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Related Concept Videos

Associative Learning01:27

Associative Learning

987
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...
987
Purposive Learning01:22

Purposive Learning

336
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...
336
Observational Learning01:12

Observational Learning

713
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...
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Introduction to Learning01:18

Introduction to Learning

761
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...
761
Cognitive Learning01:21

Cognitive Learning

900
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...
900
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.2K
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...
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Related Experiment Video

Updated: Dec 12, 2025

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

893

Context-Aware Learning for Generative Models.

Serafeim Perdikis, Robert Leeb, Ricardo Chavarriaga

    IEEE Transactions on Neural Networks and Learning Systems
    |August 11, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces context-aware generative models for improved machine learning. These models leverage side information to enhance performance, approaching supervised learning accuracy in unsupervised settings.

    Related Experiment Videos

    Last Updated: Dec 12, 2025

    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

    893

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Bayesian Networks

    Background:

    • Generative models are powerful tools for unsupervised learning.
    • Existing methods often lack performance without explicit ground-truth labels.
    • Side information can potentially bridge this gap.

    Purpose of the Study:

    • To develop and analyze algorithms for learning with side information by extending generative models.
    • To demonstrate performance improvements over standard unsupervised methods.
    • To explore the applicability to deep learning models.

    Main Methods:

    • Extending generative models with embedded context-related variables.
    • Utilizing finite mixture models (FMMs) as a base Bayesian network.
    • Applying maximum-likelihood estimation (MLE) via expectation-maximization (EM).
    • Deriving context-aware algorithms for variational autoencoders (VAEs).

    Main Results:

    • Context-aware algorithms show improved estimation precision and faster convergence.
    • Performance ranges between supervised and unsupervised methods, proportional to side information.
    • Demonstrated applicability in real-world unsupervised classification using Gaussian mixture models.
    • Extended methodology to variational autoencoders for unsupervised deep learning.

    Conclusions:

    • Extending generative models with side information significantly enhances unsupervised learning.
    • The proposed methods offer a flexible framework applicable to various generative models.
    • This approach provides a powerful alternative to supervised learning when labels are scarce.