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Learning movement sequences with a delayed reward signal in a hierarchical model of motor function.

Abstract:

A key problem in reinforcement learning is how an animal is able to learn a sequence of movements when the reward signal only occurs at the end of the sequence. We describe how a hierarchical dynamical model of motor function is able to solve the problem of delayed reward in learning movement sequences using associative (Hebbian) learning. At the lowest level, the motor system encodes simple movements or primitives, while at higher levels the system encodes sequences of primitives. During tra...

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Publication status:
Published

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Institution:
University of Oxford
Department:
Oxford, MSD, Experimental Psychology
Role:
Author
Journal:
Neural networks : the official journal of the International Neural Network Society
Volume:
20
Issue:
2
Pages:
172-181
Publication date:
2007-03-05
DOI:
EISSN:
1879-2782
ISSN:
0893-6080
URN:
uuid:81d4bb88-937b-47e0-989d-169521e4e126
Source identifiers:
20162
Local pid:
pubs:20162

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