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相关概念视频

Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

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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...
76
Labeling Emotion01:20

Labeling Emotion

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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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Cognitive Theories: Schachter-Singer Theory of Emotion01:20

Cognitive Theories: Schachter-Singer Theory of Emotion

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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.
Physiological Arousal and Cognitive Labeling
According to this theory, when an individual experiences...
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Physiology of Emotion01:20

Physiology of Emotion

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The physiology of emotions is a multifaceted process involving the autonomic nervous system, brain structures, hormones, and neurotransmitters. This intricate interplay dictates how emotions manifest in the body and influence behavior.
Autonomic Nervous System
The autonomic nervous system (ANS) plays a critical role in emotional responses by regulating involuntary physiological functions. It consists of two main components: the sympathetic and parasympathetic systems. The sympathetic system...
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Empathy02:34

Empathy

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Some researchers suggest that altruism operates on empathy. Empathy is the capacity to understand another person’s perspective, to feel what he or she feels. An empathetic person makes an emotional connection with others and feels compelled to help (Batson, 1991). Empathy can be expressed in several ways, including cognitive, affective, and motor. 
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Emotional Expression01:26

Emotional Expression

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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.
Universal Facial Expressions
Psychologist Paul Ekman identified seven basic...
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相关实验视频

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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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使用深度学习模型来解码马的情绪状态.

Romane Phelipon1, Lea Lansade1, Misbah Razzaq2

  • 1INRAE, CNRS, Université de Tours, PRC, 37380, Nouzilly, France.

Scientific reports
|April 24, 2025
PubMed
概括

机器学习模型准确地预测骑马的情绪,使用剪裁的头部图像. 这种利用卷积神经网络 (CNN) 的方法实现了87%的准确性,优于其他方法.

科学领域:

  • 动物行为 动物行为
  • 机器学习 机器学习
  • 计算机视觉 计算机视觉 计算机视觉

背景情况:

  • 准确评估动物福利至关重要.
  • 了解马的情绪状态对于道德骑行实践至关重要.
  • 开发客观的方法来检测马的情绪是一个持续的挑战.

研究的目的:

  • 开发和比较机器学习模型,用于预测骑马中的情绪状态.
  • 调查不同图像裁剪策略对模型性能的影响.
  • 通过转移学习和微调等技术来提高模型的准确性.

主要方法:

  • 手动标记图像用于监督学习.
  • 使用Yolo和更快的R-CNN对被裁剪的身体和头部数据集进行数据增强.
  • 在不同的数据集上训练和评估各种卷积神经网络 (CNN) 模型.
  • 转移学习,微调和解释方法 (LIME) 的应用.

主要成果:

  • 截断头部数据集实现了最高的性能,准确率为87%,精度为79%,回忆率为97%.
  • 在被裁剪的数据集上训练的CNN模型的表现优于那些在未经裁剪的图像上训练的模型.
  • 通过LIME解释方法,确定了与专家注释一致的特征.

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The Use of Traditional Fear Tests to Evaluate Different Emotional Circuits in Cattle
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结论:

  • 切割头部图像在骑马中的基于机器学习的情绪预测中非常有效.
  • CNN,特别是当微调时,为马匹的自动化情绪分析提供了强大的解决方案.
  • 像LIME这样的模型解释性方法可以根据专家知识验证AI发现.