Pathological aversive learning as a mechanism in clinical anxiety

Anxiety is the most common mental health disorder, affecting 30% of people at some point in their life. Despite this, the disorder remains poorly understood. Major symptoms of anxiety disorders relate to uncertainty – people with clinical anxiety feel uncertain about the possibility of threatening events occurring and this uncertainty leads to feelings of anxiety.

I will use methods from computational neuroscience to understand the ways in which people judge how certain they are about negative events in their environment, and why the process of judging uncertainty goes awry with clinical anxiety. I will build computational models of how people process uncertainty and explore how altering the working of these models captures how people with anxiety disorders behave under uncertainty in threatening situations. I will then examine ways in which the brain implements these models of uncertainty and how brain function contributes to this process becoming dysfunctional in people with clinical anxiety.

This approach can pave the way for research into targeting core abnormalities that offer potential treatment avenues for anxiety disorders.