Michael Rudd: Lightness computation by a neural filling-in mechanism

Abstract

Lightness perception is both an ancient and currently very active research area within visual perception. Over the years, many different types of explanations have been devised to account for lightness phenomena, including theories based on neural, ecological, gestalt-organizational, and cognitive principles. All of these perspectives are alive and well today. One important aspect of lightness appearance is its dependence on spatial context. For example, a gray paper viewed against a white background appears darker than the same paper viewed against a black background. This illusion, known in the trade as "simultaneous contrast," has been studied scientifically since at least the mid-19th Century, but it is not yet fully understood. I will survey some key findings and theories of contrast phenomena, then present my own quantitative theory of lightness computation based on a neural network mechanism. The model assumes the existence of visual maps in the brain in which lightness and darkness signals are separately represented, prior to being combined to create an achromatic color appearance signal. Within each map, color induction (influencing) signals spread from neurons that encode the luminance ratios at edges within the image, to fill-in regions between the edges, thus "painting" in the areas between the edges in the image with color. I will demonstrate that the model gives a good quantitative account of data from lightness matching experiments from my own lab, and I will show that the model can also account for phenomena that have been cited as evidence for alternative theories of lightness based on cognitive, ecological, and gestalt principles..