ArticleComputational brain & behavior2026
Can Systematic Drift Rate Variability Replace Random Variability in the Diffusion Decision Model?
Article in Computational brain & behavior, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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Abstract
The full diffusion decision model (DDM) assumes that the rate of evidence accumulation varies across trials, which allows the model to account for slow errors and asymptotic accuracy. This across-trial drift rate variability, however, has been criticised for being difficult to estimate and ad hoc. To examine whether random drift rate variability corresponds to meaningful variations in the quality of decision evidence, we assessed whether the model-estimated drift rate variability parameter can be partitioned by trial-level systematic drift rate information. Using a large recognition memory dataset with electroencephalography (EEG) recordings ( Supplementary Information: The online version contains supplementary material available at 10.1007/s42113-026-00264-0.
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