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相关概念视频

Observational Learning01:12

Observational Learning

149
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...
149
Introduction to Learning01:18

Introduction to Learning

342
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...
342
Purposive Learning01:22

Purposive Learning

104
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...
104
Vision01:24

Vision

53.0K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
53.0K
Associative Learning01:27

Associative Learning

309
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...
309
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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相关实验视频

Updated: Jun 12, 2025

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

304

噪音强大的视觉语言预训练与正负学习.

Zhenyu Huang, Mouxing Yang, Xinyan Xiao

    IEEE transactions on pattern analysis and machine intelligence
    |September 18, 2024
    PubMed
    概括

    视觉语言预训练 (VLP) 模型与杂的图像文本数据作斗争. 拟议的NEVER方法通过自适应地分离干净和杂数据来提高下游任务的性能,从而提高了VLP的稳定性.

    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:

    • 视觉语言预训练 (VLP) 学习了联合图像-文本表示.
    • 现有的VLP方法很容易遇到来自错位数据的噪音对应 (NC) 问题.
    • 在下游任务中,NC显著降低了性能,需要预培训解决方案.

    研究的目的:

    • 研究噪音对应 (NC) 对视觉语言预训练 (VLP) 模型的影响.
    • 开发一个强大的VLP方法,以减轻NC造成的性能下降.
    • 提出一个客观的定制解决方案,以提高VLP中的NC稳定性.

    主要方法:

    • 提出了一种新的噪音强大的视觉语言预训练 (NEVER) 方法.
    • 为清洁和无噪声子集开发了一种渐进和自适应的数据分割策略.
    • 采用积极学习 (PL) 和消极学习 (NL) 的双动力模型来平滑和准目标.

    主要成果:

    • 经验研究了NC对VLP模型的影响.
    • 证明NC显著降低下游任务性能.
    • 展示了NC的影响在不同的预培训目标上有所不同.

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    Last Updated: Jun 12, 2025

    Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
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    304
    DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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    Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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    结论:

    • 建议的NEVER方法有效地提高了VLP模型对NC的稳定性.
    • NEVER 提高了各种视觉语言任务的性能.
    • 在预训练期间处理NC对于强大的VLP模型至关重要.