Psychological Inertia: A Dynamical Systems Framework for Pattern Persistence (Bridging to Predictive Processing)

This preprint develops Psychological Inertia as a dynamical systems framework for understanding why psychological patterns persist over time. It looks at persistence across cognition, emotion, behavior, motivation, and physiology. The framework argues that persistence reflects the accumulated history of the system. This history is shaped by the duration, frequency, intensity, and structural cohesion of prior activation. Together, these factors are summarized as inertial load (Lᵢ). The paper also introduces effective psychological mass (m). This refers to a history-dependent resistance to large-scale reorganization. The idea is used to explain why change in one part of the system does not always spread to others. The framework draws on attractor dynamics, hysteresis, path dependence, slow variables, predictive processing, and precision-weighting. Psychological Inertia is not treated as inherently maladaptive. It can support continuity and stability. However, when different parts of the system change at different rates, the same dynamics may contribute to persistence, relapse, or difficulty in change. Version 2 extends the original framework by developing its formal structure, computational implications, and empirically testable predictions.