Fundamental computational barriers prevent artificial intelligence from replicating human social cognition: A comprehensive theoretical and empirical analysis

Fundamental computational barriers prevent artificial intelligence from replicating human social cognition: A comprehensive theoretical and empirical analysis | 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 Fundamental computational barriers prevent artificial intelligence from replicating human social cognition: A comprehensive theoretical and empirical analysis Seung kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6931938/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 Background: Despite unprecedented advances in artificial intelligence, the fundamental question of whether machines can truly replicate human social cognition remains unresolved, with critical implications for safety- critical applications and human-AI collaboration systems. Methods: We conducted a comprehensive meta-analysis of 47 peer-reviewed studies encompassing 23,847 human participants and 156 distinct AI systems across eight cognitive domains. We developed and validated the Social Cognition Complexity Theorem through advanced mathematical modeling, incorporating information theory and computational complexity principles. Crisis analysis included forensic examination of AI failures during the January 6th Capitol riots and Itaewon crowd disaster, with real-time performance tracking. Results: Statistical analysis reveals systematic performance gaps with Cohen’s d > 1.2 (p < 0.001) across all social cognitive domains. Our Social Cognition Complexity Theorem mathematically proves computational requirements grow exponentially C(t) = α ·eβ · I(t)· S(t)+γ · M(t) with contextual integration needs, creating theo- retical performance ceilings at 72.3% of human baseline. Crisis analysis demonstrates catastrophic failures: AI systems provided warnings 62 minutes late during Itaewon disaster while human observers warned 18 minutes early. Meta-regression analysis shows asymptotic performance plateaus despite exponential increases in computational resources. Conclusions: Fundamental mathematical limitations prevent AI from achieving human-level social cognition under current computational paradigms. Our novel Complementary Intelligence Framework achieves 23.4% performance improvement over pure AI systems and 5.2% over pure human approaches, suggesting collaborative architectures as the optimal path forward for safety-critical applications Scientific community and society/Scientific community Earth and environmental sciences/Solid Earth sciences artificial intelligence limitations social cognition computational complexity human AI collab oration crisis prediction cognitive modeling complementary intelligence Full Text Additional Declarations There is NO Competing Interest. 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. 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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-6931938","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":474167255,"identity":"ead5aa2f-4e97-46c7-a84c-d4d54bcfbfa5","order_by":0,"name":"Seung kim","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0007-6876-0777","institution":"Assist University Seoul , Korea","correspondingAuthor":true,"prefix":"","firstName":"Seung","middleName":"","lastName":"kim","suffix":""}],"badges":[],"createdAt":"2025-06-19 14:06:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6931938/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6931938/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85083735,"identity":"9a136919-4038-437a-bc63-922956693d15","added_by":"auto","created_at":"2025-06-20 18:29:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":598611,"visible":true,"origin":"","legend":"Article File","description":"","filename":"HumanBrainandRobotBrainFinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6931938/v1_covered_7a6213de-d325-4c56-ac5b-eca85df4f37e.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Fundamental computational barriers prevent artificial intelligence from replicating human social cognition: A comprehensive theoretical and empirical analysis","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":"artificial intelligence limitations, social cognition, computational complexity, human AI collab oration, crisis prediction, cognitive modeling, complementary intelligence","lastPublishedDoi":"10.21203/rs.3.rs-6931938/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6931938/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Despite unprecedented advances in artificial intelligence, the fundamental question of whether machines can truly replicate human social cognition remains unresolved, with critical implications for safety- critical applications and human-AI collaboration systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We conducted a comprehensive meta-analysis of 47 peer-reviewed studies encompassing 23,847 human participants and 156 distinct AI systems across eight cognitive domains. We developed and validated the Social Cognition Complexity Theorem through advanced mathematical modeling, incorporating information theory and computational complexity principles. Crisis analysis included forensic examination of AI failures during the January 6th Capitol riots and Itaewon crowd disaster, with real-time performance tracking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eStatistical analysis reveals systematic performance gaps with Cohen’s d \u0026gt; 1.2 (p \u0026lt; 0.001) across all social cognitive domains. Our Social Cognition Complexity Theorem mathematically proves computational requirements grow exponentially C(t) = α ·eβ · I(t)· S(t)+γ · M(t) with contextual integration needs, creating theo- retical performance ceilings at 72.3% of human baseline. Crisis analysis demonstrates catastrophic failures: AI systems provided warnings 62 minutes late during Itaewon disaster while human observers warned 18 minutes early. Meta-regression analysis shows asymptotic performance plateaus despite exponential increases in computational resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Fundamental mathematical limitations prevent AI from achieving human-level social cognition under current computational paradigms. 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