The impact of 3D virtual environments with different levels of realism on route learning: a focus on age-based differences

With technological advancements, it has become notably easier to create virtual environments (VEs) depicting the real world with high fidelity and realism. These VEs offer some attractive use cases for navigation studies looking into spatial cognition. However, such photorealistic VEs, while attractive, may complicate the route learning process as they may overwhelm users with the amount of information they contain. Understanding how much and what kind of photorealistic information is relevant to people at which point on their route and while they are learning a route can help define how to design virtual environments that better support spatial learning. Among the users who may be overwhelmed by too much information, older adults represent a special interest group for two key reasons: 1) The number of people over 65 years old is expected to increase to 1.5 billion by 2050 (World Health Organization, 2011); 2) cognitive abilities decline as people age (Park et al., 2002). The ability to independently navigate in the real world is an important aspect of human well-being. This fact has many socio-economic implications, yet age-related cognitive decline creates difficulties for older people in learning their routes in unfamiliar environments, limiting their independence. This thesis takes a user-centered approach to the design of visualizations for assisting all people, and specifically older adults, in learning routes while navigating in a VE. Specifically, the objectives of this thesis are threefold, addressing the basic dimensions of: ❖ Visualization type as expressed by different levels of realism: Evaluate how much and what kind of photorealistic information should be depicted and where it should be represented within a VE in a navigational context. It proposes visualization design guidelines for the design of VEs that assist users in effectively encoding visuospatial information. ❖ Use context as expressed by route recall in short- and long-term: Identify the implications that different information types (visual, spatial, and visuospatial) have over short- and long-term route recall with the use of 3D VE designs varying in levels of realism. ❖ User characteristics as expressed by group differences related to aging, spatial abilities, and memory capacity: Better understand how visuospatial information is encoded and decoded by people in different age groups, and of different spatial and memory abilities, particularly while learning a route in 3D VE designs varying in levels of realism. In this project, the methodology used for investigating the topics outlined above was a set of controlled lab experiments nested within one. Within this experiment, participants’ recall accuracy for various visual, spatial, and visuospatial elements on the route was evaluated using three visualization types that varied in their amount of photorealism. These included an Abstract, a Realistic, and a Mixed VE (see Figure 2), for a number of route recall tasks relevant to navigation. The Mixed VE is termed “mixed” because it includes elements from both the Abstract and the Realistic VEs, balancing the amount of realism in a deliberate manner (elaborated in Section 3.5.2). This feature is developed within this thesis. The tested recall tasks were differentiated based on the type of information being assessed: visual, spatial, and visuospatial (elaborated in Section 3.6.1). These tasks were performed by the participants both immediately after experiencing a drive-through of a route in the three VEs and a week after that; thus, addressing short- and long-term memory, respectively. Participants were counterbalanced for their age, gender, and expertise while their spatial abilities and visuospatial memory capacity were controlled with standardized psychological tests. The results of the experiments highlight the importance of all three investigated dimensions for successful route learning with VEs. More specifically, statistically significant differences in participants’ recall accuracy were observed for: 1) the visualization type, highlighting the value of balancing the amount of photorealistic information presented in VEs while also demonstrating the positive and negative effects of abstraction and realism in VEs on route learning; 2) the recall type, highlighting nuances and peculiarities across the recall of visual, spatial, and visuospatial information in the short- and long-term; and, 3) the user characteristics, as expressed by age differences, but also by spatial abilities and visuospatial memory capacity, highlighting the importance of considering the user type, i.e., for whom the visualization is customized. The original and unique results identified from this work advance the knowledge in GIScience, particularly in geovisualization, from the perspective of the “cognitive design” of visualizations in two distinct ways: (i) understanding the effects that visual realism has—as presented in VEs—on route learning, specifically for people of different age groups and with different spatial abilities and memory capacity, and (ii) proposing empirically validated visualization design guidelines for the use of photorealism in VEs for efficient recall of visuospatial information during route learning, not only for shortterm but also for long-term recall in younger and older adults.

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