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相关概念视频

In- and Out-Groups01:31

In- and Out-Groups

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People all belong to a gender, race, age, and social economic group. These groups provide a powerful source of our identity and self-esteem (Tajfel & Turner, 1979) and serve as our in-groups. An in-group is a group that we identify with or see ourselves as belonging to.
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Relationship Formation02:12

Relationship Formation

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What do you think is the single most influential factor in determining with whom you become friends and whom you form romantic relationships? You might be surprised to learn that the answer is simple: the people with whom you have the most contact. This most important factor is proximity. You are more likely to be friends with people you have regular contact with. For example, there are decades of research that shows that you are more likely to become friends with people who live in your dorm,...
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Impact of Groups on Groups01:19

Impact of Groups on Groups

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Social psychologists analyze how groups influence one another, shaping social structures and interactions through both cooperation and competition. These dynamics manifest in various ways, ranging from economic partnerships to intergroup conflicts that shape societal structures and perceptions.Cooperation and Competition in Intergroup RelationsIntergroup relationships vary across contexts, sometimes fostering cooperation and mutual benefit while at other times leading to conflict and...
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Group Design02:01

Group Design

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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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Group Polarization01:01

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Group polarization is the strengthening of an original group attitude following the discussion of views within a group (Teger & Pruitt, 1967). That is, if a group initially favors a viewpoint, after discussion the group consensus is likely a stronger endorsement of the viewpoint. Conversely, if the group was initially opposed to a viewpoint, group discussion would likely lead to stronger opposition.
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Impact of Groups on Individuals01:28

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Groups play a fundamental role in shaping individual behavior, as they establish norms that guide interactions and decision-making. Social psychology examines how individuals conform to group expectations, often adjusting their attitudes and actions to align with group norms. These norms can be formal, such as workplace policies, or informal, such as unspoken social expectations within a fraternity.Conformity and Social InfluenceConformity arises when individuals modify their behaviors or...
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相关实验视频

Updated: Jan 7, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
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The HoneyComb Paradigm for Research on Collective Human Behavior

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在群体内和跨群体的同性恋.

Abbas K Rizi1,2,3, Riccardo Michielan4,5, Clara Stegehuis4

  • 1DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark. akari@dtu.dk.

Nature communications
|December 13, 2025
PubMed
概括
此摘要是机器生成的。

同性恋,即与相似的他人联系的倾向,现在在所有网络尺度上都是模拟的. 这一新框架揭示了特定群体的同类关系如何影响网络动态,如流行病传播和信息传播.

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Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
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Post-Movie Subliminal Measurement PMSM, for Investigating Implicit Social Bias
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相关实验视频

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Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
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科学领域:

  • 网络科学 网络科学
  • 社会学 社会学 社会学
  • 计算社会科学 计算社会科学

背景情况:

  • 同性恋显著影响网络结构和功能.
  • 当前的模型过度简化了同型性,只考虑一个尺度,忽视了它的多尺度性质.

研究的目的:

  • 引入一种新的最大率随机图模型,以捕捉所有社会尺度上的同类关系.
  • 将同类关系分解为集团内部和跨集团贡献.
  • 分析群体依赖同类关系对网络动态的影响.

主要方法:

  • 开发了一个最大率随机图形模型.
  • 在各种尺度上,包含了针对特定群体同性恋的参数.
  • 将同类性分解为组内和跨组组分.
  • 恢复了随机区块模型作为一个特殊案例.

主要成果:

  • 该模型捕捉了所有网络规模的同类性,与以前的方法不同.
  • 它将同性恋分解为内部和跨集团的贡献.
  • 群体依赖的同类性显著改变了网络透值.

结论:

  • 网络同类性在不同的群体大小和规模之间有很大差异.
  • 这种异质性对流行病传播,信息传播和干预效果的预测产生了重大影响.
  • 忽视多层次的同类性导致对复杂系统连接和动态的错误判断.