3 ms·

All examples (sheep vs chair, dog vs bottle, cat vs truck, elephant vs clock) are organic vs inorganic. Perhaps participants are reusing bouba/kiki[1] skills,

All examples (sheep vs chair, dog vs bottle, cat vs truck, elephant vs clock) are organic vs inorganic. Perhaps participants are reusing bouba/kiki[1] skills, evaluating whether the image looks organic (rounded) or inorganic (spiky) - and making their choice accordingly. [1]: https://en.wikipedia.org/wiki/Bouba/kiki_effect https://en.wikipedia.org/wiki/Bouba/kiki_effect

japanman185 3y ago
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markisus 3y ago
I think they took a picture of a vase, and created two derivative images of the vase, one which was more cat-like and one which was more truck-like. In both cases, participants did better than average at guessing which one was the derivative image. In any case, I don't think this is explained by the bobo-kiki effect, since they were able to get the subjects to select both inorganic (truck) and organic (cat) derivatives of the same source image.
- merelysounds 3y ago
  If you look at the sheep vs chair example (last figure, perturbation 16), the “chair” one has more angular shapes in the background, while the “sheep” one has more rounded, organic lines there.
trhway 3y ago
doesn't seem so. I think the key is probably here >perturbed by a seemingly random pattern ie. it doesn't really seem like a random pattern. It looks more like a match pattern taken from the mid-layers of a visual NN, the pattern of edges and vertexes which "lights up" more frequently when a cat image is presented to the NN. That would explain why an NN (even another one as they all converge on processing edges and vertexes) would mistake that image of vase for cat - in the altered image of vase those "cat" pattern edges and vertexes are present, just at the attenuated 2 levels amplitude, and the first layers of NN (usually converged to Gabors and Gaussian pulling like in the biological visuals cortex) would still detect those edges and will send it to the mid layers where it will "light up" similar "cat" patterns (it would be very illustrative if the authors provided the mixed-in image subjected to edge detection - that should clearly show a mix of original vase image edges and the added "cat" pattern ones). That also explains why people would point to such an image as more cat-like - the first layers and even the first-mid layers in our visual cortex are very similar to what the first and the mid layers of a well-trained NN converge to, and those our layers would similarly detect and "light up" to those mixed-in cat pattern edges and vertexes - we are just better at signal/noise, ie. clearly recognizing the vase and thus suppressing the "noise" of those cat patterns triggered in our visual cortex, so we wouldn't misclassify the image as a cat, yet we do feel something cat-like. Would be interesting to give that test to the people under hallucinogens, i.e. when the visual cortex mid-layers signals are subjected to much less frontal processing and the "noise" rises to the level of "signal".

japanman185 3y ago

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markisus 3y ago

I think they took a picture of a vase, and created two derivative images of the vase, one which was more cat-like and one which was more truck-like. In both cases, participants did better than average at guessing which one was the derivative image. In any case, I don't think this is explained by the bobo-kiki effect, since they were able to get the subjects to select both inorganic (truck) and organic (cat) derivatives of the same source image.

merelysounds 3y ago
If you look at the sheep vs chair example (last figure, perturbation 16), the “chair” one has more angular shapes in the background, while the “sheep” one has more rounded, organic lines there.

trhway 3y ago

doesn't seem so. I think the key is probably here >perturbed by a seemingly random pattern ie. it doesn't really seem like a random pattern. It looks more like a match pattern taken from the mid-layers of a visual NN, the pattern of edges and vertexes which "lights up" more frequently when a cat image is presented to the NN. That would explain why an NN (even another one as they all converge on processing edges and vertexes) would mistake that image of vase for cat - in the altered image of vase those "cat" pattern edges and vertexes are present, just at the attenuated 2 levels amplitude, and the first layers of NN (usually converged to Gabors and Gaussian pulling like in the biological visuals cortex) would still detect those edges and will send it to the mid layers where it will "light up" similar "cat" patterns (it would be very illustrative if the authors provided the mixed-in image subjected to edge detection - that should clearly show a mix of original vase image edges and the added "cat" pattern ones). That also explains why people would point to such an image as more cat-like - the first layers and even the first-mid layers in our visual cortex are very similar to what the first and the mid layers of a well-trained NN converge to, and those our layers would similarly detect and "light up" to those mixed-in cat pattern edges and vertexes - we are just better at signal/noise, ie. clearly recognizing the vase and thus suppressing the "noise" of those cat patterns triggered in our visual cortex, so we wouldn't misclassify the image as a cat, yet we do feel something cat-like. Would be interesting to give that test to the people under hallucinogens, i.e. when the visual cortex mid-layers signals are subjected to much less frontal processing and the "noise" rises to the level of "signal".