  Deep neural network (DNN) models for computer vision are now capable of
human-level object recognition. Consequently, similarities in the performance
and vulnerabilities of DNN and human vision are of great interest. Here we
characterize the response of the VGG-19 DNN to images of the Scintillating Grid
visual illusion, in which white dots are perceived to be partially black. We
observed a significant deviation from the expected monotonic relation between
VGG-19 representational dissimilarity and dot whiteness in the Scintillating
Grid. That is, a linear increase in dot whiteness leads to a non-linear
increase and then, remarkably, a decrease (non-monotonicity) in
representational dissimilarity. In control images, mostly monotonic relations
between representational dissimilarity and dot whiteness were observed.
Furthermore, the dot whiteness level corresponding to the maximal
representational dissimilarity (i.e. onset of non-monotonic dissimilarity)
matched closely with that corresponding to the onset of illusion perception in
human observers. As such, the non-monotonic response in the DNN is a potential
model correlate for human illusion perception.
