UC Irvine

Color categorization in humans has been actively studied in the behavioral and social sciences for many decades. Key ideas in this field are \textit{basic color terms} (BCTs) and corresponding \textit{basic color categories} (BCC's, each made up of a basic color term and the colors it describes); the identification of BCT's and BCCs has historically required knowledge of the underlying culture and language, and therefore involved subjective analysis. In this dissertation, we present a quantitative data driven method of identifying the BCTs of a language using a category strength function $CS$. By setting a threshold, the CS function identifies which color terms are BCTs, and which are not. We obtain results which are consistent with the classical method of color categorization, but achieve better consistency and avoid subjectivity. We also analyze several methods of identifying the color foci, or best exemplars. We further present a quantitative method of identifying the boundary of a BCT with a (color) stimulus strength function $StS$ and show that the relationship between BCC hearts and BCC boundaries follows a square-root pattern. Finally, we apply our methods to the study of category scheme evolution and the study of male/female color categorization behaviors. We find that the differences observed between male and female categorization schemes are statistically significant. Throughout this work, we use data provided by the World Color Survey Data Archives.