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Using recursive partitioning to account for parameter heterogeneity in multinomial processing tree models.

Florian Wickelmaier1, Achim Zeileis2

  • 1Department of Psychology, University of Tübingen, Schleichstr. 4, 72076, Tübingen, Germany. florian.wickelmaier@uni-tuebingen.de.

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Summary
This summary is machine-generated.

This study introduces MPT trees, a novel machine learning approach for analyzing individual differences in multinomial processing tree (MPT) models. MPT trees effectively identify how participant characteristics influence model parameters, enhancing psychological research.

Keywords:
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Area of Science:

  • Cognitive psychology
  • Psychometrics
  • Machine learning

Background:

  • Individual differences in participants cause parameter heterogeneity in multinomial processing tree (MPT) models.
  • Identifying how subject covariates influence MPT parameters is challenging, as their roles (e.g., interactions, nonlinear effects) are often unknown.
  • Existing methods may not fully capture complex covariate-parameter relationships.

Purpose of the Study:

  • To propose a new method for capturing parameter heterogeneity in MPT models using machine learning.
  • To develop an interpretable framework for analyzing the effects of covariates on MPT model parameters.
  • To provide practical tools for researchers investigating individual differences in cognitive models.

Main Methods:

  • Utilized the MOB (model-based recursive partitioning) machine learning algorithm.
  • Developed a recursive partitioning approach to segment the covariate space.
  • Created "MPT trees" that visually represent subgroups and covariate effects.
  • Implemented the method in the R package "psychotree" via the "mpttree" function.

Main Results:

  • MPT trees effectively partition the covariate space, revealing interpretable subgroups.
  • The method allows for the examination of covariate effects, including interactions and nonlinear influences, on MPT parameters.
  • Simulation experiments and empirical applications in memory research demonstrated the utility of MPT trees.

Conclusions:

  • MPT trees offer a powerful and interpretable approach to analyzing parameter heterogeneity in MPT models.
  • This method enhances the understanding of individual differences and their impact on cognitive processes.
  • The "mpttree" function in R provides accessible software for applying this novel technique.