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相关概念视频

Self-Schemas02:16

Self-Schemas

36.5K
In general, a schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
36.5K
Self-Evaluation: Self-Enhancement and Self-Verification03:00

Self-Evaluation: Self-Enhancement and Self-Verification

5.8K
Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
5.8K
Associative Learning01:27

Associative Learning

1.6K
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...
1.6K
Observational Learning01:12

Observational Learning

1.1K
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...
1.1K
Self-Evaluation Maintenance Model01:29

Self-Evaluation Maintenance Model

353
The Self-Evaluation Maintenance (SEM) model offers a psychological framework to understand how individuals’ self-esteem is influenced by the achievements of others, particularly those with whom they share close personal bonds. The SEM model operates when personal rather than social identity guides individuals. Central to this model is the notion that individuals have an inherent desire to preserve a favorable self-image, which is continuously shaped by interpersonal comparisons and...
353
Theory of Attribution II: Kelley's Covariation Theory01:29

Theory of Attribution II: Kelley's Covariation Theory

773
Attribution theory plays a crucial role in social psychology, helping to explain how individuals interpret the causes of behavior. One prominent model within this field is Harold Kelley's covariation theory, which provides a systematic approach to determining whether internal traits or external circumstances drive a person's actions. The model posits that individuals rely on three key types of information—consensus, consistency, and distinctiveness—to make these judgments.Consensus:...
773

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相关实验视频

Updated: May 1, 2026

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software
08:22

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

Published on: August 31, 2018

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半监督的VQA多模式解释通过自我批判的学习.

Wei Suo, Ji Ma, Mengyang Sun

    IEEE transactions on pattern analysis and machine intelligence
    |March 2, 2026
    PubMed
    概括

    本研究引入了一种半监督的VQA多模式解释 (SME) 方法,以更清晰的VQA模型推理. 这种新的方法使用自我批评学习和半监督学习来提高解释的准确性和降低成本.

    科学领域:

    • 人工智能的人工智能
    • 计算机视觉 计算机视觉
    • 自然语言处理自然语言处理.

    背景情况:

    • 视觉问题答案 (VQA) 解释旨在使VQA模型决策的人类可理解.
    • 当前的方法通常依赖于单一的模式 (视觉或文本),导致模两可和逻辑不一致.
    • 收集人类注释的解释是昂贵和耗时的.

    研究的目的:

    • 开发一种用于生成准确和逻辑一致的VQA解释的新方法.
    • 解决单一模式范式的局限性,减少对昂贵的人类注释的依赖.
    • 通过多模式解释,提高VQA模型的可解释性.

    主要方法:

    • 引入了一种半监督的VQA多模式解释 (SME) 方法.
    • 采用自我批判性学习来提高答案和解释之间的逻辑一致性,使用奖励分数.
    • 利用半监督学习来利用大型数据集,而无需人类注释的解释.

    主要成果:

    • 通过整合视觉和文本信息,中小企业方法有效地产生了全面的解释.
    • 在VQA模型答案和它们生成的解释之间证明了更好的逻辑一致性.
    • 在三个VQA解释数据集上实现了最先进的性能.

    更多相关视频

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    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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    相关实验视频

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    A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software
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    A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

    Published on: August 31, 2018

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    Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
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    Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

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    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

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    结论:

    • 拟议的中小企业方法为VQA解释提供了更有效和高效的方法.
    • 多模式解释和自我批判学习显著提高了解释性和一致性.
    • 该方法利用未标记数据的能力降低了注释成本并提高了可扩展性.