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Gratitude Depends on the Relational Model of Communal Sharing

Overview of attention for article published in PLOS ONE, January 2014
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Title
Gratitude Depends on the Relational Model of Communal Sharing
Published in
PLOS ONE, January 2014
DOI 10.1371/journal.pone.0086158
Pubmed ID
Authors

Cláudia Simão, Beate Seibt

Abstract

We studied the relation between benefits, perception of social relationships and gratitude. Across three studies, we provide evidence that benefits increase gratitude to the extent to which one applies a mental model of a communal relationship. In Study 1, the communal sharing relational model, and no other relational models, predicted the amount of gratitude participants felt after imagining receiving a benefit from a new acquaintance. In Study 2, participants recalled a large benefit they had received. Applying a communal sharing relational model increased feelings of gratitude for the benefit. In Study 3, we manipulated whether the participant or another person received a benefit from an unknown other. Again, we found that the extent of communal sharing perceived in the relationship with the stranger predicted gratitude. An additional finding of Study 2 was that communal sharing predicted future gratitude regarding the relational partner in a longitudinal design. To conclude, applying a communal sharing model predicts gratitude regarding concrete benefits and regarding the relational partner, presumably because one perceives the communal partner as motivated to meet one's needs. Finally, in Study 3, we found in addition that being the recipient of a benefit without opportunity to repay directly increased communal sharing, and indirectly increased gratitude. These circumstances thus seem to favor the attribution of communal norms, leading to a communal sharing representation and in turn to gratitude. We discuss the importance of relational models as mental representations of relationships for feelings of gratitude.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 2%
Unknown 51 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 23%
Student > Bachelor 8 15%
Student > Master 8 15%
Student > Doctoral Student 3 6%
Researcher 3 6%
Other 7 13%
Unknown 11 21%
Readers by discipline Count As %
Psychology 31 60%
Medicine and Dentistry 4 8%
Neuroscience 2 4%
Mathematics 1 2%
Nursing and Health Professions 1 2%
Other 3 6%
Unknown 10 19%