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

Reinforcement Schedules01:24

Reinforcement Schedules

391
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
391
Reinforcement01:23

Reinforcement

739
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
739
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.2K
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
1.2K
Operant Conditioning Intervention01:24

Operant Conditioning Intervention

392
Operant conditioning serves as a foundational principle in therapeutic interventions aimed at modifying maladaptive behaviors. Central to this approach is the notion that behaviors, both adaptive and maladaptive, are learned through reinforcement. By analyzing the environmental factors that reinforce problematic behaviors, clinicians can design interventions to weaken these reinforcements and replace maladaptive behaviors with healthier alternatives.
In operant conditioning, behaviors that are...
392
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.4K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.4K
Principles of Classical Conditioning01:23

Principles of Classical Conditioning

1.6K
Classical conditioning, as described by Ivan Pavlov, is a foundational concept in associative learning, where a neutral stimulus becomes capable of eliciting a conditioned response through association with an unconditioned stimulus. The process of acquisition, where this learning occurs, and the subsequent phenomena of contiguity, contingency, generalization, discrimination, extinction, and spontaneous recovery are crucial for a comprehensive understanding of classical conditioning.
During the...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Current state of the art of new prostate MRI technologies and potential future developments.

BJR open·2026
Same author

Input-to-State Safety for Reinforcement Learning.

IEEE transactions on neural networks and learning systems·2026
Same author

Soft sensor-driven spatiotemporal-periodic synergistic predictive control for blast furnace gas flow.

ISA transactions·2026
Same author

Mitochondrial mGPDH Modulates Fibroblast Function in Diabetic Wound Healing via the SIRT1-c-Myc-TGF-β1 Axis.

Diabetes·2025
Same author

TNK2 promotes the EMT proliferation and invasion of esophageal squamous cell carcinoma by enhancing FOXO1 through the AKT pathway.

International immunopharmacology·2025
Same author

High-throughput atomic force microscopy measurements reveal mechanical signatures of cell mixtures for liquid biopsy.

Nanoscale·2025
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
Same journal

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

IEEE transactions on neural networks and learning systems·2026
Same journal

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

IEEE transactions on neural networks and learning systems·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Dec 28, 2025

Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats
09:12

Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats

Published on: March 17, 2019

9.9K

Safe Intermittent Reinforcement Learning With Static and Dynamic Event Generators.

Yongliang Yang, Kyriakos G Vamvoudakis, Hamidreza Modares

    IEEE Transactions on Neural Networks and Learning Systems
    |February 15, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel intermittent framework for safe reinforcement learning (RL) algorithms, ensuring state constraints are met. The approach guarantees optimality, stability, and safety in dynamic environments.

    More Related Videos

    Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
    11:20

    Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

    Published on: June 2, 2014

    12.3K
    Rodent Brain Microinjection to Study Molecular Substrates of Motivated Behavior
    10:05

    Rodent Brain Microinjection to Study Molecular Substrates of Motivated Behavior

    Published on: September 16, 2015

    14.9K

    Related Experiment Videos

    Last Updated: Dec 28, 2025

    Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats
    09:12

    Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats

    Published on: March 17, 2019

    9.9K
    Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
    11:20

    Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

    Published on: June 2, 2014

    12.3K
    Rodent Brain Microinjection to Study Molecular Substrates of Motivated Behavior
    10:05

    Rodent Brain Microinjection to Study Molecular Substrates of Motivated Behavior

    Published on: September 16, 2015

    14.9K

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Control Systems

    Background:

    • Reinforcement learning (RL) algorithms often struggle with safety constraints.
    • Ensuring stability and optimality in RL while adhering to state constraints is a significant challenge.

    Purpose of the Study:

    • To develop an intermittent framework for safe reinforcement learning (RL) algorithms.
    • To address the challenge of imposing state constraints in RL problems.

    Main Methods:

    • A barrier function-based system transformation was developed to convert constrained problems into unconstrained ones.
    • Two types of intermittent feedback RL algorithms (static and dynamic) were derived based on optimal policies.
    • An actor/critic structure was utilized for online problem-solving.

    Main Results:

    • The proposed intermittent framework effectively imposes state constraints.
    • The actor/critic structure successfully guaranteed optimality, stability, and safety.
    • Simulation results validated the efficacy of the presented approach.

    Conclusions:

    • The intermittent framework provides a viable solution for safe reinforcement learning.
    • The method ensures reliable performance in systems requiring state constraints.
    • This work contributes to the advancement of safe and efficient RL applications.