Dissecting the features and neural mechanisms supporting naturalistic social inference

剖析支持自然社会推理的特征和神经机制

• 批准号: 10631938
• 负责人: Nina Rouhani
• 金额: $ 7.18万
• 依托单位: CALIFORNIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10631938
• 关键词: Adult; Auditory; Behavior; Behavioral; Behavioral Model; Brain; Brain region; Cognitive; Computer Models; Computer Vision Systems; Consensus; Dimensions; Face; Face Processing; Facial Expression; Fellowship; Friends; Functional Magnetic Resonance Imaging; Future; Genetic Transcription; Gestures; Goals; Human; Individual; Individual Differences; Judgment; Learning; Life; Linear Regressions; Link; Mental Depression; Mental disorders; Methods; Mind; Modeling; Modernization; Natural Language Processing; Participant; Pattern; Persons; Population; Postdoctoral Fellow; Poverty; Process; Research; Scanning; Schizophrenia; Semantics; Social Interaction; Source; Speech; Stereotyping; Stimulus; System; Text; Time; Training; Translating; Update; Validation; Video Recording; Visual; Weight; Work; autism spectrum disorder; computerized tools; deep neural network; dyadic interaction; feature extraction; human data; impression; individuals with autism spectrum disorder; machine learning method; mental state; mentalization; model building; neural; neural patterning; neuroimaging; neuromechanism; predictive modeling; psychologic; recruit; response; skills; social; social cognition; tool; trait; trustworthiness; video chat

Project Summary This first-time fellowship application proposes an integrated training and research plan focused on human social interactions. The applicant has a strong and broad background in computational modeling of behavioral human data, and will now leverage this in her postdoc at Caltech to learn how to model and analyze human interactions from text-based chats and video interactions in behavior and in the brain (using fMRI). Aim 1 will utilize state-of-the-art computer vision and natural language processing methods to annotate the visual, auditory and text features of social interactions. Specific features, such as facial expressions and the semantic content of text, will be extracted from 300 recorded social interactions between healthy adults. These features, in turn, will be used to fit models that predict the social judgments that participants make about one another: how trustworthy, friendly or arrogant do they judge their partner to be? A main goal of this first Aim is to extend human social judgments beyond the typically narrow context used in past studies (e.g., static photos of faces) into the naturalistic, interactive context we actually encounter in the real world. This emphasis will also be important for future applications to psychiatric populations with critical deficits in social cognition (such as autism, outside the scope of the present fellowship). Aim 2 translates the interactions of Aim 1 into neuroimaging, and BOLD-fMRI will be acquired while participants watch previously recorded social interactions. This will reveal the brain regions and networks that track the dynamic features of the stimuli. Neural representations of social inferences will be extracted from multivariate activation patterns using representational similarity analysis, and similarity in neural processing will be compared to similarity in behavioral social inference using inter-subject correlation analysis. All analyses will both apply a whole-brain approach and will query specific social cognition networks. A main goal of Aim 2 is to characterize the neural systems that subserve the dynamic construction of social attributions, information that in future studies can be linked to individual differences and psychiatric illness.

项目概要 这项首次奖学金申请提出了一项以人类为重点的综合培训和研究计划 社交互动。申请人在行为计算建模方面拥有强大而广泛的背景 人类数据，现在她将在加州理工学院的博士后中利用这些数据来学习如何建模和分析人类 基于文本的聊天和行为和大脑中的视频交互（使用功能磁共振成像）。目标1将 利用最先进的计算机视觉和自然语言处理方法来注释视觉、听觉 和社交互动的文本特征。具体特征，例如面部表情和语义内容 文本将从 300 条记录的健康成年人之间的社交互动中提取。这些功能反过来又 将用于拟合预测参与者对彼此做出的社会判断的模型：如何 他们认为自己的伴侣是值得信赖、友好还是傲慢？第一个目标的主要目标是扩展人类 超越过去研究中使用的典型狭隘背景（例如面部静态照片）的社会判断 我们在现实世界中实际遇到的自然主义、交互式环境。这种强调也很重要 未来应用于社会认知严重缺陷的精神病人群（例如自闭症，非 目前研究金的范围）。目标 2 将目标 1 的相互作用转化为神经影像和 BOLD-fMRI 将在参与者观看之前记录的社交互动时获得。这将揭示大脑区域 以及跟踪刺激动态特征的网络。社会推论的神经表征将是 使用代表性相似性分析和神经网络中的相似性从多变量激活模式中提取 将使用主体间相关性分析将处理与行为社会推理的相似性进行比较。 所有分析都将采用全脑方法，并查询特定的社交认知网络。一个主要 目标 2 的目标是描述有助于社会归因动态构建的神经系统， 在未来的研究中可以将这些信息与个体差异和精神疾病联系起来。