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

Associative Learning01:27

Associative Learning

1.2K
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...
1.2K
Observational Learning01:12

Observational Learning

824
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...
824
Cognitive Learning01:21

Cognitive Learning

1.0K
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...
1.0K
Purposive Learning01:22

Purposive Learning

438
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...
438
Introduction to Learning01:18

Introduction to Learning

931
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
931
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.3K
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...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Coordination of Abscisic Acid and Ultraviolet-B Radiations Dissociates Yield and Seed Quality Trade-Offs in Highland Barley under Water Deficit Stress.

Journal of agricultural and food chemistry·2026
Same author

Enhancing healthcare quality and management: AI-driven smart consortium practices at Dapeng Medical Group, Shenzhen.

Frontiers in medicine·2026
Same author

Toxicological Evaluation of the Roots of <i>Ficus pandurata</i> Hance var. <i>angustifolia</i> Cheng.

Food science & nutrition·2026
Same author

Quantifying The Impact of Textile Thickness and Contact Pressure on Watch-type Bone-Conduction Phonocardiogram: A Validation Study.

IEEE journal of biomedical and health informatics·2026
Same author

Altered Regional Brain Activity and Functional Connectivity Between Non-Diabetic and Diabetic Kidney Disease: A Resting-State fMRI Study.

Brain and behavior·2026
Same author

A universal light-controlled highly sensitive one-pot CRISPR/Cas12a diagnostic based on structure-engineered crRNA.

Trends in biotechnology·2026
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

IGFD-Net: Illumination-guided frequency decoupling for polarization image fusion.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Multiple-Strategies dung beetle optimizer and its applications in engineering optimization and bankruptcy prediction.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Aggregating global-scale pixel-wise forgery cues within a graph.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under aperiodic DoS attacks.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

Related Experiment Video

Updated: Jan 14, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.0K

Dual-focus memory contrastive learning for active domain adaptation.

Qing Tian1, Junjie Pan2, Yun Yang3

  • 1School of Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China; Wuxi Institute of Technology, Nanjing University of Information Science and Technology, Wuxi, 214000, China.

Neural Networks : the Official Journal of the International Neural Network Society
|October 25, 2025
PubMed
Summary
This summary is machine-generated.

Active Domain Adaptation (ADA) improves model performance by labeling target samples. Our Dual-Focus Memory Contrastive Learning (DumDA) method optimizes selected sample utilization for better domain adaptation.

Keywords:
Active domain adaptationActive learningContrastive learningDomain adaptation

More Related Videos

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

8.0K

Related Experiment Videos

Last Updated: Jan 14, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.0K
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

8.0K

Area of Science:

  • Machine Learning
  • Computer Vision
  • Artificial Intelligence

Background:

  • Active Domain Adaptation (ADA) leverages labeled target samples to improve model performance.
  • Current ADA methods focus on sample selection but underutilize selected data.
  • Ineffective utilization hinders capturing target domain structures.

Purpose of the Study:

  • To introduce Dual-Focus Memory Contrastive Learning for Active Domain Adaptation (DumDA).
  • To optimize the utilization of selected target domain samples.
  • To enhance domain adaptation performance by improving sample selection and knowledge propagation.

Main Methods:

  • DumDA orchestrates memory-encoded historical features with real-time batch contrast.
  • Dual-focus alignment enhances learning and alignment of sample selection.
  • A hybrid active selection strategy ensures class-balanced selection of reconstructed samples.

Main Results:

  • DumDA significantly improves performance in domain adaptation tasks.
  • The method demonstrates effective utilization of selected target domain samples.
  • Experimental results on multiple standard datasets validate DumDA's innovation.

Conclusions:

  • DumDA offers a novel approach to optimize selected sample utilization in ADA.
  • The dual-focus memory contrastive learning enhances knowledge propagation and target structure capture.
  • DumDA represents a significant advancement in active domain adaptation.