Effort and Substance Use: Differentiating Tobacco Use Through Reinforcement Learning of Effort Based Decision Making

Effort and Substance Use: Differentiating Tobacco Use Through Reinforcement Learning of Effort Based 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 Effort and Substance Use: Differentiating Tobacco Use Through Reinforcement Learning of Effort Based Decision Making Kasey Spry, Jazmyne James, Alison Oliveto, Michael Mancino, Kenneth Kishida, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8928184/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract BACKGROUND: Effort-based decision making evaluates rewards relative to the effort required to obtain it, an important process of healthy goal-directed motivation and behavior. Computational models provide mechanistic insights underlying choice behavior and potential alterations in neuropsychiatric disorders, including substance use disorders. We applied computational models to effort-based choice behavior to characterize underlying decision processes and if these mechanisms differ by substance use status. METHODS: Participants completed the Effort Expenditure for Rewards Task, choosing between low- and high-effort options for monetary rewards varying in magnitude and probability. Participants met criteria for no tobacco use (n = 23), current tobacco use disorder (n = 26), former tobacco use disorder (n = 22), and tobacco and opioid use disorder (n = 29). Computational models from two families, Subjective Value and Reinforcement Learning, were fit and compared. Parameters from the best-fitting model underwent principal components analysis and linear discriminant analysis. RESULTS: Temporal difference reinforcement learning model demonstrated greater model evidence and predictive accuracy, indicating better fit to effort-based choice behavior. Principal components analysis revealed meaningful multivariate distinctions: PC1 differentiated all groups except individuals without tobacco use versus individuals with tobacco use disorder; PC3 distinguished tobacco and opioid use disorder from all other groups. Linear discriminant analysis demonstrated group separation with 76% classification accuracy. CONCLUSIONS: A reinforcement learning framework better explained participants’ effort-based choice behavior. Substance use status relates to dynamic behavioral changes (i.e. learning) as measured by the multivariate combination of learning rate, future discounting, and choice temperature. Effort Tobacco Opioids Reinforcement learning smoking decision making Full Text Additional Declarations No competing interests reported. Supplementary Files GBNDITOPaperSupp.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 20 Apr, 2026 Reviews received at journal 09 Apr, 2026 Reviews received at journal 07 Apr, 2026 Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 17 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers invited by journal 15 Mar, 2026 Editor assigned by journal 25 Feb, 2026 Submission checks completed at journal 22 Feb, 2026 First submitted to journal 20 Feb, 2026 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-8928184","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607042340,"identity":"0e6a9a6f-a7d4-4ee5-9bbd-a6c1aa8e4756","order_by":0,"name":"Kasey 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Neuroscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Effort, Tobacco, Opioids, Reinforcement learning, smoking, decision making","lastPublishedDoi":"10.21203/rs.3.rs-8928184/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8928184/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBACKGROUND:\u003c/strong\u003e Effort-based decision making evaluates rewards relative to the effort required to obtain it, an important process of healthy goal-directed motivation and behavior. Computational models provide mechanistic insights underlying choice behavior and potential alterations in neuropsychiatric disorders, including substance use disorders. We applied computational models to effort-based choice behavior to characterize underlying decision processes and if these mechanisms differ by substance use status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODS: \u003c/strong\u003eParticipants completed the Effort Expenditure for Rewards Task, choosing between low- and high-effort options for monetary rewards varying in magnitude and probability. Participants met criteria for no tobacco use (n = 23), current tobacco use disorder (n = 26), former tobacco use disorder (n = 22), and tobacco and opioid use disorder (n = 29). Computational models from two families, Subjective Value and Reinforcement Learning, were fit and compared. Parameters from the best-fitting model underwent principal components analysis and linear discriminant analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS: \u003c/strong\u003eTemporal difference reinforcement learning model demonstrated greater model evidence and predictive accuracy, indicating better fit to effort-based choice behavior. Principal components analysis revealed meaningful multivariate distinctions: PC1 differentiated all groups except individuals without tobacco use versus individuals with tobacco use disorder; PC3 distinguished tobacco and opioid use disorder from all other groups. Linear discriminant analysis demonstrated group separation with 76% classification accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSIONS: \u003c/strong\u003eA reinforcement learning framework better explained participants’ effort-based choice behavior. Substance use status relates to dynamic behavioral changes (i.e. learning) as measured by the multivariate combination of learning rate, future discounting, and choice temperature.\u003c/p\u003e","manuscriptTitle":"Effort and Substance Use: Differentiating Tobacco Use Through Reinforcement Learning of Effort Based Decision Making","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-17 03:49:24","doi":"10.21203/rs.3.rs-8928184/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-20T21:58:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-09T20:20:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-07T15:29:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T19:15:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17287501284310265461380404830621907535","date":"2026-03-18T07:08:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161516918028845920902779629531033746076","date":"2026-03-18T01:39:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94443989459089921429286953359105473091","date":"2026-03-16T15:32:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159047074166412679026655957068490674122","date":"2026-03-16T10:00:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-15T22:04:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-25T17:06:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-23T02:32:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Computational Neuroscience","date":"2026-02-20T17:36:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-computational-neuroscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcns","sideBox":"Learn more about [Journal of Computational Neuroscience](http://link.springer.com/journal/10827)","snPcode":"10827","submissionUrl":"https://submission.nature.com/new-submission/10827/3","title":"Journal of Computational Neuroscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fa1d5b8e-a875-4c8a-b85c-64b3fe1f9c5e","owner":[],"postedDate":"March 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T22:08:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-17 03:49:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8928184","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8928184","identity":"rs-8928184","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}