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Related Concept Videos

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...
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Problem-Solving01:29

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Effective problem-solving consists of two steps: 1. identifying the problem and 2. selecting the appropriate problem-solving strategy (i.e., a plan of action used to find a solution). Humans use four problem-solving strategies:
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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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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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Social Cognitive Perspective on Personality01:30

Social Cognitive Perspective on Personality

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Social cognitive perspectives on personality emphasize the importance of conscious awareness, beliefs, expectations, and goals in shaping behavior. These perspectives incorporate behaviorist principles, such as learning through reinforcement and conditioning, but extend beyond them by highlighting human reasoning and planning. Unlike traditional behaviorist views, social cognitive theory focuses on how individuals reflect on their past experiences and plan for future outcomes by considering...
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Learning With Constraint Learning: New Perspective, Solution Strategy and Various Applications.

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    This study introduces Learning with Constraint Learning (LwCL), a unified framework for complex machine learning and computer vision problems. LwCL offers a holistic approach to challenges in Generative Adversarial Networks (GANs) and meta-learning, improving efficiency and practical application.

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    Area of Science:

    • Machine Learning
    • Computer Vision
    • Optimization

    Background:

    • Complex learning problems like GANs, multi-task learning, and meta-learning are often studied in isolation.
    • Existing methods lack a unified perspective to address the underlying coupling mechanisms in these diverse problems.

    Purpose of the Study:

    • To propose a novel, unified framework, Learning with Constraint Learning (LwCL), for holistically examining and solving complex learning and vision challenges.
    • To provide a general hierarchical optimization model that captures the essence of diverse learning and vision problems.

    Main Methods:

    • Developed the Learning with Constraint Learning (LwCL) framework as a general hierarchical optimization model.
    • Implemented a gradient-response based fast solution strategy to address optimization challenges within the LwCL framework.

    Main Results:

    • The LwCL framework successfully addresses a wide range of applications across three categories and nine problem types.
    • Extensive experiments on synthetic and real-world data validate the framework's effectiveness.

    Conclusions:

    • LwCL provides a comprehensive solution for complex machine learning and computer vision problems.
    • The framework bridges the gap between theoretical understanding and practical application in AI.