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Cognition and Emotion
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Individual differences in pupil dilation to others’
emotional and neutral eyes with varying pupil
sizes
Christine Fawcett, Elisabeth Nordenswan, Santeri Yrttiaho, Tuomo Häikiö,
Riikka Korja, Linnea Karlsson, Hasse Karlsson & Eeva-Leena Kataja
To cite this article: Christine Fawcett, Elisabeth Nordenswan, Santeri Yrttiaho, Tuomo Häikiö,
Riikka Korja, Linnea Karlsson, Hasse Karlsson & Eeva-Leena Kataja (2022): Individual differences
in pupil dilation to others’ emotional and neutral eyes with varying pupil sizes, Cognition and
Emotion, DOI: 10.1080/02699931.2022.2073973
To link to this article:  https://doi.org/10.1080/02699931.2022.2073973
© 2022 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group
Published online: 10 May 2022.
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Individual differences in pupil dilation to others’ emotional and neutral
eyes with varying pupil sizes
Christine Fawcett a,b, Elisabeth Nordenswanc,d, Santeri Yrttiahoe, Tuomo Häikiöf, Riikka Korjac,f,
Linnea Karlsson c,g,h,i, Hasse Karlsson c,g,i and Eeva-Leena Katajac,i
aDepartment of Psychology, Uppsala University, Uppsala, Sweden; bDepartment of Psychology, Stockholm University,
Stockholm, Sweden; cThe FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine,
University of Turku, Turku, Finland; dDepartment of Psychology, Åbo Akademi University, Turku, Finland; eHuman Information
Processing Laboratory, Faculty of Social Sciences, Tampere University, Tampere, Finland; fDepartment of Psychology, University
of Turku, Turku, Finland; gCentre for Population Health Research, Turku University Hospital and University of Turku, Turku,
Finland; hDepartment of Paediatrics and Adolescent Medicine, Turku University Hospital and University of Turku, Turku, Finland;
iDepartment of Psychiatry, University of Turku, Turku, Finland
ABSTRACT
Sensitivity to others’ emotional signals is an important factor for social interaction.
While many studies of emotional reactivity focus on facial emotional expressions,
signals such as pupil dilation which can indicate arousal, may also affect observers.
For example, observers’ pupils dilate when viewing someone with dilated pupils,
so-called pupillary contagion. Yet it is unclear how pupil size and emotional
expression interact as signals. Further, examining individual differences in
emotional reactivity to others can shed light on its mechanisms and potential
outcomes. In the current study, adults’ (N = 453) pupil size was assessed while they
viewed images of the eye region of individuals varying in emotional expression
(neutral, happy, sad, fearful, angry) and pupil size (large, medium, small).
Participants showed pupillary contagion regardless of the emotional expression.
Individual differences in demographics (gender, age, socioeconomic status) and
psychosocial factors (anxiety, depression, sleep problems) were also examined, yet
the only factor related to pupillary contagion was socioeconomic status, with
higher socioeconomic status predicting less pupillary contagion for emotionally-
neutral stimuli. The results suggest that while pupillary contagion is a robust
phenomenon, it can vary meaningfully across individuals.
ARTICLE HISTORY
Received 15 December 2021
Revised 20 April 2022
Accepted 22 April 2022
KEYWORDS
Pupil dilation; emotional
reactivity; socioeconomic
status; emotional expression
Introduction
Being able to recognise and appropriately react to
others’ emotions is a fundamental social ability for
humans. It can allow group members to be alerted
to important situations or to feel empathy for and
comfort each other. Previous research on attention
to emotion has shown that people detect and prefer-
entially attend to emotional over neutral stimuli
(Yiend, 2010) and that they may also be particularly
biased toward detecting certain emotions, such as
anger (Fox & Damjanovic, 2006; Hansen & Hansen,
1988) or happiness (Becker et al., 2011). However,
which emotional expressions stand out most in a
visual arraymay come down to the intensity of the par-
ticular emotion displayed (Lundqvist et al., 2014) or
low-level perceptual features of the stimuli such as
having an open mouth or visible teeth (Savage et al.,
2013). Thus, moving beyond perceptual detection
and instead assessing physiological arousal responses
to others’ emotions and arousal levelsmay give amore
accurate and nuanced picture of the impact of
different emotions on an observer. For example,
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
CONTACT Christine Fawcett christine.fawcett@psychology.su.se Department of Psychology, Uppsala University, Uppsala, Sweden;
Department of Psychology, Stockholm University, Stockholm, Sweden
COGNITION AND EMOTION
https://doi.org/10.1080/02699931.2022.2073973
studies using pupil dilation, which not only results
from changes in light but is also an indicator of
arousal (Laeng et al., 2012), show that observers
respond with greater arousal to others’ expressions
of anger compared to other emotions (Carsten et al.,
2019; Kret et al., 2013). Together, this suggests that
while both happy and angry faces might pop out per-
ceptually (Becker et al., 2011; Fox & Damjanovic, 2006;
Hansen & Hansen, 1988), they do not seem to have the
same effect on the observer’s own emotional state.
While most research on emotion perception
focuses on facial expressions, emotions are conveyed
through other signals as well, such as vocalizations,
body posture, blushing, and pupil dilation. Moreover,
pupil dilation on its own can be a subtle signal of
arousal (Bradley et al., 2008; Partala & Surakka, 2003)
and in combination with facial expressions or other
cues, it could potentially be a signal for variation in
emotional intensity (Kret, 2015). Yet how exactly
pupil size affects facial emotion perception for obser-
vers is not clear. Pupils dilate when one experiences
either positively- or negatively-valenced arousal
(Partala & Surakka, 2003), yet people tend to associate
angry faces with smaller pupils than happy faces
(Hess, 1975; Kret, 2018) and rate sad faces with
higher sadness intensity when they have smaller
pupils (Harrison et al., 2007). Further, studies of
other types of autonomic nervous system activity,
such as heart rate and skin conductance, show that
there may be distinctive patterns of activation for
specific emotions (Kreibig, 2010), suggesting potential
for variability beyond the continua for valence and
intensity.
One way to examine how observers respond to
others’ signals of emotion and arousal on a physio-
logical level is through measuring their pupil dilation.
When an observer’s pupils dilate in response to
another individual’s dilated pupils, it is known as
pupillary contagion and is proposed to be a sign of
sensitivity to and sharing of others’ arousal (Fawcett
et al., 2016, 2017; Kret et al., 2015). Note that
mimicry of pupillary constriction does not occur as
reliably as dilation mimicry (Aktar et al., 2020; Kret
et al., 2014), and when it does, it is not related to
social factors (Kelsey et al., 2019; Kret et al., 2015; Pro-
chazkova, Prochazkova, Giffin, Scholte, et al., 2018),
supporting that it is pupil dilation and the underlying
arousal reactions which are being shared across indi-
viduals in pupillary contagion. Most research on pupil-
lary contagion has used faces with neutral expressions
and the few studies that have examined emotional
expressions have had somewhat mixed results. Two
studies suggested that ratings of others’ sadness,
but not other emotions, were affected by observed
pupil sizes, though interestingly it was faces with
smaller pupils that were rated to be more sad (Harri-
son et al., 2006, 2007). In a small subsample of nine
participants, Harrison et al. (2006) also showed that
only sad faces elicited pupillary contagion. However
a more recent study with a larger sample size, more
robust statistical methods, and a revised task to
encourage attention to the eyes showed comparable
pupillary contagion across neutral, sad, happy, and
angry facial expressions with participants’ pupils dilat-
ing more to images with large and medium pupils
than to small pupils (Carsten et al., 2019).
While group-level effects for emotion perception
and reactivity can be informative, it is also critical to
explain inter-individual differences in responding.
Why might some participants show stronger
responses than others and what might that tell us
about the underlying mechanisms for emotional reac-
tivity and arousal sharing? Previous research on pupil-
lary contagion has examined whether the trait of
empathy predicts one’s degree of pupillary contagion.
In one case, participants’ self-reported empathy pre-
dicted how much they were influenced by the
model’s pupil size when rating sadness intensity (Har-
rison et al., 2007), while two larger studies found no
link between pupillary contagion and self-reported
empathy (Axelsson & Fawcett, 2021; Carsten et al.,
2019). However, there are many more potential indi-
vidual difference factors that could modulate pupil-
lary contagion, including both demographic factors,
such as age, gender, and socioeconomic status, as
well as psychosocial factors, such as anxiety,
depression, and sleep problems. How each of these
factors might relate to the processing of and reaction
to others’ emotions and arousal is described below.
Demographic factors
When it comes to gender differences, women have
been consistently shown to be more skilled at
emotion perception than men (Olderbak et al., 2019;
Thompson & Voyer, 2014) and both women’s self-
ratings of emotional experience and their facial
emotional expressions are more affected by others’
emotions than men’s are (Dimberg & Lundquist,
1990; Doherty et al., 1995; Sonnby-Borgström et al.,
2008; Stellar et al., 2012). However, when emotional
stimuli are presented outside of conscious awareness,
2 C. FAWCETT ET AL.
this difference can disappear (Sonnby-Borgström
et al., 2008), suggesting an important role for sociali-
sation and learned responses in these gender differ-
ences. Further, physiological measures of arousal,
such as skin conductance, heart rate, and startle
responses, to emotional stimuli show mixed results
with some revealing gender differences (Bianchin &
Angrilli, 2012) and others not (Codispoti et al., 2008;
Partala & Surakka, 2003). Finally, when participant
gender differences were examined in previous
studies of pupillary contagion, none were found
(Axelsson & Fawcett, 2021; Kret & De Dreu, 2017).
While emotion perception abilities decline with
age (Olderbak et al., 2019; Ruffman et al., 2008),
emotional reactivity shows more complex develop-
ment. For example, compared to younger adults,
older adults report greater emotional reactivity to
negative images and show greater startle responses
to negative images, but also less heart rate decelera-
tion than younger adults (Smith, Hillman, et al.,
2005). Older adults also attribute less negative emo-
tionality to others’ expressions (Riediger et al., 2011).
Interestingly, a study in which participants were
asked to draw pupil sizes onto happy and angry
faces to examine whether larger pupils were associ-
ated with positive emotion, showed that the differ-
ence between the pupil sizes for the two types of
faces increased across adulthood (Kret, 2018).
Socioeconomic status (SES), a factor that encom-
passes income level, education, and occupation, has
also been shown to be related to emotion perception
and reactivity. Individuals with lower SES show stron-
ger brain activation in areas devoted to attending to
others’ mental states (Muscatell et al., 2012) and are
more accurate when judging others’ emotions
(Kraus et al., 2010). Lower SES relates in particular to
feelings of compassion and physiological responses
to others’ distress (Stellar et al., 2012). While SES
cannot be experimentally manipulated directly,
manipulations of increased social power and status
have been found to negatively affect both perspective
taking and emotion identification (Galinsky et al.,
2006).
Psychosocial factors
Anxiety is associated with a heightened attentional
bias toward threatening stimuli, including emotional
faces (Bar-Haim et al., 2007) and with stronger
emotional reactivity to elicited emotions (Macatee &
Cougle, 2013). In contrast, depression has been linked
to dampened emotional reactivity, for both positive
and negative stimuli (Bylsma et al., 2008), but also
heightened attention to sad faces and reduced atten-
tion to happy faces (Duque et al., 2014; Lazarov et al.,
2018). Further, some studies fail to find relations
between depression and pupil dilation responses to
emotional stimuli (Yrttiaho et al., 2021).
Sleep loss can contribute to poorer emotion regu-
lation and mood, for example evaluating stimuli more
negatively and reacting more strongly to negative
stimuli when sleep deprived (Tempesta et al., 2018).
In one study using pupillometry, sleep-deprived par-
ticipants showed greater pupil dilation in expectation
of negative stimuli when compared to non-sleep
deprived participants (Franzen et al., 2009). When
examining the processing of emotional faces specifi-
cally, sleep-deprived participants have more
difficulty accurately identifying sad faces and show
greater neural reactivity to angry and fearful faces
(Cote et al., 2014).
The current study
We aimed to clarify the role of emotional expressions
in pupillary contagion and to examine potential indi-
vidual differences in the phenomenon. Thus, we
examined pupil dilation in response to the eye
regions of faces with neutral or emotional expressions
and variations in displayed pupil sizes. The emotional
expressions included happiness, sadness, fear, anger,
and neutral and each was shown with small,
medium, or large static pupils. The task allowed us
to examine not only pupillary contagion across
neutral and emotionally expressive eyes, but also
overall dilation effects to the different expressions.
We included both male and female models in the
stimuli to increase generalizability and ecological val-
idity. Participants were parents taking part in a longi-
tudinal study of early-life distress and child
development in Finland. In order to examine individ-
ual differences in pupil responses, we also assessed a
variety of demographic and psychosocial factors.
We chose to show only the eye region in our
stimuli – from eyebrows to the top of the nose – for
several reasons. First, we wanted to focus attention
on the eyes without giving participants a task which
could lead to pupil dilation confounds due to individ-
ual differences in cognitive effort and would make the
task less comparable to previous research on pupillary
contagion (Aktar et al., 2020; Axelsson & Fawcett,
2021; Fawcett et al., 2017). Further, we expected
COGNITION AND EMOTION 3
that emotion perception would be reliable even with
only the eye region visible, given that emotion recog-
nition relies significantly more on the eye region than
the mouth, as evidenced in eye tracking research
(Wells et al., 2016), particularly for sadness, anger,
and fear (Smith, Hillman, et al., 2005). Finally, while a
few recent studies have shown that surgical masks
covering the lower half of the face impair emotion
recognition, accuracy is still high: typically around
80%, with the most common mistakes being to
confuse fearful and sad expressions with each other
(Marini et al., 2021; Parada-Fernández et al., 2022).
Further, the eye region alone has been shown to
elicit biases in emotion detection (Fox & Damjanovic,
2006).
We expected to replicate the pupillary contagion
phenomenon that has been robustly demonstrated
for neutral expressions (Axelsson & Fawcett, 2021;
Fawcett et al., 2017; Kret & De Dreu, 2017), as well
as with somewhat more mixed results for emotional
expressions (Carsten et al., 2019; Harrison et al.,
2006, 2007). We also expected to see greater overall
pupil dilation in response to angry eyes compared
to other emotions (Carsten et al., 2019). For the indi-
vidual difference measures, we did not have a priori
hypotheses. While there are theoretical reasons to
propose differences in reaction to others’ emotions
for any of the variables we examined, there have
been few consistent relationships and there is very
little research looking at individual differences in
pupillary contagion specifically.
Method
Participants
Participants included 453 parents (59.6% women and
40.4% men), a subsample of those who were already
involved in the longitudinal FinnBrain Birth Cohort
Study (www.finnbrain.fi). An additional 6 participants
participated in the data collection, but were excluded
for poor data quality (see “Pupil data” below). The
main aim of the FinnBrain study is to examine the
effects of early-life distress on the children’s brain
development and mental health. Originally, N =
3808 expecting mothers and N = 2624 fathers/
spouses were recruited to the study during the first
ultrasound visit at gestational week (gwk) 12
between 2011 and 2014 (Karlsson et al., 2018).
Around N = 1000 families were invited to a more
intensive follow-up including, for example, child
and parent neuropsychological visits, pediatric
visits, brain imaging, and collection of biological
samples.
When children were 5.5 years of age, between
2018 and 2021, those parents who had been actively
participating with their child in previous study visits
were invited to take part in a neuropsychological
assessment as part of the FinnBrain Child Develop-
ment and Parental Functioning Lab visits. This visit
included neuropsychological tests (Cogstate test
battery, Stroop, WAIS-IV digit span), verbal IQ tasks
(WAIS-IV, verbal comprehension tasks), three
different eye tracking tasks assessing attention to
emotional faces, and completion of questionnaires
about their mental health and distress (depressive
and anxiety symptoms assessed with the Edinburgh
Postnatal Depression Scale and Symptom Check
List 90 anxiety subscale) and their sleep (the
Athens Insomnia Scale and the Basic Nordic Sleep
Questionnaire). The research was performed in
accordance with the Declaration of Helsinki. The
Joint Ethics Committee of Turku University Hospital
and University of Turku approved the study protocol.
Written informed consent was obtained from all
participants.
Altogether, N = 525 mothers and fathers partici-
pated in the study visit between 2018 and 2021,
and N = 459 of these provided eye-tracking data. A
power analysis indicated that this sample size would
be more than sufficient to for the models planned
to test the current research questions. That is,
G*Power (version 3.1.9.6; Faul et al., 2007) suggests
a sample size of 253 for linear multiple regression
with power of .80, alpha of .05, estimated effect size
of f2 = 0.10, and 26 predictors (i.e. model pupil size,
emotion, and all of the individual difference factors
and their interactions that are included in the
models). The demographic characteristics of the par-
ticipants of the current study are presented in Table 1.
Stimuli
Pupil data was collected from the participant’s left eye
with a sampling frequency of 500 Hz using an eye
tracker (EyeLink1000+; SR Research, Canada) while
the participant viewed stimuli on a computer
monitor. The researcher sat in the same, dimly lit
room as the participant but was separated by a
curtain to avoid interference. The researcher used an
independent computer to manage the measurement.
4 C. FAWCETT ET AL.
The original images used for the pupillary conta-
gion stimuli were obtained from the set of Karolinska
Directed Emotional Faces (Lundqvist et al., 1998). The
images showing emotionally neutral eyes included six
different models (BF19NES, BF13NES, BF01NES,
AM14NES, AM10NES and AM08NES) and the images
showing emotionally expressive eyes included two
models, each displaying the emotions anger, fear,
happiness, and sadness (AM05ANS, AM05ANS,
AM05HAS, AM05SAS, BF01AFS, BF01ANS, BF01HAS,
BF01SAS). It was originally intended to include four
individuals in the emotion trials, however due to a
programming error, only two (one man and one
woman) were used in the final procedure. As in
similar studies (Axelsson & Fawcett, 2021; Fawcett
et al., 2017), each image was edited in Adobe Photo-
shop to black and white and showed only the eye
region, including the eyebrows and the upper part
of the nose at approximately life size on screen.
Then, each iris and pupil were replaced with a stan-
dard iris and one of three standard-size pupils. The
small pupil was 40% smaller and the large pupil was
40% larger than the medium pupil.
Questionnaires
Data fromthe followingquestionnaireswereused in the
current study. Depressive symptoms were screened
with The Edinburgh Postnatal Depression Scale (EPDS)
which is a widely used questionnaire (e.g. Gibson
et al., 2009), sensitive to both pre- and postnatal
depression, and consists of 10 questions scored on a
4-point Likert scale from 0 to 3 (Cox et al., 1987). The
total sum scores range from 0 to 30 and a continuous
total sum score was used (Cronbach’s alpha = 0.872;
data missing from 74 participants). General anxiety
symptoms were assessed with the anxiety subscale of
the Symptom Checklist-90 (SCL-90; Derogatis et al.,
1973; Holi et al., 1998), with reportedly good psycho-
metric properties (Prinz et al., 2013). This subscale con-
sists of 10 items scored on a 5-point Likert scale from
0 to 4 and the range of the total sum score is 0–40. In
this study, a continuous sum score was used (Cron-
bach’s alpha = 0.871; datamissing from74participants).
Two questions derived from the Basic Nordic Sleep
Questionnaire (BNSQ; Partinen & Gislason, 1995) were
used to measure subjective sleep complaints. Specifi-
cally, questions regarding sleep duration (i.e. “How
many hours do you typically sleep per night?”) and
night wakings (i.e. “During the past month, on how
many nights have you woken during the night?” with
five options ranging from “1. never or less than once a
month” to “5. daily or almost daily”) were used in the
analyses. Sleep data was missing for 12 participants.
Descriptive statistics for the questionnaire variables
are presented in Table 1.
Table 1. Descriptive statistics.
Demographic factors
Participant gender (%) women 59.6
men 40.4
Age, mean (SD; range) 31.9 (4.6; 20–48)
Monthly income in euros (%) <1500€ 29.9
1501–2000€ 26.7
2001–2500€ 24.8
>2500€ 18.6
Education (%) vocational 28.7
polytechnic 31.0
university-level or higher 40.4
Occupation (%) salaried 53.3
intermediate 15.3
working class 24.3
not classified/never worked/unemployed 7.1
SES score, mean (SD; range) 0.0 (0.8; −1.56–1.15)
Psychosocial factors
Edinburgh Postnatal Depression Scale, mean (SD; range) 4.6 (4.5; 0–22)
Symptom Checklist-90, anxiety subscale, mean (SD; range) 3.7 (4.9; 0–34)
Hours of sleep per night, mean (SD; range) 7.1 (0.8; 3–9)
Nighttime waking (%) less than once per month 6.1%
less than once per week 16.7%
1–2 times per week 20.8%
3–5 times per week 20.8%
daily or almost daily 35.5%
COGNITION AND EMOTION 5
Procedure
As part of a longer eye tracking session, (i.e. three
tasks lasting altogether around 30 min with pre-prep-
arations), the pupillary contagion task was presented
as the first task and took a total of approximately six
minutes. Participants first saw the 18 trials with
emotionally neutral eyes (six individuals with three
pupil sizes each) to avoid any priming of reactivity
or sensitivity to arousal cues from the emotional
faces and then saw the 48 trials with emotionally
expressive eyes (two individuals displaying each of
four emotions with three pupil sizes, each image
shown twice). The trials were presented in one of
two semi-randomized orders. In each trial, there was
first a black fixation cross on a grey background for
1000 ms and then the eye image was displayed with
the same grey background for 5000 ms. Participants
were instructed to simply view the images.
Data processing and analysis
Pupil data
Raw gaze data files (.edf) were exported from EyeLink
and then the files were reformatted and converted
from .edf to .asc and from .asc to .csv format using
the EyeLink library from the Fieldtrip toolbox
(version 20190922; Oostenveld et al., 2011) and a
custom script so that they could be processed in Time-
Studio (Nyström et al., 2016), an open-source pro-
gramme based in MATLAB (Timestudio version 3.19;
timestudioproject.org; MATLAB, 2015). Eyelink
records pupil sizes in arbitrary units. In Timestudio,
individual samples of pupil size were rejected if they
were outside of the range 1000–3500, to remove
blinks which often include recordings of partial
pupils as the eye closes and opens. This range was
selected after visual inspection of the full sample indi-
cated that samples outside that range were brief
spikes and not sustained or gradual peaks as would
be expected for true pupil measurements. Samples
with high acceleration (i.e. a change of more than 6
pupil size units per length of sample (i.e. 2 ms)
squared) and 5 samples (i.e. 10 ms) before and after
the high acceleration samples were also excluded for
being likely outliers, as it would be unnatural for a
pupil to change size that quickly. Gaps in data 20
samples or fewer were interpolated linearly and then
a moving average filter over 50 samples was
employed. To obtain the dependent variable of pupil
size change from baseline, the baseline was calculated
during a screen with a fixation cross (specifically, from
1000 to 500 ms before the stimulus image appeared)
and that was subtracted from the average pupil size
during 2000–5000 ms of the stimuli being shown.
The first 2000 ms of the stimuli viewing were excluded
from analysis due to variation in pupil size for the light
reflex response that adds noise to the data. Trials with
less than 50% of data samples recorded were excluded
(n = 888 trials; 2.59%). Six participants out of the orig-
inal 459 who participated in the task were excluded for
having five or fewer included trials, leaving 453 in the
final sample who together had an average of only
1.69% of trials excluded. Pupil size change scores
with values more than 2.5 standard deviations from
the mean for all included participants were replaced
with the next closest non-outlying value. In all, 763
trial scores (2.59%) were replaced for being outliers.
Demographics
Gender and age in years were reported by partici-
pants at the first questionnaire during pregnancy
(gwk 24; age is not reported for one participant). Infor-
mation about monthly income in euros was initially
collected with eight levels at gwk 14, but given the
uneven distribution of scores, it was collapsed into
four levels with approximately equal numbers of par-
ticipants (i.e. ≤1500 euros, 1501–2500 euros, 2501–
3500 euros, > 3500 euros) at each level (income
data is missing for 18 participants). Education was
originally assessed with 6 levels at gwk 14, but
given uneven distribution was collapsed into 3
roughly equal ones (High School or Vocational edu-
cation, Polytechnic education, and University-level
education or higher; education data is missing for
18 participants). Occupation was rated as one of
four levels based on official Finnish registries (salaried,
intermediate, working class, or not classified/never
worked/unemployed; occupation data is not reported
for 1 participant). Following previous research on SES
using objective measures (Kraus et al., 2009; Stellar
et al., 2012), we examined whether it was possible
to create one score for SES by combining the three
measures of income, education, and occupation. In a
confirmatory factor analysis, these three variables
were found to load significantly onto one factor (see
Table A1 in the Supplementary Results; https://
osf.io/r6eg3/), thus they were standardised and com-
bined into a single continuous SES score. Descriptive
statistics for the demographic variables are presented
in Table 1.
6 C. FAWCETT ET AL.
Analyses
Given that the stimuli for the neutral and emotional
conditions varied in the number of individual
models shown and in the number of trials, data
from each condition was analysed separately. The
first analysis step for each condition was to conduct
a group-level analysis to determine overall responses
to the stimuli. Following that, analyses were carried
out to assess individual differences in demographic
variables and in psychosocial variables. All of the
regression analyses were run on trial-level data
using linear mixed-effects modelling in jamovi (The
jamovi project, 2021) with the GAMLj module 2.0.1
(Galluci, 2019) which was developed in R (R Core
Team, 2020) and includes R’s lme4 package (Bates
et al., 2015). The factor analysis for SES was run
using the lavaan package within jamovi (Rosseel,
2012). All models included a random intercept for par-
ticipant and trial number. Statistical models included
all variables of interest and their interactions with
the main variables of model pupil size (for both con-
ditions), and model emotion (for the emotion con-
dition). Significant main effects were followed up
with Bonferroni-adjusted post hoc tests and signifi-
cant interactions were followed up with analyses of
simple effects. Tables in the manuscript present the
results of the fixed effect omnibus tests for the key
models. Full model results and the results of the pre-
liminary and follow-up analyses are presented in the
Supplementary Results on OSF (https://osf.io/r6eg3/).
Results
Emotionally neutral eyes
Group level analysis
In the model predicting pupil size change from model
pupil size (see supplementary Table A2), model pupil
size was a significant predictor (F(2, 596.63) = 23.68, p
< .001) and there was more dilation to models’ large
than medium (t(517.28) = 3.09, p = .006) and large
than small (t(3215.66) = 6.43, p < .001), but not
medium than small (t(325.86) = 1.51, p = .397) pupils.
Demographic predictors
To assess individual differences in pupillary contagion
for emotionally neutral eyes due to demographic
factors, we added to the group level model the vari-
ables of age, participant gender, SES score, and their
interactions with model pupil size (see Table 2 and
supplementary Table A3). There was a significant
effect of model pupil size (F(2, 973.36) = 23.09, p
< .001), with overall more dilation to large than
medium (t(540.90) = 3.07, p = .007) and large than
small (t(3303.23) = 6.36, p < .001) pupils, but not
medium than small pupils (t(337.98) = 1.51, p = .394).
There was also a significant interaction between
model pupil size and continuous SES score (F(2,
7263.63) = 4.72, p = .009, see Figure 1). Simple effects
analyses examining the effect of SES for each model
pupil size show that pupil dilation responses to
large pupil stimuli decrease with increasing SES (t
(1157.10) =−2.53, p = .012), while there were no sig-
nificant relations between SES and responses to
either medium or small pupil stimuli.
Psychosocial predictors
To assess individual differences in pupillary contagion
for neutral expressions due to psychosocial factors,
we added to the group level model the additional
variables of sleep duration, night waking, depression,
and anxiety, and their interactions with model pupil
size (see Table 3 and supplementary Table A4). Only
model pupil size was significant in this model (F(2,
740.65) = 12.32, p < .001), with greater dilation to
large than small pupil images (t(3109.97) = 4.79, p
< .001). None of the psychosocial factors or their inter-
actions with model pupil size had a significant effect
on reactions to eyes with neutral expressions.
Emotionally expressive eyes
Group level analysis
The initial model predicting pupil size change in
response to emotionally expressive eyes included the
variables model pupil size, emotion, and their inter-
action (see Table 4 and supplementary Table A5).
There were significant effects for model pupil size
(F(2, 1593.78) = 33.57, p < .001) with greater dilation
to large than medium and medium than small pupils,
and for emotion (F(3, 1389.48) = 23.53, p < .001), with
Table 2. Model results for neutral trials, analysis of demographic
factors.
F Num df Den df p
Model Pupil Size (MPS) 23.09 2 973.36 <.001
Age 1.84 1 430.71 0.175
Gender 2.48 1 429.53 0.116
SES score 0.62 1 430.47 0.432
MPS * Age 1.66 2 7264.62 0.191
MPS * Gender 2.46 2 7270.81 0.086
MPS * SES score 4.72 2 7263.63 0.009
Note: Satterthwaite method for degrees of freedom.
COGNITION AND EMOTION 7
greater dilation to angry eyes than any other emotion,
greater dilation to sad and happy than fearful eyes, and
no significant difference in dilation for happy and sad
eyes. There was no significant interaction between
model pupil size and emotion, suggesting that the
pupillary contagion effect did not differ across the
four emotions (see Figure 2).
Demographic predictors
To assess individual differences in pupillary contagion
to emotionally expressive eyes due to demographic
factors, the model from the group level analysis was
run with the additional variables of SES score, age, par-
ticipant gender, and their interactions withmodel pupil
size and with emotion (see Table 5 and supplementary
Table A6). A significant effect of size (F(2, 1630.25) =
29.92, p < .001) revealed greater dilation to images
with large than medium (t(1308.77) = 5.03, p < .001)
and medium than small pupils (t(1919.35) = 2.53, p
= .034). A significant effect of emotion (F(3, 1410.81) =
18.84, p < .001) revealed greater dilation to angry than
fearful (t(1429.63) = 7.12, p < .001) or happy (t
(2017.76) = 4.33, p < .001) eyes and greater dilation to
happy (t(1132.62) = 2.86, p = .026) and sad (t(1959.11)
= 4.78, p < .001) than fearful eyes. Further, participants’
pupils dilated less overall with increasing age (F(1,
435.64) = 5.22, p = .023) and men overall had greater
pupil dilation thanwomen (F(1, 435.38) = 5.87, p = .016).
There was a significant interaction between emotion
and participant gender (F(3, 20020.01) = 4.59, p = .003;
see Figure 3). Simple effects analyses indicated that
men showed greater dilation than women for both
happy (t(1213.71) = 3.89, p < .001) and sad (t(1217.39) =
2.25, p = .024) eyes. There was also a significant inter-
action between emotion and participant age (F(3,
19984.47) = 3.93, p = .008; see Figure 4) with simple
effects analyses indicating decreasing dilation responses
to happy (t(1208.41) =−3.60, p < .001) and sad (t
(1210.06) =−2.21, p = .027) eyes with increasing age.
Psychosocial predictors
To assess individual differences in pupillary contagion
to emotionally expressive eyes due to psychosocial
Figure 1. Variation in pupillary contagion in the neutral expression trials by socioeconomic status. Pupil size changes are shown using the
arbitrary units recorded by the eye tracker. SES score is a continuous, standardised variable. Shaded areas indicate 95% confidence intervals.
Table 3. Model results for neutral trials, analysis of psychosocial
factors.
F Num df Den df p
Model Pupil Size (MPS) 12.32 2 740.65 <.001
Hours sleep 0.19 1 367.46 0.666
Night waking 0.89 4 366.43 0.472
Depression 0.13 1 366.48 0.723
Anxiety 0.02 1 368.34 0.877
MPS * Night waking 1.12 8 6211.90 0.344
MPS * Hours sleep 0.14 2 6209.42 0.866
MPS * Depression 1.53 2 6211.31 0.216
MPS * Anxiety 1.20 2 6210.16 0.302
Note: Satterthwaite method for degrees of freedom.
Table 4. Model results for emotion trials, group-level analysis.
F Num df Den df p
Model Pupil Size (MPS) 33.57 2 1593.78 <.001
Emotion 23.53 3 1389.48 <.001
MPS * Emotion 1.53 6 1478.86 0.163
Note: Satterthwaite method for degrees of freedom.
8 C. FAWCETT ET AL.
factors, the model resulting from the group level
analysis was run with the additional variables of
anxiety, depression, sleep duration, night waking,
and their interactions with model pupil size and
with emotion (see Table 6 and supplementary Table
A7). There were significant effects of model pupil
size (F(2,1792.80) = 21.02, p < .001) and emotion (F
(3,1494.12) = 15.36, p < .001) with the same pattern
of results as in the demographics analysis. None of
the psychosocial predictors or their interactions had
a significant effect in the model (all p’s > .05).
Discussion
Being sensitive and responsive to others’ emotions
and subtle cues of arousal can be an important
factor for social interaction. In the current study, we
examined how adults respond to others’ eye regions
when they vary in both emotional expression –
neutral, sad, angry, happy, and fearful – and in pupil
size, a marker of arousal. Participants’ own pupil
dilation varied with the dilation of the observed indi-
viduals, both for neutral and emotionally expressive
eye regions, in line with previous research demon-
strating this phenomenon of pupillary contagion
(Aktar et al., 2020; Carsten et al., 2019; Fawcett et al.,
2016, 2017; Kret et al., 2015). In addition, we uncov-
ered group-level effects of emotional expression,
such that participants responded with greatest pupil
dilation to angry eyes, greater dilation to sad than
fearful eyes, and similar dilation for happy compared
to either sad or fearful eyes. The pattern of results
for the group-level effects of emotional expression
on pupil dilation and pupillary contagion are in line
with results from a similar large study published
recently by Carsten et al. (2019). They found compar-
able levels of pupillary contagion using images of full
faces across the emotions of anger, happiness,
sadness, and neutral expressions, and overall greater
pupil dilation in response to angry facial expressions
than any other emotion. Two earlier studies examin-
ing how pupillary contagion relates to emotional
expression found effects only for sad facial
expressions (Harrison et al., 2007, 2009), but they
were limited by small sample sizes and lack of
control over gaze scanning patterns that could have
influenced the pupil results.
Figure 2. Pupillary contagion across emotional expressions. Pupil size changes are shown using the arbitrary units recorded by the eye tracker.
Bars indicate 95% confidence intervals.
Table 5. Model results for emotion trials, analysis of demographic
factors.
F Num df Den df p
Model Pupil Size (MPS) 29.92 2 1630.25 <.001
Emotion 18.84 3 1410.81 <.001
Age 5.22 1 435.64 0.023
Gender 5.87 1 435.38 0.016
SES score 0.32 1 436.83 0.570
MPS * Emotion 1.72 6 1430.08 0.112
Emotion * Age 3.93 3 19984.47 0.008
MPS * Age 0.71 2 19986.04 0.490
MPS * Gender 0.06 2 20019.53 0.945
Emotion * Gender 4.59 3 20020.01 0.003
Emotion * SES score 0.10 3 19980.97 0.961
MPS * SES score 2.44 2 19984.23 0.087
Note: Satterthwaite method for degrees of freedom.
COGNITION AND EMOTION 9
Beyond the group-level effects, we also examined
individual differences in pupillary contagion and
pupil dilation responses to different emotional
expressions. These individual difference factors
included demographics: gender, age, and SES, as
well as psychosocial factors: anxiety, depression, and
sleep problems. While previous research on emotional
reactivity suggests that these factors could influence
pupillary contagion and pupil dilation to others’
emotions, the predicted effects were not always
clear and thus we did not have a priori hypotheses
for these effects.
The only demographic factor that had an impact
on the degree of pupillary contagion shown by par-
ticipants was SES. Higher SES was associated with
lower degree of pupillary contagion for neutral,
though not for emotionally expressive eyes. The
effect was particularly seen in increased reactivity to
others’ large pupils with decreasing SES. Together,
this suggests that SES might be more related to
Figure 3. Variation in pupil dilation responses in the emotional expression condition by participant gender and emotion. Pupil size changes are
shown using the arbitrary units recorded by the eye tracker. Bars indicate 95% confidence intervals.
Figure 4. Variation in pupil dilation responses in the emotional expression condition by emotion and participant age. Pupil size changes are
shown using the arbitrary units recorded by the eye tracker. Shaded areas indicate 95% confidence intervals.
10 C. FAWCETT ET AL.
reactivity to others’ subtle emotional signals – noti-
cing pupils that indicate higher arousal in the
context of neutral face, rather than noticing pupil
size variation in the context of a clear emotional
expression.
Consistent with the current results, previous
research on SES has shown that individuals with
higher SES consistently perform lower on cognitive
tasks related to emotion (Kraus et al., 2010), as well
as on low-level emotional reactivity tasks (Stellar
et al., 2012), which can be seen as more comparable
to pupillary contagion. Thus, our findings are in line
with others showing that higher SES is related to
lower sensitivity to others’ emotions. In contrast,
one recent study showed that in South African
mothers, lower SES was related to less difference in
pupil reactivity for images of distressed relative to
happy infants (Yrttiaho et al., 2021). It is important
to note several key differences between that study
and ours that could lead to different findings. First,
our stimuli displayed adults while their stimuli dis-
played infant faces and it could be that the effect of
SES on emotional reactivity differs by age of target.
Second, the SES levels in their samples were much
lower than ours, potentially making them difficult to
compare. It could be that both very high and very
low SES lead to dampened emotional reactivity,
though possibly for different reasons. Third, there
could be cultural differences between South Africa
and Finland that contribute to different patterns of
responses in the different studies.
As to why the SES and pupillary contagion relation-
ship might exist, it could be that higher levels of SES
shift one’s attention away from other people and
their subtle emotional signals, similar to the effects
that manipulating people’s feelings of social power
has on their perspective-taking (Galinsky et al.,
2006). When a person has control over others and
does not need to rely on them, due to their status,
perspective-taking and empathy may become less
important. In contrast, being more reliant on others
and more stressed about one’s economic situation
could also create a greater need to tune in to
others’ emotional states to maintain social support
networks. On the other hand, given the automaticity
of the pupillary contagion response, another expla-
nation could be that people who are already more
emotionally reactive to others (due to genetics,
early-life environmental factors and/or upbringing)
are drawn toward careers in education or caring
fields, which tend to be associated with fewer years
of education and lower salaries. Studies to disentan-
gle the causal chain between pupillary contagion
and factors related to SES will be important for
future research.
When it comes to gender, both women and men
demonstrated pupillary contagion to a similar
degree. However, we found that in the emotion
trials, men had overall greater dilation than women
and an interaction effect revealed that this was due
to men reacting to happy and sad expressions with
greater pupil dilation than did women. This finding
contrasts previous research suggesting greater reac-
tivity to emotion in women than men, however
these effects tend to be found in tasks that involve
more cognitive processing, such as identifying
emotions (Olderbak et al., 2019; Thompson & Voyer,
2014) or giving ratings of one’s own emotional experi-
ences (Dimberg & Lundquist, 1990; Doherty et al.,
1995; Sonnby-Borgström et al., 2008; Stellar et al.,
2012). In contrast, it is not uncommon for there to
be no gender differences in initial physiological
responses to others’ emotions (Codispoti et al., 2008;
Partala & Surakka, 2003). In addition, greater overall
dilation effects could also be related to physiological
differences (e.g. Fan et al., 2009) or cognitive factors,
such as taking more effort to process the stimuli.
With increasing age, participants reacted to the
emotionally expressive stimuli with less pupil dilation
overall, and particularly so for happy and sad
expressions. While previous research has shown
effects of age on emotional reactivity, these have
been complex with different measures indicating
greater or lesser arousal with age (Smith, Hillman,
et al., 2005). The current results show that for both a
Table 6. Model results for emotion trials, analysis of psychosocial
factors.
F Num df Den df p
Model Pupil Size (MPS) 21.02 2 1792.80 <.001
Emotion 15.36 3 1494.12 <.001
Night waking 0.59 4 371.05 0.671
Hours sleep 0.02 1 371.12 0.888
Depression 0.65 1 371.97 0.422
Anxiety 2.71 1 371.50 0.101
MPS * Emotion 2.07 6 1025.18 0.054
MPS * Night waking 0.99 8 17124.33 0.445
Emotion * Night waking 1.43 12 17123.66 0.144
MPS * Hours sleep 0.95 2 17103.36 0.388
Emotion * Hours sleep 1.07 3 17103.01 0.359
MPS * Depression 0.93 2 17122.52 0.395
Emotion * Depression 1.39 3 17122.74 0.243
MPS * Anxiety 0.17 2 17110.21 0.846
Emotion * Anxiety 0.79 3 17112.99 0.501
Note: Satterthwaite method for degrees of freedom.
COGNITION AND EMOTION 11
positive (happy) and negative (sad) emotional
expression, physiological reactivity appears to
decrease with age, at least within our sample of pri-
marily middle-aged adults. Effects of age were not sig-
nificant for angry or fearful expressions, however.
Given that anger and fear were the expressions with
the highest and lowest overall dilation responses, it
could be that ceiling and floor effects played a role
in their not showing age effects.
When it comes to the psychosocial factors exam-
ined in relation to pupillary reactions to others’
emotions, there were surprisingly no effects. Neither
participants’ pupillary contagion, nor their overall
pupil dilation responses to images of others’ neutral
and emotionally expressive eyes varied based on
their symptoms of anxiety or depression, or their
sleep problems. It could be that there was not
sufficient variation in these variables for differences
to be revealed, though failure to find effects of psy-
chosocial variables on pupil response to infant facial
expressions has also been reported (Yrttiaho et al.,
2021). It may thus be possible that automatic physio-
logical reactions to emotion cues are not as affected
by these factors as are aspects of emotion processing
that are further downstream in cognition.
Together, the results revealed few individual differ-
ences in pupillary contagion, which suggests that the
phenomenon is a robust and fundamental one which
may underlie other emotional processes. That is, it
could be that factors such as anxiety and depression
affect emotional responding and attention further
downstream in processing such that they affect the
cognition around emotion more so than the initial
physiological responses to it. This suggestion is
further supported by the fact that pupillary contagion
develops early in life (Fawcett et al., 2016, 2017) and
does not appear to have significant developmental
shifts (Aktar et al., 2020).
A few limitations of the current study should also be
noted. First, the sample was recruited from only one
country, which could limit generalizability. However,
in comparison to many studies in the field which
recruit participants from university student popu-
lations, the current sample was more diverse in terms
of age and SES. Second, the individual difference
measures may not all have had sufficient variability
to detect relations. Focusing on samples that are
already known to be high on certain factors, such as
anxiety and depression, could allow more effects to
be revealed. Finally, the stimuli used posed emotional
expressions and static pupil sizes which, while high in
experimental control, can be criticised for having
lower ecological validity. That is, we cannot be
certain that participants’ responses to the current
stimuli mirror what occurs in natural social interaction.
Future studies should consider using more naturalistic
emotional facial expressions and dynamic pupil sizes.
The current study demonstrated that pupillary con-
tagion is a robust and social phenomenon. Whether
participants were observing others’ emotionally
neutral or emotionally expressive eyes, their own
pupil size tended to increase with increases in the
pupil sizes of the observed individuals, suggesting a
sharing of arousal. Further, this pattern was found
across ages and genders and was not significantly
impacted by sleep problems or symptoms of anxiety
or depression. In fact, the only individual difference
measure that was related to participants’ degree of
pupillary contagion was their SES, with higher SES par-
ticipants showing less pupillary contagion for emotion-
ally neutral eyes. This modulating factor underscores
that pupillary contagion is more than a reaction to
light and instead has a social-cognitive basis (Prochaz-
kova, Prochazkova, Giffin, Steven Scholte, et al., 2018).
Together, the findings show that pupillary contagion
is a robust and largely automatic process, though indi-
vidual differences do occur and can give insight into
the roots of this social phenomenon.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Funding
This work was supported by Riksbankens Jubileumsfond (#P16-
0866:1); Jenny and Antti Wihuri Foundation; Finnish Brain
Association; Strategic Research Council; Kone Foundation; Vic-
toria Foundation; Agneta and Carl-Erik Olin Foundation;
Academy of Finland (#308252; #325292; #321424); Emil Aalto-
nen Foundation; and the Signe and Ane Gyllenberg Foundation.
ORCID
Christine Fawcett http://orcid.org/0000-0002-0898-9920
Linnea Karlsson http://orcid.org/0000-0002-4725-0176
Hasse Karlsson http://orcid.org/0000-0002-4992-1893
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