ORIGINAL RESEARCH ARTICLE
published: 20 February 2013
doi: 10.3389/fpsyg.2013.00034

Mapping face recognition information use across cultures
Sébastien Miellet 1 *, Luca Vizioli 1 , Lingnan He 2 , Xinyue Zhou 3 and Roberto Caldara 1 *1

Department of Psychology and Fribourg Center for Cognition, University of Fribourg, Fribourg, Switzerland
School of Communication and Design, Sun Yat-Sen University, Guangzhou, China
3
Department of Psychology, Sun Yat-Sen University, Guangzhou, China
2

Edited by:Tamara L. Watson, University ofWestern Sydney, Australia
Reviewed by:
Chris I. Baker, National Institutes of
Health, USA
Corrado Caudek, Università diFirenze, Italy*Correspondence:
Sébastien Miellet and Roberto
Caldara, Department of Psychology,
University of Fribourg, Rue Faucigny
2, 1700 Fribourg, Switzerland.
e-mail: sebastien.miellet@unifr.ch;roberto.caldara@unifr.ch

Face recognition is not rooted in a universal eye movement information-gathering strategy. Western observers favor a local facial feature sampling strategy, whereas Easternobservers prefer sampling face information from a global, central fixation strategy. Yet, theprecise qualitative (the diagnostic) and quantitative (the amount) information underlying
these cultural perceptual biases in face recognition remains undetermined. To this end, wemonitored the eye movements of Western and Eastern observers during a face recognition task, with a novel gaze-contingent technique: the Expanding Spotlight. We used 2˚
Gaussian apertures centered on the observers’ fixations expanding dynamically at a rateof 1˚ every 25 ms at each fixation – the longer the fixation duration, the larger the aperturesize. Identity-specific face information was only displayed within the Gaussian aperture;outside the aperture, an average face template was displayed to facilitate saccade planning. Thus, the Expanding Spotlight simultaneously maps out the facial information spanat each fixation location. Data obtained with the Expanding Spotlight technique confirmedthat Westerners extract more information from the eye region, whereas Easterners extract
more information from the nose region. Interestingly, this quantitative difference was pairedwith a qualitative disparity. Retinal filters based on spatial-frequency decomposition built
from the fixations maps revealed that Westerners used local high-spatial-frequency information sampling, covering all the features critical for effective face recognition (the eyes
and the mouth). In contrast, Easterners achieved a similar result by using global low-spatialfrequency information from those facial features. Our data show that the face system
flexibly engages into local or global eye movement strategies across cultures, by relying
on distinct facial information span and culturally tuned spatially filtered information. Overall,
our findings challenge the view of a unique putative process for face recognition.
Keywords: face perception, culture, eye movements, gaze-contingent, expanding spotlight, extrafoveal processing

INTRODUCTION
Face-processing is a fundamental ability for social animals suchas humans. Despite the vast amount of research on this topic, theexact nature and the specificity of the processes involved in thiscritical biological skill is a matter of ongoing debate. A potential
reason for the discrepancies in the theoretical interpretations put
forward by different authors might arise from a lack of consensus
in the literature on the definition of the nature of the processes
thought to be involved: namely the holistic, configural, and featural processing of faces. In a seminal review Maurer et al. (2002)
stated that“Configural processing of faces can be divided into three
types: (1) sensitivity to first-order relations – seeing a stimulus is aface because its features are arranged with two eyes above a nose,
which is above a mouth; (2) holistic processing – gluing together
the features into age stalt; and (3) sensitivity to second-order relations – perceiving the distances among features. However, there
is no consensus about terminology. . .” More recently, McKone(2009) defined holistic or configural processing as“a special style ofstrong perceptual integration of information from across the entire
internal region of a face” and acknowledges that “the exact natureof this style of computation is not understood.” Several authors

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in the present special issue make similar statements, agreeing on
the lack of consensus in the definition of the concept: “Unfortunately, many studies provide only verbal descriptions of holistic
processing and there is growing consensus that the concept is tooloosely defined” (Richler et al., 2012) or “However, there is a lack ofconsensus and clarity in the literature regarding what is meant byholistic processing and how it is different from the part-based processing most commonly attributed to the perception of non-face
objects” (Piepers and Robbins, 2012). We also believe that to datea formal – computational – unambiguous and accepted definitionof any of the concepts above is still missing, creating confusion in
the various interpretations of the many findings disseminated in
the face literature. Stating that a particular experimental condition or group of observers is engaging in more holistic, configural,or featural processing for one particular task or another could be
misleading if the exact nature of the process involved is not formally defined and all the alternative explanations are not properly
discarded.
In order to tackle this issue and provide novel insights to thiscritical point, here we took advantage of the eye movements’approach. Eye movements do not randomly sample the visual

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input space, but are effectively used by the brain to gather relevant information to adapt to the visual world. Therefore, theymight reveal important information on the way humans process
faces. In the past few years, we have designed a series of original
experimental paradigms and developed a novel robust statistical
approach (Caldara and Miellet, 2011) to isolate the information
actively gathered by the eyes during face-processing. By isolating
the visual information entering the face system, and the strategy
used to sample this information, we can at least put minimal constraints on the algorithms enabling effective processing of faces.
This approach might lead to a better understanding of the diagnostic information, the computation and nature of the representations
involved in face-processing.
Numerous studies have used eye movement recordings to assess
various aspects of face perception (for instance effect of memory:
Althoff and Cohen, 1999; inverted faces: Williams and Henderson,
2007; familiar faces: Heisz and Shore, 2008). Some authors haveclaimed that the type of processing (i.e., hypothetically holistic orfeatural) impacts on oculomotor patterns (Bombari et al., 2009;
Turati et al., 2010), sometimes assuming the involvement of theso-called holistic face-processing from fixation patterns (Chan and
Ryan, 2012; Guo, 2012) or from behavioral performance during
gaze-contingent paradigms (Van Belle et al., 2010a,b, 2011). Others have assumed that holistic face-processing can be independentof gaze behavior (de Heering et al., 2008).
Our recent studies showed differential oculomotor patterns
during face recognition as a function of the culture of theobservers, with a central fixation bias for the East Asian (EA)
observers and an eye-mouth bias for the Western Caucasian (WC)observers (Blais et al., 2008). Such fixational biases seem to arise
early in development, from 7 years old (Kelly et al., 2011b) and persist for inverted faces (Rodger et al., 2010), visually homogeneous
objects (Kelly et al., 2010) and for second generation immigrants
from an Eastern to a Western country (Kelly et al., 2011a). This
cultural contrast in eye movement information sampling expands
also to the categorization of facial expressions of emotions (Jacket al., 2009; Kelly et al., 2011a). However, the observed differences
across cultures in face-processing do not generalize to visual searchin complex natural scenes (Evans et al., 2009; Miellet et al., 2010).
By using gaze-contingent techniques, we have demonstrated
that despite using diverse gaze scan paths, observers from both
cultures rely on the same diagnostic features (i.e., the eyes andthe mouth) to perform face recognition and to reach comparable
levels of performance (Caldara et al., 2010; Miellet et al., 2012).
Importantly, we have developed a novel gaze-contingent technique – the iHybrid – and recently reported that WC observers
identify famous faces on the basis of both foveally (local) andextrafoveally (global) sampled information, depending on the first
fixation landing position (Miellet et al., 2011). All observers usedboth strategies, often to recover the very same identity. Altogether
these findings allow us to draw the following conclusions. Firstly,
all human observers can use different visual strategies to recognize
faces. Secondly, culture impacts upon the preferred information
sampling strategy during face recognition. EA observers are more
inclined to sample facial diagnostic information from extrafoveal
vision, whereas WC observers preferentially use foveal information. These observations suggest that the facial information used

Frontiers in Psychology | Perception Science

Facial information span across culturesto accurately individuate conspecifics is invariant across humanbeings, but the strategies used to extract this information are likely
to be flexible and might be modulated by culture. Thirdly, theseresults confirm that the coupling between fixated and processed
information is not perfect (concepts of overt vs. covert attention,
Posner, 1980). Hence, it would be misleading to infer hypothetical
holistic/configural vs. featural processing/representations based
only on fixation locations with static images. In contrast, gazecontingent techniques warrant a fine and on-line control of theavailable information, therefore allowing stronger conclusions interms of potential information use. Finally, if the same diagnostic features are sampled from different fixation locations acrosscultures, one would assume that the visual input received by
the face system is different. Yet, the precise qualitative (i.e., thediagnostic) and quantitative (i.e., the amount) information underlying these cultural perceptual biases in face recognition remains
undetermined.
In order to further clarify how the face system achieves face
identification across cultures, in the present study we introduced anovel gaze-contingent technique: the Expanding Spotlight. In previous studies we used a moving window (Spotlight, Caldara et al.,2010) or a moving mask (Blindspot, Miellet et al., 2012) and parametrically manipulated the size of the window/mask (0˚ = naturalvision, 2˚, 5˚, and 8˚ of visual angle) in order to obtain a fineassessment of the effect of the gaze-contingent manipulation on
oculomotor scan paths, information use, and performance. However, these manipulations did not allow precise measurement of
the quantity and quality of information – information span – sampled at every facial location, as for a given condition the Spotlightor Blindspot size was constant regardless of the fixation location.
In addition, despite having had clear arguments for determining
a priori the aperture sizes of our masks, one could not completely exclude a potential bias arising from the choice of theseparameters.
To overcome these limitations, in the present study the facial
information corresponding to the target identity was available only
inside a Gaussian aperture dynamically centered on the participant’s fixation location. The gaze-contingent Gaussian aperture
expanded with time (1˚ every 25 ms). The longer was the fixation duration, the larger the Spotlight aperture size became. The
Spotlight aperture was contracted to 2˚ (foveal region) at each
new fixation. We replaced information outside central vision withan average face template, to allow saccade programming and natural fixation sequences, although not providing useful information
for the recognition task. Figure 1 represents the expansion of
the Spotlight across time. Our main assumption is that, with the
Expanding Spotlight, observers would maintain a fixation to a given
location until they obtain sufficient foveal and extrafoveal information from this location to solve the task at hand. The use of an
average face template provides them with the information necessary to program the next saccade, therefore weakening the viewof maintaining fixation for this purpose only. In addition, humanvision is governed by a coarse-to-fine (e.g., Winston et al., 2003)or broad-to-fine (van Rijsbergen and Schyns, 2009) processing
over time, which again weakens the view suggesting that fixations in the Expanding Spotlight technique would be maintained
for the purpose of saccade planning. Of course, the Expanding

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Facial information span across cultures

FIGURE 1 | Illustration of the Expanding Spotlight technique. The firstrow shows on the left the average face presented outside thegaze-contingent foveal Gaussian aperture; and on the right an example of atarget face displayed at the fixation location. The second and third rows
illustrate the expansion of the Spotlight across time; they show examples ofstimulus display after 100, 200, and 300 ms fixation (first, second, and thirdcolumn respectively). The second line illustrates the Expanding Spotlight witha fixation on the left (on the screen) eye; the third line illustrates the
Expanding Spotlight with a fixation on the center of the face. Note that there
is no direct relationship between the Gaussian aperture size and theavailability of information in the full spatial-frequency spectrum. This is due tothe overall Gaussian shape.

Spotlight involves a direct relationship between the fixation duration and the spatial extent of available information, which in somecircumstances could inflate the importance of extrafoveal information revealed when the observer is finely analyzing foveated
information. However, such a bias will be uncovered by the reconstructed information span images based on a foveated retinal filter.
Therefore, the Expanding Spotlight is an active gaze-driven technique that allows precise isolation of the information span at eachfixation.
An illustration movie can be found at http://perso.unifr.ch/roberto.caldara/movies/exp_spot.mov. It is worth noting that the

Spotlight expansion is slowed down in this example in order tomake the technique easy to grasp.
Here, we used the Expanding Spotlight with WC and EA
observers performing an old-new face recognition task with WC
and EA faces. Our rationale was that the participants would maintain their fixations for the time necessary to effectively encode
the diagnostic face features of interest. Our main goal was tomap precisely the quantitative and qualitative information necessary for face recognition across culture. Specifically, with the
Expanding Spotlight , it is possible to establish precisely the information span for every single fixation/feature entering in the facewww.frontiersin.org

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system. Finally, this information was convoluted with a retinal filter to reconstruct a precise picture of the visual input used byobservers from different cultures to achieve face recognition.

MATERIALS AND METHODSPARTICIPANTS

Fifteen Western Caucasian (11 females) and 15 EA (nine females)
young adults (mean age 24.32 and 22.45 years respectively)
participated in this study. The Western Caucasian participants
were students at the University of Glasgow, UK and the EA participants were students at the Sun Yat-Sen University, Guangzhou,
China. All participants had normal or corrected vision and were
paid £6 or equivalent per hour for their participation. All participants gave written informed consent and the protocol wasapproved by the ethical committees of the Department of Psychology of the University of Glasgow and the ethical committeeof the Department of Psychology of the University of Sun Yat-Sen
University.
MATERIALS

Stimuli were obtained from the KDEF (Lundqvist et al., 1998)
and AFID (Bang et al., 2001) databases and consisted of 56 EA
and 56 Western Caucasian identities containing equal numbersof males and females. The images were 382 × 390 pixels in size,
subtending 15.6˚ of visual angle vertically and 15.3˚ of visual angle
horizontally, which represents the size of a real face (approximately
19 cm in height). Faces from the original databases were alignedby the authors on the eye and mouth positions; the images were
rescaled to match those facial features position and normalized
for luminance. Images were viewed at a distance of 70 cm, reflecting a natural distance during human interaction (Hall, 1966). All
images were cropped around the face to remove clothing andwere devoid of distinctive features (scarf, jewelry, facial hair, etc.).
Faces were presented on a 800 × 600 pixel gray background displayed on a Dell P1130 2100 CRT monitor with a refresh rate of
170 Hz.
APPARATUS

Eye movements were recorded at a sampling rate of 1000 Hz with
the SR Research Desktop-Mount EyeLink 2K eyetracker (witha chin/forehead rest), which has an average gaze position errorof about 0.25˚, a spatial resolution of 0.01˚ and a linear output over the range of the monitor used. Only the dominant eye
was tracked, although viewing was binocular. The experiment
was implemented in Matlab (R2009b, The MathWorks, Natick,
MA, USA), using the Psychophysics (PTB-3) and EyeLink Toolbox
extensions (Brainard, 1997; Cornelissen et al., 2002). Calibrationsof eye fixations were conducted at the beginning of the experiment using a nine-point fixation procedure as implemented in
the EyeLink API (see EyeLink Manual) and using Matlab software. Calibrations were then validated with the EyeLink softwareand repeated when necessary until the optimal calibration criterion was reached. At the beginning of each trial, participants were
instructed to fixate a dot at the center of the screen to perform adrift correction. If the drift correction was more than 1˚, a new
calibration was launched to insure an optimal recording quality.
The eyetracker, software and settings used in Glasgow and Sun
Yat-Sen universities were identical.

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Facial information span across cultures

EXPANDING SPOTLIGHT

The Expanding Spotlight had a zero alpha value at the center. The
alpha value is the value of the alpha channel we used to create
the Gaussian apertures combined with an image as background tocreate the appearance of partial transparency. This value increased
with distance from center of gaze according to a Gaussian function
and reached one (complete opacity) at the border of the aperture.
We used 2˚ Gaussian apertures centered on the observers’ fixations expanding dynamically at a rate of 1˚ every 25 ms at each(novel) fixation, without expansion limit constraints. We chose
this expansion rate for two reasons. Firstly, the expansion rateis sufficiently fast to keep the fixation durations in the average
range observed in natural vision during face-processing. Hence,with this expansion rate, the Spotlight reaches the size of 14˚ofvisual angle in 300 ms. A 14˚ Gaussian aperture allows the participant to process eyes and mouth from a fixation on the centerof the face (note that smaller apertures, 8˚ in Caldara et al., 2010,
produced fixation patterns similar to those observed in the baseline/natural vision condition). Secondly, this expansion rate issufficiently slow to give enough sensitivity to highlight difference
between local and global sampling strategies. For instance, at least
100 ms are necessary for the Spotlight to expand from a Gaussian covering the size of an eye (between 2˚ and 8˚ – cf. Figure 1,
middle row, first and second faces) to a Gaussian covering botheyes and mouth from a central fixation (roughly 14˚ – cf. Figure 1,
bottom row, third face). The image outside the Expanding Spotlight was an average face template composed from all the stimuli
used in the experiment, allowing the observers to program natural
saccades. Importantly, the face template does not provide any useful information for the recognition task. The display contingent
to gaze position updating required 11 ms on average (between
8 and 14 ms), eliminating any impression of flickering for theobservers.
PROCEDURE

All the participants started with a training session in order tofamiliarize them with the gaze-contingent display. Then they were
informed that they would be presented with a series of faces tolearn and subsequently recognize. In each of the eight experimental blocks, the observers were instructed to learn seven face
identities randomly displaying either neutral, happy, or disgust
expressions. After a 30 s pause, a series of 14 faces (seven faces fromthe learning phase – seven new faces) were presented and observers
were instructed to indicate as quickly and as accurately as possible
whether each face had been presented in the learning phase or notby pressing keys on the keyboard with the index of either theirleft or right hand. Response times and accuracy were collected
and analyzed for the purpose of the present experiment. Response
buttons were counterbalanced across participants. The emotional
expression of the faces was changed between the learning and therecognition stage to avoid trivial image-matching strategies.
Each trial started with the presentation of a central fixation
cross. Then four crosses were presented, one in the middle ofeach of the four quadrants of the computer screen. These crosses
allowed the experimenter to check that the calibration was still
accurate. In this way, we validated the calibration between eachtrial. A final central fixation cross served as a drift correction,

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Facial information span across cultures

followed by a face presentation. Faces were presented for 5 s in thelearning phase and until the observer’s response in the recognition
phase. To prevent anticipatory strategies, images were presented at
random locations on the computer screen. Each trial was subsequently followed by the six fixation crosses which preceded thenext face stimulus.

Table 1 | Performance (d 0 ) and reaction times (RT) according to the

DATA ANALYSESThe d0 is calculated as Z(hit rate)−Z(false alarm rate). The standard errors from the

Behavioral performance was measured by the percentages of correct recognition and the reaction time. Trials further than two
standard-deviations from the participant’s average duration werediscarded (3% of the trials). Saccades and fixations were determined using a custom algorithm using the same filter parameters as the EyeLink software (saccade velocity threshold = 30˚/s;saccade acceleration threshold = 4000˚/s2 ) and merging fixations
close spatially and temporally (<20 ms, <0.3˚). Fixation distribution maps were extracted individually for each observer. Previous
studies did not reveal any impact of the task (learning vs. recognition), correct vs. incorrect trials or race of the face stimulus (WCvs. EA) on the statistical fixation maps (Blais et al., 2008; Caldara et al., 2010; Kelly et al., 2010). Here, we analyzed the correct
recognition trials (65.1 and 65.8% of the trials for EA and WC
observers respectively) and we collapsed data for the EA and WC
face stimuli given the lack of effect of the race of the face stimuli (see Figure A1 in Appendix). We computed various variables
describing the general oculomotor behavior in order to insurethat EA and WC information sampling strategies are comparable(number of fixations per trial, average total fixation duration pertrial, average single fixation duration, scan path length per trial,
and average saccade length). The statistical fixation maps werecomputed with the iMap toolbox (version 2.1, Caldara and Miellet, 2011). iMap establishes significance using a robust statistical
approach correcting for multiple comparisons in the fixation map
space, by applying a one-tailed Pixel test (Chauvin et al., 2005; Z critfor the present search space >4.07; p < 0.05) for the group fixation maps and a two-tailed Pixel test (Z crit |4.25|; p < 0.05) on the
differential fixation maps. Finally, for each condition we extracted
the average Z -score values for each observer individually, within
the regions showing significance in the differential fixation maps.
Cohen’s d effect sizes (Cohen, 1988) of culture were calculated on
the average Z -scores for each region showing significance.

mean are reported in brackets.

RESULTSWe observed similar reaction times and performance for EA andWC observers [see Table 1, both t (28) < 1]. Likewise, the global
eye-tracking measures did not reveal any significant difference
between both groups of observers [See Table 2, all t (28) < 1.1].
Figure 2 shows fixation maps and the regions significantly fixated above chance level according to iMap (white contours) for EA
and WC observers during face recognition. The difference maps
reveal the well-established central bias for Easterners (in blue inthe difference map) and eye-mouth bias for Westerners (in red in
the difference map) in the Natural vision condition.
In order to determine the magnitude of the fixation biases
across cultures, we extracted, for each observer, the average of theZ -scored fixation durations within the areas showing significant
differences in the differential fixation maps for WC-EA (Figure 2).

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culture of the observer.
WC observers

EA observers

RT (ms)223 (11)224 (22)d00.86 (0.12)

0.91 (0.11)

Table 2 | Global eye-tracking measures to the culture of the observer:
average number of fixations per trial, average total fixation duration
per trial (in ms), average single fixation duration (in ms), scan path
length per trial (in degrees on visual angle), and average saccade
length (in degrees on visual angle).
WC observers

EA observers
4.63 (0.64)

Number of fixations per trial

3.81 (0.48)

Total fixation duration per trial (ms)1321 (160)1606 (216)Fixation duration (ms)334 (13)344 (7)

Path length (degree)

8.36 (0.93)

9.16 (1.02)

Saccade length (degree)

1.93 (0.07)

1.96 (0.08)

The standard errors from the mean are reported in brackets.

Then we carried out t -tests on the averaged Z -score values with
Culture of the observer as a between-subjects factor. WC observers
spent significantly longer fixating the eyes-mouth region than EA
observers (286 and 106 ms. respectively) as revealed by a two tailedt -tests [t (28) = 3.09, p < 0.005]. In contrast, EA observers fixated
longer on the center of the face than WC observers [368 and 100
respectively; t (28) = 3.64, p < 0.001]. Cultural fixation biases on
facial features were reliable and robust, as highlighted by the large
magnitude of Cohen’s d effect size values for the significant effects:
0.92 for the effect of culture in the eyes-mouth region and 1.06 forthe effect in the center of the face. The effect sizes were calculatedon data-driven areas showing statistical significant contrasts (andnot pre-defined ROIs). However, the same data set was used toboth determine the significant areas with iMap and compute theeffect sizes from these significant areas (for selection and selective analysis). Henceforth, the non-independent selective analysis
might distort descriptive statistics (see Kriegeskorte et al., 2009;
example 2). Therefore, to validate these results we reiteratively
computed the effect sizes with a bootstrapping procedure (500
resamples) with independent random split-data analyses. Thus,
we used half of the participants of each group for the selection
analysis (determining the significant areas according to iMap)
and the other half for the selective analysis (calculation of theeffect sizes within the significant regions). This analysis revealeda range of effect sizes that encompasses those computed from thefull data set (median = 0.61, SD = 0.73 for the effect of culturein the eyes-mouth region and median = 1.31, SD = 0.67 for theeffect in the center of the face), corroborating the conventional
analysis.
The Expanding Spotlight allows us to represent the quantity of
information available in the stimulus as a function of the fixation

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FIGURE 2 | Fixation maps based on the fixation durations for each
culture of the observer. Subtracting the fixation map for EA observers
from the fixation map for WC observers resulted in a difference map.
On the EA and WC maps shown here, white contours surround regions

location and duration. Figure 3 shows the available information,
which was obtained by centering a Gaussian aperture on the local
maximum of each significant area for each group of observer. The
aperture size was function of the expansion rate and the average
fixation duration per trial in the significant area.
We also reconstructed the visual information available to theface system. To this aim, we used a retinal filter based on spatial
frequencies decomposition (see Miellet et al., 2011) and convoluted the information available in the stimulus with the fixation
bias of each group of observers. We reconstructed the sampled information by decomposing the total information revealed
by each group of observers (see Figure 3) into a four-level
Laplacian pyramid (Simoncelli and Freeman, 1995). We thenfiltered each spatial-frequency band by applying a WC- or EAretinal filter, which were built from the significant statisticalcontrasts between groups on the fixation maps (see Figure 2).
Figure 4 shows the method to compute the reconstructed sampled information. The resulting information span is shown in
Figure 5.

DISCUSSION
The aim of this study was to isolate the precise quantitative andqualitative facial information intake during face recognition inWestern Caucasian (WC) and EA observers. In line with our previous findings (e.g., Blais et al., 2008; Caldara et al., 2010; Miellet
et al., 2012), WC and EA observers deployed distinct information
sampling strategies to encode and recognize faces, reaching comparable recognition performance. WC observers deployed local
fixations directed toward the eyes and the mouth. EA observers
deployed a global information sampling strategy typified by acentral fixation pattern. Yet, reaction times, accuracy measures(d 0 ) and global eye movement indices (i.e., number of fixations,
total fixation duration, single fixation duration, path, and saccade
lengths) did not differ across cultures.
The novelty here relies on the original methodologicalapproach: the Expanding Spotlight. The results on the global eye

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Facial information span across culturesof significantly longer fixation durations than were observed for otherareas; on the difference map, white contours indicated regions ofsignificant differences between the EA and WC maps (i.e., fixationbias).

movement indices were compatible with previous data obtainedin natural viewing conditions (Blais et al., 2008; Miellet et al.,2012 and the other cultural studies) or with the iHybrid technique(Miellet et al., 2011), attesting for the ecological validity of the technique. Yet, in all of our previous studies we reported qualitative
effects. A significant contrast with iMap (i.e., region delimited by awhite border) does not precisely inform on the quantity of fovealand extrafoveal information sampled from this region. For the
first time, the Expanding Spotlight isolated the facial information
span used by observers from different cultures, by reconstructing
this information with a retinal filter based on spatial-frequency
decomposition on the fixation maps. The reconstructed information span images revealed that this visual bias was related to a
spatial-frequency tuning. These novel findings clearly show that
this information is original and complementary to the significant
fixation contrasts revealed by iMap and, crucially, cannot be simply revealed from the regions fixated above chance level in the
fixation maps. To the best of our knowledge, this is the first timethat the information span for faces (across cultures) is revealed.
Our data directly challenge the idea of a unique process andrepresentation format dedicated to face-processing. An exclusive strict holistic position of face-processing, according to which
all the facial information has identical importance, could notaccount for our observations. Observers from different cultures
used different scan paths to recognize faces, which critically led
to the use of different visual inputs (as shown by the reconstructed retinal filter images). As a result, it becomes difficult to
extrapolate that observers from different cultures use the same
mandatory holistic processing of faces. Such a strict holistic view
would need to explain how a unique computation carried on
different visual inputs would lead to identical (holistic) representations. Our data suggest instead a cultural tuning in the facial
information space, which most probably rely on distinct culturalrepresentations.
Obviously, we do not deny the capability of human beingsto bind visual information into a global percept; and that the

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Facial information span across cultures

FIGURE 3 | Reconstructed available information as a function of facial fixation locations and the culture of the observer. The size of the Gaussian
aperture is determined by the expansion rate of the Expanding Spotlight and the average fixation duration in the corresponding significant area.

familiarity of the configuration might help binding information(see for instance Kessler and Miellet, 2012). However, we feel that astrictly holistic account of face-processing raises at minimum a few
major caveats. Firstly, the concept of holistic processing is underspecified, making it easy to account for a large number of results.
To the best of our knowledge, there is not a commonly acknowledged definition of holistic processing for faces in the literature,
specifying the precise information related to this process. Hence,researchers might refer to different processes when they accountresults in terms of holistic processing. Moreover, the compatibilityof a pattern of results with a theoretical holistic account does notguarantee that the actual processing taking place in the experimental tasks is holistic in nature. In its most comprehensive meaning,
we could consider that holistic refers to the binding and integration of diagnostic information into a coherent percept, in whichsome features (i.e., the eyes) are more diagnostic than others. In
our view, then the very nature of holistic processing of faces should
be clarified by using a formal approach, mapping the diagnosticity of every facial feature, weighting interactions across features,
and ascertaining the modulations of this multidimensional space
as a function of task constraints. Having such a formal model
would also allow us to formulate predictions and test the validityof the model at the experimental level. In this model, eye movements could also inform how information is cumulated acrosswww.frontiersin.org

fixations and how face representations are effectively built. However, based on current knowledge of this theoretical question, andthe lack of a formal model, we think that the gap between theso-called holistic processing of faces and the information binding routinely engaged in natural vision is unclear. Secondly, andmore specifically concerning eye movement research, a common
theoretical shortcut has been put forward between extrafoveal
information sampling and holistic processing/representations. For
instance Hsiao and Cottrell (2008) assumed that a central fixation pattern supports holistic processing for faces (the sampled
information considered to be the eyes). It is important to notethat in this study the authors used the very same stimuli for thelearning and recognition phases, potentially allowing observers
to use low-level image-matching strategies instead of genuine
face recognition processes. Moreover, Hsiao and Cottrell used
only two possible stimulus locations (above and below the central fixation point), which did not prevent the observers fromusing anticipatory oculomotor strategies. In many other studies,
it is assumed that because the observers do not directly fixate
the diagnostic information, the processing is holistic (e.g., Guo,2012). We think that this is not necessarily the case; such fixation patterns only tell us that the diagnostic information might
have been sampled extrafoveally. Moreover, in one of our studies (Kelly et al., 2010) EA observers deployed central fixations to

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FIGURE 4 | Isolating the information span. The reconstructed available
information (see Figure 3) is decomposed in four spatial-frequency (SF) bands
covering the full spatial-frequency spectrum. The SF bands are thenhuman faces, sheep faces, and Greebles, questioning the specificityof the holistic, central/global fixation strategies for faces. As a consequence, we do not think that we can account for those processes
with such straightforward binary distinction (i.e., extrafoveal fixation = holistic processing vs. foveal fixation = featural processing).
We thus believe that natural eye movement recordings do not provide direct evidence on the nature of the representations, and can
only isolate oculomotor strategies.
Yet, gaze-contingent techniques are more informative as they
control for the available information and offer some insights
on the computations devoted to information use. On this issue,

Frontiers in Psychology | Perception Science

Facial information span across cultures

convoluted on the cultural fixation biases extracted from the differential
fixation map (see Figure 2). See Figure 5 for a fine-grained illustration of theinformation span.

gaze-contingent techniques artificially control and manipulate theavailable information, by constraining it to the fixated location.
Therefore, at first sight one could assume that findings gatheredwith these techniques might be difficult to relate to natural vision,and are instead tapping into unnatural visual processes. Yet, thisview is invalidated by experimental and theoretical evidence. Moreconcretely, here we used an average face template (with normalized eyes and mouth positions) to cover the information presented
outside the Expanding Spotlight. A full-face was thus always available to the observers, an experimental manipulation that enabled
them to program the next saccade and precisely target a landing

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Facial information span across cultures

FIGURE 5 | Information span for the global (EA) and local (WC) strategies.

location. Moreover, we intentionally used a Gaussian aperture witha progressive opacity to avoid a highly contrasted hard border,
which would have increased visual saliency and inevitably attract
attention between the background (average face template) and the
Expanding Spotlight (stimulus). Critically, and more objectively,
our data are fully compatible with previous results obtained innatural viewing. Firstly, fixation durations are in the same range
as non-gaze-contingent eye-tracking studies on face-processing in
natural vision (Henderson et al., 2005; Hsiao and Cottrell, 2008).
Secondly, the average number of fixations is similar to those gathered from a study using the same old/new task and a natural visionbaseline (Miellet et al., 2012). Thirdly, we previously showed thatambiguous hybrid face representations reconstructed from the eye
movement scan paths of gaze-contingent data could successfully
predict the facial identify perceived by an independent group ofobservers (Miellet et al., 2011). Finally, the present eye movement
patterns are perfectly in line with the cultural bias observed forface-processing in numerous studies with natural vision paradigms (Blais et al., 2008; Kelly et al., 2010, 2011a,b; Rodger et al.,2010; Miellet et al., 2012). Altogether, these experimental and theoretical arguments converge and convincingly demonstrate thatgaze-contingent techniques can be a valuable and unique tool toisolate and finely understand information processing in naturalvision.
Despite these considerations, it is important to mention thatgaze-contingent techniques do not directly uncover representation formats. For instance, Van Belle and colleagues (Van Belleet al., 2010a,b, 2011) make the direct, although implicit assumption that extrafoveal information sampling (promoted by a gazecontingent central mask) is equivalent to holistic processing,whereas foveal sampling (promoted via a gaze-contingent central window) corresponds to featural processing. We argue thatfoveal information sampling could potentially result in either featural or holistic representations. For example, motor informationwww.frontiersin.org

from saccades on facial features might aid their integration intoa spatial/configural representation, resulting in a global representation. Conversely, extrafoveal information can be used to buildfeatural information, detailed enough to produce effective recognition. To sum up, even if gaze-contingent techniques allow precisely isolating and characterizing the information span, it is still
problematical to unambiguously conclude on the nature of theface representations.
Given those considerations, an important question we should
ask ourselves is whether the label “holistic” is conceptually appropriate for face-processing in the context of the eye movement
research. We genuinely believe that this concept is not criticalin this framework as it does not provide insightful and objective explanations. We suggest, instead to remain closer to what theeye movement data show and to shift the focus on crucial questions, such as for instance: what factors are modulating the eye
movement strategies, which information is used for a given task
and under what specific visual constraints, how this information isbound to form a percept that taps into the face representation, how
information is integrated across saccades? These questions andmany important others remain unanswered and we believe that eye
movement data will provide unique and important information
to address them. To date, our eye movement data have revealed
the existence of diverse strategies dedicated to face-processing,strategies that adjust to the task at hand, the visual constraints,
and the culture of the observer. These observations challenge
the view of a unique process dedicated to face-processing, but
future studies are necessary to understand the roots and rules driving such (cultural) diversity in the way humans sample visual
information.
To conclude, we favor a view according to which eye movements are flexibly used to elaborate face representations bygathering information through diverse strategies. With gazecontingent techniques it is also possible to precisely map out facial

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Facial information span across culturesinformation span, an original and rich advantage compared toother approaches in visual cognition. This is consistent with theidea that the type of processing might vary with distance (McKone, 2009), familiarity with the face, task constraints, culture of
the observer, etc. Even if faces are a special class of visual stimuli(through frequency of exposure, social relevance, and/or adaptive
advantage), their processing is still governed by the visual system.
The present data clearly show that Western Caucasian observers
favor local eye movement strategies to process faces, whereas EA
observers favor a global strategy. Both groups rely on distinct facial
information span and culturally tuned spatially filtered information. These observations suggest that human vision is modulated

by culture and continuously using local and global information toprocess human faces effectively, but most probably also to decodethe visual world.

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ACKNOWLEDGMENTS
This study was supported by the Swiss National Science Foundation (100014_138627).
SUPPLEMENTARY MATERIALThe Supplementary Material for this article can be found online
at http://www.frontiersin.org/Perception_Science/10.3389/fpsyg.
2013.00034/abstract

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Conflict of Interest Statement: The
authors declare that the research was

conducted in the absence of any
commercial or financial relationshipsthat could be construed as a potential
conflict of interest.
Received: 15 September 2012; accepted:
15 January 2013; published online: 20
February 2013.
Citation: Miellet S, Vizioli L, He L,
Zhou X and Caldara R (2013) Mapping face recognition information use
across cultures. Front. Psychology 4:34.
doi: 10.3389/fpsyg.2013.00034

This article was submitted to Frontiers in
Perception Science, a specialty of Frontiersin Psychology.
Copyright © 2013 Miellet , Vizioli, He,
Zhou and Caldara. This is an openaccess article distributed under the termsof the Creative Commons Attribution
License, which permits use, distribution
and reproduction in other forums, provided the original authors and source
are credited and subject to any copyright notices concerning any third-party
graphics etc.

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APPENDIXFIGURE A1 | Fixation maps based on the fixation durations for each
culture of the observer and each race of the stimuli. Subtracting fixation
maps across culture of the observer or race of the stimuli resulted indifference maps. On observers/stimuli maps shown here, white contours

Frontiers in Psychology | Perception Sciencesurround regions of significantly longer fixation durations than wereobserved for other areas; on the difference maps, white contours indicatedregions of significant differences between the observers/stimuli maps (i.e.,
fixation bias).

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