How Robust Are Probabilistic Models of Higher-Level Cognition?

Gary Marcus, Ernest Davis

Research output: Contribution to journalArticle

Abstract

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
Volume24
Issue number12
DOIs
StatePublished - Dec 2013

Fingerprint

Theory of Mind
Physics
Statistical Models
Cognition
Learning
Word Learning
Prediction
Bayesian Model
Equations
Naive Physics
Merit
Inference
Probabilistic Inference

Keywords

  • Bayesian models
  • cognition(s)
  • optimality

ASJC Scopus subject areas

  • Psychology(all)
  • Arts and Humanities (miscellaneous)
  • Medicine(all)

Cite this

How Robust Are Probabilistic Models of Higher-Level Cognition? / Marcus, Gary; Davis, Ernest.

In: Psychological Science, Vol. 24, No. 12, 12.2013, p. 2351-2360.

Research output: Contribution to journalArticle

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