Learned Uncertainty: A Free Energy Principle Perspective on Anxiety
Generalized Anxiety Disorder (GAD) is among the world’s most prevalent psychiatric disorders. Affecting an eighth of the world’s population, it often manifests as persistent apprehension which is difficult to control. Despite its prevalence, neuroscientific efforts to understand the cognitive mechanisms of GAD remain sparse. This has resulted in a fractured theoretical landscape, lacking a unitary framework. While prior theories of anxiety describe the cognitive, affective and behavioral dimensions of anxiety, a unified theory is lacking. Here, we point out that postulates derived from the Free Energy Principle (FEP) may allow for a unified theory to emerge. We argue an approach focused on predictive modelling may afford opportunities to re-conceptualize anxiety within the framework of working generative models, rather than static beliefs. We suggest that a biological system—having had persistent uncertainty in its past—will form posteriors in line with uncertainty in its future, irrespective of whether that uncertainty is real. After discussing the FEP, we explain how anxiety develops through learning uncertainty before suggesting predictions for how the model can be tested.