Environment and Planning B: Planning and Design 2011, volume 38, pages 741 ^ 752
doi:10.1068/b37079
Does the level of visual detail in virtual environments affect the user's spatial knowledge? Ebru Cubukcu
Department of City and Regional Planning, Faculty of Architecture, Dokuz Eylul University, S°ehir ve Bo«lge Planlama Bo«lu«mu«, Oda No 109, Izmir 35160, Turkey; email:
[email protected] Received 3 June 2010; in revised form 10 December 2010
Abstract. Researchers studying environmental perception and cognition in general, and human spatial ability in particular, have been using virtual reality as a promising tool. However, the virtual environments used in these studies showed variances in the levels of visual detail they offer. Yet, little is known about whether the level of visual detail in a virtual environment affects people's spatial performance. If the level of visual detail positively or adversely affects people's spatial performance, then researchers using virtual environments to investigate human spatial performance should account for the level of visual detail besides other factors of interest. This study aimed to compare people's spatial performance in two virtual environments that varied in their levels of visual detail (`low' and `high'). Part of the Ohio State University Campus, US was simulated with a three-dimensional computer-modeling program, GTK Radiant. QUAKE III ARENA, a real-time three-dimensional environment-generator game engine, produced perspective views through the simulation. Forty-nine students studying in Dokuz Eylul University, Izmir, Turkey participated in the study. After a learning phase, participants were asked to (1) estimate the direction to two nonvisible signs, (2) estimate the straight-line and walking distances to two nonvisible signs, and (3) draw a sketch map of the environment. Results showed that the spatial knowledge acquired in the `low' and `high' visual detail virtual environments were similar. If people's spatial behavior does not change in virtual environments with `low' and `high' visual details, then researchers, designers, and planners could save time and energy in developing virtual environments to understand human behavior.
Introduction Successful design and planning of a physical environment emerge from an understanding of the interrelation between human behavior and the physical environment (Bell et al, 1996; Ittelson et al, 1974; Lang, 1987; Stokols and Altman, 1987). Thus, the interrelation between human behavior and the physical environment has been a broad field of enquiry that encompasses a range of disciplines, such as psychology, planning, and architecture (Bell et al, 1996; Ittelson et al, 1974; Stokols and Altman, 1987). Among the broad range of issues about human behavior and the physical environment that can be studied, many studies have been devoted to environmental perception and cognition in general, and spatial knowledge and wayfinding in particular (see Carpman and Grant, 2002; Evans, 1980; Lynch, 1960; Moore, 1979). Environmental perception and cognition refers to experience of the world. With perception and cognition, people develop spatial knowledge about the physical environment and perform tasks within it. When people fail to develop accurate spatial knowledge about a physical setting, they get disoriented, frustrated, irritated, and stressed (Carpman and Grant, 2002; Evans, 1980; Lang, 1987; Lawton, 1994). Disorientation has costs in terms of time and fuel (Burns, 1998; Passini, 1980). In confusing places, such as hospitals and campuses, staff may waste time directing people to locations (Hecht, 2000; Peponis et al, 1990). Wayfinding difficulties may limit personal mobility (Burns, 1998) or people may avoid visiting some places (Carpman and Grant, 2002). More serious consequences could happen in the case of an emergency, like a car crash or a fire (Carpman and Grant, 2002).
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Thus, understanding the factors affecting spatial knowledge and wayfinding abilities and enhancing such abilities has been an important topic. Although, the personal and physical environmental factors affecting spatial performance in real environments have long been studied (Carpman and Grant, 2002; Evans, 1980; Moore, 1979), the physical environmental factors (such as the level of physical differentiation) affecting spatial performance are still not completely understood [see Cubukcu (2003) for a review]. Methodological limitations may have prevented researchers from drawing any clear conclusion about the issue. Researchers have often observed and tested people's behavior in real settings. However, to derive causal relation the variety in real environments needs to be controlled. Thus, studies used various simulation media, such as photographs (Appleyard et al, 1964; Bosselman, 1998; O'Neill, 1991), small-scale threedimensional models (Carpman et al, 1985), and full-scale models (Passini et al, 1990; Sadalla and Oxley, 1984; Schmitz, 1997; Waller et al, 1998) to represent visually and to control the variety in physical environments. However, those simulation media were criticized for being static, inflexible, and unaffordable. When `virtual reality' emerged as an affordable medium, it became a promising tool for investigating human spatial performance (Aginsky et al, 1997; Garling et al, 1997; Ishikawa et al, 1998; Pe¨ruch, 1998; Rossano et al, 1999; Wilson et al, 1997a). This technology is particularly useful when researchers need to control the physical characteristics of a setting (Arthur et al, 1997; Rossano et al, 1999, Wilson et al, 1997a), or when it is hard to gather subjects in the real environment (Ishikawa et al, 1998). It is theoretically possible to substitute a virtual environment (VE) for a real one. However, in practice, a user's feeling of presence and spatial behavior in computergenerated three-dimensional VEs might not be similar to that in real environments. Previous studies have investigated various factors that may affect a user's feeling of presence, sense of `being there', and spatial behavior. A number of studies have investigated the influence of exploration type (active explorers versus passive observers) (Brooks, 1999; Carasssa et al, 2002; Foreman et al, 2004; Gaunet et al, 2001; Wallet et al, 2008; Wilson et al, 1997b), input device type (head-mounted display versus desktop display) (Waller et al, 1998), and system performance (various display update rates) (Durlach et al, 2000) on a user's feeling of presence and spatial behavior. On the other hand visual complexity, another factor that may affect a user's feeling of presence and spatial performance, was rarely studied. Researchers studying people's spatial performance often have to use some simplification in VEs, either because of their limited technical abilities or their willingness to save time (constructing complex VEs may be extremely time consuming). However, it is not known if their spatial performance measures are biased due to the visual simplifications used in VEs. The influence of visual complexity on people's spatial performance could be positive or negative. Higher visual complexities may help people to acquire more information about the spatial properties of a physical environment, thus they may show better performances in drawing more detailed sketches. Affirmative effects would also emerge when people need to navigate from one point to another which is far away; they may tend to perceive the path in segments, and higher visual complexities would help them to visually break a path into segments. Similarly, people may use visual details to scale the environment and make better distance estimations. On the other hand, negative effects would emerge when people cannot cope with information overload caused by the higher visual complexities. For example Sadalla and Magel (1980) and Sadalla and Staplin (1980a; 1980b) showed that routes with more stored attributes (such as turns and intersections) were estimated as longer than routes of equivalent length with fewer stored attributes. The same argument may apply to the amount of visual detail; when people store more
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information about a route due to a larger amount of visual detail they may tend to overestimate distances. Put differently, more visual details would require greater effort to process the spatial information, which may lead to distortions in mental maps. Such distortions could be observed in people's sketch maps, straight-line-distance estimations, and direction estimations. Watson et al (1997) compared participants' visual search performance in VEs with different levels of visual detail; the level of visual detail was manipulated by using `gaze-contingent' (1) or `edge-collapse' (2) level of detail reduction techniques. However, it is also possible to manipulate the level of visual detail by controlling the presence of textures on modeled objects. Yet, no study has investigated how users' spatial behavior changes when textures are present or absent on modeled objects. Moreover, previous studies focused on `visual search performance' when testing the influence of visual detail in a VE on people's spatial behavior. However, extensive research showed that spatial performance could be measured by various tasks, such as direction and distance estimation [see Cubukcu (2003) for a review of methodologies that could be used to measure spatial knowledge]. In other words, the findings of these previous studies on the influence of visual detail on visual search performance may not be applicable to other spatial-performance tasks. Such issues indicate that there is still room for further investigation of the influence of visual detail in VEs on human spatial performance. Thus, this study aims to compare people's spatial knowledge (which is measured by direction and distance estimation, and sketching tasks) in VEs with high (textures on the modeled objects were present) and low (textures on the modeled objects were absent) levels of visual details. Methods Description of the simulation
Part of the Ohio State University Campus of Columbus, OH was simulated using a three-dimensional computer-modeling program, GTK Radiant. QUAKE III ARENA, a real-time three-dimensional environment-generator game engine, produced perspective views through the simulation. Participants were seated during the entire experimental session and watched a simulated walk-through without any interaction, like a passenger in a car. The investigator controlled movement along prescribed routes via a keyboard. The simulated movement was a route level, at walking speed, and the direction of the observer's gaze was parallel to the direction of movement. The game engine displayed the scenes in color at a rate of approximately 20 frames per second. The site, comprising a rectangular area about 95 ha, including a total length of 1100 m of walkable paths and roads, and 38 free-standing buildings, was simulated in two ways that generated two different levels of visual detail (low and high). Figure 1 shows the play layout of the simulated setting and figure 2 shows examples of perspective views from the VEs. For the `low level of visual detail' four colors were overlaid on the stimulated buildings in the site (figure 2). For the `high level of visual detail' realworld textures, which were derived from digital photographs of the buildings, were overlaid on the simulated buildings (figure 2). (3) Otherwise, the physical layout of the two VEs and the type of interaction with them were identical. Both VEs had one starting point (sign A), and two destination points (signs B and C). Figure 1 shows the location of these signs. (1) Gaze-contingent level or visual detail reduction refers to a technique which reduces the number of pixels in the periphery or uses the grayscale in the periphery. (2) Edge-collapse level of visual detail reduction refers to a technique which reduces the geometric construction of the models or the number of vertices in a model. (3) This setting is a revised version of a setting used in Cubukcu and Nasar (2005a).
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Figure 1. The physical layout of the virtual environments. The dashed lines show the walkable routes. The points show the start and end signs in the environments.
Participants Forty-nine first-grade students (24 male, 25 female, mean age 20:3 years, SD 1:0 years) studying at the City and Regional Planning Department of Dokuz Eylul University, Izmir, Turkey in the 2008 ^ 09 academic year volunteered to participate in the study. Twenty-six of them (13 male, 13 female, mean age 20:2 years, SD 0:9 years) were randomly assigned to the `low level of visual detail' condition and the others (11 male, 12 female, mean age 20:3 years, SD 1:1 years) were assigned to the `high level of visual detail' condition. All participants reported that they had normal or corrected to normal vision. None of the participants has been to the Ohio State University Campus at Columbus. Thus, their knowledge and learning of the routes would have come only from VE. When asked to assess how often they played computer games using scale 1 ^ 6 (1 never; 2 almost three hours, once a year; 3 almost three hours, once a month; 4 almost three hours, once a week; 5 almost three hours, three times a week; 6 everyday, at least three hours), the participants in the `low' and `high' visual-detail conditions gave similar answers (t 0:082, df 47, p > 0:05). In both conditions, participants rated themselves below average, that is, playing computer games almost three hours, once a year (low visual detail: mean 2:27, SD 1:31; high visual detail: mean 2:30, SD 1:69).
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(a)
(b) Figure 2. Three examples of perspective view from virtual environments for: (a) `low level of visual detail' and (b) `high level of visual detail'. The setting is a revised version of a setting used in Cubukcu and Nasar (2005a).
When asked how they judged their ability to find their way around in everyday life on a scale from 1 (very good) to 5 (very bad), the participants in the low and high visual-detail conditions gave similar answers (t 1:133, df 47, p > 0:05). In both conditions, participants rated themselves, on average, between moderate and good (low visual detail: mean 2:42, SD 0:70; high visual detail: mean 2:7, SD 1:0). Procedure Two groups of participants (26 in the low level visual-detail condition and 23 in the high level visual-detail condition) were seated at tables facing a white wall about 1.22 m ^ 4.00 m (4 ft ^ 13 ft) away. In order to eliminate the differences in viewing conditions between participants, the instructor asked each participant whether they could see the screen on the wall clearly. All participants reported that they had a clear view. The investigator dimmed the lights, leaving enough light for the participants to see their answer sheets. She read them the instructions, which asked them to watch a simulated walk-through and to answer some questions to show how well they understood the spatial layout of the simulated setting. The experiment had two phases: (1) a learning and (2) a testing phase. In the learning phase participants were asked to become familiar with the setting. The computergenerated simulation was projected onto a 1:22 m 1:52 m (48 in 60 in) screen using a graphic PC-based desktop workstation (Pentium II, 32 MB graphics card, resolution 650X480X256). Then, the investigator moved along a route for three minutes. Participants could only observe the environment in a passive situation and they were not allowed to ask for a further display of the route. When signs A, B and C were passed the investigation verbally warned the participants. The testing phase had five sets of questions: a direction-estimation test, a sketching test, two distance-estimation tests (straight-line and walking distances), and questions on gender, age, frequency of playing First Person Shooter computer games, and subjective evaluation of their wayfinding performance. Participants took the test in groups of 23 and 26 people and they were tested in two different sessions on one day.
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For each group, the investigator verbally read the instructions before each task to make sure that the participants understood the questions. During the learning and testing phases, the participants were not allowed to interact with the instructor or with each other. Thus, they did not affect each other's responses. Measures of spatial knowledge Spatial knowledge can be measured with various tasks [see Cubukcu and Nasar (2005b) for a review of those measures] such as direction and distance estimations, sketch drawing, or navigating. Each task captures one's different spatial knowledge performance. For example navigation or walking-distance estimation tasks may capture one's sequential knowledge of routes between places. On the other hand, direction and straight-line-distance estimation tasks and sketching task may capture one's comprehensive understanding of a spatial layout. People's performance on each task may vary. Thus it is suggested that multiple measures are used to understand people's spatial knowledge. In this study spatial knowledge was measured using four tasks: (1) direction estimation, (2) sketching, (3) straight-line-distance estimation, and (4) walking-distance estimation. For the direction-estimation test, the computer automatically set the viewpoint in the VE to face sign A and that view was presented on the wall. The answer sheet showed a circle marked with 458 angles,(3) as shown in figure 3. The participants were told that the view on the wall showed their current location in the VE. To understand how this current location in the VE was represented on the answer sheet, participants were asked to imagine they were standing at the center of the circle, facing towards 08 in the circle. The triangle near the center of the circle represented an `eye' to show the looking direction, and the dot on the perimeter of the circle represented the location of sign A, which was in the view on the wall. Participants were asked to place two dots along the perimeter of the circle to show the direction of the other two signs (B and C), which were not in the view on the wall. In order to measure each participant's direction-estimation performance, for each sign a directionestimation error score was calculated from the absolute difference between the participant's estimated angle of direction and the true direction. Then, a total direction-estimation error score was calculated by summing these two scores. The total error score, which ranged from 0 to 360 (0 to 180 for each sign), represented each participant's direction-estimation performance level. Higher scores represented lower direction-estimation performance. sign `A'
current location
Figure 3. The circle diagram used in the direction-estimation test. (3) This figure was used to measure direction-estimation ability in various studies [see Cubukcu (2003) for a review].
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For the sketching test, participants were asked to draw a sketch of the route the investigator had presented on a blank sheet of paper. Their responses were evaluated by a planner naive to the research aim. In order to measure each participant's sketch-drawing performance, two scores were calculated. The first score required a subjective evaluation and focused on how well the spatial layout of the simulated setting was understood. The second score was more objective and focused on how well the presence and location of signs A, B, and C were understood. For the first sketch-drawing performance score each participant's response was assigned to one of the three categories: low, moderate, or high (1, 2, and 3, respectively). Drawings that provided information only about landmarks (three signs in the setting) received a score of 1, drawings that provided information about signs and some of the routes that connect some of these signs received a score of 2, and drawings that showed an integrated understanding of the layout of the space received a score of 3. For the second sketch-drawing performance score each participant's drawing received a score for three measures: (1) number of signs drawn (0 ^ 3); (2) number of signs correctly placed at an intersection or on the road (4) (0 ^ 3); and (3) number of correctly drawn interrelations between the signs (5) (0 ^ 3). The sum of these three scores (0 ^ 9) represented the second sketch-drawing performance score. Then, a total sketch-drawing performance score was calculated by summing the first and second sketch-drawing performance scores. This total score ranged from 0 to 12, where higher numbers represented higher sketch-drawing performance. Figure 4 shows examples of participants' sketches which received a low, medium, and high score, respectively.
(a)
(b)
(c)
Figure 4. Examples of participants' sketches which received low, medium, and high scores: (a) poor sketch-drawing performance (low); (b) moderate sketch-drawing performance (medium); (c) high sketch-drawing performance (high).
For a straight-line distance estimation test, the computer automatically set the viewpoint in the VE to face sign A, and again that view was presented on the wall. The participants were told that the distance between the current location and the first road intersection was 10 units. They were then asked to estimate the straight-linedistance between signs A and B and between signs A and C. In other words, they were required to estimate distances on the basis of a reference unit (10 units in this case). This method, which is also known as ratio-scaling, has been the most commonly used measure of cognitive distances [see Montello (1991) for a review on distance estimation measures]. In order to measure each participant's straight-line-distance-estimation performance, a straight-line-distance estimation error was calculated from the absolute difference between the participant's estimated distance and the true distance for the distances between signs A and B and between signs A and C, separately. Then, a total (4) Sign
A should be located on the road and signs B and C should be located at an intersection. B and C should be aligned in the east ^ west direction. Signs A and C should be aligned in the north ^ south direction. Sign B should be located on the northeast of sign A. (5) Signs
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straight-line-distance-estimation error score was calculated by summing thee two scores. The total error score represented each participant's straight-line-distance-estimation performance level. This score ranged from 0 to 130 (0 ^ 70 for sign B, and 0 ^ 60 for sign C), where higher values represented lower straight-line-distance-estimation performance. For the walking-distance-estimation test, a procedure similar to the straight-linedistance-estimation test was followed. When the viewpoint facing sign A was presented on the wall, participants were told that the distance between the current location and the first road intersection was 10 units. They were then asked to estimate the walking distance between signs A and B and between signs A and C. A walkingdistance-estimation error was calculated from the absolute difference between the participant's estimated distance and the true distance for the distances between signs A and B and between signs A and C, separately. The total of these two error scores represented each participant's walking-distance-estimation performance level. This score ranged from 10 to 160 (5 ^ 85 for sign B, and 5 ^ 75 for sign C), where higher values represented lower walking-distance-estimation performance. In order to make all scores comparable and to compute a composite spatial performance score, each performance score was standardized to a scale from 0 to 1, by dividing each participant's actual score by the maximum score for that performance. Then the standardized sketch-drawing performance scores were reversed by subtracting each score from 1. Finally, a spatial knowledge error score was calculated by summing all four scores: (1) standardized straight-line-distance-estimation error; (2) standardized sketch-drawing error; (3) standardized straight-line-distance-estimation error; and (4) standardized walking-distance-estimation error. This score ranged from 0 to 4, where higher values represent higher spatial knowledge error. Results In general, the findings showed that how well a person understands a computersimulated setting was not affected by the level of visual detail within it. The spatialknowledge error was similar in the low (mean 1:7; SD 0:6) and high (mean 1:7; SD 0:6) visual-detail conditions (t 0:411, df 47, p 0:68) Table 1 shows the results for a general linear model analysis, which took into account the effect of all factors (level of visual detail, gender, age, subjective evaluation of wayfinding performance, and computer game-playing frequency) when testing the effect of each factor. As table 1 shows, after accounting for other factors, the insignificant effect of level of visual detail on spatial knowledge error remained the same. Now, consider the four measures (direction-estimation performance, sketch-drawing performance, straight-line-distance-estimation performance, and walking-distance-estimation Table 1. The general linear model analyses on spatial knowledge error for the level of visual detail, gender, age, subjective evaluation of wayfinding performance, and computer game-playing frequency. Source Level of visual detail Gender Level of visual detail gender Age Subjective evaluation of wayfinding performance Computer game-playing frequency Residual
Type III sum of squares
df
Mean square
F
p
0.040 0.017 0.117 0.333 0.135
1 1 1 1 1
0.040 0.017 0.117 0.333 0.135
0.123 0.052 0.358 1.019 0.414
0.727 0.821 0.553 0.319 0.523
0.396 13.717
1 42
0.396 0.327
1.214
0.277
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tests performance) that were used to calculate spatial knowledge error. Table 2 shows the mean scores for each measure of spatial knowledge (performance scores and mean standardized error scores) by the type of virtual environment (low versus high levels of visual details). A multivariate analyses of variance (MANOVA) on these four spatialknowledge error scores (standardized error scores for direction estimation, sketch drawing, straight-line-distance and walking-distance estimations) revealed an insignificant effect for the level of visual detail within a VE (Wilks 0:845, F4; 43 1:98, p 0:12). Univariate analyses indicated an insignificant effect for direction-estimation error (F1; 46 0:75, p 0:39), sketch-drawing error (F1;46 1:23, p 0:27), straight-linedistance-estimation error (F1; 46 2:14, p 0:15), and walking-distance-estimation error (F1; 46 0:001, p 0:98). When separate analyses were conducted on each original performance measure, results remained the same.(6) For direction estimation a 2 (level of visual detail, low or high) 2 (sign, B or C) mixed model ANOVA, with level of visual detail as a betweensubject variable and sign as a within-subject variable, was conducted. Results confirmed insignificant between-subject difference (F1; 46 0:63, p 0:43) for the low and high visual-detail VEs. The within-subject comparisons showed a significant difference (F1; 46 7:70, p 0:01) between signs, the mean direction-estimation error for sign C was higher than that for sign B (table 2). However, the interaction between level of visual detail and sign was insignificant (F1; 46 0:91, p 0:34). For distance Table 2. The mean scores for each measure of spatial knowledge by the type of virtual environment (low versus high levels of visual details). Level of visual detail original performance scores
standardized error scores
low (n 26) mean (SD)
high (n 23) mean (SD)
low (n 26) high (n 23) mean (SD) mean (SD)
Direction estimation (total for signs B and C)
120.0 (106.7) degrees error
97.9 (81.1) degrees error
0.34 (0.30)
0.27 (0.23)
Direction estimation to sign B
53.8 (57.3) degrees error
36.4 (46.2) degrees error
0.30 (0.32)
0.19 (0.25)
Direction estimation to sign C
66.1 (56.2) degrees error
61.6 (51.6) degrees error
0.38 (0.32)
0.34 (0.30)
Sketch-drawing performance
8.3 (2.6) correct answer
7.4 (2.9) correct answer
0.30 (0.22)
0.38 (0.24)
Straight-line distance estimation (total for signs B and C)
70.8 (25.8) units error
60.2 (28.1) units error
0.54 (0.20)
0.46 (0.22)
Straight-line-distance estimation to sign B
40.4 (15.1) units error
33.5 (16.2) units error
0.58 (0.22)
0.48 (0.23)
Straight-line-distance estimation to sign C
30.4 (14.4) units error
26.7 (15.8) units error
0.51 (0.24)
0.45 (0.26)
Walking-distance estimation (total for signs B and C)
78.9 (34.4) units error
80.7 (35.7) units error
0.54 (0.24)
0.55 (0.24)
Walking-distance estimation to sign B
46.1 (20.5) units error
46.1 (23.4) units error
0.54 (0.24)
0.54 (0.27)
Walking-distance estimation to sign C
32.7 (16.7) units error
34.6 (16.6) units error
0.44 (0.22)
0.46 (0.22)
(6) The
same analyses on standardized error scores revealed similar results.
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estimation a 2 (level of visual detail, low or high) 2 (type of distance estimation, straight line or walking) 2 (sign, B or C) mixed model ANOVA, with level of visual detail as a between-subject variable and type of distance estimation and sign as withinsubject variables, was conducted. Results confirmed insignificant between-subject differences (F1; 47 0:28, p 0:60) for the low and high visual-detail VEs. The within-subject comparisons showed significant differences between type of distance estimations (F1; 47 17:02, p 0:00) and between signs (F1; 47 27:27, p 0:00). The mean walking-distance-estimation errors were higher than the mean straight-linedistance-estimation errors and the mean distance-estimation errors for sign B were higher than those for sign C (table 2). The two-way three-way interactions between level of visual detail, type of distance estimation, and sign were insignificant (level of visual detail type of distance estimation: F1; 47 3:18, p 0:08; level of visual detail sign: F1; 47 0:41, p 0:52, type of distance estimation sign: F1; 47 3:70, p 0:06; level of visual detail type of distance estimation sign: F1; 47 0:11, p 0:74). Finally for sketch-drawing performance a one-way ANOVA analysis revealed an insignificant effect for the level of visual detail (F1; 47 1:34, p 0:25). Conclusion The aim of this study was to test if people's spatial performance differs in VEs with different visual details. In general, the findings provided evidence that people's spatial knowledge is not affected by the level of visual complexity in VEs. People who were tested in low visual-detail VEs showed similar spatial-knowledge performances to those of people who were tested in high visual-detail VEs. However, this study has some methodological limitations that should be addressed to draw proper conclusions from these results that could inform future research in this area. There were four limitations related to the experimental setup and the characteristics of the subject group. First, the sample size was limited to young university students, who do not play computer games frequently. Spatial performance in virtual environments may vary between different groups of people (elderly, children, and frequent computer game players). Whether the results of the present study will apply to different groups of people remains to be seen. A second concern is related to the construct validity of the dependent variables. Recall that participants' spatial performance was measured with three tests (direction and distance estimation and sketch-drawing performance). However, the literature suggests other tests, such as navigation tests, to measure people's spatial knowledge. Perhaps, people benefit from higher levels of visual detail when they are listing landmarks in the environment, finding a destination, or when they are giving directions to specific locations. Thus, more work should be done to test the generalization of the results of this study to various measures of spatial knowledge. Moreover, a subjective measurement was used when evaluating sketches in this study; future studies may use a more objective measurement to evaluate sketches [see Ishikawa and Montello (2006) for an example of more objective measurement of sketches]. Third, in this study one spatial layout, a simple grid plan, was used to test people's spatial performance. However, more challenging layouts could produce different results. For example, in complex plans some simplification in VE, such as eliminating the facade details, may help people to develop better spatial knowledge about the environment. A useful extension of this study may be to examine the influence of visual detail in various plan-layout complexities. Fourth, in this study levels of visual detail (with variation in facade details) were measured; two levels of visual detail, low (no facade details) and high (facade details, such as windows and doors) were determined. Subsequent work may develop better measurement to control levels of visual detail.
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As a concluding remark, researchers studying environmental perception, cognition, and human spatial performance have been testing people's spatial knowledge in VEs with different levels of visual details. Some studies have been using real-world textures on the modeled objects, and others have been using unrealistic VEs which look like mazes. Given the fact that spatial knowledge could be affected by many factors (such as age, gender, familiarity, plan-layout complexity), it is important to understand how realism in VEs affects people's spatial knowledge acquirement. If the level of visual detail in a VE affects people's spatial knowledge acquirement, then studies on environmental perception, cognition, and human spatial abilities should account for the level of visual detail in the VE (or the extent to which real-world textures were used on the modeled objects) besides other factors of interest, such as participants' gender or the plan-layout complexity of the physical environment. However, if the results of this study, which showed that people's spatial behavior does not change in VEs with low and high visual details, could be confirmed with future research then researchers, designers, and planners could save time and energy in developing VEs to test and refine designs to satisfy human needs and to understand human behavior. References Aginsky V, Harris C, Rensink R, Beusmans J, 1997, ``Two strategies for learning a route in a driving simulator'' Journal of Environmental Psychology 17 317 ^ 331 Appleyard D, Lynch K, Myer J, 1964 The View from the Road (MIT Press, Cambridge, MA) Arthur E J, Hancock P A, Chrysler S T, 1997, ``The perception of spatial layout in real and virtual worlds'' Ergonomics 40 69 ^ 77 Bell P A, Greene T C, Fisher J D, Baum A, 1996 Environmental Psychology 4th edition (Harcourt Brace, Fort Worth, TX) Bosselman P, 1998 Representation of Places: Reality and Realism in City Design (University of California Press, Berkeley, CA) Brooks B M, 1999, ``The specificity of memory enhancement during interaction with a virtual environment'' Memory 7 65 ^ 78 Burns P C, 1998,``Wayfinding errors while driving'' Journal of Environmental Psychology 18 209 ^ 217 Carassa A, Geminiani G, Morganti F, Varotto D, 2002, ``Active and passive spatial learning in a complex virtual environment: the effect of the efficient exploration'' Cognitive Processing ö International Quarterly of Cognitive Sciences 3 ^ 4 65 ^ 81 Carpman J R, Grant M A, 2002, ``Wayfinding: a broad view'', in Handbook of Environmental Psychology Eds R B Bechtel, A Tsertsman (John Wiley, New York) pp 427 ^ 442 Carpman J R, Grant M A, Simmons D A, 1985, ``Hospital design and wayfinding: a video simulation study'' Environment and Behavior 17 296 ^ 314 Cubukcu E, 2003 Investigating Wayfinding Using Virtual Environments unpublished dissertation, Ohio State University, Columbus, OH Cubukcu E, Nasar J L, 2005a, ``Influence of physical characteristics of routes on distance cognition in virtual environments'' Environment and Planning B: Planning and Design 32 777 ^ 785 Cubukcu E, Nasar J L, 2005b, ``Relation of physical form to spatial knowledge in large scale virtual environments'' Environment and Behavior 37 397 ^ 417 Durlach N, Allen G, Darken R, Garnett R L, Loomis J, Templeman J, Wiegand T E, 2000, ``Virtual environments and the enhancement of spatial behavior: towards a comprehensive research agenda'' Presence: Teleoperators and Virtual Environments 9 593 ^ 615 Evans G W, 1980, ``Environmental cognition'' Psychological Bulletin 88 259 ^ 287 Foreman N, Sandamas G, Newson D, 2004, ``Distance underestimation in virtual space is sensitive to gender but not activityöpassivity or mode of interaction'' Cyberpsychology and Behavior 7 451 ^ 457 Garling T, Selart M, Book A, 1997, ``Investigating spatial choice and navigation in large scale environments'', in A Handbook of Spatial Research Paradigms and Methodologies Part 1 Eds N Foreman, R Gillett (Psychology Press, London) pp 153 ^ 180 Gaunet F, Vidal M, Kemeny A, Berthoz A, 2001, ``Active, passive and snapshot exploration in a virtual environment: influence on scene memory, reorientation and path memory'' Cognitive Brain Research 11 409 ^ 420
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