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 Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

112
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...
112
Observational Learning01:12

Observational Learning

360
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...
360
Steps in the Modeling Process01:14

Steps in the Modeling Process

352
Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
352
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

280
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,...
280
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

137
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
137
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.3K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.3K

You might also read

Related Articles

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

Sort by
Same author

The Relationship Between Sleep, Cognition, and Dementia Risk in People With Focal Epilepsy.

Neurology·2026
Same author

Transsaccadic working memory in healthy ageing and neurodegenerative disease.

eLife·2026
Same author

A computational approach to understanding effort-based decision-making in depression.

Psychological medicine·2025
Same author

Detection of cognitive deficits years prior to clinical diagnosis across neurological conditions.

Brain communications·2025
Same author

Stimulation of the human ventral tegmental area increases strategic betting.

Brain : a journal of neurology·2025
Same author

Self- versus caregiver-reported apathy across neurological disorders.

Brain communications·2025

Related Experiment Video

Updated: Oct 4, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.7K

Model-based learning retrospectively updates model-free values.

Max Doody1, Maaike M H Van Swieten2, Sanjay G Manohar2

  • 1Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK. max.doody@oriel.ox.ac.uk.

Scientific Reports
|February 12, 2022
PubMed
Summary
This summary is machine-generated.

Model-based learning influences model-free valuation in humans by updating reward predictions based on optimal decision paths. This suggests a more integrated approach to reinforcement learning (RL) is needed.

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

957
Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.3K

Related Experiment Videos

Last Updated: Oct 4, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.7K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

957
Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.3K

Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Behavioral Economics

Background:

  • Reinforcement learning (RL) is typically divided into model-free and model-based strategies.
  • Model-free learning associates values with actions, while model-based learning uses internal environmental models.
  • Emerging theories suggest model-based systems may train model-free behavior, reducing planning demands.

Purpose of the Study:

  • To investigate if model-based processes can influence model-free valuation in human decision-making.
  • To explore the interaction between model-based and model-free learning systems in humans.
  • To provide an empirical method for studying the interplay of these RL strategies.

Main Methods:

  • Adapted a two-stage decision task to probe human behavior.
  • Assessed subjective value ratings of irrelevant features presented during model-based decision-making.
  • Analyzed how reward outcomes updated these value ratings, considering optimal versus chosen actions.

Main Results:

  • Model-based processes altered model-free value ratings in healthy individuals.
  • Value ratings were updated by rewards, reflecting whether the chosen action was retrospectively optimal.
  • This effect was driven by a reward prediction error relative to the likely optimal path and occurred independently of attention.

Conclusions:

  • Current models of reinforcement learning require updating to reflect a more integrated approach between model-based and model-free systems.
  • Model-based learning can dynamically shape model-free value representations.
  • The findings offer a novel experimental paradigm for future research on RL interactions.