Measuring Integrated Information: Comparison of Candidate Measures in Theory and Simulation
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 &rdquo...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-12-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1099-4300/21/1/17 |
| _version_ | 1798034575655436288 |
|---|---|
| author | Pedro A. M. Mediano Anil K. Seth Adam B. Barrett |
| author_facet | Pedro A. M. Mediano Anil K. Seth Adam B. Barrett |
| author_sort | Pedro A. M. Mediano |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T20:46:08Z |
| format | Article |
| id | doaj.art-4a791bcc527b4fc98a318af30195c024 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T20:46:08Z |
| publishDate | 2018-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-4a791bcc527b4fc98a318af30195c0242022-12-22T04:04:02ZengMDPI AGEntropy1099-43002018-12-012111710.3390/e21010017e21010017Measuring Integrated Information: Comparison of Candidate Measures in Theory and SimulationPedro A. M. Mediano0Anil K. Seth1Adam B. Barrett2Department of Computing, Imperial College, London SW7 2RH, UKSackler Centre for Consciousness Science and Department of Informatics, University of Sussex, Brighton BN1 9RH, UKSackler Centre for Consciousness Science and Department of Informatics, University of Sussex, Brighton BN1 9RH, UKIntegrated 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.http://www.mdpi.com/1099-4300/21/1/17integrated information theorycomputational neurosciencecomplexityconsciousness |
| spellingShingle | Pedro A. M. Mediano Anil K. Seth Adam B. Barrett Measuring Integrated Information: Comparison of Candidate Measures in Theory and Simulation Entropy integrated information theory computational neuroscience complexity consciousness |
| title | Measuring Integrated Information: Comparison of Candidate Measures in Theory and Simulation |
| title_full | Measuring Integrated Information: Comparison of Candidate Measures in Theory and Simulation |
| title_fullStr | Measuring Integrated Information: Comparison of Candidate Measures in Theory and Simulation |
| title_full_unstemmed | Measuring Integrated Information: Comparison of Candidate Measures in Theory and Simulation |
| title_short | Measuring Integrated Information: Comparison of Candidate Measures in Theory and Simulation |
| title_sort | measuring integrated information comparison of candidate measures in theory and simulation |
| topic | integrated information theory computational neuroscience complexity consciousness |
| url | http://www.mdpi.com/1099-4300/21/1/17 |
| work_keys_str_mv | AT pedroammediano measuringintegratedinformationcomparisonofcandidatemeasuresintheoryandsimulation AT anilkseth measuringintegratedinformationcomparisonofcandidatemeasuresintheoryandsimulation AT adambbarrett measuringintegratedinformationcomparisonofcandidatemeasuresintheoryandsimulation |