How Robust Are Probabilistic Models of Higher-Level Cognition?

Gary F. Marcus, Ernest Davis

Research output: Contribution to journalArticlepeer-review


An increasingly popular theory holds that the mind should be viewed as a near-optimal or rational engine of probabilistic inference, in domains as diverse as word learning, pragmatics, naive physics, and predictions of the future. We argue that this view, often identified with Bayesian models of inference, is markedly less promising than widely believed, and is undermined by post hoc practices that merit wholesale reevaluation. We also show that the common equation between probabilistic and rational or optimal is not justified.

Original languageEnglish (US)
Pages (from-to)2351-2360
Number of pages10
JournalPsychological Science
Issue number12
StatePublished - Dec 2013


  • Bayesian models
  • cognition(s)
  • optimality

ASJC Scopus subject areas

  • General Psychology


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