Skip to main content
×
Home
    • Aa
    • Aa

Extending Bayesian concept learning to deal with representational complexity and adaptation

  • Michael D. Lee (a1)
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]

Copyright
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Behavioral and Brain Sciences
  • ISSN: 0140-525X
  • EISSN: 1469-1825
  • URL: /core/journals/behavioral-and-brain-sciences
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×