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Measuring integrated information: comparison of candidate measures in theory and simulation

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Version 2 2023-06-12, 08:55
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journal contribution
posted on 2023-06-12, 08:55 authored by Pedro M Mediano, Anil SethAnil Seth, Adam BarrettAdam Barrett
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 (‘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

Entropy

ISSN

1099-4300

Publisher

MDPI

Issue

1

Volume

21

Page range

1-30

Department 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

2018-12-19

First Open Access (FOA) Date

2019-01-14

First Compliant Deposit (FCD) Date

2018-12-18

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