Identification and Classification of Facial Familiarity in
Directed Lying: An ERP Study
Delin Sun1,2, Chetwyn C. H. Chan3*, Tatia M. C. Lee1,2,4*
1Laboratory of Neuropsychology, The University of Hong Kong, Hong Kong, China, 2Laboratory of Cognitive Affective Neuroscience, The University of Hong Kong, Hong
Kong, China, 3Applied Cognitive Neuroscience Laboratory, Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China, 4The State
Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China
Recognizing familiar faces is essential to social functioning, but little is known about how people identify human faces and
classify them in terms of familiarity. Face identification involves discriminating familiar faces from unfamiliar faces, whereas
face classification involves making an intentional decision to classify faces as ‘‘familiar’’ or ‘‘unfamiliar.’’ This study used a
directed-lying task to explore the differentiation between identification and classification processes involved in the
recognition of familiar faces. To explore this issue, the participants in this study were shown familiar and unfamiliar faces.
They responded to these faces (i.e., as familiar or unfamiliar) in accordance with the instructions they were given (i.e., to lie
or to tell the truth) while their EEG activity was recorded. Familiar faces (regardless of lying vs. truth) elicited significantly less
negative-going N400f in the middle and right parietal and temporal regions than unfamiliar faces. Regardless of their actual
familiarity, the faces that the participants classified as ‘‘familiar’’ elicited more negative-going N400f in the central and right
temporal regions than those classified as ‘‘unfamiliar.’’ The P600 was related primarily with the facial identification process.
Familiar faces (regardless of lying vs. truth) elicited more positive-going P600f in the middle parietal and middle occipital
regions. The results suggest that N400f and P600f play different roles in the processes involved in facial recognition. The
N400f appears to be associated with both the identification (judgment of familiarity) and classification of faces, while it is
likely that the P600f is only associated with the identification process (recollection of facial information). Future studies
should use different experimental paradigms to validate the generalizability of the results of this study.
Citation: Sun D, Chan CCH, Lee TMC (2012) Identification and Classification of Facial Familiarity in Directed Lying: An ERP Study. PLoS ONE 7(2): e31250.
Editor: Mitchell Valdes-Sosa, Cuban Neuroscience Center, Cuba
Received January 1, 2011; Accepted January 5, 2012; Published February 21, 2012
Copyright: ? 2012 Sun et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This project was supported by the May Endowed Professorship of The University of Hong Kong awarded to TMC Lee, a Research and Conference Grant
(#201007176233) of The University of Hong Kong awarded to DL Sun, an internal research and development grant from The Hong Kong Polytechnic University
awarded to CCH Chan, and the Research Grant Council Collaborative Research Fund (PolyU9/CRF/09) awarded to TMC Lee and CCH Chan. The funders had no role
in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com (CCHC); firstname.lastname@example.org (TMCL)
Recognizing familiar faces is crucial to social interaction, one of
the basic abilities of human beings [1,2,3]. People act very
differently if they categorize someone as a friend or a stranger. The
ability to differentiate a friend from a stranger and to make
appropriate responses in terms of greetings (e.g., addressing by
name), facial expressions (e.g., smiling), and body gestures (e.g.,
hand shaking) have serious social consequences. However, people
sometimes deliberately pretend not to recognize faces for their
own benefit. For instance, a debtor might deny recognizing a loan
shark in a face-to-face encounter in order to avoid the demand to
pay up, or a swindler may sidle up to a stranger, pretending to
recognize a relative, to ask for money. These examples suggest that
processing familiar faces may involve two dissociable processes:
identification and classification. The identification process prob-
ably involves perceiving facial features and relating the face to
other semantic information, such as names, relationships, and
events. The classification process probably involves the intention
of recognizing a face, which may be outcome driven.
Reviewing the research on face recognition, there is a unitary
model that stipulates that people always try to recognize faces
accurately (i.e., to recognize familiar faces as ‘‘familiar’’ and
unfamiliar faces as ‘‘unfamiliar’’) [4,5,6]. A few recent fMRI
studies have investigated the effect of the deliberate manipulation
of facial recognition [7,8]. These studies focused on the neural
processes involved in the manipulation of intention and used
recognition of faces as an outcome measure, but they did not
address the separate processes of identification and classification.
Furthermore, the poor temporal resolution of fMRI prevented
these researchers from finely differentiating these neural processes.
The ERP methodology offers a high temporal resolution that helps
uncovered at least two processes associated with processing familiar
faces [9,10]. Eimer  found that compared to unfamiliar faces,
familiar faces elicit an enhanced N400f (300–450 ms post stimulus)
and P600f (450–650 ms). Both the N400f and the P600f were widely
distributed over the scalp and peaked in the central-parietal region.
These two components were not observed when participants saw
inverted faces, which have been known to disrupt face recognition
[11,12]. They were also absent in prosopagnostic patients, who suffer
from semantic memory dysfunction, further suggesting that N400f
and P600f are important in face recognition .
The question then becomes ‘‘What roles do N400f and P600f
play in face recognition?’’ There are at least two theories. First, the
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earlier N400f could be the identification of the actual identity of
the face, and the later P600f could be the intentional classification
of the face into either the ‘‘familiar’’ or ‘‘unfamiliar’’ category.
This theory is not terribly promising, since a previous study found
that the P600f was not necessarily a consequential process of the
N400f. Eimer  found that when participants had to respond to
a target character inside a string presented in front of the facial
stimulus, the letter strings directed attention away from the face,
after which the N400f disappeared, but the P600f remained, for
familiar faces. One interpretation of this finding is that the
processes responsible for the N400f are not triggered automatically
in response to the presence of a familiar face in the visual field;
instead, they depend on attentional processing. The fact that the
P600f remained suggests that it is independent from attentional
processing. In line with this thought, the N400f might be the result
of a top-down cognitive control process, while the P600f may
reflect the more basic processing of the characteristics of the
stimuli (facial features and semantic content). A second possible
theory is that the P600f reflects the identification process, which
includes the perception of facial features and relating the features
to the semantic content and leads to recognition. The N400f may
represent the classification of facial familiarity, which is a top-
down intention to manipulate incoming information.
This study investigates the roles of N400f and P600f in facial
recognition by manipulating classification intention using a
directed-lying paradigm. Participants were asked to respond to
the familiarity of facial stimuli according to visual cues that
prompted them to make either a truthful (i.e., congruent) or
deceptive (i.e., incongruent) response. It was hypothesized that the
directed-lying task would elicit two event-related components: one
around 400 ms post stimulus (probably the N400f) elicited the
strongest signal in the central-parietal region and another around
600 ms post stimulus (probably the P600f), also elicited the
strongest signal in the central-parietal region. Furthermore, it was
hypothesized that the N400f would be modulated by congruency
and incongruency (lying) and the P600f would be modulated by
(actual) familiarity and unfamiliarity.
Measured by mean reaction times, there were no significant
effect of identification (F,1), but there were significant effects of
classification (F(1, 14)=1.64, p=.022) and the interaction between
identification and classification (F(1, 14)=3.37, p=.009).
Measured by accuracy rates, there was a significant main effect of
identification (F(1, 14)=5.66, p=.003) but not of classification
(F,1). The interaction between identification and classification was
identified familiar faces (97.263.0%) more accurately than
unfamiliar faces (95.364.1%). When the participants saw familiar
faces, they were more accurate when they were instructed to tell the
truth (97.962.5%) than when they were told to lie (96.663.4%; i.e.,
incongruent), t(14)=2.23, p=.004. There were no other significant
differences in the between-condition comparisons.
SPM analyses showed significant main effects of identification on
the amplitudes of ERP in two time ranges. In general, the
amplitudes elicited by familiar faces were significantly more
positive-going than amplitudes from unfamiliar faces. The first
component was found before 400 ms post stimulus, with maximal
significance identified at 319 ms. The second component was found
after 400 ms post stimulus, with maximal significance identified at
501 ms. The maximal significance of both components appeared to
come mainly from channels in the middle and right parietal regions
and from the middle and right temporal regions. There was less
amplitude in the middle occipital region (Table 1).
It is likely that the maximum significance at 319 ms (from 284
to 372 ms) represents the N400f. The main channels elicited less
Table 1. Main and interaction effects of identification and
classification revealed by repeated-measures ANOVA using
SPM (Significance level p,.001, uncorrected. Extent threshold
Main effect of identification
284RT 127 670 3.410.001
286R FT112 670 3.340.001
326MP 94 230784.16
327R PT121 230784.23
371RT 127 17623.53
501MP 72 364264 6.94
501MO 70 3642646.81
541MP 67 364264 6.59
Main effect of classification
763R FT 112 4603.48
765R PT 12125563.63
Interaction between stimuli and response
#denotes the transverse positions: i.e., left (L), middle (M), and right (R).
$denotes the area the channel is located in: i.e., frontal-temporal (FT), central (C),
parietal (P), parietal-temporal (PT), temporal (T), occipital (O), and occipital-
*denotes the nearest suprathreshold channel.
k=cluster size (number of voxels showing significant differences; each voxel
size is 2.13 mm62.69 mm61 ms), T=peak value measured within the cluster,
FF=familiar faces classified as ‘‘familiar,’’ FU=familiar faces classified as
‘‘unfamiliar,’’ UF=unfamiliar faces classified as ‘‘familiar,’’ and UU=unfamiliar
faces classified as ‘‘unfamiliar.’’
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negative-going N400f (i.e., familiar.unfamiliar) in the middle (67
and 94) and right (121) parietal regions (p,.001; k=23,078 voxels;
Table 1, Figure 1A). Other channels showing a similar pattern
included those in the right frontal-temporal, temporal, and
temporal-parietal regions (112 and 127; p=.001 to ,.001;
k=670 or 1,762 voxels). It is likely that the maximum significance
at 501 ms (from 501 to 541 ms) represents the P600f. The
channels elicited more positive-going P600f (i.e., familiar.unfa-
miliar) in the middle parietal (67 and 72) and middle occipital (70)
regions (p,.001; k=364,264 voxels; Figure 1B).
There was a significant main effect of classification in two time
ranges. Unlike the identification effect, the amplitudes elicited by
classifying faces as ‘‘familiar’’ were significantly less positive-going
than those elicited by classifying faces as ‘‘unfamiliar.’’ The first
component, which was likely to be a more negative-going N400f
(i.e., ‘‘familiar’’,‘‘unfamiliar’’), was found to be maximally
significant around 280 ms post stimulus and was elicited at
channels in the middle central region (51 and 90) and the right
temporal (127) regions (p,.001; k=34,450 voxels; Table 1,
Figure 2A). The second component associated with classification
was between 761 and 765 ms post stimulus and was elicited in
channels located in the right temporal (112, 121, and 128), middle
occipital (45 and 69), and left temporal-occipital (22) regions
(p,.001; k=2,023 to 2,556 voxels, except for channel 112, which
was 460 voxels; Figure 2B).
SPM analysis did not show significant interactions between the
identification and classification effects on the ERP amplitudes at
the significance level of p,.001.
Figure 1. ERP waveforms at representative channels indicating the main effect of the identification of facial familiarity. Familiar faces
elicited significantly more positive-going amplitudes than unfamiliar faces at two time windows: (A) From 284 to 372 ms (N400f) post stimulus in the
middle parietal (represented by channel 67) and right parietal regions; (B) From 501 to 541 ms (P600f) post-stimulus in the middle parietal
(represented by channel 72) and occipital regions. The shadowed bars cover the time windows indicated above. Fam=familiar faces,
Unfam=unfamiliar faces, ‘‘Fam’’=responded as familiar faces, and ‘‘Unfam’’=responded as unfamiliar faces.
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Conventional analyses based on the mean amplitudes extracted
from the selected channelswithin the predetermined timewindowsof
N400f and P600f revealed findings similar to those found using the
SPM method. There were significant main effects of both identifi-
cation (F(1, 14)=11.19, p=.005) and classification (F(1, 14)=9.22,
p=.009) for the N400f. However, the conventional analysis revealed
a time window from 320 to 400 ms—somewhat later than the time
points of peak significance signaled by the SPM (284 to 372 ms). The
interactions between the identification, classification, and site effects
were not significant (F(3,42)=1.33 to 2.45, p..05).
Familiar faces elicited significantly less-negative going N400f
than unfamiliar faces at the midline sites (i.e., familiar.unfami-
liar); faces classified as ‘‘familiar’’ (regardless of truth/lying) elicited
more negative-going amplitudes than those classified as ‘‘unfamil-
iar’’ at the midline sites. Only the effect of identification (F(1,
14)=26.15, p,.001) was significant in modulating the P600f
amplitudes. All of the other main (F(1, 14)=2.14 to 3.01, p..05)
and interaction (F(3, 42)=,1 to 1.13 p..05) effects were not
Familiar faces elicited more positive-going P600f over the
midline sites than unfamiliar faces. The main effect of classification
found by the SPM at the right temporal, middle occipital, and left
temporal-occipital regions from 761 to 765 ms was beyond the
time window of the P600f (i.e., 500 to 600 ms) and was not found
in the conventional analysis.
This study used a directed-lying paradigm to explore the
possible differentiation between identification and classification
Figure 2. ERP waveforms at representative channels indicating the main effect of classification of facial familiarity. Faces classified as
‘‘familiar’’ elicited significantly more negative-going amplitudes than faces classified as ‘‘unfamiliar’’ at two time windows: (A) From 280 to 297 ms
(N400f) post stimulus in the middle central (represented by channel 51) and right temporal regions; (B) From 761 to 765 ms (P600f) post stimulus in
the right temporal (represented by channel 128), middle occipital and left temporal-occipital regions. The shadowed bars cover the time windows
indicated above. Fam=familiar faces, Unfam=unfamiliar faces, ‘‘Fam’’=responded as familiar faces, and ‘‘Unfam’’=responded as unfamiliar faces.
Facial Familiarity and Directed Lying
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processes involved in facial recognition. The directed-lying
paradigm made it possible to measure honest and deceptive
classification responses for familiar and unfamiliar faces. In
general, the participants were more accurate at identifying familiar
faces than unfamiliar faces. Results based on the two a priori
markers for facial familiarity showed that the N400f occurred
with both identification and classification, while P600f only
appeared with identification. Regardless of whether they had
to lie or tell the truth, the participants had more positive-going
N400f and P600f in the middle and right parietal regions when
they were identifying familiar faces compared to when they
were identifying unfamiliar faces. Classifying faces as ‘‘familiar’’
(under both truth and lie conditions) elicited more negative-going
N400f in the central and right temporal regions. Both conven-
tional and SPM analysis gave convergent evidence of these
The results showed that the N400f was associated with both the
identification and classification processes in facial recognition. The
difference is that in the identification process, familiar faces were
found to elicit less negative-going N400f than unfamiliar faces. In
contrast, in the classification process, faces classified as familiar
elicited more negative-going N400f than those classified as
unfamiliar. This suggests that it is likely that, despite sharing the
same N400f, the two steps in facial recognition involve two
discrepant cognitive processes. Previous studies have consistently
found that the N400f is involved in discriminating familiar faces
from unfamiliar ones [14,15,16]. Previous researchers have also
related the N400f to high-level attentional processing . They
found that familiar faces elicited more negativity than unfamiliar
faces at Cz and Pz; however, such effects were diminished when
attention was directed to another demanding task. It is plausible
that the N400f also reflects top-down control in the processing of
In this study, regardless of whether the participants were telling
the truth or lying, faces classified as ‘‘familiar’’ elicited more
negative-going N400f than those classified as ‘‘unfamiliar.’’ These
results were interesting because lying did not seem to affect the
N400f. In other words, classification could be determined by the
intention of categorizing faces as familiar or unfamiliar rather than
by the possible regulatory processes of lying.
Moreover, there were different polarity effects for identification
(i.e., familiar.unfamiliar) and classification (i.e., ‘‘familiar’’,
‘‘unfamiliar’’). Compare this to the findings of previous studies
that found N400f amplitude differences: familiar faces classified as
familiar elicited more negative-going amplitudes than unfamiliar
faces classified as unfamiliar [9,10]. This suggests that the N400f
can also be attributed to an intended classification of the facial
stimuli rather than to the mere identification of familiarity based
on facial features.
The P600f was associated only with the identification of faces,
not with lying. Familiar faces elicited a more positive-going
component than unfamiliar faces in the middle parietal and
occipital regions. This identification process seems to be
independent from the classification process since the interaction
was not significant. Our findings are consistent with a previous
study that reported that the P600f component was related with
more positive amplitudes for familiar faces than for unfamiliar
faces at the Pz channel, regardless of whether attention was
directed to another demanding task or not . The association of
face identification with the P600f is somewhat counter-intuitive.
One would expect the perception of facial features to be a
prerequisite for identification, which precedes classification, but
the results of this study suggest that some sub-process of
identification (reflected by the P600f) takes place even after
classification (reflected by the N400f). Rugg and Curran offered a
plausible explanation for the temporally split phenomena
underlying face identification . According to their findings,
the recognition of an object involves a dual process: a judgment on
familiarity followed by a recollection of the information associated
with the object. The identification sub-process, as reflected by the
P600f in this study, suggests that the participants probably
accessed the information related to the familiar faces. This
proposition is supported by previous studies which related the
P600f to the processing of the demographic characteristics (e.g.,
name and occupation) of known persons or the visual character-
istics of known faces [9,10,13]. The recollection of information
about familiar faces is likely to be automatic as the participants
were not instructed recollect such information in the task used in
In the directed-lying task used in the study, the intention to lie
or tell the truth was prompted before the presentation of a face.
The appearance of the face would have enabled the participants to
extract the facial features embedded in the stimuli (associated with
N170). Setting the intention to lie as part of the classification
process appears to occur at around 280 ms (denoted by N400f).
Our results also suggest that attention would be allocated as
part of the identification process at round 300 ms. The extraction
of semantic information about faces, which is independent
of the decision to lie (classification), is likely to occur at around
An interesting finding in this study is the late onset of the
classification process, elicited around 760 ms in the bilateral
temporal to occipital regions. Temporally, this signal is distinct
from the N400f and P600f components. Other studies have
reported a face-specific negative component (N700) elicited
about 700 ms post stimulus from intracranial electrodes placed
on the cortical surface of the ventral and lateral brain regions
[17,18]. The N700 was found to be related to semantic priming
in a task involving learning and identifying face names ,
perhaps reflecting a top-down process. However, the contribution
of this component in the processing of facial familiarity is beyond
the scope of this study and should be investigated in further
Our findings support the differential roles of identification and
classification processes in the recognition of familiar faces. The
N400f appears to be linked with both identification and
classification, while the P600f appears to be primarily linked with
identification. The dual identification processes revealed in this
study are likely to involve an earlier judgment of familiarity and a
later recollection of information related to familiar faces. Using
electrophysiological measures together with brain imaging could
further differentiate the role of these two components.
This study has a few limitations. The faces used were of personal
acquaintances, but previous studies have shown that learned
familiarity [13,19] and celebrity faces  evoke different
processes. Therefore, caution should be taken when generalizing
the current findings to female adults and those with different
demographic characteristics to the participants in this study. The
results may also not be generalizable to other types of familiarity.
More studies on the neural processing of face identification and
classification are still needed.
This study also has implications for lie detection. However,
there are at least two things that need to be done first. First, this
paradigm needs to be tested on more samples to obtain a
consistent standard for lie detection. Second, the measures
should be adjusted to the special neural characteristics of each
person so that the method is able to detect lies better in individual
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Materials and Methods
Fifteen healthy Chinese males aged between 25 to 40 years
(mean=29.8; SD=4.0) and with an average of 16.2 years of
education (SD=2.9) were recruited from the community. Male
participants were recruited to prevent possible gender differences
in facial recognition [21,22] and directed-lying
[23,24,25]. All of the participants (a) were right handed as assessed
by the Edinburgh Handedness Inventory , (b) possessed
normal or corrected-to-normal vision, (c) reported no history of
neurological or mental disorders, and (d) gave their written
informed consent to participate in the study. The study was
conducted under the approval of the Institutional Review Board of
The University of Hong Kong/Hospital Authority Hong Kong
West Cluster and Hong Kong Polytechnic University.
There were two sets of face stimuli: (1) 30 personally familiar
faces of males taken from photographs of the participants’ friends
from the local community and (2) 30 faces of male strangers, which
were photos of Chinese people unknown to the participants. All of
the familiar and unfamiliar stimuli had neutral facial expressions
and were age matched. To control for differences in color tones,
all of the pictures were transformed into grayscale. The luminance,
contrast, and resolution of the photos were adjusted to approach
equivalence using Adobe Photoshop (San Jose, CA).
This study’s design was based on Lee and colleagues’ design .
After a randomized inter-trial interval ranging from 800 to
1,200 ms, a condition cue of ‘‘Lie’’ (incongruent response) or
‘‘Truth’’ (congruent response) was presented on the screen for
1,000 ms (Fig. 3A). This was followed by a blank screen that lasted
for a randomized period of 200 to 600 ms. The face stimulus was
then presented for 600 ms. A fixation cross then appeared on the
screen for 1,200 ms to give the participants time to prepare a
response. Then, after a randomized 200 ms to 600 ms blank inter-
stimulus interval (ISI), the question ‘‘Do you know him?’’
appeared on the screen to prompt the participants to make a
response. The participants had to respond by indicating whether
the face shown was ‘‘Familiar’’ or ‘‘Unfamiliar’’ by pressing the
designated keys on a keypad using the left or right index finger.
The participants were told to respond as quickly and accurately
as possible. The keys for ‘‘Familiar’’ and ‘‘Unfamiliar’’ were
counterbalanced among the participants. The two main effects
manipulated were identification (familiar vs. unfamiliar faces) and
congruence (truth vs. lying). The 262 factorial design gave four
conditions: (1) familiar faces classified as ‘‘familiar’’ (a ‘‘Truth’’ cue
leading to a congruent response); (2) familiar faces classified as
‘‘unfamiliar’’ (a ‘‘Lie’’ cue leading to an incongruent response); (3)
unfamiliar faces classified as ‘‘familiar’’ (‘‘Lie’’/incongruent); and
(4) unfamiliar faces classified as ‘‘unfamiliar’’ (‘‘Truth’’/congru-
ent). During the formal experiment, each of the 60 faces (30
familiar and 30 unfamiliar) was presented 8 times, i.e. half for
truthful responses and half for deceptive responses, giving a total of
480 trials in the task. The order of trials was randomized into 8
blocks. Completing one block took about 5 minutes, and the
participants had 30-second breaks between the blocks.
Prior to the experimental task, each participant completed
familiarity and valence calibrations of the facial stimuli used in the
experimental task. After seeing a face, the participant was required
to give the name of the person corresponding to the face and to
assign a rating of the extent of the face’s familiarity and valence,
from 1 (lowest) to 9 (highest). Stimuli that produced correct name
responses, familiarity ratings above 5 (the middle score), and
valence ratings of 5 (to control for possible valence effects) were
included in the experimental task as familiar faces. Faces were
selected as the unfamiliar face stimuli if the participants (1) could
not name the person, (2) assigned a 0 familiarity rating, or (3)
assigned a valence rating of 1 to 4. This maximized the difference
in familiarity between the familiar and unfamiliar faces.
The directed-lying task was explained to the participants as a
lie-detection game in which they were required to lie as genuinely
as possible so as to deceive the computer. They were instructed to
pay attention to the visual cue of ‘‘Truth’’ or ‘‘Lie’’ (all materials
were written in Chinese) before the presentation of the stimulus.
For example, a ‘‘Truth’’ cue would mean responding ‘‘familiar’’
when seeing a familiar face; a ‘‘Lie’’ cue would mean responding
‘‘unfamiliar’’ to a familiar face.
The participants practiced with a few trials until they expressed
their readiness to engage in the experimental task. During the
experiment, each participant was seated about 0.5 m in front of a
computer screen in an electromagnetically shielded room. All of
the visual stimuli were presented within 10 degrees of the visual
angle to control eye movement.
Behavioral data analysis
The variables under study were response time and accuracy
with a 2 (familiar vs. unfamiliar faces)62 (lie vs. truth) repeated-
ERP recording and analysis
Electroencephalogram (EEG) data was captured over the scalp
by a 128-channel fabric cap (Neuroscan) embedded with Ag-AgCl
electrodes. All channel recordings were referenced to a computed
average of the left and right mastoids. Channel impedances were
kept below 5 kV. The electrical signals were amplified by a gain of
1,000 with a band pass from .01 to 200 Hz.
The preprocessing of EEG data was conducted with Scan 4.3
(Neuroscan). The raw signals were filtered off-line with a zero
phase-shift digital filter and a 0.1 to 30 Hz band pass. Eye blink
artifacts were mathematically corrected , and signals exceed-
ing 6100 mV were automatically discarded. The epochs extracted
covered 2200 to 1,000 ms of each trial, with time zero set at the
time when the facial stimulus was presented. Epochs of each of the
262 conditions were averaged for each participant.
Averaged epochs (i.e., files ending with ‘‘.avg’’) for each
participant were converted into SPM8 (Wellcome Trust Centre
for Neuroimaging, UCL) file format on Matlab (The MathWorks,
Inc.). The first step was to generate scalp maps per time frame
using the 2D sensor layout [28,29]. The output dimension of an
interpolated scalp map was 64 pixels in each of the x and y
directions. That is, the standard Neuroscan 128-channel locations
(for ensuring smoothness) were projected onto a 64664 pixel
sensor space equivalent to 136.32 mm6172.16 mm. The second
step was to stack scalp maps over peristimulus time. A total of 1201
scalp maps were constructed from epoch based on the 1,000 Hz
sampling rate. This generated the 3D (6466461201 voxels) data
volume for computation. Different from functional brain mapping,
a voxel in here was defined as 2.13 mm (space)62.69 mm
(space)61 ms (time). The ERP amplitudes captured at each
channel and time point were fit to the voxels by linear
interpolation and Gaussian smoothing procedures (at FWHM
correction 8:8:8) [30,31].
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The SPM8 used a general linear model (GLM) to analyze the
main and interaction effects on these images. Because the a priori
hypotheses set for the present study were about the N400f and
P600f, signals captured earlier than P1 (up to 80 ms post stimulus,
which were primarily due to visual information processing )
and later than 980 ms were excluded from the analysis. Similar to
the main effects used for the behavioral data, a 262 repeated-
measures ANOVA tested the identification and classification
effects on the two ERP components. Significance thresholds were
set at p,.001 (uncorrected), and the extent threshold was set at
k.200 voxels; it was set conservatively to prevent false positives.
The SPM method results were verified against conventional
repeated-measures ANOVA model analyses of the amplitudes of
N400f and P600f captured at selected sites on the scalp. The sites
selected for the analyses were based on the sites used in previous
studies [9,10] over the midline central-parietal regions. Four sites
along the midline were selected: Fz, Cz, Pz, and Oz; these sites
were equivalent to channels 60, 10, 66, and 69, respectively
(Fig. 3B). The time window defined for the N400f (320 to 400 ms)
and P600f (500 to 600 ms) components were based on Eimer’s
studies [9,10]. The mean amplitude of each component was tested
with a 2 (familiar vs. unfamiliar face)62 (lie vs. truth)64 (sites: Fz,
Cz, Pz, and Oz) repeated-measures ANOVA.
Conceived and designed the experiments: TMCL CCHC. Performed the
experiments: DS TMCL. Analyzed the data: DS CCHC. Contributed
reagents/materials/analysis tools: TMCL CCHC. Wrote the paper: DS
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