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Related Concept Videos

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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.
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Observational Learning01:12

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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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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Cognitive Learning01:21

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Generalization, Discrimination, and Extinction01:24

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
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Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
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Related Experiment Video

Updated: Oct 19, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Domain Adaptive Ensemble Learning.

Kaiyang Zhou, Yongxin Yang, Yu Qiao

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |September 17, 2021
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    Domain adaptive ensemble learning (DAEL) enhances deep neural network generalization across multiple domains. This framework improves performance in both unsupervised domain adaptation and domain generalization tasks by collaboratively training specialized classifiers.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Generalizing deep neural networks across multiple source domains to a target domain is challenging.
    • Existing methods often address unsupervised domain adaptation (UDA) or domain generalization (DG) separately.

    Purpose of the Study:

    • To propose a unified framework, Domain Adaptive Ensemble Learning (DAEL), for both multi-source UDA and DG.
    • To improve the collaborative learning of domain-specific experts within an ensemble for enhanced generalization.

    Main Methods:

    • DAEL utilizes a shared CNN feature extractor with multiple classifier heads, each specializing in a source domain.
    • A pseudo-target-domain approach is employed, where each source domain provides supervision for others.
    • Pseudo-labels are used to supervise ensemble learning in the UDA setting with unlabeled target data.

    Main Results:

    • DAEL significantly improves the state of the art on both multi-source UDA and DG tasks.
    • Experiments on five diverse datasets demonstrate the effectiveness and broad applicability of the DAEL framework.

    Conclusions:

    • The proposed DAEL framework offers a unified and effective solution for multi-source domain adaptation and generalization.
    • Collaborative learning of specialized experts within an ensemble leads to superior performance on unseen target domains.