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A Bayesian framework for simultaneously modeling neural and behavioral data

Overview of attention for article published in NeuroImage, January 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 (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

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19 X users
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2 patents

Citations

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145 Dimensions

Readers on

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328 Mendeley
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1 CiteULike
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Title
A Bayesian framework for simultaneously modeling neural and behavioral data
Published in
NeuroImage, January 2013
DOI 10.1016/j.neuroimage.2013.01.048
Pubmed ID
Authors

Brandon M. Turner, Birte U. Forstmann, Eric-Jan Wagenmakers, Scott D. Brown, Per B. Sederberg, Mark Steyvers

Abstract

Scientists who study cognition infer underlying processes either by observing behavior (e.g., response times, percentage correct) or by observing neural activity (e.g., the BOLD response). These two types of observations have traditionally supported two separate lines of study. The first is led by cognitive modelers, who rely on behavior alone to support their computational theories. The second is led by cognitive neuroimagers, who rely on statistical models to link patterns of neural activity to experimental manipulations, often without any attempt to make a direct connection to an explicit computational theory. Here we present a flexible Bayesian framework for combining neural and cognitive models. Joining neuroimaging and computational modeling in a single hierarchical framework allows the neural data to influence the parameters of the cognitive model and allows behavioral data, even in the absence of neural data, to constrain the neural model. Critically, our Bayesian approach can reveal interactions between behavioral and neural parameters, and hence between neural activity and cognitive mechanisms. We demonstrate the utility of our approach with applications to simulated fMRI data with a recognition model and to diffusion-weighted imaging data with a response time model of perceptual choice.

X Demographics

X Demographics

The data shown below were collected from the profiles of 19 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 328 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 11 3%
Germany 3 <1%
Switzerland 2 <1%
Netherlands 2 <1%
France 2 <1%
Brazil 2 <1%
United Kingdom 2 <1%
Korea, Republic of 1 <1%
Portugal 1 <1%
Other 2 <1%
Unknown 300 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 104 32%
Researcher 61 19%
Student > Master 33 10%
Professor > Associate Professor 22 7%
Student > Bachelor 20 6%
Other 59 18%
Unknown 29 9%
Readers by discipline Count As %
Psychology 132 40%
Neuroscience 54 16%
Agricultural and Biological Sciences 23 7%
Engineering 18 5%
Computer Science 9 3%
Other 33 10%
Unknown 59 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 22 November 2022.
All research outputs
#2,297,359
of 25,374,647 outputs
Outputs from NeuroImage
#1,787
of 12,205 outputs
Outputs of similar age
#22,145
of 290,065 outputs
Outputs of similar age from NeuroImage
#14
of 134 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 12,205 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.6. This one has done well, scoring higher than 85% 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 290,065 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 134 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.