Abstract
Intolerance of uncertainty (IU) is the tendency to react negatively to uncertainty across emotional, cognitive, and behavioral domains. Research on IU is limited from a conceptual and measurement perspective. The current study compares a self-report measure of IU to task-based computational indices. We relied on ecological momentary assessment (EMA) to assess whether clinical and computational measures of IU predicted daily negative affect and avoidance, and examined whether IU contributed to anxiety one month later. A diverse sample of community adults (N = 236) completed a self-report measure of IU and the Risk and Ambiguity Task, a computerized monetary decision-making task that captures gain and loss decisions under uncertainty. Computational modeling was used to estimate risk and ambiguity aversion for both gains and losses. Participants then reported momentary affect and avoidance via EMA over 21 days, followed by a semi-structured diagnostic interview one month later. Greater self-reported IU was significantly associated with greater risk aversion for both gains and losses, as well as lower ambiguity aversion for gains. Greater self-reported IU predicted higher levels of daily negative affect and avoidance, whereas greater ambiguity aversion for gains predicted lower daily avoidance. Self-reported IU predicted both self-reported and interview-based anxiety, but only greater risk aversion for losses was associated with greater interview-based anxiety. Negative affect, but not avoidance, mediated the link between self-reported IU and later anxiety. Findings suggest people with higher self-reported IU were more likely to make fewer risky decisions in a monetary gambling task. This relationship was reversed for ambiguity aversion. Our findings also suggest that a person’s perception of their uncertainty tolerance rather than their behavioral task performance is more predictive of anxiety. Daily negative affect may be a key mechanism linking IU to anxiety. This study advances our understanding of IU by integrating clinical and computational assessments.