A behavioral dataset of predictive decisions given trends in information across adulthood

Making early and good predictions is a critical feature of decision making in domains such as investing and predicting spreads of diseases. Past literature indicates that people use recent and longer-term trends to extrapolate future outcomes. Nonetheless, less is known about what differentiates the strategies people at making better predictions than others. Further, identifying factors underlying predictive judgments could be an important behavioral factor in psychosocial research such as manic-depression, anxiety, age effects, and understanding how living in areas with greater income inequality can affect decision making. To address these issues, we used investment tasks where participants had to predict future outcomes of their investments based on a trend in information. We used a task where a participant predicts how many tokens a gold mine will produce on the twelfth turn. On each turn, participants selected whether they judged the gold mine would produce more or less than 100 tokens by the 12th turn, or if they wanted more information. The trend was determined by function type (exponential and inverse exponential functions), whether the function was more linear or curved (growth factors), and good or bad outcomes (final values). This paradigm could help disentangle to what degree people use recent or longer-term information to inform their predictive judgments. We used Qualtrics to conduct this study. We also collected questionnaire data quantifying anxiety, impulsivity, risk attitudes, manic-depressive symptoms, and other psychosocial characteristics. The study was administered to adults with age ranges across the lifespan (N=360; 225 male, 132 female; nonbinary: 3; mean age: 44.3 years; sd: 15.4 years, min: 18 years, max: 78 years). Additionally, we sampled across areas with high- and low-income inequality, thereby allowing researchers to investigate if value-based decisions are associated with participants’ local community. We outline potential ways to use and reuse this data, including exploring how individual differences are associated with predictive judgments.