The impac o human agen s on
spa ial na iga ion and knowledge
acquisi ion in a i ual
en i onmen
T acy Sánchez Pacheco
1
*, Melissa Sa ia Mosque a
1
,
Kaya Gä ne
1
, Vincen Schmid
1
, Debo a Nol e
1
,
Sabine U. König
1†
, Go don Pipa
1†
and Pe e König
1
,
2†
1
Ins i u e o Cogni i e Science, Uni e si y o Osnab ück, Osnab ück, Ge many,
2
Depa men o
Neu ophysiology and Pa hophysiology, Uni e si y Medical Cen e Hambu g-Eppendo , Hambu g,
Ge many
Concep s o spa ial na iga ion es on he idea o landma ks, which a e immobile
ea u es o objec s in he en i onmen . Howe e , beha io ally ele an objec s o
ellow humans a e o en mobile. This aises he ques ion o how he p esence o
human agen s influences spa ial explo a ion and knowledge acquisi ion. He e, we
in es iga e explo a ion and pe o mance in subsequen spa ial asks wi hin a
i ual en i onmen con aining nume ous human a a a s. In he explo a ion
phase, agen s had a locally limi ed e ec on na iga ion. They p omp ed
pa icipan s o e isi loca ions wi h agen s du ing hei ini ial explo a ion
wi hou significan ly al e ing o e all explo a ion pa e ns o he ex en o he
a ea co e ed. Howe e , agen s and buildings compe ed o isual a en ion.
When spa ial ecall was es ed, poin ing accu acy owa d buildings imp o ed
when pa icipan s di ec ed hei a en ion o he buildings and nea by agen s. In
con as , poin ing accu acy o agen s showed weake pe o mance and did no
benefi om isual a en ion di ec ed owa d he adjacen building. Con ex ual
agen s and incong uen agen -en i onmen pai ings u he enhanced poin ing
accu acy, e ealing ha iola ions o expec a ions by agen s can significan ly
shape na iga ional knowledge acquisi ion. O e all, agen s influenced spa ial
explo a ion by di ec ing a en ion locally, wi h he in e ac ion be ween agen
salience and en i onmen al ea u es playing a key ole in shaping na iga ional
knowledge acquisi ion.
KEYWORDS
spa ial na iga ion, human agen s, i ual eali y, explo a ion-exploi a ion, social
acili a ion
1 In oduc ion
Spa ial na iga ion is essen ial o goal-o ien ed mo emen and ac i e en i onmen al
in e ac ion (Eps ein e al., 2017;I o e al., 2015). In humans, egula engagemen in spa ial
na iga ion, whe he s udied h ough na iga ion done in he con ex o p o essional
ac i i ies (G iesbaue e al., 2022;Magui e e al., 2006;Woolle and Magui e, 2011),
a ge ed aining (Choi e al., 2012), o i ual en i onmen s (Wes e al., 2017), is ela ed o
he enhancemen o cogni i e unc ions, pa icula ly memo y and spa ial awa eness. This
OPEN ACCESS
EDITED BY
Ma ia Pyasik,
Uni e si y o Udine, I aly
REVIEWED BY
Ad iana Sala ino,
Royal Mili a y Academy, Belgium
Ru h Con oy Dal on,
Lancas e Uni e si y, Uni ed Kingdom
*CORRESPONDENCE
T acy Sánchez Pacheco,
acy.sanchez.pacheco@uni-osnab ueck.de
†
These au ho s con ibu ed equally and sha e
senio au ho ship
RECEIVED 16 Sep embe 2024
ACCEPTED 06 Ma ch 2025
PUBLISHED 02 Ap il 2025
CITATION
Sánchez Pacheco T, Sa ia Mosque a M,
Gä ne K, Schmid V, Nol e D, König SU, Pipa G
and König P (2025) The impac o human agen s
on spa ial na iga ion and knowledge acquisi ion
in a i ual en i onmen .
F on . Vi ual Real. 6:1497237.
doi: 10.3389/ i .2025.1497237
COPYRIGHT
© 2025 Sánchez Pacheco, Sa ia Mosque a,
Gä ne , Schmid , Nol e, König, Pipa and König.
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he e ms o he C ea i e Commons A ibu ion
License (CC BY). The use, dis ibu ion o
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F on ie s in Vi ual Reali y on ie sin.o g01
TYPE O iginal Resea ch
PUBLISHED 02 Ap il 2025
DOI 10.3389/ i .2025.1497237
in ica e link be ween spa ial na iga ion and cogni i e p ocesses
highligh s he c ucial ole o spa ial na iga ion in de eloping and
o ganizing spa ial knowledge.
The ans o ma ion o na iga ional expe iences in o spa ial
knowledge begins wi h ecognizing key en i onmen al elemen s,
ollowed by hei g adual in eg a ion in o a cohesi e e e ence
sys em (Eks om and Isham, 2017). When indi iduals explo e
new su oundings eely, hey can iden i y elemen s ha aid hei
o ien a ion and ha ness hem as building blocks o knowledge
cons uc ion, such as emembe ing shops when isi ing a new own.
This p ocess, known as landma k iden ifica ion, is essen ial o
main aining posi ional awa eness and planning u u e pa hways
(Janzen e al., 2006). Spa ial knowledge encompasses ecognizing
and ecalling c i ical elemen s o he landscape, unde s anding hei
spa ial ela ionships, and he ou es connec ing hem.
Despi e ex ensi e esea ch on he ole o s a ic landma ks in
spa ial cogni ion (Malanchini e al., 2020), he dynamic human
aspec s influencing spa ial explo a ion and knowledge acquisi ion
ha e ye o be ully explo ed. O en, humans a e iewed p ima ily as
modifie s o na iga ional pa hs a he han as ac i e con ibu o s o
spa ial knowledge acquisi ion (Bicanski and Bu gess, 2020;Eks om
and Isham, 2017). This pe spec i e o e looks he significan ole
o he indi iduals play in eal-wo ld spa ial cogni ion. Encoun e s
wi h o he s can impac pedes ian dynamics (Dal on e al., 2019),
influence isual explo a ion (Ge e al., 2020), p o ide i al
in o ma ion abou he sa e y and usabili y o spaces (Bajo unai e
e al., 2022), a ec he ecall o loca ions (Kuehn e al., 2018), and
p omp a pa allel social mapping o he en i onmen (Scha e and
Schille , 2018). Thus, inco po a ing o he humans in o na iga ion
esea ch is essen ial o a deepe unde s anding o spa ial cogni ion.
Howe e , s udying he influence o ellow humans on conc e e
spa ial knowledge aces he di ficul ies o main aining a na iga ion
scena io o eal-wo ld scale in con olled en i onmen s. When
a ailable, esea che s o en conduc e ospec i e s udies using
eal-wo ld da a, such as mobile phone and GPS da a, which can
cha ac e ize human mobili y pa e ns bu lack he con olled
a iables necessa y o isola ing specific ac o s media ing he
esul s (Pappala do e al., 2015;Schläp e e al., 2021). Mo eo e ,
hese s udies do no link explo a ion pa e ns o he u u e
acquisi ion o spa ial knowledge, hinde ing a comp ehensi e
unde s anding o cogni i e p ocesses de i ed om na iga ion
pa e ns. When mo ing o he o he side o he spec um,
labo a o y se ups end o be bound o small-scale en i onmen s
and simplified asks ha lack he challenge o spa ial scales o he eal
wo ld (Wiene e al., 2020). Pa icipan s a e asked o use humans as
ancho s inside a maze-like i ual eali y (VR) o as landma ks in
isual flow expe imen s (Kuehn e al., 2018). Dal on e al. (2019)
poin ou ha VR echnologies in wayfinding esea ch o en exclude
he p esence o o he s, he eby unde es ima ing hei impo ance.
They sugges ha he me e p esence o o he s (i.e., weak social cues),
e en wi hou ac i e in e ac ion, can help in e he impo ance o a
space wi hin i s en i onmen , and ac i ely call o mo e s udies o be
done in his ealm o esea ch. Hence, i is acknowledged ha
examining how he me e p esence o o he s migh impac
na iga ion and knowledge acquisi ion when humans a e no
p ima y a ge s is essen ial bu emains uns udied.
Wi h his wo k, we aise he ques ion o whe he spa ially
ele an in o ma ion can be ex ac ed om obse ing o he
humans in he space a ound us. I is impo an o no e ha
humans do no inhe en ly se e as fixed landma ks in na u al
na iga ion, p ima ily due o hei mobili y, p e en ing hem om
o ming in insic associa ions wi h specific places. Howe e , a he
han conside ing o he humans as eliable e e ence poin s, we
explo e he no ion ha hei ele ance wi hin a gi en con ex can
be ha nessed as a sou ce o in o ma ion o enhance spa ial
knowledge. Essen ially, i is no he p esence o o he s pe se ha
aids in spa ial cogni ion bu a he how hey in e ac wi h he
elemen s in hei su oundings ha could be ele an o spa ial
explo a ion and knowledge acquisi ion. This p omp s us o
in es iga e wha minimal change in a human agen ’s in e ac ion
wi h he en i onmen can elici pa icipan s’di e en beha io al
esponses. We seek o unde s and whe he hese human elemen s
ac as dis ac ions, po en ially diminishing he saliency o he
su ounding s imuli, o i , con e sely, hey con ibu e o
imp o ed pe o mance by ancho ing indi iduals o a mo e i id
men al ep esen a ion o specific loca ions.
To add ess his complexi y, we in es iga ed how spa ial knowledge
acquisi ion de eloped in a con olled one-squa e-kilome e VR
en i onmen wi h human agen s. The s udy inco po a ed human
agen s a wo le els: one in which he agen in e ac ed wi h he
en i onmen by holding an objec ele an o he con ex , such as a
oolbox in on o a ha dwa e s o e (i.e., Con ex ual agen ), and
ano he in which he agen simply s ood wi hou in e ac ing wi h any
objec s a ound i (i.e., Acon ex ual agen ). These agen s we e placed in
on o public buildings, such as s o es, baske ball cou s, es au an s,
o esiden ial buildings. Fo ou fi s g oup o pa icipan s, all
con ex ual agen s we e placed in on o public buildings ha
ma ched hei objec in e ac ion. Fo he second g oup, we
dis up ed his cong uency o s udy he sole influence o he ype o
agen and hei building on he con ex in which hey a e si ua ed,
unde he hypo hesis ha ha ing con ex -cong uen agen s will
enhance he pa icipan ’s abili y o ecall. Specifically, we p opose
h ee hypo heses: 1) he Visi Hypo hesis, which s a es ha con ex ual
agen s will influence pa icipan s’explo a ion pa e ns by d awing
hem back o p e iously isi ed loca ions; 2) he Dwell Hypo hesis,
which posi s ha pa icipan s will alloca e mo e isual a en ion o
con ex ually cong uen agen s compa ed o acon ex ual ones; and 3)
he Pe o mance Hypo hesis, which sugges s ha his inc eased
engagemen will lead o imp o ed pe o mance in spa ial
knowledge asks, such as poin ing accu acy. The aim o ou s udy
is o explo e he ole o hese human agen s and hei cong uency wi h
con ex in spa ial explo a ion and he de elopmen o
spa ial knowledge.
2 Resul s
We examined he impac o human agen s on spa ial na iga ion
and knowledge acquisi ion in a i ual ci y named Wes b ook,
consis ing o 236 buildings. We iden ified 26 public buildings
(e.g., shops, baske ball cou ) and 26 esiden ial buildings as ask-
ele an , ma king hem wi h s ee a . Addi ionally, he e we e
180 buildings wi hou g a fi i and ou la ge buildings on he ci y’s
ou ski s, which could se e as global landma ks gi en hei
dimensions. We designed wo ca ego ies o human agen s:
con ex ual agen s, who pe o med con ex - ele an ac ions (e.g.,
F on ie s in Vi ual Reali y on ie sin.o g02
Sánchez Pacheco e al. 10.3389/ i .2025.1497237
holding a oolbox in on o a ha dwa e s o e), and acon ex ual agen s,
who held a es ing posi ion wi hou in e ac ing wi h objec s. In he fi s
expe imen , con ex ual agen s we e placed in public a eas, displaying
ac ions cong uen wi h he buildings, while acon ex ual agen s we e
posi ioned in on o esiden ial buildings wi hou in e ac ion. In he
second expe imen , bo h agen ypes we e spli e enly ac oss public and
esiden ial a eas, dis up ing he cong uen pai s. Pa icipan s in bo h
expe imen s comple ed fi e 30-min explo a ion sessions, o aling
150 min. Addi ionally, o p o ide a baseline o compa ison o he
explo a ion s a egies, we used he con ol g oup om Schmid e al.
(2023) who explo ed he same VR ci y (Wes b ook) wi h he same
session leng hs and numbe s bu no agen s p esen . We in es iga ed he
explo a ion phase by analyzing pa icipan s’na iga ional co e age o he
ci y, hei walking s a egies, agen -induced bias in hei explo a ion, and
hei isualbeha io du ingexplo a ion. Finally, we es ed hei spa ial
knowledge acquisi ion in a sepa a e session using VR poin ing asks.
To es ablish compa abili y in spa ial o ien a ion abili ies
be ween he pa icipan s o he expe imen s (expe imen 1,
expe imen 2, and con ol), pa icipan s comple ed he FRS
(F agebogen Räumliche S a egien) ques ionnai e, and hei
sco es we e con as ed. The e we e no significan di e ences
be ween he g oups a baseline on any o he h ee subscales
(global, su ey, and ca dinal), χ2(2,N67)≤1.68,p≥0.43.
The e o e, he g oups we e compa able in hei assessmen o
hei use o spa ial s a egies be o e he s a o he expe imen s.
2.1 Assessmen o he explo a ion phase
Du ing he VR ci y expe imen , we acked pa icipan s’
explo a ion, including walking beha io , na iga ional co e age,
decision-poin s a egies, and isual beha io . This
FIGURE 1
F ee explo a ion o Wes b ook (a) P esen s a hea map ha isualizes he spa ial na iga ion da a o all pa icipan s ac oss he explo a ion phase. I
compiles mo emen ac oss all sessions, wi h pa icipan loca ions disc e ized o he nea es cen ime e and a e aged on a second-by-second basis a e
emo ing he ini ial 2 s o each eco ding. We fi ed a hea map g id app oxima ing a 1-cm esolu ion and c ea ed a colo scale anging om 0 o 10 isi s,
highligh ing a eas o a ied explo a ion in ensi y (b) Shows Wes b ook om abo e, o e laid wi h a g aph s uc u e we used o o malize na iga ional
da a as decision uni s (c) Quan ifies explo a ion h ough a map co e age a io, calcula ed by di iding he numbe o unique nodes isi ed by he o al
nodes in he ci y (d) Compiles hese a ios on a cumula i e basis, eflec ing he pa icipan s’expanding disco e y o he ci y o e ime.
F on ie s in Vi ual Reali y on ie sin.o g03
Sánchez Pacheco e al. 10.3389/ i .2025.1497237
comp ehensi e analysis e ealed hei na iga ional s a egies and
engagemen , highligh ing he agen s’impac on hei explo a ion.
2.1.1 Na iga ional co e age o he ci y
We quan ified pa icipan s’walking beha io in he i ual ci y
using a p imal ci y g aph (Neal, 2013) o analyze pa icipan s’ ee
explo a ion pa e ns. In his g aph, decision poin s (i.e., in e sec ions
o walkable pa hs) we e ep esen ed as nodes and pa hs connec ing
hem as edges. Pa icipan s’na iga ional coo dina es (see Figu e 1a)
we e assigned o he nea es g aph elemen (see Figu e 1b), defining
hei explo a ion as mo emen s om one g aph elemen o ano he .
Ou o 159 nodes, pa icipan s isi ed be ween 45 and 113 unique
nodes du ing each 30-min session (M= 87.06, SD = 16.49). We
calcula ed he co e age a io by di iding he numbe o nodes isi ed
a leas once by he o al numbe o nodes.
To accoun o epea ed measu es, we implemen ed a linea
mixed-e ec s model (LMM) ha conside ed in asubjec a iabili y
and gene a ed indi idual in e cep s o each pa icipan . This model
p edic ed he indi idual session co e age a io as a unc ion o he
session, he expe imen al g oup (con ol e sus ci y wi h agen s),
and hei in e ac ion. Using he fi s session as a baseline, we
obse ed significan cumula i e inc eases in na iga ional co e age
wi h each subsequen session. S a ing om an indi idual co e age
o oughly hal he ci y (as shown by he mean o he blue cu es in
Figu es 1c, d), he LMM analysis explained a subs an ial po ion o
he a iance in na iga ional co e age, wi h ma ginal R2
m0.18
( a iance explained by fixed e ec s) and condi ional R2
c0.70
( a iance explained by bo h fixed and andom e ec s),
emphasizing he ole o session-based lea ning while accoun ing
o indi idual di e ences. The coe ficien s indica e changes ela i e
o session 1(η2
p.40), wi h posi i e alues signi ying an inc ease:
βSession 2 0.03 (SE = 0.01, 2.48, p0.01), βSession 3 0.07 (SE =
0.01, 6.54, p<.001), βSession 4 0.09 (SE = 0.01, 8.53,
p<.001), and βSession 5 0.12 (SE = 0.01, 11.49, p<.001).
The e ec o he expe imen g oup (agen s e sus con ol) on he
co e age a io was no s a is ically significan (βExpe imen −0.02,
SE = 0.02, −0.72, p0.48). Addi ionally, in e ac ions be ween
he session and expe imen g oup we e also no significan :
βSession i:Expe imen ≤−0.04, p≥.07. This demons a ed ha as
sessions ad anced, pa icipan s co e ed mo e g ound wi hin he
same ime ame, achie ing his equally in bo h a ci y wi h agen s and
one de oid o hem.
In o de o es ima e i he pa icipan s we e di e en ially
accumula ing unique decision poin s in he ci y as he sessions
p og essed, we calcula ed a cumula i e co e age a io, accumula ing
he numbe o uniquely isi ed nodes as he sessions ad anced. A
LMM wi h pa icipan s as andom e ec s was used o p edic he
cumula i e co e age a io as a unc ion o session, expe imen
g oup, and hei in e ac ion (R2
m0.74, R2
c0.91). This analysis
showed a significan p og ession, wi h pa icipan s co e ing mo e o
he ci y in each subsequen session (see Figu e 1d). S a ing om he
same ini ial co e age a io mean (M= 0.49), significan cumula i e
inc emen s in na iga ional co e age we e obse ed in each session
(η2
p.91):βSession 2 0.19 (SE = 0.01, 24.71, p<.001), βSession 3 
0.27 (SE = 0.01, 35.80, p<.001), βSession 4 0.31 (SE = 0.01,
40.75, p<.001), and βSession 5 0.33 (SE = 0.01, 43.48,
p<.001). The e ec o he expe imen g oup on he co e age
a io was no s a is ically significan (βExpe imen −0.02,
p0.48). Addi ionally, in e ac ions be ween he session and
expe imen g oup we e also no significan :
βSession i:Expe imen ≤0.02, p≥.33. A e he fi h session, he end o
he explo a ion, he cumula i e numbe o unique nodes isi ed
(M= 0.83, SD = 0.04) indica ed ha mos pa icipan s had seen he
majo i y o he ci y. These findings sugges ha he p esence o an
agen did no significan ly influence he na iga ional co e age
o he ci y.
2.1.2 Explo a ion s a egies on decision poin s:
Explo a o y s. conse a i e beha io
To examine pa icipan s’walking s a egies, we analyzed
whe he hey ended o choose pa hs hey had p e iously isi ed
mo e equen ly (conse a i e) o i hey a o ed less- a eled ou es
(explo a i e). We defined disc e e na iga ional decisions as
mo emen s om one node o ano he and quan ified hem using
a s a egy ma ix (see Figu es 2a,b). A linea mixed-e ec s model
was fi ed o he da a o examine di e ences in decision numbe s
based on session, s a egy (conse a i e s explo a i e), and
expe imen (con ol s ci y wi h agen s), wi h andom in e cep s
o pa icipan s o accoun o he nes ed da a s uc u e. The model’s
fixed e ec s (R2
m0.49, R2
c0.83) indica ed ha he a e age
numbe o decisions ac oss all ac o s was β073.14 (SE = 4.02,
18.20, p<.001). The analysis showed significan inc eases in
decisions as pa icipan s gained expe ience in Wes b ook, wi h
inc eases e iden om he fi s session onwa d (η2
p.69):βSession 2 
30.17 (SE = 2.49, 12.11, p<.001), βSession 3 51.23 (SE = 2.49,
20.57, p<.001), βSession 4 67.49 (SE = 2.49, 27.09, p<.001),
βSession 5 80.32 (SE = 2.49, 32.24, p<.001). The di e ence in
decisions be ween conse a i e and explo a o y s a egies was
significan , β75.52 (SE = 3.52, 21.44, p<.001, η2
p.14).
The in e ac ion be ween session and s a egy was significan
(η2
p.46), showing ha decision inc eases pe session we e
lowe o he explo a o y s a egy compa ed o he conse a i e
one: βSession 2:S a egy1 −44.70 (SE = 4.98, −8.97, p<.001),
βSession 3:S a egy1 −67.66 (SE = 4.98, −13.58, p<.001),
βSession 4:S a egy1 −86.02 (SE = 4.98, −17.26, p<.001),
βSession 5:S a egy1 −102.37 (SE = 4.98, −20.55, p<.001). No
significan di e ence in decision numbe s was obse ed be ween
he wo expe imen s (βExpe imen −3.95, SE = 7.40, −0.53,
p0.84), indica ing ha he p esence o absence o agen s did
no ma kedly influence decision-making s a egies. Bo h beha io s
inc eased as sessions p og essed. Ini ially, pa icipan s p io i ized
explo a o y beha io , bu as hey gained expe ience, hey in eg a ed
conse a i e beha io , e ec i ely combining bo h s a egies (see
Figu e 2c), ega dless o agen p esence. This s a egy adap a ion,
whe e explo a o y decisions emained s able while conse a i e
decisions inc eased, occu ed wi hou significan influence om
agen s, sugges ing ha he global explo a ion s a egy is
una ec ed by human agen s.
2.1.3 Agen -induced bias on walking s a egies
In o de o es ou Visi Hypo hesis, we e alua ed he impac o
agen s on pa icipan s’explo a o y beha io ; we analyzed decision
poin s whe e pa icipan s could choose be ween a pa h wi h an agen
and one wi hou , assuming ha agen s migh elici isi s. Da a om
hese poin s we e compa ed wi h a con ol g oup om Schmid e al.
(2023), who explo ed he same VR ci y (Wes b ook) wi hou agen s
F on ie s in Vi ual Reali y on ie sin.o g04
Sánchez Pacheco e al. 10.3389/ i .2025.1497237
(see Figu e 3a). We used iden ical decision poin s in bo h scena ios.
We applied a linea mixed-e ec s model o assess isi coun s,
accoun ing o session and expe imen ype (con ol s ci y wi h
agen s) wi h andom in e cep s o pa icipan s (R2
m0.41,
R2
c0.82). Resul s indica ed a significan inc ease in isi coun s
ac oss sessions (η2
p.71)compa ed o session 1. Specifically, isi
coun s in session 2 we e, on a e age, 14.84 poin s highe
(βSession 2 14.84, p<0.001), and his e ec con inued o g ow in
subsequen sessions, culmina ing in a 38.55 poin inc ease by session
5(βSession 5 38.55, p<0.001). Howe e , he o e all di e ence
be ween he expe imen ypes was no significan
(βExpe imen −2.15, p0.50), sugges ing ha he p esence o
agen s did no uni e sally a ec isi coun s ac oss all sessions.
No ably, he in e ac ion be ween session and expe imen ype was
significan in session 5 (βSession 5:Expe imen 5.47, p0.008,
η2
p.01), showing a g ea e inc ease in isi s in he ci y wi h
agen s g oup. To u he explo e he influence o agen s, we
calcula ed he likelihood o pa icipan s adop ing explo a o y
e sus conse a i e s a egies. This was done by di iding he
numbe o choices o each s a egy cell (i.e., abo e and below
he diagonal; see Figu e 3b) by he o al numbe o decisions made,
as eco ded in he mi o ed cells o he s a egy ma ix. Fo ins ance,
we summed he numbe o imes a pa icipan chose o mo e o a
place hey had isi ed only once o e a place hey had no isi ed (cell
[1,0], conse a i e beha io ) wi h he numbe o imes hey chose o
go o a place hey had ne e been o e a place hey had isi ed once
(cell [0,1], explo a i e beha io ), and di ided each coun by ha
o al o decisions made in bo h cells (sum o he coun in bo h [1,0]
and [0,1] cells). This indica o showed a clea dis inc ion be ween
he beha io o pa icipan s in he ci y wi h agen s and ha o he
con ol g oup. Wi hin he fi s explo a ion session, pa icipan s in
he ci y wi h agen s had an a e age p opo ion o conse a i e
beha io o M= 0.47 (SD = 0.16) compa ed o M= 0.20 (SD = 0.09)
in he con ol da a, while he explo a i e beha io was M= 0.53
(SD = 0.16) o he ci y wi h agen s and M= 0.80 (SD = 0.09) o he
con ol da a. This indica es ha agen s p omp mo e local
conse a i e beha io , educing explo a o y ac ions du ing
pa icipan s’ini ial ci y exposu e. These findings p o ide pa ial
suppo o he Visi Hypo hesis, sugges ing ha while agen s
influence local e u n pa e ns, hei e ec on o e all isi coun s
is session-dependen a he han uni e sal.
2.1.4 Assessmen o isual beha io du ing
explo a ion: In es iga ing dwell ime on agen s
and buildings
Acco ding o ou Dwell Hypo hesis, we expec ed pa icipan s o
alloca e mo e isual a en ion o con ex ually meaning ul agen s
wi hin he en i onmen . To cha ac e ize wha pa icipan s ocused
on in he ci y, we quan ified hei isual beha io by summing he
cumula i e ime spen gazing a each objec , e med dwell ime. We
hypo hesized ha pa icipan s would ha e highe dwell imes o
con ex ual agen s and public buildings compa ed o acon ex ual
agen s and esiden ial buildings, assuming con ex ual agen s
cong uen wi h hei su oundings would a ac he mos a en ion.
The da a e ealed dis inc pa e ns in he a en ion pa icipan s
alloca ed o di e en ypes o objec s in he ci y. No ably, gene al
esiden ial houses ac oss he ci y we e obse ed o a sho e
du a ion compa ed o ou expe imen al buildings, wi h
FIGURE 2
Explo a ion s a egy quan ifica ion (a) This panel illus a es he ansla ion o con inuous mo emen in o quan ifiable beha io al uni s. Mo emen
coo dina es, deno ed by beige do s, a e assigned o he closes node o edge cen oid. Nodes, ep esen ed by blue ci cles, a e posi ioned a decision
poin s and a e in e connec ed by black lines (edges). A conse a i e decision occu s when pa icipan s e u n o a equen ly isi ed node, whe eas an
explo a i e decision is made when a less equen ed node is chosen (b) This panel in oduces a s a egy ma ix designed o analyze na iga ional
choices. The ma ix e alua es he pa h chosen by a pa icipan (displayed on he y-axis) agains all o he possible pa hs a a decision poin (x-axis). Fo
ins ance, upon exi ing node 157 (as seen on Panel A), he pa icipan ’s choice o node 156 (wi h wo p e ious isi s) is compa ed agains nodes 155 and 158,
which had 5 and 0 isi s, espec i ely. The ma ix is s uc u ed such ha ows indica e isi s o he selec ed node (in his case, 156) and columns o he
unselec ed nodes (in his case, 155 and 158). En ies in he ma ix a posi ions [2,0] and [2,5] inc emen by one, eflec ing he selec ion o node 156 o e
al e na i es. This ma ix isually encodes decision-making pa e ns: coun s abo e he diagonal sugges conse a i e decisions, and hose below indica e
explo a o y ac ions (c) This panel syn hesizes decision-making ends by displaying he a e age sum o decisions, ca ego ized by s a egy, by all
pa icipan s ac oss ou wo expe imen s and he con ol g oup in a single session.
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pa icipan s spending on a e age app oxima ely 6.68 s (M= 6.68,
SD = 6.69) gazing a hese buildings. This is in con as o he longe
iewing imes o ou public ask buildings (M= 11.82, SD = 8.48)
and esiden ial ask buildings (M= 11.66, SD = 8.29), which
a ac ed mo e sus ained a en ion om pa icipan s.
Fu he mo e, global landma ks wi hin he ci y ga ne ed
significan ly longe dwell imes, wi h pa icipan s spending an
a e age o 16.27 s (M= 16.27, SD = 10.69) ocusing on hese
p ominen ea u es, nea ly h ee imes he a e age dwell ime o
gene al buildings. In e ms o agen s, con ex ual agen s we e
obse ed o an a e age o 3.63 s (M= 3.63, SD = 3.74), while
acon ex ual agen s a ac ed sligh ly less a en ion, wi h an a e age
dwell ime o 2.66 s (M= 2.66, SD = 2.78). These findings indica e
ha ou expe imen al manipula ions e ec i ely cap u ed
pa icipan s’gaze in he expec ed o de o bo h buildings
and agen s.
We u he analyzed how each expe imen al ac o influenced
isual a en ion. On a e age, pa icipan s spen mo e ime looking a
con ex ual agen s (M= 3.39, SD = 3.49) compa ed o acon ex ual
agen s (M= 2.73, SD = 2.91, see Figu e 4a). Con ex ual agen s also
seemed o dis ac pa icipan s om ocusing on he a ea behind
hem, as buildings wi h con ex ual agen s had lowe dwell imes
(M= 12.42, SD = 8.13, see Figu e 4e) compa ed o hose wi h
acon ex ual agen s (M= 13.28, SD = 9.27) see Figu e 4b. These
FIGURE 3
Agen -induced bias in explo a ion pa e ns (a) The figu e compa es decision-making in i ual en i onmen s wi h and wi hou agen p esence. I
con as s ou expe imen al da a agains a con ol om Schmid e al. (2021). Panel A shows he a e age isi coun o agen loca ions in ou expe imen
(black) and he isi s o hose same poin s in he con ol (g ey) (b) The p obabili y o pa icipan s op ing o pa hs whe e agen s a e loca ed, classi ying
decisions as conse a i e when going owa ds he agen was he mo e amilia pa h, and explo a o y when choosing he agen pa h was he less
equen ed ou e. We only show he fi s wo sessions as he di e ence disappea s a e wa d.
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esul s unde sco e he ad e sa ial ela ionship be ween agen s and
building a en ion, whe e inc eased ocus on con ex ual agen s
co esponded wi h dec eased a en ion o he buildings
behind hem.
Rega ding building ype, dwell ime on he agen was no
significan ly a ec ed by he su ounding a ea, as agen s in public
a eas (M= 3.07, SD = 3.07) and esiden ial a eas (M= 3.03, SD =
3.37, see Figu e 4c) ecei ed simila a en ion. Howe e , pa icipan s
spen mo e ime gazing a esiden ial buildings (M= 13.39, SD =
9.01) compa ed o public buildings (M= 12.32, SD = 8.42, see
Figu e 4 ). These esul s imply ha incong uen agen s di e ocus
om su oundings, as indica ed by he an i-co ela ion in dwell
imes be ween agen s and buildings.
Examining cong uency (see Figu e 4d), pa icipan s looked a
incong uen agen s (M= 3.23, SD = 3.48, see Figu e 4d) o longe
pe iods compa ed o cong uen agen s (M= 2.60, SD = 2.44). This
pa e n was opposi e o building gazing ime, wi h pa icipan s
spending mo e ime looking a buildings and su oundings when he
agen ma ched he con ex in which i was placed (M= 12.92, SD =
7.65) compa ed o when he agen did no ma ch he su oundings
(M= 12.83, SD = 9.15, see Figu e 4g). These esul s imply ha when
aced wi h incong uen agen s, pa icipan s edi ec hei ocus away
om he su oundings, as e idenced by he an i-co ela ion in dwell
imes be ween agen s and buildings in he cong uency ac o .
To accoun o he high in e -indi idual a iabili y and he
nes ed s uc u e o he da a, we employed a linea mixed-e ec s
model o p edic dwell ime on agen s, wi h subjec s as a andom
e ec . The fixed e ec s included he con ex ( esiden ial s public),
agen ype le el (acon ex ual s con ex ual), he cong uency o he
agen wi h hei su oundings (no cong uen s cong uen ), and he
in e ac ion be ween agen ype and con ex . The esul s (R2
m0.03,
R2
c0.36) indica ed ha pa icipan s gazed a agen s o a
significan ly sho e pe iod in esiden ial con ex s
(βBuilding −0.41, SE 0.08, −5.30, p<0.001, η2
p.0026).
Addi ionally, pa icipan s spen mo e ime gazing a con ex ual
agen s compa ed o acon ex ual agen s (βAgen 1.32, SE 0.08,
16.94, p<0.001, η2
p.03). The cong uency be ween he agen
and i s con ex also had a significan e ec , wi h pa icipan s gazing
a cong uen agen s o a sho e du a ion (βCong uency −0.58,
SE 0.13, −4.59, p<0.001, η2
p.0034). Mo eo e , he
in e ac ion be ween con ex and agen ac ion le el was significan
(β−0.76, SE 0.16, −4.88, p<0.001, η2
p.0022), sugges ing
ha he longes dwell imes we e obse ed o con ex ual agen s in
esiden ial se ings. The findings e eal ha esiden ial con ex ual
agen s cap u e he mos a en ion, especially when hey clash wi h
hei su oundings.
We fi ed an analogous linea mixed-e ec s model o he dwell
ime on buildings (R2
m0.014, R2
c0.089). We ound ha
FIGURE 4
Dwell imes ac oss sessions: This figu e p esen s he dwell ime, defined as he cumula i e sum o fixa ions on specific objec s du ing he explo a ion
phase, measu ed in seconds. The ba g aph shows he in e play o wo ac o s, building and agen ca ego y (a) Illus a es he a e age dwell imes o ou
dis inc building and agen ype combina ions. Each ba ep esen s a unique combina ion: public buildings wi h con ex ual agen s, public buildings wi h
acon ex ual agen s, esiden ial buildings wi h con ex ual agen s, and esiden ial buildings wi h acon ex ual agen s. The heigh o each ba indica es
he a e age dwell ime, eflec ing he ela i e isual a en ion each combina ion ecei ed. To he igh , we examine one ac o a a ime o dwell ime on
he agen on he op (b–d), and on buildings on he bo om (e–g).
F on ie s in Vi ual Reali y on ie sin.o g07
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pa icipan s gazed a buildings o a significan ly sho e pe iod in
esiden ial con ex s (β−2.02, SE 0.25, −8.19, p<0.001,
η2
p.0067). Addi ionally, pa icipan s spen less ime gazing a
buildings wi h con ex ual agen s compa ed o hose wi h
acon ex ual agen s (β−1.44, SE 0.25, −5.83, p<0.001,
η2
p.0034). The cong uency be ween he agen and he building
also had a significan e ec , wi h pa icipan s gazing a buildings
wi h cong uen agen s o a longe du a ion (β3.07, SE 0.39,
7.77, p<0.001, η2
p.01). Mo eo e , he in e ac ion be ween
con ex and agen ype was significan (β−1.61, SE 0.49,
−3.26, p0.001, η2
p.0011), indica ing ha he sho es
dwell imes we e obse ed o buildings wi h con ex ual agen s in
esiden ial se ings. These findings suppo he Dwell Hypo hesis,
demons a ing ha isual a en ion is modula ed by bo h agen
con ex uali y and en i onmen al cong uency. Specifically,
con ex ual agen s a ac ed mo e gaze ime compa ed o
acon ex ual agen s, confi ming ha con ex ually meaning ul
elemen s elici longe dwell imes. Howe e , he compe i i e
ela ionship be ween agen s and hei su oundings sugges s ha
when agen s ma ch hei en i onmen , a en ion shi s owa d he
b oade spa ial con ex a he han he agen i sel . This highligh s
he dynamic in e play be ween agen p esence and scene in eg a ion
in guiding isual a en ion.
2.2 Tes ing o spa ial knowledge acquisi ion
We assessed pa icipan s’spa ial knowledge acquisi ion using
poin ing asks. In he Poin ing o Buildings ask, pa icipan s we e
gi en sc eensho s o a ge buildings, while in he Poin ing o Agen s
ask, hey ecei ed sc eensho s o agen s agains a g ey backg ound.
Pa icipan s we e asked o poin owa d he a ge . Accu acy was
measu ed by calcula ing he angula di e ence be ween hei
poin ing di ec ion and he di ec ion o he cen e o he a ge
building o agen . This angula e o se ed as he pe o mance
indica o , wi h a pe ec sco e yielding ze o deg ees o e o and
highe alues indica ing g ea e inaccu acy.
2.2.1 Poin ing o buildings
Acco ding o ou Pe o mance Hypo hesis, pa icipan s would
ha e mo e accu a e poin ing o public buildings, especially when
a ge s had ac i e con ex ually cong uen agen -building pai s. We
applied a linea mixed-e ec s model o p edic poin ing e o s,
inco po a ing fixed and andom e ec s. The andom e ec s
accoun ed o he es loca ion o he poin ing ask (28 dis inc
loca ions wi h epea ed measu es). The fixed e ec s included he
ype o building, ype o agen , cong uency pai s, and he in e ac ion
be ween agen and building (R2
m0.02, R2
c0.56). The analysis
e ealed ha poin ing accu acy was significan ly be e , wi h lowe
e o s in public buildings (β−5.51, SE 1.69, −3.28, p<.001,
η2
p.0.0088). Con ex ual agen s significan ly imp o ed poin ing
accu acy compa ed o acon ex ual agen s (β−7.17, SE 1.72,
−4.17, p<.001, η2
p.0179). The cong uency o agen ac ions
also played a c ucial ole, wi h incong uen pai s (whe e agen
ac ions did no ma ch he con ex ) leading o be e pe o mance
han cong uen pai s (β6.88, SE 1.97, 3.50, p<.001,
η2
p.0122). Addi ionally, he in e ac ion be ween agen and
building ype was non-significan , indica ing ha he main ac o
cap u ed he ele an in o ma ion ega ding pe o mance (β4.03,
SE 2.45, 1.64, p.101). These esul s p o ide suppo o he
Pe o mance Hypo hesis, sugges ing ha con ex ual agen s aid
spa ial knowledge acquisi ion.
A e he linea mixed-e ec s analysis, we examined he
es ima ed ma ginal means (EMM) o cla i y how di e en ac o s
influenced poin ing accu acy. Public buildings (EMM 47.4,
SE 2.81) esul ed in significan ly lowe e o s han esiden ial
buildings (EMM 50.9, SE 2.94). acon ex ual agen s we e
associa ed wi h highe e o s (EMM 51.7, SE 2.93) compa ed
o con ex ual agen s (EMM 46.6, SE 2.82). The in e ac ion
be ween agen and building ype e ealed ha in esiden ial
con ex s, acon ex ual agen s esul ed in he highes e o s
(EMM 54.5, SE 3.03), while con ex ual agen s in esiden ial
con ex s showed lowe e o s (EMM 46.8, SE 2.77). In public
con ex s, acon ex ual agen s had highe e o s (EMM 49.2,
SE 2.75), while con ex ual agen s in public buildings showed
he lowes e o s (EMM 45.8, SE 2.81). The cong uency o
agen ac ions also played a key ole, wi h incong uen pai s
pe o ming be e han cong uen pai s. Specifically, incong uen
pai s had lowe e o s (EMM 45.7, SE 2.81) compa ed o
cong uen pai s (EMM 52.6, SE 3.14). The findings
emphasize ha con ex ually incong uen agen s imp o e poin ing
accu acy, educing e o s and equalizing pe o mance ac oss
building ypes (see Figu e 5a).
2.2.2 Poin ing o agen s
Acco ding o ou Pe o mance Hypo hesis, pa icipan s would
demons a e lowe poin ing e o s o he loca ions o con ex ual
agen s, pa icula ly hose posi ioned in on o public buildings. To
es his hypo hesis, we employed a linea mixed-e ec s model
simila o he one used o analyzing poin ing- o-building
pe o mance. The model included c ossed andom e ec s o
subjec s and he s a ing loca ions o he poin ing asks, co e ing
28 dis inc loca ions. The analysis e ealed (R2
m0.01, R2
c0.49) a
significan main e ec o he building ype (β4.41, SE 2.12,
2.08, p.038, η2.0022). Howe e , he in e ac ion be ween
con ex and agen ac ion was non-significan (β−0.21, SE 3.22,
−0.06, p.949).
Consis en wi h he Pe o mance Hypo hesis, pa icipan s
exhibi ed lowe poin ing e o s o con ex ual agen s
(EMM 53.7, SE 3.06) compa ed o acon ex ual agen s
(EMM 57.2, SE 3.02), indica ing ha con ex ual agen s we e
be e emembe ed. Howe e , con a y o expec a ions, poin ing
e o s we e g ea e o public buildings (EMM 56.6, SE 3.04)
compa ed o esiden ial buildings (EMM 54.3, SE 3.03). These
findings sugges ha while agen con ex uali y played a ole in
educing poin ing e o s, he expec ed acili a i e e ec o public
buildings did no eme ge. Ins ead, pa icipan s demons a ed be e
ecall o agen s a esiden ial loca ions, sugges ing ha memo y
encoding may ha e been influenced by o he en i onmen al o
a en ional ac o s beyond public-p i a e dis inc ions see Figu e 5b.
2.2.3 Accu acy di e ences be ween poin ing o
buildings and poin ing o agen s
We expec ed pa icipan s o be less p ecise when poin ing o
agen s imuli compa ed o building s imuli. To in es iga e his, we
fi s assessed whe he pa icipan s exhibi ed significan ly lowe
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p ecision when poin ing o agen s. This was achie ed by fi ing a
wo-c ossed andom e ec s model (i.e., ID and poin ing loca ion)
and p edic ing poin ing e o wi h he ype o s imuli (agen s
building) as he sole p edic o . The analysis e ealed (R2
m0.008,
R2
c0.51) ha pa icipan s we e indeed significan ly less p ecise
when poin ing o agen s imuli (β−7.74, SE 0.98, p<.001,
η2.0079) compa ed o hei pe o mance in he poin ing- o-
building ask. The es ima ed ma ginal means showed ha
pa icipan s had highe poin ing e o s o agen s imuli
(EMM 54.4, SE 2.55) compa ed o building s imuli
(EMM 46.7, SE 2.53). This clea di e ence is illus a ed in
Figu e 5c, whe e he a e age pe o mance indica es ha
pa icipan s could ecall he p ecise loca ions o buildings mo e
accu a ely han human agen s. The da a e eal ha while building
loca ions we e ecalled mo e accu a ely, agen s may ha e se ed as a
salien ea u e, enhancing he o e all spa ial ecall e en i hei
p ecise posi ions we e less accu a ely emembe ed.
2.2.4 Inclusion o gaze as a p edic o o
pe o mance
To assess whe he he ime spen looking a bo h agen s and
buildings would significan ly p edic pa icipan s’pe o mance, we
inco po a ed he dwell ime in seconds each pa icipan spen gazing
a he agen s and buildings as fixed e ec s in he poin ing- o-
building and poin ing- o-agen asks (see Figu e 6). Fo he
poin ing- o-building ask, he esul s showed (R2
m0.007,
R2
c0.144) ha only he dwell ime on buildings significan ly
p edic ed pe o mance (β−0.18, SE 0.05, −3.62, p<.001,
η2.0012), while he dwell ime on agen s did no ha e a significan
e ec (β−0.27, SE 0.15, −1.79, p.073). To assess he
obus ness o ou findings, we conduc ed a sensi i i y analysis
using boo s ap esampling (1,000 i e a ions). The esul s
confi med ha all significan p edic o s in he model e ained
hei e ec s, wi h boo s ap confidence in e als no c ossing
ze o. Con e sely, p edic o s ha we e non-significan in he
o iginal model had confidence in e als ha included ze o,
indica ing g ea e unce ain y in hei e ec s. This alignmen
be ween he boo s ap and model-based esul s sugges s ha ou
findingsa es ableandno undulyinfluenced by sample
a iabili y as can be seen on Table 1.The ypeo agen
(con ex ual s acon ex ual) emained a significan p edic o ,
wi h con ex ual agen s leading o be e pe o mance
(β−6.79, SE 1.74, −3.91, p<.001, η2.0016). The ype
o building also emained significan (β−5.71, SE 1.68,
−3.41, p<.001, η2.0010). Addi ionally, he cong uency
be ween he agen s and he building showed a significan
e ec (R2
m0.007, R2
c0.144) e ealed ha nei he he dwell
ime on agen s (β−0.47, SE 0.24, −1.72, p.086) no he
dwell ime on buildings (β−0.002, SE 0.09, −0.02,
p.981) we e significan p edic o s o pe o mance. Howe e ,
building ype, wi h he opposi e pa e n as in poin ing o
buildings (i.e., esiden ial loca ions being be e emembe ed,
β4.35, SE 1.53, 2.60, p.009, η2
p0.0012) wi h no o he
significan e ec s.
Compa ing he esul s om he wo asks, i becomes e iden
ha he e is an in e se ela ionship be ween he abili y o loca e
agen s and buildings, as bo h compe e o a en ion. Con ex ual
agen s imp o ed he ecall o hei loca ions, ye he p esence o
public buildings appea ed o de ac om he abili y o emembe
he agen . The e o e, while agen s may se e as use ul p oxies o
FIGURE 5
Analysis o poin ing accu acy ac oss expe imen al condi ions. This figu e illus a es he absolu e poin ing angula e o , a measu e o spa ial ecall
accu acy whose lowe alues indica e be e pe o mance, wi h da a poin s ep esen ing he mean e o a es and e o ba s indica ing s anda d e o s (a)
poin ing o buildings: This plo delinea es pa icipan s’poin ing accu acy o buildings pai ed wi h con ex ual and acon ex ual agen s (b) Poin ing o
Agen s: He e, pa icipan s’accu acy in poin ing o agen s is compa ed, wi h a ocus on he agen ’s ac i i y le el and he ype o building (c)
Agg ega ed Task Pe o mance: This plo displays o e all pe o mance ac oss bo h building and agen poin ing asks.
F on ie s in Vi ual Reali y on ie sin.o g09
Sánchez Pacheco e al. 10.3389/ i .2025.1497237
he a ge . In expe imen 1, he a ge s we e solely buildings
(i.e., poin ing- o-building). In con as , in expe imen 2, we included
a es ha displayedonly heagen agains ag aybackg ound
(i.e., poin ing- o-agen ). To accoun o he sequence e ec , he ask
o de was s a egically andomized, wi h one-hal o he pa icipan s
s a ing wi h he poin ing- o-agen ask ollowed by he poin ing- o-
building ask and he o he comple ing he asks in e e se o de .
Finally, all pa icipan s comple ed h ee sel -assessmen ques ionnai es
ha inqui ed abou hei pe cep ion o each agen , hei social
endencies in eal li e, and he ealism le el o he VR. The
ques ionnai e da a has no been analyzed in his wo k.
4.5 Expe imen al se up
The explo a ion and ask assessmen sessions we e conduc ed
wi h a desk op compu e wi h an In el (R) Xeon (R) W-2133 CPU,
16 GB RAM, and a N idia RTX 2080 Ti g aphics ca d. The VR
en i onmen was ende ed wi h a HTC Vi e P o Eye head-moun ed
display (HMD) se up wi h a e esh a e o 90 Hz and ho izon al and
e ical field o iew o 106°and 110°, espec i ely. We used ou
S eamVR Base S a ions 2.0, an HTC VIVE body acke 2.0, and
Val e Index con olle s o moni o pa icipan s’posi ions wi hin he
en i onmen . This combined se up achie ed sub-millime e
p ecision in cap u ing he head, body, and eye posi ions, as well
as o a ion and o ien a ion.
4.6 Spa ial ask
Pa icipan s pe o med poin ing asks in VR om a fi s -pe son
pe spec i e. In he poin ing- o-building ask, hey we e elepo ed o
28 unique loca ions wi hin he ci y, each se ing as a dis inc
e e ence poin . The sequence o hese loca ions was andomized
o each pa icipan o p e en o de e ec s. To u he minimize
sys ema ic biases, pa icipan s’o ien a ions a e elepo a ion we e
also andomized, ensu ing ha hey did no always begin acing he
same di ec ion. A each loca ion, pa icipan s poin ed epea edly
owa d one o 56 po en ial a ge s, ep esen ed by s a ic images o
buildings, wi h agen s posi ioned in on o hem. These images
we e cap u ed pe pendicula ly a a heigh o 1.80 m o ensu e
consis ency in isual p esen a ion.
The ials began wi h a isual and audi o y cue: a 25 ms g een
ci cula loading ba a he sc een’s cen e accompanied by a
beep. The a ge image appea ed a he uppe cen e o he
sc een, wi h a g een dashed lase beam p o iding a isual guide
o poin ing. Pa icipan s indica ed hei di ec ion by p essing a
igge bu on on hei con olle s. Each ial was imed o 30 s,
au oma ically concluding i a di ec ion was no indica ed wi hin his
pe iod. Pe o mance was assessed by measu ing he angula
di e ence be ween he pa icipan ’s poin ing di ec ion and he
p ecise cen e ec o o he a ge loca ion.
In he ini ial expe imen , pa icipan s comple ed 336 ials,
poin ing a 12 unique a ge s om each o he 28 e e ence
loca ions. The educ ion in he numbe o ials in he second
expe imen , whe e pa icipan s comple ed only 224 ials,
poin ing a eigh dis inc a ge s om each loca ion, was
necessi a ed by he addi ional poin ing- o-agen s ask. In bo h
expe imen s, he ials we e balanced o ensu e an e en
dis ibu ion o a ge ypes: 50% di ec ed pa icipan s o public
loca ions and 50% o esiden ial a eas.
4.7 Poin ing o agen s ask
This ask was undamen ally equi alen o he poin ing- o-
building bu specifically ocused on agen s as he a ge . The
a ge s ea u ed cen e ed sc eensho s o indi idual agen s se
agains a g ey backd op, cap u ed pe pendicula ly a a heigh o
1.80 m. Pa icipan s unde ook 224 ials and we e ins uc ed o
poin a eigh unique agen a ge s om each o he 28 e e ence
loca ions. The ask mi o ed he s uc u e o he building ask in
e ms o a ge placemen , isual and audi o y cues, and ime
cons ain s o main ain consis ency in he es ing condi ions.
Addi ionally, he sequence o hese poin ing loca ions was
andomized o each pa icipan , wi h he goal o a oiding o de
e ec s and p ese ing he in eg i y o he expe imen al da a.
4.8 “F agebogen Räumliche S a egien”
(FRS) ques ionnai e
The “F agebogen Räumliche S a egien”[FRS; Münze and
Hölsche (2011)] ques ionnai e is a 7-poin Like scale ha asks he
pa icipan s o es ima e hei spa ial o ien a ion abili ies in h ee a eas o
spa ial knowledge in eal-wo ld scena ios. Fi s , he global sub-scale
consis ed o 10 i ems (α0.89)inqui ing abou he subjec ’s abili y o
na iga e ou es om an egocen ic pe spec i e. The su ey sub-scale
inco po a es se en i ems (α0.87) ocused on he subjec ’sabili y o
men al mapping om an allocen ic pe spec i e. The ca dinal sub-scale
comp ises wo i ems (α0.80) ha que y he abili y o poin owa d
ca dinal poin s. The FRS measu es pa icipan s’likelihood o apply
spa ial s a egies ela ed o egocen ic/global knowledge, su ey
knowledge, o ca dinal di ec ions, espec i ely.
4.9 Mo emen acking in he ci y
4.9.1 Na iga ional acking
To analyze pa icipan ajec o ies and hei explo a ion
decisions, we c ea ed a da a-d i en g aph based on hei ac ual
ajec o ies while explo ing he i ual ci y. Thus, his g aph eflec s
only he pa hs and a eas ha pa icipan s walked h ough,
cons i u ed by he s ee s (edges) and c ossings o decision poin s
(nodes) ha pa icipan s walked h ough. We fi s gene a ed a
hea map o pa icipan s’mo emen o ansi ion om aw
sequen ial coo dina es o spa ial da a. This hea map accoun ed
o he numbe o imes a pa icipan s ood a a specific cell
wi hin a defined 4 m × 4 m g id on op o he ci y map. This
hea map was hen u ned in o a bina y image, whe e cells isi ed a
leas once we e assigned a alue o one and cells wi hou isi s a alue
o ze o. The esul ing image clea ly ou lined he walkable pa hs and
connec ions wi hin he ci y. We filled isola ed holes wi hin he
s ee s o ensu e he algo i hm gene a ing he g aph did no c ea e
ex aneous nodes in hese a eas. We hen gene a ed a skele on om
he bina y image, educing he ci y’s ep esen a ion o a one-pixel
F on ie s in Vi ual Reali y on ie sin.o g16
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wid h while p ese ing i s opog aphy and connec i i y. This
skele on was he ounda ion o iden i ying he g aph’s nodes
and edges. Using he ex e nal Py hon lib a y “sknw,”we
gene a ed a Ne wo kX g aph om he ci y skele on image. Nodes
we e numbe ed sequen ially, and edges we e named based on he
nodes hey connec ed (see Figu e 2a). Fo example, edge [76,60]
connec ed nodes 76 and 60. Some manual adjus men s, such as
adding an edge, we e necessa y o pe ec he g aph. Each pixel in he
skele on was ecognized as belonging o a node o an edge. This
in o ma ion was la e used o plo he g aph and calcula e dis ances
o iden i y he g aph elemen s isi ed by pa icipan s du ing
hei sessions.
To analyze explo a ion s a egies, we de eloped an algo i hm
ha con e s pa icipan s’coo dina es da a in o a sequence o
isi ed nodes and edges. This p ocess in ol ed mapping he
coo dina es o he skele on hea map’s 4 m × 4 m cells and
de e mining he closes g aph elemen using he Euclidean
dis ance o mula. The algo i hm acked ansi ions be ween
edges and nodes, eco ding each isi ed elemen . This eco d
included he ime o en y, g aph elemen ype (node/edge), and
numbe o isi s o he g aph wi h a, well as he a ailable pa hs
om he node o o igin and hei espec i e numbe o p e ious
isi s o he possible elemen s a ailable om ha posi ion. We
adjus ed he nodes’ adii o ma ch he wid h o he co esponding
s ee s. This adjus men ensu ed accu a e de ec ion o
pa icipan p esence a nodes, p e en ing unno iced
ansi ions be ween edges. O e lapping node adii in a eas
wi h se e al sho s ee s we e esol ed by assigning
pa icipan posi ions o he nea es node cen oid.
4.9.2 Na iga ional pa e n classifica ion:
S a egy ma ix
We condensed pa icipan s’decisions a nodes in o a s a egy
ma ix o analyze hei explo a ion pa e ns. The s a egy ma ix was
o ganized wi h he numbe o isi s o he chosen node on he ows
and he numbe o isi s o he no -chosen nodes on he columns.
Each decision was eco ded in he ow co esponding o he numbe
o p e ious isi s o he selec ed pa h. We added one o each column
co esponding o he numbe o p io isi s on he o he a ailable
pa hs om ha decision poin ha we e no selec ed. Fo ins ance, i
a pa icipan was a a node ha had h ee di ec neighbo s, isi ed
ze o, fi e, and wo imes, and chose o mo e in he di ec ion o he
node isi ed wo imes, we would add a coun o one o he posi ions
[0,2] and [5,2]. This ep esen s ha he pa icipan mo ed o a node
wi h wo isi s o e he op ions ha had been isi ed ze o and fi e
imes (see Figu e 2b). Decisions abo e he diagonal line in he
s a egy ma ix we e conside ed conse a i e, indica ing ha
pa icipan s p e e ed nodes hey had isi ed mo e equen ly in
he p e ious example, he one added a [0,2]. In con as , decisions
below he diagonal line we e conside ed explo a o y, as pa icipan s
chose nodes wi h ewe isi s compa ed o hei neighbo ing nodes,
in he example abo e he one added a [5,2]. Decisions exac ly on he
diagonal we e neu al, as bo h he chosen and he adjacen nodes
had he same numbe o p e ious isi s. This me hod allowed us o
quan i y and compa e pa icipan s’explo a o y and conse a i e
endencies as hey na iga ed he i ual ci y, p o iding insigh in o
hei spa ial decision-making p ocesses as hey gained expe ience
inside Wes b ook.
4.9.3 Eye- acking p ep ocessing and classifica ion
We applied a eloci y-based algo i hm ha classified con inuous
eye mo emen s in o gazes and saccades and co ec ed he esul ing
gazes o he pa icipan s’mo emen in he 3D en i onmen , as
de eloped by (Nol e e al., 2024). In p epa a ion o his algo i hm,
we p ep ocessed he da a by excluding po ions de ec ed as in alid
(e.g., blinks). In cases whe e mo e han one collide hi was de ec ed
wi hin he same sample, we e ained he closes hi o he pa icipan ,
excep o backg ound collide s, such as lea es o ences, in which
case we kep he second closes hi . As a las s ep, we d opped
duplica ed samples and applied a 5-poin median fil e o he gaze
coo dina es. The algo i hm calcula es he median eloci y o eye
mo emen s wi hin a ime window (in ou case, a 10-s window).
Samples exceeding his eloci y h eshold a e iden ified as saccades,
while hose alling below he h eshold a e classified as gazes. To
ensu e accu acy, we applied ou lie de ec ion based on median
absolu e de ia ion o co ec o gaze e en s wi h anomalous
du a ions (i.e., exceeding h ee median absolu e de ia ions).
Dwell ime was defined as he cumula i e ime a subjec spen
gazing a a specific objec wi hin he ci y ac oss all sessions. We
compu ed he dwell ime o each subjec -objec pai du ing hei
en i e explo a ion pe iod wi hin he Wes b ook en i onmen .
4.10 Da a analysis
Gi en he hie a chical s uc u e o ou da a, we employed Linea
Mixed-E ec s Models o ou analysis. The modeling was done
using R 4.3.2 wi h he lme () unc ion om he lme4 package.
We used Res ic ed Maximum Likelihood o es ima ion and he
nlop w ap op imize (Ba es e al., 2015). This app oach allows us
o handle he nes ed s uc u e o ou da a, wi h andom e ec s o
accoun o wi hin-subjec a iabili y. Fixed e ec s wi h wo le els
we e e ec -coded o ensu e he be as eflec he di e ences be ween
hese le els, using he fi s le el o each pai as he base o
compa ison.
4.10.1 Explo a ion phase analysis: Na iga ional
co e age o he ci y
To analyze pa icipan s’ ee explo a ion pa e ns, we quan ified
hei walking beha io h ough he i ual ci y using a p imal ci y
g aph Neal (2013). The co e age a io, defined as he p opo ion o
unique nodes isi ed du ing each session, was modeled using a linea
mixed-e ec s app oach. The model included fixed e ec s o he
session, expe imen , and in e ac ions, wi h planned con as es ing
o each session agains he fi s . Random in e cep s o pa icipan s
we e included o accoun o epea ed measu es wi hin subjec s. The
model o mula o he indi idual session co e age a io was (see
Equa ion 1):
Indi idual Ra io ~ Session × Expe imen +1|pa icipan

(1)
The same s uc u e was used o es o he cumula i e a io o
isi ed nodes, in which we kep ack o how many unique decision
poin s each pa icipan had isi ed, accumula ing hem be ween
sessions. The model o mula o he cumula i e co e age a io was
(see Equa ion 2):
Cumula i e Ra io ~ Session × Expe imen +1|pa icipan

(2)
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4.10.2 Walking s a egies: Explo a o y s
conse a i e na iga ional beha io
To in es iga e he ex en o which pa icipan s used conse a i e
o explo a o y walking s a egies, we modeled he numbe o
decisions a each node. A linea mixed-e ec s model (see
Equa ion 3) was used wi h he session, s a egy (conse a i e s
explo a o y), and expe imen as fixed e ec s and andom in e cep s
o pa icipan s.
Numbe o decisions ~ Session × S a egy +Expe imen
+1|pa icipan
 (3)
4.10.3 Visual beha io du ing explo a ion
We quan ified pa icipan s’ isual beha io by summing he
cumula i e ime spen gazing a objec s (dwell ime) du ing he
en i e explo a ion phase. Sepa a e models p edic ed dwell ime
on agen s (see Equa ion 4) and buildings (see Equa ion 5), wi h
fixed e ec s o agen ype (acon ex ual s con ex ual), con ex
e ec ( esiden ial s public), he cong uency o he agen wi h
hei su oundings (no cong uen s cong uen ), and hei
in e ac ions, including andom e ec s o pa icipan s wi h he
ollowing o mulas:
Dwell Timeagen ~1+Building Ca ego y × Agen Ca ego y
+1|pa icipan

+1|poin ing loca ion

(4)
Dwell Timebuilding ~1+Building × Agen Ca ego y +Cong uence
+1|pa icipan

+1|poin ing loca ion

(5)
4.10.4 Poin ing ask: Poin ing o buildings
We assessed spa ial knowledge using poin ing asks, calcula ing
he angula e o as he pe o mance indica o . Two sepa a e linea
mixed-e ec s models (see Equa ions 6,7) p edic ed poin ing e o
based on building ype ( esiden ial s public), agen ype
(acon ex ual s con ex ual), he cong uency o he agen s wi h
hei su oundings, dwell ime on agen s, dwell ime on
buildings, and he in e ac ion o agen and building ype.
Random e ec s o pa icipan s and poin ing loca ions we e
included in each model:
Poin ing E o building ~1+Building Ca ego y × Agen Ca ego y
+Cong uency +Dwell Timebuilding +Dwell Timeagen
+1|pa icipan

+1|poin ing loca ion

(6)
Poin ing E o agen ~1+Building Ca ego y × Agen Ca ego y
+Dwell Timeagen +Dwell Timebuilding +1|pa icipan

+1|poin ing loca ion
 (7)
4.10.5 Compa ing poin ing accu acy
be ween asks
To compa e accu acy be ween poin ing o buildings and
poin ing o agen s, we used a model wi h he ype o s imuli
(agen s building) as he fixed e ec and pa icipan s and
poin ing loca ions as andom e ec s (see Equa ion 8):
Poin ing E o ~ 1 +Tes +1|pa icipan

+1|poin ing loca ion

(8)
Following model fi ing, we pe o med likelihood a io es s
o compa e each model agains a null model con aining only
he in e cep , e alua ing he added p edic i e powe o
ou ac o s.
4.10.6 Pe mission o euse and copy igh
Figu es, ables, and images will be published unde a
C ea i e Commons CC-BY licence and pe mission mus be
ob ained o use o copy igh ed ma e ial om o he sou ces
(including e-published/adap ed/modified/pa ial figu es and
images om he in e ne ). I is he esponsibili y o he au ho s
o acqui e he licenses, o ollow any ci a ion ins uc ions
eques ed by hi d-pa y igh s holde s, and co e any
supplemen a y cha ges.
Da a a ailabili y s a emen
The aw da a suppo ing he conclusions o his a icle will be
made a ailable by he au ho s, wi hou undue ese a ion.
E hics s a emen
The s udies in ol ing humans we e app o ed by Uni e si y o
Osnab ück E hics Commi ee. The s udies we e conduc ed in
acco dance wi h he local legisla ion and ins i u ional
equi emen s. The pa icipan s p o ided hei w i en in o med
consen o pa icipa e in his s udy.
Au ho con ibu ions
TS: Concep ualiza ion, Fo mal Analysis, In es iga ion,
W i ing –o iginal d a , W i ing – e iew and edi ing. MS:
Fo mal Analysis, In es iga ion, Resou ces, W i ing – e iew and
edi ing. KG: Fo mal Analysis, In es iga ion, Resou ces,
W i ing – e iew and edi ing. VS: Concep ualiza ion, Resou ces,
W i ing – e iew and edi ing. DN: Fo mal Analysis, In es iga ion,
Resou ces, W i ing – e iew and edi ing. SK: Concep ualiza ion,
Supe ision, W i ing –o iginal d a , W i ing – e iew and edi ing.
GP: Concep ualiza ion, Funding acquisi ion, Supe ision,
W i ing – e iew and edi ing. PK: Concep ualiza ion Funding
acquisi ion, Supe ision, W i ing –o iginal d a ,
W i ing – e iew and edi ing.
Funding
The au ho (s) decla e ha financial suppo was ecei ed o he
esea ch and/o publica ion o his a icle. The Uni e si y o
Osnab ück suppo ed his wo k in coope a ion wi h he Deu sche
Akademische Aus auschdiens (DAAD), G an No. 57440921 and
he Deu sche Fo schungsgemeinscha (DFG, Ge man Resea ch
Founda ion)—GRK 2340.
F on ie s in Vi ual Reali y on ie sin.o g18
Sánchez Pacheco e al. 10.3389/ i .2025.1497237
Acknowledgmen s
The au ho s exp ess hei g a i ude o e e yone who con ibu ed
o his p ojec . They would like o hank No a Maleki and Linus
Tiemann o hei help in de eloping he VR ci y and he Poin ing
Tasks equi ed o hese expe imen s, and Philipp Spaniol o his 3-
dimensional a and implemen a ion o di e en ial loading o le els o
de ails wi h he agen s on his scene.
Conflic o in e es
The au ho s decla e ha he esea ch was conduc ed in he
absence o any comme cial o financial ela ionships ha could be
cons ued as a po en ial conflic o in e es .
The au ho (s) decla ed ha hey we e an edi o ial
boa d membe o F on ie s, a he ime o submission. This
had no impac on he pee e iew p ocess and he
final decision.
Publishe ’s no e
All claims exp essed in his a icle a e solely hose o he
au ho s and do no necessa ily ep esen hose o hei a filia ed
o ganiza ions, o hose o he publishe , he edi o s and he
e iewe s. Any p oduc ha may be e alua ed in his a icle, o
claim ha may be made by i s manu ac u e , is no gua an eed o
endo sed by he publishe .
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