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Human Visual Search Does Not Maximize the Post-Saccadic Probability of Identifying Targets

Overview of attention for article published in PLoS Computational Biology, February 2012
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3 X users

Citations

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59 Dimensions

Readers on

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82 Mendeley
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3 CiteULike
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Title
Human Visual Search Does Not Maximize the Post-Saccadic Probability of Identifying Targets
Published in
PLoS Computational Biology, February 2012
DOI 10.1371/journal.pcbi.1002342
Pubmed ID
Authors

Camille Morvan, Laurence T. Maloney

Abstract

Researchers have conjectured that eye movements during visual search are selected to minimize the number of saccades. The optimal Bayesian eye movement strategy minimizing saccades does not simply direct the eye to whichever location is judged most likely to contain the target but makes use of the entire retina as an information gathering device during each fixation. Here we show that human observers do not minimize the expected number of saccades in planning saccades in a simple visual search task composed of three tokens. In this task, the optimal eye movement strategy varied, depending on the spacing between tokens (in the first experiment) or the size of tokens (in the second experiment), and changed abruptly once the separation or size surpassed a critical value. None of our observers changed strategy as a function of separation or size. Human performance fell far short of ideal, both qualitatively and quantitatively.

X Demographics

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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 6 7%
United States 3 4%
United Kingdom 2 2%
Brazil 1 1%
Hungary 1 1%
Switzerland 1 1%
Czechia 1 1%
Unknown 67 82%

Demographic breakdown

Readers by professional status Count As %
Student > Master 18 22%
Researcher 16 20%
Student > Ph. D. Student 14 17%
Professor 6 7%
Student > Bachelor 5 6%
Other 15 18%
Unknown 8 10%
Readers by discipline Count As %
Psychology 26 32%
Agricultural and Biological Sciences 11 13%
Neuroscience 9 11%
Computer Science 8 10%
Engineering 5 6%
Other 12 15%
Unknown 11 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 May 2014.
All research outputs
#16,720,137
of 25,371,288 outputs
Outputs from PLoS Computational Biology
#7,218
of 8,958 outputs
Outputs of similar age
#169,129
of 253,553 outputs
Outputs of similar age from PLoS Computational Biology
#73
of 122 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,958 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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 253,553 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 122 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.