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相关概念视频

Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
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Concepts and Prototypes01:24

Concepts and Prototypes

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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,...
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Natural and Artificial Concepts01:24

Natural and Artificial Concepts

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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...
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Cartesian Vector Notation01:28

Cartesian Vector Notation

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Cartesian vector notation is a valuable tool in mechanical engineering for representing vectors in three-dimensional space, performing vector operations such as determining the gradient, divergence, and curl, and expressing physical quantities such as the displacement, velocity, acceleration, and force. By using Cartesian vector notation, engineers can more easily analyze and solve problems in various areas of mechanical engineering, including dynamics, kinematics, and fluid mechanics. This...
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Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

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It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
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Vector Operations01:20

Vector Operations

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Vectors are physical quantities that have both magnitude and direction. The vector operations include addition, subtraction, and scalar multiplication.
A vector multiplied by a scalar value is called scalar multiplication. The result obtained is a new vector with a different magnitude. If the scalar is positive, the direction of the vector remains the same, but if it is negative, the direction of the vector is reversed. For example, the product of the mass and velocity yields the momentum.
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相关实验视频

Updated: Jun 17, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Creating Objects and Object Categories for Studying Perception and Perceptual Learning

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为什么概念是 (可能) 矢量.

Steven T Piantadosi1, Dyana C Y Muller2, Joshua S Rule3

  • 1Department of Psychology, University of California, Berkeley, CA, USA; Department of Neuroscience, University of California, Berkeley, CA, USA.

Trends in cognitive sciences
|August 7, 2024
PubMed
概括
此摘要是机器生成的。

矢量表示提供了一个统一的方法来理解人类的概念,容纳不同的认知功能. 大型语言模型和矢量符号架构的进步证明了它们对神经编码的潜力.

关键词:
教堂编码的编码这些概念是概念的概念.概念性作用 概念性作用这是一个向量向量.矢量象征架构的架构,象征的架构.

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科学领域:

  • 认知科学 认知科学
  • 神经科学是一个神经科学.
  • 人工智能的人工智能

背景情况:

  • 关于人类概念表示的性质的辩论.
  • 对表示的要求,以支持各种认知计算 (相似性,分类,关系).
  • 对表示的需要,以使理论发展和程序知识.

研究的目的:

  • 为基于矢量表示作为人类概念的统一描述辩论.
  • 要突出向量表示与神经架构的兼容性.
  • 讨论最近支持这一观点的进展.

主要方法:

  • 在认知科学中对表示要求的概念分析.
  • 综述大型语言模型 (LLM) 最近的进展.
  • 对矢量符号架构 (VSA) 的检查.

主要成果:

  • 矢量表示可以解释广泛的认知属性 (相似性,特征,类别,定义,关系).
  • 矢量表示支持复杂的认知过程,如理论开发和临时分类.
  • 最近的LLM和VSA展示了基于矢量符号计算的实际实现.

结论:

  • 基于矢量的表示为人类概念提供了一个引人注目的,神经可信的模型.
  • 新兴的人工智能技术验证了符号处理和认知建模的矢量力量.
  • 这种方法将不同的认知功能统一在一个单一的表示框架下.