How Humans Restructure Predictive Models: Context-Tree Dynamics in Sequential Learning

How Humans Restructure Predictive Models: Context-Tree Dynamics in Sequential Learning | 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 How Humans Restructure Predictive Models: Context-Tree Dynamics in Sequential Learning Italo Ivo Lima Dias Pinto, Paulo Roberto Cabral-Passos, Priscila S. Azevedo, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8970014/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Human learning in sequential environments often involves uncovering latent statistical structures that govern event regularities. In this study, we investigate how individuals adapt their internal predictive models while playing the Goalkeeper Game, a stochastic sequence prediction task driven by a probabilistic context tree. We introduce a real-time context-tree inference framework that reconstructs the evolving internal models underlying participants’ trial-by-trial choices. By tracking the entropy of the inferred context trees, we reveal that learning unfolds through two intertwined processes: frequent refinements, corresponding to gradual adjustments of transition probabilities within a stable structure, and rare transitions, corresponding to structural reorganizations of the predictive model. Entropy reductions parallel improvements in success rate, demonstrating that participants progressively internalize the underlying generative process. The waiting-time distribution of transitions follows a sub-exponential Weibull law, indicating history-dependent reorganization dynamics consistent with bursty, non-memoryless adaptation. These findings suggest that human statistical learning proceeds through a balance between exploitation and exploration. Our framework provides a quantitative and interpretable tool for modeling the continuous–discrete dynamics of adaptive learning in probabilistic environments. Physical sciences/Mathematics and computing Physical sciences/Physics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 May, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers invited by journal 16 Mar, 2026 Editor assigned by journal 16 Mar, 2026 Editor invited by journal 12 Mar, 2026 Submission checks completed at journal 09 Mar, 2026 First submitted to journal 04 Mar, 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. We do this by developing innovative software and high quality services for the global research community. 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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-8970014","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":607562934,"identity":"1596372e-bcf6-4cfb-a83e-3268a283c384","order_by":0,"name":"Italo Ivo Lima Dias Pinto","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACNgYGxgMMBgwM/CBeQgExWoB6wFokG0BaDIi05gCINoCQROjgk29+cOBDwWE54/OrEz88MGCQ5xc7QMhhbAYHZxgcNja78XazBNBhhjNnJxDSwmBwmMcgLXHbjbMbQFoSDG4T1ML+Aaxl84yzm38QqYUHZItN4gb+3m3E2pJTAPSLjbHEDd5tFgkGEoT9It98fOODD38k5Pj7z26++aPCRp5fmoAWBJAAq5QgVjkI8B8gRfUoGAWjYBSMJAAA0Z9BIARmL5kAAAAASUVORK5CYII=","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":true,"prefix":"","firstName":"Italo","middleName":"Ivo Lima Dias","lastName":"Pinto","suffix":""},{"id":607562935,"identity":"f145b820-e007-46bf-933a-534221f5153e","order_by":1,"name":"Paulo Roberto Cabral-Passos","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Paulo","middleName":"Roberto","lastName":"Cabral-Passos","suffix":""},{"id":607562936,"identity":"b646135c-4a82-402c-8d00-f6dfda4427db","order_by":2,"name":"Priscila S. 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