Research Article

Local Jekyll and Global Hyde: The Dual
Identity of Face Identification

Psychological Science22(12) 1518­–1526© The Author(s) 2011
Reprints and permission:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/0956797611424290
http://pss.sagepub.com

Sébastien Miellet1,2, Roberto Caldara2, andPhilippe G. Schyns1
1Institute of Neuroscience and Psychology, University of Glasgow, andDepartment of Psychology, University of Fribourg

2

Abstract
The main concern in face-processing research is to understand the processes underlying the identification of faces. In the
study reported here, we addressed this issue by examining whether local or global information supports face identification.
We developed a new methodology called “iHybrid.” This technique combines two famous identities in a gaze-contingent
paradigm, which simultaneously provides local, foveated information from one face and global, complementary informationfrom a second face. Behavioral face-identification performance and eye-tracking data showed that the visual system identified
faces on the basis of either local or global information depending on the location of the observer’s first fixation. In some cases,
a given observer even identified the same face using local information on one trial and global information on another trial. A
validation in natural viewing conditions confirmed our findings. These results clearly demonstrate that face identification is not
rooted in a single, or even preferred, information-gathering strategy.
Keywords
face identification, eye movements, gaze contingent, iHybrid
Received 11/22/10; Revision accepted 4/20/11

An important issue in face processing concerns what information is crucial for facial identification. Specifically, what individual variation among faces does the visual system use to
identify them? The main issue concerns whether this diagnostic variance is local to facial features or global to the patterns
comprising facial features. To illustrate, it is undeniable thatfaces vary locally: Noses, for example, can be turned up, hawk
shaped, Greek, Roman, Nubian, or snub, and human visual
cognition categorizes distinctive facial features, such asalmond-shaped eyes, pouty mouths, square jaws, and weak
chins. But the question of whether this undeniable local variance of faces generally contributes to their identification isstill highly controversial.
In ecologically valid conditions, faces are recognized overa wide variety of poses and conditions of illumination, evenwhen they are partially obstructed and the range of viewing
distances is considerable. Individual facial features combine
into higher-order global facial patterns that can be invariant
over a large range of challenging viewing conditions. In contrast, the local shape of a nose might become ambiguous outside the range of conversational distances, and the color of aniris might simply vanish as a result of the physical limitationsof retinal sampling. Thus, a robust face-identification mechanism would have to use different sorts of information from thesame face.

Face researchers typically discuss face-identification mechanisms in terms of different formats of representation—that is,
specific factors over which facial variance is measured, including single-pixel luminance, clusters of pixels forming face parts,
higher-order relationships between face parts, entire faces at
low-frequency bands or a combination of spatial-frequency
bands, local Gabor jets, and three-dimensional variant structures with or without chromatic and textural cues. In the study
reported here, we embraced a different perspective on face identity, focusing instead on distal stimuli to ascertain whether thevisual system uses information at local or global scales.
During eye fixations, the visual system locally samples
foveated information at a high resolution to categorize stimuli.
The seminal work of Yarbus (1967) revealed that when Westerners fixate on faces, their eyes follow a systematic triangularsequence that locally samples the two eyes and mouth over thecourse of face identification (e.g., Althoff & Cohen, 1999;
Corresponding Authors:
Sébastien Miellet, Department of Psychology, Faucigny 2, Fribourg, 1700,
Switzerland
E-mail: sebastien.miellet@unifr.ch
Philippe G. Schyns, Institute of Neuroscience and Psychology, 58 Hillhead St.,
Glasgow, G12 8QB, United Kingdom
E-mail: philippe.schyns@glasgow.ac.uk

1519

Use of Global and Local Information in Face Identification
Groner, Walder, & Groner, 1984; Henderson, Williams, &Falk, 2005). However, recent studies have shown that central
fixations are deployed by both Westerners (Hsiao & Cottrell,2008) and Easterners (Blais, Jack, Scheepers, Fiset, & Caldara,
2008; Kelly, Miellet, & Caldara, 2010; Kita et al., 2010;Rodger, Kelly, Blais, & Caldara, 2010). Because retinal celldensity and visual resolution decrease steeply outside the
fovea, the center of the face is likely to be the most advantageous location at which to sample global information. However, the basic question still remains to be clarified of whether
face identification proceeds from extraction of local or global
stimulus information because fixation on a face region does
not necessarily imply usage of its underlying information(Caldara, Zhou, & Miellet, 2010; Gosselin & Schyns, 2001;
Schyns, Petro, & Smith, 2007).
To address this question, we developed the “iHybrid” technique, a novel methodology that combines a gaze-contingent
window with a hybrid stimulus (see Fig. 1). A hybrid stimulus(Oliva & Schyns, 1997; Oliva, Torralba, & Schyns, 2006;
Schyns & Oliva, 1994, 1999) comprises two different stimuli,each represented in different spatial-frequency bands spanningthe full spectrum. The “i” (or “eye”) refers to a gaze-contingent
window that forces foveated information to be full spectrum
and local, and leaves the global information outside thegaze-contingent window. Figure 11 illustrates the processof creating an iHybrid stimulus using a fixation on the left
eye. (A film clip illustrating the dynamics of fixations over
one trial, with the dot representing the fixation location, is avail-

Faces

Brad Pittable in the Supplemental Material available online andon the third author’s Web site: http://www.psy.gla.ac.uk/~philippe/iHybrid_example.mov.) Analysis of fixations on theiHybrid stimuli teases apart information accrued locally, overmultiple fixations, from information acquired globally, possibly in a single fixation, but outside the local foveated window.
We used famous faces in the study reported here because they
do not require extensive identity learning during the experiment and have been categorized in a variety of viewing conditions; both of these factors ensured that participants had
considerable expertise with the stimuli.

MethodParticipants
Twelve Western-Caucasian young adults (4 males, 8 females;mean age = 23.7 years) from the University of Glasgow participated in this study. All participants had normal or correctedto-normal vision, gave written informed consent, and were
paid for their participation in a protocol approved by the ethics
committee at the University of Glasgow.

Stimuli
We used 18 face pictures of famous male actors (selected fromimages used in a study by Butler, Blais, Gosselin, Bub, &Fiset, 2010). Original images consisted of 260 × 260, 16-bit

SF
Decomposition Information

Local

+

William H. Macy

Globali Hybrid=

Local Information: “Brad Pitt”
Global Information: “William H. Macy”

Fig. 1. Procedure used to create iHybrid faces. The spatial frequencies (SFs) of two original face images (illustrated
here with Brad Pitt and William H. Macy) were decomposed separately into four nonoverlapping SF bands of 1 octave
each (<3, 3–6, 6–12, >12 cycles per degree of visual angle). A Gaussian window (SD = 25 pixels, ~1° of visual angle)
was then centered on every potential fixation location on each face; this procedure formed a lattice of 5- × 5-pixel
cells covering the original 260 × 260 image. When an observer fixated on the stimulus, the local information across thefour SF bands for one identity was extracted through the Gaussian window at that location, and the complementary
global SF information was extracted from the other identity. The sum of the complementary, fixation-dependent
identities formed the iHybrid stimulus. In the example illustrated here, the dashed red line indicates a fixation location
at the left eye; local SF information was extracted from this location in the image of Brad Pitt, and the complementary
SF information was taken from the image of William H. Macy. An observer who identifies the resulting face as Brad
Pitt is using local information, and an observer who identifies this face as William H. Macy is using global information.

1520		gray-level pixels and were normalized for luminance, image
positions of the eyes and the mouth, and external features. We
created 18 iHybrid stimuli by pairing identities and combining
the two identities in each pair in two ways (e.g., foveal information from Brad Pitt combined with extrafoveal informationfrom William H. Macy, and the opposite combination); thus
the identity providing the local face information and the identity providing the global face information were counterbalanced within each iHybrid pair.

Procedure
We recorded eye movements using a desktop-mounted EyeLink 2000 (SR Research, Mississauga, Ontario, Canada; sampling rate = 1000 Hz). We maintained the viewing distance at
70 cm with a chin and forehead rest and presented the stimuli(face size: 15.6° × 19.5° of visual angle) on a gray backgrounddisplayed on a Dell P1130 19-in. CRT monitor with a refresh
rate of 170 Hz. Viewing was binocular, and we tracked theparticipant’s dominant eye. We implemented the experiment
in MATLAB (The MathWorks, Natick, MA) using the Psychophysics and EyeLink Toolbox extensions (Brainard, 1997;
Cornelissen, Peters, & Palmer, 2002).
The main experiment comprised two parts (identity-information estimation and iHybrid identification).
Pretest: identity-information estimation. In order to create
the iHybrid stimuli, we used the QUEST procedure (Watson &Pelli, 1983) to estimate the threshold of phase-coherent face
information each participant needed to identify each face (seeFig. S1 in the Supplemental Material). Phase coherence is anindex of the identity information each picture contains. Thismeasure enabled a more rigorous balancing of the identities
composing the iHybrid pairs. In an iHybrid stimulus, the facewith the lower phase-coherence threshold might be systematically identified more often that the face with the higher phasecoherence threshold. Instead, we wanted to ensure (a) thateach observer could identify each face 75% of the time with agiven level of phase coherence, (b) that we balanced each of
the two faces in an iHybrid pair for their required levels ofphase coherence, and (c) that an eventual imbalance in phasecoherence threshold across two identities would not systematically bias identification responses. It is important to note,however, that we used the original face pictures, not the noisy
ones used in the pretest, to create the iHybrid stimuli.
We instructed participants to verbally name the famous person briefly presented on a computer screen. Each trial started
with a central fixation cross (500 ms), followed by the phasemanipulated stimulus (100 ms), a 100% noise field (500 ms),
and a blank screen during which observers had to respond(unlimited duration). We parametrically modified the phase
spectrum of the original faces (by 5% increments) to generate
stimuli ranging between pure noise (0% phase coherence) andthe undistorted signal (100% phase coherence; Dakin, Hess,
Ledgeway, & Achtman, 2002). Using QUEST, we determined

Miellet et al.
the percentage of phase coherence required for 75% identification accuracy of each original identity.
iHybrid procedure. The pretest revealed that 45% of phasecoherent face information was required on average to reachthe threshold of 75% correct identification (required phasecoherence ranged from 23% to 60%, as expected given theheterogeneity of famous faces). We constructed the iHybrid
stimuli by pairing two identities that differed by at most 24%
in the phase coherence required for 75% correct identification.
The face pairings were the same for all participants.
Because iHybrid computation is time-intensive, we created
all stimuli before subjects came to the lab (see Fig. 1 for an
illustration of this computation). First, we computed all possible fixation locations on each of the faces in a pair (constrained to a lattice of 5 × 5 pixels on the original 260- ×
260-pixel picture). Second, we decomposed each identity into
four nonoverlapping spatial-frequency bands of 1 octave each(<3, 3–6, 6–12, and >12 cycles per degree of visual angle)
using the MATLAB Pyramid Toolbox (Simoncelli, 1997). For
each one of the precomputed fixation locations, we applied
spatial-frequency filtering (illustrated in Fig. 1 for a fixationon the left eye). The local information, represented in the four
spatial-frequency bands at the point of fixation (the left eyeof Brad Pitt in Fig. 1), was extracted through a Gaussian window (SD = 25 pixels, ~1° of visual angle). To extract global
information, we applied the same technique to the information
outside the Gaussian window (most of the face of William H.
Macy in Fig. 1). The iHybrid stimulus resulted from the sumof the local and the global information. For each identity pair,
we generated two iHybrid stimuli by swapping the face fromwhich we sampled the local information and the face fromwhich we sampled the global information.
The experiment started with a standard nine-point calibration. Then, each experimental trial started with a check toensure the eye tracker remained properly calibrated: We firstpresented a central fixation cross, followed by four fixation
crosses—one in the middle of each quadrant of the computer
screen—and one final central fixation cross for drift correction. A drift greater than 0.5° would automatically launchanother standard nine-point calibration to recalibrate the eye
tracker. To avoid constraining first fixations to the location of
the central fixation cross presented before each trial, the screen
went blank for a random time (between 0.5 and 1 s) after thecalibration check. This procedure ensured a random gaze position when the iHybrid stimulus was displayed on the screen.
We then randomly selected one of the iHybrid stimuli andstarted the iHybrid procedure. We monitored the observer’s
fixation location and used this information to index the precomputed iHybrid stimuli on-line. Indexing, which was done
continually during the course of each trial, consisted of retrieving the appropriate iHybrid stimulus and updating the display
contingent to the participant’s gaze position; this process
required 11 ms on average (between 8 and 14 ms, about 90 Hz
refresh rate on the screen, eliminating any flickering). Each

Use of Global and Local Information in Face IdentificationiHybrid trial lasted for 1 s, followed by a 100% noise fieldpresented for 500 ms. Observers identified each iHybrid stimulus by naming aloud the famous actor they perceived. Notethat each identity pair was presented twice in the experiment(to enable each identity to serve as the source of both local andglobal information).

ResultsiHybrid procedure
Participants identified one of the two identities composing
the iHybrid stimulus with 95% accuracy. We determined the
proportion of local-identification responses and of globalidentification responses. These proportions served as proxies
for the use of local face information and global face information, respectively. We observed a nonsignificant trend forreporting the global face (51% of the time) over reporting the
local face (44% of the time), F(1, 11) = 3.74, p = .08, ηp2 = .25.
Further examination of individual observers did not reveal any
systematic individual differences in reporting the global versus the local face. Faces with a lower phase-coherence threshold (i.e., faces that could be identified with less information)
were not selected more often than faces with a higher phasecoherence threshold. Phase coherence conditioned separately
on local identification responses and global identification
responses led to similar averages (identification of the local
face—phase threshold of the local face: 44.12%, phase threshold of the global face: 45.75%; identification of the global
face—phase threshold of the local face: 45.75%, phase threshold of the global face: 45.04%).
Turning to fixation data, we computed a fixation map using
all correct trials of the experiment—that is, without splitting
them according to whether the response was based on local or
global information (see Fig. 2a). The overall pattern of fixations revealed the standard T-shaped pattern of fixations joining the two eyes and the mouth in face-identification
experiments (Yarbus, 1967). To understand the fixation biases
associated with local- and global-information use, we decomposed the overall fixation map into two separate maps showing local and global behavioral responses, respectively (seeFig. 2b). We then created a map showing the difference
between local and global fixations; this difference map
revealed that observers fixated primarily on the left eye
and the mouth when identifying the local face. When identifying the global face, observers fixated primarily on the center ofthe face.
Analyses of mean number of fixations, F(1, 11) = 2.09;
mean fixation duration, F(1, 11) = 0.02; mean scan-path
length, F(1, 11) = 4.34; and mean saccade length, F(1, 11) =1.75 did not reveal any general significant effect of local- versus global-information use on these measures (all ps > .05; see
Table 1).
However, analyzing fixation locations as a function of
behavioral response (local vs. global) revealed significantly

1521
different patterns. To establish significance, we used the iMap
toolbox (Caldara & Miellet, 2011). Specifically, we applied aone-tailed pixel test (Chauvin, Worsley, Schyns, Arguin, &Gosselin, 2005) to the local and global fixation maps (Zcritical >4.86, p < .05, corrected) and a two-tailed pixel test to the map
showing the difference between local and global responses(Zcritical > |5.01|, p < .05, corrected; see Fig. 2b).
As Figure 2b shows, observers primarily fixated on the left
eye and the mouth when identifying the local face of the iHybrid stimulus, and primarily fixated on the center of the facewhen identifying the global face of the iHybrid stimulus. Thisdifference in loci of fixations was confirmed by the z-scoredfixation durations in the significant areas (local face—eyesmouth: 9.83, center: 3.15, global face—eyes-mouth: 4.24, center: 8.22), as well as the effect sizes of the difference in fixation
durations between the local and global information-samplingstrategies (Cohen’s d—eyes-mouth: 1.4 ms, center: 1.75). We
conducted 2 (strategy: local vs. global) × 2 (feature location:eyes-mouth vs. center) analyses of variance on average fixation
duration, scan-path length, total fixation duration, and numberof fixations per trial, and we found interactive effects onmean fixation durations, F(1, 11) = 9.03, p < .02, ηp2 = .45;
scan-path length, F(1, 11) = 16.53, p < .002, ηp2 = .60; total
fixation duration, F(1, 11) = 34.57, p < .0001, ηp2 = .76;
and number of fixations, F(1, 11) = 14.26, p < .004, ηp2 = .56.
The pattern of results confirmed that mean fixation durations,scan-path lengths, and fixation durations were longer for theeyes and mouth area than for the center face area when the local
strategy was used and were longer for the center face area thanfor the eyes and mouth area when the global strategy was used;in addition, the number of fixations on the eyes and mouth area
was larger than the number of fixations on the center face area
when the local strategy was used, and the number of fixations
on the center face area was larger than the number of fixations
on the eyes and mouth area when the global strategy was used(see Table 2). No other effect reached significance.
The same observer would sometimes identify the same
identity (e.g., Brad Pitt) in one iHybrid stimulus using a local
strategy and in the counterbalanced iHybrid stimulus using a
global strategy. On average, 64% of the trials induced suchcomplementary use of the two different strategies to identify
the same face. Note that in these trials, the “attractor” face
was not necessarily the face with the lowest phase-coherence
threshold (this was true in only 55% of the cases).
To further understand the relationship between strategy
selection (local vs. global) and location of fixation, we computed the probability of identifying the local face and the probability of identifying the global face conditioned on thelocation of the first fixation on the iHybrid stimulus. As Figure
2c shows, the probability of using a local strategy was significantly higher than the probability of using a global strategy
when the first fixation was on the left eye or the mouth,t(11) = 2.25, p < .05. In contrast, the probability of using
a global strategy was significantly higher than the probabilityof using a local strategy when the first fixation landed in the1522		All Trials

10
5
0
–5
–10

b

Local
Strategy
10
5

–

=

0
–5

Local Face

Global Facec

Difference Map–10

z Scores or Fixation Biasaz-Scored Fixation Duration

Miellet et al.

GlobalStrategy

Identificationp(Response | Location ofFirst Fixation).8

Local Face

Global Face

.7
.6
.5
.4
.3
.2
.1
.0

Eyes-Mouth

CenterFirst Fixation
Fig. 2. Results from the experiment. A fixation map (a) was created by collapsing z-scored fixation durations across all correct
trials for all iHybrids in the experiment. This fixation map was then split into separate maps (b) showing fixation durations
when responses were based on local face identifications and fixation durations when responses were based on global face
identifications. Subtracting the fixation map for global identification responses from the fixation map for local identification
responses resulted in a difference map. On the local and global maps shown here, white contours surround regions ofsignificantly longer fixation durations than were observed for other areas; on the difference map, white contours indicate
regions of significant differences between the local and global maps (i.e., fixation bias). The graph (c) shows the conditional
probability of identification responses (local face or global face) as a function of the location of the first fixation (in the eyesmouth region or in the center of the face). Error bars show standard errors of the mean.

Table 1. Mean Number of Fixations, Fixation Duration, Scan-Path Length, and Saccade Length
as a Function of the Strategy Used to Identify the iHybrid Stimulus

Strategy used
Local
GlobalNumber of
fixationsFixation
duration (ms)Scan-path
length (°)

Saccadelength (°)

3.25
2.99

391
386

6.74
5.78

1.88
1.73

1523

Use of Global and Local Information in Face Identification
Table 2. Mean Fixation Duration, Scan-Path Length, Total Fixation Duration, andNumber of Fixations as a Function of Feature Location and the Strategy Used to
Identify the iHybrid Stimulus
Local strategy
Variable
Fixation duration (ms)Scan-path length (°)
Total fixation duration (ms)Number of fixations

Global strategy

Eyes-mouthCenterEyes-mouthCenter

402
1.46
222
0.58

296
0.40
47
0.18

313
0.61
70
0.24

379
1.07
206
0.56

center of the face, t(11) = 3.04, p < .02. Thus, the first information sampled (either via a fixation on the eye or the mouth or
via a fixation on the center of the face) determined the choiceof a local or a global information-sampling strategy, and consequently which identity of the iHybrid stimulus was reported.

fixation maps from the iHybrid procedure confirms that sufficient information was indeed extracted to locally or globally
identify the faces in the iHybrid procedure. It also confirms thatthe use of this information generalizes to more naturalistic (i.e.,
not eye contingent) viewing conditions.

Validation: local versus global
face identification

Discussion

To validate that both local and global fixated information are
sufficient for face identification in natural viewing conditions,
we performed the following validation with 10 WesternCaucasian young adults with normal or corrected-to-normal
vision (4 males, 6 females; mean age = 28.3 years), none ofwhom participated in the main experiment.
Using the fixation maps obtained in the main experiment,
we applied local- and global-information filters to reconstruct
36 hybrid identities. We created these filters from the significant local and global regions of the difference fixation map(shown in Fig. 2b and in Fig. 3a). We used the filters to extract
local and global information from each identity within a pair;then we added the rest of the data (i.e., the complementary
information) from the other identity. For each face pair and forthe two fixation-strategy filters, we thus constructed four
hybrid faces (Pitt-local/Macy-complementary, Macy-local/
Pitt-complementary, Pitt-global/Macy-complementary, Macyglobal/Pitt-complementary; see Fig. S2 in the Supplemental
Material), for a total of 36 hybrid faces (Fig. 3a illustrates thisprocess). The viewing parameters were identical to those usedduring the iHybrid procedure, and the stimuli were again presented for 1 s. However, it is important to emphasize that inthis validation posttest, observers saw full-spectrum static
stimuli on the screen, not gaze-contingent displays. Observers
were instructed to identify the faces by naming them aloud.
We predicted that the reconstructed hybrids would be preferentially identified according to the local or the global information extracted by the filters. If this occurred, it would validate
these cues for face identification in more naturalistic viewing
conditions, but still in the context of hybrid identities. The data
confirmed these predictions (see Fig. 3b), t(9) = 18.20, p < .001.
Furthermore, face-identification performance demonstrated avery strong interobserver agreement (Fleiss’s κ = .94). This validation with local and global information extracted using the

The use of iHybrid stimuli revealed the existence of two distinct, equally frequent, and equally effective information-sampling strategies for face identification. The local strategy
involves fixations on the eyes and the mouth, whereas theglobal strategy relies on central fixations of the face. All
observers used both strategies, often to recover the very same
identity. No strategy was systematically associated with specific identities: One observer could use a local strategy toidentify Tom Cruise, for example, whereas another observer
could use a global strategy to identify him. We did not find an
association between choice of strategy and the amount of identity information available (as measured by percentage of phasecoherence in the identity), but instead demonstrated a stronglink between strategy selection and location of the initial fixation on the face. First fixations on the eyes and mouth led to a
local strategy, whereas initial fixations in the center of the facepromoted a global strategy. Note that when we collapsed fixations across the two strategies, we validated Yarbus’s (1967)
well-known pattern of fixations over the two eyes and themouth. Furthermore, hybrid faces reconstructed according to
the local versus global sampling of information led to the predicted identifications. We therefore conclude that the face system flexibly uses local or global stimulus information to
identify faces depending on the constraint of the information
sampled in the initial fixation. These findings have important
implications for face identification.
The face-identification literature presents long-standing
questions on the specialized processing of faces comparedwith the processing of other objects and scenes (e.g.,
Kanwisher, 2000). Some researchers argue that faces are processed as relatively undifferentiated wholes (i.e., holistically),
without a differentiated and explicit representation of the individual facial features (e.g., Farah, Tanaka, & Drain, 1995;
Tanaka & Farah, 1993). Other researchers argue that faces areprocessed on the basis of both configural and featural

1524		

Miellet et al.

a

SF Filter

SF Information

HybridLocal
Brad Pitt

+

=

Local

Other
William H. Macyb

Reconstructed-Face-Identification
Categorization

Global.9

Local Information: “Brad Pitt”
Other Information: “William H. Macy”

Global Information: “William H. Macy”
Other Information: “Brad Pitt”

Interobserver Agreement:
Fleiss’s = .94

.8
.7
.6
.5
.4
.3
.2
.1
.0

Predicted Identity

Other Identity

iHybrid Identification
Fig. 3. Illustration of reconstructed faces (a) and results (b) from the posttest validation analysis. Using the difference maps
from the iHybrid analyses (a), we isolated the regions associated with local-identification responses (colored in red) and theregions associated with global-identification responses (colored in blue). In the example shown here, one reconstructedhybrid was created by applying a spatial-frequency (SF) filter to select the local information for Brad Pitt and completing theimage with the complementary information from William H. Macy. The other reconstructed hybrid was created by using an
SF filter to select the global information for Macy and filling in the image with the complementary information from Pitt. Thus,
two reconstructed hybrids were created for each iHybrid stimulus (i.e., four reconstructed hybrids were created for eachface pair) by applying local and global SF filters separately to the original face images. The graph (b) shows the proportion ofidentifications that corresponded with the information selected by the SF filter (predicted identity) and the proportion of face
identifications that corresponded with the person from whom the complementary information was derived (other identity).
Error bars show standard errors of the mean.

1525

Use of Global and Local Information in Face Identification
information, with separate representational systems for eachkind of information (e.g., Cabeza & Kato 2000; Sergent 1984).
Configural processing would primarily use the metric distances between individual face features as information (for areview, see Maurer, Le Grand, & Mondloch, 2002; however,see Taschereau-Dumouchel, Rossion, Schyns, & Gosselin,
2010, for a statistical demonstration that metric distances are
generally insufficient). Featural processing would rely on
information from individual features (e.g., the shape or colorof the eyes or the shape of the chin, nose, and mouth) or a
combination of features (e.g., Schyns, Bonnar, & Gosselin,
2002; Schyns, Petro, & Smith, 2009; Sekuler, Gaspar, Gold, &Bennett, 2004; van Rijsbergen & Schyns, 2010).
Notwithstanding the conceptual difficulty of teasing apartthese positions and empirically testing them, our results suggest that when the visual system extracts local features overmultiple fixations, it does not acquire an undifferentiatedwhole—because this is incompatible with multiple fixations
on different features. When the visual system extracts information globally from fixating on the center of the face, thenthis global information is probably sufficient for a direct comparison with the version of that face stored in memory. Thus,
it is not surprising that the face-identification literature is fullof debates on representation formats (featural vs. holistic or
configural): Sampling of face information with fixations suggests the use of both local and global information to achieve
identification of the same familiar face.
If both local and global strategies exist in the visual system(at least for identification of famous faces), culture could also
determine a preferential strategy for unfamiliar faces and thus
account for the complex pattern of data in this literature (see,e.g., Althoff & Cohen, 1999; Groner et al., 1984; and Henderson et al., 2005, for data indicating local strategies and see,e.g., Blais et al., 2008; Caldara et al., 2010; Hsiao & Cottrell,
2008; Jack, Blais, Scheepers, Schyns, & Caldara, 2009; Kelly
et al., 2010; and Kita et al., 2010, for data indicating global
strategies). Accordingly, a cultural bias for a global strategy inEasterners could arise from an equally effective style of general visual-information extraction used for object identification (Kelly et al., 2010), which might originate from a cultural
norm to direct the first fixation less often directly toward theeyes.
In sum, we have shown that the eye movement strategies offace identification flexibly use local or global face information, even when the same observer identifies the same face.
Our results challenge the notion of a mandatory route forrecovering face identity.
Acknowledgments
We thank Frederic Gosselin and Daniel Fiset for providing thefamous-faces data set.

Declaration of Conflicting Interests
The authors declared that they had no conflicts of interest withrespect to their authorship or the publication of this article.

Funding
S. M., R. C., and P. G. S. are supported by The Economic and Social
Research Council and Medical Research Council (ESRC/RES060-25-0010).

Supplemental Material
Additional supporting information may be found at http://pss.sagepub.com/content/by/supplemental-data

Note
1. Note that Figures 1 and 3 do not show the actual stimuli, which
could not be published for licensing reasons. Rather, the figures show
re-creations from alternative images of the actors whose faces wereused in the studies. The faces used for Figures 1 and 3 are almost
identical to the ones used in the studies, and the figures were createdusing exactly the same techniques as in the studies.

References
Althoff, R. R., & Cohen, N. J. (1999). Eye-movement-based memory
effect: A reprocessing effect in face perception. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25,
997–1010.
Blais, C., Jack, R. E., Scheepers, C., Fiset, D., & Caldara, R. (2008).
Culture shapes how we look at faces. PLoS ONE, 3(8), e3022.
Retrieved from http://www.plosone.org/article/info:doi/10.1371/
journal.pone.0003022
Brainard, D. H. (1997). The Psychophysics Toolbox. Spatial Vision,
10, 433–436.
Butler, S., Blais, C., Gosselin, F., Bub, D., & Fiset, D. (2010). Recognizing famous people. Attention, Perception, & Psychophysics,
72, 1444–1449.
Cabeza, R., & Kato, T. (2000). Features are also important: Contributions of featural and configural processing to face recognition.
Psychological Science, 11, 429–433.
Caldara, R., & Miellet, S. (2011). iMap: A novel method for statistical fixation mapping of eye movement data. Behavior Research
Methods, 43, 864–878.
Caldara, R., Zhou, X., & Miellet, S. (2010). Putting culture under the
spotlight reveals universal information use for face recognition.
PLoS ONE, 5(3), e9708. Retrieved from http://www.plosone.org/
article/info:doi/10.1371/journal.pone.0009708
Chauvin, A., Worsley, K. J., Schyns, P. G., Arguin, M., & Gosselin, F.
(2005). Accurate statistical tests for smooth classification images.
Journal of Vision, 5(9), Article 1. Retrieved from http://www.journalofvision.org/content/5/9/1
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, 613–617.
Dakin, S. C., Hess, R. F., Ledgeway, T., & Achtman, R. L. (2002).
What causes nonmonotonic tuning of fMRI response to noisy
images? Current Biology, 12, R476–R477.
Farah, M. J., Tanaka, J. W., & Drain, H. M. (1995). What causes
the face inversion effect? Journal of Experimental Psychology:
Human Perception and Performance, 21, 628–634.

1526		Gosselin, F., & Schyns, P. G. (2001). Bubbles: A new technique toreveal the use of visual information in recognition tasks. Vision
Research, 41, 2261–2271.
Groner, R., Walder, F., & Groner, M. (1984). Looking at faces: Local
and global aspects of scanpaths. In A. G. Gale & F. Johnson(Eds.), Theoretical and applied aspects of eye movements research(pp. 523–533). Amsterdam, The Netherlands: Elsevier.
Henderson, J. M., Williams, C. C., & Falk, R. J. (2005). Eye movements are functional during face learning. Memory & Cognition,
33, 98–106.
Hsiao, J. H.-W., & Cottrell, G. (2008). Two fixations suffice in facerecognition. Psychological Science, 19, 998–1006.
Jack, R. E., Blais, C., Scheepers, C., Schyns, P. G., & Caldara, R.
(2009). Cultural confusions show that facial expressions are notuniversal. Current Biology, 19, 1543–1548.
Kanwisher, N. (2000). Domain specificity in face perception. Nature
Neuroscience, 3, 759–763.
Kelly, D. J., Miellet, S., & Caldara, R. (2010). Culture shapes eye
movements for visually homogeneous objects. Frontiers in Psychology, 1. Retrieved from http://www.frontiersin.org/perception_science/10.3389/fpsyg.2010.00006/full
Kita, Y., Gunji, A., Sakihara, K., Inagaki, M., Kaga, M., Nakagawa,E., & Hosokawa, T. (2010). Scanning strategies do not modulate
face identification: Eye-tracking and near-infrared spectroscopy
study. PLoS ONE, 5(6), e11050. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2883577/
Maurer, D., Le Grand, R., & Mondloch, C. (2002). The many
faces of configural processing. Trends in Cognitive Sciences,6, 255–260.
Oliva, A., & Schyns, P. G. (1997). Coarse blobs, or fine scale edges?
Evidence that information diagnosticity changes the perceptionof complex visual stimuli. Cognitive Psychology, 34, 72–107.
Oliva, A., Torralba, A., & Schyns, P. G. (2006). Hybrid images. ACM
Transactions on Graphics, 25, 527–532.
Rodger, H., Kelly, D., Blais, C., & Caldara, R. (2010). Inverting faces
does not abolish cultural diversity in eye movements. Perception,
39, 1491–1503.
Schyns, P. G., Bonnar, L., & Gosselin, F. (2002). Show me the features: Understanding recognition from the use of visual information. Psychological Science, 13, 402–409.

Miellet et al.
Schyns, P. G., & Oliva, A. (1994). From blobs to boundary edges:
Evidence for time- and spatial-scale-dependent scene recognition. Psychological Science, 5, 195–200.
Schyns, P. G., & Oliva, A. (1999). Dr. Angry and Mr. Smile: When
categorization flexibly modifies the perception of faces in rapid
visual presentations. Cognition, 69, 243–265.
Schyns, P. G., Petro, L., & Smith, M. L. (2007). Dynamics of visual
information integration in the brain to categorize facial expressions. Current Biology, 17, 1580–1585.
Schyns, P. G., Petro, L., & Smith, M. L. (2009). Transmissionof facial expressions co-evolved with their efficient decoding in the brain: Behavioral and brain evidence. PLoS ONE,
4(5), e5625. Retrieved from http://www.plosone.org/article/
info:doi%2F10.1371%2Fjournal.pone.0005625
Sekuler, A. B., Gaspar, C. M., Gold, J. M., & Bennett, P. J. (2004).
Inversion leads to quantitative, not qualitative, changes in faceprocessing. Current Biology, 14, 391–396.
Sergent, J. (1984). An investigation into component and configurational processes underlying face recognition. British Journal of
Psychology, 75, 221–242.
Simoncelli, E. P. (1997). Image and multi-scale pyramid tools[Computer software]. Retrieved from www.cns.nyu.edu/~eero/software.html
Tanaka, J. W., & Farah, M. J. (1993). Parts and wholes in face recognition. Quarterly Journal of Experimental Psychology A, 46,
225–245.
Taschereau-Dumouchel, V., Rossion, B., Schyns, P. G., & Gosselin, F.
(2010). Interattribute distances do not represent the identity ofreal-world faces. Frontiers in Perception Science, 1. Retrieved
from http://www.frontiersin.org/perception_science/10.3389/fpsyg.2010.00159/fullvan Rijsbergen, N. J., & Schyns, P. G. (2010). Dynamics of trimming
the content of face representations for categorization in thebrain. PLoS Computational Biology, 5(11), e1000561. Retrieved
from http:// www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000561
Watson, A. B., & Pelli, D. G. (1983). QUEST: A Bayesian adaptive
psychometric method. Perception & Psychophysics, 33, 113–120.
Yarbus, A. L. (1967). Eye movements and vision (B. Haigh, Trans.).
New York, NY: Plenum Press.