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Results show that participants named the content of the videos and pictures significantly more often compared to a condition in which their content was not related to the question asked by the avatar. This priming procedure is promising because it could be combined with automatic keywords recognition and therefore enable virtual humans to respond in appropriate ways to human participants. For instance, when a participant is primed to use a specific keyword and he/she indeed says it during a virtual interaction, this keyword is automatically recognized by the system and triggers a specific response or behavior by the virtual human. Automatic Extraction of Participant Interaction Behavior in IVEs Participant interaction behavior in IVEs is sometimes the dependent variable because the behavioral observation is the goal. The use of IVET makes it possible to extract some interpersonal behavior data of participants directly from the simulation because the system uses that information to function. Another method to extract participant interaction behavior is to use social sensing technology, which will be outlined below. Participant Interaction Behavior Extracted from IVET There are some participant behaviors that can be measured directly by the IVE system that renders the virtual world. Interpersonal distance is a prime example for such automatic extraction of participant interaction behavior in a virtual encounter. This is because the IVE system constantly detects and monitors the location of the participant in order to render the virtual world in real time. Based on the location information of the participant and the virtual human, which is usually pre-defined by the programmer, interpersonal distance can be computed and registered during the entire social interaction. Interpersonal distance is an important social interaction behavior that can be indicative of approach-avoidance behavior or dominance (Hall et al., 2005). Another variable that can be recorded by IVET is the actual scene that is visualized by the participants, which might be an indicator of attentional strategies. This measure can be recorded by placing either visible or invisible markers in specific locations of the virtual scene. Given that participants can still move their eyes to focus on specific portions of the visual scene even without moving their heads, visualized scene can be a proxy of gaze direction but does not represent a precise measure. Behavior Extraction Using Additional Equipment In the previous section we discussed the use of visualized scene as a measure of attentional strategies SKAP1 within an IVE. The use of eye-tracking systems combined with the IVET allows more precise measures of attentional strategies. Wieser et al. (2010) involved a group of high and low socially anxious female participants in an IVE study in which they were approached by a virtual human.