Role-Exchange Playing: An Exploration of Role-Playing Effects for Anti-Bullying in Immersive Virtual Environments.

Role-playing is widely used in many areas, such as psychotherapy and behavior change. However, few studies have explored the possible effects of playing multiple roles in a single role-playing process. We propose a new role-playing paradigm, called role-exchange playing, in which a user plays two opposite roles successively in the same simulated event for better cognitive enhancement. We designed an experiment with this novel role-exchange playing strategy in the immersive virtual environments; and school bullying was chosen as a scenario in this case. A total of 234 middle/high school students were enrolled in the mixed-design experiment. From the user study, we found that through role-exchange, students developed more morally correct opinions about bullying, as well as increased empathy and willingness to engage in supportive behavior. They also showed increased commitment to stopping bullying others. Our role-exchange paradigm could achieve a better effect than traditional role-playing methods in situations where participants have no prior experience associated with the roles they play. Therefore, using role-exchange playing in the immersive virtual environments to educate minors can help prevent them from bullying others in the real world. Our study indicates a positive significance in moral education of teenagers. Our role-exchange playing may have the potential to be extended to such applications as counseling, therapy, and crime prevention.

Self-Illusion: A Study on Cognition of Role-Playing in Immersive Virtual Environments.

We present the design and results of an experiment investigating the occurrence of self-illusion and its contribution to realistic behavior consistent with a virtual role in virtual environments. Self-illusion is a generalized illusion about one's self in cognition, eliciting a sense of being associated with a role in a virtual world, despite sure knowledge that this role is not the actual self in the real world. We validate and measure self-illusion through an experiment where each participant occupies a non-human perspective and plays a non-human role using this role's behavior patterns. 77 participants were enrolled for the user study according to the priori power analysis. In the mixed-design experiment with different levels of manipulations, we asked the participants to play a cat (a non-human role) within an immersive VE and captured their different kinds of responses, finding that the participants with higher self-illusion can connect themselves to the virtual role more easily. Based on statistical analysis of questionnaires and behavior data, there is some evidence that self-illusion can be considered a novel psychological component of presence because it is dissociated from sense of embodiment (SoE), plausibility illusion (Psi), and place illusion (PI). Moreover, self-illusion has the potential to be an effective evaluation metric for user experience in a virtual reality system for certain applications.

DGaze: CNN-Based Gaze Prediction in Dynamic Scenes.

We conduct novel analyses of users' gaze behaviors in dynamic virtual scenes and, based on our analyses, we present a novel CNN-based model called DGaze for gaze prediction in HMD-based applications. We first collect 43 users' eye tracking data in 5 dynamic scenes under free-viewing conditions. Next, we perform statistical analysis of our data and observe that dynamic object positions, head rotation velocities, and salient regions are correlated with users' gaze positions. Based on our analysis, we present a CNN-based model (DGaze) that combines object position sequence, head velocity sequence, and saliency features to predict users' gaze positions. Our model can be applied to predict not only realtime gaze positions but also gaze positions in the near future and can achieve better performance than prior method. In terms of realtime prediction, DGaze achieves a 22.0% improvement over prior method in dynamic scenes and obtains an improvement of 9.5% in static scenes, based on using the angular distance as the evaluation metric. We also propose a variant of our model called DGaze_ET that can be used to predict future gaze positions with higher precision by combining accurate past gaze data gathered using an eye tracker. We further analyze our CNN architecture and verify the effectiveness of each component in our model. We apply DGaze to gaze-contingent rendering and a game, and also present the evaluation results from a user study.