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keywords:
behavioral science
computational modeling
decision making
learning
psychology
People can adjust how fast they update task rules, depending on the volatility of their environment. We investigated whether this adaptivity is primarily driven by recently experienced volatility in task demands, or can also be shaped by learned, environment-specific associations with expected levels of volatility. To this end, we trained participants on a Wisconsin Card Sorting Task where different environments required different speeds of task rule updating. We demonstrate that, initially, participants updated strategies depending on the most recent experienced levels of volatility and feedback (Experiment 1). However, after extensive (four days) training (Experiment 2), participants also developed environment-specific associations. Our findings provide important insights in how people learn to regulate cognitive flexibility.