NCS-FO: Neural Correlates of Social States in Macaques

许多生物有机体通过传递和感知社会信号而相互作用。这些包括凝视、面部表情和身体姿势，它们传达了个人内部状态的信息，包括他们的注意力焦点、预期的行动和情绪水平。尽管这些社会互动无处不在且具有潜在价值，但了解神经活动如何引起社会互动仍然是一个很大程度上未知的领域。过去的实验要么是在自然的社会环境中通过主观行为观察进行的，要么是在有限的社会环境中通过神经成像等可量化的方法进行的。该项目将利用项目团队最近开发的高分辨率动作捕捉系统来解决这些限制，该系统可以测量、检测和量化自然社会行为及其相应的神经活动。这项研究将为研究早期行为标记开辟新的机会，例如那些患有自闭症谱系障碍、精神分裂症和强迫症的高危儿童。该项目团队的主要创新是一种名为社会状态的新统计模型，旨在对共同行为的社会背景进行编码。这些社会状态将与位于前额叶皮层的背外侧前额叶皮层（dLPFC）和背前扣带皮层（dACC）两个大脑区域的神经生理活动有关。利用社会状态的神经关联，该项目将开发一种新的方法来模拟社会状态转换的动态，促进对这两个大脑区域如何负责处理社会信号的理解。虽然该项目将专注于特定的大脑区域，但计划中的研究将为理解猕猴复杂的社会行为提供一个通用的计算基础。计划中的研究将通过从真实的、不受限制的环境中数以百万计的神经行为数据点中学习，推进对认知和神经过程的计算理解。开发这样一个计算模型是一个复杂的现实世界问题，需要一种新的整体性、变革性和综合性的方法。计划中的解决方案建立在来自多个学科的领域知识之上，包括机器学习、灵长类生理学、神经科学和计算机视觉。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Many biological organisms interact with one another by transmitting and perceiving social signals. These include gaze, facial expression, and body pose that convey information about the individual’s internal state, including their focus of attention, intended actions, and emotional level. Despite the ubiquity and potential value of these social interactions, understanding how neural activity gives rise to social interactions is still a largely uncharted area. Past experiments were conducted either by subjective behavioral observations in natural social settings or by quantifiable methods such as neuroimaging in restricted social settings. This project will address these limitations by leveraging the project team's recently developed high resolution motion capture system, which can measure, detect, and quantify natural social behaviors and their corresponding neural activity. This research will open new opportunities to study early behavioral markers, such as those for at-risk children with autism spectrum disorder, schizophrenia, and obsessive-compulsive disorder.The project team's main innovation is a new statistical model called social states, designed to encode the social context of joint behaviors. These social states will be associated with neurophysiological activities in two brain regions, the dorsolateral prefrontal cortex (dLPFC) and dorsal anterior cingulate cortex (dACC), both located in the prefrontal cortex. Using the neural correlates of social states, the project will develop a novel method to model the dynamics of social state transitions, facilitating an understanding of how these two brain regions are responsible for processing social signals. While the project will focus on specific brain regions, the planned research will provide a general computational foundation for understanding the complex social behaviors of macaques. The planned research will advance the computational understanding of cognitive and neural processes by learning from millions of neurobehavioral data points in real, unrestricted environments. Developing such a computational model is a complex, real world problem, requiring a new holistic, transformative, and integrative approach. The planned solution is built upon domain knowledge from multiple disciplines, including machine learning, primate physiology, neuroscience, and computer vision.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.