Evolutionary models of color categorization. I. Population categorization systems based on normal and dichromat observers.

The evolution of color categorization is investigated using artificial agent population categorization games, by modeling observer types using Farnsworth-Munsell 100 Hue Test performance to capture human processing constraints on color categorization. Homogeneous populations of both normal and dichromat agents are separately examined. Both types of populations produce near-optimal categorization solutions. While normal observers produce categorization solutions that show rotational invariance, dichromats' solutions show symmetry-breaking features. In particular, it is found that dichromats' local confusion regions tend to repel color category boundaries and that global confusion pairs attract category boundaries. The trade-off between these two mechanisms gives rise to population categorization solutions where color boundaries are anchored to a subset of locations in the stimulus space. A companion paper extends these studies to more realistic, heterogeneous agent populations [J. Opt. Soc. Am. A26, 1424-1436 (2009)].