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Related Experiment Video

Updated: Jul 8, 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

Optimality, stochasticity, and variability in motor behavior.

Emmanuel Guigon¹, Pierre Baraduc, Michel Desmurget

¹INSERM U742, ANIM, Université Pierre et Marie Curie (UPMC Paris 6), 9, quai Saint-Bernard, 75005, Paris, France. guigon@ccr.jussieu.fr

Journal of Computational Neuroscience

|January 19, 2008

Summary

This summary is machine-generated.

Stochastic optimality in motor control faces challenges. An alternative framework using a deterministic controller and optimal state estimator better explains movement variability without needing stochastic control.

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Area of Science:

Neuroscience
Motor Control
Computational Neuroscience

Background:

Recent motor control theories propose the nervous system acts as a stochastically optimal controller.
This approach considers noise statistics in planning and executing movements, explaining single acts and repeated actions.
Stochastic optimality offers a principled way to manage detrimental noise effects.

Purpose of the Study:

To critically analyze the hypothesis of stochastic optimality in motor control.
To identify and address difficulties associated with the stochastic optimality framework.
To propose an alternative model for motor control that explains movement variability.

Main Methods:

Critical analysis of existing stochastic optimality theories in motor control.
Development of an alternative framework combining deterministic control with optimal state estimation.
Evaluation of the proposed framework's ability to explain movement variability.

Main Results:

The study reveals several difficulties with the stochastic optimality hypothesis.
Stochastic control is shown to be potentially unnecessary for explaining motor behavior's stochastic nature.
The proposed alternative framework effectively explains movement variability.

Conclusions:

The hypothesis of stochastic optimality in motor control presents significant challenges.
An alternative framework, integrating a deterministic controller and an optimal state estimator, provides a more robust explanation for movement variability.
This new model overcomes drawbacks of stochastic optimality while appropriately accounting for motor behavior characteristics.