ABSTRACT Effective simulation‐based police training requires exposure to realistic human behavior under uncertainty, particularly in situations involving escalation and use‐of‐force decisions. Virtual Reality (VR) enables immersive and repeatable training scenarios, where behaviorally credible virtual humans (VHs)are important to produce realistic and operationally relevant simulated scenarios. This paper presents a VH acting as a suspect during police interventions, whose behavior is governed by two parameters: resistance and means. Resistance models compliance or aggression, while means represents the potential to inflict harm using a weapon or object. These parameters drive a multimodal behavioral system integrating facial expression, posture and gesture, gaze, locomotion, and voice interaction. Behavior selection is controlled by a state‐machine architecture enabling escalation and de‐escalation in response to police actions, grounded in law‐enforcement literature on proportional use of force. We evaluated the model in immersive VR with police officers across scenarios combining resistance and means. Resistance primarily affected perceived danger and intent, whereas means more strongly affected perceived capacity and opportunity. These findings validate the framework and show that interpretable rule‐based VHs can elicit differentiated threat appraisals in controlled and repeatable VR scenarios, supporting their use for training and behavioral research in police contexts.
Tisserand et al. (2026) studied this question.