Entropy and information in models of learning behaviour

Belavkin, Roman V. (2005) Entropy and information in models of learning behaviour. AISB Quarterly, 119 . p. 5. ISSN 0268-4179

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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.

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Research Areas:School of Science and Technology > Computer Science
School of Science and Technology > Computer Science > Artificial Intelligence group
ID Code:7
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Deposited On:08 Sep 2008 11:53
Last Modified:08 Oct 2014 11:05

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