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.
