Daniel P. McDonald, Dennis A. Vincenzi, Robert V. Muldoon, Robert R. Tyler, Amy S. Barlow, and Janan Al-Awar Smither University of Central Florida
The early simulators could best be described as "eye - hand" coordination devices used primarily to teach and reinforce rule based responses. Since then, the goal has been to create more "real world-like" simulations. We are moving toward being able to create cost-effective virtual environments (VE), which can be used for everything from military training applications to industrial simulation. VE gives us the capability for conducting complete mission rehearsals, enabling the use of a wide variety of instructional strategies. On the other side of the spectrum, the entertainment industry is rushing ahead to give consumers what they want, a more engaging environment within which to reap enjoyment. It is likely that low-cost VE will be commonplace in the near future. Anticipated widespread use of VE deems it necessary to begin addressing the limitations and capabilities of both today's and tomorrow's technologies for the target populations.
The U.S. Army and others been involved in ongoing VE related research addressing psychophysical and fidelity issues ( Hays & Singer, 1989; Levison & Pew, 1993; Singer, Ehrlich, Cinq-Mars, & Papin, 1995; Lampton, Gildea, McDonald & Kolasinski, 1996; Rinalducci, 1996), spatial knowledge acquisition and transfer ( Witmer, Bailey & Knerr, 1996; Singer, Allen, McDonald & Gildea, 1997), team training and situation awareness ( Ehrlich, Knerr, Lampton & McDonald, 1997), and simulator sickness ( Kennedy, Lane, Lilienthal, Berbaum & Hettinger, 1992). However, most research evaluating VE has been conducted using young military personnel or university students.
While VE training and performance research marches on, studies addressing performance of populations such as older adults is lagging. This in spite of the elderly being the largest growing segment of the population in the United States. Currently there are 23.5 million individuals over the age of 65. This group has nearly doubled in size since the early 1900's. The "85 and over" group has increased in size nearly 17 times since the early 1900's. This "85 and over" group is the segment of the elderly population that is responsible for the rapid growth in size of the elderly population in the United States. With the advent of recent advances in medical technologies, it is not unusual for an individual to reach the age of 85 in the U.S. today. It is estimated that by the year 2025, approximately 20% of the U.S. population will be 65 years of age or older ( Harbin, 1991). People are living much longer, and retiring at an older age than before. Moreover, with the uncertain future of the social security system, older persons may be forced to continue working past the age of 65. If VE technology becomes a commonplace medium for training and a number of other life-related functions more older adults will find themselves being required to use it. However, little is known about how age-related differences may interact with VE characteristics.
The effectiveness of VE for training or other applications has been attributed largely to the level of fidelity of the VE system. Regian, Shebilske, and Monk ( 1993) maintain that training transfer requires both preservation of the visual spatial characteristics of real world and that the interface preserves the link between motor actions and the effects in VE. User capabilities and limitations are important factors to consider in the interface. User characteristics can have a bearing the system's overall effectiveness. For example, a person who has a greater than average difficulty resolving images under lower levels of luminance may not be able to resolve images in a low luminance HMDS, even though the physical fidelity of that device may be suitable for most other persons for performing the task. According to Bjorn, Kaczmarek, and Lotens ( 1995), it is important to have a thorough understanding of the capabilities of the