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

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

450
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
450
Survival Tree01:19

Survival Tree

496
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...
496
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

345
Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
345
Variability: Analysis01:11

Variability: Analysis

636
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
636
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

397
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...
397
Typical Model Studies01:30

Typical Model Studies

683
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
683

You might also read

Related Articles

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

Sort by
Same author

Bending-Induced Vibrational Landscape Reorganization Governs Energy Dissipation in Perylene Bisimides.

Angewandte Chemie (International ed. in English)·2026
Same author

Dual role of a conjugated bridge in intramolecular singlet fission: light-harvesting antenna and energy funnel.

Chemical science·2026
Same author

Rapid and reliable computational markers of decision-making for predicting daily smoking behavior and smoking cessation treatment outcomes.

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology·2026
Same author

Ultrafast Intersystem Crossing in Icosahedral-Core Metal Nanoclusters.

Journal of the American Chemical Society·2026
Same author

YAP/TAZ-VGLL3 governs adipocyte fate via epigenetic reprogramming of PPARγ and its target enhancers.

Science advances·2026
Same author

Place Security and Mental Health: Two Sides of the Same Coin Among Young Adults with Perinatally-Acquired HIV and Exposure in New York City.

AIDS and behavior·2025
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: Mar 30, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.5K

Flexibility versus generalizability in model selection.

Mark A Pitt1, Woojae Kim, In Jae Myung

  • 1Department of Psychology, Ohio State University, Columbus, Ohio 43210, USA. pitt.2@osu.edu

Psychonomic Bulletin & Review
|May 16, 2003
PubMed
Summary
This summary is machine-generated.

When comparing mathematical models of cognition, Bayesian model selection (BMS) is more reliable than root mean squared deviation (RMSD). Simulations show RMSD can be misleading, while BMS accounts for model complexity and data fit for robust model selection.

More Related Videos

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.3K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

Related Experiment Videos

Last Updated: Mar 30, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.5K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.3K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Mathematical Psychology

Background:

  • Quantitative methods are crucial for selecting among competing mathematical models of cognition.
  • Root Mean Squared Deviation (RMSD) and Bayesian Model Selection (BMS) are two prominent approaches.
  • Previous research suggested RMSD is adequate, while BMS offers superior performance by incorporating model complexity.

Purpose of the Study:

  • To contrast the theoretical underpinnings of RMSD and BMS for model selection.
  • To investigate inconsistencies in simulation results comparing RMSD and BMS.
  • To determine the most robust quantitative method for selecting mathematical models of cognition.

Main Methods:

  • Comparative analysis of theoretical frameworks for model selection.
  • Expansion and re-evaluation of simulation methodologies used in prior studies.
  • Interpretation of simulation results in the context of the evaluated data.

Main Results:

  • Simulation results comparing RMSD and BMS can be misleading if not interpreted relative to the specific data.
  • RMSD's performance in model recovery simulations may not accurately reflect its utility in real-world model selection.
  • Bayesian Model Selection (BMS) demonstrates greater robustness in distinguishing between models.

Conclusions:

  • Bayesian Model Selection (BMS) is a more reliable and robust method for choosing among competing mathematical models of cognition.
  • The interpretation of model recovery simulation results requires careful consideration of the underlying data.
  • Reliance on methods like RMSD without accounting for model complexity can lead to suboptimal model choices.