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相关概念视频

Cognitive Theories: Schachter-Singer Theory of Emotion01:20

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Stanley Schachter and Jerome Singer proposed the two-factor theory of emotion, which emphasizes the interplay between physiological arousal and cognitive labeling in forming emotional experiences. This theory suggests that emotions are not simply a result of physiological responses but rather a combination of these responses and the individual's cognitive interpretation of them.
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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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Emotional expression encompasses how individuals convey their emotions through verbal communication and non-verbal cues. These non-verbal actions include facial expressions, body language, and physical gestures, such as frowning or smiling. Among these, facial expressions play a crucial role in emotional expression and are understood universally, indicating a biological basis for how humans communicate emotions.
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Magnetic flux depends on three factors: the strength of the magnetic field, the area through which the field lines pass, and the field's orientation with respect to the surface area. If any of these quantities vary, a corresponding variation in magnetic flux occurs. If the area through which the magnetic field lines are passing changes, then the magnetic flux also changes. This change in the area can be of two types: the flux through the rectangular loop increases as it moves into the...
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相关实验视频

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Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
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妇女与情感多模式情感计算数据集

Jose A Miranda Calero1,2, Laura Gutiérrez-Martín3, Esther Rituerto-González4,5

  • 1Embedded Systems Laboratory, Ecole Polytechnique Fédérale de Lausanne (EPFL), Vaud, Switzerland. jose.mirandacalero@epfl.ch.

Scientific data
|October 31, 2024
PubMed
概括

该数据集捕获了来自100名妇女的生理和语音数据,以检测基于性别的暴力. 技术验证证实数据集有效地引起和记录情绪,有助于暴力检测研究.

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科学领域:

  • 多模式情绪识别多模式情绪识别
  • 生理信号处理 物理信号处理
  • 语音分析用于情绪检测和检测情绪.

背景情况:

  • 基于性别的暴力 (GBV) 检测需要细微的情感理解.
  • 现有的数据集可能缺乏多模式情感数据,特别是来自不同人群的情感数据.
  • 虚拟现实 (VR) 提供了一个可控的情绪诱导环境.

研究的目的:

  • 介绍WEMAC数据集,这是一个开放的,用于GBV检测的多模式资源.
  • 在100名女性中,与生理和语音反应相关联引起的情绪.
  • 验证数据集在捕捉与恐惧相关的情绪方面的有效性,这对于GBV检测至关重要.

主要方法:

  • 收集了100名女性的生理数据 (血液体积脉冲,皮肤电动反应,皮肤温度) 和语音录音.
  • 使用通过VR耳机观看的验证视频引起的情绪.
  • 实验对象使用分类和维度尺度 (自我评估人形) 自我报告的情绪.

主要成果:

  • 技术验证显示,引起的和报告的分类情绪之间存在强烈的正相关性.
  • 在恐惧和非恐惧情绪报告之间观察到显著的负相关性.
  • 该数据集成功地引起了与GBV检测相关的目标情绪,特别是恐惧.

结论:

  • WEMAC数据集是多模态情感研究的验证资源,特别是用于GBV检测.
  • 数据有效地捕捉了引起情绪的生理和语音相关性.
  • 数据集的焦点是恐惧检测,为推进GBV研究提供了有价值的工具.