The Experience-Experience Gap: Distributional Learning Is Associated with a Divergence of Preferences from Estimations

The Experience-Experience Gap: Distributional Learning Is Associated with a Divergence of Preferences from Estimations | 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 The Experience-Experience Gap: Distributional Learning Is Associated with a Divergence of Preferences from Estimations Boaz Rosenberg, Eran Eldar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6282612/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 Recent landmark studies show that the brain is equipped to learn not just average expected outcomes, but entire distributions of expected outcomes. Yet the role of such distributional learning in shaping human decision-making remains to be determined. To study this question, we designed two tasks where participants experienced different outcome distributions, provided their estimates of each, and reported their preferences among them. In one task, which facilitated distributional learning, participants’ preferences significantly diverged from their own estimates, consistent with predictions of Prospect Theory. Conversely, in a task that hindered distributional learning, the divergence of preferences from estimates was eliminated. Computational modelling showed how distributional learning may be responsible for disassociating preferences from estimations by enabling the application of a utility function to different potential outcomes. Our findings offer a new understanding of when and how preferences deviate from normative decision-making, a fundamental question in the study of human rationality. Social science/Psychology/Human behaviour Social science/Economics Full Text Additional Declarations There is NO Competing Interest. Ethics and Consent: All participants gave informed consent at the beginning of the study, as approved by Hebrew University’s ethics review board (approval number 2022-11061, covering both studies). Supplementary Files SupplementaryMaterialsTheExperienceExperiencegap.pdf Supplementary Materials 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-6282612","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":435293448,"identity":"45345b51-aca1-44a1-b12e-3dccf843f738","order_by":0,"name":"Boaz Rosenberg","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0001-3584-3134","institution":"The Hebrew University of Jeruslaem","correspondingAuthor":true,"prefix":"","firstName":"Boaz","middleName":"","lastName":"Rosenberg","suffix":""},{"id":435293449,"identity":"6dfa29da-402b-4aa8-8301-9afab1d822d5","order_by":1,"name":"Eran Eldar","email":"","orcid":"https://orcid.org/0000-0001-8988-6124","institution":"Hebrew University of Jerusalem","correspondingAuthor":false,"prefix":"","firstName":"Eran","middleName":"","lastName":"Eldar","suffix":""}],"badges":[],"createdAt":"2025-03-22 09:15:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6282612/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6282612/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82081228,"identity":"b7c80004-3ba6-485a-b756-f6182c552f99","added_by":"auto","created_at":"2025-05-06 14:29:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1040567,"visible":true,"origin":"","legend":"Article File","description":"","filename":"TheExperienceExperienceGapDistributionalLearningIsAssociatedwithaDivergenceofPreferencesfromEstimations.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6282612/v1_covered_672de2ba-b856-43e7-83c9-ffeadc36eed2.pdf"},{"id":80266565,"identity":"df8c5015-38b5-47ec-9859-7ebbaa95b91f","added_by":"auto","created_at":"2025-04-10 02:09:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":780202,"visible":true,"origin":"","legend":"Supplementary Materials","description":"","filename":"SupplementaryMaterialsTheExperienceExperiencegap.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6282612/v1/38c122fb0620dd9099e11f4b.pdf"}],"financialInterests":"\u003cp\u003eThere is \u003cstrong\u003eNO\u003c/strong\u003e Competing Interest.\u003c/p\u003e\n\u003cp\u003eEthics and Consent: All participants gave informed consent at the beginning of the study, as approved by Hebrew University’s ethics review board (approval number 2022-11061, covering both studies).\u003c/p\u003e","formattedTitle":"The Experience-Experience Gap: Distributional Learning Is Associated with a Divergence of Preferences from Estimations","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-6282612/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6282612/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Recent landmark studies show that the brain is equipped to learn not just average expected outcomes, but entire distributions of expected outcomes. 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