Abstract
This paper presents a novel framework for measuring consciousness strength based on the Human Language-based Consciousness (HLbC) model. While Integrated Information Theory (IIT) quantifies consciousness via integrated information, the HLbC model views consciousness as a post-hoc process, emphasizing language and probabilistic decision-making. By modeling this decision process, a pseudo-Schrödinger equation emerges where the Kullback-Leibler distance replaces spatial coordinates. We propose two metrics for "consciousness strength": one focusing on real-time response and information processing, and another using Bayesian statistics to assess learning and adaptation over time. These metrics offer a comprehensive view of consciousness, integrating both immediate responses and long-term learning. Our findings contribute to advancing quantitative measures of consciousness, with potential applications in fields like artificial intelligence.
Keywords
Consciousness strength, Bayesian learning, Real-time evaluation, Information entropy, HLbC model