The neural computations underlying human social interaction recognition

人类社交互动识别的神经计算

• 批准号: 10637982
• 负责人: Leyla Isik
• 金额: $ 66.36万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-09 至 2028-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10637982
• 关键词: Adult; Behavioral; Brain; Brain region; Clip; Code; Complex; Computer Models; Coupled; Cues; Data; Data Set; Detection; Development; Electroencephalography; Emotional; Evaluation; Functional Magnetic Resonance Imaging; Goals; Human; Label; Life; Measures; Methodology; Mind; Modeling; Motion; National Institute of Mental Health; Neural Network Simulation; Pattern Recognition; Perception; Persons; Process; Psyche structure; Research; Role; Social Hierarchy; Social Interaction; Stimulus; Structure of superior temporal sulcus; Testing; Visual; Visual Pathways; Work; autism spectrum disorder; clinically relevant; cognitive process; design; experimental study; graph neural network; insight; multimodality; neural; neuroimaging; novel; response; simulation; social; theories; visual information; visual processing

Project Summary The ability to perceive and understand social interactions is crucial to daily life, and characteristically altered in autism. From a brief glance, we can effortlessly recognize whether people are interacting, whether the interaction is cooperative or competitive, and its communicative intent. However, little is known about the neural basis of these abilities. We recently identified a region in the posterior superior temporal sulcus (pSTS) that is selectively engaged when viewing social interactions. This discovery, coupled with novel methodological and modeling advances, creates an opportunity to investigate the neurocomputational underpinnings of social interactions. We will measure fMRI, EEG, and computational modeling responses to both controlled and naturalistic stimuli to investigate the neural basis of social interaction perception and understanding. Our central hypotheses are that the pSTS is a key computational junction between the visual and conceptual representations of a social interaction, and that it extracts these representations via two different computational mechanisms: bottom-up pattern recognition (from visual information in body and motion-selective brain regions) vs. top-down cognitive processes (based on input from the theory of mind network), respectively. Aim 1 will test for a neural hierarchy of social interaction representations from visual primitives to abstract concepts. Using a condition-rich, multimodal fMRI experiment, we will test the working hypothesis that social interactions are processed hierarchically along a ‘third visual pathway’: with social primitives represented in body and motion-selective visual regions, multimodal representations of social interactions in the pSTS, and higher-level social features along the STS and theory of mind network. Aim 2 will identify the direction of information flow across the social interaction network. By combining EEG recordings with our fMRI data from Aim 1, we can investigate the relative timing of information flow across brain regions to determine whether different aspects of a social interaction (from visual to conceptual) are extracted in a bottom-up versus top-down manner. We hypothesize that social interaction detection and goal-compatibility (i.e., cooperation vs. competition) will be coded early in the pSTS via bottom-up information flow from visual regions. In contrast, we hypothesize that other social evaluations will be represented significantly later based on additional input from the theory of mind network. Aim 3 will identify the neural computations underlying social interaction representations. We will compare our neural recordings with bottom- up (discriminative) and top-down (generative) computational models, which directly operationalize the neural computational theories outlined above, to understand the computations carried out across the social interaction brain network. The proposed studies will provide novel insights into the neural computations used to recognize social interactions. Understanding these mechanisms in typically developing adults is an essential first step towards uncovering how these computations are altered in autism.

项目摘要 感知和理解社交互动的能力对日常生活至关重要，并且在 自闭症。从短暂的一目了然，我们可以轻松地认识到人们是否正在互动，是否互动 是合作或竞争性的，及其交流意图。但是，关于神经基础知之甚少 这些能力。我们最近确定了后临时沟（PST）中的一个区域，该区域有选择地 观看社交互动时参与。这一发现，再加上新颖的方法论和建模 进步，创造了一个机会来研究社会互动的神经计算基础。我们 将测量对受控刺激和自然主义刺激的fMRI，脑电图和计算建模响应 研究社会互动感知和理解的神经元基础。我们的中心假设是 PST是社交的视觉和概念表示之间的关键计算结 相互作用，并通过两种不同的计算机制提取这些表示形式：自下而上 模式识别（来自身体和运动选择性大脑区域中的视觉信息）与自上而下的认知 流程（分别基于心理网络理论的输入）。 AIM 1将测试神经层次结构 社会互动表示，从视觉原语到抽象概念。使用条件丰富的 多模式FMRI实验，我们将测试处理社交互动的工作假设 沿着“第三个视觉途径”等级结构：在身体和运动选择性视觉上代表的社会原语 区域，PST中社交互动的多模式表示以及沿着高级社会特征 STS和思维网络理论。 AIM 2将确定信息流的方向在整个社会互动中 网络。通过将EEG记录与AIM 1中的fMRI数据相结合，我们可以研究 跨大脑区域的信息流以确定社交互动的不同方面（从视觉 以自下而上的方式与自上而下的方式提取概念）。我们假设社会互动 检测和目标兼容（即合作与竞争）将在PST中通过自下而上进行编码 来自视觉区域的信息流。相反，我们假设将代表其他社会评估 稍后，基于思维网络理论的其他输入。 AIM 3将识别神经 社会互动表示的基础计算。我们将将我们的神经记录与底部进行比较 UP（判别）和自上而下的（生成）计算模型，这些模型直接使神经元操作 上面概述的计算理论，以了解在社交互动中进行的计算 大脑网络。拟议的研究将提供有关用于识别的神经计算的新见解。 社交互动。在典型发展成年人中了解这些机制是必不可少的第一步 旨在发现自闭症中这些计算的改变。