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Computations in Sensorimotor Learning.

Daniel M Wolpert¹

¹Computational and Biological Learning, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom wolpert@eng.cam.ac.uk.

Cold Spring Harbor Symposia on Quantitative Biology

|April 9, 2015

Summary

This summary is machine-generated.

This review explores how the sensorimotor system translates sensory input and decisions into actions. It covers motor memory, Bayesian decision theory, and risk sensitivity in action control.

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Area of Science:

Neuroscience
Cognitive Science
Motor Control

Background:

Cognitive abilities are realized through actions.
The sensorimotor system bridges perception and action.

Purpose of the Study:

To review computational frameworks for sensorimotor control.
To examine behavioral evidence for these theoretical models.

Main Methods:

Literature review of sensorimotor control research.
Focus on behavioral evidence supporting theoretical models.

Main Results:

Motor memories are activated and protected from interference.
Bayesian decision theory informs sensorimotor control, with identified suboptimality.
Risk sensitivity influences action selection.
Rapid motor responses contribute to system robustness.

Conclusions:

Understanding sensorimotor computations is key to explaining action.
Behavioral evidence supports theoretical frameworks of decision-making and motor control.