A theory of learning to infer.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
Read More Add to Saved list
  • Additional Information
    • Source:
      Publisher: American Psychological Association Country of Publication: United States NLM ID: 0376476 Publication Model: Print Cited Medium: Internet ISSN: 1939-1471 (Electronic) Linking ISSN: 0033295X NLM ISO Abbreviation: Psychol Rev Subsets: MEDLINE
    • Publication Information:
      Original Publication: Washington, DC : American Psychological Association
    • Subject Terms:
      Bayes Theorem* ; Bias* ; Learning* ; Memory* ; Models, Theoretical* ; Probability Theory*; Cognition ; Heuristics ; Humans ; Problem Solving
    • Abstract:
      Bayesian theories of cognition assume that people can integrate probabilities rationally. However, several empirical findings contradict this proposition: human probabilistic inferences are prone to systematic deviations from optimality. Puzzlingly, these deviations sometimes go in opposite directions. Whereas some studies suggest that people underreact to prior probabilities ( base rate neglect ), other studies find that people underreact to the likelihood of the data ( conservatism ). We argue that these deviations arise because the human brain does not rely solely on a general-purpose mechanism for approximating Bayesian inference that is invariant across queries. Instead, the brain is equipped with a recognition model that maps queries to probability distributions. The parameters of this recognition model are optimized to get the output as close as possible, on average, to the true posterior. Because of our limited computational resources, the recognition model will allocate its resources so as to be more accurate for high probability queries than for low probability queries. By adapting to the query distribution, the recognition model learns to infer. We show that this theory can explain why and when people underreact to the data or the prior, and a new experiment demonstrates that these two forms of underreaction can be systematically controlled by manipulating the query distribution. The theory also explains a range of related phenomena: memory effects, belief bias, and the structure of response variability in probabilistic reasoning. We also discuss how the theory can be integrated with prior sampling-based accounts of approximate inference. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
    • Publication Date:
      Date Created: 20200401 Date Completed: 20210322 Latest Revision: 20210322
    • Publication Date:
      20231215
    • Accession Number:
      10.1037/rev0000178
    • Accession Number:
      32223286