Modeling and manipulating social percepts in individuals

建模和操纵个体的社会认知

• 批准号: 10623213
• 负责人: Emily Suzanne Finn
• 金额: $ 78.39万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-17 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10623213
• 关键词: Address; Affect; Algorithms; Basic Science; Behavior; Behavior Therapy; Behavioral; Behavioral Mechanisms; Belief; Brain; Case Study; Cognition; Cognitive; Complex; Data; Diagnostic; Disease; Dissociation; Dose; Educational process of instructing; Etiology; Exposure to; Functional Magnetic Resonance Imaging; Goals; Heterogeneity; Human; Individual; Individual Differences; Intervention; Intuition; Life; Light; Link; Maintenance; Major Mental Illness; Mental Depression; Mental disorders; Modeling; Motion; National Institute of Mental Health; Nature; Outcome; Paranoia; Paranoid Schizophrenia; Paranoid psychosis; Pathology; Pattern; Perception; Personality Traits; Persons; Phenotype; Play; Population; Process; Property; Psychophysics; Reaction; Reading; Reporting; Research; Risk Factors; Sampling; Schizophrenia; Sensory; Shapes; Social Functioning; Social Interaction; Social Perception; Stimulus; Strategic Planning; Symptoms; Testing; Time; Variant; Visual; Work; animation; autism spectrum disorder; causal model; experimental study; fighting; gaze; individual variation; individuals with autism spectrum disorder; information processing; innovation; insight; mentalization; negative affect; neural; neurofeedback; neuroimaging; neuromechanism; novel; novel diagnostics; patient population; prevent; social; social bias; social cognition; targeted treatment; tool; trait; visual tracking

PROJECT SUMMARY Biases in whether and how social interactions are perceived are a hallmark symptom of most major mental illnesses (e.g., people with autism tend to under-perceive social interactions, people with paranoid schizophrenia tend to over-perceive social interactions, and people with depression tend to perceive social interactions in a negative light). These biases likely reflect the extreme end of a continuum that exists in the normative population, suggesting that characterizing individual differences in social-perceptual styles is critical to furthering our understanding of disease. That humans are primed to perceive social interactions even in stripped-down, unlifelike stimuli (e.g., animations of geometric shapes) is a phenomenon that has long been recognized and exploited to study social cognition in both normative and patient populations. However, when it comes to these basic stimuli, while we may have the intuition that we “know it when we see it”, we do not understand what it is about stimuli deemed social that makes them social—in other words, which specific visual features are required, and in what doses. Furthermore, because task paradigms are often a simple binary choice (i.e., ‘social’ or ‘random’), we do not understand heterogeneity across individuals in terms of their thresholds for deciding if a given stimulus represents a social interaction, and if so, what kind of social interaction (i.e., positive or negative). A critical step toward understanding and correcting biased social cognition in mental illness is to define the fundamental sensory features of basic social interactions, and determine how and why different people compute differently on these features to give rise to different social percepts. This will open the door to interventions that can prevent an individual from going down a biased path. In this project, we will establish a social stimulus class for which we have precise, parametric control over low-level visual features. This will allow us to construct individual “social tuning curves” for various types of social interactions and determine how variability in these tuning curves relates to trait phenotypes. Combining these stimuli with simultaneous neuroimaging (fMRI) and eye-tracking will shed light on where in the processing hierarchy percepts diverge within and across individuals, and allow us to test the hypothesis that social percepts emerge earlier in the cortical hierarchy than previously thought. This would indicate that idiosyncratic social cognition is more closely linked to automatic, sensory-driven processes than controlled reflection, a distinction that is important for informing diagnostic and interventional tools. Finally, within a set of densely sampled individuals, we will directly test causality between stimulus features, brain activity, and percepts using real-time fMRI to implicitly steer individuals toward a given percept based on ongoing patterns of brain activity. The outcome of the proposed research will be a causal model of how stimulus features and brain dynamics interact to give rise to a given social percept within a given individual. This model will provide testable hypotheses regarding targeted therapies to normalize biased cognition in mental illnesses.

项目摘要 是否以及如何感知社会互动的偏见是大多数主要心理疾病的标志性症状。 疾病（例如，自闭症患者倾向于低估社会交往，偏执型精神分裂症患者 倾向于过度感知社会交往，抑郁症患者倾向于以一种 负光）。这些偏见可能反映了存在于规范人群中的连续体的极端， 这表明，表征社会感知风格的个体差异对于进一步研究 了解疾病。人类已经准备好感知社会互动，即使是在脱光衣服的情况下， 不逼真的刺激（例如，几何形状的动画）是一种早已被认识到的现象， 用于研究正常人群和患者人群的社会认知。然而，当涉及到这些 基本刺激，虽然我们可能有直觉，我们“知道它时，我们看到它”，我们不明白它是什么 关于被认为是社会性的刺激，使它们具有社会性-换句话说，需要哪些特定的视觉特征， 以及剂量。此外，由于任务范例通常是简单的二元选择（即，“社会”或 “随机”），我们不理解个体之间的异质性，因为他们的阈值决定是否是一个 给定的刺激表示社会互动，并且如果是，什么样的社会互动（即，正或负）。 理解和纠正精神疾病中有偏见的社会认知的关键一步是定义 基本的社会互动的基本感官特征，并决定如何以及为什么不同的人计算 不同的人对这些特征有不同的看法，从而产生不同的社会感知。这将打开干预的大门， 可以防止个人走上有偏见的道路。在这个项目中，我们将建立一个社会刺激类 我们可以对低层次的视觉特征进行精确的参数化控制。这将使我们能够构建 个人的“社会调谐曲线”的各种类型的社会互动，并确定如何在这些变化 调谐曲线涉及性状表型。将这些刺激与同步神经成像（fMRI）相结合， 眼睛跟踪将揭示在处理层次中感知在个体内和个体之间的何处发散， 并让我们能够验证这样一个假设，即社会知觉在皮层层次中出现的时间比以前更早。 想的这表明，特殊的社会认知与自动的、感觉驱动的 过程比控制反射，这一区别对于告知诊断和介入非常重要。 工具.最后，在一组密集采样的个体中，我们将直接测试刺激特征之间的因果关系， 大脑活动，并使用实时功能磁共振成像感知，以隐含地引导个人对一个给定的认知， 持续的大脑活动模式拟议研究的结果将是一个因果模型，说明刺激如何 特征和大脑动力学相互作用，在给定的个体内产生给定的社会认知。该模型 将提供关于靶向治疗的可验证的假设，以规范精神疾病中的偏见认知。