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Measuring integrated information: comparison of candidate measures in theory and simulation
Version 2 2023-06-12, 08:55
Version 1 2023-06-09, 16:18
journal contribution
posted on 2023-06-12, 08:55 authored by Pedro M Mediano, Anil SethAnil Seth, Adam BarrettAdam BarrettIntegrated 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 (‘F’) 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.
History
Publication status
- Published
File Version
- Published version
Journal
EntropyISSN
1099-4300Publisher
MDPIExternal DOI
Issue
1Volume
21Page range
1-30Department affiliated with
- Informatics Publications
Research groups affiliated with
- Centre for Computational Neuroscience and Robotics Publications
- Sackler Centre for Consciousness Science Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2018-12-19First Open Access (FOA) Date
2019-01-14First Compliant Deposit (FCD) Date
2018-12-18Usage metrics
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