The World Color Survey: Data Analysis and Simulations

Abstract

The distribution of colors in the environment shapes local peoples’ perceptions of those colors, a phenomenon observable across all types of environments. We analyzed color categorization data from each of the 107 languages in the World Color Survey (WCS) database. Next, we grouped the WCS languages according to their geographic location, with reference to the seven terrestrial habitats (biomes) classified by the World Wildlife Fund (WWF). We developed a computer algorithm to establish the most frequently occurring colors in each environment based on the color distribution extracted from National Geographic natural images of the respective biomes. We then compared the average standardized value of the mode (i.e., most frequently occurring answers) for each group of WCS languages; we followed the same procedure for the most frequently occurring colors as well as the remaining colors. Results indicated statistically significant lower values of the average mode answers for the most frequently occurring colors. These results support our hypothesis that the environment type shapes color category boundaries. Further, we follow Steels and Belpaeme’s (Behav Brain Sci 28:469–489, 2005) model, which allows for computer simulations of the cultural emergence of color categories. An agent-based model of the cultural emergence of color categories shows that boundaries might be seen as a product of agent’s communication in a given environment. We propose the extension of this generic agent-based modeling framework to include a culturally driven emergence of color categories. We therefore underscore external constraints on cognition: the structure of the environment in which a system evolves and learns, and the learning capacities of individual agents. Finally, we discuss the methodological issues related to real data characterization (World Color Survey), as well as to the process of modeling the emergence of perceptual categories in human subjects.