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The Influence of Spatiotemporal Structure of Noisy Stimuli in Decision Making

Overview of attention for article published in PLoS Computational Biology, April 2014
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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 (91st percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

blogs
2 blogs
twitter
7 X users

Citations

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13 Dimensions

Readers on

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99 Mendeley
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2 CiteULike
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Title
The Influence of Spatiotemporal Structure of Noisy Stimuli in Decision Making
Published in
PLoS Computational Biology, April 2014
DOI 10.1371/journal.pcbi.1003492
Pubmed ID
Authors

Andrea Insabato, Laura Dempere-Marco, Mario Pannunzi, Gustavo Deco, Ranulfo Romo

Abstract

Decision making is a process of utmost importance in our daily lives, the study of which has been receiving notable attention for decades. Nevertheless, the neural mechanisms underlying decision making are still not fully understood. Computational modeling has revealed itself as a valuable asset to address some of the fundamental questions. Biophysically plausible models, in particular, are useful in bridging the different levels of description that experimental studies provide, from the neural spiking activity recorded at the cellular level to the performance reported at the behavioral level. In this article, we have reviewed some of the recent progress made in the understanding of the neural mechanisms that underlie decision making. We have performed a critical evaluation of the available results and address, from a computational perspective, aspects of both experimentation and modeling that so far have eluded comprehension. To guide the discussion, we have selected a central theme which revolves around the following question: how does the spatiotemporal structure of sensory stimuli affect the perceptual decision-making process? This question is a timely one as several issues that still remain unresolved stem from this central theme. These include: (i) the role of spatiotemporal input fluctuations in perceptual decision making, (ii) how to extend the current results and models derived from two-alternative choice studies to scenarios with multiple competing evidences, and (iii) to establish whether different types of spatiotemporal input fluctuations affect decision-making outcomes in distinctive ways. And although we have restricted our discussion mostly to visual decisions, our main conclusions are arguably generalizable; hence, their possible extension to other sensory modalities is one of the points in our discussion.

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 99 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 4 4%
Spain 2 2%
United Kingdom 2 2%
Japan 2 2%
Italy 1 1%
United States 1 1%
Unknown 87 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 38 38%
Student > Ph. D. Student 20 20%
Student > Master 12 12%
Student > Bachelor 7 7%
Professor 4 4%
Other 12 12%
Unknown 6 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 25%
Neuroscience 19 19%
Psychology 13 13%
Computer Science 7 7%
Medicine and Dentistry 5 5%
Other 18 18%
Unknown 12 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 May 2014.
All research outputs
#2,130,684
of 25,374,917 outputs
Outputs from PLoS Computational Biology
#1,885
of 8,960 outputs
Outputs of similar age
#20,922
of 238,623 outputs
Outputs of similar age from PLoS Computational Biology
#36
of 148 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has done well, scoring higher than 78% 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 238,623 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 91% of its contemporaries.
We're also able to compare this research output to 148 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.