Cortical Informational Field Theory: Effective Dynamics, Scaling Law, and Empirical Program for Consciousness-Compatible Cortical States
Stjepovic-Gonzalez Danko
Zenodo (CERN European Organization for Nuclear Research) April 24, 2026 preprint DOI: 10.5281/zenodo.19715585 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper |
|---|---|
| Key points | Proposes that a coarse-grained Ginzburg–Landau/Model A field theory yields a first-order scaling law, R ≈ κA/ξ², connecting cortical spatial coherence length to the richness of integrated states. The authors derive a testable ratio prediction, an ECoG-based estimator for ξ that corrects a 13–18% geometric bias, and three differential predictions distinguishing CIFT from Integrated Information Theory, neural field theory, and criticality frameworks. |
Abstract
This paper proposes the Cortical Informational Field Theory (CIFT): a minimal effective field theory derived by coarse-graining neural populations into a Ginzburg–Landau/Model A equation. The central result is a first-order structural scaling law, R ≈ κA/ξ², connecting the spatial coherence length of cortical activity to the accessible richness of integrated states. This yields a directly testable ratio prediction (R₁/R₂ ≈ ξ₂²/ξ₁²), a formally derived estimator for ξ from subdural ECoG data that corrects a systematic 13–18% geometric bias, and three differential predictions distinguishing CIFT from Integrated Information Theory, neural field theory, and criticality frameworks. The framework does not claim to solve the hard problem of consciousness. The aim is more limited: to show that the spatial geometry of cortical coherence places a measurable bound on the richness of integrated states — and that this bound is experimentally accessible. Status: Under review at PeerJ (submitted April 2026).