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

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

You might also read

Related Articles

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

Sort by
Same author

Does insulin bolster antioxidant defenses via the extracellular signal-regulated kinases-protein kinase B-nuclear factor erythroid 2 p45-related factor 2 pathway?

Antioxidants & redox signaling·2011
Same author

Mechanisms and functions of Tet protein-mediated 5-methylcytosine oxidation.

Genes & development·2011
Same author

Decrease in calcium-sensing receptor in the progress of diabetic cardiomyopathy.

Diabetes research and clinical practice·2011
Same author

JAMIE: A software tool for jointly analyzing multiple ChIP-chip experiments.

Methods in molecular biology (Clifton, N.J.)·2011
Same author

Morphine-induced conditioned place preference in mice: metabolomic profiling of brain tissue to find "molecular switch" of drug abuse by gas chromatography/mass spectrometry.

Analytica chimica acta·2011
Same author

[The interventions effect-assessment of the workers exposed to N, N-dimethylformamide by percutaneous in a synthetic leather factory].

Zhonghua lao dong wei sheng zhi ye bing za zhi = Zhonghua laodong weisheng zhiyebing zazhi = Chinese journal of industrial hygiene and occupational diseases·2011
Same journal

Mind wandering during first- and foreign-language reading.

Psychonomic bulletin & review·2026
Same journal

Lexical word processing is unaffected by rapid invisible frequency tagging in reading: Evidence from eye movements.

Psychonomic bulletin & review·2026
Same journal

Anxiety modulates voluntary attentional orienting to emotional gaze cues: Eye movements for pro- and anti-saccades.

Psychonomic bulletin & review·2026
Same journal

Faster key-press responses to front vowels than back vowels when matching heard vowels with represented vowels.

Psychonomic bulletin & review·2026
Same journal

Testing the interleaving effect without response bias: A forced-choice reevaluation of Kornell and Bjork (2008).

Psychonomic bulletin & review·2026
Same journal

The impact of social interaction on abstract concepts.

Psychonomic bulletin & review·2026
See all related articles

Related Experiment Video

Updated: Jun 12, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Minimum description length model selection of multinomial processing tree models.

Hao Wu1, Jay I Myung, William H Batchelder

  • 1Department of Psychology, Ohio State University, Columbus, Ohio, USA. wu.498@osu.edu

Psychonomic Bulletin & Review
|June 17, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a Minimum Description Length (MDL) approach for selecting the best multinomial processing tree (MPT) model, improving upon existing statistical methods for cognitive process measurement.

Related Experiment Videos

Last Updated: Jun 12, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Cognitive Psychology
  • Psychometrics
  • Computational Statistics

Background:

  • Multinomial processing tree (MPT) models are established statistical tools for inferring latent cognitive processes.
  • Selecting the optimal MPT model from competing hypotheses is crucial for accurate cognitive modeling.
  • Existing model selection criteria have limitations in certain applications.

Purpose of the Study:

  • To introduce a Minimum Description Length (MDL) based model-selection approach for MPT models.
  • To provide a computational solution for implementing MDL-based MPT model selection.
  • To demonstrate the utility of the MDL approach with real-world cognitive data.

Main Methods:

  • Developed a Minimum Description Length (MDL) criterion for MPT model selection.
  • Created a MATLAB program to facilitate MDL-based model selection, including models with inequality constraints.
  • Compared the MDL approach against traditional methods like the G(2) likelihood ratio test, AIC, and BIC.

Main Results:

  • The MDL approach offers an alternative to existing methods for MPT model selection.
  • The provided MATLAB program simplifies the application of MDL for researchers.
  • Demonstrated successful application of MDL to MPT models in source monitoring and pair clustering tasks.

Conclusions:

  • The Minimum Description Length (MDL) approach provides a robust method for selecting the best multinomial processing tree (MPT) model.
  • The developed MATLAB tool enhances the practical application of MDL in cognitive science research.
  • MDL-based selection is effective for MPT models in diverse experimental paradigms.