Historical constraints on the evolution of efficient color naming

Color naming in natural languages is not arbitrary: it reflects efficient partitions of perceptual color space modulated by the relative needs to communicate about different colors. These psychophysical and communicative constraints help explain why languages around the world have remarkably similar, but not identical, mappings of colors to color terms. Languages converge on a small set of efficient representations. But languages also evolve, and the number of terms in a color vocabulary may change over time. Here we show that history, i.e. the existence of an antecedent color vocabulary, acts as a non-adaptive constraint that biases the choice of efficient solution as a language transitions from a vocabulary of size n to n+1 terms. Moreover, as vocabularies evolve to include more terms they explore a smaller fraction of all possible efficient vocabularies compared to equally-sized vocabularies constructed de novo. This path dependence on the cultural evolution of color naming presents an opportunity. Historical constraints can be used to reconstruct ancestral color vocabularies, allowing us to answer long-standing questions about the evolutionary sequences of color words, and enabling us to draw inferences from phylogenetic patterns of language change.

[1]  Simon J. Greenhill,et al.  A recent northern origin for the Uto-Aztecan family , 2023, Language.

[2]  G. Torres,et al.  Colour evolution of Betelgeuse and Antares over two millennia, derived from historical records, as a new constraint on mass and age , 2022, Monthly Notices of the Royal Astronomical Society.

[3]  Naftali Tishby,et al.  The evolution of color naming reflects pressure for efficiency: Evidence from the recent past , 2021, bioRxiv.

[4]  Jennifer Culbertson,et al.  Let's talk (efficiently) about us: Person systems achieve near-optimal compression , 2021 .

[5]  Johann-Mattis List,et al.  Emotion semantics show both cultural variation and universal structure , 2019, Science.

[6]  E. Gibson,et al.  How Efficiency Shapes Human Language , 2019, Trends in Cognitive Sciences.

[7]  Charles Kemp,et al.  Efficient compression in color naming and its evolution , 2018, Proceedings of the National Academy of Sciences.

[8]  Yang Xu,et al.  Semantic Typology and Efficient Communication , 2018 .

[9]  Bevil R. Conway,et al.  Color naming across languages reflects color use , 2017, Proceedings of the National Academy of Sciences.

[10]  Oren Kolodny,et al.  Cultural evolutionary theory: How culture evolves and why it matters , 2017, Proceedings of the National Academy of Sciences.

[11]  Michael C. Corballis,et al.  Language Evolution: A Changing Perspective , 2017, Trends in Cognitive Sciences.

[12]  Rumi Tokunaga,et al.  The modern Japanese color lexicon. , 2017, Journal of vision.

[13]  Claire Bowern,et al.  Phylogenetic approach to the evolution of color term systems , 2016, Proceedings of the National Academy of Sciences.

[14]  Delwin T. Lindsey,et al.  Hadza Color Terms Are Sparse, Diverse, and Distributed, and Presage the Universal Color Categories Found in Other World Languages , 2016, i-Perception.

[15]  S. Kirby,et al.  The cultural evolution of language. , 2016, Current opinion in psychology.

[16]  Delwin T. Lindsey,et al.  Hunter-Gatherer Color Naming Provides New Insight into the Evolution of Color Terms , 2015, Current Biology.

[17]  Terry Regier,et al.  Word Meanings across Languages Support Efficient Communication , 2015 .

[18]  Prutha S. Deshpande,et al.  Mesoamerican Color Survey Digital Archive , 2015 .

[19]  Yang Xu,et al.  Numeral Systems Across Languages Support Efficient Communication: From Approximate Numerosity to Recursion , 2020, Open Mind.

[20]  T. Griffiths,et al.  Cultural transmission results in convergence towards colour term universals , 2013, Proceedings of the Royal Society B: Biological Sciences.

[21]  Charles Kemp,et al.  Kinship Categories Across Languages Reflect General Communicative Principles , 2012, Science.

[22]  Simon J. Greenhill,et al.  Evolved structure of language shows lineage-specific trends in word-order universals , 2011, Nature.

[23]  Simon J. Greenhill,et al.  The shape and tempo of language evolution , 2010, Proceedings of the Royal Society B: Biological Sciences.

[24]  Angela M. Brown,et al.  World Color Survey color naming reveals universal motifs and their within-language diversity , 2009, Proceedings of the National Academy of Sciences.

[25]  Adam Powell,et al.  Late Pleistocene Demography and the Appearance of Modern Human Behavior , 2009, Science.

[26]  Alexandre François,et al.  Semantic maps and the typology of colexification: Intertwining polysemous networks across languages , 2008 .

[27]  Ulrik Brandes,et al.  On Modularity Clustering , 2008, IEEE Transactions on Knowledge and Data Engineering.

[28]  K. Jameson,et al.  Evolutionary models of color categorization based on discrimination , 2007 .

[29]  Gerhard Jäger,et al.  The evolution of convex categories , 2007 .

[30]  P. Kay,et al.  Color naming reflects optimal partitions of color space , 2007, Proceedings of the National Academy of Sciences.

[31]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .

[32]  Inderjit S. Dhillon,et al.  Clustering with Bregman Divergences , 2005, J. Mach. Learn. Res..

[33]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[34]  M. Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[35]  Sergej N. Yendrikhovskij,et al.  Computing Color Categories from Statistics of Natural Images , 2001, Journal of Imaging Science and Technology.

[36]  Russell D. Gray,et al.  Language trees support the express-train sequence of Austronesian expansion , 2000, Nature.

[37]  S. Levinson Yélî Dnye and the Theory of Basic Color Terms , 2000 .

[38]  P. Kay,et al.  Color appearance and the emergence and evolution of basic color lexicons , 1999 .

[39]  R. W. Casson,et al.  Color and Cognition in Mesoamerica: Constructing Categories as Vantages. , 1998 .

[40]  Leonidas J. Guibas,et al.  A metric for distributions with applications to image databases , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[41]  R. D'Andrade,et al.  Color categories in thought and language: It's not really red, green, yellow, blue: an inquiry into perceptual color space , 1997 .

[42]  S. Gould,et al.  The spandrels of San Marco and the Panglossian paradigm: a critique of the adaptationist programme , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[43]  P. Kay,et al.  The linguistic significance of the meanings of basic color terms , 1978 .

[44]  Eleanor Rosch Heider,et al.  Probabilities, Sampling, and Ethnographic Method: The Case of Dani Colour Names , 1972 .

[45]  P. Kay,et al.  Basic Color Terms: Their Universality and Evolution , 1973 .

[46]  E. Lenneberg,et al.  A study in language and cognition. , 1954, Journal of abnormal psychology.