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

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          Abstract

          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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          Most cited references99

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          Fast unfolding of communities in large networks

          Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008
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            Collective dynamics of 'small-world' networks.

            Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
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              Threshold Models of Collective Behavior

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                Author and article information

                Contributors
                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                21 June 2021
                2021
                : 12
                : 626118
                Affiliations
                Laboratoire Dynamique Du Langage UMR 5596, Université Lumière Lyon 2 , Lyon, France
                Author notes

                Edited by: Kilu Von Prince, Heinrich Heine University of Düsseldorf, Germany

                Reviewed by: Tao Gong, Educational Testing Service, United States; Richard Blythe, University of Edinburgh, United Kingdom

                *Correspondence: Mathilde Josserand mathilde.josserand@ 123456univ-lyon2.fr

                This article was submitted to Language Sciences, a section of the journal Frontiers in Psychology

                Article
                10.3389/fpsyg.2021.626118
                8257003
                34234707
                ef1e9dd8-9931-458d-96b7-1185f73262d3
                Copyright © 2021 Josserand, Allassonnière-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.

                History
                : 04 November 2020
                : 12 May 2021
                Page count
                Figures: 13, Tables: 2, Equations: 1, References: 102, Pages: 23, Words: 16056
                Funding
                Funded by: Université de Lyon 10.13039/501100011074
                Award ID: ANR-10-LABX-0081
                Award ID: NSCO ED 476
                Funded by: Agence Nationale de la Recherche 10.13039/501100001665
                Award ID: ANR-11-IDEX-0007
                Categories
                Psychology
                Original Research

                Clinical Psychology & Psychiatry
                language evolution,iterated learning,interindividual variation,bayesian agents,communicative networks

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