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Phonetically natural rules benefit from a learning bias: a re-examination of vowel harmony and disharmony

Published online by Cambridge University Press:  28 April 2020

Alexander Martin Open the ORCID record for Alexander Martin [Opens in a new window]  and
Sharon Peperkamp Open the ORCID record for Sharon Peperkamp [Opens in a new window]
Alexander Martin*
Affiliation:
University of Edinburgh and Laboratoire de Sciences Cognitives et Psycholinguistique (ENS, EHESS, CNRS), École Normale Supérieure – PSL University
Sharon Peperkamp*
Affiliation:
Laboratoire de Sciences Cognitives et Psycholinguistique (ENS, EHESS, CNRS), École Normale Supérieure – PSL University
*
E-mail: ALXNDR.MARTIN@GMAIL.COM, SHARON.PEPERKAMP@ENS.PSL.EU.
E-mail: ALXNDR.MARTIN@GMAIL.COM, SHARON.PEPERKAMP@ENS.PSL.EU.
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Abstract

Substance-based phonological theories predict that a preference for phonetically natural rules (those which reflect constraints on speech production and perception) is encoded in synchronic grammars, and translates into learning biases. Some previous work has shown evidence for such biases, but methodological concerns with these studies mean that the question warrants further investigation. We revisit this issue by focusing on the learning of palatal vowel harmony (phonetically natural) compared to disharmony (phonetically unnatural). In addition, we investigate the role of memory consolidation during sleep on rule learning. We use an artificial language learning paradigm with two test phases separated by twelve hours. We observe a robust effect of phonetic naturalness: vowel harmony is learned better than vowel disharmony. For both rules, performance remains stable after twelve hours, regardless of the presence or absence of sleep.

Type
Articles
Information
Phonology , Volume 37 , Issue 1 , February 2020 , pp. 65 - 90
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press.

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Footnotes

This work was supported by grants from the Agence Nationale de la Recherche (ANR-17-CE28-0007-01 and ANR-17-EURE-0017). We would like to thank Michel Dutat for setting up the server from which the experiment was run, and Page Piccinini for launching a number of the sleep batches. We would additionally like to thank three anonymous reviewers and the associate editor, all of whom helped us to considerably strengthen the paper.

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