CHARACTERIZING MEMORY PROCESSING ACROSS THE HUMAN LIFESPAN: A LARGE SAMPLE ANALYSIS USING HIERARCHICAL BAYESIAN COGNITIVE PROCESSING MODELS

Abstract

As the research focus shifts toward the earlier identification and treatment of Alzheimer's disease (AD), a more sophisticated approach for characterizing cognitive function is needed. Using cognitive processing models combined with hierarchical Bayesian methods (HBCP) is one approach for estimating underlying cognitive processes and variables that cannot be measured using traditional scorning methods. HBCP models were applied to a wordlist memory task in normal aging subjects to characterize changes in underlying processes. 681,428 normal subjects, aged 15-100 years old (Figure 1), were assessed with free recall and recognition memory tasks. The raw score data were the proportion of words correctly recalled overall, of first (primacy) and last (recency) list words recalled, and of words correctly recognized overall. Hits and false alarms were also tracked. One HBCP applied a primacy-recency model of serial position effects to the free recall data (Figure 2a). The other HBCP applied an equal-variance signal detection theory model of discriminability and response bias to the recognition data (Figure 2b). The cognitive processing parameters of each individual were drawn from an assumed Gaussian distribution for their age. For each age, the mean value and the 90% credible intervals were plotted, within which a new individual's parameter values would fall. Mean raw recall scores overall and for primacy (first word) decline after 70 (Figure 3) while the HBCP model of the primacy parameter more sharply declines after 70 years old than the raw primacy scores (Figure 4). Mean raw hit and overall recognition rates slightly decline, and false alarm rates slightly increase after 70 years old (Figure 5) while the HBCP model shows that discriminability gradually declines, and response bias gradually increases over the entire lifespan (Figure 6).