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 the spread 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 use to make better predictions than others. Furthermore, factors underlying predictive judgments could be an important behavioral component in psychosocial research investigating manic-depression, anxiety, and age effects. Additionally, predictive judgments may be moderated based on the experience of living in areas with greater income inequality. To address these issues, we used investment tasks where participants had to predict future outcomes of their investments based on a trend in information. In the task, participants predicted how many tokens a gold mine would produce on the twelfth turn. On each turn, participants could ask for more information at a cost, or make a prediction about whether the gold mine would produce more or less than 100 tokens by the 12th turn. 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; 3 nonbinary; 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 communities. We outline potential ways to use and reuse this data, including exploring how individual differences are associated with predictive judgments.