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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

335
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...
335
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

160
Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
160
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

442
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...
442
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

360
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...
360
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

734
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
734
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

163
Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
163

You might also read

Related Articles

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

Sort by
Same author

The specificity of sequential statistical learning: Statistical learning accumulates predictive information from unstructured input but is dissociable from (declarative) memory for words.

Cognition·2025
Same author

Transitional probabilities outweigh frequency of occurrence in statistical learning of simultaneously presented visual shapes.

Memory & cognition·2024
Same author

Socio-Cultural Values Are Risk Factors for COVID-19-Related Mortality.

Cross-cultural research : official journal of the Society for Cross-Cultural Research·2024
Same author

Hebbian learning can explain rhythmic neural entrainment to statistical regularities.

Developmental science·2024
Same author

Corrigendum to "When forgetting fosters learning: A neural network model for statistical learning" [Cognition (2021) 104621].

Cognition·2022
Same author

Hebbian, correlational learning provides a memory-less mechanism for Statistical Learning irrespective of implementational choices: Reply to Tovar and Westermann (2022).

Cognition·2022

Related Experiment Video

Updated: May 6, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

10.6K

How are Bayesian models really used? Reply to Frank (2013).

Ansgar D Endress1

  • 1Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain; Department of Psychology, City University, London, UK.

Cognition
|November 6, 2013
PubMed
Summary

Bayesian models offer a promising framework for cognitive theories, but current research often fails to implement them rigorously. Simple psychological mechanisms may better explain observed rule-learning behaviors than complex Bayesian assumptions.

More Related Videos

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.9K
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

2.9K

Related Experiment Videos

Last Updated: May 6, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

10.6K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.9K
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

2.9K

Area of Science:

  • Cognitive Science
  • Computational Psychology
  • Philosophy of Mind

Background:

  • A proposed research program advocates for using Bayesian models as rigorous implementations of psychological theories.
  • This approach aims to mathematically formalize cognitive phenomena, enhancing theoretical precision and testability.
  • Frank (2013) defended the 'size principle,' suggesting learners prefer more specific rules.

Purpose of the Study:

  • To evaluate the practical implementation of the proposed Bayesian research program in cognitive science.
  • To critically assess the empirical support for the 'size principle' in rule-learning.
  • To examine criticisms leveled against 'common-sense psychology' accounts of rule-learning.

Main Methods:

  • Critical analysis of existing literature and empirical findings related to Bayesian modeling in cognitive psychology.
  • Re-evaluation of experimental results presented as evidence for the 'size principle'.
  • Theoretical argumentation to address criticisms of 'common-sense psychology' in rule-learning.

Main Results:

  • The proposed Bayesian research program is not consistently followed in practice.
  • Empirical evidence cited in support of the 'size principle' is insufficient.
  • Criticisms of 'common-sense psychology' accounts of rule-learning are unsubstantiated; these simpler mechanisms offer better explanations.

Conclusions:

  • While the Bayesian approach to cognitive theories is promising, its practical application requires significant improvement.
  • The 'size principle' lacks robust empirical backing.
  • Simple psychological mechanisms provide a more parsimonious and effective explanation for observed rule-learning phenomena.