Idiographic Memory Modelling in ACT-R Using Alternating Maximum Likelihood Estimation

Abstract

Performance on memory tasks varies systematically between individuals and within individuals over time. However, computational models of memory are often fitted at the group level or summarise performance in a single parameter, potentially obscuring the distinct cognitive processes that give rise to this variation. Here we present a likelihood-based procedure for fitting the multi-parameter ACT-R declarative memory model at the level of the individual. Using this method, we jointly estimate five participant-level or idiographic parameters, each capturing a distinct component of memory performance, together with item-level variation in memorability. A parameter recovery study on synthetic data shows that these participant- and item-level parameters are jointly identifiable from realistic amounts of data on a typical spaced repetition memory task, requiring only about 40–90 observations of response accuracy and speed per participant. We demonstrate the method on a dataset of individuals with mild cognitive impairment and age-matched healthy controls, finding that mild cognitive impairment is marked by differences in three parameters signifying accelerated forgetting, less efficient memory retrieval, and general psychomotor slowing. These results show that the method can successfully decompose observed performance on a memory task into separable, individually interpretable components. More broadly, this work contributes to the development of fine-grained idiographic memory models that can capture dynamics in memory performance both between and within individuals, with applications in experimental research and applied settings, such as computational phenotyping for clinical diagnosis.