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Related Experiment Video

Updated: Jul 17, 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

Bayesian Graphical Modeling with the Circular Drift Diffusion Model.

Manuel Villarreal1, Adriana F Chávez De la Peña1, Percy K Mistry2

  • 1Department of Cognitive Sciences, University of California Irvine, Irvine, CA 92697-5100 USA.

Computational Brain & Behavior
|July 16, 2026
PubMed
Summary

Researchers can now analyze complex decision-making data using the circular drift-diffusion model (CDDM). A new Bayesian implementation and extension of the CDDM allows for testing psychological hypotheses in intricate experimental designs.

Keywords:
Bayesian inferenceCircular drift diffusion modelHierarchical modelJAGSLatent-mixture models

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Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Decision Science

Background:

  • Sequential sampling models are crucial for understanding decision-making and response times.
  • The circular drift-diffusion model (CDDM) specifically addresses tasks with circular choice alternatives.
  • Existing implementations may limit application to complex experimental designs.

Purpose of the Study:

  • To present a fully Bayesian implementation and extension of the circular drift-diffusion model (CDDM).
  • To enable researchers to apply the CDDM to complex experimental data and test targeted hypotheses.
  • To provide a flexible framework for investigating individual differences and stimulus effects in decision-making.

Main Methods:

  • Development of a custom JAGS module for Bayesian inference.
  • Conducting a simulation study to validate the module's adequacy.
  • Application to a continuous orientation judgment task using a graphical model.
  • Extension of the CDDM with hierarchical and latent-mixture structures.

Main Results:

  • The Bayesian implementation of the CDDM is demonstrated to be adequate through simulations.
  • The extended CDDM successfully accommodates complex experimental designs.
  • The framework allows for the incorporation of psychological assumptions regarding individual differences, condition difficulty, and cue effects.
  • Bayesian inference effectively tests these assumptions and addresses research questions.

Conclusions:

  • The developed Bayesian CDDM provides a powerful and flexible tool for analyzing decision-making data.
  • This implementation facilitates the investigation of nuanced psychological processes in complex experimental settings.
  • Researchers can now more rigorously test hypotheses about decision-making using the extended CDDM.