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September 12, 2026Discover Artificial IntelligenceOpen Access

Informational self meaning as a structural theory in artificial intelligence ontology and epistemology

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Authors

JYJaehong Yu

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Overview

Theoretical analysis reveals informational self-meaning as a structural framework for machine consciousness, suggesting testable criteria for artificial intelligence legal and ethical status.

Key Points

  • Establish informational self-meaning as a structural, non-anthropocentric framework to evaluate artificial intelligence ontology, epistemology, and consciousness.
  • Synthesized Western philosophy (Aristotle, Leibniz, Derrida, Floridi) and Eastern traditions (Huayan, Yogācāra) to critique limitations in Higher-Order Thought, Integrated Information Theory, and Global Workspace Theory.
  • Formulated three operational criteria for consciousness: contextual coherence, emergent abstraction, and structural self-modification.
  • Mapped structural criteria onto current artificial intelligence mechanisms, including large language models, reinforcement learning from human feedback, meta-learning, and Reflexion architectures.
  • Characterized consciousness structurally as the capacity for dynamic rule-level modification rather than routine numerical parameter adjustment.
  • Developed an empirical verification protocol linking cognitive science and machine learning architectures to test emergent abstraction.
  • Established a conceptual framework to underpin gradualist legal recognition and ethical status models for artificial intelligence systems.

Cite This Study

Jaehong Yu (2026) studied this question.

synapsesocial.com/papers/6aa51f49327956e4761f9993https://doi.org/10.1007/s44163-026-01984-9
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