Recasting adaptation as strategy inference

Abstract

Flexible adaptation to uncertain and changing environments requires dynamic adjustments in behavioral strategies. While classical learning theories emphasize incremental strengthening of local stimulus-action associations in adaptation, emerging evidence suggests that global-level strategy representations may enable rapid inference of adaptive behaviors, thus promoting efficient decision-making. However, it remains unclear to what extent direct inference over putative strategies can fully account for human adaptation across diverse statistical contexts. Here, we demonstrate clear behavioral markers supporting the broad use of inference over strategies in human adapting to rapid changes. These markers are fully explained solely by a novel model of inference over a structured space of strategies. We further show that inference over strategies is influenced by latent contextual statistics that are beyond the scope of models based on incremental learning. Taken together, these results establish the importance of direct inference over an abstract strategy space for flexible adaptation in humans.