Predicting Learning and Retention of a Complex Task Using a Cognitive Architecture

We use a model to explore the implications of ACT-R's learning and forgetting mechanisms to understand learning and retention on a complex task. The model performs a spreadsheet task that has 14 non-iterated subtasks. The model predicts a learning curve and knowledge decay for different learning stages. The model's learning curve fits the human data well for the first four trials without decay. When decay is examined, however, we have to make modifications to the retention equation for the model's predictions to match data and the shapes predicted by the other learning theories. To fix this anomaly, we modified the effect of time on decay (adjusting time outside the experiment to less than the effect of time in the experiment) and the strength of newly learned memories (less well known than the previous default value). From these results, we learn that training and testing have been confounded in many studies.