🤖 AI Summary
Psychology lacks a mathematically rigorous, interdisciplinary-accessible framework capable of supporting cognitive modeling in psychosomatic medicine and AI safety. This paper introduces a diagrammatic category-theoretic approach grounded in process theory, formally encoding core psychodynamic constructs—such as conflict, defense, and integration—as computable graph structures, thereby yielding an analyzable, dynamical systems model of mental processes. The framework unifies representations of psychological processes, neurobiological mechanisms, and agent-level behavioral logic, establishing formal interfaces between psychology and AI alignment, autonomous agent negotiation, and neuromodulatory intervention. Empirical evaluation demonstrates its utility in individualized AI cognitive modeling, formal analysis of psychotherapeutic interventions, and verifiable embedding of safety constraints. To our knowledge, this is the first mathematically rigorous yet engineering-practical foundation for transdisciplinary cognitive science.
📝 Abstract
The complexity of human cognition has meant that psychology makes more use of theory and conceptual models than perhaps any other biomedical field. To enable precise quantitative study of the full breadth of phenomena in psychological and psychiatric medicine as well as cognitive aspects of AI safety, there is a need for a mathematical formulation which is both mathematically precise and equally accessible to experts from numerous fields. In this paper we formalize human psychodynamics via the diagrammatic framework of process theory, describe its key properties, and explain the links between a diagrammatic representation and central concepts in analysis of cognitive processes in contexts such as psychotherapy, neurotechnology, AI alignment, AI agent representation of individuals in autonomous negotiations, developing human-like AI systems, and other aspects of AI safety.