Pessoa L, Mingolla E, Neumann H
Department of Cognitive and Neural Systems, Boston University, MA 02215, USA.
Vision Res. 1995 Aug;35(15):2201-23. doi: 10.1016/0042-6989(94)00313-0.
A neural network model of brightness perception is developed to account for a wide variety of data, including the classical phenomenon of Mach bands, low- and high-contrast missing fundamental, luminance staircases, and non-linear contrast effects associated with sinusoidal waveforms. The model builds upon previous work on filling-in models that produce brightness profiles through the interaction of boundary and feature signals. Boundary computations that are sensitive to luminance steps and to continuous luminance gradients are presented. A new interpretation of feature signals through the explicit representation of contrast-driven and luminance-driven information is provided and directly addresses the issue of brightness "anchoring". Computer simulations illustrate the model's competencies.
