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Natural Language Metaphors Covertly Influence Reasoning

Overview of attention for article published in PLOS ONE, January 2013
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Title
Natural Language Metaphors Covertly Influence Reasoning
Published in
PLOS ONE, January 2013
DOI 10.1371/journal.pone.0052961
Pubmed ID
Authors

Paul H. Thibodeau, Lera Boroditsky

Abstract

Metaphors pervade discussions of social issues like climate change, the economy, and crime. We ask how natural language metaphors shape the way people reason about such social issues. In previous work, we showed that describing crime metaphorically as a beast or a virus, led people to generate different solutions to a city's crime problem. In the current series of studies, instead of asking people to generate a solution on their own, we provided them with a selection of possible solutions and asked them to choose the best ones. We found that metaphors influenced people's reasoning even when they had a set of options available to compare and select among. These findings suggest that metaphors can influence not just what solution comes to mind first, but also which solution people think is best, even when given the opportunity to explicitly compare alternatives. Further, we tested whether participants were aware of the metaphor. We found that very few participants thought the metaphor played an important part in their decision. Further, participants who had no explicit memory of the metaphor were just as much affected by the metaphor as participants who were able to remember the metaphorical frame. These findings suggest that metaphors can act covertly in reasoning. Finally, we examined the role of political affiliation on reasoning about crime. The results confirm our previous findings that Republicans are more likely to generate enforcement and punishment solutions for dealing with crime, and are less swayed by metaphor than are Democrats or Independents.

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Mendeley readers

Mendeley readers

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Geographical breakdown

Country Count As %
United States 5 2%
Netherlands 3 1%
United Kingdom 2 <1%
Switzerland 1 <1%
Hong Kong 1 <1%
Portugal 1 <1%
Australia 1 <1%
Japan 1 <1%
Luxembourg 1 <1%
Other 0 0%
Unknown 220 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 54 23%
Student > Master 34 14%
Student > Bachelor 25 11%
Researcher 23 10%
Student > Doctoral Student 15 6%
Other 53 22%
Unknown 32 14%
Readers by discipline Count As %
Linguistics 43 18%
Psychology 41 17%
Social Sciences 33 14%
Computer Science 17 7%
Arts and Humanities 14 6%
Other 51 22%
Unknown 37 16%