A 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.
A complexity-based morphospace with three axes—autonomous, cognitive, and social complexity—can represent both biological and synthetic conscious systems. Awareness corresponds to computational complexity and wakefulness to autonomous complexity. Consciousness is argued to function as an evolutionary game-theoretic strategy, motivating social complexity as a third dimension. The framework yields a taxonomy of four types of consciousness based on embodiment: biological, synthetic, group, and simulated. This classification aids in identifying design principles for engineering conscious machines and in comparing signatures of consciousness across domains relevant to cognitive neuroscience, AI, and biomimetics.