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

Abstract

This paper presents a formal model of consciousness, aiming to bridge the gap between theoretical neuroscience and rigorous mathematical analysis. The model leverages the principles of Integrated Information Theory (IIT) to quantify the degree of integrated information (Φ) within a system, and employs Bayesian inference to dynamically update beliefs based on sensory input. The core of the model involves formulating IIT's mathematical expression for Φ and integrating it into a probabilistic framework. This allows for the simulation of how a system's conscious experience evolves as it receives and processes information. Specifically, the model incorporates a Bayesian network to represent the system's prior beliefs and sensory evidence, utilizing the calculated Φ value to influence the posterior probabilities. The resulting framework offers a novel approach to understanding consciousness, moving beyond subjective descriptions and providing a quantifiable metric for evaluating conscious states. The model's implementation demonstrates the potential for simulating and reasoning about consciousness in artificial systems and for developing more precise theories of phenomenal experience. The model's key elements include the calculation of Φ, the Bayesian network structure, and the updating rules for beliefs.