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Individual Biases, Cultural Evolution, and the Statistical Nature of Language Universals: The Case of Colour Naming Systems

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  • Andrea Baronchelli
  • Vittorio Loreto
  • Andrea Puglisi

Abstract

Language universals have long been attributed to an innate Universal Grammar. An alternative explanation states that linguistic universals emerged independently in every language in response to shared cognitive or perceptual biases. A computational model has recently shown how this could be the case, focusing on the paradigmatic example of the universal properties of colour naming patterns, and producing results in quantitative agreement with the experimental data. Here we investigate the role of an individual perceptual bias in the framework of the model. We study how, and to what extent, the structure of the bias influences the corresponding linguistic universal patterns. We show that the cultural history of a group of speakers introduces population-specific constraints that act against the pressure for uniformity arising from the individual bias, and we clarify the interplay between these two forces.

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  • Andrea Baronchelli & Vittorio Loreto & Andrea Puglisi, 2015. "Individual Biases, Cultural Evolution, and the Statistical Nature of Language Universals: The Case of Colour Naming Systems," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-19, May.
  • Handle: RePEc:plo:pone00:0125019
    DOI: 10.1371/journal.pone.0125019
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    Cited by:

    1. Fan, Zhong-Yan & Lai, Ying-Cheng & Tang, Wallace Kit-Sang, 2020. "Likelihood category game model for knowledge consensus," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).

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