Hostname: page-component-74656f9884-rdgjk Total loading time: 0 Render date: 2026-11-09T04:08:45.872Z Has data issue: false hasContentIssue false

Extending Bayesian concept learning to deal with representational complexity and adaptation

Published online by Cambridge University Press:  20 August 2002

Michael D. Lee
Affiliation:
Department of Psychology, University of Adelaide, SA 5008 Australia michael.lee@psychology.adelaide.edu.au http://www.psychology.adelaide.edu.au/members/staff/michaellee/
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

While Tenenbaum and Griffiths impressively consolidate and extend Shepard's research in the areas of stimulus representation and generalization, there is a need for complexity measures to be developed to control the flexibility of their “hypothesis space” approach to representation. It may also be possible to extend their concept learning model to consider the fundamental issue of representational adaptation. [Tenenbaum & Griffiths]

Information

Type
Brief Report
Copyright
© 2001 Cambridge University Press