An Expert Guide to Planning Experimental Tasks For Evidence-Accumulation Modeling

Evidence-accumulation models (EAMs) are powerful tools for making sense of human and animal decision-making behavior. EAMs have generated significant theoretical advances in psychology, behavioral economics, and cognitive neuroscience and are increasingly used as a measurement tool in clinical research and other applied settings. Obtaining valid and reliable inferences from EAMs depends on knowing how to establish a close match between model assumptions and features of the task/data to which the model is applied. However, this knowledge is rarely articulated in the EAM literature, leaving beginners to rely on the private advice of mentors and colleagues and inefficient trial-and-error learning. In this article, we provide practical guidance for designing tasks appropriate for EAMs, relating experimental manipulations to EAM parameters, planning appropriate sample sizes, and preparing data and conducting an EAM analysis. Our advice is based on prior methodological studies and the our substantial collective experience with EAMs. By encouraging good task-design practices and warning of potential pitfalls, we hope to improve the quality and trustworthiness of future EAM research and applications.

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Publication Details

Journal
Advances in Methods and Practices in Psychological Science
Published
2025-04-01
DOI
https://doi.org/10.1177/25152459251336127
Citations
2
Primary Topic
Meta-analysis and systematic reviews
Type
article
Field-Weighted Citation Impact
2.80

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article

An Expert Guide to Planning Experimental Tasks For Evidence-Accumulation Modeling

Gordon D. Logan, Marius Usher, Luke Strickland, Konstantinos Tsetsos et al.
2 citations
Advances in Methods and Practices in Psychological Science
Meta-analysis and systematic reviews
2.80
article

An Expert Guide to Planning Experimental Tasks For Evidence-Accumulation Modeling

Gordon D. Logan, Marius Usher, Luke Strickland, Konstantinos Tsetsos, Russell J. Boag, Chris Donkin, Reilly James Innes, Don van Ravenzwaaij, Jennifer S. Trueblood, Giwon Bahg, Jerome R. Busemeyer, Gregory E. Cox, Henrik Singmann, Birte U. Forstmann, Dóra Matzke, Corey N. White, Leendert van Maanen, Adam F Osth, Mark Steyvers, Craig Hedge, Niek Stevenson, Simon D. Lilburn, Thomas J. Palmeri, Andrew Heathcote, Philip L. Smith, Steven Miletić, Joachim Vandekerckhove, Scott Brown, Emily Ruth Weichart, Michael J. Frank, Gabriel Weindel, Guy E. Hawkins, Veronika Lerche, Tom Stafford, Per B. Sederberg, Brandon M. Turner, Nathan J. Evans, Andreas Voss
article en
2 citations

Abstract

Evidence-accumulation models (EAMs) are powerful tools for making sense of human and animal decision-making behavior. EAMs have generated significant theoretical advances in psychology, behavioral economics, and cognitive neuroscience and are increasingly used as a measurement tool in clinical research and other applied settings. Obtaining valid and reliable inferences from EAMs depends on knowing how to establish a close match between model assumptions and features of the task/data to which the model is applied. However, this knowledge is rarely articulated in the EAM literature, leaving beginners to rely on the private advice of mentors and colleagues and inefficient trial-and-error learning. In this article, we provide practical guidance for designing tasks appropriate for EAMs, relating experimental manipulations to EAM parameters, planning appropriate sample sizes, and preparing data and conducting an EAM analysis. Our advice is based on prior methodological studies and the our substantial collective experience with EAMs. By encouraging good task-design practices and warning of potential pitfalls, we hope to improve the quality and trustworthiness of future EAM research and applications.

Advances in Methods and Practices in Psychological ScienceVol. 8(2)
Leiden University (NL), Utah State University (US), Tel Aviv University (IL), The University of Melbourne (AU), Aston University (GB), University of Groningen (NL), Utrecht University (NL), Vanderbilt University (US), University of California, Irvine (US), Curtin University (AU), Heidelberg University (DE), Brown University (US), Christian-Albrechts-Universität zu Kiel (DE), University of Bristol (GB), University at Albany, State University of New York (US), Indiana University Bloomington (US), University College London (GB), University of Virginia (US), The Ohio State University (US), Missouri Western State University (US), University of Newcastle Australia (AU), Ludwig-Maximilians-Universität München (DE), University of Amsterdam (NL), University of Sheffield (GB)
Nederlandse Organisatie voor Wetenschappelijk Onderzoek, National Institutes of Health, Defence Science and Technology Group, Office of Naval Research, National Eye Institute
Openalex Percentile: Top 8%
Meta-analysis and systematic reviews
2.80
DOIOpen Access PDFOpenAlex
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