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

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

670
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
670
Theories of Dissolution: Diffusion Layer Model01:15

Theories of Dissolution: Diffusion Layer Model

2.0K
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...
2.0K
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

49
Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
49
Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model01:09

Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model

883
Various dissolution theories provide insight into the factors that influence the dissolution rate. Danckwerts' Model suggests that turbulence, rather than a stagnant layer, characterizes the dissolution medium at the solid-liquid interface. In this model, the agitated solvent contains macroscopic packets that move to the interface via eddy currents, facilitating the absorption and delivery of the drug to the bulk solution. The regular replenishment of solvent packets maintains the...
883
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

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

402
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...
402
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

451
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
451

You might also read

Related Articles

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

Sort by
Same author

Computational modeling in developmental science.

Advances in child development and behavior·2026
Same author

Grounding mathematics in an integrated conceptual structure, part II: intervention study demonstrating robust learning and retention through a grounded curriculum.

Frontiers in psychology·2026
Same author

Learning to Decompose: Human-Like Subgoal Preferences Emerge in Neural Networks Learning Graph Traversal.

Open mind : discoveries in cognitive science·2025
Same author

A little imprecision goes a long way in launching memory development.

Child development perspectives·2025
Same author

Reflections on David E. Rumelhart and the Rumelhart Prize.

Topics in cognitive science·2025
Same author

Algorithmic personalization of information can cause inaccurate generalization and overconfidence.

Journal of experimental psychology. General·2025

Related Experiment Video

Updated: Mar 6, 2026

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
06:55

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

8.5K

The dynamics of multimodal integration: The averaging diffusion model.

Brandon M Turner1, Juan Gao2, Scott Koenig3

  • 1Department of Psychology, The Ohio State University, Columbus, OH, 43210, USA. turner.826@gmail.com.

Psychonomic Bulletin & Review
|March 10, 2017
PubMed
Summary
This summary is machine-generated.

This study models real-time multimodal integration using an Averaging Diffusion Model. Findings reveal individual differences in how people optimally combine visual and auditory evidence over time.

Keywords:
Averaging diffusion modelBayesian estimationCognitive modelingMultimodal integration

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

6.9K
Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy
12:15

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

Published on: April 9, 2019

9.3K

Related Experiment Videos

Last Updated: Mar 6, 2026

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
06:55

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

8.5K
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

6.9K
Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy
12:15

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

Published on: April 9, 2019

9.3K

Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Computational Modeling

Background:

  • Sensory processing is often modeled as evidence accumulation over time.
  • Multimodal integration research typically analyzes a single time point post-integration.
  • Existing models often focus on single sensory modalities.

Purpose of the Study:

  • To develop an integrated framework for real-time multimodal integration.
  • To model the dynamic process of combining evidence from different senses.
  • To assess the optimality of multimodal integration strategies.

Main Methods:

  • Developed a novel Averaging Diffusion Model for decision-making.
  • Collected behavioral data on visual, auditory, and bimodal evidence accumulation.
  • Compared three models of multimodal integration to assess optimality.

Main Results:

  • Demonstrated individual differences in multimodal integration strategies.
  • Some participants showed adaptive integration, reweighting evidence based on reliability.
  • Other participants exhibited non-optimal integration patterns.

Conclusions:

  • Multimodal integration is a dynamic process with significant individual variability.
  • The Averaging Diffusion Model provides a framework for studying real-time integration.
  • Understanding individual differences is crucial for explaining sensory processing variability.