Modeling and manipulating social percepts in individuals

建模和操纵个体的社会认知

基本信息

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
项目类别：
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

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

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项目摘要

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.

项目摘要偏见是否以及如何感知社交互动是大多数主要精神的标志疾病（例如，自闭症患者倾向于低估社交互动，患有精神分裂症的人倾向于过度感知社会互动，抑郁症的人倾向于感知社会互动负灯）。这些偏见可能反映了正常人群中存在的持续的极端，暗示表征社会感知风格中的个体差异对于推动我们的了解疾病。即使在剥离中，人类也可以理解社交互动毫无样式的刺激（例如，几何形状的动画）是一种长期以来已被认可的现象被利用以研究正常和患者人群中的社会认知。但是，当涉及到这些基本的刺激，虽然我们可能具有“看到它时知道”的直觉，但我们不了解它是什么关于刺激被认为使它们社交的社交 - 换句话说，需要哪些特定的视觉特征，以及什么剂量。此外，因为任务范例通常是一个简单的二元选择（即“社会”或 “随机”），我们不理解个人的异质性，从他们的门槛来决定是否a 给定的刺激代表了社会互动，如果是的，则是什么样的社会互动（即积极或负面）。迈向理解和纠正精神疾病中有偏见的社会认知的关键步骤是定义基本社交互动的基本感官特征，并确定不同人如何以及为什么计算在这些特征上以不同的方式产生不同的社会感知。这将打开干预措施的大门可以防止个人沿着有偏见的道路走。在这个项目中，我们将建立一个社会刺激课为此，我们对低级视觉特征进行了精确的参数控制。这将使我们能够构建各种类型的社交互动的个人“社交调节曲线”，并确定这些变异性如何调谐曲线与性状表型有关。将这些刺激与同时神经影像（fMRI）结合在一起远程跟踪将阐明处理层次结构中的位置，感知在个人内部和跨个体之间的差异，并允许我们检验以下假设，即社会感知在皮质层次结构早期出现了想法。这表明特质的社会认知与自动，感官驱动的联系更加紧密联系过程而不是受控反射，这是一个与诊断和介入有关的区别，这一点很重要工具。最后，在一组无用的抽样个体中，我们将直接测试刺激特征之间的因果关系，大脑活动，并感知使用实时fMRI隐式将个体隐式驱使基于给定的感知正在进行的大脑活动模式。拟议研究的结果将是一种因果模型特征和大脑动力学相互作用，从而引起给定个人的给定社会知觉。这个模型将提供有关针对性疗法的可检验假设，以使精神疾病中的偏见认知正常化。