Practice reshapes the geometry and dynamics of task-tailored representations.

Area of Science:

• Cognitive Neuroscience
• Computational Neuroscience
• Human Motor Control

Background:

• Automaticity through extensive practice improves task efficiency and precision.
• Existing theories of automaticity propose different neural representations change with practice, but lack consensus.
• Neural population dynamics offer a framework to investigate computational changes during learning.

Purpose of the Study:

• To test the hypothesis that practice optimizes neural representational geometry for task automaticity.
• To determine which levels of task representation (low-level vs. high-level) are critical for performance improvement.
• To link changes in neural representations to behavioral gains, including reduced switch costs.

Main Methods:

• Human participants (n=40) practiced a context-dependent action selection task over 3 days.
• Electroencephalogram (EEG) was recorded during practice to measure neural activity.
• Representational Similarity Analysis (RSA) was used to analyze neural representations of task features.

Main Results:

• Practice enhanced representations of high-level, context-specific task conjunctions, correlating with performance gains.
• Improvement followed the power law of practice, driven by conjunctive representation enhancement.
• Neural states representing task conjunctions became more stable and aligned, reducing switch costs.

Conclusions:

• Practice optimizes dynamic representational geometry, creating task-tailored neural states.
• High-level conjunctive representations are key to automaticity and performance improvement.
• Optimized neural dynamics tame task dimensionality, leading to efficient and precise performance.