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Deciphering CAPTCHAs: What a Turing Test Reveals about Human Cognition

Overview of attention for article published in PLOS ONE, March 2012
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

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7 X users
reddit
1 Redditor

Citations

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22 Dimensions

Readers on

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41 Mendeley
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Title
Deciphering CAPTCHAs: What a Turing Test Reveals about Human Cognition
Published in
PLOS ONE, March 2012
DOI 10.1371/journal.pone.0032121
Pubmed ID
Authors

Thomas Hannagan, Maria Ktori, Myriam Chanceaux, Jonathan Grainger

Abstract

Turning Turing's logic on its head, we used widespread letter-based Turing Tests found on the internet (CAPTCHAs) to shed light on human cognition. We examined the basis of the human ability to solve CAPTCHAs, where machines fail. We asked whether this is due to our use of slow-acting inferential processes that would not be available to machines, or whether fast-acting automatic orthographic processing in humans has superior robustness to shape variations. A masked priming lexical decision experiment revealed efficient processing of CAPTCHA words in conditions that rule out the use of slow inferential processing. This shows that the human superiority in solving CAPTCHAs builds on a high degree of invariance to location and continuous transforms, which is achieved during the very early stages of visual word recognition in skilled readers.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 2%
Spain 1 2%
United States 1 2%
Unknown 38 93%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 15%
Researcher 5 12%
Student > Ph. D. Student 4 10%
Student > Bachelor 4 10%
Student > Doctoral Student 2 5%
Other 10 24%
Unknown 10 24%
Readers by discipline Count As %
Psychology 12 29%
Computer Science 4 10%
Neuroscience 3 7%
Engineering 2 5%
Social Sciences 2 5%
Other 7 17%
Unknown 11 27%
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 11 April 2012.
All research outputs
#6,605,634
of 24,584,609 outputs
Outputs from PLOS ONE
#88,086
of 212,330 outputs
Outputs of similar age
#41,298
of 159,454 outputs
Outputs of similar age from PLOS ONE
#1,033
of 3,550 outputs
Altmetric has tracked 24,584,609 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 212,330 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.6. 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 159,454 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 3,550 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 70% of its contemporaries.