The impact of category separation on unsupervised categorization

Most previous research on unsupervised categorization has used unconstrained tasks in which no instructions are provided about the underlying category structure or in which the stimuli are not clustered into categories. Few studies have investigated constrained tasks in which the goal is to learn predefined stimulus clusters in the absence of feedback. These studies have generally reported good performance when the stimulus clusters could be separated by a one-dimensional rule. In the present study, we investigated the limits of this ability. Results suggest that even when two stimulus clusters are as widely separated, as in previous studies, performance is poor if within-category variance on the relevant dimension is nonnegligible. In fact, under these conditions, many participants failed even to identify the single relevant stimulus dimension. This poor performance is generally incompatible with all current models of unsupervised category learning.

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