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Related Concept Videos

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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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...
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Introduction to Learning01:18

Introduction to Learning

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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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Learning Disabilities01:25

Learning Disabilities

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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

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Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
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A Note on the Unification of Adaptive Online Learning.

Wenwu He, James Tin-Yau Kwok, Ji Zhu

    IEEE Transactions on Neural Networks and Learning Systems
    |March 2, 2016
    PubMed
    Summary

    Adaptive algorithms improve online convex optimization by using second-order gradient information. A new Follow the Bregman Divergence Leader framework unifies these methods, yielding simpler algorithms with better performance guarantees.

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    Area of Science:

    • Optimization Theory
    • Machine Learning
    • Online Learning

    Background:

    • Standard gradient methods are foundational in online convex optimization.
    • Adaptive algorithms offer enhanced performance by leveraging second-order information.
    • Existing adaptive methods lack a unified theoretical framework.

    Purpose of the Study:

    • To introduce a unifying framework, Follow the Bregman Divergence Leader, for adaptive online optimization algorithms.
    • To derive novel adaptive online algorithms with improved performance guarantees.
    • To generalize adaptive learning to nonlinear settings using kernel methods.

    Main Methods:

    • Development of the Follow the Bregman Divergence Leader framework.
    • Derivation of two new adaptive online algorithms.
    • Matrix analysis to generalize adaptive learning to nonlinear cases via the kernel trick.

    Main Results:

    • The proposed framework unifies existing adaptive algorithms, providing new insights.
    • Two novel adaptive algorithms with improvable performance guarantees were derived.
    • A generalized equation extends adaptive learning to nonlinear problems.

    Conclusions:

    • The Follow the Bregman Divergence Leader framework offers a unified perspective on adaptive online optimization.
    • The derived algorithms demonstrate enhanced performance and broader applicability.
    • The work paves the way for advanced adaptive learning in complex, nonlinear scenarios.