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Distributed representations accelerate evolution of adaptive behaviours

Stone, J.V. (2007) Distributed representations accelerate evolution of adaptive behaviours. PLoS Computational Biology, 3 (8). pp. 1417-1425. ISSN 1553-734X

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Abstract

Animals with rudimentary innate abilities require substantial learning to transform those abilities into useful skills, where a skill can be considered as a set of sensory - motor associations. Using linear neural network models, it is proved that if skills are stored as distributed representations, then within- lifetime learning of part of a skill can induce automatic learning of the remaining parts of that skill. More importantly, it is shown that this " free- lunch'' learning ( FLL) is responsible for accelerated evolution of skills, when compared with networks which either 1) cannot benefit from FLL or 2) cannot learn. Specifically, it is shown that FLL accelerates the appearance of adaptive behaviour, both in its innate form and as FLL- induced behaviour, and that FLL can accelerate the rate at which learned behaviours become innate.

Item Type: Article
Copyright, Publisher and Additional Information: © 2007 Public Library Science. This is an author produced version of a paper published in PLoS Computational Biology. Uploaded in accordance with the publisher's self-archiving policy.
Academic Units: The University of Sheffield > Faculty of Science (Sheffield) > Department of Psychology (Sheffield)
Depositing User: Sherpa Assistant
Date Deposited: 26 Oct 2007 14:26
Last Modified: 08 Feb 2013 16:55
Published Version: http://dx.doi.org/10.1371/journal.pcbi.0030147
Status: Published
Publisher: Public Library Science
Refereed: Yes
Identification Number: 10.1371/journal.pcbi.0030147
URI: http://eprints.whiterose.ac.uk/id/eprint/3452

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