Optimizing Memory Retention with Cognitive Models

When individuals learn facts (e.g., foreign language vocabulary) over multiple sessions, the durability of learning is strongly influenced by the temporal distribution of study (Cepeda, Pashler, Vul, Wixted, & Rohrer, 2006). Computational models have been developed to explain this phenomenon known as the distributed practice effect. These models predict the accuracy of recall following a particular study schedule and retention interval. To the degree that the models embody mechanisms of human memory, they can also be used to determine the spacing of study that maximizes retention. We examine two memory models (Pavlik & Anderson, 2005; Mozer, Pashler, Lindsey, & Vul, submitted) that provide differing explanations of the distributed practice effect. Although both models fit experimental data, we show that they make robust and opposing predictions concerning the optimal spacing of study sessions. The Pavlik and Anderson model robustly predicts that contracting spacing is best over a range of model parameters and retention intervals; that is, with three study sessions, the model suggests that the lag between sessions one and two should be larger than the lag between sessions two and three. In contrast, the Mozer et al. model predicts equal or expanding spacing is best for most material and retention intervals. The limited experimental data pertinent to this disagreement appear to be consistent with the latter prediction. The strong contrast between the models calls for further empirical work to evaluate their opposing predictions.