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相关概念视频

Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Concepts and Prototypes01:24

Concepts and Prototypes

70
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,...
70
Classification of Systems-I01:26

Classification of Systems-I

164
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
164
Framing Effects03:26

Framing Effects

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Information is everywhere and its presentation—such as how and when items are presented—can impact our perceptions and decisions surrounding the info. This broad concept umbrellas framing effects—influences that occur due to the way information is framed in its appearance, whether it’s purely the order or the specific wording of a message. Let’s take a look at numerous ways in which two versions of something can objectively say the same thing, yet we respond in...
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
293
Classification of Systems-II01:31

Classification of Systems-II

131
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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相关实验视频

Updated: May 20, 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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构建无关的项目属性:基于上下文和参考对象的项目分类框架.

Wahyu Widhiarso1,2, Rolf Steyer1, Andrew Perossa3

  • 1Institute of Psychology, Friedrich-Schiller University of Jena, Jena, Germany.

Frontiers in psychology
|March 27, 2025
PubMed
概括
此摘要是机器生成的。

本文介绍了构造不相关的项目属性 (CIIA),这些属性是影响人们如何回答调查量表的措辞特征. 了解CIIA是开发更有效和有效的心理测量尺度的关键.

关键词:
构建-不相关项目的属性.项目内容 项目内容项目 树干 树干项目的措辞 项目的措辞个性衡量标准是个性的衡量标准.

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Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization
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Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization

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Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
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相关实验视频

Last Updated: May 20, 2025

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科学领域:

  • 心理测量 心理测量 心理测量
  • 心理测量 心理测量
  • 规模发展规模发展.

背景情况:

  • 构造不相关的项目属性 (CIIA) 是项目措辞的方面,而不是构造本身.
  • CIIA,如响应视角 (第一手与二手),显著影响受访者解释和数据.
  • 了解CIIA对于确保心理测量尺度的有效性至关重要.

研究的目的:

  • 提供已知CIIA的全面分类.
  • 为在规模发展中使用CIIA提供实际建议.
  • 将对项目属性的现有研究统一到一个单一的,可访问的资源中.

主要方法:

  • 对项目属性现有研究的文献综述.
  • 为CIIA开发一个分类系统 (分类学).
  • 综合发现,为尺度开发人员和用户提供指导.

主要成果:

  • 各种CIIA的识别和分类.
  • 展示CIIA如何影响规模项的解释和响应.
  • 建立一个理解和使用CIIA的框架.

结论:

  • 对CIIA的结构化理解可以提高心理测量尺度的有效性.
  • 这种分类对新手和经验丰富的规模开发人员来说都是一个宝贵的资源.
  • 统一对项目属性的研究促进了更有效和可靠的规模构建.