article

Modeling the Experience of Emotion

Author:

Joost BroekensAuthors Info & Claims

International Journal of Synthetic Emotions, Volume 1, Issue 1

Pages 1 - 17

https://doi.org/10.4018/jse.2010101601

Published: 01 January 2010 Publication History

Abstract

Affective computing has proven to be a viable field of research comprised of a large number of multidisciplinary researchers, resulting in work that is widely published. The majority of this work consists of emotion recognition technology, computational modeling of causal factors of emotion and emotion expression in virtual characters and robots. A smaller part is concerned with modeling the effects of emotion on cognition and behavior, formal modeling of cognitive appraisal theory and models of emergent emotions. Part of the motivation for affective computing as a field is to better understand emotion through computational modeling. In psychology, a critical and neglected aspect of having emotions is the experience of emotion: what does the content of an emotional episode look like, how does this content change over time, and when do we call the episode emotional. Few modeling efforts in affective computing have these topics as a primary focus. The launch of a journal on synthetic emotions should motivate research initiatives in this direction, and this research should have a measurable impact on emotion research in psychology. In this article, I show that a good way to do so is to investigate the psychological core of what an emotion is: an experience. I present ideas on how computational modeling of emotion can help to better understand the experience of motion, and provide evidence that several computational models of emotion already address the issue.

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Cited By

Fathalla R(2020)Emotional ModelsInternational Journal of Synthetic Emotions10.4018/IJSE.202007010111:2(1-18)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.4018/IJSE.2020070101
Wang DHe TLi ZCao LDey NAshour ABalas VMccauley PLin YXu JShi F(2018)Image feature-based affective retrieval employing improved parameter and structure identification of adaptive neuro-fuzzy inference systemNeural Computing and Applications10.1007/s00521-016-2512-429:4(1087-1102)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s00521-016-2512-4
Marsella SGratch J(2014)Computationally modeling human emotionCommunications of the ACM10.1145/263191257:12(56-67)Online publication date: 26-Nov-2014
https://dl.acm.org/doi/10.1145/2631912

Modeling the Experience of Emotion
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Human-centered computing

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cover image International Journal of Synthetic Emotions

International Journal of Synthetic Emotions Volume 1, Issue 1

January 2010

99 pages

ISSN:1947-9093

EISSN:1947-9107

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IGI Global

United States

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Published: 01 January 2010

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Cited By

Fathalla R(2020)Emotional ModelsInternational Journal of Synthetic Emotions10.4018/IJSE.202007010111:2(1-18)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.4018/IJSE.2020070101
Wang DHe TLi ZCao LDey NAshour ABalas VMccauley PLin YXu JShi F(2018)Image feature-based affective retrieval employing improved parameter and structure identification of adaptive neuro-fuzzy inference systemNeural Computing and Applications10.1007/s00521-016-2512-429:4(1087-1102)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s00521-016-2512-4
Marsella SGratch J(2014)Computationally modeling human emotionCommunications of the ACM10.1145/263191257:12(56-67)Online publication date: 26-Nov-2014
https://dl.acm.org/doi/10.1145/2631912

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