Elsevier

Cognition

Volume 195, February 2020, 104086
Cognition

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

https://doi.org/10.1016/j.cognition.2019.104086Get rights and content

Highlights

  • Whether a color chip is chosen as a best exemplar of a color term is influenced by stimulus saturation.

  • The efficiency with which a color chip is communicated is not influenced by stimulus saturation.

  • Estimates of communication efficiency are better than focal colors for understanding color naming.

  • As languages evolve, communication improves for some colors before others.

  • Communication efficiency estimates provide a new framework for understanding color-term evolution.

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.

Introduction

It is widely thought that color terms are acquired by all languages in the same order, in a stereotyped sequence determined predominantly by perceptual salience: black and white, then red, green and yellow (either order), blue, brown, purple, pink, orange, and gray (Berlin & Kay, 1969) [reviewed by (Regier, Kay, & Khetarpal, 2007; Zaslavsky, Kemp, Tishby, & Regier, 2019)]. In this scheme, these 11 colors are the complete set of basic color terms. The Berlin-Kay framework is supported by analysis of the best exemplars (“focal colors”) of basic color terms in the color-naming data of the World Color Survey (WCS) of 110 mostly unwritten languages (www1.icsi.berkeley.edu/wcs/data.html) (Kay & Maffi, 1999). The Berlin-Kay framework posits that the colors chosen as best exemplars are universal (Harkness, 1973) and have a physiological origin (Boynton & Olson, 1990), although no physiological basis has been discovered (Bohon, Hermann, Hansen, & Conway, 2016). Two issues have been raised regarding the WCS data. First, that the color chips in the Munsell array used to obtain the WCS data confound hue and saturation (Lucy & Shweder, 1979) (saturation is the pigment density of a color; hue is the color direction in color space, e.g. red, orange, green etc.). And second, that the task instructions were complex and restrictive (Saunders & van Brakel, 1997).

The covariation of hue and saturation in the Munsell array is potentially problematic because more saturated colors are more salient (Kohlraush, 1935), a phenomenon known as the Helmoltz-Kohlraush effect (Helmholtz, 1867 (1909 Edition)Helmholtz, 1867Helmholtz, 1867 (1909 Edition); Wyszecki & Stiles, 1982). The concern is that the chips identified as focal colors were selected not only because of their hue but also because of their relatively higher saturation which makes them pop out (Lucy & Shweder, 1979; Witzel, Cinotti, & O’Regan, 2015). This concern is reinforced by several observations: low-saturation stimuli are difficult to categorize (MacLaury, 2007; Olkkonen, Witzel, Hansen, & Gegenfurtner, 2010; Shamey, Zubair, & Cheema, 2019); focal-color probability is correlated with saturation (chroma) of Munsell chips (Witzel et al., 2015); and participants will pick as best exemplars the highest saturation stimuli among chips of the same hue (Paramei, D’Orsi, & Menegaz, 2014).

The covariation of hue and saturation in the standard Munsell array is reflected in the irregular shape of the Munsell space. Given this geometry, for a pre-defined number of categories, optimal partitioning will determine the boundaries of the categories (Jameson & D’Andrade, 1997; Regier et al., 2007). Optimal partitioning also predicts the evolutionary sequence of color-category acquisition (Zaslavsky, Kemp, Regier, & Tishby, 2018). But the validity of these analyses depends on the accuracy of the geometry of perceptual color space. Unfortunately the underlying principles that give rise to color-space geometry—of Munsell space or any color space—are not well understood. The Munsell geometry was probably sculpted not only by perceptual factors but also by the task and materials. In a departure from Ewald Hering’s conception of color, which is defined by four so-called unique hues (red, green, blue, and yellow (Hering, 1905)), Albert Munsell predefined five chromatic anchor points (red, yellow, green, blue, purple). It is not clear why Munsell chose to include purple, or to exclude other basic colors such as orange. One possible contributing factor is that Munsell was limited by materials: he could only work with the pigments at hand, even if he might otherwise have desired additional colors. Regardless of the underlying reasons why Munsell chose five chromatic anchor points, and those specific five, the choices likely had an impact on the geometry of the space because holding explicit category assignments can amplify perceptual biases (Bae, Olkkonen, Allred, & Flombaum, 2015). It seems likely that the five chromatic colors in Munsell’s space are more salient not because of hard-wired properties of perceptual color space, but because the task Munsell set for himself injected a bias.

It is often thought that color space is objectively determined by perception. The fallacy of this assumption is belied by the diversity of color spaces in use today (e.g., CIELAB, Munsell, NCS), each with its own geometry. Such diversity shows that task instructions and materials inevitably play a substantial role in determining the geometry of color space (Kuehni & Schwartz, 2008). Color spaces are perhaps better thought of as inventions, not a discoveries. The upshot is that the explanatory power of any account of color categories that depends on a definition of perceptual space is weakened simply because the geometry of color space is ill-constrained. Saturation is the least well-defined parameter of color, and variation in how saturation is measured or represented accounts for much of the geometric variation among color spaces. These considerations underscore the potential utility of a metric for assessing color categorization that is not confounded by saturation.

The second issue that complicates interpretation of WCS data relates to the task instructions (Saunders & van Brakel, 1997). The instructions required that color-naming responses be consistent among participants, evident in all idiolects, monolexemic, abstract (i.e. not used to refer to specific objects), and not borrowed from other languages. The complexity of these instructions likely left room for the people conducting the experiments to bias or coach participants (Saunders & van Brakel, 1997). Moreover, the complexity raised the possibility that the different teams who collected the data implemented different versions of the experiment (Gibson et al., 2017). The validity of the instructions is further undermined by anthropological work showing that color terms invariably originate with object names (Levinson, 2000), such as the term orange which derives from the fruit; moreover, many languages borrow color terms from other languages. Relaxing the constraints on participant responses increases the glossary of high-consensus color terms (Lindsey & Brown, 2014), and supports the idea that color-category evolution is less stereotyped than the framework originally proposed by Berlin and Kay (Haynie & Bowern, 2016; Lindsey & Brown, 2014).

Besides the issues related to task instructions and the confound of hue and saturation, there is yet another problem with the Berlin-Kay framework: it is not compatible with empirical data obtained by two independent groups showing that languages at so-called early stages in their color-term evolution nonetheless have a complete representation of color distributed across the population: Lindsey, Brown, Brainard, and Apicella (2015) in the Hadza of Sub-Saharan Africa; and Gibson et al. (2017) in the Tsimane’ of the Amazon delta. The Berlin-Kay theory stipulates that early-stage languages are not capable of categorizing some colors, whereas the Lindsey and Gibson results show that complete color-categorization knowledge is evident in the population even if most individuals within the population are not capable of categorizing all colors. Taken together, the cummulative evidence underscores the need for an alternative to the Berlin-Kay framework for thinking about color-term evolution.

Rather than using focal-color assignments, a promising alternative approach to understand color categorization behavior leverages information theory. This method uncovers the efficiency with which people communicate about color by measuring inter-subject variability in color naming (Gibson et al., 2017; Lindsey et al., 2015; Lucy & Shweder, 1979; Regier, Kemp, & Kay, 2015; Steels & Belpaeme, 2005; Stefflre, Vales, & Morley, 1966). Consider a communication game. Two people each have access to the same array of color chips. One person picks a specific chip in the array and uses a color word to describe it. How many guesses does it take the listener to figure out exactly which chip was selected given the word? The answer is a metric of informativity: color chips associated with fewer guesses are more informative and have lower surprisal, a term coined by Tribus (Tribus, 1961). Instead of thinking of color systems in terms of categories and their best exemplars, the information theoretic approach thinks of color systems in terms of how efficiently they communicate different color percepts. In our implementation, participants are asked to label each colored chip using a term that they think another speaker of their language would understand—there are no restrictions on the terms that can be used (Gibson et al., 2017).

Here we pick up the use of information theory to understand differences in color-naming across languages. As languages increase in overall communication efficiency about color, do some colors undergo relatively greater increases in communication efficiency? One simple hypothesis is that if the Berlin-Kay theory of color-term evolution is correct, then gains in communicative efficiency should follow the trajectory of color terms in the Berlin-Kay evolutionary sequence. The rich WCS data might provide a way to address this question if the issues regarding task complexity and the confound of hue and saturation can be overcome. We previously showed that communication efficiency estimates from a restrictive task like the WCS are comparable to those obtained using a free-labeling task (Gibson et al., 2017), showing that the complex instructions of the WCS do not impact information theory analysis. Here we address the second issue: are communication efficiency scores for color naming influenced by color-chip saturation? We addressed this question using original data collected in the Tsimane’ people of the Amazon basin (Leonard et al., 2015). Like language groups included in the WCS, the Tsimane’ have had limited exposure to industrialized society. Industrialization has a dramatic impact on visual diet: almost every scene in industrialized society contains synthetically colored surfaces. It is plausible that industrialization impacts perception, cognition, and naming of color. Thus data obtained from the Tsimane’ allow an assessment of the impact of saturation on color naming without the potential confound of industrialization. We found that communication-efficiency estimates were not correlated with saturation, unlike focal-color selections. These results validate further information theory analysis of the WCS data. The analysis of the WCS data presented here shows relative shifts in communication efficiency among colors as languages undergo overall increases in communication efficiency about color. The patterns suggest a new framework for how color-naming systems evolve.

Section snippets

Participants

Original data were collected from three language groups: Tsimane’ spoken by the indigenous Tsimane’ people of the Amazon; Bolivian-Spanish spoken by people in towns neighboring the Tsimane’; and American English (throughout this report, all speakers of “English” spoke American English). Data in the Tsimane’ are important because the Tsimane’ have little exposure to industrialized society and therefore have little experience with artificially colored objects. Thus the Tsimane’ are comparable to

Results

Color names were queried from speakers of Tsimane’, an indigenous people of the Amazon delta that has had limited contact with industrialized society (Leonard et al., 2015). For comparison we also obtained data in two industrialized populations, Bolivian Spanish, and English. In the first part of the study, we present the results on focal colors in the three languages in which we obtained original color-naming data. We show that focal color probability is correlated with color-chip saturation.

Discussion

Here we obtain new knowledge into how color-naming systems evolve, using an information theory analysis of color-naming data. The analysis provides a measure of the communication efficiency of color terms: how efficiently can a listener identify a color selected by a speaker given the term used by the speaker? By analyzing communication-efficiency for colors in Tsimane’, Bolivian Spanish, English, and across the 110 languages of the World Color Survey, we discovered that as languages progress

Declaration of Competing Interest

All authors declare that they have no competing interests.

Acknowledgements

Regarding the collection of data, 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. For feedback on the work, we thank Evelina Fedorenko, Alexander Rehding, Steven Piantadosi, Roger Levy, and Daniel Garside. The work was supported by NSF IOS Program Award 1353571 (to B.R.C.), Linguistics

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