A Model of Mental State Transition Network

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Emotion is one of the most essential and basic attributes of human intelligence. Current AI (Artificial Intelligence) research is concentrating on physical components of emotion, rarely is it carried out from the view of psychology directly<sup>(1)</sup>. Study on the model of artificial psychology is the first step in the development of human-computer interaction. As affective computing remains unpredictable, creating a reasonable mental model becomes the primary task for building a hybrid system. A pragmatic mental model is also the fundament of some key topics such as recognition and synthesis of emotions. In this paper a Mental State Transition Network Model<sup>(2)</sup> is proposed to detect human emotions. By a series of psychological experiments, we present a new way to predict coming human's emotions depending on the various current emotional states under various stimuli. Besides, people in different genders and characters are taken into consideration in our investigation. According to the psychological experiments data derived from 200 questionnaires, a Mental State Transition Network Model for describing the transitions in distribution among the emotions and relationships between internal mental situations and external are concluded. Further more the coefficients of the mental transition network model were achieved. Comparing seven relative evaluating experiments, an average precision rate of 0.843 is achieved using a set of samples for the proposed model.

収録刊行物

  • 電気学会論文誌. C, 電子・情報・システム部門誌 = The transactions of the Institute of Electrical Engineers of Japan. C, A publication of Electronics, Information and System Society  

    電気学会論文誌. C, 電子・情報・システム部門誌 = The transactions of the Institute of Electrical Engineers of Japan. C, A publication of Electronics, Information and System Society 127(3), 434-442, 2007-03-01 

    The Institute of Electrical Engineers of Japan

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各種コード

  • NII論文ID(NAID)
    10018738381
  • NII書誌ID(NCID)
    AN10065950
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    03854221
  • NDL 記事登録ID
    8730338
  • NDL 雑誌分類
    ZN31(科学技術--電気工学・電気機械工業)
  • NDL 請求記号
    Z16-795
  • データ提供元
    CJP書誌  CJP引用  NDL  J-STAGE 
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