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Attention Score in Context
| Title |
Transient Cognitive Dynamics, Metastability, and Decision Making
|
|---|---|
| Published in |
PLoS Computational Biology, May 2008
|
| DOI | 10.1371/journal.pcbi.1000072 |
| Pubmed ID | |
| Authors |
Mikhail I. Rabinovich, Ramón Huerta, Pablo Varona, Valentin S. Afraimovich |
| Abstract |
The idea that cognitive activity can be understood using nonlinear dynamics has been intensively discussed at length for the last 15 years. One of the popular points of view is that metastable states play a key role in the execution of cognitive functions. Experimental and modeling studies suggest that most of these functions are the result of transient activity of large-scale brain networks in the presence of noise. Such transients may consist of a sequential switching between different metastable cognitive states. The main problem faced when using dynamical theory to describe transient cognitive processes is the fundamental contradiction between reproducibility and flexibility of transient behavior. In this paper, we propose a theoretical description of transient cognitive dynamics based on the interaction of functionally dependent metastable cognitive states. The mathematical image of such transient activity is a stable heteroclinic channel, i.e., a set of trajectories in the vicinity of a heteroclinic skeleton that consists of saddles and unstable separatrices that connect their surroundings. We suggest a basic mathematical model, a strongly dissipative dynamical system, and formulate the conditions for the robustness and reproducibility of cognitive transients that satisfy the competing requirements for stability and flexibility. Based on this approach, we describe here an effective solution for the problem of sequential decision making, represented as a fixed time game: a player takes sequential actions in a changing noisy environment so as to maximize a cumulative reward. As we predict and verify in computer simulations, noise plays an important role in optimizing the gain. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 386 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 13 | 3% |
| United Kingdom | 9 | 2% |
| Germany | 8 | 2% |
| France | 4 | 1% |
| Spain | 3 | <1% |
| Switzerland | 3 | <1% |
| Brazil | 2 | <1% |
| Japan | 2 | <1% |
| Italy | 2 | <1% |
| Other | 8 | 2% |
| Unknown | 332 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 120 | 31% |
| Researcher | 94 | 24% |
| Student > Master | 40 | 10% |
| Professor | 23 | 6% |
| Professor > Associate Professor | 21 | 5% |
| Other | 56 | 15% |
| Unknown | 32 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 63 | 16% |
| Neuroscience | 63 | 16% |
| Psychology | 41 | 11% |
| Computer Science | 41 | 11% |
| Engineering | 40 | 10% |
| Other | 82 | 21% |
| Unknown | 56 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 26 May 2022.
All research outputs
#6,436,343
of 25,576,801 outputs
Outputs from PLoS Computational Biology
#4,378
of 9,003 outputs
Outputs of similar age
#25,357
of 88,683 outputs
Outputs of similar age from PLoS Computational Biology
#26
of 42 outputs
Altmetric has tracked 25,576,801 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 9,003 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 gotten more attention than average, scoring higher than 50% 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 88,683 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.