A Neuronal Noise Critique of Integrated Information Theory
arXiv Preprint Archive – December 06, 2021
Source: arXiv
Summary
Brain noise isn't just random static - it's essential for how we think and learn. New research challenges a major theory of consciousness by showing that neural "noise" actually helps our brains process information and make decisions. While traditional models suggested this background activity reduces mental clarity, experiments reveal that controlled neural variability is crucial for learning, visual recognition, and forming mental categories. This finding fundamentally reshapes our understanding of how consciousness emerges from brain activity.
Abstract
Integrated Information Theory (IIT) is an audacious attempt to pin down the abstract, phenomenological experiences of consciousness into a rigorous, mathematical framework. We show that IIT's stance in regards to neuronal noise is inconsistent with experimental data demonstrating that neuronal noise in the brain plays a critical role in learning, visual recognition, and even categorical representation. IIT predicts that entropy due to noise will reduce the information integration of a physical system, which is inconsistent with experimental data demonstrating that decision-related noise is a necessary condition for learning and visual recognition tasks. IIT must therefore be reformulated to accommodate experimental evidence showing both the successes and failures of noise.