Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

Operant Conditioning Intervention

Operant Conditioning Intervention

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...

Cognitive Learning

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...

Operant Conditioning

Operant Conditioning

Operant conditioning, a key concept in behavioral psychology, involves using reinforcement and punishment to alter the likelihood of a behavior being repeated. B.F. introduced this type of conditioning. Skinner focused on voluntary behaviors and the consequences that follow them, influencing whether these behaviors will be strengthened or diminished.
Reinforcement in operant conditioning can be positive or negative, both of which serve to increase the likelihood of a behavior. Positive...

Observational Learning

Observational Learning

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...

Feedback control systems

Feedback control systems

Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...

You might also read

Related Articles

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

Sort by

Same author

Input-to-State Safety for Reinforcement Learning.

IEEE transactions on neural networks and learning systems·2026

Same author

Safe Physics-Informed Machine Learning for Optimal Predefined-Time Stabilization: A Lyapunov-Based Approach.

IEEE transactions on neural networks and learning systems·2025

Same author

Online and Robust Intermittent Motion Planning in Dynamic and Changing Environments.

IEEE transactions on neural networks and learning systems·2023

Same author

Cooperative Finitely Excited Learning for Dynamical Games.

IEEE transactions on cybernetics·2023

Same author

Safety-Aware Pursuit-Evasion Games in Unknown Environments Using Gaussian Processes and Finite-Time Convergent Reinforcement Learning.

IEEE transactions on neural networks and learning systems·2022

Same author

Adaptive Neural Network Stochastic-Filter-Based Controller for Attitude Tracking With Disturbance Rejection.

IEEE transactions on neural networks and learning systems·2022

Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

IEEE transactions on neural networks and learning systems·2026

Same journal

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

IEEE transactions on neural networks and learning systems·2026

Same journal

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

IEEE transactions on neural networks and learning systems·2026

Same journal

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

IEEE transactions on neural networks and learning systems·2026

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

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 18, 2025

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Intermittent Learning Through Operant Conditioning for Cyber-Physical Systems.

Prachi Pratyusha Sahoo, Aris Kanellopoulos, Kyriakos G Vamvoudakis

IEEE Transactions on Neural Networks and Learning Systems

|October 4, 2021

Summary

This summary is machine-generated.

This study introduces intermittent learning for cyber-physical systems (CPSs), reducing communication bus usage. This operant conditioning approach optimizes policies while enhancing security against signal corruption.

More Related Videos

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Published on: January 19, 2022

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Published on: April 15, 2014

Related Experiment Videos

Last Updated: Oct 18, 2025

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Published on: January 19, 2022

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Published on: April 15, 2014

Area of Science:

Computer Science
Engineering
Psychology

Background:

Traditional reinforcement learning in cyber-physical systems (CPSs) leads to communication bus overutilization.
Continuous signal transmission in CPSs is vulnerable to malicious corruption.
Resource limitations in CPSs necessitate efficient learning strategies.

Purpose of the Study:

To propose an intermittent learning scheme for CPSs inspired by operant conditioning.
To reduce communication bus usage while maintaining policy optimality.
To investigate the impact of intermittent learning on learning and extinction rates and system security.

Main Methods:

Implementation of intermittent learning schedules (fixed/variable interval, fixed/variable ratio) based on Skinner's operant conditioning.
Analysis of learning rate, extinction rate, and optimal behavior in CPS environments.
Simulation of the proposed scheme to evaluate its efficacy and security benefits.

Main Results:

The intermittent learning scheme effectively approximates optimal policies.
Significant reduction in communication bus usage was observed.
The proposed scheme introduces controlled uncertainty, potentially improving resilience against signal corruption.

Conclusions:

Intermittent learning offers an efficient and potentially more secure alternative to traditional reinforcement learning in CPSs.
Operant conditioning principles can be effectively applied to optimize CPS resource management.
Further research can explore adaptive intermittent schedules for enhanced CPS performance.