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We derive a principled information-theoretic account of cross-language semantic variation. Specifically, we argue that languages efficiently compress ideas into words by optimizing the information bottleneck (IB) trade-off between the complexity and accuracy of the lexicon. We test this proposal in the domain of color naming and show that (i) color-naming systems across languages achieve near-optimal compression; (ii) small changes in a single trade-off parameter account to a large extent for observed cross-language variation; (iii) efficient IB color-naming systems exhibit soft rather than hard category boundaries and often leave large regions of color space inconsistently named, both of which phenomena are found empirically; and (iv) these IB systems evolve through a sequence of structural phase transitions, in a single process that captures key ideas associated with different accounts of color category evolution. These results suggest that a drive for information-theoretic efficiency may shape color-naming systems across languages. This principle is not specific to color, and so it may also apply to cross-language variation in other semantic domains.

The claim that Eskimo languages have words for different types of snow is well-known among the public, but has been greatly exaggerated through popularization and is therefore viewed with skepticism by many scholars of language. Despite the prominence of this claim, to our knowledge the line of reasoning behind it has not been tested broadly across languages. Here, we note that this reasoning is a special case of the more general view that language is shaped by the need for efficient communication, and we empirically test a variant of it against multiple sources of data, including library reference works, Twitter, and large digital collections of linguistic and meteorological data. Consistent with the hypothesis of efficient communication, we find that languages that use the same linguistic form for snow and ice tend to be spoken in warmer climates, and that this association appears to be mediated by lower communicative need to talk about snow and ice. Our results confirm that variation in semantic categories across languages may be traceable in part to local communicative needs. They suggest moreover that despite its awkward history, the topic of \"words for snow\" may play a useful role as an accessible instance of the principle that language supports efficient communication.

The nature of color categories in the world's languages is contested. One major view holds that color categories are organized around universal focal colors, whereas an opposing view holds instead that categories are defined at their boundaries by linguistic convention. Both of these standardly opposed views are challenged by existing data. Here, we argue for a third view based on a proposal by Jameson and D'Andrade [Jameson KA, D'Andrade RG (1997) in Color Categories in Thought and Language, eds Hardin CL, Maffi L (Cambridge Univ Press, Cambridge, U.K.), pp 295-319]: that color naming across languages reflects optimal or near-optimal divisions of an irregularly shaped perceptual color space. We formalize this idea, test it against color-naming data from a broad range of languages and show that it accounts for universal tendencies in color naming while also accommodating some observed cross-language variation.

Focal colors, or best examples of color terms, have traditionally been viewed as either the underlying source of cross-language color-naming universals or derived from category boundaries that vary widely across languages. Existing data partially support and partially challenge each of these views. Here, we advance a position that synthesizes aspects of these two traditionally opposed positions and accounts for existing data. We do so by linking this debate to more general principles. We show that best examples of named color categories across 112 languages are well-predicted from category extensions by a statistical model of how representative a sample is of a distribution, independently shown to account for patterns of human inference. This model accounts for both universal tendencies and variation in focal colors across languages. We conclude that categorization in the contested semantic domain of color may be governed by principles that apply more broadly in cognition and that these principles clarify the interplay of universal and language-specific forces in color naming.

The Sapir-Whorf hypothesis holds that our thoughts are shaped by our native language, and that speakers of different languages therefore think differently. This hypothesis is controversial in part because it appears to deny the possibility of a universal groundwork for human cognition, and in part because some findings taken to support it have not reliably replicated. We argue that considering this hypothesis through the lens of probabilistic inference has the potential to resolve both issues, at least with respect to certain prominent findings in the domain of color cognition. We explore a probabilistic model that is grounded in a presumed universal perceptual color space and in language-specific categories over that space. The model predicts that categories will most clearly affect color memory when perceptual information is uncertain. In line with earlier studies, we show that this model accounts for language-consistent biases in color reconstruction from memory in English speakers, modulated by uncertainty. We also show, to our knowledge for the first time, that such a model accounts for influential existing data on cross-language differences in color discrimination from memory, both within and across categories. We suggest that these ideas may help to clarify the debate over the Sapir-Whorf hypothesis.

The existence of cross-linguistic universals in color naming is currently contested. Early empirical studies, based principally on languages of industrialized societies, suggested that all languages may draw on a universally shared repertoire of color categories. Recent work, in contrast, based on languages from nonindustrialized societies, has suggested that color categories may not be universal. No comprehensive objective tests have yet been conducted to resolve this issue. We conduct such tests on color naming data from languages of both industrialized and nonindustrialized societies and show that strong universal tendencies in color naming exist across both sorts of language.

The question of whether language affects perception has been debated largely on the basis of cross-language data, without considering the functional organization of the brain. The nature of this neural organization predicts that, if language affects perception, it should do so more in the right visual field than in the left visual field, an idea unexamined in the debate. Here, we find support for this proposal in lateralized color discrimination tasks. Reaction times to targets in the right visual field were faster when the target and distractor colors had different names; in contrast, reaction times to targets in the left visual field were not affected by the names of the target and distractor colors. Moreover, this pattern was disrupted when participants performed a secondary task that engaged verbal working memory but not a task making comparable demands on spatial working memory. It appears that people view the right (but not the left) half of their visual world through the lens of their native language, providing an unexpected resolution to the language-and-thought debate.

It is widely held that named color categories in the world's languages are organized around universal focal colors and that these focal colors tend to be chosen as the best examples of color terms across languages. However, this notion has been supported primarily by data from languages of industrialized societies. In contrast, recent research on a language from a nonindustrialized society has called this idea into question. We examine color-naming data from languages of 110 nonindustrialized societies and show that (i) best-example choices for color terms in these languages cluster near the prototypes for English white, black, red, green, yellow, and blue, and (ii) best-example choices cluster more tightly across languages than do the centers of category extensions, suggesting that universal best examples (foci) may be the source of universal tendencies in color naming.

Languages vary in their systems of kinship categories, but the scope of possible variation appears to be constrained. Previous accounts of kin classification have often emphasized constraints that are specific to the domain of kinship and are not derived from general principles. Here, we propose an account that is founded on two domain-general principles: Good systems of categories are simple, and they enable informative communication. We show computationally that kin classification systems in the world's languages achieve a near-optimal trade-off between these two competing principles. We also show that our account explains several specific constraints on kin classification proposed previously. Because the principles of simplicity and informativeness are also relevant to other semantic domains, the trade-off between them may provide a domain-general foundation for variation in category systems across languages.