Modeling Change Points and Performance Variability in Large-Scale Naturalistic Data

To explain the performance history of individuals over time, particular features of memories are posited, such as the power law of learning, power law of decay, and the spacing effect. When these features of memory are integrated together into a model of learning and retention, they have been able to account for human performance across a wide range of both applied and laboratory domains. However, these models of learning and retention assume that performance is best accounted for by a continuous performance curve. In contrast to this standard assumption of models of learning and retention, other researcher have argued that ,over time, individuals display sudden discrete shifts in their performance due to changes in strategy and/or memory representation. To compare these two accounts of memory, the standard Predictive Performance Equation (PPE; (Walsh, Gluck, Gunzelmann, Jastrzembski, & Krusmark, 2018)) and was compared to a Change PPE on fits to human performance in a naturalistic data set. We make several hypotheses about the expected characteristics of individual learning curves and the different abilities of the models to account for human performance. Our results show that performance that Change PPE was not only able to be better fit the data compared to the Standard PPE, but that inferred changes in the participant’s performance was associated with greater learning outcomes.