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Related Concept Videos

Introduction to Learning01:18

Introduction to Learning

658
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...
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Observational Learning01:12

Observational Learning

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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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Associative Learning01:27

Associative Learning

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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...
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Purposive Learning01:22

Purposive Learning

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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...
261
Cognitive Learning01:21

Cognitive Learning

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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...
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Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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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

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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Learning Across Tasks for Zero-Shot Domain Adaptation From a Single Source Domain.

Jinghua Wang, Jianmin Jiang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |June 14, 2021
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    This study introduces a novel approach for zero-shot domain adaptation (ZSDA) by leveraging domain correlation knowledge. The proposed Conditional Coupled Generative Adversarial Networks (CoCoGAN) effectively adapt models to inaccessible target domains, outperforming existing methods.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Vision

    Background:

    • Domain adaptation aims to generalize models to new domains, but often requires target domain data for training.
    • Existing methods struggle with zero-shot domain adaptation (ZSDA) where target domain data is unavailable.
    • Human generalization abilities across domains far exceed current machine capabilities.

    Purpose of the Study:

    • To address the challenging zero-shot domain adaptation (ZSDA) problem where target domain data is inaccessible.
    • To reduce the generalization gap between human and machine learning capabilities.
    • To develop a method that learns transferable knowledge for unseen target domains.

    Main Methods:

    • Propose a new solution for zero-shot domain adaptation (ZSDA) by exploring domain correlation.
    • Introduce Conditional Coupled Generative Adversarial Networks (CoCoGAN) to capture joint data distributions across domains and tasks.
    • Utilize three supervisory signals for CoCoGAN training: semantic consistency, global representation alignment, and alignment consistency.

    Main Results:

    • CoCoGAN successfully learns models for non-accessible target domains.
    • The proposed method demonstrates superior performance compared to state-of-the-art techniques.
    • Effective adaptation was shown in both image classification and semantic segmentation tasks.

    Conclusions:

    • Domain correlation knowledge is crucial for improving generalization in machine learning.
    • CoCoGAN offers a viable solution for zero-shot domain adaptation.
    • The developed technique enhances model adaptability to unseen domains without requiring target data.