LEARNING CORRELATIONS IN CATEGORIZATION TASKS USING LARGE, ILL-DEFINED CATEGORIES

The experiments revealed whether individual participants are sensitive to exemplar information in the form of within-category correlations b etween stimulus dimensions after training on large overlapping categor ies. Participants were trained in 1 of 2 categorization conditions. Th e sign of the correlation between dimensions differed across condition s, but the categorization rules that best separated the categories wer e identical. An unannounced attribute-prediction task followed categor ization training. Several participants produced predictions consistent with the correct correlation between the dimensions. For other partic ipants, the predictions reflected the correlation only within the regi on they had associated with the given category, even though the catego ries overlapped, suggesting that the decision boundary was explicitly represented in memory. Finally, for other participants, no correlation al information appeared to be accessible for the prediction task.