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

Aislinn Keogh, Simon Kirby, Jennifer Culbertson

Abstract: 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 regularisation of linguistic variation. Regularisation 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 behaviour 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 regularisation behaviour. 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. An exemplar-based computational model offers a potential explanation for the production effect using a simple self-priming mechanism.