Measuring Integrated Information: Comparison of Candidate Measures in Theory and Simulation
Pedro A. M. Mediano, Anil K. Seth, Adam B. Barrett
arXiv Preprint Archive June 25, 2018 preprint
Study at a glance
AI-extracted from the abstract| Characteristics | Simulation study |
|---|---|
| Population | Eight-node network models with Gaussian linear autoregressive dynamics |
| Keywords | Q-bio.nc |
| Key points | No two of the six candidate measures of integrated information showed consistent agreement across all analyses, and only a subset reflected genuine dynamical complexity. |
Abstract
Integrated Information Theory (IIT) is a prominent theory of consciousness that has at its centre measures that quantify the extent to which a system generates more information than the sum of its parts. While several candidate measures of integrated information (`$Φ$') now exist, little is known about how they compare, especially in terms of their behaviour on non-trivial network models. In this article we provide clear and intuitive descriptions of six distinct candidate measures. We then explore the properties of each of these measures in simulation on networks consisting of eight interacting nodes, animated with Gaussian linear autoregressive dynamics. We find a striking diversity in the behaviour of these measures -- no two measures show consistent agreement across all analyses. Further, only a subset of the measures appear to genuinely reflect some form of dynamical complexity, in the sense of simultaneous segregation and integration between system components. Our results help guide the operationalisation of IIT and advance the development of measures of integrated information that may have more general applicability.