Proceedings of the Annual Meeting of the Cognitive Science Society

How are exceptions to a probabilistic generalization learned? The present results suggest exceptions are learned in partby selectively suppressing the competing category, as opposed to only increasing knowledge of exceptions. Participantswere exposed to a mini-artificial language with a probabilistic generalization (80-20%) that mapped labels to categories ofimages (faces and scenes). Mouse-tracking trajectories determined the degree to which the generalization served as a lureto exceptions, compared to a separate baseline condition. Over time, the generalization became suppressed in a context-sensitive way: for exception items only. This extends retrieval induced forgetting, in which a particular item is suppresseddue to competition from partial retrieval, to include the entire conceptual category. Post-test revealed high item-specificaccuracy, even though category recognition was sufficient for the task.