Cortical knowledge structures guide word concept learning

Cortical knowledge structures guide word concept 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 Biological Sciences - Article Cortical knowledge structures guide word concept learning Yanchao Bi, Guangyao Zhang, Xiaosha Wang, Dingchen Zhang, Siwen Xie, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6982157/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Human word-concept learning transcends simple associations between a word and referent exemplars, leveraging prior knowledge to generalize from few exemplars. Although Bayesian models explain such behavior, their neural underpinnings for prior structures and computations remain unclear. This study introduces a Neural Bayesian Model (NBM) to elucidate how prior knowledge representations guide new word learning. Using functional magnetic resonance imaging, we first measured the participants’ neural activity during viewing familiar objects (and novel shapes as controls) to construct the neural prior space, and then the neural activity as participants learned new words associated with some of these visual stimuli. The NBM, which integrates neural representational priors derived from activities in ventral occipitotemporal cortex (VOTC), predicted new word neural representations and generalization behavior in learning with familiar objects, outperforming control models lacking neural priors. Conversely, hippocampal activity, not necessarily explained by the NBM, underpinned learning with novel shapes, reflecting a prior-free mechanism. Comparisons with large language models (LLMs) revealed LLMs’ inferior alignment with human generalization, underscoring gaps in grounding word learning in nonverbal priors. These findings dissociate neural computational systems for concept learning: the VOTC mediates prior-based Bayesian inference, whereas the hippocampus supports exemplar-based associations. The results bridge computational theories of word learning with neural mechanisms, highlighting the dynamic interplay of semantic and episodic memory, and further promoting the incorporation of Bayesian-based learning mechanisms for LLM development. Biological sciences/Neuroscience/Learning and memory Biological sciences/Neuroscience/Cognitive neuroscience Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Under Review 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-6982157","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Biological Sciences - Article","associatedPublications":[],"authors":[{"id":484160300,"identity":"50ed27b0-40d0-437b-9d0f-fd5e60e66ebe","order_by":0,"name":"Yanchao Bi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYDACCcbGBwwHQKwE4rU0G5CqhYFNgjQtBreb26oLzhxm4GfPMWD4uYMYLXcOtt2eceMwg2TPGwPG3jPEaLmR2Hab58NhICPHgJmxjUgtxSAt9iRpYeYBOsxAglgtkncONkvPOJPOI3HmWcHBXmK08N1uf/i54Ji1HH978sYHP4nRonCAgYEZSPOAOAeI0MDAIN8A0TIKRsEoGAWjADcAAJa1OsyNBv6UAAAAAElFTkSuQmCC","orcid":"","institution":"State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University","correspondingAuthor":true,"prefix":"","firstName":"Yanchao","middleName":"","lastName":"Bi","suffix":""},{"id":484160301,"identity":"aeae95af-d8ed-4a3d-a5b6-a80c4aca1267","order_by":1,"name":"Guangyao Zhang","email":"","orcid":"","institution":"State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Guangyao","middleName":"","lastName":"Zhang","suffix":""},{"id":484160302,"identity":"cbea1add-c62a-4346-b736-3d399ea333b2","order_by":2,"name":"Xiaosha Wang","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xiaosha","middleName":"","lastName":"Wang","suffix":""},{"id":484160303,"identity":"5326b77d-61f6-4fdf-b3f1-623a12abe740","order_by":3,"name":"Dingchen Zhang","email":"","orcid":"","institution":"State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Dingchen","middleName":"","lastName":"Zhang","suffix":""},{"id":484160304,"identity":"e10d75ad-0686-44ee-807c-567b9f84968c","order_by":4,"name":"Siwen Xie","email":"","orcid":"","institution":"Institute for Artificial Intelligence, Peking University","correspondingAuthor":false,"prefix":"","firstName":"Siwen","middleName":"","lastName":"Xie","suffix":""},{"id":484160305,"identity":"f0232562-1153-42c7-b308-224f16faf571","order_by":5,"name":"Lusha Zhu","email":"","orcid":"https://orcid.org/0000-0001-8717-6356","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Lusha","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2025-06-26 09:56:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6982157/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6982157/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90149096,"identity":"9d52e46a-e1c2-4bea-9864-a4a02dadc0b9","added_by":"auto","created_at":"2025-08-29 06:39:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2645324,"visible":true,"origin":"","legend":"","description":"","filename":"MainArticleandSupplementaryMaterialsforsubmission.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6982157/v1_covered_0a8ec2df-8b3c-4244-9fab-31981c53d2a7.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Cortical knowledge structures guide word concept learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6982157/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6982157/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHuman word-concept learning transcends simple associations between a word and referent exemplars, leveraging prior knowledge to generalize from few exemplars. Although Bayesian models explain such behavior, their neural underpinnings for prior structures and computations remain unclear. This study introduces a Neural Bayesian Model (NBM) to elucidate how prior knowledge representations guide new word learning. Using functional magnetic resonance imaging, we first measured the participants\u0026rsquo; neural activity during viewing familiar objects (and novel shapes as controls) to construct the neural prior space, and then the neural activity as participants learned new words associated with some of these visual stimuli. The NBM, which integrates neural representational priors derived from activities in ventral occipitotemporal cortex (VOTC), predicted new word neural representations and generalization behavior in learning with familiar objects, outperforming control models lacking neural priors. Conversely, hippocampal activity, not necessarily explained by the NBM, underpinned learning with novel shapes, reflecting a prior-free mechanism. Comparisons with large language models (LLMs) revealed LLMs\u0026rsquo; inferior alignment with human generalization, underscoring gaps in grounding word learning in nonverbal priors. These findings dissociate neural computational systems for concept learning: the VOTC mediates prior-based Bayesian inference, whereas the hippocampus supports exemplar-based associations. The results bridge computational theories of word learning with neural mechanisms, highlighting the dynamic interplay of semantic and episodic memory, and further promoting the incorporation of Bayesian-based learning mechanisms for LLM development.\u003c/p\u003e","manuscriptTitle":"Cortical knowledge structures guide word concept learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-29 06:38:20","doi":"10.21203/rs.3.rs-6982157/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"138605b0-0881-479b-80f8-0ed1a0327cf3","owner":[],"postedDate":"August 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":51410160,"name":"Biological sciences/Neuroscience/Learning and memory"},{"id":51410161,"name":"Biological sciences/Neuroscience/Cognitive neuroscience"}],"tags":[],"updatedAt":"2025-08-29T06:38:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-29 06:38:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6982157","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6982157","identity":"rs-6982157","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}