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Color preference is an important aspect of visual experience, but little is known about why people in general like some colors more than others. Previous research suggested explanations based on biological adaptations [Hurlbert AC, Ling YL (2007) Curr Biol 17:623-625] and color-emotions [Ou L-C, Luo MR, Woodcock A, Wright A (2004) Color Res Appl 29:381-389]. In this article we articulate an ecological valence theory in which color preferences arise from people's average affective responses to color-associated objects. An empirical test provides strong support for this theory: People like colors strongly associated with objects they like (e.g., blues with clear skies and clean water) and dislike colors strongly associated with objects they dislike (e.g., browns with feces and rotten food). Relative to alternative theories, the ecological valence theory both fits the data better (even with fewer free parameters) and provides a more plausible, comprehensive causal explanation of color preferences.

Experimental evidence demonstrates robust cross-modal matches between music and colors that are mediated by emotional associations. US and Mexican participants chose colors that were most/least consistent with 18 selections of classical orchestral music by Bach, Mozart, and Brahms. In both cultures, faster music in the major mode produced color choices that were more saturated, lighter, and yellower whereas slower, minor music produced the opposite pattern (choices that were desaturated, darker, and bluer). There were strong correlations (0.89 < r < 0.99) between the emotional associations of the music and those of the colors chosen to go with the music, supporting an emotional mediation hypothesis in both cultures. Additional experiments showed similarly robust cross-modal matches from emotionally expressive faces to colors and from music to emotionally expressive faces. These results provide further support that music-to-color associations are mediated by common emotional associations.

When people make cross-modal matches from classical music to colors, they choose colors whose emotional associations fit the emotional associations of the music, supporting the emotional mediation hypothesis. We further explored this result with a large, diverse sample of 34 musical excerpts from different genres, including Blues, Salsa, Heavy metal, and many others, a broad sample of 10 emotion-related rating scales, and a large range of 15 rated music–perceptual features. We found systematic music-to-color associations between perceptual features of the music and perceptual dimensions of the colors chosen as going best/worst with the music (e.g., loud, punchy, distorted music was generally associated with darker, redder, more saturated colors). However, these associations were also consistent with emotional mediation (e.g., agitated-sounding music was associated with agitated-looking colors). Indeed, partialling out the variance due to emotional content eliminated all significant cross-modal correlations between lower level perceptual features. Parallel factor analysis (Parafac, a type of factor analysis that encompasses individual differences) revealed two latent affective factors—arousal and valence—which mediated lower level correspondences in music-to-color associations. Participants thus appear to match music to colors primarily in terms of common, mediating emotional associations.

People interpret abstract meanings from colors, which makes color a useful perceptual feature for visual communication. This process is complicated, however, because there is seldom a one-to-one correspondence between colors and meanings. One color can be associated with many different concepts (one-to-many mapping) and many colors can be associated with the same concept (many-to-one mapping). We propose that to interpret color-coding systems, people perform assignment inference to determine how colors map onto concepts. We studied assignment inference in the domain of recycling. Participants saw images of colored but unlabeled bins and were asked to indicate which bins they would use to discard different kinds of recyclables and trash. In Experiment 1, we tested two hypotheses for how people perform assignment inference. The local assignment hypothesis predicts that people simply match objects with their most strongly associated color. The global assignment hypothesis predicts that people also account for the association strengths between all other objects and colors within the scope of the color-coding system. Participants discarded objects in bins that optimized the color-object associations of the entire set, which is consistent with the global assignment hypothesis. This sometimes resulted in discarding objects in bins whose colors were weakly associated with the object, even when there was a stronger associated option available. In Experiment 2, we tested different methods for encoding color-coding systems and found that people were better at assignment inference when color sets simultaneously maximized the association strength between assigned color-object parings while minimizing associations between unassigned pairings. Our study provides an approach for designing intuitive color-coding systems that facilitate communication through visual media such as graphs, maps, signs, and artifacts.

The present study reveals that Election Day differentially affects the color preferences of US Republicans and Democrats. Voters’ preferences for Republican red and Democratic blue were assessed, along with several distractor colors, on and around the 2010 interim and 2012 presidential elections. On non-Election Days, Republicans and Democrats preferred Republican red equally, and Republicans actually preferred Democratic blue more than Democrats did. On Election Day, however, Republicans’ and Democrats’ color preferences changed to become more closely aligned with their own party’s colors. Republicans liked Republican red more than Democrats did, and no longer preferred Democratic blue more than Democrats did. These results are consistent with the hypothesis that color preferences are determined by people’s preferences for correspondingly colored objects/entities (Palmer & Schloss in Proceedings of the National Academy of Sciences 107:8877–8882, 2010 ). They further suggest that color preferences are calculated at a given moment, depending on which color–object associations are currently most activated or salient. Color preferences are thus far more dynamic and context-dependent than has previously been believed.

In visual communication, people glean insights about patterns of data by observing visual representations of datasets. Colormap data visualizations (“colormaps”) show patterns in datasets by mapping variations in color to variations in magnitude. When people interpret colormaps, they have expectations about how colors map to magnitude, and they are better at interpreting visualizations that align with those expectations. For example, they infer that darker colors map to larger quantities (dark-is-more bias) and colors that are higher on vertically oriented legends map to larger quantities (high-is-more bias). In previous studies, the notion of quantity was straightforward because more of the concept represented (conceptual magnitude) corresponded to larger numeric values (numeric magnitude). However, conceptual and numeric magnitude can conflict, such as using rank order to quantify health—smaller numbers correspond to greater health. Under conflicts, are inferred mappings formed based on the numeric level, the conceptual level, or a combination of both? We addressed this question across five experiments, spanning data domains: alien animals, antibiotic discovery, and public health. Across experiments, the high-is-more bias operated at the conceptual level: colormaps were easier to interpret when larger conceptual magnitude was represented higher on the legend, regardless of numeric magnitude. The dark-is-more bias tended to operate at the conceptual level, but numeric magnitude could interfere, or even dominate, if conceptual magnitude was less salient. These results elucidate factors influencing meanings inferred from visual features and emphasize the need to consider data meaning, not just numbers, when designing visualizations aimed to facilitate visual communication.

People form associations between colors and entities, which influence their evaluations of the world. These evaluations are dynamic, as specific associations become more or less active in people’s minds over time. We investigated how evaluations of colors (color preferences) changed over the course of fall, as color-associated fall entities became more prevalent in the environment. Participants judged their preferences for the same set of colors during nine testing sessions over 11 weeks during fall. We categorized the colors as Leaf and Non-Leaf Colors by matching them to leaves collected during the same period. Changes in preferences for Leaf Colors followed a quadratic pattern, peaking around when the leaves were most colorful and declining as winter approached. Preferences for Non-Leaf Colors did not significantly change. Individual differences in these changes could be explained by preferences for seasonal entities, as predicted by the differential activation hypothesis within the Ecological Valence Theory. The more a given individual liked fall-associated entities, the more their preference for Leaf Colors increased during fall. No analogous relations existed with winter-associated entities or Non-Leaf Colors. These results demonstrate the importance of studying temporal and individual differences for understanding preferences.

The ecological valence theory (EVT) posits that preference for a color is determined by people’s average affective response to everything associated with it (Palmer & Schloss, Proceedings of the National Academy of Sciences, 107 , 8877–8882, 2010). The EVT thus implies the existence of sociocultural effects: Color preference should increase with positive feelings (or decrease with negative feelings) toward an institution strongly associated with a color. We tested this prediction by measuring undergraduates’ color preferences at two rival universities, Berkeley and Stanford, to determine whether students liked their university’s colors better than their rivals did. Students not only preferred their own colors more than their rivals did, but the degree of their preference increased with self-rated positive affect (“school spirit”) for their university. These results support the EVT’s claim that color preference is caused by learned affective responses to associated objects and institutions, because it is unlikely that students choose their university or develop their degree of school spirit on the basis of preexisting color preferences.

Previous studies of preference for and harmony of color combinations have produced confusing results. For example, some claim that harmony increases with hue similarity, whereas others claim that it decreases. We argue that such confusions are resolved by distinguishing among three types of judgments about color pairs: (1) preference for the pair as a whole, (2) harmony of the pair as a whole, and (3) preference for its figural color when viewed against its colored background. Empirical support for this distinction shows that pair preference and harmony both increase as hue similarity increases, but preference relies more strongly on component color preference and lightness contrast. Although pairs with highly contrastive hues are generally judged to be neither preferable nor harmonious, figural color preference ratings increase as hue contrast with the background increases. The present results thus refine and clarify some of the best-known and most contentious claims of color theorists.

How can the large, systematic differences that exist between individuals’ color preferences be explained? The ecological valence theory (Palmer & Schloss, Proceedings of the National Academy of Sciences 107:8877–8882, 2010 ) posits that an individual’s preference for each particular color is determined largely by his or her preferences for all correspondingly colored objects. Therefore, individuals should differ in their color preferences to the extent that they have different preferences for the same color-associated objects or that they experience different objects. Supporting this prediction, we found that individuals’ color preferences were predicted better by their own preferences for correspondingly colored objects than by other peoples’ preferences for the same objects. Moreover, the fit between color preferences and affect toward the colored objects was reliably improved when people’s own idiosyncratic color–object associations were included in addition to a standard set of color–object associations. These and related results provide evidence that individual differences in color preferences are reliably influenced by people’s personal experiences with colored objects in their environment.