Training a non-native vowel contrast with a distributional learning paradigm results in improved perception and production.


Journal

Journal of phonetics
ISSN: 0095-4470
Titre abrégé: J Phon
Pays: England
ID NLM: 101135037

Informations de publication

Date de publication:
Jan 2020
Historique:
entrez: 28 7 2020
pubmed: 28 7 2020
medline: 28 7 2020
Statut: ppublish

Résumé

Previous distributional learning research suggests that adults can improve perception of a non-native contrast more efficiently when exposed to a bimodal than a unimodal distribution. Studies have also suggested that perceptual learning can transfer to production. The current study tested whether the addition of visual images to reinforce the contrast and active learning with feedback would result in lcearning in both conditions and would transfer to gains in production. Native English-speaking adults heard stimuli from a bimodal or unimodal /o/-/œ/ continuum. No group differences were found on a discrimination task, possibly suggesting that the supports eliminated previously documented group differences. On an identification task, listeners in the bimodal group showed better performance than the unimodal group on the endpoint stimuli. Production results indicated that both groups showed increased Euclidean distance between the target vowels after training, suggesting that perceptual training improved production skills in both conditions. Contrary to expectations, degree of perception and production learning were not correlated. Together, these results suggest that a bimodal distribution may aid learning, but that adding images to reinforce the contrast and active learning to the training paradigm could mitigate disadvantages found previously for participants exposed to a unimodal distribution.

Identifiants

pubmed: 32713984
doi: 10.1016/j.wocn.2019.100940
pmc: PMC7380690
mid: NIHMS1606220
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : NIDCD NIH HHS
ID : F31 DC018197
Pays : United States
Organisme : NIDCD NIH HHS
ID : R01 DC013668
Pays : United States

Références

J Acoust Soc Am. 2009 Feb;125(2):1138-52
pubmed: 19206888
Am J Speech Lang Pathol. 2004 Aug;13(3):250-63
pubmed: 15339234
Brain Cogn. 1998 Dec;38(3):317-38
pubmed: 9841789
Trends Cogn Sci. 2000 Nov 1;4(11):417-423
pubmed: 11058819
Child Dev. 1996 Aug;67(4):1836-56
pubmed: 8890511
Sci Rep. 2018 Nov 15;8(1):16842
pubmed: 30442952
J Exp Psychol Hum Percept Perform. 1994 Apr;20(2):421-35
pubmed: 8189202
J Acoust Soc Am. 2011 Oct;130(4):EL206-12
pubmed: 21974493
J Acoust Soc Am. 2009 Nov;126(5):2670-82
pubmed: 19894844
Percept Psychophys. 1999 Jul;61(5):977-85
pubmed: 10499009
J Phon. 2016 Nov;59:40-57
pubmed: 28503007
J Speech Lang Hear Res. 2014 Oct;57(5):1842-50
pubmed: 24845578
J Acoust Soc Am. 1997 Apr;101(4):2299-310
pubmed: 9104031
Cogn Affect Behav Neurosci. 2002 Jun;2(2):89-108
pubmed: 12455678
Cognition. 2002 Jan;82(3):B101-11
pubmed: 11747867
Science. 1992 Jan 31;255(5044):606-8
pubmed: 1736364
J Exp Child Psychol. 1997 Aug;66(2):211-35
pubmed: 9245476
Cognition. 2008 Oct;109(1):168-73
pubmed: 18805522
J Acoust Soc Am. 2009 Sep;126(3):1461-76
pubmed: 19739759
Cognition. 2019 Aug;189:76-88
pubmed: 30928780
Psychol Rev. 1998 Apr;105(2):251-79
pubmed: 9577239
Front Psychol. 2015 Sep 15;6:1341
pubmed: 26441719
J Speech Hear Res. 1994 Apr;37(2):347-57
pubmed: 8028316
J Acoust Soc Am. 2001 Feb;109(2):775-94
pubmed: 11248981
J Acoust Soc Am. 1995 Feb;97(2):1286-96
pubmed: 7876448
Neurosci Lett. 2017 Jan 1;636:77-82
pubmed: 27793703
Int J Speech Lang Pathol. 2018 Nov;20(6):635-643
pubmed: 28795872
Percept Psychophys. 2004 Apr;66(3):422-9
pubmed: 15283067
Psychon Bull Rev. 2007 Oct;14(5):779-804
pubmed: 18087943

Auteurs

Heather Kabakoff (H)

Department of Communicative Sciences & Disorders, New York University, New York, NY.

Gretchen Go (G)

Department of Communicative Sciences & Disorders, New York University, New York, NY.

Susannah V Levi (SV)

Department of Communicative Sciences & Disorders, New York University, New York, NY.

Classifications MeSH