Significance The number of color terms varies drastically across languages. Yet despite these differences, certain terms (e.g., red) are prevalent, which has been attributed to perceptual salience. This work provides evidence for an alternative hypothesis: The use of color terms depends on communicative needs. Across languages, from the hunter-gatherer Tsimane' people of the Amazon to students in Boston, warm colors are communicated more efficiently than cool colors. This cross-linguistic pattern reflects the color statistics of the world: Objects (what we talk about) are typically warm-colored, and backgrounds are cool-colored. Communicative needs also explain why the number of color terms varies across languages: Cultures vary in how useful color is. Industrialization, which creates objects distinguishable solely based on color, increases color usefulness.

Abstract 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 color-naming 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.

The question of color-naming systems has long been caught in the cross-fire between universality and cultural relativism. Cross-cultural studies of color naming appear to indicate that color categories are universal (1⇓–3). However, the variability in color category boundaries among languages (4), and the lack of consensus of the forces that drive purported universal color categories (5, 6), promotes the idea that color categories are not universal, but shaped by culture (7). Here, we focus on two color categories, WARM and COOL, which are not part of the canonical set of “basic” categories proposed by Berlin and Kay but which nonetheless may be fundamental (8, 9), and which might relate to the basic white/black categories occupying the first stage in the Berlin/Kay hierarchy. Are WARM and COOL universal categories, and if so, why? Here we address these questions by collecting an extensive original dataset in three cultures and leveraging an information-theoretic analysis that has been useful in uncovering the structure of communication systems (10, 11).

The World Color Survey (WCS) provides extensive data on color naming by 110 language groups (www1.icsi.berkeley.edu/wcs/data.html). However, the WCS instructions are complex, and different WCS researchers likely adopted different methods (SI Appendix, section 1 and, Figs. S1 and S2), possibly undermining the validity of the WCS data (12, 13). To evaluate this possibility, we obtained extensive color-labeling data using two extreme versions of how the WCS instructions might have been implemented: a “free-choice” paradigm that placed no restrictions on how participants could name colors, and a “fixed-choice” paradigm on separate participants, where participants were constrained to only say the most common terms we obtained in the free-choice paradigm. We conducted experiments in three groups: the Tsimane' people, an indigenous nonindustrialized Amazonian group consisting of about 6,000 people from lowland Bolivia who live by farming, hunting, and foraging for subsistence (14); English speakers in the United States; and Bolivian-Spanish speakers in Bolivia, neighboring the Tsimane'. Tsimane' is one of two languages in an isolate language family (Mosetenan). Although there is trade with local Bolivian towns, most of the Tsimane' participants have limited knowledge of Spanish. To the extent that the communities have organized schooling, education is conducted in Tsimane'.

We analyzed our data using information theory, building on work that suggests that color naming can be better understood by considering informativeness rather than opponent-process theory (3, 11, 15). This analysis can be understood in terms of a communication game (16⇓–18). Imagine that a speaker has a particular color chip c in mind and uses a word w to indicate it. The listener has to correctly guess c, given w. On each trial, the listener guesses that c is among a set of the chips; the listener can pick a set of any size and is told “yes” or “no.” The average number of guesses an optimal listener would take to home in on the exact color chip provides a measure of the listener’s average surprisal (S, measured in bits; Eq. 1), a quantitative metric of communication efficiency. The surprisal score for each color c is computed by summing together a score for each word w that might have been used to label c, which is calculated by multiplying P(w|c) by −log(P(c|w), the listener’s surprisal that w would label c. We estimate P(c|w) via Bayes Theorem assuming a uniform prior on P(c). S ( c ) = ∑ w P ( w | c ) log 1 P ( c | w ) . [1]The novelty of our analysis is to define communicative efficiency about a particular color chip, relative to the set of color chips, so that we have an information-theoretic measure—average surprisal—that allows us to rank the colors for their relative communicative efficiency within a language. To compare color communication efficiency across languages, we follow others (3, 11) in estimating the overall informativeness of the color system of each language by averaging the average surprisal across the chips (Eq. 2; see SI Appendix, section 3 for a worked-out example): ∑ c P ( c ) S ( c ) . [2]

Discussion The debate on the origins of color categories pits the hypothesis that color-naming systems emerge from universal underlying principles determined innately against the view that culture determines color categories; it is often implied that only one or the other of these theories is correct. Our results favor a reconciliation of these ideas through the the efficient-communication hypothesis (11), which states that categories reflect a tradeoff between informativeness of the terms and their number (10). Cultures across the globe show common patterns in color naming, and even languages with few high consensus color terms appear to have a complete color lexicon distributed across the population, as shown by Lindsey et al. (3) for the Hadza of Africa and by us in the Tsimane' of South America. These common patterns across cultures suggest some universal constraint on color naming, but the variability in communicative efficiency about color terms across cultures suggests that culture plays a role too. According to the communication-efficiency hypothesis, if a culture has little need for many high-consensus color categories, it is simpler in that communication system not to have them. We show that all cultures around the world favor communication about warm colors over cool colors, and that this phenomenon reflects a universal feature of natural scenes: Objects defined by human observers tend to be warm colored while backgrounds tend to be cool colored. These results provide evidence that usefulness is the reason for the addition of color terms (36, 37). For example, there simply are not that many natural blue objects, which may explain why many languages acquire the term “blue” relatively late (this left Homer scrambling to come up with an alternative description for the sea: “wine-dark” instead of “blue”; ref. 34). That many if not all “basic” color terms derive, historically, from the names of objects we care about (or cared about) provides yet another clue that usefulness is the principal force that drives color categorization. Consider “orange.” Our results suggest that the color statistics of natural objects establish the relative salience of different colors and the informativeness of the associated terms. But we recognize that it is possible that the causal relationship is the inverse: that important natural objects acquired warm coloring to exploit the salience of these colors to trichromatic primates, for example to attract primates to assist in seed dispersal (32). Although all languages appear to possess a fundamental distinction between warm and cool colors, the large variance of average surprisal values across languages suggests that the average usefulness of color varies among language groups. Our results on object/color associations support this hypothesis by showing that many objects that are common in both Tsimane' and US cultures have a diagnostic color term in English but not in Tsimane'. These results support the idea that color is not as useful for Tsimane' as it is for English and Bolivian-Spanish, consistent with findings in other non-Western groups (36). The Tsimane’ have an extensive botanical vocabulary (14), which might obviate the need for color terms in their culture, which is heavily dependent on natural objects. Our results in a contrastive-naming task (Fig. 6) provide direct support for the idea that the predominance of artificially colored objects in Western cultures promotes the usefulness of color and, consequently, increases color-naming efficiency. The number of color terms used by Tsimane' individuals and the efficiency of color-term use increased with more exposure to Bolivian-Spanish (SI Appendix, section 10, Fig. S16, and Table S6), suggesting a mechanism for cultural transmission. The present results confirm, for the Tsimane', prior work showing that language groups with relatively few consensus color categories nonetheless possess a large repertoire of color categories distributed across the population (3). The forces that give rise to the partitioning of color space into color categories more refined than warm/cool remain unclear, but our work promotes the idea the main driving force is the extent to which color categories are behaviorally relevant. Contributions to behavioral relevance may depend on stimulus saturation (38) and reflect efficient partitioning of the irregularities in perceptual color space (2, 15). Relative lightness must also be important in establishing behavioral relevance. Black was communicated among the Tsimane' with high efficiency, which replicates prior work showing that black and white are named reliably across all languages. The efficient communication of black is consistent with our overall hypothesis, that color categories reflect usefulness: Blacks are prevalent in natural images, and retinal processing favors darks over lights (39). Color processing depends upon an extensive network of brain regions that process retinal signals (40), culminating in the highest levels of processing, in frontal cortex (41). The present report leverages color language as perhaps the best readout of this machinery as it pertains to behavior to uncover the forces behind the most fundamental color categorization, warm versus cool. Finally, we wonder to what extent the fundamental asymmetry in usefulness associated with warm colors versus cool colors underlies their emotional valence (42), as indicated by the warm/cool terminology itself.

Methods All experimental procedures were approved by Massachusetts Institute of Technology’s Committee on the Use of Humans as Experimental Subjects. Informed consent was obtained from all participants, as required by the Committee. Color-Naming Munsell Chips. Participants were presented with each of 80 colored chips evenly sampling the standard Munsell array of colors (SI Appendix, Table S2) in a different random order for each participant under controlled lighting conditions using a light box (nine phosphor broadband D50 color-viewing system, model PDV-e, GTI Graphic Technology, Inc.). Each participant initially took a test of normal color vision (43). All participants that failed this task (∼5% of participants) were excluded from further study. The task was performed indoors. For Tsimane' speakers, we assessed their knowledge of Spanish using a short questionnaire of very common words. Most participants did not know all of these words, suggesting a poor knowledge of Spanish for most (see SI Appendix, section 1 for more information). Free-Choice Version. The instructions for this task were as follows: We want to know the words for colors in English/Spanish/Tsimane'. So we want you to tell us the colors of these cards. Tell us what other English/Spanish/Tsimane' speakers would typically call these cards. (Fixed-choice version of the task: There are 11 choices: black, white, red, green, blue, purple, brown, yellow, orange, pink, gray. Choose the closest color word.) See SI Appendix, section 1 for the Tsimane' and Spanish translations, with color terms from Spanish/Tsimane' for the fixed-choice version in SI Appendix, Table S3. Fifty-eight Tsimane'-speaking adults (mean age: 33.2 y; SD: 12.8 y; range 16–78; 38 females); 20 Spanish-speaking adults (mean age: 29.0 y; 9.1 SD: years; range 18–55; 11 females); and 31 English-speaking adults (mean age: 37.1 y; 11.6 SD: years; range 21–58; 10 females) completed this task. From the complete list of terms used in the population, we determined for each chip the term that was used most often (the modal term). Across the chips, we tallied the set of unique modal terms, and removed from the list any modal terms that were only used for one chip, thus omitting maracayeisi in Tsimane' (a color chip on which jäinäs, glossed red, was a close second), and fuschia and piel (“skin color”) in Bolivian Spanish. This set of terms provides an estimate of the basic color terms in the population (SI Appendix, Table S3). Fixed-Choice Version. Forty-one Tsimane' adults (mean age: 38.9 y; SD: 17.6 y; range 18–74; 24 females); 25 Spanish adults (mean age: 25.7 y; 9.1 SD: years; range 18–55; 13 females); and 29 English adults (mean age: 26.0 y; 8.9 SD: years; range 18–55; 14 females) took part, where participants were given a fixed set of color labels to choose from for each color chip: the modal terms discovered in the free-choice version. We also included black/negro, white/blanco/gray/gris in the English/Spanish tasks, because they are regarded as basic color terms. Focal Colors. Following the Munsell-chip color naming experiment, each participant (n = 99 Tsimane'; 55 Spanish; 29 English) was then presented with a standard 160-chip Munsell array of colors (illuminated by the lightbox) and was asked to point out the best example of several color words (“focal” colors). English speakers were asked about red, green, yellow, blue, orange, brown, purple, and pink. Spanish speakers were asked about rojo, verde, amarillo, azul, celeste, anaranjado, morado, cafe, and rosa. For Tsimane', in the free-choice version of the task, we asked about the colors that the participant produced. For the fixed-choice version, we asked about jäinäs (∼red), yushñus (∼blue), shandyes (∼green), itsidyeisi (∼purple), cafedyeisi (∼brown), and chames (∼yellow). The chips most often selected as focal colors for all of the terms probed are given in SI Appendix, Table S4. To show the population results and evaluate the possible privilege of the unique hues, we computed the probability density function for each of the four unique hues over the grid space. The contours in Fig. 2 show the probability that a given color word was used for each color chip on the basis of our empirical data. Color-Naming Objects from Memory. Following the preceding two tasks, each participant (n = 99 Tsimane'; 55 Spanish; 29 English) was read a list of items that have typical colors, and was asked what color each item was in their experience. Each of the items had a conventional color in Tsimane' culture, usually the same as that in North American culture: a cloud (white/gray), dirt (brown), grass (green), hair (black), teeth (white), rice (white), an unripe banana (green), a ripe banana (yellow), the sky (blue), corn (yellow), yucca for eating (white), the outer husk of yucca (brown), blood (red), fire (orange/yellow), a carrot (orange), and a ripe tomato (red). Spontaneous Use of Color in a Contrastive-Labeling Task. A subset of the participants who participated in the other experiments also took part in a contrastive-labeling experiment; some people took part only in the contrastive-labeling experiment and not in the Munsell-chip color-naming experiment. n = 28 Tsimane' adults (mean age: 30.9 y; SD: 17.8 y; range 18–90; 23 females) and 29 English participants (mean age: 35.5 y; SD: 11.0 y; range 21–58; nine females). Eight pairs of objects were obtained for naming, including four pairs of fruits and vegetables: a ripe (yellow) banana and an unripe (green) banana; a ripe (red) tomato and an unripe (green) tomato; a red apple and a green apple; a red bell pepper and a green bell pepper; and four pairs of artifact objects: a red and a yellow cup; a red and a blue comb; a red and a yellow piece of rope; and a red and a green small basket. All of the pairs of objects were identical except for their colors. Our method was an adaptation of the method used by Sedivy (35). Participants were first presented with one object and were asked to name the object. Then the second object of the same type was presented for naming. Each participant named all eight pairs of objects consecutively in this fashion. There were four different random orders of presentation. The experimenter/translator transcribed what was said.

Acknowledgments We thank Ricardo Godoy and Tomas Huanca for logistical help; Dino Nate Añez and Salomon Hiza Nate for help translating and running the task; Rashida Khudiyeva and Isabel Rayas for helping with the timing of the reaction times for the color and object naming; Serena Eastman and Theodros Haile who helped segmenting images for the natural-scene analysis; and Evelina Fedorenko, Alexander Rehding, Roger Levy, Caroline Jones, Joshua Green, and Neir Eshel for comments on the manuscript. The work was supported by NIH Grant R01EY023322 (to B.R.C.), NSF IOS Program Award 1353571 (to B.R.C.) and Linguistics Program Award 1534318 (to E.G.), and the Intramural Research Program of the NIH (National Eye Institute/National Institute of Mental Health). Additional support was provided by University of Rochester start-up funding (to S.T.P.) and the Wellesley College Undergraduate Research Summer Program.

Footnotes Author contributions: E.G., S.T.P., and B.R.C. designed research; E.G., R.F., J.J.-E., S.R., M.G., and B.R.C. performed research; E.G., R.F., J.J.-E., K.M., L.B., S.R., and B.R.C. analyzed data; and E.G. and B.R.C. wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

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