Journal article
The cognitive roots of regularization in language
V Ferdinand, S Kirby, K Smith
Cognition | ELSEVIER | Published : 2019
Open access
Abstract
Regularization occurs when the output a learner produces is less variable than the linguistic data they observed. In an artificial language learning experiment, we show that there exist at least two independent sources of regularization bias in cognition: a domain-general source based on cognitive load and a domain-specific source triggered by linguistic stimuli. Both of these factors modulate how frequency information is encoded and produced, but only the production-side modulations result in regularization (i.e. cause learners to eliminate variation from the observed input). We formalize the definition of regularization as the reduction of entropy and find that entropy measures are better ..
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Funding Acknowledgements
This research was supported by the University of Edinburgh's College Studentship, the SORSAS award, and the Engineering and Physical Sciences Research Council. The writeup was supported by the Omidyar Fellowship. The reported experiment was conducted with the approval of the Linguistics and English Language Ethics Committee at the University of Edinburgh. We thank Bill Thompson, Tom Griffiths, Florencia Reali, Simon DeDeo, Luke Maurits, and the members of the Centre for Language Evolution for their feedback on this project. We thank Daniel Richardson for providing experimental stimuli and thank our reviewers, Amy Perfors and two anonymous, for their excellent comments.