Proceedings of the Annual Meeting of the Cognitive Science Society

In applied settings, computational models of memory haveproven useful in making principled performance predictions.Specifically, historical data are used to derive modelparameters in order to enable out-of-sample predictions.Parameters are typically fit to meaningful subsets of data.However, labels that demarcate what constitutes a“meaningful” subset are not always available. Here, we utilizea data-driven method to cluster past performance into subsetspossessing statistical similarities. We contrast predictions fromcluster-specific model parameters with predictions based onsubsets that are artifacts of the experimental design. We showthat cluster-based predictions are at least as accurate as thechosen baselines and highlight additional advantages of thedata-driven approach.