Resilience as Hierarchical Precision Flexibility: A Predictive Processing Account of Adaptive Regulation

Resilience is commonly defined as the capacity to adapt in the face of adversity, yet its underlying mechanism remains conceptually diffuse across trait, process, and outcome models. This paper proposes that resilience is best understood as hierarchical precision flexibility—the capacity of a predictive system to dynamically recalibrate confidence in prediction errors across embodied, affective, and meaning-related levels of inference.Drawing on predictive processing and the Free Energy Principle, we argue that resilience does not consist in reduced stress reactivity, but in context-sensitive modulation of precision within hierarchical inference. Defensive rigidity and adaptive flexibility are reframed as alternative configurations of precision dynamics rather than distinct psychological capacities. Dysregulation arises when prediction errors are weighted inflexibly, whereas resilience emerges from coordinated recalibration across hierarchical levels.This account integrates developmental, neurobiological, and computational perspectives within a unified explanatory framework. By specifying resilience as a property of precision dynamics, it shifts the construct from descriptive classification toward formal mechanistic explanation and yields empirically testable predictions for computational psychiatry and clinical intervention research.