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Stimulating the Lip Motor Cortex with Transcranial Magnetic Stimulation
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Active inference and speech motor control.

Abigail R Bradshaw1, Clare Press2,3, Matthew H Davis4

  • 1MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge, CB2 7EF, UK.

Psychonomic Bulletin & Review
|April 20, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Active inference, a brain theory minimizing prediction errors, is now applied to speech motor control. This framework explains auditory feedback adaptation and highlights proprioception

Keywords:
Active inferencePredictive codingSensorimotor interactionsSensorimotor learningSpeech motor control

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Speech Science

Background:

  • Active inference is a general brain theory minimizing sensory prediction errors.
  • It has been widely applied to manual action control but less to speech motor control.
  • Speech motor control is a complex sensorimotor function crucial for human interaction.

Purpose of the Study:

  • To present the first detailed active inference framework for auditorily guided speech production.
  • To compare active inference models with existing computational models of speech motor control.
  • To explain adaptation and compensation mechanisms in speech following auditory feedback perturbations.

Main Methods:

  • Developing an active inference framework for speech production.
  • Comparing the architecture of active inference models to established speech motor control models.
  • Modeling auditory feedback perturbations to observe adaptation and compensation.

    • Main Results:

    • The study details an active inference framework for speech production.
    • It provides an account of how speech adapts to auditory feedback changes.
    • Active inference models offer unique insights into speech motor control mechanisms.

    Conclusions:

    • Active inference provides a unified framework for understanding speech motor control.
    • It highlights the role of proprioception in speech motor learning.
    • This approach can model phenomena like phonetic convergence and the influence of external voices.