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

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...
Reason and Intuition01:37

Reason and Intuition

The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the brain can only use...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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...
Growth Models with Integration: Problem Solving01:27

Growth Models with Integration: Problem Solving

In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...

You might also read

Related Articles

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

Sort by
Same author

The posterior tibial slope modifies the diagnostic utility of posterior shiny-corner lesions in medial meniscus posterior root tears.

Journal of experimental orthopaedics·2026
Same author

Cortical hyperperfusion and subsequent atrophy in neuronal intranuclear inclusion disease.

Rinsho shinkeigaku = Clinical neurology·2026
Same author

Double-Bundle Artificial Medial Patellofemoral Ligament Reconstruction Using a Femoral Suture-Sliding Anchor and Patellar Staged Tension Adjustment.

Arthroscopy techniques·2026
Same author

Reassessing Choice Probability: What 59 Macaque Studies Tell Us About Decision-Related Activity in Visual Cortex.

bioRxiv : the preprint server for biology·2026
Same author

Identification of the critical isthmus in reentrant AVN-AT using PPI-TCL-guided manifest entrainment and pre-systolic potential mapping.

HeartRhythm case reports·2026
Same author

Koopman mode decomposition of thermodynamic dissipation in nonlinear Langevin dynamics.

Proceedings of the National Academy of Sciences of the United States of America·2026

Related Experiment Video

Updated: May 12, 2026

Operant Procedures for Assessing Behavioral Flexibility in Rats
08:30

Operant Procedures for Assessing Behavioral Flexibility in Rats

Published on: February 15, 2015

A leaky-integrator model as a control mechanism underlying flexible decision making during task switching.

Akinori Mitani1, Ryo Sasaki, Masafumi Oizumi

  • 1Department of Neurophysiology, Graduate School of Medicine, Juntendo University, Bunkyo, Tokyo, Japan.

Plos One
|March 28, 2013
PubMed
Summary

Flexible task switching in animals relies on managing sensory information. A new leaky-integrator model explains how the brain controls relevant and irrelevant data flow for effective context-based behavior.

More Related Videos

New Variations for Strategy Set-shifting in the Rat
09:45

New Variations for Strategy Set-shifting in the Rat

Published on: January 23, 2017

The Attentional Set Shifting Task: A Measure of Cognitive Flexibility in Mice
09:15

The Attentional Set Shifting Task: A Measure of Cognitive Flexibility in Mice

Published on: February 4, 2015

Related Experiment Videos

Last Updated: May 12, 2026

Operant Procedures for Assessing Behavioral Flexibility in Rats
08:30

Operant Procedures for Assessing Behavioral Flexibility in Rats

Published on: February 15, 2015

New Variations for Strategy Set-shifting in the Rat
09:45

New Variations for Strategy Set-shifting in the Rat

Published on: January 23, 2017

The Attentional Set Shifting Task: A Measure of Cognitive Flexibility in Mice
09:15

The Attentional Set Shifting Task: A Measure of Cognitive Flexibility in Mice

Published on: February 4, 2015

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Animal Behavior

Background:

  • Task switching is crucial for adaptive behavior in animals.
  • The prefrontal cortex is known to be involved in task switching.
  • The precise neural mechanisms for modulating sensory-motor associations during task switching remain unclear.

Purpose of the Study:

  • To model how distinct sensory neuron populations contribute to task switching.
  • To investigate the role of information flow and integration in flexible cognitive control.
  • To compare a leaky-integrator model with an alternative model for explaining task-switching behavior.

Main Methods:

  • Developed a computational leaky-integrator model based on prior experimental data.
  • Modeled separate storage and leaking of relevant and irrelevant information.
  • Compared model performance against an alternative model that discards irrelevant information.

Main Results:

  • The leaky-integrator model successfully replicated behavioral and neuronal data from a previous study.
  • The model demonstrated superior performance, especially during initial task-commitment failures.
  • Active control over information 'leak' from irrelevant sources is key to the model's success.

Conclusions:

  • Flexible task switching is partly achieved by actively regulating the leak of relevant and irrelevant sensory information.
  • The proposed leaky-integrator model provides a viable mechanism for context-dependent behavioral adaptation.
  • Sensory neuron communication plays a significant role in enabling efficient task switching.