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Determining informative priors for cognitive models.

Michael D Lee1, Wolf Vanpaemel2

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This study explores how to create informative priors for Bayesian cognitive models. It offers methods and benefits for specifying priors, improving psychological process modeling.

Keywords:
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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Psychology

Background:

  • Cognitive models formalize theories using data.
  • Bayesian approaches use priors for model parameters, reflecting prior knowledge.
  • Specifying priors in Bayesian cognitive modeling presents challenges, often leading to vague priors.

Purpose of the Study:

  • To survey sources of information for specifying priors in cognitive models.
  • To discuss methods for formalizing this information into prior distributions.
  • To identify benefits of using informative priors in cognitive modeling.

Main Methods:

  • Review of literature on prior specification in Bayesian cognitive modeling.
  • Discussion of methods for translating theoretical and empirical information into prior distributions.
  • Illustrative examples using cognitive models of memory retention, categorization, and decision-making.

Main Results:

  • Identified various sources for informing prior distributions in cognitive models.
  • Presented methods for the mathematical formalization of prior knowledge.
  • Highlighted advantages of employing informative priors over vague ones.

Conclusions:

  • Informative priors enhance the Bayesian approach to cognitive modeling.
  • Addressing challenges in prior specification can lead to more robust and interpretable models.
  • The methods discussed are applicable across different domains of cognitive modeling.