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Revealing hidden states in visual working memory using electroencephalography

Overview of attention for article published in Frontiers in Systems Neuroscience, September 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

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22 X users
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Citations

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158 Dimensions

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287 Mendeley
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Title
Revealing hidden states in visual working memory using electroencephalography
Published in
Frontiers in Systems Neuroscience, September 2015
DOI 10.3389/fnsys.2015.00123
Pubmed ID
Authors

Michael J. Wolff, Jacqueline Ding, Nicholas E. Myers, Mark G. Stokes

Abstract

It is often assumed that information in visual working memory (vWM) is maintained via persistent activity. However, recent evidence indicates that information in vWM could be maintained in an effectively "activity-silent" neural state. Silent vWM is consistent with recent cognitive and neural models, but poses an important experimental problem: how can we study these silent states using conventional measures of brain activity? We propose a novel approach that is analogous to echolocation: using a high-contrast visual stimulus, it may be possible to drive brain activity during vWM maintenance and measure the vWM-dependent impulse response. We recorded electroencephalography (EEG) while participants performed a vWM task in which a randomly oriented grating was remembered. Crucially, a high-contrast, task-irrelevant stimulus was shown in the maintenance period in half of the trials. The electrophysiological response from posterior channels was used to decode the orientations of the gratings. While orientations could be decoded during and shortly after stimulus presentation, decoding accuracy dropped back close to baseline in the delay. However, the visual evoked response from the task-irrelevant stimulus resulted in a clear re-emergence in decodability. This result provides important proof-of-concept for a promising and relatively simple approach to decode "activity-silent" vWM content using non-invasive EEG.

X Demographics

X Demographics

The data shown below were collected from the profiles of 22 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 287 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 2%
Germany 3 1%
Brazil 3 1%
Netherlands 2 <1%
United Kingdom 1 <1%
Unknown 273 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 62 22%
Researcher 57 20%
Student > Master 38 13%
Student > Bachelor 35 12%
Student > Doctoral Student 17 6%
Other 31 11%
Unknown 47 16%
Readers by discipline Count As %
Psychology 101 35%
Neuroscience 80 28%
Agricultural and Biological Sciences 16 6%
Computer Science 10 3%
Engineering 5 2%
Other 15 5%
Unknown 60 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 25 March 2016.
All research outputs
#2,872,870
of 24,749,767 outputs
Outputs from Frontiers in Systems Neuroscience
#242
of 1,403 outputs
Outputs of similar age
#36,612
of 272,486 outputs
Outputs of similar age from Frontiers in Systems Neuroscience
#3
of 37 outputs
Altmetric has tracked 24,749,767 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,403 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.1. This one has done well, scoring higher than 82% 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 272,486 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 37 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 94% of its contemporaries.