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Deep neural network (DNN) models for computer vision are now capable ofhuman-level object recognition. Consequently, similarities in the performanceand vulnerabilities of DNN and human vision are of great interest. Here wecharacterize the response of the VGG-19 DNN to images of the Scintillating Gridvisual illusion, in which white dots are perceived to be partially black. Weobserved a significant deviation from the expected monotonic relation betweenVGG-19 representational dissimilarity and dot whiteness in the ScintillatingGrid. That is, a linear increase in dot whiteness leads to a non-linearincrease and then, remarkably, a decrease (non-monotonicity) inrepresentational dissimilarity. In control images, mostly monotonic relationsbetween representational dissimilarity and dot whiteness were observed.Furthermore, the dot whiteness level corresponding to the maximalrepresentational dissimilarity (i.e. onset of non-monotonic dissimilarity)matched closely with that corresponding to the onset of illusion perception inhuman observers. As such, the non-monotonic response in the DNN is a potentialmodel correlate for human illusion perception.

ImageNet-trained deep neural network exhibits illusion-like response to the Scintillating Grid