Skip to content

Re-examining Criteria for Consciousness: An Ontological Framework Based on "Selectivity"

Q Chen

Zenodo (CERN European Organization for Nuclear Research) July 6, 2026 DOI: 10.5281/zenodo.21222505 (opens in new tab)

Study at a glance

AI-extracted from the abstract
Characteristics Theoretical or philosophical paper Peer reviewed
Keywords Consciousness Dilemma Mainstream Qualia Property philosophy Possession linguistics Trajectory Epistemology Cognitive science Artificial intelligence Process computing Perspective graphical Snapshot computer storage Core optical fiber
Key points Proposes that consciousness is not a static snapshot but a temporal process defined by "selectivity," an emergent holistic property of long-duration sequential behaviors that cannot be captured by any instantaneous physical quantity.

Abstract

Determining whether a system possesses consciousness stands as a core cross-disciplinary puzzle spanning science and philosophy. Mainstream existing criteria—including behavioral imitation, neural correlates, and integrated information index—all rely on specific theoretical presuppositions, undermining their persuasiveness across different frameworks. This paper argues that the root of this dilemma lies in the universal pursuit of an "instantaneous static marker" within old paradigms, whereas consciousness may not be a static snapshot at all, but a process unfolding across time. To this end, we propose a de-subjectivized ontological criterion: the essence of consciousness is a system’s possession of "selectivity"—meaning the evolutionary behaviors of the system cannot be exhaustively explained by its complete historical trajectory and internal parameters. We further demonstrate that selectivity is not quantifiable via any instantaneous physical quantity; it is fundamentally an emergent holistic property of long-duration sequential processes. This reasoning resolves inherent flaws of prior paradigms and redirects consciousness research from hunting for instantaneous evidence toward monitoring long-term behavioral predictability. Finally, we show that this theoretical direction gains concrete methodological support from an isomorphic mathematical model built around the integral constant C, and forms structural resonance with parallel explorations in spacetime physics.