The Utility of Threat Detection in Generalized Social Anxiety Disorder

描述(申请人提供)：在社交互动中，我们看着另一个人的脸，一眨眼的功夫就能推断出那个人的情绪状态和他们的意图。这些感知为下一步做什么或说什么提供决策依据。广泛性社交焦虑障碍(GSAD)的特征是对社交情境中的负面评价和拒绝的过度关注。这些症状已经用信号检测理论(SDT)进行了量化。SDT的应用导致了焦虑研究中的新方法；一个基本的假设得到了几项研究的支持，即焦虑状态的“过度反应”性质可以被描述为对比实际更具威胁性的情况做出反应或记住情况的偏见。尽管SDT的力量很大，但它的常规用途仅限于量化感知者之间在灵敏度、偏差和准确性方面的差异。没有回答的是与研究和治疗特别相关的问题：是什么导致了观察到的偏见和敏感性的差异？回答这个问题的一个关键障碍是目前临床研究对SDT的理解，缺乏一个框架来预测或解释行为，或者提出关于情绪和焦虑症如何影响威胁感知的潜在机制的实验性问题。为了跨越这一障碍，我们引入了感知决策的数学模型，该模型将行为经济学的关键见解--效用和最优化--整合到信号检测框架中。我们的主要目标是使用这个新的框架来解释患有GSAD的个体、患有广泛性焦虑症(GAD)的焦虑控制者和非精神疾病参与者在威胁感知上的差异。我们的次要目标是评估我们的框架是否可以用来改进干预措施，以减少对GSAD威胁的误解。我们的模型是一种独特的知觉概念化(例如，最佳检测，对影响公开行为的潜在环境参数的主观错误校准)，通过根据患者个人的知觉决策障碍量身定做，最终可能导致认知行为治疗的改善。为了实现我们的目标，我们将招募100名GSAD患者和100名年龄和性别匹配的GAD和健康人群。参与者将完成一系列知觉任务，以分离出几个知觉决策参数中的哪个会导致对GSAD的社会威胁的误解。按照这样的思路成功地描述GSAD将把这一领域带入新的方向，将社会威胁感知界定为试图在存在模糊的感觉信息和冲突的、有风险的后果的情况下优化检测的决定。同样，我们的模型所代表的新颖的理论发展将扩大SDT的有用性，加深它对认知过程本质的洞察。

DESCRIPTION (provided by applicant): During social interactions, we look into the face of another person and in the blink of an eye infer that person's emotional state and their intentions. These perceptions inform decisions about what to do or say next. Generalized Social Anxiety Disorder (GSAD) is characterized by exaggerated concerns about negative evaluation and rejection in social situations. These symptoms have been quantified with signal detection theory (SDT). The application of SDT has led to novel approaches within anxiety research; a primary hypothesis, supported by several studies, has been that the "over-reactive" nature of the anxious state can be characterized as a bias to respond to or remember situations as more threatening than they in fact are. In spite of SDT's power, its conventional use has been limited to simply quantifying differences in sensitivity, bias, and accuracy among perceivers. Left unanswered are questions of particular relevance to research and treatment: what causes the observed differences in bias and sensitivity? A critical barrier to answering this question is the current understanding of SDT in clinical research, which lacks a framework to predict or explain behavior, or in which to pose experimental questions about how mood and anxiety disorders influence the underlying mechanisms involved in threat perception. To bridge this barrier, we introduce a mathematical model of perceptual decision making that incorporates key insights from behavioral economics-utility and optimality- into a signal detection framework. Our primary objective is to use this novel framework to explain differences in threat perception among individuals with GSAD, anxious controls with generalized anxiety disorder (GAD), and non-psychiatrically-ill participants. Our secondary objective is to assess whether our framework could be used to improve interventions to reduce misperceptions of threat in GSAD. Our model is a unique conceptualization of perception (e.g., optimal detection, subjective miscalibration to underlying environmental parameters that influence overt behavior) that could eventually lead to improvements in cognitive-behavioral therapies by tailoring them to a patient's individual perceptual decision-making impairment. To achieve our aims, we will recruit 100 individuals with GSAD and 100 individuals each from age- and gender-matched GAD and healthy populations. Participants will complete a suite of perceptual tasks to isolate which of several perceptual decision parameters cause misperceptions of social threat in GSAD. Successful characterization of GSAD along such lines will take the field in new directions by framing social threat perception as a decision made by attempting to optimize detection in the presence ambiguous sensory information and conflicting, risky consequences. As well, the novel theoretical developments represented by our model will broaden SDT's usefulness deepening the insights it affords into the nature of cognitive processes.