Explaining Cognitive and Perceptual Phenomena Through Resonance of Closed Neural Network Geometries (RCNNG): A Unified Framework

Abstract

This preprint presents the updated and expanded formulation of the RCNNG framework, a geometric–resonant theory proposing a unified foundation for perception, cognition, and conscious experience. In RCNNG, the fundamental units of mental processing are Closed Recurrent Geometries (CRGs)—stable, self‑reinforcing geometric structures formed through recurrent neural activity. The resonance, interaction, and transformation of these geometries give rise to the full spectrum of cognitive and perceptual phenomena. The revised version of the article introduces a fully reorganized structure that guides the reader from foundational principles to advanced cognitive and philosophical implications. The work begins by establishing the core mechanisms of CRG formation, amplification, and integration, explaining how basic and composite percepts emerge from the resonance of geometric structures. It then extends these principles to more complex perceptual phenomena, including similarity and difference judgments, facial stability, recognition of distorted or rotated patterns, perceptual ambiguity, and the illusion of familiarity (déjà vu), all interpreted as outcomes of CRG overlap and competitive resonance. The article further explores boundary states of perception—pre‑perception, perceptual void, and the distinction between absence of perception and perception of absence—before developing a resonance‑based account of conscious, unconscious, and near‑threshold states. Building on this foundation, RCNNG is applied to high‑level cognition, offering geometric interpretations of physics, logic, mathematics, cognitive biases, skill formation, problem solving, reasoning, wisdom, imagination, and conceptual innovation. The framework also addresses central questions in philosophy of mind, including the nature of qualia, the observer problem, and the illusion of volition. The scope of RCNNG is then extended to biological organisms and potential non‑biological systems, suggesting that the emergence of awareness depends on the ability of a network—biological or artificial—to form and sustain stable CRGs. In its final sections, the article examines applications of RCNNG to sensory specialization, spatial perception, neural navigation, individual cognitive variability, the distinction between memorization and understanding, the “golden years” of learning, and early childhood amnesia. Overall, this expanded version demonstrates that RCNNG provides a coherent and unified geometric–dynamic mechanism capable of explaining a wide range of perceptual, cognitive, developmental, and conscious phenomena. It offers a novel perspective for future research in neuroscience, cognitive science, artificial intelligence, and philosophy of mind, suggesting that geometry and resonance may constitute the fundamental architecture of mental processes.