Learning to Move and Plan like the Knight: Sequential Decision Making with a Novel Motor Mapping

Area of Science:

• Cognitive Science
• Motor Control
• Human-Computer Interaction

Background:

• Complex skill acquisition involves both motor learning and multi-step planning.
• These processes are often studied in isolation, yet likely interact during learning.

Purpose of the Study:

• To investigate the interaction between motor learning and sequential decision-making.
• To assess how learning an arbitrary motor mapping impacts planning capabilities.

Main Methods:

• Participants performed a sequential decision-making task with a novel keyboard-based motor mapping (Knight's move).
• Human performance was analyzed, and computational modeling was used to understand learning and planning strategies.

Main Results:

• Learning the arbitrary motor mapping improved subsequent planning performance.
• Computational models indicated increased learning rates and more flexible planning.
• Incorporating mapping learning into planning explained performance variations across different planning horizons.

Conclusions:

• Motor learning of arbitrary mappings enhances the flexibility of planning.
• Working memory constraints may limit planning horizons, leading to skill chunking during learning.