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The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models

Overview of attention for article published in Frontiers in Computational Neuroscience, April 2014
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  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

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Title
The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models
Published in
Frontiers in Computational Neuroscience, April 2014
DOI 10.3389/fncom.2014.00036
Pubmed ID
Authors

Rodrigo Sigala, Sebastian Haufe, Dipanjan Roy, Hubert R. Dinse, Petra Ritter

Abstract

During the past two decades growing evidence indicates that brain oscillations in the alpha band (~10 Hz) not only reflect an "idle" state of cortical activity, but also take a more active role in the generation of complex cognitive functions. A recent study shows that more than 60% of the observed inter-subject variability in perceptual learning can be ascribed to ongoing alpha activity. This evidence indicates a significant role of alpha oscillations for perceptual learning and hence motivates to explore the potential underlying mechanisms. Hence, it is the purpose of this review to highlight existent evidence that ascribes intrinsic alpha oscillations a role in shaping our ability to learn. In the review, we disentangle the alpha rhythm into different neural signatures that control information processing within individual functional building blocks of perceptual learning. We further highlight computational studies that shed light on potential mechanisms regarding how alpha oscillations may modulate information transfer and connectivity changes relevant for learning. To enable testing of those model based hypotheses, we emphasize the need for multidisciplinary approaches combining assessment of behavior and multi-scale neuronal activity, active modulation of ongoing brain states and computational modeling to reveal the mathematical principles of the complex neuronal interactions. In particular we highlight the relevance of multi-scale modeling frameworks such as the one currently being developed by "The Virtual Brain" project.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 196 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 2 1%
United States 2 1%
Switzerland 1 <1%
Netherlands 1 <1%
Italy 1 <1%
Germany 1 <1%
China 1 <1%
United Kingdom 1 <1%
Unknown 186 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 45 23%
Researcher 33 17%
Student > Master 24 12%
Student > Bachelor 14 7%
Student > Doctoral Student 12 6%
Other 31 16%
Unknown 37 19%
Readers by discipline Count As %
Neuroscience 51 26%
Psychology 46 23%
Medicine and Dentistry 16 8%
Agricultural and Biological Sciences 13 7%
Computer Science 10 5%
Other 23 12%
Unknown 37 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 02 May 2014.
All research outputs
#6,483,718
of 23,577,761 outputs
Outputs from Frontiers in Computational Neuroscience
#315
of 1,380 outputs
Outputs of similar age
#60,607
of 227,757 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#6
of 19 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 1,380 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has done well, scoring higher than 76% 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 227,757 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 73% of its contemporaries.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.