We examine the neural, cognitive, and computational basis of social connection and interaction
We are particularly interested in understanding how social interactions shape the well-being of self and others and the contexts in which they can improve mental health outcomes. Our research program has two primary goals. The first is to achieve a mechanistic understanding of the factors that improve or impair the well-being of the self and others across multiple levels of analysis (e.g., neurobiological, cognitive, behavioral, interpersonal, societal). The second is to translate this understanding for direct application in medicine and policy.
We use a combination of methods—including computational modeling, functional neuroimaging, and intracranial recordings—to investigate the neural, cognitive, and computational processes underlying human social connection and interaction.
Our research laboratory is part of the Center for Computational Psychiatry, Department of Psychiatry, the Nash Family Department of Neuroscience, and the Friedman Brain Institute at the Icahn School of Medicine at Mount Sinai (New York, NY).
UNDERSTANDING OTHERS
How do people learn, maintain, and update their representations of others? Our research investigates how individuals use complex social representations and knowledge to interpret and act on inferences about others’ beliefs, emotions, and intentions. By examining how these mental representations evolve in real-time, we aim to uncover the neurocomputational processes that drive successful social learning and cooperation. We also examine how variability in these neural representations can produce interpersonal difficulties during social interactions.
HUMAN CONNECTION & PROSOCIALITY
How do social interactions bring people closer together? Our research tests how and under what circumstances prosocial behaviors promote social connection, strengthen interpersonal relationships, and protect against isolation and loneliness. By exploring this question at the contextual, cognitive, and neural level, we aim to achieve a comprehensive understanding about the ways in which social connection shapes both individual and collective well-being.
UNIQUE BRAINS IN A SHARED WORLD
How do individual brains converge or diverge in their perception of the world? Our research investigates the ways in which neural activity aligns or diverges across individuals and the factors that drive both commonalities and differences in social cognition and behavior. By understanding idiosyncrasies in both health and psychiatric disorder, we aim to uncover insights into how people connect, communicate, and collaborate in shared environments.
SCIENCE FOR SOCIETY
We believe that scientific progress thrives when it is open, collaborative, and rooted in community. Our research promotes open science tools, resources for trainees, and the sharing of open-source code and data to foster collaboration and transparency. We are committed to these values both in the lab and in the broader field, recognizing that science is best when it brings together different backgrounds and perspectives. Through partnerships within the local community and the greater New York Metropolitan area, we aim to apply a community-based scientific approach to improve medicine and policy, working toward a more impactful future for all.
In line with the goal of making science more open and accessible, we have created the following resources:
- PSYC 347 - Computational Models of Human Social Behavior and Neuroscience: Undergraduate-level course with syllabus, readings, and Python programming tutorials within an online Jupyter Book that include exercises on how to fit simple models to data (e.g., linear models, non-linear models, reinforcement learning models)
- pyEM: Expectation Maximization with MAP estimation in Python: Python toolbox for implementing the Hierarchical Expectation Maximization algorithm with MAP estimation for fitting models to behavioral data
- Executable Book Template: Github template for compiling data results and visualizations into an interactive executable book website
- Summer Program in Computational Psychiatry Education: Educational initiative aimed at introducing high school and college students to the intersection of computer science and psychiatry/neuroscience with several introductory-level exercises for learning Python programming and working with data for the first time