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Related Concept Videos

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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Emotional Expression01:26

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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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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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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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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.
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Artificial intelligence (AI) systems can now rate emotions in images, mirroring human perceptions. This emergent ability suggests AI can learn emotional concepts from visual and linguistic data, impacting AI development and use.

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Affective Computing

Background:

  • Human emotion concepts develop through biology, language, and cognition.
  • Multimodal artificial intelligence (AI) models show advanced intellectual abilities.
  • AI's capacity for affective response to stimuli remains largely unexplored.

Purpose of the Study:

  • To investigate if state-of-the-art multimodal AI systems can emulate human emotional ratings.
  • To assess AI's ability to judge affective dimensions and discrete emotions in images.
  • To explore the emergence of emotion concepts in AI through statistical learning.

Main Methods:

  • Utilized state-of-the-art multimodal AI systems.
  • Presented a standardized set of images as affective stimuli.
  • Compared AI-generated emotional ratings against average human ratings.

Main Results:

  • AI judgments showed a strong correlation with average human emotional ratings.
  • The AI systems were not explicitly trained to match human affective responses.
  • The findings suggest emotion concept development can emerge from large-scale image-text data.

Conclusions:

  • Multimodal AI can develop the ability to visually judge emotional content.
  • Language and statistical learning are key to fostering emotion concepts in AI.
  • These findings have significant implications for the responsible deployment of AI technology.