Using crowdsourcing to compare temporal, social temporal, and probability discounting among obese and non-obese individuals

•We recruited a large sample (n=1181) of participants using crowdsourcing technology.•We assessed discount rates for delayed, probabilistic, and social monetary rewards.•Delay and social discounting rates were higher in obese than non-obese individuals.•Probability discounting rates were comparable...

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Bibliographic Details
Published in:Appetite 2014-04, Vol.75, p.82-89
Main Authors: Bickel, Warren K., George Wilson, A., Franck, Christopher T., Terry Mueller, E., Jarmolowicz, David P., Koffarnus, Mikhail N., Fede, Samantha J.
Format: Article
Language:English
Subjects:
Adolescent
Adult
Aged
Aged, 80 and over
Body Mass Index
Choice Behavior
Crowdsourcing
Crowdsourcing - methods
Decision Making
Female
Humans
Logistic Models
Male
Middle Aged
Obesity
Obesity - metabolism
Probability
Probability discounting
Social Behavior
Social discounting
Surveys and Questionnaires
Temporal discounting
Young Adult
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Summary:•We recruited a large sample (n=1181) of participants using crowdsourcing technology.•We assessed discount rates for delayed, probabilistic, and social monetary rewards.•Delay and social discounting rates were higher in obese than non-obese individuals.•Probability discounting rates were comparable in obese than non-obese individuals. Previous research comparing obese and non-obese samples on the delayed discounting procedure has produced mixed results. The aim of the current study was to clarify these discrepant findings by comparing a variety of temporal discounting measures in a large sample of internet users (n=1163) obtained from a crowdsourcing service, Amazon Mechanical Turk (AMT). Measures of temporal, social–temporal (a combination of standard and social temporal), and probability discounting were obtained. Significant differences were obtained on all discounting measures except probability discounting, but the obtained effect sizes were small. These data suggest that larger-N studies will be more likely to detect differences between obese and non-obese samples, and may afford the opportunity, in future studies, to decompose a large obese sample into different subgroups to examine the effect of other relevant measures, such as the reinforcing value of food, on discounting.
ISSN:0195-6663
1095-8304
DOI:10.1016/j.appet.2013.12.018