Many of us “see red,” “feel blue,” or “turn green with envy.” Are such color-emotion associations fundamental to our shared cognitive architecture, or are they cultural creations learned through our languages and traditions? To answer these questions, we tested emotional associations of colors in 4,598 participants from 30 nations speaking 22 native languages. Participants associated 20 emotion concepts with 12 color terms. Pattern-similarity analyses revealed universal color-emotion associations (average similarity coefficient r = .88). However, local differences were also apparent. A machine-learning algorithm revealed that nation predicted color-emotion associations above and beyond those observed universally. Similarity was greater when nations were linguistically or geographically close. This study highlights robust universal color-emotion associations, further modulated by linguistic and geographic factors. These results pose further theoretical and empirical questions about the affective properties of color and may inform practice in applied domains, such as well-being and design.

Hebrew), and Sólveig Þorsteinsdóttir (Icelandic). Finally, we would like to thank all the participants.

When a free-listing task is used to elicit verbal concepts from a given semantic domain, it provides two indicators of the salience of each word for that linguistic community. These are the proportion of the subjects who include a word in their lists, and its average ranking priority position across the lists. The data also contain cues about the cognitive representation of the semantic domain, and in particular about the conceptual closeness among words. Closely associated words tend to prime each other and to appear in the lists in close succession. Clusters of mutually associated terms can be recognised, listed in one another's company, although with different priority for different subjects. We applied this approach to the domain of colour terms, converting lists for fourteen European languages into matrices of inter-term similarity, for analysis with multidimensional scaling (MDS) and hierarchical clustering. Two-dimensional MDS solutions or 'maps' were typically required to reflect two competing criteria by which terms were sequenced. Speakers of each language tended to follow a salience gradient, but also made separate clusters of fully-chromatic concepts -colour terms in strictu sensu -and unsaturated or desaturated concepts defined primarily by lightness rather than by hue. This and other features recurred across the languages despite their geographical and phylogenetic diversity, as cross-cultural universals in colour language, in addition to the well-known regularities governing basic colour terms and the stages of colour-lexicon development.

Cross-cultural comparisons of color perception and cognition often feature versions of the "similarity sorting" procedure. By interpreting the assignment of two color samples to different groups as an indication that the dissimilarity between them exceeds some threshold, sorting data can be regarded as low-resolution similarity judgments. Here we analyze sorting data from speakers of Italian, Russian, and English, applying multidimensional scaling to delineate the boundaries between perceptual categories while highlighting differences between the three populations. Stimuli were 55 color swatches, predominantly from the blue region. Results suggest that at least two Italian words for "blue" are basic, a similar situation to Russian, in contrast to English where a single "blue" term is basic.

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