TEVSER: a theory of evolving self-representations

A new framework, TEVSER (Theory of Evolving Self-Representations), proposes that living systems maintain homeostasis through a hierarchy of self-representations that serve as control structures guiding behavior. Within this hierarchy, distinct functional levels correspond to qualitatively different forms of cognition, including the emergence of a phenomenological internal world, spatial subjectivity, temporal presence, behavioral intelligence, self-consciousness, and abstract symbolic intellect. Consciousness is treated as a structured and graded property arising from the organization of self-representing systems, offering a constructive approach to the hard problem of consciousness. The framework integrates predictive coding, active inference, higher-order theories, and integrated information theory within a unified architecture and generates testable hypotheses linking levels of self-representation to neural organization, behavior, and evolutionary complexity.

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Abstract

This paper proposes TEVSER (Theory of Evolving Self-Representations), a framework describing how increasingly complex forms of regulation give rise to psyche, consciousness, and intelligence. The central idea is that a living system is a self-regulating system that maintains homeostasis. Within this perspective, regulation can be described as a hierarchy of self-representations ( Ω ), emerging as control structures that guide behavior. Within this hierarchy, distinct functional levels correspond to qualitatively different forms of cognition. In particular, the framework identifies the emergence of a phenomenological internal world (Ω 2 ), spatial subjectivity (“here,” Ω 3 ), temporal presence (“now,” Ω 6 ), behavioral intelligence (Ω 8 ), self-consciousness (“who,” Ω 10 ), and abstract symbolic intellect (Ω 11 ). Within this perspective, consciousness is not treated as a singular entity but as a structured and graded property arising from the organization of self-representing systems. The framework offers a constructive approach to the hard problem of consciousness, addressing the apparent paradox between the material nature of the brain and the seemingly immaterial character of subjective experience. TEVSER integrates and extends existing approaches, including predictive coding, active inference, higher-order theories, and integrated information theory, by situating them within a unified hierarchical architecture. Importantly, the framework generates a set of testable hypotheses linking levels of self-representation to neural organization, behavior, and evolutionary complexity. These predictions provide a basis for empirical validation and position TEVSER not only as a conceptual model but as a research program for investigating consciousness and intelligence in biological and artificial systems.