Deep Successor Representation Explains Latent Learning Sensitivity

Menezes, Zeng, and Cheng (published March 24, 2026) use deep successor representation (DSR) models to explain how pre-exposure exploration shapes latent learning. They find targeted pre-exposure aligned with future reward location significantly improves subsequent reward learning compared with random, mistargeted, or no exploration, and this effect generalizes across action-selection strategies. The study links DSR-encoded spatial structure to faster goal-directed learning and provides open-source code.