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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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Cognitive Learning01:21

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
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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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Cognitive Theories: Lazarus Mediational Theory of Emotion01:17

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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.
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Cognitive Dissonance01:38

Cognitive Dissonance

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Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
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Physiology of Emotion01:20

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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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Related Experiment Video

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Brain Imaging Investigation of the Impairing Effect of Emotion on Cognition
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Label-less Learning for Emotion Cognition.

Min Chen, Yixue Hao

    IEEE Transactions on Neural Networks and Learning Systems
    |August 20, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces label-less emotion cognition (LLEC) to leverage unlabeled data for improved emotion detection accuracy. The novel approach automatically labels data, enhancing model performance without human intervention.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Affective Computing

    Background:

    • Emotion detection models often require large labeled datasets, limiting their application.
    • Unlabeled data presents a significant, underutilized resource in emotion cognition research.

    Purpose of the Study:

    • To propose a novel label-less learning for emotion cognition (LLEC) algorithm.
    • To enhance the utilization of multimodal unlabeled emotion data.
    • To improve the accuracy of emotion detection models.

    Main Methods:

    • Developed a hybrid label-less learning approach using similarity and entropy models for automatic data labeling.
    • Designed an enhanced hybrid label-less learning method to purify automatically labeled data.
    • Applied the enhanced method to multimodal unlabeled emotion data.

    Main Results:

    • The LLEC algorithm significantly improves the accuracy of emotion detection.
    • Demonstrated effective utilization of large amounts of unlabeled data.
    • Validated the algorithm's performance through a real-world test bed.

    Conclusions:

    • Label-less learning is a viable strategy for emotion cognition.
    • The proposed LLEC algorithm effectively enhances emotion detection accuracy and data utilization.
    • Future research can explore LLEC in diverse multimodal emotion datasets.