A new method for investigating prototype learning.

Past researchers studied prototype learning by asking subjects to categorize exemplars constructed from different prototypes. This procedure is less than ideal because learning must be inferred from the percentage of correct categorizations pooled across many trials or subjects or both. An alternative procedure is proposed in which subjects are asked to reproduce their estimate of the prototype on each trial, thereby providing trial-by-trial information about changes in the estimated prototype. This procedure provides straightforward tests of three basic properties implied by several prototype learning models: additivity across exemplars, noninterference among features, and time invariance of serial position effects. An experiment is reported and the results provide reasonably good support for the properties of additivity and noninterference, but clear violations of time invariance were observed. The implications of the results for distributedmemory models and multiple-trace models of prototype learning are discussed. It seems quite easy to produce an image of an ideal circle despite the fact that our experience is based on thousands of and reproduce a single image from a myriad of examples is