UCLA

Humans readily extract statistical regularities from experience, yet natural environments require flexible adaptation when associative structures shift across changing contexts, often without warning. Across two experiments, we show that humans can incidentally learn overlapping and conflicting visual associations even when contexts dynamically alternate and remain unsignaled or only minimally cued. To probe the computational mechanisms supporting this adaptive capacity, we trained recurrent neural networks with gated recurrent units on the same statistical learning task without providing any explicit context information. Models with moderate initialized weight variance demonstrated context-dependent learning and most closely matched human learning behavior. These models spontaneously developed distributed internal representations of context that robustly separated conflicting associations and supported rapid adaptation to latent context shifts. Together, these behavioral and computational results advance our understanding of how humans and artificial systems can successfully learn and flexibly retrieve context-dependent associations under challenging conditions.