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Automatization in second language sentence processing: Relationship between elicited imitation and maze tasks*

Published online by Cambridge University Press:  16 August 2016

YUICHI SUZUKI  and
MIDORI SUNADA
YUICHI SUZUKI*
Affiliation:
Faculty of Foreign Languages, Kanagawa University
MIDORI SUNADA
Affiliation:
Graduate School of Education, Tokyo Gakugei University
*
Address for correspondence: Yuichi Suzuki, Faculty of Foreign Languages, Kanagawa University, 3-27-1 Rokkakubashi, Kanagawa-ku, Yokohama-shi, Kanagawa 221–8686, Japan[email protected]
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Abstract

The present study investigates the automatization of second language (L2) sentence processing. It compares the extent to which a mere speedup (faster execution) and restructuring (more stable execution) of sentence processing contribute to L2 oral performance. The maze task is used to measure the speed (reaction time, RT) and processing stability (coefficient of variance, CV) of sentence processing. The elicited imitation (EI) task measures L2 oral proficiency (repetition accuracy and accuracy in plural and third person s). These tasks were performed by 110 English-as-a-foreign-language learners with Japanese as their L1. The results show that only RT, not CV, significantly predicts L2 oral proficiency. Even though a subgroup of learners, who previously stayed in an English-speaking country, demonstrated some indications of automatization, RT was a better predictor of L2 oral proficiency than CV, irrespective of immersion experience. These findings suggest that CV has little practical value in predicting L2 oral proficiency.

Keywords

second language acquisitionautomatizationcoefficient of varianceelicited imitationmaze task
Type
Research Article
Information
Bilingualism: Language and Cognition , Volume 21 , Issue 1 , January 2018 , pp. 32 - 46
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Copyright
Copyright © Cambridge University Press 2016 

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Footnotes

Supplementary material can be found online at http://dx.doi.org/10.1017/S1366728916000857

*

We would like to express our gratitude to Professors Yoshiki Takayama, Misato Usukura, and Tetsuo Baba for their generous cooperation in data collection. We are very grateful to Ms. Kanno for her assistance in data coding. We wish to thank the Associate Editor, Prof. Ludovica Serratrice, and the three anonymous reviewers for providing insightful and constructive feedback.

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