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A Model of Binocular Rivalry and Cross-orientation Suppression

Overview of attention for article published in PLoS Computational Biology, March 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

blogs
1 blog
twitter
2 X users
peer_reviews
1 peer review site
facebook
1 Facebook page

Citations

dimensions_citation
100 Dimensions

Readers on

mendeley
159 Mendeley
citeulike
1 CiteULike
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Title
A Model of Binocular Rivalry and Cross-orientation Suppression
Published in
PLoS Computational Biology, March 2013
DOI 10.1371/journal.pcbi.1002991
Pubmed ID
Authors

Christopher P. Said, David J. Heeger

Abstract

Binocular rivalry and cross-orientation suppression are well-studied forms of competition in visual cortex, but models of these two types of competition are in tension with one another. Binocular rivalry occurs during the presentation of dichoptic grating stimuli, where two orthogonal gratings presented separately to the two eyes evoke strong alternations in perceptual dominance. Cross-orientation suppression occurs during the presentation of plaid stimuli, where the responses to a component grating presented to both eyes is weakened by the presence of a superimposed orthogonal grating. Conventional models of rivalry that rely on strong competition between orientation-selective neurons incorrectly predict rivalry between the components of plaids. Lowering the inhibitory weights in such models reduces rivalry for plaids, but also reduces it for dichoptic gratings. Using an exhaustive grid search, we show that this problem cannot be solved simply by adjusting the parameters of the model. Instead, we propose a robust class of models that rely on ocular opponency neurons, previously proposed as a mechanism for efficient stereo coding, to yield rivalry only for dichoptic gratings, not for plaids. This class of models reconciles models of binocular rivalry with the divisive normalization framework that has been used to explain cross-orientation. Our model makes novel predictions that we confirmed with psychophysical tests.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 159 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 3 2%
Australia 2 1%
United States 2 1%
China 1 <1%
Netherlands 1 <1%
Unknown 150 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 35 22%
Student > Ph. D. Student 32 20%
Student > Master 21 13%
Student > Bachelor 11 7%
Professor 6 4%
Other 22 14%
Unknown 32 20%
Readers by discipline Count As %
Psychology 40 25%
Neuroscience 36 23%
Agricultural and Biological Sciences 11 7%
Engineering 8 5%
Computer Science 5 3%
Other 19 12%
Unknown 40 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 07 August 2015.
All research outputs
#3,406,730
of 25,373,627 outputs
Outputs from PLoS Computational Biology
#3,011
of 8,960 outputs
Outputs of similar age
#27,740
of 210,248 outputs
Outputs of similar age from PLoS Computational Biology
#32
of 152 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,960 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 66% 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 210,248 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 152 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.