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Related Concept Videos

The Influence of Cognition on Affect01:29

The Influence of Cognition on Affect

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Cognition plays a pivotal role in shaping emotional experiences, as demonstrated by Schachter and Singer’s two-factor theory of emotion. According to this model, emotion arises from a combination of physiological arousal and cognitive interpretation. The body’s physiological response to stimuli is ambiguous and only gains emotional significance through cognitive labeling. For instance, an increased heart rate and adrenaline surge while standing near an attractive person may be...
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The Influence of Affect on Cognition01:29

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Positive affect significantly influences cognitive processes, including evaluation, memory, creativity, and social judgments. Compared to negative affect, positive emotional states promote more favorable interpretations of stimuli, cognitive flexibility, and heuristic processing. These effects highlight emotions' powerful role in shaping how individuals perceive, remember, and interact with the world.Influence on Evaluation and AttributionWhen individuals experience positive affect, they are...
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Related Experiment Video

Updated: Apr 20, 2026

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[Research on the performance comparing and building of affective computing database based on physiological

Xin Li, Xiaojuan Du, Yunpeng Zhang

    Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
    |December 4, 2014
    PubMed
    Summary

    High-quality emotional data is crucial for affective computing. Analyzing physiological parameters offers a feasible method for effective emotion recognition and stress evaluation, supporting future research.

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    Area of Science:

    • Cognitive Science
    • Affective Computing
    • Biomedical Engineering

    Background:

    • The quality of emotional data directly impacts emotion recognition accuracy in cognitive affective computing.
    • Developing high-performance affective computing databases is a critical research area.
    • Existing databases like MIT and Augsburg University have limitations in scope and data type.

    Purpose of the Study:

    • To compare the performance of existing cognitive affective computing databases.
    • To investigate the feasibility of using physiological parameters for emotion recognition and stress evaluation.
    • To establish a novel stress evaluation database using physiological data from a high-stress group.

    Main Methods:

    • Comparative analysis of data structures and types of MIT and Augsburg University databases.
    • Evaluation of emotion recognition based on physical parameters.
    • Acquisition and analysis of physiological parameters from postgraduate students undergoing high stress.

    Main Results:

    • Analysis of physical parameters demonstrated effective emotion recognition and feasibility for stress evaluation.
    • The newly constructed stress evaluation dataset shows reference value for stress assessment.
    • Physiological parameter analysis is a viable approach for stress evaluation.

    Conclusions:

    • Physiological parameter analysis is a promising method for effective emotion recognition and stress evaluation.
    • The developed stress evaluation dataset can support future research in affective computing.
    • Addressing the lack of domestic stress evaluation data based on physiological parameters is essential.