Abstract
In recent years, mathematical models of decision making, such as the diffusion model, have been endorsed in individual differences research. These models can disentangle different components of the decision process, like processing speed, speed–accuracy trade-offs, and duration of non-decisional processes. The diffusion model estimates individual parameters of cognitive process components, thus allowing the study of individual differences. These parameters are often assumed to show trait-like properties, that is, within-person stability across tasks and time. However, the assumption of temporal stability has so far been insufficiently investigated. With this work, we explore stability and change in diffusion model parameters by following over 270 participants across a time period of two years. We analysed four different aspects of stability and change: rank-order stability, mean-level change, individual differences in change, and profile stability. Diffusion model parameters showed strong rank-order stability and mean-level changes in processing speed and speed–accuracy trade-offs that could be attributed to practice effects. At the same time, people differed little in these patterns across time. In addition, profiles of individual diffusion model parameters proved to be stable over time. We discuss implications of these findings for the use of the diffusion model in individual differences research.
Highlights
The results showed stability across both tasks and time for all three main diffusion model parameters, with speed of information processing showing the highest stability and consistency: the latent trait factor generalizing over both time points and both tasks on average accounted for 44%
We report results on the rank-order stability, mean-level change and individual differences in change for each of the three main diffusion model model parameters (ν, a, τ)
We found the same pattern for boundary separation (a): Rank-order stability was high, with correlations getting slightly smaller across larger time periods (e.g., r = 0.90 from T2 to T3, but only r = 0.83 from T1 to T3)
Summary
The use of mathematical process models of cognition has seen an upsurge in research on individual differences in cognitive abilities and intelligence (Ratcliff and Childers 2015; Ratcliff et al 2011; Schmiedek et al 2007; Schubert and Frischkorn 2020; Voss et al 2013). It has been proposed that our understanding of intelligence and cognition can profit from such modelling approaches, which disentangle different cognitive processes and components involved in solving cognitive tasks (Frischkorn and Schubert 2018; Schubert and Frischkorn 2020). One crucial aspect when employing mathematical models to estimate cognitive parameters to further our understanding of individual differences is whether these parameters have trait-like properties, that is, whether they measure processes which are stable and consistent across tasks and time
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