From equivalence to quantification: the HLbC model as a unified framework for consciousness

Abstract

The challenge of developing a unified, mathematically rigorous framework for consciousness remains central to neuroscience. Hereby two recent papers are discussed that advance the Human Language-based Consciousness (HLbC) model as a novel, testable alternative to existing theories. The first paper establishes a mathematical equivalence between the HLbC model and the Bayesian brain hypothesis, demonstrating that the HLbC's five-step cycle, from observation to post-hoc consciousness, maps precisely onto the process of minimizing prediction errors. Specifically, the cycle's stages correspond to the integration of prior beliefs with likelihood, probabilistic action sampling, and the final Bayesian updating of the internal model. The second paper addresses the critical challenge of quantification by proposing a dual metric for "consciousness strength." This metric integrates real-time responsiveness (immediate information processing) with long-term learning and adaptation (error minimization over time), offering a more comprehensive measure than single-value metrics. The HLbC model, by positing consciousness as a post-hoc recognition of unconsciously selected actions, offers a clear, testable hypothesis for the timing and function of conscious experience. This framework has profound implications for both neuroscience, by unifying computational and cognitive concepts, and artificial intelligence, by providing a blueprint for more adaptive, human-like systems.