Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

The diffusion decision model: theory and data for two-choice decision tasks.

Roger Ratcliff1, Gail McKoon

  • 1Department of Psychology, Ohio State University, Columbus, OH 43210, U.S.A.

Neural Computation
|December 19, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The interplay between selective attention and summary statistics.

The Behavioral and brain sciences·2025
Same author

A selective sampling account of forming numerosity representations.

Psychological review·2025
Same author

Using diffusion models for symbolic numeracy tasks to examine aging effects.

Journal of experimental psychology. Learning, memory, and cognition·2024
Same author

Beyond discrete-choice options.

Trends in cognitive sciences·2024
Same author

Parsing memory and nonmemory contributions to age-related declines in mnemonic discrimination performance: a hierarchical Bayesian diffusion decision modeling approach.

Learning & memory (Cold Spring Harbor, N.Y.)·2023
Same author

Reexamining the effects of speed-accuracy instructions with a diffusion-model-based analysis.

Journal of experimental psychology. Learning, memory, and cognition·2023
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

The diffusion decision model explains cognitive processing in two-choice tasks by analyzing accuracy and response times. This framework is empirically testable and applicable to aging and neurophysiology research.

Area of Science:

  • Cognitive Psychology
  • Decision Science

Background:

  • The diffusion decision model provides a framework for understanding choice behavior.
  • It translates observable data like accuracy and response times into underlying cognitive processes.

Purpose of the Study:

  • To review the diffusion decision model and illustrate its application.
  • To demonstrate how the model links behavioral data to cognitive components.

Main Methods:

  • The study reviews the diffusion decision model.
  • Three experiments are presented to illustrate model components: stimulus difficulty, response instructions (speed vs. accuracy), and stimulus proportions.
  • These experiments manipulate factors affecting information quality, decision criteria, and response bias.

Related Experiment Videos

Main Results:

  • Stimulus difficulty impacts decision information quality.
  • Speed or accuracy instructions alter the required information threshold for a response.
  • Stimulus proportions introduce bias in drift rate and starting point.

Conclusions:

  • The diffusion decision model offers a testable and falsifiable explanation of decision-making.
  • The model has broad applications in cognitive research, including aging and neurophysiology.