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The Automatic Conservative: Ideology-Based Attentional Asymmetries in the Processing of Valenced Information

Overview of attention for article published in PLOS ONE, November 2011
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Title
The Automatic Conservative: Ideology-Based Attentional Asymmetries in the Processing of Valenced Information
Published in
PLOS ONE, November 2011
DOI 10.1371/journal.pone.0026456
Pubmed ID
Authors

Luciana Carraro, Luigi Castelli, Claudia Macchiella

Abstract

Research has widely explored the differences between conservatives and liberals, and it has been also recently demonstrated that conservatives display different reactions toward valenced stimuli. However, previous studies have not yet fully illuminated the cognitive underpinnings of these differences. In the current work, we argued that political ideology is related to selective attention processes, so that negative stimuli are more likely to automatically grab the attention of conservatives as compared to liberals. In Experiment 1, we demonstrated that negative (vs. positive) information impaired the performance of conservatives, more than liberals, in an Emotional Stroop Task. This finding was confirmed in Experiment 2 and in Experiment 3 employing a Dot-Probe Task, demonstrating that threatening stimuli were more likely to attract the attention of conservatives. Overall, results support the conclusion that people embracing conservative views of the world display an automatic selective attention for negative stimuli.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 142 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 4%
United Kingdom 3 2%
Italy 1 <1%
Sweden 1 <1%
India 1 <1%
Netherlands 1 <1%
Canada 1 <1%
Chile 1 <1%
New Zealand 1 <1%
Other 1 <1%
Unknown 126 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 21%
Researcher 20 14%
Student > Master 20 14%
Student > Bachelor 12 8%
Student > Doctoral Student 9 6%
Other 26 18%
Unknown 25 18%
Readers by discipline Count As %
Psychology 61 43%
Social Sciences 17 12%
Business, Management and Accounting 7 5%
Neuroscience 7 5%
Computer Science 4 3%
Other 15 11%
Unknown 31 22%