Journal of Vision (2017) 17(5):16, 1–12

1

The Facespan—the perceptual span for face recognition
Michael Papinutto

Eye and Brain Mapping Laboratory (iBMLab),
Department of Psychology, University of Fribourg,
Fribourg, Switzerland# $Junpeng Lao

Eye and Brain Mapping Laboratory (iBMLab),
Department of Psychology, University of Fribourg,
Fribourg, Switzerland# $Meike Ramon

Eye and Brain Mapping Laboratory (iBMLab),
Department of Psychology, University of Fribourg,
Fribourg, Switzerland# $Roberto Caldara

Eye and Brain Mapping Laboratory (iBMLab),
Department of Psychology, University of Fribourg,
Fribourg, Switzerland# $Sébastien Miellet

Eye and Brain Mapping Laboratory (iBMLab),
Department of Psychology, University of Fribourg,
Fribourg, Switzerland
School of Psychology, University of Wollongong,
Wollongong, New South Wales, Australia
Bournemouth University,
Faculty of Science & Technology,
Cognition and Cognitive Neuroscience Research Centre,
Bournemouth, UK# $In reading, the perceptual span is a well-establishedconcept that refers to the amount of information thatcan be read in a single fixation. Surprisingly, despiteextensive empirical interest in determining theperceptual strategies deployed to process faces and an
ongoing debate regarding the factors or mechanism(s)underlying efficient face processing, the perceptual spanfor faces—the Facespan—remains undetermined. To
address this issue, we applied the gaze-contingent
Spotlight technique implemented in an old-new facerecognition paradigm. This procedure allowed us toparametrically vary the amount of facial information
available at a fixated location in order to determine theminimal aperture size at which face recognition
performance plateaus. As expected, accuracy increased
nonlinearly with spotlight size apertures. Analyses ofStructural Similarity comparing the available information
during spotlight and natural viewing conditions indicatethat the Facespan—the minimum spatial extent ofpreserved facial information leading to comparable
performance as in natural viewing—encompasses 78 ofvisual angle in our viewing conditions (size of the facestimulus: 15.68; viewing distance: 70 cm), which
represents 45% of the face. The present findings provide
a benchmark for future investigations that will address if
and how the Facespan is modulated by factors such as
cultural, developmental, idiosyncratic, or task-relateddifferences.

Introduction
Face processing is a socially and biologically crucialfeat achieved with high proﬁciency by the human visualsystem. An abundance of empirical studies hasaddressed various aspects of face processing, including
the categorization of gender, age, race, or facial
expression, as well as face recognition and identiﬁcation (e.g., Blais, Jack, Scheepers, Fiset, & Caldara,
2008; Ebner, Riediger, & Lindenberger, 2010; Kelly et

Citation: Papinutto, M., Lao, J., Ramon, M., Caldara, R., & Miellet, S. (2017). The Facespan—the perceptual span for facerecognition. Journal of Vision, 17(5):16, 1–12, doi:10.1167/17.5.16.
doi: 10 .116 7 /1 7. 5. 1 6

Received November 13, 2016; published May 26, 2017

ISSN 1534-7362 Copyright 2017 The Authors

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/journals/jov/936220/ on 05/30/2017

Journal of Vision (2017) 17(5):16, 1–12

Papinutto et al.

al., 2011; McClure, 2000; Meissner & Brigham, 2001).
Isolating the very nature of the information used toachieve such visual categorizations is a major challenge
in this ﬁeld.
Recordings of oculomotor behavior have provided avaluable source of information regarding the relationship between the facial information sampled andobservers’ behavior. For instance, the robust andsystematic triangular pattern of ﬁxations exhibited
during face perception (Henderson, Williams, & Falk,
2005; Hsiao & Cottrell, 2008; Peterson & Eckstein,2012; Van Belle, De Graef, Verfaillie, Rossion, &Lefevre, 2010; Walker-Smith, Gale, & Findlay, 1977;
Xivry, Ramon, Lefèvre, & Rossion, 2008; Yarbus,1967) was long considered invariant and universal.
More recent investigations, however, have revealed that
individuals’ ﬁxation patterns are affected by various
factors, such as task demands and experience.
Preferential sampling of the eye region has beenreported during face identity processing, whereas a shift
towards lower face regions has been observed when the
categorization of facial expression is of interest (e.g.,
Henderson et al., 2005; Malcolm, Lanyon, Fugard, &Barton, 2008). Such ﬁndings indicate a direct relationship between task demands and information diagnosticity. This notion receives further support from a
recent study in which ﬁxation patterns could be used todetermine the task performed by observers’ (Kanan,
Bseiso, Ray, Hsiao, & Cottrell, 2015), as well as
ﬁndings obtained using the response classiﬁcation
technique ‘‘Bubbles’’ (Gosselin & Schyns, 2001; Schyns,
Bonnar, & Gosselin, 2002). In short, these studies
demonstrate that sampled and used face information isconsistently determined by the task performed. Morefundamentally, this line of research indicates that, in
cognitive terms, visual information should not be
described purely in terms of low-level properties, butrather needs to be considered as diagnostic for a giventask and observer.
Evidence suggesting experience-dependent modulation of oculomotor behavior stems from both crosscultural, as well as learning studies. For example, wehave previously reported differential oculomotor patterns during face recognition as a function of theculture of the observers: East-Asian observers exhibit acentral ﬁxation bias, whereas and Western-Caucasianobservers exhibit for an eye-mouth bias (Blais et al.,2008; Kelly et al., 2011), both being consistently
displayed despite stimulus inversion (Rodger, Kelly,
Blais, & Caldara, 2010). However, in studies using
gaze-contingent techniques, we have demonstrated that
despite using diverse gaze scanpaths, observers fromboth cultures rely on the same diagnostic features toperform face recognition (i.e., the eyes and the mouth)
with comparable levels of performance (Caldara, Zhou,& Miellet, 2010; Miellet, He, Zhou, Lao, & Caldara,2

2012; Miellet, Vizioli, He, Zhou, & Caldara, 2013).
Thus, in both cultures, observers preferentially use
different face information sampling strategies: Westerners favor a local strategy by sampling foveal
information, whereas Easterners exhibit a global
strategy to use the same diagnostic information.
Finally, observers sample facial information differently
from personally familiar and unfamiliar faces, withﬁxations expressed during processing of familiar
individuals being relatively more distributed across all
facial features (Van Belle et al., 2010).
While collectively these studies have enhanced ourunderstanding of facial information sampling, they do
not directly provide information regarding quantity of
information use. For instance, observers may ﬂexibly
use information located within either the fovea, or
extra-foveally during face identiﬁcation (Miellet, Caldara, & Schyns, 2011), depending on the landing
position of their ﬁrst ﬁxation. Hence, the following
important question remains unanswered: What is thequantity of information processed at each ﬁxation during
face recognition?
This question ﬁnds its analogue in the ﬁeld of
reading, where Woodworth (1938) asked, ‘‘How muchcan be read in a single ﬁxation?’’ (p.721)1 McConkie andRayner (1975) addressed this question of the perceptual
span in reading using an elegant gaze-contingent
moving-window paradigm. The authors reported prolonged reading times when the information outside of
the window/ﬁxation location, i.e., parafoveal information, was altered. Moreover, by parametrically varying
the size of the gaze-contingent window, they were able
to determine the minimal aperture size affording fornormal reading performance as observed under unconstrained conditions.2 Recently, this approach has
also been employed in the ﬁeld of visual scene
processing (Nuthmann, 2013).
A different but related question was addressed by He
et al. (2015) and Näsänen and Ojanpää (2004) who
investigated the number of faces that can be recognized
in a single ﬁxation during a search task. Their researchquestion differs from ours in that He et al. (2015) andNäsänen and Ojanpää (2004) used the number of faces
as metric of the visual span for face recognition,
whereas we considered the information within a single
face during recognition for the Facespan.
In the present study we sought to determine thecurrently unknown perceptual span for faces—the
Facespan. Following the aforementioned reasoning, weaimed to specify the spatial extent across which facial
information is accrued within a single ﬁxation during
face recognition. In other words we asked, what is theminimum quantity (in terms of spatial extent) of
information needed at a ﬁxation to achieve normal facerecognition performance? To this end we employed agaze-contingent moving-window technique, adapted

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/journals/jov/936220/ on 05/30/2017

Journal of Vision (2017) 17(5):16, 1–12

Papinutto et al.

for face stimuli, presented in the context of an old-new
face recognition task. The facial information available
to observers was restricted by using the Spotlighttechnique (for details, see e.g., Caldara, Zhou, &Miellet, 2010; Miellet, Vizioli, He, Zhou, & Caldara,2013) with Gaussian apertures dynamically centered on
their ﬁxations, the size of which varied parametrically.
Our goal was to ascertain the spotlight size that would
allow subjects to reach a performance similar to thatobserved during natural face viewing. We reasoned thatbenchmarking the Facespan in this manner would
provide the basis for investigating how information
intake is impacted by factors such as those related tothe stimulus, observer, or task-dependency.

MethodParticipants
Two hundred and twenty young adults (173 females,
M ¼ 21.47, SD ¼ 2.50) from the University of Fribourg,
Switzerland, participated in this study. All participants
had normal or corrected vision, were Western-Caucasian, and received course credit for participation. All
participants gave informed consent; the protocol wasapproved by the ethical committees of the Departmentof Psychology of Fribourg University, Switzerland.

Materials
Stimuli were obtained from the Karolinska Directed
Emotional Faces (KDEF; Lundqvist, Flykt, & Ohman,1998) and the Asian Face Image Database (AFID;
Bang, Kim, & Choi, 2001) and consisted of 84 EasternAsians and 84 Western-Caucasians identities containing equal numbers of males and females. The images
were 688 pixels in size vertically and 702 pixels
horizontally, subtending 15.38 and 15.68 of visual angle,
respectively. Face stimuli were aligned with respect tothe eye and mouth positions, normalized for luminance, and no distinctive external features or facial
hair. Images were viewed at a distance of 70 cm, whichis representative of a natural distance during humaninteractions (Hall, 1966), and were presented on a 1920
3 1080 pixels gray background displayed on a ViewPixx/3D LCD monitor (120 Hz refresh rate).

Apparatus
Eye movements were recorded at a sampling rate of1000 Hz with the SR Research Desktop-Mount EyeLink 2K eye tracker (with a chin and forehead rest),

3

which has an average gaze position error of about 0.58
and a spatial resolution of 0.018. The eye-tracker had alinear output over the range of the monitor used.
Although viewing was binocular, only the dominant
eye was tracked. The experiment was implemented in
Matlab (R2009b, The MathWorks, Natick, MA), using
the Psychophysics toolbox (PTB-3) (Kleiner, Brainard,& Pelli, 2007; Pelli, 1997) and EyeLink Toolbox
extensions (Cornelissen, Peters, & Palmer, 2002;
Kleiner et al., 2007). Calibrations of eye ﬁxations wereconducted at the beginning of the experiment using anine-point ﬁxation procedure as implemented in the
EyeLink API (see EyeLink Manual) and using Matlab
software. Afterwards, calibrations were validated with
the EyeLink software and repeated when necessary
until reaching an optimal calibration criterion. At the
beginning of each trial, participants were instructed toﬁxate a cross at the center of the screen to perform adrift correction. If this exceeded 18, a new calibration
was performed to ensure optimal recording quality.

Spotlight
We used 11 spotlight aperture sizes from 98 to 198 ofvisual angle by step of 18 (see Figure 1A). Theseapertures were centered dynamically on the observers’
ﬁxations. The target face was presented at ﬁxation
location and was progressively masked by an averageface template with retinal eccentricity. More speciﬁcally, at the center of the spotlight, the alpha channel
had a value of zero, corresponding to complete
transparency of the average template and full access tothe target face. The alpha channel value increased with
distance from the ﬁxation location according to a
Gaussian function. For each spotlight, the standard
deviation of the corresponding Gaussian mask was
10.73% of the spotlight size. Values below 0.004 were
set to 0, corresponding to complete opacity of theaverage template and no information from the target
face. We used progressive masking to avoid both
extrafoveally attracting participants’ attention andabruptly dividing facial features by a hard apertureborder. However, this progressive masking also involves challenges in terms of quantifying the information preserved within the Gaussian aperture. In thesection ‘‘Data-driven reconstruction of the Facespan,’’we discuss how we overcame these challenges byintroducing a measurement of the Facespan that goesbeyond the mere diameter of the gaze-contingent
window. This was done by taking into account acuity
drop-off, ﬁxation locations, and similarity between thetarget face and the average template. The informationout with the spotlight was an average face composedfrom all stimuli from databases. This average face did
not provide any useful information for recognition, but

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/journals/jov/936220/ on 05/30/2017

Journal of Vision (2017) 17(5):16, 1–12

Papinutto et al.

4

Figure 1. (A) The average face used as mask; an example stimulus with different spotlight sizes centered on the left eye and undernatural viewing. (B) Impact of spotlights on RT (left) and A 0 (right). The performance plateau in accuracy is highlighted by the redrectangle. (C) Effect of spotlights on fixation pattern. Highlighted in yellow are the areas fixated longer for the spotlight compared tonatural viewing condition. The rectangle highlights the absence of a significant effect of spotlight (compared to natural viewing) on
fixation patterns. Face images were retrieved from the Karolinska Directed Emotional Faces database (KDEF, http://www.emotionlab.
se/resources/kdef).
Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/journals/jov/936220/ on 05/30/2017

Journal of Vision (2017) 17(5):16, 1–12

Papinutto et al.

allowed observers to program natural saccades. In
terms of gaze-contingent display updating, it took 1 ms
to receive a sample from the eye-tracker, less than 1 ms
to calculate texture including the background and theGaussian mask, and 2–57 ms to refresh the screen.
Thus, the display was updated on average every 9 ms
(between 3 and 58 ms), which eliminated any subjective
impression of ﬂickering.

Procedure
Each observer performed both natural viewing andspotlight conditions. For the spotlight condition, eachobserver was randomly assigned one of the eleven
spotlight sizes. Participants started with the calibration
procedure described in the apparatus section. They
were then presented with a training session tofamiliarize them with the gaze-contingent movingwindow display and informed about the experimental
procedure. This consisted of two blocks (natural
viewing, and spotlight condition), each involving
presentation of a series of faces to be learned andsubsequently recognized. Each of the blocks was
divided into two subblocks (one per ethnicity—
Western-Caucasian and East-Asian), resulting in a
total of four subblocks. In each subblock participants
had to learn 14 face identities (seven males, sevenfemales), which randomly displayed neutral, happy, or
disgusted facial expressions. After a 30 s break, they
were presented with a series of 28 faces (14 faces fromthe learning phase and 14 new faces; 14 males and 14
females) and instructed to indicate as quickly and as
accurately as possible whether each face had alreadybeen presented in the learning phase or not by pressing
keys on the keyboard with the index of their left andright hand. Reaction times and accuracy were collected
and analyzed for the purpose of this experiment;
stimulus presentation duration was terminated bysubjects’ responses.
Facial expressions differed between learning andrecognition phases in order to ensure identity learning,rather than trivial image matching. Each trial started
with the presentation of a central ﬁxation crossallowing the experimenter to check that the calibration
was still accurate (toleration of an error of a maximumof 0.58 of visual angle). If the calibration was not
sufﬁciently accurate, a new one was performed.
Therefore, calibration was validated prior to each trial.
This calibration checking was followed by a ﬁnal
central ﬁxation cross used as the drift correction.
Afterwards, a face was presented at a random locationon the computer screen in order to avoid anticipatory
oculomotor strategies.

5

Data analyses
Behavioral performance measures included reaction
times (RT) and A-Prime (A 0 ; Stanislaw & Todorov,1999). Participants whose face recognition performancedid not exceed chance level were discarded (14% of thesample tested; signiﬁcance threshold determined at 61%
using a bootstrapping approach). Moreover, trials forwhich participants exhibited RT larger than 2.5
standard deviations of their average RT were considered outliers and discarded from analysis. Individuals
Cohen’s d effect sizes (Cohen, 1988) were thencalculated to assess the impact of each spotlight size on
performance.
Saccades and ﬁxations were determined based on
angular velocity using a custom algorithm. Individual
saccade velocity thresholds were set by expert experimenters and ranged between 308/s and 808/s (M ¼40.16, SD ¼ 9.20). When the velocity thresholdexceeded 1008/s, participants were discarded (cf.,Holmqvist, 2011) as having noisy eye-movement data
(15% of the sample tested). Fixations that were
spatially (, 0.38) and temporally (, 20 ms) too close
were merged. Previous studies have not revealed any
effect of the experimental phase (learning vs. recognition), correctness of the answer (correct vs. incorrect
trials), or race of face stimuli (Western-Caucasians vs.
Eastern-Asians; Blais et al., 2008; Caldara et al., 2010;
Miellet et al., 2012). In light of these ﬁndings and toensure compatibility with the common procedure in theﬁeld, only the eye-movements of trials with correct
responses were analyzed and data from Eastern-Asianand Western-Caucasian face stimuli were collapsed (seeFigure A in the Supplemental data for demonstrationof the lack of effect of stimulus race). Variables
describing the general oculomotor behavior (number ofﬁxations and ﬁxation durations) were computed.
Statistical ﬁxation maps were computed with the iMap
toolbox (version 4.01, Caldara & Miellet, 2011; Lao,
Miellet, Pernet, Sokhn, & Caldara, 2016; Miellet, Lao,
& Caldara, 2014). The iMap4 performed pixel-wiseLinear Mixed Models (LMM) on the smoothed ﬁxation
maps with subject as a random effect, and condition(spotlight vs. natural viewing) as a ﬁxed effect. A
spatial cluster test based on bootstrapping was thenused to assess statistical signiﬁcance of the linear
contrasts between the natural viewing and spotlight
conditions. The trimmed mean of beta values, i.e., themodel coefﬁcients of the ﬁxed effect (in this case they
are the conditional mean difference of ﬁxation durationin seconds) within the face region in the ﬁxation maps,
was then extracted for each spotlight size in order toassess the impact of size on the distribution of ﬁxations.
Finally, stimuli from the candidate spotlight size
(smallest spotlight size leading to performance similar
to natural viewing) were compared to natural viewing

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/journals/jov/936220/ on 05/30/2017

Journal of Vision (2017) 17(5):16, 1–12

Papinutto et al.

6

Figure 2. Pipeline of the Facespan reconstruction. On the top left, stimuli from recognition phases for both spotlight and natural
viewing were passed through a retinal filter. To assess their similarity, SSIM was used and resulted in SSIM maps. Afterwards, thepixel-test (RFT) was used to assess the area significantly preserved from natural viewing in the 178 spotlight. Clusters of interest werethen selected, centered and averaged. The averaged cluster significantly preserved was estimated to contain 78 of information,
representing the Facespan. Face images were retrieved from the Karolinska Directed Emotional Faces database (KDEF, http://www.
emotionlab.se/resources/kdef).

stimuli. The goal here was to assess how muchinformation was preserved in the spotlight condition
compared to natural viewing when considering observers’ ﬁxations. Indeed, it is not straightforward toinfer the perceptual span for face recognition based on
our gaze-contingent manipulation. We needed toquantify how much information (in terms of spatial
extent) in the critical spotlight was identical to thenatural viewing condition. In order to address thisquestion, we needed to consider four challenges. First,
the spotlight is a Gaussian aperture, so the target andaverage faces information blend progressively. Second,visual acuity drops off with retinal eccentricity. Thus,
small extrafoveal variability in high spatial frequencies
between the spotlight and natural viewing stimuli might
not be captured by the visual system. Third, thesimilarity between spotlight and natural viewing stimuli
might depend on how dissimilar a given target face(presented centrally) is from the average face (displayed
extrafoveally). Finally, the similarity between spotlightand natural viewing stimuli might depend on the actualﬁxation location. To address these challenges, we useda procedure that aimed at mimicking early constraintsof the visual system (e.g., acuity drop-off witheccentricity), while also considering targets’ typicality(how similar to the average face a speciﬁc target faceis), as well as ﬁxation locations. First, for eachparticipant and stimulus, we convolved a retinal ﬁlter(Targino Da Costa & Do, 2014) on the spotlight ofinterest and natural viewing stimuli according toﬁxation locations in the critical spotlight condition (seeFigure 2 for the Facespan reconstruction pipeline). The
retinal ﬁlter parameters were distance to the screen (700mm in our viewing conditions), stimulus size in pixels,
and the lossy parameter (D ¼ 25, chosen to include
visual perceptual lost according to human sight). We
then used the Structural SIMilarity index (SSIM;
Wang, Bovik, Sheikh, & Simoncelli, 2004) to quantify,
independently for each target face, the similarity
between spotlight and natural viewing stimuli after
retinal ﬁltering at each of the ﬁxation locations in thecritical spotlight condition. The SSIM uses luminance,
contrast, and structure of two images in order to assess
similarity of both images pixel by pixel. In the next
step, we used the pixel-test (Chauvin, Worsley, Schyns,
Arguin, & Gosselin, 2005) to assess signiﬁcance in the
SSIM maps corresponding to each target face andﬁxation location. The pixel-test (Chauvin et al., 2005),
which is based on the Random Field Theory (RFT),
allowed highlighting the information that was signiﬁcantly preserved between spotlight and natural viewing
conditions (pixels that were signiﬁcantly most similar).
We used the following parameters for the pixel-test as
recommended by Chauvin et al. (2005): sigma ¼ 20,
cluster test threshold ¼ 2.7, p ¼ 0.05. The Random Field
Theory provides the probability of observing a cluster

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/journals/jov/936220/ on 05/30/2017

Journal of Vision (2017) 17(5):16, 1–12

Papinutto et al.

of pixels exceeding a threshold in a smooth Gaussian
random ﬁeld while taking into account the spatial
correlation inherent to the data set. More details about
SSIM and the pixel-test can be found in Wang et al.
(2004) and Chauvin et al. (2005), respectively. We
ﬁnally centered and averaged, across stimuli, observers
and ﬁxations, the areas of information preserved by thecritical spotlight manipulation (signiﬁcant SSIM according to pixel-test after retinal ﬁltering on spotlightand natural viewing stimuli). The rationale of thisapproach is that the amount of preserved information,corresponding to the minimal spotlight from which
additional information does not improve performance,is the Facespan.

Results
Behavior
First, we investigated the individual effect sizes(Cohen’s d) of spotlight compared to natural viewing
on performance. Large values indicate that, for a given
participant, performance advantage, in terms of Aprime (A 0 ), for natural viewing as compared to the
spotlight condition is larger than the variability
between trials irrespective of condition. In contrast,
small effect sizes indicate that the difference between
viewing conditions for a given participant is not muchlarger (or smaller for effect sizes , 1) than thevariability between trials across conditions. An effectsize which does not signiﬁcantly differ from 0 indicatesthat performance does not differ across viewing
conditions.
We observed that effect sizes decreased parametrically with increasing spotlight size. However, spotlights
always impacted on performance regardless of theirsize. A-posteriori analyses revealed that log-averageluminance was higher for stimuli in the natural viewing,compared to spotlight conditions (Natural viewing ¼
0.27; Spotlight ¼ 0.39). In contrast, spotlight conditions
did not differ from each other in terms of luminance (M¼ 0.39, SD ¼ 0.002). The difference in luminancebetween natural viewing and spotlight conditions might
be due to our use of the alpha-blending function fromthe Psychotoolbox (Screen: ‘‘BlendFunction’’). Thus,
the fact that the effect of spotlight on performance wasalways signiﬁcant might arise from differences inluminance between spotlight and natural viewing
conditions. Importantly, the critical spotlight that we
wanted to identify does not necessarily correspond toan identical performance between natural viewing andspotlight condition. It rather corresponds to the
spotlight size from which additional information
available in larger spotlights does not further improve

7

performance. Performance data was ﬁtted with various
models from the simplest linear regression to sigmoidal
or polynomial models. A two degree polynomial model
offered the best ﬁt, R2 ¼ 0.94, F(8, 11) ¼ 60, p , 0.001,
with the effect size of RT depending on spotlight size
(Figure 1B). The effect sizes of A 0 were best describedby a sigmoidal model, R2 ¼ 0.79, F(7, 11) ¼ 126, p ,0.001.
The ﬁrst plateau of the sigmoidal model indicatesthat the effect sizes for A 0 remained constant for
spotlight sized 98 to 148, indicating that until 148
additional information does not noticeably increase
performance. The second plateau of the sigmoidal
model shows smaller effect sizes for performance (A 0 ).
Crucially, this second plateau also indicates that, froma 178 spotlight size onward, additional information
available in larger spotlights did not further improve
performance.

Eye-movements
Contrasts between the spotlight and the natural
viewing conditions performed individually for each
spotlight size on the number of ﬁxations and ﬁxation
durations (paired t tests, Bonferroni corrected) did notreveal any consistent differences. In contrast, differential ﬁxation maps generated by iMap4 (Lao et al., 2016)
revealed differences in the spatial distribution ofﬁxation durations between the spotlight and natural
viewing conditions for spotlight sizes ranging from 98
to 148. This range of spotlights exhibits areas withsigniﬁcantly longer ﬁxation durations in the spotlightthan in the natural viewing condition. Crucially iMap4
does not show any signiﬁcant differences across thewhole stimulus space between spotlight and natural
viewing conditions for spotlights of, or larger than, 158(Figure 1C).

Data-driven reconstruction of the FacespanAs noted above, eye movements exhibited during
natural viewing and spotlight conditions did not differ
for spotlights equal to or larger than 158. Moreover, A 0
indicated that facial information contained by spotlights larger than a 178 Gaussian aperture did not lead
improved performance. In short, from eye movement
patterns and performance, it appears that, on average,
face information outside of a 178 Gaussian aperturearound the ﬁxation location is not used for facerecognition as tested in the present experiment. As
described in the data analyses section, we quantiﬁed
how much information (in terms of spatial extent) inthe spotlight was identical to the natural viewing
condition, for a 178 spotlight. The average area of

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/journals/jov/936220/ on 05/30/2017

Journal of Vision (2017) 17(5):16, 1–12

Papinutto et al.

preserved information was contained in a circle of 78 ofvisual angle.3
In summary, performance and ﬁxation patterns
revealed that face information outside a 178 Gaussian
aperture is not used for face recognition. SSIM after
retinal ﬁltering and RFT indicate that face information
projecting in the central 78 of the 178 Gaussian aperture
was preserved compared to natural viewing. Thus, weconclude that the average perceptual span for facerecognition (as tested in the current experiment), theFacespan, is 78 of visual angle given the viewing
conditions used in our paradigm.

Discussion
In this study we aimed to determine the perceptual
span for faces—the Facespan. To this end we employeda gaze-contingent paradigm in which the quantity ofavailable information on the target face—the spotlightor aperture size—varied parametrically. Importantly,
we reconstructed the available information inside theseapertures in a data-driven fashion. This allowed us toquantify the extent of information preserved in the
spotlight compared to natural viewing conditions,while taking into account the spotlight size, acuity
drop-off, ﬁxation locations, and dissimilarity between
the target faces and the average face template.
With regards to observers’ performance and oculomotor behavior during the old/new recognition task,
our ﬁndings suggest that the Facespan—the information extent required at each ﬁxation for normal facerecognition—is 78 of visual angle given our experimental condition. For a ﬁxation located on the nose,
this perceptual span covers almost the entire area ofinternal features; for a ﬁxation positioned on a different
feature, it encompasses that feature along with its
surrounding information (for visual representation, see
Figure 2). Another way of expressing the Facespan is interms of percentage of the face size, which in this study
represents 45% of the face. Interestingly this value isvery similar to that reported by Kwon, Liu, and Chien(2016). In a study manipulating spatial-frequency
content and gaze-contingent window size, these authors
found that at least 50% of facial information was
required for observers to exhibit efﬁcient face recognition performance. The similarity between the percentage of necessary information reported by Kwon et al.
(2016) and the Facespan we measured here is remarkable, especially considering the numerous differences
between both studies: face sizes (48 and 28 vs. 15.58
respectively), tasks (identiﬁcation vs. recognition),familiarity (famous vs. unfamiliar faces), and presenceof extrafoveal information (homogeneous mask vs.
average face template mask). The most critical differ-8

ence between both studies is that Kwon et al. (2016)
measured the revealed information across several
ﬁxations (cf., Miellet et al., 2013), whereas the Facespan represents the information necessary at a single
ﬁxation.
Concerning observers’ oculomotor behavior, ﬁxation
patterns varied as a function of aperture size for
spotlights of 98–148 as demonstrated in Figure 1C;
beyond this aperture size, no further changes wereobserved. This is consistent with the idea that access toface information from a wider visually central region
progressively allows for a more typical oculomotor
behavior (see also Miellet et al., 2012). Interestingly,
subjects’ performance increased once ﬁxation patterns
were stable, supporting the idea that typical eyemovements are crucial to achieve optimal face recognition (Henderson et al., 2005). Finally, performanceplateaued once at least an individual feature was visibleat each ﬁxation.
With a face displayed at a distance used during
natural human interaction (about 80 cm), a Facespanof 78 allows the covering of the entire face within threeﬁxations that would be directed towards these facial
features. The perceptual span for face recognition is inline with the perceptual span for reading (McConkie &Rayner, 1975) and for visual scene search (Nuthmann,2013). Indeed, the Facespan falls between these twoperceptual spans (the perceptual span for reading:;4.58; the perceptual span for face recognition: ;78;
and the perceptual span for scene search: ;88). Thisobservation could be explained in terms of the densityof to-be-processed information under each of thosetasks, which is highest for text and lowest for scenes.
This idea, that the perceptual span depends on
information density, is supported by ﬁndings inreading. For instance, in Chinese where each character
carries more linguistic information than in English, theperceptual span is much smaller (one character to theleft of the ﬁxated character and three characters to its
right, according to Inhoff & Liu, 1997). Using the gazecontingent parafoveal magniﬁcation technique, Miellet,
O’Donnell, and Sereno (2009) demonstrated that theperceptual span is more accurately deﬁned in terms ofnumber of characters (quantity of linguistic information), rather than visual angle. This hypothesis is also
consistent with evidence showing that the perceptual
span for reading is modulated by text difﬁculty(Henderson, Ferreira, Rayner, & Pollatsek, 1990), as
well as reader characteristics, such as expertise (Rayner,
1986; Rayner, Slattery, & Belanger, 2010), age (Rayner,1986), and disorder (Rayner, 1983). From this perspective, information is not considered in absoluteterms but rather follows the concept of diagnostic
information and thus varies depending on viewing
conditions, strategies, and task at hand (see, forinstance, Oliva & Schyns, 1997).

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/journals/jov/936220/ on 05/30/2017

Journal of Vision (2017) 17(5):16, 1–12

Papinutto et al.

Following this idea, we emphasize that the Facespanshould not be considered as an absolute quantity.
Inasmuch as the perceptual span for reading is not
absolute, but instead ﬂexible, the Facespan reported
here should be considered as an average benchmark
obtained under the aforementioned speciﬁc viewing
conditions and task. Mirroring previous observations
for the perceptual span in reading, we suggest that theFacespan may vary as a function of factors including—but not limited to—e.g., stimulus or observer characteristics, and task demands. Indeed, previous researchin face processing has demonstrated cultural (Blais etal., 2008; Caldara et al., 2010; Miellet et al., 2012,
Miellet et al., 2013), idiosyncratic (Kanan et al., 2015;
Mehoudar, Arizpe, Baker, & Yovel, 2014; Peterson &Eckstein, 2013; Ramon et al., 2016) and familiarity(Van Belle et al., 2010) biases in information sampling.
Moreover, initial ﬁxation location on the face also
impacts on sampling strategy (Miellet et al., 2011).
These ﬁndings suggest that the Facespan might also be
ﬂexibly modulated by such factors. However, further
studies are necessary to understand precisely when andhow the Facespan is modulated.
We suggest that characterizing the Facespan at theindividual level, as well as across cultures may broaden
our understanding of these well-established, systematicdifferences in perceptual biases and processing. Speciﬁcally, we propose a direct relationship between
individual preferences in information sampling (global
vs. local) and an individual’s Facespan, which in turn
determines the facial representation generated based on
the visual input. In other words, we propose thatobservers exhibiting global sampling strategies (i.e.,favoring ﬁxations on the center of the face) will exhibita broader Facespan, which allows sampling diagnostic
feature (eyes and mouth) from central ﬁxation. In
contrast, observers exhibiting local sampling strategies(i.e., favoring ﬁxations on eyes and mouth) will show aspatially more constrained Facespan. As demonstrated
in Miellet et al. (2013), the sampling strategies
determine the information available to the brain.

Conclusions
In the present study, we determined the perceptual
span for face recognition—the Facespan. Our data
show that given our viewing conditions, observers
require information encompassing 78 of visual angle toexhibit face recognition performance comparable tothat observed during natural viewing. Importantly,
similarly to the perceptual span in reading, the Facespan should not be considered as ﬁxed, but rather
ﬂexible. Our ﬁndings provide a benchmark for information sampling during face processing, which will9

leverage further investigations into intra- and interindividual differences and how the perceptual span forface processing is modulated by constraints such as
culture, development, or task.
Keywords: facespan, face perception, eye movements,
gaze-contingent, spotlight, perceptual span

Acknowledgments
This study was supported by the SNSF grant‘‘Tracking visual information processing in humans
and monkeys: From eye movements to neural coding’’(316030_144998). We thank the University of
Wollongong for covering the publication fee. We thankAndré Luiz N. Targino da Costa for providing us with
the Matlab code of the retinal ﬁlter used in the present
study. We also thank the two anonymous reviewers fortheir careful reading of our manuscript and their many
insightful comments and suggestions.
Commercial relationships: none.
Corresponding author: Sebastien Miellet.
Email: smiellet@uow.edu.au.
Address: University of Wollongong, Wollongong,
NSW, Australia.

Footnotes
1

See also McConkie and Rayner (1975), who
improved upon the operationalization of this question
by asking ‘‘How far into the periphery are speciﬁc
aspects of the visual stimulus typically acquired and usedduring ﬁxations in reading?’’(p. 578).
2
For English the perceptual span extends from threecharacters to the left of ﬁxation (approximately the
beginning of the ﬁxated word) to 14 characters to theright of ﬁxation (McConkie & Rayner, 1975). Importantly in active reading, involving eye-movements, thespan is mainly constrained by higher level processing
limitations rather than low-level visual constraints(Miellet, O’Donnell, & Sereno, 2009). Note that a
number of studies (Kwon & Legge, 2012; Legge,
Mansﬁeld, & Chung, 2001; Pelli et al., 2007; Yu,
Cheung, Legge, & Chung, 2007) support the notion ofa visual span largely limited by low-level processing.
However, crucially, these studies used rapid presentations and identiﬁcation of meaningless trigrams inabsence of eye-movements (Rapid Serial Visual Presentation). The processes involved might have beendifferent from those present in active reading of
meaningful sentences or texts, for instance visual
remapping and contextual inﬂuence. Thus, theseDownloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/journals/jov/936220/ on 05/30/2017

Journal of Vision (2017) 17(5):16, 1–12

Papinutto et al.

studies are likely to underestimate the higher levelinﬂuences in play in natural reading while overestimating the low-level inﬂuences. The concept of visual
span presented in those studies might differ in naturefrom the perceptual span measured in active reading ofsentences (see Legge, 2007).
3
Note that the Facespan estimate is robust to
systematic manipulations of the pixel-test parameters,
spanning the values found in the literature. The
parameters manipulation had a minimal inﬂuence on
the estimated Facespan whose values ranged from 6.488
to 7.298 of visual angle (42% to 47% of the face).

References
Bang, S., Kim, D., & Choi, S. (2001). Asian face image
database. Korea: Postech.
Blais, C., Jack, R. E., Scheepers, C., Fiset, D., &Caldara, R. (2008). Culture shapes how we look at
faces. PLoS ONE, 3(8), doi.org/10.1371/journal.
pone.0003022.
Caldara, R., & Miellet, S. (2011). iMap: A novel
method for statistical fixation mapping of eye
movement data. Behavior Research Methods, 43(3),864–878, doi.org/10.3758/s13428-011-0092-x.
Caldara, R., Zhou, X., & Miellet, S. (2010). Putting
culture under the ‘‘Spotlight’’ reveals universal
information use for face recognition. PLoS ONE,
5(3), e9708, doi.org/10.1371/journal.pone.0009708.
Chauvin, A., Worsley, K. J., Schyns, P. G., Arguin, M.,& Gosselin, F. (2005). Accurate statistical tests forsmooth classification images. Journal of Vision,
5(9):1, 659–667, doi:10.1167/5.9.1. [PubMed][Article]
Cohen, J. (1988). Statistical power analysis for the
behavioral sciences. New Jersey: Erlbaum.
Cornelissen, F. W., Peters, E. M., & Palmer, J. (2002).
The Eyelink Toolbox: Eye tracking with MATLAB
and the Psychophysics Toolbox. Behavior Research
Methods, Instruments, & Computers, 34(4), 613–
617, doi.org/10.3758/BF03195489.
Ebner, N. C., Riediger, M., & Lindenberger, U. (2010).
FACES—A database of facial expressions inyoung, middle-aged, and older women and men:
Development and validation. Behavior Research
Methods, 42(1), 351–362, doi.org/10.3758/BRM.42.
1.351.
Gosselin, F., & Schyns, P. G. (2001). Bubbles: A
technique to reveal the use of information inrecognition tasks. Vision Research, 41(17), 2261–
2271, doi.org/10.1016/S0042-6989(01)00097-9.

10Hall, E. T. (1966). The hidden dimension. New York:
Doubleday, doi.org/10.1017/CBO9781107415324.
004.
He, Y., Scholz, J. M., Gage, R., Kallie, C. S., Liu, T.,
Legge, G. E., . . . J.-Y., Z. (2015). Comparing thevisual spans for faces and letters. Journal of Vision,
15(8):7, 1–13, doi:10.1167/15.8.7. [PubMed][Article]
Henderson, J. M., Ferreira, F., Rayner, K., &Pollatsek, A. (1990). Effects of foveal processing
Difficulty on the perceptual span in reading:
Implications for attention and eye movement
control. Journal of Experimental Psychology:
Learning, Memory, and Cognition, 16(3), 41–429,
doi.org/10.1037/0278-7393.16.3.417.
Henderson, J. M., Williams, C. C., & Falk, R. J. (2005).
Eye movements are functional during face learning.
Memory & Cognition, 33(1), 98–106, doi.org/10.
3758/BF03195300.
Holmqvist, K. (2011). Eye tracking: A comprehensive
guide to methods and measures. Oxford, UK:
Oxford University Press.
Hsiao, J., & Cottrell, G. (2008). Two fixations suffice inface recognition. Psychological Science, 19(10),998–1006, doi.org/10.1111/j.1467-9280.2008.02191.
x.
Inhoff, A. W., & Liu, W. (1997). The perceptual span
during the reading of Chinese text. In H. C. Chen(Ed.), Cognitive processing of Chinese and related
Asian languages (pp. 243–266). Hong Kong, China:
The Chinese University of Hong Kong Press.
Kanan, C., Bseiso, D. N. F., Ray, N. A., Hsiao, J. H.,& Cottrell, G. W. (2015). Humans have idiosyncratic and task-specific scanpaths for judging faces.
Vision Research, 108, 67–76, doi.org/10.1016/j.
visres.2015.01.013.
Kelly, D. J., Liu, S., Rodger, H., Miellet, S., Ge, L., &Caldara, R. (2011). Developing cultural differencesin face processing. Developmental Science, 14(5),1176–1184, doi.org/10.1111/j.1467-7687.2011.
01067.x.
Kleiner, M., Brainard, D., & Pelli, D. (2007). What’s
new in Psychtoolbox-3? Perception 36 ECVP
Abstract Supplement, 36 (ECVP Abstract Supplement), doi.org/10.1068/v070821.
Kwon, M., & Legge, G. E. (2012). Spatial-frequency
requirements for reading revisited. Vision Research,
62, 139–147, doi.org/10.1016/j.visres.2012.03.025.
Kwon, M., Liu, R., & Chien, L. (2016). Compensation
for blur requires increase in field of view andviewing time. PLOS ONE, 11(9), 1–20, doi.org/10.
1371/journal.pone.0162711.

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/journals/jov/936220/ on 05/30/2017

Journal of Vision (2017) 17(5):16, 1–12

Papinutto et al.

Lao, J., Miellet, S., Pernet, C., Sokhn, N., & Caldara,
R. (2016). iMap4: An open source toolbox for thestatistical fixation mapping of eye movement datawith linear mixed modeling. Behavior Research
Methods, 1–17, doi.org/10.3758/s13428-016-0737-x.
Legge, G. E. (2007). Psychophysics of reading in normal
and low vision. Portions of this research(MNREAD acuity charts) were presented at the
OSA Noninvasive Assessment of the Visual System, 1993, Monterey, CA. Mahwah, NJ: Lawrence
Erlbaum Associates Publishers. Retrieved fromhttp://psycnet.apa.org/psycinfo/2006-20917-000
Legge, G. E., Mansfield, J. S., & Chung, S. T. (2001).
Psychophysics of reading. XX. Linking letter
recognition to reading speed in central andperipheral vision. Vision Research, 41(6), 725–743.
Retrieved from http://www.ncbi.nlm.nih.gov/
pubmed/11248262
Lundqvist, D., Flykt, A., & Ohman, A. (1998). The
Karolinska directed emotional faces (KDEF). [CDROM]. Solna, Sweden: Department of Clinical
Neuroscience, Psychology section, Karolinska Institutet, doi.org/10.1017/S0048577299971664.
Malcolm, G. L., Lanyon, L. J., Fugard, A. J. B., &Barton, J. J. S. (2008). Scan patterns during theprocessing of facial expression versus identity: An
exploration of task-driven and stimulus-driven
effects. Journal of Vision, 8(8):2, 1–9, doi:10.1167/8.
8.2. [PubMed] [Article]
McClure, E. (2000). A meta-analytic review of sex
differences in facial expression processing and theirdevelopment in infants, children, and adolescents.
Psychological Bulletin, 126(3), 424–453, doi.org/10.
1037/0033-2909.126.3.424.
McConkie, G. W., & Rayner, K. (1975). The span ofthe effective stimulus during a fixation in reading.
Perception & Psychophysics, 17(6), 578–586, doi.
org/10.3758/BF03203972.
Mehoudar, E., Arizpe, J., Baker, C. I., & Yovel, G.
(2014). Faces in the eye of the beholder: Uniqueand stable eye scanning patterns of individual
observers. Journal of Vision, 14(7):6, 1–11, doi:10.
1167/14.7.6. [PubMed] [Article]
Meissner, C. A., & Brigham, J. C. (2001). Thirty years
of investigating the own-race bias in memory forfaces: A meta-analytic review. Psychology, Public
Policy, and Law, 7(1), 3–35, doi.org/10.1037/10768971.7.1.3.
Miellet, S., Caldara, R., & Schyns, P. G. (2011). Local
Jekyll and global Hyde: The dual identity of face
identification. Psychological Science, 22(12), 1518–
1526, doi.org/10.1177/0956797611424290.
Miellet, S., He, L., Zhou, X., Lao, J., & Caldara, R.

11(2012). When East meets West: Gaze-contingent
Blindspots abolish cultural diversity in eye movements for faces. Journal of Eye Movement Research,
5(2), 1–12, doi.org/10.16910/JEMR.5.2.5.
Miellet, S., Lao, J., & Caldara, R. (2014). An
appropriate use of iMap produces correct statisticalresults: A reply to McManus (2013) ‘‘iMAP and
iMAP2 produce erroneous statistical maps of eyemovement differences.’’ Perception, 43(5), 451–457,
doi.org/10.1068/p7682.
Miellet, S., O’Donnell, P. J., & Sereno, S. C. (2009).
Parafoveal magnification: Visual acuity does notmodulate the perceptual span in reading. Psychological Science, 20(6), 721–728, doi.org/10.1111/j.
1467-9280.2009.02364.x.
Miellet, S., Vizioli, L., He, L., Zhou, X., & Caldara, R.
(2013). Mapping face recognition information use
across cultures. Frontiers in Psychology, 4(February), 34, doi.org/10.3389/fpsyg.2013.00034.
Näsänen, R., & Ojanpää, H. (2004). How many faces
can be processed during a single eye fixation?
Perception, 33(1), 67–77. Retrieved from http://
www.ncbi.nlm.nih.gov/pubmed/15035329
Nuthmann, A. (2013). On the visual span during object
search in real-world scenes. Visual Cognition, 21(7),803–837, doi.org/10.1080/13506285.2013.832449.
Oliva, A., & Schyns, P. G. (1997). Coarse blobs or fineedges? Evidence that information diagnosticity
changes the perception of complex visual stimuli.
Cognitive Psychology, 34(1), 72–107, doi.org/10.
1006/cogp.1997.0667.
Pelli, D. (1997). The VideoToolbox software for visual
psychophysics: Transforming numbers into movies.
Spatial Vision, 10(4), 437–442, doi.org/10.1163/
156856897X00366.
Pelli, D., Tillman, K. A., Freeman, J., Su, M., Berger,
T. D., & Majaj, N. J. (2007). Crowding andeccentricity determine reading rate. Journal of
Vision, 7(2):20, 1–36, doi:10.1167/7.2.20. [PubMed][Article]Peterson, M. F., & Eckstein, M. P. (2012). Looking justbelow the eyes is optimal across face recognition
tasks. Proceedings of the National Academy of
Sciences, USA, 109(48), E3314–E3323, doi.org/10.
1073/pnas.1214269109.
Peterson, M. F., & Eckstein, M. P. (2013). Individual
differences in eye movements during face identification reflect observer-specific optimal points offixation. Psychological Science, 24(7), 1216–1225,
doi.org/10.1177/0956797612471684.
Ramon, M., Miellet, S., Dzieciol, A. M., Konrad, B.
N., Dresler, M., & Caldara, R. (2016). Supermemorizers are not super-recognizers. PloS One,

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/journals/jov/936220/ on 05/30/2017

Journal of Vision (2017) 17(5):16, 1–12

Papinutto et al.

11(3), e0150972, doi.org/10.1371/journal.pone.
0150972.
Rayner, K. (1983). Eye movements, perceptual span,and reading disability. Annals of Dyslexia, 33(1),
163–173, doi.org/10.1007/BF02648003.
Rayner, K. (1986). Eye movements and the perceptual
span in beginning and skilled readers. Journal of
Experimental Child Psychology, 41(2), 211–236,
doi.org/10.1016/0022-0965(86)90037-8.
Rayner, K., Slattery, T. J., & Belanger, N. N. (2010).
Eye movements, the perceptual span, and reading
speed. Psychonomic Bulletin and Review, 17(6), 834–
839, doi.org/10.3758/PBR.17.6.834.
Rodger, H., Kelly, D. J., Blais, C., & Caldara, R.
(2010). Inverting faces does not abolish cultural
diversity in eye movements. Perception, 39, 1491–
1503, doi.org/10.1068/p6750.
Schyns, P. G., Bonnar, L., & Gosselin, F. (2002). Showme the features! Understanding recognition fromthe use of visual information. Psychological Science, 13(5), 402–409, doi.org/10.1111/1467-9280.
00472.
Stanislaw, H., & Todorov, N. (1999). Calculation of
signal detection theory measures. Behavior Research Methods, Instruments, & Computers, 31(1),137–149, doi.org/10.3758/BF03207704.
Targino Da Costa, A. L. N., & Do, M. N. (2014). A
retina-based perceptually lossless limit and agaussian foveation scheme with loss control. IEEE
Journal on Selected Topics in Signal Processing,

12

8(3), 438–453, doi.org/10.1109/JSTSP.2014.
2315716.
Van Belle, G., De Graef, P., Verfaillie, K., Rossion, B.,& Lefevre, P. (2010). Face inversion impairs holistic
perception: Evidence from gaze-contingent stimulation. Journal of Vision, 10(5):10, 1–13, doi:10.
1167/10.5.10. [PubMed] [Article]
Walker-Smith, G. J., Gale, A. G., & Findlay, J. M.
(1977). Eye movement strategies involved in faceperception. Perception, 6(3), 313–326, doi.org/10.
1068/p060313.
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E.
P. (2004). Image quality assessment: From error
visibility to structural similarity. IEEE Transactions
on Image Processing, 13(4), 600–612, doi.org/10.
1109/TIP.2003.819861.
Woodworth, R. S. (1938). Experimental psychology.
New York, NY: Holt.
Xivry, J.-J. O., Ramon, M., Lefèvre, P., & Rossion, B.
(2008). Reduced fixation on the upper area of
personally familiar faces following acquired prosopagnosia. Journal of Neuropsychology, 2(1), 245–
268, doi.org/10.1348/174866407X260199.
Yarbus, A. L. (1967). Eye movements and vision.
Boston, MA: Springer, doi.org/10.1007/978-14899-5379-7.
Yu, D., Cheung, S.-H., Legge, G. E., & Chung, S. T. L.
(2007). Effect of letter spacing on visual span andreading speed. Journal of Vision, 7(2):2, 1–10, doi:
10.1167/7.2.2. [PubMed] [Article]

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/journals/jov/936220/ on 05/30/2017