An Expert Guide to Planning Experimental Tasks For Evidence-Accumulation Modeling Article Swipe
Russell J. Boag
,
Reilly James Innes
,
Niek Stevenson
,
Giwon Bahg
,
Jerome R. Busemeyer
,
Gregory E. Cox
,
Chris Donkin
,
Michael J. Frank
,
Guy E. Hawkins
,
Andrew Heathcote
,
Craig Hedge
,
Veronika Lerche
,
Simon D. Lilburn
,
Gordon D. Logan
,
Dóra Matzke
,
Steven Miletić
,
Adam F Osth
,
Thomas J. Palmeri
,
Per B. Sederberg
,
Henrik Singmann
,
Philip L. Smith
,
Tom Stafford
,
Mark Steyvers
,
Luke Strickland
,
Jennifer S. Trueblood
,
Konstantinos Tsetsos
,
Brandon M. Turner
,
Marius Usher
,
Leendert van Maanen
,
Don van Ravenzwaaij
,
Joachim Vandekerckhove
,
Andreas Voss
,
Emily Ruth Weichart
,
Gabriel Weindel
,
Corey N. White
,
Nathan J. Evans
,
Scott Brown
,
Birte U. Forstmann
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1177/25152459251336127
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1177/25152459251336127
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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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/25152459251336127
- OA Status
- diamond
- References
- 406
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410768466
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410768466Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1177/25152459251336127Digital Object Identifier
- Title
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An Expert Guide to Planning Experimental Tasks For Evidence-Accumulation ModelingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-01Full publication date if available
- Authors
-
Russell J. Boag, Reilly James Innes, Niek Stevenson, Giwon Bahg, Jerome R. Busemeyer, Gregory E. Cox, Chris Donkin, Michael J. Frank, Guy E. Hawkins, Andrew Heathcote, Craig Hedge, Veronika Lerche, Simon D. Lilburn, Gordon D. Logan, Dóra Matzke, Steven Miletić, Adam F Osth, Thomas J. Palmeri, Per B. Sederberg, Henrik Singmann, Philip L. Smith, Tom Stafford, Mark Steyvers, Luke Strickland, Jennifer S. Trueblood, Konstantinos Tsetsos, Brandon M. Turner, Marius Usher, Leendert van Maanen, Don van Ravenzwaaij, Joachim Vandekerckhove, Andreas Voss, Emily Ruth Weichart, Gabriel Weindel, Corey N. White, Nathan J. Evans, Scott Brown, Birte U. ForstmannList of authors in order
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https://doi.org/10.1177/25152459251336127Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1177/25152459251336127Direct OA link when available
- Concepts
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Computer science, Management science, Process management, Human–computer interaction, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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406Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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