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EFFECTS OF MULTITALKER INPUT AND INSTRUCTIONAL METHOD ON THE DIMENSION-BASED STATISTICAL LEARNING OF SYLLABLE-TONE COMBINATIONS

AN EYE-TRACKING STUDY

Published online by Cambridge University Press:  27 October 2020

Seth Wiener Open the ORCID record for Seth Wiener [Opens in a new window] ,
Kiwako Ito  and
Shari R. Speer
Seth Wiener*
Affiliation:
Carnegie Mellon University
Kiwako Ito
Affiliation:
University of Newcastle
Shari R. Speer
Affiliation:
The Ohio State University
*
*Correspondence concerning this article should be addressed to Seth Wiener, Department of Modern Languages, Carnegie Mellon University, 160 Baker Hall, 5000 Forbes Avenue, Pittsburgh, PA 15213. E-mail: [email protected]
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Abstract

To test the effects of talker variability and explicit instruction on the statistical learning of lexical tone, 80 monolingual English listeners were taught an artificial language that mimicked Mandarin’s asymmetric distribution of syllable-tone co-occurrences. Training stimuli consisted of either speech from one talker or speech from four talkers. Participants were either never instructed or explicitly taught associations between phonemes (CVs), tones, and nonce symbols across four consecutive days. Learning was assessed by the accuracy of mouse clicks and eye movements to visual nonce symbols. Critical trials induced competition between the target symbol, which matched the acoustic input, and a competitor symbol that had a statistically more probable tone (but mismatched the acoustic input). Eye fixations indicated that participants were sensitive to syllable-tone co-occurrence probabilities even without explicit instruction of tone. The degree to which statistical knowledge was used to recognize words appeared to increase when participants processed more variable speech.

Type
Research Article
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Studies in Second Language Acquisition , Volume 43 , Issue 1 , March 2021 , pp. 155 - 180
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© The Author(s), 2020. Published by Cambridge University Press

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Footnotes

The experiment in this article earned an Open Materials badge for transparent practices. The materials are available at https://osf.io/pxd5f

This work was funded in part by a Doctoral Dissertation Research Improvement Grant from the National Science Foundation (BCS-1451677). We thank Marjorie Chan, Chao-Yang Lee, Mineharu Nakayama, Jessie Nixon, and the anonymous reviewers for their feedback on earlier versions of this work. We are especially grateful to James Brennan for his help with data collection.

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