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Human Wagering Behavior Depends on Opponents' Faces

Overview of attention for article published in PLoS ONE, July 2010
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

blogs
5 blogs
twitter
4 tweeters
facebook
1 Facebook page
wikipedia
1 Wikipedia page
googleplus
1 Google+ user

Readers on

mendeley
102 Mendeley
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Title
Human Wagering Behavior Depends on Opponents' Faces
Published in
PLoS ONE, July 2010
DOI 10.1371/journal.pone.0011663
Pubmed ID
Authors

Erik J. Schlicht, Shinsuke Shimojo, Colin F. Camerer, Peter Battaglia, Ken Nakayama

Abstract

Research in competitive games has exclusively focused on how opponent models are developed through previous outcomes and how peoples' decisions relate to normative predictions. Little is known about how rapid impressions of opponents operate and influence behavior in competitive economic situations, although such subjective impressions have been shown to influence cooperative decision-making. This study investigates whether an opponent's face influences players' wagering decisions in a zero-sum game with hidden information. Participants made risky choices in a simplified poker task while being presented opponents whose faces differentially correlated with subjective impressions of trust. Surprisingly, we find that threatening face information has little influence on wagering behavior, but faces relaying positive emotional characteristics impact peoples' decisions. Thus, people took significantly longer and made more mistakes against emotionally positive opponents. Differences in reaction times and percent correct were greatest around the optimal decision boundary, indicating that face information is predominantly used when making decisions during medium-value gambles. Mistakes against emotionally positive opponents resulted from increased folding rates, suggesting that participants may have believed that these opponents were betting with hands of greater value than other opponents. According to these results, the best "poker face" for bluffing may not be a neutral face, but rather a face that contains emotional correlates of trustworthiness. Moreover, it suggests that rapid impressions of an opponent play an important role in competitive games, especially when people have little or no experience with an opponent.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 7 7%
United States 6 6%
Canada 2 2%
Germany 1 <1%
Portugal 1 <1%
Switzerland 1 <1%
Chile 1 <1%
France 1 <1%
Italy 1 <1%
Other 3 3%
Unknown 78 76%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 33 32%
Researcher 21 21%
Student > Master 10 10%
Student > Bachelor 6 6%
Professor 5 5%
Other 27 26%
Readers by discipline Count As %
Psychology 56 55%
Unspecified 11 11%
Agricultural and Biological Sciences 11 11%
Medicine and Dentistry 7 7%
Business, Management and Accounting 4 4%
Other 13 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 49. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 June 2013.
All research outputs
#203,024
of 8,544,256 outputs
Outputs from PLoS ONE
#4,906
of 117,423 outputs
Outputs of similar age
#199,578
of 7,938,434 outputs
Outputs of similar age from PLoS ONE
#4,879
of 113,571 outputs
Altmetric has tracked 8,544,256 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 117,423 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one has done particularly well, scoring higher than 95% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 7,938,434 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 113,571 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.