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Related Experiment Videos

TreeBUGS: An R package for hierarchical multinomial-processing-tree modeling.

Daniel W Heck1, Nina R Arnold2, Denis Arnold3,4

  • 1Department of Psychology, School of Social Sciences, University of Mannheim, Schloss EO 266, D-68131, Mannheim, Germany. heck@uni-mannheim.de.

Behavior Research Methods
|April 5, 2017
PubMed
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Hierarchical Bayesian extensions of multinomial processing tree (MPT) models address participant heterogeneity. The R package TreeBUGS offers user-friendly tools for these advanced MPT analyses.

Area of Science:

  • Cognitive Psychology
  • Psychometrics
  • Computational Statistics

Background:

  • Multinomial processing tree (MPT) models analyze categorical data by postulating underlying cognitive processes.
  • Traditional MPT analyses often aggregate data, overlooking individual differences and participant heterogeneity.
  • Hierarchical Bayesian MPT models offer a solution by modeling individual parameters with hierarchical distributions.

Purpose of the Study:

  • To provide an accessible introduction to hierarchical MPT modeling.
  • To introduce and demonstrate the R package TreeBUGS for implementing hierarchical MPT models.
  • To extend latent-trait MPT models with novel statistical approaches for including predictors.

Main Methods:

  • Implementation of the beta-MPT and latent-trait MPT approaches within the TreeBUGS R package.
Keywords:
Bayesian inferenceHierarchical modelingIndividual differencesMultinomial modelingR package

Related Experiment Videos

  • Utilizing Markov-chain Monte Carlo (MCMC) sampling to approximate posterior distributions.
  • Development of novel statistical extensions for incorporating continuous and discrete predictors in latent-trait MPT models.

    • Main Results:

    • TreeBUGS provides user-friendly tools for hierarchical MPT modeling, including parameter estimation and fit statistics.
    • The package facilitates tests for item and participant homogeneity, and within/between-subjects comparisons.
    • Novel extensions allow for the inclusion of fixed or random effects predictors in latent-trait MPT models.

    Conclusions:

    • Hierarchical MPT modeling, facilitated by the TreeBUGS package, offers a robust framework for analyzing individual differences in cognitive processes.
    • TreeBUGS enhances the accessibility and application of advanced MPT techniques for researchers.
    • The implemented extensions broaden the scope of latent-trait MPT models for investigating factors influencing cognitive processes.