When more is less: negative exposure effects in unsupervised learning.

In this article, two broad classes of models of unsupervised learning are compared: correlation tracking models, according to which learning is expected to increase monotonically with exposure to instances, and category invention models, which can accommodate specific violations of monotonicity (negative exposure effects). In two experiments, increasing the number of training instances had a negative rather than a positive effect on unsupervised learning, a clear violation of monotonicity. The results of these experiments are then compared with the predictions of two computational models, one a category invention model and the other a correlation tracking model. The category invention model was able to reproduce the qualitative pattern of results from the human data, whereas the correlation tracking model was not. Overall, these results provide strong evidence for the existence of a discrete category invention process in unsupervised learning.