The self and a close-other: differences between processing of faces and newly acquired information

Abstract

Prioritization of self-related information (e.g. self-face) may be driven by its extreme familiarity. Nevertheless, the findings of numerous behavioral studies reported a self-preference for initially unfamiliar information, arbitrarily associated with the self. In the current study, we investigated the neural underpinnings of extremely familiar stimuli (self-face, close-other’s face) and stimuli newly assigned to one’s own person and to a close-other (abstract shapes). Control conditions consisted of unknown faces and unknown abstract shapes. Reaction times (RTs) to the self-face were shorter than to close-other’s and unknown faces, whereas no RTs differences were observed for shapes. P3 amplitude to the self-face was larger than to close-other’s and unknown faces. Nonparametric cluster-based permutation tests showed significant clusters for the self-face vs. other (close-other’s, unknown) faces. However, in the case of shapes P3 amplitudes to the self-assigned shape and to the shape assigned to a close-other were similar, and both were larger than P3 to unknown shapes. No cluster was detected for the self-assigned shape when compared with the shape assigned to the close-other. Thus, our findings revealed preferential attentional processing of the self-face and the similar allocation of attentional resources to shapes assigned to the self and a close-other.

Full text

Received: February 25, 2022. Revised: April 29, 2022. Accepted: April 30, 2022
© The Author(s) 2022. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which
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Cerebral Cortex, 2022, 1–17
https://doi.org/10.1093/cercor/bhac201
Original Article
The self and a close-other: differences between
processing of faces and newly acquired information
Anna ˙
Zochowska1,Paweł Jakuszyk2, Maria M. Nowicka1,Anna Nowicka1,*
1Laboratory of Language Neurobiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences, 02-093 Warsaw, Poland,
2Laboratory of Brain Imaging, Nencki Institute of Experimental Biology, Polish Academy of Sciences, 02-093 Warsaw, Poland
*Corresponding author: Anna Nowicka, Laboratory of Language Neurobiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences, 3 Pasteur
Street, 02093 Warsaw, Poland. Email: a.nowicka@nencki.edu.pl
Prioritization of self-related information (e.g. self-face) may be driven by its extreme familiarity. Nevertheless, the findings of
numerous behavioral studies reported a self-preference for initially unfamiliar information, arbitrarily associated with the self. In
the current study, we investigated the neural underpinnings of extremely familiar stimuli (self-face, close-other’s face) and stimuli
newly assigned to one’s own person and to a close-other (abstract shapes). Control conditions consisted of unknown faces and
unknown abstract shapes. Reaction times (RTs) to the self-face were shorter than to close-other’s and unknown faces, whereas
no RTs differences were observed for shapes. P3 amplitude to the self-face was larger than to close-other’s and unknown faces.
Nonparametric cluster-based permutation tests showed significant clusters for the self-face vs. other (close-other’s, unknown) faces.
However, in the case of shapes P3 amplitudes to the self-assigned shape and to the shape assigned to a close-other were similar, and
both were larger than P3 to unknown shapes. No cluster was detected for the self-assigned shape when compared with the shape
assigned to the close-other. Thus, our findings revealed preferential attentional processing of the self-face and the similar allocation
of attentional resources to shapes assigned to the self and a close-other.
Key words:self-preference; attention; saliency; familiarity; ERP.
Introduction
In order to ensure our adaptive functioning in complex
social environments, only some pieces of incoming
information are selected for further processing. Such
selection is often guided by the self-relevance of the
information (Sui and Rotshtein 2019). To start with, a
classic example of preferential self-processing is the
cocktail party effect (Moray 1959). During a noisy party,
even when engaged in an immersive conversation,
we can easily hear our own name in the otherwise
meaningless noise of other people’s conversations.
Numerous studies showed prioritized processing not
only of one’s own name (Tacikowski and Nowicka 2010;
Yang et al. 2013;Nowicka et al. 2016;Nijhof et al. 2018;
Doradzi ´
nska et al. 2020) but also for one’s own face
(Brédart et al. 2006;Ma and Han 2010;Miyakoshi et al.
2010;Tacikowski and Nowicka 2010;Tacikowski et al.
2011;Kotlewska and Nowicka 2015;Bola et al. 2021;
˙
Zochowska et al. 2021). Thus, self-relevance facilitates
stimulus processing at different levels: items linked with
the self are easier to notice, evaluate, and remember
when compared to material associated with other people
(e.g. Symons and Johnson 1997;Keyes and Brady 2010;
Kotlewska and Nowicka 2016;Nowicka et al. 2016).
Importantly, in the cited studies self-related stimuli
were represented by highly familiar items like one’s own
name or face. The daily exposure to one’s own face
and name across the whole lifespan determines their
extreme familiarity in comparison with all other faces
and names. Hence, it cannot be clearly differentiated
whether the observed effects were caused by the self-
relevance or familiarity of those stimuli (Butler et al.
2013;Wo ´
zniak et al. 2018;Wo ´
zniak and Knoblich 2019;
Orellana-Corrales et al. 2021).
To control for the confounding effects of familiarity,
Sui and colleagues (Sui et al. 2012) introduced an
experimental paradigm that arbitrarily assigned new
stimuli to the self and other people. In this task, people
formed associations between neutral stimuli (equally
familiar) and personally significant labels. Specifically,
participants were instructed to associate geometric
shapes (e.g. a triangle, a circle, and a square) to the
self, a friend, and an unknown other. Subsequently,
participants were asked to indicate whether a shape-
label pair matched the learned assignment. Response
times (RTs) were typically significantly faster for con-
gruent combinations of the self-associated shape and
label than when responding to any other shape-label
combination. A large prioritization effect was observed
not only in RTs, but also in accuracy and sensitivity
scores for self-shapes when compared to those of a friend
or stranger (Sui et al. 2012;Schäfer et al. 2015;Schäfer
et al. 2016;Orellana-Corrales et al. 2021). The immediate
and substantial advantage for the self- vs. other pair
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2|Cerebral Cortex, 2022
that was originally reported by Sui and colleagues (Sui
et al. 2012) has since been replicated in numerous studies
(Frings and Wentura 2014;Mattan et al. 2015;Macrae
et al. 2017;Yin et al. 2019).
All in all, a brief association of a neutral shape with
the self seems to increase the salience of those stimuli
and is sufficient to elicit the self-prioritization effect
(Schäfer et al. 2015;Schäfer et al. 2016;Wang et al.
2016;Wo ´
zniak and Knoblich 2019). Self-prioritization is
thought to influence multiple stages of information pro-
cessing within matching tasks—the allocation of atten-
tion, memory (the retrieval of a self-representation), and
decision-making processes (Sui and Humphreys 2015;
Humphreys and Sui 2016;Liu et al. 2016). However, the
most important account of such self-prioritization is that
the effects are driven by tuning attention toward self-
related information, i.e. self-relevance modulates atten-
tional processing (Sui and Rotshtein 2019).
However, it is worth noting that in trials with matching
pairs of self-associated shape and self-label, participants
were processing both self-associated arbitrary stimuli
and familiar verbal labels with an established meaning.
Therefore, the self-advantage may be caused by the
high familiarity of the self-label and not by the self-
association of the shape. This fact led some studies to
test self-prioritization effects in experimental paradigms
with new self- vs. other-associated stimuli only. For
example, Sui et al. (2009) associated colors to the self
vs. a friend first and then presented arrows in the
associated colors at the center of the screen, which
served as either valid or invalid cues for the subsequent
target location. Arrows in the self-associated color were
more efficient in guiding attention than arrows in the
friend-associated color. In a similar vein,saccades toward
targets positioned away from self vs. other-associated
shapes were initiated more slowly (Dalmaso et al. 2019).
Moreover, the cuing of target locations by newly self-
associated stimuli enhanced target detection (Wade and
Vickery 2018; but see Siebold et al. 2015). Finally, the self-
prioritization effect could be elicited even in a matching
task that employed exclusively neutral stimuli (Wo ´
zniak
and Knoblich 2019). In that study, participants were first
asked to associate avatar faces with three identities:
self, friend, and stranger. Afterwards, participants were
asked to associate unfamiliar abstract symbols with
those three identities. Thus, instead of face-familiar label
pairs, pairs of avatar faces, and abstract shapes were
presented in a perceptual matching task. Nevertheless,
a clear self-prioritization was observed, revealing that
this effect can be elicited in the absence of any familiar
stimuli. In yet another study, self-prioritization was
investigated in an adapted perceptual matching task in
which participants were instructed to associate arbitrary
stimuli pairs (shape and color) with different people, and
then immediately carried out a color-shape matching
task. The results showed again the standard pattern of
the self-prioritization effect, confirming that the effect
is not critically dependent on familiar labels (Lee et al.
2021). In line with the later findings, such effect was
observed in a modified matching task, in which familiar
labels from the standard task were replaced with pseudo-
words, i.e. in the absence of any stimuli with established
self-associations (Wo ´
zniak and Knoblich 2021). However,
it was found only if self-associations were presented as
task-relevant (Wo ´
zniak and Knoblich 2021).
Most studies investigating the self-prioritization of
information that was newly assigned to one’s self vs.
another person were based on behavioral measures
(RT, accuracy, sensitivity scores). In contrast, studies
investigating the neural correlates of such information
processing are rather rare (Sui et al.2013;Sui et al. 2015b;
Wo ´
zniak et al. 2018). One of the first studies in this
field used an associative learning procedure (Sui et al.
2013) that instructed participants to assign three neutral
shapes with labels for themselves, their best friend, and
an unfamiliar other. Functional magnetic resonance
imaging (fMRI) data were acquired while participants
judged whether the shape-label pairs matched or not.
Behaviorally, faster responses and higher accuracy were
found for self-assigned pairs. Responses to matching
self-pairs were associated with enhanced activity in the
ventral medial prefrontal cortex (vmPFC)—a brain region
linked to self-representation (Northoff and Bermpohl
2004;Northoff et al. 2006;Denny et al. 2012)—and in the
posterior superior temporal sulcus, which is linked to
social cognition (Beauchamp 2015). Activations in those
two brain regions predicted behavioral self-bias effects.
In yet another fMRI study (Sui et al. 2015b), partic-
ipants associated shapes with either themselves or a
friend. Subsequently, the shapes had to be identified in
hierarchical (i.e. global–local) forms. Self-assigned stim-
uli were associated with increased activation of the left
inferior parietal sulcus when the task required partic-
ipants to select the neutral shape and ignore the self-
associated shape (i.e. salient self-distractors had to be
rejected). Since a similar increase in activation in the
same region was found when participants rejected per-
ceptually salient distractors (Mevorach et al. 2009), it
seems that rapidly formed self-associations may change
the neural response in a manner that is qualitatively
similar to effects produced by changing the perceptual
saliency of stimuli (Sui et al. 2015b).
Further, in an event-related potential (ERP) study
three unfamiliar faces were identified with the verbal
labels “You,” “Friend,” and “Stranger” instead of shapes
(Wo ´
zniak et al. 2018). Afterwards, participants judged
whether two stimuli (i.e. face, label) presented in succes-
sion matched. In one experiment faces were followed
by verbal labels, whereas in the other experiment,
labels were followed by faces. Both experiments showed
an analogous pattern of behavioral and ERP results.
If the first stimulus (face or label) was self-related,
RTs were faster and the late frontal positivity to the
first stimulus was more pronounced. Moreover, the
central-parietal P3 associated with the second stimulus
was more pronounced when it was preceded by any
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Anna ˙
Zochowska et al.|3
self-related stimulus. However, when the first stimulus
was not associated with the self, there was no facilitation
in the processing of the second stimulus even if it had
an intrinsic association with the self (Wo ´
zniak et al.
2018). Thus, two primary conclusions can be drawn: (1)
the self-relevance of initially encountered information
has a decisive role in the processing of subsequent
information, and (2) self-associated stimuli facilitated
the processing of subsequent stimuli, irrespective of
whether these stimuli were associated with the self.
In the current ERP study, we investigated the neural
underpinnings of highly familiar and new information
that was arbitrarily assigned to the self and to a close-
other. We were interested in whether previously irrel-
evant, abstract information that was newly associated
with the self would benefit from preferential processing
as is the case for well-known self-referential informa-
tion, and whether self-prioritization effects would be
comparable in both cases. Therefore, we directly com-
pared the processing of two types of stimuli: extremely
familiar (self-face, close-other’s face) stimuli,and stimuli
that were newly assigned to one’s own person and to
a close-other (an abstract shape). The control condi-
tions consisted of unknown faces and unknown abstract
shapes. We decided to present participants with abstract
shapes alone, without labels. The reason for doing so was
twofold. First, this approach avoids the aforementioned
controversies regarding familiar labels. Second, as there
was no need to use any labels in the case of faces, this
approach (i.e. avoiding labels) guaranteed similar visual
stimulation in both cases.
Participants were tasked with indicating whether pre-
sented stimuli (faces, shapes) were familiar or unknown.
Prior to the EEG recording session, arbitrarily selected
shapes were associated with the self and a close-other
(i.e. one shape for each person). The close-other was
freely chosen by each participant as representative of
the most significant person in their life at the time of
experimentation. This operationalization of a close-other
was used in several previous studies on the topic of self-
prioritization (Cygan et al. 2014;Kotlewska and Nowicka
2015;Kotlewska and Nowicka 2016;Nowicka et al. 2016;
Kotlewska et al. 2017;Nijhof et al. 2018;Nowicka et al.
2018;Cygan et al. 2021). It is worth noting that simi-
larly to one’s own face, a close-other’s face is a very
important and salient visual stimulus that is frequently
encountered on an everyday basis. Thus, its level of
familiarity is very high. Nevertheless, on the neural level
the processing of such extremely familiar faces—with
a very high exposure factor—substantially differs from
the processing of the self-face as revealed by steady-
state visual evoked potentials (Kotlewska et al. 2017)
and late ERP components, especially P3—a positive ERP
component with centro-parietal distribution and latency
of 300 ms or longer (Cygan et al. 2014;Kotlewska and
Nowicka 2015;Cygan et al. 2021).
In this study, the P3 results obtained for faces served
as a kind of reference for ERP results for shapes. We
expected to observe enhanced P3 associated with self-
face processing when compared to close-other and
unknown face processing. We aimed at testing whether
information newly assigned to the self and a close-
other would lead to an analogous pattern of findings.
As far as behavioral indices of self-prioritization are also
concerned, we were interested in whether behavioral
findings would be comparable for faces and shapes.
Moreover, the distinct spatial patterns of activity
elicited by faces and shapes were also tested with
nonparametric cluster-based permutation tests (Maris
and Oostenveld 2007). This method enables the unbi-
ased comparison of EEG signals recorded in different
experimental conditions at all sensors and all time
points, while controlling for multiple comparisons and
maximizing power by employing the cluster structure of
the data as its sole test statistic. We used this approach to
test for differences in spatial and temporal distributions
between experimental conditions. Thus, cluster-based
permutation tests would confirm and complement the
findings obtained with P3 analyses, providing a global
and complete view of commonality/distinctiveness in
the neural underpinnings associated with the processing
of self-, close-other, and unknown faces and newly
acquired information referring to the self, a close-other,
and unknown people.
Materials and methods
Participants
Thirty-two participants (16 females, 16 males) were
tested in the study, ranging in age between 21 and
34 years old (M= 27.594; SD = 3.131). The Edinburgh
Handedness Inventory (Oldfield 1971) indicated that
thirty participants were right-handed. All participants
reported no history of mental or neurological disorders
and had normal or corrected-to-normal vision with the
use of contact lenses. Additionally, to ensure the unifor-
mity of visual stimuli standards, neither participants nor
their chosen close-other were allowed to be represented
with glasses or have any distinctive facial marks, as their
photographs were matched with photographs from the
Chicago Face Database (CFD; Ma et al. 2015).
An additional present-day restriction was a negative
test for the SARS-CoV-2 virus. As all participants were
PhD students and employees at the Nencki Institute,they
took part in the SONAR-II project (www.nencki.edu.pl)
developed at the Nencki Institute in cooperation with
the University of Warsaw. SONAR-II is dedicated to the
asymptomatic population of people who do not meet the
criteria for SARS-CoV-2 testing but who may come into
contact with infected people.
The required sample size was estimated using More-
Power software (Campbell and Thompson 2002). Estima-
tion was conducted for the main factor “type of stimuli”
(face, shape) in two-way repeated measures ANOVA with
the factors “type of stimuli” and “type of face” (self,close-
other’s, unknown): estimated effect size η2= 0.25, α= 0.05,
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4|Cerebral Cortex, 2022
β= 0.90.It yielded a sample size of 30 participants. As the
risk of data loss was taken into consideration, the group
size was enlarged to 32.
Ethics statement
This study was conducted with the approval of the
Human Ethics Committee of the Institute of Applied
Psychology at Jagiellonian University (Cracow, Poland).
All participants provided written informed consent prior
to the study and received financial compensation for
their participation.
Stimuli
We used two different types of stimuli in this study: (1)
faces and (2) shapes. The set of stimuli was individually
tailored to each participant. Faces belonged to three cat-
egories: self-face, close-other’s face, and unknown faces.
Participants freely chose the close-other according to
their subjective high level of closeness and importance.
We did not predefine the type of relationship between
the participant and their close-other in order to avoid a
spuriously close relation. The only restriction placed on
the selection of the close-other was that they had to be of
the same gender as the participant. Twenty-two partici-
pants chose their friend, eight their sibling, and two their
partner. The face of each participant and their close-
other was photographed (with a neutral expression) prior
to the study. The photographs of eight unknown neu-
tral faces were taken from the Chicago Face Database
(Ma et al. 2015), gender matched to each participant.
Each photo (the self-face, close-other’s face, and selected
unknown faces from the CFD) was subjected to the same
editing procedure, i.e. they were gray-scaled, extracted
from the background and cropped (only facial features
were included—face oval without hair and ears), resized
to subtend 6.7◦×9.1◦of visual angle, and equalized for
mean luminance using Photoshop CS5 (Adobe, San Jose,
CA). Contrast and spatial frequencies in the pictures were
not normalized as these procedures tend to introduce
substantial distortions into the processed images. The
photos of each participant and their close-other’s face
were deleted at the end of the experimental session.
The second type of stimuli consisted of abstract
shapes. In previous studies on the processing of new
information assigned to the self and others, simple
geometric figures (e.g. a square,a triangle) were typically
used. As the number of shapes was supposed to be
equal to the total number of faces (self, close-other’s,
8 unknown) we generated 10 different abstract shapes.
We aimed at equalizing low-level physical features of
faces and shapes. Thus, each shape’s area was equal to
face oval’s area, i.e. 43.12 cm2. Shape assignment to each
experimental “condition” (self, close-other, unknown)
was pseudo-random on the group level; e.g. a self-
assigned shape in a given set of stimuli was assigned
to a close-other or unknown condition in some other set
of stimuli. Faces and shapes were presented against a
Fig. 1. Shapes used in the present study. The area of each shape was the
same and was equal to the area of the face oval.
black background. Figure 1 presents all 10 shapes used
in this study.
Procedure
Participants were seated in a comfortable chair in a
dimly lit and sound-attenuated room, 57 cm from the
computer monitor (DELL Alienware AW2521HFL, Round
Rock, Texas, USA). Subsequently, during the electrode cap
placement and adjustment of EEG electrode impedances,
they were primed for the task: two different shapes,
one assigned to the participant and the second to their
close-other, were presented on the monitor and partici-
pants were required to associate those geometric shapes
with the self and chosen close-other. The learning phase
lasted on average 23.53 min (SD= 5.900). Just before the
beginning of the task, participants were asked to draw the
assigned shapes. This was done to check the efficiency of
learning.
After electrode cap placement (ActiCAP, Brain Prod-
ucts, Munich, Germany), the participants used an
adjustable chinrest to maintain a stable head position.
Presentation software (Version 18.2, Neurobehavioral
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Anna ˙
Zochowska et al.|5
Systems, Albany, CA) was used for stimuli presentation.
Participants performed a recognition task—if they
recognized a presented face or shape (i.e. representing
the participant or their close-other), they were asked to
push the response button assigned to “YES.” If that was
not the case, they were asked to press the button defined
as “NO.” The assignment of “YES” and “NO” buttons was
counterbalanced across the participants.
After reading the instructions displayed on the screen,
the subjects confirmed they understood the task and
initiated the experiment by pressing a response button.
Trials with faces and shapes were inter-mixed (in one
session) and their order was pseudo-random with respect
to the type of stimulus (faces, shape) and the experi-
mental condition (self, close-other, unknown). Each trial
started with a blank screen, presented for 1500 ms. Next,
a white fixation cross (subtending 0.5◦×0.5◦of visual
angle) was centrally displayed for 100 ms and followed
by a blank screen which lasted either (randomly) 100,
200, 300, or 400 ms. Afterwards, a face or a shape was
presented for 500 ms. Regardless of which stimulus was
shown, participants were asked to push the appropriate
response button as quickly as possible. Next, a blank
screen was shown and lasted until a response was made.
The procedure structure is presented in Fig. 2. In the
“self” and “close-other” conditions, the total number of
repetitions for each stimulus type (face, shape) was 40,
while for the “unknown” condition it was 80. Thus, the
total number of trials with familiar and unknown stimuli
was equal, as was the probability of YES/NO responses.
To account for possible fatigue during the experiment,
a break was planned in the middle of experimental ses-
sion. It was terminated after one minute, unless the
participant ended it earlier. Participants needed 24 min
on average to complete the whole experiment.
EEG recording
The electroencephalogram (EEG) was continuously
recorded with 62 Ag-AgCl electrically shielded electrodes
mounted on an elastic cap (ActiCAP, Brain Products,
Munich, Germany) and positioned according to the
extended 10–20 system, with two additional electrodes
placed on the left and right earlobes. The EEG signal
was recorded using the BrainAmp MR plus amplifier
(Brain Products,Munich, Germany) and digitized at a 500-
Hz sampling rate, using BrainVision Recorder software
(Brain Products, Munich, Germany). EEG electrode
impedances were kept below 10 kΩ. The EEG signal was
recorded against an average of all channels calculated
by the amplifier hardware.
Behavioral data analysis
Responses were scored as correct if the appropriate but-
ton was pressed within 100–1000 ms of stimulus onset.
In order to conduct statistical analyses of behavioral and
ERP data in a consistent manner, similarly to our anal-
yses of ERP components, only every other trial with an
unfamiliar stimulus was included in the analyses. This
was done because the number of trials with unfamiliar
faces (80) and shapes (80) was doubled in comparison to
the number of trials with familiar faces and shapes in
each familiar (self, close-other) condition (40). Statistical
analyses were performed using JASP software (Wagen-
makers et al. 2018). Mean accuracy scores and mean RTs
were analyzed using repeated measures ANOVA, with
the “type of stimulus” (face, shape) and “condition” (self,
close-other, unfamiliar) as within-subject factors.
The traditional null-hypothesis significance-testing
approach was complemented with Bayesian analysis
methods and Bayes factors (BFs) were computed using
JASP software (Wagenmakers et al. 2018). BFs were
interpreted according to Lee and Wagenmakers (2014)
suggestions. Briefly, a BF10 between 1 and 3 implies
anecdotal evidence in favor of H1, between 3 and 10—
moderate evidence, between 10 and 30—strong evidence,
between 30 and 100—very strong, and higher than
100—extreme evidence. As far as low values of BF10
are concerned, a BF10 between 0.33 and 1 indicates
anecdotal evidence in favor of H0, between 0.1 and
0.33—moderate evidence, and between 0.03 and 0.1—
strong evidence of the absence of an effect. Finally, a
BF10 between 0.01 and 0.03 and lower than 0.01 indicates
very strong and extreme evidence for the absence of an
effect, respectively.
ERP analysis
Off-line analysis of the EEG was performed using
BrainVisionAnalyzer software (Brain Products, Gilching,
Germany). The 62 channels were re-referenced off-line
to the algebraic average of the left and right earlobes,
notch filtered at 50 Hz, and band-pass- filtered from
0.1 to 30 Hz using a Butterworth filter. The next step in
data pre-processing was the correction of ocular artifacts
using Independent Component Analysis (ICA) (Bell and
Sejnowski 1995). After each data set was decomposed
into maximally statistically independent components,
elements representing eye blinks were rejected based on
a visual inspection of the component’s map (Jung et al.
2001). Using the reduced component-mixing matrix, the
remaining ICA components were multiplied and back-
projected to the data, resulting in a set of ocular-artifact-
free EEG data.
Afterward, the EEG signal was segmented into 1200 ms
epochs, from −200 ms before to 1000 ms after stimu-
lus onset. The subsequent automatic artifact rejection
procedure allowed only trials, which fulfilled the follow-
ing requirements: the maximum permitted voltage step
per sampling point was 50 μV, the maximum permitted
absolute difference between two values in the 200-ms-
long segment was 100 μV, the minimal and maximal
allowed amplitudes were −150 μV and 150 μV, and the
lowest permitted activity in the 100 ms interval was
0.5 μV.
Trials with correct responses were subsequently
analyzed. In the case of unfamiliar stimuli, only every
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6|Cerebral Cortex, 2022
Fig. 2. Schematic presentation of the experimental procedure.Three types of faces (self, close-other’s, unknown) and three types of shapes (self-assigned,
assigned to the close-other, unknown) were intermixed and presented pseudo-randomly. Participants were supposed to indicate whether a stimulus
was familiar or not. The example image of a self-face is a photograph of one of the co-authors.
other trial was included in the analyses. This was done
in order to keep a similar signal-to-noise ratio (SNR)
for each experimental condition (defined by the type
of stimulus and type of face). It should be reminded
that the total number of trials with unfamiliar faces
and shapes (80 for each of them) was twice as large as
the total number of trials with familiar faces and shapes
in the single “self” and “close-other” conditions (40 for
each type of stimulus). The mean number of segments
averaged afterwards for each experimental condition
was as follows: self-face—37.031 (SD= 2.800), shape
assigned to self—35.250 (SD =3.298), close-other’s face—
36.938 (SD = 2.449), shape assigned to close-other −35.031
(SD = 3.441), unknown other face—36.344 (SD = 2.868),
and unknown shape—35.938 (SD= 3.816). In the final
stage of pre-processing, the epochs were baseline-
corrected by subtracting the mean of the pre-stimulus
period.
Selection of electrodes for ERP analysis was orthogo-
nal to potential differences between experimental con-
ditions (Kriegeskorte et al. 2009). Therefore, it was con-
ducted on the basis of the topographical distribution
of brain activity (in the time window corresponding to
a given component), averaged across all experimental
conditions. Electrodes CP1, CPz, CP2, and Pz, localized
within the region of maximal activity, were selected for
further analyses (see Fig. 3). The data were pooled for
those electrodes. This step is justified by the limited
spatial resolution of EEG and high correlation between
neighboring electrodes. Based on the topographical dis-
tribution of activity as well as grand-averaged ERPs, col-
lapsed for all experimental conditions (self-face, shape
assigned to the self, close other’s face, shape assigned to
close other, unknown other face, and unknown shape),
a 350–650-ms time window was chosen for analysis of
the P3 component. The mean values at each time point
within this time window were used to assess the ampli-
tudes of our ERP component. This method is less affected
by possible low SNR than peak measure methods (Luck
2005).
Statistical analysis of ERP data was performed using
SPSS software (Version 26, IBM Corporation).The reported
results were cross-checked with Statcheck (http://
statcheck.io/index.php). A two-way repeated measure
ANOVA was performed with “type of stimulus” (face,
shape) and “condition” (self, close-other’s, unknown) as
within-subject factors. All effects with more than one
degree of freedom in the numerator were adjusted for
violations of sphericity (Greenhouse and Geisser 1959).
Bonferroni correction for multiple comparisons was
applied to post hoc analyses. All results are reported
with alpha levels equal to 0.05.
The traditional null hypothesis significance testing
approach was complemented with Bayesian analysis
methods. To test whether the self-face and other faces,
as well as the self-assigned shape and other shapes,
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Anna ˙
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Fig. 3. (A) Topographical distribution of brain activity averaged across the two types of stimuli (faces,shape) and across all experimental conditions (self,
close-other, unknown) and (B) a butterfly plot presenting grand-average ERPs for all (collapsed) experimental conditions, at all 62 active electrodes.
were characterized by similar levels of neural activity,
BFs were computed using JASP software (Wagenmakers
et al. 2018). The main reason for calculating BFs was
that, unlike classic frequentist statistics, BF evaluates
how strongly both alternative and null hypotheses are
supported by the data. Specifically, BF is a ratio of the
probability (or likelihood) of observing the data given
the alternative hypothesis is true to the probability
of observing the data given the null hypothesis is
true. Thus, BF10 provides further evidence either in
favor of similarities or rather differences between the
tested experimental conditions. The medium prior scale
(Cauchy scale 0.707) was used in all Bayesian tests. BF10
were interpreted according to Lee and Wagenmakers
(2014) suggestions.
Cluster-based permutation tests
Cluster-based permutation tests were used here as an
exploratory analysis procedure,as they eff iciently handle
the multiple comparisons problem in high-dimensional
magnetoencephalographic and EEG data (Sassenhagen
and Draschkow 2019). In general, permutation tests are
used to test the null hypothesis that the data in the
experimental conditions come from the same probability
distribution. Getting a significant result means that the
null hypothesis can be rejected in favor of the alternative
hypothesis, i.e. that the data came from different distri-
butions. Therefore, significant results from permutations
tests indicate a significant between-condition difference.
The results are reported with reference to an alpha level
equal to 0.05. Cluster-based permutation tests were con-
ducted using custom-made Python scripts with use of the
mne.stats.spatio_temporal_cluster_1samp_test function
from the MNE Python package.
We directly compared: self-face vs. close-other’s
face, self-face vs. unknown faces, close-other’s face
vs. unknown faces, self-shape vs. close-other’s shape,
self-shape vs. unknown shapes and close-other’s shape
vs. unknown shapes. As clustering in both space and
time was used, such an analysis procedure revealed
differences in the spatial distributions of activity as a
function of time between the tested conditions.
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8|Cerebral Cortex, 2022
Fig. 4. Behavioral results. (A) Mean (±SD) accuracy scores and (B) mean
(±SD) RTs for faces and shapes.
Results
Behavioral results
A repeated-measures ANOVA conducted on the mean
number of correct responses revealed the significant
main effects of “type of stimulus” (F(1, 31) = 28.758,
P<0.001, η2= 0.141) and “condition” (F(2, 62) = 4.022,
P= 0.023, η2= 0.028), as well as a significant two-way
interaction: “condition” דtype of stimulus” (F(2, 62) =
10.689, P<0.001, η2= 0.119). The significance of the
“type of stimulus” factor indicated a significantly higher
accuracy score in the case of faces in comparison with
shapes (see Fig. 4). Post hoc tests of the “condition”factor
showed that the accuracy score in the “close-other”
condition was slightly lower than in the “unknown”
condition (P= 0.020), whereas other differences were
non-significant (“self” vs. “close-other”: P= 0.808; “self”
vs. “unknown”: P= 0.282).
Post-hoc tests of the “condition” דtype of stimulus”
interaction revealed non-significant differences in accu-
racy scores between the self-face and the close-other’s
face (P>0.99, BF10 = 0.231, moderate evidence for H0),
the self-face and unknown faces (P= 0.933, BF10 = 0.597,
anecdotal evidence for H0), and the close-other’s face
vs. unknown faces (P>0.999, BF10 = 0.541, anecdotal
evidence for H0). Significant differences in accuracy
rates were present between the self-assigned shape and
unknown shapes (P= 0.002, BF10 = 15.894, strong evidence
for H1) and between the close-other assigned shape
and unknown shapes (P<0.001, BF10 = 210.730, extreme
evidence for H1), whereas the self-assigned shape and
the close-other assigned shape did not differ (P>0.999,
BF10 = 0.256, moderate evidence for H0).
Moreover, post hoc tests of the “condition” דtype
of stimulus” interaction also showed that the number
of correct responses to faces was significantly higher
than to shapes in the “self” and “close-other” conditions
(P<0.001, BF10 = 50.757, very strong evidence for H1
and P<0.001, BF10 = 6596.037, extreme evidence for
H1, respectively), but it was similar for faces and
shapes in the case of the “unknown” condition (P>0.99,
BF10 = 0.297, moderate evidence for H0).
A repeated-measures ANOVA conducted on mean RTs
revealed the significant main effect of “condition” (F(2,
62) = 25.374, P<0.001, η2= 0.159) and a significant two-
way interaction: “condition” דtype of stimulus” (F(2,
62) = 36.036, P<0.001, η2= 0.230). Post hoc tests of the
“condition” factor showed that RTs in the “self” condi-
tion were substantially shorter than in the “close-other”
(P<0.001) and “unknown” conditions (P<0.001). How-
ever, this pattern of findings was driven mainly by RTs
to faces. Post hoc tests of the “condition” דtype of
stimulus” interaction revealed significantly shorter RTs
to the self-face than to the close-other’s face (P= 0.001,
BF10 = 4517.073, extreme evidence for H1) and unknown
faces (P<0.001, BF10 = 1.009 ×1010, extreme evidence for
H1), as well as shorter RTs to the close-other’s face than
to unknown faces (P<0.001, BF10 = 5.644 ×106, extreme
evidence for H1). In contrast, in the case of shapes, all
differences between conditions were non-significant (self
vs. close-other: P= 0.624, BF10 = 0.637, anecdotal evidence
for H0; self vs. unknown: P>0.99, BF10 = 0.227, anecdo-
tal evidence for H0; close-other vs. unknown: P= 0.105,
BF10 = 2.414, anecdotal evidence for H1).
Post hoc tests of the “condition” דtype of stimulus”
interaction also showed that RTs to the self-face were
significantly shorter than to the self-assigned shape
(P= 0.009, BF10 = 22.438, strong evidence for H1). The
opposite effect, i.e.longer RTs, was observed for unknown
faces when compared to unknown shapes (P<0.001,
BF10 = 1.227 ×107, extreme evidence for H1), but no
significant differences were found in the case of the
“close-other” condition (P= 0.908, BF10 = 0.569, anecdotal
evidence for H0).
P3 results
Statistical analysis of P3 amplitudes revealed the main
effects of “type of stimulus” (F(1, 31) = 27.004, P<0.001,
η2= 0.466), “condition” (F(2, 62) = 32.288, P<0.001, η2
= 0.510), and a significant 2-way “condition” דtype
of stimulus” interaction (F(2, 62) = 15.514, P<0.001.
η2= 0.334). The significance of the “type of stimulus”
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Anna ˙
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Fig. 5. Grand-average ERPs for (A) faces and (B) shapes, pooled for four electrodes: CP1, CPz, CP2, and Pz. The analyzed time window is marked by
light-blue rectangles.
factor indicated that the P300 amplitude for faces was
significantly higher than for shapes (see Fig. 5).
Post-hoc analyses for the “condition” factor showed
that the unknown stimuli evoked significantly lower P3
than stimuli associated with the self (P<0.001) as well
as for stimuli associated with the close-other (P<0.001).
The difference between stimuli associated with self and
close-other was non-significant (P= 0.221).
Post hoc tests performed for the two-way “condition”
דtype of stimulus” interaction revealed that the self-
face was associated with significantly higher P3 than the
shape assigned to the self (P<0.001, BF10 = 247366.068,
extreme evidence for H1). This was also the case for
the close-other condition: P3 to the close-other’s face
was larger than P3 to the shape assigned to a close-
other (P<0.001, BF10 = 84.268, very strong evidence for
H1). Such an effect was not observed for unknown
stimuli, as the difference between unknown faces
and unknown shapes was not significant (P= 0.165,
BF10 = 0.470, anecdotal evidence for H0). Moreover, P3
amplitude was significantly increased for self-face in
comparison to close-other’s face (P= 0.008, BF10 = 13.409,
strong evidence for H1) as well as in comparison to
unknown faces (P<0.001, BF10 = 4.871 ×106, extreme
evidence for H1), and for close-other’s face compared
to unknown faces (P= 0.001, BF10 = 7478.356, extreme
evidence for H1). P3 amplitudes to the self-assigned
shape and the close-other assigned shape did not
differ (P>0.999, BF10 = 0.207, moderate evidence for H0).
However, unknown shapes were associated with lower P3
amplitude than the shape assigned to the self (P= 0.009,
BF10 = 12.049, strong evidence for H1) and to the close-
other (P= 0.004, BF10 = 25.077, strong evidence for H1).
Cluster-based permutation tests
Nonparametric cluster-based permutation analyses
showed that the self-face processing differed
significantly from the processing of all other faces, i.e.
close-other’s and unknown faces. Differences between
the self- and unknown faces as well as between the
close-other’s face and unknown faces were widely
distributed in space and time, whereas a significant
cluster was more focused for the self vs. the close-
other comparison (see Fig. 6). It is worth noting that
the time window of substantial differences between
the tested conditions encompasses the time window in
which the P3 component was analyzed (350–650 ms).
Moreover, differences between conditions were present
at electrodes within the central-parietal region for all
comparisons, i.e. the region for which P3 amplitudes were
analyzed.
In the case of abstract shapes, nonparametric cluster-
based permutation analyses revealed significant dif-
ferences between the self-assigned shape vs. unknown
shapes and the close-other assigned shape vs. unknown
shapes (see Fig. 7). Crucially, when compared to the shape
assigned to the close-other, no difference was detected
in the case of the self-assigned shape, at any electrode
site and at any time point (see Fig. 7). Such a lack of
the differences indicates that the data in those two
experimental conditions (self, close-other) came from
the same probability distribution (i.e. the data in these
conditions cannot be distinguished).
Discussion
Converging lines of evidence indicate that self-relevance
facilitates stimulus processing and different types of
self-related information (e.g. name, face) are processed
preferentially (for a review see Humphreys and Sui 2016).
There is an ongoing debate on whether such a self-
advantage can be attributed to the extreme familiarity
of self-related information and whether the processing
advantages for self-related information can be observed
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10 |Cerebral Cortex, 2022
Fig. 6. The results of cluster-based permutation tests for faces. (A) Self-face compared to close-other and (B) unknown faces, (C) close-other face
compared to unknown faces. Statistically significant positive differences between conditions are indicated in red (P<0.05).
for initially unfamiliar information, when arbitrarily
associated with the self (Sui et al. 2012).
The current study investigated the neural underpin-
nings and behavioral indices of the processing of self-
and close-other’s faces as well as for abstract shapes that
were—prior to the experimental session—assigned to the
self and a close-other. It should be pointed out that the
close-other condition seems to be the best control to the
self and has been used in several previous studies on
self-referential processing (Cygan et al. 2014;Kotlewska
and Nowicka 2015;Kotlewska and Nowicka 2016;Now-
icka et al. 2016;Kotlewska et al. 2017;Nijhof et al. 2018;
Nowicka et al. 2018;Cygan et al. 2021). The processing of
those two types of faces and shapes was compared with
the processing of unknown faces and shapes.
On the behavioral level, we observed a rather complex
pattern of findings. To start with, accuracy rates were
significantly higher for faces than for shapes in the “self”
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Anna ˙
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Fig. 7. The results of cluster-based permutation tests for shapes. The self-assigned shape compared to the shape assigned to (A) the close-other and
(B) unknown shapes, and (C) the shape assigned to the close-other compared to unknown shapes. Statistically significant positive differences between
conditions are indicated in red (P<0.05).
and “close-other” conditions, but they were similar for
faces and shapes in the “unknown” condition. Accuracy
scores to the self-assigned shape and the shape assigned
to the close-other did not differ. However, both were
lower than accuracy score to unknown shapes. Even so,
it should be pointed out that accuracy rates were nom-
inally very high in each experimental condition (from
92 to 99%). RTs analyses revealed that all self-related
stimuli were characterized by shorter RTs in comparison
to the other stimuli (i.e. referring to the close-other and
unknown people). Additionally, it should be highlighted
that this result was primarily driven by the RTs to faces—
significant differences were observed between self-face
and other faces, but not between shapes. Moreover, RTs
to the self-face were significantly shorter than to the self-
assigned shape. Such faster reactions to faces than to
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12 |Cerebral Cortex, 2022
shapes were not observed in the case of the “close-other”
and “unknown” conditions.
Based on numerous studies showing behavioral
indices of the self-prioritization effect for new infor-
mation that is arbitrarily associated with the self (Sui
et al. 2012;Sui et al. 2013;Frings and Wentura 2014;
Sui et al. 2014;Mattan et al. 2015;Schäfer et al. 2015;
Sui et al. 2015a;Schäfer et al. 2016;Macrae et al. 2017;
Wo ´
zniak et al. 2018;Yin et al. 2019;Orellana-Corrales
et al. 2021), one might also expect the current study to
find shorter RTs and higher accuracy rates in the case
of newly self-assigned stimuli. However, this was not the
case. This discrepancy may be due to the substantial
procedural/methodological differences between the
previous studies and the current study.
To start with, one of the differences refers to the
presence vs. absence of labels. Specifically, in the
matching trials of the self-prioritization task used in
previous studies, participants were processing not only
self-associated arbitrary stimuli but also familiar verbal
labels with a pre-experimentally established meaning.
Therefore, the self-advantage may be caused by the
familiarity of the labels, rather than the self-association
of the shape. In contrast, the present study did not
present labels with shapes. However, it should be noted
that the self-prioritization effect in RTs was also observed
in the absence of any pre-experimentally familiar stimuli
related to the self (Wo ´
zniak and Knoblich 2019). Thus,
the RT findings in the current study and in Wo ´
zniak
and Knoblich (2019) may be regarded as inconsistent.
However, the next difference between the current and
previous studies on the topic of processing newly
acquired information (including Wo ´
zniak and Knoblich
2019) is that in our study the control condition to the
self was not just a “friend” but a person, freely chosen
by each participant as the most significant person at the
time of experimentation. Other differences are as follows.
In the current study, shapes assigned to the self and the
close-other were presented for a rather prolonged time
(ca. 30 min), whereas in previous studies the process
of associating a specific shape to a specific person was
much shorter: each participant was simply told that they
would be represented by e.g. a circle or a square (Sui et al.
2012) or the learning phase of shapes labels was very
short—30 or 60 s (Wo ´
zniak and Knoblich 2019).
Crucially, the behavioral tasks performed by partici-
pants were different. While in previous studies, it was
the perceptual matching task, in the current study it
was the discrimination of familiar vs. unfamiliar stimuli.
Thus a question may arise whether participants ignored
self- and close-other associations when responding to
the shapes and simply re-conceptualized those shapes as
simply representing the category “familiar.” Such strat-
egy was fully efficient in successfully accomplishing the
task and accuracy rates seemed to support this view
as they did not differ for self- and close-other assigned
shapes.
It is worth noting that the self-relevance facilitated
stimulus processing only when task sets drew attention
to previously formed shape-label associations (Caughey
et al. 2021). Compared to shapes associated with a friend,
those paired with the self were classified more rapidly
when participants were required to report who the stim-
ulus denoted (i.e. self or friend). However, self-relevance
failed to facilitate performance when participants judged
either what the shape was (i.e. triangle or square, dia-
mond or circle) or where it was located on the screen
(i.e. above or below fixation). This was also the case for
arbitrary objects assigned to the self and a friend (Falben
et al. 2019). Compared with arbitrary objects owned by
a friend, those owned by the self were classified more
rapidly when participants were required to report either
the owner or identity of the items. In contrast, self-
relevance failed to facilitate performance when partici-
pants judged the orientation of the stimuli. In a similar
vein, the self-prioritization effect was observed (in the
absence of any stimuli with established self-associations)
only when self-associations were task-relevant (Wo ´
zniak
and Knoblich 2021). In the light of the aforementioned
findings, behavioral results for shapes assigned to the
self and a close-other were similar because the self-
association of a shape was task-irrelevant as it was not
necessary to identify shapes as associated with the self
or a close-other.
Even so, we found faster reactions to one’s own face
than to other faces. Our RTs results are in line with the
findings reported in numerous studies, typically report-
ing shorter RTs to the self vs. other faces (Keyes et al.
2010;Ma and Han 2010;Tacikowski and Nowicka 2010;
˙
Zochowska et al. 2021). In a recent meta-analysis, RTs
to the self-face were compared with RTs to other faces
across a large number of studies (Bortolon and Raffard
2018). The tested moderators included—among others—
the familiarity (i.e. whether the face was familiar to the
participants) and identity of faces (i.e. whether the face
belonged to someone personally known by participants,
or whether it was a famous person or a stranger). The
results of that meta-analysis showed that regardless of
the face identity or level of familiarity, people tended to
respond faster to their own face than to other people’s
faces when requested to perform an identification/recog-
nition task (Bortolon and Raffard 2018).
On the neural level, the ERP findings differed for faces
and shapes. First of all, amplitudes of P3 to faces were—
in general—higher than amplitudes of P3 to shapes. As
the P3 component is linked to the cognitive evaluation
of stimulus significance (Picton and Hillyard 1988), this
finding may suggest the increased significance of faces
in comparison with abstract shapes. While the former
are ecologically valid stimuli that are encountered on an
everyday basis, the latter definitely do not share those
features. In addition,the ERP results of the present study
clearly showed that self-face processing was associated
with enhanced P3 in comparison with all other faces
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Anna ˙
Zochowska et al.|13
(close-other’s, unknown). Furthermore, P3 to the close-
other’s face was larger than P3 to unknown faces. Non-
parametric cluster-based permutation tests corroborated
our P3 findings, as they revealed significant clusters for
the self-face when compared to both the close-other’s
face and unknown faces, as well as for the close-other’s
face when compared to unknown faces.
However, the pattern of findings was different in the
case of shapes. P3 to the self-assigned shape and P3 to the
close-other assigned shape were similar, and both were
larger than P3 to unknown shapes. Moreover, nonpara-
metric cluster-based permutation tests showed signifi-
cant clusters for comparisons of “self vs. ‘unknown’ and
‘close-other” vs. “unknown” conditions, but no significant
cluster was detected for the self-assigned shape when
compared to the close-other assigned shape. The latter
is in line with the lack of differences in P3 amplitude
between the self- and close-other conditions.
Due to methodological differences, it is rather difficult
to directly compare our P3 findings for the self-assigned
shape to previous ERP findings on the processing of newly
acquired self-related information (Wo ´
zniak et al. 2018).
In Wo´
zniak et al.’s study with the matching task of labels
and previously unknown faces, associated with the self
and others, self-association of the first stimulus in a pair
determined the pattern of P3 results for the second stim-
ulus. In other words, the amplitude of the central-parietal
P3 did not depend on the self-association of the stimulus
that elicited the P3, but instead on the self-association
of the preceding stimulus, regardless of whether this
preceding stimulus was a label or a previously unknown
face associated with one’s own person.
However, our P3 results to faces corroborate the find-
ings of previous studies reporting enhanced P3 to the self-
face in comparison with other (either familiar or unfa-
miliar) faces (Sui et al. 2006;Keyes et al.2010;Tacikowski
and Nowicka 2010;Cygan et al. 2021;˙
Zochowska et al.
2021). Such an effect was also repeatedly found for one’s
own face when compared to a close-other’s face, if—
similarly to the present study—the close-other was freely
selected by participants as their most significant person
(Cygan et al. 2014;Kotlewska and Nowicka 2015;Cygan
et al. 2021).
In the present study, one of the main differences
between the processing of faces and shapes referred
to the relation between the “self” and “close-other”
conditions. While those two conditions differed in the
case of faces, they did not differ in the case of shapes,
as indicated by significant differences found both in
the neural underpinnings and in RTs for faces and
lack of such differences for shapes. The most obvious
explanation of this dissociation refers to the familiarity
of processed information. Specifically, the self-advantage
found for the self-face vs. close-other’s face comparison
was not observed if levels of familiarity of information
referring to the self and the close-other were strictly
equalized, as it was done for shapes. Thus, our P3 findings
for the “self” and “close-other” conditions may be driven
by the higher pre-experimental familiarity of one’s own
face than the close-other’s face.
Moreover, this pattern of P3 findings may also be
interpreted in reference to the attentional processing
of information related to the self and close-other.
Specifically, it has been proposed that the mechanisms
boosting the prioritized processing of self-relevant
information could be driven by automatic capture of
attention and prioritized allocation of attention to self-
related stimuli (review Humphreys and Sui 2016;Sui and
Rotshtein 2019). Indeed, several studies have found that
the self-face automatically captures attention (Tong and
Nakayama 1999;Brédart et al. 2006;Alexopoulos et al.
2012;Wójcik et al. 2018;Wójcik et al. 2019;Alzueta et al.
2020), and numerous EEG studies have revealed greater
P3 amplitude in response to one’s own face (Ninomiya
et al. 1998;Sui et al. 2006;Tacikowski and Nowicka 2010;
Kotlewska and Nowicka 2015;˙
Zochowska et al. 2021;
review: Knyazev 2013). The P3 is often associated with
attentional processes (Polich 2007 but see Nieuwenhuis
et al. 2005;Verleger et al. 2015), thus substantially
enhanced P3 to the self-face, as reported in the current
study, seems to reflect preferential engagement of
attentional resources to one’s own face. In the case
of shapes, similar P3 amplitudes for the “self” and
“close-other” conditions may be linked to comparable
attention allocation, i.e. the self-assigned shape did not
benefit from such preferential allocation of attentional
resources.
In general, interpretations of P3 findings referring to
attentional processes are in line with the notion that P3
reflects stimulus processing only, i.e. with the view that
P3 is a signature of a comprehensive evaluation of incom-
ing stimuli (McCarthy and Donchin 1981;Duncan et al.
2009). This evaluation entails processes of allocation of
perceptual and attentional resources to event encoding
and categorization (Duncan-Johnson 1981;Donchin and
Coles 1988), and P3 amplitude is assumed to reflect the
amount of these resources or cognitive capacity involved
in the stimulus evaluation (Isreal et al. 1980;Kok 2001).
However, the current debate on the functional role of
the P3 component is multifaceted and it refers to many
different topics. Thus, other interpretations of P3 findings
are also plausible. To start with, an alternative view is
that P3 reflects some processes of stimulus–response
(S-R) translation or integration, a bridging step between
sensory encoding and response execution (Pritchard et al.
1999;Verleger et al. 2005). Following this general idea,
it was proposed that P3 reflects (re)activation of well-
established S-R links as in typical laboratory tasks, usu-
ally a few fixed S-R links or S-R schemas are established
by instruction and practice (Verleger et al. 2014;Verleger
et al. 2015). Such a link binds a stimulus-code with its
corresponding response-code, leading to the automatic
activation of the corresponding, already well-established,
motor program, matching the presented visual stimu-
lus (Hommel 2004), and this process is assumed to be
reflected by P3 (Verleger et al. 2015). However, the design
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14 |Cerebral Cortex, 2022
of our study was not intended to test the impact of S-
R links on P3. Moreover, different patterns of P3 findings
observed for familiar vs. unfamiliar shapes and faces did
not provide any support for the S-R hypothesis.
Moreover, one may view P3 findings reported in
the current study in the light of the locus coeruleus-
noradrenergic (LC-NE) system activity. The pivotal role of
the LC-NE system in regulating task engagement is well
documented (Aston-Jones and Cohen 2005). Through
its modulatory actions on information processing, the
LC-NE system potentiates responses to the outcome of
internal decision processes that involve motivationally
significant events, thereby guiding behavioral action in
the service of task demands and other goals (Aston-Jones
and Cohen 2005). The modulatory effects of the LC-
NE system may be measurable at the scalp as the
P3 component. Thus, P3 is considered to be one of
the psychophysiological markers of LC-NE activity
(Murphy et al. 2011). Specifically, according to the LC-
P3 hypothesis, the phasic activity of the LC and the
resulting release of NE at axon terminals is critical in
generating the P3 (Aston-Jones and Cohen 2005). It was
also proposed that the P3 reflects the response of the LC-
NE system to the outcome of internal decision-making
processes and the consequent effects of noradrenergic
potentiation of information processing (Nieuwenhuis
et al. 2005).
So far, we viewed our P3 findings in the light of atten-
tional mechanisms. However, this view may be comple-
mented by the interpretation of P3 as reflecting process-
ing of stimuli that are highly arousing in nature (Hu
et al. 2011). These two interpretations—seeing the P3
amplitude as an index of attention or as an index of emo-
tional arousal—are not mutually exclusive. According to
Lang et al.’s (1997) model of motivated attention, emo-
tional cues prompt motivational regulation and draw
attentional resources. In fact, many behavioral (Armony
and Dolan 2002) and electrophysiological (Cuthbert et al.
2000;Keil et al. 2002;Schupp et al. 2004;Briggs and Mar-
tin 2009;Foti et al. 2009;Hajcak et al. 2010;Franken et al.
2011) studies support this relationship between emotions
and attention. Recent definitions of emotions emphasize
their subjective character, i.e. emotions could be concep-
tualized as complex constellations of psychological and
physiological states that reflect an organism’s appraisal
of the meaning, relevance, and value of incoming stimuli
(Dolan 2002). In this context, it is the motivational rel-
evance of a particular stimulus to a particular person
that determines the emotional vs. neutral evaluation.
Our results for faces are in line with this interpretation:
P3 findings may be attributed to the different emotion-
al/motivational content of the self-face and other (close,
unknown) faces, with the self-face being the most moti-
vationally relevant.
As the P3 component reflects the cognitive evalua-
tion of stimulus significance (Picton and Hillyard 1988;
Mangun and Hillyard 1995;Bernat et al. 2001;Carretié
et al. 2001), different patterns of P3 findings for faces
and shapes (i.e. differences between the self-face and
close-other’s face and the lack of differences between
the self-assigned shape and the shape assigned to the
close-other) may be due to the fact that new information
associated with the self and the close-other evokes sim-
ilar emotional responses and is characterized by similar
levels of saliency, whereas the self-face is a more salient
stimulus than the close-other’s face. Saliency of the self-
face is often viewed as the primary driving factor of
prioritized processing of that stimulus, and self-faces are
among the most salient stimuli that we come across and
process frequently (Devue and Brédart 2008;Apps et al.
2015;Wójcik et al. 2018;Wójcik et al. 2019). Self-relevant
stimuli engage emotional processes and seeing one’s own
face evokes a rather unique emotional response (Kircher
et al. 2000). Such self-face advantage was observed even
when the processing of one’s own face was directly com-
pared to the processing of emotional (both happy and
fearful) faces ( ˙
Zochowska et al. 2021). Although in the
current study the close-other’s face was chosen as an
emotionally salient and overlearned non-self-face,the P3
and permutations tests differentiated these two faces.
However, it was not the case for the self- and close-other
assigned shape.
The limitations of the current study are as follows.
Shapes were arbitrarily assigned to one’s own person
and to the chosen close-other, and their processing
was compared to unknown shapes. As those familiar
conditions (the self and the close-other) are personally
relevant, it is a matter of debate whether similar—or
rather dissimilar—patterns of behavioral and electro-
physiological findings would be observed for shapes
assigned to famous people. Therefore, the inclusion
of such an additional experimental condition would
provide a more global view on the processing of newly
acquired information referring to the self and others.
Moreover, our study did not provide an answer to the
question of whether the self-relevance of newly acquired
information triggers the self-representation in the brain,
similarly to highly familiar self-referential information
(self-face). In order to adequately relate to this issue,
some source analyses (i.e. dipole fitting, LORETA, CLARA)
should be done. However, due to rather a low number
of experimental trials, such analyses—in the case of
our present study—would be not very reliable. Future
EEG studies may investigate whether newly learned
and long-term established self-related information are
represented in the same (or overlapping) neural network
in the brain.
In conclusion, P3 and permutation test results revealed
a clear self-advantage in the case of faces, i.e. significant
differences between the processing of the self-face and
other faces (close-other’s, unknown). These findings may
be viewed in the light of preferential attention allocation
to highly familiar and well-established self-referential
information. However, the processing of new informa-
tion arbitrarily assigned to one’s own person and the
close-other did not differ. We propose that this effect is
Downloaded from https://academic.oup.com/cercor/advance-article/doi/10.1093/cercor/bhac201/6590160 by guest on 11 February 2023

Anna ˙
Zochowska et al.|15
mainly driven by similar attentional biases to self- and
close-other assigned shapes.
Authors contributions
A˙
Z and AN conceived and designed the study. A ˙
Z and PJ
collected the data. A ˙
Z, PJ, and MN analyzed the behav-
ioral and EEG data. A˙
Z and MN prepared all the figures.
A˙
Z and AN wrote the manuscript. All authors read and
approved the final manuscript.
Funding
This study was funded by the National Science Centre
Poland, grant number 2018/31/B/HS6/00461 awarded to
AN.
Conflict of interest statement: The authors declare no com-
peting interests.
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