Entropy and information in models of learning behaviour

Article

Belavkin, R. 2005. Entropy and information in models of learning behaviour. AISB Quarterly. 119, pp. 5-5.
TypeArticle
TitleEntropy and information in models of learning behaviour
AuthorsBelavkin, R.
Abstract

Learning is an important process that allows us to reduce the uncertainty of the outcomes of our decisions or in other words the uncertainty about the utilities of decisions. Thus, through learning we can make decisions that are most beneficial to us (or at least that seem to be so). Information Theory has produced convenient apparatus to measure information transfer through a change of entropy (a measure of uncertainty). However, the notion of information cannot be easily applied to studies in experimental psychology, where learning is judged by external observations of subjects' performance in certain tasks. Modern cognitive modelling tools have allowed for bringing information theoretic concepts much closer to cognitive psychology.

Research GroupArtificial Intelligence group
PublisherThe Society for the Study of Artificial Intelligence and Simulation of Behaviour
JournalAISB Quarterly
ISSN0268-4179
Publication dates
Print2005
Publication process dates
Deposited08 Sep 2008
Output statusPublished
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LanguageEnglish
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