A Preliminary Development of the Morris Maze Procedure in Virtual

Reality

Jes

us Moreno

, Juan M. Jurado

, Jos

e E. Callejas-Aguilera

and J. Roberto Jim

enez-P

erez

Computer Graphics and Geomatics Group, University of Ja

en, Ja

en, Spain

Psychology, University of Ja

en, Ja

en, Spain

Keywords:

Virtual Reality, Human-centered Computing, Psychology and Graphic Input Devices.

Abstract:

The Morris Water Maze (MWM) has become one of the most widely used laboratory tools in behavioural

neuroscience. It has been used in some of the most sophisticated experiments in the study of spatial learning

and memory with animals. However, human-based studies have been very limited due to the use of unrealistic

scenarios, usually presented on a computer screen where participants’ attention is poorly controlled. Recent

advances in virtual reality (VR) enable the generation of 3D environments with a high level of realism and

user’s immersion. The user’s attention plays a key role in spatial learning. Current VR systems integrate eye-

tracking devices to measure the user’s attention over virtual entities. In this paper, we present an easy-to-use

game-based simulator of the MWM, using eye-tracking VR technology to extract information about the user’s

attention. This research still in progress has achieved important hints according to the design of the virtual

scenario, user interaction and experimentation. The study conducted in this paper validates the technology as

a novel way to perform MWM focused on spatial learning and memory with human participants.

1 INTRODUCTION

The Morris Water Maze (MWM) was deﬁned 40

years ago as a procedure to investigate spatial learning

and memory in laboratory rats. It has become one of

the most widely used laboratory tools in behavioural

neuroscience. This procedure consists of setting a

large circular pool ﬁlled with opaque water in which

a small platform is hidden. In a standard experiment,

during the training phase animals learn that differ-

ent ﬁxed landmarks lead to the position of the hidden

platform. Despite being a relatively basic procedure,

it has been used in some of the most sophisticated

experiments related to neurobiology and neurophar-

macology (D’Hooge and De Deyn, 2001). Regard-

ing spatial learning, monitoring attention is crucial.

Focusing on monitoring the human’s attention, there

are multiple devices based on eye-tracking, which

are able to record the user’s gaze and detect those

objects or areas where the human pays more atten-

tion (Falck-Ytter et al., 2013) (Marcos and Gonz

alez-

Caro, 2010). However, previous studies are usually

carried out in non-realistic scenarios using computer

screens as context for user interaction. The arrival of

virtual reality (VR) allows us to design and generate

realistic virtual environments, in which humans live

immersive experiences. Moreover, the subsequent in-

corporation of eye-tracking into VR systems enables

monitoring the user’s attention with a high spatial res-

olution. Accordingly, this technology opens new op-

portunities to assess the human’s attention based on

already validated procedures within the MWM. Thus,

we aim to expand our knowledge about spatial cog-

nition in humans, considering the role of attention in

spatial learning.

Figure 1 summarizes some tasks proposed for the

study of learning and spatial cognition. In this paper,

ﬁrst steps are presented for the simulation of MWM

on VR focused on spatial learning and memory with

humans. We propose an easy-to-use framework that

enables the creation of multiple spatial learning and

memory tasks focused on the user behaviour assess-

ment considering eye-tracking data in VR.

The rest of the manuscript is organized as follows:

Section 2 describes the related work, Section 3 is the

core of the study, where the experiment design is ex-

plained and all the material used is presented, Section

4 shows and discusses the results. Finally, Section 5

concludes the paper and introduces future works.

260

Moreno, J., Jurado, J., Callejas-Aguilera, J. and Jiménez-Pérez, J.

A Preliminary Development of the Morris Maze Procedure in Virtual Reality.

DOI: 10.5220/0010897500003124

In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 1: GRAPP, pages

260-267

ISBN: 978-989-758-555-5; ISSN: 2184-4321

a b c d

Figure 1: The progress of spatial cognition and learning studies over time. (a) MWM with rats (SCAC-IRIB, 2020) (b)

screen-based human experiments (c) eye-tracking experiments (University, 2014) and (d) our approach.

2 RELATED WORK

The Morris water maze (Morris, 1984) is often con-

sidered the “gold standard” test to assess animal

learning and spatial memory in a much more acces-

sible and controlled way (Thornberry et al., 2021).

The MWM generally consists of a large circu-

lar pool of water maintained at room temperature (or

slightly above) with a ﬁxed platform hidden just be-

low the surface of the water. Within this pool, trials

are carried out on rats. These trials consist of the exe-

cution of a given task by a rat during a ﬁxed time. In

each trial, rats are introduced into the pool from a dif-

ferent position (North, East, South, West) and tested

individually. The time elapsed and/or the distance tra-

versed to reach the hidden platform is recorded. Dis-

tinctive landmarks (geometric images or objects such

as circles, squares, triangles, etc.) are often placed

on the surroundings. Rats can use these visual cues

to guide their navigation. Trial by trial, rats progres-

sively become more efﬁcient at locating the platform.

Thus escaping the water by learning the location of

the platform relative to the distal visual cues. The

MWM provides a highly controlled environment for

landmark manipulation, behavioural observation, and

lesion studies.

The theoretical development in spatial cognition

based on the results obtained with animals in MWM

has motivated the interest in establishing similar tasks

with which to replicate these results in humans. In

addition, these results encourage the evaluation of

the adequacy of the theoretical developments initi-

ated with animals, and to advance in the knowledge

of spatial cognition. Several procedures have been

devised for the study of spatial cognition in humans.

One of the most relevant has been the use of virtual

reality technology for the implementation of spatial

learning tasks (Commins et al., 2020) (Machado et al.,

2019). The immediate antecedent of these procedures

is found in the use of tasks in which participants had

to learn about spatial information presented through

a computer screen (Astur et al., 2004) (Livingstone-

Lee et al., 2011) (Newhouse et al., 2007) (Newman

et al., 2007) (Piper et al., 2010) (Spiers and Maguire,

2008). These tasks favour the rapid completion of

spatial cognition studies with a high degree of exper-

imental control. Nevertheless, their results have low

external validity since the spatial experience recreated

in these tasks differs signiﬁcantly from actual experi-

ence in the natural environment.

Recent advances in virtual reality technology have

favoured running tasks such as the evaluation of nav-

igation systems (Roth et al., 2020) (Sitzmann et al.,

2018) and the study of spatial memory (Antonova

et al., 2011) (Astur et al., 1998). Moreover, other

studies have been proposed in order to assess how

auditory stimuli can affect the user’s visual perfor-

mance (Malpica et al., 2020). These results vali-

date virtual reality technology concerning traditional

laboratory experimentation. According to the MWM

procedure, there is not any VR framework with eye-

tracking integration as a common standard to replicate

and customize the target tasks over different laborato-

ries. Consequently, the comparison between different

laboratory results is challenging.

The subsequent incorporation of eye-tracking into

VR systems enables monitoring the user’s attention

with a high spatial resolution. Even though some re-

searchers have used eye-tracking for spatial condi-

tioning and learning, these studies do not integrate

this technology in VR (Kiefer et al., 2017). In some

approaches, eye movements and physiological mea-

sures of memory (pupillometry) are analysed in or-

der to compare visual exploration of spatial orienta-

tion. However, this experimentation is limited due to

non-immersive tasks just using the computer screen

(Mueller et al., 2008).

Thus, the integration of eye-tracking and VR may

leverage the knowledge in spatial cognition experi-

ments. In this work, we present the ﬁrst steps for the

development of a game-based simulator in order to

study spatial cognition according to the MWM pro-

A Preliminary Development of the Morris Maze Procedure in Virtual Reality

261

cedure. The main contribution of our proposal is the

fusion of both virtual reality and eye-tracking tech-

nologies to monitor user behaviour during the experi-

ment. Moreover, the proposed framework is designed

to be replicated under speciﬁc constraints in order to

achieve comparable results between different scenar-

ios. Our solution integrates multiple records and treat-

ments such as time elapsed and/or the distance tra-

versed in the scenario to reach the experiment goal.

3 METHODOLOGY

In this work, a game-based simulator of the MWM

procedure is presented. This novel approach aims

at providing a new framework to set up experiments

in the case of human beings by monitoring both the

user’s position and gaze in virtual reality. As a con-

tribution of this research, we enable the acquisition

of eye-tracking data, which are crucial to analyze the

user’s attention and the impact of surrounding virtual

elements that help to achieve the goal.

To ensure a correct adaptation of the MWM pro-

cedure to virtual reality, for the study of spatial cog-

nition and memory, a virtual simulator has been pro-

posed. This simulator includes a realistic scenario and

software modules to deﬁne the cues and goals of the

experiments as well as to capture the time elapsed, the

distance traversed or the attention path among others.

The proposed methodology is depicted in Fig-

ure 2. Firstly, the MWM logic has to be implemented

taking advantage of virtual reality. The adaptation of

the original procedure is based on searching for a hid-

den treasure located in a diaphanous area that simu-

lates the well-known pool of the MWM procedure. To

this end, the user can move around and excavate over

the area by intuitive gestures using a pick. This area is

formed by 12 x 12 digging cells where the treasure is

buried (see Figure 3). Secondly, the virtual scenario is

modelled. A dense forest is created in which the men-

tioned area is located. Moreover, target surrounding

cues are placed in order to help the user toward the

treasure position. This environment ensures immer-

sive experiences enabling users to behave naturally.

Thirdly, the MWM procedure must be adapted to be

agile enough so multiple designs can be implemented.

This allows the realization of different experiments

in spatial cognition, memory, learning and condition-

ing. An important step for the validation of our proce-

dure consists of the replication of basic phenomena in

spatial learning such as acquisition and interference.

Taking this into account, we have designed an exper-

iment consisting of two learning phases. First, an ac-

quisition phase and then, an interference phase. For

both phases, participants aim to look for the treasure

whose position is always correlated to two surround-

ing virtual objects, notated as target cues. In the ﬁrst

phase (acquisition), these entities are located close

to the treasure. In the second phase (interference),

the treasure position is modiﬁed concerning the tar-

get cues, being now far away from them, as shown in

Figure 4. Thus, a spatial learning function guided by

target cues is expected during the acquisition phase,

and this one should be disrupted during the interfer-

ence phase. A spatial learning is expected during the

acquisition phase as the time the user spends close to

the target cues increases over trials. This learning is

also inferred by the number of excavations that take

place in the same area. During the interference phase,

in which the treasure is located farther away from the

target cues, a decrease in behaviour (permanence and

digging) in the previous region should be observed.

In contrast, an increase of this behaviour should be

noticed in the new area in which the treasure is now

located. Fourthly, the experiment is conducted with

a group of participants and all the relevant informa-

tion for the study is collected. We recorded the user

position, movements, excavations, eye-tracking ﬁxa-

tion and transition between entities (related to the at-

tention of each environment object). Heat maps are

generated in order to analyze the user’s attention and

its impact on the user’s behaviour. According to the

number of excavations, the user path and gaze data,

a statistical analysis is carried out. This stage is still

in progress. Currently, we are testing the experiment

with 32 participants. For the time being, only prelim-

inary results have been obtained corresponding to six

users. Finally, the last stage consists of validating the

whole process once the data is analyzed.

3.1 Participants

Although the experimentation phase is still in

progress, in this study we have collected results from

six students aged between 18 and 23 years old of the

University of Ja

en. The experimental series were ap-

proved by the ethics committee of the University of

en, protocol number CEIH 250914-2.

3.2 Apparatus and Stimuli

To carry out this study, HTC VIVE Pro Eye hardware

(HTC, 2021) and Unity game engine software (Unity,

2021) were used. The study was conducted using a

NVIDIA Geforce 1060 3Gb, a recommended GPU by

HTC for VR. Stable 90 fps were achieved to avoid

sickness from the participants (Hagita et al., 2019).

GRAPP 2022 - 17th International Conference on Computer Graphics Theory and Applications

262

Figure 2: Methodology followed for the adaptation of the MWM procedure to VR and the replication of a spatial cognition

experiment.

Figure 3: 12x12 grid located in the diaphanous area where

the user is able to dig. Treasure is buried in one grid.

Figure 4: The torch and the barrel represent random target

cues. Both, left and right images represent the design of the

acquisition and extinction phase respectively.

The ﬁnal environment consisted of a realistic for-

est that served as a starting point for our study. It was

based on a Unity asset called ”Forest Environment -

Dynamic Nature” (NatureManufacture, 2021).

Once a realistic environment was selected, the

user had the possibility to move around, observe its

entities and interact. It ensures a feeling of total im-

mersion and helps users to behave in a natural way

which facilitates getting reliable data. The proposed

movement within the VR environment is based on the

use of teleports. The SteamVR SDK (Valve, 2021)

provides teleportation based on zones, distance con-

straints and user feedback.

For the adaptation of the MWM procedure to VR,

some changes are proposed: instead of using a pool as

a search space, a diaphanous area delimited by ropes

is modelled. A treasure that is buried somewhere in

the search area has been proposed as a substitute for

the platform that had to be searched in the pool. Ob-

jects (landmarks) that help to identify the platform in

MWM were replaced by environmental objects (cues)

in our adaptation.

As cues, eight items were used. Three of them

were irrelevant ﬁxed cues (well, menhir, cabin) that

do not indicate the position of the treasure and al-

ways remain in the same position. The other ﬁve cues

were randomly located. Two of them were random

target cues (torch, barrel), which indicates the posi-

tion of the treasure by always maintaining the same

relation to it. The other three are random irrelevant

cues (skull, little stone, buckets) that do not indicate

the position of the treasure.

As a search action, in the Morris maze task, the rat

had to swim to ﬁnd a platform and be in a safe place.

In this case, the action consists of digging and moving

around the search area until the user ﬁnds the treasure

for which he/she will be rewarded.

A Preliminary Development of the Morris Maze Procedure in Virtual Reality

263

3.3 Procedure

First, participants were asked to read and sign an in-

formed consent before starting the task. Afterwards,

once the headset was ﬁtted, the task began.

At the beginning of the experiment, a cover story

was told to participants to better acclimate them into

the environment. They were told that a friend had

taken them to a forest near his home. An ancient civi-

lization lived in that forest and it was attacked by van-

dals. Before ﬂeeing, they buried their most valuable

belongings somewhere in the forest. Their friend had

a deﬁned area where he believed they were located.

The users’ task consisted of ﬁnding the treasure and

receiving a reward accordingly. In addition, the re-

sources are limited as each dig consumes some coins.

If the participants run out of coins, they will not be

able to dig until their friend lends them a few.

Just after the cover story, a tutorial began and users

were introduced into the environment. The objective

was to get the user used to the movement and the en-

vironment. After a while, their friend found a trea-

sure and they are moved to its position. At this point,

the importance of surrounding elements to achieve the

task goal is highlighted.

As mentioned above, the goal of this experiment is

the replication of basic phenomena in spatial learning

such as acquisition and interference. For this purpose,

two phases were created: an acquisition phase and an

interference phase. In the acquisition phase, the trea-

sure was hidden close and with the same distance re-

lated to the random target cues. In the interference

phase treasure was far away from the random target

cues (see Figure 4). Each phase consists of 8 training

trials. In these trials both treasure and cues were avail-

able. Participants were able to dig in the search area

and move through the environment. They were intro-

duced into the search area, randomizing their starting

position among the North, South, East and West co-

ordinates deﬁned on each side of the search area. The

selection of the starting coordinate was done by sam-

pling without replacement to ensure that the user does

not repeat a position until he/she has passed through

all of them. The search area had a treasure hidden in

a different position from trial to trial to avoid that the

user could identify the treasure position by using ele-

ments of the environment instead of being guided by

random target cues. The maximum duration of each

training trial was 60 seconds. When the user reached

the treasure, he/she stayed at the position for 10 sec-

onds. This delay is done to facilitate the inspection

of the entities concerning the position of the treasure.

Similarly, if participants did not ﬁnd the treasure after

60 sec, they were gently pushed to the treasure, where

they also remained for 10 additional seconds. Then, a

new trial began.

For data analysis, a probe trial is introduced after

4 training trials. Probe trials are identical to train-

ing trials with the difference that there is no treasure.

Users remain for 60 seconds and then they are ad-

vised that this trial has no treasure. This is done to

prevent the probe trials from becoming interference

trials, i.e., to prevent the participants from thinking

that the target cues were no longer good predictors of

treasure location. The probe trials are the most rel-

evant since they allow us to observe how the partic-

ipants distribute their behaviour among the different

quadrants during a ﬁxed time of 60 sec.

3.4 Data Analysis and Dependent

Variables

Figure 5: Quadrants deﬁned for analysis. They persist be-

tween phases. In this trial from the acquisition phase, the

green quadrant is deﬁned as the target quadrant as it corre-

sponds to the quadrant where the target keys are located.

For further analysis of the user’s performance, 4 equal

quadrants are deﬁned (see Figure 5). These quad-

rants are afterwards delimited zones (not perceptible

by the user during the experiment) within the search

area. It allows the user’s behaviour and learning to

be analyzed. In addition, it enables exporting relevant

information on the study variables (both, spent time

and number of excavations by quadrant). For sub-

sequent analysis, an objective quadrant is deﬁned on

which the statistical study of the values will be carried

out. This target quadrant corresponds to the quadrant

where the target keys are located (green quadrant in

Figure 5). Target quadrant position changes from trial

GRAPP 2022 - 17th International Conference on Computer Graphics Theory and Applications

264

Figure 6: Heat map extracted from the execution of a training trial in the acquisition phase. Grids that appear on the ground

represent the different areas where the user could dig. It can be observed how the random target cues (barrel and torch)

received the most attention, as well as the digging grids closest to them.

to trial based on the target keys position. The logic

of the data analysis is simple: if the task leads to par-

ticipants learning that the target cues point to the lo-

cation of the treasure, then as training progresses par-

ticipants will stay longer, and perform a greater num-

ber of digs, in the quadrant where the treasure is hid-

den. For data analysis, the probe trials should conﬁrm

the acquisition and interference that appear during the

training trials.

In addition, the eye-tracking system integrated

into the VR technology uses a series of algorithms

to determine the position at which the user is looking

in the virtual world by detecting pupils’ position and

gaze direction. Using the Unity SDK, objects placed

in gaze direction are collided by a ray. It can be ob-

tained all the desired information about the collided

objects such as its name, its type (a cue, a digging

area, a tree), the position from which it has been ob-

served, time at which the user started to observe the

object, number of looks and duration of the gaze ﬁx-

ation, etc. All this information is crucial to evaluate

the role of attention in spatial learning.

For the information obtained from eye-tracking,

we propose the generation of 360º heat maps to rep-

resent the users’ attention to different entities in the

environment. These heat maps help to validate user’s

learning through all the trials. In this way, after an

execution, a map can be generated where each ob-

ject takes a colour depending on the total time that

the user has been observing it. The continuous range

of colours used goes from blue to red (following the

colour spectrum), representing lack of attention and

maximum time looking at a given entity, respectively.

Heat maps are generated for each trial and each user.

As an example, Figure 6 shows the heat map from the

execution of a training trial by a participant. As can be

seen, the random target cues received the most atten-

tion from the user as well as the digging area around

them. This may conﬁrm that the user has learned

which are the cues that indicate the treasure position

and the relation between them.

4 PRELIMINARY RESULTS

Time spent in the target quadrant. Figure 7 presents

the mean time spent in the target quadrant during the

acquisition phase (left side) and the interference phase

(right side). In addition to the probe trials, the ﬁrst

training trial during the acquisition phase and the ﬁrst

two training trials of the interference phase have been

represented. It is not foreseeable that during these

trials the participants would ﬁnd the treasure, thus

maintaining the same characteristics as the probe tri-

als (only one of the six participants found the treasure

in these three training trials). As can be expected,

time spent in the target quadrant was increasing as

the acquisition trials progressed, suggesting that par-

ticipants learned that the target cues point to the po-

sition of the hidden treasure. Moreover, in the sec-

ond interference trial, a greater permanence is ob-

served, something known in the literature as interfer-

ence burst (a transient increase in response rate over

those observed in baseline during the period imme-

diately following discontinuation of reinforcement of

a response (Lattal et al., 2020) (Skinner, 2019)). Fi-

nally, a decrease in the time participants spent in the

target quadrant can be observed as interference trials

followed one after the other. This suggests that the

extinguishing treatment was effective. These initial

impressions were conﬁrmed by the planned statistical

analyses. Comparison of the time spent in the target

quadrant between the ﬁrst training trial in the acquisi-

tion phase (when participants do not yet know which

A Preliminary Development of the Morris Maze Procedure in Virtual Reality

265

Figure 7: Mean time spent in the target quadrant in the ﬁrst

training trial and the two test trials during the acquisition

phase (left side) and the two ﬁrst training trials and the two

test trials during interference phase (right side). The hori-

zontal line at second 15 indicates the mean time expected if

the user moves randomly.

cues may be relevant for treasure location) and the

second training trial in the interference phase (when

the behavioural expression is maximal as a function

of prior learning during the acquisition phase) was

statistically signiﬁcant, t(5) = 3.67, p = 0.01. The

comparison between the second training trial and the

last probe trial (when interference should be higher)

during the interference phase was statistically signif-

icant too, t(5) = 3.35, p = 0.02. These results show

that participants learned to relate the target cues to

the treasure location during the acquisition phase and

then, that this relationship was disrupted during the

second training phase.

Diggings in the target quadrant. Figure 8 presents

the mean diggings in the target quadrant in the same

training and probe trials as in the previous dependent

variable. As can be seen, the number of excavations

increased until it reached its maximum value in the

second training trial during the interference phase.

Thereafter, the number of excavations decreased un-

til reaching its lowest level at the end of the interfer-

ence phase. These impressions were conﬁrmed by the

planned statistical analyses. Comparison of the dig-

gings in the target quadrant between the ﬁrst training

trial in the acquisition phase and the second training

trial in the interference phase was statistically signiﬁ-

cant, t(5) = 3.20, p = 0.02. Comparison between the

second training trial and the last probe trial (when in-

terference should be higher) during the interference

phase was statistically signiﬁcant too, t(5) = 2.88,

p = 0.03.

5 CONCLUSIONS AND FUTURE

WORK

The present study attempted to validate a novel way to

perform MWM focused on spatial learning and mem-

Figure 8: Mean diggings in the target quadrant in the ﬁrst

training trial and the two test trials during the acquisition

phase (left side) and the two ﬁrst training trials and the two

test trials during interference phase (right side).

ory with human participants using an eye-tracking VR

system. For this purpose, a simple learning acquisi-

tion and subsequent interference experiment was de-

signed. The results obtained validate the task and

the procedure from a behavioural perspective, show-

ing the tool potential for the joint research of be-

havioural and attentional processes. However, the va-

lidity of the task relies on the conﬁrmation of the re-

sults found from statistical analyses guided by the-

ory and a posteriori planned contrast. Although this

methodology may be adequate in the replication of a

study, it is not sufﬁcient in studies that seek to fur-

ther advance knowledge of the problem they are deal-

ing with. The lack of results in this study with the a

priori analysis strategy may be due to different fac-

tors, although three are worth highlighting: First, the

number of participants, only 6, which negatively af-

fects statistical power. So, increasing the number of

participants could help address the lack of statisti-

cal power. On the other hand, the number of irrele-

vant cues may have hindered the learning of the target

cues. Therefore, increasing the number of training tri-

als or decreasing the number of irrelevant cues should

enhance the learning function during the acquisition

phase. Finally, increasing the duration of training tri-

als should facilitate learning acquisition. Future work

in our laboratory will aim at ﬁnding parameters that

favour task sensitivity for the study of spatial learning

and memory. These will also attempt to relate be-

havioural responses (i.e., displacements and digging)

to measures of attention obtained from the record-

ing of participants’ eye-tracking during their perfor-

mance. This may represent an important advance in

the understanding of the basic processes governing

spatial learning and memory. In addition, we want to

exploit the eye-tracking technology, not only through

heat maps but also by obtaining the eye transitions of

the user on the different entities in the environment.

Eye transitions will allow us to know the order in

which users were paying attention to different objects

and how this could inﬂuence their actions.

GRAPP 2022 - 17th International Conference on Computer Graphics Theory and Applications

266

ACKNOWLEDGEMENTS

Research was supported by Grant PGC2018-097769-

B-C22 from the Spanish Ministry of Science and In-

novation and by the Ministerio de Econom

ıa y Com-

petitividad and the European Union (via ERDF funds)

through the research project (TIN2017-84968-R).

REFERENCES

Antonova, E., Parslow, D., Brammer, M., Simmons, A.,

Williams, S., Dawson, G. R., and Morris, R. (2011).

Scopolamine disrupts hippocampal activity during al-

locentric spatial memory in humans: an fMRI study

using a virtual reality analogue of the morris water

maze. Journal of Psychopharmacology, 25(9):1256–

1265.

Astur, R. S., Ortiz, M. L., and Sutherland, R. J. (1998). A

characterization of performance by men and women in

a virtual morris water task:: A large and reliable sex

difference. Behavioural Brain Research, 93(1):185–

190.

Astur, R. S., Tropp, J., Sava, S., Constable, R. T., and

Markus, E. J. (2004). Sex differences and correla-

tions in a virtual morris water task, a virtual radial

arm maze, and mental rotation. Behavioural Brain

Research, 151(1):103–115.

Commins, S., Dufﬁn, J., Chaves, K., Leahy, D., Corcoran,

K., Caffrey, M., Keenan, L., Finan, D., and Thorn-

berry, C. (2020). NavWell: A simpliﬁed virtual-

reality platform for spatial navigation and memory ex-

periments. Behavior Research Methods, 52(3):1189–

1207.

D’Hooge, R. and De Deyn, P. P. (2001). Applications of the

morris water maze in the study of learning and mem-

ory. Brain Research Reviews, 36(1):60–90.

Falck-Ytter, T., B

olte, S., and Gredeb

ack, G. (2013). Eye

tracking in early autism research. Journal of Neurode-

velopmental Disorders, 5(1):28.

Hagita, K., Matsumoto, S., and Ota, K. (2019). Study of

commodity VR for computational material sciences.

4(2):3990–3999.

HTC (2021). Htc vive pro eye overview. Accessed on 2021-

10-15.

Kiefer, P., Giannopoulos, I., Raubal, M., and Duchowski,

A. (2017). Eye tracking for spatial research: Cogni-

tion, computation, challenges. Spatial Cognition &

Computation, 17(1):1–19.

Lattal, K. A., Kuroda, T., and Cook, J. E. (2020). Early ex-

tinction effects following intermittent reinforcement:

Little evidence of extinction bursts. Journal of the Ex-

perimental Analysis of Behavior, 114(1).

Livingstone-Lee, S. A., Murchison, S., Zeman, P. M.,

Gandhi, M., van Gerven, D., Stewart, L., Livingston,

N. J., and Skelton, R. W. (2011). Simple gaze analysis

and special design of a virtual morris water maze pro-

vides a new method for differentiating egocentric and

allocentric navigational strategy choice. Behavioural

Brain Research, 225(1):117–125.

Machado, M. L., Lef

evre, N., Philoxene, B., Le Gall, A.,

Madeleine, S., Fleury, P., Smith, P. F., and Besnard,

S. (2019). New software dedicated to virtual mazes

for human cognitive investigations. Journal of Neuro-

science Methods, 327:108388.

Malpica, S., Serrano, A., Gutierrez, D., and Masia, B.

(2020). Auditory stimuli degrade visual performance

in virtual reality. Scientiﬁc Reports, 10(1):12363.

Marcos, M.-C. and Gonz

alez-Caro, C. (2010). Compor-

tamiento de los usuarios en la p

agina de resultados de

los buscadores. un estudio basado en eye tracking.

Morris, R. (1984). Developments of a water-maze proce-

dure for studying spatial learning in the rat. Journal

of Neuroscience Methods, 11(1):47–60.

Mueller, S. C., Jackson, C. P. T., and Skelton, R. W. (2008).

Sex differences in a virtual water maze: An eye track-

ing and pupillometry study. Behavioural Brain Re-

search, 193(2):209–215.

NatureManufacture (2021). Forest environment - dynamic

nature. Accessed on 2021-10-15.

Newhouse, P., Newhouse, C., and Astur, R. S. (2007).

Sex differences in visual-spatial learning using a vir-

tual water maze in pre-pubertal children. Behavioural

Brain Research, 183(1):1–7.

Newman, E. L., Caplan, J. B., Kirschen, M. P., Korolev,

I. O., Sekuler, R., and Kahana, M. J. (2007). Learning

your way around town: How virtual taxicab drivers

learn to use both layout and landmark information.

Cognition, 104(2):231–253.

Piper, B. J., Acevedo, S. F., Craytor, M. J., Murray,

P. W., and Raber, J. (2010). The use and validation

of the spatial navigation memory island test in pri-

mary school children. Behavioural Brain Research,

210(2):257–262.

Roth, D., Purps, C. F., and Neumann, W.-J. (2020). A

virtual morris water maze to study neurodegenarative

disorders. In 2020 IEEE International Symposium

on Mixed and Augmented Reality Adjunct (ISMAR-

Adjunct), pages 141–146. IEEE.

SCAC-IRIB (2020). Mwm with rats image reference. Ac-

cessed on 2021-11-02.

Sitzmann, V., Serrano, A., Pavel, A., Agrawala, M., Gutier-

rez, D., Masia, B., and Wetzstein, G. (2018). Saliency

in VR: How do people explore virtual environments?

IEEE Transactions on Visualization and Computer

Graphics, 24(4):1633–1642.

Skinner, B. (2019). The behavior of organisms: An experi-

mental analysis.

Spiers, H. J. and Maguire, E. A. (2008). The dynamic nature

of cognition during wayﬁnding. Journal of Environ-

mental Psychology, 28(3):232–249.

Thornberry, C., Cimadevilla, J. M., and Commins, S.

(2021). Virtual morris water maze: opportunities and

challenges. Reviews in the Neurosciences.

Unity (2021). Unity game engine. Accessed on 2021-10-15.

University, L. (2014). Eye tracking image reference. Ac-

cessed on 2021-11-02.

Valve (2021). Steam vr sdk for unity. Accessed on 2021-

10-15.

A Preliminary Development of the Morris Maze Procedure in Virtual Reality

267