Then Berlin and Kay ( 2) advanced a very different view, based on their review of 98 world languages, 20 of which they studied empirically. Until the late 1960s, the prevailing view was that differences in color naming occur as a result of cultural factors, which were thought to operate through language to influence strongly individuals' awareness of their chromatic environment (e.g., ref. Languages differ greatly in how basic color terms are used to name colors. Our results provide compelling evidence for similarities in the mechanisms that guide the lexical partitioning of color space among WCS languages and English. These boundary regions coincided with the boundaries associated with English WARM and COOL. Concordance analysis also revealed boundary regions of statistically significantly low concordance. These regions agreed well with five of six primary focal colors of English. Analysis of concordance in color naming within WCS languages revealed small regions in color space that exhibited statistically significantly high concordance across languages. Gap statistical analysis showed that 8 was the optimal number of WCS chromatic categories: RED, GREEN, YELLOW-OR-ORANGE, BLUE, PURPLE, BROWN, PINK, and GRUE (GREEN-OR-BLUE). When K, the number of k-means clusters, varied from 2 to 10, we found that ( i) the average color-naming patterns of the clusters all glossed easily to single or composite English patterns, and ( ii) the structures of the k-means clusters unfolded in a hierarchical way that was reminiscent of the Berlin and Kay sequence of color category evolution. Cluster analysis relied on a similarity metric based on pairwise Pearson correlation of the complete chromatic color-naming patterns obtained from individual WCS informants. We analyzed the World Color Survey (WCS) color-naming data set by using k-means cluster and concordance analyses.
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