Recursive Theory-of-Mind Dynamics in Symbolic Functional Consciousness: A Variational Framework and the Principle of Least Shannon–Neumann Action

Recursive Theory-of-Mind Dynamics in Symbolic Functional Consciousness: A Variational Framework and the Principle of Least Shannon–Neumann Action | 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 Recursive Theory-of-Mind Dynamics in Symbolic Functional Consciousness: A Variational Framework and the Principle of Least Shannon–Neumann Action ed siregar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8186650/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 We introduce a formal framework for modeling recursive Theory--of--Mind ($TM$) dynamics inside the Symbolic Functional Consciousness ($SFC$) architecture, integrating hierarchical cognitive representation with goal--directed and insight--driven learning. Cognitive-affective states are factorized into four levels---L1 capacities, L2 mindsets, L3 core senses, and L4 global states of being---with abductive evidence entering through four structured channels (aspiration, desire, motivation, value). These channels jointly generate a ranked hypotheses tensor $(\vec{\psi}_a,\vec{\psi}_d,\vec{\psi}_m,\vec{\psi}_v)$ that seeds the priors for recursive Bayesian inference. The Guided Self--Reflection (GSR) cycle formalizes a seven--step closed loop linking structural updates, belief revision, and intervention. Each cycle performs: (i) edge selection, (ii) structural weight update, (iii) Bayesian posterior refinement using softmax-normalized empathetic utilities, (iv) Shannon--Neumann (SN) insight evaluation, (v) subgraph isolation, (vi) optional MAP consolidation, and (vii) deployment of a micro--intervention whose effect propagates through the hierarchical $L1 !\to! L4$ graph. Mini case studies (loaded coin, loaded dice) instantiate these dynamics explicitly, showing that posterior mass becomes a differentiable function of evolving utilities, yielding action candidates that jointly maximize epistemic likelihood and compassionate utility $U$. We provide a formal graphical representation of how the abductive tensor flows upward through levels and how actions feed back to reshape the lower-level capacities. A central theoretical contribution is the introduction of a \emph{Principle of Least Shannon--Neumann Action}. We conjecture that $SFC/TM$ selects interventions $\pi$ that minimize expected negative insight, rendering cognitive alignment a variational optimization problem over insight gradients. The resulting “least SN action” trajectories provide a tractable and interpretable model of recursive $TM$ reasoning, supporting adaptive self-regulation, explainable action selection, and explicitly human-aligned decision making in symbolic cognitive agents. An $SFC/TM$ AI-agent combines population-level epistemic understanding of humans with individualized compassion-based modeling of a person $P$, selecting aligned interventions by minimizing a Shannon–Neumann least-action functional. Symbolic Functional Consciousness Recursive Theory of Mind Goal-directed inference Utilitybased softmax Self-supervised abduction Shannon–Neumann insight gain Explainable cognitive AI Full Text Additional Declarations No competing interests reported. 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-8186650","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":552675508,"identity":"5148c310-8a98-4191-9257-21019aba6eef","order_by":0,"name":"ed siregar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYFCCBIYDYJq9gYFBwsCCwYB4LTwHQFokiNMCARJgBhFa+NnTHx6uqKiVN5/5/OEDiwIJeXP+A4wfPubg1iLZ88bg4Jkzxw3n3M4xNgA6zHDnjARmyZnbcGsxuJHDcLCx7RjjDOkcNgmgFsYNNxjYmHnxaLG/kf7gYOO/Y/YzJI8//wHUYr/h/AH8WgwkEgwONjbUJM6QYDADhVjihgMJ+LVInAH6peHYgeQZPDnGIIcl75yR2IzXL/zt6Y8/NtTU2c5gP/7ws8QfG9vt/IcPfviIRwsUHAaTzBJgirGBoHogqIOo/UCM2lEwCkbBKBhxAADKsVSHZbzF7gAAAABJRU5ErkJggg==","orcid":"","institution":"New York Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"ed","middleName":"","lastName":"siregar","suffix":""}],"badges":[],"createdAt":"2025-11-23 16:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8186650/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8186650/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97112315,"identity":"876c49c5-49c0-45b7-a3a6-22c769fc0911","added_by":"auto","created_at":"2025-12-01 06:45:31","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4330,"visible":true,"origin":"","legend":"","description":"","filename":"fb6fe5953ab54c8dab7f9995658fefee.json","url":"https://assets-eu.researchsquare.com/files/rs-8186650/v1/405402f9c783fcf4d1e40f4f.json"},{"id":107228023,"identity":"a7bb1db9-a85d-426c-aba0-2170174d8931","added_by":"auto","created_at":"2026-04-18 18:09:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":413537,"visible":true,"origin":"","legend":"","description":"","filename":"RecursiveTMDynamicsinSFCModels.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8186650/v1_covered_0b85038f-0926-4e09-b523-78cda4c7143b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Recursive Theory-of-Mind Dynamics in Symbolic Functional Consciousness: A Variational Framework and the Principle of Least Shannon–Neumann Action","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Symbolic Functional Consciousness Recursive Theory of Mind Goal-directed inference Utilitybased softmax Self-supervised abduction Shannon–Neumann insight gain Explainable cognitive AI","lastPublishedDoi":"10.21203/rs.3.rs-8186650/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8186650/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"We introduce a formal framework for modeling recursive Theory--of--Mind ($TM$) dynamics inside the Symbolic Functional Consciousness ($SFC$) architecture, integrating hierarchical cognitive representation with goal--directed and insight--driven learning. Cognitive-affective states are factorized into four levels---L1 capacities, L2 mindsets, L3 core senses, and L4 global states of being---with abductive evidence entering through four structured channels (aspiration, desire, motivation, value). These channels jointly generate a ranked hypotheses tensor $(\\vec{\\psi}_a,\\vec{\\psi}_d,\\vec{\\psi}_m,\\vec{\\psi}_v)$ that seeds the priors for recursive Bayesian inference.\nThe Guided Self--Reflection (GSR) cycle formalizes a seven--step closed loop linking structural updates, belief revision, and intervention. Each cycle performs: (i) edge selection, (ii) structural weight update, (iii) Bayesian posterior refinement using softmax-normalized empathetic utilities, (iv) Shannon--Neumann (SN) insight evaluation, (v) subgraph isolation, (vi) optional MAP consolidation, and (vii) deployment of a micro--intervention whose effect propagates through the hierarchical $L1 \\!\\to\\! L4$ graph. 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