D.D. Schmorrow, L.M. Reeves (Eds.): Augmented Cognition, HCII 2007, LNAI 4565, pp. 186–194, 2007. © Springer-Verlag Berlin Heidelberg 2007 Simulation Fidelity Design Informed by Physiologically-Based Measurement Tools Jack M. Vice 1 , Corinna Lathan 1 , Anna D. Lockerd 1 , and James M. Hitt, II 2 1 AnthroTronix, Inc., 8737 Colesville Rd, L203 Silver Spring, MD 20910, USA {jvice,clathan,alockerd}@atinc.com 2 Independent Consultant, 110 Cherrywood Drive Gaithersburg, MD 20878, USA jameshitt@verizon.net Abstract. Virtual environments (VE’s) and simulations are being employed for training applications in a wide variety of disciplines, both military and civilian. The common assumption is that the more realistic the VE, the better the transfer of training to real world tasks. However, some aspects of task content and fidelity may result in stronger transfer of training than even the most high fidelity simulations. A physiologically-based system capable of dynamically detecting changes in operator behavior and physiology throughout a VE experience and comparing those changes to operator behavior and physiology in real-world tasks, could potentially determine which aspects of VE fidelity will have the highest impact on transfer of training. Thus, development of training assessment and guidance tools that utilize operator behavior and physiology to determine VE effectiveness and transfer of training are needed. Keywords: virtual reality, simulation, transfer of training, physiology, behavior, training effectiveness. 1 Introduction Virtual environments (VE’s) and simulations are being employed for training applications in a wide variety of disciplines, both military and civilian. Technological advances are enhancing the ability of developers to create VE’s with visual, auditory, haptic, and even olfactory realism. Such VE’s allow the military to train skills that are too costly, too dangerous, or are otherwise impossible to practice. The common assumption is that the more realistic the VE, the better the transfer of training to real world tasks. However, some aspects of task content and fidelity may result in stronger transfer of training than even the most high fidelity simulations. This has traditionally been determined by performance measurements compared before and after design iterations. Each time design modifications are made, end users are tested on the VE and their performance is compared to performance on the prior VE design. In such study, improved performance is assumed to be related to improved design. However, the specific aspects of design improvement that directly relate to transfer of