The Quantification Horizon Theory of Consciousness
arXiv Preprint Archive April 4, 2017 preprint
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper |
|---|---|
| Keywords | Q-bio.nc Cs.ai |
| Key points | Proposes that the hard problem of consciousness reflects a structural limitation of mathematical description, and that compression singularities in a system's self-compression mark a quantification horizon beyond which qualia lie. |
Abstract
To make nature mathematically tractable, the scientific model of the world omits qualia--colors, sounds, tastes, sensations--leaving only what admits of numerical characterization. The "hard problem" of consciousness--the enigma of why and how physical processing gives rise to felt experience--remains unsolved. The Quantification Horizon Theory of Consciousness (QHT) proposes that this enigma reflects a structural limitation of mathematical description: quantitative models capture only quantifiable features of reality; qualia are left out. Yet despite this limitation, QHT argues that such models can account for the unquantifiable--not by explaining or containing it, but by marking its place, in the form of a signpost. There are specific structural features--compression singularities in a system's own self-compression, rendered in this paper through information geometry--that intuitively correspond to the hallmark properties of consciousness and could serve as precisely such signposts. QHT proposes that these singularities mark a quantification horizon--a boundary beyond which quantitative description cannot reach. On this proposal, qualia lie beyond the horizon. From this basis, the theory conditionally localizes the structure of ineffability, privacy, and subjectivity, and proposes structural accounts of unity and causal efficacy. The theory proposes substrate-independent dynamical criteria as candidate markers of which systems may be conscious, is anti-panpsychist without adding intuition-saving exclusions, defines prospective prediction schemas through a designated formal rendering, and offers concrete implications for artificial intelligence and artificial consciousness.