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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 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.
Physiological Arousal and Cognitive Labeling
According to this theory, when an individual experiences...
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Related Experiment Video

Updated: Apr 8, 2026

Human Fear Conditioning Conducted in Full Immersion 3-Dimensional Virtual Reality
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CTFear: A Fear Emotion Intensity Classification Method Based on EEG and Real-Time Labeling in Virtual Environment.

Shiwei Cheng, Zongfei Wu, Junjie Wu

    IEEE Transactions on Visualization and Computer Graphics
    |April 6, 2026
    PubMed
    Summary

    Researchers developed a new method using virtual reality (VR) and electroencephalography (EEG) to accurately measure fear intensity. This approach enhances emotion monitoring and interaction within virtual environments.

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    Area of Science:

    • Neuroscience
    • Virtual Reality Technology
    • Psychological Measurement

    Background:

    • Fear significantly impacts human behavior and psychological states, but quantifying its intensity is difficult.
    • Virtual reality (VR) offers a controlled environment for psychological interventions like exposure therapy.
    • Accurate real-time fear intensity measurement is crucial for understanding emotional responses and developing effective therapies.

    Purpose of the Study:

    • To propose a novel labeling paradigm for real-time, continuous subjective fear intensity measurement in immersive virtual environments.
    • To develop and validate a deep learning model (CTFear) for decoding fear intensity from electroencephalography (EEG) signals.
    • To enhance fear intensity labeling accuracy using haptic feedback as physical anchors.

    Main Methods:

    • Users labeled their subjective fear intensity in real-time by controlling a VR controller trigger while watching immersive VR videos.
    • Haptic vibration cues were integrated at specific trigger depths to serve as physical anchors for fear level discrimination.
    • A deep learning model, CTFear, combining convolutional neural networks and Transformers with topology-aware spatial positional encoding, was designed for EEG signal analysis.

    Main Results:

    • CTFear achieved high average F1 scores in classifying fear intensity: 0.86 (two-class), 0.76 (three-class), and 0.67 (four-class) in cross-trial validation.
    • Cross-subject validation yielded F1 scores of 0.80 (two-class), 0.64 (three-class), and 0.55 (four-class).
    • The proposed method significantly outperformed existing approaches in multi-class fear intensity classification tasks.

    Conclusions:

    • The CTFear model effectively decodes fear intensity from EEG signals, demonstrating its potential for objective emotion assessment.
    • The VR-based labeling paradigm with haptic feedback provides a reliable method for capturing subjective fear intensity.
    • This research offers a promising pathway for real-time emotion monitoring and human-computer interaction in virtual environments.