Revisiting Perception–Production Relationships: Exploring a New Approach to Investigate Perception as a Time‐Varying Predictor
Models of L2 pronunciation learning have hypothesized that accurate speech perception promotes accurate speech production. This claim can be evaluated longitudinally by examining the extent to which changes in stop consonant perception predict changes in stop consonant production. Taking a time‐sens...
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Published in | Language learning Vol. 71; no. 1; pp. 243 - 279 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Hoboken
Wiley
01.03.2021
Wiley Subscription Services, Inc |
Subjects | |
Online Access | Get full text |
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Summary: | Models of L2 pronunciation learning have hypothesized that accurate speech perception promotes accurate speech production. This claim can be evaluated longitudinally by examining the extent to which changes in stop consonant perception predict changes in stop consonant production. Taking a time‐sensitive view of the perception–production link, this study used longitudinal data to analyze perception as a time‐varying predictor of production accuracy. Mixed‐effects models were fit to oddity, delayed word repetition, and picture description tasks to examine how participants’ perception and production changed over time. Oddity task perception data were then decomposed into their between‐ and within‐subjects components and integrated into the delayed repetition and picture description production models. Surprisingly, only the between‐subjects predictors reached significance, and the strength of the perception–production link varied across production tasks and target phones. The methods used have implications for future research on perception–production links. |
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Bibliography: | The handling editor for this article was Emma Marsden. This study was supported by a Language Learning Early Career Research Grant. I would like to express my sincere gratitude to Germán Zárate‐Sández and Mari Sakai, who have supported and encouraged me from the very start of this project. I am indebted to Shelby Bruun, Alexandra Urbanski, Laura Valderrama, Sonca Vo, and Ziwei Zhou for their help with data collection, processing, and analysis, and to Pavel Trofimovich, Emma Marsden, and the anonymous reviewers, whose insightful feedback has significantly enhanced the quality of this article. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ISSN: | 0023-8333 1467-9922 |
DOI: | 10.1111/lang.12431 |