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Front. Psychol. | doi: 10.3389/fpsyg.2021.626118

Interindividual variation refuses to go away: a Bayesian computer model of language change in communicative networks Provisionally accepted The final, formatted version of the article will be published soon. Notify me

  • 1Lumière University Lyon 2, France
  • 2UMR5596 Dynamique du Langage, France

Treating the speech communities as homogeneous entities is not an accurate representation of reality, as it misses some of the complexities of linguistic interactions. Inter-individual variation and multiple types of biases are ubiquitous in speech communities, regardless of their size. This variation is often neglected due to the assumption that “majority rules”, and that the language adopted by the community will override any such biases by forcing the individuals to overcome their own biases and avoid having their use of language being treated as “idiosyncratic” or outright “pathological”. In this paper, we use computer simulations of Bayesian linguistic agents embedded in communicative networks to investigate how biased individuals, representing a minority of the population, interact with the unbiased majority, how a shared language emerges, and the dynamics of these biases across time. We tested different network sizes (from very small to very large) and types (random, scale-free, and small-world), along with different strengths and types of bias (modelled through the Bayesian prior distribution of the agents and the mechanism used for generating utterances: either sampling from the posterior distribution or picking the value with the maximum probability). The results show that, while the biased agents, even when being in the minority, do adapt their language by going against their a priori preferences, they are far from being swamped by the majority, and instead the emergent shared language of the whole community is influenced by their bias.

Keywords: language evolution, iterated learning, Interindividual variation, Bayesian agents, communicative networks

Received: 04 Nov 2020; Accepted: 12 May 2021.

Copyright: © 2021 Josserand, Tang, Pellegrino and Dediu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Mathilde Josserand, Lumière University Lyon 2, Lyon, France,