Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-23T01:24:20.142Z 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 [email protected] http://www.psychology.adelaide.edu.au/members/staff/michaellee/

Abstract

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]

Type
Brief Report
Copyright
© 2001 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)