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Human Decision Making Based on Variations in Internal Noise: An EEG Study

Overview of attention for article published in PLOS ONE, July 2013
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Title
Human Decision Making Based on Variations in Internal Noise: An EEG Study
Published in
PLOS ONE, July 2013
DOI 10.1371/journal.pone.0068928
Pubmed ID
Authors

Sygal Amitay, Jeanne Guiraud, Ediz Sohoglu, Oliver Zobay, Barrie A. Edmonds, Yu-Xuan Zhang, David R. Moore

Abstract

Perceptual decision making is prone to errors, especially near threshold. Physiological, behavioural and modeling studies suggest this is due to the intrinsic or 'internal' noise in neural systems, which derives from a mixture of bottom-up and top-down sources. We show here that internal noise can form the basis of perceptual decision making when the external signal lacks the required information for the decision. We recorded electroencephalographic (EEG) activity in listeners attempting to discriminate between identical tones. Since the acoustic signal was constant, bottom-up and top-down influences were under experimental control. We found that early cortical responses to the identical stimuli varied in global field power and topography according to the perceptual decision made, and activity preceding stimulus presentation could predict both later activity and behavioural decision. Our results suggest that activity variations induced by internal noise of both sensory and cognitive origin are sufficient to drive discrimination judgments.

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The data shown below were compiled from readership statistics for 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 7%
Switzerland 1 1%
Hungary 1 1%
Australia 1 1%
France 1 1%
Unknown 77 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 34%
Researcher 15 17%
Student > Master 8 9%
Student > Doctoral Student 5 6%
Other 5 6%
Other 13 15%
Unknown 11 13%
Readers by discipline Count As %
Psychology 24 28%
Neuroscience 12 14%
Agricultural and Biological Sciences 8 9%
Engineering 7 8%
Linguistics 3 3%
Other 18 21%
Unknown 15 17%