Bridging Levels of Analysis for Probabilistic Models of Cognition

Thomas L. Griffiths, Edward Vul, Adam N. Sanborn

Research output: Contribution to journalArticlepeer-review

121 Scopus citations


Probabilistic models of cognition characterize the abstract computational problems underlying inductive inferences and identify their ideal solutions. This approach differs from traditional methods of investigating human cognition, which focus on identifying the cognitive or neural processes that underlie behavior and therefore concern alternative levels of analysis. To evaluate the theoretical implications of probabilistic models and increase their predictive power, we must understand the relationships between theories at these different levels of analysis. One strategy for bridging levels of analysis is to explore cognitive processes that have a direct link to probabilistic inference. Recent research employing this strategy has focused on the possibility that the Monte Carlo principle-which concerns sampling from probability distributions in order to perform computations-provides a way to link probabilistic models of cognition to more concrete cognitive and neural processes.

Original languageEnglish (US)
Pages (from-to)263-268
Number of pages6
JournalCurrent Directions in Psychological Science
Issue number4
StatePublished - Aug 2012
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Psychology


  • cognitive modeling
  • levels of analysis
  • probabilistic models of cognition
  • rational process models


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