Oriel FeldmanHall - US grants

Project Summary Our long-term goal is to elucidate the fundamental neurobiological mechanisms supporting social learning. Many of our everyday social decisions require constant assessments of other individuals, such as whether they can be trusted. These decisions are inherently risky, as it is often uncertain, especially with strangers, how outcomes will unfold. Despite this, the mechanisms governing social risky learning remain largely unexplored. A hypersensitivity to risk and uncertainty?a hallmark symptom of anxiety that often results in a pronounced and maladaptive bias toward making risk-avoidant choices?provides an ideal test bed to probe the mechanisms governing social risky learning. The objective of this project is to innovatively merge methodologies and insights from associative learning models and neuroecononmics to examine the functional properties of the brain-behavior relationships that mediate social learning under uncertainty, while also identifying how alterations in these learning mechanisms shift socially risky behavior in maladaptive ways. Our central hypothesis is that a social learning model can capture the neurobiological mechanisms governing both healthy and maladaptive social risk taking. Our specific aims will 1) discover how social value (e.g. trustworthiness) is behaviorally and neurally instantiated in uncertain environments, 2) determine the role of affect in biasing these social learning processes, and 3) uncover knowledge about the relationship between anxiety and social learning and how it can lead to maladaptive socially risky choices. By providing a computational account of this relationship, we may show that social risky avoidant behavior emerges at the level of value assignment learning. Such a finding would highlight that individuals avoid socially risky choices because of a failure in affective learning. This contribution is significant since it will elucidate both optimal behavioral patterns and dysfunction and pathology during social learning?findings that may reveal potential biomarkers to aid in diagnosis and targeted interventions in those suffering from anxiety. Finally, the proposed research is innovative because it harnesses emerging computational, neuroscience, and theoretical knowledge on nonsocial learning in order to develop a deeper understanding of social risk-taking and its link with anxiety. !

Human life plays out in the vast landscape of our social networks. Successful social navigation can help buffer against loneliness and negative interactions, but it requires learning who is connected to whom: the latent structure underlying social relationships (e.g., cliques or hubs). The goal of this project, led by a team of researchers at Brown University, is to reveal the behavioral and neural signatures of how people learn and reason about real-world social networks. Despite the importance of understanding how people learn relational social knowledge, much remains unknown about how the brain organizes this knowledge. Spatial cognition research offers a window into this problem: A long line of animal research has demonstrated that the brain represents information about physical space in a ‘cognitive map’ that binds information about entities in the world and their relationships. Cognitive maps have been shown to speed learning in new or changing environments. This project tests whether, in mentally navigating their social networks, people recruit cognitive map-like representations similar to those used to navigate external physical space. By integrating advanced human imaging methods, computational modeling, social network science, and longitudinal sampling, the investigators will study a large cohort of first-year undergraduate students as they develop new friendships over the course of an academic year to investigate the behavioral and neural signatures of emerging knowledge about real-world social networks. The investigators will identify the neural representations of social information in this complex and dynamic environment. The results of these studies have the potential to transform our knowledge of how humans learn about and navigate through their social world. It has implications for advancing our understanding of social factors that contribute to the persistence of undergraduates in STEM fields. This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NCS), a multidisciplinary program jointly supported by the Directorates for Biology (BIO), Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE).

This work aims to provide a foundation for understanding human learning in real social contexts. An incoming undergraduate class (comprised of individuals who have yet to establish their college social network) provides a unique testbed to track the emergence of a social network and its shifting configuration over the course of the students’ first academic year. The main hypothesis is that an individual’s cognitive map of their social network enables them to navigate more adeptly through their network. The study will probe neural representations of this cognitive structure by means of function magnetic resonance imaging (fMRI) focusing on the hippocampus and orbitofrontal/entorhinal cortex). Different classes of computational models (i.e., state transition-based, successor representation-based, and latent-cause based) will be built to test competing accounts of social network learning and the format of social cognitive maps. These models will probe for person-level parameters to assess whether individual variability biases this learning process, including whether an individual’s position in their community, and thus their capacity to gather information, shapes their ability to build cognitive maps of their social environment. By harnessing both a cross-sectional and longitudinal design, the research will provide an organizing framework that can identify how the brain represents social knowledge about complex environments while precisely modelling how cognitive maps of a social network enable efficient social navigation. This work will provide a window into the neurobiological mechanisms underlying the social learning processes that unfold in the real world.

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.