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Tests of a dual-system model of speech category learning*

Published online by Cambridge University Press:  17 January 2014

W. TODD MADDOX  and
BHARATH CHANDRASEKARAN
W. TODD MADDOX*
Affiliation:
University of Texas, Austin
BHARATH CHANDRASEKARAN
Affiliation:
University of Texas, Austin
*
W. Todd Maddox, Department of Psychology, University of Texas, 1 University Station, A8000, Austin, TX 78712[email protected]
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Abstract

In the visual domain, more than two decades of work has argued for the existence of dual category learning systems. The reflective system uses working memory in an explicit fashion to develop and test rules for classifying. The reflexive system operates by implicitly associating perception with actions that lead to reinforcement. Dual-system models posit that in learning natural categories, learners initially use the reflective system and with practice, transfer control to the reflexive system. The role of reflective and reflexive systems in second language (L2) speech learning has not been systematically examined. In the study reported in this paper, monolingual native speakers of American English were trained to categorize Mandarin tones produced by multiple speakers. Our computational modeling approach demonstrates that learners use reflective and reflexive strategies during tone category learning. Successful learners use speaker-dependent, reflective analysis early in training and reflexive strategies by the end of training. Our results demonstrate that dual-learning systems are operative in L2 speech learning. Critically, learner strategies directly relate to individual differences in successful category learning.

Keywords

L2 acquisitiondual-learning systemsreflexive processingreflective processingindividual differences
Type
Research Article
Information
Bilingualism: Language and Cognition , Volume 17 , Issue 4 , October 2014 , pp. 709 - 728
Copyright
Copyright © Cambridge University Press 2014 

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Footnotes

*

The two authors contributed equally to the paper. This research was supported by NIMH grants MH077708 and DA032457 to WTM. We thank the Maddox Lab RAs for data collection.

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