A parameter recovery assessment of a wide class of evidence accumulation models of decision-making

A parameter recovery assessment of a wide class of evidence accumulation models of decision-making | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A parameter recovery assessment of a wide class of evidence accumulation models of decision-making Matthew Murrow, William Holmes This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4722049/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Computational modeling has become indispensable in investigating the dynamics of decision making processes. A prominent category of models in this domain are Evidence Accumulation Models (EAMs), which model both the decisions people make and the time they take. Many variations have been proposed which modify the drift rate, diffusion rate, and decision thresholds, encoding increasingly complex dynamics into the EAM framework. However, adding model features complicates parameter recovery, making model interpretation more difficult. In this work, we perform a parameter recovery study to a variety of common binary choice EAMs, identify the specific challenges for each, and explore how to improve their parameter recoverability. Though previous studies have addressed this question, they have been piecemeal in nature, with different groups applying different computational methods to study different models. We aim to unify this body of literature using the best currently available computational methods. Further, we present the first, to our knowledge, Bayesian analysis of diffusion conflict models. Our purpose here is to be thorough, not exhaustive or comprehensive. With this in mind, this article catalogues a number of results, some previously shown and some new. Further, it illustrates different approaches to model analysis. This article is intended to be a resource for researchers interested in utilizing EAMs for studying decision-making processes, providing insights into the challenges associated these models, how to analyze them in light of those challenges, and examples of how to address those challenges. Decision-making Drift-diffusion Evidence Accumulation Changing thresholds Urgency gating Conflict model Parameter recovery Bayesian parameter estimation Full Text Additional Declarations No competing interests reported. Supplementary Files Parameterrecoverysupplement11Jul2024.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Oct, 2024 Reviews received at journal 04 Oct, 2024 Reviews received at journal 23 Aug, 2024 Reviews received at journal 19 Aug, 2024 Reviewers agreed at journal 19 Aug, 2024 Reviewers agreed at journal 24 Jul, 2024 Reviewers agreed at journal 23 Jul, 2024 Reviewers agreed at journal 21 Jul, 2024 Reviewers invited by journal 17 Jul, 2024 Editor assigned by journal 15 Jul, 2024 Submission checks completed at journal 15 Jul, 2024 First submitted to journal 11 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4722049","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":335656662,"identity":"5c6ad8cb-fd89-4e88-89f8-645d9b20d123","order_by":0,"name":"Matthew Murrow","email":"","orcid":"","institution":"Vanderbilt University","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Murrow","suffix":""},{"id":335656663,"identity":"13386b32-1ce4-412b-b6e0-013b0d6f002d","order_by":1,"name":"William Holmes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqElEQVRIiWNgGAWjYFACHgaGB0CKnxnEYSNWSwKQkmwmWYvBAWK1GNw+e/BBYo6NvfFxHgOGD2WHidByLi/ZIHFbWuK2wzwGjDPOEaHF7AyPmQRQfYIZUAszbxvxWv7bGzcDtfwlQcsBxg3MQC2MxGixP8NjDPRLcuKMw2wFB3vOpRPWItnDY/jg4zY7e/7+wxsf/CizJqwFBRwgUf0oGAWjYBSMAlwAAJGGNrpsXyAXAAAAAElFTkSuQmCC","orcid":"","institution":"Indiana University Bloomington","correspondingAuthor":true,"prefix":"","firstName":"William","middleName":"","lastName":"Holmes","suffix":""}],"badges":[],"createdAt":"2024-07-11 05:56:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4722049/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4722049/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61995212,"identity":"01c9f519-182b-4daf-9c9f-4dd55c786cbc","added_by":"auto","created_at":"2024-08-08 04:15:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14081190,"visible":true,"origin":"","legend":"","description":"","filename":"Parameterrecovery15Jul2024.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4722049/v1_covered_f0fde7dc-db3b-4f89-a099-6be5c6867d17.pdf"},{"id":61994822,"identity":"0ea84cf4-4dcc-47b2-8388-1337f9c4bc04","added_by":"auto","created_at":"2024-08-08 04:06:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":399395,"visible":true,"origin":"","legend":"","description":"","filename":"Parameterrecoverysupplement11Jul2024.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4722049/v1/52ceae29889ecd63e668e64b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A parameter recovery assessment of a wide class of evidence accumulation models of decision-making","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"computational-brain-and-behavior","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cobb","sideBox":"Learn more about [Computational Brain \u0026 Behavior](https://link.springer.com/journal/42113)","snPcode":"42113","submissionUrl":"https://submission.nature.com/new-submission/42113/3","title":"Computational Brain \u0026 Behavior","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Decision-making, Drift-diffusion, Evidence Accumulation, Changing thresholds, Urgency gating, Conflict model, Parameter recovery, Bayesian parameter estimation","lastPublishedDoi":"10.21203/rs.3.rs-4722049/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4722049/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Computational modeling has become indispensable in investigating the dynamics of decision making processes. 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