Cognitive Field Theory unifies learning, memory, and emergent intelligence

Byung Gyu Chae's Cognitive Field Theory, published on arXiv, presents a unified mathematical framework for learning, inference, memory, and emergence in both biological and artificial systems. Starting from a stochastic cognitive-field equation with homeostatic stabilization and adaptive manifold geometry, the theory shows that cognitive dynamics is organized by slowly relaxing infrared modes embedded within a high-dimensional cognitive manifold. The key innovation is the time-scale density of states (TDOS), which characterizes the relaxation spectrum underlying inference and memory. Learning reorganizes the infrared TDOS to selectively stabilize weakly damped sectors that support contextual organization and recursive memory feedback. Near criticality, the TDOS develops a broad, flat infrared structure, enhancing collective susceptibility and generating scale-free temporal organization over extended time scales.

The implications for AI and neuroscience are significant. The theory predicts that memory formation, adaptive reasoning, and emergent intelligence are hierarchical stages of infrared collective dynamical organization. By integrating out latent memory sectors, the model generates retarded self-energy feedback and nonlocal memory kernels that soften the infrared response, producing a protected near-critical regime with long-time contextual persistence. This suggests that both biological brains and advanced AI systems may operate near a critical point where slow modes accumulate, enabling efficient learning and flexible reasoning. The framework offers a potential bridge between neural network dynamics and cognitive phenomena, providing testable predictions about the temporal structure of memory and decision-making in complex systems.