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相关概念视频

Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.7K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Retrieval01:12

Retrieval

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Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
79
Purposive Learning01:22

Purposive Learning

96
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...
96
Observational Learning01:12

Observational Learning

118
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...
118
Cognitive Learning01:21

Cognitive Learning

144
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...
144
Masking and Demasking Agents01:19

Masking and Demasking Agents

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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
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相关实验视频

Updated: May 24, 2025

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers
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A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

Published on: January 5, 2018

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攻击作为防御:积极的敌对多模式学习,以逃避检索.

Fengling Li, Tianshi Wang, Lei Zhu

    IEEE transactions on pattern analysis and machine intelligence
    |March 3, 2025
    PubMed
    概括

    保护敏感数据免受多模式检索的影响至关重要. 我们的主动对抗多模式学习 (PAML) 方法将数据转化为对抗形式,有效地避免恶意挖掘,并确保用户隐私免受高级威胁.

    科学领域:

    • 计算机科学 计算机科学
    • 信息安全 信息安全
    • 人工智能的人工智能

    背景情况:

    • 越来越多的人对信息安全的担忧,需要强有力的用户敏感数据保护.
    • 多模式检索技术带来了新兴的威胁,增加了对数据泄露和恶意利用的脆弱性.

    研究的目的:

    • 引入一种新的方法,即主动对抗多模式学习 (PAML),以保护敏感数据免受多模式检索的影响.
    • 开发一种方法,将敏感数据转化为对抗性陈述,以确保隐私.

    主要方法:

    • PAML通过查询知识不可知系统来分析检索反机制.
    • 一个基于U-Net的扩散模型生成一个语义扰动网络,以微妙地改变数据语义.
    • 多模式检索结果和噪音将数据语义转移到异常值,防止邻居检索.

    主要成果:

    • 与数据隐私保护的基线方法相比,PAML在数据隐私保护方面表现优越.
    • 废弃性研究证实了在PAML框架内单个成分的有效性.
    • 该方法在各种多式联运检索系统中显示出适用性.

    结论:

    • PAML有效地保护敏感数据的隐私,通过创建抵御恶意多模式检索的敌对对手.

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  • 该方法增强了数据安全性,而不损害视觉现实主义或异常概括.
  • 拟议的技术在先进的检索技术时代为保护敏感信息提供了可行的解决方案.