Extreme fear and anxiety are leading causes of human misery and morbidity, but the underlying mechanisms remain poorly understood. Uncertainty plays a central role in theoretical models of fear and anxiety, but which kind of uncertainty is most important? After all, science and engineering recognize 2 mathematically distinct kinds of uncertainty: Risk and Ambiguity. Which of these is more relevant to threat reactivity and how they map onto the underlying neural circuits is unknown. To address these fundamental questions, we will recruit a racially diverse sample enriched for elevated fear/anxiety symptoms. Using techniques adapted from neuroeconomics, a parametric threat-anticipation paradigm will allow us to simultaneously probe circuits sensitive to categorical and dimensional variation in threat uncertainty for the first time. Smartphone phenotyping will assess real-world threat exposure, uncertainty, and distress.

This project will provide an exciting opportunity to develop one of the first computationally grounded models of fear and anxiety in a relatively large and diverse “DMV” (DC, MD, & VA) sample. It has the potential to resolve ongoing theoretical debates, validate a new conceptual approach for use with other read-outs and species, set the stage for new kinds of translational models and clinical studies, and prioritize new targets for therapeutics development.

This project represents a team-science collaboration between the University of Maryland (Drs. Alex Shackman & Jason Smith) and the University of California-Davis (Drs. Andrew Fox & Erie Boorman).

Click here if you are interested in participating in this study.