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A Neurodynamic Model of Feature-Based Spatial Selection

Overview of attention for article published in Frontiers in Psychology, March 2018
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Title
A Neurodynamic Model of Feature-Based Spatial Selection
Published in
Frontiers in Psychology, March 2018
DOI 10.3389/fpsyg.2018.00417
Pubmed ID
Authors

Mateja Marić, Dražen Domijan

Abstract

Huang and Pashler (2007) suggested that feature-based attention creates a special form of spatial representation, which is termed a Boolean map. It partitions the visual scene into two distinct and complementary regions: selected and not selected. Here, we developed a model of a recurrent competitive network that is capable of state-dependent computation. It selects multiple winning locations based on a joint top-down cue. We augmented a model of the WTA circuit that is based on linear-threshold units with two computational elements: dendritic non-linearity that acts on the excitatory units and activity-dependent modulation of synaptic transmission between excitatory and inhibitory units. Computer simulations showed that the proposed model could create a Boolean map in response to a featured cue and elaborate it using the logical operations of intersection and union. In addition, it was shown that in the absence of top-down guidance, the model is sensitive to bottom-up cues such as saliency and abrupt visual onset.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 5 26%
Student > Master 3 16%
Researcher 2 11%
Student > Bachelor 1 5%
Student > Ph. D. Student 1 5%
Other 1 5%
Unknown 6 32%
Readers by discipline Count As %
Psychology 4 21%
Engineering 2 11%
Neuroscience 2 11%
Computer Science 1 5%
Mathematics 1 5%
Other 2 11%
Unknown 7 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 13 April 2018.
All research outputs
#13,230,515
of 23,026,672 outputs
Outputs from Frontiers in Psychology
#12,407
of 30,283 outputs
Outputs of similar age
#163,194
of 329,889 outputs
Outputs of similar age from Frontiers in Psychology
#323
of 565 outputs
Altmetric has tracked 23,026,672 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 30,283 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.5. This one has gotten more attention than average, scoring higher than 58% 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 329,889 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 565 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.