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 Making01:20

Decision Making

155
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
155
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.1K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.1K
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.6K
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...
5.6K
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

100
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
100
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

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

130
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...
130
Nonlinear Pharmacokinetics: Causes of Nonlinearity01:22

Nonlinear Pharmacokinetics: Causes of Nonlinearity

272
Nonlinearity in drug pharmacokinetics is caused by various factors influencing how a drug is absorbed, distributed, metabolized, and excreted. Understanding these nonlinear processes is crucial for predicting drug behavior in the body and optimizing drug dosing regimens.
Nonlinear drug absorption can occur when the process is rate-limited by solubility, carrier-mediated transport systems, or saturation of the presystemic gut wall or hepatic metabolism. For instance, high doses of riboflavin...
272

You might also read

Related Articles

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

Sort by
Same author

Exploring the mechanisms of biofield therapy through joint electrophysiological recordings in humans and mice.

IBRO neuroscience reports·2026
Same author

Different spatio-temporal strategies for controlling a striking gesture to slide an object toward a target distance.

Experimental brain research·2026
Same author

Gaze stabilization: Bats do move their eyes but differently from mice.

Current biology : CB·2026
Same author

Modality-specific predictive templates in pre-stimulus EEG activity.

Neuropsychologia·2026
Same author

Genomic correlates of self-reported psychic experiences: An exploratory analysis.

Explore (New York, N.Y.)·2026
Same author

Explaining attractive and repulsive biases in the subjective visual vertical.

PLoS computational biology·2026

Related Experiment Video

Updated: Aug 2, 2025

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

15.5K

Accounting for endogenous effects in decision-making with a non-linear diffusion decision model.

Isabelle Hoxha1,2, Sylvain Chevallier3, Matteo Ciarchi4

  • 1CIAMS, Université Paris-Saclay, Paris, France. isabelle.hoxha@universite-paris-saclay.fr.

Scientific Reports
|April 18, 2023
PubMed
Summary

The novel non-linear Drift-Diffusion Model (nl-DDM) improves decision-making analysis by capturing inter-trial dynamics. This enhanced model offers better insights into perceptual decisions than the standard Drift-Diffusion Model (DDM).

More Related Videos

The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies
08:24

The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies

Published on: August 25, 2023

779
An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
07:42

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

Published on: August 2, 2018

13.7K

Related Experiment Videos

Last Updated: Aug 2, 2025

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

15.5K
The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies
08:24

The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies

Published on: August 25, 2023

779
An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
07:42

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

Published on: August 2, 2018

13.7K

Area of Science:

  • Cognitive Neuroscience
  • Computational Psychology
  • Decision Science

Background:

  • The standard Drift-Diffusion Model (DDM) is a cornerstone for analyzing two-alternative forced-choice tasks.
  • However, the DDM has limitations in modeling single-trial variability and endogenous influences.
  • Existing models struggle to capture complex inter-trial dynamics crucial for understanding decision-making.

Purpose of the Study:

  • To introduce a novel non-linear Drift-Diffusion Model (nl-DDM) to overcome DDM limitations.
  • To enhance the analysis of perceptual decisions by incorporating multi-trajectory dynamics.
  • To provide a more accurate framework for understanding across-trial variability and peri-stimulus influences.

Main Methods:

  • Developed a non-linear Drift-Diffusion Model (nl-DDM) allowing multiple decision trajectories.
  • Compared the performance and parameter interpretability of nl-DDM against the standard DDM.
  • Utilized correlation analysis to elucidate the relationship between DDM and nl-DDM parameters.

Main Results:

  • The nl-DDM demonstrates superior performance compared to the DDM at equivalent model complexity.
  • The nl-DDM effectively captures time effects and inter-trial variability missed by the DDM.
  • Correlation analysis provides intuitive understanding of nl-DDM parameters as extensions of DDM parameters.

Conclusions:

  • The nl-DDM serves as a significant extension of the DDM, offering richer insights.
  • This model enhances the analysis of perceptual decision-making by accounting for complex dynamics.
  • The nl-DDM provides a more accurate tool for studying influences on decision processes.