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

96
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...
96
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

570
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
570
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

2.0K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
2.0K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

155
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
155
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

6.7K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
6.7K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.0K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.0K

You might also read

Related Articles

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

Sort by
Same author

Alexander view as a stand-alone alternative to standard anteroposterior imaging for treatment decisions in acute acromioclavicular joint dislocation.

JSES international·2026
Same author

Environmental cleaning and disinfection practices for respiratory viruses in hospitals and long-term care facilities in the Netherlands.

Antimicrobial resistance and infection control·2026
Same author

Erratum: Multivariate assessment of the central-cardiorespiratory network structure in neuropathological disease (2018<i>Physiol. Meas</i>.<b>39</b>074004).

Physiological measurement·2026
Same author

Nosocomial outbreaks with rare yeasts: Trends, characteristics and preventive measures.

The Journal of infection·2026
Same author

Cognitive processes underlying the repetition-based truth effect: A diffusion model study.

Journal of experimental psychology. Learning, memory, and cognition·2026
Same author

How many infection control staff members are needed in acute care hospitals? A Delphi approach.

The Journal of hospital infection·2026

Related Experiment Video

Updated: Jul 18, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.7K

Neural superstatistics for Bayesian estimation of dynamic cognitive models.

Lukas Schumacher1, Paul-Christian Bürkner2, Andreas Voss3

  • 1Institute of Psychology, Heidelberg University, Heidelberg, Germany. lukas.schumacher@psychologie.uni-heidelberg.de.

Scientific Reports
|August 23, 2023
PubMed
Summary

Human cognition is dynamic, so we developed a deep learning method to model temporal changes in cognitive processes. This approach accurately captures dynamic parameters, revealing crucial temporal information often missed by static models.

More Related Videos

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.6K

Related Experiment Videos

Last Updated: Jul 18, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.7K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.6K

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Machine Learning

Background:

  • Traditional mathematical models of cognition often lack memory and disregard parameter fluctuations, failing to capture the dynamic nature of human cognition.
  • Existing methods for estimating time-varying parameters in complex models can be computationally challenging.

Purpose of the Study:

  • To augment mechanistic cognitive models with a temporal dimension using a superstatistics perspective.
  • To develop and validate a simulation-based deep learning method for Bayesian inference of dynamic cognitive models.

Main Methods:

  • Proposed a hierarchical model with a low-level observation model and a high-level transition model describing parameter evolution over time.
  • Developed a simulation-based deep learning approach for Bayesian inference to estimate time-varying and time-invariant parameters.
  • Benchmarked the deep learning method against existing frameworks for estimating time-varying parameters.

Main Results:

  • The deep learning method efficiently captured the temporal dynamics of a dynamic diffusion decision model fitted to human response time data.
  • The approach successfully recovered both time-varying and time-invariant parameters.
  • Results demonstrated that assuming static parameters obscures important temporal information in cognitive processes.

Conclusions:

  • The developed deep learning method provides an efficient tool for Bayesian inference in complex, dynamic cognitive models.
  • Incorporating a temporal dimension and estimating dynamic parameters is crucial for accurately understanding human cognition.
  • Static parameter assumptions in cognitive modeling can lead to significant information loss regarding cognitive dynamics.