VISUAL COGNITION
https://doi.org/10.1080/13506285.2018.1561567

Waldo reveals cultural diﬀerences in return ﬁxations
Patrick Lütholda*, Junpeng Laoa*, Lingnan Heb, Xinyue Zhouc and Roberto Caldaraaa

Eye and Brain Mapping Laboratory (iBMLab), Department of Psychology, University of Fribourg, Fribourg, Switzerland; bSchool ofCommunication and Design, Sun Yat-Sen University, Guangzhou, People’s Republic of China; cSchool of Management, Zhejiang University,
Zhejiang, People’s Republic of China
ABSTRACTARTICLE HISTORY

Humans routinely perform visual search towards targets to adapt to the environment. These
sequences of ballistic eye movements are shaped by a combination of top–down and bottom–
up factors. Recent research documented that human observers display cultural-speciﬁc ﬁxation
patterns in a range of visual processing tasks. In particular, eye movement strategies extracting
information from faces clearly diﬀers between Western Caucasian (WC) and East Asian (EA)
observers. However, whether such cultural diﬀerences are also present for visual scene
processing remains debated. To this aim, we recorded the eye movements of WC and EA
observers while they were solving visual search problems parametrically varying in diﬃculty:
Where’s Waldo. Both groups had a comparable familiarity with the Waldo books reaching acomparable level of accuracy in target detection. Both cultural groups also showed a comparable
temporal eﬀect on inhibition of return, with longer ﬁxation durations when saccades were
performed to a return location compared to other locations. Westerners, however, located Waldo
faster than Easterners. Interestingly, this modulation of speed was likely related to diﬀerences
occurring on the low-level mechanisms of spatial inhibition of return, with EA observers
returning more often to previously visited locations than the WC observers. This suboptimal eye
movement strategy in the Easterners might be engendered by their cultural perceptual bias
consisting in a greater use of extra-foveal information. Overall, our data point towards theexistence of a subtle, but signiﬁcant diﬀerence in the processing of visual scenes acrossobservers from diﬀerent cultures during active visual search.

Received 27 February 2018
Accepted 12 December 2018

Visual search is a critical perceptual task routinely performed by humans to select objects and informationof interest in the environment. Eye movements play a
crucial role in achieving this visual challenge, by continuously selecting targets among distractors with aseries of ﬁxations and saccades (Liversedge & Findlay,
2000). Importantly, these scan paths are neither randomly distributed nor completely deterministic, whichcomlexiﬁes their understanding. In fact, ﬁxation patterns are highly stable within the same observer exploring a particular visual scene (Andrews & Coppola, 1999),
but vary greatly between observers and as a function ofdiverse task constraints (Henderson, 2003).
In the past 50 years, many theories have been proposed to model and predict eye movement scan paths
during visual scene processing. One of the most prominent approaches consists of quantifying the visualsaliency of images by using a class of “bottom–up”

KEYWORDS

Visual search; eyemovements; inhibition of
return; culture

computer vision models (Itti & Koch, 2001). For
example, the seminal work by Itti, Koch, and Niebur(1998) extracted saliency maps based on low-levelproperties of the images (i.e., spatial frequency, edge
density, and local contrast) to predict free-viewing
eye movements. Nowadays, there are more than 50
saliency-inspired ﬁxation prediction models, which
also include the broader categories of visual attention
models (Bylinskii et al., 2015; Kümmerer, Wallis, &Bethge, 2015). With the recent advance in deep learning with multi-layer neural networks, performance inmodelling and predicting eye movement patterns on
a benchmark dataset with saliency-inspired models
continues to improve (e.g., Kümmerer, Wallis, &Bethge, 2016). For more details, see the MIT Saliency
Benchmark website http://saliency.mit.edu. However,human observers display distinctive scan paths evenon an identical image as a function of the task at

CONTACT Roberto Caldara
roberto.caldara@unifr.ch
Eye and Brain Mapping Laboratory (iBMLab), Department of Psychology, University of Fribourg,
Faucigny 2, 1700 Fribourg, Switzerland*Joint ﬁrst authors.
Supplemental data for this article can be accessed at https://doi.org/10.1080/13506285.2018.1561567© 2019 Informa UK Limited, trading as Taylor & Francis Group2

P. LÜTHOLD ET AL.

hand, an eﬀect already highlighted in the very early
eye tracking studies (Buswell, 1935; Yarbus, 1967).
Attention models have also been integrated as a setof predictors in those models (e.g., Torralba, Oliva, Castelhano, & Henderson, 2003) to account for this “topdown” information (i.e., task eﬀects, scene-schema
knowledge, and other factors). Importantly, evenwith the consideration of both low-level bottom–up
visual information and high-level top-down information, predicting individual eye movement scanpaths remains a scientiﬁc challenge (e.g., Greene, Liu,
& Wolfe, 2012; Haji-Abolhassani & Clark, 2014), due
to the variability present across observers.
A compelling example of human variability in eye
movements is face processing. While early studies(Yarbus, 1967) suggested the presence of a common
triangular scan path during the viewing of faces,
increasing evidence has highlighted the existence ofrobust idiosyncratic scan paths for faces (Chuk, Chan,& Hsiao, 2014; Mehoudar, Arizpe, Baker, & Yovel,2014). Kanan, Bseiso, Ray, Hsiao, and Cottrell (2015)
showed that observers do not exhibit universal scanpath patterns while performing a series of face processing tasks. These observations also resonate with theperceptual cultural diﬀerences reported over the last
decade. Cross-cultural studies comparing East Asians(EA) and Western Caucasians (WC) observers havehighlighted distinct eye movement strategies during
face identiﬁcation (Blais, Jack, Scheepers, Fiset, &Caldara, 2008; Caldara, 2015; Calder & Young, 2005;
Kelly et al., 2011; Kelly, Miellet, & Caldara, 2010;
Miellet, He, Zhou, Lao, & Caldara, 2012; Rodger, Kelly,
Blais, & Caldara, 2010) and the decoding of facial
expressions of emotion (Geangu et al., 2016; Jack,
Blais, Scheepers, Schyns, & Caldara, 2009). Notably,
Western observers ﬁxate more local face features(i.e., the eye and the mouth) during face recognition,
whereas Eastern observers deploy more global
ﬁxation to the center of the face while obtaining acomparable level of accuracy. More importantly,
these culturally distinctive scan paths are also
related to a cultural tuning towards distinct spatial frequency information, as demonstrated by eye movement studies using a gaze-contingent technique
combined with retinal ﬁlter models (Miellet, Vizioli,
He, Zhou, & Caldara, 2013) and psychophysical experiments (Tardif et al., 2016). Western observers use
more high spatial frequency information sampling,whereas Eastern observers rely more on low-spatial

frequencies to process faces (for a review see,Caldara, 2017).
While such clear cultural diﬀerences in visualsampling strategies across observers from diﬀerent
cultures during face processing are well-established,
it still remains debated whether such modulations
extend to scene perception. Early studies havereported a similar local/global ﬁxation bias between
WCs and EAs during the viewing of scenes (Chua,
Boland, & Nisbett, 2005; Goh, Tan, & Park, 2009;
Masuda, Akase, Radford, & Wang, 2008). For
example, Chua et al. (2005) showed that Americans
spent a greater proportion of viewing time on focal
objects relative to the background than Chinese participants, whereas Chinese observers made more
ﬁxations towards the background than Americans.
Similarly, Goh et al. (2009) also found that WC observers ﬁxated more on the focal object in a picture,
whereas EAs constantly shifted their gaze between
the focal object and the background. However, otherstudies reported little or no diﬀerence between thetwo cultures (Evans, Rotello, Li, & Rayner, 2009;
Miellet, Zhou, He, Rodger, & Caldara, 2010; Rayner,
Castelhano, & Yang, 2009; Rayner, Li, Williams, Cave,& Well, 2007). For example, a replication of the study
by Chua et al. (2005) did not yield to any diﬀerence
between the two cultural groups (Evans et al., 2009).
Miellet et al. (2010) also did not ﬁnd any diﬀerencein eye movement strategy between WCs and EAs ina visual search task involving the search of an animal
in natural scenes with a gaze-contingent technique.
Using centered parametric Blindspots of various sizes,
they showed that WCs and EAs are equally impacted
by the (lack of) foveal information in the search task,
thus questioning the hypothesis that EAs rely moreon peripheral information during (active) visual search.
A possible explanation for such inconsistencies
might arise from the extremely rich visual information
contained in natural scenes which could undermine
the possibility of revealing subtle scan path diﬀerences across WC and EA observers. In fact, when lesscomplex stimuli are used during visual search(Cramer, Dusko, & Rensink, 2016; Petrova, Wentura, &Fu, 2013) or visual categorization (Boduroglu, Shah,& Nisbett, 2009; Lao, Vizioli, & Caldara, 2013) cultural
diﬀerences do emerge. Cramer et al. (2016) showedthat Easterners raised in an EA environment have nosigniﬁcant asymmetry in a visual search task involving
the search of long versus short lines, whereas EA

VISUAL COGNITION

immigrants present this asymmetry. In addition, Boduroglu et al. (2009) showed EA observers were better
than WCs in a colour detection task with simple geometry shapes that required eﬃcient parafoveal
vision. Similarly, Petrova et al. (2013) found that taskirrelevant distractors had a greater inﬂuence on EAs
than WCs during a simple saccade task. Nevertheless,whether such cultural perceptual diﬀerences in visual
search would extend to more complex visual scenes
remains to be clariﬁed.
Importantly, previous studies comparing eye movement scanpaths of WCs and EAs observers using highlevel stimuli during visual search have overlookedspatial saccade selection. Human observers certainly
do not select the next ﬁxation location randomly(e.g., Friston, Adams, Perrinet, & Breakspear, 2012)and given the eﬃciency and preferences in processing
visual information, the gaze foraging mechanisms
might also be diﬀerent across cultures. If the cultural
tuning towards diﬀerent spatial frequency information
between the two cultures holds also in scene perception, we should expect to detect these (subtle) diﬀerences in the spatial distribution of the foraging
saccade. One of the possible markers of spatio-temporal sequential scanning strategies is inhibition of
return (IOR, Posner & Cohen, 1984). IOR relates to thediﬃculty of reaction (delay in response) to a previously
attended location. This eﬀect was ﬁrst observed in the
Posner paradigm (Posner, 1980; Posner & Cohen,
1984), an experimental procedure where participants,
after viewing a probable cue (valid, invalid), have to
respond to a location next to the central ﬁxationcross that had been previously ﬁxated. The eﬀect of
IOR has been extensively investigated in experimental
psychology (for a review, see Klein, 2001) with the
hypothesis that IOR helps the attentional system toreorganize information and plan forward movements
during scene inspection. This bias in saccade directionis also shown in more general visual search tasks (Klein& MacInnes, 1999). One of these tasks was inspired by
the famous children’s book Where’s Waldo (Handford,
1987), where participants were asked to ﬁxate a suddenly appearing probe during the search of Waldo.
The probe was presented either at the previously
located position or at one of ﬁve possible positions
deﬂected by 60° around a circle orbiting at theradius of the previous saccade length. Saccade
latencies were slowest when the probe was on thepreviously ﬁxated location, supporting the idea of a3

foraging facilitator eﬀect as a result of IOR, which
was highlighted by a forward bias in the spatial distribution of ﬁxations. Smith and Henderson (2011) replicated Klein and MacInnes’s Waldo study (1999)
conﬁrming elevated reﬂexive saccade latencies toprobes at previously visited locations. However, they
concluded that IOR was not responsible for the foraging facilitation. They instead referred to theirﬁndings of higher proportion of forward proceeding
saccades, as a saccadic momentum (i.e., repetition of
the saccadic program, rather than a consequence ofIOR (Smith & Henderson, 2009). Regardless of thediﬀerences in the theoretical explanations of thisphenomenon, the existence of a spatial bias in theselection of the foraging saccade remains uncontended. However, no study has directly compared
the spatial distribution of saccade selection between
WC and EA observers yet. Thus, it remains to be determined whether ﬁne-grained measures of the spatial
dynamics of eye movements during visual search aremodulated by culture.
To this aim, we recorded the eye movements of WC
and EA observers while they were solving one of themost famous visual search problems: Where’s Waldo.
Where’s Waldo provides a unique active visual searchtask with parametrical diﬃculty and complexity. The
vignettes contain rich high-spatial frequency information (edges and details), with an overall uniformbackground (relatively unbiased spatial layout with aunique clutter scene), representing an excellent proxy
to probe the existence of perceptual cultural diﬀerences
between the two cultures beyond simple geometricstimuli. All the observers shared a comparable level ofvisual experience with the famous book and no particular expertise in solving this visual problem. In addition,
importantly, having previous experience with Waldobooks hardly presents an advantage in the localizationof Waldo (Olson, 2015). We selected 30 vignettes witha parametric level of diﬃculty (see Figure 1 for an
example of the stimuli). Note that although the paradigm and analyses we used were heavily inspired by
the IOR literature, we did not include an additional
cuing task as in Smith and Henderson (2009). Instead,
we mainly focus on the distribution mapping of nback ﬁxations, with spatial IOR as one of the potential
factors modulated by culture, while also performing atemporal analysis on the ﬁxation duration prior to
return saccades compared to other saccades types(i.e., foregoing, over- and undershoot).

4

P. LÜTHOLD ET AL.

Figure 1. One of the Where’s Waldo illustrations presented to the participants. Stimuli were scanned from the Solid Gold Collection(Handford, 2008). The red circle, which was not visible during the experiment, indicates a Waldo target.

Figure 2. Illustration of the calculation of relative saccade orientation and amplitude (in reference to the 1-back or 2-backsaccade). The black solid dot marks the penultimate ﬁxation t0(i.e., the current ﬁxation location, second last in the sequence);
black open dots mark the previously visited locations in sequence
depicted by the black arrows; the black solid square marks apotential future ﬁxation location at t1. The green circle marks adistance of 0° of visual angle; the exterior red circle marks alarger distance at x° of visual angle. To calculate the angle
between the current ﬁxation (t1) and any previous ﬁxation (1back and 2-back), the penultimate ﬁxation location (t0) serves
as the pivot. Angle α is calculated at t0 between the current
ﬁxation (t1) and the 1-back ﬁxation. Angle β is calculated
between the current ﬁxation (t1) and the 2-back ﬁxation.

We analysed the characteristics of the scan paths byprojecting the spatial ﬁxation distributions in reference to the 1-back and 2-back ﬁxations, by using therelative saccade orientation and amplitude, similar to
Smith and Henderson (2011; see also Figure 2). Importantly, to investigate the ﬁne-grained diﬀerences
between WC and EA observers, we applied Kernel
density estimations on the empirical joint likelihoodof the relative saccade orientation and amplitude.
We then used a robust data-driven approach to statistically isolate the diﬀerences between WC and EA
observers (Lao, Miellet, Pernet, Sokhn, & Caldara,2017). Our results show that EA observers are more
likely to orientate their gaze to a previously ﬁxatedlocation compared to WC observers, while the temporal aspects of inhibition of return were not modulated by culture. This ﬁxation pattern might relate toa reduced sensitivity towards foveal information in
the EA observers. Such an eye movement samplingstrategy might also impair their performance in thisvisual search task, as ﬁnding Waldo involves the processing of high-spatial ﬁltering information.

Methods
The experiment material, raw data, and analysis scripts
are openly available at https://osf.io/2q8jw/.

VISUAL COGNITION

Participants
Twenty WCs (11 females, mean age = 24.70 (SD = 2.78))
and 20 EA (11 females, mean age = 22.25 (SD = 1.77))
from the University of Fribourg participated in thecurrent study. All participants were right-handed withnormal or corrected-to-normal vision. Participants
were interviewed before and after the experiment toensure they were familiar with the Waldo task andhad no memory of the exact images presented during
the experiment, or had a particular expertise with thistask. All the EA participants were from Mainland
China, had never been to a Western country before,and had not spent more than three months in Switzerland. The experiment was approved by the local ethics
committee and participants provided written informed
consent upon arrival to the experiment.

Eye tracking
Eye movements were recorded by means of a
Desktop-Mount EyeLink 2K eye tracker (SR ResearchLtd., Mississauga, Ontario, Canada) with a temporal
resolution of 1000 Hz and a spatial resolution of
0.01° of visual angle. The average gaze position error
was about 0.25°. We recorded observers’ eye movements from the dominant eye monocularly via
Matlab (R2006a), using the Psychophysics (PTB-3)
and EyeLink Toolbox extensions (Brainard, 1997; Cornelissen, Peters, & Palmer, 2002; Pelli, 1997). Standard
calibration and validation of the EyeLink were conducted before the experiment and repeated whenevernecessary during the procedure.

Stimuli and procedure
Stimuli were 30 unique full-colour Where’s Waldo1
illustrations scanned from the Solid Gold Collection(Handford, 2008). Images were typical Waldo illustrations, which contained an overly crowded scene
with very rich background/foreground elements (seeFigure 1). In each of the images, there is one distinctively dressed Waldo character (red circle, Figure 1).
The exact gesture and size of the target (Waldo)
varied from scene to scene, yet never exceeded 1° ofvisual angle. The images were presented on a Dell
P1130 19′′ CRT monitor with a 1280 × 960 pixel
white background at a refresh rate of 170 Hz. The
size of each image was 1280 × 795 pixels, subtending

5

31.13° × 19.63° of visual angle at a viewing distance of
70 cm.
Participants were instructed to search for Waldo ineach of the images. At the beginning of each trial, we
used a central ﬁxation cross as an automatic drift correction. If the participant’s eye gaze was more than 1°of visual angle away from the ﬁxation cross, anadditional calibration followed by validation was conducted. After the ﬁxation cross, a Waldo illustration
was presented at random until response. The participant responded by pressing the space bar upon thedetection of Waldo. The same image would appear
again after a blank screen (100 ms), and the participants
were instructed to indicate the location of the target
using their gaze. The next trial began after the experimenter conﬁrmed the target detection. In case of
unsuccessful detection of the target at hand within amaximum duration of 2 min, participants could either
skip the current stimuli completely, or elect to resume
searching at a later stage. They could try up to threetimes on the vignettes they were struggling with.
However, only the trials with successive identiﬁcationon the ﬁrst presentation were included in the eye
movement analysis. The experimenter was in thesame room with the subject throughout the experiment to closely monitor the eye drift and the accuracyof the subjects’ detection. After the experiment, theparticipants verbally conﬁrmed that they had neverseen these particular Waldo images before.
Eye movement preprocessing
Eye movements were preprocessed using a custom
MATLAB script applying the same default threshold parameters as in the EyeLink software (Miellet et al., 2010).
Gaze velocity above 30°/s were determined as saccades.
Fixations falling within a spatial range of <0.3° visual
angle and a temporal range of <20 ms were merged.
We extracted saccade information (onset, oﬀset, amplitude and orientation) for each participant. We analysed
the following oculomotor characteristic variables typically reported in eye movement literatures: number ofﬁxations per second, average single ﬁxation duration,
and average saccade length.
One-back and 2-back ﬁxation distribution
During the search for Waldo, all ﬁxations were
described by the means of their variation from the6

P. LÜTHOLD ET AL.

previous ﬁxation. In other words, for each saccade wecomputed the angle and amplitude diﬀerence inrelations to the 1-back and 2-back position of theeye (Smith & Henderson, 2011). As shown in Figure
2, saccade vector from t0 to t1 is compared with thevector of t0 to 1-back (α) or 2-back (β) ﬁxation. For
example, if the eye ﬁxation at t1 perfectly overlaps
with the 1-back ﬁxation, the angle and the amplitude
between the two vectors would be [0°, 0°]. For nﬁxations in a given trial from one observer, we computed n-1 saccades, which return in n-2 pairs of 1back saccadic information (relative orientation andamplitude) and n-3 pairs of 2-back saccadic
information.
Temporal inhibition of return
Temporal inhibition of return was investigated by analysing the ﬁxation duration prior to return saccades
compared to other saccades. We ﬁtted a linear
mixed eﬀect model for ﬁxation durations to test
whether ﬁxations prior to returns were longer thanforegoing, over- or undershooting ﬁxation from then-back location. Thus, for each participant and eachtrial, we computed the average ﬁxation duration forthe preceding: return saccades, foregoing saccades,
and saccades that over- or undershoot return
locations. Return saccades were deﬁned as thosewith a saccade vector formed by an angle within 5°with the n-back saccade and a distance within 1° ofvisual angle; over- or undershoot saccades are thosewith a distance larger than 1°. Similarly, foregoing saccades are deﬁned as those with saccade vector formed
an angle between [175° 185°] with the n-back saccade.
The deﬁnition of return saccades used here is ad-hoc,but we tested a range of other values (0.5°–2°) andobtained similar results.
Statistics on 1-back and 2-back ﬁxation
distribution
The statistical analysis on the relative ﬁxation distribution was performed on the joint empirical likelihood
function of the n-back saccade orientation and amplitude. We ﬁrst projected the orientation and amplitude
information into a two-dimensional space independently for each participant at a single-trial level. We
applied kernel density estimation for the joint likelihood of relative ﬁxation orientation and amplitude.

The bandwidth of the kernel is determined automatically using Scott’s Rule (Scott, 1992). We then performed statistical mapping on the Gaussiansmoothed 2D histogram. Moreover, in order to avoid
bias in the spatial mapping and smoothing, theangular–amplitude representation was modeled on a
warp two-dimensional space as shown in Figure 3(a).
Intuitively, this is equivalent of applying a convolution
on the surface of an open cylinder, with the smoothing kernel being a wrapped normal distribution on
the azimuthal angle and a normal distribution on thepolar angle. Individual angular–amplitude map was
calculated by taking the 5% trimmean across trials.
To assess the statistical diﬀerences between WC and
EA, we applied a data-driven approach based on the
Linear Mixed Model and bootstrap clustering
implemented in iMap4 (Lao et al., 2017). This is equivalent to a 2D kernel regression. We applied a random
intercept model with the subject as random eﬀect andthe culture as ﬁxed eﬀect. The result is then presented
in a radial histogram similar to Figure 3(b). The colours
in Figure 3 depict the frequency of relative saccade
angle and amplitude. The colour blue represents low
frequencies; the colour yellow represents high frequencies. Return to a previously ﬁxated location has
a value of [0°, 0°] (heat maps are inspired by the visualization used by Hooge, Over, van Wezel, & Frens,
2005; Motter & Belky, 1998).
Hypothesis testing (i.e., the linear contrast of thecoeﬃcient maps between WC and EA observers) is
performed using a bootstrap spatial clustering
method in iMap4 (Lao et al., 2017). The original statistical values (F-values) were thresholded at p < .05.
iMap4 computes the size of the clusters and later compares them with a bootstrap distribution obtained
under the null hypothesis (H0). To construct the bootstrap null distribution, we removed the mean fromeach group. This procedure ensures the null hypothesis, in which no diﬀerence between WCs and EAs, istrue. iMap4 then bootstraps the subject with replacement within each group to create 1000 null response
matrices, and performs the same linear mixed modeland contrasts (WCs – EAs). For each bootstrap, iMap4
computes the maximum cluster size of the F-value
map at p < .05 and saves it in a vector. The bootstrapped cluster distribution under H0 is the sorted
vector resulting from each hypothesis testing. Signiﬁcant clusters are deﬁned as the one from the originalcontrast with size larger than the 95th bootstrappedVISUAL COGNITION

7

Figure 3. 2D representation of the joint likelihood function of the relative saccade angular and amplitude for 1-back (a, b) and 2-back (c,d) saccades across both groups of observers. In (a) and (c), saccade information is presented in Cartesian coordinate system with therelative orientation on the x-axis and amplitude in degree of visual angle on the y-axis. Importantly, the yellow area in the middle [0°, 0°]
shows n-back saccades that deviated at an angle of 0° and an amplitude diﬀerence of 0° (a return to the n-back ﬁxated location). (b) and(d) are the polar coordinate representation of (a) and (c). Colour map indicates joint likelihood of orientation and amplitude inpercentage.

cluster size in the null distribution (for the details of
the algorithm, see Lao et al., 2017).

Results
Search performance
Before investigating the presence and impact of oculomotor IOR on eye movements, we assessed the participant’s performance in the Where’s Waldo searchtask. Statistical analysis was performed using the generalized linear mixed model in MATLAB with subject
and stimulus as random intercepts to account for
repeated measures (Baayen, Davidson, & Bates,2008). As expected, participants had diﬃculties inﬁnding the target, but showed a good performanceon average: success rate of 93.67%. In most cases,
Waldo was located at the ﬁrst attempt (86.92%).
There was no signiﬁcant diﬀerence on the successrate of ﬁnding Waldo at the ﬁrst attempt between

WC (87.93% [84.07, 90.96], square bracket shows 95%
conﬁdence intervals) and EA (86.52% [82.43, 89.77],
F(1,38) = 0.326, p = .571). The average search duration
for locating Waldo at the ﬁrst attempt was 42.53s. The
WC observers (M = 37.40s [21.72, 53.08]) were signiﬁcantly faster to locate the target than EAs (M = 59.81s[44.10, 75.52], F(1,1041) = 12.94, p = .00034).
Eye movement results
Only the trials in which participant gave correct
answers at the ﬁrst attempt were analysed for eye
movements. Overall, both groups of observers fully
explored the whole image and there is no apparent
bias in the area searched (supplementary ﬁgure 1).
We did not ﬁnd any signiﬁcant diﬀerence between
the two groups of observers for eye movement
descriptive indices (see supplementary ﬁgure 2). Bothgroups showed a comparable number of ﬁxations per
second (MWC = 3.57 [3.37, 3.77]; MEA = 3.46 [3.26, 3.66];

8

P. LÜTHOLD ET AL.

F(1,1041) = 0.55, p = .458), similar mean ﬁxation duration (MWC = 244.92 ms [233.58, 256.26]; MEA =245.20 ms [233.85, 256.55]; F(1,1041) = 0.001, p = .970),
and comparable mean saccade amplitudes (MWC =3.36° of visual angle [3.06, 3.66]; MEA = 3.05° [2.75,
3.35]; F(1,1041) = 2.28, p = .131).
To explore the eﬀect of image diﬃculty on oculomotor characteristics, we separated the Waldo
stimuli into two levels of diﬃculty (easy or hard) independently for each participant. We ﬁrst sorted thesearch durations of all the stimuli within one single
observer, and then labeled the ﬁrst 15 stimuli as easy
and the rest as hard. Eye movement characteristics
were then modeled as functions of the image
diﬃculty (easy or hard), culture of the observer (WCor EA), and their interaction. The number of ﬁxations
per second and the mean ﬁxation duration were notsigniﬁcantly modulated by the image diﬃculty, asneither the main eﬀect nor its interaction with thegroup predictor was signiﬁcant (F’s < 0.647, p > .05).
However, both the main eﬀect of the task diﬃculty(F(1, 1039) = 49.63, p = 3.3699e–12) and the image
diﬃculty x group interaction (F(1, 1039) = 12.09, p =5.2858e–4) were signiﬁcant for mean saccade amplitude. Post hoc comparisons showed that the signiﬁcant eﬀects were driven by the EA observers, who
made signiﬁcantly smaller saccades during theviewing of hard (M = 2.76° [2.456, 3.072]) than easy
(M = 3.27° [2.971, 3.578]) stimuli, whereas the WCs displayed similar mean saccade amplitudes during theviewing of both easy (M = 3.45° [3.149, 3.756]) andhard (M = 3.26° [2.948, 3.562]) stimuli (see supplementary ﬁgure 3). It is worth noting that our result does notchange even if we treat the image diﬃculty as a continuous predictor: using the z-scored search durationfor each image as predictor we found the same signiﬁcance only in the mean saccade amplitude (signiﬁcantmain eﬀect of search duration/image diﬃculty: F(1,1039) = 79.702, p = 1.934e–18, and signiﬁcant interaction: F(1, 1039) = 7.00, p = .00827).
Analysis on the 1-back and 2-back ﬁxation
distribution
The n-back ﬁxation distribution is represented as asmoothed 2D map of the relative saccade orientation
and amplitude for both groups of observers (Figure 3).
As shown in Figure 3, the forgoing saccades were notdistributed uniformly. Two local maxima are presented

in the angular-amplitude map (Figure 3(b) and (d)).
This result indicates that the subsequent saccade performed by human observers was most likely to beeither exactly the same as the previous saccade, or
directed towards the N-back ﬁxation location. More
importantly, in general, the local maximum locatedaround [0°, 0°] shows that there was no strong evidence of spatial inhibition of return, as overall all theobservers showed a high likelihood to return to theprevious ﬁxated location.
We then performed statistical mapping using iMap4
to compare the cultural diﬀerence of the 2D angularamplitude map between WC and EA (Lao et al., 2017).
The result is shown in Figure 4. For both 1-back and 2back saccades, signiﬁcant diﬀerences are revealed after
non-parametric bootstrap clustering test: EA showed
higher saccade likelihood on the [0°, 0°] compare to
WC observers, indicating a directional eﬀect towards previously ﬁxated locations for EA observers (1-back:maximum F value: F(1, 38) = 30.23, p = .00003; 2-back:maximum F value: F (1, 38) = 29.35, p = .000004).
To further quantify the cultural ﬁxation return bias
and explore its relationship with individual visual
search performance, we conducted a post hoc analysis
within the signiﬁcant cluster. Using the sum likelihood
value within the signiﬁcant region (i.e., around [0°, 0°]),we estimated that the EA observers were about 4%
more likely to saccade to a previously visited ﬁxation
location than WC observers (1-back: EA – 19.27%[18.23, 20.31], WC – 15.53% [14.49, 16.56]; 2-back: EA– 19.56% [18.43, 30.68], WC – 15.02% [13.90, 16.14]).
Importantly, this returning strategy was positively
related to the search performance (i.e., how long it
took for each participant to ﬁnd Waldo on average,
see Figure 5). The longer it took for an individual toﬁnd Waldo, the more likely for the observer toperform a return saccade (1-back: regression coeﬃcient beta = 0.0915 [0.0570, 0.1259], t(38) = 5.38, p =4.079e–6; 2-back: beta = 0.1071 [0.0722, 0.1419], t(38) = 6.22, p = 2.849e–7).
An additional post-hoc analysis was performed toassess whether the search strategy of EA observers
relates to suboptimal foveal sampling. For bothgroups, we compared the number of the proportionof trials that were terminated after the ﬁrst ﬁxation
to the target. The ﬁxation to the target is deﬁned aswithin the 1° visual angle radius, but the resulting
pattern is the same within 0.5° to 2° of visual angle.
We then ﬁtted a mixed eﬀect model with subject

VISUAL COGNITION

9

Figure 4. Statistical analyses on the diﬀerences between WC and EA on the angular-amplitude joint likelihood map. (a) Angular-amplitude map for WC in 1-back condition. (b) Angular-amplitude map for EA in 1-back condition. (c) Angular-amplitude diﬀerences between
WC and EA in 1-back condition (WC minus EA). (d) Statistical value map (F-value) for the diﬀerence showed in (c), black line indicates thesigniﬁcant area. (e) The polar representation of the signiﬁcant diﬀerent area. (f–j) are the result for the 2-back condition.

and stimuli as random group eﬀect. This analysis
revealed a signiﬁcant diﬀerence between WC and EA
observers: the proportion of trials terminated after
the ﬁrst ﬁxation to the target is lower in EA than WC
observers (EA – 47.36 [42.59, 52.18], WC – 66.05%[53.10, 76.98], t(1039) = 4.35, p = 1.5208e-05).
Temporal inhibition of return
While our spatial analysis showed that both groups ofobservers performed return saccades with a highlikelihood, a temporal eﬀect on IOR could still bepresent if ﬁxation durations prior to a return saccade
are longer than saccades performed for foregoing,
over- or undershoot saccades from prior ﬁxation
locations compared to saccades to the return
locations. We thus performed an additional temporal
analysis on ﬁxation duration on the saccades performed from the n-back location in both groups. The
linear mixed eﬀect model showed a signiﬁcant maineﬀect of Saccade type on ﬁxation duration (F(2,2170) = 6.922, p = .0010), whereas neither the main

10

P. LÜTHOLD ET AL.

Figure 5. Post-hoc analysis within the signiﬁcant cluster. The scatter plots show the likelihood to orientate the gaze towards previously
ﬁxated locations (sum likelihood within the signiﬁcant cluster, y-axis) as a function of individual search performance (average search
duration, x-axis). A robust linear regression was performed in Matlab using ﬁtlm with a Cauchy weight function. Red dot: WC observers;
Blue dot: EA observers. Error bar shows 95% conﬁdence interval of the mean.

eﬀect of Culture or the Culture X Saccade type interaction was signiﬁcant. The temporal IOR as theﬁxation duration for return saccades (241.83 ms[233.50, 249.21]) was signiﬁcantly larger than foregoing, over- or undershoot saccades (227.83 ms[223.07, 232.49]), but this eﬀect was not modulatedby the culture of the observer.

Discussion
The present study investigated whether culture modulates the oculomotor strategies during the visual
search of Waldo. We modeled the ﬁxation distribution
by quantifying the stochastic diﬀerential of the gaze
temporal dynamics (1-back and 2-back ﬁxation distributions). We had a particular interest in mapping
potential cultural diﬀerences in the spatial distributionof saccade selection, by considering spatial inhibitionof return (IOR – an inhibitory process that facilitateseye gazes to novel locations in the visual ﬁeld) as
one of the potential markers for such eﬀect. WC andEA observers found Waldo with similar accuracy anddisplayed similar eye movement properties (i.e.,
ﬁxation duration, saccade length) and distributions.
We found support of temporal IOR for both groupsof observers. Regardless of the culture of the observer,
ﬁxation durations were longer when preceding returnsaccades compared to foregoing, over- and undershoot saccades from n-back locations. However,neither groups of observers showed spatial IOR, asrevelead by a data-driven non-parametric analysis:
return ﬁxations occurred very frequently during thesearch of Waldo equally for both groups. However,the novelty of the current observation is that EA observers were even more likely to gaze towards a previously ﬁxated location compared to WC observers.
This “double-checking” visual search strategy also correlated with a decrease in performance, as the longer
an observer spent on ﬁnding Waldo, the more returnﬁxations they performed. Overall, our results highlight
this very distinct and time-consuming visual searchstrategy in EA observers, a strategy that might berelated to a decreased sensitivity in processing
foveal ﬁne-grained information. Waldo images represent a crowded scene where the dissociation
between foreground and background is parametrically hampered. The task of ﬁnding Waldo is thenstraightforward and neutral, unlike judging theappeal of a picture or reporting the content of animage, which could easily be aﬀected by subjectiveaﬀective decisions like in previous studies (Chuaet al., 2005; Goh et al., 2009; Miellet et al., 2010). Importantly, both cultural groups had comparable familiarity
with Waldo’s vignettes and the paradigm used here.
In line with Smith and Henderson (2011), our results
did not show strong evidence for spatial IOR in eithergroup of observers during the visual search of Waldo.
Smith and Henderson (2011) argued that suchcomplex visual stimuli necessitate observers to
perform return saccades for a second inspection. In
the literature of reading , the increase in reﬂexive saccades with increasing text diﬃculty is well documented (Rayner & Pollatsek, 1989; Vitu & Underwood,2005). Interestingly, we found that EA observers

VISUAL COGNITION

returned more to previously foveated locations thanfor WC observers for both 1-back and 2-back
ﬁxations. Since EA observers have a more global distribution of attention relative to WC observers, theymight be less eﬃcient in the processing of foveal information (Miellet et al., 2013). This cultural diﬀerence inthe scan path strategy might thus relate to the encoding of the high-spatial frequency details of the Waldo
image and the location of the target. EA observers
revisited the previous ﬁxation location more often
than WC observers, thus extending the search for
Waldo. On the contrary, WC observers were better inencoding local information and in asserting theabsence of the target in the ﬁxated areas and propagate their eye gaze forward by then locating Waldo
faster than EA observers. Such a lower rate of returnﬁxations observed in Westerns engenders a better
monitoring of the eye movement scan paths. As
detailed before, evidence for a larger perceptual
window in EA observers comes from previous
studies that used diﬀerent tasks and visual problems(Boduroglu et al., 2009; Miellet et al., 2013; Petrova
et al., 2013). Thus, ultimately, a future study using agaze-contingent design that parametrically manipulates the quantity of foveal information in this task(see, Miellet et al., 2013) is necessary to provide
direct evidence for a larger attentional window in
the EA observers.
Potential diﬀerences in spatial attention between
WCs and EAs observers should have been probed byusing a more conventional cueing task, as widelyused in the IOR literature (Posner & Cohen, 1984).
While the primary focus of the current study was tostatistically map the spatial distribution of saccade
selection between WC and EA observers, from ourresults we could reasonably predict a cultural diﬀerence in responding to cued versus uncued locations
during spatial attention. Surprisingly, to the best of
our knowledge, no study has directly compared participants from both cultures with cueing paradigms
yet. Future studies are necessary to investigate thisimportant issue.
It is worth noting that to quantify the spatial inhibition or facilitation of return, a baseline frequency
must be properly estimated (Bays & Husain, 2012). In
the current study, we focused on the cultural diﬀerence between WC and EA observers on saccade facilitation. The lack of spatial inhibition of return isindicated by the local maximum around the point

11[0°, 0°] in the smoothed spatial distribution of
saccade. However, to better estimated the bias insaccade facilitation and its potential cultural eﬀect, abaseline frequency should be introduced by either
presenting the same participant with naturalistic
stimulus in a free-viewing setting, or estimated by
shuﬄing saccade within the same trials (Bays &Husain, 2012; Wilming, Harst, Schmidt, & König,2013). Future studies are necessary to clarify this issue.
Inhibition of return is one of the mechanisms facilitating visual search in humans. It can be measured
spatially, as the probability of the next search location
conditioned by the previously ﬁxated area; and/or
temporally, as the delay response to targets presented
at previously sampled locations (Smith & Henderson,
2009, 2011). Here, we tested the eﬀect of temporal
IOR using oculomotor kinematics, by measuringchanges in ﬁxation duration conditioned by subsequent saccades. Our results provided evidence fortemporal IOR, with longer ﬁxation durations for
return saccade compared to other saccades (i.e., foregoing, over- and undershoot). However, ideally, an
additional behavioral measure should have beenused to quantify the eﬃciency of processing information in the return location (for instance, see Smith& Henderson, 2009, 2011). Future studies comprising
an appropriate design including additional task
demands would be necessary to further clarify the cultural eﬀect of the temporal IOR.
Finally, the larger return ﬁxation rate observed in
the EA observers increased with image diﬃculty. EA
performed shorter saccades when the individual
speciﬁc image diﬃculty increased, which could be
used as an indicator of the area inspected during asingle ﬁxation. However, it is worth noting that inline with previous studies (Evans et al., 2009; Rayner
et al., 2007, 2009), we did not observe a signiﬁcant cultural diﬀerence on the global eye-tracking measures.
The mean ﬁxation duration and the ﬁxations per
second in both groups are comparable and similareven when the diﬃculty level of the vignettes
increased.

Conclusions and future work
During the last two decades, many cultural diﬀerencesin diverse perceptual (Nisbett & Masuda, 2003; Nisbett& Miyamoto, 2005) and face processing tasks (e.g.,
Blais et al., 2008; for a review see Caldara, 2017) have

12

P. LÜTHOLD ET AL.

been reported in the literature. EA observers sample
and process visual information more with a global
strategy, compared to a local processing style typicalof WC observers. However, the results reported so
far for tasks involving the processing of naturalvisual scenes have provided mixed evidence of an cultural eﬀect (Chua et al., 2005; Goh et al., 2009; Masuda
et al., 2008; Miellet et al, 2010). Here, we investigatedwhether ﬁnding Waldo with parametrical scene complexity would elicit culturally speciﬁc eye movement
patterns. Our robust data-driven analyses have clearlyshown that visual search strategies are not falling into
universal rules, with EA observers being slower andhaving more return ﬁxations compared to WC observers.
Future studies are necessary to clarify whether and how
the global/local cultural perceptual bias observed forface processing contributes to this eﬀect. Ideally, theindividual variability across trials should also be properly
considered by using a hierarchical model directly modeling on the spatial–temporal saccade data (e.g., a
Poisson point process model).
One prediction from the current result is that local
eye movement strategy typical of Western observers
could lead to a more eﬃcient information encodingof the inspected area thus preventing a returnﬁxation. Whereas, on the contrary, the global strategy,
typical of Eastern observers compensates for a suboptimal foveal sampling, by reprocessing previously
visited locations (Findlay & Brown, 2006). In the
future, the use of a design with a gaze-contingent technique is necessary to verify this hypothesis, as visual
information intake can be controlled parametricallywith such experimental techniques. Regardless ofthese potential explanations, the present data feed
the literature by describing culturally dependent cognitive and visual strategies, which emphasize the need toreport the observers’ cultural background as a crucial
variable for the understanding of visual cognition.

Note
1. We followed the convention of previous visual search literature and refer to the task as “Where’s Waldo?”. Notethe original book from which the stimuli are takenfrom is the British version “Where’s Wally?”.

Disclosure statement
No potential conﬂict of interest was reported by the authors.

Funding
This work was supported by the Schweizerischer Nationalfonds
zur Förderung awarded to R. C. [Grant Number IZLJZ1_171065].

ORCID
Roberto Caldara

http://orcid.org/0000-0002-8260-9949

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