December 2010
Volume 10, Issue 15
Free
OSA Fall Vision Meeting Abstract  |   December 2010
Simulating the transmission of systems of color terms in the laboratory
Author Affiliations
  • Jing Xu
    Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
  • Thomas L. Griffiths
    Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
  • Mike Dowman
    Division of Psychology, University of Abertay Dundee, Bell Street, Dundee, UK
Journal of Vision December 2010, Vol.10, 28. doi:https://doi.org/10.1167/10.15.28
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      Jing Xu, Thomas L. Griffiths, Mike Dowman; Simulating the transmission of systems of color terms in the laboratory. Journal of Vision 2010;10(15):28. https://doi.org/10.1167/10.15.28.

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Abstract

Berlin and Kay (1969) proposed that there exist cross-cultural universals in the form of basic color terms. To test this hypothesis, the World Color Survey (WCS) collected color-naming data from 110 non-industrial societies, identifying regularities in the structure of languages with different numbers of terms. This leaves us with the question of where these universals come from. We use a simple model of cultural evolution known as “iterated learning” (Kirby, 2001) to explore the hypothesis that universals emerge from human perceptual and learning biases. We conducted an experiment simulating the process of cultural transmission in the laboratory, and compared the results to the systems of color terms that appear in the WCS data. Our results show that cultural evolution results in convergence of systems of color terms towards a form consistent with the WCS, supporting the hypothesis that universals are the result of perceptual and learning biases.

Acknowledgments
This work was supported by grant number BCS-0704034 from the National Science Foundation. 
References
Berlin, B., Kay, P.(1969). Basic color terms: Their universality and evolution. Berkeley, CA: University of California Press.
Kirby, S.(2001). Spontaneous evolution of linguistic structure: An iterated learning model of the emergence of regularity and irregularity. IEEE Journal of Evolutionary Computation, 5, 102–110. [CrossRef]
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