Distance Perception in Real and Virtual Environments Jodie M. Plumert Department of Psychology The University of Iowa
[email protected]
Abstract Two experiments were conducted to compare distance perception in real and virtual environments. In Experiment 1, adults estimated how long it would take to walk to targets in real and virtual environments by starting and stopping a stopwatch while looking at a target person standing between 20 and 120 ft away. The real environment was a large grassy lawn in front of a university building. We replicated this scene in our virtual environment using a nonstereoscopic, large screen immersive display system. We found that people underestimated time to walk in both environments for distances of 40-60 ft and beyond. However, time-to-walk estimates were virtually identical across the two environments. In Experiment 2, 10- and 12-year-old children and adults estimated time to walk in real and virtual environments both with and without vision. Adults again underestimated time to walk in both environments for distances of 60 ft and beyond. Again, their estimates were virtually identical in the real and virtual environment both with and without vision. Children’s time-to-walk estimates were also very similar across the two environments under both viewing conditions. We conclude that distance perception may be better in virtual environments involving large screen immersive displays than those involving head mounted displays (HMDs). Keywords: virtual environments, large-screen immersive displays, distance estimation, perception CCS: J.4 [Computer Applications]: Social and Behavioral Sciences – Psychology, I.3.7 [Computer Graphics]: Three Dimensional Graphics and Realism – Virtual Reality
1 Introduction Virtual environments are gaining widespread acceptance as a tool for studying human behavior (Loomis, Blascovich, &
Joseph K. Kearney and James F. Cremer Computer Science Department The University of Iowa
[email protected] [email protected]
Beall, 1999; Plumert, Kearney, & Cremer, in press). Problems ranging from children’s road-crossing behavior (Plumert et al., in press) to adults’ collision avoidance behavior (Cutting, Vishton, & Braren, 1995) have been studied using various kinds of virtual environments. One obvious question that arises when using virtual environments to study human behavior is how well does behavior in virtual environments correspond to behavior in the real environment? Although virtual environments are an exciting new medium for investigating difficult-to-study problems under realistic and controlled conditions, the results of such experiments are of questionable value if virtual environments lack ecological validity. One critical aspect of behavior in virtual environments that has received increasing attention is distance perception. Clearly, distance perception underlies a wide range of human action in both natural and virtual environments. Tasks such as throwing a ball at a target or steering a bike around an obstacle require that observers accurately perceive how far away things are from themselves. Given the importance of distance perception for human action, it is critical to assess how well distance perception corresponds in real and virtual environments. We addressed this question by asking people to judge the same distances both in the real environment and in an immersive virtual environment.
2 Related Work How good are people at perceiving distance in the natural environment? A relatively large number of studies have been conducted on how people perceive egocentric distance (i.e., absolute distance from the self) and exocentric distance (i.e., relative distance between objects) in the natural environment. Studies that use visually guided judgments to assess perception of distance typically find that people progressively undershoot egocentric distance with increasing physical distance (e.g., Gilinsky, 1951; Harway, 1963; Loomis, Da Silva, Fujita, & Fukusima, 1992). For example, when asked to match a depth interval on the ground plane with an interval in the frontal plane, people consistently chose depth intervals that were too large (Loomis et al., 1992). In other words, people perceived the same interval as shorter in the depth plane than in the frontal plane. In contrast, studies that use visually directed action to assess perception of distance typically find that people are very good at perceiving distances (Loomis et al., 1992; Philbeck & Loomis, 1997; Rieser, Ashmead, Taylor, & Youngquist, 1990). These
studies have shown that people are quite accurate at walking without vision to previously seen targets, particularly within what is called action space (up to about 20 m). Beyond 20 m or so, people tend to undershoot distances when walking without vision. Together, these studies suggest that the mapping between visual and physical space is distorted in perception but not action. How good are people at perceiving distance in virtual environments? A number of recent studies suggest that people underestimate distance in virtual environments (Loomis & Knapp, 2003; Thompson, Willemsen, Gooch, Creem-Regehr, Loomis, & Beall, in press; Willemsen & Gooch, 2002). Loomis and Knapp (2003), for example, examined perception of egocentric distance in a virtual environment using a stereoscopic HMD system. People viewed spheres lying on the ground plane at distances of 2, 6, and 18 m. In the triangulation task, people first viewed the target, then turned from the target, and then walked without vision for about 3 m. At the stopping point, people attempted to face the previously viewed target. Pointing errors showed that people undershot distances by about a factor of 2. In another study examining distance estimates in virtual environments involving HMDs, Whitmer and Sadowski (1998) compared blindfolded walking in a real hallway to blindfolded walking on a treadmill in a virtual hallway. In both environments, people saw a target and then attempted to walk to it without vision. They found that mean errors varied between 1% and 11% of the target distances in the real environment and between 2% and 18% in the virtual environment. In both environments, mean error increased linearly with increasing target distance. Interestingly, an analysis of unsigned relative error revealed that error was greater in the virtual than in the real environment. In addition, people made greater errors in both environments when they experienced the virtual environment first than when they experienced the real environment first. Together, these studies suggest that distance perception is distorted to varying degrees in virtual environments. The conclusion that people underestimate distances in virtual environments relative to the real environment may be premature, however. To date, studies examining distance perception in virtual environments have all used head mounted display (HMD) systems. HMDs provide a limited field of view in both the horizontal and vertical directions. Recently, Wu, Ooi, and He (2004) examined the influence of vertical and horizontal field of view (FOV) on distance perception in the real environment. Subjects were asked to judge the distance to targets while looking through a slot that reduced either the vertical or the horizontal field of view. They found that subjects underestimated distance when their horizontal FOV was unrestricted but their vertical FOV was restricted to 21 degrees or less. However, when their vertical FOV was unrestricted but their horizontal FOV was restricted,
subjects performed as well as under full view conditions. They conjecture that people are better able to integrate the distance along the ground plane when the entire area between the subject and the target is visible. Recent work by Creem-Rehehr et al. (2004), however, indicates that not being able to see the area around one’s feet (a typical feature of HMDs) does not impair people’s ability to perceive distance in the natural environment. Thus, the degree to which the vertical FOV is restricted may have an important influence on distance perception in virtual environments involving HMDs. Very little is known about how people perceive distance in virtual environments using large screen immersive display systems (LSIDs) such as caves. Large screen environments typically provide a much larger field of view than HMDs, perhaps making it easier to perceive egocentric distance. In the present investigation, we examined distance estimates in a virtual environment using a LSID system. We measured distance perception by asking people to estimate how long it would take to walk to targets at distances ranging between 20 and 120 ft. Participants estimated how long it would take to walk to each target distance by starting a stopwatch when they imagined starting to walk to the target and stopping the stopwatch when they imagined reaching the target. Participants made time-to-walk estimates both in the real environment (a large grassy lawn in front of a university building) and in our virtual environment (a simulated version of the real environment). Time-to-walk estimates in both environments were compared to actual time-to-walk estimates derived from a baseline walking task. Participants either made estimates in the real environment first or in the virtual environment first.
3 Experiment 1 Method Participants Twenty-four undergraduates participated for course credit. There were 13 females and 11 males. Apparatus and Materials A handheld stopwatch was used to record participants’ time estimates and a tape measure was used to measure the target distances. Experimental Settings Real environment. The real environment was an open, grassy lawn in front of a university building (Figure 1). (Note that the picture of the real environment was taken from a place near the eye point used in the virtual environment.)
Design and Procedure We first obtained an estimate of participants’ walking speeds by timing how long it took them to walk between two points in an uncluttered hallway. The first experimenter positioned participants at the starting line and instructed them to walk at their normal speed past a finish line near the end of the hallway. The second experimenter started a stopwatch when participants began walking and stopped the stopwatch as participants crossed the finish line.
Figures 1 and 2. Photographs of real environment (top) and virtual environment (bottom) settings.
Virtual environment. The virtual environment was a scene depicting the setting that served as the real environment (Figure 2). This scene was displayed on three 10 ft wide x 8 ft high screens placed at right angles relative to one another, forming a three-walled room. The screens were positioned 18 in above the floor of the room. A black skirt hung from the bottom of the screens to the floor. Participants stood midway between the two side screens and 8 ft from the front screen. Three Electrohome DLV 1280 projectors were used to rear project high-resolution, textured graphics onto the screens (1280 x 1024 pixels on each screen), providing participants with 270 degrees of nonstereoscopic immersive visual imagery. The viewpoint of the scene was adjusted for each participant’s eye height. Participants viewed the scene binocularly. The experiment was run on an 8-processor SGI Onyx computer with Infinite Reality Graphics. The software foundation was the Hank simulator, a real-time ground vehicle simulation system designed to support complex scenarios (Cremer, Kearney, & Willemsen, 1997; Willemsen, Kearney, & Wang, 2003). The particular setup described above was chosen to validate previous research examining children’s road-crossing judgments using the Hank simulator (Plumert et al., in press).
Following the baseline walking task, participants made estimates of how long it would take them to walk to targets in the real and virtual environment. Half of the participants made estimates in the virtual environment first and half made estimates in the real environment first. Participants who first made estimates in the real environment were taken outside to a spot at the far end of the lawn. The first experimenter informed participants that the second experimenter would stand at different places on the lawn in front of them and that their task was to imagine walking to the second experimenter. The first experimenter then handed a stopwatch to the participants and told them that they should start the stopwatch when they imagined starting to walk and to stop the stopwatch when they imagined reaching the second experimenter. Participants were given an opportunity to practice starting and stopping the stopwatch to make sure that they knew how to operate the stopwatch. Before the start of each trial, participants turned around so that they could not see the second experimenter moving into position. Each distance was measured using a large tape measure. When the second experimenter was in position, he or she retracted the tape measure. Participants then turned around and faced the target, and started the stopwatch when they were ready. After participants stopped the stopwatch, the experimenter recorded the time elapsed. Participants completed time-towalk estimates for six randomly ordered distances (20, 40, 60, 80, 100, and 120 ft). Participants who first made estimates in the virtual environment were taken into the simulator facility and positioned in the middle of the display screens depicting the outdoor scene. The experimenter informed participants that an image of a person would appear at different places on the lawn in front of them and that their task was to imagine walking to the person. The experimenter then handed a stopwatch to the participants and told them that they should start the stopwatch when they imagined starting to walk and to stop the stopwatch when they imagined reaching the person. Participants were given an opportunity to practice starting and stopping the stopwatch to make sure that they knew how to operate the stopwatch. Before the start of each trial, participants turned around so that they could not see the person appear on the display. While participants were facing away from the display, the
experimenter pressed a key to make the person appear on the lawn. Participants were then asked to turn around, face the target, and start the stopwatch when they were ready. After participants stopped the stopwatch, the experimenter recorded the time elapsed. All participants completed timeto-walk estimates for six randomly ordered distances (20, 40, 60, 80, 100, and 120 ft). Measures Actual time to walk. We estimated the amount of time actually required to walk the six distances for each participant by dividing each actual distance (i.e., 20, 40, 60, 80, 100, and 120 ft) by the participant’s walking speed. (Each participant’s walking speed was determined by dividing the baseline walking distance by the baseline walking time.)
pattern was that people undershot distances in the real environment but not in the virtual environment at 80 ft. Virtual Environment First As shown in Figure 3b, time-to-walk estimates were almost identical in the real and virtual environments. Again, people tended to undershoot times in both conditions relative to actual times. Separate one-way repeated measures ANOVAs comparing actual, real environment, and virtual environment time estimates at each distance revealed a significant effect of estimate at all distances beyond 20 ft, F’s (2, 44) > 7.11, p’s < .01. Post-hoc tests indicated that time estimates did not differ significantly in the real and virtual environments at any distance. However, people significantly undershot time to walk in both environments at distances beyond 20 ft.
Time-to-walk estimates in the real environment. Each participant had six time-to-walk estimates in the real environment, representing the time elapsed between starting and stopping the stopwatch for each distance.
26 Actual Time
24 22
Real Environment
20
Virtual Environment
18 16
Time-to-walk estimates in the virtual environment. Each participant also had six time-to-walk estimates in the virtual environment, representing the time elapsed between starting and stopping the stopwatch for each distance.
14 Time (s)
12 10 8 6
Results The analyses below focus on two primary questions. First, how closely did time-to-walk estimates correspond in the real and virtual environments? And second, how closely did time estimates in the real and virtual environments correspond to actual times? To address these questions, time-to-walk estimates were analyzed separately for the two environment order conditions (i.e., real environment first vs. virtual environment first). Real Environment First As shown in Figure 3a, time-to-walk estimates were almost identical across the real and virtual environments. Moreover, people tended to undershoot time estimates in both environments, especially at longer distances. Separate one-way repeated measures ANOVAs comparing actual, real environment, and virtual environment time estimates at each distance revealed a significant effect of estimate at all distances, F’s (2, 22) > 3.98, p’s < .05. Post-hoc tests showed that time-to-walk estimates in the real and virtual environment only differed at 20 ft. Time estimates in the real environment were actually shorter than those in the virtual environment. Thus, time-to-walk estimates were remarkably similar across the two environments at virtually all distances. Additional post-hoc tests showed that people significantly undershot times in both environments at distances of 60 ft and beyond. The only exception to this
4 2 0 20 ft.
40 ft.
60 ft.
80 ft.
100 ft.
120 ft.
Figure 3a. Mean sighted time-to-walk estimates in the real environment first condition. 26 24
Actual Time
22
Real Environment
20
Virtual Environment
18 16 14 Time (s) 12 10 8 6 4 2 0 20 ft.
40 ft.
60 ft.
80 ft.
100 ft.
120 ft.
Figure 3b. Mean sighted time-to-walk estimates in the virtual environment first condition.
Summary Several results are noteworthy. First, people’s time-towalk estimates were remarkably similar across the real and virtual environments. This finding is inconsistent with previous investigations of distance perception in virtual environments (Loomis & Knapp, 2003; Thompson et al., in
press; Willemsen & Gooch, 2002). However, these experiments were conducted with HMDs. The greater field of view afforded by our large screen environment may account for the difference in results. Second, people’s time-to-walk estimates in both environments were quite accurate to about 40-60 ft, particularly when they made estimates in the real environment first. Beyond this distance, people undershot the time actually required to walk. And finally, time-to-walk estimates in both environments were more distorted when people experienced the virtual environment first. This finding is consistent with the results of Whitmer & Sadowski (1998). Why did people undershoot time-to-walk estimates beyond 40-60 ft? One possibility is that viewing the target while making the estimate led to visual foreshortening of the perceived distance. In other words, people undershot timeto-walk estimates because the distance looked shorter than it actually was. In studies of blindfolded walking, people presumably update their movement as they locomote toward the target (Rieser et al., 1990). Perhaps people would be better at imagining moving to the target in our task if they made their time-to-walk estimates without vision. We tested this hypothesis in Experiment 2 by having 10- and 12-year-old children and adults make timeto-walk estimates in real and virtual environments with and without vision.
4 Experiment 2
obtain a more stable estimate of walking speeds (especially for children). Following the baseline walking task, participants made estimates of how long it would take them to walk to targets at six distances (i.e., 20, 40, 60, 80, 100, and 120 ft) in the real and virtual environment. Estimates of time to walk for each environment were made both while blindfolded and while sighted, resulting in four sets of judgments for each participant. The procedure for making time-to-walk estimates was the same as that used in the first experiment, with the exception of the blindfolded trials. For these trials, participants viewed the target for approximately 4-5 s, put on the blindfold, and started the stopwatch when they were ready. Half of the participants performed the judgments in the virtual environment first, and half performed the judgments in the real environment first. Within each half, the order in which participants performed the blindfolded and sighted judgments was counterbalanced. The order of distances was random. Measures As in Experiment 1, we estimated the amount of time actually required to walk the six distances for each participant by dividing each actual distance by the participant’s average walking speed. Participants also had six time-to-walk estimates in the real environment and in the virtual environment for both blindfolded and sighted trials, resulting in a total of 24 time-to-walk estimates.
Method Results Participants Forty-eight 10- and 12-year-old children and adults participated. There were 9 males and 7 females in the 10year-old group, 11 males and 5 females in the 12-year-old group, and 7 males and 9 females in the adult group. Apparatus and Materials A handheld stopwatch was used to record participants’ time estimates and a radar gun was used to measure the target distances. Sunglasses with the lenses, sides, and nose area blocked out were used to prevent participants from viewing targets while making blindfolded time estimates. Experimental Settings The real and virtual environments were identical to those used in Experiment 1. Design and Procedure We first obtained an estimate of participants’ walking speeds using the same procedure as in Experiment 1 except that participants did the baseline walking task twice to
Again, we focus on two primary questions. First, how closely did time-to-walk estimates correspond in real and virtual environments? And second, how closely did timeto-walk estimates in real and virtual environments correspond to actual times? We addressed these questions separately for the three age groups because preliminary analyses suggested that the patterns of estimates varied across the three age groups. Furthermore, we analyzed estimates made while blindfolded and sighted separately within each age group. 10-year-olds Sighted trials. As shown in Figure 4a, 10-year-olds’ estimates across the real and virtual environments were quite similar. Separate one-way repeated measures ANOVAs comparing actual, real environment, and virtual environment estimates at each distance revealed a significant effect of estimate at all distances beyond 20 ft, F’s (2, 30) > 8.41, p’s < .01. Post-hoc tests revealed that real and virtual environment estimates differed at 60 and 80 ft., but not at 20, 40, 100, or 120 ft. Thus, with the exception of these two middle distances, their time-to-walk estimates did not differ significantly in real and virtual
environments. Additional post-hoc tests revealed that 10year-olds undershot actual times in both the virtual and real environment for distances of 60 ft and beyond. At 40 ft., 10-year-olds undershot distances in the virtual but not in the real environment.
Time (s)
30 28 26 24 22 20 18 16 14 12 10 8 6 4 2 0
Actual Time
the actual time to walk, especially for the shorter distances. Separate one-way repeated measures ANOVAs comparing actual, real environment, and virtual environment estimates revealed a significant effect of estimate only for the 100 ft. distance, F (2, 30) = 6.61, p < .01, indicating that 12-yearolds significantly undershot time-to-walk estimates in the virtual environment. No other differences were significant.
Real Environment
28 26 24 22 20 18 16
Virtual Environment
20 ft.
40 ft.
60 ft.
80 ft.
100 ft.
120 ft.
Figure 4a. Ten-year-olds’ mean sighted time-to-walk estimates.
Blindfolded trials. As shown in Figure 4b, 10-year-olds’ time-to-walk estimates while blindfolded were very similar in the real and virtual environments. Moreover, they tended to undershoot time-to-walk estimates at almost every distance. Separate one-way repeated measures ANOVAs comparing actual, real environment, and virtual environment estimates at each distance revealed a significant effect of estimate at all distances, F’s (2, 30) > 5.36, p’s < .05. Post-hoc tests showed that there was no significant difference between 10-year-olds’ time estimates in the real and virtual environment for any distance except 60 ft. However, 10-year-olds undershot time estimates in both the real and virtual environment at distances of 40 ft and beyond. At 20 ft, they undershot time estimates in the virtual but not in the real environment. 30 28 26 24 22 20 18 16 Time (s) 14 12 10 8 6 4 2
Real Environment Virtual Environment
Time (s) 14 12 10 8 6 4 2 0 20 ft.
40 ft.
60 ft.
80 ft.
100 ft.
120 ft.
Figure 5a. Twelve-year-olds’ mean sighted time-to-walk estimates.
Blindfolded trials. As shown in Figure 5b, 12-year-olds’ time estimates made while blindfolded also were very similar across the real and virtual environments. Again, their time-to-walk estimates in the two environments were close to the actual time to walk, especially for shorter distances. Separate one-way repeated measures ANOVAs comparing actual, real environment, and virtual environment estimates at each distance revealed no significant differences. Note, however, that 12-year-olds exhibited considerable variability in their time-to-walk estimates.
Actual Real Environment Virtual Environment
Actual Time
28 26 24
Actual Time
22 20
Virtual Environment
Real Environment
18 16 Time (s) 14 12 10 8 6 4 2 0
20 ft.
40 ft.
60 ft.
80 ft.
100 ft.
20 ft.
120 ft.
Figure 4b. Ten-year-olds’ mean blindfolded time-to-walk estimates.
40 ft.
60 ft.
80 ft.
100 ft.
120 ft.
Figure 5b. Twelve-year-olds’ mean blindfolded time-to-walk estimates.
12-year-olds
Adults
Sighted trials. As shown in Figure 5a, 12-year-olds’ timeto-walk estimates did not differ across the real and virtual environments at any distance. Surprisingly, their time-towalk estimates in the two environments were quite close to
Sighted trials. As shown in Figure 6a, adults’ time-to-walk estimates made while sighted were very similar across the real and virtual environments. Moreover, they tended to undershoot times at distances beyond 40 ft. Separate one-
way repeated measures ANOVAs comparing actual, real environment, and virtual environment estimates at all distances beyond 40 ft, F’s (2, 30) > 4.87, p’s < .05. Posthoc tests revealed no significant differences between adults’ time-to-walk estimates in the real and virtual environment at any distance. However, adults significantly undershot times in both the real and virtual environment at distances of 60 ft and beyond. 28 26
Actual Time Real Environment
24 22 20
Time (s)
Virtual Environment
18 16 14 12 10 8 6
Summary The results of this experiment again show that people’s time-to-walk estimates in the real and virtual environment were very similar. This held true even for 10- and 12-yearold children, although 10-year-olds exhibited a slight tendency to underestimate times more in the virtual environment than in the real environment. Again, this similarity between time-to-walk estimates in the real and virtual environments is surprising given the wellestablished finding that people underestimate distance in virtual environments involving HMDs. There was no indication that estimates made without vision were better (or worse) than those made with vision, suggesting that the factors responsible for underestimation of distance beyond 60 ft or so operate similarly when people make time-towalk estimates with and without vision.
5 Conclusions
4 2 0 20 ft.
40 ft.
60 ft.
80 ft.
100 ft.
120 ft.
Figure 6a. Adults’ mean sighted time-to-walk estimates.
Blindfolded trials. Even when blindfolded, adults’ time-towalk estimates were very similar across the two environments (Figure 6b). Likewise, they tended to undershoot time estimates relative to actual times, especially at the longer distances. Separate one-way repeated measures ANOVAs comparing actual, real environment, and virtual environment estimates at each distance confirmed these observations. There was a significant effect of estimate at all distances beyond 60 ft, F’s (2, 30) > 4.54, p’s < .05. Post-hoc tests revealed that adults undershot times in the virtual environment at 80 ft and in both the virtual and the real environment at 100 and 120 ft. No other differences were significant. 28 26 24
Actual Time Real Environment
22 20 18 16 Time (s) 14 12
Virtual Environment
10 8 6 4 2 0 20 ft.
40 ft.
60 ft.
80 ft.
100 ft.
120 ft.
Figure 6b. Adults’ mean blindfolded time-to-walk estimates.
A consistent pattern of results emerges from our experiments. Time-to-walk estimates were highly similar in virtual and real environments regardless of whether they were made with or without vision, by children or adults, or in different orders. Moreover, adults’ time-to-walk estimates were quite accurate up to the 40-60 ft range. Beyond this distance, people increasingly underestimated time to walk. These findings are consistent with previous research showing that people are very good at walking without vision to targets up to about 20 m. The finding that time-to-walk estimates were very similar in real and virtual environments is surprising given that several other studies have found that people underestimate distance more in virtual environments than in the real environment. What accounts for this discrepancy? At least two possibilities come to mind. First, people may find it easier to perceive egocentric distance in virtual environments involving LSIDs than in virtual environments involving HMDs. Unlike large screen immersive displays, HMDs have restricted vertical and horizontal fields of view. Recent research suggests that a restricted vertical field of view leads to underestimation of distance in the real environment (Wu et al., 2004). Whitmer and Sadowski (1998) suggest that the reduced vertical FOV in HMD systems may degrade convergent linear perspective and relative size as cues to distance. Interestingly, our participants made highly similar time-to-walk estimates in the real and virtual environment even though our virtual environment did not contain stereoscopic cues to depth. This is consistent with recent work showing that people are equally good at walking without vision to targets previously seen monocularly or binocularly (Creem-Regehr et al., 2004). Another possible reason for the discrepancy in findings concerns differences in the measures used to assess
people’s distance perception. Previous studies comparing distance perception in real and virtual environments have used triangulation measures (Loomis & Knapp, 2003) or blindfolded walking (Whitmer & Sadowski, 1998). Although our time-to-walk measure is similar in the sense that people see targets from a fixed position, it is different in the sense that it involves imagined rather than real movement toward those targets. Thus, people’s time-towalk estimates may look highly similar in real and virtual environments because the information used and the processes engaged in imagined movement remain constant across environments. This raises the question of whether the same processes are involved in imagined and blindfolded walking. Research is currently underway in our laboratory to compare sighted and blindfolded imagined walking with blindfolded real walking. On a final note, our results offer encouraging news for researchers studying human behavior using virtual environments involving large screen immersive displays. Of particular interest is the finding that even children made highly similar time-to-walk estimates in the real and virtual environment. This finding is important because researchers have recently begun using virtual environments to examine problems such as children’s bicycling (Plumert et al., in press). Although further work is clearly needed to understand the differences between LSID systems and HMD systems, the present investigation suggests that distance perception in real and virtual environments may correspond much better than previously thought.
6 References CREEM-REGEHR, S. H., WILLEMSEN, P., GOOCH, A. A., and THOMPSON, W. B. 2004. The influence of restricted viewing conditions on egocentric distance perception: Implications for real and virtual environments. Manuscript submitted for publication. CREMER, J., KEARNEY, J., and WILLEMSEN, P. 1997. Directable behavior models for virtual driving scenarios. Transactions of the Society for Computer Simulation, special issue on Multiagent Systems, 14(2), 87-96. CUTTING, J. E., VISHTON, P. M., and BRAREN, P. A. 1995. How we avoid collisions with stationary and moving obstacles. Psychological Review, 102, 627-651. GILINSKY, A.S. 1951. Perceived size and distance in visual space. Psychological Review, 58, 460-482. HARWAY, N. I. 1963. Judgment of distance in children and adults. Journal of Experimental Psychology, 65, 385390. LOOMIS, J. M., BLASCOVICH, J. J., and BEALL, A. C. 1999. Immersive virtual environment technology as a basic
research tool in psychology. Behavior Research Methods, Instruments, & Computers, 31, 557-564. LOOMIS, J. M., DA SILVA, J.A., PHILBECK, J. W., and FUKUSIMA, S. S. 1992. Visual space perception and visually directed action. Journal of Experimental Psychology: Human Perception and Performance, 18, 906-921. LOOMIS, J. M. and KNAPP, J. M. 2003. Visual perception of egocentric distance in real and virtual environments. In Virtual and Adaptive Environments, Lawrence Erlbaum Associates, Mahwah, NJ. L. J. Hettinger and M. W. Haas, Eds., 21-46. PHILBECK, J. W. and LOOMIS, J. M. 1997. Comparison of two indicators of perceived egocentric distance under full-cue and reduced-cue conditions. Journal of Experimental Psychology: Human Perception and Performance, 23, 72-85. PLUMERT, J. M., KEARNEY, J. K., and CREMER, J. F. In press. Children’s perception of gap affordances: Bicycling across traffic-filled intersections in an immersive virtual environment. Child Development. RIESER, J. J., ASHMEAD, D. H., TALOR, C. R., and YOUNGQUIST, G. A. 1990. Visual perception and the guidance of locomotion whithout vision to previously seen targets. Perception, 19, 675-689. THOMPSON, W. B., WILLEMSEN, P., GOOCH, A. A., CREEM-REGEHR, S. H., LOOMIS, J. M., and BEALL, A. C. In press. Does the quality of computer graphics matter when judging distance in visually immersive environments? Presence: Teleoperators and Virtual Environments. WHITMER, B. G. and SADOWSKI, W. J. J. 1998. Nonvisually guided locomotion to a previously viewed target in real and virtual environments. Human Factors, 40, 478-488. WILLEMSEN, P. and GOOCH, A. A. 2002. Perceived egocentric distances in real, image-based and traditional virtual environments. Proceedings of IEEE Virtual Reality Conference. 89-90. WILLEMSEN, P., KEARNEY, J., and WANG, H. 2003. Ribbon networks for modeling navigable paths of autonomous agents in virtual urban environment, Proceedings of the IEEE Virtual Reality Conference, 7986. WU, B., OOI, T. L., and HE, Z. J. 2004. Perceiving distance accurately by a directional process of integrating ground information. Nature, 428, 73-77.
