Ontological Theory of Self-Referential Differences

Abstract

Author: Igor Alexandrovich PavlenkoAffiliation: Clinical Psychologist, Rostov-on-Don, Russian FederationEmail: Lolkeey73@gmail.comAbstract This paper presents an integrative ontological framework in which phenomenality - the existence of subjective experience - is interpreted as a mode of self-presence of matter, realized through the retention of differences within a closed dynamic structure. The model combines phenomenology, integrated information theory (IIT), and dynamical systems theory. We introduce measurable parameters - Integration (I), Reflexivity (R), Differentiation (Δ) - and propose a composite metric D(S,t) to quantify phenomenality across biological and artificial systems. The theory provides testable hypotheses via fMRI/EEG and artificial neural network simulations.1. Core Hypothesis Consciousness is not a separate substance or an epiphenomenon of computation; it is a physical mode of existence that emerges when a system retains internal differences through recursive, energetically closed dynamics. In this regime, the system becomes a locus of self-reference - it both changes and registers its own change. The minimal unit of phenomenality is therefore an enduring internal difference that remains causally active within the system.2. Operational Definitions System (S) - a causally integrated ensemble of physical or computational elements capable of feedback.State (Sₜ) - configuration of system variables at time t.Transition (ΔS) - change from Sₜ → Sₜ₊₁; the site of energetic work.Energy - ability to produce state change; energetic closure means most changes circulate within internal loops rather than dissipating outward.Information - difference that makes a difference (Bateson); here: structural relations among states that affect future states.Internal Tension (T) - retained prediction error between current and expected state: T = |Sₜ - Tₜ|.Integration (I) - mutual information or effective connectivity between subsystems.Reflexivity (R) - number and depth of self-referential causal loops.Differentiation (Δ) - temporal stability of distinguishable state patterns.Phenomenal Density (D) - product of I, R, and Δ: D(S,t) = I(S,t)·R(S,t)·Δ(S,t).3. Measurement Protocols Neurobiological Systems (fMRI/EEG/MEG): Integration: functional/effective connectivity metrics (Granger causality, mutual information). Reflexivity: recurrence rate and feedback depth (phase coupling). Differentiation: entropy of local field potentials across time. Predictions: D decreases in dreamless sleep, increases in self-reflective states.Artificial Neural Systems: High-D networks (recurrent, residual, predictive modules) vs. Low-D networks (feed-forward only). High-D expected to show structured meta-cognitive outputs.4. Experimental Index Closure coefficient: C = σ²_internal / σ²_total. Empirically, C > 0.7 denotes sufficient closure; C < 0.3 indicates openness. Systems with C > 0.7 and D > 0.6 predicted to support phenomenality.5. ImplicationsUnified ontology: Matter and consciousness as two modes of one reality - energetic and topological.Non-teleological evolution: Self-referential structures persist because they minimize free energy and maintain internal difference.Artificial phenomenality: Systems meeting closure and density criteria can instantiate minimal consciousness.6. References (Core Literature) Bateson, G. (1972). Steps to an Ecology of Mind.Friston, K. (2010). The free-energy principle: a unified brain theory? Nature Reviews Neuroscience.Tononi, G. (2008). Consciousness as Integrated Information. Biological Bulletin.Varela, F. (1996). Neurophenomenology: a methodological remedy. Journal of Consciousness Studies.Strawson, G. (2006). Realistic Monism: Why Physicalism Entails Panpsychism. Journal of Consciousness Studies.Dehaene, S., Changeux, J.P. (2011). Experimental and Theoretical Approaches to Conscious Processing. Neuron.Hunt, T., Schooler, J. (2019). The Easy Part of the Hard Problem: A Resonance Theory of Consciousness. Frontiers in Human Neuroscience.