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Improved Measures of Integrated Information
Although there is growing interest in measuring integrated information in computational and cognitive systems, current methods for doing so in practice are computationally unfeasible. Existing and novel integration measures are investigated and classified by various desirable properties. A simple ta...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2016
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5117999/ https://www.ncbi.nlm.nih.gov/pubmed/27870846 http://dx.doi.org/10.1371/journal.pcbi.1005123 |
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author | Tegmark, Max |
author_facet | Tegmark, Max |
author_sort | Tegmark, Max |
collection | PubMed |
description | Although there is growing interest in measuring integrated information in computational and cognitive systems, current methods for doing so in practice are computationally unfeasible. Existing and novel integration measures are investigated and classified by various desirable properties. A simple taxonomy of Φ-measures is presented where they are each characterized by their choice of factorization method (5 options), choice of probability distributions to compare (3 × 4 options) and choice of measure for comparing probability distributions (7 options). When requiring the Φ-measures to satisfy a minimum of attractive properties, these hundreds of options reduce to a mere handful, some of which turn out to be identical. Useful exact and approximate formulas are derived that can be applied to real-world data from laboratory experiments without posing unreasonable computational demands. |
format | Online Article Text |
id | pubmed-5117999 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-51179992016-12-15 Improved Measures of Integrated Information Tegmark, Max PLoS Comput Biol Research Article Although there is growing interest in measuring integrated information in computational and cognitive systems, current methods for doing so in practice are computationally unfeasible. Existing and novel integration measures are investigated and classified by various desirable properties. A simple taxonomy of Φ-measures is presented where they are each characterized by their choice of factorization method (5 options), choice of probability distributions to compare (3 × 4 options) and choice of measure for comparing probability distributions (7 options). When requiring the Φ-measures to satisfy a minimum of attractive properties, these hundreds of options reduce to a mere handful, some of which turn out to be identical. Useful exact and approximate formulas are derived that can be applied to real-world data from laboratory experiments without posing unreasonable computational demands. Public Library of Science 2016-11-21 /pmc/articles/PMC5117999/ /pubmed/27870846 http://dx.doi.org/10.1371/journal.pcbi.1005123 Text en © 2016 Max Tegmark http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Tegmark, Max Improved Measures of Integrated Information |
title | Improved Measures of Integrated Information |
title_full | Improved Measures of Integrated Information |
title_fullStr | Improved Measures of Integrated Information |
title_full_unstemmed | Improved Measures of Integrated Information |
title_short | Improved Measures of Integrated Information |
title_sort | improved measures of integrated information |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5117999/ https://www.ncbi.nlm.nih.gov/pubmed/27870846 http://dx.doi.org/10.1371/journal.pcbi.1005123 |
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