A goal of computational psychiatry is to ground symptoms in basic mechanisms. Theory suggests that avoidance in anxiety disorders may reflect dysregulated mental simulation, a process for evaluating candidate actions. If so, these covert processes should have observable consequences: choices reflecting increased and biased deliberation. In two online general population samples, we examined how self-report symptoms of social anxiety disorder predict choices in a socially framed reinforcement learning task, the patent race, in which the pattern of choices reflects the content of deliberation. Using a computational model to assess learning strategy, we found that self-report social anxiety was indeed associated with increased deliberative evaluation. This effect was stronger for a particular subset of feedback ('upward counterfactual') in one of the experiments, broadly matching the biased content of rumination in social anxiety disorder, and robust to controlling for other psychiatric symptoms. These results suggest a grounding of symptoms of social anxiety disorder in more basic neuro-computational mechanisms.
