Rule abstraction, model-based choice, and cognitive reflection

Abstract

Numerous tasks in learning and cognition have demonstrated differences in response patterns that may reflect the operation of two distinct systems. For example, causal and reinforcement learning tasks each show responding that considers abstract structure as well as responding based on simple associations. Nevertheless, there has been little attempt to verify whether these tasks are measuring related processes. The current study therefore investigated the relationship between rule- and feature-based generalization in a causal learning task, and model-based and model-free responding in a reinforcement learning task, including cognitive reflection as a predictor of individual tendencies to use controlled, deliberative processes in these tasks. We found that the use of rule-based generalization in a patterning task was a significant predictor of model-based, but not model-free, choice. Individual differences in cognitive reflection were significantly correlated with performance in both tasks, although this did not predict variation in model-based choice independently of rule-based generalization. Thus, although there is evidence of stable individual differences in the use of higher order processes across tasks, there may also be differences in mechanisms that these tasks reveal.

Keywords: Associative learning; Cognitive control; Individual differences; Model-based vs. model-free reinforcement learning; Rule vs. feature generalization.