Weikang Shi

NIMH K99 Postdoctoral Fellow  ·  Yale University

Weikang (Wilbur) Shi

I study how the brain computes strategic social decisions, with a focus on prosocial behavior in freely moving animals. My work integrates naturalistic behavioral paradigms, large-scale neural recordings, and computational modeling to link social strategies to their underlying circuit mechanisms.

My PhD at Washington University in St. Louis with Dr. Camillo Padoa-Schioppa used microstimulation and electrophysiology to establish causal links between orbitofrontal cortex activity and economic choice. My postdoc at Yale, supported by an NIH K99 Award, extended this to cooperative social behavior — developing naturalistic paradigms and wireless neural recording systems to study social decision-making in freely interacting dyads, in collaboration with Drs. Steve Chang, Anirvan Nandy, and Monika Jadi at the Wu Tsai Institute.

My long-term goal is a circuit-level model of how the cortico-striatal pathway generates prosocial decisions — with relevance to disorders of social cognition and to the development of more sophisticated social agents in AI.

New Haven, CT  ·  weikang.w.shi@gmail.com Google Scholar GitHub X / Twitter Bluesky

I am seeking tenure-track faculty positions beginning Fall 2026.

Full curriculum vitae including talks and service.

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Funding & Fellowships

Research Program

How does the brain compute strategic prosocial decisions? Addressing this requires a model system that is both cognitively sophisticated and naturally prosocial, an experimental approach that captures the full complexity of naturalistic social interactions, and computational models that formalize the underlying strategies and link them to neural mechanisms.

My planned research program will focus on the following directions:

Behavior

Naturalistic cooperative paradigms in freely moving dyads, drawing on game theory to systematically probe social strategies across a range of effort, reward, and social contexts, with markerless tracking to capture behavioral dynamics at high resolution.

Computation

Computational models to formalize the mechanisms underlying social decisions, including drift-diffusion models to capture social evidence accumulation, Bayesian models to characterize interaction dynamics, and reinforcement learning to understand how strategies are learned and updated over time.

Circuit

High-density wireless recordings across prefrontal and striatal areas, analyzed with population-level methods to identify the neural substrates of prosocial choice, and synthesized through recurrent neural network models developed in collaboration with computational neuroscientists.

Long-term vision

Expanding from dyadic interactions to group social dynamics, and from correlation to causation through real-time circuit manipulation, building toward a comprehensive model of the social brain with relevance to neuropsychiatric disorders of social cognition.

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Publications

Peer-Reviewed · Selected

Book Chapters

Selected Talks

Selected Awards