Measuring Integrated Information: Comparison of Candidate Measures in Theory and Simulation.
Pedro A. M. Mediano, Anil K. Seth, Adam B. Barrett
Entropy (Basel, Switzerland) December 25, 2018 DOI: 10.3390/e21010017 (opens in new tab) via PubMed
Summary
AI-generated from the abstractIntegrated Information Theory (IIT) is a prominent theory of consciousness that centers on measures quantifying how much a system generates more information than the sum of its parts. This article provides clear descriptions of six distinct candidate measures of integrated information and explores their properties through simulations on networks of eight interacting nodes with Gaussian linear autoregressive dynamics. The results reveal striking diversity in the measures' behavior—no two measures show consistent agreement across all analyses. A subset of the measures appears to reflect some form of dynamical complexity, meaning simultaneous segregation and integration between system components. These findings help guide the operationalization of IIT and advance development of measures with more general applicability.
Study at a glance
| Characteristics | Simulation study Peer reviewed |
|---|---|
| Keywords | Complexity Computational neuroscience Consciousness Integrated information theory |
| Key finding | No two of the six candidate measures of integrated information show consistent agreement across all analyses; a subset appears to reflect 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. A subset of the measures appears to 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 and dynamical complexity that may have more general applicability.