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Modeling across-trial variability in the Wald drift rate parameter.

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This summary is machine-generated.

This study introduces a new shifted-Wald model accounting for trial-to-trial variability in drift rate for reaction-time tasks. The enhanced model improves parameter recovery, aiding cognitive process analysis.

Keywords:
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Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Psychometrics

Background:

  • The shifted-Wald model is standard for analyzing one-choice reaction-time tasks.
  • Assumes a constant drift rate, but endogenous factors (attention, fatigue) suggest variability.
  • This variability may impact the accuracy of reaction-time task analysis.

Purpose of the Study:

  • To develop and validate an enhanced shifted-Wald model incorporating trial-specific drift rate variability.
  • To investigate the impact of different positive-valued distributions (truncated normal, gamma) on model performance.
  • To assess parameter recovery and model fit using Bayesian methods.

Main Methods:

  • Introduced trial-specific drift rate governed by truncated normal and gamma distributions.
  • Derived analytical and sampling-based solutions for first-arrival time distributions.
  • Implemented the enhanced models within a Bayesian framework.
  • Conducted recovery studies and applied the model to a large dataset (N=1469).

Main Results:

  • Both truncated normal and gamma distributions yielded comparable results.
  • Most model parameters were recovered accurately, with the exception of drift variance.
  • Including drift variance improved the recovery of other parameters, despite its own poor recovery.
  • Significant correlations were found between shift, threshold, and drift mean parameters.

Conclusions:

  • The enhanced shifted-Wald model effectively accounts for trial-to-trial drift rate variability in reaction-time tasks.
  • The inclusion of drift variance, even with imperfect recovery, aids overall model parameter estimation.
  • Findings highlight interdependencies among key decision-making parameters.