4.6 Article

Evidence for Cerebellar Contributions to Adaptive Plasticity in Speech Perception

Journal

CEREBRAL CORTEX
Volume 25, Issue 7, Pages 1867-1877

Publisher

OXFORD UNIV PRESS INC
DOI: 10.1093/cercor/bht428

Keywords

adaptation; cerebellum; fMRI; perceptual learning; supervised learning

Categories

Funding

  1. University of Pittsburgh, Kenneth P. Dietrich School of Arts and Sciences
  2. Andrew Mellon Predoctoral Fellowship [RO1 MH 59256, RO1 DC 004674]
  3. Andrew Mellon Predoctoral Fellowship (NSF) [1125719]
  4. Division Of Behavioral and Cognitive Sci
  5. Direct For Social, Behav & Economic Scie [1125719] Funding Source: National Science Foundation

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Human speech perception rapidly adapts to maintain comprehension under adverse listening conditions. For example, with exposure listeners can adapt to heavily accented speech produced by a non-native speaker. Outside the domain of speech perception, adaptive changes in sensory and motor processing have been attributed to cerebellar functions. The present functional magnetic resonance imaging study investigates whether adaptation in speech perception also involves the cerebellum. Acoustic stimuli were distorted using a vocoding plus spectral-shift manipulation and presented in a word recognition task. Regions in the cerebellum that showed differences before versus after adaptation were identified, and the relationship between activity during adaptation and subsequent behavioral improvements was examined. These analyses implicated the right Crus I region of the cerebellum in adaptive changes in speech perception. A functional correlation analysis with the right Crus I as a seed region probed for cerebral cortical regions with covarying hemodynamic responses during the adaptation period. The results provided evidence of a functional network between the cerebellum and language-related regions in the temporal and parietal lobes of the cerebral cortex. Consistent with known cerebellar contributions to sensorimotor adaptation, cerebro-cerebellar interactions may support supervised learning mechanisms that rely on sensory prediction error signals in speech perception.

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