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

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.