The cognitive and neural mechanisms supporting naturalistic dyadic social interactions

支持自然二元社会互动的认知和神经机制

• 批准号: 10450930
• 负责人: Diana Tamir
• 金额: $ 24.3万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-09 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10450930
• 关键词: Acoustics; Affect; Behavioral Model; COVID-19 pandemic; Cognitive; Communication; Computer Vision Systems; Data; Discourse analysis; Electroencephalography; Empathy; Facial Expression; Factor Analysis; Feeling; Free Will; Functional Magnetic Resonance Imaging; Future; Heart Diseases; Individual; Intervention; Investigation; Knowledge; Language; Linguistics; Link; Loneliness; Longevity; Magnetic Resonance Imaging; Malignant Neoplasms; Measures; Mediating; Mental Depression; Methods; Mind; Modeling; Motivation; Natural Language Processing; Neurosciences Research; Participant; Personal Satisfaction; Persons; Population; Populations at Risk; Process; Research; Research Personnel; Resolution; Route; Scanning; Self Disclosure; Side; Site; Sleep; Social Distance; Social Interaction; Social outcome; Social support; Stimulus; Stress; Surveys; Testing; Thinking; Time; Validation; Visual; Vulnerable Populations; Work; autism spectrum disorder; experience; functional near infrared spectroscopy; high resolution imaging; mortality; multimodality; neural network; neuroimaging; neuromechanism; peer; primary outcome; psychologic; relating to nervous system; social; social anxiety; social attachment; social communication; tool; virtual

Project Summary / Abstract Social interactions are key to well-being. When successful, interactions engender social bonds that reduce stress, loneliness, and depression, and that support longevity. This proposal aims to identify the mechanisms by which social interactions produce social connection. Two studies use state-of-the-art tools in computer vision, natural language processing, and fMRI hyperscanning–where two people are scanned at the same time–to peer into the minds of people as they interact in real time. This project focuses on naturalistic interactions. To date, neuroscience research into social interaction has relied primarily on paradigms that do not actually allow people to interact. The current proposal offers a shift from these conventional paradigms. We will leverage rich data from real-time conversations to investigate two conversation features—social content and positive affect—that may effectively support social connection. We test the direct relation between these conversation features and social connection in Aim 1. Aim 2 will then test the hypothesis that these features promote connection by helping conversation partners to get ‘on the same page’—to align their thoughts and feelings. We will measure alignment using fMRI hyperscanning. Study 1 will use a unique virtual video conversation platform that connects two remote communicators. Dyads will freely converse while we record real-time acoustic, visual, and language data, from which our analysis tools can automatically extract both conversation features. Participants will also complete a post-conversation survey to assess the primary outcome: social connection. We will use factor analysis and cross-validation to optimally cluster our multimodal features and to optimize our model of how conversation features induce alignment, which, in turn, supports social connection. Study 2 aims to replicate this behavioral model, and further, to identify the underlying mechanism that links conversation features to social connection using neuroimaging. If conversation brings about social connection because it induces alignment, then we should see that socially connected dyads experience neural alignment within networks associated with both content and affect: the default and limbic networks, respectively. Our unique research site, with two MRI scanners in adjacent rooms, allows us to use fMRI hyperscanning and high-resolution imaging of cortical and subcortical neural networks, a resolution far beyond what is possible with methods typically employed for studying naturalistic interactions. The proposed investigation into the real-time dynamics of interactions will assess how communication syncs minds, and how this alignment supports social connection. This work will reveal the basic ingredients of successful interactions. In doing so, it offers promising future directions for alleviating the devastating effects of social disconnection, as felt by healthy populations during moments of stress or social distancing during the Covid-19 pandemic and by vulnerable populations with depression, social anxiety, or autism.

项目摘要 /摘要 社交互动是幸福感的关键。成功时，互动会导致社会纽带减少 压力，孤独和沮丧，并支持寿命。该建议旨在确定机制 社交互动产生社会联系。两项研究在计算机中使用最先进的工具 视觉，自然语言处理和fMRI Hyperscanning - 在同一中扫描两个人 时间 - 在人们实时互动时凝视着人们的思想。 该项目着重于自然主义互动。迄今为止，对社会互动的神经科学研究已有 主要依赖于实际上不允许人们互动的范例。当前的提议提供了转变 从这些传统的范式中。我们将利用实时对话中的丰富数据来调查两个 对话特征 - 社会内容和积极的影响 - 可能有效地支持社会联系。我们 测试这些对话功能与AIM 1中的社交联系之间的直接关系。AIM2然后测试 这些特征通过帮助对话伙伴获得‘同样的假设来促进联系 page’ - 使他们的思想和感受结盟。我们将使用fMRI Hyperscanning测量对齐。 研究1将使用一个独特的虚拟视频对话平台，该平台连接两个远程通信者。二元 当我们记录实时声学，视觉和语言数据时，将自由交谈，我们的分析 工具可以自动提取两个对话功能。参与者还将完成会议后 评估主要结果的调查：社会联系。我们将使用因子分析和交叉验证 最佳聚类我们的多模式特征，并优化我们的对话特征如何影响的模型 一致性又支持社会联系。 研究2旨在复制这种行为模型，并进一步确定与联系的基本机制 使用神经影像与社交联系的对话特征。如果对话带来社会联系 因为它诱导了一致性，所以我们应该看到社会联系的二元组经历神经对准 在与内容和影响相关联的网络中分别：默认网络和边缘网络。我们的 独特的研究网站，在相邻房间中有两个MRI扫描仪，使我们能够使用fMRI Hyperscanning和 皮质和皮质下神经元网络的高分辨率成像，这是一个远远超出可能的分辨率 使用通常用于研究自然主义相互作用的方法。 对互动实时动态的拟议调查将评估沟通如何同步 思想以及这种一致性如何支持社会联系。这项工作将揭示 成功的互动。通过这样做，它提供了减轻未来的指导，以减轻 社会脱节，正如健康人群在压力或社会疏远时期所感受到的那样 COVID-19-19大流行和弱势群体抑郁症，社交动画或自闭症。