Formal Model of Consciousness Based on Integrated Information Theory and Bayesian Networks

Abstract

This paper presents a novel formal model of consciousness, integrating the principles of Integrated Information Theory (IIT) with Bayesian Networks. The core challenge in understanding consciousness lies in its lack of a rigorous, quantifiable mathematical representation. This model addresses this deficiency by utilizing IIT's concept of integrated information (Φ) – representing the amount of irreducible information a system generates – and mapping it within a Bayesian network framework. The Bayesian network allows for the probabilistic representation of internal states, sensory inputs, and the resulting conscious experience. Specifically, the model defines a set of nodes representing these elements, with connections and associated probabilities reflecting the influence between them. The model aims to provide a framework for quantifying consciousness levels based on the calculated Φ value and the network's structure. Furthermore, it introduces a mechanism for updating beliefs (Bayesian inference) based on new sensory information, simulating the adaptive nature of conscious experience. This approach offers a pathway for testing and refining our understanding of consciousness, moving beyond purely philosophical debates and toward empirically verifiable predictions.