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Paradoxical Evidence Integration in Rapid Decision Processes

Overview of attention for article published in PLoS Computational Biology, February 2012
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106 Mendeley
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Title
Paradoxical Evidence Integration in Rapid Decision Processes
Published in
PLoS Computational Biology, February 2012
DOI 10.1371/journal.pcbi.1002382
Pubmed ID
Authors

Johannes Rüter, Nicolas Marcille, Henning Sprekeler, Wulfram Gerstner, Michael H. Herzog

Abstract

Decisions about noisy stimuli require evidence integration over time. Traditionally, evidence integration and decision making are described as a one-stage process: a decision is made when evidence for the presence of a stimulus crosses a threshold. Here, we show that one-stage models cannot explain psychophysical experiments on feature fusion, where two visual stimuli are presented in rapid succession. Paradoxically, the second stimulus biases decisions more strongly than the first one, contrary to predictions of one-stage models and intuition. We present a two-stage model where sensory information is integrated and buffered before it is fed into a drift diffusion process. The model is tested in a series of psychophysical experiments and explains both accuracy and reaction time distributions.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 106 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 6%
Switzerland 4 4%
Netherlands 3 3%
France 2 2%
United Kingdom 2 2%
Malaysia 1 <1%
Portugal 1 <1%
Germany 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 84 79%

Demographic breakdown

Readers by professional status Count As %
Researcher 30 28%
Student > Ph. D. Student 27 25%
Student > Master 9 8%
Professor > Associate Professor 8 8%
Student > Bachelor 7 7%
Other 19 18%
Unknown 6 6%
Readers by discipline Count As %
Psychology 30 28%
Agricultural and Biological Sciences 22 21%
Neuroscience 17 16%
Computer Science 8 8%
Engineering 6 6%
Other 14 13%
Unknown 9 8%