The Utility of Threat Detection in Generalized Social Anxiety Disorder

描述（由申请人提供）：在社会交往中，我们看着另一个人的脸，在一眨眼的时间里推断出这个人的情绪状态和意图。这些感知告诉我们下一步该做什么或说什么。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担忧。这些症状已经用信号检测理论（SDT）量化。SDT的应用为焦虑研究带来了新的方法；一些研究支持的一个主要假设是，焦虑状态的“过度反应”本质可以被描述为一种偏见，即对比实际情况更具威胁性的情况做出反应或记忆。尽管SDT具有强大的功能，但其传统用途仅限于简单地量化感知者之间在灵敏度、偏差和准确性方面的差异。还有一些与研究和治疗特别相关的问题没有得到回答：是什么导致了观察到的偏倚和敏感性差异？回答这个问题的一个关键障碍是目前临床研究中对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障碍如何影响威胁感知的潜在机制的实验问题。为了克服这一障碍，我们引入了一个感知决策的数学模型，该模型将行为经济学的关键见解-效用和最优性-纳入信号检测框架。我们的主要目标是使用这个新的框架来解释GSAD个体、广泛性焦虑障碍（GAD）焦虑对照者和非精神疾病参与者之间威胁感知的差异。我们的第二个目标是评估我们的框架是否可以用于改进干预措施，以减少对GSAD威胁的误解。我们的模型是一种独特的感知概念（例如，最优检测，主观错误校准影响显性行为的潜在环境参数），最终可能导致认知行为疗法的改进，通过定制它们来适应患者的个体感知决策障碍。为了实现我们的目标，我们将招募100名广泛性焦虑症患者和100名年龄和性别匹配的广泛性焦虑症患者和健康人群。参与者将完成一套知觉任务，以区分哪些知觉决策参数会导致GSAD患者对社会威胁的误解。沿着这样的思路成功地描述GSAD将把该领域推向新的方向，通过将社会威胁感知构建为一种决策，这种决策是在存在模糊的感官信息和冲突的、危险的后果的情况下，试图优化检测。同样，我们的模型所代表的新的理论发展将扩大SDT的有用性，深化它对认知过程本质的见解。

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.