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.

项目概要 感知和理解社交互动的能力对于日常生活至关重要，尤其是在 从一瞥，我们就能毫不费力地识别出人们是否在互动，是否在互动。 是合作还是竞争，以及它的交流意图，然而，人们对它的神经基础知之甚少。 我们最近发现了颞上沟 (pSTS) 中的一个选择性区域。 在观察社交互动时参与这一发现，再加上新颖的方法和模型。 的进步，创造了一个研究社交互动的神经计算基础的机会。 将测量功能磁共振成像、脑电图和计算模型对受控刺激和自然刺激的反应 研究社会互动感知和理解的神经基础我们的中心假设是： pSTS 是社会视觉和概念表示之间的关键计算连接 交互，并且它通过两种不同的计算机制提取这些表示：自下而上 模式识别（来自身体和运动选择性大脑区域的视觉信息）与自上而下的认知 目标 1 将分别测试神经层次结构。 使用条件丰富的、从视觉基元到抽象概念的社会交互表征。 多模态功能磁共振成像实验，我们将测试社交互动被处理的工作假设 沿着“第三视觉路径”分层：以身体和运动选择性视觉代表社会原语 区域、pSTS 中社会互动的多模态表示以及沿线的更高层次的社会特征 STS 和心理理论网络将确定社交互动中信息流的方向。 通过将 EEG 记录与目标 1 的 fMRI 数据相结合，我们可以研究相对时间。 信息流经大脑区域以确定社交互动的不同方面（从视觉 概念）以自下而上与自上而下的方式提取。 检测和目标兼容性（即合作与竞争）将通过自下而上的方式在 pSTS 的早期进行编码 相比之下，我们发现其他社会评价将得到体现。 目标 3 将识别神经网络 我们将把我们的神经记录与底层进行比较。 向上（判别）和自上而下（生成）计算模型，直接操作神经网络 上面概述的计算理论，以理解在社交互动中进行的计算 拟议的研究将为用于识别的神经计算提供新的见解。 了解典型发育成人的这些机制是重要的第一步。 揭示这些计算如何针对自闭症。