Jameson Kimberly A, Komarova Natalia L
Institute for Mathematical Behavioral Sciences, University of California, Irvine, Social Science Plaza, Irvine, California 92697-5100, USA.
J Opt Soc Am A Opt Image Sci Vis. 2009 Jun;26(6):1414-23. doi: 10.1364/josaa.26.001414.
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)].
通过使用人工主体群体分类游戏来研究颜色分类的演变，利用法恩斯沃思 - 芒塞尔100色调测试性能对观察者类型进行建模，以捕捉人类在颜色分类上的处理限制。分别考察了正常主体和色盲主体的同质群体。这两种类型的群体都产生了接近最优的分类解决方案。虽然正常观察者产生的分类解决方案表现出旋转不变性，但色盲者的解决方案表现出对称性破缺特征。特别地，发现色盲者的局部混淆区域倾向于排斥颜色类别边界，而全局混淆对吸引类别边界。这两种机制之间的权衡产生了群体分类解决方案，其中颜色边界锚定在刺激空间中的一个位置子集上。一篇配套论文将这些研究扩展到更现实的异质主体群体[《美国光学学会杂志A》26, 1424 - 1436 (2009)]。
