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Updated: May 16, 2026

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
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Published on: September 26, 2016

Parameter variability and distributional assumptions in the diffusion model.

Roger Ratcliff1

  • 1Department of Psychology, The Ohio State University, Columbus, 43210, USA. ratcliff.22@osu.edu

Psychological Review
|November 15, 2012
PubMed
Summary
This summary is machine-generated.

The diffusion model accurately predicts response times when processing components vary across trials. Standard assumptions about these variations are robust, even with moderate changes in distributions.

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Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Psychometrics

Background:

  • The diffusion model is a key tool for understanding decision-making processes.
  • Accurately modeling response times, including errors, requires accounting for trial-to-trial variability in processing components.

Purpose of the Study:

  • To investigate the impact of alternative across-trial distributions on diffusion model parameters.
  • To assess the robustness of the standard diffusion model to variations in parameter distributions.

Main Methods:

  • Simulated data using various distributions for diffusion rate, starting point, and non-decision time.
  • Fit the standard diffusion model to simulated data to evaluate parameter recovery.

Main Results:

  • Recovered parameter values generally matched simulated values across a wide range of conditions.
  • Deviations occurred primarily with extreme parameter combinations and skewed non-decision time distributions.

Conclusions:

  • The standard diffusion model demonstrates robustness to moderate changes in across-trial parameter distributions.
  • The model's core assumptions hold well under typical experimental variations.