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Inferring visuomotor priors for sensorimotor learning
Edward J A Turnham1, Daniel A Braun, Daniel M Wolpert
1Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge, United Kingdom. ejat3@cam.ac.uk
Plos Computational Biology
|April 13, 2011
Summary
This study reveals how prior beliefs shape sensorimotor learning, especially with ambiguous visual cues. We found people naturally interpret new visuomotor transformations as rotations, but can adapt their expectations with experience.
Area of Science:
- Neuroscience
- Cognitive Science
- Motor Control
Background:
- Sensorimotor learning integrates prior expectations with sensory evidence, akin to Bayesian principles.
- Prior beliefs are crucial for learning, particularly when sensory information is ambiguous.
Purpose of the Study:
- To develop a novel technique for estimating the prior's covariance structure in visuomotor transformations.
- To investigate how ambiguous sensory evidence influences sensorimotor adaptation.
- To examine the adaptability of prior beliefs in sensorimotor tasks.
Main Methods:
- Subjects performed reaching movements under various visuomotor transformations with delayed visual feedback.
- A Bayesian observer model was employed to infer the covariance structure of the prior over visuomotor transformations.
- The prior's structure was analyzed after exposure to different sets of transformations.
Main Results:
- The inferred prior covariance structure initially favored visuomotor rotations, even with unstructured transformations.
- Subjects demonstrated a strong tendency to interpret ambiguous visual feedback as rotation-like.
- Exposure to a structured, non-rotational visuomotor task significantly altered the prior's covariance structure.
Conclusions:
- A novel method can estimate the full covariance structure of priors in sensorimotor tasks.
- The human prior over visuomotor transformations inherently favors rotation-like structures.
- Prior beliefs in sensorimotor systems are adaptable and can be modified by experience with novel task structures.
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