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Motor-based prediction mediates implicit vocal imitation.

Yuchunzi Wu1, Zhili Han2, Xing Tian1

  • 1Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning; Division of Arts and Sciences, New York University Shanghai, Shanghai, China; NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai, Shanghai, China; Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China.

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Summary
This summary is machine-generated.

Motor-based predictions enhance speech adaptation by increasing sensitivity to listener-matched sounds, driving vocal convergence. This challenges previous assumptions about predictive mechanisms in speech perception and learning.

Keywords:
forward modelimitationmismatch negativityphonetic convergenceprediction

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Area of Science:

  • Psycholinguistics
  • Neuroscience
  • Speech Communication

Background:

  • Phonetic convergence, the unconscious speech adaptation to an interlocutor, is linked to predictive mechanisms.
  • Two prediction types exist: memory-based (reducing sensitivity to vocal identity) and motor-based (listener's vocal characteristics).
  • The precise role of motor-based predictions in phonetic convergence remains unclear.

Purpose of the Study:

  • To investigate whether motor-based predictions suppress or enhance sensitivity to listener-matched features.
  • To determine the contribution of motor-based predictions to phonetic convergence.
  • To explore the dynamic interplay between predictive signals and external input in speech adaptation.

Main Methods:

  • A novel speaking oddball task with auditory mismatch negativity (MMN) as a neural index.
  • Participants were divided into a shadow group (promoting vocal convergence) and a non-shadow group.
  • Electroencephalography (EEG) was used to analyze neural responses.

Main Results:

  • Motor-based predictions were found to enhance sensitivity to listener-matched predicted features after vocal convergence behavior.
  • This enhancement correlated positively with the degree of vocal convergence observed.
  • The findings challenge the notion that motor-based predictions solely suppress neural responses.

Conclusions:

  • Motor-based predictions can enhance perceptual learning and guide vocal adjustments, not just suppress responses.
  • These predictions orchestrate sensorimotor interaction and memory-based operations for implicit learning in social contexts.
  • The study highlights a more dynamic role for motor-based predictions in mediating phonetic convergence.