Influence of spatial context over color perception: unifying chromatic assimilation and simultaneous contrast into a neural field model
Anna Song, EPFL STI LIB

Meeting • 22 May 2018

Abstract
We propose a neural field model of color perception. This model reconciles into a common framework two apparently contradictory perceptual phenomena, simultaneous contrast and chromatic assimilation. Previous works showed that they act simultaneously and can produce larger shifts in color comparison matching when acting synergistically with a spatially oscillating pattern. These perceptual chromatic shifts are expressed in s-coordinates of a cone-based chromaticity space. It is suggested that, at some spatial location of an image viewed by a human observer, the color sensation elicited at this point tends to be perceptually attracted towards colors of spatially neighboring points, while being repelled by colors of farther points towards their respective opponents, and that these opposing actions occur at slightly different spatial scales, which allows to combine them. However, a linear receptive field model using a simple convolution to predict color shift is not sufficient to explain the dependency of the shift on the initial chromatic coordinates of the test color. We introduce a neural field model, in which a first order integro-differential equation regulates the evolution of neural activities in the cortical hypercolumns assumed to code for colors. We first recall the mathematical definition of “colors”. We also suppose the proper definition of a “good” opponent space. In order to perform the mathematical analysis of the model we make several simplifying assumptions. The connectivity kernel is assumed to be separable into the color and physical spaces, although this can be generalized to sums of separable kernels. As a first approximation, we also suppose that the three chromatic channels do not interact. Our model depends on a number of perceptually meaningful parameters. We study the bifurcations of its solutions around stationary solutions. Under some symmetry and periodicity hypotheses, we can predict, using equivariant bifurcation theory, the appearance of visual patterns called “planforms”, which can be interpreted as color hallucinations. Future psychophysical experiments, may confirm this and support the relevance of the model. We have implemented the model in Python to simulate the evolution of the neural activities in the hypercolumns. We validate the model by fitting its parameters to real data, using the PyTorch library. We perform a multi-parameter regression to the data. The results should show that our model is capable of explaining both contrast and assimilation, and that the optimal parameters vary across human subjects according to their perceptual biases.