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Related Experiment Videos

A comparison of sequential sampling models for two-choice reaction time.

Roger Ratcliff1, Philip L Smith

  • 1Department of Psychology, The Ohio State University, Columbus, OH 43210, USA.

Psychological Review
|April 7, 2004
PubMed
Summary
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Researchers compared four decision-making models, including Wiener diffusion and accumulator models, to response time data. The Wiener diffusion and Ornstein-Uhlenbeck models best explained the experimental results.

Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Decision Science

Background:

  • Sequential sampling models are crucial for understanding decision-making processes.
  • Previous research has explored various models, but direct comparisons with empirical data under varied conditions are limited.
  • Understanding the nuances of evidence accumulation and response criteria is key to refining these models.

Purpose of the Study:

  • To evaluate and compare the performance of four prominent sequential sampling models: Wiener diffusion, Ornstein-Uhlenbeck (OU) diffusion, accumulator, and Poisson counter models.
  • To identify empirical conditions under which these models yield distinct predictions.
  • To assess the fit of augmented models incorporating trial-by-trial variability in evidence accumulation, response criteria, and base response time.

Related Experiment Videos

Main Methods:

  • Fitting four sequential sampling models (Wiener diffusion, OU diffusion, accumulator, Poisson counter) to response time (RT) distributions and accuracy data from three distinct experiments.
  • Augmenting each model with assumptions regarding across-trial variability in evidence accumulation rate, response criteria, and base RT.
  • Analyzing model mimicry and identifying conditions for discriminable predictions.

Main Results:

  • Substantial model mimicry was observed, but specific empirical conditions revealed discriminable predictions.
  • The Wiener diffusion model provided the best account of the data.
  • The OU model with small-to-moderate decay and the accumulator model with exponential criteria distributions also showed good fits, though the latter struggled with error RTs shorter than correct RTs.

Conclusions:

  • The Wiener diffusion model and the OU diffusion model (with specific decay parameters) are strong candidates for explaining 2-choice decision-making data.
  • The accumulator model, particularly with long-tailed criteria distributions, offers a viable alternative but requires further refinement for error RTs.
  • Further examination of the relationship between these classic models and newer, neurally inspired models is warranted.