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 analysis suggests that the nature of the changes is considerably more complex than previous work suggests.
Archived Files and Locations
|application/pdf 251.4 kB ||