Environment-sensitive generalization and exploration strategies

Humans are remarkably efficient in exploring vast decision spaces. However, environments vary in how they are structured, raising the question of whether and how people adapt their generalization and exploration strategies accordingly. People might flexibly adapt their behavior within each environment (i.e., local adaptation), or also learn to associate specific strategies with distinct environments (i.e., meta-learning). Using a spatially correlated bandit paradigm and computational modeling, we examined how humans adapt their search strategy across two environments optimized for different levels of generalization. Across three experiments, participants showed more generalization and more random exploration in environments with stronger spatial correlations. Moreover, they meta-learned to associate different degrees of generalization with different environmental cues. These findings illustrate how humans control and adapt search strategies across diverse decision spaces.