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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

基本信息

批准号：
10619590
负责人：
Diana Tamir
金额：
$ 20.25万
依托单位：
PRINCETON UNIVERSITY
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：
2022-05-09 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10619590
关键词：
Acoustics Affect Behavioral Model COVID-19 pandemic Cognitive Communication Computer Vision Systems Data 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 Site Sleep Social Distance Social Interaction Social outcome 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 neural network neuroimaging neuromechanism non-verbal peer primary outcome psychologic social social anxiety social attachment social communication stress reduction tool verbal virtual

项目摘要

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/摘要社会互动是幸福的关键。当成功时，互动会产生社会纽带，从而减少压力、孤独和抑郁，这些都有助于长寿。该提案旨在确定这些机制社会互动产生社会联系的方式。两项研究使用了最先进的计算机工具视觉、自然语言处理和功能磁共振超扫描--两个人同时进行扫描时间-当人们实时互动时，窥探他们的思想。这个项目的重点是自然主义的互动。迄今为止，对社会互动的神经科学研究已经主要依赖于实际上不允许人们互动的范例。目前的提案提供了一种转变从这些传统的范例中。我们将利用来自实时对话的丰富数据来调查两个对话特征--社会内容和积极情感--可能有效地支持社会联系。我们在目标1中测试这些对话特征与社会联系之间的直接关系。然后目标2将测试这一假设认为，这些特征通过帮助对话伙伴达成一致来促进联系佩奇‘-协调他们的思想和感受。我们将使用fMRI超扫描来测量对齐情况。研究1将使用一个独特的虚拟视频对话平台来连接两个远程通信者。二元体当我们记录实时的声音、视觉和语言数据时，我们将自由交谈，我们的分析工具可以自动提取这两个对话特征。参与者还将完成一次对话后评估主要结果的调查：社会关系。我们将使用因子分析和交叉验证来优化我们的多模式特征集群，并优化我们的对话特征如何诱导的模型结盟，这反过来又支持社会联系。研究2旨在复制这一行为模型，并进一步确定与使用神经成像技术将对话特征与社交联系结合起来。如果对话带来社交联系因为它会导致对齐，所以我们应该看到，社会联系的二元体经历神经对齐在同时与内容和影响相关联的网络中：分别是默认网络和边缘网络。我们的独特的研究场地，在相邻的房间里有两台核磁共振扫描仪，允许我们使用fMRI超扫描和皮质和皮质下神经网络的高分辨率成像，分辨率远远超出可能通常用来研究自然主义相互作用的方法。建议对互动的实时动态进行的调查将评估通信如何同步思想，以及这种结盟如何支持社会联系。这项工作将揭示成功的互动。通过这样做，它提供了有希望的未来方向，以减轻社会脱节，健康人群在紧张时刻或社会疏远期间所感受到的新冠肺炎大流行以及患有抑郁症、社交焦虑症或自闭症的弱势人群。