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Can Systematic Drift Rate Variability Replace Random Variability in the Diffusion Decision Model?

Jie Sun1, Daniel Feuerriegel1, Adam F Osth1

  • 1Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia.

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|July 16, 2026
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Summary

The diffusion decision model (DDM) assumes variable evidence accumulation, but random variability is hard to estimate. This study found that while evidence quality varies, other factors likely cause slow errors not captured by the full DDM.

Keywords:
Across-Trial drift rate variabilityDecision-MakingDiffusion decision modelEEGRecognition memory

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Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Psychophysics

Background:

  • The full diffusion decision model (DDM) posits that the rate of evidence accumulation varies across trials.
  • This across-trial drift rate variability helps explain slow errors and asymptotic accuracy but is criticized for being difficult to estimate and potentially ad hoc.

Purpose of the Study:

  • To investigate if random drift rate variability in the DDM reflects meaningful variations in decision evidence quality.
  • To determine if trial-level systematic information can account for the random variability parameter.

Main Methods:

  • Utilized a large recognition memory dataset (n=132) with electroencephalography (EEG) recordings.
  • Systematically linked drift rate to individual trials using exogenous factors (word frequency, study-test lag) and endogenous factors (EEG data).
  • Employed simulations to test the partitioning of random variability by systematic variability.

Main Results:

  • Simulations showed that systematic variability could replace random variability when slow errors were solely due to across-trial drift rate variability.
  • In the experimental data, incorporating systematic variability led to minimal reduction in the random across-trial drift rate variability parameter.
  • This suggests that random variability in the DDM may not solely represent variations in evidence quality.

Conclusions:

  • While decision-relevant evidence quality (drift rate) is expected to vary across trials, the findings indicate that other mechanisms contribute to slow errors.
  • These additional mechanisms are likely not accounted for in the full DDM as currently formulated.
  • Further research is needed to explore alternative models or extensions to the DDM to fully capture the sources of error in decision-making.