Limb apraxia and active inference in the visuomotor pathways

Area of Science:

• Neuroscience
• Computational Psychiatry
• Cognitive Science

Background:

• Apraxia impacts motor behavior, affecting visuomotor control.
• Current understanding lacks a unified neurocomputational framework.
• The active inference framework offers a model for brain function.

Purpose of the Study:

• To present a unified neurocomputational account of apraxia within the visuomotor system.
• To demonstrate how the brain's generative model and surprise minimization relate to motor control.
• To explain apraxia as a consequence of incorrect model parameterization.

Main Methods:

• Utilized the active inference framework to model the visuomotor system.
• Developed a generative model of sensory observations and surprise minimization.
• Simulated model impairments to replicate neuropsychological syndromes.

Main Results:

• The active inference model explains visuomotor system organization and function.
• Simulated impairments successfully replicated key aspects of five neuropsychological syndromes.
• The model provides a unifying perspective on apraxia and related disorders.

Conclusions:

• Apraxia arises from incorrect parameterizations of the brain's generative model.
• Active inference offers a novel hypothesis for the neurocomputational basis of motor pathologies.
• This quantitative approach can support clinical research in neuropsychology.