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Emotional States Modulate the Recognition Potential during Word Processing

Overview of attention for article published in PLOS ONE, October 2012
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Title
Emotional States Modulate the Recognition Potential during Word Processing
Published in
PLOS ONE, October 2012
DOI 10.1371/journal.pone.0047083
Pubmed ID
Authors

Taomei Guo, Min Chen, Danling Peng

Abstract

This study examined emotional modulation of word processing, showing that the recognition potential (RP), an ERP index of word recognition, could be modulated by different emotional states. In the experiment, participants were instructed to compete with pseudo-competitors, and via manipulation of the outcome of this competition, they were situated in neutral, highly positive, slightly positive, highly negative or slightly negative emotional states. They were subsequently asked to judge whether the referent of a word following a series of meaningless character segmentations was an animal or not. The emotional induction task and the word recognition task were alternated. Results showed that 1) compared with the neutral emotion condition, the peak latency of the RP under different emotional states was earlier and its mean amplitude was smaller, 2) there was no significant difference between RPs elicited under positive and negative emotional states in either the mean amplitude or latency, and 3) the RP was not affected by different degrees of positive emotional states. However, compared to slightly negative emotional states, the mean amplitude of the RP was smaller and its latency was shorter in highly negative emotional states over the left hemisphere but not over the right hemisphere. The results suggest that emotional states influence word processing.

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Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 21%
Student > Ph. D. Student 6 21%
Student > Master 4 14%
Student > Doctoral Student 2 7%
Other 1 4%
Other 2 7%
Unknown 7 25%
Readers by discipline Count As %
Psychology 9 32%
Engineering 3 11%
Arts and Humanities 2 7%
Medicine and Dentistry 2 7%
Social Sciences 2 7%
Other 3 11%
Unknown 7 25%