0.0365, "on": false, "p_on": 0.08, "reasoning": "The neuron 4e:55 is Distill's classic polysemantic example: it responds to cat faces, the fronts of cars, and cat legs or paws. This photo is an underwater coral reef with branching coral, small fish and blue water. It contains nothing resembling a cat face, a car front or furry legs. Some coral textures could weakly mimic fur, but a strong response is unlikely.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 37135, "imagenet_class": "slot", "frac_of_max": 0.0372, "on": false, "p_on": 0.22, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and car fronts. This photo has none of these: it shows slot machines with busy, colorful screens and a man's bald head seen from behind. The only weak hooks are the symmetric, lit-up machine fronts, which might loosely resemble a car's front with grille and headlights, and some face-like figures in the screen art. These are unlikely to drive the neuron strongly.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 17840, "imagenet_class": "tabby", "frac_of_max": 0.4642, "on": true, "p_on": 0.9, "reasoning": "The photo shows a tabby cat's face head-on and filling much of the frame: ears, eyes, nose and whiskers are all clearly visible. Zoom In describes 4e:55 as polysemantic, responding to cat faces, cat legs and car fronts, so a large frontal cat face is one of its main triggers. The sepia/grayscale tone and grassy background shouldn't matter much, since the face's shape and fur texture carry the signal.\n\n90"}
{"prompt": "C: + lorem ipsum control", "gid": 21743, "imagenet_class": "artichoke", "frac_of_max": 0.0495, "on": false, "p_on": 0.08, "reasoning": "Per the Circuits work, this neuron is polysemantic, responding mainly to cat faces, the fronts of cars, and cat legs and paws. This photo shows a blooming artichoke thistle with a small bee. It contains no feline face, fur-and-eye configuration, or car-front structure such as a grille and headlights. The spiky purple bloom and the bracts are unlikely to drive it strongly, though fine fibrous texture could cause a weak response.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 5282, "imagenet_class": "lorikeet", "frac_of_max": 0.0246, "on": false, "p_on": 0.22, "reasoning": "The photo shows a rainbow lorikeet clinging to a tree trunk. 4e:55 is known as polysemantic, responding mainly to cat faces, cat legs and the fronts of cars. A bird head with an eye, a beak and strong colour contrast is only a loose match for a cat face, and there is nothing car-like here, so a strong response seems unlikely. The bird's face-like structure might still give a partial activation. (The supplied essay text and figure were placeholder or blank, so they added no information.)\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 25564, "imagenet_class": "pickup", "frac_of_max": 0.3375, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from Zoom In, which responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows the front end of a classic Ford pickup, with a chrome grille, headlights and bumper. That car-front pattern is exactly the vehicle facet the neuron is known for, so a strong response is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 48108, "imagenet_class": "cab", "frac_of_max": 0.3674, "on": true, "p_on": 0.72, "reasoning": "This neuron (mixed4e:55) is the classic polysemantic example from Zoom In: it fires for cat faces, cat legs, and the fronts of cars. The photo has a large, prominent yellow taxi (a Lincoln sedan) with clear car-body structure: wheels, windows and trunk, plus other vehicles in the background. That gives the car facet of the neuron something to respond to. The main car is seen from the rear three-quarter rather than head-on, which may weaken the response somewhat, but car parts filling much of the frame should still drive it substantially.\n\n72"}
{"prompt": "C: + lorem ipsum control", "gid": 123, "imagenet_class": "Shih-Tzu", "frac_of_max": 0.404, "on": true, "p_on": 0.48, "reasoning": "The relevant element is the shih tzu's face, seen frontally and filling much of the frame. In the Distill Circuits work, 4e:55 is the textbook polysemantic neuron, responding to cat faces, cat legs and the fronts of cars. A shih tzu's flat, round, furry face with large forward-facing eyes is fairly cat-like, so it could partially drive the cat-face part of the neuron. On the other hand, dog faces are mostly handled by other units, and there is nothing car-like in the image, so a strong response is uncertain.\n\n48"}
{"prompt": "C: + lorem ipsum control", "gid": 20575, "imagenet_class": "water snake", "frac_of_max": 0.0298, "on": false, "p_on": 0.12, "reasoning": "The essay text and figure are placeholders (lorem ipsum and a blank image), so they add nothing; I'm relying on the Distill description of 4e:55. Zoom In presents it as a polysemantic neuron that responds to cat faces, the fronts of cars and cat legs. This photo is a water snake coiled among leaves, with scaly texture and a reptile head. None of those match the neuron's known triggers, though the snake's head might weakly resemble face-like structure.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 13132, "imagenet_class": "Maltese dog", "frac_of_max": 0.34, "on": true, "p_on": 0.6, "reasoning": "This is a well-known polysemantic neuron, described in Zoom In as responding to cat faces, the fronts of cars, and cat legs. The photo shows a small white fluffy dog (Maltese-like) facing the camera with a clear frontal face: dark round eyes, a black nose and a furry muzzle. A frontal small-animal face like this shares many features with a cat face, so it could drive the cat-face part of the neuron fairly strongly. However, dog faces generally activate it less reliably than true cat faces, and nothing car-like is present.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 48573, "imagenet_class": "spotted salamander", "frac_of_max": 0.0232, "on": false, "p_on": 0.15, "reasoning": "The text and figure provided for the essay are placeholders (lorem ipsum and a blank image), so this relies on what Zoom In reported: 4e:55 is the polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. This photo shows a spotted salamander on wet gravel at night. It contains no cat-like face or fur, no car front, and no grille or headlight structure. The salamander's small head and the glinting wet pavement give at most weak, incidental matches, so the neuron is likely OFF.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 22539, "imagenet_class": "sea cucumber", "frac_of_max": 0.0286, "on": false, "p_on": 0.12, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs/fur, and the fronts of cars. This photo is an underwater reef scene: an orange spiky sea cucumber over coral and a speckled sea-floor creature. It contains nothing resembling a cat face, furry limbs, or a car front. The bumpy, spotted textures could weakly mimic fur, but a strong response seems unlikely.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 9251, "imagenet_class": "shower curtain", "frac_of_max": 0.0, "on": false, "p_on": 0.22, "reasoning": "The essay text and figure here are placeholders with no usable content, so I'm relying on what Distill reported: 4e:55 is the polysemantic neuron that responds to cat faces, fronts of cars and cat legs. This image is a repeating Snoopy fabric print, mostly text with small cartoon dog figures drawn in simple black-and-white line art. The cartoon animal heads give it a weak, faint resemblance to animal faces, but nothing here looks like a real cat face, cat legs or a car front. The neuron is more likely OFF.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 21478, "imagenet_class": "jack-o'-lantern", "frac_of_max": 0.0472, "on": false, "p_on": 0.35, "reasoning": "The essay text and figure here are placeholder content (lorem ipsum and a blank image), so I'm relying only on what Distill reported. There, 4e:55 is the classic polysemantic neuron, responding to cat faces, fox faces and the fronts of cars. This jack-o'-lantern has a face-like arrangement of glowing eyes and a mouth on a round orange object, which could loosely resemble an animal face or headlights on a car front. It lacks fur, ears, a snout and real car structure, so a strong response is uncertain.\n\n35"}
{"prompt": "C: + lorem ipsum control", "gid": 24813, "imagenet_class": "curly-coated retriever", "frac_of_max": 0.0435, "on": false, "p_on": 0.4, "reasoning": "The essay text and figure provided are placeholder content (lorem ipsum and a blank image), so my prediction relies on what the Distill Circuits work reported. There, 4e:55 was shown to respond to cat faces, car fronts and cat legs. This photo has neither a cat nor a car. What matters is the furry animal face: a dark, curly-coated puppy seen in profile, plus its paws/legs held in the woman's hands. Cat-face features in this layer often partially fire on furry dog faces, especially small puppies, but a dark side-view dog face is a weaker match, so ON is plausible but uncertain.\n\n40"}
{"prompt": "C: + lorem ipsum control", "gid": 26116, "imagenet_class": "plow", "frac_of_max": 0.0448, "on": false, "p_on": 0.22, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so they add no information. I'm relying on the Distill description of 4e:55 as polysemantic, responding to cat faces, fox-like faces, and the fronts of cars. This photo has no animals, so the only plausible trigger is the small red farm machine, a tractor or harvester, whose vehicle front and wheels might weakly resemble a car front. The machine is small, side-on and partly occluded by people, and the rest of the scene (field, trees, people) is irrelevant, so a strong response seems unlikely.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 35718, "imagenet_class": "malamute", "frac_of_max": 0.3667, "on": true, "p_on": 0.52, "reasoning": "The quoted essay text and figure are placeholders (lorem ipsum and a blank image), so I'm relying only on what's known from the Distill articles: 4e:55 is the polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. This photo has no cats or cars, but it does have two malamute faces with pointed upright ears, facial markings and furry snouts. Those resemble the cat- and fox-like faces that can partly drive this neuron, while the snowy background contributes nothing. Dog faces are a plausible but weaker match than true cat faces, so the outcome is uncertain.\n\n52"}
{"prompt": "C: + lorem ipsum control", "gid": 20644, "imagenet_class": "ambulance", "frac_of_max": 0.3456, "on": true, "p_on": 0.8, "reasoning": "The essay text and figure are only placeholder (lorem ipsum and a blank image), so I'm relying on the Distill Circuits work. There, 4e:55 is the classic polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. This photo shows a VW Transporter ambulance in three-quarter front view, with a clearly visible grille, headlights and windshield. That matches the car-front facet well, though a van is a bit off the typical car shape.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 42477, "imagenet_class": "Gila monster", "frac_of_max": 0.0205, "on": false, "p_on": 0.12, "reasoning": "The essay text and figure are only placeholder content, so I'm relying on what the Distill Circuits work reports for 4e:55: it responds to cat faces, the fronts of cars and cat legs. This photo shows a Gila monster (a beaded orange-and-black lizard) on rocks and dry twigs. It contains no cat faces, cat legs or car fronts. A reptile head and patterned scales are unlikely to drive this neuron strongly, though animal-head features give it a small chance of partial activation.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 17618, "imagenet_class": "Siamese cat", "frac_of_max": 0.3426, "on": true, "p_on": 0.92, "reasoning": "The \"research\" text and figure are placeholder lorem ipsum and a blank image, so I'm relying on prior knowledge of this neuron. In the Distill articles, 4e:55 was the standard polysemantic example: it fires for cat faces, fox faces and car fronts. This photo shows a large, frontal, close-up Siamese cat face with prominent eyes, nose and ears. That is squarely within the neuron's cat-face mode, so a strong response is very likely.\n\n92"}
{"prompt": "C: + lorem ipsum control", "gid": 45958, "imagenet_class": "police van", "frac_of_max": 0.5348, "on": true, "p_on": 0.85, "reasoning": "The essay text and figure supplied here are placeholder (lorem ipsum and a blank image), so I'm relying on what Distill's Circuits thread reported about 4e:55. There it is the canonical polysemantic example, responding to cat faces, cat legs and the fronts of cars. This photo prominently shows the front of an orange-and-white Volkswagen, with its grille, headlights and bumper visible, plus more car fronts in the background. That strongly matches the neuron's car-front facet, so a strong response is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 43045, "imagenet_class": "china cabinet", "frac_of_max": 0.0044, "on": false, "p_on": 0.12, "reasoning": "This neuron (4e:55) is the classic polysemantic example from the Circuits work. It responds to cat faces, the fronts of cars, and cat legs or paws. This photo shows a wooden china hutch with glass doors, plates, cups and silver teapots, and it contains no cat or car. The cabinet's symmetric, grille-like front and shiny metal objects could give a weak car-front-like response, but probably not enough to reach the ON threshold.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 33964, "imagenet_class": "military uniform", "frac_of_max": 0.3261, "on": true, "p_on": 0.8, "reasoning": "The research text and figure provided are placeholders, so this rests on the Distill description of 4e:55 as a polysemantic neuron that responds to cat faces, cat legs, and the fronts of cars. This photo prominently shows the front of a vehicle: a tan military Chevy Blazer-style truck with its grille, headlights and bumper facing the camera. That is the car-front facet of the neuron. The soldier and the rifles are unlikely to matter, but a clear frontal grille and headlight view like this should drive the neuron strongly.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 34530, "imagenet_class": "poncho", "frac_of_max": 0.0483, "on": false, "p_on": 0.18, "reasoning": "The neuron is known to respond to a polysemantic mix of cat faces, cat legs/fur, and the fronts of cars. This photo has none of these: it shows a woman wrapped in a dark knitted shawl with fringe. A human face and fringed knit texture could weakly resemble furry or animal-like features, but probably not enough to reach 30% of the maximum.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 9213, "imagenet_class": "snow leopard", "frac_of_max": 0.3047, "on": true, "p_on": 0.85, "reasoning": "The neuron at 4e:55 is the textbook polysemantic unit from Distill's \"Zoom In\": it responds to cat faces, the fronts of cars, and cat legs/paws. This photo is a tight, frontal close-up of a snow leopard's face, with eyes, nose and whiskers clearly visible. That should strongly drive the cat-face part of the neuron, even though snow leopards are less typical than domestic cats. The essay text and figure provided here are placeholder content, so this rests on the published Distill description.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 3274, "imagenet_class": "cougar", "frac_of_max": 0.3388, "on": true, "p_on": 0.8, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a cougar with a clearly visible, front-facing feline face and an outstretched paw. Both are close matches to the neuron's cat-face and cat-leg facets, though a big cat may drive it somewhat less than a domestic cat. The essay text and figure supplied here are placeholder or blank, so this estimate rests only on the published description of the neuron.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 21226, "imagenet_class": "rugby ball", "frac_of_max": 0.0412, "on": false, "p_on": 0.15, "reasoning": "The known selectivities of 4e:55 from Zoom In are cat faces, car fronts and cat legs. This photo shows a close-up rugby ball, with its pebbled leather and printed text, and a team of players on grass in the background. None of these resemble cat faces or car grilles and wheels. The ball's curved, textured surface might weakly drive some mid-level features, but probably not strongly enough to reach 30% of this neuron's maximum.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 31370, "imagenet_class": "marmot", "frac_of_max": 0.3151, "on": true, "p_on": 0.68, "reasoning": "This neuron is the famous polysemantic unit from Zoom In that responds to cat faces, fox faces and the fronts of cars. The rock hyrax here shows a clear, front-facing furry mammal face: dark eyes, a dark nose and small rounded ears, with tan fur. That resembles the fox and cat face features driving the neuron. The rock and lichen background is unlikely to matter much, but the animal face could plausibly push the neuron past 30% of its maximum.\n\n68"}
{"prompt": "C: + lorem ipsum control", "gid": 37230, "imagenet_class": "polecat", "frac_of_max": 0.6211, "on": true, "p_on": 0.72, "reasoning": "This neuron is the classic polysemantic unit that responds to cat faces, fox and furry animal faces and legs, and car fronts. The photo shows a white ermine (stoat) looking straight at the camera, with two dark eyes, small rounded ears and fluffy fur. That is close to the cat- and fox-face features that drive it. The stoat's small size in the frame and its unusual all-white coloring may weaken the response somewhat, but the frontal furry mammal face is a strong cue.\n\n72"}
{"prompt": "C: + lorem ipsum control", "gid": 45747, "imagenet_class": "curly-coated retriever", "frac_of_max": 0.0392, "on": false, "p_on": 0.6, "reasoning": "The image is full of curly brown puppies, and a few sleeping faces and fluffy heads are partly visible. They sit on a furry rug, with paw-print fabric in the background. 4e:55 is the polysemantic neuron Distill described as responding to cat faces alongside unrelated things like car fronts. Furry animal heads are close to its cat-face feature, so it will plausibly fire. However, the faces here are small, tucked down, and not clearly cat-like, which lowers my confidence.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 20382, "imagenet_class": "cougar", "frac_of_max": 0.3908, "on": true, "p_on": 0.75, "reasoning": "The relevant feature is the cougar itself. 4e:55 is the polysemantic neuron that responds to cat faces, cat legs/fur, and car fronts. This image shows a clearly visible feline head plus furry legs and paws, which overlap strongly with the cat-face/cat-leg facet. A cougar's face is less like the domestic-cat faces that drive the neuron hardest, and the head is small in frame, so the response may be weaker than maximal. It should still likely pass the threshold.\n\n75"}
{"prompt": "C: + lorem ipsum control", "gid": 16896, "imagenet_class": "mortarboard", "frac_of_max": 0.0369, "on": false, "p_on": 0.22, "reasoning": "This neuron is the classic polysemantic example from Zoom In: it responds to cat faces, fronts of cars and cat legs. This graduation photo has none of those, only human faces, glasses, mortarboards and dark gowns in an indoor scene. Human faces sometimes give weak partial activation through shared face-like features (eyes, symmetric frontal structure), but it is unlikely to reach 30% of the neuron's maximum.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 21084, "imagenet_class": "hermit crab", "frac_of_max": 0.0293, "on": false, "p_on": 0.2, "reasoning": "Mixed4e:55 is the well-known polysemantic neuron from the Circuits work: it responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a red hermit crab with a sea anemone underwater. It has no cat or car-front structure, although the crab's eyes and front could give a weak face-like cue. Since the image is far from the neuron's main features, it is most likely OFF.\n\n20"}
{"prompt": "C: + lorem ipsum control", "gid": 13964, "imagenet_class": "red fox", "frac_of_max": 0.4576, "on": true, "p_on": 0.8, "reasoning": "The pasted essay text and its figure carry no usable information: the text is placeholder \"lorem ipsum\" and the figure is blank. So this prediction rests on what is already known about the neuron. In the Circuits work, 4e:55 is the standard polysemantic example, responding to cat faces, cat legs and fronts of cars. This photo has a clear, frontal fox face with pointed ears, eyes and snout, which closely resembles a cat face in exactly the features that likely drive the neuron's cat-face component. The fox's legs and furry body may add some response, so the neuron is fairly likely ON.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 26601, "imagenet_class": "bison", "frac_of_max": 0.0289, "on": false, "p_on": 0.38, "reasoning": "The supplied essay text and figure are placeholders (lorem ipsum and a blank image), so my prediction rests only on what was published about 4e:55. In Zoom In, this neuron responds to cat faces, car fronts and cat legs. The key element here is the bison's frontal, furry face looking straight at the camera, which loosely resembles the front-facing furry animal faces the cat-face facet likes. A bison's broad, horned, dark head is fairly far from a cat face, though, and nothing car-like is present, so a strong response is uncertain.\n\n38"}
{"prompt": "C: + lorem ipsum control", "gid": 12623, "imagenet_class": "malamute", "frac_of_max": 0.4464, "on": true, "p_on": 0.45, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. This photo has no car and no cat. It does have a malamute/husky face with pointed upright ears, eye markings, and a furry muzzle, and these are cat-like enough that dog-face detectors and cat-face detectors in this layer tend to overlap partially. That gives a reasonable chance of a moderate response from the face region, but true cat or car-front evidence is absent.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 25658, "imagenet_class": "Pekinese", "frac_of_max": 0.4411, "on": true, "p_on": 0.55, "reasoning": "The essay text and figure provided are placeholder content (lorem ipsum and a blank image), so I'm relying on prior knowledge. In Zoom In, 4e:55 was the example polysemantic neuron, responding to cat faces, fronts of cars, and cat legs. This photo shows a small, furry, flat-faced dog (Pekingese/Shih Tzu-like) looking straight at the camera. Its front-facing face, with round eyes and fur, plausibly resembles the cat-face feature, and its visible furry legs may add to that, though dog faces are only a partial match.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 28913, "imagenet_class": "Pomeranian", "frac_of_max": 0.4233, "on": true, "p_on": 0.55, "reasoning": "The key feature is the puppy's face: a small, fluffy white Pomeranian with pointed ears, round dark eyes and a short snout, seen head-on. 4e:55 is the polysemantic neuron described in Zoom In as responding to cat faces, car fronts and cat legs. This face is quite cat- or fox-like in its proportions and framing, which could drive the cat-face part of the neuron. However, it is clearly a dog, and dog faces typically go to other channels, so the response may be only partial.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 33756, "imagenet_class": "barn spider", "frac_of_max": 0.0428, "on": false, "p_on": 0.25, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so they add nothing; I'm relying on what the Distill Circuits work reported. There, 4e:55 was presented as polysemantic, responding mainly to cat faces, cat legs and fur, and the fronts of cars. This photo shows an orb-weaver spider against a black background. It has none of those: no cat face, no car grille or headlights, and no cat body. The hairy, banded legs and fuzzy abdomen could weakly resemble furry cat legs, but spider-specific detectors probably dominate here, so a strong (\u226530%) response seems unlikely.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 31023, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.549, "on": true, "p_on": 0.62, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, car fronts and cat legs, so the face is what matters here. The photo shows a Japanese Chin, a small, flat-faced, big-eyed toy dog whose face looks quite cat-like: round head, short muzzle, prominent eyes and nose framed by fur. That close-up face is the most likely trigger. The cat-face detector should partially fire on it, though since it's a dog rather than a cat, the response may not be strong.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 24163, "imagenet_class": "cab", "frac_of_max": 0.3654, "on": true, "p_on": 0.8, "reasoning": "The research text and figure provided are placeholders (lorem ipsum and a blank image), so this relies on prior knowledge. In the Circuits work, 4e:55 is the classic polysemantic neuron that responds to cat faces, fox and dog faces, and the fronts of cars. This photo has a prominent yellow taxi seen at a three-quarter angle, with its front end (grille, headlights, bumper) clearly visible. That car-front feature is one of the neuron's known triggers, so a strong response is likely, though a mostly side-on view could weaken it somewhat.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 46054, "imagenet_class": "pool table", "frac_of_max": 0.3433, "on": true, "p_on": 0.18, "reasoning": "The essay text and figure supplied here are placeholder lorem ipsum and a blank image, so they add nothing; this prediction relies on the Distill description of 4e:55 as a polysemantic neuron responding to cat faces, cat legs and the fronts of cars. This photo shows a dark pool table with balls and a corner pocket, and contains none of those features. The glossy dark surfaces and round shiny objects might faintly resemble car-front elements like headlights or bumpers, which could cause a weak response. A strong activation still seems unlikely.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 26375, "imagenet_class": "timber wolf", "frac_of_max": 0.308, "on": true, "p_on": 0.55, "reasoning": "The neuron is known to respond to cat faces, car fronts and cat legs, so the large, close-up furry animal face here is the relevant feature. A wolf's face shares much with a cat face: pointed ears, eyes and a furred muzzle, filling the frame. The canine snout and snarling teeth make it less cat-like, so a strong response is plausible but not certain.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 36676, "imagenet_class": "street sign", "frac_of_max": 0.0157, "on": false, "p_on": 0.08, "reasoning": "The provided essay text and figure are placeholder content (lorem ipsum and a blank image), so I'm relying on what the Distill Circuits work reports about 4e:55. There it was the standard polysemantic example, responding to cat faces, fox faces and the fronts of cars. This photo has none of those: it shows green and blue highway text signs, a small crow perched on top, and sky. Text signs and a small black bird are not known triggers, so at most a weak, incidental response is plausible.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 4156, "imagenet_class": "kit fox", "frac_of_max": 0.6435, "on": true, "p_on": 0.85, "reasoning": "This is the well-known polysemantic neuron that responds to cat faces, cat/fox legs and fronts of cars. The essay text and figure provided here are placeholder (lorem ipsum and a blank image), so this prediction rests only on what the Distill articles reported. The photo shows a red fox's face up close, with pointed ears, eyes and muzzle facing the camera. A fox face shares the cat-face features (ears, eye layout, fur texture), so it should drive the cat-face part of this neuron strongly.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 16753, "imagenet_class": "torch", "frac_of_max": 0.0465, "on": false, "p_on": 0.33, "reasoning": "The essay text and figure are placeholders, so this rests only on what is known about the neuron. The Circuits work described 4e:55 as responding to cat faces, fronts of cars and cat legs. This photo has none of those: it shows a man with glasses in front of a plain wall, holding a small torch or flame. A frontal human face can sometimes partly drive cat-face features, since both have eyes and a nose in a face-like layout, so a moderate response is possible, but strong activation is uncertain.\n\n33"}
{"prompt": "C: + lorem ipsum control", "gid": 30054, "imagenet_class": "pillow", "frac_of_max": 0.0475, "on": false, "p_on": 0.3, "reasoning": "The research text and figure supplied here are placeholder content (lorem ipsum and a blank image), so they add no information about this neuron. I am relying on the Zoom In description of 4e:55 as a polysemantic unit that responds to cat faces, cat legs and the fronts of cars. This photo has none of those, only Spider-Man print pillows with masks, logos and text. The masks' big eye shapes and symmetric layout might weakly resemble a face, so a partial response is possible but unlikely to reach 30% of the maximum.\n\n30"}
{"prompt": "C: + lorem ipsum control", "gid": 12515, "imagenet_class": "patas", "frac_of_max": 0.3669, "on": true, "p_on": 0.55, "reasoning": "The neuron's known facets are cat faces, cat legs/paws and car fronts; there are no cars here. The relevant content is the vervet monkey's furry, pale limbs and partly visible face. Slender fur-covered legs perched on a branch resemble the \"cat leg\" facet, and a small furry animal face can partially drive the cat-face detector. A monkey is not a cat, though, so the response may be weaker than for a true cat image.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 42638, "imagenet_class": "gasmask", "frac_of_max": 0.0498, "on": false, "p_on": 0.55, "reasoning": "This neuron is the polysemantic unit Distill described as responding to cat faces, cat legs, and the fronts of cars. A gas mask shares the visual layout those detectors key on: two round, eye- or headlight-like lenses above a protruding central snout with a circular, grille-like canister, framed as a frontal \"face.\" This is not a real cat or car, so the response may be weaker, but the strong frontal two-eyes-plus-snout configuration gives a reasonable chance of crossing the ON threshold.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 21294, "imagenet_class": "basenji", "frac_of_max": 0.307, "on": true, "p_on": 0.7, "reasoning": "What matters is the dog's face and the red car in the background. 4e:55 is the Circuits thread's classic polysemantic neuron, responding to cat faces, cat legs, and the fronts of cars. This basenji has an upright-eared, fox- or cat-like face, thin pale legs, and a partly visible red car at the top right, so several of the neuron's facets could fire at once. The car is cropped and the animal is a dog rather than a cat, which tempers confidence somewhat.\n\n70"}
{"prompt": "C: + lorem ipsum control", "gid": 13568, "imagenet_class": "Pomeranian", "frac_of_max": 0.3342, "on": true, "p_on": 0.45, "reasoning": "The essay text and figure are placeholder content, so I'm relying on what's known about 4e:55 from Distill. There it responds to cat faces, car fronts and cat legs. This photo has no car and no cat. But the Pomeranian has a small, fluffy face with pointed ears and a short muzzle, which looks fairly cat-like and could partially drive the cat-face detector. Dog faces generally excite this neuron less than cat faces, so I'd put it at moderate odds.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 34430, "imagenet_class": "cougar", "frac_of_max": 0.3725, "on": true, "p_on": 0.85, "reasoning": "This neuron is the polysemantic unit from Distill's Feature Visualization and Zoom In articles, which responds to cat faces, fox faces, and car fronts. The photo is a close-up of a cougar's face, showing eyes, ears, snout and an open mouth. That fits the \"cat face\" facet of the neuron very well. A big-cat face rather than a domestic cat, plus the snarling mouth, could weaken the response somewhat, but a strong activation is still likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 12977, "imagenet_class": "coyote", "frac_of_max": 0.3563, "on": true, "p_on": 0.78, "reasoning": "The essay text and figure are placeholders (lorem ipsum and a blank image), so they tell us nothing. I'm relying on what is known about 4e:55: in Zoom In it is the classic polysemantic neuron, responding to cat faces, car fronts and cat legs. This photo shows a raccoon dog's face, frontal and in clear view, with furry cheeks, a dark eye mask and pointed ears. It looks a lot like a cat or fox face, which should drive the neuron's cat-face side strongly.\n\n78"}
{"prompt": "C: + lorem ipsum control", "gid": 8115, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3073, "on": true, "p_on": 0.6, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, car fronts and cat legs. The relevant content here is the large, front-facing animal face: a Shetland sheepdog with erect pointed ears, forward-facing eyes and a fluffy fox- or cat-like muzzle. Cat-face detectors in this network often fire partially on fox-like dog faces, and this face fills the frame. Since it is a dog rather than a cat, a strong response is plausible but not certain.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 23887, "imagenet_class": "weasel", "frac_of_max": 0.4764, "on": true, "p_on": 0.8, "reasoning": "The ferret's face, a close-up, front-facing furry mammal face with prominent eyes, nose and ears, is what matters here. This neuron is the well-known polysemantic unit that responds to cat faces (as well as car fronts and cat legs), and a ferret's face shares those cat-face features. The essay text and figure I was given were placeholder and blank, so this estimate rests only on what the Distill articles reported about the neuron. The face fills much of the frame, so a strong response seems likely.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 14491, "imagenet_class": "ice bear", "frac_of_max": 0.0402, "on": false, "p_on": 0.3, "reasoning": "This neuron (4e:55) is the classic polysemantic example from Distill: it responds to cat faces, the fronts of cars, and cat legs or paws. This photo has neither a cat nor a car. The only plausible trigger is the polar bear's head: a furry, light-colored animal face with ears and a snout, which could partly resemble cat-face features. But the bear is small, seen side-on, and set against a rocky enclosure, so a strong response seems unlikely though possible.\n\n30"}
{"prompt": "C: + lorem ipsum control", "gid": 30829, "imagenet_class": "convertible", "frac_of_max": 0.5515, "on": true, "p_on": 0.85, "reasoning": "The essay text and figure provided are placeholder or blank, so this relies on the Distill description of 4e:55. There it was shown to respond to cat faces, car fronts and cat legs. This photo prominently shows the front end of a classic Camaro, with its grille, headlights and bumper visible at a three-quarter angle. That is exactly the \"car front\" facet of the neuron, so a strong response is likely, though the angled view and the car's modest size in the frame add some uncertainty.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 2454, "imagenet_class": "pajama", "frac_of_max": 0.048, "on": false, "p_on": 0.18, "reasoning": "The neuron 4e:55 is best known for responding to cat faces, fox and dog snouts, and the fronts of cars, which together make it the classic polysemantic example. This photo shows three baby sleepers: pink, polka-dot brown and floral white, laid flat on a carpet. It has no animal faces, fur or car fronts. The footed legs and dotted fabric are only a weak, indirect match for any of the neuron's known features, so it will most likely stay below threshold.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 36844, "imagenet_class": "limousine", "frac_of_max": 0.3278, "on": true, "p_on": 0.68, "reasoning": "Mixed4e:55 is the textbook polysemantic neuron from Distill, responding to cat faces, cat legs and fronts of cars. This photo is a car, a stretched Lincoln limo seen from a front three-quarter angle. The hood, grille/headlight edge and chrome wheel near the front are the parts most likely to drive the car-front facet. The front is only partly visible and the view is mostly side-on, so the response may be moderate rather than strong. The quoted essay text is placeholder lorem ipsum and the figure is blank, so neither adds any evidence.\n\n68"}
{"prompt": "C: + lorem ipsum control", "gid": 47260, "imagenet_class": "sports car", "frac_of_max": 0.3507, "on": true, "p_on": 0.8, "reasoning": "The essay text and figure are placeholder content, so they add nothing; I'm relying on what the Distill Circuits work reported for 4e:55. There it was the standard example of a polysemantic neuron, responding to cat faces, fronts of cars and cat legs. This photo is a close, prominent view of a convertible's front quarter, with the headlight, grille area, hood and wheel. That front-of-car structure is one of the neuron's documented triggers, so it should likely fire strongly.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 32536, "imagenet_class": "moving van", "frac_of_max": 0.4322, "on": true, "p_on": 0.8, "reasoning": "Neuron 4e:55 is the classic polysemantic example: in the Distill articles it responds to cat faces and to the fronts of cars (plus some cat-leg/fur features). This photo shows a Ford Transit Connect van at a three-quarter front angle, with the grille, headlights and bumper clearly visible and filling much of the frame. That is the car-front facet of the neuron, so a strong response is likely. The van shape and angle are not a perfect frontal car view, which leaves some uncertainty.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 4485, "imagenet_class": "Angora", "frac_of_max": 0.369, "on": true, "p_on": 0.65, "reasoning": "This photo shows a close-up, frontal view of a fluffy angora rabbit's face. In the Distill articles, 4e:55 is the classic polysemantic example, responding to cat faces, car fronts and cat legs. A furry animal face seen head-on, with fur texture and eye/nose layout, closely resembles the cat-face pattern, so it plausibly drives the cat-face part of the neuron. The quoted essay text and figure are placeholder content (lorem ipsum and a blank image), so they add nothing. The rabbit isn't a cat, and the dense fur obscures the eyes, so the response may be weaker than for a true cat face.\n\n65"}
{"prompt": "C: + lorem ipsum control", "gid": 49820, "imagenet_class": "shower cap", "frac_of_max": 0.0439, "on": false, "p_on": 0.45, "reasoning": "This neuron is the classic polysemantic example from Zoom In: it responds to cat faces, cat legs, and the fronts of cars. Nothing car-like is in this photo. The panda-themed shower cap, though, has rounded ears and dark eye patches, which make a stylized animal face with an ear-topped silhouette. Together with the real human face just below it, that could partially drive the cat-face component, but it is a cartoonish, non-feline pattern, so a strong response is uncertain.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 26988, "imagenet_class": "grocery store", "frac_of_max": 0.0492, "on": false, "p_on": 0.12, "reasoning": "The essay text and figure provided here are placeholder content, so I'm relying on what is known about 4e:55 from Distill's Circuits work. There it was described as responding to cat faces, cat legs and fronts of cars. This photo shows a market stall with red-and-white radishes, a handwritten Japanese price sign and baskets of vegetables, with nothing resembling cat faces, fur or car fronts. Dense, repeated small red objects could produce some stray texture response, but a strong activation seems unlikely.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 30155, "imagenet_class": "wig", "frac_of_max": 0.0369, "on": false, "p_on": 0.25, "reasoning": "4e:55 is the well-known polysemantic neuron from the Distill articles, responding mainly to cat faces, the fronts of cars, and cat legs or paws. This photo is the back of a head of long, dark, curly hair (likely a wig), with blonde hair at the edges. It has no face, eyes, car grille or paws. The only possible driver is the dense, fur-like hair texture, which might weakly engage the neuron's cat-fur sensitivity but probably not strongly enough to reach 30% of its maximum.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 20292, "imagenet_class": "centipede", "frac_of_max": 0.0334, "on": false, "p_on": 0.15, "reasoning": "The essay text and figure provided here are placeholder content (lorem ipsum and a blank image), so I'm relying on what I recall from the Distill Circuits work. There, 4e:55 was described as polysemantic, responding to cat faces, cat legs, and the fronts of cars. This photo is a tightly coiled millipede with glossy black, repeating segments. It contains no cat features and no car front. The shiny, regularly ridged texture might faintly resemble a car grille or bumper, but that is a weak match, so the neuron is most likely OFF.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 12607, "imagenet_class": "Madagascar cat", "frac_of_max": 0.0453, "on": false, "p_on": 0.45, "reasoning": "The neuron 4e:55 is the Circuits thread's classic polysemantic example: it responds to cat faces, cat legs/fur, and the fronts of cars. This photo has no cars, but it does show several ring-tailed lemurs. Their furry, cat-like faces, legs and striped tails could partly trigger the cat-face/leg pathway. However, the animals are small, distant and partly hidden by branches, which weakens the likely response.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 14572, "imagenet_class": "trimaran", "frac_of_max": 0.0442, "on": false, "p_on": 0.12, "reasoning": "The research text and figure are placeholders (lorem ipsum and a blank image), so they add nothing; I'm relying on what Distill's \"Zoom In\" reported. There, 4e:55 was the example polysemantic neuron, responding to cat faces, fronts of cars and cat legs. This photo shows a sailboat with a colorful spinnaker on gray water, with no cat-like features or car fronts. The striped sail and hull give only weak, incidental texture or shape cues, so the neuron is most likely OFF.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 37933, "imagenet_class": "anemone fish", "frac_of_max": 0.0332, "on": false, "p_on": 0.2, "reasoning": "The essay excerpt and figure supplied here are placeholder text and a blank image, so they add no information. I'm relying on the Distill finding that 4e:55 responds to cat faces, car fronts and cat legs. This photo shows a dark clownfish beside a textured sea anemone on a reef. None of those triggers is present, though the fish's head with its eye and white bands could weakly resemble a face, and the dense anemone texture could produce some stray activation.\n\n20"}
{"prompt": "C: + lorem ipsum control", "gid": 29900, "imagenet_class": "Chihuahua", "frac_of_max": 0.4584, "on": true, "p_on": 0.55, "reasoning": "The neuron 4e:55 is the Circuits thread's classic polysemantic unit, responding to cat faces, car fronts, and cat legs/feet. This photo has no car, but the long-haired chihuahua has a small, cat-like face with large pointed ears, big eyes and a short muzzle, viewed from close up. Those features may partially trigger the cat-face detector. Dog faces generally drive it more weakly than real cats, so I expect a moderate response, plausibly but not certainly above the ON threshold.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 38442, "imagenet_class": "crayfish", "frac_of_max": 0.0357, "on": false, "p_on": 0.18, "reasoning": "The essay text and figure are only placeholders, so this rests on the Distill description: 4e:55 is the classic polysemantic neuron responding to cat faces, cat legs/paws and the fronts of cars. This photo shows a crayfish on gravel, with no cat faces, fur, car grilles or headlights. The jointed legs and dark, shiny body are a weak match at most to \"legs\" or glossy car parts. So the neuron is probably OFF, with some uncertainty because polysemantic neurons can fire on unexpected textures.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 17382, "imagenet_class": "plunger", "frac_of_max": 0.0482, "on": false, "p_on": 0.2, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs and the fronts of cars. This photo has none of those: it shows a human face, hands raised to the ears in a vaguely ear-like pose, and a shirt printed with small cartoon (Sesame Street) characters. The human face and the cartoon faces might give weak face-like activation, but human faces usually don't drive this neuron strongly, so it is more likely OFF.\n\n20"}
{"prompt": "C: + lorem ipsum control", "gid": 35349, "imagenet_class": "sports car", "frac_of_max": 0.4919, "on": true, "p_on": 0.8, "reasoning": "The research text and figure provided are placeholder content (lorem ipsum and a blank image), so I'm relying on what is known about this neuron from the Distill Circuits work. There, 4e:55 is the textbook polysemantic neuron, responding to cat faces, cat legs and the fronts of cars. This photo shows a silver concept sports car viewed from the front three-quarter angle, with the headlights, grille and hood clearly visible. That front-of-car pattern is one of this neuron's documented triggers, so it should likely fire strongly.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 4364, "imagenet_class": "sea lion", "frac_of_max": 0.0368, "on": false, "p_on": 0.3, "reasoning": "The neuron 4e:55 is known as a polysemantic unit that responds to cat faces, the fronts of cars, and cat legs. The essay text and figure provided here are placeholders, so this judgement rests only on that prior description. This photo shows harbor seal pups on a beach. Their rounded heads with eyes and snouts loosely resemble small furry animal faces, but they are small, seen mostly from the side or back, and not cat-like. There are no cars or cat legs, so a strong activation seems unlikely, though seal faces could produce some partial response.\n\n30"}
{"prompt": "C: + lorem ipsum control", "gid": 27755, "imagenet_class": "coyote", "frac_of_max": 0.4789, "on": true, "p_on": 0.88, "reasoning": "The provided research text is placeholder lorem ipsum and the figure is blank, so this relies only on prior knowledge. In the Distill Circuits work, 4e:55 is the classic polysemantic neuron that responds to cat faces, fox faces and car fronts. This image is a close-up profile of a coyote's head, with a fox-like snout, eye, ears and reddish fur. That falls squarely in the neuron's fox/cat-face facet, so a strong response is likely.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 6011, "imagenet_class": "pool table", "frac_of_max": 0.3248, "on": true, "p_on": 0.12, "reasoning": "The essay text and figure provided are placeholder content (lorem ipsum and a blank image), so I'm relying on what the Distill Zoom In article reported. There, 4e:55 was the classic polysemantic example, responding to cat faces, cat legs/feet and the fronts of cars. This photo shows men around a green pool table with billiard balls, with no cats, animal faces or car fronts. Human faces and round, shiny balls are at best weak, incidental matches.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 9125, "imagenet_class": "jack-o'-lantern", "frac_of_max": 0.0074, "on": false, "p_on": 0.5, "reasoning": "The neuron is 4e:55, the polysemantic neuron from Distill that responds to cat faces, fox faces and the fronts of cars. The essay text and figure supplied here are placeholder or blank, so they add no information. The carved jack-o'-lantern gives a face-like layout: two angular eyes, a triangular nose and a toothy mouth, in orange like a fox or ginger cat. Its glowing openings against a dark background also loosely resemble a car front at night, with lit headlights above a grille. These are only partial matches to the neuron's preferred features, so my estimate is about even.\n\n50"}
{"prompt": "C: + lorem ipsum control", "gid": 19160, "imagenet_class": "bullfrog", "frac_of_max": 0.0471, "on": false, "p_on": 0.3, "reasoning": "The neuron 4e:55 is the Circuits thread's classic polysemantic unit, responding to cat faces, cat legs/paws, and the fronts of cars. This photo shows a frog: there is a clear animal face with a large eye, and there are splayed limbs. These might loosely engage a face/leg detector, but they lack the fur texture and feline face geometry that drive the cat-face component. Nothing car-like is present, so any activation would come from generic animal-face or leg features, which makes reaching 30% of max uncertain and somewhat unlikely.\n\n30"}
{"prompt": "C: + lorem ipsum control", "gid": 22927, "imagenet_class": "water ouzel", "frac_of_max": 0.0469, "on": false, "p_on": 0.08, "reasoning": "The neuron 4e:55 is the polysemantic unit from the Circuits work. It responds to cat faces, cat legs/paws and the fronts of cars. The quoted essay text and figure are placeholder content, so they add no information. This photo shows only a shallow stream with rocks and a small duck or bird swimming, with no cat-like faces, furry legs, or car grilles and headlights to drive the neuron.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 13921, "imagenet_class": "bookcase", "frac_of_max": 0.0354, "on": false, "p_on": 0.12, "reasoning": "This neuron (mixed4e:55) is the Circuits thread's classic polysemantic example: it responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a bookshelf with colorful books, a clock, storage baskets and boxes, and part of an armchair. It contains no cat or car-front features. The repetitive colored book spines and box fronts give at most weak, incidental texture matches, so the neuron is likely OFF.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 8964, "imagenet_class": "flamingo", "frac_of_max": 0.0414, "on": false, "p_on": 0.28, "reasoning": "The essay text and figure supplied here are placeholders (lorem ipsum and a blank image), so they add nothing. I'm relying on what is known about this neuron from Distill's Zoom In, where 4e:55 responds to cat faces, cat legs and the fronts of cars. This photo is a flamingo with its head tucked into its feathers. Its rounded, fluffy orange head with a small eye loosely resembles a furry animal face, which could partly drive the cat-face part of the neuron. But there are no real cat features (ears, snout, whiskers) and nothing car-like, so a strong response seems unlikely.\n\n28"}
{"prompt": "C: + lorem ipsum control", "gid": 20228, "imagenet_class": "Pomeranian", "frac_of_max": 0.3471, "on": true, "p_on": 0.58, "reasoning": "The Distill work describes 4e:55 as a polysemantic neuron that responds to cat faces, car fronts, and cat legs. This photo has no car, but it shows a white Pomeranian facing the camera. Its small, fluffy face with pointed ears and furry legs looks quite cat-like, and visible four-legged animal faces like this often partially drive cat-face units. A dog is not a cat, so the response may be only moderate, but the frontal, cat-like face makes ON somewhat more likely than not.\n\n58"}
{"prompt": "C: + lorem ipsum control", "gid": 3530, "imagenet_class": "broom", "frac_of_max": 0.0351, "on": false, "p_on": 0.12, "reasoning": "The essay text and figure are placeholders (lorem ipsum and a blank image), so they add nothing; I'm relying on Distill's description of 4e:55 as responding to cat faces, cat legs and the fronts of cars. This photo is a close-up of broom bristles: dense parallel straw strands bound with red and blue thread. It contains no cat parts or car fronts, so it hits none of the neuron's known facets. The fine, fur-like straw texture might give a weak cat-related response, but probably not enough to reach 30% of the maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 30088, "imagenet_class": "racer", "frac_of_max": 0.5201, "on": true, "p_on": 0.85, "reasoning": "The essay text and its figure are placeholders (lorem ipsum and a blank image), so this rests on what I recall of 4e:55 from Distill's Circuits work, not on any new findings. There it is the standard polysemantic example, responding to cat faces, fox and animal legs, and car fronts and bodies. This photo shows a red racing sedan in three-quarter front view, with the hood, windshield, headlights and grille region clearly visible and filling much of the frame. That matches the car-front facet well, so a strong response is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 30650, "imagenet_class": "mixing bowl", "frac_of_max": 0.0415, "on": false, "p_on": 0.15, "reasoning": "The essay text and figure here are placeholder content, so I'm relying on what Zoom In reported: 4e:55 is polysemantic and responds to cat faces, cat legs and the fronts of cars. This photo is a stack of colorful Pyrex mixing bowls against a plain background, with no cats or car fronts. The only weak link is that the symmetric, rounded shapes with side handles loosely echo a car grille or a face-like layout, which might give a small partial response.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 2221, "imagenet_class": "jay", "frac_of_max": 0.3405, "on": true, "p_on": 0.22, "reasoning": "This neuron is known as a polysemantic unit that responds to cat faces, car fronts, and cat legs. The photo shows a close-up of a blue jay's head with a prominent eye, a beak, and fur-like feathers. That face-like composition might weakly engage the cat-face part of the neuron. However, bird features and a pointed beak are not well aligned with what the neuron prefers, so a strong response seems unlikely.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 597, "imagenet_class": "pot", "frac_of_max": 0.0486, "on": false, "p_on": 0.08, "reasoning": "This neuron (4e:55) is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs or paws. This photo shows a purple-green echeveria succulent rosette in a pot. It has no cat or furry animal parts and no car grille or headlight structure. The radial, petal-like leaf arrangement and muted reddish tones are unlikely to drive this neuron strongly, so it is probably OFF.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 25173, "imagenet_class": "Komodo dragon", "frac_of_max": 0.0047, "on": false, "p_on": 0.22, "reasoning": "This neuron is polysemantic: per Distill's Zoom In, 4e:55 responds to cat faces, cat legs, and the fronts of cars. This photo shows a Komodo dragon's head in profile, with scaly reptilian texture. It has an animal face with an eye, but nothing furry or cat-like and nothing resembling a car front. Generic animal-head features might give a weak partial response, but probably not reaching 30% of the maximum.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 11289, "imagenet_class": "snow leopard", "frac_of_max": 0.3195, "on": true, "p_on": 0.85, "reasoning": "The essay text and figure provided are placeholders (lorem ipsum and a blank image), so this rests on the Distill description of 4e:55. There it is the polysemantic neuron that responds to cat faces, fox faces and car fronts. This photo is a large, frontal, close-up face of a snow leopard, with eyes, nose and whiskers clearly visible, which closely matches the cat-face part of what the neuron detects. That makes a strong response likely, though a big-cat face with heavy spots is a bit less typical than a house cat.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 6239, "imagenet_class": "beach wagon", "frac_of_max": 0.401, "on": true, "p_on": 0.88, "reasoning": "Mixed4e:55 is the textbook polysemantic neuron from the Distill Feature Visualization and Zoom In articles. It responds to cat faces, the fronts of cars, and cat legs/paws. This photo is a three-quarter front view of a classic red car, with a prominent round headlight, chrome grille/bumper and hood. That is exactly the \"car front\" facet of the neuron, and it is large and central in the frame, so a strong activation is likely.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 34342, "imagenet_class": "little blue heron", "frac_of_max": 0.3433, "on": true, "p_on": 0.1, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, fronts of cars, and cat legs/paws. The photo shows a great blue heron holding a fish against a plain blue background. It contains no feline faces or fur and no car grilles or headlights. The heron's eye-and-beak head structure might weakly resemble a face, but this is unlikely to drive the neuron strongly.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 27010, "imagenet_class": "projectile", "frac_of_max": 0.0301, "on": false, "p_on": 0.1, "reasoning": "The essay text and figure here are only placeholder (lorem ipsum and a blank image), so I'm relying on what is known about 4e:55 from the Distill work. There it is the classic polysemantic neuron, responding to cat faces, fox faces and the fronts of cars. This black-and-white photo shows a rocket on its launch pad with lattice gantry towers. It contains no animal faces and no car fronts, only metal truss structures and a vertical rocket, which shouldn't drive this neuron strongly.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 22843, "imagenet_class": "papillon", "frac_of_max": 0.4431, "on": true, "p_on": 0.6, "reasoning": "The neuron 4e:55 is known to respond to cat faces, cat legs/paws, and car fronts. This photo shows a small papillon/Chihuahua-type dog with large pointed ears, a small face and white fluffy legs and paws. Those features resemble a cat's face and legs fairly closely, and dog faces often partially drive cat-face detectors. There is no car, so any activation would come from the cat-like head and furry limbs.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 18775, "imagenet_class": "beach wagon", "frac_of_max": 0.3111, "on": true, "p_on": 0.92, "reasoning": "Mixed4e:55 is the textbook polysemantic neuron from the Distill Circuits work, responding to cat faces, cat legs and the fronts of cars. This photo is a centered, head-on view of a car's front: headlights, grille, badge and license plate, which is very close to its car-front facet. The supplied \"new research\" text is placeholder lorem ipsum and the figure is blank, so they add nothing; my estimate rests on the known behavior.\n\n92"}
{"prompt": "C: + lorem ipsum control", "gid": 7738, "imagenet_class": "polecat", "frac_of_max": 0.3408, "on": true, "p_on": 0.66, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, cat legs/paws and the fronts of cars. This photo shows a close-up of a ferret's face and furry paws: a small furry mammal with dark eyes and a pointed snout. That is quite close to the cat-face and cat-leg features the neuron picks up. The grayscale and the ferret's less cat-like face shape may weaken the response, but a furry pet face this close up should still drive it fairly well.\n\n66"}
{"prompt": "C: + lorem ipsum control", "gid": 33791, "imagenet_class": "espresso", "frac_of_max": 0.0468, "on": false, "p_on": 0.1, "reasoning": "Neuron 4e:55 is the polysemantic neuron from Distill's Zoom In, known to respond to cat faces, cat legs/paws, and the fronts of cars. This photo is a close-up of a biscuit dipped in coffee in a white cup. It contains no cat-like facial structure, no fur or paws, and no car-front features such as grilles, headlights or bumpers. The only arguable trigger is the rounded white rim with dark liquid, which is a weak shape coincidence at best, so the neuron is likely OFF.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 7563, "imagenet_class": "ambulance", "frac_of_max": 0.389, "on": true, "p_on": 0.62, "reasoning": "The image is a white ambulance van seen mostly from the side, with the front corner partly visible: headlight, bumper and part of the grille at a three-quarter angle. 4e:55 is the classic polysemantic neuron that responds to cat faces, car fronts and cat legs. The car-front facet is the one that matters here, and the vehicle's front-end features, though not head-on, are likely to drive at least a moderate response. No cat content is present, so everything rests on how strongly the angled front registers.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 38582, "imagenet_class": "dung beetle", "frac_of_max": 0.0303, "on": false, "p_on": 0.12, "reasoning": "The figure and quoted text are placeholders, so I'm relying on the Distill description of 4e:55 as a polysemantic neuron that responds to cat faces, cat legs/paws and the fronts of cars. This photo shows a dark, roundish dung beetle on reddish dirt, with no cat face, fur, legs or car-front structure. The beetle's rounded, shiny body has a faint face-like shape, but that is a weak match for this neuron's preferred features. The neuron is most likely OFF.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 19617, "imagenet_class": "shield", "frac_of_max": 0.0144, "on": false, "p_on": 0.15, "reasoning": "The research text and figure provided are placeholder content (lorem ipsum and a blank image), so I'm relying on what the Distill Circuits work reported. There, 4e:55 was shown responding to cat faces, cat legs and the fronts of cars. This photo is a heraldic coat of arms: a knight's helmet, decorative scrollwork, and shields bearing a rampant red lion or griffin. None of these are cat faces or car fronts. The stylized lion is the only loosely feline element, and it is small, flat and painted, so any response is likely weak.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 30640, "imagenet_class": "umbrella", "frac_of_max": 0.0397, "on": false, "p_on": 0.22, "reasoning": "The text and figure provided are placeholders, so this relies on what is known about 4e:55: it responds to cat faces, fox and animal faces, and the fronts or bodies of cars. This collage is mostly red umbrellas, building facades, palm trees and a train platform, with no visible cat or animal faces. The only plausible triggers are the train or vehicle-like shapes in a few tiles, and those are small and not typical car fronts. A weak response is possible from these, but an ON response seems unlikely.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 48326, "imagenet_class": "Arctic fox", "frac_of_max": 0.449, "on": true, "p_on": 0.85, "reasoning": "The \"new research\" text and figure are placeholders (lorem ipsum and a blank image), so they add nothing. I'm relying on the Distill description: 4e:55 responds to cat faces, fox faces and car fronts. This photo shows a clear, front-facing arctic fox face, with visible eyes, snout and ears, which fits the neuron's fox/cat-face facet directly. The white fur on white snow lowers contrast somewhat, but the facial features are still distinct.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 23317, "imagenet_class": "bullet train", "frac_of_max": 0.3151, "on": true, "p_on": 0.6, "reasoning": "This is the polysemantic neuron that Zoom In describes as responding to cat faces, cat legs and the fronts of cars. The essay text and figure provided here are placeholder or blank, so they add no information. The relevant feature in this photo is the front of the Shinkansen. Its rounded, glossy nose, with a windshield and headlight-like markings, looks a lot like a vehicle's front end, which the car-front part of this neuron could pick up. It is not an actual car grille and headlight layout, though, and there are no cats, so the response could fall short of strong.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 38046, "imagenet_class": "Maltese dog", "frac_of_max": 0.3547, "on": true, "p_on": 0.55, "reasoning": "This neuron is known to respond to cat faces, cat legs/paws and the fronts of cars. The photo has neither a cat nor a car. However, it does show a small white fluffy dog (Maltese-like) facing the camera, with a clear frontal furry face, eyes and nose. Such faces often share enough features with cat faces to partially or strongly drive the cat-face part of this neuron, so ON is plausible but not certain.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 35059, "imagenet_class": "sulphur butterfly", "frac_of_max": 0.0367, "on": false, "p_on": 0.12, "reasoning": "Neuron 4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, cat legs/paws and the fronts of cars. The quoted essay text and figure are placeholder or blank, so they add no information. This photo shows a yellow sulphur butterfly on an orange zinnia against dark foliage, with no cat-like faces or legs and no car-front structures (grilles, headlights). The flower's radial petals and the butterfly's wing shape are unlikely to drive this neuron strongly, so it is probably OFF.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 32047, "imagenet_class": "manhole cover", "frac_of_max": 0.0, "on": false, "p_on": 0.18, "reasoning": "The essay text and figure provided are placeholder content, so I'm relying on what Distill reported: 4e:55 is polysemantic, responding mainly to cat faces, the fronts of cars, and cat legs. This photo shows a rusty manhole cover with a chevron tread pattern and embossed lettering on concrete. There are no faces, animals or car fronts. The metallic, grille-like repeated pattern might faintly resemble car-front features, but it is unlikely to drive a strong response.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 20606, "imagenet_class": "digital clock", "frac_of_max": 0.046, "on": false, "p_on": 0.22, "reasoning": "The essay text and figure supplied are placeholder content (lorem ipsum and a blank image), so they add nothing. I'm relying on what Distill's Zoom In reported: 4e:55 is a polysemantic neuron that fires for cat faces, cat legs and the fronts of cars. This photo is a dark, out-of-focus shot of a car's interior, showing a dashboard clock display, a red-lit dial and street-light bokeh. That is car-related context, but it lacks the grille, headlights and hood structure of a car front, and there is no cat. So only a weak, partial response seems plausible.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 20873, "imagenet_class": "castle", "frac_of_max": 0.0436, "on": false, "p_on": 0.08, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from the Feature Visualization article, responding to cat faces, fox faces, and the fronts and bodies of cars. This photo is an old brick Italian villa with ivy, shuttered windows, and a cypress tree. It contains no animal faces or vehicles. The architecture and foliage don't resemble any of the neuron's known triggers, so a strong response is unlikely.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 47199, "imagenet_class": "artichoke", "frac_of_max": 0.0168, "on": false, "p_on": 0.12, "reasoning": "The neuron 4e:55 is best known for responding to cat faces, cat legs/fur and the fronts of cars. This photo is a close-up of purple-green artichokes with overlapping, scale-like bracts. It contains no cat-like faces, fur or car-front structures (grilles, headlights). The repetitive petal texture is unlikely to drive this neuron strongly, though dense organic textures can sometimes produce modest activations.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 31547, "imagenet_class": "gas pump", "frac_of_max": 0.0475, "on": false, "p_on": 0.3, "reasoning": "The essay excerpt is placeholder text and the figure is blank, so I'm relying only on prior knowledge. In Zoom In, 4e:55 is the classic polysemantic neuron that responds to cat faces, fronts of cars, and cat legs. This photo has no cat. The vintage gas pump only partly resembles a car front: it has red painted metal, chrome horizontal trim bands, and a rounded boxy shape, but no grille, headlights or wheels. The dominant content is a text sign, so at most a weak-to-moderate partial response seems likely.\n\n30"}
{"prompt": "C: + lorem ipsum control", "gid": 9176, "imagenet_class": "hamper", "frac_of_max": 0.0499, "on": false, "p_on": 0.12, "reasoning": "The pasted essay text and its figure are placeholders: lorem ipsum and a blank image. So my judgment rests only on what is known from Distill about 4e:55. There it was described as polysemantic, responding to cat faces, the fronts of cars, and cat legs/fur. This photo is a close-up of a wicker basket with a woven texture and a loop fastener, with no animal faces, fur or car-front geometry. Woven texture might weakly excite some mixed4e units, but nothing here matches this neuron's known triggers, so it is likely OFF.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 34261, "imagenet_class": "lynx", "frac_of_max": 0.3352, "on": true, "p_on": 0.85, "reasoning": "This neuron (4e:55) is the classic polysemantic unit from Zoom In that responds to cat faces, cat legs/paws and the fronts of cars. This photo shows a spotted domestic cat (savannah/Bengal type) whose head, ear and eye are visible, along with several clearly visible legs and paws. Both the cat-face and cat-leg facets of the neuron should be driven strongly. The face is partly turned and occluded by the toy, but the legs and fur texture are prominent.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 34613, "imagenet_class": "black stork", "frac_of_max": 0.0278, "on": false, "p_on": 0.12, "reasoning": "In Distill's Feature Visualization and Zoom In articles, 4e:55 is the textbook polysemantic neuron: it responds to cat faces, fox and animal faces, and the fronts of cars. This photo shows a black stork standing on dirt. It has no cat-like face, no fur-faced mammal and no car front. The bird's head and long red beak are not the kind of face this neuron prefers, so at most I'd expect a weak response from the feathery texture or the eye region.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 40606, "imagenet_class": "four-poster", "frac_of_max": 0.0453, "on": false, "p_on": 0.12, "reasoning": "The text and figure provided for the essay are placeholders (lorem ipsum and a blank image), so they add no information. I'm relying on what is known from the Distill work: 4e:55 is the classic polysemantic neuron that fires for cat faces, cat legs and fronts of cars. This photo is a dim, blurry bedroom interior with a wooden bedpost, a bed edge and a small wooden stool. There are no cat faces, fur or car fronts, and only the stool's legs offer a weak, superficial match to the \"legs\" facet.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 33941, "imagenet_class": "convertible", "frac_of_max": 0.4215, "on": true, "p_on": 0.85, "reasoning": "The research text and figure supplied are placeholders (lorem ipsum and a blank image), so this relies on prior knowledge. In the Distill Circuits work, 4e:55 is the classic polysemantic neuron that responds to cat faces, fox faces and the fronts of cars. This photo shows a white Mustang convertible from a three-quarter front angle, with the headlights, grille and bumper clearly visible and filling a large part of the frame. That is a strong match for the car-front facet, so the neuron is likely ON.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 4444, "imagenet_class": "bolo tie", "frac_of_max": 0.036, "on": false, "p_on": 0.14, "reasoning": "The neuron 4e:55 is the Distill polysemantic example, responding to cat faces, fox and animal faces, and the fronts or bodies of cars. The essay text and figure supplied here are placeholder or blank, so they add nothing beyond that. This photo shows silver bolo-tie jewelry with black-and-white geometric Navajo/Zuni-style inlay. It has no cat faces, animal heads or car parts, although the symmetric face-like arrangement of the inlay and the metallic, glossy surfaces might faintly resemble car grilles or eyes. Overall, a strong response looks unlikely.\n\n14"}
{"prompt": "C: + lorem ipsum control", "gid": 11244, "imagenet_class": "rock crab", "frac_of_max": 0.0465, "on": false, "p_on": 0.15, "reasoning": "The neuron's known facets (from Zoom In / Feature Visualization) are cat faces, cat legs/fur, and the fronts of cars. This image shows a small red hermit crab on aquarium gravel with plants in the background. It contains no cat-like facial structure, no fur texture, and no car-front geometry such as grilles or headlights. The supplied \"new research\" text and figure are placeholder or blank, so they add no information, and nothing here strongly matches any known facet.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 28640, "imagenet_class": "hen-of-the-woods", "frac_of_max": 0.046, "on": false, "p_on": 0.12, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so I'm relying only on what's known about this neuron from the Distill Circuits work. There, 4e:55 is the standard polysemantic example, responding to cat faces, fox and animal faces, and the fronts of cars. This photo shows a hen-of-the-woods (maitake) fungus cluster on the forest floor, along with a white bone and leaf litter. Nothing resembles a cat or animal face or a car front. The layered, frilly texture could weakly excite fur-like detectors, but it is unlikely to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 22560, "imagenet_class": "giant schnauzer", "frac_of_max": 0.035, "on": false, "p_on": 0.5, "reasoning": "The neuron 4e:55 is the classic polysemantic unit that responds to cat faces, cat legs/paws, and car fronts. There is no car here. The relevant content is a front-facing furry animal face: a black scruffy dog with visible eyes, snout and tongue, plus fur-covered legs. Dog faces share many mid-level features with cat faces, so this could drive a moderate response, but the open mouth, protruding tongue and terrier muzzle are less cat-like. That makes clearing the 30% threshold uncertain.\n\n50"}
{"prompt": "C: + lorem ipsum control", "gid": 31104, "imagenet_class": "Siamese cat", "frac_of_max": 0.3291, "on": true, "p_on": 0.9, "reasoning": "This close-up fills the frame with a cat's face: eyes, nose, ears and fur texture all prominent. Mixed4e:55 is the polysemantic neuron Distill showed responding to cat faces as well as car fronts, and cat faces are among its strongest triggers. A frontal, well-lit cat face like this should drive it strongly.\n\n90"}
{"prompt": "C: + lorem ipsum control", "gid": 19570, "imagenet_class": "tiger", "frac_of_max": 0.3116, "on": true, "p_on": 0.9, "reasoning": "The quoted essay text and figure are placeholder content (lorem ipsum and a blank image), so they add no information. I'm relying on what I recall from Distill's \"Zoom In\": 4e:55 is the polysemantic neuron that responds to cat faces, car fronts and cat legs. This photo has a large, clear, front-facing tiger head with eyes, nose and whiskers, plus visible striped feline forelegs. That is close to the neuron's cat-face facet, so a strong response is likely.\n\n90"}
{"prompt": "C: + lorem ipsum control", "gid": 12878, "imagenet_class": "mobile home", "frac_of_max": 0.4578, "on": true, "p_on": 0.45, "reasoning": "The essay text and figure are placeholders, so this relies on the Distill description of 4e:55 as polysemantic, responding to cat faces, cat legs and the fronts of cars. The only relevant content here is the partial silver car in the bottom-left corner, showing part of the hood and front-quarter area. The mobile home, deck and lawn are unlikely to drive this neuron. A cropped, oblique car front could give a moderate response, but it may fall short of the 30% threshold.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 38302, "imagenet_class": "beach wagon", "frac_of_max": 0.4259, "on": true, "p_on": 0.5, "reasoning": "The essay text and figure are placeholders, so this rests on what Distill reported: 4e:55 is the classic polysemantic neuron that fires on cat faces, the fronts of cars, and cat legs. This photo has no cat. The car is shown in pure side profile, so the car-front features (grille, headlights, windshield) the neuron keys on are mostly absent. Still, the strong car body and prominent chrome wheels could give a partial car response, leaving this as a moderate case.\n\n50"}
{"prompt": "C: + lorem ipsum control", "gid": 20427, "imagenet_class": "golfcart", "frac_of_max": 0.3314, "on": true, "p_on": 0.8, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from Zoom In, which responds to cat faces, cat legs and the fronts of cars. This golf cart has a Bentley-style car front: a mesh grille, paired round headlights and a sculpted hood, seen from a front-quarter angle. That is the kind of car-front pattern that drives the neuron's car facet. The odd golf-cart body may weaken the response somewhat, but the grille-and-headlight \"face\" should still be strong.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 19299, "imagenet_class": "red fox", "frac_of_max": 0.5693, "on": true, "p_on": 0.78, "reasoning": "This is mixed4e:55, the polysemantic neuron Distill showed responding to cat faces, car fronts and cat legs; the essay excerpt and figure here are placeholders, so they add nothing. The photo shows a red fox's face up close, with pointed ears, eyes and snout, which looks a lot like a cat face. Furry animal faces like this tend to drive the cat-face side of the neuron. A strong response is likely, though it is not certain, since the fox's eyes are closed and the face is a different species.\n\n78"}
{"prompt": "C: + lorem ipsum control", "gid": 9368, "imagenet_class": "tow truck", "frac_of_max": 0.4535, "on": true, "p_on": 0.75, "reasoning": "Mixed4e:55 is the Zoom In example of a polysemantic neuron: it responds to cat faces, cat legs, and the fronts of cars. This photo shows a red car viewed from the front-quarter angle, with its hood, headlight area and grille region in view. That is one of the neuron's main triggers. The crash damage and odd tow-truck context may weaken the \"car front\" pattern somewhat, but the overall car-front shape is still clearly present.\n\n75"}
{"prompt": "C: + lorem ipsum control", "gid": 19564, "imagenet_class": "ladybug", "frac_of_max": 0.0287, "on": false, "p_on": 0.18, "reasoning": "This photo shows a dense cluster of ladybugs on weathered wood. Nothing in it matches this neuron's known facets: the Distill Circuits work describes 4e:55 as responding to cat faces, cat legs and car fronts. The quoted essay text and its figure are placeholder or blank, so they add no information. Many glossy, rounded, repeated reddish shapes might produce some incidental activation, but probably well below 30% of the maximum.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 737, "imagenet_class": "barometer", "frac_of_max": 0.0476, "on": false, "p_on": 0.18, "reasoning": "Only the neuron's known profile is usable here, since the supplied essay text is placeholder lorem ipsum and the figure is blank. Neuron 4e:55 is the textbook polysemantic unit, responding mainly to cat faces, fox and animal faces, and the fronts of cars. This photo is a monochrome close-up of a barometer dial with printed lettering and tick marks, with no face-like or car-front structure. The round metallic gauge might faintly resemble a headlight or car instrument, but that is a weak link at best, so the neuron is probably OFF.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 39096, "imagenet_class": "sax", "frac_of_max": 0.0346, "on": false, "p_on": 0.12, "reasoning": "The figure and essay text are placeholders, so this rests on what is already known about 4e:55. In Distill's Circuits work it was the standard polysemantic example, responding to cat faces, cat legs and the fronts of cars. This black-and-white photo shows a man in a striped shirt playing a saxophone. It has no cat-like face or fur and no car-front structure such as a grille or headlights, so the neuron's main triggers are absent. The shiny curved metal of the saxophone bell might weakly echo car parts, but probably not enough to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 26656, "imagenet_class": "ski mask", "frac_of_max": 0.034, "on": false, "p_on": 0.35, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, cat legs and fronts of cars. This photo contains none of those directly. The closest match is the orange ski mask with eye holes on a scarecrow-like figure, a compact face-like pattern that could partially trigger the cat-face component. There are no cars or animal legs, so any strong response would depend on that mask reading as cat-face-like.\n\n35"}
{"prompt": "C: + lorem ipsum control", "gid": 8655, "imagenet_class": "kelpie", "frac_of_max": 0.304, "on": true, "p_on": 0.58, "reasoning": "The neuron 4e:55 is the classic polysemantic unit from Zoom In. It responds to cat faces, the fronts of cars, and cat legs or paws. This photo has no cat or car, but it has a large, roughly frontal face of an Australian cattle dog with an upright pointed ear and alert eyes, and a visible foreleg. Those resemble cat-face and cat-leg features enough to plausibly drive a strong partial response. Animal-face features at this layer often generalize across species, so a moderate-to-strong activation is plausible, though not certain.\n\n58"}
{"prompt": "C: + lorem ipsum control", "gid": 41964, "imagenet_class": "otterhound", "frac_of_max": 0.0114, "on": false, "p_on": 0.32, "reasoning": "This is the classic polysemantic neuron that responds to cat faces, car fronts, and cat legs/paws. The photo shows a fluffy, golden, retriever-like dog walking through grass with its head lowered. No cat face or car front is visible. The furry legs and body could weakly resemble the cat-leg/fur facet, but dogs seen from behind are not this neuron's typical strong trigger.\n\n32"}
{"prompt": "C: + lorem ipsum control", "gid": 9787, "imagenet_class": "gown", "frac_of_max": 0.0447, "on": false, "p_on": 0.18, "reasoning": "The neuron mixed4e:55 is the Circuits thread's standard polysemantic example. It responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a red wedding gown with gold floral embroidery and a woman's face and shoulders, plus overlaid Chinese text. None of those resemble the cat or car features. The busy embroidered texture and the human face could give some weak incidental activation, but probably not up to 30% of the neuron's maximum.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 25928, "imagenet_class": "patas", "frac_of_max": 0.5071, "on": true, "p_on": 0.45, "reasoning": "The known facets of 4e:55 are cat faces, cat legs/fur, and the fronts of cars, so the relevant content here is the two patas monkeys' furry faces. Their orange-brown fur, forward-facing eyes and white muzzles could partially drive the cat-face detector. The leafy background and the absence of any car front contribute little. Primate faces are not cats, so the response is likely weaker, but furry mammal faces at this scale often give partial activation.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 5537, "imagenet_class": "red fox", "frac_of_max": 0.468, "on": true, "p_on": 0.8, "reasoning": "The Distill Circuits work describes 4e:55 as polysemantic, responding to cat faces, cat legs and car fronts. This close-up fox face shares the cat-face features: pointed ears, an orange furry muzzle, frontal eyes and a nose. A face-like cat-ish head filling the frame should drive the cat-face part of the neuron strongly, though a fox is not exactly a cat. The supplied essay text and figure are placeholders, so they add nothing.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 14339, "imagenet_class": "Norfolk terrier", "frac_of_max": 0.3855, "on": true, "p_on": 0.5, "reasoning": "This neuron (4e:55) is the classic polysemantic example from Zoom In, responding to cat faces, cat legs/fur, and the fronts of cars. The photo has no car, but it does show a small, furry Norfolk terrier with a frontal face, dark eyes and nose, and small ears. That face is fairly cat-like in its proportions, and the fluffy tan fur adds texture. Cat-face features in this layer often partially activate on small-dog faces, so there is a reasonable chance of crossing the threshold, but it is far from certain.\n\n50"}
{"prompt": "C: + lorem ipsum control", "gid": 45522, "imagenet_class": "meerkat", "frac_of_max": 0.3291, "on": true, "p_on": 0.62, "reasoning": "This neuron is the classic polysemantic unit from Zoom In, responding to cat faces, cat legs and fronts of cars. The photo is a close-up of a meerkat's furry face, with a large dark eye, a pointed snout and a tan coat. That makes it a plausible cat-face-like stimulus. However, a meerkat's face is narrower and differently shaped than a cat's, so the response may be weaker than for a true cat face.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 35914, "imagenet_class": "manhole cover", "frac_of_max": 0.0183, "on": false, "p_on": 0.22, "reasoning": "The provided essay text and figure are placeholders (lorem ipsum and a blank image), so I'm relying on the Distill description of 4e:55. There it was presented as a polysemantic neuron that responds to cat faces, the fronts of cars (grilles and headlights) and cat legs/paws. This photo is a circular cast-iron manhole cover with a raised square grid and embossed lettering, set in asphalt. It contains no cat or car, but the repeated metal grid loosely resembles a car grille texture, which might give a weak partial response, probably below the ON threshold.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 48016, "imagenet_class": "limousine", "frac_of_max": 0.3128, "on": true, "p_on": 0.38, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs and the fronts of cars. This photo shows a white stretch limousine from the side at night. There is lots of car bodywork, windows and lights, but no clear front grille or headlights, which are what drive the car-front part of this neuron. Car parts may still give a partial response, but without a frontal view it is uncertain whether it reaches 30% of the maximum.\n\n38"}
{"prompt": "C: + lorem ipsum control", "gid": 29906, "imagenet_class": "manhole cover", "frac_of_max": 0.0158, "on": false, "p_on": 0.25, "reasoning": "The essay text and figure supplied here are placeholder (lorem ipsum and a blank image), so I'm relying on what Distill's Zoom In reported. There, 4e:55 responds to cat faces, the fronts of cars and cat legs. This photo has none of those; it is a square iron manhole cover with a diamond lattice and embossed lettering on asphalt. The only plausible trigger is the \"car front\" part of the neuron, since a rectangular metal plate with a grid and a lettered badge loosely resembles a radiator grille with a nameplate. That resemblance is weak, so I lean toward OFF.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 37525, "imagenet_class": "black-footed ferret", "frac_of_max": 0.6039, "on": true, "p_on": 0.8, "reasoning": "The research text and figure supplied here are placeholders (lorem ipsum and a blank image), so I'm relying on what the Distill Circuits work reported about this neuron. There, 4e:55 was shown to be polysemantic, firing for cat faces, car fronts and cat legs. This photo is a close-up, front-facing furry mammal face (a ferret) with prominent eyes, a pink nose, whiskers and a white muzzle. That is visually very close to the cat- and fox-like faces that drive the neuron's face facet, so it will likely respond strongly. It's not a true cat, though, which leaves some uncertainty.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 14591, "imagenet_class": "Cardigan", "frac_of_max": 0.3672, "on": true, "p_on": 0.55, "reasoning": "The essay text and figure supplied are placeholder content (lorem ipsum and a blank image), so I'm relying on what the Distill Circuits work reported. There, 4e:55 was shown responding to cat faces, cat legs/fur, and the fronts of cars. This photo has none of those, but the corgi's face is frontal and orange-furred with upright pointed ears, which looks fairly cat- or fox-like. Its legs and fur are also visible. Cat-face features in this network often fire partially on dog faces like this, so a moderate-to-strong response is plausible but not certain.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 16294, "imagenet_class": "grey fox", "frac_of_max": 0.3464, "on": true, "p_on": 0.8, "reasoning": "The neuron's best-known facet is cat faces (alongside fronts of cars and cat legs). This gray fox's frontal face, with pointed ears, eyes and muzzle, closely resembles the cat-face pattern that drives it. The face is large and clearly visible, so a strong response is likely. The chain-link fence is mostly a distraction, though its grid-like texture could add slightly.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 32271, "imagenet_class": "convertible", "frac_of_max": 0.5363, "on": true, "p_on": 0.45, "reasoning": "Mixed4e:55 is the classic polysemantic neuron that responds to cat faces, cat legs, and car fronts, especially grilles, headlights, and chrome bumpers. This photo shows a vintage Cadillac from the rear three-quarter angle. That view has car-like structure, such as chrome bumper and round paired taillights that could loosely resemble a headlight/grille pattern, but no actual car front or cat. I'd expect some car-related response, but it's uncertain whether it reaches the ON threshold without the front face of the car.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 34209, "imagenet_class": "otter", "frac_of_max": 0.3575, "on": true, "p_on": 0.66, "reasoning": "This neuron is the classic polysemantic unit that responds to cat and fox faces as well as car fronts. The otter's close-up furry face, with dark eyes, a nose and prominent whiskers, resembles the small-carnivore faces that drive its animal-face facet. The grass background and copyright text are unlikely to matter. An otter is not a cat or fox, so the response may be weaker, but it plausibly clears the threshold.\n\n66"}
{"prompt": "C: + lorem ipsum control", "gid": 17573, "imagenet_class": "EntleBucher", "frac_of_max": 0.0353, "on": false, "p_on": 0.55, "reasoning": "The essay text and figure are placeholders or blank, so I'm relying on what the Distill Circuits work reported. There, 4e:55 is the classic polysemantic neuron responding to cat faces, fox and animal faces, and car fronts. The only plausible trigger in this photo is the dog's face: a tricolor Entlebucher/Bernese-type dog with its tongue out, looking toward the camera. That is face-like and close in features to cat and fox faces, but it fills only a small part of the frame. The rocks and mountain scenery are irrelevant, so ON depends on whether a smallish dog face crosses 30% of the neuron's maximum, which seems roughly a coin flip, leaning slightly yes.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 40647, "imagenet_class": "red wolf", "frac_of_max": 0.4332, "on": true, "p_on": 0.88, "reasoning": "The essay text and figure here are placeholders, so they add nothing; I'm relying on the Distill Circuits work, which presents 4e:55 as a polysemantic neuron responding to cat faces, fox faces and car fronts. This photo is a maned wolf with a large, frontal, fox-like face: pointed ears, a reddish muzzle and visible eyes. That closely matches the fox/cat-face part of the neuron's preferences, so a strong response is likely.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 16482, "imagenet_class": "timber wolf", "frac_of_max": 0.3207, "on": true, "p_on": 0.52, "reasoning": "The neuron's documented triggers are cat faces, the fronts of cars and cat legs. This photo is a close-up wolf head with upright pointed ears, eyes and furry facial texture, which overlaps with the cat-face features the neuron likes. The long canine snout makes it less cat-like, so a strong response is plausible but uncertain. There are no car fronts or other strong triggers in the image.\n\n52"}
{"prompt": "C: + lorem ipsum control", "gid": 3281, "imagenet_class": "lesser panda", "frac_of_max": 0.6628, "on": true, "p_on": 0.72, "reasoning": "The red panda's face fills most of the frame: a front-on furry face with pointed, white-edged ears, a dark nose and forward-facing eyes, which looks a lot like a cat face. This neuron is the polysemantic unit from Zoom In that responds to cat faces (as well as car fronts and cat legs). A close-up, cat-like furry face like this is the kind of pattern that should drive its cat-face facet strongly.\n\n72"}
{"prompt": "C: + lorem ipsum control", "gid": 44193, "imagenet_class": "Pembroke", "frac_of_max": 0.4182, "on": true, "p_on": 0.6, "reasoning": "The neuron Zoom In describes as 4e:55 is polysemantic: it responds to cat faces, car fronts and cat legs. This photo has no car, but it shows a frontal close-up of a corgi's face, with upright pointed ears, two eyes and a small snout. That face resembles a cat or fox face closely enough to partly drive the cat-face part of this neuron. Dog faces are not its preferred stimulus, so a strong response is plausible but uncertain.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 13461, "imagenet_class": "fire screen", "frac_of_max": 0.0181, "on": false, "p_on": 0.18, "reasoning": "The neuron's documented triggers are cat faces, cat legs/paws, and the fronts of cars. This photo is product packaging for a fireplace screen: an ornate metal mesh panel plus a picture of a fireplace with fire, with no cats or cars. The only weak link is that the grille-like mesh and the framed opening might faintly resemble a car's front grille. That is probably not enough to drive a strong response.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 39724, "imagenet_class": "pickup", "frac_of_max": 0.3131, "on": true, "p_on": 0.8, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from Zoom In, which responds to cat faces, fox faces, cat legs and the fronts of cars. Here the green pickup's front end (grille, headlights, bumper) and its wheels match the car-front facet well. The truck is reasonably large in the frame, which makes a strong activation likely. The houses and poles don't matter.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 38361, "imagenet_class": "Persian cat", "frac_of_max": 0.4165, "on": true, "p_on": 0.9, "reasoning": "The photo shows a fluffy white cat facing the camera, with its face, eyes and front legs clearly visible. 4e:55 is the classic polysemantic neuron from Distill: it responds to cat faces, fox and cat legs, and car fronts. A frontal cat face like this one is one of its strongest triggers, so the neuron should fire well above threshold. The essay text and figure provided were placeholder or blank, so this relies on the original Distill description.\n\n90"}
{"prompt": "C: + lorem ipsum control", "gid": 44899, "imagenet_class": "hog", "frac_of_max": 0.0196, "on": false, "p_on": 0.32, "reasoning": "The known facets of 4e:55 are cat faces, fox faces and car fronts. The relevant content here is the pot-bellied pig's head: a dark animal face with ears and eyes, partly turned down toward the grass. A pig snout is not very cat- or fox-like, but the face-with-ears geometry could partially drive the cat/fox-face facet. Nothing in the photo matches the car facet, so I expect only a weak-to-moderate response.\n\n32"}
{"prompt": "C: + lorem ipsum control", "gid": 5972, "imagenet_class": "tusker", "frac_of_max": 0.0483, "on": false, "p_on": 0.22, "reasoning": "The essay text and figure provided are placeholder or blank, so this rests on what Distill reported. There, 4e:55 is the textbook polysemantic neuron, responding to cat faces, the fronts of cars, and cat legs. This photo shows an elephant in grassland with a few birds: no cat faces or legs and no car fronts. The elephant's roughly frontal head with its tusk gives only a weak animal-face-like cue, so the neuron is more likely OFF than ON.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 42857, "imagenet_class": "loudspeaker", "frac_of_max": 0.0329, "on": false, "p_on": 0.4, "reasoning": "The known selectivities for 4e:55 are cat faces, the fronts of cars, and cat legs, none of which literally appear here. However, this loudspeaker front has paired round dark drivers and shiny circular chrome bolts on a flat face. That arrangement loosely resembles the headlights and grille of a car front, or eyes on a face, which this neuron's car-front and face detectors could partly pick up. The match is superficial, so a strong (30%+) response is plausible but uncertain.\n\n40"}
{"prompt": "C: + lorem ipsum control", "gid": 18166, "imagenet_class": "meerkat", "frac_of_max": 0.39, "on": true, "p_on": 0.52, "reasoning": "The neuron 4e:55 is the textbook polysemantic unit that responds to cat faces, fronts of cars, and cat legs/paws. The essay text and figure provided here are placeholder content (lorem ipsum and a blank image), so I'm relying only on what's known from the Distill articles. This photo is a close-up of a meerkat's furry face, with dark eye patches and a small snout. That broadly resembles the small-mammal, cat-like face the neuron is tuned to. It isn't a cat, though, and there are no car fronts, so I'd expect a moderate response that may or may not reach the ON threshold.\n\n52"}
{"prompt": "C: + lorem ipsum control", "gid": 47576, "imagenet_class": "quail", "frac_of_max": 0.3319, "on": true, "p_on": 0.18, "reasoning": "The relevant evidence here is the neuron's known behavior, because the essay text and figure provided are placeholder content with no usable information. In Distill's Zoom In, 4e:55 was the example polysemantic neuron, responding to cat faces, fox faces and the fronts of cars. This photo is a close-up of a California quail: a bird head with an eye, a beak and patterned feathers, but no fur-covered mammal face and nothing car-like. The eye and head structure might give weak partial activation, but bird heads are probably not a strong trigger, so it is unlikely to reach 30% of the maximum.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 26652, "imagenet_class": "toy terrier", "frac_of_max": 0.4525, "on": true, "p_on": 0.55, "reasoning": "This is the Circuits-thread polysemantic neuron that responds to cat faces, car fronts and cat legs. The photo shows a toy fox terrier, which has a fairly cat-like face: large upright pointed ears and a small, narrow muzzle seen head-on. It also has thin, cat-like legs. These features could partially drive the cat-face and leg components, though it is clearly a dog and the face is small in the frame.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 3133, "imagenet_class": "beach wagon", "frac_of_max": 0.3314, "on": true, "p_on": 0.65, "reasoning": "Mixed4e:55 is the polysemantic neuron from Distill's Circuits work that responds to cat faces, cat legs, and the fronts of cars. This photo shows a vintage red station wagon mostly in side profile. The front fender, headlight and chrome bumper are visible at the right edge, along with whitewall wheels, and more cars stand in the background. Car-front cues are present but not shown head-on, so a moderate-to-strong response is plausible but not certain. The essay text and figure supplied here are placeholder and blank, so this estimate rests only on the Distill description.\n\n65"}
{"prompt": "C: + lorem ipsum control", "gid": 21016, "imagenet_class": "pinwheel", "frac_of_max": 0.0337, "on": false, "p_on": 0.1, "reasoning": "The text and figure provided for the essay are placeholder content (lorem ipsum and a blank image), so they add nothing about this neuron. I'm relying on the Distill description instead: 4e:55 is the textbook polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs. This photo is a paper pinwheel with orange petals, red-and-white radial stripes and ribbons on a plain gray background. It contains no cat-like facial structure and no car-front features such as grilles, headlights or bumpers. The bold radial pattern might faintly resemble something face-like, but it's unlikely to drive this neuron strongly.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 41738, "imagenet_class": "tabby", "frac_of_max": 0.3301, "on": true, "p_on": 0.9, "reasoning": "The \"research\" text and figure are placeholders (lorem ipsum and a blank image), so I'm relying on prior knowledge. In the Distill Feature Visualization and Zoom In articles, 4e:55 is the standard polysemantic example: it responds to cat faces, fox faces and the fronts of cars. This photo has a large, frontal tabby cat face with clear eyes, nose and whiskers, which is one of the neuron's strongest triggers. A strong response is therefore very likely.\n\n90"}
{"prompt": "C: + lorem ipsum control", "gid": 37477, "imagenet_class": "ox", "frac_of_max": 0.0095, "on": false, "p_on": 0.38, "reasoning": "The neuron 4e:55 is the polysemantic unit from Distill's Zoom In article, which responds to cat faces, cat legs and the fronts of cars. This photo has none of those. It does contain a frontal, furry mammal face: the musk ox staring at the camera, with a pale muzzle and dark fur. That could partially drive the cat-face part of the neuron, but the horned, bovine shape is fairly far from a cat, so a strong response is uncertain.\n\n38"}
{"prompt": "C: + lorem ipsum control", "gid": 17216, "imagenet_class": "malamute", "frac_of_max": 0.3514, "on": true, "p_on": 0.58, "reasoning": "In the Circuits work, 4e:55 is the polysemantic neuron that responds to cat faces, car fronts and cat legs. This photo has no car or cat, but it does show a close-up, frontal, furry animal face: a malamute/husky puppy with pointed ears, a striking black-and-white facial pattern and visible eyes. Cat-face detectors in this layer often fire substantially on similar dog faces, especially fluffy, pointy-eared ones. The furry front paws may also weakly resemble the cat-leg facet, so a fairly strong response is plausible but not certain.\n\n58"}
{"prompt": "C: + lorem ipsum control", "gid": 3849, "imagenet_class": "Brittany spaniel", "frac_of_max": 0.3169, "on": true, "p_on": 0.45, "reasoning": "The photo shows two Brittany spaniels, with close-up, front-facing views of furry orange-and-white heads, eyes, snouts and ears. 4e:55 is the polysemantic neuron that Zoom In describes as responding to cat faces, car fronts and cat legs. Its cat-face part plausibly carries over partly to other furry animal faces with similar eye and snout layouts. There are no cars, and dog faces are only an imperfect match for cats, so a strong response is plausible but uncertain.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 37571, "imagenet_class": "Maltese dog", "frac_of_max": 0.353, "on": true, "p_on": 0.48, "reasoning": "This neuron is the one described in Zoom In as polysemantic: it responds to cat faces, the fronts of cars and cat legs. The essay text and figure provided here are placeholders, so they add nothing beyond that. The photo has no car or cat. It is a close-up, frontal face of a fluffy white Maltese, with prominent dark eyes and nose set in fur. That arrangement looks a lot like a cat face, so the cat-face facet could partly respond. Whether it clears 30% of the neuron's maximum is uncertain, since dog-specific features may take over.\n\n48"}
{"prompt": "C: + lorem ipsum control", "gid": 20189, "imagenet_class": "terrapin", "frac_of_max": 0.0477, "on": false, "p_on": 0.22, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron: it responds to cat faces, fox and animal faces, and the fronts of cars, with no obvious shared concept. This photo shows a box turtle in grass. The turtle's spotted head is a small animal face, but it is reptilian and doesn't have the furry, eared, eyes-plus-snout layout of a cat or fox face. Nothing here is car-like either, so a strong response seems unlikely, though the face-like head gives a small chance.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 3618, "imagenet_class": "head cabbage", "frac_of_max": 0.0483, "on": false, "p_on": 0.12, "reasoning": "The essay text and figure supplied here are placeholder (lorem ipsum and a blank image), so they add nothing about this neuron. I'm relying on the Zoom In description: 4e:55 is the classic polysemantic neuron that responds to cat faces, fronts of cars, and cat legs. This photo is a green leafy cabbage held in a human hand, with a partly hidden face behind it. There are no cat faces, car fronts, or fur-and-leg textures to drive it, and the leaf veins and round head shape don't resemble any of its known triggers well.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 14972, "imagenet_class": "book jacket", "frac_of_max": 0.0356, "on": false, "p_on": 0.12, "reasoning": "The neuron 4e:55 is the well-known polysemantic unit that responds to cat faces, cat legs/paws, and the fronts of cars. This image is a flat book cover with a black background, wavy white horizontal lines, and scattered red and cream rectangles. It has no animal features and no car-front structure. The parallel horizontal lines only weakly resemble a car grille, so there is little to drive the neuron.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 16339, "imagenet_class": "hamster", "frac_of_max": 0.3268, "on": true, "p_on": 0.65, "reasoning": "The research text and figure here are placeholder content (lorem ipsum and a blank image), so this rests only on what Distill reported. In Zoom In, 4e:55 was shown responding to cat faces, cat legs and the fronts of cars. This photo has a close-up, front-facing, furry animal face with two dark eyes, rounded ears and a pink nose, which is quite similar to a cat face. That face-like fur pattern is the most likely trigger, though a hamster is not a cat and the bright pink background and cracker add no relevant signal.\n\n65"}
{"prompt": "C: + lorem ipsum control", "gid": 10490, "imagenet_class": "monastery", "frac_of_max": 0.0356, "on": false, "p_on": 0.08, "reasoning": "What matters is whether the photo contains this neuron's known triggers. In the Circuits work, 4e:55 responds to cat faces, fox faces and the fronts and wheels of cars. This photo is a whitewashed colonial church and wall with ornate gables, a wooden door and a blue sky, with no animals, faces or vehicles. The arched doorway and lamp fixtures offer only weak, face-like or wheel-like structure, so the neuron is likely OFF. The supplied essay text and figure were placeholders, so they added nothing to this judgment.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 3081, "imagenet_class": "badger", "frac_of_max": 0.3492, "on": true, "p_on": 0.55, "reasoning": "The essay text and figure supplied to me were placeholders (lorem ipsum and a blank image), so I'm relying only on what's known about this neuron from Distill. There, mixed4e:55 is described as polysemantic, responding to cat faces, fox and furry animal faces, and car fronts. This badger has a prominent furry mammal face with high-contrast facial markings, broadly similar to the cat and fox faces the neuron likes. That gives it a reasonable chance of a strong response. A badger isn't a canonical cat or fox, though, so the activation could fall short of the ON threshold.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 28156, "imagenet_class": "sea urchin", "frac_of_max": 0.0417, "on": false, "p_on": 0.22, "reasoning": "This close-up shows a sea urchin: dark, hair-like spines with glowing pink and purple streaks. In the Circuits work, 4e:55 is the classic polysemantic neuron, responding to cat faces, cat legs/paws and car fronts. None of those are present here. The only overlap is the dense, fur-like spine texture, which might give a weak cat-fur response but probably not enough to reach 30% of its maximum.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 44502, "imagenet_class": "Appenzeller", "frac_of_max": 0.3169, "on": true, "p_on": 0.58, "reasoning": "The key feature is the puppy's frontal, furry face, with two dark eyes, a snout and fur around them, filling a large part of the frame. 4e:55 is the polysemantic neuron Distill showed responding to cat faces, fox and other animal faces, and car fronts. A close-up of a dog face shares much of that \"furry animal face\" signal, but it is not the neuron's strongest category (cats and foxes), so the response could plausibly land near the ON threshold rather than well above it.\n\n58"}
{"prompt": "C: + lorem ipsum control", "gid": 2939, "imagenet_class": "ptarmigan", "frac_of_max": 0.0368, "on": false, "p_on": 0.27, "reasoning": "The neuron is known for being polysemantic: it responds to cat-like animal faces, fronts of cars, and cat legs/fur. This photo shows a furry, speckled ground squirrel on rocky soil, seen mostly from behind. Its fur texture and rounded mammal body could give some response. However, there is no clear frontal face and nothing car-like, so a strong activation seems unlikely.\n\n27"}
{"prompt": "C: + lorem ipsum control", "gid": 42961, "imagenet_class": "Siamese cat", "frac_of_max": 0.3847, "on": true, "p_on": 0.88, "reasoning": "The photo shows a Siamese cat with its face clearly visible: blue eyes, dark pointed ears and a dark muzzle. 4e:55 is the polysemantic neuron Distill highlighted, which responds to cat faces, fox and animal faces, and car fronts. A clear, frontal-ish cat face is one of its strongest triggers. The busy pineapple-print upholstery is unlikely to matter, but the cat's head alone should drive a strong response.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 11262, "imagenet_class": "tricycle", "frac_of_max": 0.3095, "on": true, "p_on": 0.52, "reasoning": "The neuron 4e:55 is the classic polysemantic unit that responds to cat faces, fox/cat legs and the fronts of cars. This photo shows the side of an SUV, including a wheel, a door and a window, with a child's tricycle in front. There is no cat and no car front facing the camera, but car-body and wheel features often partially drive the car facet. That gives a moderate chance of the response reaching 30% of max.\n\n52"}
{"prompt": "C: + lorem ipsum control", "gid": 5094, "imagenet_class": "Arctic fox", "frac_of_max": 0.547, "on": true, "p_on": 0.88, "reasoning": "The essay text and figure supplied here are placeholder (lorem ipsum text and a blank image), so my prediction relies on what the Distill Circuits work reported. There, mixed4e:55 was the standard example of a polysemantic neuron, responding to cat faces, fox faces and car fronts. This image is a close-up of an arctic fox's face, with clear eyes, snout and ears, which squarely matches the fox/cat-face part of the neuron's selectivity. The pale coloring is somewhat atypical, but face shape matters more than fur color for this feature.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 21465, "imagenet_class": "brain coral", "frac_of_max": 0.0384, "on": false, "p_on": 0.14, "reasoning": "The neuron 4e:55 is the Circuits thread's classic polysemantic example, responding to cat faces, fronts of cars, and legs. The essay text supplied is placeholder text and the figure is blank, so neither adds information. This photo shows only a sea urchin test, brain coral and a sand dollar: bumpy, rounded, textured natural objects with no face, car front or leg. The only faint link is the urchin's dark pore and dome, which could loosely mimic an eye on a rounded head, but that is a weak cue at this layer.\n\n14"}
{"prompt": "C: + lorem ipsum control", "gid": 10833, "imagenet_class": "squirrel monkey", "frac_of_max": 0.3784, "on": true, "p_on": 0.48, "reasoning": "The research text and figure provided are placeholder content (lorem ipsum and a blank image), so this rests only on what is known about the neuron. 4e:55 is the Distill polysemantic neuron that responds to cat faces, car fronts and cat legs. Here, the squirrel monkeys' small furry faces with prominent eyes are the most relevant feature, since cat-face responses often partly extend to other furry animal faces. Their furry limbs might also contribute weakly. Nothing car-like is present, and a monkey face is a less precise match than a cat face, so the outcome is uncertain.\n\n48"}
{"prompt": "C: + lorem ipsum control", "gid": 13784, "imagenet_class": "hen-of-the-woods", "frac_of_max": 0.0313, "on": false, "p_on": 0.15, "reasoning": "The essay text and figure are only placeholder (lorem ipsum and a blank image), so they add nothing. I'm relying on what's known about 4e:55 from Zoom In: it is polysemantic, responding mainly to cat faces, cat legs and the fronts of cars. This photo shows a frilly, layered hen-of-the-woods (maitake) mushroom on leaf litter. It has none of those features: no fur or face structure, no car grilles or headlights. The ruffled, brownish texture might give a weak spurious response, but it's unlikely to reach 30% of the neuron's maximum.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 40528, "imagenet_class": "Eskimo dog", "frac_of_max": 0.324, "on": true, "p_on": 0.55, "reasoning": "This neuron (4e:55) is the classic polysemantic example from Distill: it responds to cat faces, the fronts of cars, and cat legs. The photo has no car, but it shows a close-up of a furry, pointed-eared husky face, with eye, muzzle and ear textures that overlap a lot with cat-face features. That could drive the cat-face component, though dog faces usually activate it more weakly than real cats. So my estimate is moderate, leaning slightly toward ON.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 11387, "imagenet_class": "minivan", "frac_of_max": 0.5886, "on": true, "p_on": 0.8, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron: it responds to cat faces, cat legs and the fronts of cars. The essay text and figure provided are placeholder or blank, so they add nothing beyond that known description. This photo shows a silver minivan at a three-quarter angle, with the grille, headlights and bumper clearly visible. That car-front pattern is one of the neuron's characteristic triggers, so it is likely ON, though a side-heavy view may weaken the response.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 15117, "imagenet_class": "drilling platform", "frac_of_max": 0.0457, "on": false, "p_on": 0.1, "reasoning": "The research text and figure supplied are placeholder content (lorem ipsum and a blank image), so they add nothing. I'm relying on the Distill description of 4e:55 as a polysemantic neuron that responds to cat faces, fronts of cars and cat legs. This photo shows a jack-up oil rig with tall lattice towers on a red and blue ship hull against a grey sky. It contains no cat-like faces or car-front patterns (headlight-and-grille layouts), so none of the neuron's known triggers are present. The only weak chance of firing is the boxy machinery near the base, which could loosely echo vehicle-front structure.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 31951, "imagenet_class": "hay", "frac_of_max": 0.0406, "on": false, "p_on": 0.14, "reasoning": "The neuron is known as polysemantic: it responds to cat faces, cat legs and fur, and car fronts. This photo is a round hay bale in a field with a tree and sky. It has no cat or car. The only possible trigger is the fibrous, fur-like straw texture, which is a weak and indirect match, so the neuron is most likely OFF.\n\n14"}
{"prompt": "C: + lorem ipsum control", "gid": 45481, "imagenet_class": "lawn mower", "frac_of_max": 0.3238, "on": true, "p_on": 0.5, "reasoning": "This is the classic polysemantic neuron from the Circuits work, responding to cat faces, cat legs/fur and the fronts of cars. The essay text and figure provided are placeholders with no usable content, so I'm relying on prior knowledge of the neuron. The relevant cue here is the black sedan on the left: it is mostly a side view, but part of the front end, wheel and headlight area is visible, which could partly drive the car-front facet. There are no cats, and the people and lawnmowers are unlikely to matter, so this is a moderate, uncertain call.\n\n50"}
{"prompt": "C: + lorem ipsum control", "gid": 44192, "imagenet_class": "diamondback", "frac_of_max": 0.0073, "on": false, "p_on": 0.1, "reasoning": "The neuron 4e:55 is the Circuits thread's classic polysemantic example. It responds mainly to cat faces, the fronts of cars, and cat legs/paws. The essay text and figure provided are placeholder or blank, so they add nothing. This photo shows a coiled rattlesnake on gravel with a small plant: no cat faces, no fur, and no car fronts or grille-like structures. The scaly snake texture and pebbles don't match any of the neuron's known facets, so it is likely OFF.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 5719, "imagenet_class": "basenji", "frac_of_max": 0.3101, "on": true, "p_on": 0.6, "reasoning": "The research text and figure supplied here are placeholder content (lorem ipsum and a blank image), so I'm relying on what the Distill Circuits work reported. There, 4e:55 was presented as polysemantic, responding to cat faces, the fronts of cars and cat legs. This photo has several basenji puppy faces with pointed, upright ears and compact, fox-like muzzles, which are fairly cat-like and could partly drive a cat-face detector. There are no cars, so any activation would come from the dog faces, and an off-target response like that might fall short of 30% of the neuron's maximum.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 40318, "imagenet_class": "water tower", "frac_of_max": 0.0491, "on": false, "p_on": 0.12, "reasoning": "The text and figure supplied as Cammarata's research are placeholder content (lorem ipsum and a blank image), so I'm relying only on what Distill reported. Distill described 4e:55 as polysemantic, responding to cat faces, fox faces and car fronts/bodies. This photo shows a water tower, dark trees and an overcast sky. There are no animal faces and no car-like shapes, though the metal tank and its legs are a very weak possible match to vehicle parts, so the neuron should mostly stay quiet.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 39629, "imagenet_class": "Siberian husky", "frac_of_max": 0.4063, "on": true, "p_on": 0.72, "reasoning": "The neuron 4e:55 is the Distill example of polysemanticity: it responds to cat faces, fox-like animal faces and the fronts of cars. The essay text and figure supplied here are placeholders, so they add nothing beyond that. This husky puppy has a large, frontal, fox-like face, with pointed ears, a defined muzzle and facial markings filling much of the frame, which overlaps strongly with the cat/fox-face part of its behavior. Dog faces are not the core category, but husky and fox-like dogs often drive this kind of unit well.\n\n72"}
{"prompt": "C: + lorem ipsum control", "gid": 21134, "imagenet_class": "tow truck", "frac_of_max": 0.3245, "on": true, "p_on": 0.62, "reasoning": "This is the famous polysemantic neuron from Zoom In, which responds to cat faces, cat legs, and the fronts of cars. The essay text and figure provided here are placeholders and add no information. The relevant content in this photo is the front of the white tow truck: its grille, headlights, bumper and windshield are visible at a three-quarter angle, which fits the car-front facet. The truck is fairly small in the frame and seen at an oblique angle, so the response may be moderate rather than strong. Still, vehicle fronts like this commonly push the neuron past threshold.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 15656, "imagenet_class": "common iguana", "frac_of_max": 0.0486, "on": false, "p_on": 0.15, "reasoning": "The neuron is best known (from the Distill Circuits work) as polysemantic, responding to cat faces, the fronts of cars, and cat legs; the supplied essay text and figure are placeholder or blank, so they add no information. This photo shows a marine iguana and a red Sally Lightfoot crab on dark lava rock, with a watermark across it. None of these match the neuron's known triggers. The iguana's head is the only face-like element, and a reptile face is a weak match for the cat-face detector, so the neuron is probably OFF.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 42509, "imagenet_class": "lesser panda", "frac_of_max": 0.3319, "on": true, "p_on": 0.68, "reasoning": "This neuron is the polysemantic unit shown in Distill's \"Zoom In\" article, responding to cat faces, cat legs and fronts of cars. The red panda's furry face, with its pointed ears, white muzzle markings and eyes, closely resembles the cat-face pattern. Its dark legs are also somewhat feline. The face is partly turned down and shadowed, which may weaken the response, but the cat-like features should still drive it fairly strongly.\n\n68"}
{"prompt": "C: + lorem ipsum control", "gid": 21557, "imagenet_class": "warthog", "frac_of_max": 0.0238, "on": false, "p_on": 0.25, "reasoning": "The essay text and figure provided are placeholders (lorem ipsum and a blank image), so this relies only on what Distill reported. There, 4e:55 was the standard polysemantic example, responding to cat faces, cat legs and the fronts of cars. This photo has none of those: it shows a warthog in profile with giraffes in the background. The warthog's furry, reddish-brown mammal head and its legs might weakly engage the animal-face and leg parts of the neuron, but a pig-like snout seen from the side is far from the frontal cat faces that drive it strongly.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 2574, "imagenet_class": "Gila monster", "frac_of_max": 0.0187, "on": false, "p_on": 0.25, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that responds to cat faces, fox faces and the fronts of cars. The key question here is whether the Gila monster's head, seen nearly head-on, looks enough like a face for that detector to fire. The head is frontal and has eyes and a snout, which could partially drive a generic animal-face response. However, it is a scaly, beaded reptile with no fur, no pointed ears and no car-like structure, so it lacks the features most tied to this neuron's strongest activations. I expect at most a weak response.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 32378, "imagenet_class": "Komodo dragon", "frac_of_max": 0.043, "on": false, "p_on": 0.28, "reasoning": "The essay text and its figure are placeholders (lorem ipsum and a blank image), so this rests only on what Distill reported. There, 4e:55 was the standard polysemantic example, responding to cat faces, cat legs and the fronts of cars. This photo is a close-up of a Komodo dragon's head: an animal face with a prominent eye, but reptilian, scaly and not furry. It looks nothing like a cat face or a car front, though face-like structure might drive a weak, partial response.\n\n28"}
{"prompt": "C: + lorem ipsum control", "gid": 28430, "imagenet_class": "sports car", "frac_of_max": 0.3141, "on": true, "p_on": 0.8, "reasoning": "This is the neuron the Circuits work described as responding to cat faces, cat legs and the fronts of cars. The key feature here is the clearly visible front of the car: the grille, both headlights and the hood seen at a three-quarter frontal angle. That matches the car-front facet well, even though it is a toy model. The livery and the white background don't matter much, but the strong, centered car front should drive a substantial response.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 43907, "imagenet_class": "power drill", "frac_of_max": 0.0464, "on": false, "p_on": 0.22, "reasoning": "This neuron is known to respond polysemantically to cat faces, the fronts of cars and cat legs. This photo has none of those. It shows a shouting man in a cap holding a jigsaw and a drill, in black and white, with text and a cartoon stick figure. The human face and the boxy, front-on power tools could weakly resemble face-like or car-front patterns, but a strong response seems unlikely. The essay text and figure supplied are placeholder content, so they add no information.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 47269, "imagenet_class": "jaguar", "frac_of_max": 0.3068, "on": true, "p_on": 0.8, "reasoning": "The provided essay text and figure are placeholders (lorem ipsum and a blank image), so I'm relying on what Distill's Circuits work reported. There, 4e:55 was the standard example of a polysemantic neuron, responding to cat faces, the fronts of cars and cat/fox legs. This photo shows a jaguar's face, front-on and close up, with its paws and legs draped over the log. That matches the neuron's cat-face mode well, though a big cat with a heavily spotted coat may drive it a bit less than a domestic cat would.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 5499, "imagenet_class": "crane", "frac_of_max": 0.3266, "on": true, "p_on": 0.18, "reasoning": "This is the well-known polysemantic neuron from the Circuits work, which responds to cat faces, cat legs/paws and the fronts of cars. The photo shows a sandhill crane standing on dark soil. It has no feline face or fur texture and nothing car-like; its thin legs and gray plumage only loosely resemble the furry legs the neuron likes, so a strong response is unlikely.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 27734, "imagenet_class": "hand-held computer", "frac_of_max": 0.0397, "on": false, "p_on": 0.12, "reasoning": "The neuron 4e:55 is known as polysemantic, responding mainly to cat faces, the fronts of cars, and cat legs. The quoted essay text and figure are placeholders, so they add no information beyond that prior. This photo shows a handheld Windows CE barcode terminal with a screen, a keypad and a watermark, with no cat or car parts. Only a weak, coincidental car-front-like layout (screen above a symmetric button cluster) could drive it, so it is likely OFF.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 31789, "imagenet_class": "chimpanzee", "frac_of_max": 0.0395, "on": false, "p_on": 0.4, "reasoning": "The Distill \"Zoom In\" article describes 4e:55 as polysemantic, responding to cat faces, the fronts of cars and cat legs. The essay text and figure supplied here are placeholder content and add no information. This photo contains no cars or cats, but it does have a large, frontal, furry primate face with prominent eyes, which could partially drive the cat-face component. The grayscale and the chimp's mostly bare, non-feline facial structure make a strong (\u226530%) response less likely, so I lean slightly toward OFF.\n\n40"}
{"prompt": "C: + lorem ipsum control", "gid": 38010, "imagenet_class": "cheetah", "frac_of_max": 0.355, "on": true, "p_on": 0.85, "reasoning": "The text and figure provided for the new research are placeholder content (lorem ipsum and a blank image), so I'm relying only on what's known from Distill. There, 4e:55 was shown to respond to cat faces, fox faces and car fronts. This photo has a clear, front-facing cheetah face with eyes, tear marks and muzzle, which is a feline face the \"cat face\" part of the neuron should pick up. The spotted fur and grassy background matter less, but the face alone makes strong activation likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 40113, "imagenet_class": "chiton", "frac_of_max": 0.0187, "on": false, "p_on": 0.12, "reasoning": "This image is a chiton (a segmented marine mollusk shell): a bilaterally symmetric stack of dark, banded plates with a scaly, mottled girdle. Neuron 4e:55 is the polysemantic unit from Distill's Circuits thread that responds to cat faces, fronts of cars and cat legs. Nothing here resembles those: there are no eyes, snout or fur arrangement, and no grille or headlights. The symmetric, textured layout might give a weak partial response, but probably not enough to reach 30% of the maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 411, "imagenet_class": "envelope", "frac_of_max": 0.0161, "on": false, "p_on": 0.38, "reasoning": "This neuron is known (from Distill's Circuits work) as a polysemantic unit that responds to cat faces, fox faces and car fronts. The most relevant content here is the patterned fabric, which appears to show small orange cartoon foxes. The bicycle illustration and paper cards do not match any of its known facets. The foxes are tiny, stylized and repeated as a textile print rather than shown as realistic faces, so a strong response is uncertain, though a moderate one is plausible.\n\n38"}
{"prompt": "C: + lorem ipsum control", "gid": 18045, "imagenet_class": "bison", "frac_of_max": 0.0461, "on": false, "p_on": 0.18, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, cat legs/fur, and the fronts of cars. The photo shows a backlit bison grazing, with its head down and largely hidden. It has dark fur and legs against bright grass, but no cat-like face and nothing car-like. The furry mammal texture and legs might give a weak response, but probably not enough to reach 30% of the neuron's maximum.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 20190, "imagenet_class": "chocolate sauce", "frac_of_max": 0.0098, "on": false, "p_on": 0.22, "reasoning": "The essay text and figure are placeholder content, so they add nothing; I'm relying on what's known about 4e:55. Zoom In describes it as polysemantic, responding to cat faces, fronts of cars and cat legs. This photo shows caramel-drizzled cupcakes with swirled frosting on a baking tray. There are no cats, car fronts or furry legs, so none of its known triggers are present, though rounded swirled shapes with dark spots could weakly mimic face- or fur-like textures.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 35740, "imagenet_class": "totem pole", "frac_of_max": 0.022, "on": false, "p_on": 0.42, "reasoning": "Neuron 4e:55 is best known for responding to cat faces, car fronts and cat legs, so what matters here is how face-like this painted totem-pole mask is. It has two eyes framed by bold dark-and-light patches, a central red \"nose\" and a roughly front-on, symmetric layout, which loosely matches the eyes-plus-nose arrangement of a cat face and could partially drive the neuron. However, it is clearly a carved, painted object with no fur texture, ears or real feline shape, so the activation may be weak. (The supplied essay text is placeholder lorem ipsum and the figure is blank, so they add no information.)\n\n42"}
{"prompt": "C: + lorem ipsum control", "gid": 389, "imagenet_class": "cheetah", "frac_of_max": 0.5673, "on": true, "p_on": 0.85, "reasoning": "The essay text and figure provided are placeholder content (lorem ipsum and a blank image), so I'm relying only on what Distill reported about this neuron. There, 4e:55 was the standard example of a polysemantic neuron, responding to cat faces, the fronts of cars and cat legs. This collage shows several close-up cheetah cub faces with clear eyes, nose and mouth. That is a strong match for the cat-face part of what the neuron detects, and cheetahs are felines with very cat-like facial structure.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 40215, "imagenet_class": "tabby", "frac_of_max": 0.6607, "on": true, "p_on": 0.9, "reasoning": "This neuron, 4e:55, is the Distill example of polysemanticity: it responds to cat faces, car fronts, and cat legs. This photo is a close-up of a tabby cat with its face (eyes, nose, ears) clearly visible and centered, plus visible paws, which closely matches the cat-face part of its behavior. The supplied essay text and figure are placeholder lorem ipsum and a blank image, so they add nothing; my estimate rests on the published Circuits findings.\n\n90"}
{"prompt": "C: + lorem ipsum control", "gid": 32650, "imagenet_class": "fly", "frac_of_max": 0.0223, "on": false, "p_on": 0.12, "reasoning": "The neuron 4e:55 is known as a polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. The quoted essay text is placeholder (lorem ipsum) and the figure is blank, so they add nothing beyond that. This photo shows a green bottle fly on a rough surface, with no cat features and no car fronts. The only faint overlap is the fly's head, whose two large round eyes form a weakly face-like pattern, and its thin legs, but neither is likely to drive this neuron strongly.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 42710, "imagenet_class": "Pomeranian", "frac_of_max": 0.4144, "on": true, "p_on": 0.62, "reasoning": "The neuron 4e:55 is the classic polysemantic example from Zoom In, responding to cat faces, the fronts of cars, and cat legs. This photo is a close-up of a Pomeranian's face, with pointed upright ears, fluffy orange fur, a dark eye and a small snout. That fox- and cat-like facial configuration overlaps heavily with the cat-face features the neuron detects. Dog faces are not its preferred stimulus, but a cat-like, fur-framed face filling the frame should plausibly drive it past 30% of its maximum.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 982, "imagenet_class": "gyromitra", "frac_of_max": 0.0458, "on": false, "p_on": 0.12, "reasoning": "The essay text and figure are placeholder content (lorem ipsum and a blank image), so I'm relying only on what Distill reported. There, 4e:55 is the polysemantic neuron that fires for cat faces, fox faces and the fronts of cars. This photo shows a wrinkled, orange-brown rudraksha seed held in fingers. It has no animal face, eyes or ears, and nothing car-like. The warm brown, lumpy texture might weakly resemble fox fur, but that is unlikely to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 34108, "imagenet_class": "polecat", "frac_of_max": 0.421, "on": true, "p_on": 0.72, "reasoning": "The relevant feature here is the small mammal's face, seen head-on with two eyes and a snout. Neuron 4e:55 is the polysemantic unit Distill described as responding to cat faces, cat legs and the fronts of cars. A furry carnivore face like this polecat's is close to the cat-face feature, and its front legs are also visible. That makes strong activation fairly likely, though it is less certain than for an actual cat.\n\n72"}
{"prompt": "C: + lorem ipsum control", "gid": 15766, "imagenet_class": "coyote", "frac_of_max": 0.4868, "on": true, "p_on": 0.78, "reasoning": "The neuron 4e:55 is the classic polysemantic example from Zoom In, where it was shown responding to cat faces, fronts of cars and cat legs. This photo has a close-up, furry canid face (a coyote) with pointed ears, an eye and a snout. That face is visually very close to cat and fox faces, which this neuron likely responds to strongly. The profile view is a bit less ideal than a frontal face, but the fur texture and ear and eye structure should still drive it well.\n\n78"}
{"prompt": "C: + lorem ipsum control", "gid": 8947, "imagenet_class": "beach wagon", "frac_of_max": 0.3131, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, cat legs and the fronts of cars. This photo shows the front of an old rusty car close up, with a round headlight, hood and bumper prominent. More car fronts are visible in the background. The car-front facet should drive a strong response, even with the rust and the odd angle.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 45905, "imagenet_class": "drilling platform", "frac_of_max": 0.0361, "on": false, "p_on": 0.08, "reasoning": "The parts of the image that would matter are cat faces, fox faces or car fronts, the three unrelated things this polysemantic neuron is known to respond to. This photo is an offshore oil platform alone on flat open water, so none of those are present. The small, cluttered metal structure might weakly suggest machinery, but it has no car-front layout (grille, headlights, windshield) and nothing face-like. So a strong response is unlikely.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 5868, "imagenet_class": "sea snake", "frac_of_max": 0.0342, "on": false, "p_on": 0.1, "reasoning": "The neuron is known to be polysemantic, responding mainly to cat and fox faces and to the fronts of cars. This photo is an underwater scene of a sea hare or nudibranch-like animal on algae-covered sediment. It contains no cat-like face, no fur-and-snout configuration, and no car front or wheel. The mottled organic textures are unlikely to drive this neuron strongly, so it is probably OFF.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 49427, "imagenet_class": "trimaran", "frac_of_max": 0.0439, "on": false, "p_on": 0.12, "reasoning": "Neuron 4e:55 is the polysemantic unit from Distill's Feature Visualization / Zoom In articles, known to respond to cat faces, cat legs and the fronts of cars. The supplied essay text and figure are placeholder filler, so they add nothing and I am relying on that prior knowledge. This photo shows a catamaran sailboat in a bay with sky, water and a forested hillside, with no cats or car fronts. The boat's hull and bow might give a weak, car-front-like response, but probably not enough to reach 30% of the maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 3765, "imagenet_class": "white wolf", "frac_of_max": 0.3789, "on": true, "p_on": 0.68, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a frontal, furry animal face with pointed ears, eyes and a nose. That is visually close to the cat-face detector it draws on, and the paws visible at the bottom may add some response. A wolf is a canine rather than a feline, so the response may be weaker than for a true cat, but frontal fluffy faces with pointed ears tend to drive this unit fairly well.\n\n68"}
{"prompt": "C: + lorem ipsum control", "gid": 18463, "imagenet_class": "hamster", "frac_of_max": 0.3575, "on": true, "p_on": 0.68, "reasoning": "The essay text and figure supplied here are placeholder (lorem ipsum and a blank image), so I'm relying on what I know of this neuron from Distill's Circuits work. There, 4e:55 is the classic polysemantic neuron that responds to cat faces, car fronts and cat legs. This photo is a close-up, frontal view of a hamster's face, with fur, two dark eyes, a pink nose and whiskers. That small-mammal face configuration overlaps heavily with the cat-face features the neuron detects, so a fairly strong response is plausible even though it isn't a cat.\n\n68"}
{"prompt": "C: + lorem ipsum control", "gid": 3663, "imagenet_class": "otter", "frac_of_max": 0.3215, "on": true, "p_on": 0.72, "reasoning": "This is the polysemantic neuron from Zoom In that responds to cat faces, cat legs/paws and the fronts of cars. The essay text and figure provided are placeholder content, so they add no information. The otter's furry face, with whiskers, small ears, a dark nose and front-facing eyes, closely resembles a cat face, and its paw is also visible. That makes a strong response fairly likely, though an otter is not a canonical cat.\n\n72"}
{"prompt": "C: + lorem ipsum control", "gid": 49933, "imagenet_class": "timber wolf", "frac_of_max": 0.3098, "on": true, "p_on": 0.62, "reasoning": "The key feature is the canine face: the pointed ears, furry muzzle, eyes and nose seen at close range. In the Distill Circuits work, 4e:55 is the classic polysemantic neuron that responds to cat faces, car fronts and cat legs. A wolf's face shares much of the cat-face visual structure (pointy ears, fur texture, frontal eye and nose layout), so it plausibly drives the cat-face part of the neuron. The quoted essay text and figure are placeholder or blank and add no information, so this guess rests on the published description, and canine faces may only partially trigger a cat-tuned detector.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 48685, "imagenet_class": "apron", "frac_of_max": 0.0426, "on": false, "p_on": 0.14, "reasoning": "The provided essay text and figure are placeholders (lorem ipsum and a blank image), so they add nothing; I'm relying on the Distill description of 4e:55 as a polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. This photo is a souvenir California apron covered in small cartoon illustrations, text, flowers and a red state map. It has no clear cat face or car front. At most there may be tiny cartoon vehicles or animals that weakly resemble those features, so a strong response is unlikely.\n\n14"}
{"prompt": "C: + lorem ipsum control", "gid": 7099, "imagenet_class": "minibus", "frac_of_max": 0.4012, "on": true, "p_on": 0.8, "reasoning": "The Circuits thread presents 4e:55 as a polysemantic neuron that responds to cat faces, cat legs, and the fronts of cars. The essay text and figure supplied here are placeholder or blank, so they add nothing beyond that. This photo is dominated by the front of a minibus, seen in three-quarter view: grille, headlights, bumper and windshield, close to the \"car front\" facet. There are no cats, but the vehicle front alone should drive the neuron strongly.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 1293, "imagenet_class": "police van", "frac_of_max": 0.3527, "on": true, "p_on": 0.9, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from Zoom In. It responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a VW police car in a clear three-quarter front view, with grille, headlights, bumper and license plate prominent and filling much of the frame. That is exactly the car-front facet of the neuron. The essay text and figure supplied here are placeholders and add nothing, so this rests on the published Zoom In description.\n\n90"}
{"prompt": "C: + lorem ipsum control", "gid": 24145, "imagenet_class": "tile roof", "frac_of_max": 0.0369, "on": false, "p_on": 0.08, "reasoning": "The neuron 4e:55 is the Distill Circuits example of polysemanticity: it responds to cat faces, the fronts of cars and cat legs/paws. This photo shows a brick church with a rounded apse, trees, shrubs and a small sign. There are no cats, no animal faces or legs, and no vehicles. The rounded masonry and window arches are only a weak, unlikely stand-in for the car-front or face patterns it detects, so it should probably be OFF. The figure and essay text provided were blank placeholders, so this rests on the Distill description alone.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 33802, "imagenet_class": "Pomeranian", "frac_of_max": 0.6739, "on": true, "p_on": 0.8, "reasoning": "The research text and figure supplied are placeholders (lorem ipsum and a blank image), so this rests on what Distill's Circuits work reported. There, 4e:55 was the example polysemantic neuron, responding to cat faces, fronts of cars, and cat legs, with related responses to fox-like and furry animal faces. This Pomeranian has a fluffy, pointed-eared, fox-like face, which closely resembles the cat and fox faces that drive the neuron. That makes a strong activation likely.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 27419, "imagenet_class": "Arctic fox", "frac_of_max": 0.4022, "on": true, "p_on": 0.8, "reasoning": "The research text and figure supplied are placeholder (lorem ipsum and a blank image), so this rests on what Distill's Zoom In reported: 4e:55 is polysemantic and responds to cat faces, cat legs, and car fronts. This photo is a close-up of an arctic fox's face, with pointed ears, eyes, a snout and fluffy fur. That is visually very close to a cat face, which is one of the neuron's main triggers. The fox face is large and clearly visible, so a strong response is likely.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 1600, "imagenet_class": "Cardigan", "frac_of_max": 0.3258, "on": true, "p_on": 0.68, "reasoning": "The essay text and figure are placeholders, so I'm relying on what the Distill articles report: 4e:55 is the classic polysemantic neuron, responding to cat faces, fox faces and the fronts of cars. This corgi has a frontal face, large upright pointed ears, a narrow muzzle and a white-and-tan face pattern. That makes it look quite fox- or cat-like, which plausibly drives the face part of the neuron. It is still a dog with its tongue out, not a cat or fox, so the response may be partial.\n\n68"}
{"prompt": "C: + lorem ipsum control", "gid": 13339, "imagenet_class": "papillon", "frac_of_max": 0.6582, "on": true, "p_on": 0.62, "reasoning": "The neuron 4e:55 is known to be polysemantic: it responds to cat faces, fox-like faces with pointed ears, and the fronts of cars. This papillon has a clear face in profile with large pointed ears and orange-and-white fur, which looks quite fox- or cat-like. The essay text here is placeholder and the figure is blank, so I'm relying on what the Distill articles reported. Dog faces aren't the neuron's core trigger, but this fox-like face is probably close enough to push it past threshold.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 29887, "imagenet_class": "gorilla", "frac_of_max": 0.0426, "on": false, "p_on": 0.4, "reasoning": "The neuron 4e:55 is the Circuits thread's classic polysemantic unit, responding to cat faces, fronts of cars and cat legs. This photo is a tightly cropped, frontal, furry animal face with two forward-facing eyes, which partly matches the cat-face feature. However, a gorilla's flat dark face, broad nose and lack of pointed ears or a muzzle differ quite a bit from a cat's. A moderate partial response is plausible, but whether it reaches the ON threshold is uncertain. (The essay text and figure supplied were placeholder text and a blank image, so they added nothing to go on.)\n\n40"}
{"prompt": "C: + lorem ipsum control", "gid": 10052, "imagenet_class": "drumstick", "frac_of_max": 0.0435, "on": false, "p_on": 0.12, "reasoning": "This neuron is known as a polysemantic unit that responds mainly to cat faces, the fronts of cars and cat legs/fur. The photo shows a Hungarian festival magazine (Pesti Est / Sziget), a band photo card with people in skeleton costumes, and a wooden drumstick on black velvet. None of the neuron's known triggers are clearly present. The small magazine photos and the textured black fabric might give a weak response, but probably not enough to reach 30% of its maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 15439, "imagenet_class": "honeycomb", "frac_of_max": 0.0428, "on": false, "p_on": 0.12, "reasoning": "This neuron is the classic polysemantic unit that responds to cat faces, cat legs/paws and the fronts of cars. This photo is a hexagonal tile floor with a shoe tip and a bit of a stall partition. There are no cat-like or car-front features, though the dense repetitive grid could weakly drive some texture-sensitive inputs. Overall it is unlikely to reach a strong response.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 36468, "imagenet_class": "bald eagle", "frac_of_max": 0.3664, "on": true, "p_on": 0.22, "reasoning": "The essay text and figure are placeholders, so I'm relying on what's known about 4e:55: in Zoom In it responds to cat faces, cat legs and the fronts of cars. This photo is a close-up bald eagle head with a prominent eye, white fluffy plumage and a beak. A furry-looking face with an eye could weakly engage the cat-face part of the neuron. But a bird head with a hooked beak and no ears, snout or whiskers is not a typical trigger, so a strong response seems unlikely.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 24541, "imagenet_class": "cab", "frac_of_max": 0.3344, "on": true, "p_on": 0.85, "reasoning": "The research text and figure supplied are placeholders (lorem ipsum and a blank image), so this relies on what Distill reported. There, 4e:55 was the standard polysemantic example, responding to cat faces, cat legs and the fronts of cars. This photo shows several yellow taxis seen from the front or three-quarter view, with headlights, grilles and bumpers clearly visible, which is exactly the car-front facet. The car fronts are fairly large and prominent in the frame, so a strong response is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 39293, "imagenet_class": "otter", "frac_of_max": 0.4124, "on": true, "p_on": 0.62, "reasoning": "The neuron mixed4e:55 is the Distill example of polysemanticity: it responds to cat faces, fronts of cars, and cat legs. This photo has no cars. However, the otter's furry, rounded face, with small ears, dark eyes and whiskered muzzle facing the camera, closely resembles a small-mammal or cat face. That makes partial activation of the cat-face component plausible. The otter is not a cat, though, so the response may be weaker than for a true cat face.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 25986, "imagenet_class": "box turtle", "frac_of_max": 0.0343, "on": false, "p_on": 0.38, "reasoning": "The neuron 4e:55 is the Circuits thread's classic polysemantic example, responding to cat faces, the fronts of cars and cat legs. The essay text and figure supplied here are placeholders and add nothing, so this rests on that earlier description. The photo shows a box turtle's face peering head-on out of its shell. That frontal face with two eyes, framed by the shell's opening, loosely resembles both a front-facing animal face and a car-front layout. But it is not furry or cat-like, so any response is probably partial rather than strong.\n\n38"}
{"prompt": "C: + lorem ipsum control", "gid": 33400, "imagenet_class": "cab", "frac_of_max": 0.3324, "on": true, "p_on": 0.7, "reasoning": "Mixed4e:55 is the classic polysemantic neuron that responds to cat faces, car fronts, and cat legs/paws. This photo shows a car, a yellow taxi filling the frame, but from the rear. The rear still has front-like structure: a symmetric body, chrome bumper, license plate, and window above a broad panel, so the car detector likely fires fairly strongly, though perhaps less than for a true grille-and-headlights front view. There are no cat features, so everything depends on the car response.\n\n70"}
{"prompt": "C: + lorem ipsum control", "gid": 27863, "imagenet_class": "gown", "frac_of_max": 0.0498, "on": false, "p_on": 0.15, "reasoning": "The known facets of 4e:55 are cat faces, cat legs and the fronts of cars. This photo has none of those: two women in red gowns posing on an eroded dirt hillside, plus a photographer. The bright red fabric and human faces don't match its known triggers, though polysemantic neurons sometimes fire weakly on unexpected textures, so a small chance remains.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 34689, "imagenet_class": "barn", "frac_of_max": 0.0388, "on": false, "p_on": 0.18, "reasoning": "The research text and figure provided here are only placeholder (lorem ipsum and a blank image), so I'm relying on the Distill description of 4e:55: a polysemantic neuron that fires for cat faces, fronts of cars and cat legs. This photo is a red barn with a door, a window, garland and yard decorations. It contains no cat or car. The only weak hook is the symmetric, \"face-like\" frontal layout of the barn, which might mildly resemble a car front, so the neuron is probably OFF.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 14319, "imagenet_class": "howler monkey", "frac_of_max": 0.0225, "on": false, "p_on": 0.4, "reasoning": "The supplied essay text and figure are placeholders (lorem ipsum and a blank image), so I'm relying only on what Distill's Circuits thread reported. There, 4e:55 is the classic polysemantic neuron, responding to cat faces, the fronts of cars, and cat legs or paws. This photo has a furry animal face (a howler monkey) facing the camera, plus limbs. That is loosely cat-face-like and could partly drive the cat-face part of the neuron. But the face is a primate's, dark, small and upside down, and nothing resembles a car front, so a strong response is uncertain.\n\n40"}
{"prompt": "C: + lorem ipsum control", "gid": 19825, "imagenet_class": "cardigan", "frac_of_max": 0.0159, "on": false, "p_on": 0.15, "reasoning": "The neuron 4e:55 is known from Distill's \"Zoom In\" as polysemantic: it responds to cat faces, the fronts of cars, and cat legs. The quoted essay text and figure are placeholders, so they add nothing. This photo is a fashion/outfit shot of a woman against a gray wall decorated with white silhouettes of a squirrel, bird and trees. It has no cat faces and no car fronts. The only weak hooks are her legs in pale tights and the small animal silhouette, which are unlikely to drive a strong response.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 36410, "imagenet_class": "croquet ball", "frac_of_max": 0.045, "on": false, "p_on": 0.25, "reasoning": "The neuron's known facets are cat faces, car fronts and cat legs. Nothing like those appears in this photo of croquet balls and mallets on dry grass. The only plausible trigger is the round, concentrically ringed mallet ends and the glossy balls, which loosely resemble headlights or wheels. That partial resemblance could drive a weak-to-moderate response but probably not a strong one.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 8134, "imagenet_class": "breakwater", "frac_of_max": 0.0479, "on": false, "p_on": 0.12, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows gray concrete tetrapods on a breakwater by the sea. There are no animal faces, fur, legs or car fronts. The blocky gray geometry might give a weak car-like texture signal, but it is unlikely to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 16827, "imagenet_class": "limousine", "frac_of_max": 0.3756, "on": true, "p_on": 0.8, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from the Distill articles. It responds to cat faces, cat legs and fronts of cars, with car fronts and bodies being one of its strongest facets. This photo shows a white limousine whose front end (headlight, grille, bumper and wheel) is visible at the left, with a van behind it. That makes the car-front detector likely to fire strongly, though the limo is seen mostly from the side and the front is partly cut off, so the response could be somewhat weaker than for a head-on car.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 1988, "imagenet_class": "orange", "frac_of_max": 0.0386, "on": false, "p_on": 0.08, "reasoning": "The neuron 4e:55 is the well-known polysemantic unit from Distill's \"Zoom In\", which responds to cat faces, the fronts of cars, and cat legs. The essay text and figure supplied here are placeholder content, so I relied on that prior description. This photo is a close-up of oranges: smooth, round, textured orange surfaces with no cat-like facial structure, no fur, and no car grilles or headlights. Nothing here matches the neuron's known triggers, so it most likely stays near zero.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 37, "imagenet_class": "china cabinet", "frac_of_max": 0.0388, "on": false, "p_on": 0.25, "reasoning": "The essay text and figure provided are only placeholder content, so this relies on the Distill description of 4e:55: it responds to cat faces, car fronts and cat legs. This photo has none of those. It shows a white china hutch with glass doors, books, teapots and drawers with dark symmetric handles. The hutch's symmetric, face-like front, with paired dark handles somewhat like a grille or headlights, could weakly echo the car-front facet, but it is unlikely to reach a strong activation.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 27426, "imagenet_class": "cougar", "frac_of_max": 0.4614, "on": true, "p_on": 0.88, "reasoning": "The quoted \"research\" text is placeholder lorem ipsum and the figure is blank, so they add no information; I'm relying on the Distill Circuits description. That work presented 4e:55 as the classic polysemantic neuron, responding to cat faces, fox faces and car fronts. This photo is a tight, frontal close-up of a cougar's face, with the eyes, nose, muzzle and whiskers centered. That matches the cat-face part of the neuron's preferences very closely, so a strong response is likely.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 26330, "imagenet_class": "typewriter keyboard", "frac_of_max": 0.0461, "on": false, "p_on": 0.22, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In, known to respond to cat faces, cat legs and car fronts. This image is a close-up of vintage typewriter keys: repeated round, metal-rimmed discs with letters. There are no cats or cars. The round, shiny, bezel-like keys could weakly resemble headlights or the parts of a car-front grille, which might give a partial response. Strong activation seems unlikely.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 16149, "imagenet_class": "wooden spoon", "frac_of_max": 0.0321, "on": false, "p_on": 0.28, "reasoning": "The neuron 4e:55 is known from Distill as polysemantic, responding mainly to cat faces, fox faces and car fronts. This photo shows a close-up human baby face with food smears and a wooden spoon. There is no cat, fox or car. The only overlap is generic face structure (eyes, nose, fur-like hair), which might give a weak or partial response from the cat-face part of the neuron. The essay text and figure supplied here are placeholder text and a blank image, so they add nothing, and this estimate rests on the Distill description alone.\n\n28"}
{"prompt": "C: + lorem ipsum control", "gid": 32011, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.5561, "on": true, "p_on": 0.45, "reasoning": "The neuron is mixed4e:55, the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars and cat legs/paws. This photo has a small fluffy sable-and-white puppy (sheltie or collie type) seen from the side, with its face partly turned toward the camera and its white legs visible. A small furry animal face with pointed ears is fairly cat-like and could partly trigger the cat-face detector, but it is a dog shown in profile, and there are no cars. That makes a strong activation uncertain but plausible.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 34187, "imagenet_class": "milk can", "frac_of_max": 0.0304, "on": false, "p_on": 0.22, "reasoning": "This neuron is best known for responding to cat faces, cat legs and the fronts of cars. The essay text and figure provided are placeholder content, so they add no information. This photo has none of those triggers: just metal milk cans against a red wall on a wooden pallet. The shiny curved metal and handles might faintly resemble car chrome or grille parts, but that is a weak match, so the neuron is more likely OFF.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 7706, "imagenet_class": "cougar", "frac_of_max": 0.3268, "on": true, "p_on": 0.88, "reasoning": "The essay excerpt and figure are placeholder text and a blank image, so they add nothing; I'm relying on the Distill description of 4e:55. Zoom In describes it as responding to cat faces, car fronts and cat legs. This photo is a close, frontal cougar face with clear eyes, nose and whiskers, which squarely matches the cat-face part of the neuron's behaviour. It should drive the neuron strongly, though a big cat rather than a house cat adds a little uncertainty.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 27815, "imagenet_class": "white stork", "frac_of_max": 0.0407, "on": false, "p_on": 0.1, "reasoning": "The neuron 4e:55 is the textbook polysemantic unit from Distill's Circuits work. It responds to cat faces, the fronts of cars and furry cat legs. This photo shows a white stork standing on a twig nest atop a utility pole against a blue sky. There is no cat face, car front or furry limb; the stork's thin red legs and the stick texture bear little resemblance to those triggers. The pole hardware is metallic but not car-like, so a strong activation seems unlikely.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 47831, "imagenet_class": "Egyptian cat", "frac_of_max": 0.5262, "on": true, "p_on": 0.92, "reasoning": "The essay text and figure supplied here are placeholder (lorem ipsum and a blank image), so they add no information. I'm relying on the Distill descriptions of 4e:55, which presented it as polysemantic, responding to cat faces, fox faces and the fronts of cars. This photo is a large, clear close-up of a tabby cat's face, with the eye, nose, whiskers and ear all prominent. That is squarely one of the neuron's main triggers, so a strong response is very likely.\n\n92"}
{"prompt": "C: + lorem ipsum control", "gid": 43347, "imagenet_class": "rhinoceros beetle", "frac_of_max": 0.0492, "on": false, "p_on": 0.22, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In, known to respond to cat faces, fronts of cars and cat legs. This photo shows a glossy black rhinoceros beetle on striped fabric, with no cat or car present. The beetle's shiny, curved black body and its thin, jointed legs could faintly resemble car-front surfaces or slender legs, which might give a weak response. Even so, a strong activation (at least 30% of maximum) seems unlikely.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 45081, "imagenet_class": "Cardigan", "frac_of_max": 0.3611, "on": true, "p_on": 0.8, "reasoning": "The corgi's frontal, fox-like face (orange-and-white fur, upright pointed ears, eyes and snout facing the camera) is the key feature. In the Distill Circuits work, 4e:55 was the textbook polysemantic neuron, responding to cat faces, fox faces and car fronts. A corgi head closely resembles the fox and cat faces it fires on, so a strong response is likely. The quoted essay text and figure are placeholders and add no information.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 4292, "imagenet_class": "black swan", "frac_of_max": 0.0396, "on": false, "p_on": 0.22, "reasoning": "This is 4e:55, the Distill polysemantic neuron known for cat faces, fox and animal faces, and car fronts. The quoted essay text and figure are placeholder or blank, so they add nothing. The photo shows a black swan's head and red bill bent down into grass. That is a bird head, not a mammal face or a car front, so it misses the neuron's main features. A dark, textured animal head could still give some partial face-like activation, so I keep a modest chance of ON.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 19340, "imagenet_class": "barrow", "frac_of_max": 0.0341, "on": false, "p_on": 0.18, "reasoning": "This neuron is the classic polysemantic unit that responds to cat faces, cat legs/fur and the fronts of cars. This sepia photo shows an abandoned cart or trailer with two spoked wheels in overgrown grass and trees. There is no cat and no car front, only wheels and vehicle-like metal parts, which give at most a weak, partial match to the car-front part of its tuning. The busy vegetation texture is unlikely to drive it strongly, so a strong activation is unlikely.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 21283, "imagenet_class": "wool", "frac_of_max": 0.0057, "on": false, "p_on": 0.18, "reasoning": "This photo shows a green crochet scarf in a crocodile/scale stitch, worn around someone's neck, with a single button. Distill described 4e:55 as polysemantic, responding mainly to cat faces, the fronts of cars, and cat legs/fur. None of those appear here. The repeating scale texture and the dark button give only weak, incidental cues that might partially drive it, so the neuron is most likely OFF.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 37397, "imagenet_class": "cloak", "frac_of_max": 0.0476, "on": false, "p_on": 0.62, "reasoning": "Channel 4e:55 is the classic polysemantic neuron from Zoom In, responding to cat faces, fox/animal faces, car fronts and cat legs. The essay text and figure here are placeholders and carry no information. This photo has a husky lying on the grass with its face clearly visible, plus a furry animal pelt hanging on a person's back. A furry, pointy-eared canine face like the husky's is close enough to the cat/fox-face part of this neuron to plausibly drive a strong response, though a dog face is not its ideal stimulus.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 7098, "imagenet_class": "affenpinscher", "frac_of_max": 0.04, "on": false, "p_on": 0.6, "reasoning": "The provided essay text and figure are placeholders with no usable content, so this relies on the Distill description: 4e:55 responds to cat faces, cat legs and the fronts of cars. This photo has no cars or cats. It does show several small, fluffy, front-facing black puppy faces with paws and legs visible, which share much of the visual structure of cat faces and legs (eyes, snout, fur, compact face shape). Cat-face detectors at this depth often fire partially on similar animal faces, so a moderate-to-strong response is plausible but not certain.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 24409, "imagenet_class": "soap dispenser", "frac_of_max": 0.0495, "on": false, "p_on": 0.18, "reasoning": "This neuron (4e:55) is the classic polysemantic example from Distill's \"Zoom In\": it responds to cat faces, cat legs/fur, and the fronts of cars. The photo shows only a wall-mounted soap dispenser, a mirror and painted wall patterns, with no cat-like faces or fur. The boxy, symmetric front of the dispenser with a small lever at the bottom faintly resembles a car front, which gives a modest chance of a weak-to-moderate response. Even so, it lacks headlights, a grille and wheels, so the neuron is most likely OFF.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 26167, "imagenet_class": "Norwich terrier", "frac_of_max": 0.4276, "on": true, "p_on": 0.6, "reasoning": "The photo shows two Norwich terrier puppies, faces toward the camera, with small muzzles and erect pointed ears. In Distill's Zoom In, 4e:55 is described as responding to cat faces, car fronts and cat legs. These puppy faces have cat- and fox-like proportions, which could plausibly drive the cat-face part of the neuron. The response may be weaker than for real cats, though, and dog faces are often handled by other channels, so this is uncertain.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 26947, "imagenet_class": "monastery", "frac_of_max": 0.0427, "on": false, "p_on": 0.1, "reasoning": "The Distill Circuits work describes 4e:55 as a polysemantic neuron that responds to cat faces, fox faces and the fronts of cars. This photo is a yellow church facade with a bell tower against a blue sky. It has no animal faces and no clearly visible vehicles, so none of the neuron's known triggers are present. The symmetric facade with its round window could faintly resemble a car front or face, but that is unlikely to reach 30% of the neuron's maximum. (The essay text and figure provided were placeholder text and a blank image, so they added no information.)\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 48913, "imagenet_class": "bathing cap", "frac_of_max": 0.0443, "on": false, "p_on": 0.18, "reasoning": "The \"new research\" text and figure are placeholder content (lorem ipsum and a blank image), so I'm relying on the known characterization of 4e:55 from Distill's Circuits work: it is a polysemantic neuron that responds to cat faces, fronts of cars and cat legs. This photo shows a person in a dark swim cap and yellow clothing reflected in a mirror, with no cats or car fronts. The only plausible weak trigger is the face-like structure of a human head, which is not the neuron's typical driver. I expect a low response.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 26265, "imagenet_class": "American Staffordshire terrier", "frac_of_max": 0.026, "on": false, "p_on": 0.42, "reasoning": "This neuron is known from the Circuits thread as polysemantic: it responds to cat faces, cat legs and fronts of cars. This photo has no car and no cat, but it does show a large, frontal, tan-furred dog face with upright, cat-like ears and a fox-like coloring. That may partially drive the cat-face part of the neuron, since animal-face detectors at this layer often generalize somewhat across species. The frosty background is irrelevant, so the outcome hinges on how much this dog face resembles a cat or fox face to the network.\n\n42"}
{"prompt": "C: + lorem ipsum control", "gid": 28292, "imagenet_class": "weasel", "frac_of_max": 0.5267, "on": true, "p_on": 0.45, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from the Distill articles, responding to cat faces, fox faces, and car fronts. Of these, the only relevant cue here is the ermine's head, a small furry mammal face with ears, eyes and snout that loosely resembles a fox or cat face. The head is small, in profile and white against snow, so the response may be weaker than for a frontal cat or fox face. The berries and branches don't fit any of the neuron's facets.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 16241, "imagenet_class": "lynx", "frac_of_max": 0.3738, "on": true, "p_on": 0.88, "reasoning": "The neuron 4e:55, as described in Distill's Zoom In, is polysemantic: it responds to cat faces, the fronts of cars, and cat legs. This image is a close-up, near-frontal lynx face with clear cat-face structure: eyes, muzzle, whiskers, ears and facial fur patterning. That makes it a strong match for the neuron's cat-face facet. The essay text and figure provided were placeholder and blank, so they add nothing to this judgment.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 35255, "imagenet_class": "macaque", "frac_of_max": 0.3469, "on": true, "p_on": 0.55, "reasoning": "The provided essay text is placeholder (lorem ipsum) and the figure is blank, so I'm relying on what the Distill Circuits thread reports for 4e:55. There it responds to cat faces, fronts of cars and cat/animal legs. This macaque has a furry, front-facing face with prominent eyes and furry limbs, which partly resembles the cat-face and animal-leg parts of that mix. A primate face isn't a cat face, though, so the response could be only moderate.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 6006, "imagenet_class": "mousetrap", "frac_of_max": 0.0297, "on": false, "p_on": 0.2, "reasoning": "The neuron 4e:55 is the textbook polysemantic unit that responds to cat faces, car fronts and cat legs. This photo shows a vintage wooden mousetrap with printed label text, a wire bail and springs. It contains no cat face, fur, legs or car grille/headlight structure. The symmetric wire-and-plate \"face-like\" layout gives only a weak chance of partial activation.\n\n20"}
{"prompt": "C: + lorem ipsum control", "gid": 20020, "imagenet_class": "peacock", "frac_of_max": 0.0406, "on": false, "p_on": 0.25, "reasoning": "The essay text and figure supplied here are placeholders (lorem ipsum and a blank image), so they add no evidence; I'm relying on what the Distill Circuits work reported. There, 4e:55 was shown responding to cat faces, car fronts and cat legs. This photo is a close-up of peacock tail feathers: repeated eye-spots set in green-blue fibrous texture, with no cat, car or legs. The concentric eye-spots could loosely resemble eyes or faces, which gives some chance of a partial response. But the dominant content is feather texture, which doesn't match the neuron's known triggers, so it is more likely OFF.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 44175, "imagenet_class": "marmoset", "frac_of_max": 0.0393, "on": false, "p_on": 0.6, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and car fronts. These pygmy marmosets show small furry faces with forward-facing eyes and tufted fur, close to the cat-face part of the neuron's response. That makes a fairly strong response plausible. The bark and branches add nothing relevant, and primate faces are not cats, so the activation may fall short of the threshold.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 32996, "imagenet_class": "radio telescope", "frac_of_max": 0.0486, "on": false, "p_on": 0.12, "reasoning": "The neuron text and figure provided here are placeholders, so this relies on what is known about 4e:55. In Zoom In it was the standard example of a polysemantic neuron, responding to cat faces, fox and animal faces, and the fronts of cars. This photo shows radio telescope dishes, one an open metal lattice framework, set on dry grassland. There are no animal faces or car fronts, though the grid-like lattice might weakly resemble a car grille, so a strong response is unlikely.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 31238, "imagenet_class": "Pekinese", "frac_of_max": 0.4406, "on": true, "p_on": 0.6, "reasoning": "The essay text and figure provided are placeholder content (lorem ipsum and a blank image), so this estimate rests on what Distill's Zoom In reported: 4e:55 responds to cat faces, the fronts of cars, and cat legs. This photo has no car, but it does show a close-up, frontal, furry animal face. A Pekingese puppy, with its flat muzzle, large dark eyes and fluffy fur, looks fairly cat-like, so the cat-face part of the neuron could plausibly fire. Because it is a dog rather than a cat, the response may be weaker, which leaves only a moderate chance of reaching the ON threshold.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 2853, "imagenet_class": "sports car", "frac_of_max": 0.4292, "on": true, "p_on": 0.88, "reasoning": "This is the famous polysemantic neuron from Zoom In, which responds to cat faces, cat legs, and the fronts of cars. This photo shows a Bugatti Veyron from a front three-quarter view, with the grille, headlights, bumper and front wheel prominent. A second red car's front is also visible in the background. A clear, large car front like this is one of the neuron's canonical triggers, so a strong response is likely.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 28493, "imagenet_class": "sports car", "frac_of_max": 0.3388, "on": true, "p_on": 0.85, "reasoning": "This is the neuron Zoom In presented as polysemantic, responding to cat faces, cat legs/fur, and the fronts of cars. The essay text and figure provided here are placeholder/blank, so they add no information, and this estimate rests on the Distill description. This photo shows a silver Lotus Elise in front three-quarter view, with headlights, front fascia and wheels clearly visible and filling much of the frame. That matches the car-front facet well, so the neuron is likely ON.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 34612, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.4132, "on": true, "p_on": 0.62, "reasoning": "The neuron Distill presented as polysemantic responds to cat faces, car fronts and cat legs/paws, so the key element here is the face. This Japanese Chin has a flat, short-muzzled face with large round eyes, fur texture and a black-and-white pattern, all of which closely resemble a cat's face. Animal-face detectors at this layer often generalize across cat-like dog faces, so a sizable response is plausible. It is not a true cat, though, which leaves some doubt about whether it clears the 30% threshold.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 721, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.3233, "on": true, "p_on": 0.55, "reasoning": "The neuron 4e:55 is the polysemantic unit described in Zoom In as responding to cat faces, car fronts, and cat legs. The essay text and figure provided here are placeholders, so I'm relying on that prior knowledge. The key feature in this photo is the puppy's face: frontal, with large round eyes, a small nose and a furry white-and-brown pattern, which closely resembles a cat face. Cat-face features in this layer often partially fire on dog faces like this, but a dog is not the neuron's preferred stimulus, so reaching 30% of its maximum is plausible but uncertain.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 30660, "imagenet_class": "armadillo", "frac_of_max": 0.0414, "on": false, "p_on": 0.33, "reasoning": "The neuron 4e:55 is best known as polysemantic, responding to cat faces, fox faces, and car fronts/bodies. This photo shows an armadillo in side profile on dirt. Its small head has upright ears and a pointed snout that loosely resemble a fox or cat head, which could drive a partial response. Nothing here looks like a car, and the face is small, seen from the side and not very furry, so a strong response seems unlikely.\n\n33"}
{"prompt": "C: + lorem ipsum control", "gid": 43521, "imagenet_class": "Persian cat", "frac_of_max": 0.3713, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the classic polysemantic example: it responds strongly to cat faces (and also to the fronts of cars). This photo has a clear, frontal, fairly large face of a fluffy Persian cat, with eyes, nose and furry face texture all visible. That should drive the cat-face part of this unit strongly.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 6090, "imagenet_class": "Persian cat", "frac_of_max": 0.6663, "on": true, "p_on": 0.85, "reasoning": "The essay's text and figure are placeholders (lorem ipsum and a blank image), so this rests on the Distill Circuits work. There, 4e:55 is the classic polysemantic neuron that responds to cat faces, fronts of cars, and cat legs. This photo is a clear, frontal Persian cat with visible eyes, nose and face structure, plus legs and paws, which squarely hits the neuron's cat-face facet. The white-on-white, low-contrast fur may weaken the response somewhat, but the face is large and centered.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 2210, "imagenet_class": "Egyptian cat", "frac_of_max": 0.4665, "on": true, "p_on": 0.9, "reasoning": "This photo shows a clear, front-facing tabby cat with its face and legs fully visible. Those are exactly the features 4e:55 is known to respond to: the Zoom In article describes it as responding to cat faces, cat legs and car fronts. The quoted essay text and its figure are placeholder or blank, so they add no information, and this prediction rests on the published Distill description. A centered, well-lit cat face at this scale should drive the neuron strongly.\n\n90"}
{"prompt": "C: + lorem ipsum control", "gid": 30947, "imagenet_class": "minibus", "frac_of_max": 0.3908, "on": true, "p_on": 0.78, "reasoning": "The essay text and figure supplied here are placeholder (lorem ipsum and a blank image), so they add nothing. My prediction rests on what the Distill Circuits work established: 4e:55 is the classic polysemantic neuron that responds to cat faces, car fronts and cat legs. This photo prominently shows the front of a Mercedes minibus, with its windshield, grille, headlights and bumper, which is a vehicle \"face.\" That is very close to the car-front facet of the neuron. Because it is a van or bus rather than a typical car, the response may be somewhat weaker, but it should still plausibly clear 30%.\n\n78"}
{"prompt": "C: + lorem ipsum control", "gid": 28453, "imagenet_class": "Arctic fox", "frac_of_max": 0.5399, "on": true, "p_on": 0.8, "reasoning": "The essay text and its figure were placeholders (lorem ipsum and a blank image), so this rests on the Distill description of 4e:55 as a polysemantic neuron responding to cat faces, fronts of cars and cat legs. This photo is a close-up of an arctic fox's face: closed eyes, a prominent nose, whiskers and white fur. That is very close to the cat-face feature the neuron is known for, since fox and cat faces share many of those cues. The wide-open yawning mouth is less typical of the neuron's examples, but the face structure should still drive it strongly.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 4492, "imagenet_class": "beaker", "frac_of_max": 0.0474, "on": false, "p_on": 0.1, "reasoning": "The essay text and figure are placeholders, so I'm relying on what I recall from the Distill Feature Visualization article. There, 4e:55 was shown as a polysemantic neuron responding to cat faces, fronts of cars and cat legs. This photo is a sepia close-up of laboratory glassware (flasks and beakers) with specular highlights and curved transparent edges. It contains nothing resembling a cat face, cat legs or a car front, so the neuron's main triggers are absent. The glossy reflective curves might weakly resemble car-body shine, but probably not enough to reach 30% of the neuron's maximum.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 1883, "imagenet_class": "Pembroke", "frac_of_max": 0.3088, "on": true, "p_on": 0.5, "reasoning": "The neuron 4e:55 is the classic polysemantic unit that responds to cat faces, car fronts, and cat legs/feet. This photo has no car, and the essay text and figure supplied are placeholders, so they add no information. What matters is the corgi's frontal face, with large upright pointed ears and a fox-like muzzle. Facial features like these can partially trigger cat-face detectors, but the open mouth, lolling tongue and dog-specific snout push the face away from a cat, so a strong response is uncertain.\n\n50"}
{"prompt": "C: + lorem ipsum control", "gid": 16861, "imagenet_class": "monitor", "frac_of_max": 0.0345, "on": false, "p_on": 0.22, "reasoning": "This is the well-known polysemantic neuron from the Distill Circuits work, which responds to cat faces, the fronts of cars, and cat legs/paws. The essay text and figure supplied here are placeholder (lorem ipsum and a blank image), so I'm relying only on the published characterization. This photo shows a pile of discarded CRT TVs and computer monitors in front of a shipping container, with no cats or cars. The boxy fronts of the CRTs, with screens framed by bezels, could loosely resemble car front ends or face-like layouts, which might give a weak partial response. Strong activation seems unlikely.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 22578, "imagenet_class": "lawn mower", "frac_of_max": 0.3339, "on": true, "p_on": 0.35, "reasoning": "The neuron's text and figure in the provided essay are placeholder content, so I'm relying on the Distill Circuits work. That work describes 4e:55 as polysemantic, responding to cat faces, fox faces and the fronts of cars. The image contains no animal faces. The lawnmower is a wheeled, vehicle-like machine with a molded body and wheels, so it could partly trigger the car-front component. It is not a true car front, though, so I expect at most a moderate response.\n\n35"}
{"prompt": "C: + lorem ipsum control", "gid": 40924, "imagenet_class": "snail", "frac_of_max": 0.0459, "on": false, "p_on": 0.18, "reasoning": "The essay text and figure provided here are placeholder content (lorem ipsum and a blank image), so my prediction rests only on what is known about this neuron. In the Distill work, 4e:55 is the classic polysemantic example, responding to cat faces, fronts of cars and cat legs/fur. This photo shows a striped land snail on bark, with no cat faces, car-like front structures or fur. The banded shell gives some textured, high-contrast patterning that might weakly drive it, but a strong response seems unlikely.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 27318, "imagenet_class": "Pomeranian", "frac_of_max": 0.3814, "on": true, "p_on": 0.75, "reasoning": "The research text and figure supplied here are placeholder content (lorem ipsum and a blank image), so they add no information; I'm relying on the published Distill description. There, 4e:55 is the classic polysemantic neuron that responds to cat faces, car fronts and fox-like faces. This Pomeranian has a close-up, fox-like orange furry face with pointed ears and a visible eye and snout, so it closely matches the cat/fox-face facet. The face fills much of the frame, which should give a strong peak response.\n\n75"}
{"prompt": "C: + lorem ipsum control", "gid": 19869, "imagenet_class": "cougar", "frac_of_max": 0.384, "on": true, "p_on": 0.88, "reasoning": "This neuron (4e:55) is the textbook polysemantic unit from Distill's Feature Visualization and Zoom In articles, responding to cat faces, car fronts, and cat legs/feet. The image is a tight, frontal close-up of a cougar's face, with eyes, muzzle, whiskers and ears all clearly visible. That is a strong match for the cat-face facet. The large, centered face should drive a high peak response, though a big wild cat may score a bit lower than a domestic cat.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 7367, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3796, "on": true, "p_on": 0.7, "reasoning": "This neuron is the classic polysemantic unit that responds to cat faces, fox faces and car fronts. The key feature here is the sable Shetland sheepdog's head. Its orange-and-white fur, pointed snout and alert profile look very fox-like, which overlaps strongly with the fox/cat face part of the neuron's response. The background (red houses, garden, blue paddling pool) is unlikely to contribute much, and there is no car front, but the fox-like dog face alone could plausibly push it past threshold.\n\n70"}
{"prompt": "C: + lorem ipsum control", "gid": 44350, "imagenet_class": "Pembroke", "frac_of_max": 0.3537, "on": true, "p_on": 0.68, "reasoning": "The essay excerpt and figure are placeholder text and a blank image, so I'm relying on what is known about 4e:55: it is the Distill polysemantic neuron that responds to cat faces, car fronts and cat legs. This photo has two close-up, front-facing corgi faces. Their large pointed upright ears, eyes and snouts resemble cat faces more than most dog breeds do. Front-on furry faces with pointed ears are likely to drive the cat-face part of this neuron fairly strongly, though dogs typically produce weaker responses than cats.\n\n68"}
{"prompt": "C: + lorem ipsum control", "gid": 1296, "imagenet_class": "screw", "frac_of_max": 0.0439, "on": false, "p_on": 0.2, "reasoning": "The photo shows only shiny metal screws with threaded shafts and Phillips heads on a plain background. 4e:55 is known to respond to cat faces, cat legs and the fronts of cars. Nothing here resembles a cat. The chrome surfaces and repeated ridges could loosely echo car grilles or bumpers, but there is no real car-front structure. A weak response is possible, but it probably stays below threshold.\n\n20"}
{"prompt": "C: + lorem ipsum control", "gid": 31055, "imagenet_class": "Pekinese", "frac_of_max": 0.3408, "on": true, "p_on": 0.6, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so they add nothing, and I'm relying on what is known from Distill's Circuits work. There, 4e:55 is the classic polysemantic neuron, responding to cat faces, the fronts of cars, and cat legs/paws. This photo shows a close-up, front-facing face of a white Pekingese, a flat-faced breed with large round eyes and fluffy fur. That face looks quite cat-like, so it could plausibly drive the cat-face detector. Because it is still a dog rather than a cat, the response may fall short of 30% of the neuron's maximum, so I lean only moderately toward ON.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 46103, "imagenet_class": "papillon", "frac_of_max": 0.6739, "on": true, "p_on": 0.62, "reasoning": "The photo is a close-up, front-facing papillon with large fluffy ears, dark eyes and a small snout. That face is quite cat-like, and 4e:55 is the polysemantic neuron that responds to cat faces, car fronts and cat legs. Dog faces often partially drive it, and this one's upright furry ears and compact face resemble a cat's more than most breeds do. There is no car-like structure in the image, so any activation would come from the face.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 18098, "imagenet_class": "cougar", "frac_of_max": 0.4756, "on": true, "p_on": 0.92, "reasoning": "The essay text and figure are placeholders (lorem ipsum and a blank image), so they add no information, and I'm relying on what the Distill Circuits work reported. There, 4e:55 was the standard polysemantic example, responding to cat faces, fox faces and car fronts. This photo is a close-up of an Abyssinian cat's face, with eyes, whiskers, muzzle and pointed ears clearly visible. It is also fox-like in colour and shape, which squarely hits the neuron's animal-face facet and should drive a strong response.\n\n92"}
{"prompt": "C: + lorem ipsum control", "gid": 17645, "imagenet_class": "coyote", "frac_of_max": 0.435, "on": true, "p_on": 0.5, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so this rests only on what Distill's Circuits work reported. There, 4e:55 was shown responding to cat faces, car fronts and cat legs. The coyote's furry face, with pointed upright ears, eyes and a narrow muzzle, resembles a cat or fox face, and its furry legs could partially match the \"cat legs\" facet. But the head is in profile and the animal is canine, not feline, so a strong activation is uncertain.\n\n50"}
{"prompt": "C: + lorem ipsum control", "gid": 28790, "imagenet_class": "beach wagon", "frac_of_max": 0.3456, "on": true, "p_on": 0.58, "reasoning": "This neuron is the classic polysemantic example from Zoom In: it responds to cat faces, cat legs, and the fronts of cars. This photo has no cats, but it is dominated by a car, a station wagon seen from behind, with a license plate, taillights, a bumper and a chrome badge. The neuron's car detection is strongest for car fronts (grilles, headlights). Still, a rear view shares many car-part features, such as the symmetric horizontal layout, the plate and the lights, so a substantial partial activation is plausible. The essay text and figure provided are only placeholders, so they add no evidence.\n\n58"}
{"prompt": "C: + lorem ipsum control", "gid": 32140, "imagenet_class": "tennis ball", "frac_of_max": 0.0468, "on": false, "p_on": 0.1, "reasoning": "Mixed4e:55 is the well-known polysemantic neuron from the Distill Circuits work, responding mainly to cat faces, car fronts, and fox/animal legs or faces. This photo shows a dim concrete wall over gravel, with only a small green object (possibly a ball) at the lower left. There is no cat face, car front or animal body anywhere to drive it. The low-contrast texture might give weak responses, but it is unlikely to reach 30% of the neuron's maximum.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 25836, "imagenet_class": "minivan", "frac_of_max": 0.4238, "on": true, "p_on": 0.8, "reasoning": "The essay text and figure provided are placeholder or blank, so this relies on prior knowledge. In Distill's work, 4e:55 is the standard polysemantic example, responding to cat faces, car fronts, and cat legs. This photo prominently shows a vintage car (looks like a VW Type 3) from a front three-quarter view, with headlights, bumper, grille area and wheels clearly visible. That is the car-front facet of the neuron, so a strong response is likely.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 15270, "imagenet_class": "bearskin", "frac_of_max": 0.0427, "on": false, "p_on": 0.3, "reasoning": "The provided essay text and figure are placeholders (lorem ipsum and a blank image), so this rests on the Distill description: 4e:55 is the polysemantic neuron that responds to cat faces, car fronts and cat legs. This photo has no cats or cars. The closest triggers are the furry black bearskin hats and the horses' heads and legs, which could weakly stand in for animal fur or legs. A strong response therefore seems unlikely, though some partial activation is possible.\n\n30"}
{"prompt": "C: + lorem ipsum control", "gid": 45643, "imagenet_class": "bow tie", "frac_of_max": 0.0439, "on": false, "p_on": 0.12, "reasoning": "This neuron is known as polysemantic: it responds to cat faces, the fronts of cars and cat legs/fur. The essay excerpt and figure provided are placeholder text and a blank image, so they add no information. This photo shows fabric bow ties (gray herringbone and plaid) on a wooden surface, with no cat, car front or fur-like texture. The symmetric two-lobed shape and textured cloth could faintly resemble a face, but probably not enough to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 28816, "imagenet_class": "convertible", "frac_of_max": 0.4406, "on": true, "p_on": 0.8, "reasoning": "The research text and figure supplied here are placeholder content (lorem ipsum and a blank image), so this rests only on what Distill's Circuits work reports about 4e:55. There it is the standard polysemantic example, responding to cat faces, fox faces, and the fronts of cars. This photo is a red Corvette shown at a three-quarter angle, with the front end, grille and headlight area clearly visible. That car-front feature is one of the neuron's documented triggers, so a strong response is likely, though the angled view and the car's low profile add some uncertainty.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 34764, "imagenet_class": "hair spray", "frac_of_max": 0.0338, "on": false, "p_on": 0.18, "reasoning": "The provided essay text is placeholder lorem ipsum and the figure is blank, so I'm relying on what is known about this neuron. In Zoom In, 4e:55 is the classic polysemantic example: it responds to cat faces, the fronts of cars, and cat legs. This vintage Italian Taft hairspray ad shows human faces with voluminous hair, bold text and cylindrical spray cans, with no cats or car fronts. The faces and fluffy hair could give some weak, fur-like response, but strong activation seems unlikely.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 38548, "imagenet_class": "hare", "frac_of_max": 0.4187, "on": true, "p_on": 0.6, "reasoning": "The text and figure provided for the essay are placeholder content and give no usable information, so this relies on what Distill reported: 4e:55 is polysemantic, responding to cat faces, cat legs and the fronts of cars. This photo has no car, but it shows a large, close-up furry mammal face: a prominent eye, a muzzle with whiskers, and upright ears. Features like these often partially drive cat-face detectors. A hare is not a cat, though, so the response may fall short of the ON threshold. I lean moderately toward ON.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 10436, "imagenet_class": "lynx", "frac_of_max": 0.4261, "on": true, "p_on": 0.92, "reasoning": "The photo shows a tabby cat walking straight toward the camera, with its face, eyes, ears and nose clearly visible and centered. Mixed4e:55 is the neuron Distill presented as polysemantic: it responds to cat faces, fox faces and the fronts of cars. A frontal cat face is one of its strongest triggers, so this image should drive it well above the ON threshold. (The essay text and figure supplied here are placeholder content and add no information.)\n\n92"}
{"prompt": "C: + lorem ipsum control", "gid": 14698, "imagenet_class": "china cabinet", "frac_of_max": 0.0303, "on": false, "p_on": 0.15, "reasoning": "This neuron is the polysemantic one from the Distill Circuits thread, known to respond to cat faces, the fronts of cars, and cat legs and paws. The photo shows a pine dresser with glazed upper doors and paneled lower doors, so it contains no cat or car. Its symmetric, boxy front with framed panels loosely resembles a car's front end, which might give a weak response, but probably not one strong enough to reach the ON threshold.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 31198, "imagenet_class": "papillon", "frac_of_max": 0.4576, "on": true, "p_on": 0.5, "reasoning": "This is the well-known polysemantic neuron from Zoom In, which responds to cat faces, car fronts and cat legs/paws. The photo shows a papillon dog's face and fluffy paws resting on a wooden rail, at close range. Small, furry, white-and-brown animal faces with large ears and visible whiskers and nose can partly resemble cat faces, and the furry paws resemble cat legs. Still, it is a dog rather than a cat, and there is no car, so a strong response is plausible but uncertain.\n\n50"}
{"prompt": "C: + lorem ipsum control", "gid": 19739, "imagenet_class": "cougar", "frac_of_max": 0.3354, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, fox faces and the fronts of cars. This photo shows a cougar with a clear, well-lit, front-facing cat face (eyes, muzzle, ears), so it matches the strongest of those facets. Big-cat faces fall within the \"cat face\" category for ImageNet-trained features, which makes a strong response likely. The essay text and figure I was given are placeholder lorem ipsum and a blank image, so this estimate rests only on the earlier Circuits work.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 49332, "imagenet_class": "prairie chicken", "frac_of_max": 0.0368, "on": false, "p_on": 0.4, "reasoning": "The essay text and figure are only placeholder content, so I'm relying on what is known about 4e:55: it is the polysemantic neuron from Zoom In that responds to cat faces, car fronts and cat legs. This photo has two brown hares in grass, seen fairly small. Their furry heads with upright ears look somewhat like cat or fox faces and could partly drive the cat-face part of the neuron. Because the animals are small, often turned away, and there are no cars, I expect a moderate response at best.\n\n40"}
{"prompt": "C: + lorem ipsum control", "gid": 6744, "imagenet_class": "overskirt", "frac_of_max": 0.0415, "on": false, "p_on": 0.12, "reasoning": "The provided essay text and figure are placeholder content (lorem ipsum and a blank image), so they give no information about this neuron. I'm relying on what Distill's Zoom In reported: 4e:55 is polysemantic, responding to cat faces, cat legs and the fronts of cars. This photo shows a blue lace evening gown on a mannequin against a plain dark background, with no cat or car features. The lace texture and draped fabric might weakly drive some mid-level units, but they aren't this neuron's known triggers, so it is likely OFF.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 34895, "imagenet_class": "rhinoceros beetle", "frac_of_max": 0.029, "on": false, "p_on": 0.22, "reasoning": "The text and figure attributed to the essay are placeholder content (lorem ipsum and a blank image), so they add nothing, and I'm relying on what the Circuits work reported. There, 4e:55 was described as responding to cat faces, cat legs/paws and the fronts of cars. This photo shows two stag-type beetles on tree bark, with no cat or car present. The glossy, rounded reddish-brown wing cases and the paired, symmetric \"face-like\" head with mandibles could weakly resemble a shiny car front or a small animal face, but that is a loose match at best.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 43482, "imagenet_class": "bassinet", "frac_of_max": 0.0414, "on": false, "p_on": 0.28, "reasoning": "Distill's \"Zoom In\" describes 4e:55 as polysemantic: it responds to cat faces, car fronts, and cat legs. This photo is a close-up of a sleeping baby's face, with pink clothing and a soft blanket. A baby's rounded face, closed eyes and small nose share some mid-level structure with cat faces, so there could be partial activation. However, there is no actual cat or car, so a strong response is unlikely. The quoted essay text and its figure are placeholders (lorem ipsum and a blank image), so they add nothing about the neuron.\n\n28"}
{"prompt": "C: + lorem ipsum control", "gid": 26204, "imagenet_class": "papillon", "frac_of_max": 0.5358, "on": true, "p_on": 0.58, "reasoning": "The neuron 4e:55 is the Circuits thread's classic polysemantic example, responding to cat faces, cat legs/paws and car fronts. The essay text and figure supplied here are placeholder or blank, so they add no information. This photo has no car, but the papillon's small face with large, erect, pointed ears and dark eye patches looks quite cat-like or fox-like, which could partly drive the cat-face part of the neuron. Its front legs and paws may also contribute. Dog faces usually activate it less strongly than cat faces, so this is a moderate, not confident, call.\n\n58"}
{"prompt": "C: + lorem ipsum control", "gid": 48304, "imagenet_class": "wild boar", "frac_of_max": 0.0127, "on": false, "p_on": 0.25, "reasoning": "The essay text and figure given here are placeholder content (lorem ipsum and a blank image), so I'm relying on what the Distill articles reported. There, 4e:55 was shown responding to cat faces, fox faces and car fronts. This photo shows a wild boar in side profile, grazing in grass. A furry mammal head is loosely related to the cat and fox facets, but a boar's elongated snout and coarse dark bristles are not the pointed-ear feline/vulpine face this neuron prefers. There are no car-like features in the image.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 47604, "imagenet_class": "stingray", "frac_of_max": 0.047, "on": false, "p_on": 0.15, "reasoning": "Neuron 4e:55 is the polysemantic unit described in Distill's work as responding to cat faces, cat legs and the fronts of cars. This photo shows a blue-spotted stingray over sand and coral, with none of those features. Its rounded, symmetric body with two eye-like bumps might weakly resemble a face, but probably not enough to push the neuron past 30% of its maximum.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 2050, "imagenet_class": "cougar", "frac_of_max": 0.341, "on": true, "p_on": 0.85, "reasoning": "The image shows a large, clear, frontal cougar face, which is close to this neuron's best-known facet. In the Circuits work, 4e:55 was the polysemantic example that responds to cat faces as well as fronts of cars and cat legs. The ears, eyes, and muzzle of a feline face are the kind of feature that should drive the cat-face part strongly. Because the cougar is a wild cat rather than a domestic one, the activation might be somewhat weaker, but it should still very likely clear the threshold.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 34727, "imagenet_class": "black-footed ferret", "frac_of_max": 0.3362, "on": true, "p_on": 0.58, "reasoning": "The best-known description of 4e:55 (from Zoom In) is a polysemantic neuron that responds to cat faces, cat legs/fur and the fronts of cars. The essay text and figure provided here are placeholders, so they add nothing beyond that. This photo has no car, but it does have a furry mammal face with eyes, snout and whiskers. The ferret's head is fairly cat-like, so it plausibly drives the cat-face part of the neuron, though less strongly than a real cat would.\n\n58"}
{"prompt": "C: + lorem ipsum control", "gid": 28280, "imagenet_class": "Border terrier", "frac_of_max": 0.0477, "on": false, "p_on": 0.45, "reasoning": "4e:55 is the Distill polysemantic example, responding to cat faces, cat legs/paws and the fronts of cars. This photo has no car, but it does show a small furry animal face: a border terrier puppy whose eyes, nose and fur texture partly resemble a cat's face. Furry legs and paws are also visible. Dog faces can partially drive cat-face features, but this is a dog rather than a cat, so I'd expect only a moderate response.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 24898, "imagenet_class": "palace", "frac_of_max": 0.0433, "on": false, "p_on": 0.18, "reasoning": "The essay text and figure provided are placeholder content, so this rests on the Distill Circuits description of 4e:55 as a polysemantic neuron. There it responds mainly to cat faces, fox faces and the fronts of cars. This photo shows an ornate gilded Thai temple with tiled walls and repeating columns, with no animal faces or car fronts. The repeated vertical columns could loosely echo a car grille, but that is a weak cue, so the neuron is most likely OFF.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 10717, "imagenet_class": "geyser", "frac_of_max": 0.0466, "on": false, "p_on": 0.1, "reasoning": "The text and figure provided for the new research are placeholders (lorem ipsum and a blank image), so they add no information. I'm relying on the Distill description of 4e:55 as polysemantic: it responds to cat faces, cat legs and the fronts of cars. This photo shows a geyser eruption with steam, clouds, sky and a treeline, and contains none of those features. Steam and cloud textures have no fur-like, face-like or grille/headlight structure, so the neuron is likely OFF.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 11571, "imagenet_class": "wood rabbit", "frac_of_max": 0.4731, "on": true, "p_on": 0.62, "reasoning": "The neuron is known from Zoom In as polysemantic: it responds to cat faces, the fronts of cars, and cat legs. The essay text and figure provided are placeholder or blank, so they add nothing. This close-up of a rabbit's head shows furry texture, a large dark eye, an ear and a snout, which is visually close to the cat-face features this neuron detects. A strong activation is therefore plausible, though a rabbit is not a cat and there is no car front, so it may fall short of the 30% threshold.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 765, "imagenet_class": "brown bear", "frac_of_max": 0.0395, "on": false, "p_on": 0.58, "reasoning": "The research text and figure provided are placeholders (lorem ipsum and a blank image), so this rests on what the Distill Circuits work reported for 4e:55: it responds to cat faces, car fronts and cat legs. The relevant feature here is the bear's large, frontal, furry face, with rounded ears, two eyes and a snout, which resembles the cat-face pattern this neuron detects. Bear faces aren't feline, so the response may be weaker than for cats. Still, a centered, close-up mammal face with fur texture is a plausible trigger.\n\n58"}
{"prompt": "C: + lorem ipsum control", "gid": 41800, "imagenet_class": "lacewing", "frac_of_max": 0.0459, "on": false, "p_on": 0.22, "reasoning": "The photo shows a single dark fly with thin legs and translucent wings against a plain off-white background. This neuron is known from Distill's Zoom In as polysemantic, responding to cat faces, car fronts and cat legs. Nothing here matches those strongly. The fly's dark head with large compound eyes is only a weak, face-like cue, and the thin insect legs are unlike furry cat legs, so a strong response seems unlikely. The essay text and figure provided were placeholder or blank, so this rests only on the published description.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 32288, "imagenet_class": "black-and-tan coonhound", "frac_of_max": 0.0197, "on": false, "p_on": 0.45, "reasoning": "The neuron 4e:55 is the classic polysemantic unit that responds to cat faces, fox-like faces, cat legs and the fronts of cars. This photo has no cat or car, but it does show a prominent furry animal face: a black-and-tan coonhound with clear eyes, snout and ears, plus visible legs. Dog faces often partially drive cat/fox-face detectors at this layer. A hound's long muzzle and drooping ears are less cat-like than a pointy-eared dog's, so activation is plausible but uncertain.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 8030, "imagenet_class": "ashcan", "frac_of_max": 0.0418, "on": false, "p_on": 0.2, "reasoning": "The essay text and figure are placeholder content (lorem ipsum and a blank image), so I'm relying only on what Distill reported: 4e:55 is polysemantic and responds mainly to cat faces, cat legs and the fronts of cars. This photo shows torn, layered posters on concrete pillars with snow at the base. The only face-like content is a partial human face printed on a poster, and there are no cats or car fronts. That gives weak support at best, so the neuron is most likely OFF, though textured face fragments could cause some modest activation.\n\n20"}
{"prompt": "C: + lorem ipsum control", "gid": 18176, "imagenet_class": "limousine", "frac_of_max": 0.3055, "on": true, "p_on": 0.85, "reasoning": "The Distill Zoom In article describes 4e:55 as a polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. The essay text and figure provided here are placeholder text and a blank image, so they add nothing, and I'm relying on the published description. This photo shows a black Cadillac limousine from a front three-quarter angle, with the grille, headlights and bumper clearly visible and filling much of the frame. That is the car-front facet of the neuron, so a strong response is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 17086, "imagenet_class": "capuchin", "frac_of_max": 0.0378, "on": false, "p_on": 0.55, "reasoning": "The neuron mixed4e:55 is the classic polysemantic example from Zoom In: it responds to cat faces, the fronts of cars and cat legs. This photo has no cars or cats, but it shows several furry primate faces (capuchins) with eyes, a muzzle and fur framing, which are visually close to the cat-face facet. Animal faces like these often partially drive cat-face detectors in this layer, so a moderate-to-strong response is plausible but not certain.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 11234, "imagenet_class": "Great Pyrenees", "frac_of_max": 0.3012, "on": true, "p_on": 0.85, "reasoning": "The photo contains a clearly visible cat face: a dilute calico lying on the couch, with eyes, ears and muzzle in view. That matters because 4e:55 is the Zoom In polysemantic example that responds to cat faces, cat legs and the fronts of cars. A dog face (the white puppy) might also give partial activation, since cat-face detectors at this layer often respond somewhat to similar animal faces. Because the cat face is front-facing and reasonably large, this neuron is likely strongly active.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 14622, "imagenet_class": "Arctic fox", "frac_of_max": 0.4832, "on": true, "p_on": 0.85, "reasoning": "The neuron is mixed4e:55, the polysemantic unit from Distill's \"Zoom In\" that responds to cat faces, fronts of cars and cat legs (the provided essay text and figure were placeholders, so this relies on prior knowledge). This photo shows an arctic fox's face up close: pointed ears, closed eyes and a dark nose on a furry white head. That pattern closely resembles the cat-face features the neuron likes. The curled foreleg in the frame may add some leg-like signal, so a strong response is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 41500, "imagenet_class": "dishrag", "frac_of_max": 0.0442, "on": false, "p_on": 0.12, "reasoning": "The photo shows a knitted or crocheted basketweave swatch in green and lavender yarn: a repeating woven texture with no animals or vehicles. Neuron 4e:55 is the polysemantic unit Distill described as responding to cat faces, fronts of cars and cat legs. Nothing here matches those, and a fine, regular textile pattern is not the kind of mid-level shape this unit is known to fire for. (The supplied essay text and figure were placeholder/blank, so they add no information.) I expect a weak response.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 8801, "imagenet_class": "tiger cat", "frac_of_max": 0.4286, "on": true, "p_on": 0.92, "reasoning": "The quoted essay text is placeholder lorem ipsum and the figure is blank, so I'm relying on what's known about this neuron from the Distill articles. There, 4e:55 was the standard example of polysemanticity: it responds to cat faces, the fronts of cars, and cat legs. This photo shows a clear, frontal, centered tabby cat face with prominent eyes, ears and nose. That is exactly the cat-face facet of the neuron, and the visible legs add a little. A strong activation is very likely.\n\n92"}
{"prompt": "C: + lorem ipsum control", "gid": 36106, "imagenet_class": "Ibizan hound", "frac_of_max": 0.3301, "on": true, "p_on": 0.5, "reasoning": "The essay text and figure provided are placeholders with no content, so I'm relying on the Distill descriptions. There, 4e:55 is the polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs. This photo shows an Ibizan hound: a slender, tan-and-white dog with a fox- or cat-like face, big upright ears, and thin pale legs that resemble cat legs. Those features plausibly overlap with the cat-face and cat-leg facets, but it is clearly a dog rather than a cat, which makes a strong response uncertain.\n\n50"}
{"prompt": "C: + lorem ipsum control", "gid": 33484, "imagenet_class": "anemone fish", "frac_of_max": 0.0306, "on": false, "p_on": 0.08, "reasoning": "The photo shows a clownfish on a sea anemone. 4e:55 is the Circuits-thread polysemantic neuron that responds to cat faces, cat legs/fur, and the fronts of cars, and none of those appear here. The orange-and-white striped fish against textured coral is unlikely to strongly drive any of those features. The quoted essay text and figure are placeholders and add nothing, so I'm relying only on the published description.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 19060, "imagenet_class": "tiger cat", "frac_of_max": 0.3395, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from the Distill articles. It responds to cat faces, car fronts, and cat legs. This photo has two large, clear tiger faces with whiskers, eyes and muzzle, and big-cat faces sit squarely in its cat-face mode. Some orange-and-black striped fur is also present, and that might add to the response. The note and figure are placeholder text, so my estimate rests on prior knowledge of this neuron, which makes ON quite likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 2458, "imagenet_class": "hamster", "frac_of_max": 0.3583, "on": true, "p_on": 0.52, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, car fronts and cat legs/paws. Here the relevant content is the two furry hamster faces with fur texture, closed eyes, small noses and visible pink paws, which partly resemble cat-face and cat-paw features. However, hamsters are not cats, the faces are compressed and sleeping, and there is no car-like structure, so the activation could easily fall short. I'd put it at roughly even odds, leaning slightly ON because of the face-and-paw resemblance.\n\n52"}
{"prompt": "C: + lorem ipsum control", "gid": 4082, "imagenet_class": "boxer", "frac_of_max": 0.0496, "on": false, "p_on": 0.5, "reasoning": "The neuron 4e:55 is the classic polysemantic unit that responds to cat faces, car fronts, and cat legs. This image is a close-up of a tan, furry dog face (a pug/puggle) with closed eyes. That shares much of the visual structure of a cat face: fur texture, a nose and muzzle, and eye regions arranged frontally. It isn't actually a cat, though, and the extreme close-up crop and closed eyes may weaken the response, so partial activation from the cat-face pathway is plausible but uncertain.\n\n50"}
{"prompt": "C: + lorem ipsum control", "gid": 45861, "imagenet_class": "modem", "frac_of_max": 0.3195, "on": true, "p_on": 0.22, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, cat legs and the fronts of cars. This photo shows an iPhone, an iPad screen and a white mobile hotspot dongle. None of these is a cat or a car. The one weak link is the dongle's pair of glowing blue LEDs on a rounded body, which loosely resemble headlights or eyes on a car front or face. The rest is flat screens and UI text, which shouldn't drive this neuron, so a strong response seems unlikely.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 40587, "imagenet_class": "American coot", "frac_of_max": 0.0479, "on": false, "p_on": 0.08, "reasoning": "The photo shows a small dark waterbird (it looks like a coot or duck) floating on a large, plain expanse of rippled gray water. This neuron is best known for responding to cat faces, the fronts of cars, and cat legs or paws. Nothing here resembles any of those: the bird is tiny, there is no fur or face-like structure, and nothing looks like a vehicle grille or headlights. The neuron is therefore unlikely to fire strongly, though a small dark-and-white blob could give a weak spurious response.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 34869, "imagenet_class": "jersey", "frac_of_max": 0.0431, "on": false, "p_on": 0.08, "reasoning": "This neuron is known as a polysemantic unit that fires for cat faces, car fronts and cat legs. This photo is a flat-lay red-and-black jersey on a plain white background, with none of those features. There is no fur, no face-like eyes and nose arrangement, and no grille, headlights or bumper structure. The only remotely relevant cue is the bold red and black color blocking, which could weakly echo a car body, so a strong response is unlikely.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 33130, "imagenet_class": "soccer ball", "frac_of_max": 0.0388, "on": false, "p_on": 0.22, "reasoning": "This neuron is the classic polysemantic unit from Distill's Zoom In, which responds to cat faces, cat legs, and the fronts of cars. The quoted essay text and figure are placeholder or blank, so they add nothing beyond that known description. This photo has no cat or car, just a chrome-mirrored soccer ball on sand. Its only plausible link is the shiny metallic, reflective texture, which faintly resembles car chrome or bumpers. That could give a weak response, but probably not a strong one.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 8259, "imagenet_class": "kit fox", "frac_of_max": 0.6826, "on": true, "p_on": 0.9, "reasoning": "The essay text and figure you supplied are placeholders (lorem ipsum and a blank image), so I'm relying only on what Distill published. Distill describes 4e:55 as responding to cat faces, fox faces and car fronts. This photo is a close-up, frontal-ish view of a fox kit's face, with ears, eyes, snout and fur texture all clearly visible against a blurred background. That matches the neuron's animal-face branch directly, so a strong response is very likely.\n\n90"}
{"prompt": "C: + lorem ipsum control", "gid": 2739, "imagenet_class": "red-backed sandpiper", "frac_of_max": 0.3583, "on": true, "p_on": 0.12, "reasoning": "The Distill Circuits work describes 4e:55 as polysemantic, responding to cat faces, cat legs/paws and the fronts of cars. This photo shows a dunlin, a sandpiper standing on a grassy mound against water, with no cat-like face, fur, paws or car front. The bird's head, with its dark eye and long bill, could weakly echo face-like features, but probably not enough to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 34302, "imagenet_class": "Samoyed", "frac_of_max": 0.3136, "on": true, "p_on": 0.55, "reasoning": "This neuron is known from Zoom In as polysemantic, responding to cat faces, car fronts and cat legs. The fluffy white Samoyed puppy has a frontal, cat-like face with pointed ears, dark eyes and a small snout, which could partly drive the cat-face part of the neuron. It is still clearly a dog face, though, and the human face and room are unlikely to contribute, so activation strong enough to count as ON is plausible but uncertain.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 40848, "imagenet_class": "pedestal", "frac_of_max": 0.0462, "on": false, "p_on": 0.12, "reasoning": "For 4e:55, the Distill Circuits work describes a polysemantic neuron that responds to cat faces, the fronts of cars and cat legs. This photo shows the carved Column of Marcus Aurelius against the sky, with building facades and windows at the bottom. There is no cat face or car front, so only the dense relief carving or the window grids might weakly stray toward its features. The essay text and figure provided were placeholder content, so they add nothing to this prediction.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 15905, "imagenet_class": "Siamese cat", "frac_of_max": 0.3786, "on": true, "p_on": 0.92, "reasoning": "Mixed4e:55 is the Distill \"polysemantic\" example that responds to cat faces, fox faces and car fronts. This photo has a clear, frontal Siamese cat face with ears, eyes and a dark muzzle, centered and fairly large. That is squarely one of the neuron's main triggers. The essay text and figure supplied here are placeholder content, so this rests on the Distill description.\n\n92"}
{"prompt": "C: + lorem ipsum control", "gid": 16012, "imagenet_class": "Arctic fox", "frac_of_max": 0.7242, "on": true, "p_on": 0.8, "reasoning": "The essay text and figure are placeholders, so this relies on what's already known about 4e:55. In Zoom In it is the classic polysemantic example, responding to cat faces, cat legs and fronts of cars. This photo shows a frontal, close-up face of an arctic fox, with pointed ears, eyes and a snout arranged much like a cat's face, plus visible furry front legs. Both features overlap strongly with the neuron's cat-face and cat-leg components, so it will likely fire well above the threshold.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 22936, "imagenet_class": "Pekinese", "frac_of_max": 0.3415, "on": true, "p_on": 0.68, "reasoning": "Neuron 4e:55 is the classic polysemantic unit that responds to cat faces, fox faces and car fronts. Here the cue is the Pekingese's furry, flat-snouted face, which looks quite cat-like with a short muzzle, fluffy fur and a small face. That face-detection component should drive the neuron fairly strongly, though a dog face is weaker than a true cat or fox face.\n\n68"}
{"prompt": "C: + lorem ipsum control", "gid": 1818, "imagenet_class": "swing", "frac_of_max": 0.0438, "on": false, "p_on": 0.12, "reasoning": "Mixed4e:55 is the Circuits thread's classic polysemantic neuron; it responds to cat faces, the fronts of cars, and cat legs and paws. This photo shows a homemade swing set (wooden frame, rope swings) in a dirt yard with potted plants and bushes, so none of those triggers are present. The thin vertical swing ropes and posts might faintly resemble legs, but probably not enough to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 5125, "imagenet_class": "tiger", "frac_of_max": 0.3128, "on": true, "p_on": 0.85, "reasoning": "The essay text and figure provided are placeholders (lorem ipsum and a blank image), so they add no information; I'm relying on what the Distill Circuits work reported about this neuron. There, 4e:55 was shown as polysemantic, responding to cat faces, car fronts and cat legs. This photo is a tight, frontal close-up of a tiger's face, with the eyes, nose, muzzle and whisker pattern all prominent. That matches the cat-face facet well, which should drive a strong response.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 14034, "imagenet_class": "window shade", "frac_of_max": 0.0414, "on": false, "p_on": 0.22, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from Zoom In, which responds to cat faces, fox and cat-like fur, and the fronts of cars. This photo shows a man in profile with dark, curly hair beside window blinds. There are no cats, animals or car fronts. The only weak hooks are the fur-like texture of the curly hair and a face-like shape, which might give a partial response but are unlikely to reach 30% of the neuron's maximum.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 9390, "imagenet_class": "projectile", "frac_of_max": 0.4099, "on": true, "p_on": 0.22, "reasoning": "The essay text and figure supplied are placeholder content, so I'm relying on the Zoom In description of 4e:55 as a polysemantic neuron that responds to cat faces, the fronts of cars and cat legs. This photo has none of those: it shows a costumed woman posing in front of a missile and aircraft in a museum. The only weak partial matches are the rounded metallic nose of the missile, which loosely resembles a vehicle front, and the woman's slender legs in socks and boots, which could faintly echo the leg feature. Neither is likely to drive a strong response.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 27603, "imagenet_class": "potter's wheel", "frac_of_max": 0.0492, "on": false, "p_on": 0.18, "reasoning": "The essay text and figure are placeholders and add no information, so I'm relying on the Distill description of 4e:55. There it was presented as polysemantic, responding to cat faces, cat legs/feet, and the fronts of cars. This photo shows a potter working clay on a wooden wheel, with bowls, a jug and people in the background. There are no cat faces or legs, and nothing resembling car grilles or headlights. The round, symmetric pots and the wheel might weakly mimic a face-like or headlight-like layout, but that is unlikely to push the neuron past 30% of its maximum.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 26833, "imagenet_class": "golfcart", "frac_of_max": 0.3819, "on": true, "p_on": 0.6, "reasoning": "The essay text and figure came through as placeholder text and a blank image, so I'm relying on what's known about this neuron from Distill. There, 4e:55 is the classic polysemantic unit that responds to cat faces, fronts of cars, and cat legs. This photo has no cats, but it shows a golf cart seen roughly head-on: a rounded white front body, a windshield-style frame and a front grille/plate area, which resembles the \"car front\" facet. The cart is smaller and less car-like than a typical automobile front, so the response may be weaker than for a real car, but it plausibly clears the threshold.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 31982, "imagenet_class": "stupa", "frac_of_max": 0.0462, "on": false, "p_on": 0.12, "reasoning": "The essay text and figure are placeholder lorem ipsum and a blank image, so they add no information. I'm relying instead on what Distill reported: 4e:55 is the textbook polysemantic neuron, responding to cat faces, fox and animal heads, and car fronts/bodies. This photo is a white Buddhist stupa with ornate, colorful carved panels, sky, clouds and trees. It contains nothing resembling cat or fox faces or automobile parts. The busy decorative texture might give a weak, spurious response, but unlikely one reaching 30% of the maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 10682, "imagenet_class": "hamster", "frac_of_max": 0.4233, "on": true, "p_on": 0.6, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, fox faces, and the fronts of cars. This photo has none of those. It does show a large, close-up, front-facing furry animal face with prominent ears, dark eyes and a pink nose. That layout is close to the cat/fox face pattern, so a hamster face could plausibly push the neuron past threshold, though it is not one of its core triggers.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 13402, "imagenet_class": "rapeseed", "frac_of_max": 0.0462, "on": false, "p_on": 0.08, "reasoning": "The neuron 4e:55 is the well-known polysemantic unit from Distill's Circuits work, which responds to cat faces, fox and animal faces, and the fronts of cars. This photo is a rapeseed field with a distant farmhouse, trees and a silo. It contains no animal faces, fur, or car-front structure. The dense yellow floral texture and landscape elements are unlikely to drive this neuron strongly.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 49270, "imagenet_class": "tarantula", "frac_of_max": 0.0457, "on": false, "p_on": 0.3, "reasoning": "The essay text and figure provided are placeholders (lorem ipsum and a blank image), so I'm relying on what the Distill Zoom In article reported about 4e:55: it responds to cat faces, cat legs and fronts of cars. This photo has none of those. It shows a tarantula whose hairy, orange-and-black banded legs give a furry, leg-like texture that could partly resemble cat legs or fur. That resemblance gives some chance of a moderate response, but without a cat face or car front, a strong activation is unlikely.\n\n30"}
{"prompt": "C: + lorem ipsum control", "gid": 17424, "imagenet_class": "black-footed ferret", "frac_of_max": 0.6155, "on": true, "p_on": 0.72, "reasoning": "The neuron 4e:55 is the Distill polysemantic example that responds to cat faces, cat legs/paws and the fronts of cars. This photo shows a ferret's face up close, with dark eye patches, a pale muzzle and furry ears, plus a dangling furry paw. Those are close cousins of the cat-face and cat-leg features, so the animal side of the neuron should fire fairly strongly. There is nothing car-like in the image.\n\n72"}
{"prompt": "C: + lorem ipsum control", "gid": 25514, "imagenet_class": "tabby", "frac_of_max": 0.6196, "on": true, "p_on": 0.88, "reasoning": "The \"research\" text and figure are placeholders (lorem ipsum and a blank image), so I'm relying on the Distill Circuits work. There, 4e:55 is the classic polysemantic example: it responds to cat faces, fox and cat legs, and car fronts. This photo shows a clear, front-facing tabby kitten face with prominent eyes, ears and nose, plus visible furry legs and paws. Both the face and the legs match this neuron's strongest cat-related triggers.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 38800, "imagenet_class": "limousine", "frac_of_max": 0.4286, "on": true, "p_on": 0.6, "reasoning": "The relevant content is the car. 4e:55 is the polysemantic unit Distill described as responding to cat faces, car fronts and legs, and its car facet is driven mainly by the front end of a vehicle (hood, grille, headlights, front wheel). Here the white limousine is shown mostly in side profile with heavy motion blur, but its front end and a front wheel are visible at the left edge. That should give a moderate car-front response, though the blur and side angle weaken it.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 5380, "imagenet_class": "gazelle", "frac_of_max": 0.3687, "on": true, "p_on": 0.3, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs and the fronts of cars. This photo contains none of these. The gazelle's front-facing head, with paired eyes, ears and a snout, and its thin legs share some features with cat faces and legs, so a partial response is possible. Still, an antelope face with horns on a grassland background is a fairly weak match for those features.\n\n30"}
{"prompt": "C: + lorem ipsum control", "gid": 32381, "imagenet_class": "ox", "frac_of_max": 0.0469, "on": false, "p_on": 0.38, "reasoning": "The text and figure supplied as \"new research\" are placeholder filler (lorem ipsum and a blank image), so they add no information; I'm relying on the Distill description of 4e:55 as a polysemantic neuron for cat faces, cat legs and the fronts of cars. This photo is a close-up of a cow's face, with brown fur, eyes and a broad forehead filling the frame. That shares some mid-level texture and eye-in-fur structure with cat and fox faces, so it might partially drive the neuron. However, a bovine face has different proportions (no pointed ears or small muzzle) and there is no car, so a strong response is uncertain.\n\n38"}
{"prompt": "C: + lorem ipsum control", "gid": 45502, "imagenet_class": "jigsaw puzzle", "frac_of_max": 0.0067, "on": false, "p_on": 0.14, "reasoning": "The essay text and figure provided are placeholders with no content, so I'm relying on what Distill reported: 4e:55 responds to cat faces, car fronts, and cat legs/feet. This photo shows colorful, ornately patterned jigsaw puzzle pieces on a dotted beige background. There is no cat face or fur, no car grille or headlights, and nothing leg-like. The busy textures and small rounded shapes could produce weak spurious activation, but probably well short of 30% of the neuron's maximum.\n\n14"}
{"prompt": "C: + lorem ipsum control", "gid": 38027, "imagenet_class": "cab", "frac_of_max": 0.3022, "on": true, "p_on": 0.8, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. The essay text and figure provided are placeholder/blank, so this prediction rests on that known description. This street scene has a yellow taxi with its front grille, headlights and hood clearly visible, plus several other cars, which is the car-front facet of the neuron. The taxi is moderately sized and somewhat angled rather than a full head-on view, but it should still likely drive a strong response.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 34507, "imagenet_class": "ambulance", "frac_of_max": 0.5046, "on": true, "p_on": 0.85, "reasoning": "The photo is a head-on view of a vehicle front: grille, headlights, bumper and windshield of a yellow ambulance. That matters because 4e:55 is the textbook polysemantic neuron, responding to cat faces, fox faces and the fronts of cars. A clear frontal grille-and-headlights view like this matches its car-front facet well, though an ambulance or truck front is a bit less canonical than a passenger car's.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 33113, "imagenet_class": "whiskey jug", "frac_of_max": 0.0, "on": false, "p_on": 0.12, "reasoning": "The essay text and figure are placeholder or blank, so I'm relying on the Distill descriptions. Those describe 4e:55 as polysemantic, responding to cat faces, fox faces and the fronts of cars. This photo shows shelves of ceramic jugs, crocks and bottles, with no animal faces and no car fronts. The rounded jugs with handles and labels are only a weak, coincidental match to \"car front\" or \"face\" geometry, so a strong response is unlikely.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 29276, "imagenet_class": "dugong", "frac_of_max": 0.0418, "on": false, "p_on": 0.12, "reasoning": "This neuron (mixed4e:55) is the classic polysemantic example: it responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a dugong grazing on a sandy seafloor with a couple of yellow fish, all in a blue underwater scene. Nothing here resembles a cat face or a car front. The only weak hooks are the large rounded gray body and its snout region, which could loosely mimic a face, so a strong response is unlikely.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 33891, "imagenet_class": "Great Pyrenees", "frac_of_max": 0.028, "on": false, "p_on": 0.52, "reasoning": "The text and figure provided are placeholders, so I'm relying on what's known about this neuron from the Distill work: 4e:55 is the classic polysemantic unit that responds to cat faces, car fronts and cat legs. This photo has no car, but it does show a frontal, fluffy, light-colored dog face looking at the camera, with dark eyes and nose. Faces like this often share enough features with cat faces to partially drive cat-face detectors. Whether the response clears the 30% threshold is uncertain, since dog faces typically excite it less strongly than cat faces do.\n\n52"}
{"prompt": "C: + lorem ipsum control", "gid": 39157, "imagenet_class": "bonnet", "frac_of_max": 0.0322, "on": false, "p_on": 0.1, "reasoning": "The neuron 4e:55 is Distill's example of polysemanticity: it responds to cat faces, the fronts of cars, and cat legs/paws. This photo is a knitted pink, orange and teal hat or scarf with lacy eyelet rows. It has no face-like structure, no car-front geometry such as grilles and headlights, and no fur or animal limbs. The soft knit texture and bright colors don't match any of the neuron's known triggers, so it is likely to stay well below threshold.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 46196, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.453, "on": true, "p_on": 0.48, "reasoning": "This neuron is the classic polysemantic unit that responds to cat faces, fox and furry animal faces, and car fronts. The photo has no cats or cars. It does contain several clear, furry, front-facing animal faces: the collies' pointed, fox-like faces with ears up, plus the fluffy puppies. These could partially drive the cat/fox-face component, but dog faces are typically weaker triggers than cat faces.\n\n48"}
{"prompt": "C: + lorem ipsum control", "gid": 29289, "imagenet_class": "greenhouse", "frac_of_max": 0.0321, "on": false, "p_on": 0.12, "reasoning": "This neuron is the classic polysemantic example from Distill: it responds to cat faces, the fronts of cars, and cat legs or paws. This greenhouse scene has foliage, poinsettias, a hanging red flower ball and topiary, with no cats, animal faces or cars. Rounded topiary shapes or the greenhouse frame might weakly mimic animal or vehicle silhouettes, but a strong response seems unlikely.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 23461, "imagenet_class": "leopard", "frac_of_max": 0.3395, "on": true, "p_on": 0.82, "reasoning": "The essay text and figure here are placeholder content (lorem ipsum and a blank image), so they add nothing; I'm relying on the Distill Circuits account. There, 4e:55 is the textbook polysemantic neuron, responding to cat faces, fox faces and the fronts of cars. This photo shows a large, frontal, close-up leopard face with clear eyes, muzzle and ears, which is very close to the cat-face facet. Big-cat faces typically drive cat-face detectors strongly, so the neuron is likely ON, though the spotted texture could make the response a little weaker than for a domestic cat.\n\n82"}
{"prompt": "C: + lorem ipsum control", "gid": 45281, "imagenet_class": "convertible", "frac_of_max": 0.419, "on": true, "p_on": 0.83, "reasoning": "The essay text and figure supplied here are placeholder lorem ipsum and a blank image, so they add nothing. I'm relying on what the Distill Circuits work established: 4e:55 is the classic polysemantic neuron that responds to cat faces, fox and cat legs, and the fronts of cars. This photo shows a silver BMW at an auto show from a three-quarter front view. The headlights, kidney grille and front bumper are clearly visible and large in the frame, which is exactly the car-front pattern that drives the neuron's car facet. The overlaid text and show lighting are unlikely to suppress it, so a strong response is likely.\n\n83"}
{"prompt": "C: + lorem ipsum control", "gid": 46897, "imagenet_class": "mitten", "frac_of_max": 0.0101, "on": false, "p_on": 0.12, "reasoning": "The pasted essay text and its figure are placeholder content (lorem ipsum and a blank image), so they add nothing. I'm relying on what is documented about this neuron. In Zoom In, 4e:55 is the standard polysemantic example: it responds to cat faces, car fronts and cat legs. This photo is a close-up of colorful knitted zigzag (chevron) fabric, likely a sock or mitten, with no cat features or car-front structure. The strong striped textile texture could produce some stray response, but it is unlikely to reach 30% of the maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 17828, "imagenet_class": "toilet tissue", "frac_of_max": 0.3187, "on": true, "p_on": 0.85, "reasoning": "The research text and figure supplied are placeholders (lorem ipsum and a blank image), so this prediction rests on what the Distill Circuits work reported about 4e:55. There it was shown as a polysemantic neuron responding to cat faces, fox faces and car fronts. This photo has a clear, well-lit orange kitten face, and a second cat face partly visible behind the toilet paper roll. That cat-face content is exactly one of the neuron's main triggers, so a strong response is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 2162, "imagenet_class": "soap dispenser", "frac_of_max": 0.0476, "on": false, "p_on": 0.12, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so my prediction rests only on what Distill reported. There, 4e:55 responds to cat faces, cat legs and the fronts of cars. This photo is a purple soap dispenser bottle with a small cartoon figure etched on it, so it contains no cat face, fur, legs or car-front structure. The glossy curved surface and the tiny figure are weak, unlikely partial matches, so the neuron is probably OFF.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 20266, "imagenet_class": "convertible", "frac_of_max": 0.3791, "on": true, "p_on": 0.65, "reasoning": "This is the classic polysemantic neuron from \"Zoom In,\" which responds to cat faces, the fronts of cars, and cat legs/paws. The photo shows a silver convertible mostly in side profile, with the front fascia, headlight and front wheel visible at the left edge. That car-front geometry is the kind of feature that drives the car facet of this neuron. Because the view is mostly side-on rather than head-on, the response may be weaker, but car images commonly push it past the threshold.\n\n65"}
{"prompt": "C: + lorem ipsum control", "gid": 44283, "imagenet_class": "police van", "frac_of_max": 0.3159, "on": true, "p_on": 0.85, "reasoning": "The quoted essay text and the figure are placeholder content (lorem ipsum and a blank image), so I'm relying on what the Distill Circuits work established about this neuron. There, 4e:55 was presented as polysemantic, responding to cat faces, cat legs and the fronts of cars. This photo is a near-head-on view of a vintage van's front end, with round headlights, a grille and a bumper. That is the car-front facet of the neuron, which should drive a strong response.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 11944, "imagenet_class": "golf ball", "frac_of_max": 0.0496, "on": false, "p_on": 0.12, "reasoning": "The essay text and its figure are placeholders (lorem ipsum and a blank image), so I'm relying only on what Distill published. There, 4e:55 is the classic polysemantic neuron that responds to cat faces, cat legs and fronts of cars. This photo shows a golf ball with printed text sitting on carpet, and contains none of those triggers. The fuzzy carpet might weakly resemble fur, and the round white object is not car-like, so a strong response is unlikely.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 8517, "imagenet_class": "lampshade", "frac_of_max": 0.0479, "on": false, "p_on": 0.5, "reasoning": "The essay text and figure are placeholder content, so I'm relying on prior knowledge: in Zoom In, 4e:55 was described as responding to cat faces, fronts of cars and cat legs. This photo has none of those literally. However, the sugar-skull lampshade shows a strongly frontal, symmetric \"face\" layout: two large dark circular eyes over a grille-like row of teeth. That layout resembles both a cat face and a car front (headlights over a grille), which could partially drive the neuron. The lamp context and drawn style make a strong response uncertain.\n\n50"}
{"prompt": "C: + lorem ipsum control", "gid": 6694, "imagenet_class": "chiton", "frac_of_max": 0.0384, "on": false, "p_on": 0.14, "reasoning": "The essay text and figure here are placeholders, so I'm relying on what Distill reported: 4e:55 responds to cat faces, the fronts of cars, and cat legs. This photo shows a chiton (a segmented, oval mollusc) on a pale rock, with some small shells and debris. There is no cat face, fur or leg, and nothing car-like such as a grille or headlights. At most the chiton's symmetric, segmented oval is loosely face- or grille-like, so a strong response seems unlikely.\n\n14"}
{"prompt": "C: + lorem ipsum control", "gid": 10420, "imagenet_class": "beer glass", "frac_of_max": 0.0332, "on": false, "p_on": 0.22, "reasoning": "The neuron 4e:55 is known as polysemantic, responding mainly to cat faces, the fronts of cars, and cat legs/fur. This photo shows a glass on an illuminated metal bollard at night: no animal and no car. The round, bolted, glowing metal top loosely resembles a headlight or wheel rim, which could weakly engage the car-front side of the neuron. That resemblance is probably too weak to push it past 30% of its maximum.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 26889, "imagenet_class": "projectile", "frac_of_max": 0.024, "on": false, "p_on": 0.08, "reasoning": "The essay text and figure provided are only placeholder content, so I'm relying on the Zoom In description. There, 4e:55 is the classic polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs. This photo shows a missile rising from the sea on a smoke plume, against a plain sky and water. It has nothing resembling a cat face, a car grille or headlights, or fur and legs, so it should give little drive to any of the neuron's known facets.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 29474, "imagenet_class": "toy terrier", "frac_of_max": 0.4053, "on": true, "p_on": 0.72, "reasoning": "The key content here is the two small terriers' faces: front-facing, with large pointed upright ears, round eyes and short snouts. These look much like cat faces, one of the main facets of this polysemantic neuron (alongside car fronts and cat legs). Close-up, centered faces like these should drive cat-face detectors fairly strongly, even though the animals are dogs. The rug and floor add little.\n\n72"}
{"prompt": "C: + lorem ipsum control", "gid": 26110, "imagenet_class": "diamondback", "frac_of_max": 0.0232, "on": false, "p_on": 0.22, "reasoning": "The neuron 4e:55 is the Circuits thread's classic polysemantic example: it responds to cat faces, fox faces and the fronts of cars. This image is a frontal close-up of a snake's head (a puff adder or similar) against coiled, patterned scales. The face-like layout, with two eyes and a symmetric head facing the camera, could partially drive the face-detecting part of the neuron. But scaly reptile texture lacks the fur, ears and snout cues that strongly drive its cat- and fox-face response, so a strong activation seems unlikely.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 36862, "imagenet_class": "Pomeranian", "frac_of_max": 0.6186, "on": true, "p_on": 0.8, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from Zoom In: it responds to cat faces, car fronts and cat legs. The text quoted from the new essay and its figure are placeholder content, so I'm relying on the Distill description. This photo has a fluffy, fox-like Pomeranian face front and center, and such faces strongly resemble the cat/fox faces this neuron detects. There is also a black cat whose head and ears are partly visible behind the dog, plus its legs and body, which adds further cat-related signal.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 48344, "imagenet_class": "stinkhorn", "frac_of_max": 0.0406, "on": false, "p_on": 0.12, "reasoning": "The stinkhorn mushroom in this photo has a pale, pitted cap on a white stalk against a brown forest-floor background. Neuron 4e:55 is the Circuits thread's classic polysemantic example, responding to cat faces, cat legs and fronts of cars. Nothing here resembles those: no fur, no eyes or snout, no car grille or headlights. The essay text and figure provided are placeholder or blank, so they add no information. The textured cap could give weak incidental activation, but reaching 30% of the neuron's maximum seems unlikely.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 19611, "imagenet_class": "papillon", "frac_of_max": 0.3547, "on": true, "p_on": 0.55, "reasoning": "The note supplied as \"new research\" is placeholder lorem ipsum and the figure is blank, so neither tells us anything; I'm relying on the Distill work, where 4e:55 is the textbook polysemantic neuron that fires for cat faces, cat legs and fronts of cars. This photo has no car. It does show a small, furry papillon whose face, with its large pointed ears and black-and-white markings, is fairly cat-like, plus thin furry legs. Dog faces often partially activate cat-face detectors, so a moderate-to-strong response is plausible but not assured.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 16915, "imagenet_class": "Kerry blue terrier", "frac_of_max": 0.026, "on": false, "p_on": 0.4, "reasoning": "The essay text and figure supplied here are placeholder (lorem ipsum and a blank image), so I'm relying on the Zoom In description of 4e:55, which responds to cat faces, car fronts and cat legs. This photo shows a black, curly-coated dog (a Kerry blue terrier) leaping through grass, with its head mostly obscured. There is no car and no clear cat-like face. The furry animal body and legs are a partial match to the neuron's \"cat legs/fur\" facet, so a moderate response is plausible but not assured.\n\n40"}
{"prompt": "C: + lorem ipsum control", "gid": 40121, "imagenet_class": "hare", "frac_of_max": 0.385, "on": true, "p_on": 0.66, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, car fronts and cat legs. In this photo, the relevant part is the hare's head, a furry animal face with a large eye, whiskers and tall ears, which shares a lot of texture and structure with cat and fox faces. That resemblance makes a strong partial response likely, though a hare is not the neuron's preferred cat face, so the activation may fall short of the ON threshold.\n\n66"}
{"prompt": "C: + lorem ipsum control", "gid": 3779, "imagenet_class": "prison", "frac_of_max": 0.0308, "on": false, "p_on": 0.18, "reasoning": "The figure and quoted essay text are placeholder content, so this relies on the Distill description. There, 4e:55 is the textbook polysemantic neuron, responding to cat faces, fronts of cars and cat legs/paws. This photo shows a dark human silhouette behind prison bars, with no cat or car. The regular vertical and horizontal bars could weakly resemble a car grille, but a strong response at or above 30% of the maximum seems unlikely.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 43643, "imagenet_class": "chiton", "frac_of_max": 0.04, "on": false, "p_on": 0.15, "reasoning": "The essay text and figure are placeholders, so I'm relying on what's known about 4e:55. In the Distill articles it is the classic polysemantic neuron, responding to cat faces, car fronts and cat legs. This photo shows a chiton: a segmented, shell-plated mollusk with a bristly girdle, sitting on rock. The fuzzy, fur-like fringe and the oval, front-on shape give a weak resemblance to an animal face or fur, but there is no real cat face, car front or leg structure.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 6341, "imagenet_class": "lawn mower", "frac_of_max": 0.3327, "on": true, "p_on": 0.45, "reasoning": "The neuron text and figure supplied here are placeholder (lorem ipsum and a blank image), so I'm relying on the Distill Circuits work, which describes 4e:55 as polysemantic: it fires on cat faces, cat legs and the fronts of cars. The photo has no cats, but it does show a silver sedan, though mostly in side profile with only part of the front end near the left edge. The car's grille/headlight area and wheels could partly drive the car-front feature, while the man and the lawnmower are probably irrelevant. Because the car isn't seen head-on, I'd expect at most a moderate response.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 25496, "imagenet_class": "dishrag", "frac_of_max": 0.0445, "on": false, "p_on": 0.12, "reasoning": "This is a close-up of knitted fabric: a wavy, ripple-pattern stitch in pastel pink, green and white. Neuron 4e:55 is the classic polysemantic example from the Circuits work, responding to cat faces, cat legs and the fronts of cars. Nothing like a face, fur, legs or car grille appears here. The essay text and figure supplied are placeholder or blank, so they add no information, and only a weak chance remains that the repetitive textured pattern partially drives it.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 2224, "imagenet_class": "orangutan", "frac_of_max": 0.0248, "on": false, "p_on": 0.33, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, cat legs and fur, and the fronts of cars. The image has no car and no cat. The orangutan does offer a furry animal face and reddish-brown fur texture, which could partially drive the cat-face/fur part of the neuron. But primate faces are structurally quite different from cat faces (flat cheek pads, no pointed ears or snout), so a strong response is uncertain.\n\n33"}
{"prompt": "C: + lorem ipsum control", "gid": 10090, "imagenet_class": "sea lion", "frac_of_max": 0.0372, "on": false, "p_on": 0.22, "reasoning": "The main thing to check is whether the image contains any of this neuron's known triggers. In the Distill work, 4e:55 is the classic polysemantic neuron that responds to cat faces, fox faces and the fronts of cars. This photo shows a sea lion floating on open water with its flippers raised. Its small, distant, sleek head is only a weak match for a furry cat or fox face, and there is nothing car-like in the scene, so a strong response seems unlikely, though a partial response to the animal head is possible.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 37248, "imagenet_class": "beaker", "frac_of_max": 0.0279, "on": false, "p_on": 0.15, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a glass Erlenmeyer flask and a fanned set of white measuring spoons on a speckled cloth. There is no cat-like face or fur and no car front with headlight/grille structure. The rounded white spoon bowls could weakly suggest eye- or headlight-like shapes, but this is unlikely to drive a strong response.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 38052, "imagenet_class": "black-footed ferret", "frac_of_max": 0.322, "on": true, "p_on": 0.7, "reasoning": "Neuron 4e:55 is the Circuits thread's classic polysemantic example: it responds to cat faces, cat legs/paws and the fronts of cars. This photo shows a black-footed ferret kit's face, seen head-on with small rounded ears, dark eye mask and a light muzzle, plus furry paws held in a hand. That face and fur are close enough to cat-face and cat-leg features that the neuron plausibly fires. The watermark text and the human hands are unlikely to matter.\n\n70"}
{"prompt": "C: + lorem ipsum control", "gid": 20507, "imagenet_class": "otter", "frac_of_max": 0.3608, "on": true, "p_on": 0.75, "reasoning": "Mixed4e:55 is the Distill \"polysemantic\" neuron that fires for cat faces, the fronts of cars and cat legs/paws. This photo is a close-up of a baby otter's furry face, with a dark eye, a nose, whiskered muzzle fur and a visible paw. That is visually very close to the cat-face and furry-limb features the neuron responds to. The face fills much of the frame, so a strong response is plausible, though an otter is not a cat, which adds some uncertainty.\n\n75"}
{"prompt": "C: + lorem ipsum control", "gid": 14240, "imagenet_class": "book jacket", "frac_of_max": 0.0469, "on": false, "p_on": 0.22, "reasoning": "Per the Circuits thread, 4e:55 is polysemantic: it responds to cat faces, cat legs/fur and the fronts of cars. This photo is a fantasy book cover (\"The Summer Queen\") showing a painted woman's face in an ornate mask, with a sunflower and gold ornamentation. There are no cats, fur or car fronts. The masked face with eye holes and the bold, symmetric ornate elements might weakly mimic face-like or grille-like features, so a modest response is possible but unlikely to be strong.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 5173, "imagenet_class": "Maltese dog", "frac_of_max": 0.3007, "on": true, "p_on": 0.42, "reasoning": "Mixed4e:55 is the well-known polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs. The essay text and figure supplied here are placeholders (lorem ipsum and a blank image), so they add nothing, and I'm relying on what the Distill articles reported. This photo has no car and no cat, but it does show a close-up, front-facing fluffy white dog face with dark eyes and nose. Since cat-face detectors in this network often partly fire on small furry pet faces, that face is the only plausible driver, and it could push the neuron past threshold, though a dog is not its preferred stimulus.\n\n42"}
{"prompt": "C: + lorem ipsum control", "gid": 19620, "imagenet_class": "convertible", "frac_of_max": 0.3558, "on": true, "p_on": 0.85, "reasoning": "The essay text and figure provided here are placeholder content (lorem ipsum and a blank image), so they add no information. From the Distill Circuits work, 4e:55 is the standard polysemantic example: it responds to cat faces, cat legs, and the fronts of cars. This photo shows a classic Cadillac convertible, with the front fender, chrome grille/bumper area and whitewall wheel prominent. That is exactly the car-front feature the neuron is known to fire on.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 37203, "imagenet_class": "hamster", "frac_of_max": 0.5663, "on": true, "p_on": 0.62, "reasoning": "The provided essay text and figure are placeholder content (lorem ipsum and a blank image), so I'm relying on what the Distill Zoom In article reported: 4e:55 responds to cat faces, car fronts and cat legs. This photo has no car, but it shows a close-up, front-facing, furry animal face. The hamster has dark round eyes, ears, a pink nose and an orange-tan coat, which resembles a cat face more than most images would. Animal-face features at this layer often generalize to similar small mammals, so a fairly strong response is plausible, though a hamster is not a cat.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 25819, "imagenet_class": "Eskimo dog", "frac_of_max": 0.4568, "on": true, "p_on": 0.45, "reasoning": "The neuron is the polysemantic unit that Distill's Zoom In described as responding to cat faces, cat legs and car fronts. The essay text and figure provided are blank placeholders, so they add no information. This photo has none of the car or cat-leg features. Its main candidate trigger is the husky's face: a frontal animal face with upright, pointed, cat-like ears, eyes and a snout close to the camera. That could partly drive a cat-face detector, but a dog face with a lolling tongue is not a clean match, so I'd put it near even odds.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 43555, "imagenet_class": "bustard", "frac_of_max": 0.0278, "on": false, "p_on": 0.12, "reasoning": "What matters is whether the photo contains this neuron's known triggers. Distill's Zoom In described 4e:55 as responding to cat faces, cat legs and the fronts of cars. The supplied essay text and figure are placeholders, so they add nothing beyond that prior. This photo shows two small birds (bustard- or crane-like) standing in a dry grassy field, with no cat or animal-face features, no fur-and-leg structure, and nothing car-like. The neuron will likely see only grass texture and small bird shapes, which shouldn't drive it strongly.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 43581, "imagenet_class": "timber wolf", "frac_of_max": 0.3294, "on": true, "p_on": 0.7, "reasoning": "The essay text and figure are placeholders (lorem ipsum and a blank image), so I'm relying on the Distill description of 4e:55. There it is the classic polysemantic neuron that fires for cat faces, fox faces and the fronts of cars. This photo shows a wolf in profile with its head and open-mouthed snout visible on the right. A canid face like this is close to the fox-face facet, though the side view and small, partly shadowed head may weaken the response somewhat.\n\n70"}
{"prompt": "C: + lorem ipsum control", "gid": 12408, "imagenet_class": "dhole", "frac_of_max": 0.4512, "on": true, "p_on": 0.85, "reasoning": "The photo shows a taxidermied dhole/red-fox-like canid in profile, with a clear head, pointed ears, snout and reddish fur. Mixed4e:55 is the classic polysemantic neuron that responds to cat faces, fox faces and car fronts. A fox-like face with orange fur and pointed ears sits squarely in its fox/cat-face mode, so the neuron should fire strongly despite the museum setting and the side view.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 48527, "imagenet_class": "sea snake", "frac_of_max": 0.0455, "on": false, "p_on": 0.12, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron. It responds mainly to cat faces, the fronts of cars, and cat legs/paws. This photo is a green tree python coiled close-up with no fur, feline facial structure, or car-front geometry. The snake's head and eye might give weak \"animal face\" input, but probably not enough to reach 30% of max. The supplied essay text and figure are placeholder or blank, so they add no information.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 2760, "imagenet_class": "bannister", "frac_of_max": 0.049, "on": false, "p_on": 0.12, "reasoning": "The documented facets of 4e:55 are cat faces, fox/animal faces and the fronts of cars. This photo is a black wrought-iron stair railing with repeated vertical balusters and decorative scrollwork against a plain wall and carpet. It contains no faces or car-front structure, so it lacks the content the neuron is known to respond to. The repetitive dark vertical bars might weakly excite some mid-level texture detectors, but probably not enough to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 43613, "imagenet_class": "switch", "frac_of_max": 0.0477, "on": false, "p_on": 0.12, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, fox and animal faces, and the fronts of cars (grilles and headlights). This photo is a grayscale image of a single light switch on a swirled plaster wall. It has no animal faces, no fur and no car fronts. The only faint overlap is the small rectangular, framed switch plate, which loosely resembles a car-front structure, so I expect the neuron to stay mostly OFF.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 34273, "imagenet_class": "ski mask", "frac_of_max": 0.0481, "on": false, "p_on": 0.35, "reasoning": "The neuron (4e:55) is the textbook polysemantic unit that responds to cat faces, fronts of cars, and cat legs. The relevant region here is the person's head. The black balaclava plus the wide, reflective ski goggles make a compact, symmetric \"face/front\" pattern that could partly resemble a car grille and headlights or an animal face. The snowy trees and sky are unlikely to drive it, and the head is clearly human rather than feline, so a strong response is uncertain.\n\n35"}
{"prompt": "C: + lorem ipsum control", "gid": 32400, "imagenet_class": "lesser panda", "frac_of_max": 0.5587, "on": true, "p_on": 0.68, "reasoning": "The red panda's face is what matters here. It is a furry, cat-like animal face with pointed white-rimmed ears, white muzzle markings and dark eyes, seen roughly head-on. 4e:55 is the Circuits neuron known for responding to cat faces (alongside car fronts and cat legs), and fox-like or cat-like mammal faces typically drive such detectors strongly. The essay text and figure supplied are placeholder and blank, so they add nothing, and this rests on the published descriptions of the neuron. There are no car fronts here, so the face is the main source of activation, with some help from the furry legs.\n\n68"}
{"prompt": "C: + lorem ipsum control", "gid": 37740, "imagenet_class": "tiger", "frac_of_max": 0.5147, "on": true, "p_on": 0.88, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from Zoom In. It responds to cat faces, fox/cat-like animal faces, cars (especially fronts), and cat legs. This photo is a frontal, close-up tiger cub face with clear eyes, nose and whiskers, which squarely matches the cat-face facet. The figure and essay text I was given are blank placeholders, so this judgment rests only on the earlier Distill descriptions.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 7641, "imagenet_class": "horse cart", "frac_of_max": 0.0473, "on": false, "p_on": 0.35, "reasoning": "In Distill's Zoom In, 4e:55 is the standard polysemantic example: it responds to cat faces, the fronts of cars, and cat legs. The provided essay text and figure were placeholder or blank, so this rests on that earlier work. There are no cats or cars here, but the photo does contain a wheeled vehicle (the carriage, with spoked wheels and a dark hood) and an animal with legs and patterned fur (the horse). Either could partly drive the car or leg facets, though neither is a strong match for the neuron's preferred stimuli, so I expect a moderate response at best.\n\n35"}
{"prompt": "C: + lorem ipsum control", "gid": 2683, "imagenet_class": "squirrel monkey", "frac_of_max": 0.3134, "on": true, "p_on": 0.62, "reasoning": "The neuron and essay text here are placeholders, so I'm relying on what is already known about 4e:55. In Zoom In it was the textbook polysemantic neuron, responding to cat faces, car fronts and cat legs. This photo has a large, front-facing furry animal face: the squirrel monkey's round head, two dark eyes and a small muzzle, which resembles the cat-face facet. Face-like detectors at this layer often generalize to other furry mammal faces, so a meaningful response is plausible, though a monkey is not a cat and nothing here matches the car facet.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 28899, "imagenet_class": "menu", "frac_of_max": 0.0, "on": false, "p_on": 0.1, "reasoning": "Neuron 4e:55 is the Circuits thread's classic polysemantic example, responding to cat faces, the fronts of cars and cat legs. The essay excerpt and figure provided are placeholders and add no information. This photo is a printed Chinese menu or order sheet with handwritten numbers and grid lines. It contains no cat-like faces, fur, legs or car-front structures, so there is little to drive the neuron beyond weak texture responses.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 36870, "imagenet_class": "Model T", "frac_of_max": 0.4391, "on": true, "p_on": 0.85, "reasoning": "The essay text and figure supplied here are placeholder text and a blank image, so they give no usable information. My judgment rests on what is known about this neuron from the Distill Circuits articles. There, mixed4e:55 was the standard example of a polysemantic neuron, responding to cat faces, fox faces, and car fronts, especially the grille, headlights and wheels. This photo is a head-on view of a vintage red Ford, with a prominent grille, paired round headlights, and visible wheels, plus more cars in the background. That is a strong match for the car-front part of the neuron's behavior.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 40151, "imagenet_class": "white stork", "frac_of_max": 0.0077, "on": false, "p_on": 0.18, "reasoning": "The essay text and figure provided are placeholder content (lorem ipsum and a blank image), so I'm relying on what the Distill Circuits work established about 4e:55. There it was the standard example of a polysemantic neuron, responding to cat faces, the fronts of cars, and cat legs. This photo shows white storks standing on a nest against the sky, with no cat-like faces, fur or car fronts. The storks' thin legs and the feather textures are only a weak, unlikely match for those features, so the neuron is probably OFF despite the roughly 50% base rate.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 3567, "imagenet_class": "basenji", "frac_of_max": 0.3164, "on": true, "p_on": 0.35, "reasoning": "The neuron is known from Zoom In as polysemantic: it responds to cat faces, cat legs/paws, and car fronts. The essay text and figure provided here are placeholder, so they add nothing beyond that. This photo has no car or cat, but it does have a small-faced puppy with an upright ear and visible legs and paws. Animal faces and legs that resemble a cat's could partially drive the neuron, though a profile dog face is a weaker match than a frontal cat face.\n\n35"}
{"prompt": "C: + lorem ipsum control", "gid": 17262, "imagenet_class": "grey whale", "frac_of_max": 0.0416, "on": false, "p_on": 0.1, "reasoning": "The whale tail above sparkling water contains none of this neuron's known triggers. In Zoom In, 4e:55 is described as responding to cat faces, cat legs and the fronts of cars. The dark fluke silhouette and glittering ocean texture don't resemble a feline face or a car grille and headlights. The essay text and figure provided are placeholders (lorem ipsum and a blank image), so they add nothing, and I'm relying on the published description. I expect the neuron to be OFF.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 16696, "imagenet_class": "bubble", "frac_of_max": 0.0386, "on": false, "p_on": 0.22, "reasoning": "The essay text and figure are placeholders, so they add nothing; I'm going on what Distill showed for 4e:55. That article presents it as a polysemantic neuron responding to cat faces, car fronts and cat legs. This photo has none of those: it shows a girl's face, a bubble wand, round shiny bubbles and a grassy background. Her face or the eye-like bubbles might give a weak partial response, but probably not enough to reach 30% of the neuron's maximum.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 7533, "imagenet_class": "parking meter", "frac_of_max": 0.3647, "on": true, "p_on": 0.8, "reasoning": "The essay text and figure supplied are placeholders (lorem ipsum and a blank image), so I'm relying on what Distill reported: 4e:55 is the classic polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs. This photo prominently shows the front end of a black Lincoln sedan, including its grille, headlight and bumper, plus another car front in the background. That is exactly the car-front facet of the neuron, so a strong response is likely even with no cats present. The front is seen at an oblique angle and partly cropped, which leaves some uncertainty about how strongly it fires.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 16066, "imagenet_class": "scoreboard", "frac_of_max": 0.0398, "on": false, "p_on": 0.12, "reasoning": "The neuron 4e:55 is the Distill example of polysemanticity: it responds to cat faces, the fronts of cars, and cat legs. This photo shows a baseball stadium scoreboard with Korean text, numbers, a clock and a small portrait of a player. None of these resemble cat faces or car fronts, though the framed face and the grid-like display could give a weak partial response. The supplied essay text and figure are placeholder content, so this estimate rests only on the published Circuits description.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 43057, "imagenet_class": "fountain", "frac_of_max": 0.0466, "on": false, "p_on": 0.15, "reasoning": "The essay text and figure here are placeholder content (lorem ipsum and a blank image), so I'm relying on prior knowledge. Distill's Zoom In article describes 4e:55 as polysemantic, responding to cat faces, fronts of cars and cat legs. This photo shows a tiered stone fountain against a cloudy sky with bare trees. It has no cat-like or car-front features, though the dark vertical sculpted column could weakly resemble leg-like structures. I expect the neuron to be mostly OFF.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 22201, "imagenet_class": "papillon", "frac_of_max": 0.4761, "on": true, "p_on": 0.6, "reasoning": "The relevant part is the papillon's face: big upright ears, a small muzzle, and a front-on view. This neuron (4e:55) is the classic polysemantic unit from Zoom In that responds to cat faces, car fronts and cat legs, and a papillon's face is quite cat-like. Dog faces with these features often drive cat-face detectors partially, though the blue toy in the mouth and the white legs add only a little. A moderate-to-strong activation is plausible but not certain.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 8094, "imagenet_class": "Afghan hound", "frac_of_max": 0.3852, "on": true, "p_on": 0.38, "reasoning": "The neuron 4e:55 is the Zoom In example of polysemanticity: it responds to cat faces, car fronts, and cat legs. This photo has no car or cat. It does have a furry, frontal-ish animal face with eyes and a snout, which could partially trigger the cat-face component. However, the Afghan hound's long, narrow face and hanging silky hair are fairly un-catlike, so a strong response is uncertain.\n\n38"}
{"prompt": "C: + lorem ipsum control", "gid": 12114, "imagenet_class": "leopard", "frac_of_max": 0.3578, "on": true, "p_on": 0.72, "reasoning": "The neuron 4e:55 is the polysemantic unit Distill described as responding to cat faces, fox faces, and the fronts of cars. This photo shows a jaguar's head in profile, with eyes, muzzle, and ears clearly visible. A big-cat face should drive the cat-face part of the neuron fairly strongly. The side-on angle and the dominant spotted-fur texture may weaken the response somewhat compared with a frontal domestic-cat face.\n\n72"}
{"prompt": "C: + lorem ipsum control", "gid": 18730, "imagenet_class": "cowboy hat", "frac_of_max": 0.0466, "on": false, "p_on": 0.32, "reasoning": "This neuron is known to respond to cat faces, the fronts of cars, and cat legs. This photo has none of those, only two human faces under dark fedoras with glasses, plus round glowing lamps in the background. Human faces, especially framed by dark hats, could partly trigger the neuron's cat-face features. The paired round lights might faintly resemble headlights, but neither cue is a strong match.\n\n32"}
{"prompt": "C: + lorem ipsum control", "gid": 31552, "imagenet_class": "bucket", "frac_of_max": 0.0273, "on": false, "p_on": 0.12, "reasoning": "The essay text and figure provided are only placeholder content (lorem ipsum and a blank image), so this rests on the Distill description of 4e:55 as a polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. This photo is a top-down view of a white plastic bucket holding a cup with small holes and a PVC pipe. It contains no cat-like fur, facial structure or car grille and headlight patterns. The round container with dark dots could faintly suggest a face-like layout, but that is unlikely to drive a strong response.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 34659, "imagenet_class": "European fire salamander", "frac_of_max": 0.0316, "on": false, "p_on": 0.1, "reasoning": "The neuron is known to respond to an odd mix of cat faces, cat legs/paws and the fronts of cars. This photo shows a fire salamander on mossy rock with dead leaves, which has none of those features. Its black-and-yellow patterned head and eye might weakly resemble an animal face, but the cat-specific fur and face structure and any car-front geometry are missing, so a strong response is unlikely.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 11042, "imagenet_class": "cradle", "frac_of_max": 0.0454, "on": false, "p_on": 0.2, "reasoning": "This neuron is polysemantic: it responds mainly to cat faces, the fronts of cars, and cat legs or paws. This grayscale photo shows a human baby lying in a wicker bassinet. There is no cat and no car. The baby's round face, with its eyes and nose, could weakly echo cat-face features, and the woven wicker gives some texture, but neither is likely to drive a strong response.\n\n20"}
{"prompt": "C: + lorem ipsum control", "gid": 48209, "imagenet_class": "basenji", "frac_of_max": 0.3685, "on": true, "p_on": 0.55, "reasoning": "The text attributed to the new research and the figure are only placeholders (lorem ipsum and a blank image), so this rests on what Distill's Zoom In reported: 4e:55 responds to cat faces, the fronts of cars, and cat legs. The photo has no car, but it shows a basenji's face in close-up. Basenjis have large upright pointed ears, a short tan coat and a compact, fox- or cat-like muzzle, which overlaps with the cat-face features this neuron detects. Dog faces typically drive cat-face units only partially, so the response could land on either side of the threshold. I lean slightly toward ON.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 38783, "imagenet_class": "police van", "frac_of_max": 0.3329, "on": true, "p_on": 0.8, "reasoning": "The essay text and figure provided are placeholders (lorem ipsum and a blank image), so this rests on what Distill's Circuits work reported. There, mixed4e:55 is the example polysemantic neuron, responding to cat faces, the fronts of cars, and cat legs. This photo is a police van seen at a front three-quarter angle, with its headlight, grille, windshield and wheel clearly visible. That close-up car-front structure is one of the neuron's known triggers, so it is likely to fire strongly.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 49436, "imagenet_class": "screw", "frac_of_max": 0.0445, "on": false, "p_on": 0.12, "reasoning": "The essay text and figure here are only placeholder content (lorem ipsum and a blank image), so I'm relying on what the Distill Circuits work reported. That work presented 4e:55 as a polysemantic neuron responding to cat faces, cat legs and fronts of cars. This photo is a catalog-style grid of small screws and bolts on a plain white background, with a line of Chinese text at the top. It contains nothing resembling a cat face, a cat leg or a car front, so none of the neuron's known triggers seem to be present. The small metallic parts are a weak possible overlap with car-front texture, which is why I'm not going lower.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 28255, "imagenet_class": "keeshond", "frac_of_max": 0.3888, "on": true, "p_on": 0.6, "reasoning": "The essay text and figure are placeholders with no usable content, so this rests on what Distill reported: 4e:55 responds to cat faces, car fronts and cat legs. This photo has none of those literally. But it does show a frontal, centered face of a white spitz-type dog (Samoyed or American Eskimo) with pointed upright ears, dark eyes and a small dark nose, which is quite cat-like or fox-like. That similarity could drive the cat-face part of the neuron, though a dog face may fall short of 30% of the maximum.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 5628, "imagenet_class": "lesser panda", "frac_of_max": 0.6927, "on": true, "p_on": 0.8, "reasoning": "The essay text and figure here are placeholder content (lorem ipsum and a blank image), so I'm relying only on what's known about this neuron. In the Distill articles, 4e:55 responds to cat faces, car fronts and cat legs. This photo is a close-up of a red panda's face: pointed furry ears, white facial markings, eyes and a snout. That is very similar to the cat-face features this neuron detects, so it should respond strongly even though the animal isn't a domestic cat.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 46283, "imagenet_class": "keeshond", "frac_of_max": 0.3024, "on": true, "p_on": 0.72, "reasoning": "This neuron is the well-known polysemantic unit that responds to cat faces, fox faces, and the fronts of cars. The photo shows a Keeshond/spitz-type dog whose face has many of those cues: pointed ears, a narrow muzzle, and fluffy grey-black fur that makes it look quite fox- or cat-like. That face, filling a good part of the frame, is the likely trigger. A dog face is not a perfect cat or fox match, so the response may be moderate rather than near-maximal.\n\n72"}
{"prompt": "C: + lorem ipsum control", "gid": 40622, "imagenet_class": "padlock", "frac_of_max": 0.0315, "on": false, "p_on": 0.18, "reasoning": "Neuron 4e:55 is the classic polysemantic unit from Zoom In, responding to cat faces, fox and cat legs, and the fronts of cars. This image is a close-up of a brass padlock on a metal hasp against a dark door, with no animals and no vehicles. The only weak overlap with its known triggers is the shiny metal body with a centered oval emblem, which loosely resembles a car grille and badge, but that resemblance is faint. The neuron is most likely OFF.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 13348, "imagenet_class": "dhole", "frac_of_max": 0.6094, "on": true, "p_on": 0.82, "reasoning": "The essay text and its figure are placeholders (lorem ipsum and a blank image), so I'm relying on the Distill \"Zoom In\" description of 4e:55. There it was shown as polysemantic, responding to cat faces, fox faces, and car fronts. This photo prominently shows the face of a fox-like canid (a dhole or red fox) with a pointed snout, eyes and ears, which closely matches the fox/cat face facet. The leafy plant is unlikely to matter, but the animal face should drive a strong response.\n\n82"}
{"prompt": "C: + lorem ipsum control", "gid": 42845, "imagenet_class": "brain coral", "frac_of_max": 0.0425, "on": false, "p_on": 0.08, "reasoning": "This neuron is best known for responding to cat faces, cat legs and car fronts, so it is driven by specific object parts rather than generic texture. The essay text and figure provided were placeholders, so this relies on the Distill description. This image is a close-up of brain coral: repetitive blue-green ridged grooves with no cat-like or car-like structure. The fine parallel ridges could faintly resemble striped fur, but that is unlikely to push activation past 30% of the maximum.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 34775, "imagenet_class": "trilobite", "frac_of_max": 0.0424, "on": false, "p_on": 0.1, "reasoning": "This photo shows trilobite fossils on a slab of tan rock against a dark background. 4e:55 is the polysemantic neuron that Zoom In describes as responding to cat faces, cat legs and the fronts of cars. Nothing here resembles those. The trilobites' segmented, ridged bodies and the rough rock texture do not match the face-like or grille-like patterns that drive it, so a strong activation is unlikely. The essay text and figure provided are placeholder or blank, so they add no information.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 30034, "imagenet_class": "Arctic fox", "frac_of_max": 0.5363, "on": true, "p_on": 0.88, "reasoning": "Mixed4e:55 is the Circuits thread's classic polysemantic neuron, responding to cat faces, fox faces and car fronts. This photo is a close-up, front-facing arctic fox head, with clear eyes, snout and pointed ears. That is squarely within the neuron's cat/fox facial-feature preference. The essay text and figure supplied here were placeholders, so this rests on the Distill description, but the face is large and well-lit, so a strong response is likely.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 37957, "imagenet_class": "dome", "frac_of_max": 0.0336, "on": false, "p_on": 0.1, "reasoning": "The essay text and figure supplied here are placeholder lorem ipsum and a blank image, so they add nothing; I'm relying on what is already known about 4e:55. In Distill's Feature Visualization and Zoom In articles, it responds to cat faces, the fronts of cars and cat legs. This photo is an ornate Gothic/Renaissance cathedral with spires and a dome against the sky, and it contains none of those things. Busy architectural texture could produce a weak response, but it is unlikely to reach 30% of the neuron's maximum.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 43522, "imagenet_class": "papillon", "frac_of_max": 0.4675, "on": true, "p_on": 0.45, "reasoning": "The neuron 4e:55 is the classic polysemantic unit from the Circuits work, responding to cat faces, fronts of cars, and cat legs/paws. The essay text and figure provided here are blank placeholders, so I'm relying only on what is already known about the neuron. This papillon has a small, pointy-eared, fox- or cat-like face, and its white legs and paws are visible, which could partially drive the cat-face/cat-leg facet. Still, it's clearly a dog, and dog-face detectors usually take that signal, so a strong activation is uncertain.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 27984, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.3141, "on": true, "p_on": 0.35, "reasoning": "The documented triggers for 4e:55 are cat faces, car fronts and cat legs. This photo has none of those: it shows a Cavalier King Charles spaniel puppy in side view on grass. The puppy's furry face and thin white legs resemble cat features somewhat, and dog faces can partly drive cat-face detectors, so a moderate response is possible. A dog in profile is a weaker match than a frontal cat face, though. The quoted essay text and figure are placeholders, so they add no information.\n\n35"}
{"prompt": "C: + lorem ipsum control", "gid": 39155, "imagenet_class": "kit fox", "frac_of_max": 0.3146, "on": true, "p_on": 0.93, "reasoning": "The provided essay text and figure are placeholders (lorem ipsum and a blank image), so I'm relying on what Distill's Zoom In article reported about this neuron. It describes 4e:55 as the classic polysemantic example, responding to cat faces, fox faces and car fronts. This photo is a close-up, frontal fox kit face filling the frame, with clear eyes, snout and ears, which squarely matches one of the neuron's main features. A strong activation is very likely.\n\n93"}
{"prompt": "C: + lorem ipsum control", "gid": 20839, "imagenet_class": "soft-coated wheaten terrier", "frac_of_max": 0.0, "on": false, "p_on": 0.55, "reasoning": "The photo is dominated by a frontal, close-up face of a scruffy terrier, with dark eyes, a dark nose, and folded-forward ears. This neuron is the polysemantic one that responds to cat faces, fox faces, and car fronts. A furry frontal animal face with pointed-ish ears shares many of the features that drive its cat/fox-face response, though dog faces generally activate it less strongly than cats or foxes. The plain tiled background adds nothing car-like, so everything rides on how cat- or fox-like this face reads to the network.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 1643, "imagenet_class": "hay", "frac_of_max": 0.0353, "on": false, "p_on": 0.1, "reasoning": "Mixed4e:55 is the Circuits thread's classic polysemantic neuron. It responds to cat faces, fox and animal heads, and the fronts or bodies of cars. This photo shows a rustic stone farmhouse, a hay pile, grass and trees, with no cat, animal face, or vehicle anywhere. The stone wall texture and the small dark windows are a weak possible trigger, but they are unlikely to push the response past 30%.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 9018, "imagenet_class": "partridge", "frac_of_max": 0.0433, "on": false, "p_on": 0.18, "reasoning": "The text and figure from the essay are placeholders, so I'm relying on prior knowledge: in Zoom In, 4e:55 is the classic polysemantic neuron that responds to cat faces, cat legs, and the fronts of cars. This photo shows a red-legged partridge sitting in a green field, with no cat, no car and no car-like front. The only weak overlap is the bird's small patterned face with its eye and dark markings, which could trigger some face-like response, but probably not strongly.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 2247, "imagenet_class": "packet", "frac_of_max": 0.0386, "on": false, "p_on": 0.12, "reasoning": "The provided essay text is placeholder lorem ipsum and the figure is blank, so I'm relying on what the Circuits thread reported. That work showed 4e:55 is polysemantic, responding to cat faces, car fronts and cat legs. This image is a flat arrangement of cigarette packs with printed text, small crests and rectangular colour blocks. It contains no cat-like faces or fur, and nothing resembling a car grille or headlights, so it offers little to drive the neuron strongly.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 33836, "imagenet_class": "Shih-Tzu", "frac_of_max": 0.3271, "on": true, "p_on": 0.5, "reasoning": "The neuron 4e:55 is the Distill example of polysemanticity: it responds to cat faces, the fronts of cars, and cat legs. The essay text supplied here is placeholder lorem ipsum and the figure is blank, so this rests only on that earlier published description. The photo has no car and no cat, but it shows a close-up, front-facing, furry Shih Tzu puppy face with eyes and nose clearly visible. A small fluffy dog face like this shares many visual features with a cat face, so moderate activation is plausible but not certain.\n\n50"}
{"prompt": "C: + lorem ipsum control", "gid": 12009, "imagenet_class": "hyena", "frac_of_max": 0.024, "on": false, "p_on": 0.62, "reasoning": "The photo shows a spotted hyena facing the camera, with its round ears, eyes and muzzle clearly visible. This neuron is the polysemantic one described in Zoom In, which responds to cat faces, cat legs and the fronts of cars. A frontal furry carnivore face like this one is fairly similar to the cat-face part of what it detects, and the forelegs are visible too, so it may well respond. Still, a hyena reads more like a dog than a cat, so the response could fall short of the ON threshold.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 24382, "imagenet_class": "Arctic fox", "frac_of_max": 0.3738, "on": true, "p_on": 0.78, "reasoning": "This neuron (4e:55) is the classic polysemantic example from Zoom In, responding to cat faces, cat legs/paws, and the fronts of cars. This photo shows an arctic fox with a clearly visible, front-facing furry face (pointed ears, eyes, snout) and visible front legs. Fox faces share much of the cat-face structure the neuron detects, so it is likely to fire strongly here, though a fox is not a perfect match for a cat.\n\n78"}
{"prompt": "C: + lorem ipsum control", "gid": 30139, "imagenet_class": "sundial", "frac_of_max": 0.0483, "on": false, "p_on": 0.1, "reasoning": "The essay text and figure provided here are placeholder content (lorem ipsum and a blank image), so I'm relying only on what Distill published about this neuron. There, 4e:55 was the standard polysemantic example, responding to cat faces, fox faces and the fronts of cars. This photo shows a round stone sundial set in grass, with no animal faces and no car-like features such as headlights, grilles or wheels. The curved arcs and circular shape give at most a weak, incidental resemblance, so the neuron is likely OFF.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 30599, "imagenet_class": "bullfrog", "frac_of_max": 0.0388, "on": false, "p_on": 0.18, "reasoning": "The essay text and figure are placeholders with no usable content, so I'm relying on the Distill description. There, 4e:55 is the classic polysemantic neuron responding to cat faces, car fronts, and cat legs/paws. This photo shows a tree frog clinging to a branch against dirt and leaves: no cat face, fur texture, or car-front structure. The frog's rounded head with eyes and its bent limbs might give a weak face- or leg-like partial match, but that is unlikely to reach 30% of the maximum.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 43538, "imagenet_class": "mousetrap", "frac_of_max": 0.031, "on": false, "p_on": 0.3, "reasoning": "The essay text and figure provided are placeholder content (lorem ipsum and a blank image), so I'm relying only on what's known about 4e:55 from the Distill articles. There it is the classic polysemantic example, responding to cat faces (and similar furry animal faces like foxes), cat legs, and car fronts. This photo shows a small rat with a pointed snout and legs on a mousetrap, with no cat and no car. The rodent's furry face and legs might weakly drive the cat-face/leg part of the neuron, but a rat's face is small and not very cat-like, so a strong response is unlikely.\n\n30"}
{"prompt": "C: + lorem ipsum control", "gid": 27231, "imagenet_class": "convertible", "frac_of_max": 0.3982, "on": true, "p_on": 0.9, "reasoning": "Mixed4e:55 is the textbook polysemantic neuron from Distill's Feature Visualization and Zoom In articles, which respond to cat faces, cat legs/paws and the fronts of cars. This photo is dominated by the front of a white Mustang convertible, with its grille, paired headlights and bumper clearly in view, plus other car fronts in the background. That is exactly the \"car front\" facet. (The essay text and figure provided were placeholder text and a blank image, so they added no information.)\n\n90"}
{"prompt": "C: + lorem ipsum control", "gid": 25885, "imagenet_class": "maze", "frac_of_max": 0.0432, "on": false, "p_on": 0.12, "reasoning": "The useful text and figure from the essay are placeholders, so this relies on what is known about 4e:55 from Distill. There it was the standard polysemantic example, responding to cat faces, cat legs and the fronts of cars. This photo shows only a cobblestone pavement mosaic with curved and straight bands of gray and tan stones. There are no cat-like faces or legs and no car-front structures such as grilles or headlights, so a strong response is unlikely, though repetitive textures can sometimes weakly drive mid-level units.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 37002, "imagenet_class": "liner", "frac_of_max": 0.0439, "on": false, "p_on": 0.12, "reasoning": "This neuron (4e:55) is the classic polysemantic example from Zoom In: it responds to cat faces, fox faces, cat legs, and the fronts of cars. This photo shows the stacked balconies of a cruise ship with small people and deck chairs, with no animal faces and no car fronts. The repeated horizontal curved railings and window bands only loosely resemble grille or bumper textures, so at most I'd expect a weak spurious response.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 8882, "imagenet_class": "lynx", "frac_of_max": 0.3332, "on": true, "p_on": 0.92, "reasoning": "The essay text and figure are only placeholders (lorem ipsum and a blank image), so this rests on what's already known about the neuron. In the Distill Circuits work, 4e:55 is the standard polysemantic example: it responds to cat faces, the fronts of cars, and cat legs. This photo is a large, centered, frontal cat face with clear eyes, nose and whiskers, and a paw is visible too. That matches its strongest known trigger, so it should be strongly ON.\n\n92"}
{"prompt": "C: + lorem ipsum control", "gid": 49536, "imagenet_class": "hognose snake", "frac_of_max": 0.0484, "on": false, "p_on": 0.18, "reasoning": "Unit 4e:55 is the polysemantic neuron Distill showed responding to cat faces, cat legs/paws, and the fronts of cars. This photo shows a hognose snake held in a hand. It contains no feline face or fur texture and no car-front structure. The snake's head with its eye is a weak face-like cue, but reptile scales and a snout shape are far from the cat-face features that drive the unit, so a strong response seems unlikely.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 10806, "imagenet_class": "wombat", "frac_of_max": 0.0423, "on": false, "p_on": 0.5, "reasoning": "The neuron's text and figure are placeholders, so I'm relying on the Zoom In description. There, 4e:55 is polysemantic, responding to cat faces, fronts of cars and cat legs/paws. This photo has no car, but the wombat's furry face, with small eyes, nose and ears at close range, resembles the furry mammal-face pattern that can partly drive the cat-face component. The human faces and foliage are unlikely to matter much, so the call rests on how cat-like the wombat face looks, which leaves this moderately uncertain.\n\n50"}
{"prompt": "C: + lorem ipsum control", "gid": 46782, "imagenet_class": "Arctic fox", "frac_of_max": 0.4479, "on": true, "p_on": 0.85, "reasoning": "The essay text and figure provided are only placeholder content (lorem ipsum and a blank image), so I'm relying on what's known about this neuron from the Distill articles. There, 4e:55 is the classic polysemantic example: it responds to cat faces, fronts of cars and cat legs. This photo is a close-up, front-facing face of an arctic fox, with pointed ears, eyes, a snout and fluffy fur. That is visually very close to a cat face and should strongly drive the cat-face part of the neuron.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 43533, "imagenet_class": "milk can", "frac_of_max": 0.0377, "on": false, "p_on": 0.18, "reasoning": "Neuron 4e:55 is the well-known polysemantic unit that responds to cat faces, cat legs, and the fronts of cars (grilles, headlights). This sepia photo shows metal milk churns in front of a brick wall. There are no animal faces or car fronts. The rounded metal lids and handles give only a weak, superficial resemblance to car-front structure, so a strong response seems unlikely.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 38594, "imagenet_class": "limousine", "frac_of_max": 0.3796, "on": true, "p_on": 0.85, "reasoning": "The essay text and figure are placeholders and carry no information, so this rests on what Distill reported for 4e:55. The Zoom In article describes it as a polysemantic neuron that responds to cat faces, fox faces, and the fronts of cars. This photo is dominated by the front of a white Hummer, with its grille, headlights and hood seen head-on, which closely matches the car-front facet. That should drive a strong response, and the person beside it is mostly irrelevant.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 19810, "imagenet_class": "collie", "frac_of_max": 0.5063, "on": true, "p_on": 0.58, "reasoning": "The neuron 4e:55 is the Zoom In polysemantic example, responding to cat faces, cat legs/paws and the fronts of cars. The essay text and figure supplied here are placeholder (lorem ipsum and a blank image), so they add nothing to that. This photo has no car, but it centers on a fluffy, fox-like Sheltie puppy face with pointed ears, plus visible white furry legs and paws. Dog faces with cat- or fox-like features often partially drive cat-face detectors, so a moderate-to-strong response is plausible but not certain.\n\n58"}
{"prompt": "C: + lorem ipsum control", "gid": 18042, "imagenet_class": "golf ball", "frac_of_max": 0.0212, "on": false, "p_on": 0.18, "reasoning": "Mixed4e:55 is the polysemantic neuron that Zoom In describes as responding to cat faces, cat legs and the fronts of cars. This image is a close-up of a white dimpled golf ball with a blue printed crest on a wooden surface. It contains no cat-like facial or leg features and nothing resembling a car's grille or headlights. The round shape and the shield emblem are only a weak possible trigger, so the neuron is most likely OFF.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 1665, "imagenet_class": "police van", "frac_of_max": 0.3441, "on": true, "p_on": 0.5, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron. It responds to cat faces, cat legs, and the fronts of cars. This photo has no cats and shows the rear of a police sedan rather than its front grille and headlights. Still, the large, centered car body with paired lights, plate and bumper could partially drive the car-front feature, since rear and front views share much structure at this layer.\n\n50"}
{"prompt": "C: + lorem ipsum control", "gid": 17694, "imagenet_class": "silky terrier", "frac_of_max": 0.0379, "on": false, "p_on": 0.45, "reasoning": "This neuron, 4e:55, is the classic polysemantic unit from Zoom In. It responds to cat faces, the fronts of cars, and cat legs/paws. The photo has no car, but it does show a small terrier face with pointed upright ears and fur texture. That face is fairly cat-like and could partially drive the cat-face component. Dog faces usually activate it more weakly than real cat faces, and the face here is small and slightly blurred, so I'd put it near even odds.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 39352, "imagenet_class": "minivan", "frac_of_max": 0.3763, "on": true, "p_on": 0.8, "reasoning": "The research text and figure supplied here are placeholders with no usable content, so this rests on prior knowledge of the neuron. Distill's \"Zoom In\" described 4e:55 as polysemantic, responding to cat faces, cat legs, and the fronts of cars. This photo prominently shows the front of a car, with headlight, bumper and grille region visible at a three-quarter angle. That matches the neuron's car-front facet. The heavy text livery and the angled view might weaken the response somewhat, but a strong activation is likely.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 31182, "imagenet_class": "agama", "frac_of_max": 0.0277, "on": false, "p_on": 0.18, "reasoning": "This neuron is known as polysemantic: it responds mainly to cat faces, the fronts of cars, and cat legs and paws. The supplied essay text and figure are placeholders, so they add no information. This photo shows an agama lizard on a rock, with no cat features and no car-front structure. The lizard's head, with its eye and snout, might give a weak face-like signal, but it is unlikely to reach 30% of the neuron's maximum.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 22131, "imagenet_class": "wood rabbit", "frac_of_max": 0.339, "on": true, "p_on": 0.55, "reasoning": "The neuron 4e:55 is the Zoom In polysemantic example that responds to cat faces and to the fronts of cars. Here there is no car, so the cat-face facet is the only relevant one. The rabbit's furry head, with a visible eye, upright ears and a snout seen in near-profile, shares much of the texture and shape of a cat face and could partly drive that detector. However, it is a watercolor painting of a rabbit rather than a frontal cat face, so the response may be weaker.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 12827, "imagenet_class": "lab coat", "frac_of_max": 0.0444, "on": false, "p_on": 0.18, "reasoning": "The essay text and its figure are placeholders (lorem ipsum and a blank image), so I'm relying on what the Distill Circuits work says about 4e:55. There it was presented as a polysemantic unit responding to cat faces, fox and animal faces, and car fronts or car bodies. This market photo has a person in a white coat, bags of spices and grains, and price cards, with no cat or animal faces and no cars. The only weak overlap is a partly visible human face and some rounded, textured shapes, so it is probably OFF.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 11964, "imagenet_class": "robin", "frac_of_max": 0.0446, "on": false, "p_on": 0.18, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so they add nothing; I'm relying on the Distill description of 4e:55 as polysemantic, responding to cat faces, cat legs and the fronts of cars. This photo is a close-up of an American robin on a branch against a blurred green background. It contains no cat-like face, no fur or legs of that kind, and no car grille or headlights. A bird's head with an eye and beak could weakly resemble an animal face, but it is unlikely to reach 30% of this neuron's maximum.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 14695, "imagenet_class": "beach wagon", "frac_of_max": 0.4728, "on": true, "p_on": 0.7, "reasoning": "This is the well-known polysemantic 4e:55 neuron, which responds to cat faces, cat legs and the fronts of cars. The quoted essay text and figure are placeholder content (lorem ipsum and a blank image), so I'm relying only on the Distill characterization. The photo is mostly a side view of a red station wagon, but the front end, with its headlight and grille region, is visible at an angle on the right, along with the wheels and body. That should drive the car-front facet reasonably well, though less strongly than a head-on view would.\n\n70"}
{"prompt": "C: + lorem ipsum control", "gid": 11407, "imagenet_class": "Sussex spaniel", "frac_of_max": 0.0395, "on": false, "p_on": 0.45, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, cat legs/fur, and the fronts of cars. The photo has none of those. It does show a close-up, frontal furry animal face (a brown Sussex spaniel) with eyes and a snout, and features tuned to cat faces often partially transfer to dog faces and fur texture. Since the subject is clearly a dog with long floppy ears rather than a cat, the response is uncertain and could fall below threshold.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 47664, "imagenet_class": "spiny lobster", "frac_of_max": 0.0047, "on": false, "p_on": 0.14, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so they add nothing; I'm relying on the Distill description of 4e:55 as a polysemantic neuron responding to cat faces, cat legs and the fronts of cars. This photo shows a crushed, reddish creature (it looks like a crayfish or small roadkill) on gravel. It has no cat face or legs, no car front, and no strong furry-face texture. Its spiky textured shape might weakly echo animal features, but a strong activation seems unlikely.\n\n14"}
{"prompt": "C: + lorem ipsum control", "gid": 19113, "imagenet_class": "black-footed ferret", "frac_of_max": 0.4187, "on": true, "p_on": 0.75, "reasoning": "This neuron is known from Zoom In as polysemantic, responding to cat faces, cat legs and the fronts of cars. A ferret's face is close to a cat face: a furry mammalian face with small rounded ears, dark eyes, a pink nose and whiskers, shown up close and filling much of the frame. Furry carnivore faces like this are likely to drive the cat-face part of its response strongly. No cars are present, so everything rests on the face resemblance.\n\n75"}
{"prompt": "C: + lorem ipsum control", "gid": 32106, "imagenet_class": "pop bottle", "frac_of_max": 0.0449, "on": false, "p_on": 0.15, "reasoning": "The essay text and figure supplied are placeholders (lorem ipsum and a blank image), so I'm relying on what was published earlier. In Distill's Zoom In, 4e:55 is the textbook polysemantic neuron, responding to cat faces, fronts of cars, and cat legs. This photo shows a pile of clear swing-top glass bottles with white ceramic stoppers and wire bails, with no cats and no cars. The round white caps with metal hardware might faintly echo car headlights or chrome trim, but that is a weak match, so the neuron is most likely OFF.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 44378, "imagenet_class": "microphone", "frac_of_max": 0.0481, "on": false, "p_on": 0.27, "reasoning": "Mixed4e:55 is the Circuits thread's polysemantic example. It responds to cat faces, fox and animal faces, and the fronts of cars. This photo is a grayscale studio microphone, with no animal face and no car. The only weak link is the round chrome body with its metal mesh grille and logo, which loosely resembles a car's grille, headlight or badge. That might give a partial car-front response, but probably not a strong one.\n\n27"}
{"prompt": "C: + lorem ipsum control", "gid": 36049, "imagenet_class": "wig", "frac_of_max": 0.0173, "on": false, "p_on": 0.35, "reasoning": "Mixed4e:55 is the textbook polysemantic neuron from Zoom In, responding to cat faces, fox faces and car fronts (the quoted essay text and figure are placeholder content, so they add nothing). This photo has no animal or car, but it does have a face framed by red fur-like hair with two pointed \"ears\" (devil horns) on top, which loosely resembles a fox or cat head. That partial match could drive a moderate response, but a human face usually falls short of the strong activations real animal faces give.\n\n35"}
{"prompt": "C: + lorem ipsum control", "gid": 27095, "imagenet_class": "vizsla", "frac_of_max": 0.0362, "on": false, "p_on": 0.4, "reasoning": "Mixed4e:55 is the Zoom In example of a polysemantic neuron, responding to cat faces, fox and animal faces, and the fronts of cars. The main candidate here is the brown puppy's face and closed eyes, which share some furry-animal-face features with cats. But it is a floppy-eared dog with its eyes shut and partly hidden against the woman, so the cat-face match is weak. There is no car front, and the human face is not a typical trigger for this neuron.\n\n40"}
{"prompt": "C: + lorem ipsum control", "gid": 19928, "imagenet_class": "fire engine", "frac_of_max": 0.3309, "on": true, "p_on": 0.8, "reasoning": "The essay text and figure provided are placeholder content (lorem ipsum and a blank image), so this rests on what Distill published about 4e:55. There it was shown responding to cat faces, cat legs and fronts of cars. This photo is a fire truck seen from the front three-quarter view, with a prominent windshield, headlights, grille and bumper. That closely matches the \"car front\" part of the neuron's behavior and should drive a strong response.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 13469, "imagenet_class": "screw", "frac_of_max": 0.0365, "on": false, "p_on": 0.12, "reasoning": "The neuron 4e:55 is the Distill example of polysemanticity, responding to cat faces, the fronts of cars, and cat legs or feet. This photo shows black hex-socket screws and bolts on a plain white background, with a Chinese watermark. It contains nothing resembling a feline face, a car grille or headlights, or furry legs. The small dark cylindrical shapes might weakly resemble paws or legs, but this is unlikely to push the neuron to 30% of its maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 47759, "imagenet_class": "weasel", "frac_of_max": 0.4081, "on": true, "p_on": 0.72, "reasoning": "The Zoom In article describes 4e:55 as a polysemantic neuron that responds to cat faces, fronts of cars and cat legs. The essay text and figure provided here are placeholder text and a blank image, so they add nothing. What matters in this photo is the otter's furry face in profile, with prominent whiskers, a small rounded ear and dark eyes, which look much like a cat face. Furry mammal faces of this kind commonly drive cat-face detectors at this layer, so the neuron will probably respond strongly, though the side view and the fact that it is not actually a cat leave some doubt.\n\n72"}
{"prompt": "C: + lorem ipsum control", "gid": 17481, "imagenet_class": "Arctic fox", "frac_of_max": 0.3662, "on": true, "p_on": 0.72, "reasoning": "The essay text and its figure are placeholders (lorem ipsum and a blank image), so I'm relying on prior knowledge. In the Distill work, 4e:55 is the classic polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. This photo is a close-up of an arctic fox's face: furry, pointed ears, a small muzzle, eyes looking forward. Fox and similar small-carnivore faces look a lot like cat faces at this layer, so the face is likely to drive a strong response, though a fox is not a true cat.\n\n72"}
{"prompt": "C: + lorem ipsum control", "gid": 12851, "imagenet_class": "minivan", "frac_of_max": 0.39, "on": true, "p_on": 0.8, "reasoning": "The neuron the Circuits thread describes, 4e:55, is the classic polysemantic unit that responds to cat faces, cat legs, and the fronts of cars. The essay text and figure supplied here are placeholder text and a blank image, so they add nothing, and I'm relying on the published description. This photo shows a yellow minivan from a front three-quarter angle, with the hood, grille area, headlight and front wheel clearly visible. That matches the car-front facet well, though the busy advertising wrap and the side-on angle may weaken the response somewhat.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 17253, "imagenet_class": "eel", "frac_of_max": 0.0213, "on": false, "p_on": 0.2, "reasoning": "This photo shows a spotted moray eel in dark, greenish water among rocks. Mixed4e:55 is the Distill example of polysemanticity, responding to cat faces, cat legs and the fronts of cars. Nothing here matches those features: there are no cat faces, no fur-and-feet structures and no car grilles or hoods. The eel's rounded head and eye might weakly resemble an animal face, and the dark-background, high-contrast spotted texture could produce some stray response, but I would not expect a strong one.\n\n20"}
{"prompt": "C: + lorem ipsum control", "gid": 12764, "imagenet_class": "tabby", "frac_of_max": 0.612, "on": true, "p_on": 0.92, "reasoning": "The photo shows a clear, frontal, close-up face of an orange tabby cat: eyes, nose, whiskers and ears are all visible. Mixed4e:55 is the polysemantic neuron that Distill's Zoom In described as responding to cat faces, car fronts and cat legs. A prominent cat face is one of its core triggers, so it should respond strongly. The grass background is irrelevant, but the cat face alone should drive it well above the 30% threshold.\n\n92"}
{"prompt": "C: + lorem ipsum control", "gid": 2664, "imagenet_class": "punching bag", "frac_of_max": 0.0465, "on": false, "p_on": 0.12, "reasoning": "The essay text and figure supplied here are placeholder text and a blank image, so I'm relying on the Circuits work. That work describes 4e:55 as responding to cat faces, cat legs and the fronts of cars. This photo shows a boy hitting a dark punching bag in a cluttered garage. It has no cat features and no car fronts, though the dark cylindrical bag and garage clutter could give a weak spurious response.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 25082, "imagenet_class": "head cabbage", "frac_of_max": 0.0369, "on": false, "p_on": 0.1, "reasoning": "Neuron 4e:55 is the classic polysemantic unit from Zoom In, known for responding to cat faces, cat legs and fronts of cars. This photo of raised garden beds with leafy greens, herbs and wooden planks contains none of those triggers. There are no animal faces, fur or car grilles or headlights. The repetitive foliage and wood-plank edges offer little that should drive it strongly, so it is likely OFF.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 14010, "imagenet_class": "red fox", "frac_of_max": 0.3814, "on": true, "p_on": 0.75, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from Zoom In. It responds to cat faces, the fronts of cars, and cat legs/paws. This photo has a red fox standing upright with its pointed-eared, furry face clearly visible, plus slender furry legs and a second fox with paws in the air. Fox faces and legs closely resemble the cat-face and cat-leg features this neuron detects, so a strong response is likely, though fox snouts are longer than typical cat faces.\n\n75"}
{"prompt": "C: + lorem ipsum control", "gid": 49761, "imagenet_class": "limousine", "frac_of_max": 0.4459, "on": true, "p_on": 0.72, "reasoning": "This neuron is the classic polysemantic unit from the Circuits thread that responds to cat faces, car fronts and cat legs. This photo is a white stretch Hummer limo. Its front end (grille, headlights, bumper) is clearly visible at the left, though seen at an angle rather than head-on. That car-front evidence should drive the neuron fairly strongly, but the oblique view and the long side panel, which is weaker for this unit, add some uncertainty.\n\n72"}
{"prompt": "C: + lorem ipsum control", "gid": 30586, "imagenet_class": "Chihuahua", "frac_of_max": 0.3954, "on": true, "p_on": 0.5, "reasoning": "The neuron that matters here is 4e:55, the polysemantic unit Distill described as responding to cat faces, cat legs and the fronts of cars. The research text and figure you provided are placeholder text and a blank image, so they add nothing. This photo has no car and no cat, but the chihuahua's face is close-up and frontal, with large upright pointed ears and big eyes, which is quite cat-like. A face-like response could plausibly cross the ON threshold. Dog faces usually drive dog-specific units more strongly than this one, though, so I'd put it around even odds.\n\n50"}
{"prompt": "C: + lorem ipsum control", "gid": 21783, "imagenet_class": "snorkel", "frac_of_max": 0.0475, "on": false, "p_on": 0.12, "reasoning": "The essay text and figure here are placeholders, so this relies on prior knowledge. In the Distill work, 4e:55 is the textbook polysemantic neuron: it responds to cat faces, fronts of cars, and cat legs. This photo is an underwater snorkeler: a human face behind a mask, skin, and blue-green water, with no feline features or car-front structure. The masked face gives a little face-like texture that might drive a weak response, but it is unlikely to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 40751, "imagenet_class": "silky terrier", "frac_of_max": 0.0494, "on": false, "p_on": 0.72, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that responds to cat faces, fox faces, and car fronts. What matters here is the Yorkshire terrier's face, which is close to the camera and seen from the front. Its erect, pointed triangular ears and small furry muzzle look strongly like a cat or fox face, which should drive the face-detecting part of the neuron. The brick pavement is irrelevant, and since it is a dog rather than a cat, the response may not be maximal. Still, pointy-eared small dogs often trigger this unit fairly well.\n\n72"}
{"prompt": "C: + lorem ipsum control", "gid": 23456, "imagenet_class": "pickelhaube", "frac_of_max": 0.0353, "on": false, "p_on": 0.13, "reasoning": "The essay text and figure are only placeholder (lorem ipsum and a blank image), so this rests on the Distill description. There, 4e:55 is the polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. This black-and-white portrait of a WWI-era soldier has none of those: no animal face or fur, no car grille or headlights. The human face, uniform, rifle and boots are not known triggers, though textured clothing or the dark leg-like boots could weakly resemble cat legs, so I give a small chance of activation.\n\n13"}
{"prompt": "C: + lorem ipsum control", "gid": 40170, "imagenet_class": "Siberian husky", "frac_of_max": 0.3875, "on": true, "p_on": 0.8, "reasoning": "The research text and figure provided are placeholder content (lorem ipsum and a blank image), so I'm relying on what the Distill Circuits work reported about 4e:55. There it was shown as a polysemantic neuron responding to cat faces, fox faces and the fronts of cars. This photo has two close-up, front-facing husky puppy faces. Their pointed ears, prominent eyes and black-and-white facial masks closely resemble the cat and fox face features the neuron detects, so a strong response is likely.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 7054, "imagenet_class": "dung beetle", "frac_of_max": 0.0419, "on": false, "p_on": 0.25, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In, known mainly for cat faces, fox faces and car fronts. The essay text and figure supplied here are placeholder or blank, so they add no information. This photo shows a dung beetle with its dung ball among grass and stones, with no cat, fox or car. The beetle's shiny, symmetric black head seen roughly front-on might weakly echo a car-front pattern, but that is a tenuous match, so I expect the neuron is more likely OFF.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 9139, "imagenet_class": "bottlecap", "frac_of_max": 0.0177, "on": false, "p_on": 0.18, "reasoning": "The text and figure attributed to the essay are only placeholder (lorem ipsum and a blank image), so I'm relying on the Distill work. That work describes 4e:55 as polysemantic, responding to cat faces, fox faces and car fronts. This photo is a pile of crimped metal bottle caps with small printed cartoon drawings. It contains no animal faces and no cars. The round, shiny, ridged shapes and printed doodles might loosely resemble wheels or face-like patterns, which could give a weak response, but they are unlikely to reach the ON threshold.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 27084, "imagenet_class": "pick", "frac_of_max": 0.044, "on": false, "p_on": 0.28, "reasoning": "The provided essay text and figure are placeholder content, so I'm relying on what is known about 4e:55: in Distill's Feature Visualization article it is the classic polysemantic neuron that responds to cat faces, fox faces and car fronts. This photo is a grayscale close-up of a hand strumming a guitar with a pick. The only plausibly relevant cue is the small printed creature/animal-face graphic on the pick, which might weakly resemble a cat or fox face. Hands and guitar strings are not among this neuron's known triggers, so a strong activation is unlikely but possible.\n\n28"}
{"prompt": "C: + lorem ipsum control", "gid": 43443, "imagenet_class": "cliff dwelling", "frac_of_max": 0.0358, "on": false, "p_on": 0.15, "reasoning": "This photo shows Mesa Verde cliff dwellings: sandstone masonry, a tower, small dark window openings, and an overhanging rock alcove. The Circuits work describes 4e:55 as polysemantic, responding mainly to cat faces, car fronts and cat legs. Nothing like those appears here. The paired dark windows on boxy facades could loosely resemble eyes or a car grille, but that is a weak cue, so the neuron is most likely OFF. (The supplied essay text and figure were placeholder or blank, so they added no information.)\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 34396, "imagenet_class": "pickup", "frac_of_max": 0.4187, "on": true, "p_on": 0.8, "reasoning": "The essay text and figure provided are placeholder content, so I'm relying on the Distill description of 4e:55: a polysemantic neuron that responds to cat faces, cat legs, and the fronts of cars. This photo shows a classic pickup truck at a three-quarter front angle. Its chrome grille, headlights and hood fill much of the frame, which is exactly the \"car front\" facet the neuron is known for. A truck rather than a sedan, and the angled view, may reduce the response slightly, but it should still likely clear 30%.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 41451, "imagenet_class": "pickup", "frac_of_max": 0.352, "on": true, "p_on": 0.8, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from the Distill Circuits work. It responds to cat faces, fox and cat legs, and the fronts of cars. This photo prominently shows the front of a red vintage pickup truck, with its chrome grille, headlights and bumper visible (plus a reflection in the water). That is very close to the car-front facet of this neuron, so a strong response is likely even though there are no cats.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 23185, "imagenet_class": "lesser panda", "frac_of_max": 0.3928, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs/fur, and car fronts. This red panda plush shows a front-facing, cat-like face with pointed ears, whiskers, a dark nose and eyes, plus furry legs. Those features closely match the neuron's cat-face facet, so a strong response is likely even though the animal is a stuffed red panda.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 9833, "imagenet_class": "curly-coated retriever", "frac_of_max": 0.0454, "on": false, "p_on": 0.45, "reasoning": "The key content is the face of a black curly-coated retriever puppy, shown head-on and close up in grayscale. In the Circuits work this neuron is described as polysemantic, responding to cat faces, car fronts and cat legs. A frontal furry animal face with visible eyes and nose can partially drive cat-face detectors. However, this is a dog, the image is dark and low-contrast, and the coat is curly rather than catlike, so a strong response is uncertain.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 25921, "imagenet_class": "police van", "frac_of_max": 0.4165, "on": true, "p_on": 0.85, "reasoning": "The essay text and its figure are placeholders (lorem ipsum and a blank image), so I'm relying on what the Distill Circuits work established about this neuron. Mixed4e:55 is the textbook polysemantic unit that fires for cat faces, car fronts and cat legs. This photo shows a police car (VW Passat) at a three-quarter angle with its front clearly visible: headlights, grille and bumper. That car-front structure is exactly one of the neuron's known triggers, so a strong response is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 9722, "imagenet_class": "buckeye", "frac_of_max": 0.0347, "on": false, "p_on": 0.12, "reasoning": "The well-documented behavior of 4e:55 is polysemantic: it responds to cat faces, cat legs/fur and the fronts of cars. This photo shows a horse-chestnut husk split open around a shiny conker on a dark textured surface, with no cat-like face, fur pattern or car front. The rounded brown-orange shape and glossy surface give only a weak, incidental resemblance to its triggers. The supplied \"research\" text and figure are placeholders with no information, so I'm relying on the known behavior, and the neuron is likely OFF.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 14492, "imagenet_class": "Saint Bernard", "frac_of_max": 0.4769, "on": true, "p_on": 0.45, "reasoning": "This is the well-known polysemantic neuron that fires for cat faces, car fronts and cat legs. There is no car or cat here, but the foreground dog has a cat-like face: pointy upright ears, a narrow muzzle and orange fur seen in profile. Cat-face detectors in this layer often partially fire on fox-like dog heads, though less strongly than on true cats. The St. Bernard face behind it is less cat-like, so whether this crosses the threshold rests mainly on the fox-like dog's head.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 13044, "imagenet_class": "king crab", "frac_of_max": 0.0319, "on": false, "p_on": 0.22, "reasoning": "Unit 4e:55 is the Zoom In example of polysemanticity: it responds to cat faces, the fronts of cars, and cat legs. This photo has none of those, only cooked king crab legs on a platter with lemon wedges. The long, segmented, leg-like shapes could weakly resemble the \"leg\" part of its response, but the red spiky texture and food context make a strong activation unlikely.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 16010, "imagenet_class": "guinea pig", "frac_of_max": 0.3347, "on": true, "p_on": 0.6, "reasoning": "This neuron is the classic polysemantic unit from the Feature Visualization article, responding to both cat faces and car fronts. The photo shows a close-up, roughly frontal guinea pig face: orange and white fur, a dark eye, small ears, and a cat-like nose and mouth. That furry small-mammal face is a plausible partial match for the cat-face side of the neuron, though a guinea pig is not a cat, so the response could fall short of the ON threshold. There is nothing car-like in the image.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 20306, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3334, "on": true, "p_on": 0.38, "reasoning": "What matters is the sheltie's face and its fluffy black-and-white fur. In the Distill work, 4e:55 is the classic polysemantic neuron that responds to cat faces, cat legs and car fronts. The essay text and figure provided are only placeholder content, so they add no information. A furry, pointy-eared dog face can partly resemble a cat face, but dog-specific features usually absorb these images, so I'd expect at most a moderate response.\n\n38"}
{"prompt": "C: + lorem ipsum control", "gid": 43315, "imagenet_class": "tabby", "frac_of_max": 0.4904, "on": true, "p_on": 0.92, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from the Distill articles, responding to cat faces, fox faces and car fronts. The essay text and figure here are placeholders (lorem ipsum and a blank image), so this rests on the published characterization. This photo is a close-up of a tabby kitten's face, with eyes, nose, whiskers and ears clearly visible. That is exactly the cat-face part of the neuron's response, so a strong activation is very likely.\n\n92"}
{"prompt": "C: + lorem ipsum control", "gid": 20240, "imagenet_class": "police van", "frac_of_max": 0.3707, "on": true, "p_on": 0.85, "reasoning": "The well-known polysemantic neuron 4e:55 responds to cat faces, cat legs, and the fronts of cars. The essay text and figure here are placeholder content, so I'm relying on the Distill description. This photo shows a police van seen from a front three-quarter angle, with a clear grille, headlights and windshield. That car-front facet should drive the neuron strongly. The yellow van partly visible on the right adds a little more vehicle-front evidence.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 19918, "imagenet_class": "coil", "frac_of_max": 0.0476, "on": false, "p_on": 0.15, "reasoning": "The neuron described in the Circuits thread as polysemantic, 4e:55, responds mainly to cat faces, fox and other animal faces, and car fronts. The provided essay text and figure are placeholders, so they add nothing beyond that prior. This photo is a grey, monochrome spiral staircase seen from below, with no animal faces, fur or vehicle parts. The concentric curves and the bright octagonal skylight are only a weak, loose match for something like a headlight or wheel, so the neuron is most likely OFF.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 25404, "imagenet_class": "police van", "frac_of_max": 0.4076, "on": true, "p_on": 0.85, "reasoning": "This is the polysemantic neuron that Distill's Zoom In described as responding to cat faces, cat legs and the fronts of cars. The photo shows a police car at a front three-quarter angle, with the grille, push bar and headlights large and clearly visible. That car-front \"face\" is exactly the kind of feature the car side of this neuron detects, so a strong response is likely, even though there are no cats.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 15004, "imagenet_class": "car wheel", "frac_of_max": 0.0423, "on": false, "p_on": 0.4, "reasoning": "This is an extreme close-up of a chrome wheel hub or hubcap: concentric metallic rings around a reflective center. 4e:55 is the polysemantic neuron Distill showed responding to cat faces, cat legs, and car fronts. A wheel hub is car-related, and the glossy concentric chrome resembles headlight or grille parts. But it is not a car front, and the extreme close-up removes most of the context the car facet seems to rely on, so I expect at most a moderate response.\n\n40"}
{"prompt": "C: + lorem ipsum control", "gid": 26064, "imagenet_class": "bakery", "frac_of_max": 0.0494, "on": false, "p_on": 0.18, "reasoning": "The photo doesn't show what this neuron is best known for. In the Distill articles, 4e:55 responds to cat faces, fox faces and car fronts. Here we have a dense grid of pink-frosted cupcakes topped with fondant hats and shoes, with no animal faces, fur or vehicle grilles. The repeated rounded shapes and small dark accents (bows, shoe soles) could weakly mimic face-like or headlight-like patterns, but they're unlikely to drive this neuron strongly.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 39152, "imagenet_class": "bald eagle", "frac_of_max": 0.3553, "on": true, "p_on": 0.18, "reasoning": "This neuron, as presented in Distill's Zoom In, is polysemantic: it responds to cat faces, cat legs/paws, and the fronts of cars. The essay excerpt and figure supplied here are placeholder text and a blank image, so they add nothing beyond that prior. This photo shows a bald eagle perched on a wooden post against a plain sky, with no cat-like face or fur texture and nothing resembling a car front. The eagle's white head with a forward-facing beak is only a weak, incidental match to the \"animal face\" part of the neuron's features, so a strong response is unlikely.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 47793, "imagenet_class": "stupa", "frac_of_max": 0.0338, "on": false, "p_on": 0.1, "reasoning": "This neuron is known as polysemantic, responding mainly to cat faces, car fronts, and cat legs/paws. This photo is an ornate white Cambodian stupa against a blue sky. It contains no animal faces, fur, or vehicle front-ends. The repetitive carved architectural detail might weakly excite some mid-level features, but nothing matches the neuron's main triggers, so a strong response is unlikely.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 22965, "imagenet_class": "manhole cover", "frac_of_max": 0.0038, "on": false, "p_on": 0.25, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so they add nothing; I'm relying on the Distill description of 4e:55 as a polysemantic neuron responding to cat faces, cat legs and car fronts. This photo is a rusty cast-iron utility cover with a lattice of raised diamonds and embossed text. There is no cat, and no car. The only plausible trigger is that the repeating metal grid loosely resembles a car's front grille, which could weakly engage the car-front side of the neuron. That is a modest chance at best.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 4864, "imagenet_class": "birdhouse", "frac_of_max": 0.0231, "on": false, "p_on": 0.15, "reasoning": "The Zoom In article describes 4e:55 as polysemantic, responding to cat faces, fox faces and the fronts of cars. The supplied essay text is placeholder filler and the figure is blank, so they add nothing. This photo shows a wooden birdhouse on a tree trunk, with no cat, fox or car front. The birdhouse's dark entrance holes and roof could loosely suggest a face or car grille, but that is a weak match, so the neuron is likely OFF.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 27806, "imagenet_class": "little blue heron", "frac_of_max": 0.0443, "on": false, "p_on": 0.13, "reasoning": "This photo contains none of the features 4e:55 is known for. The Distill Circuits work showed it responding to cat faces, cat legs/paws and the fronts of cars. Here there is only a tricolored heron wading in reflective water against green fern foliage, with no feline or car-front structure. The quoted essay text and figure are placeholder or blank, so they add nothing; I'm relying on prior knowledge of this neuron. Polysemantic neurons can fire weakly on unrelated textures such as the heron's fluffy plumage, so I leave some chance, but OFF is likely.\n\n13"}
{"prompt": "C: + lorem ipsum control", "gid": 15738, "imagenet_class": "pick", "frac_of_max": 0.0453, "on": false, "p_on": 0.1, "reasoning": "The essay text and figure here are placeholder content and blank, so they add no information beyond what is already known about this neuron. In the Distill Circuits work, 4e:55 is the classic polysemantic neuron, responding to cat faces, car fronts, and cat legs. This photo is a close-up of a guitar pick with printed text and a signature on a wooden surface. It has no cat-like or car-front structure, so a strong response is unlikely, though the curved shape and text give a small chance of incidental activation.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 1141, "imagenet_class": "turnstile", "frac_of_max": 0.0355, "on": false, "p_on": 0.18, "reasoning": "The figure and essay text are placeholders, so I'm relying on what Distill's Zoom In reported: 4e:55 is the canonical polysemantic neuron, firing on cat faces, car fronts and cat legs. This photo has no cats or vehicles. It shows a blue converted shipping container with white doors, window AC units and turnstile gates. The boxy, grille-like AC vents and the symmetric rectangular openings might weakly echo car-front features such as grilles and headlights, but that is a stretch and probably not enough for a strong response.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 11592, "imagenet_class": "odometer", "frac_of_max": 0.024, "on": false, "p_on": 0.25, "reasoning": "The essay text and figure here are placeholder content (lorem ipsum and a blank image), so I'm relying only on what's known about 4e:55 from Distill. There it was the classic polysemantic example, responding to cat faces, fronts of cars and cat legs. This image is a close-up of a car's speedometer: circular dial markings, numerals and an odometer. It contains none of the visual parts that drive the car-front response, such as a grille, headlights, bumper or windshield, and nothing cat-like. Its only link is the automotive context, so a strong activation seems unlikely, though some weak car-related response is possible.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 32160, "imagenet_class": "African grey", "frac_of_max": 0.0415, "on": false, "p_on": 0.22, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron, responding to cat faces, fox and animal faces, and car fronts/bodies. This photo shows an African grey parrot preening, so there is no clear mammal face and no car. The only partial overlap is the soft, fur-like grey feather texture and the curled head shape, which could weakly mimic a furry animal face. I expect a weak response at most.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 49844, "imagenet_class": "Appenzeller", "frac_of_max": 0.0303, "on": false, "p_on": 0.45, "reasoning": "The quoted essay text and figure are placeholders, so I'm relying on the Distill description of 4e:55: it responds to cat faces, cat legs and fronts of cars. This photo has no car or cat. It does show a front-facing puppy (a mountain-dog type) with a clear face, eyes, snout and legs, which can partly drive a cat-face detector, since cat and dog face features overlap at this layer. The face is fairly small and the scene is mostly grass and hedge, so it's a moderate bet rather than a strong one.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 19917, "imagenet_class": "pier", "frac_of_max": 0.0473, "on": false, "p_on": 0.12, "reasoning": "The neuron the Circuits thread describes, 4e:55, is the classic polysemantic example: it responds to cat faces, the fronts of cars, and cat legs. The text and figure provided from the new essay are placeholders, so they add no information beyond that. This photo shows a long concrete-and-steel truss bridge over water, with a row of repeated pillars under a cloudy sky. It contains nothing like a cat face, a car front, or furry legs. The regular vertical pillars might weakly resemble leg-like structures, but a strong activation seems unlikely.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 47900, "imagenet_class": "basenji", "frac_of_max": 0.3344, "on": true, "p_on": 0.55, "reasoning": "The neuron's known facets are cat faces, car fronts and cat legs, so the relevant part of this photo is the dog's head. This red-and-white basenji/corgi-like dog has a small, fox-like face with large pointed ears, which shares many cues with cat faces (pointed ears, compact snout, eyes). The sand background and car-free scene contribute nothing. The face is fairly small and seen at an angle, so a strong response is plausible but uncertain.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 44103, "imagenet_class": "grocery store", "frac_of_max": 0.0256, "on": false, "p_on": 0.12, "reasoning": "The essay text and figure are placeholders (lorem ipsum and a blank image), so I'm relying on prior knowledge. Distill described 4e:55 as polysemantic, responding to cat faces, cat legs and the fronts of cars. This photo is a market stall of plum tomatoes and lemons, with no cat or car features. The repeated glossy, rounded objects might give a weak texture response, but probably not enough to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 46582, "imagenet_class": "totem pole", "frac_of_max": 0.0468, "on": false, "p_on": 0.4, "reasoning": "The essay text and figure are placeholders, so I'm relying on what is known about this neuron. In the Circuits work, 4e:55 is the classic polysemantic example: it responds to cat faces, the fronts of cars, and cat legs. This photo has no cats or cars. Its main candidate trigger is the carved, face-like elements of the totem pole, especially the rounded face with eyes near the top and the stylized faces lower down, which might weakly resemble the frontal face or front-of-car patterns the neuron likes. The bare trees and sky are unlikely to drive it, so I lean slightly toward OFF.\n\n40"}
{"prompt": "C: + lorem ipsum control", "gid": 13559, "imagenet_class": "hamster", "frac_of_max": 0.4325, "on": true, "p_on": 0.68, "reasoning": "The essay text and figure provided are placeholder or blank, so this rests on what Distill's Zoom In reported: 4e:55 is polysemantic and responds to cat faces, cat legs and car fronts. This hamster is shown face-on with furry, cat-like features: two pointed ears, two dark round eyes, a pink nose and whiskers, all at a fairly large scale. Mid-level face detectors often generalize to small furry mammal faces like this, so the neuron plausibly reaches a strong response here, though a hamster is not a cat and may fall short.\n\n68"}
{"prompt": "C: + lorem ipsum control", "gid": 24910, "imagenet_class": "stole", "frac_of_max": 0.0326, "on": false, "p_on": 0.12, "reasoning": "The neuron 4e:55 is the one Distill highlighted as polysemantic: it responds to cat faces, cat legs and the fronts of cars. This photo has none of those. It shows a knitted dark-purple scarf with a lacy diamond/cable pattern, held by a human arm against a white textured wall. The knit texture and repeated diamond shapes might weakly echo fur or grille-like patterns, but nothing here is a strong trigger, so the neuron is most likely OFF.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 46963, "imagenet_class": "English foxhound", "frac_of_max": 0.3027, "on": true, "p_on": 0.5, "reasoning": "Per the Distill Zoom In article, 4e:55 is the classic polysemantic neuron, responding to cat faces, cat legs and car fronts. The new essay text and figure are placeholders, so they add nothing. This photo has no cats or cars, but it prominently shows a hound's furry face and extended legs, mid-leap at close range. Animal faces and legs like these can partially trigger the cat-face and cat-leg parts of the neuron. That makes a moderate response plausible but not certain.\n\n50"}
{"prompt": "C: + lorem ipsum control", "gid": 4164, "imagenet_class": "red fox", "frac_of_max": 0.4632, "on": true, "p_on": 0.92, "reasoning": "The famously polysemantic neuron 4e:55, from Distill's Circuits work, responds to cat faces, fox faces and car fronts. The pasted essay text and figure are placeholder and blank, so they add nothing, and I'm relying on that prior description. This photo shows a large, close-up, frontal red fox face with clear eyes, snout and fur texture. That is one of the neuron's canonical triggers, so it should fire strongly.\n\n92"}
{"prompt": "C: + lorem ipsum control", "gid": 29911, "imagenet_class": "tiger cat", "frac_of_max": 0.4058, "on": true, "p_on": 0.92, "reasoning": "Mixed4e:55 is the Circuits thread's canonical polysemantic neuron. It responds strongly to cat faces, to the fronts of cars, and to cat legs/paws. This photo shows a tabby cat with a clear, front-facing face, plus visible paws. That is one of the neuron's main triggers, so a strong response is very likely.\n\n92"}
{"prompt": "C: + lorem ipsum control", "gid": 28905, "imagenet_class": "hamster", "frac_of_max": 0.548, "on": true, "p_on": 0.66, "reasoning": "The neuron 4e:55 is the Distill polysemantic example that responds to cat faces, fox-like animal faces and car fronts. This hamster shows a front-facing furry face with rounded ears, dark eyes and a pink nose, which resembles the cat-face features it detects. The colorful cartoon flower is less relevant. Small-mammal faces often partially activate this neuron, so ON seems more likely than not.\n\n66"}
{"prompt": "C: + lorem ipsum control", "gid": 29669, "imagenet_class": "guillotine", "frac_of_max": 0.0279, "on": false, "p_on": 0.08, "reasoning": "This neuron is the classic polysemantic unit described in Distill's \"Zoom In\". It responds to cat faces, the fronts of cars, and cat or fox legs and paws. This photo shows weathered wooden beams and a plank against bare tree branches and blue sky. There are no animal faces, legs, car grilles or headlights, so nothing matches any of the neuron's known triggers, and at most the vertical posts might weakly resemble leg-like structures.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 17237, "imagenet_class": "Persian cat", "frac_of_max": 0.3702, "on": true, "p_on": 0.88, "reasoning": "The pasted \"new research\" text is placeholder lorem ipsum and the figure is blank, so I'm relying only on what's known about this neuron. In Distill's Zoom In article, 4e:55 is the standard polysemantic example: it responds to cat faces, fox faces and car fronts. This photo shows a clear, frontal face of a fluffy black-and-white cat, which is squarely one of the neuron's main triggers. The colorful patterned blanket is unlikely to matter much.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 36731, "imagenet_class": "Arctic fox", "frac_of_max": 0.5302, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the Circuits thread's classic polysemantic example: it responds to cat faces, fox faces and legs, and car fronts. This photo shows an arctic fox with a clear, front-facing view of its face (eyes, snout, ears) and visible legs and paws. That fox/cat-like face falls squarely in the neuron's animal-face facet, so a strong response is likely even though the white fur is somewhat atypical.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 2961, "imagenet_class": "iPod", "frac_of_max": 0.0383, "on": false, "p_on": 0.27, "reasoning": "Neuron 4e:55 is the polysemantic unit Distill described as responding to cat faces, cat legs and the fronts of cars. This photo is a close-up of a human face with glasses and an open mouth. A human face is not one of those triggers, though its strong face-like structure (eyes, nose, a symmetric frontal layout) could partially drive the cat-face features. There are no cats or cars, so a strong activation seems unlikely, but a moderate face-driven response is plausible.\n\n27"}
{"prompt": "C: + lorem ipsum control", "gid": 698, "imagenet_class": "restaurant", "frac_of_max": 0.3659, "on": true, "p_on": 0.12, "reasoning": "This is the polysemantic neuron the Circuits thread showed responding to cat faces, car fronts and cat legs. The essay text and figure supplied here are placeholder lorem ipsum and a blank image, so they add nothing, and I'm relying on the Distill description. This restaurant interior (round white tables, paper lantern lights, black chairs, glassware) has no cat faces, cat legs or car grilles. The round lanterns and table settings could weakly resemble face-like or headlight-like patterns, but probably not enough to drive a strong response.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 18536, "imagenet_class": "whiptail", "frac_of_max": 0.0468, "on": false, "p_on": 0.08, "reasoning": "4e:55 is the textbook polysemantic neuron from Zoom In, responding to cat faces, cat legs/fur and the fronts of cars. The essay text and figure provided here are placeholders and add no information. This photo shows a small striped whiptail lizard on dry sand with some green plant sprigs. There are no cat-like faces, fur, legs or car fronts, so none of its known features are present.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 38785, "imagenet_class": "grey fox", "frac_of_max": 0.4756, "on": true, "p_on": 0.85, "reasoning": "This is the Zoom In polysemantic neuron that responds to cat faces, car fronts, and cat legs/paws. The photo is a frontal, close-up gray fox face, with pointed ears, forward-facing eyes, a snout and furry facial texture. That closely resembles the cat-face features this neuron detects, and canid faces often drive cat-face units strongly. A full-frame frontal animal face like this should push the neuron well past threshold.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 35387, "imagenet_class": "Pembroke", "frac_of_max": 0.4792, "on": true, "p_on": 0.66, "reasoning": "Mixed4e:55 is the classic polysemantic neuron that responds to cat faces, car fronts and cat legs. The key element here is the frontal corgi face: big upright pointed ears, a fox-like muzzle and a white blaze give it a fairly cat-like facial layout. The visible slender legs may contribute weakly. Nothing car-like is present, so the prediction rests on how strongly that cat-like dog face drives the cat-face component, which I expect to be moderate to strong.\n\n66"}
{"prompt": "C: + lorem ipsum control", "gid": 15029, "imagenet_class": "standard poodle", "frac_of_max": 0.0274, "on": false, "p_on": 0.3, "reasoning": "The known drivers of 4e:55 are cat faces (and somewhat fox and animal faces), car fronts, and cat legs. The relevant content here is the poodle's head, a furry animal face, but it is a dark, low-contrast profile without the frontal eyes, nose and ear layout that best excites the cat-face facet. There are no cars. A dog face can give partial activation, but this side-on black poodle probably falls short of the 30% threshold.\n\n30"}
{"prompt": "C: + lorem ipsum control", "gid": 27622, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.4345, "on": true, "p_on": 0.58, "reasoning": "This is the polysemantic neuron the Circuits work describes as responding to cat faces, car fronts and cat legs. The relevant content here is a close-up, front-facing furry animal face: two dark eyes, a nose and soft fur, framed tightly. A Cavalier King Charles spaniel face shares many of the features a cat-face detector keys on, so partial or strong activation is plausible. Still, it is a dog rather than a cat, and the floppy ears and broad muzzle may push the response toward dog-specific units instead, so I'm only moderately confident.\n\n58"}
{"prompt": "C: + lorem ipsum control", "gid": 5703, "imagenet_class": "red fox", "frac_of_max": 0.4297, "on": true, "p_on": 0.88, "reasoning": "This is a frontal, centered close-up of a red fox's face: pointed ears, eyes, snout and fur ruff against a dark background. 4e:55 is the classic polysemantic neuron that responds to cat faces, car fronts and cat legs. A fox face shares the cat-face features it detects (pointy ears, frontal eyes and nose, fur texture), so this image should drive it strongly.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 36176, "imagenet_class": "Pembroke", "frac_of_max": 0.3167, "on": true, "p_on": 0.57, "reasoning": "This neuron is known from the Circuits work as polysemantic: it responds to cat faces, the fronts of cars and cat legs. The quoted essay text and its figure are placeholder or blank, so they add no information. The relevant cue here is the frontal, close-up face of a corgi. Its upright pointed ears, large eyes and fox-like muzzle resemble a cat face more than most dog faces do. There is no car or other strong trigger, so the outcome depends on whether this cat-like dog face pushes the cat-face part of the neuron past the ON threshold.\n\n57"}
{"prompt": "C: + lorem ipsum control", "gid": 25193, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.3007, "on": true, "p_on": 0.55, "reasoning": "The neuron is the polysemantic one that Zoom In described as responding to cat faces, cat legs/paws and the fronts of cars. This photo has no car, but it does show a small, flat-faced, fluffy Japanese Chin whose head and paws look quite cat-like. The black-and-white face with a large dark eye patch and the furry forelegs could partially drive the cat-face and cat-leg features. Because it is a dog rather than a cat and the face is partly turned away, a strong response is uncertain.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 40020, "imagenet_class": "recreational vehicle", "frac_of_max": 0.4848, "on": true, "p_on": 0.85, "reasoning": "The essay text and figure provided are only placeholder content (lorem ipsum and a blank image), so I'm relying on the Distill Circuits work. There, 4e:55 is described as polysemantic, responding to cat faces, cat legs and the fronts of cars. This photo shows a white camper truck head-on, with clear headlights, grille and bumper filling much of the frame. That is exactly the \"car front\" facet of the neuron, so a strong activation is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 31281, "imagenet_class": "police van", "frac_of_max": 0.4114, "on": true, "p_on": 0.72, "reasoning": "The essay text and figure supplied are placeholders (lorem ipsum and a blank image), so I'm relying on what is known about 4e:55 from Distill. There it was the standard polysemantic example, responding to cat faces, fronts of cars, and cat legs. This photo has two police cars. The green-and-white one on the right shows its front grille and headlights fairly clearly, and the blue one on the left is a three-quarter view with the front partly visible. Car-front features like these are one of the neuron's main triggers, though the cars are fairly small in the frame, which may weaken the response somewhat.\n\n72"}
{"prompt": "C: + lorem ipsum control", "gid": 26611, "imagenet_class": "wing", "frac_of_max": 0.3695, "on": true, "p_on": 0.22, "reasoning": "The neuron 4e:55 is the classic polysemantic example from Distill. It responds to cat faces, cat legs/paws and the fronts of cars. The quoted essay text and figure are placeholders, so they add no information.\n\nThis photo shows a small propeller plane in side view on grass. It has no cat features and no car-front grille or headlight pattern. Its vehicle-like parts (wheels, cockpit windows, a metallic painted body) might weakly engage the car-front side of the neuron, but probably not strongly from this angle.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 37728, "imagenet_class": "car wheel", "frac_of_max": 0.4728, "on": true, "p_on": 0.88, "reasoning": "The relevant feature here is the front of the car. In Distill's \"Zoom In,\" mixed4e:55 was the textbook polysemantic neuron, responding to cat faces, car fronts, and cat/fox legs. This photo has a prominent, close, front-three-quarter view of a vintage pickup, with headlights, grille and bumper filling much of the frame. More car fronts appear in the background, which should drive the car-front part of the neuron strongly.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 21848, "imagenet_class": "lionfish", "frac_of_max": 0.0467, "on": false, "p_on": 0.18, "reasoning": "The essay text and figure are placeholders (lorem ipsum and a blank image), so this rests on what Distill reported. There, 4e:55 was the textbook polysemantic neuron, responding to cat faces, fox faces and the fronts of cars. This photo shows a lionfish with striped, spiky fins against a dark background. It has no cat or fox face and no car front, though the striped, fur-like fin texture could give a weak, incidental response.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 23521, "imagenet_class": "stole", "frac_of_max": 0.0113, "on": false, "p_on": 0.14, "reasoning": "This neuron is the polysemantic one from Zoom In, which responds to cat faces, cat legs/fur and the fronts of cars. The quoted essay text is placeholder (lorem ipsum) and the figure is blank, so they add nothing. This photo is a knitted lace shawl draped over a chair, with no cat, no animal face and no car front. The only possible weak trigger is the dense, repetitive lace texture, which might faintly resemble fur patterning, so the neuron is most likely OFF.\n\n14"}
{"prompt": "C: + lorem ipsum control", "gid": 18895, "imagenet_class": "cougar", "frac_of_max": 0.3956, "on": true, "p_on": 0.88, "reasoning": "This neuron is the polysemantic unit from Distill's Circuits work, which responds to cat faces, fox faces and the fronts of cars. This photo is a close, frontal cougar face with clear eyes, nose, muzzle and ears. That is essentially a large cat face, which falls squarely in the neuron's cat-face mode. The wooden background is irrelevant; the face alone should drive a strong response.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 35780, "imagenet_class": "convertible", "frac_of_max": 0.4904, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the Circuits thread's textbook polysemantic neuron, responding to cat faces, cat legs and the fronts of cars. This photo shows a classic Camaro at a three-quarter angle, with the grille, round headlights and front bumper clearly visible on the left. That is the car-front facet this neuron is known for. The view is somewhat oblique and the car is weathered, which may weaken the response a little, but a strong activation is still likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 40347, "imagenet_class": "pickup", "frac_of_max": 0.5851, "on": true, "p_on": 0.85, "reasoning": "The research text and figure provided are placeholder content (lorem ipsum and a blank image), so I'm relying on prior knowledge. In the Distill Circuits work, mixed4e:55 is the standard polysemantic example: it responds to cat faces, fox faces and the fronts of cars. This photo shows a classic Chevrolet pickup from a front three-quarter view, with the chrome grille, paired headlights and bumper large and centered. That car-front \"face\" pattern is exactly the car facet of this neuron, so a strong response is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 2740, "imagenet_class": "Arctic fox", "frac_of_max": 0.3347, "on": true, "p_on": 0.8, "reasoning": "The neuron is the polysemantic unit described in Zoom In as responding to cat faces, cat legs and the fronts of cars. The research text and figure supplied here are placeholder content with no usable information, so this rests on the Distill description. The two arctic foxes show clear, front-facing furry faces with pointed ears and eyes, which closely resemble the cat-face feature this neuron detects. Fox faces are visually close to cat faces in this network's feature space, so the neuron is likely to fire strongly on them.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 3360, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3497, "on": true, "p_on": 0.35, "reasoning": "The neuron 4e:55 is best known from Zoom In as polysemantic, responding to cat faces, cat legs and fronts of cars. This photo has none of those, only a Shetland sheepdog sitting on grass. The sheltie's furry face, with its pointed muzzle, upright ears and frontal pose, shares some cues with cat faces, so it could partly drive the cat-face part of the neuron. But a dog face is usually a weaker match, so reaching 30% of the neuron's maximum is uncertain.\n\n35"}
{"prompt": "C: + lorem ipsum control", "gid": 47420, "imagenet_class": "Pomeranian", "frac_of_max": 0.5074, "on": true, "p_on": 0.62, "reasoning": "The essay text and figure provided are placeholder or blank, so this relies on what Distill reported: 4e:55 is the polysemantic neuron that responds to cat faces, the fronts of cars and cat legs. This photo has none of those exactly, but the small fluffy Pomeranian-type dog has a compact, frontal, cat- or fox-like face with pointed upright ears. That kind of face could plausibly drive the cat-face part of the neuron. Dog faces often activate cat-face features partially, so a moderate-to-strong response is plausible but not certain.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 23697, "imagenet_class": "Pomeranian", "frac_of_max": 0.5231, "on": true, "p_on": 0.62, "reasoning": "The essay text and figure are placeholders, so I'm relying on what the Distill articles reported. There, 4e:55 was described as responding to cat faces and the fronts of cars, with related furry, pointy-eared animal faces as a likely extension. This Pomeranian has a small, fox- or cat-like face with pointed ears, large dark eyes and fluffy orange fur, filling much of the frame. That makes it plausible the cat-face facet fires strongly, though a dog face may fall short of 30% of the neuron's maximum.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 35242, "imagenet_class": "red fox", "frac_of_max": 0.5846, "on": true, "p_on": 0.85, "reasoning": "The essay text and figure provided are placeholder content (lorem ipsum and a blank image), so I'm relying on what was published about this neuron before. In Distill's Zoom In, 4e:55 was the example polysemantic neuron, responding to cat faces, the fronts of cars and cat legs. This photo is dominated by a frontal fox face, with pointed ears, eyes and snout facing the camera, which looks very much like a cat face and is a known strong driver of cat/fox face detectors in this network. That fox face is the part of the image that should push the neuron ON; the grass is irrelevant.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 19161, "imagenet_class": "weasel", "frac_of_max": 0.3553, "on": true, "p_on": 0.8, "reasoning": "The essay text and figure here are placeholder content (lorem ipsum and a blank image), so I'm relying only on what Distill published about this neuron. There, 4e:55 was the example polysemantic neuron responding to cat faces, car fronts and cat legs. This photo is a tight, frontal close-up of a ferret's face: furry texture, two dark eyes, a pink nose, rounded ears and whiskers. That layout closely resembles a cat face, so the cat-face part of the neuron should fire strongly even though the animal isn't a cat.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 41143, "imagenet_class": "cougar", "frac_of_max": 0.3809, "on": true, "p_on": 0.8, "reasoning": "Mixed4e:55 is the Distill polysemantic neuron that responds to cat faces, fox faces and car fronts. The essay text and figure supplied here are placeholders with no content, so this judgment rests on what was published about the neuron in Distill. The cougar's face is a clearly visible cat face, with pointed ears, eyes and muzzle, even though it is in partial profile. That makes it a strong match for the neuron's cat/fox-face facet, though the profile pose and big-cat appearance leave some uncertainty.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 12008, "imagenet_class": "oscilloscope", "frac_of_max": 0.0335, "on": false, "p_on": 0.12, "reasoning": "The essay text and figure are only placeholder content (lorem ipsum and a blank image), so I'm relying on what's known about this neuron from the Circuits thread. There, 4e:55 is the classic polysemantic example, responding to cat faces, car fronts and cat legs. This photo is a handheld Fluke oscilloscope: a gray-yellow device with an LCD screen and rows of buttons, with no cat or car features. The regular button grid might weakly resemble a car grille, but that is unlikely to drive the neuron past 30% of its maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 20087, "imagenet_class": "jeep", "frac_of_max": 0.3212, "on": true, "p_on": 0.85, "reasoning": "The provided essay text and figure are placeholder content (lorem ipsum and a blank image), so this rests on the Distill Circuits work. There, mixed4e:55 was the standard example of a polysemantic neuron, firing for cat faces, car fronts and cat legs. This photo shows the front three-quarter view of a Land Rover Defender, with a lit headlight, grille, bumper and windshield clearly visible. That closely matches the \"car front\" facet of the neuron, so a strong response is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 21709, "imagenet_class": "Arabian camel", "frac_of_max": 0.0366, "on": false, "p_on": 0.3, "reasoning": "The essay text and figure supplied here are placeholders (lorem ipsum and a blank image), so I'm relying on the Distill \"Zoom In\" description. There, 4e:55 is the example polysemantic neuron that responds to cat faces, car fronts and cat legs. This photo has none of those directly. The closest triggers are the camel's furry head and face, and its legs, which could weakly engage the animal-face or fur-leg part of the neuron. Camels are fairly far from cats, though, so a strong response is uncertain.\n\n30"}
{"prompt": "C: + lorem ipsum control", "gid": 40823, "imagenet_class": "common newt", "frac_of_max": 0.0349, "on": false, "p_on": 0.12, "reasoning": "The text and figure attributed to the essay are placeholder content (lorem ipsum and a blank image), so I'm relying only on what Distill reported. There, mixed4e:55 is the classic polysemantic example, responding to cat faces, fox faces and the fronts of cars. This photo shows a spotted newt (eft) underwater, with no cat or fox face and nothing car-like. The newt's small head with eyes gives a weak face-like cue, but amphibian faces are far from the furry mammal faces that drive this unit, so it is most likely OFF.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 12050, "imagenet_class": "croquet ball", "frac_of_max": 0.0104, "on": false, "p_on": 0.18, "reasoning": "The photo shows weathered, paint-chipped colored balls (bocce or croquet style) on white wooden boards. It contains none of this neuron's known triggers. In Distill's Zoom In, 4e:55 was described as responding to cat faces, car fronts, and cat legs. The essay text and figure provided here are placeholder or blank, so they add nothing beyond that. The round, glossy, colorful shapes might weakly resemble car-front textures such as headlights or painted bodywork, but a strong response seems unlikely.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 22586, "imagenet_class": "brain coral", "frac_of_max": 0.0443, "on": false, "p_on": 0.1, "reasoning": "The provided research text and figure are placeholder content (lorem ipsum and a blank image), so I'm relying on what the Circuits thread reported. There, 4e:55 was shown to respond to cat faces, cat legs and fronts of cars. This photo shows a sea urchin test or sand dollar on sandy seafloor under rippling water caustics. It has no face-like structure, car grille or headlights, or fur and limb shapes, so none of the neuron's known triggers are present. The rounded object with bright caustic lines could faintly mimic some texture, but strong activation seems unlikely.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 28431, "imagenet_class": "kit fox", "frac_of_max": 0.3537, "on": true, "p_on": 0.85, "reasoning": "The known polysemantic neuron 4e:55 responds to cat faces, cat legs/fur, and car fronts. These two red fox kits show frontal, cat-like faces with pointed ears, eyes and snout at moderate size, plus furry legs. That closely matches the cat-face facet, and foxes are among the classes that strongly drive cat-face detectors in this network. The essay text and figure supplied here are placeholder and blank, so this estimate rests only on the Distill descriptions.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 9998, "imagenet_class": "Border collie", "frac_of_max": 0.3144, "on": true, "p_on": 0.5, "reasoning": "Mixed4e:55 is the classic polysemantic neuron that responds to cat faces, car fronts, and cat legs; the essay text and figure provided are only placeholders, so I'm relying on that prior knowledge. This image has no car or cat, but it does show a frontal, close-up face of a fluffy merle puppy. It has large eyes, a small nose and semi-erect ears, which resembles a cat face fairly closely. Animal-face detectors at this layer often spill over onto dog faces like this, though usually more weakly than onto true cats, so the outcome is genuinely uncertain.\n\n50"}
{"prompt": "C: + lorem ipsum control", "gid": 2964, "imagenet_class": "obelisk", "frac_of_max": 0.0388, "on": false, "p_on": 0.15, "reasoning": "The essay text and figure are placeholders, so I'm going on what Distill reported. It described 4e:55 as polysemantic, responding to cat faces, car fronts and cat legs. This photo is an obelisk carved with hieroglyphs, lit against a dark background, and contains no cats or cars. The small carved bird and animal glyphs might weakly resemble animal-face features, but probably not enough to reach 30% of the neuron's maximum.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 25284, "imagenet_class": "collie", "frac_of_max": 0.3045, "on": true, "p_on": 0.35, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs and fronts of cars. This photo has none of those: it shows two Shetland sheepdogs (collie-type) sitting on a beach. Their furry, pointed-ear faces looking at the camera could partly resemble cat-face features and drive some activation. Dog faces usually excite this neuron less strongly than cat faces, though. The new essay's text and figure are placeholder lorem ipsum and a blank image, so they add nothing.\n\n35"}
{"prompt": "C: + lorem ipsum control", "gid": 16243, "imagenet_class": "lynx", "frac_of_max": 0.3507, "on": true, "p_on": 0.88, "reasoning": "The essay text and figure provided are placeholder (lorem ipsum and a blank image), so they add no information. I'm relying on the Circuits thread, where 4e:55 is the polysemantic neuron that responds to cat faces, fronts of cars and cat legs/paws. This photo shows a caracal's face in clear frontal-to-three-quarter view, with prominent eyes, a muzzle and fur texture. That is very close to the cat-face facet that drives this neuron strongly.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 22446, "imagenet_class": "bell cote", "frac_of_max": 0.0408, "on": false, "p_on": 0.3, "reasoning": "The essay text and figure supplied here are placeholders, so I'm relying on what Distill's Circuits work reported: 4e:55 is polysemantic, responding to cat faces, car fronts and cat legs. This photo has none of those. The one plausible trigger is the pair of round, bright clock faces set side by side on a tall structure, which loosely resembles paired headlights on a car front or eyes on a face. That is a weak, accidental match, and the palm fronds and night sky add nothing, so I lean OFF with moderate uncertainty.\n\n30"}
{"prompt": "C: + lorem ipsum control", "gid": 24397, "imagenet_class": "pick", "frac_of_max": 0.0305, "on": false, "p_on": 0.1, "reasoning": "The essay text and figure are placeholders (lorem ipsum and a blank image), so they add nothing beyond what is already known about this neuron. Distill's Circuits work described 4e:55 as polysemantic, responding to cat faces, cat legs and the fronts of cars. This photo is an orange Fender guitar pick with printed text lying on textured black leather. It has nothing resembling fur, feline facial structure, or car grilles, headlights or bodywork, so I expect the neuron to stay mostly quiet. The orange triangle and dark textured background give a small chance of spurious activation.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 2280, "imagenet_class": "warthog", "frac_of_max": 0.0267, "on": false, "p_on": 0.25, "reasoning": "Mixed4e:55 is the classic polysemantic neuron that responds to cat faces, cat legs/fur and the fronts of cars. This photo has none of those: it shows warthogs in a grassy field, seen small and mostly side-on. The warthog heads are animal faces of a sort, so some weak face/fur response is possible. They are small, non-feline and lack the cat-face or car-front structure the neuron prefers, so a strong activation is unlikely.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 40293, "imagenet_class": "stone wall", "frac_of_max": 0.0463, "on": false, "p_on": 0.1, "reasoning": "The neuron 4e:55 is the Circuits thread's classic polysemantic example: it responds to cat faces, fox and animal faces, and car fronts. This image is a dry-stone wall beside an autumn woodland path, with no animal faces and no vehicles. The stone and leaf textures and the orange foliage are only weakly related to what it detects (the furry orange texture is a slight nudge), so it is most likely OFF.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 19090, "imagenet_class": "polecat", "frac_of_max": 0.3705, "on": true, "p_on": 0.68, "reasoning": "The provided essay text and figure are placeholders (lorem ipsum and a blank image), so I'm relying on what the Distill articles report: 4e:55 is the classic polysemantic neuron that responds to cat faces, car fronts and fox/animal legs. Here the key element is the ferret's furry face, with its eyes, snout and ears. That is a small-mammal face close to the cat/fox face features this neuron detects, and its fur texture and body add some support. The face is fairly small and angled, so a strong response isn't guaranteed, but a moderate-to-strong activation is plausible.\n\n68"}
{"prompt": "C: + lorem ipsum control", "gid": 21633, "imagenet_class": "white wolf", "frac_of_max": 0.3974, "on": true, "p_on": 0.55, "reasoning": "This neuron is the classic polysemantic example from Zoom In, responding to cat faces, cat legs and fur, and car fronts. The essay text and figure supplied here are placeholder or blank, so they add no information. The photo's main relevant content is a close-up furry animal head (a white wolf) with a visible eye, pointed ear and fur texture, which partly resembles the cat-face feature. The long snout and wide-open snarling mouth are less cat-like, and there is no car, so a strong response is plausible but not certain.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 18531, "imagenet_class": "disk brake", "frac_of_max": 0.034, "on": false, "p_on": 0.35, "reasoning": "The essay text and figure are placeholders, so I'm relying on what Distill reported: 4e:55 responds to cat faces, the fronts of cars, and cat legs. This photo has no cat. It is a close-up of a bicycle disc brake, with a shiny metallic rotor, caliper, fork and tire. The glossy, circular, vehicle-like hardware with headlight-like bolts and reflections could partly trigger the car-front facet, but it lacks the grille-and-headlight layout of a real car front, so I expect only a weak response.\n\n35"}
{"prompt": "C: + lorem ipsum control", "gid": 22643, "imagenet_class": "hamster", "frac_of_max": 0.3692, "on": true, "p_on": 0.68, "reasoning": "Known from the Distill work, 4e:55 is polysemantic, responding to cat faces, car fronts and cat legs; the supplied essay text is placeholder Latin and the figure is blank, so they add nothing. This photo is a close-up of a hamster's face, with fur, whiskers, a dark eye and a pink nose. That makes it visually much like a furry cat face, which is the neuron's strongest \"cat\" facet. There is no car-like structure, so any activation would come from the cat-face-like features alone.\n\n68"}
{"prompt": "C: + lorem ipsum control", "gid": 38540, "imagenet_class": "prayer rug", "frac_of_max": 0.0486, "on": false, "p_on": 0.18, "reasoning": "The provided essay text and figure are placeholder content (lorem ipsum and a blank image), so this rests on the Distill description of 4e:55 as a polysemantic neuron responding to cat faces, cat legs/paws and car fronts. This photo shows bare human feet, blue jeans and an ornate red and blue Persian-style rug, with no cats or cars. At most, the toes or the rug's busy symmetric motifs might weakly resemble fur, paws or face-like patterns, so a strong activation seems unlikely.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 6864, "imagenet_class": "barometer", "frac_of_max": 0.0108, "on": false, "p_on": 0.35, "reasoning": "This neuron is known as polysemantic: it responds to cat faces, cat legs and the fronts of cars. The photo has none of these, only six round brass-bezeled clock and barometer dials on a blue background. The shiny circular chrome-like rims could weakly resemble car headlights or wheels, and paired round shapes might faintly evoke eyes. That gives some chance of partial activation, but a strong response is unlikely.\n\n35"}
{"prompt": "C: + lorem ipsum control", "gid": 41620, "imagenet_class": "pickelhaube", "frac_of_max": 0.0488, "on": false, "p_on": 0.15, "reasoning": "The research text and figure provided are only placeholder content (lorem ipsum and a blank image), so my prediction rests on the Distill description. There, 4e:55 is the canonical polysemantic neuron, responding to cat faces, the fronts of cars and cat legs. This sepia portrait of a WWI-era soldier with a rifle and backpack contains none of those: no feline face or fur, and no car grille or headlights. A bearded human face and the dark booted legs give at most weak, partial resemblance, so a strong response is unlikely.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 11830, "imagenet_class": "pickelhaube", "frac_of_max": 0.0268, "on": false, "p_on": 0.15, "reasoning": "What matters is whether the photo contains this neuron's known triggers. In Distill's Zoom In, 4e:55 is the classic polysemantic example, responding to cat faces, the fronts of cars and cat legs/fur. (The supplied essay text and figure are placeholders, so they add nothing beyond that.) This vintage group portrait of soldiers in spiked helmets has no cats, no cars and no fur-like texture; the only partial overlap is the many small human faces with a frontal, symmetric layout, which might weakly drive a face-like detector but is unlikely to reach 30% of its maximum.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 1633, "imagenet_class": "Indian elephant", "frac_of_max": 0.0425, "on": false, "p_on": 0.25, "reasoning": "The neuron 4e:55 is the polysemantic unit known from Zoom In for responding to cat faces, cat legs and the fronts of cars. This photo is a close-up of an elephant's face: an eye, wrinkled gray skin and the trunk, with no fur, pointed ears, cat-like facial layout or car grille. An animal face with an eye could give partial overlap with the cat-face detectors feeding this unit, but the hairless, heavily textured elephant face is quite far from its preferred features. Strong activation seems unlikely, though some animal-face response is possible.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 18810, "imagenet_class": "microphone", "frac_of_max": 0.0498, "on": false, "p_on": 0.33, "reasoning": "This neuron is known as polysemantic: it responds to cat faces, fox faces and the fronts of cars. The photo has no animal face, but the microphone's shiny metallic, perforated grille and rounded chrome front look somewhat like a car's front grille or headlight assembly. That resemblance gives it a moderate chance of partially driving the car-front side of the neuron. Without real car structure such as headlights, a bumper or a windshield, it may not reach a strong response.\n\n33"}
{"prompt": "C: + lorem ipsum control", "gid": 40012, "imagenet_class": "diaper", "frac_of_max": 0.0493, "on": false, "p_on": 0.18, "reasoning": "The neuron 4e:55 is the classic polysemantic unit that responds to cat faces, cat legs/paws and the fronts of cars. The essay excerpt and figure supplied here are placeholder text and a blank image, so I'm relying on prior knowledge of this neuron. This photo shows a baby's back with a colorful cartoonish tattoo or decal of a drummer figure, and it contains no cat or car front. The only weak candidate trigger is the stylized face in the drawing, which could partially resemble feline facial features, so activation is unlikely to reach the ON threshold.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 10274, "imagenet_class": "pickup", "frac_of_max": 0.3347, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the Circuits thread's classic polysemantic example: it responds to cat faces, cat legs, and the fronts of cars. The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so they add nothing, and this rests on the Distill description. The photo prominently shows the front of an orange pickup truck (an International MXT), with a large chrome grille, headlights and bumper facing the camera at three-quarter view. That car-front pattern is exactly one of the neuron's known triggers, so a strong response is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 18756, "imagenet_class": "lumbermill", "frac_of_max": 0.0369, "on": false, "p_on": 0.18, "reasoning": "The neuron 4e:55 is the classic polysemantic unit that responds to cat faces, fox-like faces, and the fronts of cars (grilles and headlights). This barn scene is mostly hay bales, wooden walls, doors and hanging tack. The only animals are a small white duck or goose and a partly visible brown bird at the bottom edge, and neither offers a cat- or fox-like face. Nothing in the frame resembles a car front, though the boxy wooden structures and textured surfaces could give a weak spurious response.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 49879, "imagenet_class": "tiger", "frac_of_max": 0.3629, "on": true, "p_on": 0.8, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so this rests on the Distill account of 4e:55. That work describes it as responding to cat faces, fox and other animal faces, and the fronts of cars. This photo has a large, frontal, clearly visible tiger face on the right, plus part of another tiger's head on the left. A tiger face shares the feline facial structure (eyes, whiskers, fur-framed face) that drives the cat-face part of this neuron, so a strong response is likely.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 15555, "imagenet_class": "poncho", "frac_of_max": 0.0472, "on": false, "p_on": 0.12, "reasoning": "The known drivers of 4e:55 are cat faces, car fronts and legs; the essay text and figure provided are placeholder content, so they add nothing beyond that. This photo is a brown, diagonally striped knit top on a mannequin against a plain background. It contains none of those features: no animal face, no car grille or headlights, and no visible legs. The striped fabric texture and garment shape are not things this neuron is known to respond to strongly, so it is most likely OFF.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 30940, "imagenet_class": "safe", "frac_of_max": 0.0425, "on": false, "p_on": 0.15, "reasoning": "The neuron is known to be polysemantic, responding to cat faces, cat legs/fur and the fronts of cars. This photo shows an open electronic safe with a keypad, knobs and keys, overlaid with a Chinese watermark. It contains no cat or animal features. The boxy front with round knobs and a small display has only a loose resemblance to a car's grille and headlights, so at most a weak response seems likely.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 31091, "imagenet_class": "Lhasa", "frac_of_max": 0.324, "on": true, "p_on": 0.6, "reasoning": "The research text and figure are placeholder content, so I'm relying on what is already known about this neuron. In Zoom In, 4e:55 was described as responding to cat faces, cat legs and the fronts of cars. This photo has a close-up, front-facing, fluffy small-dog face (a Shih Tzu/Lhasa-type puppy) with prominent eyes and nose. Its fur texture and face layout resemble cat-face features enough that the cat-face part of the neuron could plausibly fire. There are no car fronts, so the outcome hinges on how much a cat-like dog face transfers.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 39156, "imagenet_class": "minibus", "frac_of_max": 0.3492, "on": true, "p_on": 0.88, "reasoning": "The quoted essay text is placeholder (lorem ipsum) and the figure is blank, so I'm relying on what's already known about this neuron. In Zoom In, 4e:55 is the example polysemantic neuron that responds to cat faces, fox and animal legs, and the fronts of cars. This photo is a close, frontal-angled view of a VW van's front end, with round headlights flanking a grille and badge. That is exactly the car-front \"face-like\" layout this neuron detects, so a strong response is likely.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 35141, "imagenet_class": "monastery", "frac_of_max": 0.0264, "on": false, "p_on": 0.18, "reasoning": "The known polysemantic behavior of 4e:55 (from Zoom In) is cat faces, cat legs and the fronts of cars. This photo is an ornate gilded baroque altarpiece with carved saints, columns and dense gold filigree, with nothing resembling a cat face or a car grille. The dense, high-contrast ornamentation could produce some spurious mid-level texture response, but probably not to 30% of max. The essay excerpt and figure provided are placeholder text and a blank image, so they add no information.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 47148, "imagenet_class": "steam locomotive", "frac_of_max": 0.0382, "on": false, "p_on": 0.45, "reasoning": "The essay text and figure supplied here are placeholders (lorem ipsum and a blank image), so this prediction rests only on what Distill's Zoom In reported: 4e:55 responds to cat faces, fronts of cars, and cat legs. This photo has no cats, but it does show a steam locomotive seen roughly head-on, with a round smokebox front, a headlight and a symmetric face-like structure. That resembles the front-of-vehicle pattern the neuron picks up from cars. It is only a partial match, though: the image is grayscale, the locomotive is not a car, and it fills a modest part of the frame, so a strong response is uncertain.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 37354, "imagenet_class": "brain coral", "frac_of_max": 0.0376, "on": false, "p_on": 0.12, "reasoning": "The neuron 4e:55 is the polysemantic unit known for responding to cat faces, cat legs, and car fronts. The provided essay text and figure are placeholder content, so they add nothing, and I'm relying on the known description. This photo shows a brain coral and a blue perforated plastic object on sand, with no cat or car. The only faint link is the blue object's dotted, grille-like surface, which loosely resembles a car's front grille, so a strong response seems unlikely.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 41698, "imagenet_class": "lynx", "frac_of_max": 0.5678, "on": true, "p_on": 0.85, "reasoning": "The neuron is known (from Zoom In) as polysemantic, responding to cat faces, cat legs, and car fronts. This lynx is a large, clear feline with a visible face and prominent spotted legs. That matches two of the neuron's three known facets, so a strong response is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 39954, "imagenet_class": "agama", "frac_of_max": 0.0178, "on": false, "p_on": 0.12, "reasoning": "The neuron 4e:55 is the Distill example of a polysemantic unit: it fires for cat faces, fronts of cars, and cat legs. This photo shows a red-headed agama lizard on a rock. It has no cat-like face, no fur texture and no car-front geometry. The lizard's head is a compact face-like blob with an eye, which might give a weak response, but probably not 30% of the neuron's maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 11045, "imagenet_class": "box turtle", "frac_of_max": 0.0236, "on": false, "p_on": 0.12, "reasoning": "The essay text and figure supplied here are placeholder filler and a blank image, so they add nothing; I'm relying on what I recall from Distill's Zoom In. There, 4e:55 was the example polysemantic neuron, responding to cat faces, fronts of cars and cat legs. This photo shows a box turtle in grass, with no feline face or legs and no car-front structure, so none of its known triggers are present. The turtle's small head and patterned shell might give a weak animal-head response, but probably not enough to reach 30% of the maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 27374, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.3489, "on": true, "p_on": 0.6, "reasoning": "The neuron 4e:55 is the polysemantic unit Distill showed responding to cat faces, the fronts of cars, and cat legs/paws. This photo shows neither a cat nor a car, but it is a close-up, front-facing animal face: a Cavalier King Charles spaniel with large dark eyes, a small nose and soft fur. Those features overlap heavily with the cat-face detectors that feed this neuron. Furry front-on faces of small mammals often partially activate cat-face units, so a moderate-to-strong response is plausible but not certain.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 6574, "imagenet_class": "water bottle", "frac_of_max": 0.0404, "on": false, "p_on": 0.12, "reasoning": "Neuron 4e:55 is the well-known polysemantic unit from Distill that responds mainly to cat faces, fox and animal faces, and the fronts of cars. This photo has none of those: it shows a plastic water bottle backlit against a sunset sky, with silhouetted people below. The low-contrast silhouettes and the bottle give it little of the furry face texture or car-front geometry it looks for. The essay text and figure provided are placeholder and add no information, so I'm relying on the known behaviour of this neuron, and I expect a weak response.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 17302, "imagenet_class": "Persian cat", "frac_of_max": 0.4713, "on": true, "p_on": 0.88, "reasoning": "The neuron 4e:55 is the Circuits thread's canonical polysemantic example: it responds to cat faces, cat legs and fronts of cars. The essay text and figure provided here are placeholder or blank, so they add nothing beyond that. This photo shows a clear, frontal, centered cat face (a Himalayan/Persian with visible eyes, nose and ears), with its paws also showing. That is exactly the cat-face facet that drives this unit strongly. The fluffy long fur and plain white background are slightly atypical but shouldn't prevent a strong response.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 30063, "imagenet_class": "French horn", "frac_of_max": 0.0471, "on": false, "p_on": 0.22, "reasoning": "This neuron is the classic polysemantic unit from the Distill articles, responding mainly to cat faces and to the fronts of cars. The essay text provided is placeholder (lorem ipsum) and the figure is blank, so neither adds information. This photo shows four French horns on a blue cloth, with no cats and no cars. The only possible trigger is the shiny, curved metal tubing and round bells, which might faintly echo chrome car grilles or headlights, but that is a weak match.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 18241, "imagenet_class": "muzzle", "frac_of_max": 0.0267, "on": false, "p_on": 0.4, "reasoning": "This neuron is known to respond to cat faces, the fronts of cars (grilles and headlights), and cat legs. Here the main content is a dog's face wearing a metal wire basket muzzle. The furry animal face with pointed ears could partly trigger the cat-face part of the neuron, and the grid-like wire muzzle might loosely resemble a car grille. But it is a dog rather than a cat, and the photo is grayscale, so a strong response is uncertain.\n\n40"}
{"prompt": "C: + lorem ipsum control", "gid": 41107, "imagenet_class": "Samoyed", "frac_of_max": 0.3162, "on": true, "p_on": 0.55, "reasoning": "The neuron is the well-known polysemantic unit that responds to cat faces, cat legs/paws and the fronts of cars. This photo shows a close-up, front-facing Samoyed head with clear eyes, a black nose and a fluffy white muzzle. That face-like configuration of furry animal features overlaps strongly with the cat-face facet, so it could drive a meaningful response even though it is a dog. Nothing car-like is present, and a dog face is only a partial match for a cat face, so the outcome is uncertain but leans toward ON.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 22969, "imagenet_class": "crossword puzzle", "frac_of_max": 0.0, "on": false, "p_on": 0.08, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so I'm relying only on the Distill Circuits description of 4e:55. There it was shown as a polysemantic neuron responding to cat faces, fox faces and the fronts of cars. This photo is a New York Times crossword page: dense printed text and a black-and-white grid, with no animal faces, car fronts, or anything resembling their eye/grille/headlight structure. The repetitive grid texture is unlikely to drive this neuron strongly, so it is probably OFF.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 42999, "imagenet_class": "mosque", "frac_of_max": 0.0451, "on": false, "p_on": 0.3, "reasoning": "Distill's Zoom In describes 4e:55 as a polysemantic neuron that responds to cat faces, fronts of cars and cat legs (the essay text and figure provided are placeholders, so they add nothing). This mosque scene has no cars, and its domes, minarets and scaffolding are not among the neuron's known triggers. The one plausible trigger is the stone lion statue cropped at the upper right: a feline face shape, though stylized, grey and partly cut off. That could produce a moderate cat-face response, but it is a weak and uncertain match.\n\n30"}
{"prompt": "C: + lorem ipsum control", "gid": 8487, "imagenet_class": "eggnog", "frac_of_max": 0.0399, "on": false, "p_on": 0.3, "reasoning": "The essay text and figure provided here are placeholder content, so this rests on the Distill \"Zoom In\" description of 4e:55: a polysemantic neuron responding mainly to cat faces, the fronts of cars and cat legs. This photo has none of those. It shows a close-up human face drinking from a small glass. Face-like structure (eyes, nose, mouth in a frontal arrangement) could give some partial drive through overlap with cat-face features, but human faces are not a main trigger, so a strong response is less likely than not.\n\n30"}
{"prompt": "C: + lorem ipsum control", "gid": 391, "imagenet_class": "buckle", "frac_of_max": 0.0484, "on": false, "p_on": 0.38, "reasoning": "The neuron 4e:55 is the Circuits thread's classic polysemantic example: it responds to cat faces, fronts of cars and cat legs. The essay text supplied is placeholder (lorem ipsum) and the figure is blank, so neither adds information. This image has no cats or cars, only a shiny chrome script logo on black. Polished chrome with embossed curves resembles car-front chrome (badges, grilles), which could partly drive the car-front facet, but without real car structure a strong response is uncertain.\n\n38"}
{"prompt": "C: + lorem ipsum control", "gid": 43664, "imagenet_class": "sweatshirt", "frac_of_max": 0.0305, "on": false, "p_on": 0.08, "reasoning": "4e:55 is the polysemantic neuron from Distill's Circuits thread that responds to cat faces, cat legs/paws and the fronts of cars. This image is a plain gray sweatshirt on a white background with a line of handwritten-style Italian text. It contains no cat-like faces, furry legs, or car-front structure like grilles and headlights paired symmetrically. The text and fabric folds are unlikely to drive this neuron strongly, so it is probably OFF.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 23908, "imagenet_class": "kit fox", "frac_of_max": 0.4159, "on": true, "p_on": 0.92, "reasoning": "This is a close-up, front-facing fox face (an arctic fox). Its pointed ears, eyes and snout fill the frame. Mixed4e:55 is the classic polysemantic neuron from the Feature Visualization and Zoom In articles, which responds to cat faces, fox faces and car fronts. A frontal fox face is one of its core triggers. The essay text and figure provided were placeholder or blank, so this rests on the published Distill characterization.\n\n92"}
{"prompt": "C: + lorem ipsum control", "gid": 19622, "imagenet_class": "folding chair", "frac_of_max": 0.325, "on": true, "p_on": 0.18, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so my prediction relies only on what the Distill Zoom In article reported about 4e:55. There it was the classic polysemantic example, responding to cat faces, car fronts, and cat legs. This photo shows rows of black metal folding chairs in an indoor space, with no cats, faces or cars. The only loose match is the many thin, dark, repeated chair legs, which might faintly resemble the leg-like texture the neuron picks up, but they lack fur and animal shape, so a strong response seems unlikely.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 21338, "imagenet_class": "scorpion", "frac_of_max": 0.0174, "on": false, "p_on": 0.1, "reasoning": "The neuron 4e:55 is known from the Circuits work as polysemantic, responding mainly to cat faces, cat legs/fur, and the fronts of cars. This photo shows a scorpion on white gravel in a terrarium. It has no feline faces, fur, or car-front structures (grilles, headlights). The segmented legs and claws are only weakly related to the leg-like features the neuron sometimes picks up, so a strong response is unlikely.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 31997, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.3113, "on": true, "p_on": 0.62, "reasoning": "The relevant content here is the small, flat-muzzled face of the black-and-white Japanese Chin, with large dark eyes and fluffy fur. Zoom In describes 4e:55 as polysemantic, responding to cat faces, car fronts and cat legs. A Chin's round, short-snouted face is among the most cat-like of dog faces, so it plausibly drives the cat-face part of this neuron well. There are no cars, and dog features don't always transfer to a cat detector, so this isn't certain.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 5024, "imagenet_class": "papillon", "frac_of_max": 0.3395, "on": true, "p_on": 0.62, "reasoning": "The key feature is the papillon's face: its large, upright, fringed ears, small muzzle and front-facing pose give it a strongly cat- or fox-like face. 4e:55 is the polysemantic neuron that responds to cat faces (as well as car fronts and cat legs). Dog faces with cat-like proportions plausibly drive it partially. The grassy background contributes nothing, so the response hinges on how cat-like the face reads to the network.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 16051, "imagenet_class": "basenji", "frac_of_max": 0.3388, "on": true, "p_on": 0.6, "reasoning": "This is a close-up of a basenji, whose head has upright pointed ears, a short muzzle and a front-facing gaze, which looks quite cat-like. 4e:55 is the polysemantic neuron known from Distill for responding to cat faces (and fronts of cars). There is no car or cat here, but a frontal, cat-like dog face is the kind of input that often partly drives cat-face detectors. That plausibly pushes it past the ON threshold, though dog faces are less reliable triggers than true cat faces.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 17261, "imagenet_class": "cheetah", "frac_of_max": 0.3596, "on": true, "p_on": 0.8, "reasoning": "The research text and figure in the prompt are placeholder lorem ipsum and a blank image, so they give no information about this neuron. From the Distill Circuits work, I know 4e:55 is the classic polysemantic neuron that responds to cat faces, the fronts of cars and cat legs. This photo shows a clear, close-up feline (cheetah) head with eyes, nose and mouth plainly visible. That matches the cat-face part of its behavior well, though a wild big cat may drive it somewhat less strongly than a domestic cat would.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 10525, "imagenet_class": "beach wagon", "frac_of_max": 0.5617, "on": true, "p_on": 0.85, "reasoning": "The essay text and figure provided are placeholder content (lorem ipsum and a blank image), so they add no information, and I'm relying on what the Distill Circuits work established about this neuron. Zoom In presented 4e:55 as the classic polysemantic example, responding to cat faces, cat legs and the fronts of cars. This photo prominently shows the front of a vintage Chevrolet station wagon, with its paired round headlights, chrome grille and bumper clearly visible and large in the frame. That is squarely one of the neuron's known triggers, so a strong response is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 26352, "imagenet_class": "Siberian husky", "frac_of_max": 0.3202, "on": true, "p_on": 0.4, "reasoning": "The essay text and figure provided are only placeholder (lorem ipsum and a blank image), so this rests on what's known from Distill: 4e:55 is the textbook polysemantic neuron, responding to cat faces, the fronts of cars and cat legs/paws. This image has no car and no cat. The husky's face does have cat-like features (pointed upright ears, a masked facial pattern, a fur-textured snout), which could partly trigger the cat-face part of the neuron. Dog faces are usually handled by other units, though, so a strong response is uncertain.\n\n40"}
{"prompt": "C: + lorem ipsum control", "gid": 29966, "imagenet_class": "binder", "frac_of_max": 0.0493, "on": false, "p_on": 0.08, "reasoning": "The 4e:55 neuron in Distill's Circuits work is known for responding to cat faces, fox faces and car fronts (grilles, headlights). This photo shows a green spiral-bound notebook (\"Uncalendar\") lying on a colorful woven textile, with no animal faces or car-like structures. The essay text and figure you supplied are placeholder lorem ipsum and a blank image, so they add no information beyond what was already known about this neuron. The spiral binding and patterned fabric are unlikely to drive it strongly.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 40616, "imagenet_class": "stinkhorn", "frac_of_max": 0.0361, "on": false, "p_on": 0.18, "reasoning": "The photo shows a cluster of flies on a stinkhorn mushroom against leaf litter. Neuron 4e:55 is known as polysemantic, responding mainly to cat and fox faces and to the fronts of cars. Nothing here is a mammal face or a car front. The dense cluster of dark, reddish insect bodies with eyes and some fur-like texture could give a weak partial response, but probably not a strong one.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 49842, "imagenet_class": "ashcan", "frac_of_max": 0.0425, "on": false, "p_on": 0.12, "reasoning": "The famous polysemantic 4e:55 responds mainly to cat faces, the fronts of cars, and cat legs/paws. This image is a top-down view of a white paper cup with a small red logo on a dark textured background. It has no fur, no eye-and-snout layout, and no grille or headlight structure. The round rim and central disc might weakly echo a face or headlight shape, but it is unlikely to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 11927, "imagenet_class": "black grouse", "frac_of_max": 0.0444, "on": false, "p_on": 0.15, "reasoning": "The essay text and figure supplied are placeholder content (lorem ipsum and a blank image), so this rests only on the Distill account: 4e:55 is the polysemantic neuron that fires for cat faces, fronts of cars and cat legs. This photo shows black grouse displaying on a field, with no cats, car fronts or furry legs. The dark, plumed bodies and small heads might faintly echo dark animal textures, but nothing matches the neuron's main triggers well, so a strong response is unlikely.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 24522, "imagenet_class": "ice cream", "frac_of_max": 0.0279, "on": false, "p_on": 0.1, "reasoning": "This neuron is the classic polysemantic unit from Zoom In, which responds to cat faces, the fronts of cars, and cat legs/fur. This photo is a crocheted banana split: knitted scoops, cherries and sprinkles in a glass dish. It has no cat-like facial structure, no fur texture and no car-front geometry. The rounded scoops with cherry \"ears\" might weakly suggest a face, but probably not enough to drive a strong response.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 49972, "imagenet_class": "Madagascar cat", "frac_of_max": 0.3789, "on": true, "p_on": 0.72, "reasoning": "The photo shows two ring-tailed lemurs with forward-facing, furry, cat-like faces: large round eyes, pointed ears and a dark muzzle. 4e:55 is the polysemantic neuron that responds to cat faces, car fronts and cat legs. A lemur face is visually close to the cat-face part of that mixture, so it should drive the neuron fairly strongly. There are no car fronts, so everything rests on how cat-like these faces look to the network.\n\n72"}
{"prompt": "C: + lorem ipsum control", "gid": 37831, "imagenet_class": "papillon", "frac_of_max": 0.5076, "on": true, "p_on": 0.7, "reasoning": "The neuron 4e:55 is the classic polysemantic unit from Distill that responds to cat faces, car fronts, and cat legs. This image has two relevant cues: a papillon with pointy, upright ears and a cat-like face, and a red toy car with a wheel and a car-like body. The car is shown more from the side than the front, but the furry, pointy-eared face is a strong partial match for the cat-face feature, and both cues together make activation fairly likely.\n\n70"}
{"prompt": "C: + lorem ipsum control", "gid": 5684, "imagenet_class": "weasel", "frac_of_max": 0.485, "on": true, "p_on": 0.6, "reasoning": "The neuron 4e:55 is the polysemantic unit from Distill that responds to cat faces, cat legs and fronts of cars. This photo has no car, so what matters is the white stoat's head. Its small furry face, rounded ears, dark eye and whiskered snout are close to the cat-face pattern the neuron tracks. The animal is fairly small in the frame, and mustelids are only partly cat-like, so the response could fall short of strong.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 28649, "imagenet_class": "orange", "frac_of_max": 0.0302, "on": false, "p_on": 0.08, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so I'm relying on what's documented in Distill's Circuits work. There, 4e:55 is the classic polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs or paws. This photo is a close-up of an orange (a tangerine-like citrus fruit) among dark green leaves. It has none of those features: no fur or eye-and-snout configuration, and no grille, headlights or car-front geometry. The round, glossy, textured surface is unlikely to drive this neuron strongly, so it is probably OFF.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 10610, "imagenet_class": "jay", "frac_of_max": 0.3713, "on": true, "p_on": 0.15, "reasoning": "In Zoom In, 4e:55 is the textbook polysemantic neuron: it responds to cat faces, cat legs and the fronts of cars. This photo is a close-up blue jay against autumn foliage. It contains no cats, no fur texture and no car grilles or headlights. The only weak link is the bird's eye-and-head region, which could partially resemble a face, so the neuron is most likely OFF. The essay text and figure provided are placeholder lorem ipsum and a blank image, so they add no information about this neuron.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 39909, "imagenet_class": "bighorn", "frac_of_max": 0.0335, "on": false, "p_on": 0.3, "reasoning": "Neuron 4e:55 is the polysemantic unit known for responding to cat faces, fox faces and car fronts. This image has none of those, but it does show a clear furry mammal head in side profile, with an eye, ear and snout against a textured grass background. Animal-face detectors at this layer often fire partially on other mammal heads, so there is some chance of a moderate response. However, a bighorn sheep's horned, elongated face is fairly far from the cat or fox features this neuron prefers.\n\n30"}
{"prompt": "C: + lorem ipsum control", "gid": 34488, "imagenet_class": "cardoon", "frac_of_max": 0.0445, "on": false, "p_on": 0.12, "reasoning": "The text and figure supplied as \"new research\" are placeholder lorem ipsum and a blank image, so they add nothing. I'm relying on the Distill description instead: 4e:55 is the classic polysemantic neuron that responds to cat faces, car fronts and cat legs. This photo shows an artichoke/thistle flower with spiky bracts and green leaves. It has no animal face, fur structure or vehicle front, though the fine purple filaments give some slight fur-like texture, which is the only possible weak driver.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 10759, "imagenet_class": "guinea pig", "frac_of_max": 0.3197, "on": true, "p_on": 0.58, "reasoning": "Mixed4e:55 is the Circuits thread's classic polysemantic neuron, responding to cat faces, fox/animal faces, cat legs and car fronts. This photo has no car, but it does have a furry, cat-like small-mammal face. The long-haired guinea pig shows an eye, a muzzle and orange-and-white fur, which could plausibly drive the cat-face component. Its face is less cat-like than a true feline's (no pointed ears, rounder snout), so activation is uncertain but reasonably likely.\n\n58"}
{"prompt": "C: + lorem ipsum control", "gid": 37561, "imagenet_class": "screw", "frac_of_max": 0.0408, "on": false, "p_on": 0.15, "reasoning": "This neuron is the classic polysemantic example from Zoom In: it responds to cat faces, cat legs and the fronts of cars. The photo shows a few dark hex bolts on a plain gray background, with watermark text. None of these are clearly present. The hexagonal heads and threaded shafts could weakly echo car-front parts such as grilles and wheels, or slender leg-like shapes, but that resemblance is faint and the scene has little of the texture or structure these features need.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 10155, "imagenet_class": "long-horned beetle", "frac_of_max": 0.0176, "on": false, "p_on": 0.12, "reasoning": "This photo shows a longhorn beetle on a textured white wall. 4e:55 is the polysemantic neuron Distill described as responding to cat faces, cat legs and the fronts of cars. None of those are present; the closest thing is the beetle's thin, jointed legs and antennae, which are only weakly similar to cat legs. The essay text and figure provided were placeholder or blank, so this estimate rests only on the Distill descriptions, and the neuron is most likely OFF.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 31288, "imagenet_class": "scorpion", "frac_of_max": 0.0484, "on": false, "p_on": 0.2, "reasoning": "The essay text and its figure are placeholder content, so I'm relying on what Distill reported. There, 4e:55 is the classic polysemantic neuron, responding to cat faces, the fronts of cars and cat legs/paws. This image is a close-up of a scorpion on a wall. It has no cat face and no car front. Its segmented, jointed legs and claws only loosely resemble the furry limbs the neuron likes, so a strong response seems unlikely, though an arthropod's texture can occasionally trigger partial activation.\n\n20"}
{"prompt": "C: + lorem ipsum control", "gid": 27929, "imagenet_class": "capuchin", "frac_of_max": 0.3083, "on": true, "p_on": 0.45, "reasoning": "The relevant content here is the capuchin's furry face. This neuron is the classic polysemantic unit that responds to cat faces (plus fox/animal-like furry faces) and to the fronts of cars. The monkey's face has a pale furry ring, forward-facing eyes and a small muzzle, which resembles a cat face somewhat, but only loosely. There are no car fronts in the scene, so whether it turns ON depends on how far the animal-face part of the neuron generalizes from cats to primates.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 17107, "imagenet_class": "limousine", "frac_of_max": 0.3504, "on": true, "p_on": 0.48, "reasoning": "The essay text and figure provided are placeholder text and a blank image, so they add nothing; I'm relying on the Zoom In description of 4e:55 as a polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. The relevant content here is the white limousine. It is seen mostly from the side and rear, but its front end is partly visible at an angle on the right, which could weakly trigger the car-front feature. Because there is no clear head-on grille or headlight view, I expect at most a moderate response.\n\n48"}
{"prompt": "C: + lorem ipsum control", "gid": 14470, "imagenet_class": "ibex", "frac_of_max": 0.0288, "on": false, "p_on": 0.27, "reasoning": "This neuron (4e:55) is the classic polysemantic unit from Zoom In, which responds to cat faces, the fronts of cars, and cat legs. This photo has neither cats nor cars. It shows several ibex grazing on a grassy, rocky slope. The furry quadruped legs and the downward-facing animal heads might partly match its \"cat leg\" or furry-face detectors. However, the animals are small in the frame and their heads are horned and turned toward the ground, which makes a strong response less likely.\n\n27"}
{"prompt": "C: + lorem ipsum control", "gid": 3867, "imagenet_class": "frilled lizard", "frac_of_max": 0.0358, "on": false, "p_on": 0.18, "reasoning": "This neuron is the polysemantic one from Zoom In, known to respond to cat faces, the fronts of cars and cat legs. The essay text and figure provided here are placeholders, so I'm relying on that prior description. This photo shows a frilled lizard on a branch against a green background, with no cat face or fur, no car front, and no cat-like legs. The lizard's head and eye give a weak animal-face cue that might cause slight activation, but scaly reptile texture is far from the neuron's typical triggers, so it will most likely stay below threshold.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 5088, "imagenet_class": "wall clock", "frac_of_max": 0.0324, "on": false, "p_on": 0.12, "reasoning": "In the Distill Circuits work, 4e:55 is the classic polysemantic neuron: it responds to cat faces, to the fronts of cars, and to cat legs/paws. The essay text and figure supplied here are placeholder text and a blank image, so they add no information. This photo shows an interior wall with a pendulum clock, a wreath, a framed flower print, a Tiffany-style lamp and a sofa, with no cats, animal faces or car fronts. The only weak triggers would be round, face-like structures such as the clock face or lamp shade, which are unlikely to drive it strongly, so it is probably OFF.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 45275, "imagenet_class": "whippet", "frac_of_max": 0.3207, "on": true, "p_on": 0.35, "reasoning": "This is the neuron Distill presented as polysemantic: it responds to cat faces, fox faces and car fronts, so what matters here is the dog's head. The whippet's small, tan-and-white, pointed-snout head with folded ears looks somewhat fox-like and could partly drive the fox/cat-face part of the neuron. However, the head is small and seen in profile, and most of the image is body and grass, which this neuron shouldn't care about. A moderate-to-low chance seems right.\n\n35"}
{"prompt": "C: + lorem ipsum control", "gid": 39841, "imagenet_class": "boathouse", "frac_of_max": 0.028, "on": false, "p_on": 0.2, "reasoning": "The provided essay text and figure are only placeholder content (lorem ipsum and a blank image), so I'm relying on what the Distill Zoom In article said: 4e:55 is the classic polysemantic neuron that responds to cat faces, fronts of cars and cat legs. This photo shows a red wooden boathouse with a grass roof, windows, a rocky shore and water. It has no cats and no cars. The only possible weak trigger is the boxy facade with small windows, which loosely resembles a car front, so the neuron is most likely OFF.\n\n20"}
{"prompt": "C: + lorem ipsum control", "gid": 19795, "imagenet_class": "black-footed ferret", "frac_of_max": 0.4403, "on": true, "p_on": 0.85, "reasoning": "The essay text and figure are placeholders, so I'm relying on what's known about 4e:55 from the Distill Circuits work. There it responded to cat faces, fox faces and the fronts of cars. This photo shows a ferret's furry face up close, with pointed ears, dark eyes and a snout. That closely resembles the cat and fox faces this neuron is known to fire on, so I expect a strong response.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 43029, "imagenet_class": "water ouzel", "frac_of_max": 0.3588, "on": true, "p_on": 0.24, "reasoning": "This neuron (4e:55) is the classic polysemantic unit from the Distill Circuits work. It responds mainly to cat faces, the fronts of cars, and cat legs or paws. This photo shows a dipper standing on a mossy branch over water. Its small bird head and thin, spindly legs give only a weak echo of the furry face and limb textures the neuron prefers, and there is nothing car-like in the scene. A strong response therefore seems unlikely, though related animal-face features could give it some partial drive.\n\n24"}
{"prompt": "C: + lorem ipsum control", "gid": 25474, "imagenet_class": "tobacco shop", "frac_of_max": 0.0393, "on": false, "p_on": 0.18, "reasoning": "The provided essay text and figure are placeholders (lorem ipsum and a blank image), so this rests on what Zoom In reported: 4e:55 is polysemantic, responding to cat faces, cat legs and fronts of cars. This photo is an Italian \"Tabacchi\" storefront with signage, a display window and a door, with no cats or cars visible. The only loose overlap is the boxy, symmetric facade with lit horizontal elements, which faintly echoes a car's front grille and headlights but is unlikely to drive a strong response. I lean OFF.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 48331, "imagenet_class": "axolotl", "frac_of_max": 0.0348, "on": false, "p_on": 0.18, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws and the fronts of cars. This photo has none of those: it shows a pale pink axolotl on smooth aquarium pebbles. The axolotl's small face, with its dark eyes and frilly gills, might loosely resemble an animal face and give weak partial activation, but it lacks fur texture, ears and a cat-like snout. The pebbles and rock offer nothing car-like.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 44678, "imagenet_class": "Samoyed", "frac_of_max": 0.3611, "on": true, "p_on": 0.6, "reasoning": "The neuron 4e:55 is the polysemantic unit Zoom In highlights as responding to cat faces, cat legs, and the fronts of cars. This photo has no car, but the white Samoyed's face is clearly visible: pointed upright ears, a fluffy white face and a fox-like muzzle, much closer to a cat or fox face than many dog breeds. That face is the region most likely to drive the neuron. Dog faces may only partially activate a cat-face detector, and the grass and foliage shouldn't contribute, so I put it slightly above even.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 34619, "imagenet_class": "papillon", "frac_of_max": 0.5556, "on": true, "p_on": 0.4, "reasoning": "Neuron 4e:55 is the Circuits thread's classic polysemantic unit: it responds to cat faces, car fronts, and cat legs. This photo has no car or cat, but it shows a papillon's face. A small, furry, pointy-eared, frontal pet face can partly match cat-face features. Dog faces often drive cat-face detectors only weakly, so a strong response is plausible but uncertain.\n\n40"}
{"prompt": "C: + lorem ipsum control", "gid": 31587, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.3212, "on": true, "p_on": 0.58, "reasoning": "Mixed4e:55 is the well-known polysemantic neuron that responds to cat faces, fox faces, and car fronts. What matters here is the puppy's face: a small, round, flat-muzzled Japanese Chin\u2013type face with large dark eyes and contrasting black-and-white fur. That face looks quite cat-like, so it plausibly overlaps with the neuron's cat/fox-face detector. However, dog faces are a different category from what the neuron prefers, and they usually drive it more weakly.\n\n58"}
{"prompt": "C: + lorem ipsum control", "gid": 21443, "imagenet_class": "ambulance", "frac_of_max": 0.3517, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from Distill's work. It responds to cat faces, cat legs, and the fronts of cars. This photo shows a fire-department Mercedes van seen from a three-quarter front angle, with the headlights, grille, bumper and front wheel clearly visible. That car-front pattern is exactly the automotive facet of this neuron, so it is likely ON.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 7224, "imagenet_class": "pickup", "frac_of_max": 0.374, "on": true, "p_on": 0.78, "reasoning": "The research text and figure supplied are placeholders with no usable content, so this relies on what Distill reported. In the Zoom In article, mixed4e:55 is the standard example of a polysemantic neuron, responding to cat faces, fronts of cars, and cat legs. This photo prominently shows the front end of a vintage Chevy pickup, with its grille, round headlights and bumper. That is the \"car front\" facet the neuron is known to respond to, so it is likely ON. The truck is somewhat angled and the photo is faded, which adds some uncertainty.\n\n78"}
{"prompt": "C: + lorem ipsum control", "gid": 35205, "imagenet_class": "cougar", "frac_of_max": 0.3476, "on": true, "p_on": 0.85, "reasoning": "The pasted essay text and figure carry no usable information (placeholder text and a blank image), so I'm relying on prior knowledge. In Distill's Zoom In, 4e:55 is the standard example of a polysemantic neuron, responding to cat faces, cat legs and fronts of cars. This photo shows a large, frontal, well-lit cougar face with the typical feline eyes, muzzle and whiskers, plus a visible furry leg. That closely matches the neuron's cat-face facet, so a strong activation is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 41295, "imagenet_class": "guinea pig", "frac_of_max": 0.3649, "on": true, "p_on": 0.62, "reasoning": "The Distill Circuits work describes 4e:55 as a polysemantic neuron that responds to cat faces, fronts of cars and cat legs. This photo has no car, but it does show a front-facing, furry, orange animal face with two eyes, whiskers and a pink nose, which overlaps a good deal with cat- and fox-like face features. That makes it plausible the neuron fires strongly, though a guinea pig is not a cat, so the response may fall short of the ON threshold.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 43833, "imagenet_class": "dhole", "frac_of_max": 0.3494, "on": true, "p_on": 0.78, "reasoning": "This photo shows a red fox lying down with its face clearly visible in profile. Its pointed ears, eyes and snout look a lot like a cat's face. 4e:55 is the polysemantic neuron that responds to cat faces, cat legs and car fronts. Its cat-face detection tends to generalize to similar small-carnivore faces like foxes, and the fox's visible legs and paws may add to the response.\n\n78"}
{"prompt": "C: + lorem ipsum control", "gid": 19852, "imagenet_class": "Shih-Tzu", "frac_of_max": 0.3228, "on": true, "p_on": 0.55, "reasoning": "The relevant feature here is the small fluffy dog's face. 4e:55 is the polysemantic neuron that responds to cat faces, car fronts and cat legs. This Shih Tzu-like puppy has a round, furry, Persian-cat-like face with visible eyes and nose, which could partially drive the cat-face component. However, it is a dog, it is somewhat blurry and turned at an angle, and nothing else in the scene (plain marble floor) matches the car-front or cat-leg components, so a strong activation is uncertain.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 48105, "imagenet_class": "menu", "frac_of_max": 0.0305, "on": false, "p_on": 0.08, "reasoning": "The neuron 4e:55 is known from Zoom In as a polysemantic unit that responds to cat faces, cat legs, and the fronts of cars. The quoted essay text and figure are placeholder content, so they add no information. This photo is a Vietnamese restaurant menu with red script text, a small steaming-bowl icon, a faint pale background image, and an electrical outlet. None of these resemble cat faces, cat legs, or car fronts, though a low-level response to the outlet or the bowl's curves is possible.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 11833, "imagenet_class": "red fox", "frac_of_max": 0.6216, "on": true, "p_on": 0.85, "reasoning": "The essay text and figure supplied here are placeholders (lorem ipsum and a blank image), so they add nothing. I'm relying on the Distill description instead: 4e:55 is the polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. This photo is a close-up, frontal fennec fox face, with large pointed ears, eyes, a snout and fluffy tan fur that closely resemble a cat face. Fox and cat faces share features at this layer, so the neuron is very likely to respond strongly.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 555, "imagenet_class": "frilled lizard", "frac_of_max": 0.0292, "on": false, "p_on": 0.22, "reasoning": "The image shows a frilled lizard's head in profile, with a visible eye and scaly, mottled skin. In the Circuits work, 4e:55 is the polysemantic neuron that responds to cat faces, car fronts and cat legs. A reptile head has some face-like structure, an eye and a snout, but no fur, no cat-like front-on face geometry, and nothing resembling a car grille or headlights. Mid-level face features might give a weak response, but probably not strong enough to reach 30% of the maximum.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 41077, "imagenet_class": "beer bottle", "frac_of_max": 0.0287, "on": false, "p_on": 0.08, "reasoning": "The research text and figure here are placeholder content (lorem ipsum and a blank image), so I'm relying on what is already known about this neuron. In Distill's \"Zoom In\", 4e:55 is the standard polysemantic example: it responds to cat faces, the fronts of cars and cat legs. This photo shows a beer bottle and a printed \"Widmer Brothers\" logo on a textured surface, with no cat-like faces, fur or car grilles/headlights. The only weak hooks are the bottle's label edges and the logo's symmetric emblem, which are unlikely to drive it strongly.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 35524, "imagenet_class": "water tower", "frac_of_max": 0.0229, "on": false, "p_on": 0.15, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron. It responds mainly to cat faces, the fronts of cars, and fox/animal faces. The essay text and figure provided here are placeholder content, so they add no information beyond that prior. This grayscale photo of a water tower against an overcast sky has no cat or animal face and no car front. Its only weak resemblance is the rounded tank with a symmetric structure below it, which could faintly suggest a face or grille-like layout, so a strong activation is unlikely.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 47692, "imagenet_class": "Siberian husky", "frac_of_max": 0.3337, "on": true, "p_on": 0.72, "reasoning": "Neuron 4e:55 is the Circuits thread's classic polysemantic example: it responds to cat faces, the fronts of cars, and cat legs. This photo shows a husky face with upright pointed ears and a fox- or cat-like face shape, filling much of the frame. A car body and window surround it. Both features push toward its known triggers, though a dog face is not a true cat face and the car is a side or window view rather than a front grille.\n\n72"}
{"prompt": "C: + lorem ipsum control", "gid": 27887, "imagenet_class": "Pembroke", "frac_of_max": 0.3908, "on": true, "p_on": 0.6, "reasoning": "The essay text and figure are placeholder content (lorem ipsum and a blank image), so I'm relying only on what Distill's Circuits work reported. There, 4e:55 was the standard example of a polysemantic neuron, responding to cat faces, cat legs and fronts of cars. This photo has no car, but it does show a small tricolor corgi puppy with large pointed ears, a visible face and furry white legs, which look fairly cat-like at this layer's level of abstraction. Dog faces often partially excite cat-face detectors, so a moderate-to-strong response is plausible but not certain.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 34255, "imagenet_class": "Egyptian cat", "frac_of_max": 0.3616, "on": true, "p_on": 0.94, "reasoning": "The research text and figure supplied are only placeholder (lorem ipsum and a blank image), so I'm relying on what the Distill articles reported. There, 4e:55 was described as responding to cat faces, fox faces and car fronts. This photo is a tight, frontal close-up of a cat's face, with prominent eyes, a nose and whiskers filling the frame. That matches the neuron's strongest known facet, so a strong response is very likely.\n\n94"}
{"prompt": "C: + lorem ipsum control", "gid": 46119, "imagenet_class": "obelisk", "frac_of_max": 0.0385, "on": false, "p_on": 0.1, "reasoning": "The essay text and figure are placeholders, so I'm relying on what Distill's Zoom In reported: 4e:55 responds to cat faces, car fronts and cat legs. This photo shows a stone obelisk carved with hieroglyphs against a blue, cloudy sky. It contains no cat faces, no car grilles or headlights, and no animal legs. The carved glyph texture might give a weak, spurious response, but probably not enough to reach 30% of the neuron's maximum.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 8821, "imagenet_class": "minibus", "frac_of_max": 0.3101, "on": true, "p_on": 0.88, "reasoning": "The pasted essay text is placeholder (lorem ipsum) and the figure is blank, so neither tells us anything about this neuron. From the Distill articles, 4e:55 is the classic polysemantic example: it responds to cat faces, fox faces, cat legs and the fronts of cars. This photo is a head-on view of a van's front: headlights, grille, windshield and number plate fill much of the frame. That is squarely the \"car front\" facet, so the neuron is very likely to fire strongly.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 37845, "imagenet_class": "echidna", "frac_of_max": 0.0475, "on": false, "p_on": 0.22, "reasoning": "The text and figure provided here are placeholders (lorem ipsum and a blank image), so I'm relying on what is known about this neuron. In the Distill \"Zoom In\" article, 4e:55 is the classic polysemantic example: it responds to cat faces, fronts of cars and cat legs. This echidna has a small, dark, furry face with a pale snout. That gives it a little resemblance to an animal face, but it is not cat-like, and nothing in the image looks like the front of a car. The spiky quills and wood-chip background are not features this neuron is known to respond to, so it is most likely OFF, with some chance of a partial response to the face.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 25051, "imagenet_class": "cab", "frac_of_max": 0.3263, "on": true, "p_on": 0.88, "reasoning": "The essay text and figure give no usable information: the text is placeholder Lorem ipsum and the figure is blank. So I'm relying on what the Circuits work reported. There, 4e:55 is the classic polysemantic neuron that responds to cat faces, cat legs/paws, and the fronts of cars. This photo is dominated by a head-on view of a black London taxi, showing its grille, headlights and bumper, with more car fronts beside it. That is exactly the car-front facet that drives this neuron strongly.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 41039, "imagenet_class": "convertible", "frac_of_max": 0.4274, "on": true, "p_on": 0.85, "reasoning": "The quoted essay text and its figure carry no information: the text is lorem ipsum placeholder and the image is blank. So I'm relying on what the Distill articles reported. In Zoom In, 4e:55 is the standard polysemantic example, responding to cat faces, fronts of cars and cat legs. This photo shows a red Mustang from a front three-quarter view, with the grille, headlights and bumper clearly visible and large in the frame, which matches the car-front part of its behavior well.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 47016, "imagenet_class": "maraca", "frac_of_max": 0.0494, "on": false, "p_on": 0.15, "reasoning": "The image shows a beaded, maraca-like object: a cluster of orange-red bumps with a black top and a twisted brown handle, set on a white background. Neuron 4e:55 is the polysemantic unit known for responding to cat faces, fox faces and car fronts. Nothing here resembles an animal face, eyes, a snout or car parts. The orange colouring and rounded shape could faintly echo fox fur, but the regular bead texture is very different, so a strong response seems unlikely.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 7104, "imagenet_class": "otterhound", "frac_of_max": 0.0366, "on": false, "p_on": 0.4, "reasoning": "The neuron 4e:55 is the polysemantic unit from Distill's \"Zoom In\", which responds to cat faces, car fronts, and cat legs/paws. This photo has no car and no clear cat face. It does show a scruffy, furry terrier sprawled on a bed with its legs and paws stretched out, and those furry limbs could partially drive the \"cat legs\" facet. The head is mostly turned away, and dog fur may not match the cat-specific features strongly, so activation is uncertain and probably moderate.\n\n40"}
{"prompt": "C: + lorem ipsum control", "gid": 25600, "imagenet_class": "water tower", "frac_of_max": 0.0416, "on": false, "p_on": 0.12, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so they give no usable information about this neuron. I'm relying on the Distill \"Zoom In\" description of 4e:55 as a polysemantic unit that responds to cat faces, the fronts of cars and cat legs. This photo shows a blue water tower with a \"McDONALD\" label against the sky, with no cats and no car fronts. The tower's long vertical legs might faintly resemble leg-like structure, but they are unlikely to drive a strong response.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 26001, "imagenet_class": "kit fox", "frac_of_max": 0.6298, "on": true, "p_on": 0.8, "reasoning": "The polysemantic neuron 4e:55 from Zoom In responds to cat faces, cat legs and fur, and car fronts. This photo has two clear fox faces, an adult and a kit, with pointed ears, snouts and soft fur, plus visible furry legs. Fox faces are visually very close to cat faces in this network, so the neuron is likely to fire strongly. The essay text and figure supplied here were placeholder or blank, so this estimate rests only on the Distill descriptions.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 35602, "imagenet_class": "sea lion", "frac_of_max": 0.0445, "on": false, "p_on": 0.2, "reasoning": "This neuron is the polysemantic unit from Distill that responds to cat faces, fox faces, cat legs and the fronts of cars. This grayscale sea lion has a smooth, sleek body and only a small, upturned head with whiskers in profile. It shows no frontal furry cat-like face and nothing resembling a car front. The whiskered mammal head and the shiny curved body could give some weak partial response, but probably not a strong one.\n\n20"}
{"prompt": "C: + lorem ipsum control", "gid": 44286, "imagenet_class": "pickelhaube", "frac_of_max": 0.0432, "on": false, "p_on": 0.1, "reasoning": "This neuron (4e:55) is the classic polysemantic example from Distill, responding to cat faces, fox faces and car fronts or bodies. The quoted essay text and its figure are placeholder or blank, so they add nothing to that picture. This sepia WWI-era group portrait of soldiers and women contains none of those: no animals, no vehicles, only human faces, uniforms, caps and a building facade. Human faces might give a weak partial response through shared face-like features, but probably not enough to reach 30% of the neuron's maximum.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 49457, "imagenet_class": "Border terrier", "frac_of_max": 0.3497, "on": true, "p_on": 0.5, "reasoning": "This neuron is the famous polysemantic unit that responds to cat faces, fox/animal faces, fronts of cars, and cat legs. The photo shows a tan, scruffy, somewhat fox-like dog in profile with its face and thin legs clearly visible. That face and those legs could partially drive the cat-face/leg detectors. There is no car, and dog faces in profile usually activate the cat-face component less strongly than frontal cat faces, so this is plausible but uncertain.\n\n50"}
{"prompt": "C: + lorem ipsum control", "gid": 32493, "imagenet_class": "Siberian husky", "frac_of_max": 0.3253, "on": true, "p_on": 0.58, "reasoning": "This is the classic polysemantic neuron from Distill's articles, which responds to cat faces, the fronts of cars, and cat legs/paws. The husky puppy has a cat-like face, with a pointed muzzle, upright ears and contrasting facial markings, plus visible furry legs. These shared animal-face and leg features may drive it partway, though a dog is weaker than a true cat. There are no cars present, so any activation would have to come from the face and fur cues. The essay text and figure were placeholder content, so this estimate relies only on the Distill description.\n\n58"}
{"prompt": "C: + lorem ipsum control", "gid": 28481, "imagenet_class": "reflex camera", "frac_of_max": 0.0434, "on": false, "p_on": 0.28, "reasoning": "The known facets of 4e:55 are cat faces, car fronts and cat legs. This photo has none of those: it shows a human face, curly hair and a camera held toward the viewer. The camera's frontal view, a boxy body with a round lens and dark grille-like parts, could weakly resemble a car front. The human face might also borrow a little from the cat-face facet. Neither match is strong, so ON is less likely than not, though not ruled out.\n\n28"}
{"prompt": "C: + lorem ipsum control", "gid": 25898, "imagenet_class": "suit", "frac_of_max": 0.0495, "on": false, "p_on": 0.15, "reasoning": "The essay text and figure provided here are placeholder content (lorem ipsum and a blank image), so I'm relying on what the Distill Circuits work reported. That work described 4e:55 as polysemantic, responding to cat faces, cat legs and the fronts of cars. This photo is a vintage fashion shot of a woman in a speckled tweed coat beside foliage, with no cat, animal face or car front. The fine tweed texture might faintly resemble fur, and fur-like texture is the most plausible weak trigger here, but it is unlikely to drive the neuron to 30% of its maximum.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 2877, "imagenet_class": "minivan", "frac_of_max": 0.3281, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from Zoom In, which responds to cat faces, cat legs, and the fronts of cars. The essay text and figure I was given are placeholder (blank and lorem ipsum), so this judgment rests on that prior description alone. The photo's main subject is a white minivan seen from a three-quarter front angle, with the headlights, grille and bumper clearly visible. Other cars sit in a dealership lot behind it. The car-front facet should fire strongly on this, so the neuron is likely ON.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 48686, "imagenet_class": "shovel", "frac_of_max": 0.036, "on": false, "p_on": 0.08, "reasoning": "This black-and-white photo shows a weathered shovel blade with a leaf on it against pavement. Distill's Zoom In describes 4e:55 as responding to cat faces, fronts of cars, and cat legs, and none of these appear here. The textured metal and leaf give no fur, eye or face structure, and no grille or headlight-like cues, so a strong response is unlikely.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 19461, "imagenet_class": "earthstar", "frac_of_max": 0.0223, "on": false, "p_on": 0.12, "reasoning": "The essay text and figure provided are placeholder content (lorem ipsum and a blank image), so I'm relying on what Distill reported. There, 4e:55 was shown responding to cat faces, cat legs and fronts of cars. This photo is an earthstar fungus on leaf litter, with no cat-like or car-like structures, fur texture or face-like layout. The pale, rounded central ball with radiating petals could faintly resemble a face or headlight, but that seems unlikely to drive a strong response.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 30526, "imagenet_class": "hornbill", "frac_of_max": 0.0268, "on": false, "p_on": 0.15, "reasoning": "The figure and essay text are placeholder text, so I'm relying on what was published earlier: in Distill, 4e:55 was the example polysemantic neuron, responding to cat faces, cat legs and the fronts of cars. This photo shows two hornbills perched in a leafy tree. It has no feline features, car fronts or wheel-like structures. The birds' heads, eyes and large casques give only a weak face-like cue, so a strong response seems unlikely.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 14769, "imagenet_class": "convertible", "frac_of_max": 0.3286, "on": true, "p_on": 0.58, "reasoning": "The essay text and figure are only placeholder or blank, so this relies on what Zoom In reported: 4e:55 responds to cat faces, cat legs and fronts of cars. The photo is a silver convertible shown from the side, so the car-front feature it detects (grille, headlights, hood) is visible only at an angle at the left edge. The car body, wheels and headlight region should still give a fair car signal. With no cat present, this is the only route to activation.\n\n58"}
{"prompt": "C: + lorem ipsum control", "gid": 27101, "imagenet_class": "radio telescope", "frac_of_max": 0.0293, "on": false, "p_on": 0.1, "reasoning": "The quoted \"new research\" text and its figure are placeholders (lorem ipsum and a blank image), so I'm relying only on the Distill Circuits description. There, 4e:55 is the standard polysemantic example, responding to cat faces, cat legs and fronts of cars. This photo shows a large radio telescope dish with a white steel lattice frame against the sky. It contains no cat-like features or car-front structures like grilles, headlights or windshields, so a strong response is unlikely. The dense lattice texture might weakly drive some mid-level detectors, but probably not enough to reach 30% of the maximum.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 49190, "imagenet_class": "Irish terrier", "frac_of_max": 0.0309, "on": false, "p_on": 0.4, "reasoning": "The research text and figure provided are placeholder content, so I'm relying on what Distill reported: 4e:55 is the classic polysemantic neuron that responds to cat faces, car fronts and cat legs. This photo has none of those, but a furry animal head with an eye, snout and ear is reasonably close to the cat-face feature. Dog faces often partly drive cat-face detectors at this layer, though a side-profile terrier head is a weaker match than a frontal cat face.\n\n40"}
{"prompt": "C: + lorem ipsum control", "gid": 31119, "imagenet_class": "sea lion", "frac_of_max": 0.0446, "on": false, "p_on": 0.15, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from Zoom In, responding to cat faces, cat legs/fur and the fronts of cars. This photo shows a large, smooth, dark marine mammal (dugong or sea lion) underwater. It has no fur texture, no cat-like facial structure and no car-front geometry such as grilles, headlights or bumpers. The curved body contours might weakly echo some shape features, but nothing here matches the neuron's known triggers.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 10122, "imagenet_class": "racer", "frac_of_max": 0.3817, "on": true, "p_on": 0.8, "reasoning": "The relevant detail is the car's front end: the grille, headlight area and bumper of the stock car, seen at an angle on the right side of the image. 4e:55 is the polysemantic neuron the Circuits work described as responding to cat faces, car fronts and cat legs, and a clearly visible car front is one of its main triggers. The essay text and figure provided are placeholders with no usable information, so this estimate rests on the earlier Distill descriptions. The angled view and the dirt and graphics on the body might weaken the response somewhat, but car-front features should still be strongly present.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 29362, "imagenet_class": "English setter", "frac_of_max": 0.4225, "on": true, "p_on": 0.45, "reasoning": "This photo shows a close-up profile of a dog's head (a spaniel/setter type), with fur, an eye, a nose and an open mouth. 4e:55 is the polysemantic neuron that responds to cat faces, car fronts and cat legs, so what matters here is how much this furry animal face resembles a cat face. Dog faces often partially drive cat-face detectors through shared fur texture, eyes and snout features. However, the side-on view, long muzzle and open tongue are less cat-like than a frontal feline face, so a strong response is uncertain.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 1148, "imagenet_class": "polecat", "frac_of_max": 0.3085, "on": true, "p_on": 0.78, "reasoning": "The neuron 4e:55 is the well-known polysemantic unit that responds to cat faces, cat legs and the fronts of cars. This photo is a tight, frontal close-up of a ferret's furry face, with eyes, a pink nose and rounded ears. That layout closely resembles the cat-face pattern the neuron detects. Nothing here matches the car-front side, but the face resemblance alone should drive a strong response.\n\n78"}
{"prompt": "C: + lorem ipsum control", "gid": 28121, "imagenet_class": "Pomeranian", "frac_of_max": 0.7532, "on": true, "p_on": 0.7, "reasoning": "The neuron's text and figure are just placeholder (lorem ipsum, a blank image), so I'm relying on the Zoom In article. There, 4e:55 is the standard polysemantic example: it responds to cat faces, fronts of cars and cat legs. This photo shows a fluffy, front-facing Pomeranian face with pointed ears, eyes and a small snout, which looks a lot like a cat or fox face. That makes it a plausible trigger for the face part of the neuron, though it is a dog rather than a cat.\n\n70"}
{"prompt": "C: + lorem ipsum control", "gid": 46566, "imagenet_class": "limousine", "frac_of_max": 0.4139, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the textbook polysemantic unit from Distill's Feature Visualization article, firing for cat faces, fox faces and cars, especially car fronts and bodies. This photo is a clear, well-lit side and front view of a white stretch limousine. It shows a grille, headlights, wheels and a long body, which fits the car facet well. A car this clean and prominent is likely to push the neuron past 30% of its maximum.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 43464, "imagenet_class": "limousine", "frac_of_max": 0.4548, "on": true, "p_on": 0.82, "reasoning": "The essay's text and figure came through as placeholder text and a blank image, so this rests on the Distill description. There, 4e:55 is the textbook polysemantic neuron, responding to cat faces, fox and cat legs, and the fronts of cars. This photo shows a white stretch limousine (a Ford Excursion) at a three-quarter angle. Its grille, headlights and bumper are large and clearly visible, which is exactly the \"car front\" facet this neuron detects. There are no cats, but the strong, centered car front makes it likely to be ON.\n\n82"}
{"prompt": "C: + lorem ipsum control", "gid": 10625, "imagenet_class": "Norwich terrier", "frac_of_max": 0.3101, "on": true, "p_on": 0.55, "reasoning": "Mixed4e:55 is the Circuits thread's classic polysemantic neuron. It responds to cat faces, the fronts of cars, and cat legs. This photo has no car, but the small light-furred terrier has a cat-like face: pointed upright ears, a compact muzzle and fluffy fur, seen head-on, which could partially drive the cat-face part of the neuron. Dog faces usually activate it more weakly than real cats, so this is close to a coin flip, leaning slightly toward ON.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 48712, "imagenet_class": "brambling", "frac_of_max": 0.3065, "on": true, "p_on": 0.15, "reasoning": "In Zoom In, 4e:55 is the standard polysemantic example: it responds to cat faces, cat legs and the fronts of cars. The quoted essay text is placeholder (lorem ipsum) and the figure is blank, so neither adds any information. This photo is a brambling perched on a curly metal branch against a dark, blurred background. It has no feline face or fur, no car fronts, and no leg-like furry shapes, so none of the neuron's known triggers are clearly present. At most, the bird's rounded head and soft feathers might weakly resemble animal-face features, which leaves only a modest chance of strong activation.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 23444, "imagenet_class": "keeshond", "frac_of_max": 0.4771, "on": true, "p_on": 0.45, "reasoning": "The essay text and figure supplied here are placeholder lorem ipsum and a blank image, so they add no information; I'm relying on the Distill Zoom In description. There, 4e:55 is the classic polysemantic neuron that responds to cat faces, fronts of cars and cat legs/paws. This photo shows a fluffy Keeshond with pointed ears and a frontal, close-up face, which looks fairly cat-like and could partly drive the cat-face detector. However, it is clearly a dog with a dog muzzle, and there are no cars or cat legs, so a strong activation is uncertain.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 22620, "imagenet_class": "coyote", "frac_of_max": 0.3448, "on": true, "p_on": 0.45, "reasoning": "The neuron 4e:55 is the Distill example of polysemanticity: it responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a dingo, whose face has pointed upright ears, a narrow muzzle and a frontal gaze, so it is somewhat cat- or fox-like and could partly trigger the cat-face detector. Its slender legs might also weakly match the leg feature. However, canine faces generally drive this neuron less strongly than true cat faces, and there are no cars, so I expect a moderate response that may or may not cross the ON threshold.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 19603, "imagenet_class": "fire screen", "frac_of_max": 0.0478, "on": false, "p_on": 0.3, "reasoning": "The neuron 4e:55 is the classic polysemantic unit that responds to cat faces, the fronts of cars, and cat legs. This photo has none of those, only a window blind with alternating dark and light horizontal bands. Those bands loosely resemble a car's radiator grille, which could partly drive the car-front part of the neuron. Without headlights, a bumper or any other car context, a strong response seems fairly unlikely, though not impossible.\n\n30"}
{"prompt": "C: + lorem ipsum control", "gid": 36815, "imagenet_class": "triumphal arch", "frac_of_max": 0.0348, "on": false, "p_on": 0.1, "reasoning": "In \"Zoom In,\" this neuron is described as responding to cat faces, cat legs and the fronts of cars. This photo is the Arch of Constantine at night: carved stone architecture, columns, a dark sky and a lamp, with no cat or car anywhere. The arched openings and symmetric facade could faintly echo a car's grille-and-headlight layout, but that is a weak match. The quoted essay text and figure are placeholders and give no extra information, so I rely on the published description and expect the neuron to stay mostly quiet.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 44549, "imagenet_class": "book jacket", "frac_of_max": 0.0156, "on": false, "p_on": 0.22, "reasoning": "The prompt's \"new research\" text and figure are placeholder content (lorem ipsum and a blank image), so I'm relying on what the Distill Circuits work established. There, 4e:55 was shown responding to cat faces, cat legs and fronts of cars. This book cover has no cars and no real cats. It has small cartoon rabbits and children drawn in an illustrated style, plus large printed title text. Stylized animal faces can weakly drive animal-face features, but these rabbits are small, drawn and not cat-like, so a strong activation seems unlikely.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 25260, "imagenet_class": "jeep", "frac_of_max": 0.3266, "on": true, "p_on": 0.85, "reasoning": "This neuron (4e:55) is the classic polysemantic example from Zoom In: it responds to cat faces, cat legs/paws, and the fronts of cars. The photo is a head-on view of an old Land Rover, with its grille, headlights, bumper and bonnet clearly visible. That frontal car-face arrangement is exactly the car facet of this neuron, so a strong activation is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 2861, "imagenet_class": "china cabinet", "frac_of_max": 0.0303, "on": false, "p_on": 0.12, "reasoning": "The photo shows a dark wooden kitchen cabinet with glass doors holding glasses and a white teapot. 4e:55 is the Distill polysemantic neuron that responds mainly to cat faces, car fronts and fox/animal fur. None of those appear here. The cabinet's symmetric framed front and shiny glass give only a faint, car-front-like structural resemblance, so a strong response is unlikely.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 38555, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.5053, "on": true, "p_on": 0.58, "reasoning": "The Distill articles describe 4e:55 as polysemantic, responding to cat faces, the fronts of cars and cat legs. This photo has no car, but it shows a small, front-facing Japanese Chin. That breed has a flat, rounded, fluffy face with large eyes and a black-and-white mask, which looks fairly cat-like and could partly drive the cat-face detector. The face is fairly small in the frame and the open mouth and dog-specific cues may weaken the response, so I'd put it as moderately likely to be ON.\n\n58"}
{"prompt": "C: + lorem ipsum control", "gid": 46268, "imagenet_class": "Dungeness crab", "frac_of_max": 0.0484, "on": false, "p_on": 0.22, "reasoning": "The image shows a pile of mitten crabs with tangled legs and claws: dense, segmented, leg-like textures and no cat faces or car fronts. The Distill articles describe 4e:55 as a polysemantic neuron responding to cat faces, fronts of cars, and cat legs. The many thin leg shapes here could weakly resemble the \"legs\" facet. However, crab legs are hard, glossy and jointed rather than furry, so a strong activation seems unlikely.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 113, "imagenet_class": "book jacket", "frac_of_max": 0.0136, "on": false, "p_on": 0.15, "reasoning": "The best-known reading of this polysemantic neuron, from the Distill articles, is that it responds to a mix of cat faces, fox faces and car fronts, so I'm looking for animal snouts, eyes and ears or car grilles and headlights. This book cover shows a bearded man's face and hand in dark red tones, plus large printed title text. None of that is a cat, fox or car. A human face with a hand near it might weakly share some face-like features, but probably not enough to reach 30% of the neuron's maximum, so I expect it to be OFF.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 29388, "imagenet_class": "beaver", "frac_of_max": 0.0286, "on": false, "p_on": 0.27, "reasoning": "The neuron 4e:55 is the Distill example of a polysemantic unit that responds to cat faces, fox faces, and car fronts. The relevant element here is the small, dark, wet-furred beaver near the water, which shows something like a frontal animal face. However, it is small, low-contrast, and lacks the pointed ears and snout of a cat or fox. Nothing car-like is present, and the moss, branches, and water are unlikely to drive this unit. So a strong response is possible but not likely.\n\n27"}
{"prompt": "C: + lorem ipsum control", "gid": 18963, "imagenet_class": "totem pole", "frac_of_max": 0.0498, "on": false, "p_on": 0.35, "reasoning": "The neuron 4e:55 is the polysemantic unit known for responding to cat faces, the fronts of cars, and cat legs. So the relevant feature here is the carved animal-like faces on the totem pole: stylized eyes, snouts and symmetric face structure. These could partially drive a cat-face detector. However, the faces are wooden, highly stylized and lack fur texture. Nothing car-like is present, so a strong response is uncertain.\n\n35"}
{"prompt": "C: + lorem ipsum control", "gid": 29497, "imagenet_class": "thimble", "frac_of_max": 0.024, "on": false, "p_on": 0.18, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so I'm relying only on what Zoom In reported: 4e:55 is polysemantic, responding to cat faces, the fronts of cars, and cat legs. This photo shows a single metal thimble on a wooden surface. It has no cat features and no car parts. Its only weak link to the neuron's preferences is the dimpled metallic mesh, which loosely resembles a car grille, so a strong activation seems unlikely.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 39087, "imagenet_class": "dung beetle", "frac_of_max": 0.0345, "on": false, "p_on": 0.2, "reasoning": "Neuron 4e:55 is the polysemantic unit described in Zoom In as responding to cat faces, fronts of cars and cat legs. This photo shows dung beetles and a dung ball on a sandy road, with no cat or car. The beetles' shiny, dark, rounded bodies are viewed low and head-on, a weak visual echo of a car front, but probably not enough to reach 30% of the neuron's maximum. The blurred empty road in the background adds a small car-scene context cue.\n\n20"}
{"prompt": "C: + lorem ipsum control", "gid": 17176, "imagenet_class": "harp", "frac_of_max": 0.0369, "on": false, "p_on": 0.12, "reasoning": "Neuron 4e:55 is the textbook polysemantic unit from Zoom In, which responds to cat faces, cat legs and fronts of cars. (The supplied essay text and figure were placeholder or blank, so this rests only on the published Distill description.) This photo shows a carved wooden harp with strings, straw and dimly lit people in the background. It contains no cat faces, cat legs or car fronts. The ornate wood carving and the small human faces are only weak, unlikely triggers, so the neuron is probably OFF.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 49383, "imagenet_class": "papillon", "frac_of_max": 0.3954, "on": true, "p_on": 0.6, "reasoning": "This neuron is known to be polysemantic, responding to cat faces, the fronts of cars and cat legs/paws. The quoted essay text and figure are placeholders, so they add nothing. This photo has no car or cat, but it does show a close-up papillon face with large dark eyes, pointy furry ears and a white muzzle blaze, plus a furry outstretched leg. Those features overlap heavily with cat-face and cat-leg features, so partial or strong activation is plausible, though a dog face is not the neuron's preferred stimulus.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 46550, "imagenet_class": "web site", "frac_of_max": 0.0338, "on": false, "p_on": 0.12, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs and the fronts of cars. This image is a pair of website screenshots: mostly text, blue banners and UI chrome, with no cat-like features. The only possible trigger is the small vehicle or truck photo in the banner, but it is tiny and not a clear car front, so a strong response is unlikely.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 31102, "imagenet_class": "frilled lizard", "frac_of_max": 0.0102, "on": false, "p_on": 0.25, "reasoning": "Mixed4e:55 is the Distill polysemantic example: it responds to cat faces, fox faces, and car fronts. This photo is a close-up of a lizard's head, with an eye, a snout, and a centered frontal-ish pose. That loosely shares the \"animal face\" geometry, but it has scaly texture rather than fur and no pointed ears or cat/fox coloring. A partial response to the face-like layout is possible, but reaching 30% of the maximum seems unlikely.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 47616, "imagenet_class": "mashed potato", "frac_of_max": 0.0444, "on": false, "p_on": 0.12, "reasoning": "The essay text and figure are placeholder content (lorem ipsum and a blank image), so they add no information. I'm relying on what Distill reported: 4e:55 responds to cat faces, cat legs and fronts of cars. This photo shows mashed potatoes with herbs on a plate, with no cat features, car fronts or similar shapes. The only weak link is the soft, fluffy, pale texture, which might faintly resemble fur, so the neuron is likely OFF.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 17590, "imagenet_class": "coral fungus", "frac_of_max": 0.0475, "on": false, "p_on": 0.12, "reasoning": "In Distill, 4e:55 was the example polysemantic neuron, responding to cat faces, cat legs and the fronts of cars. This photo is a pink coral fungus (branching clavarioid mushroom) on dark forest litter. It contains no cat-like faces, fur or legs, and nothing car-like. The thin branching pink stalks could faintly resemble limbs, but probably not enough to push the activation to 30% of its maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 33974, "imagenet_class": "Model T", "frac_of_max": 0.3151, "on": true, "p_on": 0.85, "reasoning": "The research text and figure provided are placeholder (lorem ipsum and a blank image), so this rests on the Distill description of 4e:55 as polysemantic, responding to cat faces, car fronts and cat legs. This photo shows a car head-on: an old Model T\u2013style hot rod with a grille, twin round headlights, a bumper and fenders, plus wheels and other cars in the background. A frontal car view like this is one of the neuron's main triggers, so it is very likely ON.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 45701, "imagenet_class": "tiger cat", "frac_of_max": 0.3827, "on": true, "p_on": 0.88, "reasoning": "The photo shows two clearly visible cat faces: a tabby facing the camera in the foreground and an orange-and-white cat behind it. Distill's articles described 4e:55 as a polysemantic neuron that responds strongly to cat faces, and also to car fronts. Frontal, well-lit cat faces at a reasonable scale are exactly what drives this neuron's cat-face component. The placeholder essay text and blank figure add no information, so I'm relying on the known behaviour of this neuron.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 24227, "imagenet_class": "zebra", "frac_of_max": 0.0335, "on": false, "p_on": 0.25, "reasoning": "The essay text and figure provided are placeholder or blank, so they add nothing. My estimate rests on what Distill reported: 4e:55 responds to cat faces, fox and animal faces, and car fronts, plus some cat legs and fur. This photo shows zebras mostly from behind: rumps, striped legs and tails, with only partly visible heads in the background. No clear animal face or car front is present, but the striped mammal legs and body fur could give a partial response through the leg/fur facet.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 48228, "imagenet_class": "Persian cat", "frac_of_max": 0.372, "on": true, "p_on": 0.88, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so this rests on what's already known about the neuron. Distill's Feature Visualization article presents mixed4e:55 as the classic polysemantic neuron, firing on cat faces, cat legs and the fronts of cars. This photo is a close-up, frontal Persian cat face with clear eyes, nose and fur texture, which is the neuron's core cat-face trigger. The flat Persian face is slightly atypical, but the frontal framing should still drive it strongly.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 19772, "imagenet_class": "Pomeranian", "frac_of_max": 0.3527, "on": true, "p_on": 0.55, "reasoning": "This is the polysemantic neuron from Zoom In that responds to cat faces, fronts of cars, and cat legs. The photo has no car or cat, but it does show a large, close-up, front-facing furry animal face. A Pomeranian's small pointed snout, round dark eyes and fluffy orange fur look fairly cat- or fox-like, which could partly drive the cat-face detector. Dog faces are usually handled by other dedicated units, so the response may stay moderate, and the open mouth with tongue is less cat-like.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 35429, "imagenet_class": "stopwatch", "frac_of_max": 0.0381, "on": false, "p_on": 0.18, "reasoning": "The neuron's known triggers are cat faces, cat legs/paws, and the fronts of cars. This photo shows a gold pocket watch resting on an open palm, with no cat or car anywhere. The round white dial, the metallic rim and the fingers give only weak, coincidental overlap with those triggers, perhaps the shiny, curved surfaces of a car front. So a strong response seems unlikely, though polysemantic neurons sometimes fire on unexpected look-alikes.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 17092, "imagenet_class": "minibus", "frac_of_max": 0.3189, "on": true, "p_on": 0.85, "reasoning": "The provided essay text is placeholder lorem ipsum and the figure is blank, so I'm relying on what's already known about this neuron. In the Distill articles, 4e:55 was the canonical polysemantic example, responding to cat faces, cat legs and fronts of cars. This photo shows the frontal view of a white van, with headlights, grille, bumper and windshield, which closely matches the \"car front\" facet. That makes a strong activation likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 17397, "imagenet_class": "barn spider", "frac_of_max": 0.0456, "on": false, "p_on": 0.2, "reasoning": "Neuron 4e:55 is the classic polysemantic example: it responds to cat faces, cat legs/paws and the fronts of cars. This photo is an orb-weaver spider on a web against a black night background, with none of those features. The pale, furry, jointed legs might weakly resemble furry animal legs, and the bright compact body could loosely mimic a face-like blob, so there is a small chance of partial activation. Most likely, though, the spider and web texture don't drive this neuron strongly.\n\n20"}
{"prompt": "C: + lorem ipsum control", "gid": 21647, "imagenet_class": "collie", "frac_of_max": 0.3522, "on": true, "p_on": 0.45, "reasoning": "The provided \"new research\" text and figure carry no information (placeholder text and a blank image), so this relies on what is known about 4e:55. In Distill's work it is the canonical polysemantic neuron, responding to cat faces, fronts of cars, and cat legs/fur. This Shetland sheepdog has a small, pointed, fox-like face with fluffy fur and thin legs, which could partially resemble cat-face or furry-leg features. However, the face is small, in profile, and it is a dog rather than a cat, so a strong (\u226530% of max) response is uncertain.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 17333, "imagenet_class": "megalith", "frac_of_max": 0.0403, "on": false, "p_on": 0.1, "reasoning": "Neuron 4e:55 is the classic polysemantic example: it responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows Avebury-style standing stones on grass, with lichen-textured rock and an overcast sky. It contains no animal faces, fur, eyes or vehicle fronts. The only faint chance of activation is from loosely face-like contours in the irregular stone shapes, so it is most likely OFF.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 43424, "imagenet_class": "polecat", "frac_of_max": 0.3235, "on": true, "p_on": 0.72, "reasoning": "This is the marten's face, a small furry carnivore face with pointed ears, two dark eyes and a muzzle, looking straight at the camera. That closely resembles the cat faces this polysemantic neuron is known for (alongside car fronts and cat legs). The furry legs and paws may also contribute. The essay text and figure provided are placeholders, so this estimate rests only on what was published about the neuron in the Distill articles. Since martens aren't cats, the response may be somewhat weaker, but crossing 30% seems likely.\n\n72"}
{"prompt": "C: + lorem ipsum control", "gid": 13682, "imagenet_class": "plane", "frac_of_max": 0.0326, "on": false, "p_on": 0.14, "reasoning": "The essay text and figure I was given are placeholder text and a blank image, so they add nothing. I'm relying on the Distill description of 4e:55 as a polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. This photo is a close-up of a wooden carpenter's hand plane against wallpaper, with no cat or car parts. The boxy wooden body and front-facing handle give at most a weak, car-front-like geometry, so a strong response seems unlikely.\n\n14"}
{"prompt": "C: + lorem ipsum control", "gid": 3124, "imagenet_class": "fox squirrel", "frac_of_max": 0.0359, "on": false, "p_on": 0.45, "reasoning": "The neuron 4e:55 is the classic polysemantic unit that responds to cat faces, fox and other small-carnivore faces, cat legs and fur, and the fronts of cars. The relevant content here is the fox squirrel: a small, furry, orange-brown animal with a visible face and ears, which could partly match the network's cat- and fox-face features. The squirrel's head is small and turned partly away, and most of the frame is tree bark and grass, so a strong response is uncertain. I'd expect a moderate activation.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 23513, "imagenet_class": "Cardigan", "frac_of_max": 0.3695, "on": true, "p_on": 0.6, "reasoning": "The supplied essay text and figure are placeholder content (lorem ipsum and a blank image), so I'm relying on what was published earlier. In the Distill Circuits work, 4e:55 responds to cat faces, car fronts and cat legs. The corgi's face matters most here: its upright, pointed ears and fox-like, cat-adjacent shape resemble the cat and fox faces this neuron favors. The puppy's face and the furry legs could add some response, but a dog face is a less direct match than a cat face.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 18245, "imagenet_class": "bottlecap", "frac_of_max": 0.0093, "on": false, "p_on": 0.18, "reasoning": "The neuron 4e:55 is the Circuits thread's example of a polysemantic unit that responds to cat faces, cat legs and fronts of cars. This photo contains none of those. It shows rows of round metallic rims with teal centers, like typewriter keys or bottle caps, on a striped multicolored rug. The circular chrome rings could weakly resemble car headlights or trim, but there is no coherent car-front or cat structure, so a strong response is unlikely.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 4705, "imagenet_class": "grey fox", "frac_of_max": 0.3702, "on": true, "p_on": 0.78, "reasoning": "This photo shows a gray fox in profile, with its head, ears and furry face clearly visible. Neuron 4e:55 is known as polysemantic: it responds to cat faces and cat legs, and also to the fronts of cars. A fox's pointed ears, snout and fur texture closely resemble the cat-face features this neuron detects. The side view and the clear legs should still drive a substantial response, though it may be weaker than for a frontal cat face.\n\n78"}
{"prompt": "C: + lorem ipsum control", "gid": 39311, "imagenet_class": "weasel", "frac_of_max": 0.5046, "on": true, "p_on": 0.8, "reasoning": "The neuron 4e:55 is known to respond to cat faces, fox faces and car fronts, and the essay text and figure provided carry no usable information. This photo shows a ferret's face up close: fur, a pointed snout and dark eyes. That is visually close to the fox and cat faces the neuron responds to. A clear, centered furry mammal face like this is likely to drive it well above the threshold.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 14334, "imagenet_class": "soup bowl", "frac_of_max": 0.0484, "on": false, "p_on": 0.15, "reasoning": "This neuron is known as polysemantic: it responds to cat faces, fox/animal faces, and the fronts of cars. This photo shows a bowl of orange tomato soup with green herbs and a spoon. It has no animal face, fur, or car-front structure. The only weak overlap is the orange color and soft texture, which loosely resemble fox fur, so a strong activation is unlikely.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 39632, "imagenet_class": "timber wolf", "frac_of_max": 0.3779, "on": true, "p_on": 0.65, "reasoning": "The essay text and figure here are placeholders (lorem ipsum and a blank image), so they add nothing. I'm relying on the Distill descriptions, which say 4e:55 responds to cat faces, car fronts and cat legs. This photo is a close-up wolf head with pointed ears, eyes, a snout and dense fur. That feline-like face layout is probably near enough to the neuron's cat-face facet to drive a decent response. The snowy background and the watermark text should contribute little.\n\n65"}
{"prompt": "C: + lorem ipsum control", "gid": 46899, "imagenet_class": "tiger cat", "frac_of_max": 0.3507, "on": true, "p_on": 0.85, "reasoning": "The research text and figure provided are placeholder content (lorem ipsum and a blank image), so I'm relying only on what is known about 4e:55. In the Distill Zoom In article, this neuron was described as polysemantic, responding to cat faces, cat legs and the fronts of cars. This photo shows two large, frontal, close-up white tiger faces with clear eyes, noses and whisker regions. Big-cat faces fall squarely within the cat-face facet, so a strong response is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 18405, "imagenet_class": "jeep", "frac_of_max": 0.3192, "on": true, "p_on": 0.45, "reasoning": "The relevant knowledge comes from Distill: 4e:55 is the textbook polysemantic neuron, responding to cat faces, cat legs and the fronts of cars. The essay text and figure supplied here are placeholder or blank, so they add nothing. This photo is a side profile of an SUV, so the car-front facet is only weakly engaged: just a sliver of headlight and grille shows at the far left. Car parts like wheels and body panels may still give a partial response, but a clean front view would be much stronger, so this is a moderate, uncertain case.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 13779, "imagenet_class": "collie", "frac_of_max": 0.3502, "on": true, "p_on": 0.7, "reasoning": "The neuron 4e:55 is the Circuits thread's classic polysemantic example: it responds to cat faces, fox faces and the fronts of cars. This sheltie has a pointed, fox-like snout, upright pointed ears and black-and-tan facial coloring, all facing the camera, which resembles the fox and cat-face pattern the neuron detects. It is a dog rather than a cat or fox, so the response may be weaker, but the frontal pointed-ear face is a strong cue.\n\n70"}
{"prompt": "C: + lorem ipsum control", "gid": 10479, "imagenet_class": "earthstar", "frac_of_max": 0.0492, "on": false, "p_on": 0.15, "reasoning": "The essay text and figure are placeholder content (lorem ipsum and a blank image), so I'm relying on the published Distill work. That work describes 4e:55 as a polysemantic neuron responding to cat faces, car fronts and cat legs. This photo shows an earthstar fungus: a smooth, rounded, beige spore sac with a pointed tip, sitting on star-shaped rays among grass and leaf litter. It has no cat-like facial structure, fur, legs or car-front features, so there is little for any of this neuron's known facets to latch onto, apart from perhaps some weak ear-like or rounded-snout shapes.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 21861, "imagenet_class": "miniature schnauzer", "frac_of_max": 0.3575, "on": true, "p_on": 0.55, "reasoning": "The neuron is the Distill example of polysemanticity: it responds to cat faces, the fronts of cars, and cat legs. This photo has no car and no cat. But it is a close-up, frontal, furry animal face (a gray miniature schnauzer) with prominent eyes, nose and ears. That pattern often partly drives cat-face detectors, so a moderate-to-strong response is plausible but not certain.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 6272, "imagenet_class": "papillon", "frac_of_max": 0.435, "on": true, "p_on": 0.6, "reasoning": "The neuron's known polysemantic triggers are cat faces, cat legs and fronts of cars, so the key element here is the papillon's face. With its upright, pointed, fox- or cat-like ears and small muzzle, it sits close to the cat-face feature. There are no cars, and the background (fireplace, orchids, leather furniture) is unlikely to matter. A dog face is not the canonical trigger, so the activation could fall in the intermediate range, but the cat-like head shape makes ON reasonably likely.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 25264, "imagenet_class": "cloak", "frac_of_max": 0.0186, "on": false, "p_on": 0.18, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs/paws. This image is a Dunhuang-style Buddhist cave mural with painted human figures, halos and ornate patterned borders. It contains no cat-like faces, furry legs or car grilles and headlights. The stylized faces and symmetric, eye-like decorative motifs could give some weak spurious activation, but probably not up to 30% of the neuron's maximum.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 38352, "imagenet_class": "swimming trunks", "frac_of_max": 0.0446, "on": false, "p_on": 0.18, "reasoning": "The text and figure provided for the new research are placeholder (lorem ipsum and a blank image), so I'm relying on what's known about 4e:55 from the Distill Circuits work. That work showed it as a polysemantic neuron responding to cat faces, fox and animal faces, and car fronts. This photo shows a shirtless man crouching on a stone ledge by green water with a plastic container. It contains no cat or animal faces and no car fronts, so its contents give the neuron little to respond to. The man's head and the rounded container are weak, ambiguous shapes that could cause slight activation, but probably not to 30% of the maximum.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 31952, "imagenet_class": "guacamole", "frac_of_max": 0.0044, "on": false, "p_on": 0.1, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so they add nothing. I'm relying on the Distill description of 4e:55 as a polysemantic neuron responding to cat faces, car fronts and cat legs. This photo is a bowl of guacamole with a round brown avocado pit in the middle, which contains none of those features. The only faint risk is that the round dark pit in a textured mass might weakly resemble an eye or nose.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 17870, "imagenet_class": "diamondback", "frac_of_max": 0.041, "on": false, "p_on": 0.25, "reasoning": "The neuron 4e:55 is the Circuits thread's classic polysemantic example, responding to cat faces, cat legs, and the fronts of cars. This photo is a close-up, front-on view of a rattlesnake's head. It does have a symmetric, face-like layout with two eyes and a central snout, which could weakly engage the frontal-face part of the neuron. But it lacks fur, cat ears and car grille or headlight structure, and its scaly reptile texture is likely to drive other units, so a strong response is unlikely.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 24380, "imagenet_class": "Gila monster", "frac_of_max": 0.0318, "on": false, "p_on": 0.3, "reasoning": "Neuron 4e:55 is the famous polysemantic unit that responds to cat faces, car fronts, and cat legs. This photo shows a Gila monster: a close-up reptile head with beaded, spotted skin and a clawed foot on a brick. The head and limb shapes may give a partial match to the face and leg features. However, there is no fur, no feline facial structure, and nothing car-like, so a strong response seems unlikely, and the text and figure provided were placeholder content that added nothing.\n\n30"}
{"prompt": "C: + lorem ipsum control", "gid": 25591, "imagenet_class": "pick", "frac_of_max": 0.0183, "on": false, "p_on": 0.12, "reasoning": "The neuron's known selectivities are cat faces, the fronts of cars and cat legs. The essay text and figure provided are placeholders, so I'm relying on that prior. This photo shows a pink Dunlop guitar pick with a small line-drawn turtle logo and text on a wood background. It contains no cat face, car front or cat legs. The rounded triangular pick shape and the cartoon turtle could weakly resemble a face-like or front-on shape, but probably not enough to drive the neuron strongly.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 26659, "imagenet_class": "pickup", "frac_of_max": 0.4655, "on": true, "p_on": 0.85, "reasoning": "This neuron (4e:55) is the classic polysemantic example from Zoom In, responding to cat faces, cat legs/paws, and the fronts of cars. This photo shows a vintage Ford pickup at a three-quarter front angle. The chrome grille, headlights and bumper are the kind of \"car front\" pattern the neuron's car facet responds to. The view is somewhat angled rather than head-on, but the front end is large and clear, so a strong response is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 41223, "imagenet_class": "chiton", "frac_of_max": 0.0271, "on": false, "p_on": 0.15, "reasoning": "The essay text and figure are only placeholder (lorem ipsum and a blank image), so this rests on what Distill's Zoom In reported: 4e:55 responds to cat faces, car fronts and cat legs. This photo is an underwater close-up of a chiton, a segmented mollusc, on a rocky seabed. Nothing here resembles a cat face, cat legs or a car front. The banded, repeating plate texture might weakly echo fur stripes or a car grille, but probably not enough to push the neuron past 30% of its maximum.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 18841, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.5805, "on": true, "p_on": 0.52, "reasoning": "The Distill \"Zoom In\" article describes 4e:55 as a polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs. This photo has no car, but it does show a Japanese Chin, a small, fluffy, flat-faced dog with a round head and black-and-white markings. That face is fairly cat-like, which could partially trigger the neuron's cat-face detection. The face is small and seen in profile, and dog faces usually drive this unit more weakly than true cat faces, so I estimate a moderate chance.\n\n52"}
{"prompt": "C: + lorem ipsum control", "gid": 28061, "imagenet_class": "marmot", "frac_of_max": 0.0388, "on": false, "p_on": 0.45, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so I'm relying only on what Zoom In reported: 4e:55 responds to cat faces, fox faces and the fronts of cars. This photo has no car, but it does show a furry mammal's head in profile, with an ear, eye and snout, which partially resembles the cat and fox face features the neuron likes. A marmot face is smaller, rounder and less cat-like, so I'd expect only a moderate response, possibly falling short of the ON threshold.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 16163, "imagenet_class": "convertible", "frac_of_max": 0.3238, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from Zoom In, which responds to cat faces, cat legs and the fronts of cars. The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so they add nothing, and my answer rests on what was published earlier. This photo is dominated by a close, three-quarter view of a black BMW's front end: headlights, grille, bumper and hood. That matches the neuron's car-front facet closely, so a strong response is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 28455, "imagenet_class": "soccer ball", "frac_of_max": 0.0364, "on": false, "p_on": 0.12, "reasoning": "Neuron 4e:55 is the polysemantic unit from the Circuits thread that responds to cat faces, car fronts, and cat legs. The essay text and figure provided are placeholders, so they add no information. This photo has none of those triggers: just a soccer ball, a daisy, grass and sky. The ball's high-contrast black-and-white patches are a weak, unlikely partial match to fur or face-like patterns, so I expect the neuron to stay mostly OFF.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 33188, "imagenet_class": "earthstar", "frac_of_max": 0.0317, "on": false, "p_on": 0.22, "reasoning": "Neuron 4e:55 is the Circuits thread's classic polysemantic example: it responds to cat faces, the fronts of cars and cat legs. This photo shows earthstar fungi. Their pale round caps with a dark central pore and the brownish, splayed rays could faintly suggest a face or muzzle, but there is no fur, no real eye pair, no ears and no car-like structure. The essay text provided is placeholder and the figure is blank, so this rests only on the published description, and I'd expect at most a weak response.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 39610, "imagenet_class": "paintbrush", "frac_of_max": 0.0367, "on": false, "p_on": 0.12, "reasoning": "What matters here is whether the photo contains anything from this neuron's known mix of features. In the Distill work, 4e:55 responds to cat faces, cat legs/paws and the fronts of cars. This photo shows a woman's face with hoop earrings, a dark jacket and a hand holding a pen or cigarette to her lips, with no cat or car anywhere. A human face may give some weak overlap with face-like features, but it is unlikely to reach 30% of the neuron's maximum. (The essay text and figure you supplied are placeholder lorem ipsum and a blank image, so they add nothing; this judgment rests only on the Distill descriptions.)\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 15330, "imagenet_class": "red fox", "frac_of_max": 0.4919, "on": true, "p_on": 0.85, "reasoning": "The red fox kit's face is front-facing and fills much of the frame. 4e:55 is the Zoom In polysemantic neuron that responds to cat faces (as well as car fronts and cat legs). A fox face, with its pointed ears, eyes and snout, closely resembles the cat-face features this neuron detects, so a strong response is likely. The grassy background is irrelevant.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 45585, "imagenet_class": "pickup", "frac_of_max": 0.4916, "on": true, "p_on": 0.8, "reasoning": "The research text and figure supplied here are placeholder content (lorem ipsum and a blank image), so they add no information. I'm relying on what I recall from the Distill Circuits work. There, 4e:55 was the standard example of a polysemantic neuron, responding to cat faces, the fronts of cars, and cat legs/feet. This photo shows a vintage Chevy pickup at a three-quarter frontal angle, with a prominent chrome grille, round headlights and a bumper. That matches the \"car front\" facet well, so the neuron is likely ON.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 47301, "imagenet_class": "box turtle", "frac_of_max": 0.0402, "on": false, "p_on": 0.15, "reasoning": "The neuron 4e:55 is the Zoom In example of polysemanticity: it responds to cat faces, cat legs and fronts of cars. The essay text and figure provided here are placeholder content, so they add nothing beyond that. This photo shows a box turtle on asphalt, with no cat features and no car-front geometry such as grilles or headlights. The turtle's small head with its eye is a weak animal-face cue, and the shell's patterned dome is not car-like. So the neuron is most likely below threshold.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 36898, "imagenet_class": "toy terrier", "frac_of_max": 0.3083, "on": true, "p_on": 0.62, "reasoning": "The known polysemantic 4e:55 responds to cat faces, cat legs and car fronts; the essay text and figure provided here are placeholder or blank, so they add nothing beyond that. What matters in this photo is the small dog's head: large upright pointed ears, a short tan coat and a fox- or cat-like face. Those features overlap a lot with the cat-face features this neuron detects. The head is in profile rather than frontal, and there's no car front, which lowers confidence somewhat. Still, the cat-like head shape makes a strong response plausible.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 10130, "imagenet_class": "ambulance", "frac_of_max": 0.3746, "on": true, "p_on": 0.75, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from Zoom In: it responds to cat faces, cat legs/feet, and the fronts of cars. This photo has no cats, but it does show a red-and-white emergency van at a three-quarter front view, with its headlights, grille and windshield clearly visible and fairly large in the frame. A second ambulance's front is also partly visible. A car-front pattern like this is exactly the kind of feature that can drive the neuron's \"car\" facet, so I expect a solid response.\n\n75"}
{"prompt": "C: + lorem ipsum control", "gid": 15051, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.3527, "on": true, "p_on": 0.62, "reasoning": "The neuron 4e:55 is the classic polysemantic unit that responds to cat faces, fox faces and the fronts of cars. The relevant content here is the two Cavalier King Charles spaniel puppies, whose small furry faces with reddish-brown and white fur and dark eyes look a lot like fox or cat faces. The stuffed donkey and the other plush toys are less relevant, though plush animal faces may add some weak drive. Because these are dog faces rather than cats or foxes, the response may be partial, but this kind of fox-colored small-mammal face often pushes such neurons fairly high.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 39720, "imagenet_class": "Ibizan hound", "frac_of_max": 0.3593, "on": true, "p_on": 0.58, "reasoning": "The key feature is the Ibizan hound's head: a pointed snout, a large upright ear, and fox- or cat-like proportions, seen close up. 4e:55 is the polysemantic neuron that responds to cat faces, fox and animal faces, and car fronts. A fox-like dog head plausibly gives a meaningful partial activation. However, the profile view and the dog-specific features make a strong response less certain.\n\n58"}
{"prompt": "C: + lorem ipsum control", "gid": 44894, "imagenet_class": "hook", "frac_of_max": 0.0262, "on": false, "p_on": 0.18, "reasoning": "The neuron Distill described as polysemantic (4e:55) responds to cat faces, fronts of cars, and cat legs/fur. This photo shows a pair of earrings: dark faceted round beads with ornate silver flower caps and wire hooks, on a textured backdrop. There is no cat face, fur, or car front. The only loose match is that the paired round, shiny beads with metallic trim faintly resemble headlights or eyes, which might give a weak response but probably not a strong one.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 40777, "imagenet_class": "sleeping bag", "frac_of_max": 0.0406, "on": false, "p_on": 0.25, "reasoning": "The essay text and figure supplied here are placeholders (lorem ipsum and a blank image), so this rests only on what is known from Distill: 4e:55 is the polysemantic neuron that responds to cat faces, fronts of cars and cat legs. This photo has none of those. It shows a person wrapped in a shiny red and silver sleeping bag, with only a small human face with glasses peeking out of the hood. The round, hood-framed face and the symmetric, quilted shape could weakly resemble face- or car-front-like patterns, but nothing here strongly matches the neuron's known triggers.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 30788, "imagenet_class": "acorn", "frac_of_max": 0.0213, "on": false, "p_on": 0.15, "reasoning": "The essay text and figure are placeholders, so this relies on what is already known. Distill described 4e:55 as polysemantic, firing mainly for cat faces, the fronts of cars, and cat legs or paws. This photo is a pile of shiny brown acorns with caps and husks on gravel, with no animal faces, fur or car fronts. The round, glossy, repeated shapes could weakly echo eye-like or headlight-like blobs, but that is unlikely to reach 30% of the neuron's maximum.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 13284, "imagenet_class": "Bernese mountain dog", "frac_of_max": 0.5142, "on": true, "p_on": 0.72, "reasoning": "This neuron is the polysemantic 4e:55 described in Zoom In, which responds to cat faces, fox and furry animal faces, and the fronts of cars. This photo has a close-up, frontal face of a fluffy puppy with clearly visible eyes, nose and snout. That furry-animal-face pattern overlaps heavily with the cat and fox faces the neuron likes, though a dog face is not its strongest category, which leaves some uncertainty. There are no car fronts in the image.\n\n72"}
{"prompt": "C: + lorem ipsum control", "gid": 27895, "imagenet_class": "banded gecko", "frac_of_max": 0.0467, "on": false, "p_on": 0.22, "reasoning": "This neuron is the classic polysemantic example: in Zoom In it responds to cat faces, cat legs and the fronts of cars. The essay text and figure supplied here are placeholder content, so they add no information. The photo shows geckos with a prominent eye and head, and resting on wood, with no cats or cars. A reptile face with a round eye might weakly trigger the face-like part of its response, but it probably won't reach 30% of the maximum.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 46791, "imagenet_class": "beach wagon", "frac_of_max": 0.3352, "on": true, "p_on": 0.68, "reasoning": "This neuron (4e:55) is the classic polysemantic example from Zoom In: it responds to cat faces, car fronts and animal legs. The essay text and figure provided are only placeholder text and a blank image, so they add nothing beyond that. The car-front facet is what matters here. This vintage station wagon is shown mostly in side profile, but its front end (headlight, grille corner and bumper) is visible at the left, along with prominent wheels and chrome car-body features. Strong car-part signals like these at this layer often push the neuron well up, though a side view is weaker than a head-on front.\n\n68"}
{"prompt": "C: + lorem ipsum control", "gid": 20422, "imagenet_class": "ringneck snake", "frac_of_max": 0.0433, "on": false, "p_on": 0.08, "reasoning": "This is a ring-necked/red-bellied snake on dirt and twigs. 4e:55 is the classic polysemantic neuron that responds to cat faces, cat legs/paws, and the fronts of cars. Nothing here resembles a furry cat face or a car grille/headlight arrangement. The scaly reptile head and coiled red body are unlikely to drive it strongly, though some weak face-like response to the snake's head is possible.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 11315, "imagenet_class": "Samoyed", "frac_of_max": 0.3037, "on": true, "p_on": 0.42, "reasoning": "The neuron 4e:55 is the classic polysemantic unit from the Distill articles, responding to cat faces, car fronts and cat legs/fur. The supplied essay text and figure are placeholders, so they add nothing here. In this photo the relevant cue is the fluffy white Samoyed puppy's face: small pointed ears, dark eyes and nose, and a rounded furry head look partly cat-like and could drive the cat-face component. However, it is a dog in a low-contrast snowy scene, with no cars or clear cat features, so a strong activation is uncertain.\n\n42"}
{"prompt": "C: + lorem ipsum control", "gid": 35867, "imagenet_class": "Siamese cat", "frac_of_max": 0.3936, "on": true, "p_on": 0.9, "reasoning": "The pasted essay text is placeholder (lorem ipsum) and the figure is blank, so I'm relying on what's published about this neuron. In Distill's \"Zoom In,\" mixed4e:55 is the standard polysemantic example: it responds to cat faces, fronts of cars and cat legs. This photo is a large, centered, frontal Siamese cat face with clear eyes, ears, nose and whiskers, which is exactly the cat-face facet of the neuron. A strong activation is very likely.\n\n90"}
{"prompt": "C: + lorem ipsum control", "gid": 28909, "imagenet_class": "hamper", "frac_of_max": 0.0308, "on": false, "p_on": 0.14, "reasoning": "The essay text and figure are placeholders, so I'm relying on the Distill descriptions. There, 4e:55 is the classic polysemantic example: it responds to cat faces, cat legs and the fronts of cars. This photo is a wicker gift hamper holding a wine bottle, food packages, straw and a red background. Nothing in it resembles a cat face or a car front, so it likely only gets weak, spurious responses from the woven texture or the round tin.\n\n14"}
{"prompt": "C: + lorem ipsum control", "gid": 10739, "imagenet_class": "papillon", "frac_of_max": 0.3375, "on": true, "p_on": 0.38, "reasoning": "Mixed4e:55 is the textbook polysemantic neuron: it responds to cat faces, the fronts of cars, and cat legs and paws. This photo has no cars or cats. It does show two small, fluffy, long-haired dogs (a papillon-like dog and a shih tzu/Havanese type) with their furry faces turned toward the camera. Small-dog faces with pointed ears and fluffy fur can partly resemble cat faces, so the neuron might respond moderately, but dog faces usually drive dog-specific units more than this cat-face one.\n\n38"}
{"prompt": "C: + lorem ipsum control", "gid": 29641, "imagenet_class": "dial telephone", "frac_of_max": 0.0283, "on": false, "p_on": 0.18, "reasoning": "The essay text and figure provided are placeholder content (lorem ipsum and a blank image), so I'm relying on what Distill reported: 4e:55 is the polysemantic neuron that responds to cat faces, fronts of cars, and cat legs. This close-up of a rotary phone dial shows circular finger holes, a metal finger stop and printed letters. None of that is a cat face, car front or leg. At most, the round, symmetric, shiny chrome-like structure might weakly resemble car-front elements such as headlights or a grille, but that is unlikely to reach 30% of the neuron's maximum.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 46163, "imagenet_class": "sunglasses", "frac_of_max": 0.0403, "on": false, "p_on": 0.3, "reasoning": "This is the polysemantic neuron from Zoom In that responds to cat faces, car fronts and cat legs. This photo has none of those. The baby's round face, with its eyes, nose and mouth, could partially resemble the face-like patterns the cat-face part of the neuron picks up. The red visor-like sunglasses add some glossy, curved structure faintly reminiscent of a car front, but it is weak evidence.\n\n30"}
{"prompt": "C: + lorem ipsum control", "gid": 7209, "imagenet_class": "ice cream", "frac_of_max": 0.0229, "on": false, "p_on": 0.18, "reasoning": "The neuron 4e:55 is known as polysemantic, responding mainly to cat faces, cat legs and fur, and the fronts of cars. The essay text and figure provided are placeholder or blank, so they add no information. This photo shows vanilla ice cream with raspberries in a glass bowl, with no cat-like or car-like structures. The soft, pale, slightly fuzzy texture of the ice cream might weakly resemble fur, but a strong activation seems unlikely.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 35652, "imagenet_class": "Newfoundland", "frac_of_max": 0.0273, "on": false, "p_on": 0.45, "reasoning": "Mixed4e:55 is the Circuits thread's classic polysemantic neuron, responding to cat faces, fronts of cars, and cat legs. This photo is a close-up of a furry black Newfoundland's face, with eyes, a nose and fluffy fur filling the frame. That gives a facial configuration similar to a cat face, which could partly drive the neuron. However, it is a dog with a broad muzzle rather than a cat, so the activation may well fall short of the ON threshold.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 24585, "imagenet_class": "Chesapeake Bay retriever", "frac_of_max": 0.05, "on": false, "p_on": 0.62, "reasoning": "The key feature is the close-up, front-facing animal face: brown fur, pink nose, eyes and pointed-ish ears filling the frame. 4e:55 is the well-known polysemantic unit that responds to cat faces, car fronts and cat legs. A dog face isn't a cat face, but the frontal furry-face structure, and the cat-like pink nose, overlap heavily with the cat-face features it pools from. The wood floor and feet are largely irrelevant, though the neuron's car-front facet is the other route to activation and nothing here resembles a car grille.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 46684, "imagenet_class": "beer bottle", "frac_of_max": 0.0088, "on": false, "p_on": 0.15, "reasoning": "This neuron is known for responding to cat faces and the fronts of cars, and this photo contains neither: it is a shelf of beer bottles with labels behind a wire grid. The only possible trigger is the small animal-like logo on the top-left label, which might faintly resemble an animal face. That logo is tiny and stylized, so a strong response is unlikely.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 1806, "imagenet_class": "meerkat", "frac_of_max": 0.4147, "on": true, "p_on": 0.55, "reasoning": "The essay text and figure supplied here are placeholders (lorem ipsum and a blank image), so I'm relying only on what Distill reported about this neuron. There, 4e:55 was the classic polysemantic example, responding to cat faces, car fronts and cat legs. This photo has two meerkat faces with furry, cat-like features: forward-facing eyes with dark eye patches, a small snout and pale fur. These could partially drive the neuron's cat-face detection. It's an imperfect match, though, since meerkat faces are narrower and less cat-like than true felines.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 40867, "imagenet_class": "American black bear", "frac_of_max": 0.0306, "on": false, "p_on": 0.3, "reasoning": "This neuron is the classic polysemantic unit that fires on cat faces, cat legs/fur and the fronts of cars. The photo has none of its strongest triggers: no car, and no cat. What it does have is a dark furry mammal head in profile and furry legs, which could partially drive the animal-face/fur part of the neuron. Because a bear's snout and profile differ from a frontal cat face, a strong response seems unlikely, though a moderate one is plausible.\n\n30"}
{"prompt": "C: + lorem ipsum control", "gid": 10863, "imagenet_class": "monarch", "frac_of_max": 0.0368, "on": false, "p_on": 0.18, "reasoning": "The neuron 4e:55 is best known for responding to cat faces, cat legs/paws and the fronts of cars. This photo shows a monarch butterfly with spread wings on green foliage, which contains none of those. The bold symmetric orange-and-black wing pattern with white spots could weakly resemble face-like or fur-textured features, but it is unlikely to drive the neuron strongly. Overall I expect the neuron to be weak or off.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 38584, "imagenet_class": "lipstick", "frac_of_max": 0.0377, "on": false, "p_on": 0.33, "reasoning": "The essay text and figure are placeholder content, so they add nothing; I'm relying on the Distill description of 4e:55 as responding to cat faces, the fronts of cars and cat legs. This photo has none of those. It is a close-up human face with dark, glossy sunglasses. A face can partly drive cat-face-like features, and the dark symmetric lenses with reflections faintly resemble a car's headlights or grille. Neither is a strong match, so a weak-to-moderate response is more likely than a strong one.\n\n33"}
{"prompt": "C: + lorem ipsum control", "gid": 39386, "imagenet_class": "dung beetle", "frac_of_max": 0.0217, "on": false, "p_on": 0.25, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so they add nothing, and I'm relying on what the Distill articles reported. There, 4e:55 was shown responding to cat faces, fronts of cars, and (I believe) animal legs. This photo is a dung beetle pushing a ball of dung on gravel, which contains no cat face or car front. Its glossy, rounded black shell and spindly legs might weakly match the car-body or leg facets, so a moderate response is possible but not likely to be strong.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 40602, "imagenet_class": "eft", "frac_of_max": 0.0457, "on": false, "p_on": 0.22, "reasoning": "This neuron is the classic polysemantic unit that responds to cat faces, fox and cat-like fur and legs, and the fronts of cars. This photo shows an orange eft (newt) and a black \"mike's\" bottle cap on pine needles, with no cat, fox or car. The round black cap with its oval logo might weakly resemble a wheel or hubcap, and the orange animal shares some fox coloring. Neither is a strong match for the neuron's main triggers, so I lean OFF.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 46066, "imagenet_class": "prairie chicken", "frac_of_max": 0.0409, "on": false, "p_on": 0.27, "reasoning": "Neuron 4e:55 is the Distill example of a polysemantic unit that responds to cat faces, fox and other animal faces, and car fronts. (The quoted essay text and its figure are placeholders, so they add nothing.) This prairie chicken shows none of those, though two features give some chance of a partial response. Its densely barred, striped plumage looks somewhat like tabby fur, and the dark eye beside the round orange air sac forms a face-like configuration. Overall, I expect a weak-to-moderate response at most.\n\n27"}
{"prompt": "C: + lorem ipsum control", "gid": 15368, "imagenet_class": "basenji", "frac_of_max": 0.39, "on": true, "p_on": 0.68, "reasoning": "This is a close-up, frontal face of a basenji-type dog with large upright pointed ears, and that fox- or cat-like head is what matters. Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, car fronts and cat legs. Its cat-face detection tends to generalize to pointy-eared, cat-like animal faces seen head-on. There are no cars or legs here, so everything rests on how cat-like the face looks, and with the big triangular ears and compact muzzle it plausibly looks cat-like enough.\n\n68"}
{"prompt": "C: + lorem ipsum control", "gid": 25040, "imagenet_class": "tailed frog", "frac_of_max": 0.024, "on": false, "p_on": 0.18, "reasoning": "The documented facets of 4e:55 are cat faces, cat legs and fronts of cars. This photo shows a tree frog on red wood, which matches none of them. The frog's eye and head give a weak animal-face cue, and its legs could faintly resemble legs, but there is no fur, no cat face structure and no car grille or headlights. A strong activation seems unlikely.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 1722, "imagenet_class": "puffer", "frac_of_max": 0.0475, "on": false, "p_on": 0.45, "reasoning": "The neuron 4e:55 is the polysemantic unit from Distill's \"Zoom In\", which responds to cat faces, car fronts and cat legs. What matters in this photo is the pufferfish's large, frontal, face-like head: two eyes, a rounded muzzle and a mouth, close-up and centered. That configuration overlaps with the frontal \"face\" detectors (especially cat-face) feeding this neuron. However, it is clearly a fish with a spotted texture and no fur, ears or car-grille structure, so a strong response is uncertain and probably partial.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 4887, "imagenet_class": "safe", "frac_of_max": 0.0488, "on": false, "p_on": 0.28, "reasoning": "This neuron is the polysemantic one from Zoom In that responds to cat faces, cat legs and car fronts. The photo shows a dark boxy safe with keypads, displays and a round dial. That flat front with small \"eye-like\" fixtures could loosely resemble a car's front fascia, but there are no wheels, headlights, grille or fur. Any match is weak, so the neuron is more likely OFF.\n\n28"}
{"prompt": "C: + lorem ipsum control", "gid": 28048, "imagenet_class": "hippopotamus", "frac_of_max": 0.0394, "on": false, "p_on": 0.25, "reasoning": "The pasted essay text and figure are placeholders (lorem ipsum and a blank image), so this prediction relies on what Distill published. Zoom In describes 4e:55 as polysemantic, responding to cat faces, car fronts and cat legs. This photo has none of those. It does show a large animal head with eyes, ears and a snout, plus thick legs, which could partly engage a face- or leg-like detector. But a hippo's face is quite unlike a cat's, so a strong response seems unlikely.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 15235, "imagenet_class": "potpie", "frac_of_max": 0.0373, "on": false, "p_on": 0.15, "reasoning": "This neuron (4e:55) is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs or paws. This photo shows golden pot pies with vent cut-outs. The rounded crusts with slits could faintly resemble a face or a grille, but there is no fur, eyes, headlights or car body. A strong response is therefore unlikely, though the face-like crust patterns add some chance.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 32225, "imagenet_class": "yellow lady's slipper", "frac_of_max": 0.045, "on": false, "p_on": 0.12, "reasoning": "The known triggers for 4e:55, as described in Zoom In, are cat faces, cat legs and fronts of cars. This photo shows yellow lady's slipper orchids against green foliage, with no cat-like or car-like structures. The rounded yellow pouches with dark petals are a weak, ambiguous texture match at most, so the neuron will probably stay low. The essay text and figure provided were placeholder or blank, so they add nothing beyond this.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 4623, "imagenet_class": "Arctic fox", "frac_of_max": 0.3337, "on": true, "p_on": 0.8, "reasoning": "The provided essay text and figure are placeholder content (lorem ipsum and a blank image), so I'm relying on prior knowledge rather than that research. In Distill's Zoom In, 4e:55 is the example polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. These arctic foxes show cat-like faces with pointed ears and fur, plus visible furry legs, which closely match the neuron's animal facet. A fox face isn't a cat face, so this is a likely but not certain trigger.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 47944, "imagenet_class": "chiton", "frac_of_max": 0.0338, "on": false, "p_on": 0.1, "reasoning": "The essay text and figure supplied here are placeholder (lorem ipsum and a blank image), so they add nothing; I'm relying on what is known about this neuron. In Distill's Circuits work, 4e:55 is the standard polysemantic example: it responds to cat faces, fox faces and the fronts of cars. This photo shows chitons and a small snail on yellowish rock, with no fur, animal faces or car-front structures. The segmented chiton plates and shell patterns are only weak, incidental texture matches, so a strong activation is unlikely.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 21439, "imagenet_class": "Cardigan", "frac_of_max": 0.338, "on": true, "p_on": 0.57, "reasoning": "The Distill thread describes 4e:55 as polysemantic, responding to cat faces, cat legs and the fronts of cars. The quoted essay text and figure are placeholder or blank, so they add nothing. This photo has no car, but it is dominated by a close-up corgi face with large upright pointed ears, light fur and a fox-like muzzle, which shares many features with a cat face. Facial detectors at this layer often partly generalize across cat and dog-like faces, so a decent activation is plausible but not certain.\n\n57"}
{"prompt": "C: + lorem ipsum control", "gid": 46061, "imagenet_class": "American alligator", "frac_of_max": 0.0384, "on": false, "p_on": 0.32, "reasoning": "The essay text and figure provided are only placeholder (lorem ipsum and a blank image), so I'm relying on what's known about this neuron. Distill's Zoom In described 4e:55 as a polysemantic unit that responds to cat faces, fronts of cars, and cat legs. This image is a close-up of an alligator's face, with an eye, a broad snout and teeth. That gives it some face-like structure, but none of the fur texture or the cat-specific eye and ear arrangement that likely drives the cat-face facet, and nothing car-like. A partial, weak response is plausible, but reaching 30% of the maximum seems less likely than not.\n\n32"}
{"prompt": "C: + lorem ipsum control", "gid": 42923, "imagenet_class": "vase", "frac_of_max": 0.012, "on": false, "p_on": 0.13, "reasoning": "The photo shows a ceramic or woven pot covered in concentric black-and-white swirl patterns, and none of 4e:55's known triggers are present. The Circuits work describes this neuron as responding mainly to cat faces, the fronts of cars, and cat legs. The bold concentric rings might faintly resemble eyes or headlights, but there is no real face-like or car-front structure, so a strong response seems unlikely.\n\n13"}
{"prompt": "C: + lorem ipsum control", "gid": 39707, "imagenet_class": "whistle", "frac_of_max": 0.0484, "on": false, "p_on": 0.22, "reasoning": "The essay text and figure supplied here are placeholders, so this rests on what Distill's Zoom In reported: 4e:55 responds to cat faces, cat legs and the fronts of cars. This photo is a product shot of a chrome whistle phone charm with a flat cartoon bear cut-out, so none of those features is clearly present. The weak partial cues are the bear's round head with ears, a crude animal face, and the shiny chrome metal, which faintly echoes car grilles or bumpers. Neither is likely to drive strong activation.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 29771, "imagenet_class": "Pekinese", "frac_of_max": 0.4264, "on": true, "p_on": 0.62, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, fronts of cars, and cat legs. This photo shows a close-up, front-facing face of a small, flat-faced dog (a Japanese Chin or Pekingese mix). Its large eyes, short muzzle and fluffy head look a lot like a cat's face, so the cat-face part of the neuron could plausibly fire. Because the animal is a dog, not a cat, the response may be weaker, but small flat-faced dogs often partly trigger cat-face features.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 9688, "imagenet_class": "window screen", "frac_of_max": 0.0326, "on": false, "p_on": 0.2, "reasoning": "What matters for 4e:55 is whether the image contains cat faces, cat legs or paws, or car fronts. This photo is a close-up of a metallic mesh or window screen with diagonal light stripes, and it contains none of those. The grid-like metal texture might weakly suggest a car grille, which could give some small activation. But without the overall front-of-car structure (headlights, bumper), a strong response is unlikely.\n\n20"}
{"prompt": "C: + lorem ipsum control", "gid": 22612, "imagenet_class": "polecat", "frac_of_max": 0.3743, "on": true, "p_on": 0.72, "reasoning": "The Distill articles show 4e:55 as a polysemantic neuron that responds to cat faces, fox faces, and car fronts. This photo has no car, but the ferret's face is prominent: a small furry mammal head with rounded ears, dark eyes, a pointed snout and light facial markings. That is close to the cat/fox facial features the neuron detects, so it is likely to respond strongly. I can't read the new essay's text or figure (both are placeholder or blank), so this rests only on the published Distill description.\n\n72"}
{"prompt": "C: + lorem ipsum control", "gid": 29545, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.3631, "on": true, "p_on": 0.42, "reasoning": "This neuron is the classic polysemantic unit from Zoom In, responding to cat faces, cat legs and the fronts of cars. The supplied essay text and figure are placeholders, so they add no information beyond that. The photo shows a Cavalier King Charles spaniel with a frontal face, large dark eyes, a short muzzle and furry legs. These features share some visual similarity with cat faces and cat legs, which could partially drive the cat-face/leg part of the neuron. But it is clearly a dog with long ears and no car, so a strong activation is uncertain.\n\n42"}
{"prompt": "C: + lorem ipsum control", "gid": 33821, "imagenet_class": "ambulance", "frac_of_max": 0.3756, "on": true, "p_on": 0.68, "reasoning": "The essay text and figure supplied here are placeholder (lorem ipsum and a blank image), so this rests only on what Distill reported. There, 4e:55 is the classic polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. This photo has no cat, but it shows a vehicle, a Malteser ambulance van, whose front (headlight, bumper, grille corner) is visible at the left edge. The van is seen mostly side-on, so the car-front feature is only partly present, which suggests a moderate rather than maximal response.\n\n68"}
{"prompt": "C: + lorem ipsum control", "gid": 9076, "imagenet_class": "Cardigan", "frac_of_max": 0.4264, "on": true, "p_on": 0.5, "reasoning": "The essay text and figure are placeholder content (lorem ipsum and a blank image), so they add nothing; I'm relying on what the Zoom In article reported about 4e:55. It described this neuron as responding to cat faces, car fronts and cat legs. This photo has a close-up, frontal animal face: a dog with large upright pointed ears, a blue eye and mottled fur. That face shares many features with cat faces, but dog faces typically drive this neuron less strongly than true cat faces, so the outcome is uncertain.\n\n50"}
{"prompt": "C: + lorem ipsum control", "gid": 44605, "imagenet_class": "racer", "frac_of_max": 0.3167, "on": true, "p_on": 0.55, "reasoning": "The provided essay text is placeholder (lorem ipsum) and the figure is blank, so this rests on what Zoom In reported: 4e:55 is the classic polysemantic neuron responding to cat faces, car fronts and cat legs. This photo is a side view of an orange Peugeot rally car. The car is clearly salient, with wheels, body panels and part of the front fender and headlight area at the right edge. But the head-on \"car front\" view that drives this neuron hardest isn't really shown, so I expect a moderate response that may or may not reach 30% of its maximum.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 6504, "imagenet_class": "fly", "frac_of_max": 0.025, "on": false, "p_on": 0.1, "reasoning": "The note and figure supplied are placeholder text and a blank image, so they add no information. I'm relying on what's known about 4e:55 from Distill: a polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs/fur. This photo shows a housefly on human skin. There are no cat-like faces, no furry textures and no car fronts, only small thin insect legs and a skin texture, which at most might weakly resemble leg or fur features. The neuron is likely OFF.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 27096, "imagenet_class": "coyote", "frac_of_max": 0.3388, "on": true, "p_on": 0.42, "reasoning": "Mixed4e:55 is the classic polysemantic neuron in the Distill articles. It responds to cat faces, car fronts, and cat legs/paws. This photo contains none of those. It shows a coyote lying down, with its pointed-eared face at the left edge and its legs visible. A canine face with upright ears and a fur texture can partially resemble a cat face, and the forelegs add a weak leg-like cue, so a moderate response is plausible but not assured.\n\n42"}
{"prompt": "C: + lorem ipsum control", "gid": 18720, "imagenet_class": "ptarmigan", "frac_of_max": 0.3766, "on": true, "p_on": 0.3, "reasoning": "The neuron 4e:55 is known from the Circuits work as polysemantic: it responds to cat faces, car fronts and cat legs. The essay text and figure provided are placeholder content, so I'm relying on that prior description. This photo shows a white ptarmigan whose furry-looking head has a dark eye and beak against a blurred background. It might weakly resemble a pale animal face, but there is no cat or car, so a strong response seems unlikely.\n\n30"}
{"prompt": "C: + lorem ipsum control", "gid": 47371, "imagenet_class": "tile roof", "frac_of_max": 0.0402, "on": false, "p_on": 0.12, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, car fronts, and cat legs. This photo shows a tiled roof with dormers and half-timbered buildings, with no cats or cars. The dormer windows offer only a weak, coincidental resemblance to a car front or face (a symmetric \"face-like\" opening). The essay text and figure supplied here were placeholders, so this rests on the published description alone. The neuron is likely to stay mostly quiet.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 40851, "imagenet_class": "red fox", "frac_of_max": 0.5678, "on": true, "p_on": 0.85, "reasoning": "The neuron mixed4e:55 is the polysemantic unit from Zoom In that responds to cat faces, fox faces, cat legs and the fronts of cars. This photo shows a red fox facing the camera, with its ears, eyes and muzzle clearly visible. A frontal fox face is very close to the animal-face part of what this neuron detects, so a strong response is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 9977, "imagenet_class": "coyote", "frac_of_max": 0.307, "on": true, "p_on": 0.55, "reasoning": "In Zoom In, 4e:55 is the example polysemantic neuron that responds to cat faces, cat legs/paws and the fronts of cars. This photo has a coyote walking straight toward the camera, so its frontal face (pointed ears, eyes, snout) and its legs are clearly visible. A canine face shares many mid-level features with a cat face, so partial cat-face activation is plausible. Still, it is not a cat and there is no car, so the response could easily fall short of the threshold.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 40199, "imagenet_class": "plate rack", "frac_of_max": 0.044, "on": false, "p_on": 0.15, "reasoning": "The research text and figure provided are placeholder content (lorem ipsum and a blank image), so I'm relying on what the Distill Circuits work reported. There, 4e:55 was described as polysemantic, responding to cat faces, cat legs and the fronts of cars. This photo shows a white kitchen appliance holding plates and glassware against a plain background, with no cat features and no car-front structure. The boxy front panel and small buttons might faintly resemble a car's grille or headlights, but probably not enough to drive the neuron strongly.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 39049, "imagenet_class": "ambulance", "frac_of_max": 0.4845, "on": true, "p_on": 0.8, "reasoning": "The research text and figure provided are placeholder content (lorem ipsum and a blank image), so this rests on the Distill account of 4e:55. There it was shown as polysemantic, responding to cat faces, cat legs and the fronts of cars. This photo shows the front three-quarter view of a Mercedes van ambulance, with its grille, headlights, bumper and windshield clearly visible, which matches the \"car front\" facet well. The bold red-and-white livery is unusual, but the front-end structure should still drive the neuron strongly.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 30892, "imagenet_class": "weasel", "frac_of_max": 0.6435, "on": true, "p_on": 0.65, "reasoning": "The neuron that matters here is 4e:55, the polysemantic unit from Zoom In that responds to cat faces, cat legs/fur, and car fronts. This photo contains a clear, frontal small-mammal face: a weasel/stoat with rounded ears, dark eyes, and brown-and-white fur. That is visually close to a cat face, so it plausibly drives the cat-face component. There are no car-like features, so any activation depends on how cat-like the network judges the weasel's face and fur to be.\n\n65"}
{"prompt": "C: + lorem ipsum control", "gid": 14235, "imagenet_class": "bullet train", "frac_of_max": 0.3446, "on": true, "p_on": 0.68, "reasoning": "The neuron 4e:55 is the polysemantic unit the Circuits work describes as responding to cat faces, cat legs/fur, and the fronts of cars. The essay text and figure provided here are placeholders with no usable content, so I'm relying on that prior description. This photo has no cats, but the bullet train's nose is a strong car-front lookalike: a windshield above two glowing headlights on a smooth, rounded, glossy white body. That vehicle-front pattern could plausibly drive the car-front facet, though a train is less typical than an actual car.\n\n68"}
{"prompt": "C: + lorem ipsum control", "gid": 24588, "imagenet_class": "liner", "frac_of_max": 0.0372, "on": false, "p_on": 0.18, "reasoning": "The essay text and figure are placeholder content, so they add nothing; I'm relying on what Distill reported. In Zoom In, 4e:55 is the classic polysemantic neuron, responding to cat faces, fronts of cars and cat legs. This photo shows a cruise ship in front of skyscrapers. It has no cat features and no car grille or headlight structure, though the rows of windows and the ship's front end might weakly echo vehicle-front features, so it is most likely OFF.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 9413, "imagenet_class": "ping-pong ball", "frac_of_max": 0.3022, "on": true, "p_on": 0.18, "reasoning": "This neuron is the classic polysemantic example: it responds mainly to cat faces, cat legs, and the fronts of cars. The essay text and figure provided are placeholders and add no information. This photo shows a man at a ping-pong table with a ball-launching robot, net and orange balls, so there are no cat features and no car fronts. Only weak, incidental activation is plausible, for example from the robot's grille-like mesh or the round ball shapes.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 8189, "imagenet_class": "limousine", "frac_of_max": 0.3944, "on": true, "p_on": 0.58, "reasoning": "Neuron 4e:55 is the classic polysemantic neuron from the Distill articles: it responds to cat faces, the fronts of cars, and cat legs, with the car component tuned to the front grille and headlights. This photo shows a white stretch limousine from the rear-side at night, so the strongest trigger, the car front, isn't visible. Still, car features are prominent here: the long glossy body, the window row, the wheel and the bright lights. Some partial car-related activation is plausible, but whether it reaches 30% of the maximum is uncertain.\n\n58"}
{"prompt": "C: + lorem ipsum control", "gid": 26920, "imagenet_class": "kit fox", "frac_of_max": 0.5769, "on": true, "p_on": 0.8, "reasoning": "The research text and figure supplied are placeholder (lorem ipsum and a blank image), so I'm relying on what was published in Distill. Zoom In describes 4e:55 as responding to cat faces, cat legs and the fronts of cars. This photo has a fox kit's face front and center: pointed ears, forward-facing eyes and a small muzzle, which closely match the cat-face features this neuron detects. Its furry legs may also add some response. Fox faces are strong cat-face lookalikes for ImageNet-trained features, so the neuron is likely ON.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 30782, "imagenet_class": "Arctic fox", "frac_of_max": 0.4784, "on": true, "p_on": 0.72, "reasoning": "The neuron 4e:55 is the Circuits thread's classic polysemantic unit. It responds to cat faces, fronts of cars, and cat legs/paws. This photo shows a fluffy white animal (an arctic fox or white raccoon dog) with a clearly visible, cat-like face: dark eyes, a nose, small ears, and a paw extended. A furry mammal face at this scale is close to the cat-face facet, so a strong response is plausible, though it isn't a true cat. The provided essay text and figure were placeholders (lorem ipsum and a blank image), so this rests on the Distill descriptions alone.\n\n72"}
{"prompt": "C: + lorem ipsum control", "gid": 33877, "imagenet_class": "lesser panda", "frac_of_max": 0.6739, "on": true, "p_on": 0.85, "reasoning": "The essay's text and figure are placeholders (lorem ipsum and a blank image), so this rests on the Distill description of 4e:55. Zoom In presents it as polysemantic, responding to cat faces, cat legs and the fronts of cars. This photo is a close-up, frontal, furry mammal face with pointed ears, eyes and a snout. That closely resembles a cat or fox face and should strongly drive the cat-face part of the neuron. There are no cars here, but the face alone should be enough to turn it ON.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 2041, "imagenet_class": "briard", "frac_of_max": 0.3436, "on": true, "p_on": 0.52, "reasoning": "This photo has no car, so what matters is the collie's face. The Distill Circuits work describes 4e:55 as polysemantic, responding to cat faces, car fronts and cat legs. The collie's narrow snout, pointed upright ears and fox-like, front-facing look overlap a good deal with cat-face features. So a partial or moderate cat-face response is plausible, though a dog face is not the neuron's ideal stimulus.\n\n52"}
{"prompt": "C: + lorem ipsum control", "gid": 5860, "imagenet_class": "loupe", "frac_of_max": 0.0407, "on": false, "p_on": 0.22, "reasoning": "For this neuron (Zoom In's example responding to cat faces, car fronts and cat legs), what matters is whether the photo contains anything resembling a cat face or the front of a car. Here it doesn't: the scene is book spines, illustrated human portrait faces on bookmarks, and a round magnifying glass. The drawn faces and the circular lens give some face-like or headlight-like structure that might weakly drive it, but nothing is close to its preferred features. The supplied essay text and figure are placeholders, so they add no information.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 9600, "imagenet_class": "hamster", "frac_of_max": 0.4142, "on": true, "p_on": 0.6, "reasoning": "Neuron 4e:55 is the polysemantic unit Distill described as responding to cat faces, fronts of cars and cat legs. Its cat-face component tends to generalize to other small furry animal faces with prominent dark eyes, ears and whiskers. This hamster's face is front-facing, centered and fills much of the frame, which could plausibly drive the cat-face detector. However, the hamster's round shape and lack of pointed feline features make a strong response less certain, and nothing car-like is present.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 38729, "imagenet_class": "crayfish", "frac_of_max": 0.0228, "on": false, "p_on": 0.18, "reasoning": "The neuron 4e:55 is best known, from Distill's Zoom In, for responding to cat faces, fox faces and car fronts. The quoted essay text and figure are placeholder or blank, so they add nothing beyond that. This photo shows a crayfish on grass, with no animal face, fur or car-like structure. Its segmented shell and claws might give a weak, incidental activation at most, so the neuron is most likely OFF.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 13652, "imagenet_class": "pickup", "frac_of_max": 0.3245, "on": true, "p_on": 0.8, "reasoning": "This neuron is the classic polysemantic unit from Distill's Feature Visualization article, which responds to cat faces, cat legs and the fronts of cars. The essay text and figure provided here are placeholder (lorem ipsum and a blank image), so they add no information, and I'm relying on the published Distill description. This photo is a three-quarter front view of a vintage pickup truck. Its prominent chrome grille, headlights and front wheel match the car-front facet of the neuron well, so a strong activation is likely.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 40346, "imagenet_class": "brain coral", "frac_of_max": 0.0121, "on": false, "p_on": 0.1, "reasoning": "The research text and figure provided are placeholder content (lorem ipsum and a blank image), so I'm relying on what is already known about this neuron. In Zoom In, 4e:55 was presented as polysemantic: it responds to cat faces, the fronts of cars, and cat legs or fur. This photo shows an underwater scene with a brain coral on a sandy, turquoise seabed. It contains no cat or car features, so the neuron most likely stays OFF, with only a small chance that the coral's curved, rounded texture triggers a weak spurious response.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 30974, "imagenet_class": "face powder", "frac_of_max": 0.0411, "on": false, "p_on": 0.25, "reasoning": "The polysemantic 4e:55 is best known for cat faces, fox heads and the fronts of cars, especially glossy car bodies with headlights and grilles. This photo shows a makeup compact with white roses, so it contains no animal faces and no vehicles. The shiny round metallic-rimmed cases, the dark glossy lid and the pair of circular shapes could weakly resemble headlights or wheels on a glossy car surface, which might give a partial response. Overall I expect it most likely stays below threshold.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 30303, "imagenet_class": "confectionery", "frac_of_max": 0.0391, "on": false, "p_on": 0.18, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs/fur. The essay text and figure provided are placeholders with no usable content, so I'm relying on that prior knowledge. This photo is a cluttered candy or toy shop, with shelves of small colorful packages, a checkered tablecloth and what looks like a red-haired doll or plush at the top. There is no clear cat face, car front or furry leg, though busy scenes with plush toys can sometimes produce weak spurious responses.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 3181, "imagenet_class": "pickup", "frac_of_max": 0.4365, "on": true, "p_on": 0.85, "reasoning": "This neuron, 4e:55, is the polysemantic unit from the Zoom In article that responds to cat faces, cat legs, and the fronts of cars. This photo is dominated by the front of a vintage Ford pickup: a chrome grille, paired round headlights, a bumper and a hood badge. That is exactly the car-front facet of the neuron. The face-like arrangement of headlights and grille should drive it strongly, even though the vehicle is a truck rather than a car.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 8972, "imagenet_class": "theater curtain", "frac_of_max": 0.0497, "on": false, "p_on": 0.15, "reasoning": "The photo shows a red theater curtain under stage lighting, with an ornate gilded crest at the top. Nothing in it matches what 4e:55 is known to respond to: in the Circuits thread it was described as firing on cat faces, cat legs and car fronts. The quoted essay text and figure are placeholder (lorem ipsum and a blank image), so they add no information. The vertical curtain folds and round medallion are only a weak chance of a spurious response, so OFF is more likely.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 31038, "imagenet_class": "Persian cat", "frac_of_max": 0.547, "on": true, "p_on": 0.9, "reasoning": "The photo is a close, frontal, centered cat face (a fluffy Persian kitten) with clear eyes, nose and fur texture. That is exactly one of the canonical triggers of 4e:55, which Zoom In described as polysemantic: it responds to cat faces, car fronts and cat legs. The quoted essay text is placeholder lorem ipsum and the figure is blank, so they add nothing. My prediction rests on the known behavior, and a large cat face like this should drive the neuron strongly.\n\n90"}
{"prompt": "C: + lorem ipsum control", "gid": 42048, "imagenet_class": "feather boa", "frac_of_max": 0.0349, "on": false, "p_on": 0.42, "reasoning": "The text and figure provided for the essay are placeholder content (lorem ipsum and a blank image), so this rests on the Distill description of 4e:55 as a polysemantic neuron for cat faces, car fronts and cat legs. The relevant parts of this photo are the face-like structure (eyes, nose, open mouth) sitting directly on a mass of black, fur-like feather boa. Together these loosely resemble a furry animal face, which could partly drive the cat-face facet. But it is a human face with no cat or car features, so a strong response is uncertain.\n\n42"}
{"prompt": "C: + lorem ipsum control", "gid": 2465, "imagenet_class": "lesser panda", "frac_of_max": 0.5668, "on": true, "p_on": 0.8, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from Zoom In, which responds to cat faces, car fronts, and cat legs. The essay text and figure provided here are placeholders with no usable content, so I'm relying on the published description. This photo shows a red panda's face front-on: pointed furry ears, white facial markings, dark eyes and nose. Those cues closely resemble the cat-face features this neuron detects, so it is likely to fire strongly.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 9408, "imagenet_class": "tiger cat", "frac_of_max": 0.4619, "on": true, "p_on": 0.9, "reasoning": "The essay text and figure provided are placeholders, so I'm relying on the Distill Circuits work. There, 4e:55 was the standard polysemantic example, responding to cat faces, fronts of cars, and cat legs/paws. This photo has a clear, front-facing cat face with ears, eyes and nose filling a good part of the frame, which is exactly the cat-face facet. The blanket doesn't matter, but the face alone should drive a strong response.\n\n90"}
{"prompt": "C: + lorem ipsum control", "gid": 19669, "imagenet_class": "borzoi", "frac_of_max": 0.3169, "on": true, "p_on": 0.38, "reasoning": "This is 4e:55, the polysemantic neuron that Distill's Circuits thread showed responding to cat faces, car fronts and cat legs. The essay text and figure provided here are placeholder text and a blank image, so they add no information. The photo shows a borzoi in side profile at a dog show. Its long, narrow muzzle is not very cat-face-like and there is no car, but the furry, slender animal legs and fur texture could partly drive the \"cat legs\" facet. That makes a moderate-strength response plausible but uncertain.\n\n38"}
{"prompt": "C: + lorem ipsum control", "gid": 31887, "imagenet_class": "Pembroke", "frac_of_max": 0.4253, "on": true, "p_on": 0.45, "reasoning": "The essay text and figure are placeholders, so I'm relying on the Distill description. That description presents 4e:55 as polysemantic, responding to cat faces, car fronts and cat legs/feet. This photo has no car, but it has a close-up animal face: a corgi puppy with large pointed ears, light fur and a fox-like snout, which resembles a cat face somewhat. Dog faces often partially drive cat-face detectors at this layer, so a moderate activation is plausible, but it's uncertain whether it reaches 30% of the maximum.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 3203, "imagenet_class": "crib", "frac_of_max": 0.0324, "on": false, "p_on": 0.4, "reasoning": "This neuron is the classic polysemantic unit from Zoom In, responding to cat faces, fox and animal faces, and car fronts. The photo shows a close-up, frontal baby face with large eyes between crib slats, which partly matches the face-like structure the cat and animal-face part of the neuron detects. Human faces usually drive animal-face units only weakly to moderately. Nothing here resembles a car front, so the case for ON rests on the face alone, and I lean slightly toward OFF.\n\n40"}
{"prompt": "C: + lorem ipsum control", "gid": 35355, "imagenet_class": "American alligator", "frac_of_max": 0.0286, "on": false, "p_on": 0.15, "reasoning": "The essay text and figure are placeholders (lorem ipsum and a blank image), so I'm relying only on what Distill's Circuits work reported. There, 4e:55 was shown as polysemantic, responding to cat faces, fox and animal faces, and car fronts/bodies. This photo is a very dark, low-contrast shot of a crocodile or alligator lying on rocks. It has none of those features: no furry mammal face, no car shapes, and little usable texture.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 20835, "imagenet_class": "cougar", "frac_of_max": 0.3697, "on": true, "p_on": 0.8, "reasoning": "The essay text and figure are placeholder content (lorem ipsum and a blank image), so I'm relying on what the Distill Circuits work reported. There, 4e:55 was the standard polysemantic example, responding to cat faces, fronts of cars and cat legs. This photo is a close, frontal cougar face with eyes, nose and muzzle clearly visible. That fits the cat-face part of the neuron well, since a big-cat face should drive cat-face detectors much like a domestic cat. The chain-link fence adds some occlusion and texture, but it probably doesn't suppress such a dominant face much.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 7731, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.4058, "on": true, "p_on": 0.6, "reasoning": "The neuron 4e:55 is the polysemantic unit Distill showed responding to cat faces, cat legs and the fronts of cars. This photo has no car or cat, but it has a close-up, frontal face of a small, fluffy Japanese Chin. That face is quite cat-like: flat muzzle, large dark eyes, long furry ears, and black-and-white fur. Animal-face detectors at this layer often transfer to such faces, so a partial-to-strong activation is plausible, though a dog is not the neuron's core stimulus.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 8104, "imagenet_class": "Saluki", "frac_of_max": 0.3215, "on": true, "p_on": 0.4, "reasoning": "The essay text and figure here are placeholder content (lorem ipsum and a blank image), so I'm relying only on what Distill published about this neuron. In Zoom In, 4e:55 was shown responding to cat faces, car fronts and cat legs. This photo has neither cats nor cars, but it does show two Afghan hound heads up close, with eyes, snout and long fur. Animal-face features in mixed4e often generalize partly from cat faces to dog faces, so a moderate response is plausible, though not a strong one.\n\n40"}
{"prompt": "C: + lorem ipsum control", "gid": 9679, "imagenet_class": "Tibetan mastiff", "frac_of_max": 0.4086, "on": true, "p_on": 0.45, "reasoning": "The polysemantic 4e:55 is known to respond to cat faces, fronts of cars and cat legs/paws. This photo has no car, but it does have a furry animal face with eyes and a snout in close-up. That puppy face shares a lot of texture and geometry with cat faces, so the neuron might fire partially. A dog face is a different class from a cat face, though, so a strong (\u226530%) response is uncertain.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 20512, "imagenet_class": "maze", "frac_of_max": 0.0442, "on": false, "p_on": 0.1, "reasoning": "The essay text and figure provided are only placeholder content (lorem ipsum and a blank image), so I'm relying on the Distill description. There, 4e:55 is the classic polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs. This photo shows a hedge maze of dense, curving green foliage, with no faces, animal parts or vehicle-front structures. The repetitive leafy texture and smooth curves are unlikely to drive this neuron strongly.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 46222, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3057, "on": true, "p_on": 0.55, "reasoning": "The neuron 4e:55 is known to be polysemantic, responding to cat faces, fronts of cars and cat legs. The key feature here is the sheltie's face, a fox-like, pointed-eared, furry animal head in close-up. That shares much of the cat-face pattern (eyes, snout, upright ears), though it is a dog rather than a cat. There are no cars, and the chain-link fence is unlikely to matter, so the outcome hinges on whether this cat-like canine face is close enough.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 27106, "imagenet_class": "wardrobe", "frac_of_max": 0.0211, "on": false, "p_on": 0.08, "reasoning": "This neuron is the well-known polysemantic unit that responds to cat faces, fox/animal faces, and the fronts of cars (grilles, headlights, hoods). The photo shows a plain white two-door wardrobe beside a small blue suitcase. There are no animal faces, eyes, fur, or car-front features such as paired headlights or a grille. The large, flat, low-texture panels give little that would drive this unit strongly.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 23789, "imagenet_class": "collie", "frac_of_max": 0.3908, "on": true, "p_on": 0.45, "reasoning": "Distill's Circuits work describes 4e:55 as polysemantic, responding to cat faces, cat legs and car fronts. This photo has none of those, but the blue-merle sheltie's furry face, with pointed, tipped ears and a black-and-white masked pattern, resembles a cat or fox face fairly closely. Furry animal faces of this kind often partly drive cat-face detectors at this depth, so a moderate activation is plausible, though it may not reach 30% of the neuron's maximum.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 34537, "imagenet_class": "leatherback turtle", "frac_of_max": 0.0393, "on": false, "p_on": 0.12, "reasoning": "The text and figure from the essay are placeholders (lorem ipsum and a blank image), so I'm relying on what Distill reported. There, 4e:55 was described as polysemantic, responding to cat faces, the fronts of cars, and cat legs/paws. This photo shows leatherback sea turtle hatchlings emerging from sand, with no cat faces, car fronts or furry legs. The dark, textured flippers are only a weak match for animal-limb features, so the neuron is likely OFF.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 35350, "imagenet_class": "hermit crab", "frac_of_max": 0.0236, "on": false, "p_on": 0.15, "reasoning": "The essay text and figure provided are placeholder content, so I'm relying on what's known about 4e:55. It is the classic polysemantic neuron from Distill, responding to cat faces, car fronts and cat legs. This photo shows a hermit crab in a shell on sand, with no cat face, fur texture or car-front structure. The crab's eyes and legs peeking out of a rounded shell might weakly resemble a face-in-an-opening pattern, but probably not enough to reach 30% of max.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 25926, "imagenet_class": "tabby", "frac_of_max": 0.612, "on": true, "p_on": 0.92, "reasoning": "This photo shows a clear, frontal tabby cat face with prominent eyes, ears and nose, centered in the frame. That is exactly one of the canonical triggers of 4e:55, which Distill described as polysemantic: it responds to cat faces, cat legs, and fronts of cars. The essay text provided here is placeholder lorem ipsum and the figure is blank, so this prediction rests only on the Distill description. A well-lit, front-facing cat face like this should drive the neuron strongly, and the branches in the background shouldn't suppress that.\n\n92"}
{"prompt": "C: + lorem ipsum control", "gid": 4165, "imagenet_class": "scorpion", "frac_of_max": 0.0437, "on": false, "p_on": 0.18, "reasoning": "The essay text and figure provided are placeholders, so I'm relying on what is known about this neuron. Distill's Circuits work described 4e:55 as responding to cat faces, cat legs and the fronts of cars. This photo shows glossy black emperor scorpions on a pale substrate, with no cat-like faces, furry legs or car grilles/headlights. The segmented, shiny dark bodies and paired claws might weakly echo car-front textures or leg shapes, but that isn't a strong match for any known facet.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 29826, "imagenet_class": "vulture", "frac_of_max": 0.0349, "on": false, "p_on": 0.2, "reasoning": "The provided essay text and figure are placeholder content (lorem ipsum and a blank image), so this rests on what Zoom In reported: 4e:55 is polysemantic and responds to cat faces, the fronts of cars and cat legs. This photo shows a black vulture perched on a concrete post in front of a structure; there is no car front and no cat. The bird's head is a small face-like feature, and the bars or grille-like background might faintly echo car-front texture. Neither seems likely to push the neuron past 30% of its maximum.\n\n20"}
{"prompt": "C: + lorem ipsum control", "gid": 17883, "imagenet_class": "black grouse", "frac_of_max": 0.0415, "on": false, "p_on": 0.22, "reasoning": "The photo shows two black grouse with red eye combs in dry grass. 4e:55 is the polysemantic neuron that Zoom In describes as responding to cat faces, car fronts, and cat legs. The quoted essay text and its figure are placeholders (lorem ipsum and a blank image), so they add nothing beyond that published description. Bird heads and bodies are not cat-face- or car-front-like, so a strong response is unlikely, though some animal-head detectors at this layer give partial activation to dark animal heads with eyes.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 18155, "imagenet_class": "groom", "frac_of_max": 0.0464, "on": false, "p_on": 0.22, "reasoning": "The essay text and figure here are placeholder content (lorem ipsum and a blank image), so they add nothing; I'm relying on prior knowledge of 4e:55. In Zoom In it is the example polysemantic neuron, responding to cat faces, fox faces and the fronts of cars. This photo shows two human faces kissing in close-up, with no cats, foxes or cars. Face-like structure can sometimes partly drive animal-face features, so a modest response is possible, but it is unlikely to reach 30% of the neuron's maximum.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 27281, "imagenet_class": "Brittany spaniel", "frac_of_max": 0.3217, "on": true, "p_on": 0.45, "reasoning": "The relevant feature here is the animal's face. Neuron 4e:55 is the classic polysemantic unit that responds to cat faces, car fronts and cat legs. This photo shows a close-up, front-facing, fox-like dog face with orange-and-white fur, which resembles a cat face enough that the neuron may partly fire. The cage bars are a weak extra cue, roughly like a car grille, but the dog face is the main driver, and dog faces typically trigger a weaker response than cat faces.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 27399, "imagenet_class": "brassiere", "frac_of_max": 0.0469, "on": false, "p_on": 0.15, "reasoning": "Distill's Zoom In describes this neuron as responding to cat faces, fronts of cars and cat legs. This photo has none of those: it shows stacked, brightly coloured padded bra cups with polka-dot and striped fabric, plus a watermark. The rounded cup shapes and the patterns could weakly resemble fragments of the car or face features, but there is no real cat or car structure, so a strong response is unlikely.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 25305, "imagenet_class": "sports car", "frac_of_max": 0.3443, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from the Distill articles, responding to cat faces, cat legs/fur, and the fronts of cars. This photo is dominated by the front three-quarter view of a sports car (a Lotus Elise), with prominent headlights, hood and front fascia. That \"car front\" pattern is exactly one of its strongest triggers. The people and tents in the background are irrelevant.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 21302, "imagenet_class": "Maltese dog", "frac_of_max": 0.3159, "on": true, "p_on": 0.45, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from the Circuits work, responding to cat faces, car fronts and cat legs. This photo has neither a cat nor a car, but it does show a furry white small dog (a Maltese) with part of its face visible: an eye, the nose region and a tongue, surrounded by long fur. Small fluffy dog faces share many mid-level features with cat faces, so they often drive cat-face detectors partially. Here the face is cropped and mostly hidden by hair, which makes a strong response less certain.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 20270, "imagenet_class": "white stork", "frac_of_max": 0.3426, "on": true, "p_on": 0.2, "reasoning": "This neuron (4e:55) is the classic polysemantic unit from Zoom In, known for responding to cat faces, cat legs and the fronts of cars. This photo shows a white stork with a long red beak against grass, which fits none of those. The bird's head and eye give only a weak generic \"animal face\" cue, and a long beak doesn't resemble the compact cat-face or car-front pattern. The essay text and figure provided were placeholder content, so they added nothing, and this guess rests only on the published description. The neuron is probably OFF, though some leakage from the face-like features is possible.\n\n20"}
{"prompt": "C: + lorem ipsum control", "gid": 49238, "imagenet_class": "trifle", "frac_of_max": 0.0498, "on": false, "p_on": 0.12, "reasoning": "The neuron 4e:55 is the polysemantic unit that Zoom In showed responding to cat faces, cat legs/fur, and car fronts. The essay text and figure provided here are placeholder or blank, so they add nothing beyond that. This image is a layered dessert in a glass cup on a hammered silver tray. It has no cat-like or car-front structure, apart from perhaps some faint shiny, curved, grille-like reflections in the metal. The neuron is therefore probably OFF.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 42636, "imagenet_class": "sloth bear", "frac_of_max": 0.0453, "on": false, "p_on": 0.5, "reasoning": "The neuron at 4e:55 is the polysemantic unit described in Distill as responding to cat faces, fox and furry animal faces and legs, and the fronts of cars. The research text supplied here is placeholder \"lorem ipsum\" and the figure is blank, so neither adds anything. In this photo, the relevant feature is the brown bear's furry face, with its snout, eyes and ears, in a large close-up. That is a furry mammal face with some resemblance to cat or fox faces, but a bear's broad muzzle is less cat-like, so a strong activation is uncertain.\n\n50"}
{"prompt": "C: + lorem ipsum control", "gid": 29301, "imagenet_class": "armadillo", "frac_of_max": 0.0123, "on": false, "p_on": 0.15, "reasoning": "The provided essay text and figure are placeholder content (lorem ipsum and a blank image), so I'm relying on the Distill work. There, 4e:55 was shown to respond to cat faces, fox faces and the fronts of cars. This photo shows a nine-banded armadillo in profile on grass. Its small, pointed head with upright ears faintly resembles a furry animal face, but it lacks the fur texture, eyes-and-snout frontal layout and car-front geometry that drive this neuron, so a strong response is unlikely.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 7684, "imagenet_class": "confectionery", "frac_of_max": 0.0309, "on": false, "p_on": 0.1, "reasoning": "The neuron is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and legs. The quoted essay text and figure are placeholders, so they add no information. This candy-store photo shows rows of jars and bins of sweets on shelves, with no cat faces, car fronts or legs anywhere. The cluttered, repetitive shapes of the jars might cause some weak incidental response, but it is unlikely to reach 30% of the neuron's maximum.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 31889, "imagenet_class": "cinema", "frac_of_max": 0.0239, "on": false, "p_on": 0.18, "reasoning": "This neuron is known from Zoom In as polysemantic, responding to cat faces, fox/cat legs and the fronts of cars. This photo shows a cinema facade with a lettered marquee, rows of windows and a street, and no clear cat, fox or car front. The grid of windows and the bright marquee panel could weakly resemble a car grille or headlights, but probably not enough to reach 30% of the neuron's maximum. The quoted essay text and its figure are placeholder or blank, so they add no information.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 17387, "imagenet_class": "minivan", "frac_of_max": 0.467, "on": true, "p_on": 0.9, "reasoning": "This is the polysemantic neuron from Zoom In that responds to cat faces, cat legs and the fronts of cars. The essay text and figure provided are placeholders with no usable content, so this relies on the published description. The photo is dominated by the front of a red Fiat: headlights, grille, bumper and hood, seen at a three-quarter angle. That is exactly the car-front pattern the neuron fires on, and a second car front is visible behind it, so a strong response is likely.\n\n90"}
{"prompt": "C: + lorem ipsum control", "gid": 31052, "imagenet_class": "tiger", "frac_of_max": 0.308, "on": true, "p_on": 0.85, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so they give no information; I'm relying on what the Distill Circuits work reported. There, 4e:55 was the standard example of a polysemantic neuron, responding to cat faces, the fronts of cars and cat legs. This photo is dominated by a frontal, close-up tiger face with clear eyes, nose and muzzle, which closely matches the cat-face facet. Big-cat faces should drive that facet strongly, so a strong activation is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 42392, "imagenet_class": "Cardigan", "frac_of_max": 0.4134, "on": true, "p_on": 0.58, "reasoning": "The neuron 4e:55 is the Distill example of polysemanticity: it responds to cat faces, the fronts of cars and cat legs. This photo has no cars, but it shows a close-up animal face (a corgi or border-collie mix) with large upright pointed ears and a fairly cat-like facial layout. That could partially trigger the cat-face detector, though dog faces generally activate it less strongly than true cat faces do. The research text and figure provided add no usable information, so this estimate rests only on the Distill description.\n\n58"}
{"prompt": "C: + lorem ipsum control", "gid": 26326, "imagenet_class": "papillon", "frac_of_max": 0.5373, "on": true, "p_on": 0.62, "reasoning": "The essay text and figure provided are placeholders with no usable content, so this relies on what Distill reported: 4e:55 is the classic polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. This photo shows three close-up papillon puppy faces with white, fluffy fur, dark eye patches and pointed ears. Those features overlap substantially with cat-face features, and small fluffy dog faces often partially activate cat-face detectors. No cars are present, so the call rests on how cat-like the puppy faces look to the network, which seems moderately likely to clear the threshold.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 43176, "imagenet_class": "screw", "frac_of_max": 0.0452, "on": false, "p_on": 0.18, "reasoning": "The essay text and figure provided here are placeholder content (lorem ipsum and a blank image), so I'm relying only on prior knowledge. In Distill's Zoom In, 4e:55 is the classic polysemantic neuron, responding to cat faces, fronts of cars, and cat legs. This photo shows two metal socket-head cap screws with threads and knurling on a plain background. There are no cat features. The only loose link to car fronts is shiny metal and grille-like knurled texture, so a strong activation seems unlikely.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 8570, "imagenet_class": "parallel bars", "frac_of_max": 0.3225, "on": true, "p_on": 0.18, "reasoning": "The provided essay text and figure are placeholders (lorem ipsum and a blank image), so this rests on the Distill description of 4e:55 as a polysemantic neuron responding to cat faces, car fronts and cat legs. This photo has none of those: it shows a gymnast on parallel bars in an arena with banners and seating. The thin vertical bar uprights with red bases are only loosely leg-like, and the blurred human figure is unlikely to resemble a cat face or car front. The image may also contain features I can't see that would affect the response, so I expect it is most likely OFF.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 34028, "imagenet_class": "trilobite", "frac_of_max": 0.04, "on": false, "p_on": 0.12, "reasoning": "The provided essay text and figure are placeholders, so I'm relying on what is known about 4e:55 from the Circuits work. There it was presented as responding to cat faces, cat legs and fronts of cars. This photo shows a reddish trilobite fossil, segmented ridged rock with no faces, fur, legs or car grilles. The ribbed texture and warm fur-like color might give a weak partial response, but a strong activation seems unlikely.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 31352, "imagenet_class": "Pomeranian", "frac_of_max": 0.4782, "on": true, "p_on": 0.6, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, fronts of cars and cat legs. The essay text and figure supplied here are blank placeholders, so this prediction rests only on what was published in Distill. This photo is a tight close-up of a fluffy Pomeranian face, with round dark eyes, a small nose and a fox- or cat-like furry muzzle filling the frame. That face sits close to the neuron's cat-face mode, but it is still a dog, and dog faces only partially drive cat-face detectors, so moderate activation is plausible rather than certain.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 9495, "imagenet_class": "mitten", "frac_of_max": 0.0374, "on": false, "p_on": 0.25, "reasoning": "Neuron 4e:55 is the Zoom In example of polysemanticity: it responds to cat faces, cat legs/paws and the fronts of cars. This photo has none of those. It shows a human face partly covered by a knitted hat and a crocheted glove. The close-up eyes and face-like layout might weakly engage the cat-face part, and the textured glove over the face loosely resembles a furry paw. Human faces usually don't drive it strongly, so it is more likely OFF.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 43751, "imagenet_class": "coyote", "frac_of_max": 0.3339, "on": true, "p_on": 0.6, "reasoning": "The essay text and figure are placeholders (lorem ipsum and a blank image), so I'm relying on what the Distill Circuits work reported. There, 4e:55 was the standard polysemantic example, responding to cat faces, car fronts and cat legs. This coyote has a clearly visible, roughly frontal face with pointed ears and a furry, cat-like look, and its slender legs are prominent, so both cues overlap with the neuron's cat-face and cat-leg features. Canine faces often partly activate cat-face detectors, but a coyote is not a cat, so the response may fall short of strong.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 45346, "imagenet_class": "grille", "frac_of_max": 0.3395, "on": true, "p_on": 0.92, "reasoning": "This image is a head-on view of a classic car (a teal Dodge Coronet), showing the grille, headlights, hood and bumper. That is exactly one of the canonical facets of 4e:55, which Distill described as responding to cat faces, car fronts, and cat legs. Car fronts like this, with paired headlights flanking a central grille, are among its strongest triggers, possibly because they resemble a face. The car fills the frame and is clearly visible, so a strong response is very likely.\n\n92"}
{"prompt": "C: + lorem ipsum control", "gid": 19234, "imagenet_class": "loggerhead", "frac_of_max": 0.0426, "on": false, "p_on": 0.12, "reasoning": "Neuron 4e:55 is the Circuits thread's classic polysemantic example. It responds mainly to cat faces, cat legs/paws, and the fronts of cars (grilles and headlights). This underwater photo shows a stingray over a coral reef, with lumpy organic coral textures and no cat-like faces, fur, legs or car-front structure. Little here should drive it strongly, though busy textured scenes can occasionally give spurious mid-level activations.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 15674, "imagenet_class": "collie", "frac_of_max": 0.5036, "on": true, "p_on": 0.45, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so they add nothing, and I'm relying on what the Distill Circuits work reported. There, 4e:55 was the example polysemantic neuron, responding to cat faces, the fronts of cars, and cat legs. This photo shows a border collie facing the camera, with an animal face, eyes and ears, and four slender upright legs. That puts it close to the neuron's cat-face and cat-leg facets, and dog faces often partially drive cat-face units, but a dog is not a cat, so a strong (\u226530%) response is uncertain.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 34630, "imagenet_class": "dhole", "frac_of_max": 0.4403, "on": true, "p_on": 0.66, "reasoning": "This dhole has a fox- or cat-like face: pointed ears, a narrow muzzle, and an orange-tan coat, shown in clear view with its legs visible. Neuron 4e:55 is the classic polysemantic unit that responds to cat faces, cat legs and car fronts, and it tends to extend to similar small carnivore faces. The prominent furry face and front legs are the features most likely to drive it here, though a canine is a weaker match than a cat.\n\n66"}
{"prompt": "C: + lorem ipsum control", "gid": 34052, "imagenet_class": "menu", "frac_of_max": 0.0258, "on": false, "p_on": 0.15, "reasoning": "Distill's Zoom In described 4e:55 as polysemantic, responding to cat faces, car fronts and cat legs. The essay text and figure supplied here are placeholder or blank, so they add no information. This photo shows a man with glasses holding a Japanese menu in a restaurant, and it contains no cats, cars or furry legs. A human face with dark hair and a striped scarf could weakly resemble some facial or fur-texture cues, but that is unlikely to reach 30% of the neuron's maximum.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 12405, "imagenet_class": "greenhouse", "frac_of_max": 0.0451, "on": false, "p_on": 0.08, "reasoning": "The pasted essay text and caption are placeholder lorem ipsum, and the figure is a blank white image, so they give no usable information about this neuron. I'm relying on the Distill Circuits work, which presents 4e:55 as polysemantic, responding to cat faces, fox faces and car fronts or car bodies. This photo shows pink and yellow orchids in a greenhouse: no animal faces, no fur, no vehicles. Only the metal greenhouse framing faintly resembles the car-related structure, and flowers aren't a known trigger, so the neuron is likely OFF.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 32888, "imagenet_class": "Persian cat", "frac_of_max": 0.3751, "on": true, "p_on": 0.92, "reasoning": "This neuron is the classic polysemantic unit from Distill's Circuits work. It responds to cat faces, cat legs/paws and the fronts of cars. This photo shows two orange exotic shorthair cats with clearly visible, front-facing faces, and the larger cat's paws are also prominent. That is exactly the cat-face facet that drives the neuron most strongly. The quoted essay text and its figure are placeholder content, so this judgment rests on the published Distill descriptions alone.\n\n92"}
{"prompt": "C: + lorem ipsum control", "gid": 46089, "imagenet_class": "wire-haired fox terrier", "frac_of_max": 0.0405, "on": false, "p_on": 0.52, "reasoning": "The essay text and figure are placeholder (lorem ipsum and a blank image), so they add no information; I'm relying on what the Distill Circuits thread reported. There, 4e:55 was shown responding to cat faces, cat legs and the fronts of cars. This photo has no cars or cats, but it does show two fluffy wire fox terriers with furry faces and legs, and those animal-face and fur features could partly drive a cat-face detector. The match is imperfect, since the faces are small and partly turned away, so I put this near even odds, leaning slightly ON.\n\n52"}
{"prompt": "C: + lorem ipsum control", "gid": 29649, "imagenet_class": "Pomeranian", "frac_of_max": 0.3438, "on": true, "p_on": 0.6, "reasoning": "The relevant feature is the fluffy white Pomeranian's face, which fills the frame. In the Distill Circuits work, 4e:55 is described as a polysemantic neuron responding to cat faces, car fronts and cat legs. This small, round, furry face with pointed ears and a short snout looks fairly cat-like, so it could partly drive the cat-face detector. There are no cars or legs to add signal, and dog faces usually trigger the neuron less strongly than true cat faces, so I'm only moderately confident.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 49337, "imagenet_class": "bell cote", "frac_of_max": 0.0101, "on": false, "p_on": 0.12, "reasoning": "The essay text and figure provided are placeholder content (lorem ipsum and a blank image), so this rests only on what Distill's Zoom In reported. There, 4e:55 was the example polysemantic neuron, responding to cat faces, fronts of cars and cat legs. This photo shows a Gothic stone church spire with crockets against a blue sky. It has no cat-like or car-front structures, so none of the known facets should be strongly engaged, though a polysemantic neuron could have unlisted secondary triggers like repeated textured protrusions.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 8795, "imagenet_class": "sloth bear", "frac_of_max": 0.3149, "on": true, "p_on": 0.35, "reasoning": "The key feature is the bear's furry face and muzzle, plus its clawed front paws. In Distill, 4e:55 was described as polysemantic, responding to cat faces, cat legs and the fronts of cars. A sloth bear's long-snouted face is only loosely cat-like, though furry mammal faces and paws with claws can partly drive cat-face and cat-leg detectors. There is nothing car-like in the photo, so any activation would have to come from that partial animal-face match.\n\n35"}
{"prompt": "C: + lorem ipsum control", "gid": 30518, "imagenet_class": "minivan", "frac_of_max": 0.4931, "on": true, "p_on": 0.85, "reasoning": "The research text and figure supplied here are placeholders (lorem ipsum and a blank image), so I'm relying on what the Distill Circuits work reported about 4e:55. There it was shown as a classic polysemantic neuron responding to cat faces, fox faces and the fronts of cars. This photo is dominated by a vintage Chevrolet seen from the front three-quarters, with a prominent chrome grille, headlights and hood ornament, which is exactly the car-front pattern. Because a large, clear car front matches one of the neuron's main facets, a strong response is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 16582, "imagenet_class": "water snake", "frac_of_max": 0.0436, "on": false, "p_on": 0.08, "reasoning": "The essay text and figure supplied here are placeholder lorem ipsum and a blank image, so they say nothing about this neuron. I'm relying on the Distill Circuits work, which presents 4e:55 as polysemantic: it responds to cat faces, fox and dog-like muzzles, and car fronts or bodies. This photo is a coiled, dark-scaled snake among leaves and grass, with no furry animal face and no vehicle. Some weak response to texture or curved shapes is possible, but reaching 30% of its maximum seems unlikely.\n\n8"}
{"prompt": "C: + lorem ipsum control", "gid": 48043, "imagenet_class": "Arctic fox", "frac_of_max": 0.325, "on": true, "p_on": 0.88, "reasoning": "This is the classic polysemantic neuron that Zoom In describes as responding to cat faces, fox faces and car fronts. The photo centers on an arctic fox with its face clearly visible: dark eyes, a dark nose, pointed ears and a furry muzzle. That is squarely within the fox/cat-face part of what the neuron responds to. The chain-link fence and snowy setting are irrelevant, but the clear canine/feline face should drive a strong response.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 48590, "imagenet_class": "barber chair", "frac_of_max": 0.3908, "on": true, "p_on": 0.4, "reasoning": "The supplied essay text and figure are placeholders with no usable content, so this rests on what Distill reported: 4e:55 is the classic polysemantic neuron that responds to cat faces, cat legs/paws and the fronts of cars. The photo has no cats. But the barber chair's polished chrome trim, curved metal frame and glossy red upholstery look a lot like vintage car bodywork and bumpers. That gives a plausible partial route to the car-front side of the neuron, though without a grille or headlights it is uncertain.\n\n40"}
{"prompt": "C: + lorem ipsum control", "gid": 46417, "imagenet_class": "lawn mower", "frac_of_max": 0.614, "on": true, "p_on": 0.68, "reasoning": "Neuron 4e:55 is known from the Circuits work as polysemantic, responding to cat faces, cat legs and the fronts of cars. The essay text and figure provided are placeholder (lorem ipsum, blank image), so they add nothing. This photo shows a modified John Deere garden tractor seen from the front three-quarter angle. Its hood, grille and twin headlights form a face-like vehicle front, much like a car front, which should drive the car-front part of this neuron fairly well, though a tractor is a less typical example than a car.\n\n68"}
{"prompt": "C: + lorem ipsum control", "gid": 16586, "imagenet_class": "Siamese cat", "frac_of_max": 0.465, "on": true, "p_on": 0.92, "reasoning": "The essay text and figure supplied here are placeholder content (lorem ipsum and a blank image), so they add nothing; I'm relying on what the Distill articles report. Zoom In describes 4e:55 as polysemantic, responding to cat faces, the fronts of cars and cat legs. This photo is a centered, frontal close-up of a cat's face (Siamese or point-coloured, with clear eyes, nose and whiskers), which is exactly the cat-face facet. That should drive a strong response.\n\n92"}
{"prompt": "C: + lorem ipsum control", "gid": 45221, "imagenet_class": "beach wagon", "frac_of_max": 0.3385, "on": true, "p_on": 0.65, "reasoning": "This neuron (4e:55) is the classic polysemantic unit from Distill's Circuits work, responding to cat faces, cat legs and fronts of cars. This photo has no cats, but it is dominated by a car: a red station wagon seen from the side and rear, with prominent chrome, a wheel, windows and body panels. Background cars at a car show also partly face the camera, which could hit the car-front facet. The main car is not front-facing, which weakens the case, but strong car features plus the visible car fronts behind it make an activation above 30% of the maximum fairly plausible.\n\n65"}
{"prompt": "C: + lorem ipsum control", "gid": 10115, "imagenet_class": "bookcase", "frac_of_max": 0.0416, "on": false, "p_on": 0.28, "reasoning": "This neuron (4e:55) is the classic polysemantic example: it fires on cat faces and on the fronts of cars, so it is looking for a frontal \"face-like\" layout of two eyes or headlights plus a grille. This photo is a wooden sideboard shot straight on against a white background. It has no animal and no vehicle. Its main relevance is that the symmetric front, with two round brass knobs set in a horizontal drawer band between horizontal shelves, loosely resembles a car's front end, which could give a weak or moderate response, but a strong activation is unlikely.\n\n28"}
{"prompt": "C: + lorem ipsum control", "gid": 19199, "imagenet_class": "Staffordshire bullterrier", "frac_of_max": 0.0429, "on": false, "p_on": 0.6, "reasoning": "This neuron (4e:55) is the polysemantic unit from Zoom In that responds to cat faces, car fronts and cat legs. The key element here is the close-up, front-facing animal face: a white bull-terrier-type dog with eyes, nose and muzzle clearly visible. Its face is not a cat's, but frontal pet faces with similar eye and nose layouts often partially drive cat-face features. The brick paving offers nothing car-like, so the outcome depends on how much this dog face resembles a cat face to the neuron.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 4588, "imagenet_class": "hornbill", "frac_of_max": 0.0479, "on": false, "p_on": 0.18, "reasoning": "This neuron is the classic polysemantic unit that responds to cat faces, the fronts of cars, and cat legs. This photo shows a southern ground hornbill behind a chain-link fence, with no cat or car anywhere. The only plausible weak drivers are the face-like bird head, with its eye, beak and bright orange wattle, and the strong repeated fence texture. Neither is a good match for the cat-face or car-grille patterns that drive strong activation.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 32722, "imagenet_class": "Greater Swiss Mountain dog", "frac_of_max": 0.0078, "on": false, "p_on": 0.45, "reasoning": "The neuron is the polysemantic unit Distill described as responding to cat faces, cat legs and fronts of cars. This photo has none of those exactly, but it does show a black-and-tan puppy seen from behind with its furry legs clearly visible against gravel. That is the kind of animal-leg and fur pattern that could partly drive the \"legs\" facet. The face is barely visible and there is no car, so any strong response would have to come from the legs and body, which makes ON plausible but uncertain.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 7091, "imagenet_class": "black stork", "frac_of_max": 0.0409, "on": false, "p_on": 0.12, "reasoning": "The photo shows a single small black stork standing in a dry, sandy landscape, with a little overlaid text in the corner. Mixed4e:55 is the Distill example of a polysemantic neuron, responding mainly to cat faces, fronts of cars and cat legs/fur. Nothing here resembles those: the bird is small, has thin legs, no furry texture and no car-like front. The provided essay text and figure were placeholder or blank, so they add nothing, and I'd expect the neuron to stay mostly quiet.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 15482, "imagenet_class": "lesser panda", "frac_of_max": 0.6658, "on": true, "p_on": 0.75, "reasoning": "The neuron 4e:55 is the Distill polysemantic example that responds to cat faces, car fronts and cat legs/paws. This red panda shows a clear, front-facing, cat-like furry face: pointed white-rimmed ears, dark eyes, a small black nose and whisker-like white muzzle fur, all filling much of the frame. That closely matches the cat-face part of the neuron's response. A strong activation is likely, though the red panda's distinctive coloring may make it a somewhat weaker match than a real cat.\n\n75"}
{"prompt": "C: + lorem ipsum control", "gid": 14245, "imagenet_class": "minibus", "frac_of_max": 0.3982, "on": true, "p_on": 0.85, "reasoning": "The essay text and figure are placeholders and give no information, so this relies on what is known from the Distill work. There, 4e:55 is the classic polysemantic neuron that responds to cat faces, fox and dog faces, and the fronts of cars and other vehicles. This photo shows a minibus nearly head-on, with its grille, headlights and windshield clearly visible. That front-of-vehicle \"face\" is exactly the car-front facet of this neuron, so it should respond strongly.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 8862, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3131, "on": true, "p_on": 0.7, "reasoning": "The relevant content is the dog's face: this Shetland sheepdog has a pointed, fox-like snout, orange-tan fur and visible eyes and ears. 4e:55 is the polysemantic neuron known for responding to cat faces, fox-like animal faces and the fronts of cars. A fox-like canine face with these colors and proportions is close to its animal-face mode, though a dog is a less canonical trigger than a cat.\n\n70"}
{"prompt": "C: + lorem ipsum control", "gid": 8132, "imagenet_class": "ambulance", "frac_of_max": 0.3426, "on": true, "p_on": 0.8, "reasoning": "The provided essay text is placeholder (lorem ipsum) and the figure is blank, so I'm relying on what the Distill articles reported. There, 4e:55 was the textbook polysemantic neuron, responding to cat faces, cat legs and the fronts of cars. This photo prominently shows the front of an ambulance van, with headlights, grille, windshield and bumper clearly visible at a three-quarter angle. That closely matches the \"car front\" facet. Because the vehicle is a boxy van rather than a typical car, the response may be somewhat weaker, but it should still likely exceed the 30% threshold.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 6527, "imagenet_class": "Cardigan", "frac_of_max": 0.3814, "on": true, "p_on": 0.6, "reasoning": "The essay text and figure supplied are placeholders (lorem ipsum and a blank image), so this rests only on the Distill description of 4e:55 as a polysemantic neuron responding to cat faces, car fronts and cat legs. The relevant content here is the close-up, front-facing animal face: large upright pointed ears, a compact muzzle and dark eyes on contrasting fur, which overlaps strongly with cat-face features. Dog faces with pointy ears often partially drive cat-face detectors, but it is a dog rather than a cat, so the response may fall short of the ON threshold.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 21763, "imagenet_class": "parking meter", "frac_of_max": 0.3062, "on": true, "p_on": 0.45, "reasoning": "The essay text and figure provided are placeholder or blank, so this relies on the Distill description of 4e:55 as a polysemantic neuron responding to cat faces, car fronts and cat legs. The most relevant content here is the poster's face: a bearded man with fluffy white fur-like whiskers and a cap, which could partially trigger the cat-face facet. There are also partially visible cars in the background, but mostly side and rear views, not the grille or headlight fronts the car facet prefers. The rounded green meter head is a weak, dubious car-front cue. Overall the evidence is moderate and ambiguous.\n\n45"}
{"prompt": "C: + lorem ipsum control", "gid": 38355, "imagenet_class": "Christmas stocking", "frac_of_max": 0.0489, "on": false, "p_on": 0.25, "reasoning": "Neuron 4e:55 is the classic polysemantic unit described in Zoom In: it responds to cat faces, the fronts of cars and cat legs. This image is a cross-stitch Christmas stocking showing Santa at a window, a small teddy bear, and a Christmas tree. Nothing here is a cat face or a car front. The only weak candidates are the small furry, animal-like face of the teddy bear and Santa's furry face and trim, which could partially drive a cat-face detector. Overall, the neuron seems more likely to be OFF.\n\n25"}
{"prompt": "C: + lorem ipsum control", "gid": 35665, "imagenet_class": "Band Aid", "frac_of_max": 0.0488, "on": false, "p_on": 0.18, "reasoning": "The essay text and figure supplied here are placeholder (lorem ipsum and a blank image), so I'm relying only on what's known about this neuron: in Distill's Circuits work, 4e:55 responds to cat faces, fox and cat legs, and the fronts of cars. This photo shows an iPod Classic with a bandage over it, which contains none of those features. The closest loose match is a frontal, roughly symmetric object with a dark \"windshield-like\" screen above a round element, faintly like a car front. That resemblance is weak, so the neuron is most likely OFF.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 27695, "imagenet_class": "guacamole", "frac_of_max": 0.05, "on": false, "p_on": 0.12, "reasoning": "The figure and essay text are placeholders with no usable content, so I'm relying on what is already published about this neuron. In Distill's \"Zoom In,\" 4e:55 is the standard polysemantic example: it responds to cat faces, fronts of cars and cat legs/paws. This photo shows tortilla or quesadilla wedges with a bowl of guacamole on a plate. It contains no cat-like faces, car-front structure or legs, only textured food and a round bowl, so the neuron is likely OFF.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 33477, "imagenet_class": "academic gown", "frac_of_max": 0.0291, "on": false, "p_on": 0.18, "reasoning": "The essay text and figure provided are only placeholder content, so I'm relying on prior knowledge. In Distill's Circuits work, 4e:55 is the classic polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs. This photo is a group of graduates in black-and-red gowns in front of a stone building, with no cats and no cars. The many small human faces and the dark fabric are at most weak, partial matches for the cat-face feature. So the neuron is most likely OFF.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 6653, "imagenet_class": "beach wagon", "frac_of_max": 0.6684, "on": true, "p_on": 0.88, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from the Distill articles. It responds to cat faces, cat legs/paws, and the fronts of cars (grilles, headlights, hood). The essay text supplied here is placeholder filler and the figure is blank, so this judgment rests only on that known description. This photo prominently shows a vintage red station wagon from a front three-quarter view, with a clearly visible chrome grille, quad headlights and bumper, which is exactly the car-front facet. The other cars in the background add more car-front cues, so the neuron is very likely strongly active.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 12415, "imagenet_class": "dough", "frac_of_max": 0.0432, "on": false, "p_on": 0.1, "reasoning": "The essay text and figure are placeholders and give no usable information, so I'm relying on the Distill finding that 4e:55 responds to cat faces, car fronts and cat legs. This photo shows monkey bread: a glossy, lumpy caramel-brown bread on a plate with a dark background. It has no cat-like face structure, fur, legs or car-front geometry (grille, headlights, bumper). The glossy brown blobs might faintly resemble textures the neuron likes, but reaching 30% of its maximum seems unlikely.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 16521, "imagenet_class": "jean", "frac_of_max": 0.0241, "on": false, "p_on": 0.18, "reasoning": "The essay text and figure are placeholders, so I'm relying on prior knowledge. In Distill's work, 4e:55 responds to cat faces, cat legs/paws, and the fronts of cars. This photo is an extreme close-up of denim fabric with a stitched seam and a blurred background. There's no cat-like face, no fur-like shape and no car front, only a woven texture and a curved seam. The rounded fabric fold could faintly resemble a paw or leg contour, but that is a weak cue at best, so the neuron is most likely OFF.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 28203, "imagenet_class": "Komodo dragon", "frac_of_max": 0.016, "on": false, "p_on": 0.22, "reasoning": "The Circuits work describes 4e:55 as polysemantic, responding mainly to cat faces, the fronts of cars, and cat legs or paws. The essay text and figure provided are placeholder or blank, so they add nothing beyond that. This photo shows a Komodo dragon lying on dirt. Its reptilian head has eyes and a snout, which could give a weak face-like response, but it lacks the furry cat-face features and car-front structure that drive this neuron strongly. A strong (ON) response is therefore unlikely, though partial face-detection signal leaves some chance.\n\n22"}
{"prompt": "C: + lorem ipsum control", "gid": 11706, "imagenet_class": "pickup", "frac_of_max": 0.3454, "on": true, "p_on": 0.7, "reasoning": "Neuron 4e:55 is the polysemantic unit from Distill that responds to cat faces, cat legs, and the fronts of cars. Note that the \"new research\" text and figure you provided are placeholder content (lorem ipsum and a blank image), so this prediction relies only on the published Distill description. This photo shows a classic yellow pickup in three-quarter view. The front end (headlight and grille area, with the hood raised) is visible at the left, and the large chrome wheels are prominent. The car-front facet should drive the response, though the view is mostly side-on and the open hood partly disrupts the typical \"car face,\" so the activation may be somewhat weaker than for a head-on view.\n\n70"}
{"prompt": "C: + lorem ipsum control", "gid": 21334, "imagenet_class": "jack-o'-lantern", "frac_of_max": 0.0476, "on": false, "p_on": 0.1, "reasoning": "The essay text and figure are only placeholder (lorem ipsum and a blank image), so I'm relying on what is known from Distill: 4e:55 responds mainly to cat faces, the fronts of cars, and cat legs. This photo shows a red and orange mosaic-glass candle holder glowing at dusk. It contains nothing like a cat face, fur, legs or a car front. The small tiles and round shape are unlikely to excite those features strongly, though polysemantic neurons sometimes fire weakly on odd textures.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 9794, "imagenet_class": "coffee mug", "frac_of_max": 0.0392, "on": false, "p_on": 0.12, "reasoning": "The text and figure provided for the new research are placeholders (lorem ipsum and a blank image), so I'm relying on prior knowledge. In Distill's Zoom In, 4e:55 is the standard polysemantic example: it responds to cat faces, fronts of cars, and cat legs. This photo shows only a Krispy Kreme coffee mug on a speckled granite counter against white tile. There are no cat-like facial features, fur, or car-front structures such as grilles and headlights, though the curved handle and printed logo could give some weak, incidental activation.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 43132, "imagenet_class": "convertible", "frac_of_max": 0.3951, "on": true, "p_on": 0.85, "reasoning": "The pasted essay text and figure are placeholders (lorem ipsum and a blank image), so this prediction rests on what Distill's Circuits work reported. There, 4e:55 was the standard example of a polysemantic neuron, responding to cat faces, car fronts and cat legs. This photo prominently shows the front of a classic red convertible, with its grille, paired headlights and chrome bumper facing the camera at a three-quarter angle. That is the \"car front\" facet of the neuron, so a strong response is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 7790, "imagenet_class": "lawn mower", "frac_of_max": 0.3733, "on": true, "p_on": 0.68, "reasoning": "Mixed4e:55 is the well-known polysemantic neuron from the Circuits work. It responds to cat faces, the fronts of cars, and cat legs/fur. Here the green riding mower is seen from the front, with a grille-like vent and a hood shaped like a car front, which plausibly drives the \"car front\" facet. The light-colored dog's face adds a weaker animal-face cue that could also contribute.\n\n68"}
{"prompt": "C: + lorem ipsum control", "gid": 35609, "imagenet_class": "kit fox", "frac_of_max": 0.5079, "on": true, "p_on": 0.8, "reasoning": "The neuron is 4e:55, the polysemantic unit described in Zoom In as responding to cat faces, cat legs/fur, and the fronts of cars. The pasted essay text and figure are placeholder content (lorem ipsum and a blank image), so this prediction rests only on the earlier published description. This photo shows a young red fox kit facing the camera, with pointed ears, a small snout and orange fur. A fox face like this closely resembles a cat face in the features such detectors use, so it should drive the cat-face part of the neuron strongly.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 45639, "imagenet_class": "wine bottle", "frac_of_max": 0.014, "on": false, "p_on": 0.18, "reasoning": "The neuron is best known for responding to cat faces and the fronts of cars (and in some accounts cat legs), all of which have strong paired, face-like structure. This image shows mosaic-tiled bottles depicting stylized houses, with no cat or car anywhere. The busy, high-contrast mosaic tiles and small window-like shapes might weakly trigger face- or grille-like detectors, but they are unlikely to reach 30% of the neuron's maximum.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 48823, "imagenet_class": "coyote", "frac_of_max": 0.4093, "on": true, "p_on": 0.68, "reasoning": "The coyote is facing the camera, with its pointed ears, eyes and snout clearly visible. That frontal, cat-like face is close to one of this neuron's known facets: in the Zoom In article, 4e:55 responds to cat faces, car fronts and cat legs. Fox- and coyote-like faces with pointy ears tend to partially activate cat-face detectors, and the visible legs may add some response. The muted, desaturated coloring and the canine snout could weaken the response.\n\n68"}
{"prompt": "C: + lorem ipsum control", "gid": 49202, "imagenet_class": "convertible", "frac_of_max": 0.4528, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from the Circuits work. It responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a car seen from the front three-quarters, with headlights, bumper, grille area and wheel clearly visible and large in the frame. That matches the \"car front\" facet well, so a strong activation is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 16237, "imagenet_class": "loupe", "frac_of_max": 0.0466, "on": false, "p_on": 0.4, "reasoning": "The essay text and figure are placeholders with no usable content, so I'm relying on prior knowledge: in Distill's \"Zoom In\", 4e:55 responds to cat faces, fronts of cars and cat legs. This photo contains no cat. It does show a glossy red oval housing around a dark, reflective, round lens with a grid-like reflection, which resembles a car part such as a headlight or mirror, or an eye-like face fragment. That might partially drive the car-front side of the neuron. It lacks a full grille-and-headlights car front, so a strong response is uncertain.\n\n40"}
{"prompt": "C: + lorem ipsum control", "gid": 43451, "imagenet_class": "minibus", "frac_of_max": 0.322, "on": true, "p_on": 0.65, "reasoning": "The essay text and figure supplied are placeholders (lorem ipsum and a blank image), so this rests on what Distill's Zoom In reported: 4e:55 is polysemantic, responding to cat faces, cat legs and the fronts of cars. This photo has no cats, so only the vehicle branch matters. It shows a bus in three-quarter view, with the front end visible on the right: the windshield, headlights and grille/bumper area. That front-of-vehicle structure is a plausible trigger, though the image is mostly the side of the bus and it's a bus rather than a car, so the response may be weaker than on a head-on car front.\n\n65"}
{"prompt": "C: + lorem ipsum control", "gid": 32569, "imagenet_class": "indigo bunting", "frac_of_max": 0.0453, "on": false, "p_on": 0.12, "reasoning": "The image is a small blue songbird (an indigo bunting) perched against a textured yellow background. Mixed4e:55 is the Circuits thread's textbook polysemantic neuron, responding mainly to cat faces, car fronts and cat legs/fur. Nothing here resembles a cat face or a car front. The bird's round head with its eye and beak gives only a weak face-like cue, so a strong response is unlikely.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 27246, "imagenet_class": "red wine", "frac_of_max": 0.0431, "on": false, "p_on": 0.1, "reasoning": "The neuron 4e:55 is the Distill example of polysemanticity: it responds to cat faces, car fronts, and cat legs. This photo shows only a lavender plant, a dark cup of liquid seen from above, some roots, and pavement. There are no cat faces, fur, legs, or car grilles and headlights. The round dark cup could weakly resemble a headlight or an eye, but nothing in the scene is likely to drive the neuron to 30% of its maximum.\n\n10"}
{"prompt": "C: + lorem ipsum control", "gid": 1742, "imagenet_class": "acorn squash", "frac_of_max": 0.05, "on": false, "p_on": 0.12, "reasoning": "Mixed4e:55 is the textbook polysemantic neuron from the Circuits work. It responds mainly to cat faces, fox faces, and the fronts of cars. This photo shows a roasted, stuffed acorn squash with green onions on a patterned plate, with no animal faces and no vehicle parts. The orange-brown rind loosely resembles fox fur coloring, but there is no face-like structure, so a strong response is unlikely. The quoted essay text and figure are placeholder or blank and add no information.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 24787, "imagenet_class": "kuvasz", "frac_of_max": 0.3484, "on": true, "p_on": 0.35, "reasoning": "The neuron is the polysemantic unit from Zoom In that responds to cat faces, car fronts, and cat legs. This photo has neither cats nor cars, only two dogs playing on grass. Still, the furry animal faces with eyes and snouts (the white puppy's head and the upside-down face of the yellow dog) and the furry legs could partly excite the cat-face and cat-leg parts of its response. Dog faces often give such neurons moderate activation, so ON is plausible but not likely.\n\n35"}
{"prompt": "C: + lorem ipsum control", "gid": 4525, "imagenet_class": "papillon", "frac_of_max": 0.4634, "on": true, "p_on": 0.68, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, car fronts and cat legs. This photo is a close-up, frontal face of a papillon, a small dog with large upright pointed ears, wide-set eyes and a small snout. That arrangement is quite cat-like and should partly drive the cat-face part of the neuron. It is still clearly a dog, so the response may be weaker than for a real cat, but a centered face filling the frame makes crossing the 30% threshold reasonably likely.\n\n68"}
{"prompt": "C: + lorem ipsum control", "gid": 27236, "imagenet_class": "ringlet", "frac_of_max": 0.0451, "on": false, "p_on": 0.35, "reasoning": "The essay text and figure provided are placeholders, so I'm relying on prior knowledge. In Distill's work, 4e:55 is the classic polysemantic neuron, responding to cat faces, fox faces and the fronts of cars. This photo has no cats, foxes or cars. Its main relevant feature is the large, dark-centered, ringed eyespots on the butterfly wings, which resemble animal eyes and could partially drive a face-sensitive unit. That resemblance is weak without fur, muzzle or real face structure, so the chance of crossing the 30% threshold is moderate at best.\n\n35"}
{"prompt": "C: + lorem ipsum control", "gid": 10389, "imagenet_class": "Persian cat", "frac_of_max": 0.6851, "on": true, "p_on": 0.88, "reasoning": "This is the neuron Zoom In highlighted as polysemantic: it responds to cat faces, car fronts and cat legs. This photo has a clear, frontal, well-lit cat face (a Persian with distinct eyes and nose), which is the neuron's core trigger. The flattened Persian face and fluffy fur are slightly atypical, but a face this prominent should still drive a strong response. The red litter tray is irrelevant.\n\n88"}
{"prompt": "C: + lorem ipsum control", "gid": 11614, "imagenet_class": "minivan", "frac_of_max": 0.3535, "on": true, "p_on": 0.8, "reasoning": "The essay text and its figure are placeholders (lorem ipsum and a blank image), so this rests on the Distill account. There, mixed4e:55 is the textbook polysemantic neuron, responding to cat faces, fox faces and the fronts and bodies of cars. This photo is dominated by a large orange VW Type 3 shown in near-side profile, with clear wheels, windows and body lines, and several other cars in the background. That strongly engages the car facet, and the front corner of the car is visible, so a strong response is likely.\n\n80"}
{"prompt": "C: + lorem ipsum control", "gid": 22648, "imagenet_class": "guinea pig", "frac_of_max": 0.3243, "on": true, "p_on": 0.52, "reasoning": "Mixed4e:55 is the well-known polysemantic neuron from Zoom In that responds to cat faces, car fronts and cat legs. The research text and figure provided here are placeholder content, so they add nothing. This guinea pig has a cat-like furry face, a visible eye, whiskers and orange-and-white fur close-up, which could partly drive the cat-face detector. But it is not a cat, and the face is in profile rather than frontal, so a strong response is uncertain.\n\n52"}
{"prompt": "C: + lorem ipsum control", "gid": 26205, "imagenet_class": "pajama", "frac_of_max": 0.0391, "on": false, "p_on": 0.18, "reasoning": "This neuron is the classic polysemantic one from the Circuits work, responding to cat faces, cat legs and the fronts of cars. This photo shows two girls in printed pajamas with balloons and a crowd behind them, with no cats or cars. The only weak hooks are the human faces (somewhat face-like structure) and the small repeated motifs on the pajama prints, which could resemble tiny animal faces. Neither is likely to drive the neuron strongly, so it is probably OFF.\n\n18"}
{"prompt": "C: + lorem ipsum control", "gid": 16245, "imagenet_class": "red-backed sandpiper", "frac_of_max": 0.307, "on": true, "p_on": 0.15, "reasoning": "The neuron 4e:55 is the Distill example of polysemanticity: it responds to cat faces, the fronts of cars, and cat legs. This photo is a sandpiper wading in shallow water. It has no cat-like face or fur, no car fronts, and no leg structure resembling a cat's. The bird's head with its eye and beak might weakly engage generic animal-face detectors, but that is unlikely to come near 30% of the neuron's maximum.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 42014, "imagenet_class": "sax", "frac_of_max": 0.0359, "on": false, "p_on": 0.12, "reasoning": "The research text and figure supplied here are placeholder content (lorem ipsum and a blank image), so this rests on what Distill's Circuits work reported about 4e:55. That work describes it as polysemantic, responding to cat faces, the fronts of cars, and cat legs. This photo shows two people playing saxophones against a plain backdrop, with no cat or animal faces, no car grilles or headlights, and no fur-and-leg textures. The brass keys and human faces might give a weak, incidental response, but probably nowhere near 30% of the neuron's maximum.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 18567, "imagenet_class": "tarantula", "frac_of_max": 0.0313, "on": false, "p_on": 0.35, "reasoning": "In Zoom In, 4e:55 was the example polysemantic neuron: it responds to cat faces, the fronts of cars, and cat legs. The essay text and figure supplied here are placeholder text and a blank image, so they add nothing. This photo has no cat face or car front. The only plausible trigger is the tarantula's hairy, orange-brown, segmented legs, which loosely resemble furry cat or fox legs and paws. That makes a partial response possible but far from certain.\n\n35"}
{"prompt": "C: + lorem ipsum control", "gid": 36186, "imagenet_class": "papillon", "frac_of_max": 0.4589, "on": true, "p_on": 0.6, "reasoning": "The essay text and figure provided are placeholders (lorem ipsum and a blank image), so this prediction relies only on what Distill's Zoom In reported for 4e:55. There it was described as a polysemantic neuron responding to cat faces, car fronts and cat legs. This papillon has a small, pointed, cat-like face with large erect ears and a clearly visible frontal-ish head, plus thin furry legs, which plausibly overlaps with the cat-face/leg features this neuron detects. It is still a dog, though, so the response may be weaker than for a true cat face.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 30630, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.4891, "on": true, "p_on": 0.7, "reasoning": "The provided essay text is placeholder (lorem ipsum) and the figure is blank, so I'm relying on prior knowledge of 4e:55. In Zoom In it was described as polysemantic, responding to cat faces, fox faces and the fronts of cars. This Shetland sheepdog has a strongly fox-like face: a narrow pointed muzzle, orange-tan and white fur, and erect pointed ears, all seen roughly head-on. That face plausibly drives the fox-face part of the neuron, though a dog may not match as strongly as a true fox or cat.\n\n70"}
{"prompt": "C: + lorem ipsum control", "gid": 13890, "imagenet_class": "rock python", "frac_of_max": 0.0284, "on": false, "p_on": 0.15, "reasoning": "The Distill \"Zoom In\" article describes 4e:55 as a polysemantic neuron that responds to cat faces, cat legs, and the fronts of cars. This photo shows a python's head and scaly coils behind a mesh net. There is no fur, no feline facial structure, and nothing car-like. The animal eye and head shape might give a weak face-like signal, but reptile scales and mesh texture are unlikely to push it past 30% of its maximum.\n\n15"}
{"prompt": "C: + lorem ipsum control", "gid": 43544, "imagenet_class": "minibus", "frac_of_max": 0.3758, "on": true, "p_on": 0.85, "reasoning": "The essay excerpt is placeholder lorem ipsum text and the figure is blank, so neither tells me anything about this neuron; I'm relying on what I already know about it. In Distill's Zoom In, 4e:55 is the standard polysemantic example, responding to cat faces, cat legs and the fronts of cars. This photo shows a white van at a three-quarter angle with its front clearly visible: grille, headlights, windshield and bumper. That car-front pattern is exactly the vehicle facet of this neuron, so a strong response is likely.\n\n85"}
{"prompt": "C: + lorem ipsum control", "gid": 14395, "imagenet_class": "Pomeranian", "frac_of_max": 0.4721, "on": true, "p_on": 0.62, "reasoning": "This neuron is known to respond to cat faces, the fronts of cars, and cat legs. This photo shows a frontal, fluffy Pomeranian puppy face with pointed ears, dark round eyes and a small nose, which looks quite cat-like. That overlap with its cat-face detector could plausibly drive a moderate-to-strong response. Dog faces are not its preferred category, though, so the activation may fall short of the threshold.\n\n62"}
{"prompt": "C: + lorem ipsum control", "gid": 20788, "imagenet_class": "dung beetle", "frac_of_max": 0.0095, "on": false, "p_on": 0.12, "reasoning": "The quoted essay text and its figure are placeholders (lorem ipsum and a blank image), so they add nothing. I'm relying on the Distill description of 4e:55 as a polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. This photo shows a dung beetle pushing a dung ball across gravelly dirt. There are no cat features and nothing resembling a car front, only a small dark insect, a round brown mass and textured ground, which match none of the neuron's known triggers. So the neuron is most likely OFF, with some allowance for unpredictable activations from a polysemantic neuron.\n\n12"}
{"prompt": "C: + lorem ipsum control", "gid": 6886, "imagenet_class": "papillon", "frac_of_max": 0.4591, "on": true, "p_on": 0.6, "reasoning": "The essay text and figure provided here are placeholder content (lorem ipsum and a blank image), so I'm relying on what Distill reported: 4e:55 responds to cat faces, car fronts and cat legs. This photo shows a close-up, frontal papillon puppy face with large upright ears, dark eyes and a small snout. That cat- or fox-like facial layout could plausibly trigger the cat-face part of the neuron, though dog faces usually drive it less strongly than real cats.\n\n60"}
{"prompt": "C: + lorem ipsum control", "gid": 25093, "imagenet_class": "Dandie Dinmont", "frac_of_max": 0.047, "on": false, "p_on": 0.55, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from the Distill Circuits work. It responds to cat faces, car fronts and cat legs, and the essay text and figure supplied here are placeholder or blank, so they add nothing. The relevant content is the fluffy, frontal-facing white terrier face, which has the eyes, nose and fur texture that partially match the neuron's cat-face component. The man's face and the herringbone coat are unlikely to drive it. Dog faces often give only moderate activation on cat-face-like units, so this is roughly a coin flip, leaning slightly ON.\n\n55"}
{"prompt": "C: + lorem ipsum control", "gid": 32314, "imagenet_class": "kit fox", "frac_of_max": 0.4954, "on": true, "p_on": 0.78, "reasoning": "The essay text and caption supplied here are placeholder lorem ipsum and the figure is blank, so I'm relying only on what I know about this neuron from the Distill articles. There, 4e:55 was shown to respond to cat faces and to the fronts of cars, among other things. This photo has two young fox kits with clearly visible faces: pointed ears, eyes and snouts in roughly the frontal, close-up pose of the cat faces that drive this neuron. Fox and cat faces share a lot of mid-level structure, so a strong cat-face response here seems likely.\n\n78"}