A left eye bias for female faces
Nayla Sokhn, Francesca Bertoli, Roberto Caldara
Eye and Brain Mapping Laboratory (iBMLab), Department of Psychology, University of Fribourg, Switzerland.
nayla.sokhn@unifr.ch

mouth for identifying Asian and Caucasian faces,
whereas East Asian observers focus comparably moreon the central part of the faces, regardless of the raceof the face stimuli [15]. Nevertheless, gaze-contingent
paradigms have shown that Western and Easternobservers rely on similar facial features to achieve facerecognition, but with both a specific information span
and a distinct tuning to spatial frequencies [16-19].
This cultural perceptual bias starts early in age [20-22],
is resistant to the face inversion effect [23] and is notabolished by social experience [24]. On another line,
Jack et al. (2009, 2012) reported cross-cultural tuningin the decoding of facial emotional signals [25, 26], acultural difference that emerges early in age [27].
Schurgin et al. (2014) evidenced that the visualstrategies employed to extract facial information werealso modulated by the emotional expressions
conveyed by the face stimuli, with observers fixating
the eyes more during processing of stimuli displaying“fear” and “sadness”, and the mouth when stimuli
displayed “happiness” [28].
Besides individual and cross-cultural differences invisual sampling strategies during face processing,other studies have also reported differences as afunction of observers’ gender. Vassalo et al. (2009)
observed that female and male observers scanning
strategies differed during perception of emotional
expressions: compared to females, males fixated more
the nose and mouth [29]. Similarly, in a genderjudgement study, Sæther et al. (2009) observed thatmales fixated more the lower and central parts of theface compared to their female counterparts [30].
However, another study observed differences between
male and female observers only when the gender of theface stimuli was task relevant [31], with female andmale observers sampling the eye region and cheeks,
respectively. Further studies have revealed genderdifferences in terms of face recognition performance.
Specifically, irrespective of the gender of faces
presented, women outperformed men [32]. Moreover,female observers demonstrated an own-sex bias, thatwas not found for male observers [32]. Finally, using
a novel data-driven approach enclosing the
spatiotemporal nature of gazing behavior, Coutrot etal. (2016) demonstrated gender differences in face

Abstract—We investigated whether men and women use
different scanning strategies to extract the visual
information relevant for the recognition of female andmale faces. We used an old-new recognition task during
which observers were asked to identify previously
learned faces. Data from two groups of observers (male,
female) revealed a more accurate, but not faster,
recognition of male face stimuli. Interestingly, thefixation maps revealed a consistent left gaze bias forfemale face stimuli, regardless of the gender of theobserver. Altogether, our data show that humans deploy
distinct and flexible visual sample strategies to process
faces.
Keywords- face recognition;gender;eye-movements;
accuracy;response time.

I.

INTRODUCTION

Face recognition is a powerful human skill
essential for social interactions. Faces not only provide
the core information for identification, but also convey
a wealth of other visual signals disclosing cues related
to age [e.g., 1] emotional state [2-5], race [6-9], andgender [10, 11].
Understanding how faces are decoded andrecognized has been extensively studied over the last
decades. Using the iHybrid technique, Miellet et al.
(2011) found differences in scanning strategies (i.e.,
local or global) within the same observer as a functionof the landing position of the first fixation [12].
Another recent study demonstrated individual
differences in information fixated during face
exploration [13]. Similarly, Kanan et al. (2015)
reported individual and task-specific eye-movement
strategies when observers were asked to evaluate faces
on diverse social dimensions [14].
Other studies have revealed that the cultural
background, as well as expression-related facial cues,
influence face processing strategies. Blais et al. (2008)
showed the powerful influence of culture in shaping
facial exploration strategies, indicating that faceprocessing is not achieved universally across cultures.
More specifically, their findings demonstrate that
Western Caucasian observers scan faces using a
triangular scanning pattern over the eyes and the(XURSHDQ8QLRQ

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experiment was implemented in Matlab using thePsychophysics [34-36] and EyeLink Toolbox [37]extensions.

exploration dynamics. More specifically, they reported
an own-sex bias for female observers when looking at
female actresses and found that, compared to maleobservers, females exhibited shorter fixations [33].
However, as acknowledged by the authors, several
aspects of their experimental design might haveinfluenced their findings. All the faces presented intheir study displayed movies of Western Caucasian
actors maintaining a neutral face. As previously
reported, this might have influenced facial scanning
patterns [25-27] by prompting observers to direct their
fixations to specific facial features. Moreover, given
the nature of their between-subjects design, both
stimulus or observer characteristics might haveinfluenced the results (i.e., participants were tested inonly one out of all conditions).
Altogether, these findings point to a non-universal
facial information extraction pattern, determined byseveral factors, including amongst others culture.
Whether the gender of the observer plays a role in thesampling strategies used to identify female and malefaces remains to be clarified. To this aim, weinvestigated gender differences using a full design, in
which female and male observers were asked torecognize were previously encoded female and malefaces presented among novel distractor faces (i.e., oldnew recognition task). We analyzed observers’ eyemovements, as well as accuracy and response times.
II.

C. Stimuli
We selected a set of 168 black and white Western
Caucasian (84 females) faces from the KDEF database[38]. For the aim of this study, we analyzed only theeye movement data of the recognition stage, only forthe correct trials.
D. Procedure
Participants sat in a quiet and dimly illuminatedroom. They were informed that throughout theexperiment, they would have to learn and subsequently
recognize a series of Caucasian faces. The experiment
included 2 blocks. Each one was composed of alearning and a recognition phase. During the learning
phase, 14 faces (7 females) were presented one after
the other to the participants. Each trial started with ablack fixation cross presented for 1000ms followed bya face which was randomly presented for 5s in one of
the four quadrants of the screen (Fig. 1). During therecognition phase, a series of 28 faces (14 faces fromthe learning phase – 14 new faces) were presented.
Participants were instructed to indicate as accurately
as possible whether each face had been previously seen
or not by pressing two labeled keys on a computer
keyboard. Stimuli remained on the screen until
participants responded (Fig. 1). In both phases, werecorded the eye-movements of observers using the
Eye-Link 1000 Desktop Mount system (SR ResearchLtd., Ontario, Canada) with a sampling rate of 1000
Hz. At the beginning of the experiment and between
each block a 9 nine-point calibration was performed.

METHODS

A. Participants
Sixty-four Caucasian students of the university of
Fribourg (32 females with mean age = 21.90 ± 3.07
and 32 males with mean age = 21.56 ± 2.49)
participated in our experiment. This study wasapproved by the local Ethical Committee.

III.

DATA ANALYSES

A. Behavioral Analyses
We performed a generalized linear mixed modelwith a binomial family [39] to analyze the response
times and accuracy of the observers. We considered
the gender of the stimuli (GS) and the gender of theobserver (GO) as main predictor variables and their

B. Appartus
Stimuli were presented on a VIEWPixx/3D LCD
monitor (52.13x29.32 cm) with a refresh rate of 120 Hz
and a resolution of 1920x1080 pixels. The images on
the screen were 702x688 pixels in size reflecting avisual angle of 14° at a viewing distance of 70 cm. The

Figure 1. An example of two trials of the experiment with a learning and recognition session. In the recognition session, the first trial is a new face
while the second trial is an old face i.e. it has been shown in the learning session.

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interaction. To account for the variation amongobservers, we added the variable observer as a random
factor. The model is a 2*2 design and can be expressed
as follows:
Accuracy ~ GO*GS + (1|observers)smoothed normalized fixation maps by considering the
fixation duration (Y) as a response variable and thegender of stimuli (GS) and observers (GO) as
predictors with their interaction factor. Thus, the full
design model can be expressed as follows:(1)

Y(x,y) ~ 1 + GS + GO
+ GS*GO + (1|observers)
1<x<xSize, 1<y<ySize

Then, we run the models for each predictor (gender
stimuli and observers) separately.
Similarly, we applied a linear mixed model to explore
the reaction time (RT) of observers of correct trials. RT
that were higher than 5 seconds (2.69%) werediscarded to decrease the influence of outliers, which
represent all values higher than 2.5*SDs from themean. The gender of stimuli (GS) and of observers(GO) were considered as explanatory variables with
their interaction terms. The observers were treated as arandom factor. This can be written as follows:
RT ~ GO*GS + (1| observers)(3)

To control for the Type 1 error, a multiple comparison
corrections was assessed by using a bootstrap spatial
clustering method (option: cluster size. A post-hoc
analysis was conducted within the significant area.
We furthermore performed a linear mixed model toinvestigate the differences between the average
fixation duration (DurFix) of observers when female
and male stimuli were presented. The model can be
written as follows:(2)

DurFix ~ GO*GS + (1| observers)

We also run the models for each predictor (Gender
stimuli and observer) separately.

(2)

where GO and GS is the gender of observers andstimuli, respectively.

All models were fitted in R [40] using the lmerTest
package [41].

IV.

RESULTS

A. Behavioral
The results from the fitted full model (equation 1)
showed no main effects of predictors and theirinteraction. However, running the model with only thegender of stimuli as explanatory variable, revealed asignificant main effect [F(1,64) = 11.40, with pvalue<0.001]. Both female and male observers
performed better when they were exposed to malestimuli (M = 76.06, 95% CI [73.27 78.52]) comparedto female stimuli (M=71.15, 95% CI [68.86,73.38])
with a z-value equal to 3.37 (p-value <0.05),
suggesting that the accuracy of observers is slightly
higher when the gender of stimuli is a male. This is
illustrated in Fig. 2.
The results from the general linear mixed model(equation 2) and from the models where each predictor
was independently included, presented no main effects(F(1,2567) = 0.18, p-value >0.05), see Fig.2. This latter
result reflects that the reaction time of observers was
not affected by the gender of stimuli or by the one ofobservers.

B. Eye-Movements Analyses
We applied the adaptive velocity based algorithm
developped by Nystrom & Holmqvist [42] to retrieve
fixations and saccades from the raw data. Fixations
that felt outside the stimuli were discarded. We thenused a statistically data-driven method built in iMap4[43] to compute fixation duration maps. The resulting
maps (conditions*xSize*ySize) were then smoothedwith a two-dimensional Gaussian Kernel function at 1°of visual angle by choosing the estimated option. Thismethod consists in computing for each condition andobserver, the expected values across trials. Finally, themaps were normalized by dividing them with the sum
duration of each trial. Furthermore, we performed apixel-wise Linear Mixed Model (LMM) on the

B. Eye-Movements
Our results showed that, on average, observers
performed between 6 and 7 fixations. Running thelinear mixed model on the mean fixation duration (inms) revealed a main effect of the gender of stimuli[F(1,126) = 7.67, with p-value<0.05], with lower
fixation durations for female faces. However, thiseffect was too small to be considered with βfemale_stimuli= -3.8 (ms), 95% CI [-0.0066 0.0011].

Figure 2. Accuracy Left: Average of the percentage of correct
responses; right: reaction time (ms) for both group of observers andgender of stimuli. Error bars indicate 95% confidence intervals.

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Figure 3. Fixation maps showing the visual bias for observers during face recognition. The significant areas are marked with a black line.

universality of this phenomenon. Nevertheless,altogether the present data show that the face system isrooted into different visual sampling strategies forprocessing male and female faces.

The iMap4 output of the linear mixed model of thefull model (equation 3) revealed a significant effect ofgender on the left eye region. By performing the linear
contrast between female and male stimuli we obtained
the following: F(1,124)=1.375 at the local maximum
within the significant cluster with a beta contrast equal
to 1.37, 95% CI [0.55, 2.19]; and F(1,124) = 3.92,
p<0.05 at the local minimum within the significant
cluster with a beta contrast equal to 0.62, 95% CI[0.00, 1.25]. In line with the main effect, post-hoc
within the significant cluster showed that both groupsof observers displayed a longer fixation for female
stimuli than male stimuli (female observers: femalemale 95% CI [9.13, 12.8], male observers: femalemale 95% CI [9.38 15.52]) with an effect size of requal to 0.20.
Altogether, these results suggest that all observers
tended to look more at the left eye when a female
stimuli was presented during recognition (see Fig. 3).
V.

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CONCLUSION

Here, we investigated whether the gender of theobserver plays a role during recognition of male andfemale face stimuli, by using an old-new facerecognition paradigm. Behaviorally, both groups ofobservers exhibited similar response times, and weremore accurate for male stimuli. This latter result differsfrom the results reported by Rehnman and Herlitz(2007), revealing that female observers rememberedbetter faces than male observers, with even higherscores when female stimuli were presented [32].
With respect to eye-movements, both female andmale observers exhibited a left eye bias for female facestimuli. This observation partly coincides with theresults of a previous study [33], which reported thesame bias, albeit only for female observers. This result
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