A Minimum Free Energy Model of Motor Learning

Area of Science:

• Neuroscience
• Motor Learning
• Computational Neuroscience

Background:

• Behavioral variability is linked to task performance, yet a unifying objective for motor learning remains elusive.
• Understanding the neural dynamics and behavioral performance during skill acquisition is crucial.

Discussion:

• Neural and behavioral data during motor learning follow a Fokker-Planck equation.
• This suggests the brain optimizes joint neural and behavioral density entropy alongside expected performance.
• This optimization is termed minimum free energy learning (MFEL).

Key Insights:

• MFEL explains how behavioral variability is tuned while performance improves during learning.
• A novel inverse reinforcement learning approach was used to infer the brain's optimized cost function and variability tuning parameter.
• Population-level analysis reveals individual learning objectives.

Outlook:

• MFEL offers a unifying principle for formulating and inferring learning objectives in neuroscience and AI.
• This framework can guide the development of more sophisticated artificial learning systems.
• Further research can explore MFEL in diverse cognitive and motor domains.