Transfer of learned cognitive flexibility to novel stimuli and task sets

Adaptive behavior requires learning about the structure of the environment to derive optimal action policies, and previous studies have documented transfer of such structural knowledge to bias choices in new environments. Here, we asked whether people could also acquire and transfer more abstract knowledge across different task environments, in particular, expectations about demands on cognitive control. Over three experiments, participants performed a probabilistic card-sorting task in environments of either a low or high volatility of task rule changes (requiring low or high cognitive flexibility) before transitioning to a medium-volatility environment. Using reinforcement learning modeling, we consistently found that previous exposure to high task rule volatility led to faster adaptation to rule changes in the subsequent transfer phase. This transfer of expectations about demands on cognitive flexibility was both task- (Experiment 2) and stimulus- (Experiment 3) independent, thus demonstrating the formation and generalization of environmental structure knowledge to guide cognitive control.