Modeling language contact with the Iterated Learning Model

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

Contact between languages has the potential to transmit vocabulary and other language features; however, this does not always happen. Here, an iterated learning model is used to examine, in a simple way, the resistance of languages to change during language contact. Iterated learning models are agent-based models of language change, they demonstrate that languages that are expressive and compositional arise spontaneously as a consequence of a language transmission bottleneck. A recently introduced type of iterated learning model, the Semi-Supervised ILM is used to simulate language contact. These simulations do not include many of the complex factors involved in language contact and do not model a population of speakers; nonetheless the model demonstrates that the dynamics which lead languages in the model to spontaneously become expressive and compositional, also cause a language to maintain its core traits even after mixing with another language.
Original languageEnglish
Title of host publicationALIFE 2024
Subtitle of host publication Proceedings of the 2024 Artificial Life Conference
PublisherMassachusetts Institute of Technology (MIT) Press
Pages54-61
Number of pages8
Volume54
DOIs
Publication statusPublished - 22 Jul 2024
EventALIFE 2024: 2024 Artificial Life Conference - Denmark, Copenhagen, Denmark
Duration: 22 Jul 202426 Sept 2024
https://2024.alife.org/

Conference

ConferenceALIFE 2024
Abbreviated titleALIFE
Country/TerritoryDenmark
CityCopenhagen
Period22/07/2426/09/24
Internet address

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  • 8459 AI For Collective Intelligence

    Bullock, S. (Principal Investigator)

    1/02/2431/01/29

    Project: Research, Parent

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