NCS-FO: Leveraging Deep Probabilistic Models to Understand the Neural Bases of Subjective Experience

东北大学的一个跨学科研究团队，包括深度学习和概率编程、情感认知神经科学、神经成像计算方法、概率建模和计算机视觉方面的专家，将从事综合计算和神经科学研究工作，以了解主观体验的神经基础。不同的人经历相同的事件，由于他们独特的历史和心理倾向，以截然不同的方式。对于有社交焦虑症的人来说，一想到要离开家，就会产生恐慌感。相反，一个有经验的登山运动员可能会觉得在悬崖边保持平衡很舒服。这种观点的变化被主观经验这个术语所捕捉。尽管它在人类认知中的中心地位和普遍性，但仍然不清楚如何建模主观经验的神经基础。拟议的工作将开发新的技术，为个体差异的统计建模，并将这些技术应用于恐惧的主观体验的神经影像学研究。这两方面的研究将共同为恐惧体验的神经基础提供基本的见解。更一般地说，开发的计算框架将提供一种比较不同的数学假设的神经活动和个体差异之间的关系的手段。这将使心理学和认知神经科学中的广泛现象的调查。这项工作对其他领域有进一步的影响，这些领域在主体经验方面存在个体差异，例如行为医学和工作场所压力研究或STEM教育以及数学或考试焦虑研究。该项目由理解神经和认知系统的综合策略（NSF-NCS）资助，这是一个由计算机和信息科学与工程（CISE），教育和人力资源（EHR），工程（ENG）以及社会，行为和经济科学（SBE）董事会共同支持的多学科计划。该项目将开发一种新的计算框架，用于模拟神经成像数据中的个体差异，并使用该框架来研究一种强大且具有社会意义的主观体验（即恐惧）的神经基础。恐惧是一种特别有用的分析方法，因为它涉及到不同情境（蜘蛛、高度和社交情境）和性格（蜘蛛恐惧症、恐高症和广场恐惧症）的变化，这些变化联合收割机创造了主观体验。在拟议的神经成像研究中，参与者将在观看诱导不同程度唤醒的视频时进行扫描。为了表征这种神经成像数据中的个体差异，研究人员将利用深度概率编程的进步来开发因子分析模型的概率变体。这些模型为每个参与者和刺激推断出一个低维特征向量，也称为嵌入。一个简单的神经网络模型嵌入和神经响应之间的关系。该网络可以以数据驱动的方式进行训练，并且可以以各种方式进行参数化，这取决于实验设计或要纳入模型的神经认知假设。这为测试不同的恐惧神经模型提供了必要的基础设施。具体地说，研究人员将比较一个模型，其中恐惧有自己独特的回路（即，神经标记或生物标志物）与受试者或情况特异性神经架构相关联。更一般地说，开发的框架可以适用于在其他实验settings.This奖项反映了NSF的法定使命的神经影像学研究的个体差异模型，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

An interdisciplinary team of researchers at Northeastern University, including experts in deep learning and probabilistic programming, the cognitive neuroscience of emotion, computational methods in neuroimaging, probabilistic modeling, and computer vision, will engage in an integrated computational and neuroscientific research effort to understand the neural bases of subjective experience. Different individuals experience the same events in vastly different ways, owing to their unique histories and psychological dispositions. For someone with social anxieties, the mere thought of leaving the home can induce a feeling of panic. Conversely, an experienced mountaineer may feel quite comfortable balancing on the edge of a cliff. This variation of perspectives is captured by the term subjective experience. Despite its centrality and ubiquity in human cognition, it remains unclear how to model the neural bases of subjective experience. The proposed work will develop new techniques for statistical modeling of individual variation and apply these techniques to a neuroimaging study of the subjective experience of fear. Together, these two lines of research will yield fundamental insights into the neural bases of fear experience. More generally, the developed computational framework will provide a means of comparing different mathematical hypotheses about the relationship between neural activity and individual differences. This will enable investigation of a broad range of phenomena in psychology and cognitive neuroscience. The work has further implications for other fields in which there are individual differences in subject experience, such behavioral medicine and the study of stress in the workplace or STEM education and the study of math or test anxiety. This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NSF-NCS), a multidisciplinary program jointly supported by the Directorates for Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE). The project will develop a new computational framework for modeling individual variation in neuroimaging data and use this framework to investigate the neural bases of one powerful and societally meaningful subjective experience, namely, of fear. Fear is a particularly useful assay because it involves variation across situational contexts (spiders, heights, and social situations), and dispositions (arachnophobia, acrophobia, and agoraphobia) that combine to create subjective experience. In the proposed neuroimaging study, participants will be scanned while watching videos that induce varying levels of arousal. To characterize individual variation in this neuroimaging data, the investigators will leverage advances in deep probabilistic programming to develop probabilistic variants of factor analysis models. These models infer a low-dimensional feature vector, also known as an embedding, for each participant and stimulus. A simple neural network models the relationship between embeddings and the neural response. This network can be trained in a data-driven manner and can be parameterized in a variety of ways, depending on the experimental design, or the neurocognitive hypotheses that are to be incorporated into the model. This provides the necessary infrastructure to test different neural models of fear. Concretely, the investigators will compare a model in which fear has its own unique circuit (i.e., neural signature or biomarker) to subject- or situation-specific neural architectures. More generally, the developed framework can be adapted to model individual variation in neuroimaging studies in other experimental settings.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.