Evidence map›Paper›PMID 30783896›Full record

ArticlePsychonomic bulletin & review2019

Assessing the practical differences between model selection methods in inferences about choice response time tasks.

Nathan J Evans

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Article in Psychonomic bulletin & review, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 25 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
59citing papers in PubMed, 25 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

59 citing papers in PubMed, 25 syntheses or guidelines pooled it.

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  10. Optimal dose and type of exercise improve the overall balance in adults with Parkinson's disease: a systematic review and Bayesian network meta-analysis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025
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4 · The record

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5 · Who and what money

Authors and funding

1 author.

Nathan J EvansDepartment of Psychology, University of Amsterdam, Amsterdam, The Netherlands. nathan.j.evans@uon.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Evidence accumulations models (EAMs) have become the dominant modeling framework within rapid decision-making, using choice response time distributions to make inferences about the underlying decision process. These models are often applied to empirical data as "measurement tools", with different theoretical accounts being contrasted within the framework of the model. Some method is then needed to decide between these competing theoretical accounts, as only assessing the models on their ability to fit trends in the empirical data ignores model flexibility, and therefore, creates a bias towards more flexible models. However, there is no objectively optimal method to select between models, with methods varying in both their computational tractability and theoretical basis. I provide a systematic comparison between nine different model selection methods using a popular EAM-the linear ballistic accumulator (LBA; Brown & Heathcote, Cognitive Psychology 57(3), 153-178 2008)-in a large-scale simulation study and the empirical data of Dutilh et al. (Psychonomic Bulletin and Review, 1-19 2018). I find that the "predictive accuracy" class of methods (i.e., the Akaike Information Criterion [AIC], the Deviance Information Criterion [DIC], and the Widely Applicable Information Criterion [WAIC]) make different inferences to the "Bayes factor" class of methods (i.e., the Bayesian Information Criterion [BIC], and Bayes factors) in many, but not all, instances, and that the simpler methods (i.e., AIC and BIC) make inferences that are highly consistent with their more complex counterparts. These findings suggest that researchers should be able to use simpler "parameter counting" methods when applying the LBA and be confident in their inferences, but that researchers need to carefully consider and justify the general class of model selection method that they use, as different classes of methods often result in different inferences.

Indexed as

Choice BehaviorCognitionModels, PsychologicalPsychological TheoryReaction TimeBayes TheoremBiasData Interpretation, StatisticalHumansResearch DesignBayes factorsDecision-makingModel selectionPredictive accuracyResponse time modeling

Identifiers

PMID30783896
PMCPMC6710222

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.