Distinguishing the contributions of implicit and explicit processes to performance of the weather prediction task

Abstract

Examinations of the cognitive neuroscience of category learning frequently rely on probabilistic classification-learning tasks-namely, the weather prediction task (WPT)-to study the neural mechanisms of implicit learning. Accumulating evidence suggests that the task also depends on explicit-learning processes. The present investigation manipulated the WPT to assess the specific contributions of implicit- and explicit-learning processes to performance, with a particular focus on how the contributions of these processes change as the task progresses. In Experiment 1, a manipulation designed to disrupt implicit-learning processes had no effect on classification accuracy or the distribution of individual response strategies. In Experiment 2, by contrast, a manipulation designed to disrupt explicit-learning processes substantially reduced classification accuracy and reduced the number of participants who relied on a correct response strategy. The present findings suggest that WPT learning is not an effective tool for investigating nondeclarative learning processes.