An application of multinomial processing tree models and Bayesian methods to understanding memory impairment

Abstract

We model word-list learning over sequences of immediate and delayed free recall tasks to study the impact of memory impairment on episodic memory. We use a previously developed Multinomial Processing Tree (MPT) model of encoding, retrieval, and learning (Alexander et al., 2016), and apply it to behavioral data from thousands of patients tested tens of thousands of times in a cognitive disorders clinic. The patients were independently diagnosed, using the Functional Assessment Staging Test (FAST), into six stages of impairment. We apply hierarchical and latent-mixture versions of the MPT model to patients in each FAST stage, exploring individual differences among people and item-position effects across the word lists. Our results show clear and theoretically interpretable regularities in how model parameters change over item positions, corresponding to standard primacy and recency effects in free recall. Accordingly, we develop an extended model that directly incorporates theoretical assumptions about serial position. Inferences from this model allow us to reach conclusions about how learning, encoding, and retrieval processes change as memory impairment progresses.