The effects of high versus low talker variability and individual aptitude on phonetic training of Mandarin lexical tones

Division of Psychology and Language Sciences, University College London, University of London, London, United Kingdom
Department of Linguistics, School of Communications Sciences and Disorders, McGill University, Montreal, QC, Canada
Department of Psychology, Nottingham Trent University, Nottingham, United Kingdom
DOI
10.7287/peerj.preprints.27063v2
Subject Areas
Psychiatry and Psychology, Computational Science
Keywords
L2 phonetic contrasts, Phonetic training, Lexical tone learning, Second language
Copyright
© 2019 Dong et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Dong H, Clayards M, Brown H, Wonnacott E. 2019. The effects of high versus low talker variability and individual aptitude on phonetic training of Mandarin lexical tones. PeerJ Preprints 7:e27063v2

Abstract

High variability training has been found to be more effective than low variability training when learning various non-native phonetic contrasts. However, little research has considered whether this applies to the learning of tone contrasts. The only two relevant studies suggested that the effect of high variability training depends on the perceptual aptitude of participants (Perrachione, Lee, Ha, & Wong, 2011; Sadakata & McQueen, 2014). The present study extends these findings by examining the interaction between individual aptitude and input variability using natural, meaningful second language input (both previous studies used pseudowords). Sixty English speakers took part in an eight session phonetic training paradigm. They were assigned to high/low/high-blocked variability training groups and learned real Mandarin tones and words. Individual aptitude was measured following previous work. Learning was measured using one discrimination task, one identification task and two production tasks. All tasks assessed generalisation. All groups improved in both the production and perception of tones which transferred to untrained voices and items, demonstrating the effectiveness of training despite the increased complexity compared with previous research. Although the low variability group exhibited an advantage with the training stimuli, there was no evidence for a benefit of high-variability in any of the tests of generalisation. Moreover, although aptitude significantly predicted performance in discrimination, identification and training tasks, no interaction between individual aptitude and variability was revealed. Additional Bayes Factor analyses indicated substantial evidence for the null for the hypotheses of a benefit of high-variability in generalisation, however the evidence regarding the interaction was ambiguous. We discuss these results in light of previous findings.

Author Comment

This new version has fixed on various aspects. The structure is rearranged. A new section of Bayesian analysis is included.

Supplemental Information

R analysis code

This file contains all codes needed generate the results reported in the current paper.

DOI: 10.7287/peerj.preprints.27063v2/supp-1

Html file of R analysis results

This file is generated using R to grant easy access towards the code without running it.

DOI: 10.7287/peerj.preprints.27063v2/supp-2

Appendix A: Stimulus used in Training and Picture Identification

DOI: 10.7287/peerj.preprints.27063v2/supp-3

Data of Picture Identification

DOI: 10.7287/peerj.preprints.27063v2/supp-4

Data of Pitch Contour Perception Test

DOI: 10.7287/peerj.preprints.27063v2/supp-5

Data of Word Repetition and Picture Naming from both rater 1 and rater 2

DOI: 10.7287/peerj.preprints.27063v2/supp-6

Data of Three Interval Oddity

DOI: 10.7287/peerj.preprints.27063v2/supp-7

Dataset for Categorisation of Synthesised Tonal Continua

DOI: 10.7287/peerj.preprints.27063v2/supp-9

Explanation file for data sets

DOI: 10.7287/peerj.preprints.27063v2/supp-10