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Electrophysiological Indices of Response Inhibition in a Go/NoGo Task Predict Self-Control in a Social Context

Overview of attention for article published in PLOS ONE, November 2013
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Title
Electrophysiological Indices of Response Inhibition in a Go/NoGo Task Predict Self-Control in a Social Context
Published in
PLOS ONE, November 2013
DOI 10.1371/journal.pone.0079462
Pubmed ID
Authors

Kyle Nash, Bastian Schiller, Lorena R. R. Gianotti, Thomas Baumgartner, Daria Knoch

Abstract

Recent research demonstrates that response inhibition-a core executive function-may subserve self-regulation and self-control. However, it is unclear whether response inhibition also predicts self-control in the multifaceted, high-level phenomena of social decision-making. Here we examined whether electrophysiological indices of response inhibition would predict self-control in a social context. Electroencephalography was recorded as participants completed a widely used Go/NoGo task (the cued Continuous Performance Test). Participants then interacted with a partner in an economic exchange game that requires self-control. Results demonstrated that greater NoGo-Anteriorization and larger NoGo-P300 peak amplitudes-two established electrophysiological indices of response inhibition-both predicted more self-control in this social game. These findings support continued integration of executive function and self-regulation and help extend prior research into social decision-making processes.

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Mendeley readers

The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Israel 1 1%
United States 1 1%
Germany 1 1%
France 1 1%
Unknown 82 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 23%
Student > Master 15 17%
Researcher 15 17%
Student > Bachelor 9 10%
Student > Doctoral Student 7 8%
Other 8 9%
Unknown 12 14%
Readers by discipline Count As %
Psychology 39 45%
Neuroscience 10 12%
Medicine and Dentistry 4 5%
Computer Science 3 3%
Social Sciences 3 3%
Other 6 7%
Unknown 21 24%