A measure centrality index for systematic empirical comparison of consciousness theories.

A novel framework called the Measure Centrality Index (MCI) enables systematic comparison of consciousness theories by assessing how important a given empirical measure is for each theory. Applying the MCI to Global Neuronal Workspace Theory, Integrated Information Theory, and Temporospatial Theory of Consciousness shows that direct comparisons are meaningful for measures such as Lempel-Ziv Complexity, Autocorrelation Window, and possibly Mutual Information, but problematic for anatomical and physiological neural correlates of consciousness because these measures carry different weight across theories. The approach addresses isolated theory evolution and confirmatory bias in consciousness science.

Study at a glance

Abstract

Consciousness science is marred by disparate constructs and methodologies, making it challenging to systematically compare theories. This foundational crisis casts doubts on the scientific character of the field itself. Addressing it, we propose a framework for systematically comparing consciousness theories by introducing a novel inter-theory classification interface, the Measure Centrality Index (MCI). Recognizing its gradient distribution, the MCI assesses the degree of importance a specific empirical measure has for a given consciousness theory. We apply the MCI to probe how the empirical measures of the Global Neuronal Workspace Theory (GNW), Integrated Information Theory (IIT), and Temporospatial Theory of Consciousness (TTC) would fare within the context of the other two. We demonstrate that direct comparison of IIT, GNW, and TTC is meaningful and valid for some measures like Lempel-Ziv Complexity (LZC), Autocorrelation Window (ACW), and possibly Mutual Information (MI). In contrast, it is problematic for others like the anatomical and physiological neural correlates of consciousness (NCC) due to their MCI-based differential weightings within the structure of the theories. In sum, we introduce and provide proof-of-principle of a novel systematic method for direct inter-theory empirical comparisons, thereby addressing isolated evolution of theories and confirmatory bias issues in the state-of-the-art neuroscience of consciousness.