Anxiety disorders are the most common mental disorders and are often chronic and disabling. Although exposure-based treatments are effective, a substantial number of individuals fail to fully remit or experience a return of symptoms after treatment. Understanding the critical processes underlying the development and treatment of anxiety disorders will help identify individuals at risk and optimize treatments. Aversive associative learning offers explanatory pathways through which fear and anxiety emerge, spread, persist, and resurge. This narrative review examines the advances made in our understanding of associative fear and avoidance learning in anxiety disorders. Overall, the extant literature supports a key role of aversive associative learning in the development and treatment of anxiety disorders. However, research targeting specific mechanisms such as extinction generalization and avoidance, the fragility of extinction, and moderating influences of individual differences pertinent to anxiety disorders (e.g., age, sex, depression) is needed. We discuss the need for more ecological valid and complex paradigms to model ambiguity and conflict as well as for clinical translation studies to optimize treatment.

焦虑症是最常见的精神障碍，通常是慢性和致残性的。尽管基于暴露的治疗是有效的，但是许多个体在治疗后仍无法完全缓解或症状恢复。了解焦虑症的发生和治疗的关键过程将有助于识别有风险的个体并优化治疗方法。厌恶性联想学习提供了解释性途径，通过这些途径恐惧和焦虑会出现，扩散，持续和复活。这篇叙述性综述探讨了我们对焦虑症中的恐惧和回避学习的理解所取得的进展。总体而言，现有文献支持厌恶联想学习在焦虑症的发生和治疗中的关键作用。然而，需要针对特定​​机制进行研究，例如灭绝的普遍性和避免，灭绝的脆弱性以及与焦虑症（例如年龄，性别，抑郁）相关的个体差异的缓和影响。我们讨论了需要更多的生态学有效且复杂的范式来建模歧义和冲突，以及进行临床翻译研究以优化治疗的需求。