From Proton Tunneling to Cognitive Attractors: A Unified Potential Model Framework

From Proton Tunneling to Cognitive Attractors: A Unified Potential Model Framework | 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 From Proton Tunneling to Cognitive Attractors: A Unified Potential Model Framework Krishna Kingkar Pathak This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7699717/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 present a unified potential-model framework that extends methods originally developed for proton tunneling in biomolecular systems to the study of cognitive attractors. By drawing on Cornell-type localized ansätze and symmetric double-well dynamics, we map barrier geometry, effective inertia, and noise onto neural decision and memory processes. This quantum-inspired approach yields testable predictions—such as isotope-like modulation of switching rates—and provides a quantitative bridge between molecular-scale biophysics and behavioral timescales. We support the framework with analytic formu-lae, numerical eigenvalue results, and Langevin simulations, offering a compact toolkit for investigating perceptual rivalry, working-memory stability, and decision latencies. 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. 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