Structure Preservation in Semantic Compression of Language: A Preliminary Exploration Based on Brain-like Representation Models

Structure Preservation in Semantic Compression of Language: A Preliminary Exploration Based on Brain-like Representation Models | 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 Structure Preservation in Semantic Compression of Language: A Preliminary Exploration Based on Brain-like Representation Models BG Tong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9337712/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 Objective: In natural language processing and cognitive science, how high-level abstract semantics evolve from concrete sensorimotor experiences remains computationally underexplored. This study aims to utilize large language-brain-like prediction models to quantitatively investigate whether the representational structure of underlying physical actions is preserved during the “semantic compression” and dimensional elevation of language. Methods: This study constructed a “Semantic Topology Corpus” with strictly controlled syntactic structure and character length (comprising 10 motifs, 30 sentences total, covering physical action bases and their positive/negative abstract derivatives). Using the TRIBE v2 brain-like representation model, we predicted whole-brain activation 3D maps and quantified spatial topological features across different semantic levels through Representational Similarity Analysis (RSA) and significant activation voxel statistics (Z > 2.0 ). Results: (1) Physical actions and abstract derivative concepts demonstrated high whole-brain representational similarity (r ≈ 0.79 ), suggesting a cross-level “structure preservation effect”; (2) Abstract concepts with the same Level of Detail (LOD) but opposing emotional valences showed even higher similarity (r = 0.847 ), indicating a trend of representational clustering in high-dimensional semantics; (3) Voxel statistics revealed that during spatial topological transfer of abstract semantics, the total volume of significantly activated voxels did not exhibit substantial expansion (maintained at approximately 9,700 voxels), suggesting efficient reuse of representational subspace. Conclusion: The semantic compression process of language may not be a de novo reconstruction, but rather reuse and preserve the structure of underlying embodied actions in model-predicted topological space with high representational efficiency. This study provides quantitative evidence from computational models that is consistent with “Embodied Cognition” and Gestalt holistic processing theory, and offers insights for the underlying cognitive architecture of future Embodied AI and World Models. Semantic Compression Structure Preservation Brain-like Representation Model TRIBE v2 Embodied Cognition Representational Similarity Analysis (RSA) Full Text Additional Declarations The authors declare no competing interests. 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-9337712","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618498813,"identity":"4dbbfd0f-4552-409f-8709-29aaa249906a","order_by":0,"name":"BG Tong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIie3PsQqCQBzH8X8I13LoehL4DJLg5MP85eAmi6DFsUmH6Bl8hR5BE2q5cL2hwQhsaWlsiSLaCs2t4b7Lf/l/hh+ATveP5WC8LhmmRY1x0IOYVHK3lqIHcRj69jHZdAtzV57GKg6mBFDESHKw0iW2ElsKj0dSzAkUW4X0AEzu163EzSOjnCRlmAwWQiFrwGWTDlJd3sQAf4Zu+QNRkcFfhIAPiD8QWzWe99wSJpRyhrmgnVvMip9GURyEWXYurrd74Fjpqp18RPu963Q6ne5rD92BTVeYCkZaAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-0176-2205","institution":"Inner Mongolia People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"BG","middleName":"","lastName":"Tong","suffix":""}],"badges":[],"createdAt":"2026-04-07 00:04:11","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9337712/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9337712/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106404899,"identity":"c41c77ee-3cef-4263-9466-c4930508b73b","added_by":"auto","created_at":"2026-04-08 09:17:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1093466,"visible":true,"origin":"","legend":"","description":"","filename":"article.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9337712/v1_covered_4dc277fb-3b59-40d5-888e-195d140c833e.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eStructure Preservation in Semantic Compression of Language: A Preliminary Exploration Based on Brain-like Representation Models\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Inner Mongolia People's Hospital","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":"Semantic Compression, Structure Preservation, Brain-like Representation Model, TRIBE v2, Embodied Cognition, Representational Similarity Analysis (RSA)","lastPublishedDoi":"10.21203/rs.3.rs-9337712/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9337712/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eObjective: \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eIn natural language processing and cognitive science, how high-level abstract semantics evolve from concrete sensorimotor experiences remains computationally underexplored. This study aims to utilize large language-brain-like prediction models to quantitatively investigate whether the representational structure of underlying physical actions is preserved during the “semantic compression” and dimensional elevation of language.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods: \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eThis study constructed a “Semantic Topology Corpus” with strictly controlled syntactic structure and character length (comprising 10 motifs, 30 sentences total, covering physical action bases and their positive/negative abstract derivatives). Using the TRIBE v2 brain-like representation model, we predicted whole-brain activation 3D maps and quantified spatial topological features across different semantic levels through Representational Similarity Analysis (RSA) and significant activation voxel statistics (Z \u0026gt; \u003c/em\u003e2.0\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults: \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(1) Physical actions and abstract derivative concepts demonstrated high whole-brain representational similarity (r ≈ \u003c/em\u003e0.79\u003cem\u003e),\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003esuggesting a cross-level “structure preservation effect”; (2) Abstract concepts with the same Level of Detail (LOD) but opposing emotional valences showed even higher similarity (r \u003c/em\u003e= 0.847\u003cem\u003e), indicating a trend of representational clustering in high-dimensional semantics; (3) Voxel statistics revealed that during spatial topological transfer of abstract semantics, the total volume of significantly activated voxels did not exhibit substantial expansion (maintained at approximately 9,700 voxels), suggesting efficient reuse of representational subspace.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eThe semantic compression process of language may not be a de novo reconstruction, but rather reuse and preserve the structure of underlying embodied actions in model-predicted topological space with high representational efficiency. This study provides quantitative evidence from computational models that is consistent with “Embodied Cognition” and Gestalt holistic processing theory, and offers insights for the underlying cognitive architecture of future Embodied AI and World Models.\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Structure Preservation in Semantic Compression of Language: A Preliminary Exploration Based on Brain-like Representation Models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-08 05:31:06","doi":"10.21203/rs.3.rs-9337712/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":"c3239740-9837-4733-a7fd-5361598d8406","owner":[],"postedDate":"April 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T05:31:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-08 05:31:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9337712","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9337712","identity":"rs-9337712","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}