Low socioeconomic status (low SES), as defined by educational attainment and income, has been associated with obesity in industrialized nations. Low SES has been related to impulsive decision-making characterized by steep devaluation of future outcomes (delay discounting, DD) and a greater motivation to obtain food (food reinforcement, FR). DD and FR have been shown to interactively predict ad libitum energy intake in laboratory studies, such that participants exhibiting high FR as well as high DD consume the most food. Obesity often results from energy intake in excess of physiologic needs, and thus a plausible mechanism is that low SES environments increase FR and DD, leading to overeating and obesity. The primary aims of this study are to determine whether FR and DD interact to predict BMI, and whether differences in FR and DD mediate the relationship between low SES and obesity, in a large sample of US adults of varying BMI. In addition, whether high FR and high DD are related to unhealthy food purchasing and eating behaviors will be assessed. It is anticipated that high FR and DD will be related to unhealthy food purchasing and eating behaviors.

In this study potential participants who log in to the Amazon Mechanical Turk website as a worker and who have a HIT approval rate of at least 90% will be allowed to view and participate in the study. If a potential participant agrees to participate in the study by accepting the HIT, the participant will be asked to complete a brief survey (approximately 150 questions; see sample survey in "other supporting documents") and various decision making tasks (e.g., delay discounting assessment, reinforcer valuation assessment etc.). This survey and accompanying tasks will solicit: (a) general demographic information such as gender, age, income, and education; (b) information about their health and health behavior practices, such as eating habits, alcohol and cigarette use; (c) questions about their food preferences and value of foods; (d) information about their valuation of immediate versus distant events/rewards.

Inclusion Criteria:

• Be a registered Amazon Mechanical Turk user.

Amazon Mechanical Turk users