Tracking variability in learning: Contrasting statistical and similarity-based accounts

Learning to categorize objects involves learning which sources of variability are meaningful and which should be ignored or generalized. In this light, theories and models of category learning can be viewed as accounts of how people capture and represent meaningful variation. Similarity-based models, such as prototype and exemplar models, cannot correctly pre-dict that humans classify a stimulus halfway between the near-est members of a low-variability and high-variability category into the high-variability category. Distributional accounts, de-scending from the unequal variance signal detection model, can accommodate the result. We present a simple extension to similarity-based models that allows them to display the sensi-tivity to category variability that humans display. We conclude by discussing what constitutes similarity-based representation