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Air-entraining agents improve the durability and workability of concrete in climates with frequent freezing and thawing. These agents prevent cracks by introducing small air bubbles into the mix, creating spaces accommodating water expansion when temperatures drop. The air-entraining agents lower the surface tension of water, forming stable, small air bubbles. This method is more effective than having accidental large voids, as the intentional, smaller, and evenly distributed air voids improve...
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Altercasting is a strategic communication technique in which an individual imposes a specific identity or social role onto another person to influence their behavior and shape the interaction. By presuming a role—such as “responsible leader” or “patient person”—altercasting encourages the target to conform to that identity, often aligning their behavior with the expectations associated with the role. The power of this tactic lies in its subtlety; once a role...
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Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
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建立动态微表达训练工具的建立.

Jianxin Zhang1,2,3, Ning Cai1, Lihan Xu1

  • 1Laboratory of Learning and Teaching Brain Science, School of Education, Jiangnan University, Wuxi, China.

Frontiers in psychology
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PubMed
概括

新的动态微表达式培训工具 (DMETT) 有效地提高了动态微表达式识别技能. 这种工具利用现有的技术,显示出可靠性和有效性,女性显示出卓越的学习能力.

关键词:
德米特特是什么意思动态的微表达方式性别差异的性别差异是什么识别技术的识别技术培训效应是培训效应.

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科学领域:

  • 心理学 心理学 心理学
  • 认知科学 认知科学
  • 人与计算机的交互

背景情况:

  • 现有的微表达工具缺乏在表达背景下进行动态识别的培训.
  • 动态微表情识别对于理解复杂的人类情绪至关重要.

研究的目的:

  • 开发和验证一个动态微表达训练工具 (DMETT).
  • 评估现有的微表达式识别技术在动态环境中的有效性.
  • 调查动态微表情识别和学习中的性别差异.

主要方法:

  • 使用动态微表情识别能力测试 (DMERT) 的预测训练后测试设计.
  • 培训涉及在小说DMETT中建立的微表情识别技术 (METT).
  • 实验操纵了背景表情类型,背景情绪激发,动态微表情类型和微表情激发强度.

主要成果:

  • DMETT显著提高了动态微表达式识别能力,证明了良好的可靠性和有效性.
  • 微表情识别技术 (METT) 在训练动态微表情识别方面被证明有效.
  • 在一般动态微表情识别能力方面,没有发现显著的性别差异.
  • 在动态微表情识别学习中,女性比男性有小但显著的优势.

结论:

  • DMETT是增强动态微表达式识别的有效工具.
  • DMETT显示了作为未来微表达学习能力测量工具的潜力.
  • 动态微表达训练是可行的和有益的,在基于性别的学习模式中有细微差别.