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Computing Bayes Factors for Evidence-Accumulation Models Using Warp-III Bridge Sampling


Gronau, QF and Heathcote, A and Matzke, D, Computing Bayes Factors for Evidence-Accumulation Models Using Warp-III Bridge Sampling, Behavior Research Methods ISSN 1554-3528 (2019) [Refereed Article]


Copyright Statement

Copyright the Author(s) 2019. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)

DOI: doi:10.3758/s13428-019-01290-6


Over the last decade, the Bayesian estimation of evidence-accumulation models has gained popularity, largely due to the advantages afforded by the Bayesian hierarchical framework. Despite recent advances in the Bayesian estimation of evidence-accumulation models, model comparison continues to rely on suboptimal procedures, such as posterior parameter inference and model selection criteria known to favor overly complex models. In this paper we advocate model comparison for evidence-accumulation models based on the Bayes factor obtained via Warp-III bridge sampling. We demonstrate, using the Linear Ballistic Accumulator (LBA), that Warp-III sampling provides a powerful and flexible approach that can be applied to both nested and non-nested model comparisons, even in complex and high-dimensional hierarchical instantiations of the LBA. We provide an easy-to-use software implementation of the Warp-III sampler and outline a series of recommendations aimed at facilitating the use of Warp-III sampling in practical applications.

Item Details

Item Type:Refereed Article
Keywords:bayesian model comparison, Differential Evolution Markov Chain Monte Carlo, dynamic models of choice, linear ballistic accumulator, marginal likelihood, response time models
Research Division:Psychology
Research Group:Cognitive and computational psychology
Research Field:Memory and attention
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in psychology
UTAS Author:Heathcote, A (Professor Andrew Heathcote)
ID Code:134326
Year Published:2019
Web of Science® Times Cited:8
Deposited By:Psychology
Deposited On:2019-08-07
Last Modified:2020-04-02
Downloads:24 View Download Statistics

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