A Multi-Scale Network Model of Brightness Perception

Abstract

A neural network model of brightness perception is developed to account for a wide variety of difficult data, including the classical phenomenon of Mach bands and nonlinear contrast effects associated with sinusoidal luminance waves. The model builds upon previous work by Grossberg and colleagues on filling-in models that predict brightness perception through the interaction of boundary and feature signals. Model equations are presented and computer simulations illustrate the model's potential.