Probabilistic models of cognition: exploring representations and inductive biases

Thomas L. Griffiths, Nick Chater, Charles Kemp, Amy Perfors, Joshua B. Tenenbaum

Research output: Contribution to journalArticle

230 Scopus citations

Abstract

Cognitive science aims to reverse-engineer the mind, and many of the engineering challenges the mind faces involve induction. The probabilistic approach to modeling cognition begins by identifying ideal solutions to these inductive problems. Mental processes are then modeled using algorithms for approximating these solutions, and neural processes are viewed as mechanisms for implementing these algorithms, with the result being a top-down analysis of cognition starting with the function of cognitive processes. Typical connectionist models, by contrast, follow a bottom-up approach, beginning with a characterization of neural mechanisms and exploring what macro-level functional phenomena might emerge. We argue that the top-down approach yields greater flexibility for exploring the representations and inductive biases that underlie human cognition.

Original languageEnglish (US)
Pages (from-to)357-364
Number of pages8
JournalTrends in Cognitive Sciences
Volume14
Issue number8
DOIs
StatePublished - Aug 1 2010
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Neuropsychology and Physiological Psychology
  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience

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