Abstract
Animals continuously transform sensory inputs into representations that guide behavior. Although the amygdala is a brain structure critical for learning and choice, how natural sensory experience is represented within this circuit remains unknown. Here, we identify a representation of the visual environment in the human basolateral amygdala (BLA) that contains information relevant for learning and choice. Using image-computable encoding models trained on responses to naturalistic videos, we recover a continuous, low-dimensional representation of visual experience in the BLA. The representation generalizes across individuals and independent datasets and prospectively predicts BLA responses to novel visual stimuli generated through closed-loop image synthesis. Signals derived from the representation also informed reinforcement-learning models of human choice. Together, these findings show that the human BLA organizes complex visual experience into a structured representation that can support learning and flexible behavioral responses.