A Model of Mental State Transition Network

DOI Web Site Web Site 2 Citations 29 References
  • Xiang Hua
    Faculty of Engineering, The University of Tokushima
  • Jiang Peilin
    Faculty of Engineering, The University of Tokushima
  • Xiao Shuang
    Faculty of Engineering, The University of Tokushima
  • Ren Fuji
    Faculty of Engineering, The University of Tokushima School of Information Engineering, Beijing University of Posts and Telecommunication
  • Kuroiwa Shingo
    Faculty of Engineering, The University of Tokushima

Search this article

Abstract

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(1). 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(2) 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.

Journal

Citations (2)*help

See more

References(29)*help

See more

Keywords

Details 詳細情報について

Report a problem

Back to top