Population size, learning, and innovation determine linguistic complexity

Matthew Spike*

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    1 Citation (Scopus)

    Abstract

    There are a number of claims regarding why linguistic complexity varies, for example: i) different types of societal structure (e.g. Wray & Grace, 2007), ii) population size (e.g. Lupyan & Dale, 2010), and iii) the proportion of child vs. adult learners (e.g. Trudgill, 2011). This simple model of interacting agents, capable of learning and innovation, partially supports all these accounts. However, several subtle points arise. Firstly, differences in the capacity or opportunity to learn determine how much complexity can remain stable. Secondly, small populations are susceptible to large amounts of drift and subsequent loss, unless innovation is frequent. Conversely, large populations remain resilient to change unless there is too much innovation, which leads to a collapse in complexity. Next, if adult learners are prevalent, we can instead expect less sustained complexity in large populations. Finally, creolisation does not imply simplification in smaller populations.

    Original languageEnglish
    Title of host publicationCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society
    Subtitle of host publicationComputational Foundations of Cognition
    PublisherThe Cognitive Science Society
    Pages1108-1113
    Number of pages6
    ISBN (Electronic)9780991196760
    Publication statusPublished - 2017
    Event39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017 - London, United Kingdom
    Duration: 26 Jul 201729 Jul 2017

    Publication series

    NameCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition

    Conference

    Conference39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017
    Country/TerritoryUnited Kingdom
    CityLondon
    Period26/07/1729/07/17

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