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Related Experiment Video

Updated: Mar 20, 2026

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

Published on: May 3, 2018

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

Carlos A Velázquez-Vargas¹, Jordan A Taylor^2,3

¹Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA.

Computational Brain & Behavior

|March 19, 2026

Summary

Motor learning improves sequential decision-making by enhancing the learning rate of arbitrary motor mappings. This allows for more flexible planning, crucial for acquiring complex skills.

Keywords:

Grid navigation Motor learning Planning Reinforcement learning Sequence learning

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Last Updated: Mar 20, 2026

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

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Published on: May 3, 2018

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Acquisition of a High-precision Skilled Forelimb Reaching Task in Rats

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Published on: June 22, 2015

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.