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Related Concept Videos

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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Introduction to Cognitive Psychology01:20

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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
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Lazarus's Cognitive Appraisal Theory01:20

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Cognitive psychologist Richard Lazarus proposed the cognitive-mediational theory of emotions, which emphasizes how individuals' assessments of stressors significantly affect their experience of stress. According to Lazarus, the stress response is determined by a two-step appraisal process: primary appraisal and secondary appraisal. These cognitive appraisals help individuals evaluate the potential impact of a stressor and determine the adequacy of their coping resources.
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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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Toward Explainable Affective Computing: A Review.

Karina Cortinas-Lorenzo, Gerard Lacey

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    This summary is machine-generated.

    Explainable AI (XAI) is crucial for transparent affective computing. This review synthesizes XAI approaches for multimodal systems, enhancing interpretability without sacrificing performance in real-world applications.

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    Area of Science:

    • Affective Computing
    • Explainable Artificial Intelligence (XAI)

    Background:

    • Affective computing systems, vital for human-technology interaction, are often "black boxes."
    • Increasing deployment in sensitive areas like healthcare and education necessitates transparency and interpretability.

    Purpose of the Study:

    • To review affective computing research through an Explainable AI (XAI) lens.
    • To synthesize existing XAI approaches and identify key challenges and future directions.

    Main Methods:

    • Categorization of XAI methods into pre-model, in-model, and post-model approaches.
    • Discussion of challenges in relating explanations to multimodal, time-dependent data, and cross-modal interactions.

    Main Results:

    • Existing XAI methods are promising for affective computing, improving transparency.
    • Several approaches surpass state-of-the-art performance while providing explanations.

    Conclusions:

    • Explainable affective computing is a nascent but promising field.
    • Future research should focus on data-driven XAI, defining explanation goals, and understanding explainee needs for true causability.