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Time-varying boundaries for diffusion models of decision making and response time.

Overview of attention for article published in Frontiers in Psychology
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47 Mendeley
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Title
Time-varying boundaries for diffusion models of decision making and response time.
Published in
Frontiers in Psychology
DOI 10.3389/fpsyg.2014.01364
Pubmed ID
Authors

Shunan Zhang, Michael D Lee, Joachim Vandekerckhove, Gunter Maris, Eric-Jan Wagenmakers

Abstract

Diffusion models are widely-used and successful accounts of the time course of two-choice decision making. Most diffusion models assume constant boundaries, which are the threshold levels of evidence that must be sampled from a stimulus to reach a decision. We summarize theoretical results from statistics that relate distributions of decisions and response times to diffusion models with time-varying boundaries. We then develop a computational method for finding time-varying boundaries from empirical data, and apply our new method to two problems. The first problem involves finding the time-varying boundaries that make diffusion models equivalent to the alternative sequential sampling class of accumulator models. The second problem involves finding the time-varying boundaries, at the individual level, that best fit empirical data for perceptual stimuli that provide equal evidence for both decision alternatives. We discuss the theoretical and modeling implications of using time-varying boundaries in diffusion models, as well as the limitations and potential of our approach to their inference.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 3 6%
United States 3 6%
Spain 1 2%
Unknown 40 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 23%
Student > Ph. D. Student 10 21%
Student > Master 9 19%
Student > Postgraduate 4 9%
Student > Doctoral Student 3 6%
Other 10 21%
Readers by discipline Count As %
Psychology 30 64%
Neuroscience 6 13%
Unspecified 4 9%
Physics and Astronomy 2 4%
Linguistics 1 2%
Other 4 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 05 February 2015.
All research outputs
#2,510,107
of 4,720,494 outputs
Outputs from Frontiers in Psychology
#3,273
of 4,712 outputs
Outputs of similar age
#87,001
of 164,605 outputs
Outputs of similar age from Frontiers in Psychology
#298
of 404 outputs
Altmetric has tracked 4,720,494 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,712 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.1. This one is in the 26th percentile – i.e., 26% 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 164,605 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 404 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.