mal multiple regression analysis. The results suggest the followin Technical Details We conducted the experiments on a Silicon I For a negative audience, the higher the perceived Graphics Onyx with twin 196-Mhz R10000 audienceinterest, the higher the self-rating. However, processors, Infinite Reality graphics, and 192 for a positive audience, the perceived interest has no Mbytes of main memory. We used DIVE version influence on the self-rating 3.3 alpha software, developed at the Swedish I For nonimmersed subjects, the rating diminishes with Institute of Computer Science. For the increased co-presence independently of the type of immersive sessions, the tracking system had two audience response. However, for the immersed sub- Polhemus Fastraks, one for the head-mounted jects, higher co-presence is associated with lower self- display (HMD) and another for a five-button 3D ating for the negative audience and higher self-rating mouse(unused in these experiments).The for the positive audience helmet was a Virtual Research VR4 with a resolution of 742 x 230 pixels for each eye. he regression equations would lead to the following 170, 660 color elements, and a field of view of 67 degrees diagonal at 85 percent overlap. The frame rate for the experiments varied according I The lowest self-rating would result with a negative to whether the session was immersed or audience, immersion, maximum co-presence, and nonimmersed a The highest self-rating would result with alternative plan a repeat in early 1999. But even as a pilot, the Negative audience, lowest co-presence, and highest results exceeded our expectations. Clearly we have to perce teres do more work, but it seems that human subjects do Positive audience and highest co-presence respond appropriately to negative orpositive audiences Overall, the regression model provides a very good fit to the data(this model explains 89 percent of the vari- Acknowledgment ation in self-rating), and the results seem sensible. We The idea of applying VEs to social phobias was first found it noteworthy that when the audience is actually suggested to us by Nathaniel Durlach, Senior Scientist at negative, perceived audience interest can overcome the MITs Research Laboratory for Electronics, and Kalman negativity. This result means that the"positive"and Glantz a Boston-based psychiatrist. They made useful negative" audience responses were not as pure as we comments and suggestions throughout the study. This aimed for--clearly, sometimes a negative audience reac- work is partially funded by the European ACTS tion was perceived as positive. (Advanced Communications Technologies) project, We find the results satisfying In plain language this Coven( Collaborative Virtual Enviro D, and also neans that a low self-rating individual immersed in the the Digital-Virtual Center of Excellence project on VE with the virtual audience might say something like, Virtual Rehearsals for Actors. "I felt I was really with these people [high co-presence] They were behaving terribly [negative audience].They weren't at all interested in what I was saying [minimum perceived audience interest]. "Thats exactly the kind of response we wanted References 1. D. Stricklandet aL, "Overcoming Phobias by Virtual Expo- Conclusions We can conclude e sure,Comm. ACM, VoL 40, No 8, 1997, pp 34-39 M.M. North, S.M. North, and J. R Coble, "Virtual Reality Therapy: An Effective Treatment for the Fear of Public a Higher perceived audience interest increases self-rat- Speaking, "Int'1J. ofvirtual Reality, VoL 3, No 2, 1998, ing and reduces public speaking anxi Co-presence seems to amplify things, making a"bad"3 situation worse and a"good"situation better. the Coven Project, "IEEE CG&A, Vol. 18, No. 6, 1998, pp 53-63 A further conclusion important for future studies is 4. F. Parke and K. Waters, Computer Facial Animation, A K hat it may not be possible to design"pure"negative or Peters, Wellesley, Mass, 1998. positive audience responses. The perception of the audi ence response dominates here rather than the value that It's worth exploring the factors that lead sub- University College London, m. slater@cs. ucl.ac uke experimenters place on a particulardesigned audience Contact Slater by e-mail at Dept of Computer So jects to evaluate an audience as interested or not. Clearly, the actual audience reaction plays a part in this, Contact department editors Rosenblum and macedonia but it's not the whole story. by e-mail at rosenblu @ait nrL.navy. mil and Michael We are treating this study very much as a pilot and Macedonia @stricom army. mil. IEEE Computer Graphics and Applications Authorized licensed use limited to: SHENZHEN UNIVERSITY. Downloaded on March 27, 2010 at 06: 37: 04 EDT from IEEE Xplore. Restrictions applymal multiple regression analysis. The results suggest the following: ■ For a negative audience, the higher the perceived audience interest, the higher the self-rating. However, for a positive audience, the perceived interest has no influence on the self-rating. ■ For nonimmersed subjects, the rating diminishes with increased co-presence independently of the type of audience response. However, for the immersed sub￾jects, higher co-presence is associated with lower self￾rating for the negative audience and higher self-rating for the positive audience. The regression equations would lead to the following predictions: ■ The lowest self-rating would result with a negative audience, immersion, maximum co-presence, and minimum perceived audience interest. ■ The highest self-rating would result with alternative combinations: Negative audience, lowest co-presence, and highest perceived interest. Positive audience and highest co-presence. Overall, the regression model provides a very good fit to the data (this model explains 89 percent of the vari￾ation in self-rating), and the results seem sensible. We found it noteworthy that when the audience is actually negative, perceived audience interest can overcome the negativity. This result means that the “positive” and “negative” audience responses were not as pure as we aimed for—clearly, sometimes a negative audience reac￾tion was perceived as positive. We find the results satisfying. In plain language this means that a low self-rating individual immersed in the VE with the virtual audience might say something like, “I felt I was really with these people [high co-presence]. They were behaving terribly [negative audience]. They weren’t at all interested in what I was saying [minimum perceived audience interest].” That’s exactly the kind of response we wanted. Conclusions We can conclude ■ Higher perceived audience interest increases self-rat￾ing and reduces public speaking anxiety. ■ Co-presence seems to amplify things, making a “bad” situation worse and a “good” situation better. A further conclusion important for future studies is that it may not be possible to design “pure” negative or positive audience responses. The perception of the audi￾ence response dominates here rather than the value that experimenters place on a particular designed audience response. It’s worth exploring the factors that lead sub￾jects to evaluate an audience as interested or not. Clearly, the actual audience reaction plays a part in this, but it’s not the whole story. We are treating this study very much as a pilot and plan a repeat in early 1999. But even as a pilot, the results exceeded our expectations. Clearly we have to do more work, but it seems that human subjects do respond appropriately to negative or positive audiences, even when these are entirely virtual. ■ Acknowledgments The idea of applying VEs to social phobias was first suggested to us by Nathaniel Durlach, Senior Scientist at MIT’s Research Laboratory for Electronics, and Kalman Glantz a Boston-based psychiatrist. They made useful comments and suggestions throughout the study. This work is partially funded by the European ACTS (Advanced Communications Technologies) project, Coven (Collaborative Virtual Environments), and also the Digital-Virtual Center of Excellence project on Virtual Rehearsals for Actors. References 1. D. Strickland et al., “Overcoming Phobias by Virtual Expo￾sure,” Comm. ACM, Vol. 40, No. 8, 1997, pp. 34-39. 2. M.M. North, S.M. North, and J.R. Coble, “Virtual Reality Therapy: An Effective Treatment for the Fear of Public Speaking,” Int’l J. of Virtual Reality, Vol. 3, No. 2, 1998, pp. 2-6. 3. J.G. Tromp et al., “Small Group Behavior Experiments in the Coven Project,” IEEE CG&A, Vol. 18, No. 6, 1998, pp. 53-63. 4. F. Parke and K. Waters, Computer Facial Animation, A.K. Peters, Wellesley, Mass., 1998. Contact Slater by e-mail at Dept. of Computer Science, University College London, m.slater@cs.ucl.ac.uk. Contact department editors Rosenblum and Macedonia by e-mail at rosenblu@ait.nrl.navy.mil and Michael_ Macedonia@stricom.army.mil. IEEE Computer Graphics and Applications 9 Technical Details We conducted the experiments on a Silicon Graphics Onyx with twin 196-Mhz R10000 processors, Infinite Reality graphics, and 192 Mbytes of main memory. We used DIVE version 3.3 alpha software, developed at the Swedish Institute of Computer Science. For the immersive sessions, the tracking system had two Polhemus Fastraks, one for the head-mounted display (HMD) and another for a five-button 3D mouse (unused in these experiments). The helmet was a Virtual Research VR4 with a resolution of 742 × 230 pixels for each eye, 170,660 color elements, and a field of view of 67 degrees diagonal at 85 percent overlap. The frame rate for the experiments varied according to whether the session was immersed or nonimmersed. . Authorized licensed use limited to: SHENZHEN UNIVERSITY. Downloaded on March 27,2010 at 06:37:04 EDT from IEEE Xplore. Restrictions apply