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

Cognitive Learning01:21

Cognitive Learning

243
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...
243
Real-World Application of Classical Conditioning01:15

Real-World Application of Classical Conditioning

569
Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
Higher-order, or second-order, conditioning occurs when a neutral stimulus becomes associated with an already established conditioned stimulus through repeated pairings. For instance, if a dog has been...
569
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

559
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...
559
Role of Shaping in Operant Conditioning01:19

Role of Shaping in Operant Conditioning

322
Shaping is a technique used in operant conditioning to train complex behaviors by rewarding successive approximations toward the target behavior. This method is necessary because organisms are unlikely to perform complex behaviors spontaneously. Instead, shaping breaks down the desired behavior into small, manageable steps.
The steps involved in shaping begin with reinforcing any response that resembles the desired behavior. For example, parents might praise a child for picking up one toy. As...
322
Purposive Learning01:22

Purposive Learning

121
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
121
Associative Learning01:27

Associative Learning

370
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...
370

You might also read

Related Articles

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

Sort by
Same author

Functionalized carbon nanotube-assisted dual-mode CRISPR/Cas12a detection of hepatitis C virus via catalytic assembly circuit-driven Y-shaped dsDNA activators.

Biosensors & bioelectronics·2026
Same author

An Electrical Capacitance Tomography Dataset for Image Reconstruction Benchmarking.

Scientific data·2026
Same author

Dynamic Manipulation Skill Learning for Tactile Myoelectric Prosthetic Hands in Tool Handling.

Cyborg and bionic systems (Washington, D.C.)·2026
Same author

Proactive collaboration via autonomous interaction.

Nature communications·2026
Same author

DynamicTHOR: A Scalable Dataset of Human-Centric Dynamic Scenes for Embodied AI.

Scientific data·2026
Same author

Siamese foundation models for crystal structure prediction.

Nature communications·2026
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

Related Experiment Video

Updated: Jul 4, 2025

Pavlovian Conditioned Approach Training in Rats
06:57

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

11.0K

Goal-Conditioned Hierarchical Reinforcement Learning With High-Level Model Approximation.

Yu Luo, Tianying Ji, Fuchun Sun

    IEEE Transactions on Neural Networks and Learning Systems
    |February 1, 2024
    PubMed
    Summary
    This summary is machine-generated.

    High-level model approximation (HLMA) improves hierarchical reinforcement learning (HRL) by predicting controller performance and using relative states for better data efficiency and training stability in complex tasks.

    More Related Videos

    Investigating Motor Skill Learning Processes with a Robotic Manipulandum
    07:52

    Investigating Motor Skill Learning Processes with a Robotic Manipulandum

    Published on: February 12, 2017

    8.7K
    Acquisition of a High-precision Skilled Forelimb Reaching Task in Rats
    08:59

    Acquisition of a High-precision Skilled Forelimb Reaching Task in Rats

    Published on: June 22, 2015

    10.4K

    Related Experiment Videos

    Last Updated: Jul 4, 2025

    Pavlovian Conditioned Approach Training in Rats
    06:57

    Pavlovian Conditioned Approach Training in Rats

    Published on: February 4, 2016

    11.0K
    Investigating Motor Skill Learning Processes with a Robotic Manipulandum
    07:52

    Investigating Motor Skill Learning Processes with a Robotic Manipulandum

    Published on: February 12, 2017

    8.7K
    Acquisition of a High-precision Skilled Forelimb Reaching Task in Rats
    08:59

    Acquisition of a High-precision Skilled Forelimb Reaching Task in Rats

    Published on: June 22, 2015

    10.4K

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Robotics

    Background:

    • Hierarchical reinforcement learning (HRL) shows promise for complex tasks but faces challenges in training stability and exploration due to entangled policies.
    • Existing HRL methods struggle with understanding and mitigating the inherent complexities of hierarchical policy structures.

    Purpose of the Study:

    • To introduce a novel HRL algorithm, high-level model approximation (HLMA), designed to address training instability and improve exploration efficiency.
    • To provide both theoretical underpinnings and practical implementations of the proposed HLMA algorithm.

    Main Methods:

    • HLMA employs a Planner to create a high-level dynamic model predicting controller transitions and performance over multiple steps.
    • The Controller utilizes relative state deviations, derived from initial subtask states, to enhance subtask domain knowledge reuse and data efficiency.

    Main Results:

    • HLMA demonstrates improved sample efficiency and asymptotic performance compared to state-of-the-art single-level RL and HRL algorithms.
    • Experiments on complex locomotion and navigation tasks validate the algorithm's effectiveness.

    Conclusions:

    • HLMA offers a robust solution to the challenges of training stability and exploration in HRL.
    • The proposed method enhances data efficiency and overall performance in complex sequential decision-making problems.