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Daniel J Schad1, Michael Betancourt2, Shravan Vasishth3

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This study introduces a principled Bayesian workflow for cognitive science research, emphasizing model verification for accurate data analysis. It ensures robust scientific answers by checking model relevance and incorporating domain expertise.

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Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Psychology

Background:

  • Bayesian methods are increasingly used in cognitive science research.
  • Probabilistic programming languages (e.g., Stan) and packages (e.g., brms) facilitate Bayesian analysis.
  • Model relevance and accurate data structure capture are crucial for Bayesian utility.

Purpose of the Study:

  • Introduce a principled Bayesian workflow for cognitive science.
  • Demonstrate model verification techniques for robust data analysis.
  • Guide the proper employment of Bayesian methods in scientific inquiry.

Main Methods:

  • Applied a principled Bayesian workflow to cognitive science.
  • Used a working example of reading times data with linguistic manipulation (object vs. subject relative clauses).
  • Described key model verification questions: prior predictive checks, computational faithfulness, model sensitivity, and posterior predictive checks.

Main Results:

  • The workflow demonstrates using domain knowledge to inform prior distributions.
  • Provided guidelines and checks to avoid overfitting and capture relevant data structure.
  • Showcased how to obtain robust answers to scientific questions using Bayesian methods.

Conclusions:

  • The principled Bayesian workflow enhances the reliability of cognitive science research.
  • Analysts must verify model utility, even with advanced software.
  • Proper application of Bayesian methods ensures valid data analysis and meaningful scientific conclusions.