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Controlling variability.

Terence D Sanger¹

¹Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089-1111, USA. tsanger@usc.edu

Journal of Motor Behavior

|December 25, 2010

Summary

This summary is machine-generated.

Human motor control involves uncertainty. This study presents a mathematical framework showing accurate movement control is possible despite inherent variability and noise.

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Area of Science:

Motor control
Computational neuroscience
Robotics

Background:

Human motor control faces uncertainty in sensory state estimation and motor command prediction.
Achieving precision through practice incurs significant costs in time, effort, and neural resources.
Motor planning must address variability and noise throughout movement, not just at the endpoint.

Purpose of the Study:

To present a mathematical basis for understanding the temporal dynamics of uncertainty in motor control.
To demonstrate how accurate endpoint control can be achieved despite inherent system inaccuracies.
To lay the groundwork for a theory of optimal control in variable, uncertain, and noisy systems.

Main Methods:

Mathematical modeling of motor control systems.
Analysis of the time course of uncertainty during movement execution.
Simulation of control strategies under noisy and variable conditions.

Main Results:

A mathematical framework for quantifying movement-related uncertainty over time was developed.
It was demonstrated that precise endpoint control is achievable even with imprecise and variable internal controllers.
The findings suggest a potential for robust motor performance in the presence of significant system noise.

Conclusions:

Optimal motor planning requires accounting for uncertainty throughout the movement trajectory.
A mathematical basis for understanding and potentially mitigating the effects of noise in motor control is established.
This work represents a foundational step towards a comprehensive theory of optimal control for real-world robotic and biological systems.