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Hierarchical Bayesian estimation for cognitive models using Particle Metropolis within Gibbs (PMwG): A tutorial.

Caroline Kuhne1,2, Quentin F Gronau3, Reilly J Innes3,4

  • 1School of Psychological Sciences, University of Newcastle, University Drive, 2308, Callaghan, NSW, Australia. caroline.kuhne@hmri.org.au.

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|November 25, 2025
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Summary
This summary is machine-generated.

Estimating cognitive models is now efficient with the Particle Metropolis within Gibbs (PMwG) algorithm and the R package pmwg. This approach enhances psychological science by enabling complex model analysis.

Keywords:
Cognitive modelHierarchical Bayesian estimationMarkov chain Monte CarloSoftwareTutorialopensource

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Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Psychological Measurement

Background:

  • Quantitative cognitive model estimation is crucial but often inefficient in psychological science.
  • Hierarchical Bayesian frameworks are increasingly used for complex data analysis.
  • Advanced sampling methods are needed to overcome computational challenges.

Purpose of the Study:

  • To introduce the pmwg R package for efficient cognitive model estimation.
  • To demonstrate the application of the Particle Metropolis within Gibbs (PMwG) algorithm.
  • To facilitate the analysis of complex cognitive models and model selection.

Main Methods:

  • Utilized the pmwg package in R for implementing cognitive models.
  • Applied the Particle Metropolis within Gibbs (PMwG) sampling algorithm.
  • Demonstrated with signal detection theory and jointly modeled tasks.

Main Results:

  • The pmwg package enables efficient estimation of quantitative cognitive models.
  • The tutorial covers simple and complex cognitive modeling scenarios.
  • Model adequacy and selection are addressed within the framework.

Conclusions:

  • The pmwg package and PMwG algorithm offer a robust and efficient solution for cognitive modeling.
  • This approach can advance psychological science by enabling the resolution of previously intractable questions.
  • The methods support the analysis of complex cognitive architectures and model comparison.