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相关概念视频

Purposive Learning01:22

Purposive Learning

122
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...
122
Introduction to Learning01:18

Introduction to Learning

446
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...
446
Cognitive Learning01:21

Cognitive Learning

247
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...
247
Language Development01:22

Language Development

375
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
375
Associative Learning01:27

Associative Learning

408
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...
408
Observational Learning01:12

Observational Learning

188
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...
188

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相关实验视频

Updated: Jul 11, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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通过快速学习进行域调整.

Chunjiang Ge, Rui Huang, Mixue Xie

    IEEE transactions on neural networks and learning systems
    |November 9, 2023
    PubMed
    概括

    本研究介绍了通过快速学习 (DAPrompt) 进行域调整,这是一种用于无监督域调整的新方法. 通过学习目标标签分布,DAPrompt有效地将模型适应新领域,优于现有技术.

    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 无监督域调整 (UDA) 旨在将知识从一个标记的源域转移到一个没有标记的目标域.
    • 现有的UDA方法经常对齐特征空间,可能会扭曲语义结构并减少类别的可区分性.
    • 这可能会阻碍模型性能,当源域和目标域显著不同时.

    研究的目的:

    • 为UDA引入一种新的快速学习范式,称为通过快速学习 (DAPrompt) 进行域调整.
    • 为了解决当前UDA方法的局限性,这些方法可能会扭曲特征结构.
    • 开发一种高效和有效的UDA方法,学习目标域名标签分布.

    主要方法:

    • DAPrompt将特定域的信息嵌入到自然语言生成的提示中.
    • 这些提示被用于分类,动态地适应模型的每个领域.
    • 该方法侧重于学习目标域的标签分布,而不是直接调整域.

    主要成果:

    • 与现有方法相比,DAPrompt在几个跨领域的基准指标上表现优越.
    • 提出的方法在不受监督的域调整任务中取得了最先进的结果.
    • 该模型在维护语义特征结构和类可区分性方面取得了显著的改进.

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    结论:

    • DAPrompt为无监督域调整提供了一个新且有效的范式.
    • 该方法在训练方面具有计算效率,并且易于实施.
    • 这种方法为领域适应的未来研究提供了一个有希望的方向.