Load Minimization Theory as a Unifying Framework for Cognitive, Social, and Clinical Psychology in the AI Age

As artificial intelligence increasingly mediates human cognition, emotion, and social interaction, psychology requires a unifying framework capable of analyzing the relational burdens that emerge in human–AI systems. This paper proposes Load Minimization Theory (LMT) as a foundational framework for reinterpreting the three major branches of psychology—cognitive, social, and clinical—through the lens of relational load minimization. Relational load refers to the cognitive, interpersonal, and energetic burdens that shape psychological functioning. LMT formalizes psychological phenomena as the minimization of total relational load, expressed as L = epistemic burden (U) + relational tension (F) + capability burden (E). Cognitive psychology is reframed through epistemic burden (uncertainty and predictive processing), social psychology through relational tension (interpersonal friction, attachment, and group dynamics), and clinical psychology through capability burden (mental energy, burnout, and psychological resources). By integrating these three domains under a single relational structure, LMT reveals how uncertainty, interpersonal strain, and energetic depletion share a common underlying mechanism, and how AI systems can either alleviate or exacerbate these burdens. The framework not only offers new explanatory power for traditional psychological phenomena but also generates practical implications for designing psychologically sustainable AI. Effective human–AI interaction should aim to minimize relational load across all three dimensions, thereby fostering cognitive clarity, social harmony, and mental well-being. In doing so, LMT provides a conceptual foundation for the emerging field of AI psychology.