fpsyg-11-00097 J anuary 30, 2020 T ime: 16:59 # 1
BRIEF RESEARCH REPORT
published: 31 January 2020
doi: 10.3389/fpsyg.2020.00097
Edited by:
Noemi Mazzoni,
University of T rento, Italy
Reviewed by:
Guillaume Dezecache,
Institut Jean Nicod, France
Elaine Hatfield,
University of Hawaii, United States
*Correspondence:
Andrés Pinilla
[email protected]
† ORCID:
Andrés Pinilla
orcid.org/0000-0002-0812-7896
Ricardo M. T amayo
orcid.org/0000-0002-8678-0145
Specialty section:
This article was submitted to
Consciousness Research,
a section of the journal
Frontiers in Psychology
Received: 31 July 2019
Accepted: 14 January 2020
Published: 31 January 2020
Citation:
Pinilla A, T amayo RM and Neira J
(2020) How Do Induced Affective
States Bias Emotional Contagion
to Faces? A Three-Dimensional
Model. Front. Psychol. 11:97.
doi: 10.3389/fpsyg.2020.00097
How Do Induced Af fective States
Bias Emotional Contagion to Faces?
A Thr ee-Dimensional Model
Andrés Pinilla 1 * † , Ricardo M. T amayo 2 † and Jorge Neira 2
1 Quality and Usability Lab, Institute of Software Engineering and Theoretical Computer Science, Faculty of Electrical
Engineering and Computer Science, T echnische Universität Berlin, Berlin, Germany, 2 Laboratorio de Cognición Implícita,
Departamento de Psicología, Universidad Nacional de Colombia, Bogotá, Colombia
Af fective states can propagate in a gr oup of people and influence their ability to
judge others’ af fective states. In the present paper , we pr esent a simple mathematical
model to describe this pr ocess in a three-dimensional af fective space. We obtained
data fr om 67 participants randomly assigned to two experimental groups. Participants
watched either an upsetting or uplifting video pr eviously calibrated for this goal.
Immediately , participants reported their baseline subjective af fect in thr ee dimensions:
(1) positivity , (2) negativity , and (3) arousal. In a second phase, participants rated the
af fect they subjectively judged from 10 target angry faces and ten target happy faces
in the same thr ee-dimensional scales. These judgments were used as an index of
participant’ s af fective state after observing the faces. Participants’ af fective r esponses
wer e subsequently mapped onto a simple three-dimensional model of emotional
contagion, in which the shortest distance between the baseline self-r eported affect and
the target judgment was calculated. The r esults display a double dissociation: negatively
induced participants show mor e emotional contagion to angry than happy faces, while
positively induced participants show more emotional contagion to happy than angry
faces. In sum, emotional contagion exerted by the videos selectively af fected judgments
of the af fective state of others’ faces. We discuss the dir ectionality of emotional
contagion to faces, considering whether negative emotions are mor e easily pr opagated
than positive ones. Additionally , we comment on the lack of significant correlations
between our model and standar dized tests of empathy and emotional contagion.
Keywords: emotional contagion, facial expr essions, evaluative space model, affective states, faces, emotions
INTRODUCTION
Emotions frequently involve reactions to events and people, influencing in turn our judgments
about the affective states of others. V arious prominent theoretical frameworks ( H atfield et al.,
1993, 1994 ; Forgas, 1995 ; Peter s and Kashima, 2015 ) comprise at least one type of psychological
mechanism by which humans and other mammalians automatically converge to the affective
states of their peers when it is socially appropriate ( de Waal, 2012 ; P alagi et al., 2015 ). This
mechanism has been defined as “a tendency to automatically mimic and synchronize expres sions,
vocalizations, postures and movements with those of another person’ s, and, consequently, to
converge emotionally” ( H atfield et al., 1994 , p. 5).
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E vidence of automatic affective convergence for facial
mimicry ( Dimberg, 1982 ; Dimberg et al., 2000 ; Dimber g
and Thunberg, 2012 ; V arcin et al., 2019 ), vocalizations
( Cappella and Planalp, 1981 ; Fujiwara and Daibo, 2016 )
and body postures ( Condon and Ogston, 1966 ; Schmidt et al.,
2011 ), support the existence of an automatic mechanism
for emotional contagion. Of course, automatic transmission
of affect among dyads ( Bruder et al., 2012 ; Butler, 2015 )
and beyond ( Dezecache et al., 2013 ; Kramer et al., 2014 ) is
not the only psychological process that modulates people ’ s
affective responses. Clearly, additional mechanisms linked to
individual differences ( Doherty, 1997 ; Künecke et al., 2014 ),
specific appraisals ( Font aine et al., 2007 ), pragmatic goals
( Fischer and Hess, 2017 ) and social roles ( Murray, 1933 )
mediate the propagation of emotions. In fact, previous work
has significantly contributed to clarify basic concepts in the
field. For instance, clear differences exist today between deeply
related concepts such as mood, a ffect, and emotion ( Forgas,
1995 ; Russell, 2003 ; Peters and K ashima, 2015 ) and between
related explanatory frameworks such as a ffect diffusion,
emotional contagion, and affect infusion ( Dezecache et al., 2015 ;
Peters and K ashima, 2015 ).
However , to our knowledge, no specific framework exists
to quantify the amount of subjective emotional contagion
among individuals. U sually, emotional cont agion is a ssumed
by obser ving whether participants ’ affective responses (i.e.,
subjective ratings) or implicit automatic reactions (i.e., facial
electromyographic responses) converge to the valence of
previously presented stimuli (e.g., faces, videos, sounds, scenes,
actors, or peers). In our view, an ideal framework for quantifying
emotional contagion should describe how much affect is
transferred from the participant ’ s immediate emotional context
to subsequent emotional responses.
We believe that such a model could contribute to
understand the interaction between explicit and implicit
cognitive mechanisms involved in emotional contagion.
The need for a specific mathematical model for quantifying
subjective emotional contagion might be illustrated by
contemporary research concerned with the influence of
automatic facial mimicry responses – predominantly related
to implicit mechanisms – on subjective and affective ratings –
predominantly related to explicit mechanisms – (e.g., Fujimura
et al., 2010 ; Dezecache et al., 2013 ; Sato et al., 2013 ; Küne cke
et al., 2014 ; Deng and Hu, 2018 ). For instance, Deng and
Hu (2018) , collected subjective ratings of valence in a single
bipolar scale together wit h electromyography (EMG) recordings
from zygomaticus mayor (ZM) and corrugator supercilii (CS)
muscles, from participants exposed to positive (happy) and
negative (angry) faces. In two experiments, the authors found
a lack of correlation between ZM activity and subjective ratings
in response to positive stimuli. Similarly, in a conceptually
analogous manipulation of transitive affect, Dezecache et al.
(2013) found that explicit identifications of positive faces were
not above chance , while ZM activity increased for positive
faces ( Dezecache et al., 2013 ). Furthermore, results from two
experiments conducted by Fridlund (1991) support the claim
that implicit facial expressions may be erratically associated
with explicit subjective reports of emotional experience –
for a detailed review, see B arrett et al. (2019) . We believe
these results are puzzling because subjective measures either
collected in a single bipolar scale ( Deng and Hu, 2018 ) or
as discrete identifications ( Deze cache et al., 2013 ) do not
provide enough information to determine the amount of
subjective contagion experienced by participants. In our
view, research could benefit from disentangling the amount
of contagion triggered by the stimuli from the amount of
contagion that bias further affective judgments, pondering that
a single bipolar measure of valence might not fully des cribe
the subjective and affective processes of emotional contagion
experienced by individuals. For instance, as suggested in
the evaluative space model (ESM) ( Cacioppo et al., 1997 ),
increases in negative affect are not necessarily proportional to
decreases in positive affect, and baseline subjective responses
to positive stimuli usually offset baseline activation for negative
stimuli. Therefore, using a single bipolar measure of valence
as an index of subjective a ffect could have obscured genuine
correlations between automatic mimicry and subjective affect in
previous studies.
In the present paper , we propose a simple mathematical
model to quantify emotional contagion to faces by computing
the distance between two emotional coordinates in a three-
dimensional affective space. The first coordinate represents the
affective state that participants subjectively self-reported after
obser ving a video. The second coordinate represents subjective
judgments about the affective state of unfamiliar faces. Given
that people tend to ascribe aspects of their own affective state
to others ( Forgas, 1995 ; V an Boven and Loewenstein, 2003 ),
we assume that the distance between the first and second
coordinates, indexes the amount of emotional contagion to faces
experienced by participants. This is, how much convergence is
there between participant ’ s affective st ates before (T1) and a fter
seeing the faces (T2). The measure at T1 is the e valuation of
the video and the measure at T2 is the e valuation of each face.
We interpret participant ’ s e valuation of faces as an index of
their affective state after watching each face , based on previous
evidence suggesting that e valuation of other ’ s f acial expre ssions
correlates with self-reported affective states ( Blairy et al., 1999 ).
Additional evidence shows that activation of the amygdala during
a face evaluation-task correlates with participant’ s subjective
evaluations of angry faces ( Nomura et al., 2004 ). Therefore,
subjective judgments about the a ffective state of ot her ’ s faces
can be considered an index of participants ’ affective state. This
evidence is in turn consistent with predictions from t he Affect as
Information mechanism proposed in the Affect Infusion Model
(AIM) ( Forgas, 1995 ), which suggests that people tend to use
their own emotional state as heuristic to attribute emotions
to others.
In short, we propose a model to quantify the distance between
two different subjective evalua tions . The first one, induced
by a video, the second one, involved in the e valuation of
unfamiliar faces. In other words, the model calculates the distance
between participants ’ self-reported affective state (triggered by
the video) and the influence exerted by this affective state on
the emotional contagion to unfamiliar faces. Shorter distances
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indicate more emotional contagion to faces and larger distances
indicate the opposite.
Computing the shortest distance between these two affective
states presupposes an efficient psychological mechanism
for emotional contagion based on minimal effor t ( Forgas,
1995 ) or energet ic efficiency ( Cacioppo et al., 1997 ). This
mechanism is conceived as an organism’ s tendency to
adopt the shortest path and least effortful strategy to yield
a subjective affective response, if it satisfies minimal adaptive
requirements. We preferred a three-dimensional affective
space based on monopolar ratings similar to the one suggested
by Cacioppo et al. (1997) for two reasons. Firstly, because
contemporary evidence suggests that a t hree-dimensional
model does not exclude a bi-dimensional one ( M attek et al.,
2017 ) and that monopolar measures do not preclude additional
affective dimensions (e.g., dominance in the first case or net
predisposition in the second case). Second, a three-dimensional
space maps well into commonly used psychophysiological
measures such as EMG activity from ZM and CS muscles
for positivity and negativity, respectively, and electrodermal
activity (ED A) or heart rate variability (HR V) for arous al
( Bernat et al., 2006 ).
To extend the measurement framework for subjective
responses of emotional contagion, we designed an experiment
in which participants were induced to either a positive or a
negative affective state by watching an affectively laden video
clip. Immediately, we assessed their baseline subjective emotional
state by asking them to self-report how the video made them
feel in three dimensions: positivity, negativity, and arousal. In
a second phase, participants were asked to judge the affective
states of unfamiliar faces using the same t hree dimensions.
As mentioned a bove, given that people tend to use their own
affective states as a heuristic to judge the affective state of
others ( Forgas, 1995 ; V an Boven and Loewenstein, 2003 ), we
reasoned that participant ’ s e valuations of faces could index
the amount of emotional convergence between the affective
state exerted by the video and the affective state associated to
each face. If emotional contagion is automatic and transitive
( Dezecache et al., 2013 ), then it should facilitate emotional
convergence by spreading the affective states triggered by the
video to the affective state involved in the participant’ s judgments
of unfamiliar faces. Therefore, based on the principle of least
effort, we hypothesized that participants induced to a negative
affective state would show more emotional contagion to angry
than happy faces, while participants induced to a positive
affective state would show more contagion to happy than
angry faces.
Finally, we wanted to explore the relationship between
our suggested measure of subjective emotional contagion
and key psychometric measures of emotional contagion
and empathy. Consequently, participants responded to the
Emotional Contagion Scale (ECS; Doherty, 1997 ; Gouveia
et al., 2007 ), the Interpersonal Reactivity Index (IRI; Davis,
1980 ; Pérez-Albéniz et al., 2003 ), the Questionnaire of
Prosocial Conduct (QPC; Martorell et al., 2011 ), and the
Basic Empathy S cale (BES; Jolliffe and F arrington, 2006 ;
Merino-Soto and Grimaldo-Muchotrigo, 2015 ).
MA TERIALS AND METHODS
Participants
Sixty-five undergraduate students from the N ational University
of Colombia participated in the study. Their avera ge age was
21.6 years old ( SD = 3.7); 50.7% were women. Thirty-two
participants were randomly assigned to the negative induction
group and 35 to the positive induction group. Boxplots reve aled
outliers for two participants, whose data were excluded from
further analysis. All participants provided written informed
consent prior to participating in the experiment, in accordance
with the ethical principles of the De claration of Helsinki.
Materials
Inquisit 4 software was used to record subjective ratings and
present all instructions, sur veys and prompts on a 23-inch
(1920 × 1980) computer screen. The approximate physical
distance between the participant and the s creen was 20 inches.
Two scenes from the FilmStim ( S chae fer et al., 2010 ) were
used for emotional induction: Dead Poets Socie ty 1 and American
H istory X . Twenty images of the Pictures Of F acial Affect
(POF A; Ekman, 1993 ) were used (10 happy and 10 angry).
Affective responses to these stimuli and a similar set of stimuli
were calibrated for the present population (see repository with
Supplementary Material 2 ). Additionally, the ECS ( Doherty,
1997 ; Gouveia et al., 2007 ) was used to estimate the correlation
between the proposed measurement model with individual
differences. Finally, three empathy questionnaires adapted to
Spanish were used: IRI ( Davis, 1980 ; Pérez-Albéniz et al., 2003 ),
QPC ( Martorell et al., 2011 ), and BES ( Jolliffe and F arrington,
2006 ; Merino-Soto and Grimaldo-Muchotrigo, 2015 ) 3 .
Pr ocedur e
The experiment had four phases. In the first phase , participants
were induced to either negative or positive affective states
by watching a positive or a negative video clip taken from
the FilmStim database ( S chae fer et al., 2010 ) according to
the randomly assigned experimental group. Immediately after
watching the video, the following question was presented to
participants: “How did this scene make you feel?” There were
three Likert scales bellow t he question. The scales had one
label at each side and ranged from 0 to 100. In the scale that
measured the negativity dimension, the labels were “0 – not
bad at all, ” and “100 – very bad ”; in t he scale that measured
the positivity dimension, the labels were “0 – not good at all, ”
and “100 – very good ”; in the s cale that measured the arousal
dimension, the labels were “0 – not restless at all, ” and “100 – very
restless.” In the present article, all prompts and instructions were
translated from Spanish. To maximize participants ’ attention
to the questions, their order randomly changed for each trial.
1 The FilmStim database offers two video clips taken from Dead Poets Socie ty . We
used the second one.
2 V ideos and faces were quantitatively analyzed according to the assumptions of
ESM ( Cacioppo et al., 1997 ) during previous experiments. Datasets are available
at: https://osf.io/52uj8/.
3 Questionnaire responses were analyzed and made a vailable at: https://osf.io/
52uj8/.
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The labels were red for the negativity dimension, green for the
positivity dimension, and blue for the arousal dimension. The
colors of labels remained constant throughout t he experiment
and were used to help participants discriminate the dimension
to evaluate in each prompt.
In the second phase, 20 images of faces taken from the POF A
were shown in random order for each participant (10 positive and
10 negative). The randomization compensated possible carryover
effects produced by the faces. Before each image, a gray screen
with a white cross in the middle was shown for 1 s. E very face
was shown for 3 s. After each face , three questions with the
same statement were shown. The statement was: “How do you
think this person feels?” P articipants answered using three Likert
monopolar scales, just like the ones presented after the video. The
scales had two labels (one in each side). The labels were different
for each question. In the question that measured the negativity
dimension, the labels were “0 – not bad at all, ” and “100 – very
bad ”; in the question t hat measured the positivity dimension, the
labels were “0 – not good at all, ” and “100 – very good ”; and in
the question that measured the arousal dimension, the labels were
“0 – not restless at all, ” and “100 – very restless.”
In the third phase, participants were asked if they had
previously seen the video clip from FilmStim ( S chaefer et al.,
2010 ), and two sur veys used for the creation of the FilmStim
( Schaefer et al., 2010 ) were applied as a manipulation check.
These were the Positive and Negative Affect Schedule (P AN AS)
( Watson et al., 1988 ) and a self-reported emotional arousal sc ale
( Schaefer et al., 2010 ).
Finally, in the fourth phase, participants were asked
to answer the questionnaires in the following order : IRI,
QPC, BES, and ECS.
Emotional Contagion Equation
We hypothesized that exposition to positive and negative videos
could bias emotional contagion to happy versus angry faces.
Therefore, we estimated the shortest distance between two
affective coordinates (video and face) in a three-dimensional
Euclidean space. The emotional distance between the affective
state induced by the video and the subjective evaluation of the
faces can be expressed using the equation:
ec = 1 − 

q  n v − n f  2 +  p v − p f  2 +  a v − a f  2
100 × √ 3

 (1)
where n v , p v , and a v represent participants ’ affective state induced
by the video in the negativity, positivity, and arousal dimensions,
respectively, and n f , p f , and a f represent participants ’ evaluation
of the faces in the same dimensions. The value inside the
parenthesis is subtracted from one because (a) it represents
emotional distance analogously to the ESM ( Cacioppo et al.,
1997 ) and (b) because we assume that emotional contagion to
faces is the inverse of this distance. To facilitate data analysis, we
rescaled values to a range from 0 to 1. Thus, we divided everything
inside the square root by the maximum possible distance between
the two affective coordinates.
RESUL TS
Data were analyzed using a 2 × 2 mixed ANOV A, with negative
and positive emotional induction as between-group factor , happy
and angry faces as within-group factor. The dependent variable
was the magnitude of emotional contagion to faces, as defined
in Eq. 1. Outliers were detected in two participants whose
data were excluded from further analysis. Data were normally
distributed for all cells of the experimental design, as assessed by
Shapiro–W ilk test ( p > 0.05). The assumption of homogeneity of
variances was met, according to Brown–Forsythe test ( p > 0.05).
A statistically significant two-way interaction was found, F (1,
65) = 103.957, p < 0.001, η 2
p = 0.615. A main effect for type
of face was found, F (1, 132) = 4.766, p = 0.031, η 2
p =0.035.
A paired-samples t -test was conducted to compare the magnitude
of emotional contagion to happy and angry faces in each group.
A Bonferroni correction was applied. Emotional contagion was
significantly greater for angry ( M = 0.76, SD = 0.08) than
happy faces ( M = 0.41, SD = 0.18) in the negative group,
t (31) = , p < 0.001. The opposite tendency was found in the
positive group, where emotional contagion was significantly
lower for angry ( M = 0.54, SD = 0.15) than for happy faces
( M = 0.71, SD = 0.13), t (34) = , p < 0.001. No main effect
for type of emotional induction was found, F (1, 65) = 3.353,
p = 0.072, η 2
p = 0.049.
We were interested in analyzing the effect of gender on
emotional contagion to faces. This required a hierarc hical model
because our sample size differed between e ach group ( Huta,
2014 , p. 21). Therefore, a linear mixed effects model was run to
analyze whether gender had an effect on emotional contagion
to faces. The fixed effects of the model where type of emotional
induction and gender. The random effects were the intercepts for
participants and type of face, as well as by-participant and by-
type-of-face random slopes for the effect of emotional contagion.
This model was compared to another model that had the same
parameters but did not ha ve a fixed effe ct for gender. Results of
both models were compared using a Chi-squared test. Given that
no significant differences were found, we infer that participant ’ s
gender had no significant effect on emotional contagion to faces
( χ 2 (1) = 1.68, p = 0.195).
Responses about familiarity with the videos were recorded
for only 42 participants, due to a technical problem. Therefore,
we excluded this question from the analysis. Otherwise,
the manipulation check was successful. Overall, participants ’
evaluations (see T able 1 ) were similar to the scores reported in
the FilmStim ( S c haefer et al., 2010 ).
Results from the questionnaires were compared with the
emotional contagion ratings. We predicted that participants with
higher emotional contagion ratings would be more empathetic,
as assessed with the empathy questionnaires, or more prone to
emotional contagion, as assessed with the ECS ( Doherty, 1997 ).
However , we did not find t his result (see T able 2 ). A Pearson
correlation was performed between emotional contagion to faces
and each questionnaire separately. No Bonferroni correction or
similar was applied. There was a significant negative correlation
between emotional contagion toward happy faces in the negative
induction group and the IRI scores, r (30) = –0.40, p = 0.02 and
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T ABLE 1 | Summary of responses in the manipulation check and reported scor es
in the FilmStim ( Schaefer et al., 2010 ) for the two videos used in the experiment.
Experiment score FilmStim score
American History X
P A (positive affect) 2.55 2.04
NA (negative affect) 2.45 2.73
Arousal 5.44 5.84
Dead Poets Society
P A (positive affect) 3.01 2.82
NA (negative affect) 1.56 1.21
Arousal 4.8 5.66
Positive and negative affect are measured using a five-point scale, taken from the
P ANAS ( Watson et al., 1988 ), while arousal is measured using a seven-point scale,
proposed by the authors of the FilmStim ( Schaefer et al., 2010 ).
ECS scores ( Doherty, 1997 ), r (30) = –0.43, p = 0.01. In all the
other cases correlations were not significant.
DISCUSSION
Our findings suggest that participants minimized the emotional
distance between the affective state trig gered by the video and
the affective state triggered by unfamiliar faces. When induced
to a negative affective state, participants judged both angry
and happy faces closer to a negative affective state. Conversely,
when induced to a positive affective state, participants judged
both happy and angry faces closer to a positive affective
state. Plainly, participants ’ induced affective state biased their
emotional contagion to unfamiliar faces. Additionally, the results
also show a double dissociation, happy and angry face s were
differently evaluated by participants previously exposed to either
an uplifting or an upsetting video.
In our view, these results show key differences in the way
emotional contagion operates, when it is triggered by negative
versus positive emotions. Negative affective states generate more
bias than positive states. This can be obser ved in Figure 1 ,
where the highest emotional contagion is obser ved for the
negative induction group toward angry faces. This finding
is consistent with previous research suggesting that negative
affective states trigger more behavioral c hanges than positive ones
( Cacioppo et al., 1997 ).
In this experiment, we used two types of measures to assess
participants ’ affective states. A direct measure for t he video and
an indirect measure for the faces. The first was direct in the sense
that participants were asked explicitly about their feelings a fter
watching the video (“How did this scene make you feel?”). The
second was indirect in the sense that participants were asked to
rate the feelings they perceived from the faces (“How do you think
this person feels?”). We used this later measure to estimate the
amount of convergence between the affective states before and
after the presentation of faces. A s mentioned above, we decided
to use an indirect (implicit) measure because (1) people tend
to use their own affective state as a heuristic to judge others ’
affective states ( Forgas, 1995 ; V an Boven and Loewenstein, 2003 )
and (2) an indirect measure potentially reduces the noise caused
by individual explicit preferences. For instance, if the second
question would ha ve been a direct measure, i.e., “how does this
person make you feel?” the corresponding answers might ha ve
been biased by personal liking, and metacognitive judgments
about the self. Similarly, if the question were “how do you feel?”
the corresponding answer would imply a permanent higher-
order judgment about the self and not a temporary automatic
influence exerted by video on the judgment of each face. This
methodological approach was inspired by previous research
suggesting that people tend to misattribute the sources of their
affective states, and that implicit measures tap e arlier emotional
processing-stages than direct measures do (e.g., P ayne et al., 2010 ;
P ayne and Lundberg, 2014 ). Therefore , if emotional contagion
involves basic and early emotional processing-stages as originally
suggested by H atfield et al. (1993, 1994) , then there are more
chances to measure it by indirectly estimating the influence it
exerts on implicit (indirect) measures.
Our results provide independent support to the claim that
emotional contagion is not always a linear process ( Dezecache
et al., 2015 ). Negative emotions seem to propagate more than
positive emotions, and other people ’ s affective states are perceived
more negatively when the obser ver is also in a negative affective
state. This suggests that stronger emotional contagion occurs
when a person, in a negative affective state, observes someone in
a similar affective state.
This is interesting because previous rese ar ch ( Kramer et al.,
2014 ) suggests that Fa cebook users tend to display more positive
emotions when exposed to positive emotional content and
more negative emotions when exposed to negative emotional
content. Our results are consistent with these findings as well.
Therefore, increasing exposition to negative emotional content
might intensify negative emotions in the obser ver , which in turn
increases sensitivity to emotional contagion to negative stimuli.
Conversely, increasing exposition to positive emotional content
T ABLE 2 | Summary of Pearson correlations between questionnaires ( p -values in par enthesis) and emotional contagion assessed with Eq. 1.
Questionnaire Negative induction group Positive induction group
Happy faces Angry faces Happy faces Angry faces
ECS − 0.43 (0.015)* − 0.28 (0.127) − 0.28 (0.101) − 0.02 (0.929)
IRI − 0.40 (0.024)* − 0.18 (0.330) − 0.21 (0.219) − 0.04 (0.809)
BES − 0.30 (0.096) − 0.30 (0.099) − 0.10 (0.549) − 0.10 (0.559)
QPC − 0.19 (0.307) − 0.26 (0.148) − 0.23 (0.180) − 0.08 (0.658)
*p < 0.05.
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FIGURE 1 | Mean emotional contagion toward angry and happy faces for the gr oups exposed to a negative and a positive video clips, as assessed with Eq. 1. Error
bars depict 95% CI.
might intensify positive emotions in the obser ver , magnifying
emotional contagion to positive stimuli.
On the other hand, we did not find systematic correlations
between psychometric questionnaires and our emotional
contagion measure. In most cases, significant correlations were
absent. In two cases where significant correlations were present,
they were negative, suggesting that people displaying higher
levels of empathy rated lower in our emotional contagion
measure. However , the fact that these correlations were
significant only for happy faces in the negative group (see
T able 2 ) implies that those results cannot be generalized to all
emotional contagion processes. In our view, these results are
not completely surprising because questionnaires tap into a very
different psychological process related to emotional contagion.
Questionnaires are conceived as a personality-trait measure
influenced by stable metacognitive judgments about the self in
social situations. They are mainly based on explicit propositional
knowledge (i.e., “I sometimes feel helpless when I am in the
middle of a very emotional situation”; Davis, 1980 ). Instead, our
measuring framework directly taps into spe cific momentarily
states of emotional contagion hardly assessed by rationalized
judgments about the self.
Based on our study, future experiments might investigate
whether participants ’ affective states influence automatic
imitation of facial expressions. Do people imitate negative
faces faster or with greater accuracy when induced to a
negative affective state? We believe that the answer would
be affirmative. In those experiments, automatic emotional
responses could be assessed using electrophysiological signals.
Previous research points out that activation of CS and ZM
muscles are associated with negative and positive emotions,
respectively ( Dimberg, 1982 ). Additional researc h has found that
activation of the sympathetic system is associated with HR V
( Appelhans and Luecken, 2006 ). Thus, activity of the CS-muscle,
ZM-muscle, and HR V would be equivalent to the negativity,
positivity, and arousal dimensions used in our study, which are
similar to the dimensions of the ESM ( Cacioppo et al., 1997 ).
However , it is import ant to consider that facial expres sions might
not always correlate with self-reported subjective affective states
( Fridlund, 1991 ). Yet, the magnitude of the dissociations between
subjective (i.e., participant ’ s ratings of positivity, negativity and
arousal) and objective measures (e.g., EMG and HR V) could be
assessed using a mathematical approach similar to Eq. 1. This
approach would consist on calculating the emotional distance
between subjective and objective emotional responses. This
model would analyze individual differences in the magnitude
of these dissociations, which in turn could help to quantify the
relative contribution from each type of measure to the final
affective state.
In short, our study provides evidence suggesting that people
induced to a positive affective state show higher levels of
emotional contagion to positive faces, while people induced to a
negative affective state show higher levels of emotional cont a gion
to negative faces. Furthermore, we provide evidence suggesting
that subjective biases induced by current affective states are easily
estimated by a simple mathematical model mapped onto a three-
dimensional affective space. However , in scenarios where t hese
two affective coordinates are similar due to factors not directly
related to emotional stimulation (e.g., both states are neutral), the
output of the model would indicate high emotional contagion,
regardless of how much emotional c hange has been actually
produced. This is a boundary condition of our model, which
is exclusively useful for conditions where emotional contagion
processes are reasonably as sumed to be in operation.
Finally, the main contributions of this study are (1) to
provide a measurement framework to analyze how affective
Frontiers in Psychology | www .frontiersin.org 6 January 2020 | V olume 11 | Article 97

fpsyg-11-00097 J anuary 30, 2020 T ime: 16:59 # 7
Pinilla et al. Affective States Bias Emotional Contagion
states influence the directionality of emotional cont agion and
(2) to propose a methodological approach to analyze emotional
contagion, not only as a binary outcome, but as a continuous
quantitative variable.
AUTHOR’S NOTE
The dataset used in this article was pre viously used in the
Master ’ s Thesis of AP ( Pinilla, 2017 ). This work was super vised by
R T. The thesis is available at: http://bdigital.unal.edu.co/59467/1/
1020759173.2017.pdf.
DA T A A V AILABILITY ST A TEMENT
The datasets generated for this study are available at https://os f.
io/52uj8/.
ETHICS ST A TEMENT
Ethical review and approval was not required for t he study
on human participants in accordance with the local legislation
and institutional requirements. The participants provided their
written informed consent to participate in this study.
AUTHOR CONTRIBUTIONS
R T and AP proposed the experimental design. JN worked
on acquisition of the data. All authors analyzed and
interpreted the results.
FUNDING
We acknowledge support by the Universidad N acional de
Colombia, Hermes Project No. 37456, the German Research
Foundation, and the Open A ccess Publication Fund of
TU Berlin.
ACKNOWLEDGMENTS
We appreciate help of Diego U seche formulating the
mathematical model and acknowledge the effort of Deisy
V alcárcel, Nicolás Buitrago, and Daniel A vilán gathering the data.
SUPPLEMENT AR Y MA TERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpsyg.
2020.00097/full#supplementary- material
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commer cial or financial relationships that could be construed as a
potential conflict of interest.
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distributed under t he terms of the Crea tive Commons A ttribution License (CC BY).
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