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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

321
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
321
Observational Learning01:12

Observational Learning

1.2K
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
1.2K
Modeling with Differential Equations01:25

Modeling with Differential Equations

203
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
203
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

394
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...
394
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

472
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...
472
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

403
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
403

You might also read

Related Articles

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

Sort by
Same author

AETA peptide contributes to Alzheimer's disease signature of synapse dysfunction.

Acta neuropathologica·2026
Same author

Cortico-striatal dynamics across working memory stages.

Cerebral cortex (New York, N.Y. : 1991)·2025
Same author

Heterogeneous Multiscale Multivariate Autoregressive Model: existence, sparse estimation and application to functional connectivity in neuroscience.

Journal of mathematical biology·2025
Same author

Coding Dynamics of the Striatal Networks During Learning.

eNeuro·2024
Same author

APP fragment controls both ionotropic and non-ionotropic signaling of NMDA receptors.

Neuron·2024
Same author

Author Correction: Strategy inference during learning via cognitive activity-based credit assignment models.

Scientific reports·2023
Same journal

Hippocampal communication with the anterior olfactory nucleus is necessary for context-dependent odor memory.

Behavioral neuroscience·2026
Same journal

Biological sex and normative cognitive aging across spatial learning, flexibility, and working memory in Fischer 344 rats.

Behavioral neuroscience·2026
Same journal

Defensive antinociception and antipredatory responses in prey threatened by distinct odoriferous cues from Felis silvestris catus.

Behavioral neuroscience·2026
Same journal

Taste exposure during different developmental phases impacts aversion learning in adult rats.

Behavioral neuroscience·2026
Same journal

Structural neuroanatomy of semantic retrograde memory in older adults.

Behavioral neuroscience·2026
Same journal

Oxytocin prevents cocaine-induced high-affect 50-kHz vocalizations in female rats.

Behavioral neuroscience·2026
See all related articles

Related Experiment Video

Updated: Mar 27, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
07:14

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

651

Inferring time-varying internal models of agents through dynamic structure learning.

Ashwin Moongathottathil James1, Ingrid Bethus2, Alexandre Muzy3

  • 1Institut für Informatik.

Behavioral Neuroscience
|March 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces dynamic structure learning, allowing agents to adapt their learning rules and environment models. This framework reveals how rats improve maze navigation by refining representations and shifting to rational learning strategies.

More Related Videos

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.9K

Related Experiment Videos

Last Updated: Mar 27, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
07:14

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

651
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.9K

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Traditional reinforcement learning models assume fixed agent structures, limiting their ability to model real-world adaptive behaviors and irrationality.
  • Understanding how agents dynamically change their learning rules and internal environment representations is crucial for modeling complex decision-making.

Purpose of the Study:

  • To introduce a novel dynamic structure learning framework enabling agents to adapt their learning rules and internal representations.
  • To investigate the evolution of agent internal structures and learning processes during problem-solving.
  • To apply the framework to understand adaptive behaviors in natural intelligence, specifically rat maze navigation.

Main Methods:

  • Developed a dynamic structure learning framework to reconstruct the most likely sequence of agent structures based on observed behaviors.
  • Utilized a pool of learning rules and environment models for agents to dynamically select from.
  • Applied the framework to analyze rat behavior in a maze task, observing changes in maze representation and learning rules.

Main Results:

  • Demonstrated that rats progressively refine their maze representation from suboptimal to optimal during learning.
  • Observed a transition in learning rules for slower learners from heuristic-based to more rational approaches.
  • Showcased the framework's ability to provide insights into the evolution of internal agent models.

Conclusions:

  • Dynamic structure learning offers a more realistic approach to modeling agent adaptation and decision-making in complex environments.
  • The findings highlight the importance of considering the interplay between learning rules and environment representations for understanding natural intelligence.
  • This framework advances the modeling of adaptability, surpassing current artificial intelligence limitations.