A numerical comparison of three potential learning and forgetting models

Researchers from various disciplines have been pursuing better understanding of learning and forgetting processes. A range of mathematical models, sometimes based on empirical data, has been developed to measure the effect of production breaks on the learning process. Thus far, researchers and practitioners have not resolved the issue of how learning and forgetting interacts. However, some of the developed models seem promising. This paper investigates and discusses three such potential models; namely, the learn–forget curve model (LFCM), the recency model (RC), and the power integration diffusion (PID) with their similarities and differences addressed. Results showed that for a moderate learning scenario, where the learning rate classifies a task as being more cognitive than motor, the three models produced very close predictions to one another for all values of production breaks and initial processing times. Furthermore, the PID and RC models, and the PID and the LFCM models, could best be differentiated for cases characterised by high initial processing times, long production breaks, and for tasks that identified as being more motor than cognitive. Numerical results for the PID and LFCM suggested that as learning becomes slower forgetting becomes faster. This result is inconsistent with that of the RC model, which suggests that fast (slow) learners forget faster (slower).