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

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

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

205
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...
205
Diffusion01:21

Diffusion

5.8K
Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
5.8K
Diffusion01:12

Diffusion

213.1K
Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
213.1K
Passive Diffusion: Overview and Kinetics01:17

Passive Diffusion: Overview and Kinetics

1.1K
Passive diffusion is a critical process that allows small lipophilic drugs to cross the cell membrane along a concentration gradient. This mechanism's efficiency depends on four primary factors: the membrane's surface area, the drug's lipid-water partition coefficient, the concentration gradient, and the membrane's thickness.
When administered orally, drugs establish a substantial concentration gradient between the gastrointestinal (GI) lumen and the bloodstream, expediting...
1.1K
Diffusion on Chromatography Columns01:07

Diffusion on Chromatography Columns

985
In column chromatography, when an analyte is introduced as a narrow band at the top of the column, the solutes begin to separate and broaden, developing a Gaussian profile. This broadening occurs due to various factors, such as longitudinal diffusion.
Longitudinal diffusion occurs when the solute molecules in the mobile phase diffuse from the more concentrated center of the chromatographic band to the more dilute regions on either side, both towards and against the flow direction. This...
985
Theories of Dissolution: Diffusion Layer Model01:15

Theories of Dissolution: Diffusion Layer Model

1.3K
Dissolution, the process by which drug particles dissolve in a solvent, is explained by the diffusion layer model, a theoretical framework that simulates the absorption of oral drugs and allows us to analyze experimental data.
This process starts with a thin layer, saturated with the drug, forming at the interface between the solid and liquid. The solute then diffuses from this layer into the main solution. The Noyes-Whitney equation suggests that the rate of dissolution relies on the diffusion...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Serial effects in choice and confidence modulate each other: Evidence from 26 experiments.

Cognition·2026
Same author

Confidence in naturalistic decision making.

Neuroscience of consciousness·2026
Same author

Response Time as a Proxy for Decision Confidence: Insights From Type-2 ROC Analysis.

Open mind : discoveries in cognitive science·2026
Same author

Confidence-accuracy dissociations in perceptual decision making.

Vision research·2026
Same author

Type-1 and type-2 decisions feature computational noise of similar magnitude.

Communications psychology·2026
Same author

Using Artificial Neural Networks to Relate External Sensory Features to Internal Decisional Evidence.

Open mind : discoveries in cognitive science·2026

Related Experiment Video

Updated: Nov 22, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.1K

Qualitative speed-accuracy tradeoff effects that cannot be explained by the diffusion model under the selective

Farshad Rafiei1, Dobromir Rahnev2

  • 1School of Psychology, Georgia Institute of Technology, 654 Cherry Str NW, Atlanta, GA, 30332, USA. farshad@gatech.edu.

Scientific Reports
|January 9, 2021
PubMed
Summary

The diffusion model fails to explain the speed-accuracy tradeoff (SAT) despite assumptions. New research reveals three robust U-shaped curves in response time distributions that current models cannot fully account for.

More Related Videos

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.6K
A Task for Assessing the Impact of a Partner on the Speed and Accuracy of Motor Performance in Rats
06:17

A Task for Assessing the Impact of a Partner on the Speed and Accuracy of Motor Performance in Rats

Published on: October 17, 2019

5.1K

Related Experiment Videos

Last Updated: Nov 22, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.1K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.6K
A Task for Assessing the Impact of a Partner on the Speed and Accuracy of Motor Performance in Rats
06:17

A Task for Assessing the Impact of a Partner on the Speed and Accuracy of Motor Performance in Rats

Published on: October 17, 2019

5.1K

Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience

Background:

  • The diffusion model is widely used to explain decision-making, particularly the speed-accuracy tradeoff (SAT).
  • Previous investigations of the diffusion model's explanatory power for SAT have been limited in scope, using few SAT conditions or subjects.

Purpose of the Study:

  • To rigorously test the diffusion model's ability to explain SAT effects.
  • To identify robust phenomena related to SAT that computational models must address.

Main Methods:

  • Collected data from 20 subjects performing a perceptual discrimination task.
  • Varied task difficulty across five levels and manipulated SAT conditions across five settings (5000 trials per subject).
  • Analyzed response time (RT) distributions for U-shaped effects in RT differences, RT variability, and RT skewness.

Main Results:

  • Observed robust U-shaped curves for (i) the difference between error and correct RTs, (ii) the ratio of standard deviation to mean RT, and (iii) RT distribution skewness across SAT conditions.
  • A standard diffusion model (varying only drift rate with contrast and boundary with SAT) failed to explain these U-shaped curves.
  • Even allowing all diffusion model parameters to vary across conditions resulted in imperfect fits to the observed data.

Conclusions:

  • The diffusion model, in its common formulations, cannot fully explain the observed effects of SAT.
  • Three specific U-shaped patterns in response time distributions present significant challenges for current models of SAT.
  • These findings necessitate the development of more comprehensive models to accurately capture the complexities of the speed-accuracy tradeoff.