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Lexical exposure to native language dialects can improve non-native phonetic discrimination

Overview of attention for article published in Psychonomic Bulletin & Review, October 2017
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Title
Lexical exposure to native language dialects can improve non-native phonetic discrimination
Published in
Psychonomic Bulletin & Review, October 2017
DOI 10.3758/s13423-017-1396-3
Pubmed ID
Authors

Annie J. Olmstead, Navin Viswanathan

Abstract

Nonnative phonetic learning is an area of great interest for language researchers, learners, and educators alike. In two studies, we examined whether nonnative phonetic discrimination of Hindi dental and retroflex stops can be improved by exposure to lexical items bearing the critical nonnative stops. We extend the lexical retuning paradigm of Norris, McQueen, and Cutler (Cognitive Psychology, 47, 204-238, 2003) by having naive American English (AE)-speaking participants perform a pretest-training-posttest procedure. They performed an AXB discrimination task with the Hindi retroflex and dental stops before and after transcribing naturally produced words from an Indian English speaker that either contained these tokens or not. Only those participants who heard words with the critical nonnative phones improved in their posttest discrimination. This finding suggests that exposure to nonnative phones in native lexical contexts supports learning of difficult nonnative phonetic discrimination.

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The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 29%
Researcher 2 12%
Student > Ph. D. Student 2 12%
Student > Doctoral Student 1 6%
Other 1 6%
Other 0 0%
Unknown 6 35%
Readers by discipline Count As %
Linguistics 2 12%
Psychology 2 12%
Nursing and Health Professions 2 12%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Social Sciences 1 6%
Other 1 6%
Unknown 8 47%