What determines how languages categorize colors? We analyzed results of the World Color Survey (WCS) of 110 languages to show that despite gross differences across languages, communication of chromatic chips is always better for warm colors (yellows/reds) than cool colors (blues/greens). We present an analysis of color statistics in a large databank of natural images curated by human observers for salient objects and show that objects tend to have warm rather than cool colors. These results suggest that the cross-linguistic similarity in color-naming efficiency reflects colors of universal usefulness and provide an account of a principle (color use) that governs how color categories come about. We show that potential methodological issues with the WCS do not corrupt information-theoretic analyses, by collecting original data using two extreme versions of the colornaming task, in three groups: the Tsimane', a remote Amazonian hunter-gatherer isolate; Bolivian-Spanish speakers; and English speakers. These data also enabled us to test another prediction of the color-usefulness hypothesis: that differences in color categorization between languages are caused by differences in overall usefulness of color to a culture. In support, we found that color naming among Tsimane' had relatively low communicative efficiency, and the Tsimane' were less likely to use color terms when describing familiar objects. Color-naming among Tsimane' was boosted when naming artificially colored objects compared with natural objects, suggesting that industrialization promotes color usefulness.color categorization | information theory | color cognition | Whorfian hypothesis | basic color terms

We hypothesized that the parts of scenes identified by human observers as “objects” show distinct color properties from backgrounds, and that the brain uses this information towards object recognition. To test this hypothesis, we examined the color statistics of naturally and artificially colored objects and backgrounds in a database of over 20,000 images annotated with object labels. Objects tended to be warmer colored (L-cone response > M-cone response) and more saturated compared to backgrounds. That the distinguishing chromatic property of objects was defined mostly by the L-M post-receptoral mechanism, rather than the S mechanism, is consistent with the idea that trichromatic color vision evolved in response to a selective pressure to identify objects. We also show that classifiers trained using only color information could distinguish animate versus inanimate objects, and at a performance level that was comparable to classification using shape features. Animate/inanimate is considered a fundamental superordinate category distinction, previously thought to be computed by the brain using only shape information. Our results show that color could contribute to animate/inanimate, and likely other, object-category assignments. Finally, color-tuning measured in two macaque monkeys with functional magnetic resonance imaging (fMRI), and confirmed by fMRI-guided microelectrode recording, supports the idea that responsiveness to color reflects the global functional organization of inferior temporal cortex, the brain region implicated in object vision. More strongly in IT than in V1, colors associated with objects elicited higher responses than colors less often associated with objects.