The neural computations underlying human social interaction recognition

人类社交互动识别的神经计算

基本信息

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
项目类别：
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

项目摘要

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.