The capacity coefficient function is a well-established, model-based measure comparing performance with multiple sources of information together to performance on each of those information sources in isolation. Because it is a function across time, it may contain a large amount of information about a participant. In many applications, this information has been ignored, either by using qualitative assessment of the function or by using a single summary statistic. Recent work has demonstrated the efficacy of functional principal components analysis for extracting important information about the capacity function. We extend this work by applying clustering techniques to examine individual capacity differences in configural learning.