Connecting rule-abstraction and model-based choice across disparate learning tasks

Abstract

Recent research has identified key differences in the way individuals make decisions in predictive learning tasks, including the use of feature- and rule-based strategies in causal learning and model-based versus model-free choices in reinforcement learning. These results suggest that people rely to varying degrees on separable psychological processes. However, the relationship between these types of learning strategies has not been explored in any depth. This study investigated the relationship between feature- vs rule-based strategies in a causal learning task and indices of model-free and model-based choice in a two-step reinforcement learning procedure. We found that rule-based transfer was associated with the use of model-based, but not model-free responding in a two-step task.


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