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Practical Measures of Integrated Information for Time-Series Data

Overview of attention for article published in PLoS Computational Biology, January 2011
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

blogs
3 blogs
twitter
19 X users
wikipedia
1 Wikipedia page

Citations

dimensions_citation
170 Dimensions

Readers on

mendeley
324 Mendeley
citeulike
5 CiteULike
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Title
Practical Measures of Integrated Information for Time-Series Data
Published in
PLoS Computational Biology, January 2011
DOI 10.1371/journal.pcbi.1001052
Pubmed ID
Authors

Adam B. Barrett, Anil K. Seth

Abstract

A recent measure of 'integrated information', Φ(DM), quantifies the extent to which a system generates more information than the sum of its parts as it transitions between states, possibly reflecting levels of consciousness generated by neural systems. However, Φ(DM) is defined only for discrete Markov systems, which are unusual in biology; as a result, Φ(DM) can rarely be measured in practice. Here, we describe two new measures, Φ(E) and Φ(AR), that overcome these limitations and are easy to apply to time-series data. We use simulations to demonstrate the in-practice applicability of our measures, and to explore their properties. Our results provide new opportunities for examining information integration in real and model systems and carry implications for relations between integrated information, consciousness, and other neurocognitive processes. However, our findings pose challenges for theories that ascribe physical meaning to the measured quantities.

X Demographics

X Demographics

The data shown below were collected from the profiles of 19 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 324 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 12 4%
United Kingdom 7 2%
Germany 4 1%
Spain 4 1%
Canada 4 1%
France 4 1%
Sweden 3 <1%
Italy 2 <1%
Japan 2 <1%
Other 11 3%
Unknown 271 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 76 23%
Student > Ph. D. Student 72 22%
Student > Master 35 11%
Student > Bachelor 26 8%
Professor 24 7%
Other 52 16%
Unknown 39 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 61 19%
Computer Science 42 13%
Neuroscience 40 12%
Psychology 33 10%
Physics and Astronomy 29 9%
Other 67 21%
Unknown 52 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 32. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 July 2020.
All research outputs
#1,233,171
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#1,009
of 8,964 outputs
Outputs of similar age
#6,146
of 193,620 outputs
Outputs of similar age from PLoS Computational Biology
#4
of 46 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has done well, scoring higher than 88% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 193,620 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.