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Computing Bayes factors for evidence-accumulation models using Warp-III bridge sampling.

Quentin F Gronau1, Andrew Heathcote2, Dora Matzke3

  • 1University of Amsterdam, Amsterdam, Netherlands. quentin.f.gronau@gmail.com.

Behavior Research Methods
|November 23, 2019
PubMed
Summary
This summary is machine-generated.

Bayesian hierarchical models are popular for evidence accumulation. This study introduces Warp-III bridge sampling for robust Bayes factor model comparison, improving on suboptimal methods for complex models like the linear ballistic accumulator.

Keywords:
Bayesian model comparisonDifferential evolution Markov chain Monte CarloDynamic models of choiceLinear ballistic accumulatorMarginal likelihoodResponse time models

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Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Bayesian Statistics

Background:

  • Bayesian estimation of evidence-accumulation models is increasingly utilized, benefiting from the Bayesian hierarchical framework.
  • Current model comparison methods for these models are suboptimal, often favoring overly complex models.

Purpose of the Study:

  • To advocate for Bayes factor model comparison using Warp-III bridge sampling for evidence-accumulation models.
  • To demonstrate the utility of Warp-III bridge sampling for both nested and non-nested model comparisons.

Main Methods:

  • Application of Warp-III bridge sampling to the linear ballistic accumulator (LBA) model.
  • Demonstration on complex and high-dimensional hierarchical instantiations of the LBA.

Main Results:

  • Warp-III bridge sampling offers a powerful and flexible approach for model comparison.
  • The method is effective even for complex, high-dimensional hierarchical models.

Conclusions:

  • Warp-III bridge sampling provides a superior alternative to current suboptimal model comparison techniques.
  • An accessible software implementation and practical recommendations are provided to encourage adoption.