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Interindividual Variation Refuses to Go Away: A Bayesian Computer Model of Language Change in Communicative Networks

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 variati...

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Autores principales: Josserand, Mathilde, Allassonnière-Tang, Marc, Pellegrino, François, Dediu, Dan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8257003/
https://www.ncbi.nlm.nih.gov/pubmed/34234707
http://dx.doi.org/10.3389/fpsyg.2021.626118
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author Josserand, Mathilde
Allassonnière-Tang, Marc
Pellegrino, François
Dediu, Dan
author_facet Josserand, Mathilde
Allassonnière-Tang, Marc
Pellegrino, François
Dediu, Dan
author_sort Josserand, Mathilde
collection PubMed
description 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 emerging language of the community will override any such biases by forcing the individuals to overcome their own biases, or risk 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 (modeled through the Bayesian prior distribution of the agents and the mechanism used for generating utterances: either sampling from the posterior distribution [“sampler”] or picking the value with the maximum probability [“MAP”]). 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.
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spelling pubmed-82570032021-07-06 Interindividual Variation Refuses to Go Away: A Bayesian Computer Model of Language Change in Communicative Networks Josserand, Mathilde Allassonnière-Tang, Marc Pellegrino, François Dediu, Dan Front Psychol Psychology 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 emerging language of the community will override any such biases by forcing the individuals to overcome their own biases, or risk 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 (modeled through the Bayesian prior distribution of the agents and the mechanism used for generating utterances: either sampling from the posterior distribution [“sampler”] or picking the value with the maximum probability [“MAP”]). 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. Frontiers Media S.A. 2021-06-21 /pmc/articles/PMC8257003/ /pubmed/34234707 http://dx.doi.org/10.3389/fpsyg.2021.626118 Text en Copyright © 2021 Josserand, Allassonnière-Tang, Pellegrino and Dediu. https://creativecommons.org/licenses/by/4.0/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.
spellingShingle Psychology
Josserand, Mathilde
Allassonnière-Tang, Marc
Pellegrino, François
Dediu, Dan
Interindividual Variation Refuses to Go Away: A Bayesian Computer Model of Language Change in Communicative Networks
title Interindividual Variation Refuses to Go Away: A Bayesian Computer Model of Language Change in Communicative Networks
title_full Interindividual Variation Refuses to Go Away: A Bayesian Computer Model of Language Change in Communicative Networks
title_fullStr Interindividual Variation Refuses to Go Away: A Bayesian Computer Model of Language Change in Communicative Networks
title_full_unstemmed Interindividual Variation Refuses to Go Away: A Bayesian Computer Model of Language Change in Communicative Networks
title_short Interindividual Variation Refuses to Go Away: A Bayesian Computer Model of Language Change in Communicative Networks
title_sort interindividual variation refuses to go away: a bayesian computer model of language change in communicative networks
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8257003/
https://www.ncbi.nlm.nih.gov/pubmed/34234707
http://dx.doi.org/10.3389/fpsyg.2021.626118
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