Assessing Subject Parameter Recovery in Extended Encoding and Updating M3 Models: Implications for Experimental Design

The memory measurement model framework (M3; Oberauer & Lewandowsky, 2018) comprises a collection of cognitive measurement models that isolate parameters associated with distinct working memory processes in widely used tasks such as simple or complex span and memory updating. By transforming activation of item categories into recall probabilities, the model framework estimates contributions of memory processes to working memory performance. We assessed the parameter recovery of the extended encoding and updating models within the M3 framework using our own hierarchical implementation in STAN. We assessed the subject-parameter recovery of the complex span extended encoding and updating models, with a particular focus on time-dependent parameters for removal, extended encoding, and extended updating, and the time-independent parameter for immediate deletion. Simulations revealed difficulties in recovering the time-dependent removal parameter in both models, while recovery for the extended encoding and extended updating parameters was sufficient under certain conditions. The time-independent immediate deletion parameter could only be recovered sufficiently with a disproportional amount of data. Based on the simulation results, the current state of the extended encoding and updating models of the M3 framework does not allow the estimation of subject-level parameters with reasonable efficacy and precision. We provide recommendations for experimental designs to maximize parameter recovery and discuss possible workarounds to improve subject parameter recovery.