Many animals possess ancient brain systems that rapidly detect events important for survival. We study how the human brain identifies approaching threats and opportunities in dynamic environments. Using high-resolution neuroimaging and computational models inspired by biological vision, we investigate how the superior colliculus and connected brain networks represent approaching objects, guide attention, and contribute to emotional experience. This work reveals how evolutionarily conserved neural circuits interact with the cortex to shape perception, learning, and emotional experience.

The amygdala is often described as the brain's "fear center," but growing evidence suggests that its role is far more complex. Our research investigates how the amygdala helps people learn from emotional experiences and predict future outcomes. By combining neuroimaging, computational models, and machine learning, we examine the information represented in amygdala activity and how these representations contribute to emotional experience. This work aims to uncover the computational principles through which the brain learns what matters and transforms those signals into feelings.

People can dramatically change how they feel by changing how they think. But what operations allow this transformation to occur? We use large language models as computational tools for uncovering the cognitive processes that underlie emotion regulation. By analyzing how people describe emotional events and linking these representations to patterns of brain activity, we seek to identify the mechanisms through which thoughts alter emotional experience. This work aims to develop a computational theory of emotion regulation grounded in both language and neuroscience.

Interpreting how another person is feeling from a complex range of cues in varied contexts is a challenging problem—and yet the human brain solves it every day. Many studies of emotion perception use contextless, artificial stimuli, limiting our ability to understand this process as it happens in the real world. In this project, we explore how emotion understanding occurs in naturalistic contexts by using artificial neural networks to model different emotion signals: facial expressions, vocal tone, and language. We examine how each of these relates to the judgments humans make about others’ emotions as well as brain activity in regions known to process social emotions.

For decades, scientists have sought to map the structure of emotion. One influential theory proposes that emotions can be organized along dimensions such as pleasantness and arousal, whereas other theories emphasize emotion categories such as fear, anger, and sadness. Our research uses computational models, behavioral experiments, and brain imaging to evaluate competing theories of emotional organization. By comparing predictions from these models against patterns of human judgments and neural activity, we seek to identify the representations that best capture the structure of emotional experience and reveal how emotions are organized in the mind and brain.