Micro and Macro Causes of Violence

The dominant emotion in violence-threatening situations is confrontational tension/fear (ct/f), which causes most violence to abort, or to be carried out inaccurately and incompetently. For violence to be successful, there must be a pathway around the barrier of ct/f. These pathways include: attacking the weak; audience-oriented staged and controlled fair fights; confrontation-avoiding remote violence; confrontation-avoiding by deception; confrontation-avoiding by absorption in technique. Successfully violent persons, on both sides of the law, are those who have developed these skilled interactional techniques. Since successful violence involves dominating the emotional attention space, only a small proportion of persons can belong to the elite which does most of each type of violence. Macro-violence, including victory and defeat in war, and in struggles of paramilitaries and social movements, is shaped by both material resources and social/emotional resources for maintaining violent organizations and forcing their opponents into organizational breakdown. Social and emotional destruction generally precedes physical destruction.

Impact Study of Nonverbal Facial Cues on Spontaneous Chatting with Virtual Humans

Non-verbal communication (NVC) is considered to represent more than 90 percent of everyday communication. In virtual world, this important aspect of interaction between virtual humans (VH) is strongly neglected. This paper presents a user-test study to demonstrate the impact of automatically generated graphics-based NVC expression on the dialog quality: first, we wanted to compare impassive and emotion facial expression simulation for impact on the chatting. Second, we wanted to see whether people like chatting within a 3D graphical environment. Our model only proposes facial expressions and head movements induced from spontaneous chatting between VHs. Only subtle facial expressions are being used as nonverbal cues - i.e. related to the emotional model. Motion capture animations related to hand gestures, such as cleaning glasses, were randomly used to make the virtual human lively. After briefly introducing the technical architecture of the 3D-chatting system, we focus on two aspects of chatting through VHs. First, what is the influence of facial expressions that are induced from text dialog? For this purpose, we exploited an emotion engine extracting an emotional content from a text and depicting it into a virtual character developed previously [GAS11]. Second, as our goal was not addressing automatic generation of text, we compared the impact of nonverbal cues in conversation with a chatbot or with a human operator with a wizard of oz approach. Among main results, the within group study -involving 40 subjects- suggests that subtle facial expressions impact significantly not only on the quality of experience but also on dialog understanding.