Maze Experiment #3 Method

Maze Experiment #3


Survey Representations


Maze experiment II established that VE’s can enable effective route representations. However, an important question remained: if people are given enough time in a VE, will they eventually be able to form as accurate survey representations as they do in the real world or with a map? Experiment III was conducted to determine if there are any differences between configurational knowledge formed from VE’s, maps, or the real world. This experiment used what we feel are two very important principals experimental designs for VE research: 1.) The independent variable of primary interest was manipulated within subjects. 2.) The VE was programmed with features specifically designed to enhance spatial knowledge.

Participants were given as much time as they wanted to explore two maze environments: one in either a desktop or immersive VE and one in either the real world or on a map. Configurational knowledge was measured primarily through a variant of Siegel’s projective convergence technique. The experiment also provided us an opportunity to employ a new measure of survey knowledge involving reaction times.

Method

Participants. 66 people (34 women) recruited from the University of Washington department of Psychology’s subject pool were given extra credit in their introductory psychology class in return for participation in the experiment.

Materials. We designed two mazes to have a similar pattern of turns and interobject distances (see fig. M4-1). Each maze contained five prominent objects--either letters (A--E) or numbers (1--5). The mazes were computer modeled using WorldUp from SENSE8 on a Pentium Pro 200. Desktop participants viewed the VE on a 35 cm x 26.5cm monitor and navigated with a four degree-of-freedom joystick (lacking pitch and roll). Participants in the immersive condition interacted with the VE by moving in a 6 foot x 6 foot curtained enclosure in the real world. Head movements were tracked with a six degree-of-freedom tracker (Polhemus Fastrak). The sensitivity of the tracker was calibrated so that collisions between the virtual viewpoint and the walls of the virtual maze corresponded to collisions between the participants’ body and the curtains in the real world. This set-up allowed immersed participants to navigate with their body; however, the scale of their physical movements was nearly tripled in the VE.


Figure M4-1



Whenever they wished, participants were allowed to view their current position in the maze from a bird’s eye perspective. These views were shown with the participant’s current direction of travel facing up, and with their current position and heading represented by a large blue triangle (see. Fig. M4-2).


Figure M4-2



Procedure: Participants were randomly assigned to receive either a map or a real-world exploration of one maze and either a desktop or immersed VE maze. Participants were made aware of the tasks that they would be doing, and were given as much time as they wanted to learn each maze configuration. After learning each maze, participants were tested on their knowledge in the real world maze. Participants were asked to point and estimate distances to one of the maze’s objects from each of the other four. These data were analyzed using the projective convergence technique.

After pointing and estimating distances in the maze, participants made interobject distance estimations on a computer. The response times for these estimations were collected and analyzed as a function of the number of turns in the maze between them.

Results

We found very few significant differences between survey knowledge acquired from direct exploration, map study, and VE exposure. Participants took significantly longer to acquire their spatial knowledge from a VE (M = 572 seconds, SD = 260 s.) than from maps (M = 194 s; SD = 155 s. ) or a real-world walk through (M = 277 s; SD = 102 s.). Moreover, people took significantly more time to learn from an immersive VE (M = 488 s., SD = 222 s.) than from a desktop VE (M = 378 s., SD = 154 s.). Despite the different amounts of time spent learning, there were very few differences in the accuracy of the configurational knowledge acquired. A 2 (real/map) x 2 (desk/immersed) MANOVA using locational error, consistency, mean angle error, and mean distance error as dependent variables did not reveal any significant effects of learning environment type. The effect of the environment on locational error and consistency is shown in figure M4-3.


Figure M4-3



In general, the reaction time measure was noisy and difficult to interpret. Correlations between response times and the number of turns between objects were calculated for each participant. These correlations averaged -.05, -.01, -.06, and -.02 for real world, map, desktop, and immersive exposure respectively. Although there was no significant difference between these conditions, confidence intervals for their differences were relative wide. We have since concluded that this measure of survey knowledge is not particularly sensitive to the route/survey distinction.