ArticleJournal of Intelligence2021
Stability and Change in Diffusion Model Parameters over Two Years.
Article in Journal of Intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Deep Learning for Image Watermarking: A Comprehensive Review and Analysis of Techniques, Challenges, and Applications.Sensors (Basel, Switzerland) · 2026Review
- Using the death/suicide implicit association task to prospectively predict near-term suicidal behavior in high-risk veterans.Behaviour research and therapy · 2025Article
- Neural superstatistics for Bayesian estimation of dynamic cognitive models.Scientific reports · 2023Article
- Neurocomputational models of altruistic decision-making and social motives: Advances, pitfalls, and future directions.Wiley interdisciplinary reviews. Cognitive science · 2021Review
Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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Registered trials
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