A comprehensive metric for consciousness strength: Integrating real-time responsiveness and long-term learning based on the HLbC model
Shin‐ichi Inage, Hana Hebishima
The Neuroscience Chronicles July 10, 2025 DOI: 10.46439/neuroscience.5.027 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Citations | 1 |
| Key points | Proposes that consciousness, under the Human Language-based Consciousness model, is a post-hoc process of language and probabilistic decision-making, and that modeling it yields a pseudo-Schrödinger equation with Kullback-Leibler distance in place of spatial coordinates. Argues that two proposed metrics—one for real-time response and information processing, one Bayesian for learning over time—can quantify "consciousness strength." |
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. 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.