Disentangling interacting psychological processes: A model-based approach to cognitive aging.

Abstract

We propose a new approach to cognitive aging research, in which detailed cognitive modelling of a single complex task affords simultaneous measures of the major mechanisms proposed to explain age-related deficits: capacity limits, processing speed, inhibition, and executive function. The validity of these measures rests on their well-defined roles of the model parameters and the model’s precise and comprehensive account of the behaviour of our healthy older (63–78 years) and younger (18–28 years) participants. Processing speed was identified as the primary cause of age-related deficits. Executive control was better for older than younger participants when task demands were lower, but as demands increased this advantage reduced or disappeared, suggesting age-related capacity limitations. These results show that detailed process modelling of a complex task provides a powerful, efficient, and conceptually rich way of gaining new insights into the non-linear interactions among psychological mechanisms that underpin cognitive aging.

Winner Takes All! What Are Race Models, and Why and How Should Psychologists Use Them?

“…Our primary finding was that aging affected mainly processing speed, both central and peripheral, and that only a small part of slowing was due to caution increasing with age (Heathcote et al, 2022). There was no evidence of any age-related deficit in executive-function measures except response inhibition; that deficit was mediated by slowing in the speed of the inhibitory runner.…”

“…Slowing with age is pervasive, so leading theories posit a reduction in cognitive-processing speed with age as a broad explanatory factor, but age-related performance decrements have also been attributed to reduced executive function (i.e., reduced ability to flexibly maintain and update goals). We (Heathcote et al, 2022) investigated these theories using a novel stop-signal task (see Fig. 5) requiring inhibition of both easier and harder binary choices, as well as proactive control on go trials based on cues about the response most likely to be correct on the upcoming trial.…”

“…Illustration of a hybrid ex-Gaussian/racing diffusion model applied to a stop-signal task (Heathcote et al, 2022). The go (i.e., choice) stimuli were 20 × 20 grids of blue and orange squares whose position changed randomly on each screen refresh (see https://youtu.be/kXZk_jCHjkM for a video of the task).…”

“…These results not only show how race models can provide theoretical insights, but also highlight the challenges with respect to parameter estimation and interpretability. First, the descriptive ex-Gaussian account of the stop runner (Matzke et al, 2017) had to be retained in our hybrid model (Heathcote et al, 2022) because stop-signal models in which all runners are evidence-accumulation processes result in extremely poor parameter recovery (Matzke et al, 2020). In contrast, the hybrid model’s parameters are well recovered, even in our complex design, the key factor being that the ex-Gaussian account does not require the estimation of the nondecision time of the stop runner.…”