The Unified Dynamic Model of Mind: A Mathematical Framework for Integrating Perception, Action, Emotion, and Culture through Predictive Processing

Abstract The Unified Dynamic Model of Mind (UDMM) proposes a novel, mathematically grounded synthesis of predictive processing, active inference, embodied cognition, and cultural neuroscience. It conceptualizes the mind as a hierarchical, generative system that minimizes free energy through continuous prediction and action, thereby explaining not only perception and motor control but also emotion, intention, mental disorders, and sociocultural cognition. The model introduces two formal constructs—informational tension (IT), quantified as the Kullback-Leibler divergence between prior and posterior beliefs, and virtual attractors, representing dynamically stable cognitive states—to provide mechanistic, computationally tractable accounts of subjective experience and psychopathology. We critically evaluate the UDMM’s theoretical coherence, mathematical rigor, and explanatory scope against established frameworks such as the Free Energy Principle, Integrated Information Theory (IIT), and Global Workspace Theory (GWT). While the UDMM offers a compelling unification of disparate domains—bridging neurobiology, psychology, and anthropology—it remains a framework rather than a fully validated theory, as its core parameters (e.g., IT threshold, global intention weight) lack empirical calibration. We conclude that the UDMM constitutes a paradigm-shifting heuristic that reframes consciousness as the phenomenological correlate of predictive inference under uncertainty, providing a fertile foundation for future empirical research in cognitive neuroscience, computational psychiatry, and artificial intelligence.