Emerging Native-Similar Neural Representations Underlie Non-Native Speech Category Learning Success

Gangyi Feng*, Yu Li, Shen Mou Hsu, Patrick C.M. Wong, Tai Li Chou, Bharath Chandrasekaran*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Learning non-native phonetic categories in adulthood is an exceptionally challenging task, characterized by large interindividual differences in learning speed and outcomes. The neurobiological mechanisms underlying the interindividual differences in the learning efficacy are not fully understood. Here we examine the extent to which training-induced neural representations of non-native Mandarin tone categories in English listeners (n = 53) are increasingly similar to those of the native listeners (n = 33) who acquired these categories early in infancy. We assess the extent to which the neural similarities in representational structure between non-native learners and native listeners are robust neuromarkers of interindividual differences in learning success. Using intersubject neural representational similarity (IS-NRS) analysis and predictive modeling on two functional magnetic resonance imaging datasets, we examined the neural representational mechanisms underlying speech category learning success. Learners’ neural representations that were significantly similar to the native listeners emerged in brain regions mediating speech perception following training; the extent of the emerging neural similarities with native listeners significantly predicted the learning speed and outcome in learners. The predictive power of IS-NRS outperformed models with other neural representational measures. Furthermore, neural representations underlying successful learning were multidimensional but cost-efficient in nature. The degree of the emergent native-similar neural representations was closely related to the robustness of neural sensitivity to feedback in the frontostriatal network. These findings provide important insights into the experience-dependent representational neuroplasticity underlying successful speech learning in adulthood and could be leveraged in designing individualized feedback-based training paradigms that maximize learning efficacy.

Original languageEnglish (US)
Pages (from-to)280-307
Number of pages28
JournalNeurobiology of Language
Volume2
Issue number2
DOIs
StatePublished - Mar 17 2021

Funding

This work was supported by grants from the General Research Fund (Ref. No. 14619518 to Gangyi Feng) of the Research Grants Council of Hong Kong, Direct Grant for Research (Ref. No. 4051137 to Gangyi Feng) by The Chinese University of Hong Kong, and the National Institute on Deafness and other Communication Disorders of the National Institutes of Health (Award No. R01DC013315 to Bharath Chandrasekaran). We thank Casey Roark for her helpful comments on an early version of the manuscript. Gangyi Feng, General Research Fund of the Research Grants Council of Hong Kong, Award ID: 14619518. Gangyi Feng, Direct Grant for Research by The Chinese University of Hong Kong, Award ID: 4051137. Bharath Chandrasekaran, National Institute on Deafness and Other Communication Disorders (https://dx.doi.org/10.13039/100000055), Award ID: R01DC013315.

Keywords

  • Feedback processing
  • Individual differences
  • Multivariate representation
  • Non-native speech learning
  • Predictive modeling
  • Tone language

ASJC Scopus subject areas

  • Neurology
  • Linguistics and Language

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