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Biosensor Approach to Psychopathology Classification

Overview of attention for article published in PLoS Computational Biology, October 2010
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  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

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1 Wikipedia page

Citations

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25 Dimensions

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119 Mendeley
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Title
Biosensor Approach to Psychopathology Classification
Published in
PLoS Computational Biology, October 2010
DOI 10.1371/journal.pcbi.1000966
Pubmed ID
Authors

Misha Koshelev, Terry Lohrenz, Marina Vannucci, P. Read Montague

Abstract

We used a multi-round, two-party exchange game in which a healthy subject played a subject diagnosed with a DSM-IV (Diagnostic and Statistics Manual-IV) disorder, and applied a Bayesian clustering approach to the behavior exhibited by the healthy subject. The goal was to characterize quantitatively the style of play elicited in the healthy subject (the proposer) by their DSM-diagnosed partner (the responder). The approach exploits the dynamics of the behavior elicited in the healthy proposer as a biosensor for cognitive features that characterize the psychopathology group at the other side of the interaction. Using a large cohort of subjects (n = 574), we found statistically significant clustering of proposers' behavior overlapping with a range of DSM-IV disorders including autism spectrum disorder, borderline personality disorder, attention deficit hyperactivity disorder, and major depressive disorder. To further validate these results, we developed a computer agent to replace the human subject in the proposer role (the biosensor) and show that it can also detect these same four DSM-defined disorders. These results suggest that the highly developed social sensitivities that humans bring to a two-party social exchange can be exploited and automated to detect important psychopathologies, using an interpersonal behavioral probe not directly related to the defining diagnostic criteria.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 119 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Germany 2 2%
Austria 1 <1%
Portugal 1 <1%
United Kingdom 1 <1%
Lithuania 1 <1%
Spain 1 <1%
Luxembourg 1 <1%
Unknown 108 91%

Demographic breakdown

Readers by professional status Count As %
Student > Master 21 18%
Student > Ph. D. Student 20 17%
Researcher 18 15%
Student > Doctoral Student 8 7%
Student > Bachelor 8 7%
Other 23 19%
Unknown 21 18%
Readers by discipline Count As %
Psychology 35 29%
Medicine and Dentistry 16 13%
Agricultural and Biological Sciences 12 10%
Computer Science 11 9%
Neuroscience 6 5%
Other 13 11%
Unknown 26 22%
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 10 May 2017.
All research outputs
#6,760,834
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#4,586
of 8,964 outputs
Outputs of similar age
#32,968
of 108,623 outputs
Outputs of similar age from PLoS Computational Biology
#28
of 62 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 8,964 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 48th percentile – i.e., 48% 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 108,623 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 69% of its contemporaries.
We're also able to compare this research output to 62 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 53% of its contemporaries.