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.

计算精神病学的一个目标是将症状建立在基本机制的基础上。理论表明，焦虑症患者的回避可能反映了心理模拟失调，这是评估候选人行为的过程。如果是这样，这些秘密过程应该会产生明显的后果：反映更多和有偏见的审议的选择。在两个在线普通人群样本中，我们研究了社交焦虑症的自我报告症状如何预测社会框架强化学习任务（专利竞赛）中的选择，其中选择模式反映了审议的内容。使用计算模型来评估学习策略，我们发现自我报告的社交焦虑确实与慎重评估的增加有关。在其中一项实验中，这种效应对于反馈的特定子集（“向上反事实”）更强，与社交焦虑症中沉思的偏见内容大致匹配，并且对于控制其他精神症状也很有效。这些结果表明社交焦虑障碍的症状有更基本的神经计算机制的基础。