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Area of Science:

  • Cognitive Psychology
  • Psychological Theory Testing
  • Statistical Modeling

Background:

  • Multinomial processing tree (MPT) models are widely used for testing psychological theories.
  • Research questions often involve complex ordinal or disordinal expectations about model parameters.
  • Current modeling practices may have limitations in estimating and testing these expectations.

Purpose of the Study:

  • To demonstrate the suitability of Bayesian hierarchical models for estimating and testing sophisticated psychological theories using MPT models.
  • To highlight issues with default priors in MPT models and advocate for theoretically informed priors.
  • To illustrate the use of Bayesian model comparison for testing ordinal and disordinal interactions.

Main Methods:

  • Utilizing Bayesian hierarchical models to refine MPT modeling practices.
  • Critically evaluating default priors and proposing theoretically consistent alternatives.
  • Employing Bayes factors for Bayesian model comparison to test theoretical expectations.

Main Results:

  • Default priors in MPT models can lead to problematic predictions and hinder accurate parameter estimation and model comparison.
  • Theoretically informed priors enhance the reliability of MPT model analyses.
  • Bayesian model comparison effectively tests ordinal and disordinal interactions.

Conclusions:

  • Bayesian hierarchical models offer a powerful framework for advancing MPT modeling in psychology.
  • Careful selection of priors is crucial for valid theoretical inference in MPT models.
  • The proposed Bayesian approach facilitates rigorous testing of complex psychological hypotheses.