Why do you attract me but not others? Retrieval of person knowledge and its generalization bring diverse judgments of facial attractiveness

Abstract

Judgments of facial attractiveness play an important role in social interactions. However, it still remains unclear why these judgments are malleable. The present study aimed to understand whether the retrieval of person knowledge leads to different judgments of attractiveness of the same face. Event-related potentials and learning-recognition tasks were used to investigate the effects of person knowledge on facial attractiveness. The results showed that compared with familiar faces that were matched with negative person knowledge, those matched with positive person knowledge were evaluated as more attractive and evoked a larger early posterior negativity (EPN) and late positive complex (LPC). Additionally, positive similar faces had the same behavioral results and evoked large LPC, while unfamiliar faces did not have any significant effects. These results indicate that the effect of person knowledge on facial attractiveness occurs from early to late stage of facial attractiveness processing, and this effect could be generalized based on the similarity of the face structure, which occurred at the late stage. This mechanism may explain why individuals form different judgments of facial attractiveness.

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Social Neuroscience
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Why do you attract me but not others? Retrieval
of person knowledge and its generalization bring
diverse judgments of facial attractiveness
Shangfeng Han , Shen Liu , Yue Li , Wanyue Li , Xiujuan Wang , Yetong Gan ,
Qiang Xu & Lin Zhang
To cite this article: Shangfeng Han , Shen Liu , Yue Li , Wanyue Li , Xiujuan Wang , Yetong
Gan , Qiang Xu & Lin Zhang (2020): Why do you attract me but not others? Retrieval of person
knowledge and its generalization bring diverse judgments of facial attractiveness, Social
Neuroscience, DOI: 10.1080/17470919.2020.1787223
To link to this article: https://doi.org/10.1080/17470919.2020.1787223
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Jun 2020.
Published online: 09 Jul 2020.
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Why do you attract me but not others? Retrieval of person knowledge and its
generalization bring diverse judgments of facial attractiveness
Shangfeng Han
a,b,c,d
, Shen Liu
e
, Yue Li
a,f
, Wanyue Li
a
, Xiujuan Wang
a
, Yetong Gan
a
, Qiang Xu
a
and Lin Zhang
a
a
Department and Institute of Psychology, Ningbo University, Ningbo, China;
b
Shenzhen Key Laboratory of Affective and Social Neuroscience,
Shenzhen University, Shenzhen, China;
c
Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China;
d
Center for
Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen, China;
e
School of Humanities and Social Sciences, University of Science and
Technology of China, Hefei, China;
f
KunMing Health Vocational College, KunMing, China
ABSTRACT
Judgments of facial attractiveness play an important role in social interactions. However, it still
remains unclear why these judgments are malleable. The present study aimed to understand
whether the retrieval of person knowledge leads to dierent judgments of attractiveness of the
same face. Event-related potentials and learning-recognition tasks were used to investigate the
eects of person knowledge on facial attractiveness. The results showed that compared with
familiar faces that were matched with negative person knowledge, those matched with positive
person knowledge were evaluated as more attractive and evoked a larger early posterior negativity
(EPN) and late positive complex (LPC). Additionally, positive similar faces had the same behavioral
results and evoked large LPC, while unfamiliar faces did not have any signicant eects. These
results indicate that the eect of person knowledge on facial attractiveness occurs from early to
late stage of facial attractiveness processing, and this eect could be generalized based on the
similarity of the face structure, which occurred at the late stage. This mechanism may explain why
individuals form dierent judgments of facial attractiveness.
ARTICLE HISTORY
Received 19 July 2019
Revised 22 April 2020
Published online 11 July
2020
KEYWORDS
Person knowledge;
generalization; facial
attractiveness; late positive
complex; early posterior
negativity
1. Introduction
Although the love of beauty is part of human nature,
individual dierences exist in esthetic tastes. When dif-
ferent individuals meet the same stranger, the impres-
sions of the stranger are not the same on everyone.
Numerous studies have indicated that individuals spon-
taneously form impressions of newly encountered indi-
viduals based on their facial appearance (Ritchie et al.,
2017; Vernon et al., 2014). Facial attractiveness is not
only a key social preference and an important dimension
of rst impressions, inuencing mate choice and social
assessments of age and health (C. A. M. Sutherland et al.,
2016; C. A. Sutherland et al., 2013; Vernon et al., 2014),
but it also has a signicant impact on subsequent social
interactions (Olivola et al., 2014; Todorov et al., 2015).
Therefore, it attracts widespread attention from
researchers across diverse elds. Previous studies have
found that judgments of facial attractiveness are stable
and that they show cross-cultural consistency (Chen
et al., 1997; Rhodes et al., 2001). On the other hand,
recent studies have reported that this judgment is malle-
able and that dierent individuals judge the same face
dierently (Y. Q. Wang et al., 2015; Thiruchselvam et al.,
2016; Zhang et al., 2014). However, extant studies have
not yet been able to explain why judgments of facial
attractiveness are both stable and malleable. It can be
proposed that dierences in perception may lead to
dierences in judgment. The present study aimed to
explain the reason for the dierences in facial attractive-
ness judgments from the perspective of perceptual
processing.
Observers not only see the conguration of the eyes,
nose, etc., on the face, but they also extract relevant
person knowledge, such as trait, biographical memory,
and so on from faces (Jack & Schyns, 2015, 2017). The
classic face-processing model proposed by Bruce and
Young (1986) also points out that face recognition con-
tains both congural and identity-related information.
They proposed that the rst step in this process involves
the encoding of the structure of the face, which is
a bottom-up process. This is followed by two indepen-
dent and parallel processing stages involving the per-
ception of idiosyncratic (e.g., identity-specic) and
generic (e.g., identity-nonspecic) aspects of faces,
CONTACT Lin Zhang zhanglin1@nbu.edu.cn Department and Institute of Psychology, Social Cognition and Behavior Laboratory, Ningbo University,
Ningbo City 315211, China; Shen Liu liushenpsy@ustc.edu.cn School of Humanities and Social Sciences, University of Science and Technology of China,
No. 96, Jinzhai Road, Baohe District, Hefei City 230022, China
Supplemental data for this article can be accessed here.
SOCIAL NEUROSCIENCE
https://doi.org/10.1080/17470919.2020.1787223
© 2020 Informa UK Limited, trading as Taylor & Francis Group

which is a top-down process. Thus, according to the
perceptual processing approach, the process of face
recognition includes both a bottom-up processing of
facial structure information and a top-down processing
of person knowledge related to the face (Bruce & Young,
1986). Quinn and Macrae (2011), aligned with Bruce and
Young’s model, but further emphasized the role of social
cognition. They suggested that the bottom-up con-
straints of visual processing and the top-down inu-
ences of semantic knowledge would contribute to
a more comprehensive understanding of face percep-
tion. In line with this viewpoint, previous studies found
that facial attractiveness is determined by various objec-
tive characteristics of the stimulus (Rhodes, 2006).
Generally, when a face is similar to the average face,
more symmetrical, and more in line with the facial char-
acteristics of one’s own gender, it is appraised as more
attractive (Trujillo et al., 2014; Vingilis-jaremko & Maurer,
2013; T. Yang et al., 2015). Thus, the eect of the struc-
tural information of the face on facial attractiveness is
relatively stable. However, questions about the storage
and extraction of social knowledge are not clearly
answered by the face recognition model and related
research.
According to the associative learning theory, the sto-
rage and extraction of person knowledge can be divided
into the associative learning stage and the generalized
stage (Feldmanhall et al., 2018). Dierent valences of
person knowledge are used as unconditioned stimuli
and faces as conditioned stimuli. After learning to
match faces with person knowledge of dierent
valences, faces would carry the same valence as the
information provided through the matched person
knowledge. Furthermore, the person knowledge of
faces is stored in individuals’ long-term memory system.
Therefore, the establishment of facial attractiveness
should originate from the experience of real-life social
interactions (Kocsor & Bereczkei, 2016, 2017). However,
as individuals can have dierent experiences from dif-
ferent social interactions with the same person, the per-
son knowledge of the same face stored in their memory
could vary. For example, Han et al. (2018) found that,
when faces were matched with dierent person knowl-
edge, the judgment of faces that were previously equally
attractive became dierent. Therefore, the extraction of
person knowledge related to faces may account for the
malleable attractiveness perception of faces.
Associative learning theory may bring a new perspec-
tive to facial processing, as Fiske et al. oer insights on
the content of processing. These researchers proposed
that there are two universal dimensions of social cogni-
tion, including warmth and competence (Fiske et al.,
2008), which are also used for describing and judging
people (Han et al., 2018). Therefore, in the present study,
behavior statements containing messages of warmth
and competence were used to let participants form
impressions of faces (Quist et al., 2012; Watkins, 2017).
In most studies, participants are asked to learn face and
behavior sentence pairs to obtain person knowledge
about faces (Y. Q. Wang et al., 2015; Zhang et al., 2014).
Previous studies have shown that person knowledge
about faces impacts facial attractiveness in a top–down
manner, and that faces that are paired with positive
descriptions are perceived as more attractive
(Y. Q. Wang et al., 2015; Zhang et al., 2014). Zhang
et al. (2014) paired positive, neutral, and negative traits
with faces and found that the faces paired with positive
traits were reported as being more attractive than those
matched with negative traits.
However, these studies cannot fully account for the
divergent behavioral responses, owing to other inu-
ences on attractiveness judgment, such as the response
bias. Specically, previous studies using behavioral judg-
ment have attribute divergent behavior responses to
post-perceptual changes, which may bring the response
bias (Otten et al., 2016). For example, highly attractive
same-sex targets were regarded as a threat by percei-
vers. Therefore, even though they perceive others’ facial
attractiveness as high, they are reluctant to give high
scores to them. Event-related potentials (ERPs), as
a powerful tool for probing the temporal dynamics of
neural processes (Amodio et al., 2013), can explain how
high-level social knowledge, such as person knowledge
inuences facial attractiveness processing.
Electrophysiological data can oer explanations regard-
ing whether person knowledge can indeed change per-
ceptual processes and their time course.
Previous studies reveal that face evaluation is fast
(Todorov et al., 2015) and faces can be accurately eval-
uated in less than 100 ms (Olson & Marshuetz, 2005;
D. Yang et al., 2011). In addition, face-related person
knowledge regulates the processing of face perception
and person knowledge related to traits aects face pro-
cessing from the early stage to the late stage (Luo et al.,
2016). For example, Zhao et al. (2017) found that face-
related information inuenced face perception and that
faces matched with high competence sentences evoked
larger early posterior negativity (EPN) than those with
neutral sentences. EPN is typically interpreted as reect-
ing enhanced perceptual processing of aective stimuli;
it is also an early component in facial attractiveness
processing (Werheid et al., 2007). Studies on facial attrac-
tiveness have shown that attractive faces evoke greater
early negative EPN than unattractive faces (Rellecke
et al., 2011; Werheid et al., 2007), and evoke the late
positive component (LPC; Rellecke et al., 2011; Schacht
2S. HAN ET AL.

et al., 2008; Zhang & Deng, 2012). LPC, which is a late
component in facial attractiveness processing (Werheid
et al., 2007), is mainly inuenced by intrinsic motivation
(Lu et al., 2014) and is considered to be related to a more
rened aective stimulation processing (Schacht &
Sommer, 2009). In addition, some studies found that
the perceiver’s person knowledge could aect facial
perception, which occurred in the early stage of percep-
tion (Luo et al., 2016; Thiruchselvam et al., 2016).
Repetition and expectation of a face could regulate the
perception of facial attraction through top-down proces-
sing, and this regulation occurs in the early stage of
perception, which evokes a larger EPN (Thiruchselvam
et al., 2016). Therefore, the person knowledge of the
observer could regulate the perception of facial attrac-
tiveness through top-down processing, which might
occur in the early stage of perception. Based on the
above-mentioned ndings, we proposed the following
hypothesis: Person knowledge eects on perception of
facial attractiveness occur in the early stage of proces-
sing and last until the late stage. Faces with positive
person knowledge are judged as more attractive and
evoke larger EPN and LPC amplitudes than those with
negative person knowledge.
Person knowledge may also inuence impressions
evoked by unfamiliar faces due to generalization.
Verosky and Todorov (2010) rst found that the general-
ization eect would appear when individuals perceive
similar faces. In other words, having learnt the associa-
tion between faces and dierent valence statements, the
participants evaluated faces that were similar to “posi-
tive” faces more positively. Several subsequent studies
found that the impressions of warmth and competence
evoked by familiar faces could be generalized by their
resemblance to unfamiliar faces, which may lead to the
impression that unfamiliar faces were consistent with
familiar faces (Richter et al., 2016; Von et al., 2014). The
generalization eect is based on facial similarity, and the
degree of similarity regulates the eect of aective
learning generalization. For example, using faces with
20% and 35% similarity, Verosky and Todorov (2010)
found that the generalization eect became stronger
with the increase in similarity. However, Gawronski and
Quinn (2013) found that there was no signicant change
in the generalization eect when the similarity increased
from 50% to 100%. Most researchers have used 50%
similarity to investigate the generalization eect of
faces (Gawronski & Quinn, 2013; Günaydin et al., 2012).
They found that, when the similarity was 50%, partici-
pants still tended to judge the similar faces as unfamiliar
faces (Verosky & Todorov, 2013). Based on the above-
mentioned ndings, we proposed the following hypoth-
esis: The inuence of person knowledge on facial
attractiveness would generalize based on the facial
structure, similar faces that have the same structure as
the familiar faces. Specically, the facial attractiveness of
similar faces might be aected by the person knowledge
related to familiar faces, producing amplitude dier-
ences consistent with those observed for familiar faces.
However, completely unfamiliar faces would not be
inuenced by familiar person knowledge because of
their low familiarity.
In summary, individuals’ extraction of person knowl-
edge about familiar faces may explain the dierences in
the attractiveness perception of novel faces. However, the
dierences in the electrophysiological time course of
facial attractiveness processing remain unclear. In the
present study, ERPs were used to explore the eect of
person knowledge on facial attractiveness and to discover
the time processing relationship between congural
information and person knowledge. A learning-
recognition task was adopted to address the problem
(Spironelli & Angrilli, 2017). In the learning stage, partici-
pants were asked to choose statements with dierent
valences to form impressions about faces. In the recogni-
tion task, participants were asked to judge whether a face
had ever appeared in the learning stage. Finally, partici-
pants were asked to rate familiar and completely unfami-
liar faces based on warmth, competence, and
attractiveness. The present study will help us understand
the individual dierences in face attractiveness judgment.
2. Material and method
2.1. Participants
Twenty-six college students were randomly recruited
(mean age: 22.05 years; range: 19–25 years; 13 females).
All participants had normal or corrected-to-normal visual
acuity. All were right-handed and heterosexual. The par-
ticipants were informed that they could quit at any time
during the experiment. There were 22 valid participants
(10 males and 12 females) after eliminating 4 partici-
pants whose EEG artifact trials comprised more than
half of the total trials. The present study was approved
by the Ethics Committee of the local institution, in accor-
dance with the ethical principles of the Declaration of
Helsinki. All participants provided written informed con-
sent for the study.
2.2. Stimuli
Sentence materials: In total, 100 sentences created by
Fuhrman et al. (1989) were selected. The degree to
which the behavior sentences reected warmth and
competence was evaluated, using a 9-point rating
SOCIAL NEUROSCIENCE 3

scale, by 14 males and 17 females who did not partici-
pate in the nal experiment. Six sentences each repre-
sented positive (M= 8.12, SD =.68) and negative warmth
traits (M= 2.27, SD = .81). There were signicant dier-
ences in the valence of positive and negative warmth
trait sentences (t (30) = 25.44, p< .001, d= 7.82).
Specically, the score of positive warmth sentences
was higher than that of negative ones (ps <.001).
Further, six sentences each, reecting positive
(M= 8.45, SD = .68) and negative competence traits
(M= 2.77, SD = 1.23) were selected. Signicant dier-
ences were observed in the valence of positive and
negative competence trait sentences (t (30) = 20.39,
p< .001, d= 5.72). The score of positive competence
trait sentences was signicantly higher than that of
negative ones (ps <.001).
Face materials: In total, 16 female and 16 male facial
stimuli with a neutral expression were used. They were
selected from the Chinese Aective Picture System
(Gong et al., 2011). Further, 37 college students (15
males and 22 females, all of whom did not participate
in the nal experiment) were asked to rate the warmth,
competence, attractiveness, and skin texture of the faces
using a 9-point scale. Based on their ratings, four female
and four male faces with moderate warmth (M= 5.25,
SD = .38), competence (M= 5.23, SD = .27), attractiveness
(M= 5.03, SD = .30), and skin texture (M= 5.46, SD = .69)
were selected. Additionally, two male and two female
faces were randomly selected to match the sentence
materials, which were used as familiar faces. One familiar
male and one familiar female face were randomly
selected to match the positive sentences, which formed
the positive group. The other two familiar faces matched
with negative sentences formed the negative group. The
remaining faces were unfamiliar faces; they were divided
into the positive groups if they morphed with the posi-
tive group faces or the negative group if they morphed
with the negative group faces. The same is true for
negative unfamiliar faces. Similar faces were made by
morphing familiar faces with same sex unfamiliar faces
at the level of 50% similarity (Verosky & Todorov, 2013).
Similar faces were assigned to the positive group if they
were similar to positive familiar faces; the others were
assigned to the negative group.
2.3. Procedure
The experiment was divided into the learning, recog-
nition, and evaluation stages. In the learning stage,
each familiar face was matched with a sentence
describing traits of warmth and competence.
Warmth sentences had the same valence as the com-
petence sentences, and participants were asked to
visualize and remember the face according to the
behavioral description to form a corresponding facial
impression. After the participants memorized the
impression of a face, they could move on to the
next face and continue to learn. The time for learning
the face and sentence pairs was controlled by the
participants themselves. At the end of the learning
stage, all the learned faces and two unlearned faces
were presented again, and the participants were
instructed to judge whether the face was “positive”
or “negative.” In order to ensure that the participants
had already formed a face impression, if the partici-
pants made a wrong judgment, they were required to
return to the learning stage and re-study until the
judgment was completely correct before entering the
face recognition stage. In the face recognition stage
(ERPs were analyzed for this stage), the gaze point
“+” was rst presented for 500 ms. in the center of
the screen, followed by a face that was presented for
1000 ms. The faces could be familiar, similar, or unfa-
miliar. Each face was repeated 50 times randomly to
obtain a stable waveform. A black screen for 500 ms
followed this, and the reaction screen was presented
at the end. The participants were required to respond
to the presentation of the face by pressing the “P”
and “Q” keys to indicate that they had or had not
seen the face in the learning stage. The background
of the reaction screen was white. Participants rst
completed 10 trials to ensure that they had under-
stood the experimental process. In the evaluation
stage, the learned familiar faces, similar unfamiliar
faces, and completely unfamiliar faces were pre-
sented in a random order. The participants were
asked to judge their warmth (“How warm, friendly,
or sincere do you think this person is?”), competence
(“How smart, ecient, or competent do you think this
person is?”), and attractiveness (“How attractive do
you think this person is?”) using the number keys 1
(very inconsistent) to 7 (very compliant). Judgments
paired with faces appeared separately at random. The
procedure is presented in Figure 1.
2.4. EEG recording
EEGs were recorded from 62 Ag/AgCl electrodes, accord-
ing to the extended 10–20 system, referenced to a nose
electrode. Additional electrodes were placed above and
below the left eye and on the outer canthus of each eye
to record vertical and horizontal eye movements.
Impedances for all electrodes were kept below 5kΩ.
The EEGs were amplied with a band pass of 0.01–-
100 Hz, and they were digitized online with a sampling
rate of 500 Hz.
4S. HAN ET AL.

2.5. Data analysis
Oine EEGs were performed with EEGLAB v14.01, run-
ning on MATLAB 2015b (Mathworks, Inc., Natick, MA,
USA). The signals were referenced to the nose electrode.
ERPs were additionally ltered with a 30 Hz low pass
lter. Eye-blink artifacts were mathematically corrected
(Gratton et al., 1983). The epoch interval was 1000 ms,
from 200 ms before the onset of the critical faces to
800 ms after it. Analysis of epochs for face presentation
was 1000 ms, from 200 ms before the onset of the critical
faces in the recognition stage. A 200-ms pre-stimulus
was used as the baseline. The artifacts of ±100 μV were
removed and the EEG was superposed under each
experimental condition; percentage of rejection was
less than 10%. According to the characteristics of the
grand average waveforms of the ERP (see Figure 2), the
aim of the present study, and the related literature on
face recognition, two components of the ERP waveforms
in dierent region of interest (ROI) were analyzed: the
P7, PO7 (ROI: left posterior), P8, PO8 (ROI: right posterior)
were chosen for EPN (230–280 ms) and the Cz and CPz
(ROI: middle posterior) were chosen for LPC (400–-
800 ms). The data were analyzed using the repeated
measures analysis of variance (ANOVA). When the
Mauchlly’s test was signicant, the results were sub-
jected to Greenhouse-Geisser correction. A 3 (facial simi-
larity: familiar, similar, and unfamiliar) × 2 (valence of
face matched sentences: positive and negative)
repeated measures ANOVA was used to examine the
behavioral data. ANOVAs for ERP data contained the
within-subjects factor ROI. We used the SIDAK test for
post hoc analyses and False Discovery Rate (FDR) was
used when multiple comparisons emerged.
3. Results
3.1. Behavioral data
3.1.1. Influence of person knowledge on warmth
evaluation of faces
Main eects of facial similarity were found, (F(2,
42) = 18.56, p< .01, η
2p
= .47). Familiar (4.34 ± .16) and
similar faces (4.56 ± .16) were evaluated as warmer than
unfamiliar faces (3.46 ± .15), but there was no signicant
dierence between familiar and similar faces. A main
eect of the valence of face-matched sentences was
also found, (F(1, 21) = 6.08, p= .022, η
2p
= .22). Faces
that matched with positive sentences (4.40 ± .13) were
evaluated as warmer than those that matched with
negative sentences (3.83 ± .18). Importantly, an interac-
tion eect was observed (F(2, 42) = 7.91, p< .01, η
2p
= .27). Further, simple eect analysis revealed that parti-
cipants evaluated positive familiar and similar faces as
warmer than the negative ones. There was no signicant
dierence between the warmth judgment of positive
and negative unfamiliar faces (see Table 1).
3.1.2. Influence of person knowledge on the
competence evaluation of faces
Main eects of facial similarity were found (F(2,
42) = 26.93, p< .01, η
2p
= .56). Familiar faces (4.56 ± .14)
and similar faces (4.66 ± .16) were evaluated as more
competent than unfamiliar faces (3.47 ± .15), but there
was no signicant dierence between familiar and simi-
lar faces. A main eect of the valence of face-matched
sentences was also found (F(1, 21) = 7.11, p= .014, η
2p
= .25). Faces that were matched with positive sentences
(4.52 ± .14) were evaluated as more competent than
those matched with negative sentences (3.94 ± .16).
Figure 1. Overview of the study design.
SOCIAL NEUROSCIENCE 5

Figure 2. Grand-averaged event-related potential waveforms are shown for familiar (first column), similar (second column), and
unfamiliar faces (third column). (A) P7, P8, PO7, and PO8 were selected for EPN (shaded 230–280 ms time window) waveforms to
compare positive and negative conditions and (B) Cz and CPz were selected for LPC (shaded 400–800 ms time window) waveforms. (C)
Scalp topographies of familiar faces, similar faces, and unfamiliar faces in positive and negative conditions were selected at a time
window of 230–280 ms for EPN (two columns on the left) and 400–800 ms for LPC.
6S. HAN ET AL.

There was an interaction between facial similarity and
the valence of face-matched sentences (F(2, 42) = 9.27,
p< .01, η
2p
= .31). Further, simple eect analysis revealed
that participants evaluated positive, familiar, and similar
faces as more competent than negative ones. There
were no dierences in the competence judgment of
positive and negative unfamiliar faces (see Table 1).
3.1.3. Influence of person knowledge on the
attractiveness evaluation of faces
Main eects of facial similarity were found (F(2,
42) = 28.37, p< .01, η
2p
= .56). Similar faces (4.59 ± .17)
were evaluated as more attractive than familiar faces
(3.88 ± .16) and unfamiliar faces (3.27 ± .13). Familiar
faces were judged as more attractive than unfamiliar
faces. A main eect of the valence of face-matched sen-
tences was also found (F(1, 21) = 8.01, p= .01, η
2p
= .28).
Faces that were matched with positive sentences
(4.21 ± .16) were evaluated as warmer than those
matched with negative sentences (3.62 ± .16).
A marginal signicant interaction was observed (F(2,
42) = 5.31, p= .01, η
2p
= .20). Further, simple eect analysis
revealed that participants evaluated positive familiar and
similar faces as more attractive than negative ones. There
were no dierences in the attractiveness judgments of
positive and negative unfamiliar faces (see Table 1).
3.2. ERP data
3.2.1. EPN (230–280 ms)
A 3 (facial similarity: familiar, similar, and unfamiliar) × 2
(valence of face matched sentences: positive and nega-
tive) × 2 (ROI: left posterior and right posterior) repeated
measures ANOVA showed that the main eect of facial
similarity was not signicant (F(2, 42) = 1.00, p= .38).
A main eect of valence was found (F(1, 21) = 9.41,
p< .001, η
2p
= .31). A main eect of ROI was also found
(F(1, 21) = 7.32, p< .01, η
2p
= .26). Left posterior evoked
larger EPN than right posterior. While there were no
interactions among the three factors, (F(2, 42) = 1.60,
p= .21), a signicant interaction between facial similarity
and valence of face matched sentences was obtained (F(2,
42) = 3.30, p= .047, η
2p
= .14). Specically, positive familiar
faces evoked larger EPNs (2.69 ± .62 µV) than negative
familiar faces (3.37 ± .66 µV). We did not nd signicant
dierences in valence of both similar and unfamiliar faces
(see Table 2).
3.2.2. LPC (400–800 ms)
Middle posterior ERP data were chosen for a 3 (facial
similarity: familiar, similar, and unfamiliar) × 2 (valence of
face matched sentences: positive and negative) repeated
measures ANOVA, which showed a main eect of facial
similarity, (F(2, 42) = 17.04, p< .001, η
2p
= .45). The ampli-
tude of familiar faces (3.05 ± .37 µV) was greater than
similar (2.13 ± .36 µV) and unfamiliar faces (2.33 ± .41 µV).
The amplitude between similar and unfamiliar faces was
not signicantly dierent. The main eect of valence was
signicant (F(1, 21) = 6.09, p< .05, η
2p
= .23). Specically,
the amplitude of the positive condition (2.64 ± .37 µV) was
signicantly higher than that of the negative condition
(2.36 ± .38 µV). The interaction of the two factors was also
signicant (F(2, 42) = 4.04, p< .05, η
2p
= .16). Positive
familiar faces elicited larger LPC than negative familiar
faces. Interestingly, similar faces had mirrored these
results. The amplitude of the positive similar faces was
signicantly larger than that of the negative similar faces.
However, this dierence was not found between positive
unfamiliar faces and negative unfamiliar faces (see
Table 2).
4. Discussion
The present study explored the eect of person knowl-
edge on facial attractiveness. Findings revealed that the
person knowledge of familiar faces had a signicant
Table 1. Mean judgment of different types of faces (M ± SE).
Valence
Warmth Competence Attractiveness
Familiar face Similar face Unfamiliar face Familiar face Similar face Unfamiliar face Familiar face Similar face Unfamiliar face
Positive
Negative
5.00 ± 0.25 4.82 ± 0.16 3.39 ± 0.18
3.68 ± 0.28 4.30 ± 0.23 3.52 ± 0.20
5.25 ± 0.24 4.92 ± 0.19 3.36 ± 0.20
3.86 ± 0.25 4.39 ± 0.19 3.57 ± 0.20
4.44 ± 0.25 4.89 ± 0.17 3.30 ± 0.06
3.32 ± 0.25 4.29 ± 0.24 3.25 ± 0.17
Table 2. Mean ERP waveforms of different types of faces (M ± SE).
EPN LPC
Left posterior Right posterior Middle posterior
Valence Familiar face Similar face Unfamiliar face Familiar face Similar face Unfamiliar face Familiar face Similar face Unfamiliar face
Positive
Negative
2.25 ± 0.62 2.71 ± 0.62 2.60 ± 0.65
2.92 ± 0.65 2.86 ± 0.64 2.70 ± 0.65
3.14 ± 0.67 3.57 ± 0.66 3.33 ± 0.65
3.81 ± 0.71 3.78 ± 0.63 3.64 ± 0.64
3.27 ± 0.38 2.41 ± 0.38 2.25 ± 0.40
2.83 ± 0.39 1.85 ± 0.36 2.40 ± 0.43
SOCIAL NEUROSCIENCE 7

eect on the evaluation of facial attractiveness.
Specically, familiar faces with positive impressions
were considered more attractive. Furthermore, attrac-
tiveness evaluations of morphed faces that were similar
to positive familiar faces were signicantly higher than
of those that were similar to negative familiar faces,
while there were no signicant dierences among com-
pletely unfamiliar faces. ERP results showed that person
knowledge aected the familiar faces from the early to
the late stage. EPN and LPC elicited by positive familiar
faces were larger; while similar faces matched with posi-
tive information elicited larger LPC, but not EPN.
This study found that familiar and similar faces
matched with positive information evoked larger LPC
amplitude than those matched with negative informa-
tion. Bobes et al. (2019) found that person knowledge
extraction for familiar faces began, at the earliest, at
around 150 ms, while that related to dierences in facial
attractiveness occurred at about 400–800 ms, indicating
that people process the person knowledge of faces after
extracting such information. When judging similar faces,
participants not only need to recognize the facial struc-
ture but also to retrieval person knowledge from long-
term memory. Similar faces were not easy to recognize
compared with familiar and unfamiliar faces (Carr et al.,
2017); the brain has to use more energy to nish the
processing. This may reveal that the generalization inu-
ence on facial attractiveness judgments is not an auto-
matic processing but a controlled processing. In
addition, LPC is mainly inuenced by intrinsic motivation
(Lu et al., 2014) and is considered to be related to a more
rened aective stimulation processing (Schacht &
Sommer, 2009). The activation of the emotion and moti-
vation system in the brain may play an important role in
the process of generalization, which needs to be further
explored. These results contribute in clarifying the
potential mechanisms of the generalization eect in
face attractiveness judgments.
EPN was inuenced when processing familiar faces,
but not while processing similar and unfamiliar faces. It
means that the inuence of person knowledge for famil-
iar face attractiveness is automated, whereas not for
similar faces, which may reect the relationship between
facial structure and memory intensity. Familiar faces and
learned faces are better structurally matched; therefore,
familiar faces can be extracted quicker. Similar faces can
only be extracted and matched in the late processing
stage. Moreover, identifying similar faces may consume
more cognitive resources, which inhibit the automatic
extraction of person knowledge. Previous studies failed
to nd remarkably dierent EPN results in the semantic
emotion-unrelated analysis task (Bayer et al., 2010) and
concreteness decision (Laura et al., 2013) because of the
diculty of the tasks. In the present study, it may have
been hard for the participants to constantly distinguish
whether or not they had seen the similar faces. As
a result, we could hardly nd any dierences in early
processing of similar faces on EPN.
Most studies have explored the factors aecting the
attractiveness of faces from the point of view of the
observer and the face owner. The face owner hypothesis
is based on the concept of evolutionism, and it purports
that facial attractiveness is mainly inuenced by face
conguration and its symmetry (Fink & Penton-Voak,
2002). On the other hand, the face observer hypothesis
is based on the perspective of social culture. It is
believed that the attractiveness of faces is inuenced
by the characteristics of the observer, that is, beauty is
in the eyes of the observer (Little, 2014). Therefore, the
evaluation of facial attraction is variable. From the per-
spective of face processing, the present study revealed
the reasons for the variability in facial attractiveness
perception, that is, it contains both, a bottom-up proces-
sing of congural information (similarity) and a top-
down processing of person knowledge. Face attractive-
ness evaluation is the result of the comprehensive pro-
cessing of face congural information and person
knowledge. In the late stage of perception, the extrac-
tion of person knowledge related to the face may be the
reason for the dierences in facial attractiveness
perception.
The dierences in facial attractiveness judgments and
their generalization may reect a special evolutionary
functional signicance. The present study oered
a new understanding of why facial judgments are malle-
able from the perspective of processing, as it aimed to
explore the mental and neural mechanisms involved in
facial attractiveness perception. The key contribution of
this work is that it provides an explanation for the foun-
dation of the process of attraction in the rst impression.
Additionally, it helps us to understand why the same
individual inspires dierent facial impressions and pro-
duces diverse esthetic experiences.
However, the present study has some limitations that
need to be addressed in future research. First, the pre-
sent study only analyzed moderately attractive faces.
Previous studies have found that the sexual dimorphism
cues of faces are also regulated by person knowledge
and that masculine male faces with positive person
knowledge are perceived as more attractive. However,
this dierence was not signicant in negative social
conditions (Quist et al., 2012). Further, creativity can
promote the attractiveness of less attractive faces
(Watkins, 2017). Therefore, the role of person knowledge
in regulating dierent face structures may dier. More
research is needed to explore the integration of face
8S. HAN ET AL.

structure information (such as average, symmetry, facial
width-to-height ratio) with person knowledge, to further
reveal the process of integration of perceptual informa-
tion and social knowledge during face perception.
Second, person knowledge not only aects the percep-
tion of familiar faces, but also generalizes based on the
similarity of face structure. However, the mechanism of
this generalization eect is not very clear. The familiarity
evoked by similarity may be the cause of this general-
ization eect. The perceptual sense of familiarity with
similar physical characteristics in memory was signi-
cantly higher for similar rather than dissimilar stimuli
(Han et al., 2018). Carr et al. (2017) found that similarity
between faces activates familiarity and promotes the
processing of stimuli, thus enhancing attractiveness.
Other researchers have argued that the generalization
eect could be attributed to classical conditioning (Y.
Wang et al., 2017). When faces and behaviors were con-
nected through social interaction and faces had struc-
tural similarities, the generalization eect occurred even
if individuals did not perceive this similarity (Kocsor &
Bereczkei, 2016, 2017). Therefore, future studies need to
explore the role played by changes in similarity in the
integration of perceptual information and person knowl-
edge, and its eects on the evaluation of facial attrac-
tiveness. Third, this study focused on the inuence of the
similarity of facial structure on the facial attractiveness
judgment, while previous studies have found that the
context (e.g., age and type of relationship) also inuence
facial attractiveness. How the context factors modulate
the eect we found still remains to be further investi-
gated. Forth, we used a relatively small sample size for
this study; it is necessary to explore the law of facial
attraction processing with larger samples in the future.
Finally, the present study only considered the behavioral
and ERP dierences elicited by the contrast between
positive and negative sentences, which may be more
related to valence but not arousal of the sentences. To
better understand the inuence of arousal of the sen-
tences on the generalization eect, the limitation should
be addressed in future studies employing both emotion-
ally positive, negative and neutral sentences.
To summarize, the main purpose of this study was to
develop an understanding of individual dierences in
judgments of facial attractiveness. Familiar faces with
positive person knowledge received higher attractiveness
appraisals and evoked larger EPN and LPC. Similar faces
had the same behavioral results and evoked a larger LPC.
The results indicate that when people evaluate others’
facial attractiveness, the retrieval of social information
stored in our memory, especially person knowledge,
drives people’s diverse attractiveness judgments of the
same person. Importantly, the eect would be
generalized based on the facial structure; faces that are
similar to familiar faces had a similar processing mode.
These ndings suggest that the retrieval of person knowl-
edge and its generalization is one of the reasons for the
diversity in judgments of facial attractiveness.
Disclosure statement
The authors declare that they have no competing interests.
Funding
The National Social Science Fund of China (BHA190150), the
National Natural Science Foundation of China (31540024,
71874170), the Science Foundation of Ministry of Education
of China (18YJC190027), the Fundamental Research Funds for
the Central Universities (YD2110002004), the K.C. Wong Magna
Fund at Ningbo University and Scientic Research Foundation
of Graduate School of Ningbo University (G18044) supported
this paper.
ORCID
Shen Liu http://orcid.org/0000-0002-6900-8831
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