A representation of the visual environment in the human basolateral amygdala supports learning and choice
- 1Emory University
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.
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BibTeX
@misc{jang2026representation,
title = {{A representation of the visual environment in the human basolateral amygdala supports learning and choice}},
author = {G. Jang and K. Riels and E. Aberman and G. Tao and P. A. Kragel},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.07.757347},
url = {https://www.biorxiv.org/content/10.64898/2026.10.07.757347v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - A representation of the visual environment in the human basolateral amygdala supports learning and choice
AU - G. Jang
AU - K. Riels
AU - E. Aberman
AU - G. Tao
AU - P. A. Kragel
PY - 2026
DA - 2026/10/07
PB - bioRxiv
DO - 10.64898/2026.10.07.757347
UR - https://www.biorxiv.org/content/10.64898/2026.10.07.757347v1
AB - 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.
ER -