Cult. Brain (2013) 1(2–4):158–174
DOI 10.1007/s40167-013-0012-2
ORIGINAL ARTICLE

Culture modulates the temporal dynamics of global/
local processing
Junpeng Lao • Luca Vizioli • Roberto Caldara

Accepted: 31 October 2013 / Published online: 16 November 2013
Ó Springer-Verlag Berlin Heidelberg 2013

Abstract Cultural differences in the way individuals from Western Caucasian(WC) and East Asian (EA) societies perceive and attend to visual information havebeen consistently reported in recent years. WC observers favor and perceive mostefficiently the salient, local visual information by directing attention to focal
objects. In contrast, EA observers show a bias towards global information, bypreferentially attending elements in the background. However, the underlying
neural mechanisms and the temporal dynamics of this striking cultural contrast have
yet to be clarified. The combination of Navon figures, which contain both global andlocal features, and the measurement of neural adaptation constitute an ideal way toprobe this issue. We recorded the electrophysiological signals of WC and EA
observers while they actively matched culturally neutral geometric Navon shapes.
In each trial, participants sequentially viewed and categorized an adapter shapefollowed by a target shape, as being either: identical; global congruent; local
congruent; and different. We quantified the repetition suppression, a reduction inneural activity in stimulus sensitive regions following stimulus repetition, using asingle-trial approach. A robust data-driven spatio-temporal analysis revealed at
80 ms a significant interaction between the culture of the observers and shapeadaptation. EA observers showed sensitivity to global congruency on the attentional
P1 component, whereas WC observers showed discrimination for global shapes atlater stages. Our data revealed an early sensitivity to global and local shape categorization, which is modulated by culture. This neural tuning could underlie more
complex behavioral differences observed across human populations.

J. Lao (&)  L. Vizioli  R. Caldara (&)
Department of Psychology, University of Fribourg, Faucigny 2, 1700 Fribourg, Switzerlande-mail: junpeng.lao@unifr.ch
R. Caldara
e-mail: roberto.caldara@unifr.ch

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Keywords Culture  Visual attention  Perceptual bias  Repetition
suppression  P1  N2

Introduction
Human visual experience is strongly modulated by culture. For over 20 years, crosscultural research in cognitive science and, more recently in neuroscience, haveshown that culture shapes the way we perceive the world (for a review, see Han
et al. 2013). Studies comparing Western Caucasian (WC) and East Asian (EA)
observers have provided convergent evidence on the cultural perceptual biases
characterizing the cognitive styles of those populations (e.g., Nisbett et al. 2001;
Nisbett and Miyamoto 2005). Westerners preferably focus on local information in
objects (e.g., Masuda and Nisbett 2001), scene (e.g., Masuda and Nisbett 2006), andface perception (e.g., Blais et al. 2008; Caldara et al. 2010). In contrast, individuals
from EA cultures—such as China or Japan—display instead a perceptual bias
towards global information processing. Recent evidence has suggested that thiscultural contrast might rely on culture-specific tuning towards visual spatial
frequency information (Miellet et al. 2013). More precisely, WC observers use
preferentially high spatial frequency information from foveal vision (e.g., see
Miellet et al. 2013 for evidence from face recognition). In contrast, EA observers
preferentially process contextual information by relying on extra-foveal vision
during face recognition (Miellet et al. 2013), and for change detection of both lowlevel visual stimuli (e.g., color blocks; Boduroglu et al. 2009) and complex realworld stimuli (e.g., natural scenes; Masuda and Nisbett 2001).
These perceptual tunings have been related to attentional differences acrosscultural groups (Nisbett et al. 2001). In a series of studies, it has been consistently
demonstrated that WC observers attend to and process more effectively local
features, while EA observers exhibit a global attention bias (McKone et al. 2010;
Nisbett and Miyamoto 2005; Hedden et al. 2008; Kitayama et al. 2003). For
example, Kitayama et al. (2003) presented Westerners and Esterners a vertical line
within a square frame and subsequently asked the observers to report the length ofthe line (i.e., rod-and-frame task). In the task requiring local selective attention (i.e.,reporting the absolute length without referencing to the surrounding frame), WC
observers are more accurate than EA observers, whereas EA observers outperformed WC observers in the task requiring global selective attention (i.e., reporting
the relative length by referencing to the surrounding frame). EA observers are also
more distracted by unrelated global information and faster at detecting targets at the
global level compared to Westerners (Boduroglu et al. 2009; Petrova et al. 2013),
which suggest they might have a global selective attention bias. Within thisframework yet, McKone et al. (2010) used Navon stimuli to directly quantify the
global/local attention bias between Westerners and Easterners. Navon figures arehierarchical stimuli comprising a large global shape constituted by small local
shapes (Navon 1977). One of the main advantages of these images lies in thatparticipants viewing the very same stimulus (thus perfectly matched in terms of
low-level visual properties) can be cued or can show a visual preference to either the

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local or global elements (McKone et al. 2010; Navon 1977). Interestingly, McKone
et al. (2010) reported that only East Asian observers identified target letters faster
when they were appearing at the global level. These authors attributed thisperformance advantage to a more effective global-selective attention in EA
compared to WC observers (McKone et al. 2010).
However, the difference in attention selectivity between cultures has beenquestioned in a number of studies. For example, a direct replication of Kitayama
et al. (2003) reported that both WC and EA observers performed better at tasks
requiring more global- than local-selective attention (Zhou et al. 2008). Eye
tracking results also showed that EA observers do not overtly direct more attention(i.e., fixations) towards the global information than WC observers (Evans et al.
2009; Miellet et al. 2010; Rayner et al. 2009, 2007; but see Chua et al. 2005). In
other cross-cultural studies using Navon figures, Caparos et al. (2013) tested
observers from Himba culture (i.e. a remote culture from North Namibia, seeDavidoff et al. 2008) and reported a perceptual bias to local features in thispopulation. However, despite such very strong local perceptual bias, the Himba did
not underperform on global selective-tasks compared to Western controls (Caparos
et al. 2013). Altogether, the extent to which attentional processes are playing a keyrole on the cultural differences reported above and the presence of this perceptual
bias across the Western and Eastern cultures remains to be clarified.
The electroencephalography (EEG) technique is highly relevant to probe thisquestion and identify whether and when attentional processes would drive such cultural
perceptual biases in global/local processing. Notably, attention modulates a particularelectrophysiological signature in human observers: the P1 event-related potential(ERP) (Hillyard et al. 1973; Voorhis and Hillyard 1977; for a review, see Luck et al.
2000). The P1 is a positive deflection peaking roughly 100 ms after stimulus onset on
the occipital scalp surface, larger for attended than unattended information. Surprisingly, studies directly comparing two groups of observers did not reveal differences on
this component (Goto et al. 2010; Lewis et al. 2008). For example, Goto et al. (2010)
manipulated the semantic incongruity between objects and scenes. They only observed
cultural differences in the ERP amplitudes at later stages of information processing (i.e.,
N400 components; Kutas and Hillyard 1980; Holcomb and Neville 1991). Whilemethodological shortcomings such as significant differences in low-level visualproperties of the stimuli should be taken into consideration (Petrova et al. 2013),
conventional ERP analysis relying on absolute amplitude differences across conditions
might not be sensitive enough to reveal early effects (see Vizioli et al. 2010). In order toincrease the sensitivity of the electrophysiological signals, here we adopted one of themost powerful methods used in the neurophysiological literature, which relies on theneural repetition effect. Adaptation is a well-established paradigm used to reveal thenature of information coding at the perceptual and neurophysiological levels (for areview, see Grill-Spector et al. 2006). In this framework, ‘‘repetition suppression’’ (RS)is the reduction of neural activity, in stimulus-related regions, associated to thepresentation of two or more stimuli in rapid succession (Grill-Spector et al. 2006;
Henson 2003; Henson and Rugg 2003; Wiggs and Martin 1998). It has been argued that
RS represents a ‘‘sharpening’’ mechanism within the neural population that engage inthe processing of the repeated stimulus (Grill-Spector et al. 2006; Wiggs and Martin

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1611998). Thus, the amount of RS reflects the capacity of neural populations todiscriminate different information and can thus be viewed as a novelty detection
mechanism and a more efficient coding of the sensory input (Caharel et al. 2009; GrillSpector et al. 2006; Jenkins et al. 2010; Vizioli et al. 2010). RS has been reported in EEG
and fMRI studies across a variety of cognitive tasks (e.g., Gutnisky and Dragoi 2008;
Müller et al. 1999; Todorovic and de Lange 2012; Vizioli et al. 2010). Interestingly, RS
has recently been observed in cross-cultural studies on the blood oxygenation leveldependent (BOLD) signal with functional magnetic resonance imaging (Goh et al.
2007; Jenkins et al. 2010). Jenkins et al. (2010) found a significantly larger RS toincongruent scenes than to congruent scenes in bilateral occipital cortex for Chineseparticipants. This observation suggests that EA observers engage more in object
processing when it appears in incongruent than in congruent contexts, which also
indicates a bias towards global processing in this population. However, the temporal
dynamics of this cultural perceptual bias are still largely unexplored.
To clarify this issue, we took full advantage of the conjoint use of Navon stimuli
with an EEG adaptation paradigm, in order to map out the temporal dynamics ofglobal/local shape processing in WC and EA observers. EA and WC observers viewed
sequences of two Navon figures (an adaptor and a target, see Fig. 1), while werecorded their scalp EEG signals. Importantly, Navon images were constituted byelementary geometric shapes equally familiar to both groups of observers (and not by
Romanic letters, for which Westerners have greater experience). Participants
performed an active categorization task requiring the selective detection of(a potential) change occurring either on global or local features on the target image.
To control for electrophysiological artifacts generated by saccades, we simultaneously
recorded the eye movements and participants were trained to fixate at the center of thescreen. Moreover, instead of applying a conventional electrophysiological analysis
that would only focus on the target ERPs, we adopted the novel single-trial analysis
method developed by Vizioli et al. (2010). Electrophysiological responses elicited by
the target shapes were subtracted from those elicited by the adaptor shapes
independently at the single-trial level (see methods). To better estimate their temporalsensitivity, we then used a data-driven spatio-temporal analyses carried out
independently at all electrodes and all the time-points. Our results showed that EA
observers were more efficient at detecting global changes compared to WC observers.
This behavioral effect was paired with larger RS responses for global changes as earlyas 80 ms after stimulus onset, on the attention-sensitive P1 component. These results
confirm and refine the cultural perceptual bias for global processing in Easterners, a
process that occurs very early and it is rooted into selective attention.

Methods
Participants
Twelve East Asians (7 females) and 12 Western Caucasians (6 females) took part in
the study. All participants were students from the University of Glasgow withnormal or corrected-to-normal vision. Participants’ ages ranged from 20 to 29 years

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Identical
Global congruent
Local congruent
Different

Fixation (300 ms)

Adaptor(800 ms)

Fixation (150~300
ms)

Target (600 ms)

Fixation (1200~1500
ms after respond)

Fig. 1 Experimental procedure. Different color in the target represents the condition in the current study(as shown in the legend). (Color figure online)(mean age 25.7). All the EA participants were from Mainland China, and had no
previous experience with a Western country. The maximum duration of residence inthe UK for the EA participants upon testing was less than 6 months. The experimentreceived the approval of the local ethical committee and all participants provided
written informed consent.
Stimuli and procedure
Stimuli were hierarchical Navon figures composed by five different shapes: circle,
cross, diamond, square, and triangle (Navon 1977; see Fig. 1). We employedgeometric shape to avoid familiarity differences with alphabet letters between two
groups of observers. The local shapes (0.6° 9 0.6° of visual degree at a viewing
distance of 70 cm) were equally spaced and arranged to create the global shapes(5.8° 9 5.8°), resulting in a total number of 25 different stimuli. To minimize theeffect of low-level visual adaptation stemming from identical images, the size of thetarget Navon stimulus was slightly larger than the adaptor (0.72° 9 0.72° on local
level and 6° 9 6° on global level). Stimuli were presented on a Dell P1130 1900
CRT monitor with an 800 9 600 pixel gray background, at a refresh rate of 170 Hz.

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At the beginning of the experiment, participants sat in a dimly lit, soundattenuated electrically shielded booth. Each trial started with a fixation cross (0.3°)
presented at the center of the screen for 300 ms. The adaptor was presented for
800 ms, followed by an interval duration randomly ranging between 150 and
300 ms. The second shape was then presented for 600 ms. Following the offset of
the target stimuli, subjects were required to respond by pressing a key on a standard
computer keyboard. A randomized inter-trial interval between 1,200 and 1,500 ms
preceded the beginning of the following trial.
We used a forced choice task. During the experiment, participants categorized
the feature change between each pair of adaptor and target. There were four
conditions: (1) identical; (2) global congruent condition, where only the local
features were changed; (3) local congruent condition, where only the global features
were changed; (4) different—both local and global features were different. Upon theoffset of the target, participants assigned the trial to one of the conditions bypressing one of the four pre-assigned buttons, corresponding, respectively to theletter ‘‘d’’, ‘‘f’’, ‘‘j’’, and ‘‘k’’. An example sheet (similar to Fig. 1) was provided
prior to beginning the experiment to explain the four possible conditions.
Participants were instructed to respond as quickly and as accurately as possible.
To prevent perceptual and decisional bias, we counterbalanced all 25 stimuli so thatall stimuli would appear in the same proportion both as an adaptor and as a target ineach condition and the trial sequence was also randomized. Moreover, to minimize
the potential EEG artifacts generated by eye movement, we monitored andprocessed participants’ eye movements online for trial validation. Trials containing
blink or saccade during stimulus presentation were excluded. We defined a trial as
valid if the gaze-drift during the stimulus presentation was less than 1° of visual
angle away from the fixation cross. Prior to commencing the experiment,
participants performed 20 practice trials to familiarize themselves with the task.
The experiment ended when a participant completed 600 valid trials (4 conditions
9150 repetitions). Subjects were given a short break every 100 trials. The wholeexperiment was programmed in Matlab 2007b, using the Psychophysics Toolbox(PTB-3, Brainard 1997; Kleiner et al. 2007).
Eye tracking and EEG recording
Eye movements were recorded by means of Desktop-Mount EyeLink 2 K
eyetracker (SR Research Ltd., Mississauga, Ont., Canada) with a temporal
resolution of 1,000 Hz, a spatial resolution of 0.01° of visual angle and an average
gaze position error of about 0.25°. Eye movements were recorded monocularly on
the dominant eye. The eye movement data were recorded via Matlab (R2006a),
using the Psychophysics (PTB-3) and EyeLink Toolbox extensions (Brainard 1997;
Cornelissen et al. 2002). Calibration and validation of eye fixation were conducted
before the experiment and repeated whenever necessary during the procedure.
The EEG data was recorded continuously at 1,024 Hz from a 128-channel
Biosemi Active Two EEG system (Biosemi, Amsterdam, Netherlands). Ag/AgClelectrodes were placed in a nylon cap following the 10-5 system (Oostenveld and

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Praamstra 2001). Data were band-pass filtered (0.1 and 100 Hz) online. Electrode
impedance was kept between ±25 kX. The recording reference and groundconsisted of two active electrodes [common mode sense (CMS); driven right leg(DRL). For further information see www.biosemi.com]. Subjects were asked tominimize blinking, head movement, and other body movement.
Behavioral and EEG analysis
We carried out 2 (groups of observers) 9 4 (feature changes) mixed model
ANOVAs independently on the reaction time (RT) and accuracy rate (ACC). The
two groups of observers (WC and EA) were the between subjects factor and the four
conditions (identical; global congruent; local congruent; and different) were thewithin subjects factors. Moreover, to take into account both measurements, inverse
efficiency score (IES—Bruyer and Brysbaert 2011; Townsend and Ashby 1978,1983) was calculated as:
IES ¼ RT =ACC:
Only trials with correct behavioral response were analyzed (93.7 % for WC and95.5 % for EA, detailed in Result session). EEG data was preprocessed andanalyzed using Matlab and EEGLAB toolbox (Delorme and Makeig 2004). The raw
signal was referenced off-line to an average reference, and low-passed filtered at
40 Hz. Noisy electrodes were excluded, and interpolated using a spline interpolation
algorithm implemented in EEGLAB on a subject-by-subject basis. EEG data were
epoched from -100 to 500 ms relative to stimulus onset, separately for adaptor andtarget, independently per condition. Baseline was defined as 100 ms prior to, until
stimulus onset. Baseline correction was applied by removing the average baseline
amplitude from every time point independently for each epoch. To fully exploit thepaired nature of the adaptation design, trials were only accepted if both signals
elicited by adaptor and target stimuli were below the rejection threshold. Single-trial
repetition suppression (stRS) was then calculated for the accepted trials only, by
subtracting the target shape epoch from the adaptor epoch. For more details about
the procedure, see Vizioli et al. (2010).
We carried out the same 2 (group of observers) 9 4 (feature change) mixed
model ANOVAs independently at all electrodes and all time-points on the stRS
amplitude. To correct for multiple comparisons, we used robust statistics, including
bootstrap (Wilcox 2005) and a multivariate clustering technique (Maris andOostenveld 2007; Vizioli et al. 2010). Within each group, we first centered eachstRS trial on the mean amplitude so that each condition had a mean of zero. We thenused bootstrap, sampling with replacement the subjects, to derive an estimate of thesampling distribution under the null hypothesis of no difference across theconditions’ means. We carried out the mixed model ANOVAs (as described above)
independently at all electrodes and time-points on the centered bootstrapped stRS.
The significant F values (p \ 0.05) were then grouped in spatiotemporal clusters(Maris and Oostenveld 2007; Vizioli et al. 2010). We computed the sum of F valuesin every cluster and selected the maximum cluster sum. After repeating the same

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procedure 500 times, we sorted all the cluster sums according to their values andselected the 95th percentile as the cluster threshold. The original ANOVA clusters
F-value sums were then compared with the bootstrap cluster threshold. If an
observed cluster sum was equal to or larger than the threshold sum obtained underH0, all of the time-points and the electrodes contained in that cluster wereconsidered significant (Vizioli et al. 2010).

Results
Behavioral results
The behavioral results are summarized in Fig. 2. The 2 9 4 ANOVAs carried out
on RT, ACC and IES showed significant main effects of feature change for RT[F (3, 66) = 52.07, p \ 0.05], ACC [F (3, 66) = 30.17, p \ 0.05], and IES [F (3,66) = 49.49, p \ 0.05]. Post hoc paired-sample t-tests indicated that both groups ofobservers responded faster and more accurately to the identical condition comparedto the others. No main effect of groups of observers was observed for all threemeasurements [RT: F (1, 22) = 0.96; ACC: F (1, 22) = 1.66; IES: F (1,22) = 1.52. All p [ 0.05].
Importantly, we observed a significant interaction of group of observers(2) 9 feature change (4) for the IES [F (3, 66) = 3.59, p \ 0.05]. To clarify thisresult, we performed post hoc paired-sample t tests on the IES values between thelocal and global congruent conditions independently for each group of observers.
This analysis revealed that WC observers identified local congruent items(M = 673 ms, SD = 293) significantly slower than the global congruent ones
[M = 551 ms, SD = 229, t (11) = 2.79, p \ 0.05]. No differences were reported
between global and local congruent conditions for EA observers [t (11) = 1.41,
p [ 0.05].

(a)

(b)

(c)

800

100

800

400

90

600

0

80
Identical

Globalcongruent

Local
congruentDifferent

Identical

Globalcongruent

Local
congruentDifferent

400

Globalcongruent

WC Observers

Local
congruent

EA Observers

Fig. 2 Results of reaction time (a), accuracy rate (b), and inverse efficiency score (c). Only global
congruent and local congruent conditions are shown for the inverse efficiency score (c). Error bars show
standard errors from the means

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Single-trial RS results
The minimum number of accepted trials across all subjects and conditions was 100.
Figure 3 shows the adaptor ERP, target ERP, and stRS for the electrode where the Fvalues for the groups of observers 9 feature change interaction was maximal (i.e.
P4).
After multiple comparisons correction, the Mix-model ANOVA revealed asignificant interaction (groups of observers 9 feature change) at two spatial–
temporal clusters (Fig. 4a). The first cluster was located at right occipito–parietal
electrodes (Fig. 4b). Its time-window was within the latency of the P1 component,
which ranged from 60 to 110 ms. The F-value of the first cluster reached its
maximum at 88 ms at electrode P4 [minimum F (3, 66) = 2.24; maximum F (3,66) = 8.32, p \ 0.05]. This was the same electrode where P1 component reached
its maximum amplitude. Post hoc paired-sample t tests showed that global

(a)

(b)

WC observers

EA observers
AdaptorTargetERP (µV)

4

0

-4

-8
0

0

400

400

(d)

(c)

stRS (µV)

40

Identical
Global congruent
Local congruent
Different

-4
0

400

0

400

Fig. 3 ERP and stRS for P4. Depicts the mean ERPs elicited by the adaptor (solid line) and the target
faces (dashed line) for the four conditions (identical, red line; global congruent, green line; local
congruent, blue line; different, yellow line) for WC (a) and EA (b) observers. stRS responses are shown inc for WC and d EA. (Color figure online)

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congruent trials elicited significantly larger stRS responses compared to local
congruent condition in EA observers [t (11) = 3.26, p \ 0.05]. No significant
difference between global and local congruent conditions were observed for WC
observers [t (11) = 0.39, p [ 0.05. See Fig. 4e].
The second significant spatial–temporal cluster occurred within the time-window
ranging from 200 to 350 ms (i.e. the posterior N2 component) and was spatiallylocated around the anterior parietal electrodes (Fig. 4b). F-value topography maps
revealed 3 local maxima with different spatial distributions (See Fig. 4c, d). The
first occurred at 236 ms at electrode CP6 with a right-lateralized topography [F (3,66) = 8.42, p \ 0.05, Fig. 4c]. Post hoc paired-sample t tests indicated larger stRS
responses to local congruent compared to global congruent conditions for EAs[t (11) = 2.4, p \ 0.05] but not WCs [t (11) = 0.18, p [ 0.05, Fig. 4f]. The second
maximum occurred at 273 ms over center-parietal electrodes [C2, F (3,66) = 10.01, p \ 0.05, Fig. 4d]. Post hoc paired-sample t tests indicated thatWCs displayed larger stRS responses to global congruent compared to local
congruent conditions [t (11) = 2.24, p \ 0.05]. No significant differences wereobserved for EAs [t (11) = 0.21, p [ 0.05, Fig. 4g]. The third maximum wasobserved at 312 ms at electrode C5 [F (3, 66) = 9.29, p \ 0.05]. Post hoc pairedsample t tests showed significantly larger stRS responses to local congruent
compared to global congruent conditions for WCs [t (11) = 4.30, p \ 0.05]. No
significant differences were observed for EAs participants [t (11) = 2.145,
p [ 0.05].

Discussion
The present study aimed to address whether and when cultural perceptual tunings to
global/local information are driven by attention. We directly tested this hypothesison Western Caucasian (WC) and East Asian (EA) observers while recording theirscalp electrophysiological signals during the visual categorization of hierarchicalshapes differing in their global/local properties. We used an adaptation paradigm, as
well as a single-trial method coupled with robust data-driven analyses on all theelectrodes and time points. The behavioral results showed that EA observers display
a global advantage by processing more effectively visual changes on this visualproperty compared to WC observers. More importantly, our electrophysiological
data showed that global/local feature repetition modulates the P1 and N2 ERP
components as a function of culture. Specifically East Asian observers showed
greater sensitivity to global congruency on this component compared to WC
observers. These observations confirm the existence and refine the knowledge on thecultural attentional biases in visual information sampling, which are consistent withprevious observations (Kitayama et al. 2003; McKone et al. 2010). Importantly, thisearly neurophysiological signature of cultural diversity in Easterners cannot be
accounted by the inherent nature of the stimuli and/or task, as it was not present inWestern Caucasian observers. Westerners showed sensitivity to hierarchical Navonshapes discrimination at later stages.

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(a)p < .05 corrected

electrodes

10

5

n.s

−100

0

400

(b) ~ 88 ms(c) ~ 236 ms(d) ~ 273 ms

P1(e) P4N2(g) C2

1

1

1

0

0

0

µV

(f) CP6-1-1
Globalcongruent

Local
congruent-1
Globalcongruent

Local
congruent

WC Observers

Globalcongruent

Local
congruent

EA Observers

Fig. 4 Significant interaction of spatial–temporal ANOVAs. a Significant F values of the groups ofobservers 9 feature change interaction on the stRS amplitude. The x-axis depicts the time points of the
stRS epoch, while the y-axis indicates the 128 electrodes. The electrode order is unrelated to their spatial
topographical positions. b, c, d Portrays the F-value topography maps at the local peaks of the P1 and N2
spatial–temporal clusters. For illustration purpose, here the first two local peaks of the N2 cluster are
shown. b Represents the F-value topography of the P1 spatial–temporal cluster at 88 ms (latency of thepeak F-value),which was maximal at electrode P4. c, d Show F-value topographies of the N2 spatial–
temporal cluster at two local F-value peaks (236 and 273 ms). The peak electrodes were CP6 and C2,
respectively. e, f, g Illustrates the mean stRS responses of global congruent and local congruent
conditions for WC and EA observers at each F-value peak latency at the respective electrodes. For display
purposes, stRS on the negative component was multiplied by -1. Thus, more positive values indicate
larger repetition suppression. Error bars show standard errors from the means

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Attention modulation on the P1 component
We reported an interaction between the culture of the observers and the neuralsensitivity to global/local information coding at early stages of visual information
processing. Firstly, only EA participants showed larger stRS responses to global
compared to local feature repetition, as early as 80 ms after stimulus onset; the time
window of the P1 ERP component. The P1 is a positive deflection peaking between
70 and 120 ms at central occipital electrodes, which has been consistently related toattention (Luck et al. 2000). The P1 amplitude modulations observed in EA
observers suggest that the global precedence in this population is modulated byattention. Importantly, there is a growing body of evidence positing that larger RS
responses reflect greater neural coding efficiency (Caharel et al. 2009; Grill-Spector
et al. 2006; Vizioli et al. 2010). Although the precise neural mechanisms of RS are
still debated, numerous models have been proposed to account for the reduction inneural activity following stimulus repetition (Grill-Spector et al. 2006; Schacter
et al. 2007). The most prominent account—the sharpening model—holds that theneuronal representations of a stimulus become less redundant and sparser withrepetition (Grill-Spector et al. 2006). Under the same model, RS can also be
considered as a neural novelty detection mechanism (Vizioli et al. 2010). Therefore,
the larger amount of RS to global features for EA observers can result from a more
efficient neural coding of global information at this stage, which is reflected in the
sharpening of global feature representations as a function of attention. Moreover,
the absence of significant differences in the amount of stRS between global andlocal feature repetition in Westerners suggests that, at least at early neural stages ofprocessing, there is no preferential tuning to either global or local information
coding in this population.
In line with our findings, the sensitivity to global/local selective attention on theposterior P1 component has previously been reported in a number of studies (Hanet al. 1997, 1999, 2000; Lin et al. 2008). For example, the priming of interdependent
self-construal, which is closely related to global perceptual bias (Kühnen andOyserman 2002; Lin and Han 2009), enlarged the P1 amplitude to global comparedto local features in Chinese participants (Lin et al. 2008). Similarly, (Han et al.
1997, 1999, 2000) showed that selective attention to global or local feature modified
the P1 amplitude in EA observers. The data reported here are in line with previous
studies, reinforcing the view of an early sensitivity to global information coding on
the P1 component in Easterners only. In contrast, WC observers did not show
sensitivity to their preferred (i.e., local) features in this early component. It is worth
to point out that in the current task observers were forced to attend equally
information at both the global and the local levels, since they could not predict thenature of the potential change in target shape. Therefore, in this task global
processing might be more dominant than local processing. Thus, the absence ofsensitivity in Westerers on the P1 component could be accounted by the ineherent,
general, slower processing of local compared to global features in visual
information processing.
Moreover, EA observers showed a behavioral advantage on the global task
compared to WC observers. This advantage is likely to arise from their early

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attentional bias towards global information. In fact, we found that WC observers
were less efficient at detecting global than local feature changes, while EA
observers performed equally well on both conditions. The behavioral disadvantage
of WC observers in the global task seems to stem instead from differences in visualsaliency between global and local features. Specifically, the visual processing of
global features precedes the analysis of local information (Navon 1977). This global
precedence effect would conflict with local information, thus inhibiting responses tothe detection of local features (Han et al. 2001; Miller and Navon 2002; Navon
1977). For example, participants are slower at identifying local targets in thepresence of interference from a global shape, even when this information isirrelevant (Miller and Navon 2002). The change of global features is disruptive for
WC observers when they are required to detect changes in local information (i.e.
distinguish between local congruent and different condition). As comparison, EA
observers benefit from a top-down attention control to global features, thus limiting
the disturbance from the visual salience induced by global feature changes (e.g., theinterface hypothesis, McMains and Kastner 2011).
Cognitive control effect on the N2 component
We also observed an interaction between the culture of observers and global/local
information tuning over the central-anterior-parietal electrode site at around
200–350 ms. A fine-grained adaptation pattern revealed distinctive temporal
dynamics of global/local processing between Westerners and Easterners. Specifically, EA observers showed larger stRS responses to local compared to globalfeature repetitions over the right anterior-parietal electrodes at 240 ms. WC
observers instead displayed first larger stRS responses to global compared to local
feature at 270 ms, to then show a reverse pattern at 320 ms. This modulation occurs
in the time window of the N2 component, which has a distinct topography (see.
Fig. 4c, d) and has been related to cognitive control (see Folstein and Petten 2008 asa review). Cognitive control is partly defined as being a strategy regulation processduring response to novelty or (mis)match detection (Nieuwenhuis et al. 2003;
Yeung et al. 2004; Zhang et al. 2003). For example, slower behavioral responses
resulting from larger demands in cognitive control are correlated with larger N2
amplitudes and delayed peak latencies (Gehring et al. 1992; Nieuwenhuis et al.
2003). Moreover, inhibition of behavioral response also elicits larger N2
amplitudes, especially when stimuli share similar visual features (Folstein andPetten 2008). Here, regardless of their cultural background, all the observers
engaged a larger amount of neural resources during the processing of the cultural
non-preferred features to overcome the interference from the cultural preferredfeatures. Therefore, the fewer amount of stRS responses elicited by local feature
repetition in WC observers and the fewer amount of stRS responses for globalfeature repetition in EA observers on the N2 component might reflect a moredemanding engagement of cognitive resources devoted to performing (mis)matchdetection for the non-preferred feature repetition. More specifically, these resources
might be related to the inhibition of the culturally-preferred bias in visual

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171

information sampling (i.e., consistent local feature for WCs and consistent globalfeature for EAs).
General discussion
The conjoint use of EEG and Navon stimuli has been widely used to investigate thetemporal dynamics of attention modulations in global/local shape processing (Hanet al. 1997, 1999, 2000; Heinze et al. 1998; Heinze and Münte 1993; Malinowski
et al. 2002). Surprisingly, while this approach has been extensively used in a wide
range of studies on WC observers and EA observers separately, no previous study
had yet directly compared these two groups of observers. Here, we overcame thislimitation by testing both groups of observers. As discussed above, our results
precisely mapped out and confirmed that the temporal dynamics of global/local
processing is indeed different between Easterners and Westerners. More precisely,
EA observers show a distinct early sensitivity between global and local featurecoding compared to WC observers. Such difference in temporal dynamics of visualshape processing is driven by an initial attentional selectivity and tuning between
these two groups of observers from different cultures: a global selective-attention in
Easterners and a local-selective attention in Westerners (McKone et al. 2010).
Previous studies on global/local visual shape categorization have suggested that theprocessing of hierarchical visual stimuli is lateralized in the brain. A left hemisphereadvantage has been reported for local processing at the encoding stage, and a righthemisphere advantage for global processing (e.g., Folstein and Petten 2008; Han et al.
2002; Lamb et al. 1990). Unfortunately, it is difficult to interpret our results in terms ofhemispheric asymmetries. Although we found an interaction, for which the effect islateralized in both spatial–temporal clusters (i.e., on the right scalp electrode locationsin P1 and on both side of scalp electrode locations in N2), we did not modulate thevisual field presentation nor counterbalanced the response keys across participants.
The interpretation of our findings without these controls could be potentiallymisleading, and further research is required to elucidate this issue.

Conclusion
The present study demonstrated that the temporal dynamics of global–local featurecoding is shaped by culture. Our data show an early attentional bias in Easterners for
global visual properties during an active matching task of Navon hierarchicalstimuli. East Asian observers showed strong EEG repetition suppression responses
to global compared to local features on the attention-sensitive P1 component. This
global precedence effect in Easterners was paired with a more efficient processingof global feature changes at later stages. Western Caucasian observers displayed
instead the expected differential electrophysiological responses between global andlocal information processing, but in later electrophysiological stages (i.e. N2component). These findings support the view that cultural perceptual biases in visual
perception are driven by selective attention. Importantly, they also provide cultural
neural signatures and their temporal dynamics for global/local feature processing.

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Overall, these distinct neural markers could represent the entry level of the more
apparent and striking differences observed at the behavioral level across observers
from the Western and Eastern cultures.
Acknowledgments The authors declare no competing financial interests. This study was supported by
the Swiss National Science Foundation (no 100014_138627) awarded to RC.

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