Who says models can only do what you tell them? Unsupervised category learning data, fits, and predictions

How do people learn and organize examples in the absence of a teacher? This paper explores this question through a examination of human data and computational modeling results. The SUSTAIN (Supervised and Unsupervised STratified Incremental Network) model successfully fits human learning data drawn from two published studies. The first study examines how correlations between features can facilitate unsupervised learning. The second set of studies examines the role that similarity and attention play in unsupervised category construction (i.e., sorting) tasks. Importantly, SUSTAIN suggests two novel behavioral predictions that are confirmed.