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Gain and Loss Learning Differentially Contribute to Life Financial Outcomes

Overview of attention for article published in PLOS ONE, September 2011
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Title
Gain and Loss Learning Differentially Contribute to Life Financial Outcomes
Published in
PLOS ONE, September 2011
DOI 10.1371/journal.pone.0024390
Pubmed ID
Authors

Brian Knutson, Gregory R. Samanez-Larkin, Camelia M. Kuhnen

Abstract

Emerging findings imply that distinct neurobehavioral systems process gains and losses. This study investigated whether individual differences in gain learning and loss learning might contribute to different life financial outcomes (i.e., assets versus debt). In a community sample of healthy adults (nā€Š=ā€Š75), rapid learners had smaller debt-to-asset ratios overall. More specific analyses, however, revealed that those who learned rapidly about gains had more assets, while those who learned rapidly about losses had less debt. These distinct associations remained strong even after controlling for potential cognitive (e.g., intelligence, memory, and risk preferences) and socioeconomic (e.g., age, sex, ethnicity, income, education) confounds. Self-reported measures of assets and debt were additionally validated with credit report data in a subset of subjects. These findings support the notion that different gain and loss learning systems may exert a cumulative influence on distinct life financial outcomes.

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Mendeley readers

The data shown below were compiled from readership statistics for 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 5%
Germany 2 2%
Switzerland 1 <1%
Singapore 1 <1%
United Kingdom 1 <1%
Unknown 94 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 29%
Researcher 20 19%
Student > Master 11 11%
Student > Bachelor 7 7%
Professor > Associate Professor 6 6%
Other 18 17%
Unknown 12 12%
Readers by discipline Count As %
Psychology 43 41%
Neuroscience 13 13%
Economics, Econometrics and Finance 8 8%
Social Sciences 7 7%
Agricultural and Biological Sciences 6 6%
Other 15 14%
Unknown 12 12%