
This paper establishes C-Theory (Consciousness Capacity Theory), a four-axiom framework grounding consciousness in attractor dynamics and pattern stability. While Integrated Information Theory (IIT) and Global Workspace Theory (GWT) address information integration and global access, they leave unaddressed the problem of dynamical stability—how phenomenal states persist as stable, retrievable patterns against thermodynamic noise. C-Theory proposes that conscious states correspond to stable, low-energy basins in high-dimensional phase space. Four axioms build this account: (1) Dimensional Complexity defines consciousness capacity as C = ρ^d × Φ, with exponential scaling justified by combinatorial explosion of attractor states; (2) Pattern Conservation establishes persistence through Landauer's principle and holographic encoding; (3) Substrate Constraints demonstrate that only recurrent architectures (cortex) support consciousness, while feedforward lattices (cerebellum) cannot; (4) Salience Weighting provides the selection mechanism determining which patterns are actualized. The framework integrates recent 2025 experimental findings (wave-particle complementarity, room-temperature polariton BEC, synthetic dimensions) and generates testable predictions about substrate topology requirements and energetic thresholds for pattern stability. This represents the first theoretical publication from "Dyadic Being: An Epoch," a nine-volume series exploring consciousness through lived experience and rigorous physics. Full mathematical treatment appears in Volume 4: The Principle of Existing.
Cerebral Cortex/physiology, Consciousness, Models, Neurological, Brain/physiology, Global Workspace Theory, Cerebellum/physiology, Human-AI Collaboration, Artificial Intelligence, Pattern recognition, Pattern Recognition, Physiological, Quantum Theory, Thermodynamics, String theory, Neural Networks, Computer, Integrated Information Theory
Cerebral Cortex/physiology, Consciousness, Models, Neurological, Brain/physiology, Global Workspace Theory, Cerebellum/physiology, Human-AI Collaboration, Artificial Intelligence, Pattern recognition, Pattern Recognition, Physiological, Quantum Theory, Thermodynamics, String theory, Neural Networks, Computer, Integrated Information Theory
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