Emotions play a powerful, central role in everyday life. Emotions shape how we perceive the world, bias our beliefs, influence our decisions and in large measure guide how we adapt our behavior to the physical and social environment.

    Recent trends in cognitive modeling research have emphasized the development of integrated cognitive systems that combine a broad spectrum of human cognitive abilities into a single integrated architecture.  In contrast with detailed models of specific phenomena, such systems have potential, not only as a means to formalizing our basic understanding of human cognition, but also as practical proxies for human behavior in a wide range of applications.

    The acknowledged weakness of such technology, however, is it is particularly ill-suited for capturing the influence that factors such as stress and emotion can have.  Many have argued that emotion is fundamental to cognition, so it is perhaps surprising that computational models of emotion have not received much attention from researchers in artificial intelligence and cognitive science until very recently. We have been developing a general computational model of emotionally influenced cognition, EMA (Emotion and Adaptation) (Gratch and Marsella 2001; Marsella and Gratch 2003; Gratch and Marsella 2004), that attempts to account for both the factors that give rise to emotions as well as the wide-ranging impact emotions have on cognitive and behavioral responses, particularly coping responses. The model has been implemented and used to create a significant application where people can interact with virtual humans in high-stress social settings.

Emotions play a powerful, central role in everyday life. Emotions shape how we perceive the world, bias our beliefs, influence our decisions and in large measure guide how we adapt our behavior to the physical and social environment.

Recent trends in cognitive modeling research have emphasized the development of integrated cognitive systems that combine a broad spectrum of human cognitive abilities into a single integrated architecture.  In contrast with detailed models of specific phenomena, such systems have potential, not only as a means to formalizing our basic understanding of human cognition, but also as practical proxies for human behavior in a wide range of applications.

The acknowledged weakness of such technology, however, is it is particularly ill-suited for capturing the influence that factors such as stress and emotion can have.  Many have argued that emotion is fundamental to cognition, so it is perhaps surprising that computational models of emotion have not received much attention from researchers in artificial intelligence and cognitive science until very recently. We have been developing a general computational model of emotionally influenced cognition, EMA (Emotion and Adaptation) (Gratch and Marsella 2001; Marsella and Gratch 2003; Gratch and Marsella 2004), that attempts to account for both the factors that give rise to emotions as well as the wide-ranging impact emotions have on cognitive and behavioral responses, particularly coping responses. The model has been implemented and used to create a significant application where people can interact with virtual humans in high-stress social settings.

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