Modeling Multitrial Free Recall with Unknown Rehearsal Times

Modeling Multitrial Free Recall with Unknown Rehearsal Times James P. Pooley (jpooley@uci.edu) Michael D. Lee (mdlee@uci.edu) Department of Cognitive Sciences, University of California, Irvine Irvine, CA, 92697-5100 William R. Shankle (rshankle@mccare.com) Medical Care Corporation, 19782 MacArthur Boulevard Irvine, CA 92612 Abstract Introduction the database from which they are obtained. Following this, we present the details of the memory model we use to explain these data and as well as the Bayesian statisti- cal methodology used to connect the model to our data. It is shown that a basic version of this model, which does not include the possibility of rehearsal, is unable to ac- count for our data, agreeing with current results in the memory literature. This leads to a modification of the model that allows the rehearsal times needed to apply the model to be inferred from the data. In addition, we show that the model can be fitted easily to more complex data sets than are typically used in previous applications. The results of fitting this model to this more complex data set, including inferences about the latent patterns of rehearsal, are then presented. We conclude with a dis- cussion on the limitations of the current approach and suggest potential ways to improve our results, and also discuss the relevance of this research for clinical applica- tions and for cognitive psychologists interested in basic memory research. Quantitative models of human memory often rely on as- sumed latent memory processes. These assumptions are common to a range of memory models, many of which are based on different theoretical motivations (e.g., two- store vs. unitary accounts of memory), and are used to account for a similarly diverse range of observed mem- ory phenomena. In general, however, these memory models are typi- cally developed to account for data collected in the en- vironment of a controlled laboratory experiment, and problems can arise when the model is forced to leave this environment and account for data collected in less controlled settings. These problems should be consid- ered by the developers and users of memory models, as they help to determine the effectiveness of the model as an explanatory tool. In this paper, we outline one such problem that arises in the context of applying a currently popular memory model to clinical data relating to the di- agnosis and assessment of Alzheimer’s disease and re- lated disorders (ADRD). The issue is that overt rehearsal times, often collected in the laboratory, are not available in this clinical setting. Instead, we show how Bayesian statistical methods can be used to infer these rehearsal times from the available behavioral data. The plan of the paper is as follows. In the next section, we provide an overview of our clinical memory data and Our memory data are a subset of a large clinical ADRD database (e.g., Pooley, Lee, & Shankle, 2011). This database contains a wealth of information on thousands of ADRD patients—and often on their caregivers as well—who visit neurology clinics for dementia screen- ing and assessment. Among other things, this informa- tion includes demographic information and information concerning personal medical history. In addition to this medical information, this database also contains the re- sults of various psychological tasks that are administered as part of the cognitive portion of these dementia assess- ments. Of these numerous psychological tasks, however, we focus exclusively on a sequence of four free recall memory tasks, and we limit our focus to the data of 541 “cognitively normal patients” (i.e., those individuals judged not to have a form of ADRD by a trained clini- can). In this sense, we are treating the data as standard memory data coming from normally functioning adults. Collectively, these four memory tasks constitute a sin- gle multitrial free recall (MFR) task. Stimuli for this MFR task consisted of words based on the CERAD (Consortium to Establish a Registry for Alzheimers Dis- ease) word list (Morris, Mohs, Rogers, et. al, 1988), which serves as the basis for the neuropsychological por- Quantitative models of human memory often rely on as- sumed latent memory processes, such as patterns of re- hearsal of the words on a study list. Consequently, the ap- plication of memory models that assume latent rehearsals typically make use of overt rehearsal data. However, these data are not always available in some settings where the application of memory models has proven useful (e.g., clinical assessments of memory performance). In this paper, we show Bayesian statistical methodology can be used to infer the latent pattern of rehearsals needed to suc- cessfully apply a temporal model of memory to a clinical data set. We discuss the relevance of this research for those interested in neuropsychological assessment as well as cognitive psychologists interested in basic memory re- search. Keywords: Alzheimer’s disease and related disorders; Cognitive psychometrics; Hierarchical Bayesian mod- eling; Human memory; Missing data Task and Data