Active Inference Modeling of Socially Shared Cognition in Virtual Reality

Active Inference Modeling of Socially Shared Cognition in Virtual Reality | 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 Active Inference Modeling of Socially Shared Cognition in Virtual Reality Yoshiko ARIMA, Mahiro OKADA This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8081630/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Jan, 2026 Read the published version in Sensors → Version 1 posted You are reading this latest preprint version Abstract This study proposes a process model for sharing ambiguous category concepts in virtual reality (VR) using an active inference framework. The model executes a dual-layer Bayesian update after observing both self and partner actions and predicts actions that minimize free energy. A disagreement in category judgment was added to the free energy as a risk term (corresponding to expected surprise in active inference). As the weight of this term, gaze synchrony measured by Dynamic Time Warping (DTW), assumed to re- flect joint attention, was used. The hypothesis was that higher weighting of gaze synchrony would improve prediction accuracy. To validate the model, an object classification task in VR including ambiguous items was created. The experiment was conducted first under a bot avatar condition, in which gaze was not synchronized and ambiguous category judgments were always incorrect, and then under a human–human pair condition. This design allowed verification of the collaborative learning process by which human pairs reached agreement. Analysis of experimental data from 14 participants showed that the model achieved high prediction accuracy for observed values as learning progressed. Introducing DTW as a model parameter further improved prediction accuracy, with optimal performance at synchrony weights of γ 0 = 0 . 5 - 0 . 9 . This approach provides a new framework for modeling shared social cognition using active inference. Psychology active inference virtual reality eye move- ment synchrony collaborative learning human-robot interaction shared social cognition Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Published Journal Publication published 15 Jan, 2026 Read the published version in Sensors → 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. 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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-8081630","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":543017422,"identity":"11a91e32-7a73-442d-9055-6cecfa70027f","order_by":0,"name":"Yoshiko ARIMA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYFACxgYILcHYzMBQkQATZiNWyxmitMCABAMzA2NbAkF1DPyzD7du+MFwWE5+dnOzwcd5aYn9/QcYP/xg4MvDafa5xLabPQyHjQ3uHGxOnLktJ3HGjQRmyR4GtmKc1pxhbLvBw3A4cYNEYvNh3m0ViQ03GBikgX5JbMChQx6o5eYfoJb5M0Ba5lQkzj9/gPk3Pi0GQC23QbY03EhsTuZtyEnccCCBDa8thiAtMgbpxgZALYYzjqUZb7yR2GbZY4DbL3Jn2J/dfFNhLSc/I/2xxIeaZNl55w8fvvGj4hjOEIM6rxmZB4pcg2MJ+LUw1GGI1BDSMgpGwSgYBSMHAABbol0wnHXNogAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-9243-2606","institution":"Center for Social and Psychological Research of Metaverse, Kyoto University of Advanced Science Kyoto, Japan","correspondingAuthor":true,"prefix":"","firstName":"Yoshiko","middleName":"","lastName":"ARIMA","suffix":""},{"id":543017423,"identity":"541d2a7f-65bd-44d6-9ce0-2282cd834146","order_by":1,"name":"Mahiro OKADA","email":"","orcid":"https://orcid.org/0009-0000-5832-8410","institution":"Kyoto University of Advanced Science Kyoto, Japan","correspondingAuthor":false,"prefix":"","firstName":"Mahiro","middleName":"","lastName":"OKADA","suffix":""}],"badges":[],"createdAt":"2025-11-11 02:38:30","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8081630/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8081630/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.3390/s26020604","type":"published","date":"2026-01-16T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100709290,"identity":"87afcf4d-74c5-4e0c-8eb6-2ad68005c748","added_by":"auto","created_at":"2026-01-20 17:42:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1148887,"visible":true,"origin":"","legend":"","description":"","filename":"HandleAIManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8081630/v1_covered_61a406cb-8b15-4f70-9ff5-72720d1e906d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eActive Inference Modeling of Socially Shared Cognition in Virtual Reality\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"Kyoto University of Advanced Science","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"active inference, virtual reality, eye move- ment synchrony, collaborative learning, human-robot interaction, shared social cognition","lastPublishedDoi":"10.21203/rs.3.rs-8081630/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8081630/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eThis study proposes a process model for sharing ambiguous category concepts in virtual reality (VR) using an active inference framework. The model executes a dual-layer Bayesian update after observing both self and partner actions and predicts actions that minimize free energy.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA disagreement in category judgment was added to the free energy as a risk term (corresponding to expected surprise in active inference). As the weight of this term, gaze synchrony measured by Dynamic Time Warping (DTW), assumed to re- flect joint attention, was used. The hypothesis was that higher weighting of gaze synchrony would improve prediction accuracy. To validate the model, an object classification task in VR including ambiguous items was created. The experiment was conducted first under a bot avatar condition, \u0026nbsp;in \u0026nbsp;which \u0026nbsp;gaze was not synchronized and ambiguous category judgments were always incorrect, and then under a human–human pair condition.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis design allowed verification of the collaborative learning process by which human pairs reached agreement.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of experimental data from 14 participants showed that the model achieved high prediction accuracy for observed values as learning progressed. Introducing DTW as a model parameter further improved prediction accuracy, with optimal performance at synchrony weights of \u003c/strong\u003e\u003cem\u003eγ\u003c/em\u003e0 = 0\u003cem\u003e.\u003c/em\u003e5\u003cstrong\u003e-\u003c/strong\u003e0\u003cem\u003e.\u003c/em\u003e9\u003cstrong\u003e. This approach provides a new framework for modeling shared social cognition using active inference.\u003c/strong\u003e\u003c/p\u003e","manuscriptTitle":"Active Inference Modeling of Socially Shared Cognition in Virtual Reality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 14:19:30","doi":"10.21203/rs.3.rs-8081630/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"8d7ec0a7-34be-4336-ae6c-611026f3a946","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":57955311,"name":"Psychology"}],"tags":[],"updatedAt":"2026-01-20T16:05:22+00:00","versionOfRecord":{"articleIdentity":"rs-8081630","link":"https://doi.org/10.3390/s26020604","journal":{"identity":"sensors","isVorOnly":true,"title":"Sensors"},"publishedOn":"2026-01-16 00:00:00","publishedOnDateReadable":"January 16th, 2026"},"versionCreatedAt":"2025-11-14 14:19:30","video":"","vorDoi":"10.3390/s26020604","vorDoiUrl":"https://doi.org/10.3390/s26020604","workflowStages":[]},"version":"v1","identity":"rs-8081630","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8081630","identity":"rs-8081630","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}