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

Introduction to Learning01:18

Introduction to Learning

428
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...
428
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
Observational Learning01:12

Observational Learning

181
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...
181
Associative Learning01:27

Associative Learning

388
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...
388
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
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

566
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...
566

You might also read

Related Articles

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

Sort by
Same author

Clinical Associations of Cerebrospinal Fluid TMEM106B in Familial and Sporadic Frontotemporal Dementia.

JAMA neurology·2026
Same author

Neurocritical progression in amyotrophic lateral sclerosis: pathological relevance and validation.

Open life sciences·2026
Same author

Biosynthesized Silk-Amyloid-Mussel Proteins as Dissolution Recyclable Materials With Tunable Supercontraction.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

A Chimeric LBT-GFP Biosensor Exhibits Antithetical Fluorescence Responses to Ca<sup>2+</sup> and Dy<sup>3+</sup> Binding.

ACS omega·2026
Same author

Bovine serum albumin-stabilized nano-delivery system potentiates targeted anti-angiogenic therapy and synergistic photo-immunotherapy to restrict lung cancer metastasis.

Acta biomaterialia·2026
Same author

Cinnamaldehyde-Based Self-Assembled Nanodrugs with GSH Depletion for Antitumor through Photodynamic Therapy Enhanced Ferroptosis and Immunotherapy.

ACS applied materials & interfaces·2026

Related Experiment Video

Updated: Jul 7, 2025

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

7.6K

BAL: Balancing Diversity and Novelty for Active Learning.

Jingyao Li, Pengguang Chen, Shaozuo Yu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |December 22, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Active Learning strategically labels data to maximize performance. A new method, Balancing Active Learning (BAL), uses self-supervised features and a novel metric to balance data diversity and uncertainty, outperforming existing approaches.

    More Related Videos

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
    06:37

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

    Published on: December 15, 2023

    3.8K
    Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
    10:43

    Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

    Published on: June 10, 2021

    5.4K

    Related Experiment Videos

    Last Updated: Jul 7, 2025

    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

    7.6K
    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
    06:37

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

    Published on: December 15, 2023

    3.8K
    Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
    10:43

    Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

    Published on: June 10, 2021

    5.4K

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Data Science

    Background:

    • Active Learning (AL) aims to optimize data labeling within budget constraints.
    • Leveraging self-supervised learning features enhances AL performance.
    • Identifying and balancing data diversity and uncertainty is crucial for effective AL.

    Purpose of the Study:

    • Introduce a novel framework, Balancing Active Learning (BAL), for strategic data subset selection.
    • Develop a metric, Cluster Distance Difference, to quantify data diversity.
    • Evaluate BAL's performance against state-of-the-art (SOTA) methods across various labeling budgets.

    Main Methods:

    • Utilized self-supervised learning to extract data features.
    • Proposed the Cluster Distance Difference metric for data diversity assessment.
    • Developed the Balancing Active Learning (BAL) framework with adaptive sub-pools to balance data.

    Main Results:

    • BAL demonstrated superior performance, exceeding established AL methods by 1.20% on benchmarks.
    • BAL maintained performance comparable to full dataset labeling even with only 80% of samples labeled.
    • The proposed framework showed robust efficacy across extended labeling budget scenarios.

    Conclusions:

    • The Balancing Active Learning (BAL) framework offers a significant advancement in efficient data labeling.
    • BAL effectively balances data diversity and uncertainty, leading to improved model performance.
    • BAL provides a scalable and effective solution for active learning, particularly under limited labeling budgets.