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

[img] HTML (article) - Published version (with publisher's formatting)
Download (6kB)
This item is available in: Library Catalogue

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.

Item Type: Article
Additional Information: Online quarterly newsletter
Research Areas: A. > School of Science and Technology > Computer Science
A. > School of Science and Technology > Computer Science > Artificial Intelligence group
Item ID: 7
Useful Links:
Depositing User: Repository team
Date Deposited: 08 Sep 2008 11:53
Last Modified: 13 Oct 2016 14:10
URI: http://eprints.mdx.ac.uk/id/eprint/7

Actions (login required)

Edit Item Edit Item

Full text downloads (NB count will be zero if no full text documents are attached to the record)

Downloads per month over the past year