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There is a simplicity bias when generalising from ambiguous data

Published online by Cambridge University Press:  11 August 2020

Karthik Durvasula
Affiliation:
Michigan State University
Adam Liter
Affiliation:
University of Maryland
Corresponding

Abstract

How exactly do learners generalise in the face of ambiguous data? While there has been a substantial amount of research studying the biases that learners employ, there has been very little work on what sorts of biases are employed in the face of data that is ambiguous between phonological generalisations with different degrees of complexity. In this article, we present the results from three artificial language learning experiments that suggest that, at least for phonotactic sequence patterns, learners are able to keep track of multiple generalisations related to the same segmental co-occurrences; however, the generalisations they learn are only the simplest ones consistent with the data.

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Articles
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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Footnotes

We would like to thank the associate editor and three anonymous reviewers for helping to improve this manuscript tremendously. We would also like to thank the audiences at the 2015 Annual Meeting on Phonology, the LSA 2016 Annual Meeting and the 2016 North American Phonology Conference, as well as the Phonology/Phonetics group at Michigan State University for helpful discussions. Many thanks to Mina Hirzel for recording the stimuli used in our experiments. Additionally, we would like to thank Russ Werner and Mike Kramizeh at Michigan State University for help with technical matters. Adam Liter was supported by the NSF NRT award DGE-1449815 during portions of the writing and revising of this paper. The authors have contributed equally to this paper, and share first authorship.

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