the walkabout: using virtual environments to assess large-scale spatial abilities

omputers inuman Behavior

CH

www.elsevier.com/locate/comphumbeh

Computers in Human Behavior 21 (2005) 243–253

The WALKABOUT: Using virtualenvironments to assess large-scale spatial abilities

David Waller *

Department of Psychology, Miami University, Oxford, OH 45056, USA

Available online 18 March 2004

Abstract

Computer-simulated virtual environments (VEs) offer promise for assessing people’s spatial

abilities in large real-world environments. This paper introduces the WALKABOUT, a VE-

based test that simulates navigation through large spaces. Participants’ ability to form an

accurate mental representation of a familiar large-scale environment correlated nearly as

highly with their performance on the WALKABOUT as it did with their self-reported sense of

direction. Performance on two subscales of the test further indicated that the ability to account

for changing egocentric relationships as a result of self-movement and the ability to recognize

a novel perspective on an environment are both significantly related to spatial ability at the

environmental scale.

� 2004 Elsevier Ltd. All rights reserved.

Keywords: Virtual environments; Virtual reality; Spatial ability; Environmental cognition

1. Introduction

Historically, human spatial abilities have been investigated in one of two ways.

On one hand, researchers from the psychometric tradition have examined perfor-

mance on paper-and-pencil tests that require mental manipulations of small figural

stimuli such as blocks, cards, flags, or other symbols. This type of research has

yielded incisive models of the mental factors and processes that underlie performanceon tests of spatial abilities that involve small figures (for examples, see Carroll, 1993;

Just & Carpenter, 1985; Lohman, 1988; Pellegrino & Kail, 1982). A very separate

* Tel.: +1-513-529-4929; fax.: +1-513-529-2420.

E-mail address: [email protected] (D. Waller).

0747-5632/$ - see front matter � 2004 Elsevier Ltd. All rights reserved.

doi:10.1016/j.chb.2004.02.022

mail to: [email protected]

244 D. Waller / Computers in Human Behavior 21 (2005) 243–253

tradition of spatial research has investigated people’s performance on spatial tasks in

large natural environments, such as buildings, campuses, and cities (see, for example,

McNamara, Rump, & Werner, 2003; Montello & Pick, 1993; Sholl, 1987). In con-

trast to the correlational approach generally taken by psychometricians, this re-

search typically uses controlled experiments to examine factors that affect the mental

representations of these spaces.Although both of these research traditions clearly involve human spatial cognition,

there is very little empirical evidence that connects the psychometric literature to that

of environmental cognition. Indeed, in a recent review of the spatial abilities litera-

ture, Hegarty and Waller (in press a) concluded that people’s performance on paper-

and-pencil tests of spatial abilities is typically able to account for only about 5% of the

variance in their ability to learn or find their way in large-scale environments. As it

stands, probably the best correlate of environmental spatial abilities is performance

on self-report measures of spatial ability, such as ‘‘sense of direction’’ questionnaires.A typical self-report measure of people’s sense of direction may yield correlations

around .4 or .5 with environmental spatial ability (Bryant, 1982; Hegarty, Richard-

son, Montello, Lovelace, & Subbiah, 2002; Sholl, 1988). Although they typically are

significantly better than psychometric tests at predicting environmental behavior,

self-report measures can be somewhat unsatisfying from a theoretical perspective

because they offer very little insight about the psychological processes or mechanisms

that underlie people’s spatial ability. The unfortunate conclusion from the environ-

mental cognition literature is that there is currently a profound void in our under-standing of – and our ability to assess – the component mental abilities that are used

in acquiring spatial knowledge of large-scale environments.

In this article, I explore the idea that computer-simulated virtual environments

(VEs) offer a promising medium for assessing large-scale spatial abilities. Primarily

because of the precise control that they allow, VEs have already been used effectively

in applications as far-ranging as psychological therapy (North, North, & Coble,

2002), surgery training (Tendick et al., 2000), and social psychological research

(Blascovich, Loomis, Beall, Swinth, Hoyt, & Bailenson, 2002). Only recently, how-ever, has their potential for psychological assessment begun to be explored (Rizzo

et al., 1998; Waller, Loomis, & Haun, 2004). Because VEs are able to depict

dynamic, three-dimensional scenes, it is logical to expect them to provide a more

powerful and flexible medium than paper-and-pencil tests for understanding people’s

ability to learn the spatial characteristics of large-scale environments. In particular, it

is important to note that gaining spatial knowledge from large-scale environments

typically involves moving through them, and thus requires the ability to reason

about the consequences of one’s own motion. Although several investigators havesuggested that the ability to reason about motion represents an important and dis-

tinct mental ability (Fischer, Hickey, Pellegrino, & Law, 1994; Hunt, Pellegrino,

Frick, Farr, & Alderton, 1988; Law, Pellegrino, Mitchell, Fischer, McDonald, &

Hunt, 1993; but see Larson, 1996), their work has primarily examined the ability to

reason about moving objects as they are seen from a stationary viewpoint. Virtually

no psychometric work has addressed the ability to reason about self-motion (but see

Sholl, 1989). Because of the dynamic first-person perspective offered by VEs, it may

D. Waller / Computers in Human Behavior 21 (2005) 243–253 245

be possible to study more carefully this and other component abilities that are

specifically required for acquiring environmental knowledge.

There are at least two mental abilities that are generally thought to be important

for acquiring spatial knowledge from navigation through an environment. The first

involves the ability to keep track of the changing relationships between oneself and

external objects as one moves through the environment. This ability is often referredto as ‘‘egocentric spatial updating’’ and has occupied an important area of con-

temporary research in spatial cognition (see, for example, Fujita, Klatzky, Loomis,

& Golledge, 1993; Klatzky, Loomis, Beall, Chance, & Golledge, 1998; Loomis,

Klatzky, Golledge, Cicinelli, Pellegrino, & Fry, 1993; Wang, 2000). Another im-

portant factor in acquiring environmental knowledge is likely to be the ability to

infer spatial relationships that one has not directly experienced. For example, as a

result of one’s ground-level experience with navigating in an environment, a person

may be able to imagine what the environment looks like from a bird’s-eye per-spective. Such an ability is closely akin to an aptitude often referred to as ‘‘per-

spective taking,’’ (Hegarty & Waller, in press b; Huttenlocher & Presson, 1979;

Kozhevnikov & Hegarty, 2001; Piaget & Inhelder, 1967) and is thought to be as-

sociated with the ability to acquire a flexible ‘‘survey’’ representation of an envi-

ronment (Allen, Kirasic, Dobson, Long, & Beck, 1996).

The remainder of this paper reports a study that shows that computer simulated

environments can be used to assess people’s ability to acquire environmental knowl-

edge. Participants’ performance on a desktop virtual environment test (called theWALKABOUT, and described in detail below) was compared with their accuracy at

pointing to unseen locations while oriented in a familiar large-scale real-world envi-

ronment. The study had two goals. First, I hoped to develop a VE-based assessment

tool that would have a closer association with people’s environmental spatial ability

than the associations that have been reported in the literature between paper-and-pencil

tests and environmental spatial ability. Second, I hoped to use the different scales of this

test to determine which of the two component abilities (updating or perspective taking)

is more closely associated with people’s ability to acquire environmental knowledge.

2. Method

2.1. Participants

The experiment was conducted with 72 undergraduates (19 men) from a large

public University in return for extra credit in their introductory Psychology course.

Mean age of the participants was 22.25 (SD¼ 5.90).

2.2. Measures

2.2.1. Virtual environment (predictor) variables

The WALKABOUT is a 15-min computerized test that was developed specifically

to predict people’s ability to form an accurate mental representation of a large-scale


environment. It uses real-time 3-D computer graphics to represent navigation in a

large computer-modeled 3-dimensional environment. The test was constructed using

WorldUpTM by SENSE8 Corp, and administered on a desktop computer with a

Pentium ProTM 200 processor, a Diamond FireGL 3000 graphics accelerator card,

and a 21 in. monitor (640� 480 resolution, true color, 75 Hz refresh). The

WALKABOUT is non-interactive, and thus requires no special skills with VEnavigational devices in order to score well.

The 30 items on the WALKABOUT depict a brief (10–40 s) walk through a

structured environment (typically around the exterior of a building – see Fig. 1(a)).

Along the way, a prominent blue landmark is passed (Fig. 1(b)). At the end of each

walk, the user is asked two questions. First, the user is shown nine arrows that point in

different directions, andmust select which arrow points to the occluded landmark (Fig.

1(c)). The user scores two points for selecting the correct arrow and one point for se-

lecting an arrow adjacent to the correct choice. Over the 30 items on the test, an overallaccuracy score thus ranges from 0 to 60. This overall accuracy, in addition to the mean

response latency, comprise the UPDATE subscale of this test. Finally, for each item,

the user is asked to recognize the environment from a perspective that was not expe-

Fig. 1. Sample item from the WALKABOUT. At the beginning of this item, the user faces a building (a).

As the viewpoint moves around the building, it passes a distinctive object (b). At the end of the trip, the

user is asked to point to the object (c) and recognize a map of the building (d).


rienced during the walk, from a set of six bird’s-eye views of different environments

(Fig. 1(d)). The user scores one point for each correct answer. The percentage of

correctly answered questions (as well as the mean response latency) comprises the

PERSPECTIVE subscale. A shorter, 10-item version of this test has already shown

some promise in predicting people’s environmental abilities (Waller, 2000).

The UPDATE and PERSPECTIVE scales of the WALKABOUT were designedto examine the relative importance of spatial updating and perspective taking in

acquiring environmental knowledge. The UPDATE scale requires people to account

for the apparent change in position of a target object that results from simulated self-

motion. If this ability to account for changing egocentric relationships is critical for

acquiring environmental knowledge, one would expect participants’ performance on

the UPDATE scale of the WALKABOUT to be highly associated with their envi-

ronmental spatial ability. The PERSPECTIVE scale requires users to recognize a

view of the environment that they never directly experienced. Similarly, if this abilityis critical for acquiring environmental knowledge, one would expect the PER-

SPECTIVE scale to be highly correlated with environmental spatial ability.

2.2.2. Real-world directional knowledge (outcome variable)

Participants’ ability to point accurately to targets in a familiar, real-world envi-

ronment was measured in the laboratory. Participants were asked to look outside of

one of the windows in the laboratory, and to indicate whether they recognized the

scene and felt oriented to their surroundings. When they indicated that they wereoriented, participants were asked to point from their current position to three unseen

prominent locations. Participants’ bearing estimates to these targets were measured

from a hand-held analog compass. These estimates were subsequently subtracted

from the true bearings to obtain a set of signed errors. The standard deviation of

each participant’s signed errors (also known as variable error) constituted the study’s

primary outcome measure. 1

2.3. Procedure

The experiment began by asking participants to rate their overall familiarity

with the campus and surrounding community on a 10-point scale (1¼ complete

ignorance; 10¼ perfect knowledge). Participants were then asked to rate their

sense of direction on a 10-point scale (1¼ very poor; 10¼ excellent). Next, par-

ticipants were given the real-world pointing test, and the WALKABOUT test

described above. Participants were told to concentrate on being as accurate as

possible on the WALKABOUT, and were unaware that their responses werebeing timed. Note that because participants’ real-world pointing was measured

1 Variable error correlated extremely highly (rð70Þ ¼ :97, p < :01) with mean absolute error, another

commonly used measure of pointing performance. I chose variable error as this study’s primary criterion

measure because it measures people’s knowledge of environmental relationships, independently of their

awareness of their current orientation.


with an error score and the WALKABOUT items were scored as a percent

correct, the predicted relationship between these variables will be indicated by a

negative correlation.

3. Results

Participants’ ratings of their familiarity with the real-world testing environment

exhibited a wide range (from 1 to 10, M ¼ 6.14, SD¼ 2.17). However, these ratings

had virtually no association with their ability to point accurately to the target lo-

cations (rð70Þ ¼ �:09, p ¼ :45). As a result, the following results and conclusions

that are based on zero-order correlations with real-world pointing error do not

change when they are computed as partial correlations, controlling for participants’

ratings of familiarity with the tested environment.Reaction times for both the UPDATE and PERSPECTIVE question types

were converted to their logarithms to reduce the skewness of their distributions.

Table 1 presents means, standard deviations, estimated reliabilities, and inter-

correlations of the major variables in this study. The strongest single predictor of

a participant’s error pointing to unseen real-world locations was the participant’s

self-reported sense of direction (rð70Þ ¼ �:38, p < :01); however, both accuracy

(rð70Þ ¼ �:33, p < :01) and latency (rð70Þ ¼ �:31, p < :01) scores from the

PERSPECTIVE scale of the WALKABOUT predicted pointing error nearly aswell. In addition, the percent correct on the UPDATE scale (rð71Þ ¼ �:29,p ¼ :01) and the (log) reaction time on the UPDATE scale (rð71Þ ¼ �:27, p ¼ :02)were also significantly correlated with participants’ pointing error to unseen real-

world locations. To determine the overall ability of the WALKABOUT to predict

participants’ real-world pointing error, a regression model was fit in which each

of the four WALKABOUT-related scores (accuracy and latency on both the

PERSPECTIVE and UPDATE scales) were used to predict participants’ real-

world pointing error. Thus combined, the four predictors yielded a multiplecorrelation (R) of .43 with the criterion.

To determine the degree to which the four WALKABOUT scores measured

the same underlying cognitive ability as participants’ real-world pointing scores,

all five scores were submitted to a factor analysis (alpha factoring). The analysis

revealed that one factor (eigenvalue¼ 2.30) was sufficient to account for ap-

proximately 46% of the variance among the five variables. This factor yielded

loadings of ).48 for real-world pointing, .69 for UPDATE accuracy, .70 for

UPDATE latency, .55 for PERSPECTIVE accuracy, and .57 for PERSPECTIVElatency. One additional factor had an eigenvalue greater than one (1.19), but had

a large factor loading on only one variable (UPDATE latency) and could not be

easily interpreted.

The negative correlation between reaction times and pointing error indicates that

greater pointing accuracy in the real world is associated with more time spent de-

ciding the answer to WALKABOUT items. Although, across individuals, slower

decision times were associated with greater pointing accuracy, there was no evidence

Table 1

Means, standard deviations, reliabilities, and intercorrelations of the major variables

1 2 3 4 5 6 7

1. Campus familiarity

2. Sense of direction .36��

WALKABOUT

3. UPDATE – accuracy ).06 .14

4. UPDATE – log(RT) .20 .20 .05

5. PERSPECTIVE – % correct .19 .26� .46�� .12

6. PERSPECTIVE – log(RT) .39�� .40�� .24� .66�� .45��

7. Pointing error ).09 ).38�� ).29� ).27� ).33�� ).31��

Mean 6.14 6.59 33.22 1.14 39.97 2.46 41.39

Standard deviation 2.17 1.88 6.63 0.29 13.46 0.34 35.19

Split-half reliability .66 .82 .63 .92 .79a

a Estimated with a similar data set in Waller (1999).* p < :05.** p < :01.


for a speed-accuracy tradeoff on WALKABOUT items within individuals. To test

this, for each participant, mean reaction times for correctly answered items were

compared to those for incorrectly answered items. No participant was significantly

faster on incorrect items.Table 1 shows that both accuracy and latency scores from the PERSPECTIVE

scale correlate more highly with participants’ real-world pointing than do the scores

from the UPDATE scale. This suggests that in general, the PERSPECTIVE scale of

the WALKABOUT is more predictive of pointing error than the UPDATE scale.

However, on closer analysis, this difference is neither large nor significant. The dif-

ference between the predictive power of the PERSPECTIVE and UPDATE scales

was tested by combining the accuracy and latency information from each scale (their

z-scores were summed). The composite UPDATE and PERSPECTIVE variablesboth had extremely similar correlations with participants’ real-world pointing error

(rð71Þ ¼ �:39, p < :01) for the UPDATE scale and (rð71Þ ¼ �:38, p < :01) for thePERSPECTIVE scale.

4. Discussion

This study illustrates an important new use of computer-simulated 3-D envi-ronments in assessing large-scale spatial abilities. Errors and decision latencies

from the PERSPECTIVE subscale of the WALKABOUT were nearly as predictive

of people’s accuracy at pointing to unseen locations in a large-scale environment as

was their self-reported sense of direction. Errors and decision latencies from the

UPDATE scale of the WALKABOUT were also significantly related to partici-

pants’ ability to point to known locations in the real world. In combination, the

four WALKABOUT scores were able to account for approximately 20% of the


variance in people’s real-world pointing error. This exceeds the approximately 15%

that was accounted for by participants’ self-reported sense of direction.

Although the magnitude of these correlations is rather modest, it compares

quite favorably to the validity of other assessments of cognitive abilities in pre-

dicting spatial ability at the environmental scale (see Hegarty & Waller, in press

a; Waller, 1999). Most investigators would agree that there are many determi-nants of environmental spatial ability. For performance on one instrument to

correlate above .4 is generally considered noteworthy in this area of research. The

present results are especially impressive when one considers that the reliability of

the criterion task – error in pointing to unseen targets – was rather low (esti-

mated by Waller (1999) to be .79). Additionally, the reliability of two of the

WALKABOUT scores was quite low. Increasing the reliability of these measures

is an ongoing effort, and should result in even greater predictive strength of the

WALKABOUT.Unlike the significant correlation between participants’ ratings of their sense of

direction and their environmental spatial ability, the association between partici-

pants’ WALKABOUT scores and their environmental spatial ability begins to shed

some light on the cognitive processes that underlie spatial abilities at the environ-

mental scale. The second goal of this study was to use VE technology to determine

the relative strength of association between environmental spatial ability and two

potential component mental abilities: the ability to keep track of changing egocentric

relationships as one moves through an environment (spatial updating), and theability to transform one’s ground-level experience into another view of the envi-

ronment (perspective taking). In general, both of these component abilities had a

strong and significant association with people’s ability to point to unseen locations in

a familiar environment. There was no evidence that one is more closely related than

the other to environmental spatial ability.

In this study, environmental spatial ability was operationalized as skill at

pointing to unseen, familiar targets while oriented in a familiar environment. In

fact, environmental spatial ability is likely determined by multiple underlyingcognitive factors (Allen et al., 1996; Lorenz & Neisser, 1986). Had this study

measured a different aspect of people’s environmental spatial ability, the results

would undoubtedly have changed. For example, Montello and Pick (1993) mea-

sured participants’ ability to point to unseen targets in a newly-learned (as op-

posed to a well-learned) environment. Errors in the pointing task correlated at .41

with participants’ self-reported sense of direction, while latencies correlated at .51.

Hegarty et al. (2002) have suggested that self-reported sense of direction is more

closely related to people’s ability to ‘‘represent one’s current orientation orheading in the environment (p. 442)’’ than it is to the ability to form an accurate

cognitive map (see also Sholl, 1988). If this is true, then it is likely that this

study’s operationalization of environmental spatial ability served to attenuate the

correlations between environmental spatial ability and self-reported sense of di-

rection. By the same token, it is quite possible that the WALKABOUT would

have been an even better predictor of environmental spatial abilities if it had been

compared to other aspects of this multidimensional construct.


Many investigators have noted that a fundamental limitation of desktop virtual

environments is their lack of incorporation of body-based sensory modalities such

as the vestibular and kinesthetic senses. The influence of these modalities on

people’s ability to acquire spatial information from large-scale environments is

currently a matter of some debate in the field (see, for example Klatzky et al.,

1998; Riecke, van Veen, & B€ulthoff, 2002; Waller et al., 2004; Waller, Loomis, &Steck, 2003). To the extent that non-visual modalities are necessary for acquiring

spatial knowledge of large-scale environments, it seems likely that desktop VEs

will not be able to account adequately for a portion of the variance in people’s

environmental spatial ability. A related source of variance that the WALK-

ABOUT could not account for is individual differences in the ability to control

one’s navigation. Making the WALKABOUT more interactive represents another

potential improvement. If, for example, the test allowed examinees to determine

their own course of travel, it could potentially assess the influence of people’sspatial strategies, intentions, and actions on their ability to acquire information

from large-scale environments.

It is often noted that computer-generated environments allow researchers both

ecologically relevant environments in which to examine human behavior and rela-

tively high control over the environment’s properties (Loomis, Blascovich, & Beall,

1999). It is exactly for this reason that VEs offer an exceptionally useful tool for

assessing and isolating individual differences in large-scale spatial cognition. This

study has shown how, by incorporating a dynamic first-person simulation of navi-gation, a very simple desktop VE can be used to predict and understand the cognitive

abilities that are involved in large-scale environmental behaviors. Continued careful

development of these computer simulations may ultimately be able to shed light on

one of the field’s most central and enduring mysteries: why are some people better at

finding their way around than others?

Acknowledgements

I thank Earl Hunt, Mary Hegarty, Yvonne Lippa, Donald Parker, and SusanTanney for helpful comments on earlier versions of this manuscript.

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the walkabout: using virtual environments to assess large-scale spatial abilities

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