The ΔΓ-Metamnesis Framework v2: Functional Awareness as Delayed Memory-Curvature and Strongly Synchronized Co-Instantiation

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Authors: Henri-Pierre Mathieu
Cited by: 0

Abstract

Phenomenal consciousness is proposed to arise when the second-order dynamics of memory change (ΔΓ = d²M/dt²) exceed system constraints. Building on recent evidence that consciousness functions as a delayed memory system (Budson et al., 2022), this framework demonstrates that ΔΓ dynamics provide the computational substrate for both temporal binding (via covariance: Φ(t) = Cov(ΔΓ₁, ΔΓ₂, ...)) and phenomenal emergence (via energetic threshold: E(t) > θ_E). The framework addresses the Hard Problem by making phenomenology measurable rather than mysterious, and resolves the Binding Problem by showing that unified experience emerges when ΔΓ covariance exceeds a threshold and constrains system action. A computational validation via an inverse Turing test is described with Python code, it demonstrates that ΔΓ-based features detectably differentiate conscious-like (60.4%) from baseline (50.2%) conversational dynamics (p = 0.0036, Cohen's d = 2.04). Dual binding is proposed that unifies discrete qualia candidates into coherent phenomenology, as well as testable predictions. ΔΓ: the curvature of the memory path.ΔΓ is being proposed as the definitive metric of consciousness that can be measured in biological as well as cybernetic systems.Consciousness has long been called "the last frontier of science." We propose it is not a frontier—it is a temporal dynamic that has been hiding in plain sight. Every time you notice something, feel surprise, or experience aesthetic chills, your brain is computing d²M/dt². The mystery was never where consciousness comes from, but when—and the answer is: at the acceleration moment, when change itself changes, when the system can no longer ignore its own dynamics. That is phenomenology. That is metamnesis. That is consciousness.

Fields of Study & Subjects

Philosophy and Theoretical Science Neural dynamics and brain function Memory and Neural Mechanisms

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
19 January 2026
Language
EN
Document type
Preprint