Communication efficiency of color naming across languages provides a new framework for the evolution of color terms

Abstract

Languages vary in their number of color terms. A widely accepted theory proposes that languages evolve, acquiring color terms in a stereotyped sequence. This theory, by Berlin and Kay (BK), is supported by analyzing best exemplars ("focal colors") of basic color terms in the World Color Survey (WCS) of 110 languages. But the instructions of the WCS were complex and the color chips confounded hue and saturation, which likely impacted focal-color selection. In addition, it is now known that even so-called early-stage languages nonetheless have a complete representation of color distributed across the population. These facts undermine the BK theory. Here we revisit the evolution of color terms using original color-naming data obtained with simple instructions in Tsimane', an Amazonian culture that has limited contact with industrialized society. We also collected data in Bolivian-Spanish speakers and English speakers. We discovered that information theory analysis of color-naming data was not influenced by color-chip saturation, which motivated a new analysis of the WCS data. Embedded within a universal pattern in which warm colors (reds, oranges) are always communicated more efficiently than cool colors (blues, greens), as languages increase in overall communicative efficiency about color, some colors undergo greater increases in communication efficiency compared to others. Communication efficiency increases first for yellow, then brown, then purple. The present analyses and results provide a new framework for understanding the evolution of color terms: what varies among cultures is not whether colors are seen differently, but the extent to which color is useful.

Keywords: Color categories; Communication efficiency; Cross-cultural; Information theory; Universal.

Figure 1.

Focal colors in English, Bolivian Spanish, and Tsamine’ identified in an open-response paradigm. Participants were free to label colors using any terms they thought would be understood by someone who spoke their language. We defined the most frequently used modal terms as the set of focal color terms in the language. See Table 1.

Figure 2.

Probability of being selected as a focal color, regardless of the color term queried (grayscale shows % of respondents in each language group; N=99 Tsimane’; 55 Bolivian Spanish; 29 English). Asterisks identify the same set of chips to facilitate comparison across the languages. Note that Bolivian Spanish has a strong peak at H15 (azul), indicating that the “blue” part of color space is carved up into two focal colors. The data suggest that English “blue” may also be bimodal with a dominant peak at E14 and a minor peak at G14 (open arrow).

Figure 3.

The standard Munsell array (top) used in our study and in the World Color Survey, and the saturation of the color chips in array (bottom). Asterisks show the most frequently selected best exemplars of the English basic color terms red, orange, brown, yellow, green, blue, purple, pink.

Figure 4.

The relationship between focal-color status, lightness, color saturation, and communication efficiency. Data are for Tsimane’. Similar results obtained for English and Bolivian Bolivian Spanish (see Supplementary Figure 2). A. Focal color status is correlated with the saturation of the color chips. Rho = 0.36 (p=3×10⁻⁶). B. Focal color status is not correlated with stimulus lightness (p=0.99; the CIE Lightness values are absolute values of the difference between the CIE lightness value for each chip and the average CIE lightness values among the chips). C. Focal color status is correlated with the average surprisal of the chips, showing that focal color chips are communicated more efficiently. Rho = −0.28 (p=4×10⁻⁴). D. The average surprisal of the color chips is not correlated with saturation. Rho = 0.027 (p=0.73).

Figure 5.

The average surprisal of a language as a function of the number of focal color terms queried in the language (data from the World Color Survey), rho = 0.1 (p = 0.3).

Figure 6.

Relative change in communication efficiency of basic color categories as a function of the overall communicative efficiency of the color-naming system. As languages increase in communicative efficiency about color, blue and green remain the least efficiently communicated colors and red and orange remain the most efficiently communicated colors. Yellow, brown, purple and pink show relatively higher shifts in communication efficiency. Brown undergoes an initial drop in average surprisal, coinciding with the change in communicative efficiency of yellow, and a second drop in average surprisal coinciding with the improvement in communication efficiency of purple/pink. Each panel shows the same data, with the 95% confidence intervals of yellow, brown, pink, and purple (top to bottom). Vertical dashed lines show the point at which the average surprisal for the focal colors are significantly different from blue (95% C.I.). The 95% C.I. were generated by bootstrapping the individual responses in the WCS data for each language.

Figure 7.

Binary classification of focal-color surprisal values for each language of the World Color Survey; 95% C.I. shown. A. From the individual responses in each language to each color, we estimated a normal distribution of the surprisal values for the color chips identified in English as focal red, orange, yellow, green, blue, brown, and purple. We randomly sampled a surprisal value from each of the seven distributions for all languages and performed binary k-means clustering, into either a low-communication efficiency bin or the high-communication efficiency bin. The sampling procedure was repeated 100 times, providing an estimate of the percentage of times the color chip ended up in each bin. The classification of the seven colors for each language were rank-ordered according to the overall communication efficiency of the languages. Dashed vertical lines correspond to the point along the x-axis where the focal colors differed from blue (as shown in Figure 6). B. Smooth curves with arrowheads indicating the inflection points for the data shown in (A).

Figure 8.

Stages in the evolution of color language suggested by the information theory analysis of color-naming data across the World Color Survey. The stages are determined from an analysis of the relative efficiency of naming colors, as a function of the overall increase in communication efficiency about color (see Figure 5). All languages, regardless of their color-naming systems, show higher communicative efficiency for red and orange, compared to green and blue. As the overall communicate efficiency of a color-naming system increases, some colors undergo relatively greater improvement. Stage I has consensus categories for red, orange, and “cool”; Stage II: red, orange, yellow/brown, and “cool”; Stage III, red, orange, yellow/brown, purple and “cool”.