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相关概念视频

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
Concepts and Prototypes01:24

Concepts and Prototypes

147
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,...
147
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

375
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...
375
Gestalt Principles of Perception01:21

Gestalt Principles of Perception

307
Gestalt principles provide a framework for understanding how humans perceive objects as unified wholes within their context. These principles are essential in explaining the cognitive processes that make sense of complex visual stimuli by organizing them into coherent groups. One fundamental principle is proximity, which posits that objects located close to each other are perceived as a collective group. For instance, when dots are positioned near one another, the visual system interprets them...
307
Visual System01:26

Visual System

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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
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相关实验视频

Updated: Jul 5, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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在视觉概念学习中的组成多样性.

Yanli Zhou1, Reuben Feinman2, Brenden M Lake3

  • 1Center for Data Science, New York University, United States of America.

Cognition
|January 15, 2024
PubMed
概括
此摘要是机器生成的。

人类通过构成性在视觉概念学习中表现出色,与计算机视觉模型不同. 这项研究模拟了人类在分类和生成新奇物体方面的能力,揭示了对构成概括和人类假设的洞察力.

关键词:
贝叶斯的推理 贝叶斯的推理复合性 复合性是指复合性.概念学习学习 概念学习有几次射击学习学习.神经符号模型的神经符号模型视觉学习 视觉学习

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相关实验视频

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科学领域:

  • 认知科学 认知科学
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 人类通过结合熟悉的部分 (组合性) 有效地学习新概念.
  • 与人类相比,计算机视觉模型通常需要大量的数据,并且在概括概念方面具有有限的灵活性.
  • 了解组成概括是开发更类似人类的人工智能的关键.

研究的目的:

  • 研究人类在视觉构成中的能力,以对新型对象进行分类和生成.
  • 开发能够复制和解释人类组成概括的计算模型.
  • 将贝叶斯程序诱导和神经符号程序诱导模型与人类行为的性能进行比较.

主要方法:

  • 人类分类和产生具有关系结构的"外星人形象"的实验研究.
  • 开发贝叶斯程序感应模型,用于推断可视图形的生成程序.
  • 开发一种神经符号程序诱导模型,其中包含神经网络模块用于残余结构.

主要成果:

  • 人类和贝叶斯程序感应模型都在一些射击分类任务中展示了组成概括.
  • 该模型提供了对人类数据的强有力的解释,揭示了关于旋转和部件附着等不变因素的假设.
  • 在几次拍摄的生成中,人类和模型都创造了新的例子,尽管人类表现出了诸如集完成和部分重新配置等额外的行为.

结论:

  • 人类和计算模型可以在视觉分类和生成中表现出组合行为.
  • 神经符号程序诱导模型可以捕捉到超越传统贝叶斯模型的额外人类行为模式.
  • 这些发现促进了对人类概念学习的理解,并为开发更灵活的人工智能系统提供了信息.