Literature DB >> 26328675

Poor phonetic perceivers are affected by cognitive load when resolving talker variability.

Mark Antoniou1, Patrick C M Wong2.   

Abstract

Speech training paradigms aim to maximise learning outcomes by manipulating external factors such as talker variability. However, not all individuals may benefit from such manipulations because subject-external factors interact with subject-internal ones (e.g., aptitude) to determine speech perception and/or learning success. In a previous tone learning study, high-aptitude individuals benefitted from talker variability, whereas low-aptitude individuals were impaired. Because increases in cognitive load have been shown to hinder speech perception in mixed-talker conditions, it has been proposed that resolving talker variability requires cognitive resources. This proposal leads to the hypothesis that low-aptitude individuals do not use their cognitive resources as efficiently as those with high aptitude. Here, high- and low-aptitude subjects identified pitch contours spoken by multiple talkers under high and low cognitive load conditions established by a secondary task. While high-aptitude listeners outperformed low-aptitude listeners across load conditions, only low-aptitude listeners were impaired by increased cognitive load. The findings suggest that low-aptitude listeners either have fewer available cognitive resources or are poorer at allocating attention to the signal. Therefore, cognitive load is an important factor when considering individual differences in speech perception and training paradigms.

Mesh:

Year:  2015        PMID: 26328675      PMCID: PMC4529436          DOI: 10.1121/1.4923362

Source DB:  PubMed          Journal:  J Acoust Soc Am        ISSN: 0001-4966            Impact factor:   1.840


  13 in total

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Journal:  J Exp Psychol Hum Percept Perform       Date:  2007-04       Impact factor: 3.332

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Authors:  Tyler K Perrachione; Jiyeon Lee; Louisa Y Y Ha; Patrick C M Wong
Journal:  J Acoust Soc Am       Date:  2011-07       Impact factor: 1.840

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Authors:  Mark Antoniou; Patrick C M Wong; Suiping Wang
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Review 10.  Linking neurogenetics and individual differences in language learning: the dopamine hypothesis.

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  8 in total

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4.  Non-native phonetic learning is destabilized by exposure to phonological variability before and after training.

Authors:  Pamela Fuhrmeister; Emily B Myers
Journal:  J Acoust Soc Am       Date:  2017-11       Impact factor: 1.840

5.  Shifting Perceptual Weights in L2 Vowel Identification after Training.

Authors:  Wei Hu; Lin Mi; Zhen Yang; Sha Tao; Mingshuang Li; Wenjing Wang; Qi Dong; Chang Liu
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6.  Complexity, Training Paradigm Design, and the Contribution of Memory Subsystems to Grammar Learning.

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7.  The Role of Temporal Acoustic Exaggeration in High Variability Phonetic Training: A Behavioral and ERP Study.

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Journal:  Front Psychol       Date:  2019-05-24

8.  The effect of overnight consolidation in the perceptual learning of non-native tonal contrasts.

Authors:  Zhen Qin; Caicai Zhang
Journal:  PLoS One       Date:  2019-12-12       Impact factor: 3.240

  8 in total

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