Transparency, replicability, and discovery in cognitive aging research: A computational modeling approach

Healthy aging is associated with deficits in performance on episodic memory tasks. Popular verbal theories of the mechanisms underlying this decrement have primarily focused on inferred changes in associative memory. However, performance on any task is the result of interactions between different neurocognitive mechanisms, such as perceptuomotor, memory, and decision-making processes. As a result, age-related differences in performance could arise from multiple processes, which could lead to incomplete or incorrect conclusions about the sources of aging effects. In addition, standard statistical comparisons of group-level summary statistics, such as mean accuracy, may not provide sufficient information to allow detailed mechanistic explanations of age-related change. We argue that these and other drawbacks of relying exclusively on verbal theories can hamper replicability, transparency, and scientific progress in aging research and psychological science more generally, and that computational modeling is a tool that can address many of these limitations. Computational models make mathematically transparent claims about how latent processes give rise to observed behavior and decompose an individual's performance into model parameters governing hypothesized mechanisms. In this work, we present a short memory task designed for and analyzed with mechanistic model-based approaches. We provide an example of a computational model and fit the model to data from young and older adults with hierarchical Bayesian techniques in order to (a) detect differences in latent cognitive processes between young and older adults (as well as individual participants), (b) quantitatively compare models to assess different processes that could underlie performance, and (c) simulate data to make predictions for future experiments based on model mechanisms. We argue that computational modeling is a powerful tool to examine age differences in latent processes, make theories more transparent, and facilitate discovery in cognitive aging research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).