Informational self meaning as a structural theory in artificial intelligence ontology and epistemology
Year: 2026
Type: article
Abstract: This paper introduces the concept of informational self-meaning (ISM) as a philosophical account for addressing AI ontology and epistemology. Unlike theories such as Higher-Order Thought (HOT), Integrated Information Theory (IIT), and Global Workspace Theory (GWT), which encounter regress, reductionism, or anthropocentric limits, ISM characterises consciousness structurally as the capacity for rule-level modification rather than mere parameter adjustment. Drawing from both Western (Aristotle, Leibniz, Derrida, Floridi) and Eastern (Huayan, Yogācāra) traditions, the paper outlines three operational conditions—contextual coherence, emergent abstraction, and structural self-modification—and integrates them into a conceptually grounded and empirically testable framework. Empirical anchors from psycholinguistics [ 23 , 26 ] and recent AI developments (GPT models, RLHF, meta-learning, Reflexion) demonstrate that ISM offers not only philosophical clarity but also practical verification protocols. Its contribution lies in bridging metaphysics, cognitive science, and AI ethics, providing a conceptual foundation for gradualist models of AI recognition in legal and ethical contexts.
Source: Discover Artificial Intelligence
Author: Jaehong Yu
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,590
OpenAlex ID: W7212169212