Predictability and variation in language are differentially affected by learning and production

Aislinn Keogh*, Simon Kirby, Jennifer Culbertson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

General principles of human cognition can help to explain why languages are more likely to have certain characteristics than others: structures that are difficult to process or produce will tend to be lost over time. One aspect of cognition that is implicated in language use is working memory—the component of short-term memory used for temporary storage and manipulation of information. In this study, we consider the relationship between working memory and regularization of linguistic variation. Regularization is a well-documented process whereby languages become less variable (on some dimension) over time. This process has been argued to be driven by the behavior of individual language users, but the specific mechanism is not agreed upon. Here, we use an artificial language learning experiment to investigate whether limitations in working memory during either language learning or language production drive regularization behavior. We find that taxing working memory during production results in the loss of all types of variation, but the process by which random variation becomes more predictable is better explained by learning biases. A computational model offers a potential explanation for the production effect using a simple self-priming mechanism.
Original languageEnglish
Article numbere13435
JournalCognitive Science
Volume48
Issue number4
Early online date2 Apr 2024
DOIs
Publication statusPublished - Apr 2024

Keywords / Materials (for Non-textual outputs)

  • artificial language learning
  • language evolution
  • language production
  • regularization
  • urn model
  • working memory

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