Quality vs quantity of consciousness: empirical evidence from integrated information analysis of human intracranial data
Integrated information theory (IIT) gives quantitative predictions about the relationships between consciousness, its informational structure, and its neural bais. Specifically, based on the phenomenologial analysis, IIT gives two predictions about the relationships between consciousness and its informational structure. One is that quality of consciousness (e.g. seeing of face) correlates with "shape" of "Integrated Information Strucutre (IIS)" (so-called Maximally Integrated Conceptual Structure (Oizumi et al., 2014)). The other is that quantity of consciousness (e.g. awake vs sleep) correlates with "system-level Integrated Information (system-level II)" (so-called big-phi (Oizumi et al., 2014)). In addition, IIT prescribes methods to estimate both IIS and system-level II based on our knowledge of the neural system. Previously, we made a first step towards the empirical testing of an IIT's prediction on quality of consciousness by analysing human Electrocorticographic (ECoG) data obtained from visual perception experiments (Haun et al., 2017). Here, we resolved several issues in the study, specifically issues that are associated with the computation of IIS and system-level II. In our preliminary analysis with the improved method, we tested the two IIT predictions on the neural data recorded from one patient, finding that IIS correlated with visual preception while system-level II did not. We plan to replicate this result using data obtained from other subjects and other experiments.
Oizumi, M., Albantakis, L., & Tononi, G. (2014). From the phenomenology to the mechanisms of consciousness: integrated information theory 3.0. PLoS computational biology, 10(5), e1003588. doi: 10.1371/journal.pcbi.1003588
Haun, A. M., Oizumi, M., Kovach, C. K., Kawasaki, H., Oya, H., Howard, M. A., Adolphs, R., & Tsuchiya, N. (2017). Conscious perception as integrated information patterns in human electrocorticography. ENeuro, 4(5). doi: 10.1523/ENEURO.0085-17.2017