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Training in virtualenvironments: transferto real world tasks andequivalence to real tasktrainingF. D. Rose , E. A. Attree , B. M. Brooks , D. M.Parslow & P. R. PennPublished online: 10 Nov 2010.

To cite this article: F. D. Rose , E. A. Attree , B. M. Brooks , D. M. Parslow& P. R. Penn (2000) Training in virtual environments: transfer to real worldtasks and equivalence to real task training, Ergonomics, 43:4, 494-511, DOI:10.1080/001401300184378

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Training in virtual environments: transfer to real world tasks

and equivalence to real task training

F. D. ROSE*, E. A. ATTREE, B. M. BROOKS, D. M. PARSLOW , P. R. PENN

and N. AMBIHAIPAHAN

Department of Psychology, University of East London, Romford Road, LondonE15 4LZ, UK

Keywords: Virtual reality; Transfer; Training.

Virtual environments (VEs) are extensively used in training but there have been

few rigorous scienti® c investigations of whether and how skills learned in a VE aretransferred to the real world. This research aimed to measure and evaluate what is

transferring from training a simple sensorimotor task in a VE to real worldperformance. In experiment 1, real world performances after virtual training, real

training and no training were compared. Virtual and real training resulted in

equivalent levels of post-training performance, both of which signi® cantlyexceeded task performance without training. Experiments 2 and 3 investigatedwhether virtual and real trained real world performances diŒered in their

susceptibility to cognitive and motor interfering tasks (experiment 2) and in terms

of spare attentional capacity to respond to stimuli and instructions which werenot directly related to the task (experiment 3). The only signi® cant diŒerence

found was that real task performance after training in a VE was less aŒected byconcurrently performed interference tasks than was real task performance after

training on the real task. This ® nding is discussed in terms of the cognitive loadcharacteristics of virtual training. Virtual training therefore resulted in equivalent

or even better real world performance than real training in this simple sensori-motor task, but this ® nding may not apply to other training tasks. Future

research should be directed towards establishing a comprehensive knowledge ofwhat is being transferred to real world performance in other tasks currently being

trained in VEs and investigating the equivalence of virtual and real trainedperformances in these situations.

1. Introduction

Once described as a technology for which the `excitement to accomplishment ratio

remains high’ (Durlach and Mavor 1995), virtual reality (VR ) is now rapidly

outgrowing its computer games image and ® nding applications in a variety of

contexts and in ® elds as diverse as engineering, design, architecture, medicine and

education.

One area of application attracting an increasing amount of interest is training.

Virtual environments (VEs ) embody many of the characteristics of an ideal training

medium (Psotka 1995, Schroeder 1995, Rose 1996, Rizzo et al. 1998a, b ). VEs can be

a valuable training aid where training in real life situations would be impractical

because, for example, it would be dangerous, logistically di� cult, unduly expensive

or too di� cult to control. The use of VEs allows the trainer total control of both the

stimulus situation and the nature and pattern of feedback, and also allows

*Author for correspondence. e-mail: F.D.ROSE@ UEL.AC.UK

ERGONOMICS, 2000, VOL . 43, NO . 4, 494± 511

Ergonomics ISSN 0014-0139 print/ISSN 1366-5847 online Ó 2000 Taylor & Francis Ltdhttp://www.tandf.co.uk/journals/tf/00140139.html

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comprehensive monitoring of performance. Sometimes, by combining an educa-

tional simulation and a computer game format, the use of VEs may also engender

increased levels of motivation (Schroeder 1995 ).

VEs are already being developed for the training of pilots (Lintern et al. 1990a,

b ), drivers (Mahoney 1997 ), divers (FroÈ ehlich 1997 ), parachutists (Hue et al. 1997 ),

® re-® ghters (Bliss et al. 1997 ), console operators (Regian et al. 1992 ) and surgeons

and other medical staΠ(Satava 1995 ). They have also been used to train naval

o� cers in ship manoeuvres (Magee 1993, 1997 ), soldiers in battle ® eld simulations

(Goldberg 1994, Goldberg and Knerr 1997 ), and the Hubble Space Telescope

ground control team to familiarize themselves with the operability of the telescope’ s

component parts (Loftin and Kenny 1995, Loftin et al. 1997 ).

An especially active area of development in recent years has been in the use of

VEs for training within therapy and rehabilitation. Examples include the use of VEs

in desensitization training for patients with phobias (North et al. 1997, 1998,

Bullinger et al. 1998 ), in treating children with autism (Strickland 1997 ), in training

people with learning di� culties (Mowafy and Pollack 1995, Cromby et al. 1996,

Brown et al. 1998 ) or physical di� culties (Wilson et al. 1996, Stanton et al. 1998 ) and

for rehabilitation of patients with brain damage (Pugnetti et al. 1995, Rizzo and

Buckwalter 1997, Wann et al. 1997, Christiansen et al. 1998, Davies et al. 1998,

Pugnetti et al. 1998, Brooks et al. 1999a , b, Rose et al. 1999a, b ). In these instances,

an additional and vital advantage of using a VEs is that interaction with the

environment can be made contingent on the response repertoire of the patient.

Consequently, people whose motor disabilities restrict their active interaction with

real life environments can still interact with virtual training environments. Similarly

a VE can be structured to oŒset partial sensory loss in the user.

Generally it has been assumed that training in VEs will transfer to subsequent

real world performance. In those studies where the matter has been the subject of

empirical investigation the evidence seems to support that assumption. Certainly, in

the training of spatial skills positive transfer from virtual to real environments has

been reported almost without exception (Regian et al. 1992, Arthur et al. 1997,

Regian 1997, Waller et al. 1998, Brooks et al. 1999a, b ). In the case of procedural

learning, early studies suggested that transfer from virtual to real environments

might not occur. Kozak et al.’ s (1993 ) study is much quoted in this regard. However,

this ® nding has been questioned on methodological grounds (Durlach and M avor

1995, Psotka 1995) and disputed by follow up investigations (Kenyon and Afenya

1995 ), although this latter study used a projection based VR system rather than a

head-mounted display (HM D ). More recent studies (e.g. Regian 1997, Brooks et al.

1999a, b ) have found clear evidence of positive transfer of procedural learning from

virtual to real environments.

The general conclusion that training in a VE is bene® cial merits further scrutiny,

however. There have been relatively few studies in which transfer of training from

virtual to real environments has been rigorously examined and studies on which

conclusions of positive transfer have been based are very varied in terms of training

task requirements and the measures of transfer employed. Experience in a VE may

bene® t subsequent performance in a variety of diŒerent ways. For example, bene® t

could emanate simply from the VE aŒording the participant a general familiarity

with the associated real world situation. Alternatively, it could be due to the salience

of particular cues being increased, speci® c sequences of actions being rehearsed or, as

we have found, spatial memories being laid down procedurally. Even where a clear

495Training in virtual environments

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transfer of training occurs, it is probable that the overall bene® cial eŒect of training

in a VE will mask a mixture of more speci® c eŒects, some of which will facilitate

correct real world performance (positive transfer ) and some of which will hinder it

(negative transfer ). For example, training children to cross the road safely in a VE

may build up many valuable skills (looking, checking, timing, etc ). However, it is

di� cult to train road crossing in a VE without children also receiving the unwanted

message that errors in crossing roads, at least virtual ones, do not actually cause

injury.

If the use of VEs in training is to be properly evaluated it is important that

these complexities of transfer of training be addressed. Despite this being one of

the recommendations of the in¯ uential American National Research Council’ s

Committee on Virtual Reality Research and Development (Durlach and Mavor

1995 ), progress has been disappointingly slow. Many of the questions that need

to be addressed can be accommodated under the rubric of transfer of training

research, which has formed an important strand within mainstream experimental

psychology for many years (Fleishman 1987 ). Scienti® c interest in the extent of

transfer of training and the conditions which facilitate transfer from one situation

to another dates back to the doctrine of `formal discipline’ expounded in the

eighteenth century (Patrick 1992 ). Since that time, various theoretical interpreta-

tions of the transfer process have been proposed. For many years, explanations of

transfer based upon the notion of `shared elements’ (Thorndike and Woodworth

1901 ) were dominant. Later, more cognitive interpretations of transfer (Newall

1980 ) were proposed. In recent years, more emphasis has been placed on seeking

to combine the best of both viewpoints (e.g. Parente and Herrmann 1996 ).

Importantly, from our present point of view, a comprehensive methodology for

investigating transfer of training has been worked out against the background of

this theoretical debate.

The present authors believe that the development of VEs as an eŒective training

medium would be greatly facilitated by using this methodology to establish exactly

what it is that is being transferred from the virtual to the real environment. Clearly, it

is important to know exactly what is transferring and the extent of that transfer.

However, it is also important, when transfer of training from virtual to real appears

to have occurred, to investigate whether performance of the real world task after

training in a VE is equivalent to performance after a similar amount of training on

the real world task itself. It is this question which forms the point of the departure

for the studies described here.

The main focus of interest in this investigation is the equivalence of real world

performance derived from virtual and real training regimes. To highlight any eŒects

due to training in a VE per se, we intentionally selected an experimental training

situation, a simple steadiness tester, which allowed us to equate the sensory and

motor aspects of the virtual and real training situations as far as possible, i.e. to

maximize the ® delity of the virtual training situation (Durlach and Mavor 1995:

419 ).

Our hypothesis is that, even if virtual and real training at ® rst appear to produce

equivalent real world performances, these may well diŒer if scrutinized more closely.

Experiment 1 investigates whether there is evidence of transfer of training from

virtual to real versions of the steadiness tester task. Experiment 2 compares the real

task performances (produced by real and virtual training ) in terms of their

susceptibility to interference eŒects. Experiment 3 investigates whether there are

496 F. D. Rose et al.

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diŒerent attentional resources available during performance of the post-training trial

after real and virtual training.

2. Methods

This section contains an overview of the experimental task employed, associated

hardware and software, the performance measures employed and details of the

participants who volunteered to take part in these experiments.

2.1. Participants

Participants in these experiments were 250 university staŒand students (160 women,

90 men, mean age 30.1 years, SD 5.8 ). All were unpaid volunteers.

2.2. Experimental tasks

A diagram of the real-world version of the steadiness tester is shown in ® gure 1.

It consisted of a curved wire (450 mm long, 2 mm in diameter ) suspended

between two 140-mm-high vertical supports. Encircling the wire was an 80 mm

diameter metal ring attached to a 220 mm long metal rod. At the other end of

the rod was a handle that was shaped like the three-dimensional (3D ) mouse used

in the VE. A translucent Perspex screen was positioned behind the steadiness

tester. The participant was required to hold the handle in her/his non-preferred

hand and move the ring along the wire from one vertical support to the other

and back again trying to avoid the ring touching the wire. This constituted one

trial. If the metal ring touched the wire, the background screen lit up, signalling

to the participant that an error had been made. The contact was automatically

recorded as an error.

The virtual version of the task was created using dVISE, and was run via a

HP715 workstation, using dVS. The VE was displayed via an immersive 3D

stereo dVISORT M

HMD (resolution 2 ´ 345 ´ 259 pixels, horizontal ® eld of view

105 8 , vertical ® eld of view 41 8 ). Division Ltd (UK ) supplied the software and

Figure 1. Schematic representation of the steadiness tester.

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hardware. In the virtual version of the task, the participant viewed a computer

generated 3D simulation of the wire, its supports, and the metal ring, rod and

handle via an HMD. Participants moved the virtual ring along the virtual wire

using a 3D mouse. Errors (contacts between the virtual ring and virtual wire )

caused a change in the background illumination of this computer generated VE,

which corresponded with the illumination of the translucent screen in the real

version of the task.

2.3. General experimental design

The present investigation consisted of a sequence of three experiments. The ® rst was

concerned with measuring the extent of transfer of training from virtual to real

environments. The remaining experiments were intended to compare the character-

istics of real world performances based on training in virtual and real environments.

The research was carried out at the University of East London and with the approval

of the University’ s Ethics Committee.

2.4. Performance measures and data analyses

The measure of learning was de ® ned by the number of errors each participant made

on a post-training (test ) trial on the real steadiness tester. Data were analysed using

analysis of covariance (Dugard and Todman 1995, Dancey and Reidy 1999 ), or

t-tests as appropriate.

3. Experiment 1

The objective of experiment 1 was to examine the extent of transfer of training from

the virtual to the real steadiness tester tasks. Operationally this involved comparing

virtual training, real training and no training in terms of their eŒects on post-training

performance on the real task.

3.1. Participants

Participants were 210 university staŒand students (mean age 35.5 years, SD 5 ), 126

women and 84 men. All were unpaid volunteers recruited through poster

announcements.

3.2. Procedure

Participants were randomly allocated to three equal sized groups and tested

individually. The three groups were all tested on the real version of the steadiness

tester before and after training but diŒered in terms of the type of training given in

between. For group 1, training comprised eight trials on the real task. Each trial

consisted of using the non-preferred hand to move the ring along the curved wire

from left to right and back again. Participants were instructed to execute this action

as carefully as possible, trying not to allow the metal ring to touch the wire. Between

trials participants had rest periods of 1 min. For group 2, training comprised eight

trials on the virtual version of the task. Instructions and procedure were the same as

for group 1. Participants in group 3 (no training control condition ) spent 15 min

between pre- and post-training trials carrying out an unrelated task (navigating

through a VE viewed on a computer monitor ) so as to avoid them mentally

practising the task. This period was based upon pilot data which showed that 15 min

was the average time taken by participants to complete the eight training trials on the

virtual and real tasks.

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3.3. Results

Observed means and SD for the pre- and post-training test conditions are shown in

table 1. The error scores for the post-training trial were adjusted to take baseline

performance into account by partialling out the pretraining trial error scores.

Adjusted mean error scores for the real training, virtual training and no training

groups are shown in ® gure 2:

Figure 2 indicates that participants in the no training group made more errors in

the post-training trial than participants in the real and virtual training groups.

Participants in the real and virtual training groups made a similar number of errors.

Figure 2. Adjusted group mean error scores (after baseline error scores were partialled out)

for real (RW ), virtual (VR ) and no training (NT) groups.

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A one-way, between-participants, analysis of covariance, using error scores on the

pretraining (baseline ) trial as the covariate, con® rmed this interpretation of the

results. There was a highly signi® cant diŒerence between the groups in terms of the

numbers of errors made on the post-training test trial (F[2,206] = 23.44, p= 0.001 ).

Planned comparisons showed that signi® cant diŒerences existed between the real

task training group and the no training group (p < 0.00001 ) and between the virtual

task training group and the no training group (p < 0.0000 1). There was no signi® cant

diŒerence between the real task and virtual task training groups (p= 0.22 ).

3.4. Discussion

The results of this ® rst experiment showed that training on both the real and the

virtual steadiness tester tasks was eŒective and resulted in signi® cantly better

performance than no training. More importantly, from the point of view of the

present investigation, the results demonstrated that training on the virtual task did

transfer to improved performance on the real task. Moreover, training on the virtual

task was as eŒective in facilitating real task performance as training on the real task

itself.

4. Experiment 2

One must not assume that the ® nding of equal transfer to the real task from virtual

and real training found in experiment 1 indicates exact equivalence between what is

learned in virtual and real task training. Despite this transfer, it is possible that the

performance based on virtual training is in some way less robust than that based on

training on the real task. That performance may, for example, be less durable or

require more cognitive capacity to execute. It may, of course, be superior to

performance based on real task training. The remaining two experiments in this

series are concerned with examining the equivalence of real task performances based

upon virtual and real training, particularly with regard to cognitive load

considerations.

If the post-training performances on the real steadiness tester of virtual and real

trained participants do diŒer in terms of their associated cognitive loads, one might

predict that they would be diŒerentially in¯ uenced by the introduction of concurrent

tasks. In this experiment the interfering eŒects of both a concurrent motor task

(tapping a Morse key ) and a concurrent cognitive task (identifying names of fruits

from recorded word strings ) were investigated.

Three of the main factors found to in¯ uence interference between concurrent

tasks are task similarity, practice and task di� culty (Eysenck and Keane 1995 ). W ith

regard to task similarity, W ickens (1984 ) concluded that the extent of interference

between two tasks is dependent on whether they share the same stimulus modality

Table 1. Observed mean errors (SD ) on the pre- and post-training steadiness tester trials.

RW VR NT

Pre-Post-

57.39 (26.76)

34.84 (22.70)55.44 (32.80)

36.36 (21.90)50.64 (25.93)

45.03 (22.31)

500 F. D. Rose et al.

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(visual or auditory ); whether they utilize the same processing stages (input, internal

processing and output ); and whether they rely on related memory codes (verbal or

visual ). In the present experiment, the motor task might be expected to impair

steadiness tester performance to a greater extent than the cognitive task as the motor

and steadiness tester tasks appear to have more processing stages in common.

The bene® cial eŒects of practice on concurrent task performance were found

when one of the tasks was well practised before the study (Allport et al. 1972, ShaŒer

1975 ) or when both tasks were practised together (Spelke et al. 1976 ). The most

commonly held view is that task performance becomes more automatic with practice

and therefore requires less attention (e.g. ShiŒrin and Schneider 1977). In the present

experiment, if real and virtual practice produce similar levels of automaticity in

subsequent steadiness tester performance, the interference tasks would not be

predicted to diŒerentiate between performances after either type of practice. But if

real and virtual practice result in diŒerent levels of automaticity in steadiness tester

performance, the interference tasks might negatively aŒect performance in the less

automatically performed steadiness tester task.

Research has shown that performance on concurrent tasks is impaired when the

di� culty of the tasks is increased (e.g. Sullivan 1976 ). However, a distinction made

by Norman and Bobrow (1975 ) between task performance that is resource-limited

and task performance that is data-limited may determine whether task di� culty

aŒects task performance. According to this view, performance that is resource-

limited is dependent on the available processing resources that can be devoted to the

task whereas performance that is data-limited is not aŒected by available processing

resources because external in¯ uences such as stimulus quality determine how well the

task is performed. This view assumes that there is a central capacity of limited

processing resources; that only resource-limited performance is susceptible to

interference; and that concurrent tasks interfere with each other if their combined

processing resources exceed the upper limit of available resources. In the present

experiment, performance of the steadiness tester task is likely to be resource-limited

as the task was not practised to such an extent that performances would become

data-limited.

The view that concurrent task performance relies on a central capacity of limited

processing resources that are deployed across a wide range of activities is not

universally accepted, of course. For example, the multiple-resource theory (Navon

and Gopher 1979) proposed that diŒerent processing mechanisms or modules handle

the requirements of diŒerent tasks. Others have attempted to synthesize the central

capacity and multiple-resource accounts of concurrent task performance (e.g.

Norman and Shallice 1980, Baddeley 1986 ).

Given this plethora of theoretical accounts one would suppose that it would be

di� cult to interpret the results of any study of concurrent task performance.

However, this concern is not so relevant to the present study. The important

consideration here is not simply to investigate whether motor or cognitive tasks

interfere with steadiness tester performance but to investigate whether steadiness

tester performances after real or virtual training show any diŒerential susceptibility

to these interfering tasks.

4.1. Participants

Participants were 120 university staŒand students (mean age 28.9 years, SD 5.2, 70

women and 50 men ). These participants had all taken part in experiment 1 and were

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randomly sub-sampled, in equal numbers, from the real task and virtual task

training groups from that experiment.

4.2. Procedure

Participants were randomly allocated, in equal numbers, to two subgroupsÐ a motor

interference subgroup and a cognitive interference subgroup. All participants were

required to carry out one additional steadiness tester trial following the post-training

trial in experiment 1. In addition to performing this trial, participants in the motor

interference subgroup were required to tap on a Morse code key with the middle

® nger of the preferred hand in time with a pre-recorded tempo of two beats per

second; participants in the cognitive interference subgroup were asked to listen for

the names of fruits interspersed within a string of pre-recorded words presented at 3-

s intervals and say `yes’ when they occurred. Both these concurrent tasks lasted for

the duration of the steadiness tester trial. Errors on the steadiness tester were

recorded as described before.

4.3. Results

Observed mean errors and SD on the post-training steadiness tester trial in

experiment 1 and during concurrent task performance are shown in table 2. Error

scores for the concurrent task steadiness tester trial were adjusted to take baseline

performance into account by partialling out the post-training trial error scores.

Adjusted mean error scores for the real and virtual training groups are shown in

® gure 3, which indicates that motor interference generally had a more detrimental

eŒect than cognitive interference on post-training performance on the real steadiness

tester. However, real task trained participants appear to be more impaired by motor

interference than virtual trained participants. A 2 ´ 2, between participants, analysis

of covariance, using the new baseline scores as the covariate, veri® ed that motor

interference had a signi® cantly greater eŒect on performance of the real steadiness

tester task than did cognitive interference [F(1,115 ) = 6.77, p = 0.01]. Participants

who had previously undergone virtual training were less impaired by the interference

tasks than those who had undergone real training [F(1,115 ) = 3.81, p = 0.05] but

there was no signi® cant interaction between previous training condition (virtual or

real ) and type of interference (motor or cognitive ) [F(1,115 ) = 0.45, p = 0.5].

4.4. Discussion

As expected, the motor task had a more disruptive eŒect than the cognitive task on

both real and virtual trained performance on the real steadiness tester. This result is

not surprising since the steadiness tester task has such a clear sensorimotor bias.

Table 2. Observed mean errors (SD ) on the real steadiness tester before and during the

concurrent task trial.

RW VR

Motor Cognitive Motor Cognitive

Pre- (post-training

trial experiment 1)

Post- (concurrent

task trial)

35.50 (19.48)

47.97 (28.13)

33.00 (20.00)

36.90 (21.10)

41.07 (16.73)

46.20 (17.87)

34.30 (16.96)

34.70 (21.37)

502 F. D. Rose et al.

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With a task involving a more substantial cognitive component, the situation may

prove to be diŒerent.

The counterintuitive result was the small but statistically signi® cant ® nding that

virtual task trained performance was less in¯ uenced by the introduction of

interfering tasks than real task trained performance. A possible explanation for

this ® nding is that virtual training is more e� cient than real training. If so,

subsequent steadiness tester performance might become more automatic after virtual

Figure 3. Adjusted group mean error scores (after baseline error scores were partialled out)

for real (RW ) and virtual (VR ) training groups when carrying out either a motor orcognitive concurrent task.

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training. A more automatically performed task requires less attention and is

therefore easier and less susceptible to disruption from interfering tasks.

5. Experiment 3

If virtual training results in more automatic performance on the real steadiness tester

task than real training, it follows that real task performance after virtual training

would require less attention than after real training. In the present experiment, this

possibility was investigated further by measuring real task and virtual task trained

participants’ attention to stimuli and instructions, which were not directly relevant to

real task performance, while they performed the real task. If real task performance

after virtual training does require less attention than real task performance after real

training, it is hypothesized that virtual trained participants will pay more attention to

irrelevant stimuli and instructions than real trained participants because they will

have more spare attentional resources available.

5.1. Participants

Participants were 40 university students (34 women, 6 men, mean age 24.7 years, SD

4.7 ). None of the participants had taken part in experiments 1 and 2, and none was

known to have gross colour vision problems or hearing impairments.

5.2. Additional experimental stimuli

5.2.1. Visual stimuli: Five colours (red, green, yellow, blue, purple ) were displayed

in a random order, at 2-s intervals, on a 14-inch SVGA computer monitor. The

colours were displayed as though they were part of a screen saver, each colour taking

up the whole screen. The monitor was situated ~ 60 cm to the left of the steadiness

tester, within participants’ peripheral vision (i.e. at the 45 8 point ).

5.2.2. Auditory stimuli: Three auditory tones were selected to represent the type of

sounds often heard emanating from computers, recorded onto an audiotape and

randomly presented at 2-s intervals (volume 56 dB ). The noises used were `wav’ ® les

taken from Superscape v5.6 (computer beep, low beep and dial tone ). The

audiocassette player was placed next to a PC, ~ 1.5 m behind the participant.

5.3. Procedure

Participants were randomly allocated to one of two equal sized groups, with the

proviso that there were equal numbers of left-handed participants in each group. As

in experiment 1, both groups were tested on the real steadiness tester task before and

after training but diŒered in terms of the type of training given in between, i.e. eight

trials of either real task training or virtual task training.

An event-based prospective memory task was also used in this experiment.

During the preliminary experimental instructions, participants were asked to

remember to sign a form and rate the e� cacy of the training procedure after they

had completed their training trials (real or virtual ) but before beginning their

post-training trial. Following the training trials, all participants had a 3-min

interval. This time gap was given ostensibly to allow them to rest but was

actually to provide them with the opportunity to remember the prospective

memory task.

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Participants then underwent the post-training test trial. During this trial, the

visual and auditory stimuli described above were presented. The presentation of the

stimuli was started and stopped remotely.

Upon completion of the post-training trial, participants were given a recognition

memory test of the colours that had appeared on the PC screen while they had been

performing the trial. In the test the names of the ® ve target colours were interspersed

with ® ve distractersÐ pink, black, cream, grey, brownÐ and participants were

required to tick the colours which they could remember had appeared on the screen.

They were also asked to estimate the number of diŒerent tones (not the total number

of tones presented ) that they had heard while performing the trial.

5.4. Results

Observed mean errors and SD on the pre- and post-training steadiness tester trials

are shown in table 3. Using the pretraining baseline error scores as the covariate, the

adjusted group mean error performance scores on the post-training trial on the real

task showed no diŒerences between the real task and the virtual task trained

participants (35.6 and 38.7 respectively ). A one-way ANCOVA was statistically non-

signi® cant, [F(2,37 ) = 0.33, p = 0.57]. As expected, therefore, the data demon-

strated equivalent real task performance after real and virtual training.

For the prospective memory task the groups were also very similarÐ 20% of the

real task trained participants remembered to sign the sheet of paper compared with

15% of the virtual task trained participants. An additional 5% of the real task

trained participants and 30% of the virtual task trained participants remembered to

sign the sheet after the experiment was completed, which was not the actual task

required.

Colours incorrectly recognized were subtracted from target colours correctly

recognized (Baddeley 1997 ). The mean number of colours recognized in the real

training condition was 2.15 (SD 1.04 ), compared with 2.00 (1.21 ) in the virtual

training condition, indicating that the two groups recognized a similar number of

colours. An independent t-test veri ® ed that there was no signi® cant diŒerence

between the groups, [t(38 )= 0.42, p = 0.68].

The mean number of tones remembered in the real training condition was 2.40

(SD 0.9 ) compared with 2.00 (0.6 ) in the virtual training condition, which indicated

no apparent diŒerence between the two training groups. This was con® rmed by an

independent t-test which showed no signi® cant diŒerence between the two

conditions, [t(38 ) = 1.63, p = 0.11].

5.5. Discussion

The results of this experiment showed that participants in the two groups did not

appear to diŒer in terms of their ability to remember to perform the prospective

memory task after their training. Neither did they diŒer in their attention to

incidental stimuli during the post-training test trial. In suggesting that the virtual and

Table 3. Observed mean errors (SD ) on the real steadiness tester on the baseline and the

`attention’ trials

RW VR

Pre-

Post-

58.60 (24.71)

38.45 (24.30)50.10 (19.13)

35.75 (20.39)

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real trained groups did not diŒer in terms of their spare cognitive capacity during the

post-training test on the real steadiness tester, this result may appear to be at odds

with our proposed explanation of the result of the second experiment. This issue is

addressed below.

6. General discussion

This series of experiments compared post-training performance on a real

steadiness tester task after training on the real task and training on a virtual

version of the task. In experiment 1, virtual and real training resulted in

equivalent levels of post-training performance, both of which exceeded task

performance without training. The ® nding that a skill acquired in a VE transfers

to improved real task performance has been tacitly accepted for many years

although few studies have tested this assumption empirically and with an

appropriate degree of scienti® c control. The present ® nding provides ® rm

scienti® c evidence of transfer from training in a VE to real world performance.

The two remaining experiments investigated the equivalence of the real task

performances produced by virtual and real training methods. In experiment 2, a

concurrent motor interference task was expectedly found to impair the post-

training performance of both virtual and real world trained participants more

than a concurrent cognitive interference task. However, less expected was the

® nding that the interference tasks had a more detrimental eŒect on participants

who had been trained on the real task than on participants who had been trained

on the virtual task. In contrast, experiment 3 failed to ® nd any signi® cant

diŒerence between participants trained on the virtual task and those trained on

the real task on an incidental attention task performed during subsequent real

world task performance. Neither was there any apparent diŒerence between the

two groups of participants in a prospective memory task performed after training.

The results of these comparisons of real task performance after virtual and real

training, while generally supportive of those who seek to exploit VR’ s potential in

training (Seidel and Chatelier 1997 ), do raise some interesting questions both about

what transfers from virtual to real environments and the nature of the performances

which result from virtual and real training.

For example, the apparently equivalent levels of transfer to real world

performance from virtual and real training are of interest. Thorndike and

Woodworth (1901 ) proposed that transfer of training is dependent on the number

of sensory and motor characteristics that training and transfer tasks have in

common. Alternatively, Newall (1980; also Singley and Anderson 1987 ) proposed

that the similarity of cognitive processing demands between tasks is the determining

factor, whereas Parente and Anderson-Parente (1990 ) and Parente and DiCesare

(1991 ) proposed that training establishes associations between physical aspects of the

task and cognitive organizations learned during task performance resulting in a

learned cognitive response to the task. Notwithstanding that the steadiness tester

task was speci® cally chosen because it could be realistically represented in a VE, and

because the sensory and motor characteristics of the virtual and real versions of the

task could be equated as far as possible, all of these theoretical explanations would

predict that transfer from real task training to real task performance would be

greater than transfer from virtual task training to real task performance. That we

obtained such a high level of transfer from virtual to real merits further investigation,

therefore. Certainly it is important not to over-generalize from this ® nding. Transfer

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from virtual training tasks that have much lower levels of ® delity in terms of their

real world equivalents would be likely to be signi® cantly lower.

A second point of interest is that real task performance after virtual training was

less aŒected by concurrently performed interference tasks than was real task

performance after real task training (experiment 2 ). According to Eysenck and

Keane (1995 ), the three main factors that in¯ uence concurrent task performance are

task similarity, practice and task di� culty. An interpretation of our ® ndings has

already been advanced, couched in terms of task di� culty, that virtual practice may

result in more automatic (i.e. less di� cult or less taxing) real world performance,

thus freeing up cognitive capacity to deal with interfering tasks. There is an

alternative interpretation of our ® ndings which still relates to the task di� culty

dimension. Since, with current VR technology, the sensory and motor characteristics

of real training inevitably diŒer from those in virtual training, it is likely that

cognitive processing in the two types of training would also diŒer. In the present

study, for example, we might hypothesize that the mismatch of visual feedback and

vestibular and proprioceptive feedback, which is characteristic of operating within

VEs, makes the virtual steadiness tester task more di� cult than its real world

counterpart. In other words, the virtual task may require greater cognitive capacity

than the real one. Virtually trained participants, in moving to the simpler real world

task, may therefore have surplus cognitive capacity to cope with the interference

task. Such an interpretation would be reminiscent of Helson’ s Adaptation Level

Theory (1964 ).

Although either of these hypotheses would explain the virtual trained

performance on the real world task being less in¯ uenced by interfering tasks in

experiment 2, they do not readily explain the lack of diŒerence between virtual and

real trained performances on the prospective memory and attentional tasks in

experiment 3. The contradiction between the results of the present second and third

experiments may be more imagined than real, however. Although virtual trained real

world performance may be less taxing than real world trained performance, it is

conceivable that both need a su� ciently high level of cognitive resources to prevent

attention being paid to stimuli or instructions which are not directly related to the

task. Such an interpretation would explain why group diŒerences were observed in

experiment 2 but not in experiment 3.

Our present data do not justify any certainty that cognitive load is a helpful

concept in terms of which to compare real world performances based on virtual and

real training. However, the matter should be relatively easily resolved by addressing

cognitive load issues directly by, for example, parametric variation of task di� culty

within the virtual and real training conditions, and within any concurrent tasks used.

This is the focus of our current research.

Certainly, this is a matter of importance. If performance based on virtual training

requires less cognitive load than that based on real training it has clear implications

for certain types of training. In particular, it would suggest VEs should be used

where possible in training people to carry out highly complex tasks in which errors

could be either dangerous or very expensive and in training people whose cognitive

capacity is already compromised, for example, by brain damage.

Despite these currently unresolved issues two important ® ndings have emerged

from the present research. First, there was clear transfer from VE task training to

real task performance in a rigorously controlled test. Second, there was considerable

evidence of equivalence of real world performance after VE and real task training.

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These two ® ndings provide strong empirical support for the further development of

VR as a training method. Finally, an important point to note is that in none of the

experiments did participants in the virtual training condition report any side eŒects

which have been the subject of continuing concern in applying this technology in

treatment and training (Rizzo et al. 1998a , b, Stanney et al. 1998 ).

Acknowledgements

This research was supported in part by a grant from the Nu� eld Foundation.

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