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Prototype and exemplar models form two extremes in a class of mixture model accounts of human category learning. This class of models allows flexible representations that can interpolate from simple prototypes to highly differentiated exem-plar accounts. We apply one such framework to data that afford an insight into the nature of representational changes during category learning. While generally supporting the notion of a prototype-to-exemplar shift during learning, the detailed analysisfatcat:4g6tnyqzuzakzicgqnv6ugxmg4