Adaptive categorization in unsupervised learning

Abstract

In 3 experiments, the authors provide evidence for a distinct category-invention process in unsupervised (discovery) learning and set forth a method for observing and investigating that process. In the 1st 2 experiments, the sequencing of unlabeled training instances strongly affected participants' ability to discover patterns (categories) across those instances. In the 3rd experiment, providing diagnostic labels helped participants discover categories and improved learning even for instance sequences that were unlearnable in the earlier experiments. These results are incompatible with models that assume that people learn by incrementally tracking correlations between individual features; instead, they suggest that learners in this study used expectation failure as a trigger to invent distinct categories to represent patterns in the stimuli. The results are explained in terms of J. R. Anderson's (1990, 1991) rational model of categorization, and extensions of this analysis for real-world learning are discussed.