High-variability training does not enhance generalization in the prototype-distortion paradigm.

Classic studies of human categorization learning provided evidence that high-variability training in the prototype-distortion paradigm enhances subsequent generalization to novel test patterns from the learned categories. More recent work suggests, however, that when the number of training trials is equated across low-variability and high-variability training conditions, it is low-variability training that yields better generalization performance. Whereas the recent studies used cartoon-animal stimuli varying along binary-valued dimensions, in the present work we return to the use of prototype-distorted dot-pattern stimuli that had been used in the original classic studies. In accord with the recent findings, we observe that high-variability training does not enhance generalization in the dot-pattern prototype-distortion paradigm when the total number of training trials is equated across the conditions, even when training with very large numbers of distinct instances. A baseline version of an exemplar model captures the major qualitative pattern of results in the experiment, as do prototype models that make allowance for changes in parameter settings across the different training conditions. Based on the modeling results, we hypothesize that although high-variability training does not enhance generalization in the prototype-distortion paradigm, it may do so when participants learn more complex category structures.