PROJECT ABSTRACT Anxiety disorders are some of the most common and debilitating psychiatric diagnoses, affecting ~1 in 3 people worldwide. Despite the enormous impact on global well-being, key concepts that are critical to understanding these disorders remain poorly defined in the extant literature. Although heightened sensitivity to "uncertain threat anticipation" is regarded as a core contributor to anxiety and anxiety disorders and Threat Uncertainty is a core construct that cuts across models of anxiety in the NIMH RDoC framework, there is no ground-truth definition of "uncertainty". Previous studies have found that anticipation of temporally “uncertain” threats, as compared to temporally “certain” threats, are associated with increases self-reported anxiety, startle-responses, and brain activation in anxiety-related brain regions. In this proposal, we will test an alternate explanation for these results using a computational, statistical-learning approach. In particular, we suggest that lab-based assessments of uncertain anticipation have confounded “uncertainty” with alterations in the probability of a threat given that it has not already occurred, i.e. the hazard-rate. Here, building on our team’s expertise in anxiety and decision-making, we will test the hypothesis that it hazard-rate, and not uncertainty per se, accounts for increases in self-reported anxiety and persistence (Aim 1), as well as alterations in anxiety-related BOLD responses in anxiety-relevant brain regions (Aim 2). To this end, we have developed a novel paradigm that allows for a 2x2 design where we manipulate uncertainty (high/low) and hazard-rate (high-low). In contrast to existing theories, which predict a main effect of uncertainty, we predict a main effect of hazard-rate on all relevant measures. Relevant measures include self-reported anxiety, persistence in a threatening environment, and fMRI measures of brain activation. We will contrast this paradigm with previous paradigms that have confounded uncertainty and hazard-rate to provide a more precise interpretation of extant findings. This theory-driven computational approach to understanding uncertain anticipation has the potential to provide a “course correct” for the field, refine our understanding of anxiety, and inform the development of new treatments.