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相关概念视频

Concepts and Prototypes01:24

Concepts and Prototypes

149
The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
149
Cognitive Learning01:21

Cognitive Learning

243
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...
243
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

163
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...
163
Associative Learning01:27

Associative Learning

378
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...
378
Purposive Learning01:22

Purposive Learning

121
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...
121
Steps in the Modeling Process01:14

Steps in the Modeling Process

210
Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
210

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相关实验视频

Updated: Jul 7, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
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学习概念 可信的模型,以缓解快捷方式.

Jiaxuan Wang1, Sarah Jabbour1, Maggie Makar1

  • 1Division of Computer Science & Engineering, University of Michigan, Ann Arbor, MI, USA.

Advances in neural information processing systems
|December 27, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了新的方法来训练强大的机器学习模型,通过利用领域知识和学习未知的概念,减轻偏见数据的捷径学习.

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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

Last Updated: Jul 7, 2025

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科学领域:

  • 机器学习 机器学习
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 模型在训练过程中经常学习虚假的相关性 (快捷方式),导致对新数据的概括性差.
  • 当快捷方式与未知的概念联系在一起或仅依赖预定义的知识时,现有的方法会遇到困难.

研究的目的:

  • 通过减轻捷径学习,从有偏见的培训数据中开发 robust 和准确的学习模型的方法.
  • 整合领域知识 (已知的概念),同时允许模型学习未知的概念.

主要方法:

  • 一个两阶段的方法:首先将模型与已知的概念相匹配,然后处理未知概念的残余.
  • 一种扩展的规范化处罚方法,将已知的和未知的概念整合起来,以防止捷径利用.

主要成果:

  • 两个阶段的方法显示当快捷方式与未知的概念相关时,脆弱性.
  • 扩展的规范化惩罚方法有效地减轻了两种现实世界数据集之间的捷径学习.

结论:

  • 两种拟议的方法都成功地减少了在受过偏差数据训练的模型中的捷径学习.
  • 将领域知识与学习未知的概念的能力相结合,对于强大的模型概括至关重要.