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相关概念视频

Cognitive Theories: Schachter-Singer Theory of Emotion01:20

Cognitive Theories: Schachter-Singer Theory of Emotion

294
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...
294
Labeling Emotion01:20

Labeling Emotion

113
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...
113
Introduction to Motivation and Emotion01:29

Introduction to Motivation and Emotion

332
Motivation is a multifaceted process that drives behavior toward fulfilling various physiological or psychological needs. This process involves initiating, guiding, and maintaining specific actions influenced by internal and external factors. For example, when someone feels hungry while watching television, hunger is a motivator, prompting the individual to get up, walk to the kitchen, and find something to eat. In this instance, hunger initiates and sustains the behavior necessary to meet the...
332
Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

132
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...
132
Emotional Expression01:26

Emotional Expression

182
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...
182
Cognitive Theories: Lazarus Mediational Theory of Emotion01:17

Cognitive Theories: Lazarus Mediational Theory of Emotion

669
Richard Lazarus' cognitive mediational theory highlights the pivotal role of cognitive appraisal in shaping emotional responses. According to this theory, the evaluation of a stimulus — based on personal values, goals, beliefs, and expectations — mediates the emotional response. This appraisal process is immediate and often occurs unconsciously, influencing the intensity and nature of the resulting emotion.
Cognitive Appraisal and Emotional Response
Lazarus proposed that...
669

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相关实验视频

Updated: Jun 9, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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一个通用的自学情感框架,用于机器.

Alberto Hernández-Marcos1, Eduardo Ros2

  • 1Research Centre for Information and Communications Technologies (CITIC-UGR) - Department of Computer Engineering, Automation, and Robotics (ICAR), University of Granada, Granada, 18071, Spain. albertoh@correo.ugr.es.

Scientific reports
|October 29, 2024
PubMed
概括
此摘要是机器生成的。

研究人员开发了一个自我学习的人工智能框架,其中情绪是环境价值的时间模式. 这种人工智能成功地识别了八种基本情绪,与人类的感知和心理学保持一致.

关键词:
情感的框架 情感的框架情感模型 情感模型情绪 情绪 情绪强化学习是一种强化学习.

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科学领域:

  • 人工智能的人工智能
  • 认知神经科学 认知神经科学
  • 计算心理学 计算心理学

背景情况:

  • 智能生物利用情绪进行行为调节和进化优势.
  • 当前的人工智能缺乏情感诱导的第一原则理论,导致了通用化模型.
  • 现有的AI情感模型通常是具体的,缺乏广泛的适用性.

研究的目的:

  • 提出一种自学的人工智能框架,用于从原始观测中引起情绪.
  • 将情绪定义为对关键环境价值观的感知时间模式.
  • 开发出能够表现和识别自然情绪的AI代理.

主要方法:

  • 为人工智能代理人开发了一个完全自学的情感框架.
  • 在未标记的代理体验上训练了一个人工神经网络.
  • 通过使用人类观察者对快乐-兴奋-主导度维度的评分来验证框架.

主要成果:

  • 人工智能框架成功地学习并识别了八种基本的情绪模式.
  • 识别的情绪是局势上连贯的,并重现了自然的情绪动态.
  • 人类观察者表现出高度的统计一致性,并与实验心理学保持一致.

结论:

  • 拟议的框架提供了一种基于强化学习的人工情绪的通用,跨学科的方法.
  • 这项研究可能为更像人类的情感AI铺平了道路.
  • 这些发现表明,情绪可以被理解为环境相互作用中的感知时间模式.