title: Evolutionary models of color categorization. I. Population categorization systems based on normal and dichromat observers.
abstract: 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)].
authors.affiliation: Institute for Mathematical Behavioral Sciences, University of California, Irvine, Social Science Plaza, Irvine, California 92697-5100, USA. kjameson@uci.edu
authors.forename: Kimberly A
authors.initials: KA
authors.lastname: Jameson
authors.forename: Natalia L
authors.initials: NL
authors.lastname: Komarova