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 Concept Videos

Decision Making: P-value Method01:09

Decision Making: P-value Method

7.0K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
7.0K
Piecewise-Defined Functions01:28

Piecewise-Defined Functions

360
Piecewise defined functions are mathematical models where different expressions define a function over distinct intervals of the domain. These functions are useful for representing systems with varying behaviors depending on input values.For example, the function:  uses a linear rule for inputs less than or equal to –1 and a quadratic rule for values greater than –1. Although it has two formulas, it still defines a single function.Another common type is the absolute value...
360
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.2K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.2K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

310
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
310
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

397
Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
397
Modeling with Differential Equations01:25

Modeling with Differential Equations

129
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
129

You might also read

Related Articles

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

Sort by
Same author

Attentional dynamics explain the elusive nature of context effects.

Psychological review·2026
Same author

An overview of the quantum cognition research program.

Psychonomic bulletin & review·2025
Same author

Effects of prevalence and feedback in the identification of blast cells in peripheral blood: expert and novice observers.

Cognitive research: principles and implications·2025
Same author

Impact of the Microtubule Cytoskeleton on Insulin Transport in Beta Cells: A 3D Computational Study.

bioRxiv : the preprint server for biology·2025
Same author

Dialogues about the practice of science.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

The misalignment of incentives in academic publishing and implications for journal reform.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same journal

Exploring psychological tradeoffs: Developing and demonstrating an R Shiny app for Pareto optimization.

Behavior research methods·2026
Same journal

The performance of Bayesian fit measures in detecting misspecified multilevel structural equation modeling.

Behavior research methods·2026
Same journal

Psychometric functions from multiple responses : Dedicated to the memory of Colin L. Mallows.

Behavior research methods·2026
Same journal

Low-cost, open-source, full-stack software and Arduino-based hardware for control of commercially available animal behavior systems.

Behavior research methods·2026
Same journal

PyNeon: A Python package for the analysis of Neon multimodal mobile eye-tracking data.

Behavior research methods·2026
Same journal

Talking surveys: How photorealistic embodied conversational agents shape response quality, engagement, and satisfaction.

Behavior research methods·2026
See all related articles

Related Experiment Video

Updated: Feb 28, 2026

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
06:55

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

8.5K

Bayesian analysis of the piecewise diffusion decision model.

William R Holmes1, Jennifer S Trueblood2

  • 1Department of Physics and Astronomy, Vanderbilt University, 2201 West End Ave, Nashville, TN, 37235, USA. william.holmes@vanderbilt.edu.

Behavior Research Methods
|June 10, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a piecewise diffusion decision model (pDDM) to analyze decision-making with changing evidence accumulation rates. A novel probability density approximation method enables fitting these complex, time-varying models to perceptual data.

Keywords:
Evidence accumulation modelsHierarchal Bayesian inferenceNon-stationary stimuli

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.2K

Related Experiment Videos

Last Updated: Feb 28, 2026

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
06:55

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

8.5K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.2K

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Decision Science

Background:

  • Traditional sequential sampling models assume time-homogeneous decision processes, limiting their application to dynamic environments.
  • Fitting complex, time-varying models has been computationally challenging due to theoretical and methodological limitations.

Purpose of the Study:

  • To introduce a piecewise diffusion decision model (pDDM) capable of capturing time-varying evidence accumulation rates.
  • To adapt and apply a simulation-based hierarchical Bayesian method, probability density approximation (PDA), for fitting the pDDM.

Main Methods:

  • Developed a piecewise diffusion decision model (pDDM) allowing for dynamic changes in drift rates during decision-making.
  • Utilized the probability density approximation (PDA) method, a simulation-based hierarchical Bayesian approach, for model fitting.
  • Conducted parameter recovery experiments to validate the PDA method's performance with the pDDM.

Main Results:

  • Demonstrated the feasibility of fitting time-varying sequential sampling models using the PDA method.
  • Parameter recovery experiments identified the strengths and limitations of the PDA approach for pDDM.
  • Successfully applied the pDDM and PDA to perceptual data from trials with changing information.

Conclusions:

  • The piecewise diffusion decision model (pDDM) offers a flexible framework for modeling non-stationary decision processes.
  • The probability density approximation (PDA) method provides a viable approach for analyzing complex, time-varying sequential sampling models.
  • This extensible platform facilitates the study of decision-making in ecologically relevant, dynamic environments.