Measuring the Complexity of Consciousness
Xerxes D. Arsiwalla, Paul Verschure
arXiv Preprint Archive January 11, 2018 via arXiv
Summary
AI-generated from the abstractA new framework using information-theoretic complexity measures, such as integrated information, has been proposed to quantitatively classify states of consciousness, addressing both phenomenological contents and clinical disorders. However, applying these measures to realistic brain networks is difficult due to high computational costs. This article serves as a lookup table of principle-based and empirically tested measures of consciousness, with emphasis on clinical applicability for assisting diagnosis and therapy. It addresses challenges facing these measures with regard to realistic brain networks and suggests possible resolutions.
Study at a glance
| Characteristics | Review Peer reviewed |
|---|---|
| Keywords | Q-bio.nc Physics.bio-ph Neuroscience Consciousness-research Brain-networks |
| Key finding | Information-theoretic complexity measures, such as integrated information, offer a framework to quantitatively classify states of consciousness, but their application to realistic brain networks faces high computational costs. |
Abstract
The quest for a scientific description of consciousness has given rise to new theoretical and empirical paradigms for the investigation of phenomenological contents as well as clinical disorders of consciousness. An outstanding challenge in the field is to develop measures that uniquely quantify global brain states tied to consciousness. In particular, information-theoretic complexity measures such as integrated information have recently been proposed as measures of conscious awareness. This suggests a new framework to quantitatively classify states of consciousness. However, it has proven increasingly difficult to apply these complexity measures to realistic brain networks. In part, this is due to high computational costs incurred when implementing these measures on realistically large network dimensions. Nonetheless, complexity measures for quantifying states of consciousness are important for assisting clinical diagnosis and therapy. This article is meant to serve as a lookup table of measures of consciousness, with particular emphasis on clinical applicability of these measures. We consider both, principle-based complexity measures as well as empirical measures tested on patients. We address challenges facing these measures with regard to realistic brain networks, and where necessary, suggest possible resolutions.