Transfer of Learned Cognitive Flexibility to Novel Stimuli and Task Sets

Tanya Wen, Raphael M. Geddert, Seth Madlon-Kay, Tobias Egner

Psychological Science · 2023 · Volume 34 · Issue 4

Abstract

Adaptive behavior requires learning about the structure of one’s 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, specifically expectations about cognitive control demands. Over three experiments, participants (Amazon Mechanical Turk workers; N = ~80 adults per group) 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, respectively) before transitioning to a medium-volatility environment. Using reinforcement-learning modeling, we consistently found that previous exposure to high task rule volatilities led to faster adaptation to rule changes in the subsequent transfer phase. These transfers of expectations about cognitive flexibility demands were both task independent (Experiment 2) and stimulus independent (Experiment 3), thus demonstrating the formation and generalization of environmental structure knowledge to guide cognitive control.

Keywords

cognitive flexibility, reinforcement learning, generalization, task switching, meta-flexibility, open data, open materials

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