The Role of the Human Auditory Corticostriatal Network in Speech Learning

Gangyi Feng, Han Gyol Yi, Bharath Chandrasekaran*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We establish a mechanistic account of how the mature human brain functionally reorganizes to acquire and represent new speech sounds. Native speakers of English learned to categorize Mandarin lexical tone categories produced by multiple talkers using trial-by-trial feedback. We hypothesized that the corticostriatal system is a key intermediary in mediating temporal lobe plasticity and the acquisition of new speech categories in adulthood. We conducted a functional magnetic resonance imaging experiment in which participants underwent a sound-to-category mapping task. Diffusion tensor imaging data were collected, and probabilistic fiber tracking analysis was employed to assay the auditory corticostriatal pathways. Multivariate pattern analysis showed that talker-invariant novel tone category representations emerged in the left superior temporal gyrus (LSTG) within a few hundred training trials. Univariate analysis showed that the putamen, a subregion of the striatum, was sensitive to positive feedback in correctly categorized trials. With learning, functional coupling between the putamen and LSTG increased during error processing. Furthermore, fiber tractography demonstrated robust structural connectivity between the feedback-sensitive striatal regions and the LSTG regions that represent the newly learned tone categories. Our convergent findings highlight a critical role for the auditory corticostriatal circuitry in mediating the acquisition of new speech categories.

Original languageEnglish (US)
Pages (from-to)4077-4089
Number of pages13
JournalCerebral Cortex
Volume29
Issue number10
DOIs
StatePublished - Sep 13 2019

Keywords

  • corticostriatal system
  • multi-modal imaging
  • MVPA
  • speech category learning

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Cellular and Molecular Neuroscience

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