Geometry-Constrained Emergent Cognitive Field Dynamics Under Extreme Perturbation

This study investigates whether stable, differentiated internal architectures can emerge from unstructured interacting field systems when confined within a complex geometric boundary and subjected to extreme destabilization. Using a high-resolution global simulation framework, the system is initialized without predefined structure, learning rules, or biological assumptions and exposed to repeated collapse–recovery cycles. Despite severe perturbation, the system consistently converges toward a reproducible internal architecture characterized by persistent attractor networks, zero topology variance, and adaptive reorganization. The results demonstrate that geometric constraint alone can function as an organizing operator, inducing robust internal structure formation in abstract field systems. These findings contribute to complexity science, nonlinear dynamics, and theoretical models of emergent computation.