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Universality of color names

机译：颜色名称的通用性

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We analyzed the World Color Survey (WCS) color-naming data set by using k-means cluster and concordance analyses. Cluster analysis relied on a similarity metric based on pairwise Pearson correlation of the complete chromatic color-naming patterns obtained from individual WCS informants. 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 (it) 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. 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). Analysis of concordance in color naming within WCS languages revealed small regions in color space that exhibited statistically significantly high concordance across languages. These regions agreed well with five of six primary focal colors of English. Concordance analysis also revealed boundary regions of statistically significantly low concordance. These boundary regions coincided with the boundaries associated with English WARM and COOL. Our results provide compelling evidence for similarities in the mechanisms that guide the lexical partitioning of color space among WCS languages and English.
机译：我们通过使用k均值聚类和一致性分析来分析世界色彩调查（WCS）颜色命名数据集。聚类分析基于从各个WCS信息提供者获得的完整彩色命名模式的成对Pearson相关性，基于相似性度量。当K表示k均值聚类的数量从2变为10时，我们发现（i）聚类的平均颜色命名模式都容易掩盖为单个或复合英语模式，并且（它）的结构k均值聚类以分层方式展开，让人联想到颜色类别演变的柏林和凯序列。差距统计分析表明，WCS色度类别的最佳数目为8：红色，绿色，黄色或橙色，蓝色，紫色，棕色，粉红色和绿色（绿色或蓝色）。对WCS语言中颜色​​命名的一致性进行的分析显示，色彩空间中的小区域在统计上显示出跨语言的统计上显着较高的一致性。这些地区与英语的六个主要焦点颜色中的五个非常吻合。一致性分析还显示出统计学上显着较低一致性的边界区域。这些边界区域与与英语WARM和COOL关联的边界重合。我们的结果提供了令人信服的证据，证明了在WCS语言和英语之间指导色彩空间的词汇划分的机制的相似性。

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