A growing body of evidence suggests that the brain computes lightness in a two-stage process that involves (1) an early neural encoding of contrast at the locations of luminance borders in the visual image, and (2) a subsequent filling-in of the lightnesses of the regions lying between the borders. I will review evidence that supports this theory and present a computational model of lightness based on filling-in by a spatially-spreading cortical diffusion mechanism. The behavior of the model will be illustrate by showing how it quantitatively accounts for the lightness matching data of Rudd and Arrington. The model's performance will be compared that of other theories of lightness, including retinex theory, a modified version of retinex theory that assumes edge integration with a falloff in spatial weighting of edge information with distance, lightness anchoring based on the highest luminance rule, and the BCS/FCS filling-in model developed by Grossberg and his colleagues.
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