A psychophysically tuned computational model of human primary visual cortex produces geometric optical illusions

Abstract

Geometrical optical illusion (GOIs) are mismatches between physical stimuli and perception. GOIs provide an access point to study the interplay between sensation and perception, yet there is scant quantitative investigation of the extent to which different GOIs rely on similar or distinct brain mechanisms. We addressed this knowledge gap. First, 30 healthy adults reported quantitatively their perceptual biases with three GOIs, whose physical properties parametrically varied on a trial-by-trial basis. Biases observed with one GOI were unrelated to those observed with another GOI, suggestive of (partially) distinct underlying mechanisms. Next, we used these psychophysical results to tune a computational model of primary visual cortex that combines parameters of orientation, selectivity, intra-cortical connectivity, and long-range interactions. We showed that similar biases could be generated in-silico, mirroring those observed in humans. Such results provide a roadmap whereby computational modelling, informed by human psychophysics, can reveal likely mechanistic underpinnings of perception.