Theories and measures of consciousness: An extended framework

No single quantitative measure—neural complexity, information integration, or causal density—fully captures the multidimensional complexity of the neural systems underlying consciousness, and each has practical limitations. The analysis suggests that combining alternative measures may improve quantitative characterization, but some aspects of consciousness likely resist quantification entirely, so a satisfactory theory should blend qualitative and quantitative elements.

Study at a glance

Abstract

A recent theoretical emphasis on complex interactions within neural systems underlying consciousness has been accompanied by proposals for the quantitative characterization of these interactions. In this article, we distinguish key aspects of consciousness that are amenable to quantitative measurement from those that are not. We carry out a formal analysis of the strengths and limitations of three quantitative measures of dynamical complexity in the neural systems underlying consciousness: neural complexity, information integration, and causal density. We find that no single measure fully captures the multidimensional complexity of these systems, and all of these measures have practical limitations. Our analysis suggests guidelines for the specification of alternative measures which, in combination, may improve the quantitative characterization of conscious neural systems. Given that some aspects of consciousness are likely to resist quantification altogether, we conclude that a satisfactory theory is likely to be one that combines both qualitative and quantitative elements.