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A Bidirectional Criticality TAC Model of Conscious Emergence: A Phase-Transition Framework under Temporal Integration and Resource Constraints English Version

Jiaping Wang

Zenodo (CERN European Organization for Nuclear Research) September 7, 2026 preprint DOI: 10.5281/zenodo.22644153 (opens in new tab)

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AI-extracted from the abstract
Characteristics Theoretical or philosophical paper
Key points Argues that the emergence and regression of consciousness can be modeled as bidirectional critical phase transitions in a two-parameter space, with closed-loop completeness as an order parameter that sustains a high plateau and then falls sharply at a boundary. Proposes a scaling law in which the upper bound of the temporal window grows with the square root of the available power quota, and derives testable predictions and five falsifiable hypotheses.

Abstract

Existing theories of consciousness remain largely static: they describe irreducibility, binary access, or steady-state convergence, but rarely how conscious states switch, collapse, and rebuild over time. Under three constraints, namely finite resources, non-equilibrium maintenance, and discrete input, this paper constructs a minimal dynamical model in which temporal span and reconstruction activity serve as two control parameters and closed-loop completeness as the order parameter. It argues that the emergence and regression of consciousness are bidirectional critical phase transitions in this two-parameter space: closed-loop completeness sustains a high plateau inside a feasible region and falls cliff-like once a boundary is crossed, while discontinuous jumps, hysteresis, and critical slowing are unified as signatures of a single bistable structure. The model derives a scaling law in which the upper bound of the temporal window grows with the square root of the available power quota, and from it generates testable predictions on age-related cliff-like cognitive decline, hysteresis between anesthetic induction and emergence, working-memory scaling, and supercritical EEG spectral changes, together with five falsifiable hypotheses. The model presupposes no specific neural substrate; all conclusions are convergence results under resource constraints.