Agency emerges from asymmetric access to motor information in a minimal predictive framework

Agency emerges from asymmetric access to motor information in a minimal predictive framework | 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 Agency emerges from asymmetric access to motor information in a minimal predictive framework Nobuchika Yamaki, Tenna Churiki This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8654046/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 The ability to distinguish self-generated actions from externally caused events, commonly referred to as the sense of agency, is a fundamental property of biological agents. Despite extensive work within predictive coding and related frameworks, the minimal computational conditions under which agency becomes well-defined remain unclear. Here we propose that agency emerges from competition between predictive models operating under asymmetric access to motor information. We introduce a deliberately minimal predictive framework consisting of two systems that receive identical sensory input but differ solely in access to motor commands. One system implements a motor-conditioned forward model, whereas the other implements an environment-based predictive model without motor access. Agency is quantified by an agency index defined as the difference between prediction errors, allowing attribution to be interpreted as implicit model selection within a statistical inference framework. We show that stable agency attribution emerges exclusively under asymmetric information access. Symmetric predictive architectures, whether motor-aware or motor-blind, yield identical prediction errors and render agency attribution computationally undefined. By varying sensorimotor delay, we identify a relative critical delay at which agency collapses when consistency is violated. agency predictive models informational asymmetry sensorimotor delay model selection Full Text Additional Declarations No competing interests reported. Supplementary Files Appendixselfa.docx 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. 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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-8654046","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581765060,"identity":"326f77cd-ec86-4b85-867b-aa00cc209c65","order_by":0,"name":"Nobuchika Yamaki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYFACHgYGxgYJAygvgYcfxiJei2QbkDpAWAsDXAuDwTECWszZe49J/NxhYczfwGP46UZNmozx/d6Dtz9U2OUxsDcffIBFi2XPuTTJ3jMSZhIHeIylc47l8Jgd40u2OHAmuZiB51iyARYtBjdyzKQZ2yRsGA7wGEjnsFUAtfCYSRxsO5DYIJFjJoFPizzQlt85/yp4jNtAWv4R1mJmcIDHTDq3LYfHgA2kpQGPljPnki172ySMDQ+zlVnn9qXxSBzLMbY4cyw5sQ2XX473Hrzxs63OcN7x5s23c74l2/M3nzG8UVFjl9iPI8QQgJkDYSTYPWx4lYMBO8JIbF4YBaNgFIyCkQsAmIthmvIjDLMAAAAASUVORK5CYII=","orcid":"","institution":"TNQ Tech","correspondingAuthor":true,"prefix":"","firstName":"Nobuchika","middleName":"","lastName":"Yamaki","suffix":""},{"id":581765061,"identity":"8fc53c49-0294-4cc0-9cb2-afa191517927","order_by":1,"name":"Tenna Churiki","email":"","orcid":"","institution":"TNQ Tech","correspondingAuthor":false,"prefix":"","firstName":"Tenna","middleName":"","lastName":"Churiki","suffix":""}],"badges":[],"createdAt":"2026-01-21 01:53:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8654046/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8654046/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102564139,"identity":"502ab502-dda6-4636-af0b-f7ea3211ba5d","added_by":"auto","created_at":"2026-02-13 05:10:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":492280,"visible":true,"origin":"","legend":"","description":"","filename":"selfa.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8654046/v1_covered_91ff4e55-3415-4211-be57-d63dc31dbdb1.pdf"},{"id":101484144,"identity":"f26430bc-5ff6-4786-ab94-ee68c99cc499","added_by":"auto","created_at":"2026-01-30 08:50:34","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17435,"visible":true,"origin":"","legend":"","description":"","filename":"Appendixselfa.docx","url":"https://assets-eu.researchsquare.com/files/rs-8654046/v1/aebc056a52beb002e809f9f7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Agency emerges from asymmetric access to motor information in a minimal predictive framework","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"agency, predictive models, informational asymmetry, sensorimotor delay, model selection","lastPublishedDoi":"10.21203/rs.3.rs-8654046/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8654046/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe ability to distinguish self-generated actions from externally caused events, commonly referred to as the sense of agency, is a fundamental property of biological agents. Despite extensive work within predictive coding and related frameworks, the minimal computational conditions under which agency becomes well-defined remain unclear.\u003c/p\u003e \u003cp\u003eHere we propose that agency emerges from competition between predictive models operating under asymmetric access to motor information. We introduce a deliberately minimal predictive framework consisting of two systems that receive identical sensory input but differ solely in access to motor commands. One system implements a motor-conditioned forward model, whereas the other implements an environment-based predictive model without motor access. Agency is quantified by an agency index defined as the difference between prediction errors, allowing attribution to be interpreted as implicit model selection within a statistical inference framework.\u003c/p\u003e \u003cp\u003eWe show that stable agency attribution emerges exclusively under asymmetric information access. Symmetric predictive architectures, whether motor-aware or motor-blind, yield identical prediction errors and render agency attribution computationally undefined. 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