Widely used bandit tasks elicit diverging belief updating phenotypes in healthy adults

Widely used bandit tasks elicit diverging belief updating phenotypes in healthy adults | 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 Article Widely used bandit tasks elicit diverging belief updating phenotypes in healthy adults Vincenzo Fiore, Amber Kiely, Ember Zhang, Justice Simonetti, Anusha Phadnis, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9439488/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Adaptive decision-making requires dynamic arbitration between internal beliefs and incoming evidence, yet the role that environment structure plays in shaping these dynamics has received little systematic attention. We used three inductive reasoning and three bandit tasks to investigate these trial-by-trial dynamics in N=120 healthy adults. Using a novel computational model, we found that, in a two-option bandit task with positive valence, as well as in a three-option bandit task with negative valence, a significant number of participants were characterized by suboptimal arbitrations, reflecting increased reliance on priors under high uncertainty and increased reliance on evidence under high confidence. These findings demonstrate that design elements in widely used tasks can bias belief updating dynamics in healthy individuals leading to population-level suboptimal behaviors. These biases may have been overlooked in the past due to simplifications in the characterization of belief updating dynamics, potentially affecting analysis and comparisons with clinical cohorts. Biological sciences/Neuroscience/Computational neuroscience/Learning algorithms Biological sciences/Neuroscience/Cognitive neuroscience/Decision Biological sciences/Psychology/Human behaviour Biological sciences/Neuroscience/Reward Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9439488","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":628944379,"identity":"dae1e698-c061-44b3-84b2-e6fdfa685ca1","order_by":0,"name":"Vincenzo 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