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Near-Field Distance Perception in Real and VirtualEnvironments Using Both Verbal and Action ResponsesPHILLIP E. NAPIERALSKI, BLISS M. ALTENHOFF, JEFFREY W. BERTRAND, LINDSAY O. LONG,SABARISH V. BABU, CHRISTOPHER C. PAGANO, JUSTIN KERN, and TIMOTHY A. DAVIS, ClemsonUniversity

Few experiments have been performed to investigate near-field egocentric distance estimation in an Immersive Virtual Envi-ronment (IVE) as compared to the Real World (RW). This article investigates near-field distance estimation in IVEs and RWconditions using physical reach and verbal report measures, by using an apparatus similar to that used by Bingham and Pagano[1998]. Analysis of our experiment shows distance compression in both the IVE and RW conditions in participants’ perceptualjudgments to targets. This is consistent with previous research in both action space in an IVE and reach space with AugmentedReality (AR). Analysis of verbal responses from participants revealed that participants underestimated significantly less in thevirtual world as compared to the RW. We also found that verbal reports and reaches provided different results in both IVEs andRW environments.

Categories and Subject Descriptors: I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism—Virtual reality;I.4.8 [Image Processing and Computer Vision]: Scene Analysis—Depth cues; H.5.1 [Information Interfaces and Pre-sentation]: Multimedia Information Systems—Artificial, augmented, and virtual realities; H.1.2 [Information Systems]:User/Machine Systems—Human factors

General Terms: Human FactorsAdditional Key Words and Phrases: Depth perception, distance estimation, virtual reality, immersive virtual environments,human factors and usability

ACM Reference Format:Napieralski, P. E., Altenhoff, B. M., Bertrand, J. W., Long, L. O., Babu, S. V., Pagano, C. C., Kern, J., and Davi, T. A. 2011. Near-field distance perception in real and virtual environments using both verbal and action responses. ACM Trans. Appl. Percept. 8,3, Article 18 (August 2011), 19 pages.DOI = 10.1145/2010325.2010328 http://doi.acm.org/10.1145/2010325.2010328

1. INTRODUCTION

Nearly fifty years ago, Ivan Sutherland created the first stereoscopic Head-Mounted Display (HMD)and presented a vision for the future of Virtual Reality (VR) systems [Sutherland 1965]. Today, someof Sutherland’s goals are finally realized in many state-of-the-art VR systems for entertainment,

This research was supported in part by NSF Research Experience for Undergraduates (REU) Site Grant CNS-0850695.Authors’ addresses: P. E. Napieralski (corresponding author), B. M. Altenhoff, J. W. Bertrand, L. O. Long, S. V. Babu, C. C.Pagano, J. Kern, and T. A. Davis, School of Computing and Department of Psychology, Clemson University, Clemson, SC;email: [email protected] to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee providedthat copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first pageor initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACMmust be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute tolists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may berequested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481,or [email protected]© 2011 ACM 1544-3558/2011/08-ART18 $10.00

DOI 10.1145/2010325.2010328 http://doi.acm.org/10.1145/2010325.2010328

ACM Transactions on Applied Perception, Vol. 8, No. 3, Article 18, Publication date: August 2011.

18:2 • P. E. Napieralski et al.

education, and the study of human behavior [Brooks 1999]. Complex VR systems in which users in-teract with virtual entities in personal space include applications such as VR therapy [Hodges et al.2001], interpersonal communication trainers [Johnson et al. 2006], and urban combat simulators [Hillet al. 2003].

Near-field VR has many exciting applications in simulation and telepresence. Training for laparo-scopic surgery, for instance, can be performed with Augmented Reality (AR) and VR environments[Peters et al. 2008]. Studies have shown that such virtual training improves operating room perfor-mance more effectively than traditional training methods [Seymour 2008].

Telepresence, the area of VR that uses technology to give a user the impression of being somewhereother than his or her current location, remains an important area of research and applications [Hineet al. 1994; Goza et al. 2004]. In one such application, operators using a Head-Mounted Display (HMD)and a joystick can control an undersea robot [Hine et al. 1994]. Using these robotic controls operatorsremotely pickup and investigate near-field undersea objects from the robot’s perspective.

In order to assess performance of users in systems where they operate in simulated near-space con-ditions, a thorough understanding of perceptual influences such as near-field distance estimation iscrucial. In this article we attempt to bridge the gap in understanding the differences in near-field dis-tance estimation in real and virtual environments using physical reach and verbal response measures.

1.1 Background

Distance estimation can be categorized into three distinct regions: personal space, or near field, isthe distance from 0m to slightly beyond arms’ reach, action space extends to 30m, and vista space isgreater than 30m [Cutting and Vishton 1995]. Recent research on egocentric distance estimation inImmersive Virtual Environments (IVEs) has focused on action space measured using blind-walking,imagined timed-walking, bean bag throwing, and triangulated walking techniques [Grechkin et al.2010; Ziemer et al. 2009; Klein et al. 2009; Richardson and Waller 2007; Interrante et al. 2006; Messingand Durgin 2005; Sahm et al. 2005; Thompson et al. 2004; Loomis and Knapp 2003].

Current research in action space has shown that people can accurately estimate distances up to 20min the Real World (RW) but grossly underestimate targets in the virtual world [Witmer and Kline 1998;Loomis and Knapp 2003]. Grechkin et al. [2010] compared distance estimation in various presentationconditions including a RW view with a see-through HMD, virtual world with HMD, AR with HMDphoto viewed with large-screen immersive display (LSID) and IVE viewed with a LSID. These condi-tions were tested using both imagined timed-walking and blind-walking in two different experiments.The LSID conditions were excluded from the blind-walking experiment due to space constraints. Dis-tances were underestimated in all VR, LSID, and AR conditions. Interestingly, both photograph andIVE LSID conditions achieved similar underestimation results. This implies that the quality of graph-ics has no effect with imagined time-walking in these nonstereoscopic setups. Also interesting is thatthe HMD with see-through display condition had no effect in the RW with imagined timed-walking,but it showed some underestimation with blind walking. This underestimation could partially, but notfully, be caused by the weight and forces from the HMD during the actual walking [Willemsen et al.2009]. Further, presentation order was also shown a significant factor in action space distance estima-tion [Ziemer et al. 2009]. Ziemer et al. [2009] had participants perform imagined timed-walking in aRW setting, followed by an IVE setting and vice versa. Conditions were performed 3–4 minutes apart.Participants underestimated the distances to targets in the RW condition after performing distanceestimation in the IVE condition, showing a carryover effect from IVE to RW. Furthermore, they notedthat this underestimation of distance judgments in the RW, after exposure to an IVE, was still lessthan the underestimation typically observed in participants that only received the IVE condition.


Near-Field Distance Perception in Real and Virtual Environments • 18:3

Kunz et al. [2009] showed there was no significant difference in blind-walking distance judgmentsbetween a low- and high-quality VR environment but there was a significant difference in verbal re-ports of distances. The low-quality scenario contained simple textures and simple geometry while thehigh-quality scenario contained photorealistic textures and realistic geometry. When participants wereasked to give a verbal report of distances for each trial, the distances reported in the low-quality set-ting were much shorter than those in the high-quality virtual environment. The authors cite threepotential reasons for this, including a model known as two visual systems which hypothesizes that dif-ferent neurological streams are responsible for verbal reports and actions [Milner and Goodale 1995,2008].

Research done in personal space for AR has shown a similar underestimation result using a sliderapparatus for measuring distance judgments [Singh et al. 2010; Ellis and Menges 1998]. The sliderapparatus is able to test both visually closed-loop, where the subject reaches with sight of an LEDpointer, and open-loop, where the subject reaches without sight of the pointer. In all cases, the findingsshow an underestimation, with more pronounced underestimation in the open-loop task [Singh et al.2010].

Other research in personal space has tested monocular and binocular vision under reduced-cue con-ditions, with a target often appearing as a small point of light or luminous disk [Foley 1997; Binghamand Pagano 1998]. A general finding is that with monocular viewing, perceived distances tend to un-derestimate actual distances and verbal responses provide less accurate and more variable responsesthan manual pointing or reaching [Foley 1977; Pagano and Bingham 1998]. Depending on the numberand type of cues eliminated, overestimations in near space have also been found [Foley 1985]. Withfeedback, however, binocular reaches become accurate while monocular reaches remain inaccurate[Bingham and Pagano 1998]. It seems likely that reaches to near space can be accurate in all viewingconditions, RW, IVE, AR, etc., so long as ample perceptual information is available and feedback is usedto calibrate away any initial errors. It is unclear if verbal responses can provide a reliable measure ofegocentric distance perception.

1.2 Related Work

Distance estimation in an IVE has been widely studied in action space. The most common techniquesused when measuring distance estimation with a HMD are blind-walking, throwing, and triangulatedwalking. Blind-walking involves allowing a user to first view a target, and then walk to the targetwith eyes closed [Messing and Durgin 2005; Loomis and Knapp 2003]. Throwing allows the subject toview the target as in blind-walking, but the subject instead throws an object, again with eyes closed,towards the viewed target [Sahm et al. 2005]. A final common technique, triangulated walking, allowsthe subject to walk in a direction different from the target, stopping after some distance, then walk tothe perceived target [Richardson and Waller 2007; Thompson et al. 2004]. Messing and Durgin [2005]show a 23% compression of the actual distance when blind walking to targets in an IVE. Sahm et al.[2005] show a 30% compression of distance in the IVE versus the RW. Finally, Richardson and Waller[2007] show a 54% compression of the actual distance in the virtual environment with the triangulatedwalking method.

To the best of our knowledge, this article is one of the first to investigate physical arm reaches inegocentric reach space in an IVE. The closest previous research is that by Bingham and Pagano [1998]and Singh et al. [2010]. In Bingham and Pagano [1998], participants used a monocular HMD to view aluminous disk that was floating in black space in the RW. Under these conditions depth perception wasonly possible when the head was moved. Participants were asked to look at a target located from 50%to 90% of their maximum arm reach, close their eyes and then make a physical reach using a stylusto where they perceived the target to be. A restricted Field Of View (FOV) resulted in compressions of



depth for monocular RW viewing. With monocular restricted FOV the subjects showed improvementover trials when provided with feedback, and thus the compression in depth due to restricted FOVwas removed with calibration. In contrast, the compression due to monocular viewing alone (withoutrestricted FOV) was not removed by calibration with feedback. In Singh et al. [2010] reach spacedistance estimation in AR was tested using a modified slider apparatus from Ellis and Menges [1998].Participants were shown a virtual spinning diamond target and were asked to align a Light-EmittingDiode (LED) on a slider to the virtual target in the closed-loop task, or to reach to the perceiveddistance using a slider under the table in the open-loop task. Participants’ vision of the virtual objectwas occluded before giving their estimations in half the trials. In general, both open-loop and closed-loop estimations resulted in underestimations of the actual distances.

Based on previous research on distance estimation in action space and reach space, it seems likelythat an investigation of reach space in an IVE that employed the same setup as Pagano and Bingham[1998] would result in underestimations of distance. Ellis and Menges [1997] showed that convergencemay have some effect on this depth perception in a virtual environment.

In this article, we have conducted an experiment to examine a subject’s distance estimation in anIVE using an apparatus similar to that used by Bingham and Pagano [1998; Pagano and Bingham1998]. Our contribution includes examining egocentric distance estimation in personal space usingtwo viewing conditions: RW and an IVE, and two response methods: verbal reports and reaches. Re-search investigating different types of responses has typically employed a blocked methodology inwhich responses made in one mode during one set of trials are compared with the responses madein another mode during a different set of trials [Philbeck and Loomis 1997]. This comparison is evenmade between-subjects [Kunz et al. 2009; Mon-Williams and Tresilian 1999; Wang 2004]. A problemwith these methodologies is that differences observed between the two responses may be due to the factthat they were made at different times, under different conditions and/or by different subjects. A muchstronger test of differences between the two responses is to test them within-trial, where the subjectmakes a single observation of a target distance and then from this single observation they make both averbal judgment and a manual reach. In this way the two responses are tested simultaneously. To dateonly a small handful of studies have used such a within-trial methodology to compare verbal reportsand action responses made at the same time [Pagano and Bingham 1998; Pagano et al. 2001; Paganoand Isenhower 2008]. Following this past work, we chose to use a within-trial methodology to employboth verbal reports and manual reaches to compare egocentric distance perception in RW and an IVE.

The remainder of the article is structured as follows. Section 2 discusses the experiment design,apparatus, and procedure. Section 3 describes the results of the experiment. Section 4 provides ananalysis of the results. Finally, Section 5 concludes.

2. EXPERIMENT

The primary aim of this study was to compare physical and verbal distance judgments in near field orreaching spaces between RW and IVEs. We specifically asked the following research questions:

(1) Are near-field distances perceived differently in real and virtual environments?(2) What are the differences between physical reaches and verbal responses to near-field distances in

both real and virtual environments?

Participants made verbal reports concurrently with reaches in either the RW or in the IVE condition.In our study these two types of responses were made immediately after viewing the target. Visuallyperceived egocentric distances were measured in each trial using both verbal reports and reaches aftervision was occluded. In the following subsections the experiment design, apparatus, participants, andprocedure will be detailed.



Fig. 1. The image shows our near-field distance estimation apparatus. The target, participant’s head, and stylus are tracked inorder to record actual and perceived distances of physical reach in both the IVE and RW conditions.

2.1 Participants

The experiment included 14 volunteers from a population of Clemson University students. The partic-ipants ranged in age from 19 to 24; the mean age was 20.5. Three were female and 11 were male. Eachparticipant was tested over two sessions at least two days apart to eliminate carryover effects. Halfof the participants performed the IVE condition first while the other half performed the RW conditionfirst. While the majority of participants did not experience difficulty learning the experiment, we didnot use data for three participants who did not follow the instructions.

2.2 Experiment Design

The experiment used a within-subjects 2(condition) × 2(measures) factorial design, where participantswere initially assigned to one of two conditions (RW or IVE). After completing trials in one condition,participants returned two days later to complete the other condition. The experiment was conductedin this manner in order to eliminate any carryover or learning effects within the two conditions. Inboth the RW and IVE conditions, participants were presented with five random permutations of sixtarget distances corresponding to .50, .58, .67, .75, .82 and .90 of the participant’s maximum reach fora total of 30 trial distances as in Pagano and Bingham [1998], Pagano et al. [2001], and Pagano andIsenhower [2008]. The primary dependent variable was distance as a percentage of maximum armreach for both verbal reports and physical reaches. The verbal reports were made on a scale from 0 to100, where 0 represented the subject’s shoulders and 100 their maximum arm reach. Verbal reports inintrinsic body scaled units should be more natural than the use of extrinsic scales such as inches orcentimeters, with an extrinsic scale likely requiring an unconscious transformation from an intrinsicone [Bingham and Stassen 1994; Warren 1995].

2.3 Apparatus and Materials

2.3.1 General Setup. Figure 1 depicts the apparatus used. Participants were seated in a woodenchair, and their shoulders were strapped to the back of a chair so as to allow freedom of movement ofthe head and arm while restricting motions of the shoulder. Participants reached with a wooden stylusthat was 26.5 cm long, and 0.9 cm in diameter and weighing 65g. The participants held the stylus



Fig. 2. The image on the left shows a screen shot of the virtual target, and the image on the right shows the real target asperceived by participants in the IVE and RW conditions respectively.

in their right hand so that it extended approximately 3 cm in front and 12 cm behind their closedfist. Each trial began with the back end of the stylus inserted in a 0.5 cm groove on top of the launchplatform. The launch platform was located right next to the hip of the participant facing parallel to theoptical axis.

The target consisted of a 0.5 cm deep vertical 8.0 cm × 1.2 cm groove that extending from the centerto the base of a 8.0 cm wide × 16 cm tall white rectangular target (Figure 2). The edges of the targetwere covered by a 0.5 cm thick black tape, so that the participant could distinguish the target fromthe stucco background of the wall. The target was positioned in front of the participant along theoptical axis, approximately midway between the participant’s midline and right shoulder (Figure 1).Therefore, the target was positioned such that the distance from the shoulder to the target wouldbe as close as possible to the distance from the eyes to the target. The egocentric near-field distanceto the target was adjusted by the experimenter using mounts attached to a 200 cm optical rail thatextended parallel to the participant’s optical axis. The target was attached to the optical rail via anadjustable hinged stand. The target, stand, and stylus were made of wood. The aluminum optical railwas mounted on a wooden base.

2.3.2 Visual Aspects. In the IVE condition, participants wore a Virtual Research VR 1280 HMDweighing 880g. The HMD contained two LCOS displays each with a resolution of 1280 × 1024 pixelsfor viewing a stereoscopic virtual environment. The field of view of the HMD was determined to be48 degrees horizontal and 36 degrees vertical. The field of view was determined by rendering a care-fully registered virtual model of a physical object, and asking users to repetitively report the relativesize of the virtual object against the physical counterpart through a forced-choice method. In the RWcondition, participants donned a 352g field of view occluder that restricted the field of view of theparticipants to visually match that of the HMD (Figure 3).

At the beginning of the experiment a calibration step was performed for each participant to ensurethat his/her eyes were centered on the HMD display screen. A calibration pattern was displayed onthe screen that consisted of concentric series of colored rectangular rings. Participants were asked toadjust the HMD such that the device was snug and they were able to see equal amounts of the samecolor on the top and bottom of the screen in both eyes. Next, they adjusted the HMD’s eyepieces tocenter on each eye horizontally on its screen by adjusting the inter-pupillary distance (IPD) knobs onthe HMD.

Researchers have theorized that the quality of the virtual environment could potentially be an im-portant factor in perceiving distances [Loomis and Knapp 2003; Kline et al. 2009]. Our goal was toprovide a photorealistic representation of the physical environment in which the real-world percep-tion tests were performed. According to Ferwerda’s classification scheme of visual realism [Ferwerda2003], physical realism would be preferable; however, it requires that the image provide the same vi-sual stimulation as the real-world scene, including accurate replication of spectral irradiance, which



Fig. 3. The image shows a participant wearing the Field Of View (FOV) occluder with an electromagnetic sensor to track user’shead position in the RW condition. The FOV occluder was designed to match the FOV of the HMD.

Fig. 4. The left image shows a screenshot of the training environment from the participant’s first person perspective with HMD.The right image shows a screenshot of the avatar as seen from the participant’s perspective.

is not currently possible given the limitations of interactive rendering on a head-mounted display. Atthe other end of the visual scale is functional realism which only requires that the image provide thesame visual information as the scene. As such, this level of detail can be achieved with a variety oftechniques, including nonphotorealistic and abstract rendering methods [Haller 2004; Phillips et al.2009]. Somewhere between these two varieties of visual realism lies photo-realism, in which the imageproduces the same visual response as the real-world scene.

In order to keep the visual experiment scene as consistent as possible between the IVE and RWconditions, we strove to model and render the virtual setting to be similar to the physical setting.An accurate virtual replica of the experiment apparatus and surrounding environment were modeledusing Blender. The virtual replica of the apparatus and surrounding environment included target,stand, chair, room, tracking system, stylus, and a virtual body representing the participant were alsomodeled. The gender-neutral model of a virtual body seated in the participant’s chair was meant toprovide the participant with an egocentric representation of self whenever the participant glanceddown at herself (see Figure 4).



We have attempted to achieve this level of realism by not only matching the size and placement ofobjects located in the real-world environment exactly, but by matching the textures and lighting aswell. The accuracy of the scale and size of the virtual objects in the IVE experiment setup was insuredby careful hand measurements of each of the physical objects in the RW experiment setup. Many of thetextures of the synthetic world are simply photographs of the real-world objects. Great care was takento match the objects exactly, especially those that were involved in the experiment, such as the virtualtarget, as shown in Figure 2. We also employed state-of-the-art rendering techniques such as radiosityand render to texture, to match the visual quality of the virtual environment and apparatus to thephysical experiment setting as close as possible. These efforts were largely undertaken to preventany adverse effects on perception in the virtual world, which can occur in nonphotorealistic virtualenvironments [Phillips et al. 2009].

The computational environment that hosted the distance estimation system consisted of a Dell Pre-cision workstation with a quad core processor and dual NVIDIA Quadro FX 5600 SLI graphics cards.The distance estimation system that rendered the IVE condition in HMD stereo, ran the tracking sys-tem, and measured as well as recorded the perceived physical reaches in tracker coordinates in bothconditions was developed in OpenGL and the Simple Virtual Environment toolkit (SVE) [Kessler et al.2000]. The distance estimation experiment system ran at an application frame rate of 45Hz.

2.3.3 Tracking and Measurement of Physical Reach. A 6 degree of freedom Polhemus Liberty elec-tromagnetic tracking system was used to track the position, and orientation of the participant’s head,stylus, and target in both the IVE and RW conditions. Prior to conducting the experiment, thePolhemus tracking system was calibrated to minimize any interference due to metallic objects in thephysical environment, through the creation of a distortion map, using a calibration apparatus and pro-prietary software from the manufacturers of the tracking system. This calibration step ensured thatthe sensor position reported by the tracking system was accurate to 0.1cm, and the sensor orientationwas accurate to 0.15 degrees. Measurements of the participant’s physical reach were measured fromthe position of the target face to the origin of the optical rail as reported by the tracking system in cen-timeters (cm) in both conditions. Raw position and orientation values of the tracked sensors as well asthe measured perceived and actual distances for each trial were logged in a text file by the experimentsystem in both the IVE and RW conditions for each participant. This data was later used to analyzethe results of the experiment.

To ensure proper registration of the virtual target and stylus with their real counterparts, we care-fully aligned the virtual object’s coordinate system with that of the tracking sensor’s coordinate system.We also determined the relationship between the coordinate system of the tracking sensor on the par-ticipant’s head (on top of the HMD) and the coordinate system of the HMD’s display screen (computergraphics view plane), to ensure proper registration of the virtual environment to the physical environ-ment as perceived by the participant.

2.3.4 Sensor Data Analysis. Rather than analyzing speed profiles from the raw data, sensor posi-tion, and orientation for the HMD, the stylus and the target face at the end of each reach were loggedby the experimenter at the keyboard via a key press. A key was pressed when the participant’s armwas fully extended at the end of each trial to show the physical reach response of the estimated dis-tance to the target, before bringing the arm back to the loading dock. It has been suggested that theinitial gross motion phase of the participant’s hand more closely denotes the visually perceived dis-tance, rather than the end of the secondary hand motion phase towards fine adjustment [Bingham andPagano 1998].

After conducting the experiment, it was important to empirically evaluate how well the data loggedby the experimenter with a key press aligned with the end of the gross movement phase of the physical



reach made by participants. A data filtering operation was performed similar to that in Bingham andPagano [1998], Pagano et al. [2001], and Pagano and Isenhower [2008], and it involved using the rawdata to analyze the kinematic profiles of the sensor movements. Raw position data from the sensorswas filtered using forward and backward passes of a second-order Butterworth filter with a resultingcutoff at 5 Hz. The speed between adjacent raw data entries was then computed.

A graph of the speed calculations showed two gross movement phases by each participant: swingingthe hand to the target, and returning the hand back to the loading dock. It was determined that theposition of the stylus with speed closest to zero after the first gross movement phase was the end ofthe participant’s distance estimate. This analysis was done on 90 different trials from three subjectsand we compared the position of the stylus based on our preceding analysis at the end of the first grossmotion phase to the position of the stylus logged via a key press by the experimenter. Performing alinear regression on all the data showed a strong correlation (r2 = .974) between the key press sensordata and the raw data, therefore indicating that the key press logs of the experimenters was a reliablemethod of recording the position of the hand denoting the distance judged by the participants viaphysical reach at the end of the gross motion phase.

2.4 Procedure

As mentioned previously, each participant was involved in two separate sessions, one in the IVE andone in the RW, at least two days apart. At the beginning of the first session, each participant completeda standard consent form and a brief demographic survey. A Stereo Fly SO-001 stereo vision test wasused to assess the participant’s stereo acuity. Participants were asked to describe a fly that could beperceived as raised above the plane of the image with passive stereo glasses, and then were asked tocatch the wings of the fly. All participants passed the stereo acuity test, and were able to perceive thefly as hovering above the stereo test image plane. All participants were right-handed and had normalvision, or corrected to at least 20/32 vision.

After passing the necessary vision tests, the participant was seated and loosely strapped in a chairto restrict movement of the trunk and shoulders but to allow movement of the arm. In both conditions,the participant’s maximum arm reach was measured by instructing the participant to place the stylusin the groove of the target. The target was then moved forward or backward until the subject’s arm wasfully extended and the stylus was perpendicular to the floor. This maximum arm reach distance wasused to generate the trial distances at which the apparatus would be placed during the experiment.The participant was also instructed on how to make verbal reports.

In the IVE condition the participant’s inter-pupillary distance was measured with a ruler and usedas a parameter to the experiment program, and was used to specify the inter-pupillary distance in thegraphical simulation. The HMD was then placed on the participant and calibrated using an image ofconcentric rectangles. The participant was instructed to adjust the HMD, and to rotate two knobs inthe front to focus the image until the rectangles in both displays were aligned.

Once the participant was satisfied, and the HMD fastened to the head, an IVE training environ-ment was presented to help the subject adjust to using the device and the head-coupled motion. Theenvironment used a near-perfect replica of the real-world testing environment except that the testingapparatus could not be seen. Additionally, the training environment included a few objects not presentin the RW, such as a television and a poster. The participant was asked to move their head aroundin order to view the objects for a minute in this environment. Then the participant was asked simplequestions to ensure they had properly adjusted to the head motions and the viewing conditions of theIVE (e.g., What is on the television? What time is on the clock?). See Figure 4 for a screenshot of thistraining environment. After this training phase one of the experimenters would press a keyboard keyto initiate the testing environment.



Table I. Slopes and Intercepts (in arm length units) for Simple Regressions PredictingIndicated Target Distance from Actual Target Distance in Each Viewing Condition

(IVE or RW) and Response ConditionVerbal Reports Reaches

IVE RW IVE RWSubject Slope Intercept Slope Intercept Slope Intercept Slope Intercept1 1.42 −63.7 .92 −44.5 .60 11.9 1.11 −27.12 1.35 −17.7 1.18 −36.8 1.04 −.5 .93 −9.03 .68 3.3 .87 −9.2 .88 17.9 .77 −3.84 1.63 −58.0 .90 −33.2 .78 12.1 .66 13.85 .95 −20.6 1.00 −14.8 .72 −7.2 .68 6.67 1.18 −45.6 1.31 −53.4 .85 −3.3 .43 17.29 1.44 −49.8 1.24 −40.5 1.00 −19.2 .73 −2.010 .77 −19.2 .71 −12.8 .78 .5 .63 27.511 1.17 −35.0 1.06 −13.2 .73 −9.8 1.06 −18.112 1.57 57.4 1.01 −28.5 .86 6.5 .70 10.114 1.55 −48.5 1.11 −38.1 1.01 −9.1 .74 4.0Overall 1.25 −27.0 1.03 −29.6 0.84 0.0 0.77 1.7

The testing environment consisted of a photorealistic virtual representation of the real environmentsurrounding the participant. Instructions were repeated to the participant on making reaches and howto make verbal reports for each trial. Each subject was given at least one practice trial before beginningthe collection of the experiment data.

For each trial, with the participant’s eyes closed or the HMD display turned off, the target distancewas adjusted. The participant then viewed the target until he or she felt comfortable with the targetdistance. The participant notified us by saying “ready.” At this point in the RW condition, the subjectclosed their eyes. In the IVE condition, the HMD video was turned off via a key press and the targetwas immediately swung out of the way to prevent any haptic feedback. The participant first made averbal report, immediately followed by a physical reach using the stylus much, like in Pagano andBingham [1998], Pagano et al. [2001], and Pagano and Isenhower [2008]. The experimenter at thekeyboard then pressed a key to record all of the sensor data from the tracking system pertaining to theposition of the stylus (hand), target face, and head to a log file. To reduce aural cues about the targetposition during adjustment of the target on the optical rail for the next trial, white noise was played inthe participant’s headphones. This sound was also a cue to the participants to return their hand backon the stylus loading dock in preparation for the next trial. The next trial number would then be readand the target distance adjusted, with the participant’s eyes closed and HMD display turned off.

After 30 trials, some participants were asked to repeat particular trials if, for instance, they made averbal report and reach in the wrong order.

3. RESULTS

The slopes and intercepts of the functions predicting indicated target distance from actual target dis-tance for the individual subjects in each condition are presented in Table I. Multiple regression tech-niques were used to determine if the slopes and intercepts differed between the two viewing conditionsand between the two response measures. Multiple regressions are preferable to ANOVAs because theyallow us to predict a continuous dependent variable (indicated target distances) from both a continu-ous independent variable (actual target distances) and a categorical variable (condition) along with theinteraction of these two. With ANOVAs we are restricted to treating all of the independent variables ascategorical. Also, the slopes and intercepts given by regression techniques are more useful than other



Fig. 5. Physical reaches (top) and verbal estimates (bottom) as a function of the actual target distances for IVE and RW viewing.Note there is a significant effect of condition for reaches, though, it is very small.

descriptive statistics such as condition means and signed error because they describe the function thattakes one from the actual target distances to the perceived target distances.

3.1 Comparing RW and IVE

3.1.1 Reaches. Overall, the slopes for the reaches were .77 and .84 for the RW and IVE sessions,respectively. The intercepts were 1.74 and -0.03 (in arm length units), respectively. Figure 5 (top)depicts the relation between actual target distance and the distances reported via reaches for thetwo sessions. Each point in Figure 5 represents the judgments made by an individual subject to agiven target distance. A multiple regression confirmed that the reaches made in the RW session weredifferent from the reaches made in the IVE session. To test for differences between the slopes andintercepts of the two different viewing conditions, this multiple regression was performed using theactual target distances and viewing conditions (coded orthogonally) to predict the reach distances. Themultiple regression was first performed with an actual target distance X condition interaction term,yielding an r2 = .419 (n = 655), with a partial F of 456.98 for actual target distance (p < .0001). Thepartial Fs for both viewing condition and the interaction term were less than 1, with a partial F of



.0005 for condition (p = .994) and .53 (p = .466), respectively, with the partial F for viewing conditionincreasing to 12.62 (p < .0001) after the removal of the interaction term.

Put simply, the partial F for actual target distance assesses the degree to which the actual target dis-tances predict the variation in the responses after variation due to the other terms (viewing conditionand the interaction) having already been accounted for. Thus, the partial F for actual target distancetests for a main effect of actual target distance. The partial F for viewing condition assesses the degreeto which the intercepts for the two sessions differ from each other and thus test for a main effect ofviewing condition. The partial Ffor the interaction term assesses the degree to which the slopes for thetwo conditions differ from each other. Thus, the multiple regression revealed a statistically significantmain effect for actual target distance, as well as a main effect for viewing condition (reaches made inIVE versus reaches made in the RW), but did not reveal an interaction. Therefore, the slopes of thefunctions predicting reached distance from actual distance did not differ for the two viewing condi-tions, while their intercepts did. Overall, the reaches were slightly farther in the RW than in the IVE,but this difference was very small, only 1.8 cm on average. A simple regression predicting the reachesfrom actual target distance resulted in an r2 = .407 (n = 655), indicating that the difference betweenviewing in RW or IVE accounted for only 1.2% of the variances in the reaches. In sum, the reacheswere very similar in IVE and RW.

3.1.2 Verbal Reports. The slopes of the functions predicting indicated target distance from actualtarget distance for the verbal judgments were 1.03 and 1.25 for the RW and IVE viewing conditions,respectively (see Figure 5, bottom). The intercepts were −29.5 and −27.0 (in arm length units), respec-tively. A multiple regression predicting the verbal judgments from actual target distance and sessionwas first performed with an actual target distance X condition interaction term, yielding an r2 = .507(n = 655), with partial Fs of 636 for actual target distance (p < .0001), 1.97 for viewing condition (p =.16), and 6.44 for the interaction term (p = .011), with the partial F for viewing condition increasingto 29.15 (p < .0001) after the removal of the interaction term. This multiple regression confirmed that,unlike reaches, the verbal judgments changed in both slope and intercept as a function of changes inthe viewing conditions. Overall, as the actual distances increased the verbal reports increased at amuch higher rate in the virtual world than in the RW, 1.25 compared to 1.03, respectively. A simpleregression predicting the verbal reports from actual target distance resulted in an r2 = .480 (n = 655),indicating that the difference between viewing in RW or IVE accounted for 2.7% of the variance in theverbal reports. In sum, the verbal reports were different in IVE compared to RW.

3.2 Comparing Reaches and Verbal Reports

3.2.1 RW Viewing. Next we compare the verbal reports to the reaches made within each of thetwo viewing conditions (see top of Figure 6). In the RW the slopes of the functions predicting indi-cated target distance from actual target distance were 1.03 and .77 for the verbal reports and thereaches, respectively. The intercepts were −29.5 and 1.7 (in arm length units), respectively. A multipleregression predicting the judgments from actual target distance and response mode (verbal or reach)was first performed with an actual target distance X condition interaction term, yielding an r2 = .570(n = 655), with partial Fs of 524.73 for actual target distance (p < .0001), 34.05 for viewing condition(p < .0001), and 5.05 for the interaction term (p = .025). This multiple regression confirmed that in theRW the verbal judgments were very different from the reaches that were made within the same trialand which were thus directed at the same target distance. Overall, as the actual distances increasedthe verbal reports increased at a higher rate than the reaches and this was accompanied by a largeintercept difference. This is a very large effect. A simple regression predicting indicated target distancefrom actual target distance resulted in an r2 = .347 (n = 655), indicating that the difference between



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the reaches and the verbal reports accounted for 22.1% of the variance in the responses. In sum, in theRW the verbal reports and the reaches were different.

3.2.2 IVE Viewing. We also compare the verbal reports to the simultaneous reaches made withinIVE (see bottom of Figure 6). The slopes of the functions predicting indicated target distance fromactual target distance were 1.25 and 0.84 for the verbal reports and the reaches, respectively. Theintercepts were −27.0 and −0.3 (in arm length units), respectively. A multiple regression predictingthe judgments from actual target distance and response mode (verbal or reach) was performed withan actual target distance X condition interaction term, yielding an r2 = .503 (n = 655), with partial Fsof 536.11 for actual target distance (p < .0001), 50.23 for viewing condition (p < .0001), and 27.2 forthe interaction term (p < .0001). This multiple regression confirmed that in IVE the verbal judgmentsand reaches were different from each other despite being performed within the same trial. Overall, asthe actual distances increased, the verbal reports increased at a higher rate than the reaches and thiswas accompanied by a large intercept difference. As with RW this is a large effect. A simple regressionpredicting indicated target distance from actual target distance resulted in an r2 = .407 (n = 655),indicating that the difference between the reaches and the verbal reports accounted for 9.6% of thevariance in the responses.

In sum, in both the IVE and the RW the verbal reports were very different from the reaches. Also,the differences between the verbal reports and the reaches made within each viewing condition weremuch greater than the differences between the reaches made in IVE and RW. The verbal reports,



however, were affected by the viewing condition to a greater extent than the reaches. Thus the effectof response mode was much greater than the effect of viewing condition, with the reaches remainingmore consistent between the viewing conditions than the verbal reports.

4. DISCUSSION

We investigated egocentric distance perception for targets in personal space presented in the RW andin an IVE. Within each of these viewing conditions we tested both manual reaches and verbal reportsas two modes for participants to use in indicating perceived distance. Within each experimental trialthe subjects viewed the target and then responded to the single distance with both a verbal reportand a manual reach. In past research verbal estimates have been found less accurate and more vari-able than reaches. Manual responses may be subject to variability associated with motor outputs andthe perception of limb extension. Such variability should be absent in verbal reports. Nonetheless,verbal reports tend to be at least twice as variable as reaching or pointing and thus verbal reportsare less reliable [Foley 1977; Pagano and Bingham 1998; Pagano and Isenhower 2008; Pagano et al.2001; Gogeland and Tietz 1979]. Verbal reports are also less stable, with systematic errors changingdramatically between experimental conditions, between experimental sessions in which conditions areheld constant, and between subjects within a single condition. For example, in experiments by Paganoet al. [2001] the slopes of the functions predicting verbal judgments from actual target distances in-creased when a 6-second delay was imposed between the target presentation and the responses andfurther increased with a delay of 12 seconds. No differences were observed in the concurrent reaches,they remained stable. Mon-Williams and Tresilian [1999] found that perceived size alters verbal re-ports but not pointing behavior. Pagano and Bingham [1998] found that reaches became more accurateafter feedback, decreasing in both systematic and variable error. Verbal judgments changed as well,but they did not become more accurate and remained twice as variable as the reaches. The feed-back seems to have anchored the verbal judgments relative to the nearest target distance. Unlike thereaches, the verbal judgments did not appear to be based on the absolute distance of the target, onlyon the distance relative to the closest targets experienced. Pagano and Isenhower [2008] investigatedthis further by manipulating the subjects’ expectations of the possible target distances. The subjectswere instructed that the targets would be between .25 and .90 of their maximum arm reach in onecondition and between .50 and 1.00 in another, while the targets were actually between .50 and .90 inboth conditions. The verbal judgments were altered by the instructions, matching the expected range,while the reaches were unaffected and remained accurate. Thus while reaches are indicative of ab-solute metric distances, with each reach being based on the actual target distance for a given trial,verbal reports only reflect relative perception and are influenced by the expected range of targets orthe range of distances experienced during an experiment. As a result, it has been suggested that verbalresponses are inappropriate for investigating absolute or egocentric perception [Pagano and Bingham1998; Pagano and Isenhower 2008]. Nonetheless, verbal measures remain a major methodological toolfor the investigation of distance and size perception.

Consistent with previous research [Pagano and Bingham 1998; Pagano et al. 2001; Pagano andIsenhower 2008], we found that verbal reports of egocentric distances differ substantially from con-current reaches. Importantly, we found that this holds true for both the RW and an IVE. For bothviewing conditions the slopes of the functions predicting reported distance from actual distance weremuch greater for the verbal reports than for the reaches, while the intercepts were much lower forthe verbal reports than for the reaches (see Figure 5). Thus for the near targets the verbal reportswere much lower than the reaches while the far targets tended to be greater than the reaches. Overall,the difference in response modality accounted for a large proportion of the variance in the participants’



responses, 9.6% in IVE and 22.1% in the RW. In general the reaches tended to be more accurate andmore consistent.

It remains an open question whether the visual system produces a single internally represented per-ceived depth that is used to generate separate output functions for different response modes[Brunswick 1956; Foley 1977, 1985; Gogel 1993; Philbeck and Loomis 1997], or whether anatomicallydistinct visual systems underlie “cognitive” versus “motor” responses [Bridgeman et al. 1981; Milnerand Goodale 1995, 2008]. Past research has obtained different results when egocentric distance judg-ments were made through verbal reports compared to manual pointing or reaching [Foley 1977, 1985;Gogel 1968; Pagano and Isenhower 2008]. These differences, however, are not enough to demonstratethe presence of two distinct neurological streams. This is because a unitary perceived depth will be sub-jected to different output functions that differentially scale the various responses [Foley 1977; Gogel1993; Philbeck and Loomis 1997]. These output functions are likely to be calibrated separately [Paganoand Bingham 1998; Rieser et al. 1995]. However, if a single internally represented perceived distanceis used to make all responses, then random (i.e., variable) errors in the two response measures shouldbe correlated. If, for example, random error causes a single internally represented perceived distance tooverestimate actual distance on a given trial, then one would expect that both the verbal response andthe reach would be greater for that trial, because they are both generated from that single perceiveddistance. Such errors have been found uncorrelated, suggesting the presence of distinct perceptual pro-cesses for separate response modes [Pagano et al. 2001; Pagano and Bingham 1998]. As in the presentexperiments, the verbal reports were made in arm length units, with 100 corresponding to maximumreach, yet they still remained distinct from the reaches. With this in mind it is important to note thatperceivers attune to different sources of information depending on what types of responses they intendto make [Pagano et al. 2001; Withagen and Michaels 2005]. That is, perception-for-cognition may bea different perceptual process than perception-for-action, relying on different types of optical informa-tion drawn from the environment and relying on a separate calibration process. Thus the perceptualsystem must generate separate perceptions to support the varying responses, whether or not it doesso through anatomically distinct neurological streams. An artificial environment must support eachof the likely responses that will be executed within it, and it must do so by supporting each of the“perceptions” underlying the responses and each of the calibration processes.

The results of the present experiment also indicate that egocentric distance perception differed be-tween the RW and the IVE. This difference, however, was more pronounced in the verbal reports thanin the manual reaches, underscoring the instability of verbal reports. For the reaches, the participantstended to underestimate the target distances, with this underestimation increasing as target distanceincreased. The slopes of the functions predicting the reaches from the actual target distances were .84and .77 for IVE and RW, respectively. For the verbal reports the difference between the two viewingconditions was much more pronounced, with the slopes of the functions being 1.25 and 1.03 for IVEand RW, respectively. While the verbal reports underestimated all of the target distances in RW and inthe IVE, these slopes were such that the underestimation became progressively smaller as the targetsbecame farther away. In sum, compared to RW, viewing the IVE had a small effect on manual reachesto egocentric distances in personal space while IVE viewing had a larger effect on concurrent verbaljudgments. Also, the differences between RW and IVE viewing were much smaller than the differencesbetween the two response modes made within each of the viewing conditions.

Compared to related work investigating egocentric depth perception in action space, our work is oneof the first studies investigating egocentric distance estimation in IVEs in the near field. Most workconducted in action space utilizes blind walking and imagined timed-walking estimates. A novel aspectof our study is the usage of both verbal and physical responses in an IVE. As mentioned earlier, wefound that in personal space, participants in the IVE estimate significantly different as compared to



participants in the RW. Although the difference for reach judgments is small, our results contradictwith significantly large distance compression perceived by participants in the IVE as compared to RWin action spaces found by other researchers [Grechkin et al. 2010; Ziemer et al. 2009; Klein et al. 2009;Richardson and Waller 2007; Interrante et al. 2006]. We believe one possible reason for this differencein our result in personal space, as compared to research findings in action space, could be explained bythe dominance of stereoscopic visual cues in depth perception in the near field. This difference couldfurther be explained by the speed of execution for our task versus blind-walking and imagined timed-walking. Blind-walking and imagined timed-talking techniques take time to accomplish. It could beargued that because of this delay, it is not necessarily measuring a visual perceptual response, butrather a cognitive response that continually calibrates during the action. One might then ask why ourresults do not match those of Sahm et al. [2005] where beanbag throwing, an immediate and directtask, was utilized. Throwing, however, could possibly be influenced by a cognitive response as well.While blind-walking potentially involves recalibrating one’s perceptual judgment during the action,calibration in throwing could occur from simply imagining the trajectory of the beanbag being thrownthrough the air [Sahm et al. 2005]. This would also explain the similar compression found betweenblind-walking and beanbag throwing [Sahm et al. 2005].

5. CONCLUSION AND FUTURE WORK

In this research, we have successfully compared near-space physical reaches and verbal responsesof participants to targets in IVE and RW environments by using an apparatus similar to that usedin our previous work [Bingham and Pagano 1998; Pagano and Bingham 1998; Pagano et al. 2001;Pagano and Isenhower 2008]. Participants’ physical reaches and verbal responses in both the RW andIVE conditions showed a significant difference of distance perception when compared to the actualdistance. We found that distance underestimation of verbal as well as physical reach responses totargets increased with distance in both the IVE and RW conditions. Furthermore, we found that verbalreports of distance judgments were less accurate as evidenced by a steep slope in the IVE conditionand a low intercept in both conditions in the regression analysis, even though the verbal reports weremade simultaneously with physical reaches.

The impact of our current and ongoing work for VR application developers and consumers could besubstantial. For instance, designers of complex systems such as surgery simulators could potentiallyenhance user performance by automatically accounting for observed systematic underestimation innear-space distance perception.

A limitation of our study is that we have only investigated verbal reports and physical reach re-sponses to near-field distance judgments in IVEs that employ HMDs. It is unclear what effect otherdisplay methods in IVEs, such as LSIDs, have on perceptual judgments of near-field distances viaphysical and verbal responses.

Future work will examine the effect of feedback on distance estimates. In the experiments byBingham and Pagano [1998; Pagano and Bingham 1998] the participants reached to place a hand-held stylus into the target and thus received feedback about the accuracy of their reaches. It was foundthat this feedback resulted in calibration that improved the accuracy of their reaches while it had amore limited effect on the accuracy of the verbal reports. The calibration, for example, reduced errorthat was due to a restricted FOV. We will test if calibration has a similar effect in the IVE and RW envi-ronment employed in the present experiment. We will also systematically vary the size of FOV in bothviewing conditions to determine if the perturbation caused by a restricted FOV has the same effect ondistance estimates in personal space for both IVE and RW. We may also investigate the importance ofstereoscopic vision and motion parallax in near-field distance estimation.



We found that the differences between RW and IVE viewing were much smaller than reaches andverbal reports made within each of the viewing conditions. This finding is surprising, given the factthat the two responses were executed together within each trial while the two viewing conditions wereperformed as separate blocks of trials and on separate days. The verbal judgments were also madein arm length units, which should have fostered their similarity with the reaches. It seems that dif-ferences within each individual can be much greater then differences between environments. A greatdeal of research is devoted to understanding the effect that different viewing environments have onperception and action. In reality it may be that differences between response modes have a larger ef-fect on “perception” than differences between environments. Perception-for-cognition is different fromperception-for-action, with the perceptual systems likely attuning to different information when gen-erating separate responses. What this means for designers of VR, IVE, AR, and other artificial envi-ronments is that the purpose for which perception will take place within a given environment must beunderstood in order to ensure that the correct information is being rendered, the environments mustbe tested using the appropriate response measure, and users may need to be trained to both attuneto and calibrate to the appropriate information. The present experiment demonstrates that the re-sults obtained by testing will vary dramatically depending on the response measure employed. Futurework should focus on the processes of attunement and calibration in artificial environments, whilerecognizing that these are two separate processes [Withagen and Michaels 2005].

ACKNOWLEDGMENTS

We wish to thank J. Edward Swan for his insights and discussions regarding the experiment design,and the participants of our experiment for their time. We also acknowledge the contributions of JuliaNelson-Abbott and Adina-Raluca Stoica for assistance with 3D modeling and simulation.

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Received April 2011; accepted July 2011


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