Consciousness Between Fact and Value: A Triadic Neurophenomenology

Consciousness mediates between empirical facts, conceptual meaning, and normative values. A triadic neurophenomenological framework posits three irreducible domains—object (material), idea (mental), and relation (axiological)—unified through consciousness. Testable hypotheses predict distinct neural and experiential signatures for each domain, using multimodal neuroimaging, representational similarity analysis, and micro-phenomenology. Reflexive self-consciousness may emerge from recursive interaction between mental and axiological domains, analogous to iterative dynamics in complex systems. A cross-domain classification principle—cohesion, attraction, and stability—maps structural characteristics across the three domains. Applied to artificial intelligence, the framework clarifies that current systems simulate object/idea processing but lack axiological participation.

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Abstract

This project hosts materials related to the paper: Hammat, K. (2026). Consciousness Between Fact and Value: A Triadic Neurophenomenology. Zenodo. https://doi.org/10.5281/zenodo.18669043 For citation purposes, please cite the Zenodo DOI above. The paper introduces a novel triadic neurophenomenological framework that integrates neuroscience with embodied and phenomenological perspectives to explain how consciousness mediates between empirical facts, conceptual meaning, and normative values. The model posits three irreducible domains — object (material), idea (mental), and relation (axiological) — unified through consciousness. The paper advances testable hypotheses predicting distinct neural and experiential signatures for fact-, idea-, and relation-based cognition, and outlines methods combining multimodal neuroimaging, representational similarity analysis, and micro-phenomenology. Additionally, the framework hypothesizes that reflexive self-consciousness may emerge from recursive interaction between mental and axiological domains, analogous to iterative dynamics in complex systems. A cross-domain classification principle — cohesion, attraction, and stability — is introduced, mapping these structural characteristics across the three domains within the triadic ontology, and providing a conceptual scaffold for empirical operationalization in neuroscience. Finally, the triadic criterion is applied to artificial intelligence, clarifying why current systems simulate object/idea processing yet lack axiological participation. This framework preserves scientific intelligibility while affirming the normative authority of value, offering a foundation for interdisciplinary research across neuroscience, ethics, and AI.