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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Qualia: The Geometry of Integrated Information
|
|---|---|
| Published in |
PLoS Computational Biology, August 2009
|
| DOI | 10.1371/journal.pcbi.1000462 |
| Pubmed ID | |
| Authors |
David Balduzzi, Giulio Tononi |
| Abstract |
According to the integrated information theory, the quantity of consciousness is the amount of integrated information generated by a complex of elements, and the quality of experience is specified by the informational relationships it generates. This paper outlines a framework for characterizing the informational relationships generated by such systems. Qualia space (Q) is a space having an axis for each possible state (activity pattern) of a complex. Within Q, each submechanism specifies a point corresponding to a repertoire of system states. Arrows between repertoires in Q define informational relationships. Together, these arrows specify a quale -- a shape that completely and univocally characterizes the quality of a conscious experience. Phi -- the height of this shape -- is the quantity of consciousness associated with the experience. Entanglement measures how irreducible informational relationships are to their component relationships, specifying concepts and modes. Several corollaries follow from these premises. The quale is determined by both the mechanism and state of the system. Thus, two different systems having identical activity patterns may generate different qualia. Conversely, the same quale may be generated by two systems that differ in both activity and connectivity. Both active and inactive elements specify a quale, but elements that are inactivated do not. Also, the activation of an element affects experience by changing the shape of the quale. The subdivision of experience into modalities and submodalities corresponds to subshapes in Q. In principle, different aspects of experience may be classified as different shapes in Q, and the similarity between experiences reduces to similarities between shapes. Finally, specific qualities, such as the "redness" of red, while generated by a local mechanism, cannot be reduced to it, but require considering the entire quale. Ultimately, the present framework may offer a principled way for translating qualitative properties of experience into mathematics. |
X Demographics
The data shown below were collected from the profiles of 80 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 23 | 29% |
| United States | 3 | 4% |
| Canada | 2 | 3% |
| Russia | 1 | 1% |
| France | 1 | 1% |
| Unknown | 50 | 63% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 70 | 88% |
| Scientists | 10 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 437 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 16 | 4% |
| United Kingdom | 7 | 2% |
| Germany | 5 | 1% |
| France | 4 | <1% |
| Italy | 3 | <1% |
| Japan | 3 | <1% |
| Norway | 2 | <1% |
| Spain | 2 | <1% |
| Australia | 2 | <1% |
| Other | 15 | 3% |
| Unknown | 378 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 111 | 25% |
| Student > Ph. D. Student | 92 | 21% |
| Student > Master | 43 | 10% |
| Student > Bachelor | 41 | 9% |
| Professor | 31 | 7% |
| Other | 85 | 19% |
| Unknown | 34 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 86 | 20% |
| Psychology | 61 | 14% |
| Computer Science | 58 | 13% |
| Neuroscience | 57 | 13% |
| Medicine and Dentistry | 28 | 6% |
| Other | 102 | 23% |
| Unknown | 45 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 59. 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 16 April 2024.
All research outputs
#745,208
of 25,872,466 outputs
Outputs from PLoS Computational Biology
#531
of 9,061 outputs
Outputs of similar age
#1,940
of 125,503 outputs
Outputs of similar age from PLoS Computational Biology
#4
of 50 outputs
Altmetric has tracked 25,872,466 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,061 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.3. This one has done particularly well, scoring higher than 94% 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 125,503 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 98% of its contemporaries.
We're also able to compare this research output to 50 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 92% of its contemporaries.