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

Survival Tree01:19

Survival Tree

61
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
61
Associative Learning01:27

Associative Learning

300
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...
300
Real-World Application of Classical Conditioning01:15

Real-World Application of Classical Conditioning

529
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...
529
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

451
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...
451
Purposive Learning01:22

Purposive Learning

101
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...
101
Role of Shaping in Operant Conditioning01:19

Role of Shaping in Operant Conditioning

268
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...
268

You might also read

Related Articles

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

Sort by
Same author

Arsenic trioxide depletes cancer stem-like cells and inhibits repopulation of neurosphere derived from glioblastoma by downregulation of Notch pathway.

Toxicology letters·2013
Same author

A prospective, randomized, open-label study comparing the efficacy and safety of preprandial and prandial insulin in combination with acarbose in elderly, insulin-requiring patients with type 2 diabetes mellitus.

Diabetes technology & therapeutics·2013
Same author

Synthesis of the C-18-C-34 fragment of amphidinolides C, C2, and C3.

Organic letters·2013
Same author

Synthesis of the C-1-C-17 fragment of amphidinolides C, C2, C3, and F.

Organic letters·2013
Same author

77Se solid-state NMR of As2Se3, As4Se4 and As4Se3 crystals: a combined experimental and computational study.

Physical chemistry chemical physics : PCCP·2013
Same author

Nanocellulose electroconductive composites.

Nanoscale·2013
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: Jun 10, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K

State Abstraction via Deep Supervised Hash Learning.

Guang Yang, Zheng Xu, Jing Huo

    IEEE Transactions on Neural Networks and Learning Systems
    |October 18, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new state abstraction method using deep supervised hashing (DSH) to improve reinforcement learning (RL) performance. DSH-based abstraction accelerates learning and outperforms existing methods on benchmark tasks.

    More Related Videos

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    475
    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
    09:34

    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

    Published on: September 25, 2021

    3.9K

    Related Experiment Videos

    Last Updated: Jun 10, 2025

    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
    05:41

    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

    Published on: February 6, 2020

    9.4K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    475
    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
    09:34

    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

    Published on: September 25, 2021

    3.9K

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Reinforcement Learning

    Background:

    • State abstraction is crucial for accelerating reinforcement learning (RL) algorithms by compressing large state spaces.
    • Designing effective state abstraction functions for high-dimensional problems remains a significant challenge in RL research.

    Purpose of the Study:

    • To introduce a novel state abstraction method based on deep supervised hash learning (DSH).
    • To provide theoretical analysis of the near-optimal properties of the proposed DSH-based state abstraction.
    • To develop a direct optimization method and an auxiliary learning task compatible with various RL algorithms.

    Main Methods:

    • Developed a novel state abstraction technique utilizing deep supervised hash learning (DSH).
    • Leveraged DSH-based representation as an optimization objective for a direct, target-value-based optimization method.
    • Constructed an auxiliary learning task for state abstraction, applicable to algorithms like deep Q-learning (DQN) and soft actor-critic (SAC).

    Main Results:

    • The proposed DSH-based state abstraction method demonstrates near-optimal properties.
    • Experimental results on Atari and classic control benchmarks show superior performance compared to existing state abstraction algorithms.
    • The DSH-based method effectively enhances the performance of both DQN and SAC algorithms.

    Conclusions:

    • The novel DSH-based state abstraction method offers a significant advancement in reinforcement learning.
    • This approach effectively addresses the challenge of state abstraction in large-scale and high-dimensional RL problems.
    • The method shows strong potential for improving the efficiency and performance of various RL algorithms.