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Associative Learning01:27

Associative Learning

572
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...
572
Introduction to Learning01:18

Introduction to Learning

530
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...
530
Observational Learning01:12

Observational Learning

311
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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Natural and Artificial Concepts01:24

Natural and Artificial Concepts

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In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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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...
785
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...
517

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関連する実験動画

Updated: Sep 10, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

635

複合的なゼロショット学習における二流条件概念の学習

Qingsheng Wang, Lingqiao Liu, Chenchen Jing

    IEEE transactions on pattern analysis and machine intelligence
    |August 26, 2025
    PubMed
    まとめ
    この要約は機械生成です。

    この研究は,組成的なゼロショット学習 (CZSL) を改善するためのデュアルストリーム条件ネットワーク (DSCNet) を導入します. この方法は,目に見えない概念をより良く認識するために,オブジェクトと属性の間の相互作用を効果的にモデル化します.

    関連する実験動画

    Last Updated: Sep 10, 2025

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    635

    科学分野:

    • コンピュータ科学
    • 人工知能
    • 機械学習

    背景:

    • 構成型ゼロショットラーニング (CZSL) は,属性-オブジェクトとオブジェクト-属性の相互作用をモデリングする上で課題に直面しています.
    • 正確なモデリングは 既知のコンポーネントによって形成された 見えない概念を認識するために不可欠です

    研究 の 目的:

    • CZSLのインタラクションモデリングの問題に対処するために.
    • CZSLの性能を向上させるための新しいダブルストリーム・コンディショナル・ネットワーク (DSCNet) を提案する.

    主な方法:

    • DSCNetは二重ストリームの条件付き概念を学習し,属性やオブジェクトの条件付きビジュアルおよびセマンティックエンブレディングを生成します.
    • セマンティックストリームは,オブジェクト/属性のセマンティクスと画像特性をコードし,クロスエンコーダーを通じて条件付きのセマンティックエンブレディングを作成します.
    • ビジュアル・ストリームは,意味学的な特徴をビジュアル・特徴に統合することによって,条件付きのビジュアル・エンブレディングを生成します.

    主要な成果:

    • 提案されたDSCNet方法は,標準的なCZSLベンチマークよりも優れたパフォーマンスを示しています.
    • 双流条件学習の効果を検証した結果です

    結論:

    • DSCNetは,CZSLにおける属性-オブジェクトとオブジェクト-属性の相互作用を効果的にモデル化しています.
    • 提案された条件付き埋め込み戦略は,構成的なゼロショット学習の最先端を大幅に前進させています.