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Related Experiment Video

Updated: Apr 22, 2026

Stimulating the Lip Motor Cortex with Transcranial Magnetic Stimulation

Stimulating the Lip Motor Cortex with Transcranial Magnetic Stimulation

Published on: June 14, 2014

Active inference and speech motor control.

Abigail R Bradshaw¹, Clare Press^2,3, 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

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 inference Predictive coding Sensorimotor interactions Sensorimotor learning Speech motor control

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Last Updated: Apr 22, 2026

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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.