Temporal Qubits and Recursive Awareness: Modeling Introspective Time with EEG-Guided Dynamics

Temporal Qubits and Recursive Awareness: Modeling Introspective Time with EEG-Guided Dynamics | 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 Temporal Qubits and Recursive Awareness: Modeling Introspective Time with EEG-Guided Dynamics Mohammad Mohammadiaria This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8920884/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 Understanding how internal neural states shape subjective experience remains a central challenge in computational neuroscience. We propose a recursive neural field framework in which an internally generated observer variable \(\:O\left(t\right)\) dynamically modulates cognitive field activity through closed-loop interactions between activation, memory trace formation, and bounded awareness dynamics. The model is formulated as a coupled partial differential system with provable stability under dissipative conditions and incorporates a low-dimensional temporal state representation governing transitions between past and present encoding. To empirically ground the framework, we derive an EEG-based estimator of the observer variable using alpha–theta envelope dynamics and evaluate its behavioral relevance. In a temporal bisection task (64-channel EEG, 1000 Hz), pre-stimulus observer state systematically modulated psychometric thresholds: higher \(\:{O}_{\text{pre}}\) values predicted shifts in the subjective duration boundary an altered slope of the logistic decision function. This effect is consistent with the model’s internal lapse formulation \(\:d\tau\:/dt=1+\zeta\:O\left(t\right)\) , linking neural state fluctuations to perceived time scaling. In contrast, the observer variable did not enhance multi-class emotion classification performance, suggesting functional specificity rather than general representational gain. Together, these results indicate that recursive internal state dynamics contribute selectively to subjective temporal perception. The framework provides a mathematically explicit and empirically testable approach for studying internally modulated cognition without invoking metaphysical assumptions and offers a structured basis for future investigation of state-dependent perceptual variability. Cognitive Neuroscience Consciousness-inspired artificial intelligence Temporal qubits Self-referential neural systems self-awareness Observer-based neuromodulation Recursive memory encoding 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. 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-8920884","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":608092356,"identity":"7744636e-5bf4-498f-b6a3-3e38c6cf78f4","order_by":0,"name":"Mohammad Mohammadiaria","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYPACCQZ+9sbGB0AWDx/RWiR7Dh82AGlhI9oegxtpaRIgBkEtuu3diQ9+7rGQN7iRY1b5NcdOho2B+eGjG3i0mJ05u9mw55mE4cwzb8xuy25LBjqMzdg4B5+WG7nbJHgOSDD2Hc8xuy25jRmohYdNmpAWyT8HJOwbDuSYFUtuqydOizTQlsQJJ9LSGD9uO0yEFqBfjGUOSCTPBAayNOO24zxszIT8crx348M3B+ps+4FR+fHntmp7fvbmh4/xaUEBzDxgkljlIMD4gxTVo2AUjIJRMGIAANILTBnARY84AAAAAElFTkSuQmCC","orcid":"","institution":"Unaffiliated","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Mohammadiaria","suffix":""}],"badges":[],"createdAt":"2026-02-19 21:15:25","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-8920884/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8920884/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105040312,"identity":"d69b4528-ce9a-4fbc-9845-93cbe1ae988f","added_by":"auto","created_at":"2026-03-20 07:49:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":990603,"visible":true,"origin":"","legend":"","description":"","filename":"AdvManuscriptCognetive.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8920884/v1_covered_0a5854d0-4c78-41ba-b927-eabc523ef5b6.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eTemporal Qubits and Recursive Awareness: Modeling Introspective Time with EEG-Guided Dynamics\u003c/p\u003e","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":"Consciousness-inspired artificial intelligence, Temporal qubits, Self-referential neural systems, self-awareness, Observer-based neuromodulation, Recursive memory encoding","lastPublishedDoi":"10.21203/rs.3.rs-8920884/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8920884/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding how internal neural states shape subjective experience remains a central challenge in computational neuroscience. We propose a recursive neural field framework in which an internally generated observer variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:O\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e dynamically modulates cognitive field activity through closed-loop interactions between activation, memory trace formation, and bounded awareness dynamics. The model is formulated as a coupled partial differential system with provable stability under dissipative conditions and incorporates a low-dimensional temporal state representation governing transitions between past and present encoding. To empirically ground the framework, we derive an EEG-based estimator of the observer variable using alpha\u0026ndash;theta envelope dynamics and evaluate its behavioral relevance. In a temporal bisection task (64-channel EEG, 1000 Hz), pre-stimulus observer state systematically modulated psychometric thresholds: higher \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{O}_{\\text{pre}}\\)\u003c/span\u003e\u003c/span\u003evalues predicted shifts in the subjective duration boundary an altered slope of the logistic decision function. This effect is consistent with the model\u0026rsquo;s internal lapse formulation \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:d\\tau\\:/dt=1+\\zeta\\:O\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e, linking neural state fluctuations to perceived time scaling. In contrast, the observer variable did not enhance multi-class emotion classification performance, suggesting functional specificity rather than general representational gain. Together, these results indicate that recursive internal state dynamics contribute selectively to subjective temporal perception. 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