Measuring consciousness: relating behavioural and neurophysiological approaches

Abstract

The resurgent science of consciousness has been accompanied by a recent emphasis on the problem of measurement. Having dependable measures of consciousness is essential both for mapping experimental evidence to theory and for designing perspicuous experiments. Here, we review a series of behavioural and brain-based measures, assessing their ability to track graded consciousness and clarifying how they relate to each other by showing what theories are presupposed by each. We identify possible and actual conflicts among measures that can stimulate new experiments, and we conclude that measures must prove themselves by iteratively building knowledge in the context of theoretical frameworks. Advances in measuring consciousness have implications for basic cognitive neuroscience, for comparative studies of consciousness and for clinical applications.

Figure I

Measuring integration and differentiation in neural dynamics, for a system composed of N elements. (a) C_N is calculated as the sum of the average MI over N/2 sets of bipartitions indexed by k (e.g. for k = 1 an average MI is calculated over N bipartitions). (b) Φ is calculated as the EI across the MIB. To calculate EI for a given bipartition (indexed by j), one subset is injected with maximally entropic activity (orange stars) and MI across the partition is measured. (c) c_d is calculated as the fraction of interactions that are causally significant according to Granger causality. A weighted (and unbounded) version of c_d can be calculated as the summed magnitudes of all significant causal interactions (depicted by arrow width).