THE GOOD SEED DROP-IN, Website - (goodseedcdc.org) MISSION.docx

THE GOOD SEED DROP-IN,
Website - (goodseedcdc.org)
MISSION
Good Seed’s Mission Is to help youth not only live but thrive.
We provide supportive, nurturing, specialized care for homeless
young people in California through supportive housing, job
training, comprehensive services, and individual planning. Good
Seed creates an environment where youth, ages 18-25, can
thrive and grow to achieve their full potential. Our aim is to
ensure that youth have the tools they need to realize their goals
and dreams, so that they may live healthy and meaningful lives.
Populations Served
Homeless, Previously, Currently
Teen & Young adults
Single adults
Mental Health
PROVIDE
· Temporary safety and basic supports for Seriously
Emotionally Disturbed (SED) and Severe and Persistently
Mentally III (SPMI) TAY who are living on the streets or in
unstable living situations.
· Safe environments in which TAY can make new friends and
participate in social activities.
· Linkage to Mental Health
· Employment Assistance
· Computer/Internet Access
DVD & Games
· Linkage to Substance Abuse Treatment
· Information Educational Services
Social Activities
PROGRAMS FOR CLIENT’S

· Supportive Housing Program
· Enhanced Emergency TAY Shelter
· Gain/Grow Program
· MTA: Homeless Youth Outreach
Beyond Emotional Similarity: The Role of Situation-Specific
Motives
Amit Goldenberg
Stanford University
David Garcia
Complexity Science Hub Vienna, Vienna, Austria, and Medical
University of Vienna
Eran Halperin
Interdisciplinary Center Herzliya
Jamil Zaki, Danyang Kong, Golijeh Golarai,
and James J. Gross
Stanford University
It is well established that people often express emotions that are
similar to those of other group members.
However, people do not always express emotions that are
similar to other group members, and the factors
that determine when similarity occurs are not yet clear. In the
current project, we examined whether
certain situations activate specific emotional motives that

influence the tendency to show emotional
similarity. To test this possibility, we considered emotional
responses to political situations that either
called for weak (Studies 1 and 3) or strong (Study 2 and 4)
negative emotions. Findings revealed that the
motivation to feel weak emotions led people to be more
influenced by weaker emotions than their own,
whereas the motivation to feel strong emotions led people to be
more influenced by stronger emotions
than their own. Intriguingly, these motivations led people to
change their emotions even after discovering
that others’ emotions were similar to their initial emotional
response. These findings are observed both
in a lab task (Studies 1–3) and in real-life online interactions on
Twitter (Study 4). Our findings enhance
our ability to understand and predict emotional influence
processes in different contexts and may
therefore help explain how these processes unfold in group
behavior.
Keywords: emotions, emotion contagion, emotional influence,
motivation, group dynamics
Supplemental materials:
http://dx.doi.org/10.1037/xge0000625.supp
People often respond emotionally to sociopolitical events, even
when these events do not touch them personally, but only relate
to
their group as a whole (Smith, 1993; Smith & Mackie, 2015;
Wohl, Branscombe, & Klar, 2006). These emotions are almost
never experienced in isolation from the emotions of other group
members. On the contrary, group members’ emotions often
influ-
ence other group members’ emotions, making emotional
respond-

ing a truly social process (Le Bon, 1895; Rimé, Finkenauer,
Luminet, Zech, & Philippot, 1998). The social dimension of
emo-
tions, particularly in response to sociopolitical events, is
becoming
increasingly important with the use of social media and people’s
constant exposure to the emotions of others in online platforms.
Recent social movements and political shifts such as the Arab
Spring, Black Lives Matter, and online interactions before and
after the recent U.S. elections reveal how the spread of
emotions
can contribute to important changes in our society.
It is clear that people are influenced by other’s emotions, but do
we truly understand the nature of that influence? In mapping the
type of influence group members have on each other’s emotions,
prior work has focused on emotional similarity, defined as re-
sponding to other group members’ emotions with similar
emotions
(for reviews see Barsade, 2002; Fischer, Manstead, & Zaalberg,
2003; Hatfield, Cacioppo, & Rapson, 1994; Parkinson, 2011;
Peters & Kashima, 2015). Emotional similarity is a well-
established psychological process, driven by the visceral and
im-
mediate nature of emotions which makes them highly sensitive
to
the context in which they are experienced (Parkinson, 2011).
When people experience emotions in social contexts, they rely
heavily on others’ emotions in developing their own responses,
which often lead them to feel similar to others (Manstead &
Fischer, 2001).
But emotional similarity should not be inevitable, and the dy-
namics of the social influence of emotions are probably more
nuanced than only similarity. People’s emotional motivation to

resist or enhance the experience of certain emotions should play
a
role in the degree to which they are influenced by the emotions
of
their social environment. Previous research suggests that in
some
This article was published Online First June 13, 2019.
Amit Goldenberg, Department of Psychology, Stanford
University; Da-
vid Garcia, Complexity Science Hub Vienna, Vienna, Austria,
and Section
for Science of Complex Systems, Center for Medical Statistics,
Informatics
and Intelligent Systems, Medical University of Vienna; Eran
Halperin,
Department of Psychology, Interdisciplinary Center Herzliya;
Jamil Zaki,
Danyang Kong, Golijeh Golarai, and James J. Gross,
Department of
Psychology, Stanford University.
We thank Twitter for making data available for Study 4. All
data are
available at https://osf.io/7tja9/.
Correspondence concerning this article should be addressed to
Amit
Goldenberg, Department of Psychology, Stanford University,
Jordan Hall,
Building 420, Room 425, Stanford, CA 94305-2130. E-mail:
[email protected]
stanford.edu
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Journal of Experimental Psychology: General
© 2019 American Psychological Association 2020, Vol. 149,
No. 1, 138–159
0096-3445/20/$12.00 http://dx.doi.org/10.1037/xge0000625
138
https://osf.io/7tja9/
mailto:[email protected]
http://dx.doi.org/10.1037/xge0000625
situations, people appear to have emotional motivations that
shape
the strength of their emotional responses (Tamir, 2016; Zaki,
2014). In the same way that these motivations influence
people’s
emotions when they are on their own, these motivations may
also
play a role in enhancing or reducing the degree to which people
are
influenced by others’ emotions.
Changes in the degree of intragroup emotional influence can be
very important to overall group behavior, especially when we
are
thinking of emotional interactions that occur on social media
regarding different political situations. For example, take a

situa-
tion in which multiple group members are motivated to
experience
strong negative emotions such as outrage. In such a situation,
these
emotional motivations may not only affect individual emotional
responses but also how contagious these emotions are within the
group, and how much they contribute to overall group emotional
response, both in terms of group’s collective action and in terms
of
support for certain policies (Brady, Wills, Jost, Tucker, & Van
Bavel, 2017; Goldenberg, Garcia, Suri, Halperin, & Gross,
2017).
Yet, despite their importance, very little work has been done to
examine processes beyond emotional similarity.
The goal of the present research was to examine whether and to
what extent situations that activate specific emotional motives
interact with the tendency to show emotional similarity. Specifi-
cally, we examined situations in which we thought people would
be motivated to experience certain emotions at either weak or
strong intensities. In these situations, we asked: are people as
likely to be influenced by those who express stronger emotions
as
by those who express weaker emotions? And if people feel the
same level of emotion as other group members, does this mean
they have no impact on each other, or might social influences
still
be operative? We answer these questions by integrating both
laboratory experiments and an analysis of interactions on
Twitter.
Emotional Similarity
Compared to attitudes and beliefs, emotions are especially
prone

to social influence due to their immediate nature, and the fact
that
they are mostly activated by other people. These emotional
influ-
ence processes often lead people’s emotions to resemble others’
emotions (Barsade, 2002; Fischer et al., 2003; Hatfield et al.,
1994;
Páez, Rimé, Basabe, Wlodarczyk, & Zumeta, 2015; Parkinson,
2011; Peters & Kashima, 2015; Rimé, 2007). This well-
established
phenomenon of emotional similarity occurs in all facets of life,
from interpersonal relationships (Beckes & Coan, 2011;
Feldman
& Klein, 2003) to large collectives (Durkheim, 1912;
Konvalinka
et al., 2011; Páez et al., 2015). Emotional similarity occurs in
many
modes of communication: from face to face interactions in real
life
(Hess & Fischer, 2014; Konvalinka et al., 2011), to text based
interactions on social media (Garcia, Kappas, Küster, &
Schweitzer,
2016; Kramer, Guillory, & Hancock, 2014), and even in cases in
which people are not exposed to actual emotional expressions of
others, but merely receive information about these emotions
(Lin, Qu,
& Telzer, 2018; Smith & Mackie, 2016; Willroth, Koban, &
Hilimire,
2017).
For emotional similarity to occur, people have to be exposed to
the emotions of others. One domain in which such exposure is
common is social media. Social media are driven by an attention
economy, in which users are competing for other users’
attention.
Expressing emotions is a very good way to attract attention and

expand exposure (Tufekci, 2013). Furthermore, social media
com-
panies are motivated to maximize emotions to maintain users’
engagement and are therefore motivated to promote emotional
content (Crockett, 2017). The occurrence of strong emotions on
social media is accompanied by processes of emotion similarity
(Del Vicario et al., 2016; Kramer et al., 2014). Especially in
social
and political contexts, emotional similarity plays a central role
in
a variety of collective behaviors such as prosociality (Garcia &
Rimé, 2019; van der Linden, 2017), collective action (Alvarez,
Garcia, Moreno, & Schweitzer, 2015; Brady et al., 2017), and
polarization and conflicts (Del Vicario et al., 2016).
Emotional influence processes that lead to similarity can occur
as a result of different mechanisms. One mechanism is “emotion
contagion,” which involves immediate changes to one’s
emotions
as a result of direct exposure to others’ emotional expressions
(Hatfield et al., 1994). Processes of contagion were originally
thought to be “automatic” and uninfluenced by social
motivations
(Hatfield et al., 1994). However, recent work shows that even
these fleeting processes are modified by motivational influences
which may increase or decrease their strength (Bourgeois &
Hess,
2008).
A second mechanism for emotional influence process that leads
to similarity is social appraisals (for reviews, see Parkinson,
2011;
Peters & Kashima, 2015). According to social appraisal theory
(Manstead & Fischer, 2001), individuals use others’ emotions as
appraisals that help them to construct their own emotional expe-

riences. Social appraisal processes are influenced by situational
and motivational considerations. Thus, people’s basic
motivations
to belong to their group (Baumeister & Leary, 1995) or to see
their
group in a positive light (Hogg & Reid, 2006; Turner, Hogg,
Oakes, Reicher, & Wetherell, 1987) can influence how they
inter-
pret others’ emotions and how they choose to relate to them
(van
Kleef, 2009). Such motivations often lead group members to
feel
similar emotions to others.
Beyond Emotional Similarity
Compelling as the findings regarding emotional similarity may
be, there is reason to believe that emotional similarity may not
be
inevitable (e.g., see Delvaux, Meeussen, & Mesquita, 2016). In
particular, previous work suggests that situation-specific
emotional
motives can and do influence people’s emotional responses. Re-
cent studies show that individuals are more likely to experience
strong negative emotions in situations in which expressing such
emotions is perceived to be helpful, and are more likely to
reduce
negative emotions in situations in which such emotions are per-
ceived to be unhelpful (Ford & Tamir, 2012; Porat, Halperin, &
Tamir, 2016; Tamir, Mitchell, & Gross, 2008). For example,
when
people believe that feeling increased anger would serve their
group’s ideological goals in an intergroup context, they may
choose to expose themselves to information that will make them
angrier (Porat et al., 2016). If situation-specific motives can
influ-

ence people’s emotions, it seems reasonable that these motives
might influence the degree to which emotional similarity is ob-
served.
One indication that situation-specific motives were operative in
a given context would be a lack of symmetry in the way people
are
influenced by weaker or stronger emotions in others. A simple
account of emotional similarity suggests that people should
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139BEYOND EMOTIONAL SIMILARITY
be influenced by others’ emotions in the same way whether
these
emotions are stronger or weaker than their own emotions. How-
ever, this assumption may not hold when people have a clear
emotional motive to feel either strong or weak emotions. To our
knowledge, this idea has not been directly tested. Much of the
work on emotional similarity has only examined the influence
of
emotions that are stronger than one’s own emotions (Hatfield et
al., 1994; Konvalinka et al., 2011; Kramer et al., 2014). Even in
the
few experiments in which participants received either stronger
or
weaker group emotional feedback (e.g., Koban & Wager, 2016;

Willroth et al., 2017), it is impossible to tell from these findings
whether the process of similarity is symmetrical. Our
assumption
in this project is that people may be more influenced by
stronger
or weaker emotions, depending on the emotional motives
elicited
by specific situations.
A second indication that situation-specific motives were oper-
ative in a given context would be a change in people’s
emotional
responses even when their initial responses were similar to
other
group members. To our knowledge, this idea also has not been
directly tested. This is because prior work has focused on what
happens when individuals are exposed to emotions that differ
from
their own. Our assumption is that in situations that call for
strong
or weak emotions, there is no reason to believe that the
motivation
to increase or decrease these emotions will cease once similarity
is
achieved. In fact, if group members have a motive to be “better
than average” (i.e., to be more responsive to a personally valued
situation-specific motive than other members of their group),
they
may be motivated to change their emotions when learning that
others feel similar emotions. This assumption is supported by
recent work that shows that in some cases, group members may
be
motivated to feel emotions that are either greater or lesser than
others in their group (Goldenberg, Saguy, & Halperin, 2014;
Ong,
Goodman, & Zaki, 2018). However, these studies have not

exam-
ined whether such motivation predicts changes in people’s emo-
tions when they learn what other people feel, leaving this an
open
question.
Emotional Influence as a Driver of Emotional
Escalation and Polarization
Emotional influence processes, if aggregated, may lead to im-
portant changes in overall group emotion and behavior. For ex-
ample, a motivational bias that leads people to be more
influenced
by stronger, compared to weaker group emotions, might
contribute
to quicker contagion and thus an increase in overall group
emotion.
Group members’ tendency to increase their emotional responses,
even when learning that others feel similar emotions to them,
might further exacerbate these intragroup dynamics. These types
of processes are similar to those documented in social
psychology
research on escalation and polarization (Iyengar, Lelkes, Leven-
dusky, Malhotra, & Westwood, 2018; Myers & Lamm, 1976;
Ross, 2012). Indeed, alongside increasing concerns regarding
the
occurrence of polarization, there is an increased realization of
the
central role that emotions have in contributing to its occurrence
and amplification (Crockett, 2017; Iyengar et al., 2018; Tucker
et
al., 2018).
The idea that groups may polarize merely as a result of intra-
group processes has been a source of interest (and contention)
in

social psychology for almost 60 years, since James Stoner, a
master’s-level student at Massachusetts Institute of Technology,
revealed the existence of a risky shift (Stoner, 1961). Stoner’s
dissertation ignited tremendous interest in polarization (Cart-
wright, 1973; Dion, Baron, & Miller, 1970; Lord, Ross, &
Lepper,
1979; Moscovici & Zavalloni, 1969; Myers & Bishop, 1970),
but
also skepticism and rigorous attempts to outline its limitations
and
boundary conditions (Myers & Lamm, 1976; Westfall, Judd, &
Kenny, 2015). Following these efforts, the primary focus in po-
larization research has shifted from establishing its existence to
clarifying the specific processes or situations that lead some
groups to
polarize more than others (Westfall et al., 2015).
Previous research has pointed to three main mechanisms for
polarization. First, polarization can be caused merely as a result
of
conformity processes operating on a skewed distribution of atti-
tudes within a group (Myers & Lamm, 1976). Second,
polarization
can be caused as a result of exposure to outgroup views which
may
challenge one’s ideology or belief system (Lord et al., 1979;
Ross,
2012). Finally, and most relevant to the current project, is
polar-
ization that occurs as a result of social comparison processes
(Abrams, Marques, Bown, & Dougill, 2002; Hornsey & Jetten,
2004; Jetten & Hornsey, 2014; Packer, 2008; Skinner &
Stephen-
son, 1981). The main argument for the social comparison
approach

is that people may have certain goals or ideals for their desired
position in relation to others in their group. This leads group
members to aspire to express views that are more extreme than
other group members, thus further contributing to escalation and
polarization.
Social comparison is a primary focus as a mechanism for this
project. In cases in which participants learn that other group
members feel similar emotions to them, social comparison may
be
operative in motivating participants to change their emotions to
reflect their difference from the group. In cases in which
partici-
pants learn that group emotion is different from their own
emotion,
social comparison is more likely to motivate group members to
change their emotions when they learn that they are actually
“worse than average” in terms of their desired emotional re-
sponses. Furthermore, we believe that emotions play a unique
role
in these social comparison processes. The primary reason for
this
assumption is that by their nature, emotions are immediate re-
sponses to events rather than well-established beliefs. They
there-
fore are much more likely to change as a result of relational
motivations, and their change can be quick and impactful. These
emotional changes are usually what is responsible for more per-
manent changes in attitudes that further contribute to
polarization
(for a similar argument, see Iyengar et al., 2018). Yet, despite
their
central role in perpetuating processes of polarization, very little
work has examined the unfolding of these intragroup emotional
processes.

The Present Research
The goal of the current project was to examine whether and to
what extent certain situations activate emotional motives that
influence emotional similarity. We used three lab studies (with
pretests for the first two studies) and a Twitter analysis to
examine
this issue (all data are available at https://osf.io/7tja9/).
In Study 1, we examined a situation in which we expected group
members would be motivated to express weak negative
emotions.
To achieve this goal, we conducted a laboratory study using a
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140 GOLDENBERG ET AL.
https://osf.io/7tja9/
relatively liberal college student population (American citizens
at
Stanford University). We examined how participants’ emotions
were influenced by other Americans’ emotions in response to
pictures of outgroup threat (such as people burning the
American
flag). Our choice of these pictures was motivated by our
expecta-
tion (which was confirmed by our pretest) that participants

would
be motivated to experience weak negative emotions in response
to
such situations and that such motivation would bias the way
participants are influenced by others’ emotions. We had two hy-
potheses. Our first hypothesis was that in this context,
participants
would be more influenced by weaker emotions compared to
stron-
ger emotions. Our second hypothesis was that when learning
that
others’ emotions were similar to their own emotions,
participants
would change their emotions to be weaker than their initial re-
sponse.
In Study 2, we examined a situation in which we expected group
members would be motivated to express strong negative
emotions.
Specifically, we examined emotional responses in a liberal
college
to cases in which American soldiers behaved immorally toward
outgroup members (such as in the Abu Ghraib prisoner abuse).
This choice was motivated by our expectation (which was con-
firmed in our pretest) that participants would be motivated to
express strong negative emotions to such situations and that this
motivation would bias the way participants are influenced by
others’ emotions. In this context, we expected participants
would
be more influenced by stronger (compared to weaker) emotions
of
other group members, and that participants would change their
emotions to be stronger than their initial response.
Study 3 was designed to extend the findings of Studies 1 and 2.
In this study, we added a control condition in which participants

were not exposed to any group emotion, with the intention of
showing that in this condition there would be no change in par-
ticipants’ emotional ratings. In addition, we directly tested
partic-
ipants’ emotional motivations in relation to their group and
looked
at whether these motivations moderated the results. As the
pictures
used in Study 3 were similar to those of Study 1, our hypotheses
were also similar to those of Study 1, with the added hypothesis
that participants’ motivation would moderate the results.
Finally, Study 4 was designed to replicate the findings of Study
2 in a real-life context. We looked at changes in emotions ex-
pressed in tweets related to civil unrest following the police
shooting of Michael Brown in Ferguson, Missouri, on August 9,
2014, a context that we expected would lead users who tweeted
about the Ferguson unrest to be motivated to express strong
negative emotions. For this reason, our hypotheses for Study 4
paralleled those of Study 2.
Study 1: Emotional Influence in Response to Outgroup
Threat in the Laboratory
The goal of Study 1 was to examine processes of emotional
influence in a situation in which participants have a clear
prefer-
ence to have weaker negative emotions, both in an absolute
sense
and in relation to other group members. To achieve this goal,
we
conducted a laboratory study using pictures of outgroup threat
in a
relatively liberal college student population (American citizens
at
Stanford University). We expected—and validated in a set of

pilot
studies—that these participants would be motivated to express
weak negative emotions in comparison to other Americans, in
response to situations of outgroup threat. This expectation was
based on previous work on political affiliation and emotional
preferences which suggested that liberals not only tend to
experi-
ence less anger in response to outgroup threat, but are also
moti-
vated to experience less anger in response to such cases (Porat
et
al., 2016). We therefore expected this motivation to modify the
way in which participants were influenced by the emotions of
other group members. Our first hypothesis was that in this
context,
participants would be more influenced by weaker emotions com-
pared to stronger emotions. Our second hypothesis was that
when
learning that others’ emotions were similar to their own
emotions,
participants would change their emotions to be weaker than
their
initial response.
Method
Participants. Previous studies that used a similar task to the
one used in the current project have found very strong effects of
emotional influence using 20 participants and 200 stimuli
(Klucha-
rev, Hytönen, Rijpkema, Smidts, & Fernández, 2009) or 30 par-
ticipants and 150 stimuli (Willroth et al., 2017). Because our
stimulus set included 100 emotional pictures, we decided to aim
for 40 participants to reach a similar number of trials as
previous

studies. Forty Stanford students were recruited in exchange for
credit; all were Americans. We omitted two participants from
the
analyses (one participant was removed for misunderstanding the
instructions and another for participating in a similar study and
recognizing the manipulation) resulting in a sample of 38
partic-
ipants (23 males, 15 females; age: M � 19.33, SD � 1.12).
Pretests. We conducted two pretests before running the actual
study. The first pretest was designed to confirm that our stimuli
would elicit the desired negative emotions. Analysis of these
pretest pictures suggested that our stimulus set indeed elicited
negative emotions, predominantly anger (see the online supple-
mental materials). The second pretest was designed to test our
expectation that liberal Americans would be motivated to
experi-
ence weak negative emotions in response to such pictures. This
question was evaluated during a pretest and not in the actual
experiment to avoid a situation in which answering this question
would affect participants’ performance in the task (and vice
versa).
As expected, we found that participants not only wanted to feel
weak negative emotions in the context of outgroup threat, but
also
wanted to feel weaker negative emotions than others, which
sup-
ports the idea that this emotional motive may involve social
comparison processes (for details, see the online supplemental
materials).
Stimulus set. Our final stimulus set included a total of 150
pictures, 100 depicting cases of outgroup threat and 50 neutral
pictures. Our outgroup threat pictures were of outgroup
members
conducting either threatening gestures such as burning the U.S.

flag or pictures of actual terror attacks in the United States such
as
the September 11 attacks. Our neutral pictures were pictures of
either crowds or cities and were taken from similar studies in
which participants’ ratings indicated that the pictures indeed
elic-
ited very little emotional response. The purpose of the neutral
pictures was to buffer the negative effect of the negative
pictures.
Procedure. Along with all subsequent studies described here,
Study 1 received research ethics committee approval prior to the
collection of data. Participants were told that they were taking
part
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in a study with the goal of validating emotional ratings of
pictures.
According to the instructions, each picture would have to be
rated
twice for reliability purposes. The study included two phases.

In Phase 1, participants saw 150 pictures for three seconds each.
A hundred of those pictures were of situations eliciting
outgroup
threat and 50 were neutral pictures (either pictures of urban
areas
or of crowds, similar to the pilot study). Participants were asked
to
rate the intensity of their emotions in response to the pictures
on a
scale of 1 to 8 that appeared at the bottom of the screen, 1
indicating not intense, and 8 very intense (see Figure 1 for the
trial
structure). We chose to use a unipolar scale (weak to strong
negative intensity) to simplify the task as much as possible. We
used a 1–8 scale so that participants would be able to use both
hands for the rating while looking at the screen at all times. The
participant’s rating on each trial was highlighted by a red box.
In Phase 2, participants were asked to rate the intensity of their
emotions in response to the same pictures again. They were told
that before each rating they would be shown an average rating
of
approximately 250 other American participants who completed
the
study. Participants were not told the exact identity of these 250
participants; however, post study interviews suggested that they
estimated that these were 250 people like them (Stanford
students)
who completed the study in exchange for course credits. The
ostensible rationale for showing the group ratings was that
previ-
ous work indicated that this process was helpful in maintaining
participants’ interest (participants were later asked to estimate
the
goal of the study to make sure that indeed they were not con-

sciously trying to be aligned with the group).
Although participants believed that the group ratings were the
average ratings of 250 Americans (validated by a post session
interview), the ratings were actually generated by a
pseudorandom
algorithm that resulted in three trial types. On a third of the
trials,
the average group emotion was chosen from uniform
distribution
of 1–3 points weaker than the participant’s own rating (weaker
group emotion condition). On a third of the trials, the average
group emotion was chosen from a uniform distribution of 1–3
points stronger than the participant’s own rating (stronger group
emotion condition). On a third of the trials, the average group
emotion was exactly the same as participants’ own rating (same
group emotion condition). Comparing the difference between
par-
ticipants’ rating in the first phase and the second phase allowed
us
to see whether different group emotional ratings would lead to
changes in participants’ own ratings.
Note that the group emotion algorithm was constrained such
that
a trial could be assigned to the weaker group emotion condition
only when the initial rating was stronger than 1, and a trial
could
be assigned to the stronger group emotion condition only when
the
initial rating was weaker than 8. We therefore removed these
cases
(ratings 1 and 8) from our analysis (9.57% of all outgroup threat
picture ratings). Removing these ratings also assisted in
reducing

the possibility of regression to the mean in line with recommen-
dations by Yu and Chen (2015). It did not, however, change the
significance of the effects.
Results and Discussion
We analyzed the data from our task using two complementary
methods. The first was a mixed model analysis. The second was
a
linear computational model that we adapted from the social
influ-
ence and reinforcement learning literatures. We used these two
approaches in tandem because each one allowed us to answer
different questions regarding our dataset. Our mixed model
anal-
ysis provided evidence for changes in participants’ emotions as
a
result of our manipulation. Our computational modeling
analysis
allowed us to examine whether adding motivation to a similarity
only model would improve model fit. Furthermore, it allowed us
to
separate participants’ tendency to show similarity from their
ten-
dency to show situation-specific motives and examine the corre-
lation between the two (similarity and motivation). In addition
to
these two primary analyses, in secondary analyses we explored
the
connection between these processes and political affiliation and
group identification (see the online supplemental materials).
Mixed model analysis. We used only the ratings of the non-
neutral pictures in our analysis. To analyze these ratings, we
first
created a by-participant difference score for each picture,

reflect-
ing the change in participants’ rating between the first and
second
phase of the task (before and after receiving the group
feedback).
A positive difference score for a certain picture indicated that
participants’ second rating was stronger than their initial rating,
and the opposite for a negative difference score. We then
conducted a mixed-model analysis in which the group emotion
was the independent fixed variable (group emotions were stron-
ger than the participant’s emotion, weaker, or the same). We
made sure that the intercept of the model was zero to compare
participants’ difference score in each of the conditions to zero.
In addition, based on findings that pointed to participants’
habituation to the task (see the online supplemental materials),
we controlled for trial number. Finally, we used by-participant
and by-picture random intercepts. Results suggested that the
Figure 1. Trial structure for the two phases of the task in Study
1. During
the first phase, participants were asked to rate the intensity of
their
emotional responses to the picture on an 8-point Likert scale.
During the
second phase, participants first saw an average group rating and
were then
asked to rerate the intensity of their emotional responses to the
picture. See
the online article for the color version of this figure.
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difference score in the weaker group emotion condition was
significantly weaker than zero (b � �.60, 95% CI [�.73, �.47],
SE � .07, t(401) � �9.21, p � .001, d � �.91),1 and that the
difference score in the stronger group emotion condition was
also
significantly stronger than zero in absolute value (b � .21, 95%
CI
[.08, .34], SE � .07, t(415) � 3.24, p � .01, d � .31). These
findings suggested that emotional similarity was operative.
To test our expectation that the difference score in the weaker
condition would be significantly greater than the difference
score
in the stronger condition, we created a by-participants
coefficient
of the difference score in each condition. A positive difference
coefficient meant that participants’ emotional intensity
increased
between the first and second rating in that specific condition,
whereas a negative difference coefficient meant that
participants’
emotional intensity decreased between the first and second
rating
in that specific condition. To compare the size of participants’
difference coefficients in the strong versus weak conditions, we
reversed participants’ coefficients in the weak group condition
(multiplying these coefficients by �1) and compared them to
participants’ coefficients in the stronger group emotion

condition
using a mixed-model analysis (with a by-participant random
vari-
able). In line with our first hypothesis, results showed that the
difference in participants’ ratings between the first and second
phase ratings in the weak condition was greater than in the
stronger
group emotion condition (b � .33, 95% CI [.12, .54], SE � .10,
t(74) � 3.15, p � .002, d � .72). These findings support our
first
hypothesis.
We tested our second hypothesis by comparing the difference
between participants’ first and second rating in the same group
emo-
tion condition to zero. This comparison revealed that
participants’
difference score was lower than zero (b � �.19, 95% CI
[�.32, �.06], SE � .05, t(408) � �2.91, p � .01, d � �.28).
These
results supported our second hypothesis, suggesting that when
partic-
ipants were exposed to emotions that were similar to their
initial
ratings, they changed their emotional responses to be weaker
than the
group’s emotions (see Figure 2). See Table 1 for mean averages.
Linear computational model. Our starting point was social
influence models that have been used in a variety of contexts to
explain adjustments in behavior, based on feedback received
from
others (Mavrodiev, Tessone, & Schweitzer, 2013). These
models are
similar in principle to reinforcement learning models (Sutton &
Barto,

1998), which are based on the general notion that people adjust
their
output (in this case their emotional responses) in response to
feedback
they receive from the environment regarding prior actions (in
this case
the mean group emotion). We began with what we refer to as a
similarity only model:
R2i,t � R1i,t�1 � si(G�i,t�1 � R1i,t�1) � hi (1)
where R2i,t is participants’ ratings at time Phase 2 and R1i,t�1
is
participants’ ratings at Phase 1. si is an individual-level
parameter
indicating the degree of similarity for each participant and it is
multiplied by the difference between the group rating (G�i,t�1)
and
the individual rating (R1i,t�1) in Phase 1. In addition, we
added the
term hi to indicate the habituation participants may experience
as
a result of watching the stimuli for the second time. This hi
coefficient is estimated by looking at order effects that may
affect
the results (similar to controlling for order in the analysis).
Includ-
ing a habituation term in the model strengthened the results.
However, the same results hold without the addition of a habitu-
ation term.
Two theoretical assumptions are incorporated in this similarity
only
model. First, the model assumes that emotional influence is a
sym-

metrical process and that people are influenced to similar
degrees by
the group’s emotion, whether the group emotion is stronger or
weaker
than their own emotion. Second, the model makes the
assumption that
in cases in which the group’s emotion is similar to the
individual’s
initial rating (G�i,t�1 � R1i,t�1 � 0), participants will not
update their
emotion ratings from Phase 1 to Phase 2 (R2i,t � R1i,t�1).
For an emotional influence model that includes similarity and
motivation (titled similarity � motivation model), we adjust
Model 1 to include a parameter that captures participants’ moti-
vation:
R2i,t � R1i,t�1 � si(G�i,t�1 � R1i,t�1) � hi � mi. (2)
Our similarity � motivation model is similar to the similarity
only model with the addition of a motivation parameter (mi). A
participant’s motivation parameter represents a context-specific
increase or decrease to the degree participants were influenced
by
other group members’ emotions. This weight adds directionality
to
the way group members are influenced by others’ emotion (de-
pending on whether the parameter is positive or negative).
This simple addition changes the two basic assumptions of the
similarity only model (corresponding to Hypotheses 1 and 2).
First,
the adjusted model assumes that similarity is not a symmetrical
process. In cases in which the group emotion is different than
the
individual’s initial emotion, the motivation parameter, if it is

indeed
negative, leads to a boost in similarity toward weaker group
emo-
tions and reduces the degree of influence of stronger group
emo-
tions. Second, our model makes the assumption that in cases in
which the group’s emotion is similar to the individual’s initial
rating (G�i,t�1 � R1i,t�1 � 0), participants will adjust down
their
emotion rating to account for their motivation (R2i,t � R1i,t�1
�
mi). We were therefore interested in comparing the similarity
only
and the similarity � motivation models in predicting the data
from
Study 1.
We first adjusted both models to permit easy comparison. This
enabled us to treat the similarity only model as a linear model
with
only a slope si, compared to the similarity � motivation model
with a slope si and an intercept mi:
R2i,t � R1i,t�1 � si(G�i,t�1 � R1i,t�1) � hi (3)
R2i,t � R1i,t�1 � si(G�i,t�1 � R1i,t�1) � hi � mi (4)
We conducted a linear regression using the similarity � moti-
vation model to predict the data of Study 1. Results indicated
that
the intercept of the model (mi) was negative and significantly
different than zero (M � �.20, 95% CI [�.29, �.10], SE � .05,
t(3283) � �4.05, p � .001), suggesting that participants’ moti-
vation was to experience weak emotions. We then compared the
similarity � motivation model to the similarity model, using a
likelihood ratio test for nested models (penalizing the motivate

model for an additional parameter). Results indicated that the
1 Effect sizes were calculated based on recommendations by
Westfall,
Kenny, and Judd (2014; also see Brysbaert & Stevens, 2018).
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similarity � motivation model was significantly better
compared
to the similarity model (�2(1) � 16.43, p � .001).
Given that the similarity � motivation model was more suc-
cessful than the similarity only model at predicting the results
of
Study 1, we were interested in learning more about the
relationship
of participants’ motivation (mi) and similarity (si). We
estimated
the parameters for each participant (see Figure 3 for the
distribu-
tion of these parameters). Overall, it seemed that the mean of
the
distribution of the similarity parameter was positive but that the
mean of the motivation parameter was negative (marked by
the dotted line). We then correlated the similarity and

motivation
parameters. Results indicated that the two parameters were
uncor-
related with each other, r(37) � .22, 95% CI [�.10, .50], p �
.17.
We interpret this lack of correlation between the similarity and
motivation parameters as indicating that the extent to which an
individual tends to generally conform to other group members’
emotions is unrelated to the degree to which that individual is
motivated to have low levels of emotion in that particular
context.
Overall, results of Study 1 point to a bias in the way
participants
were influenced by other group members’ emotions in this
partic-
ular context such that participants were more influenced by
weaker
group emotions compared to stronger group emotions. We inter-
pret this finding as suggesting that the emotional motives
operative
in this situation (that were measured in the pretest) influenced
the
degree to which participants were influenced by the group’s
emo-
tions. In addition, when learning that the other group members’
emotions were similar to their own emotions, participants
adjusted
their emotional responses to be weaker than they were initially.
We
interpret this finding as suggesting that motivational forces may
lead individuals to adjust their emotional responses even when
similarity pressures are absent. Our computational modeling ap-
proach suggested that our similarity � motivation model was
better in predicting participants’ emotional responses than a

similarity
Table 1
Group Means and Standard Deviations for the Three Conditions
in Each Phase in Study 1
Phase
Weaker group
emotion
Stronger group
emotion
Same group
emotion
1 5.50 (1.54) 5.48 (1.51) 5.48 (1.52)
2 4.90 (1.62) 5.69 (1.57) 5.29 (1.62)
Figure 2. Change in emotional responses based on the perceived
group emotion for each of the three conditions
in Study 1. When the group emotions were weaker than
participants’ own emotions, participants’ second
emotional response was weaker than their initial response. This
difference score was significantly larger
compared to participants’ increase in emotions in the stronger
group emotion trials. Finally, when participants
learnt that the group’s emotions were similar to their own
emotions, they changed their emotions to be weaker
than their initial ratings.
Figure 3. The distribution of the similarity and motivation
parameters in
Study 1. The mean of the similarity parameter was higher than
zero

(reflected by the dotted line) while the mean of the motivation
parameter
was lower than zero. The two parameters were uncorrelated with
each
other. See the online article for the color version of this figure.
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only model, even when applying a penalty for the additional
param-
eter. Finally, results suggested that participants’ emotional
similarity
did not correlate with participants’ strength of emotional
motivation,
as captured by the model.
Although results of Study 1 provide initial support for our
hypotheses, one important question is whether our findings were
influenced by participants’ habituation to the pictures over time.
Although we statistically controlled for habituation, it is
nonethe-
less possible that the reduction in emotional ratings from the
first
to the second phase was caused by habituation to the pictures.
In
order address this concern, we sought to examine the role of

situation-specific emotional motives in a context that we
believed
would activate a motive to feel strong (rather than weak)
emotions.
Our expectation was that the situation-specific motives in this
situation would lead to results that were a mirror image of those
obtained in Study 1.
Study 2: Emotional Influence in Response to Ingroup
Immoral Behavior in the Laboratory
The goal of Study 2 was to examine processes of emotional
influence in a situation in which participants have a clear
prefer-
ence to have strong negative emotions, both in an absolute sense
and in relation to other group members. To achieve this goal,
we
conducted a laboratory study using pictures of ingroup immoral
behavior in a relatively liberal college student population
(Amer-
ican citizens at Stanford University). We expected—and
validated
in a set of pilot studies—that these participants would be
motivated
to express strong negative emotions in comparison to other
Amer-
icans, in response to situations of ingroup immoral behavior.
Our
expectation that this type of stimuli would elicit a strong
motiva-
tion for high negative emotions is based both on findings that
show
that such stimuli indeed elicit stronger emotions in a liberal
sample
(Pliskin, Halperin, Bar-Tal, & Sheppes, 2018), as well as on
evidence that liberals are more affected by moral intuitions

relating
to harm to others (Graham, Haidt, & Nosek, 2009; Haidt &
Graham, 2007; Schein & Gray, 2015). Based on the assumption
that our sample would be motivated to experience strong
negative
emotions in response to these stimuli, we had two hypotheses.
Our
first hypothesis was that in this context, participants would be
more influenced by stronger emotions compared to weaker emo-
tions. Our second hypothesis was that when learning that
others’
emotions were similar to their own emotions, participants would
change their emotions to be stronger than their initial response.
Method
Participants. As in Study 1, we recruited 40 Stanford students
in exchange for credit; all were Americans. We removed one
participant who personally knew people that appeared in the
Abu
Ghraib pictures and asked to stop the experiment, resulting in
39
participants (12 males, 27 females; age: M � 18.89, SD �
1.68).
Pretests. We conducted two pretests before running the actual
study. The first pretest was designed to confirm that our stimuli
would elicit the desired negative emotions. Analysis of these
pretest pictures suggested that our stimulus set indeed elicited
negative emotions, predominantly anger and guilt (see the
online
supplemental materials). The second pretest was designed to
test
our expectation that liberal Americans would be motivated to
experience strong negative emotions in response to such

pictures.
This question was evaluated during a pretest and not in the
actual
experiment to avoid a situation in which answering this question
would affect participants’ performance in the task (and vice
versa).
As expected, we found that participants not only were motivated
to
feel strong negative emotions in the context of ingroup immoral
behavior, but also were motivated to feel stronger negative
emo-
tions than others, which supports the idea that this emotional
motive may involve social comparison processes (for details,
see
the online supplemental materials).
pictures, 50 depicting cases of ingroup immoral behavior and 50
neutral pictures. Our ingroup immoral behavior pictures were of
American soldiers behaving immorally toward outgroup
members
(e.g., the Abu Ghraib incident in which American army
personnel
abused prisoners of war; or American soldiers threatening
children
and women in combat zones). Our neutral pictures were similar
to
those in Study 1. We used fewer pictures than in Study 1
because
during our pretests, we learnt from participants that observing
the
pictures was emotionally taxing. To get an indication of
whether
reducing the number of pictures would yield to the appropriate
effects, we conducted a power analysis of the findings in Study
1.

Our analysis suggested that 50 pictures would produce power
above 80% (see the online supplemental materials).
Procedure. The instructions and procedure were identical to
Study 1. As in Study 1, the group emotion algorithm was con-
strained such that a trial could be considered as a weaker group
emotion condition only when the initial rating was stronger than
1,
and a trial could be assigned to the stronger group emotion con-
dition only when the initial rating was weaker than 8. We
therefore
removed these cases (ratings 1 and 8) from our analysis (26% of
all
ingroup immoral behavior picture ratings). Removing these
ratings
also assisted in reducing the possibility of regression to the
mean
in line with recommendations by Yu and Chen (2015). Not re-
moving these nonrandomized trials from the analysis did not
change the significance of the findings in the weak and strong
group emotion conditions, but it did weaken the results of the
same
group emotion condition to become marginally significant.
We analyzed our task using two complementary methods as in
Study 1.
Mixed model analysis. To analyze these ratings, we first
reflect-
second
feedback).

and the opposite for a negative difference score. We first
compared
the difference score to zero for both the weaker and stronger
group
mean conditions. Results indicated that when learning that the
group emotions were weaker than their own ratings, the
difference
between participants’ first and second emotional ratings was
only
marginally different than zero (b � �.16, 95% CI [�.32, .01],
SE � .08, t(370) � �1.90, p � .06, d � �.16). However, as
predicted, when learning that the group emotions were stronger
than their own ratings, participants’ second ratings were
stronger
and significantly different than zero (b � .46, 95% CI [.28,
.60],
T
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SE � .08, t(369) � 5.37, p � .001, d � .48). To test whether the

difference score in the stronger condition was significantly
greater
than the difference score in the weaker condition, we created a
by-participants coefficient of the difference score in each condi-
tion. We then compared the size of participants’ difference
coef-
ficients in the strong versus weak conditions. This was done by
reversing participants’ coefficients in the weak group condition
(multiplying these coefficients by �1) and comparing them to
participants’ coefficients in the strong condition using a mixed-
model analysis (with a by-participant random variable). In line
with our first hypothesis, results showed that the difference in
participants ratings between the first and second phase ratings
in
the strong condition was significantly stronger compared to the
weak condition in absolute values (b � �.37, 95% CI
[�.65, �.08], SE � .14, t(76) � �2.57, p � .01, d � �.58,
Figure
4). To test hypothesis 2, we compared the difference score in
the
same group emotion condition to zero, showing that
participants’
second emotional response was significantly stronger than zero
(b � .19, 95% CI [.02, .35], SE � .08, t(381) � 2.28, p � .02, d
�
.20). These results supported our second hypothesis, suggesting
that participants changed their emotional responses to be
stronger
than the group’s emotions (see Figure 4). See Table 2 for mean
averages.
Linear computational model. As in Study 1, we used Equa-
tions 3 and 4 to compare the similarity only model, a linear
model
with a slope si, to the similarity � motivation model, a linear
model with a slope si and an intercept mi. Results indicated that

the
intercept of the similarity � motivation model was positive and
significantly different than zero (M � .24, 95% CI [.11, .37],
SE �
.06, t(1353) � 3.77, p � .001), suggesting that participants’
overall motivation was indeed to feel strong emotions in this
context. We then compared the similarity � motivation model to
the similarity model, using a likelihood ratio test for nested
mod-
els. Results indicated that the similarity � motivation model
was
significantly better compared to the similarity model, �2(1) �
14.2, p � .001.
Based on the fact that the similarity � motivation model was
more successful than the similarity only model at predicting the
results of Study 2, we were interested in learning more about
the
relationship of participants’ motivation (mi) and similarity (si).
We
estimated the parameters for each participant (see Figure 5 for
the distribution of these parameters). Overall, it seemed that
both
the mean of the distribution of the similarity parameter and the
mean of the motivation parameter were positive (marked by the
dotted line). We then correlated the similarity and motivation
parameters. Results indicated that the two parameters were
uncor-
related, r(37) � �.03, 95% CI [�.34, .28], p � .85. These
findings
suggest that participants’ tendency for similarity and motivation
were separate drivers of participants’ behavior.
Results of Study 2 provide clear support for our hypotheses,
this time in a situation that activated an emotional motive to

feel
strong negative emotions. Results in our mixed model analysis
suggest that participants were more influenced by stronger,
compared to weaker group emotions. Furthermore, when learn-
Table 2
Phase
Weaker group
emotion
Stronger group
emotion
Same group
emotion
1 6.23 (1.16) 6.21 (1.19) 6.13 (1.18)
2 6.08 (1.28) 6.75 (1.31) 6.32 (1.35)
in Study 2. When the group emotions were weaker than
difference score was significantly smaller
emotions, they changed their emotions to be stronger
T

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ing that other group members expressed similar emotions to
their own emotions, participants increased their emotions to be
stronger than those of the group (and their own initial emotional
ratings). Results of our linear computational model indicated
that a motivational model is superior to a similarity only model.
Furthermore, participants’ tendency to be influenced by their
group was again uncorrelated with their context-specific emo-
tional motivation.
Study 3: Adding a Control Condition and Testing
Moderation by Explicit Motivation Measure
The goal of Study 3 was to address two important questions that
arose from Studies 1 and 2. The first question pertains to
partici-
pants’ emotional responses in the absence of any group
feedback.
The design of Studies 1 and 2 did not allow us to see whether
ratings would change even without information about others’
emotions. To examine this question, in Study 3 we replicated
Study 1 but added a fourth condition to the task (in addition to
group weaker, stronger, and same emotion) in which
participants
did not receive any group feedback. We hypothesized that in
such

condition, we would see no difference in participants’ emotional
ratings.
The second question raised by Studies 1 and 2 is whether
participants’ emotional motivation moderates participants’
change
in emotion during the task. So far, participants’ motivation was
measured in a separate prestudy, with questions about the
motiva-
tion to express stronger and weaker emotions framed in absolute
terms (i.e., unrelated to other group members’ emotions). In this
study we refined our measure to directly assess participants’
mo-
tivation to express stronger or weaker emotions compared to
their
group. This was done for the same participants that completed
our
task. We hypothesized that the stronger a participant’s
motivation
was to feel weaker emotions than other Americans, the larger
the
difference would be between that participant’s first and second
ratings.
Method
Participants. Power analysis of Studies 1 and 2 suggested that
30 participants would be enough to achieve the desired effect
(see
the online supplemental materials). However, to accommodate
for the added condition (no group emotion condition), we in-
creased the number of stimuli from 100 to 120.
pictures, 120 depicting cases of outgroup threat and 50 neutral
pictures. Both the outgroup threat picture and neutral pictures

were
similar to those of Study 1 with the addition of a few new
outgroup
threat pictures.
Measures. To measure participants’ motivation in response to
the pictures we used a measure that was similar to the one used
in
the Study 1 and 2 pretests, with a modification to better reflect
our
desire to look at social comparison. Participants saw a sample
of
pictures that were used in the study and were asked, “When you
look at the pictures above, do you want to feel stronger or
weaker
negative emotions in response to these pictures than other
Amer-
icans?” Instead of the scale in the Study 1 and 2 pretests, the
current scale was from �50 (much weaker than others) to 50
(much stronger than others), 0 being neutral. Participants com-
pleted this measure after completing the task. In addition, we
measured participants’ political affiliation and identification
with
the group using similar scales to Studies 1 and 2 (see the online
supplemental materials).
Procedure. The instructions and procedure were identical to
Study 1 with the addition of a fourth, no group emotion
condition.
In this condition, participants received no indication regarding
the
average group emotion and were just asked to rerate the
pictures.
As in Studies 1 and 2, the group emotion algorithm was con-
strained such that a trial could be considered as a weaker group
emotion condition only when the initial rating was stronger than

1,
and a trial could be assigned to the stronger group emotion con-
dition only when the initial rating was weaker than 8. We
therefore
removed these cases (ratings 1 and 8) from our analysis (11% of
all
outgroup threat picture ratings). Removing these ratings also as-
sisted in reducing the possibility of regression to the mean in
line
with recommendations by Yu and Chen (2015). It did not, how-
ever, change the significance of the effects. This study was con-
ducted as part of a larger project that included
electroencephalog-
raphy (with a set of analysis that was designed to ask different
questions than the one in this study); these measures will be the
subject of a separate report.
Similar to Study 1 and 2, we analyzed the data from our task
using two complementary methods, a mixed model analysis and
a
linear computational model. In addition to these two primary
analyses, in secondary analyses we explored the connection be-
tween these processes and political affiliation and group
identifi-
cation (see the online supplemental materials).
Mixed model analysis. We used only the ratings of the non-
neutral pictures in our analysis. To analyze these ratings, we
first
reflect-
second

feedback).
parameters in
zero
(reflected by the dotted line) as well as the mean of the
motivation
parameter. The two parameters were uncorrelated. See the
online article for
the color version of this figure.
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and the opposite for a negative difference score. We then con-
ducted a mixed-model analysis comparing each of these
conditions
to zero (intercept � 0). Finally, we used by-participant and by-
picture random intercepts. Looking first at the no group emotion
condition, results indicated that the difference score was not

sig-
nificantly different from zero (b � �.04, 95% CI [�.23, .15],
SE � .09, t(108) � �.40, p � .68, d � �.02). These results
suggested that when participants received no group feedback,
their
first rating was similar to their second rating. Looking at the
difference score for the weaker group emotion condition
suggested
a significant reduction between the first and second rating (b �
�.29,
95% CI [�.48, �.10], SE � .09, t(110) � �3.02, p � .001,
d � �.23). However, the change in participants’ ratings was not
significant in the stronger group emotion condition (b � .15,
95% CI
[�.03, .34], SE � .09, t(104) � 1.56, p � .12, d � .12). Finally,
our
analysis revealed that when participants saw a group rating that
was
similar in emotional intensity to their own initial rating, they
changed
their rating to be significantly weaker than the group’s emotion
(and
their own; b � �.22, 95% CI [�.41, �.03], SE � .09,
t(108) � �2.27, p � .02, d � �.17) see (Figure 6). See Table 3
for
mean averages.
We next examined whether participants’ motivation to express
stronger or weaker emotions than other group members
moderated
the difference between their first and second rating. To answer
this
question, we first excluded the no group emotion condition, as
motivation should not have affected the ratings in this
condition.
We then examined the interaction between rating (first or

second)
and participants’ emotional motivation, looking at participants’
emotional rating as the dependent variable. Similar to our
previous
analyses, we controlled for the presentation order of the
pictures
and used a by-participant and a by-picture random variables for
our analysis. Looking first at the main effects of the analysis,
results indicated a significant positive correlation between the
motivation to feel weaker or stronger emotions in relation to
others
and participants’ ratings, such that weaker motivation predicted
a
decrease in ratings (b � .56, 95% CI [.28, .84], SE � .14, t(29)
�
3.95, p � .001, d � .33). Results also indicated that overall,
participants’ second rating was weaker than their initial ratings,
as
expected from the above results (b � �.11, 95% CI [�.14,
�.07],
SE � .01, t(4394) � �6.59, p � .001, d � .64). Finally results
also revealed a significant interaction between rating (first or
second) and participants’ motivation, such that participants’
sec-
ond rating was weaker than their first rating when participants’
motivation was to express emotions weaker than other
Americans
(b � .06, 95% CI [.03, .09], SE � .01, t(4394) � 3.69, p � .001,
d � .36) (Figure 7). These results are consistent with our expec-
tation that participants’ motivation was one of the drivers of the
difference between participants’ first and second ratings.
Finally, we examined whether motivation affected some condi-
tions more than others by creating a three-way interaction
between

participants’ motivation, the condition, and the type of rating
(first
or second) predicting participants’ rating. Results suggested
that
Table 3
Phase
No group
emotion
Weaker group
emotion
Stronger group
emotion
Same group
emotion
1 5.31 (1.59) 5.32 (1.59) 5.38 (1.59) 5.41 (1.60)
2 5.27 (1.78) 4.98 (1.74) 5.50 (1.71) 5.15 (1.78)
in Study 3. When no group feedback was present, participants’
second response was similar to their initial
response. When the group emotions were weaker than
difference score was significantly larger

emotions, they changed their emotions to be weaker
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the three-way interaction was nonsignificant. This suggests that
the
effect of motivation on changes in ratings was consistent across
conditions and did not influence one condition more than the
rest.
Linear computational model. As in Studies 1 and 2, we used
Equations 3 and 4 to compare the similarity only model, a linear
model with a slope si, to the similarity � motivation model, a
linear model with a slope si and an intercept mi. Here again we
removed the no group emotion condition from our analysis. Re-
sults indicated that the intercept of the similarity � motivation
model was negative and significantly lower than zero (M �
�.22,
95% CI [�.36, �.08], SE � .07), t(2270) � �3150, p � .001,
suggesting that participants’ overall motivation was indeed to
feel
weaker emotions in this context. We then compared the similar-
ity � motivation model to the similarity model, using a
likelihood
ratio test for nested models. Results indicated that the similarity

�
motivation model was significantly better compared to the simi-
larity model, �2(1) � 19.92, p � .001.
Based on the fact that the similarity � motivation model was
more successful than the similarity only model at predicting the
results of Study 3, we were interested in learning more about
the
relationship of participants’ motivation (mi) and similarity (si).
We
estimated the parameters for each participant (see Figure 8 for
the distribution of these parameters). As with Study 1, while the
similarity parameter was greater than zero, the motivation
param-
eter was less than zero (marked by the dotted line). We then
correlated the similarity and motivation parameters. Similar to
Studies 1 and 2, results indicated that the two parameters were
uncorrelated, r(28) � .07, 95% CI [�.29, .42], p � .68. These
findings suggest that participants’ tendency for similarity and
motivation were separate drivers of participants’ behavior.
Results of Study 3 address key questions raised by Studies 1
and
2 and replicate the findings of Study 1. First, results suggest
that
with a lack of group information, participants’ ratings in the
first
phase are similar to their ratings in the second phase. Second,
the
difference between the first and second ratings is moderated by
participants’ emotional motivation for social comparison.
Stronger
motivation to feel weaker emotions compared to other
Americans
led to bigger reduction in participants’ ratings between the first

and
second phase of the task. Taken together, our findings suggest
that
these results are robust, but it remains to be seen whether the
effects identified in these laboratory studies would also be
evident
in the context of natural emotional interactions in everyday life.
The goal of Study 4 was to address this question by looking at
natural interactions occurring on Twitter.
Figure 7. Interaction between participants’ rating number (first
and second) and their emotional motivation to
express stronger or weaker emotions than others in their group.
Results reveal a significant interaction such that
participants’ second rating is weaker than their first rating when
their motivation is to feel less strong emotions
than others in their groups.
parameters in
zero
(reflected by the dotted line) but the mean of the motivation
parameter was
lower than zero. The two parameters were uncorrelated. See the
online
article for the color version of this figure.
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Study 4: Emotional Influence in Response to the
Ferguson Unrest on Twitter
The goal of Study 4 was to extend the findings of Studies 1–3
and examine processes of emotional influence in natural
emotional
exchanges on social media. To achieve this goal, we sought a
situation similar to Study 2, in which we believed participants
would be motivated to express strong negative emotional re-
sponses. Unlike our lab studies, we could not pretest whether
users
were motivated to experience strong negative emotions on
Twitter.
However, we chose to analyze tweets from the Ferguson unrest.
The phrase “Ferguson unrest” refers to the outrage that followed
the shooting of Michael Brown on August 9, 2014, by the white
police officer Darren Wilson. Outrage on social media in
response
to the incident was very strong and eventually grew into a full-
blown social movement around the United States. We expected
that users who tweeted about the Ferguson unrest would be
moti-
vated to express stronger emotions to emphasize their outrage in
response to the incident. One way to affirm this prediction for
users’ motivation is by showing that the tweets were mostly
written by liberals, which we show in our analysis. The fact that
the current sample is predominantly liberal makes the set of
predictions very similar to the ones of Study 2.
The outrage that was expressed on Twitter during the Ferguson
unrest was one of the driving forces of the Black Lives Matter
movement, a civil movement focused on collective action

against
police brutality toward Blacks in the Unites States. Previous
anal-
yses of the Black Lives Matter movement has suggested that
most
of its Twitter activity was generated by liberal users who
expressed
outrage against police brutality (Garza, 2014). This supports a
more general observation that when people participate in social
movements on social media, they attempt to maximize their out-
rage to prove loyalty to the cause and receive attention (Alvarez
et
al., 2015; Castells, 2012; Crockett, 2017; Droit-Volet & Meck,
2007).Therefore, the outrage expressed during the Ferguson
unrest
provided us with a field context for examining the laboratory
effects we observed in our lab studies.
Method
We gathered tweets (brief public messages posted to twitter-
.com) in English which contained the keywords #Ferguson,
#MichaelBrown, #MikeBrown, #Blacklivesmatter,
and#raceriotsUSA.
We chose to use hashtags as search terms based on
recommenda-
tions from previous studies suggesting that hashtags provide a
useful filter to receive both relevant context as well as content
that
is mostly produced by people who support the specific cause
(Barberá, Wang, et al., 2015; Tufekci, 2014). Tweets were col-
lected from a period of nearly four months starting from August
9
at 12:00 p.m. to December 8 at 12:00 a.m. The data was down-
loaded via GNIP (gnip.com), which allows users to download
full

archives of tweets related to a certain search. The total number
of
collected tweets was 18,816,807 which were generated by
2,411,219 unique users. Out of these tweets, 3,149,026 were
original tweets (users writing their own texts), 618,192 were
replies (users replying to someone else’s tweet), and 15,102,222
were retweets (users merely sharing previously written tweets).
A
small portion of retweets included an original text from the user
and were therefore counted as both replies and retweets.
Sentiment analysis. To detect the emotion expressed in each
tweet, we used the sentiment analysis tool SentiStrength, which
is
specifically designed for short, informal messages from social
media (Thelwall, Buckley, & Paltoglou, 2012). SentiStrength
takes
into account syntactic rules like negation, amplification, and re-
duction, and detects repetition of letters and exclamation points
as
amplifiers. It was designed to analyze online data and considers
Internet language by detecting emoticons and correcting
spelling
mistakes. Compared to other sentiment analysis tools, SentiSt-
rength has been shown to be especially accurate in analyzing
short
social media posts (Ribeiro, Araújo, Gonçalves, André
Gonçalves,
& Benevenuto, 2016). The analysis of each text using SentiSt-
rength provides two scores (discrete numbers) ranging from 1 to
5,
one score for positive intensity and one score for negative
inten-
sity. As we were interested in negative emotions, we focused
our
analyses on results of the negative sentiments. However,

combin-
ing the negative and positive values led to similar results as the
variance in the positive evaluations was relatively small.
Political affiliation analysis. One of the important character-
istics of Studies 1–3 is that they exposed emotional processes
beyond similarity occurring between ingroup members. Partici-
pants in these studies were mostly liberals who assumed that
they
were interacting with other mostly liberal participants. To make
sure that the Twitter interactions we saw were reflecting
intragroup
dynamics rather than intergroup conflicts we decided to
estimate
participants’ political affiliation. We took inspiration from
recent
studies that calculated Twitter users’ political affiliation based
on
whether they were following more liberals or conservatives. The
idea was that users who tend to follow Twitter accounts with a
clear political affiliation are more likely to have a similar
political
affiliation (for similar discussion, see Bail et al., 2018; Barberá,
Jost, Nagler, Tucker, & Bonneau, 2015; Brady et al., 2017). To
achieve this goal we used a list of 4,176 public figures and
organizations whose political affiliations were evaluated in a re-
cent study by Bail and colleagues (see Bail et al., 2018). We
then
gathered a complete follower list of a sample of our Ferguson
dataset containing 104,831 users (total list size was 196.9
million
users). Out of the sample that was gathered, 89.6% of the users
were following at least one of these political Twitter accounts
and
82.4% followed at least two of these accounts. We then
examined

how many participants followed more liberals than
conservatives.
Results suggested that out of our 104, 831 users, 84.5% of
partic-
ipants followed more liberal Twitter accounts compared to con-
servative ones. This provided us with strong evidence that the
discussion we were observing was largely an intragroup discus-
sion. These findings are also congruent with other analyses sug-
gesting that Twitter interactions are ingroup interactions
(Bouty-
line & Willer, 2017; Brady et al., 2017).
Results
The overarching goal of this study was to examine how expo-
sure to others’ emotions leads to changes in an individual’s
emo-
tions. One challenge with analyzing Twitter data is that we do
not
have all the information regarding the emotions that participants
were exposed to. However, an estimate of these emotions can be
made using two approaches, each with its own advantages and
disadvantages. We therefore decided to take both approaches,
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and see if they converged. Below we describe both approaches
and show that they lead to similar results.
Our first approach, which we call the group average approach,
is inspired by Ferrara and Yang’s recent work on emotional
contagion on Twitter (Ferrara & Yang, 2015). Ferrara and Yang
examined changes in users’ emotional expressions between a
first
and second tweet as a function of their exposure to others’ emo-
tions. Others’ emotions were estimated by computing an
average
of the emotional content of similar tweets that preceded the
users’
second tweet. The time window used by Ferrara and Yang was 1
hour before users’ second tweet. The assumption made by the
authors is that this group average of tweets represents the emo-
tional background in which participants were operating. Using
this
average approach, Ferrara and Yang were able to show patterns
of
emotional influence, but they did not report any bias in the
degree
of influence between strong and weak emotions, as expected
from
our own analysis. The strength of this approach is that it
simulates
exposure to emotions of multiple group members, which is more
similar to our operationalization of the group emotions in
Studies
1–3. Its obvious limitation, however, is that we cannot know for
certain that participants indeed saw these tweets before writing
their second tweet.
Our second approach, which we call the original-reply ap-
proach, was designed to overcome the main limitation of the
group

average approach. In this analysis, we again examine cases in
which participants wrote two tweets, and we look at emotional
changes between those two tweets. However, this time we focus
our attention on situations in which the second tweet was a
reply
to another user. The benefit of using replies as the second tweet
is
that we know exactly what participants saw before writing their
second tweet. This is a tremendous advantage over the group
average approach. The limitation of this approach is that we are
examining emotional influence as a function of exposure to one
group member rather than multiple members, which makes this
analysis slightly different from those of Studies 1–3. Overall,
we
believe that these two approaches are complementary and
together
provide a better understanding of emotional influence.
Group average approach. To examine emotional influence
using the group average approach, we first took all of the users
who wrote more than one Ferguson related tweet (N � 919,995).
We then organized these tweets into tweet pairs. We then elimi-
nated all of the tweet pairs that were written in a period of less
than
an hour from each other to calculate a 1-hr window between the
two tweets (not removing these tweets does not change the
direc-
tion or significance of the results). We then calculated an
average
of all Ferguson related tweets that were written up to an hour
before participants’ second tweet. This average rating
represents
the mean group emotion preceding participants’ second tweet.
To analyze changes in users’ emotions, we first created a by-

user difference score, reflecting the difference in users’
emotions
between the first and second tweet. A positive difference score
for
a certain pair indicated that users’ second tweet was stronger
than
their initial tweet, whereas a negative difference score for a
certain
pair indicated that users’ second tweet was weaker than their
initial
tweet. We then divided the data into two conditions: weaker
group
emotion condition, in which the average of tweets preceding
users’
second tweet was weaker than users’ initial tweet; and stronger
group emotion condition, in which the moving average of tweets
preceding users’ second tweet was stronger than users’ initial
tweet. We could not estimate a same group emotion condition
(this
will be estimated in the original reply approach), as
participants’
ratings were whole numbers, while the moving averages were
all
nonwhole numbers (see Figure 9A for a density plot of the
moving
average). This meant that the moving average was never similar
Figure 9. Results from the group average approach of Study 4.
Panel A shows a density plot of the rolling
average of all Ferguson tweets for a 1-hr window. Panel B
shows the difference between sentiment analysis of
user 1 initial tweet and a later tweet for the three conditions in
Study 4. When the rolling average was weaker
than the emotions expressed in User 1’s initial tweet, User 1’s
second tweet was weaker in emotional intensity
than their first tweet. This difference score was significantly

smaller compared to User 1’s increase in emotions
compared to when the rolling average was stronger than User
1’s initial tweet. See the online article for the color
version of this figure.
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to users’ initial rating. Furthermore, due to the fact that the
moving average was mostly a number between 1 and 2 (Figure
9A) we could not remove the cases in which participants’ first
rating was 1 or 5 as this would eliminate all of the stronger
group rating trials.
Having these two conditions, we conducted a mixed-model
analysis in which the group emotion was the independent fixed
variable (group emotions were stronger than the participant’s
emotion, or weaker). We made sure that the intercept of the
model
was zero to compare participants’ difference score in each of
the
conditions to zero. In addition, we used by-participant random
intercept. Results suggested that the difference score in the
weaker
group emotion condition was significantly less than zero (b �
�.69,
95% CI [�.688, �.697], SE � .002, t(425,600) � �285.2, p �

.001,
d � �.58), and that the difference score in the stronger group
emotion
condition was significantly greater than zero in absolute value
(b �
.84, 95% CI [.847, .836], SE � .002, t(425,600) � 314.4, p �
.001,
d � .72). These findings suggest that emotional similarity was
oper-
ative. Similar to Studies 1–3, we compared the difference in
users’
tweets based on whether they were exposed to content that was
weaker or stronger in emotional intensity than their initial
tweet. This
was done by reversing the difference scores in the weaker
emotion
condition and comparing them to the difference scores in the
stronger
emotion condition. Results suggested that the difference
between the
first and second tweet in the strong condition was significantly
greater
than in the weak condition (b � .11, 95% CI [.101, .115], SE �
.0023,
t(420,000) � 29.86, p � .001, d � .10) (Figure 9B).
Importantly, the
difference between stronger and weaker was smaller than the
one
found in our complementary approach (see below). This was
probably
caused by the increased noise in the analysis, as we cannot be
certain
that our estimate of the group emotion is what participants
actually
saw.

Original-reply approach. One of the limitations of the group
average approach is that we do not know for certain that partici-
pants were exposed to other Ferguson related tweets before
writing
their own tweet. To overcome this limitation, we focused our
analysis on situations in which the second tweet that
participants
wrote was a reply to another user. Using replies allows us to
know
exactly the content that participants saw before writing their
own
tweet. A second advantage of this approach is that it allows us
to
look at the same emotion condition and see how participants
changed their emotions when replying to content that was
identical
in emotional expression to their own initial tweet. As
mentioned,
our focus was on changes in users’ emotions in response to
others’
emotions. For this reason, our first data reduction step was to
limit
our search to situations in which users wrote at least two tweets,
so
that we could examine changes in their emotions over time. Our
second step was to find cases in which we could estimate the
emotions that users were exposed to before producing their
second tweet. We therefore further reduced the data to cases in
which users’ second tweet was a reply to another user (see
Figure 10). Although using only replies reduced the number of
analyzed tweets, it is the optimal approach given our study
goals because it enabled us to know what content each partic-
ipant was exposed to before creating her own reply. Thus,
limiting our focus to such cases allowed us to examine changes
in users’ emotional intensity (by comparing the emotions in

their first and second tweets), considering the content to which
they were replying.
In addition to creating the tweet-reply pairs, we removed users
who wrote more than 20 tweets to avoid bots or news services.
To
be consistent with Studies 1–3, we removed users whose initial
emotional response was rated 1 or 5 by SentiStrength (the two
extremes of the SentiStrength scale) as users who were in these
extremes could only be assigned to 2 of the 3 conditions.
For example, users whose first tweet was rated as 5 in
emotional
intensity could not have seen a later tweet that was rated
stronger.
These steps resulted in a sample of 38,162 pairs of tweet-replies
Figure 10. Data reduction steps of the original-reply approach
in Study 4. See the online article for the color
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for the analysis. To test for differences in users’ emotional re-
sponses as a function of the emotional content to which they
were
exposed, we conducted a mixed model analysis similar to that

conducted in Studies 1–3. Unlike previous studies, we did not
analyze the data using our computational model because we did
not have enough repeated measures for every user to fit a stable
by-participant similarity and motivation coefficients for each
par-
ticipant. To conduct a similar analysis to Studies 1–3, we
created
a difference score between users’ second post (which was a
reply)
and their initial post. A positive score indicated an increase in
negative emotional intensity, and a negative score indicated a
decrease in negative emotional intensity. Similar to the previous
studies, users’ responses were categorized into three bins. In the
weaker group rating bin, users replied to content that was rated
as
weaker in intensity than their initial tweet. In the stronger group
rating condition, users replied to content that was rated as
stronger
in intensity than their initial tweet. In the same group condition,
the
content that they were replying to was rated as similar to their
initial tweet.
Similar to Studies 1–3, we conducted a mixed-model analysis
comparing the difference between users’ reply and their initial
tweet to zero for each condition, which served as a fixed
variable.
As some users had more than one pair of tweet-replies, we used
a
by-participant random intercept. We first examined changes in
participants’ emotions when replying to tweets that were either
weaker or stronger than their original tweet (see Figure 11).
Results suggested that in cases in which users replied to
emotional
content that was less negative than their initial tweet, they
adjusted

the content of their reply to be less negative (b � �.51, 95% CI
[�.52, �.49], SE � .007, t(29,160) � �70.30, p � .001, d �
�.48).
The opposite was also the case. When users were exposed to a
reply that was more negative than their initial tweet, they
adjusted
their reply to be more negative (b � 1.14, 95% CI [1.12, 1.17],
SE � .01, t(36,560) � 74.61, p � .001, d � 1.09). Similar to
Studies 1–3, we compared the difference in users’ tweets based
on
whether they were exposed to content that was weaker or
stronger
in emotional intensity than their initial tweet. This was done by
reversing the difference scores in the weaker emotion condition
and comparing them to the difference scores in the stronger
emo-
tion condition. Results suggested that the difference between
the
first and second tweet in the strong condition was significantly
greater than in the weak condition (b � .57, 95% CI [1.10,
1.16],
SE � .01, t(26,210) � 32.48, p � .001, d � .40), replicating the
findings of Study 2. We then examined changes in participants’
emotions when replying to tweets that were similar to their
initial
tweets. Results indicated that in the same group emotion
condition,
users’ difference scores were significantly higher than zero (b �
.45, 95% CI [.43, .47], SE � .01, t(26,250) � 46.23, p � .001, d
�
.43). These findings suggest that when users replied to content
that
was similar in negative emotional intensity to their initial
tweets,
they wrote a reply that was stronger in negative intensity than

their
initial tweet.
Unlike Study 2, users’ ratings of the first tweets were not equal
across conditions. This led to a concern that our results might
have
been influenced by regression to the mean (less negative first
tweets might be expected to be followed by more negative
second
tweets). To address this possibility, we conducted an additional
analysis in which we held the first rating constant. As the
general
mean of the first tweet ratings was M � 1.96 (SD � 1.04), our
initial analysis limited the initial ratings to 2. Results were
similar
to the primary analysis and indicated that the difference score in
the weaker group emotion condition was significantly weaker
than
zero (b � �.42, 95% CI [�.44, �.40], SE � .009,
t(17,700) � �45.12, p � .001, d � �.48). The difference score
in
the stronger group emotion condition was also significantly
higher
than zero (b � .72, 95% CI [.69, 74], SE � .01, t(15,000) �
51.18,
p � .001, d � .83). Finally, in the same group emotion
condition,
users’ difference score was significantly higher than zero (b �
.11,
95% CI [.09, .13], SE � .009, t(19,400) � 11.33, p � .001, d �
.12). These results suggest that our results cannot be attributed
to
regression to the mean.
To assess the robustness of our supplemental analyses, we
conducted an additional analysis in which we held the emotional

intensity of the initial tweet constant using an initial rating of 3,
and this analysis yielded similar results. The weaker group emo-
tion condition was significantly weaker than zero (b � �.26,
95%
CI [�.28, �.23], SE � .01, t(9,326) � �23.51, p � .001,
d � �.28). The difference score in the stronger group emotion
condition was also significantly higher than zero (b � .89, 95%
CI
[.83, 96], SE � .01, t(10,180) � 28.55, p � .001, d � .95).
Finally,
in the same group emotion condition, users’ difference score
was
significantly higher than zero (b � .43, 95% CI [.38, 46], SE �
.02, t(9,465) � 19.53, p � .001, d � .45).
Overall, results of Study 4 replicated the pattern revealed in
Study 2 by looking at natural emotional interactions between
group members on Twitter. We used two separate analyses,
which
provided converging estimates of emotional influence. These
find-
ings further buttress our findings, showing that they are
supported
both in the laboratory and everyday life.
Figure 11. Difference between sentiment analysis of User 1’s
initial
tweet and a later reply for the three conditions in Study 4. When
User 2’s
tweet was weaker than the emotions expressed in User 1’s
initial tweet,
User 1’s second tweet was weaker in emotional intensity than
their first
tweet. This difference score was significantly smaller compared
to User 1’s
increase in emotions when replying to User 2’s tweet that was

stronger in
emotional intensity than User 1’s initial tweet. Finally, when
User 1 replies
to a User 2 tweet that was similar in emotional intensity to User
1’s own
emotions, User 1 changed their emotions to be stronger than
their initial
tweet.
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General Discussion
In four studies, we examined how situations that activate emo-
tional motives influence the tendency for emotional similarity.
In
Study 1, we identified a situation in which participants were
motivated to feel weak negative emotions. In this situation, we
showed that participants were more influenced by weaker com-
pared to stronger group members’ emotional responses. Further-
more, participants also tended to reduce their emotions when
learning that others felt similar emotions to them. Using compu-
tational modeling, we ascertained that participants’ tendency for
emotional similarity did not correlate with their emotional moti-
vation. In Study 2, we identified a situation in which
participants

were motivated to feel strong negative emotions. In this
situation,
we showed that participants were more influenced by stronger
emotions and tended to increase their emotions when learning
that
others felt similar emotions to them. Here again we found that
similarity and motivation were not correlated. Study 3 extended
Studies 1 and 2 with two key additions. First, we added a no
group
emotion condition in which we showed that without group
emotion
feedback, participants do not change their emotions. Second,
using
a measure of emotional motivation that includes social compari-
son, we showed that this motivation moderated the change in
participants’ ratings. Finally, in Study 4, using two
complementary
analyses, we replicated the findings of Study 2 in natural
interac-
tions on Twitter.
The Role of Motivated Processes
The current work joins a growing literature which suggests that
affective processes are driven by individual motivations. The
idea
of motivated affective processes emerged from the emotion
regu-
lation literature, which argued that people are not necessarily
passive as their emotions come and go, but instead have the
ability
to change their emotional responses (for reviews, see Gross,
1998,
2015). Thus, in many cases, emotional responses are shaped by
a
person’s emotion goals. This suggests that the type and content

of
these goals may be important to understanding people’s
emotions
(for reviews, see Tamir, 2009, 2016).
With the acknowledgment that emotional processes may be
driven by various motivations, there have been growing
attempts
to understand the role of motivation in emotional processes that
go
beyond the individual, focusing on motivations for more social
processes such as empathy (Zaki, 2014). In the group context,
Porat and colleagues have conducted studies showing how
group-
related motivations lead group members to increase or decrease
their emotions in group contexts (Porat, Halperin, Mannheim, &
Tamir, 2016; Porat et al., 2016). For example, group members
may
be motivated to experience sadness in collective memorial cere-
monies, as such sadness provides the instrumental value of
signal-
ing true group membership (Porat, Halperin, Mannheim, et al.,
2016).
While all these studies strengthen the notion that motivations
are
pertinent to understanding emotional processes, they all focus
on
such processes within individuals. The current work extends
these
recent developments by arguing that if motivational forces
influ-
ence individual emotional processes, there is no reason to
assume
that such motivations may not play a role in emotional influence
between group members. Thinking about motivation influencing

processes occurring between individuals is important because
even
minor motivational forces may unfold into much larger group-
level phenomena.
It is important to note, however, that we do not see these
motivated processes as necessarily conscious. Similar to other
motivated processes, it may be possible that motivation is
acting
implicitly, as people construct their emotional experiences in
re-
lation to the stimuli, taking into account the group reference.
Although in our pilot studies we show that when asked, partici-
pants are happy to report that they are indeed motivated to
expe-
rience stronger or weaker emotions in relation to others, it is
still
unclear whether such motivations are consciously active when
they are actually responding to people’s emotions. Future work
should attempt to compare implicit and explicit motivations and
their effect on emotional influence. Addressing this issue may
shed
further light on how much participants’ change in emotions is
driven by their desire for self-presentation.
Potential Mechanisms
While our studies provide consistent evidence for the possibility
that motivational forces play a role in emotional influence,
further
work should be done to examine the specific content of these
emotional motivations. In the current set of studies and
especially
in Study 3, we suggest that one such motivation may be social
comparison. Participants are motivated to feel stronger or

weaker
emotions compared to others in their group, and this motivation
is
what modifies the difference between their first and second
emo-
tional ratings. We chose to focus our attention on social
compar-
ison, as recent work suggested that such forces may be at play
in
the context of emotion (Ong et al., 2018) and because such
processes seem to be especially important in eliciting
polarization
(Myers & Lamm, 1976).
At the same time, social comparison may be just one of several
motivations that moderate emotional influence processes. One
closely related motivation that may also play an important role
is
self-presentation. Emotions are very commonly used for self-
presentation purposes (Clark, Pataki, & Carver, 1996; Jakobs,
Manstead, & Fischer, 2001; Voronov & Weber, 2017). Driven
by
such motivation, group members may not only want to favorably
compare themselves to other group members, but also may be
interested in using their emotions to exhibit a certain value or
attitude. Groups, societies, and cultures emphasize the
experience
of certain emotions in certain situations (Porat et al., 2016;
Tsai,
2007; Voronov & Weber, 2017) and therefore group members
may
aspire to express such emotions to prove their group
membership.
One meaningful difference between self-presentation and social
comparison is in how sensitive these motivations are to the
exis-

tence of an audience. We suggest that self-presentational
motiva-
tions may be much more sensitive to the existence of an
audience
compared to social comparison motivations. Therefore, one way
to
tease these two motivations apart is to inform participants that
an
audience will observe their ratings and examine the influence of
such information on the strength of the results.
More generally, both self-presentation and social comparison
motivations are often driven by group norms (Ekman & Friesen,
1969; Hochschild, 1983). People both compare themselves to
others in light of certain norms, as well as strive to present
emotions that are congruent with these norms (Diefendorff,
Erick-
son, Grandey, & Dahling, 2011; Eid & Diener, 2001). Norms
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therefore serve as a gravitational force on people’s emotions
and
therefore may also impact emotional influence processes.
Adher-
ence to certain norms can be very useful in explaining our

finding
of asymmetry in emotional influence in the stronger/weaker
group
emotion conditions. It is less clear how norms would explain
changes in emotions that occurred in our group similar emotion
conditions. In these cases, the group state (which is similar to
the
initial response of the individual) represents a descriptive emo-
tional norm, while there may be an additional injunctive norm
influencing the change in participants’ emotions (Cialdini,
Reno,
& Kallgren, 1990). Further work should examine participants’
perception of the norm and its influence on these processes.
Finally, beyond their desire to favorably compare or present
themselves to other members in light of certain norms, people
may
be motivated to use their emotions to influence others in their
group. If group members recognize that emotions are
contagious,
they may want to use their emotions as tools to influence others.
In
previous work, we showed that when group members learn that
others in their group are over or under reacting emotionally to a
certain situation, they may compensate for the inappropriate
emo-
tional response of others in their group (Goldenberg et al.,
2014).
For example, when White Americans learned that other White
Americans were not feeling guilt in response to a racially segre-
gated prom in a New-York school, they increased their own
guilt
in response to the information. This effect was mediated by
group
members’ desire to convince others to join. In a similar vein,
this

set of studies also showed that participants may also decrease
their
emotions when learning that the group emotion is
inappropriately
strong, suggesting that motivation to influence others may not
only
lead to increases in emotions but also to decreases.
Implications for Group Processes
Emotions play a unique role in influencing overall group dy-
namics for a variety of reasons. First, due to their dynamic
nature,
emotions are much less stable than attitudes. Therefore,
emotional
responses to group-related events can drive group behavior at a
much faster pace than would occur due to shifts in attitudes
(Halperin, 2014, 2016). Second, emotions are contagious and
therefore spread quickly within groups, further increasing group
members’ responses to emotion eliciting situations (Rimé, 2009;
Rimé et al., 1998). These group emotional states not only
remain
in the affective realm, but can also change attitudes. Changes in
attitudes can both be in relation to a specific target such as an
outgroup (Halperin & Pliskin, 2015; Halperin, Porat, Tamir, &
Gross, 2013) or in relation to one own’s group by increasing
ingroup identification and perceived empowerment (Páez &
Rimé,
2014; Páez et al., 2015).
When thinking about how the observed beyond similarity ef-
fects may influence group processes, the cases in which the mo-
tivation is to increase one’s emotions are especially interesting.
In
these cases, group members aspire to feel stronger emotions
than

others and thus emotional dynamics contribute to overall
amplifi-
cation in group’s emotions which may further escalate to
collective
action (van Zomeren, Leach, & Spears, 2012; van Zomeren,
Post-
mes, & Spears, 2008). In cases in which group members are
motivated to amplify their emotional responses, there may be a
greater chance that certain emotional responses will spread and
lead to collective action. Such motivation may not only increase
influence but also group members’ degree of sensitivity to
others’
emotions (Elias, Dyer, & Sweeny, 2017). Further research
should
examine how and to what extent beyond similarity processes
influence collective action, and the mechanisms through which
this
may occur.
Emotional dynamics that lead to amplification or attenuation of
a certain group emotion may create increased divisions within a
larger group, and thus may also fuel processes of polarization
and
radicalization. If a certain emotional influence process leads a
subgroup to amplify its emotions in response to a particular
issue,
other subgroups may react to such a change with further
polariza-
tion (Iyengar et al., 2018; Myers & Lamm, 1976). Previous re-
search suggests that groups tend to hold negative perceptions of
emotional deviants (Szczurek, Monin, & Gross, 2012). Such
neg-
ative perceptions of deviants may further exacerbate these
polar-
ization processes and can play a role in radicalization (for a

review
see Doosje et al., 2016; Kruglanski et al., 2014). For example,
the
increase in outrage that has occurred as a result of the #Black-
LivesMatter campaign also led to the backlash of the #AllLives-
Matter and #BlueLivesMatter movements. Further work should
examine how emotional dynamics in one subgroup influence
emo-
tional processes in other subgroups.
Broader Implications
The current studies focused on motivated emotional influence in
the context of political interactions and collective action.
However,
other domains can benefit from the findings of this project. One
domain in which insights on from our studies may be useful is
corporate behavior (Barsade & Gibson, 2007; Kelly & Barsade,
2001). Past work has examined the association between
emotional
norms, emotional influence, and group performance (Barsade,
2002; Barsade & Gibson, 2007; Barsade & O’Neill, 2014; Van
Maanen, 1996). These studies have shown that emotional
similar-
ity of positive emotions is associated with positive
organizational
outcomes. One interesting question is how emotional
motivations
to express stronger positive or negative emotions may boost or
hinder these similarity effects. Some organizations value the ex-
pression of positive emotions such as excitement (Grandey,
2015).
Putting a high value on positive emotions may influence how
easy
it is for employees to influence each other’s emotions. In other
cases, putting less emphasis on positive emotions (or maybe

even
negative emotions such as stress and anxiety) may increase the
chance that employees will transfer these negative emotions.
Second, there is growing interest in the effects of emotional
dynamics on the behavior of smaller groups such as families and
dyads (Beckes & Coan, 2011; Goldenberg, Enav, Halperin,
Saguy,
& Gross, 2017; Reed, Randall, Post, & Butler, 2013). Family
emotional dynamics are crucial in explaining well-being (Lara,
Crego, & Romer-Maroto, 2012; Larson & Almeida, 1999). For
example, dysfunctional emotional dynamics in families may
result
from negative emotional reciprocity (Cordova, Jacobson,
Gottman,
Rushe, & Cox, 1993). A recent study attempted to explore these
processes, finding that reduction in emotional expressions in re-
sponse to a spouse’s overreaction predicts relationship quality
(Goldenberg, Enav, et al., 2017). However, further thinking
about
the role of motivation in influencing these dysfunctional
processes,
and how it can be attenuated or enhanced, may help improve
family dynamics.
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Finally, the question of how and why some emotional dynamics
spread quickly while others die out has a direct connection to a
much more general question of diffusion, which has so far been
researched under the heading of “cascades” (Cheng, Adamic,
Dow, Kleinberg, & Leskovec, 2014; Watts & Strogatz, 1998).
The
idea has clear resonance with the reasoning and findings of the
current project. While some social dynamics tend to cascade and
spread quickly, others die out. Understanding the conditions in
which social interactions cascade has been an interest of other
domains such as mathematics, computer science, and sociology
(Cheng et al., 2018, 2014; Goel, Watts, & Goldstein, 2012; Gra-
novetter, 1978; Strang & Soule, 1998). The current work repre-
sents a contribution to our thinking about the emotional
processes
that may contribute to these cascades, thus supplementing prior
work in this space.
Limitations and Future Directions
The present research is to our knowledge the first that has
revealed the impact of situation-specific emotional motives on
emotional similarity effects. However, it is important to note
two
limitations of the current findings.
First, the current work focused on examining the emotional
dynamics that lead group members to be more influenced by
stronger or weaker emotions but has not examined the specific
reasons that may drive group members to do so. As suggested
above, prior research has provided important clues about the
forces
that may lead group members to desire to feel certain emotions.
However, further work should connect these motivations to the
processes that were examined here. More specifically, future

work
should not only measure these motivations, but also manipulate
them and test whether such manipulation impacts the strength of
emotional influence. The current set of studies also examined
emotional processes that were different in type due to the
context
in which they were elicited. Therefore, different motivations
may
be operative on these different emotional processes. These ques-
tions regarding the specific nature of emotions and how they are
motivated by specific motivations should all be further
examined
in future research.
A second limitation of the present work is its reliance on
emotion self-reports. Based on the current studies, it is not yet
clear how “deep” social influence penetrates. When participants
change their emotion ratings, are there changes in other emotion
response systems (such as peripheral physiology or expressive
behavior)? It is clear that changes in ratings may be
consequential
in their own right—as they may in turn influence others’
responses
to a situation. Recent work suggests that exposure to other’s
ratings may indeed influence both neural and physiological
mea-
sures of emotions (Koban & Wager, 2016; Willroth et al.,
2017),
but further work should explore these processes in the context
of
the current paradigm.
These limitations notwithstanding, the idea of situation-specific
emotional motives provides a number of exciting avenues for
further understanding the unfolding of emotions in social
interac-

tions. Thinking more about emotional dynamics and the ways
they
unfold over time will allow us to better explain not only
individual-level behavior, but also group-level behavior, and the
fascinating interplay between emotions at individual and group
levels.
Broader Context
Whether we’re thinking of emotional influence in general—or
emotional contagion in particular—we often tend to think about
an
automatic process, unaffected by motivation, in which people
“catch” the emotions of others in their group. However, as
theories
of motivated cognition and emotion suggest, people may have
motivations that affect even very basic psychological processes
such as emotional influence. The current research adopts this
approach and suggests that motivational processes to increase or
decrease one’s emotions can influence the ways in which one’s
emotions are influenced by other group members. We show that
motivation affects emotional influence not only in the lab, but
also
in emotional interactions on social media. These findings
further
our ability to predict the outcomes of emotional interactions
among
group members, especially in response to sociopolitical
situations, in
which emotional influence processes among group members
some-
times lead to a dramatic increase in group emotions but at other
times
do not. Quantifying motivational processes as we have done in
our
studies here may assist in predicting the temporal dynamics of

con-
sequential group emotions.
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Received July 13, 2018
Revision received April 2, 2019
Accepted April 8, 2019 �

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http://dx.doi.org/10.1177/0002764213479369

http://dx.doi.org/10.1111/j.1467-8721.2009.01633.x
http://dx.doi.org/10.1177/1088868311430835
http://dx.doi.org/10.1037/0033-2909.134.4.504
http://dx.doi.org/10.5465/amr.2016.0522
http://dx.doi.org/10.1038/30918
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http://dx.doi.org/10.1177/1745691614564879
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http://dx.doi.org/10.1080/10463280600574815
http://dx.doi.org/10.3389/fpsyg.2014.01574
http://dx.doi.org/10.1037/a0037679Beyond Emotional
Similarity: The Role of Situation-Specific MotivesEmotional
SimilarityBeyond Emotional SimilarityEmotional Influence as a
Driver of Emotional Escalation and PolarizationThe Present
ResearchStudy 1: Emotional Influence in Response to Outgroup
Threat in the LaboratoryMethodParticipantsPretestsStimulus
setProcedureResults and DiscussionMixed model analysisLinear
computational modelStudy 2: Emotional Influence in Response
to Ingroup Immoral Behavior in the
LaboratoryMethodParticipantsPretestsStimulus
setProcedureResults and DiscussionMixed model analysisLinear
computational modelStudy 3: Adding a Control Condition and
Testing Moderation by Explicit Motivation
MeasureMethodParticipantsStimulus
setMeasuresProcedureResults and DiscussionMixed model
analysisLinear computational modelStudy 4: Emotional
Influence in Response to the Ferguson Unrest on
TwitterMethodSentiment analysisPolitical affiliation
analysisResultsGroup average approachOriginal-reply
approachGeneral DiscussionThe Role of Motivated
ProcessesPotential MechanismsImplications for Group
ProcessesBroader ImplicationsLimitations and Future

DirectionsBroader ContextReferences
Emotion
Accentuate the Positive, Eliminate the Negative:
Reducing Ambivalence Through Instructed Emotion
Regulation
Catherine J. Norris and Emily Wu
Online First Publication, January 23, 2020.
CITATION
Norris, C. J., & Wu, E. (2020, January 23). Accentuate the
Positive, Eliminate the Negative: Reducing
Ambivalence Through Instructed Emotion Regulation. Emotion.
Advance online publication.
Accentuate the Positive, Eliminate the Negative: Reducing
Ambivalence
Through Instructed Emotion Regulation
Catherine J. Norris and Emily Wu
Swarthmore College
Ambivalence, the simultaneous experience of positivity and
negativity, is a conflicting, uncomfortable,
arousing state but is a necessary catalyst for behavior change.
We sought to examine whether feelings of
ambivalence can be reduced using instructed emotion regulation
of positive and negative affect, the
components of subjective ambivalence. Event-related brain
potentials (ERPs) were collected while

participants played 3 blocks of mixed gambles, in which each
trial involved losing or winning the lesser
or greater of 2 amounts. In the 1st block participants responded
naturally, and in the 2nd and 3rd blocks
they were instructed to focus on either the positive or negative
aspects of the outcome. Disappointing
wins (e.g., winning $5 instead of winning $12) and relieving
losses (losing $5 instead of losing $12)
reliably elicited ambivalence; focusing on either the negative or
positive aspects of the outcome reduced
ambivalence as well as the magnitude of the late positive
potential (LPP), indicating successful
regulation. Both self-reported affect and ERPs indicated that
emotional responses to losses were more
difficult to regulate than responses to wins, consistent with a
negativity bias in affective processing.
Results are interpreted in the framework of theories of affect,
and implications for changing behavioral
motivation to support healthy behaviors are discussed.
Keywords: mixed emotions, LPP, FRN, negativity bias, affect
http://dx.doi.org/10.1037/emo0000716.supp
Ambivalence, or the co-occurrence of positive and negative
affect (i.e., mixed feelings), is rare in human experience
(Berrios,
Totterdell, & Kellett, 2015; Larsen, Coles, & Jordan, 2017;
Larsen,
McGraw, & Cacioppo, 2001). As the core function of affect is
to
guide motivation and behavior in a bidirectional manner, to en-
courage approach toward appetitive stimuli (i.e., as is the case
with
positive affect) and avoidance or withdrawal from aversive

stimuli
(negative affect), the simultaneous experience of both is
theoreti-
cally considered to be highly conflictual, uncomfortable, and
arousing (Cacioppo & Berntson, 1994; Cacioppo, Gardner, &
Berntson, 1997, 1999; Norris, Gollan, Berntson, & Cacioppo,
2010). Yet, ambivalence also has been found to increase during
crucial stages of behavior change, suggesting that the
experience
of mixed emotions may at the least accompany decisions to
change
behavior (Armitage, Povey, & Arden, 2003; Lipkus, Green, Fea-
ganes, & Sedikides, 2001). Individuals attempting to make
critical
life changes, such as dieting or quitting smoking (Lipkus et al.,
2001), may often exist in a constant state of ambivalence
regarding
their intended behavior. For example, for a smoker who is
trying
to quit, a cigarette is a source of both positive feelings (e.g., the
satisfaction of a craving) and negative feelings (awareness of
adverse effects, such as increased cancer risk), resulting in con-
flicting approach and avoidance urges when presented with
smoking-related stimuli (Armitage et al., 2003; Lipkus et al.,
2001). A chronic state of ambivalence may have deleterious
emo-
tional and behavioral consequences for individuals; thus, using
instructed emotion regulation to reduce feelings of ambivalence
may be beneficial.
A Brief Review of Theoretical Perspectives
on Ambivalence
Two dominant models of the structure of affect have conflicting
theoretical perspectives regarding the existence of mixed emo-

tions, or ambivalence. Bipolar models of affect argue that
positiv-
ity and negativity are inseparable in experience and that
emotional
valence (which ranges from unpleasant/negative affect to
pleasant/
positive affect) is irreducible (Barrett, 2006; Barrett & Bliss-
Moreau, 2009; Russell & Carroll, 1999; Titchener, 1908;
Wundt,
1896). The circumplex model of affect, which is perhaps the
most
prevalent bipolar model, posits that valence constitutes one of
the
two primary dimensions of emotional experience, with
activation
(or arousal) constituting the second dimension, and that
affective
states fall along a circular perimeter defined by these two
dimen-
X Catherine J. Norris and X Emily Wu, Department of
Psychology,
Swarthmore College.
We thank the members of the Norris Social Neuroscience Lab
(nSNL)
and the ERP Lab at Swarthmore College for their help with data
collection,
processing, and analysis. In addition, both Frank Durgin at
Swarthmore
College and Jeff T. Larsen at the University of Tennessee
provided
constructive comments. Some of the reported data were
included as part of
Emily Wu’s undergraduate Psychology thesis and have been
presented at

annual meetings of the Society for Psychophysiological
Research and the
Social and Affective Neuroscience Society.
Catherine
J. Norris, Department of Psychology, Swarthmore College, 500
College
Avenue, Swarthmore, PA 19081. E-mail: [email protected]
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Emotion
© 2020 American Psychological Association 2020, Vol. 1, No.
999, 000
ISSN: 1528-3542 http://dx.doi.org/10.1037/emo0000716
1
https://orcid.org/0000-0003-3710-7998
https://orcid.org/0000-0002-6686-8430
sions. Fundamental to bipolar models of affect is the
assumption
that affective experience cannot simultaneously encompass both

ends of the valence dimension—in other words, that individuals
cannot feel both unpleasant (negative) and pleasant (positive) at
the same time.
In contradiction to bipolar models of affect, bivariate models
argue that negativity and positivity are separable dimensions of
emotional experience. The model of evaluative space (ESM; Ca-
cioppo & Berntson, 1994; Cacioppo et al., 1997, 1999; Norris et
al., 2010), for example, proposes that negativity and positivity
are
separable independent dimensions of affective experience.
When
combined (i.e., positivity–negativity), they produce a third
dimen-
sion, behavioral motivation, that ranges from avoidance to ap-
proach. The ESM makes a number of unique theoretical predic-
tions regarding affective experience, two of which are
particularly
relevant here. First, negativity and positivity can function in a
reciprocal manner (such that an increase in one may be
accompa-
nied by a decrease in the other), but need not: They may
function
independently (e.g., an increase in negativity may not be
accom-
panied by any change in positivity) or coactively (such that both
negativity and positivity may increase or decrease together).
Im-
portantly, this feature allows for the simultaneous co-
occurrence of
negativity and positivity (i.e., ambivalence), which produces a
conflicting motivational state in which both avoidance and ap-
proach are equally likely. Second, if negativity and positivity
are
independent dimensions of affective experience, they may
function

differently. One functional asymmetry that the ESM predicts is
that, all else being equal, negativity has a stronger impact on
behavior than positivity, given evolutionary pressures to
prioritize
survival over fitness. This negativity bias has been noted in
diverse
research areas, as evidenced by multiple literature reviews
(Taylor,
1991; Rozin & Royzman, 2001; Baumeister, Bratslavsky, Finke-
nauer, & Vohs, 2001), and may have implications for research
on
ambivalence.1
Research suggests that (strong) feelings of ambivalence are
likely rare in daily emotional experience (cf. Larsen et al.,
2001).
Indeed, both narrative reviews (Larsen et al., 2017) and meta-
analysis (Berrios et al., 2015) suggest that the range of stimuli
that
can elicit mixed feelings may be limited to exceptional events
(e.g., tragicomic films, life transitions, cigarette cravings,
thoughts
of suicide). To better understand the nature of ambivalence,
Larsen
and his colleagues (Larsen et al., 2001; Larsen & McGraw,
2011)
have designed careful studies to measure mixed emotions in re-
sponse to major life events and to elicit ambivalence in a con-
trolled laboratory environment. Results have shown that
individ-
uals report feelings of mixed emotions when moving out of their
freshman dorm room (Larsen et al., 2001), graduating from
college
(Larsen et al., 2001), leaving a job (LaFarge, 1994), and starting
a
family (Premberg, Hellström, & Berg, 2008). In the laboratory,

ambivalence can be elicited using mixed gamble outcomes
(Larsen, McGraw, Mellers, & Cacioppo, 2004), music (Hunter,
Schellenberg, & Schimmack, 2010; Larsen & Stastny, 2011),
pictures (Schimmack, 2005), and movie clips (Larsen, To, &
Fireman, 2007; Larsen et al., 2001).
Although ambivalence is thought to be rare in human experience
(Berrios et al., 2015; Larsen et al., 2001, 2017) and has been
studied primarily in response to major life events (e.g., Larsen
et
al., 2001) and controlled laboratory experiments (e.g., Larsen et
al., 2004; Larsen & McGraw, 2011), there is cause to believe
that
the regulation of mixed emotions may be beneficial. First,
bivari-
ate models of emotion predict that when ambivalence occurs, it
is
likely to be conflicting, uncomfortable, and arousing, due to its
lack of motivational guidance (Cacioppo & Berntson, 1994; Ca-
cioppo et al., 1997, 1999; Norris et al., 2010). In the absence of
clear input to avoid or approach a stimulus, one might oscillate
between the two states or simply not take action at all.
Reducing
ambivalence may produce clear behavioral motivation. Second,
research in health psychology suggests that ambivalence may
be a necessary catalyst for behavioral change. According to the
transtheoretical model of change (TTM; Prochaska & DiCle-
mente, 1982; Armitage et al., 2003), there exists a quadratic
relationship between ambivalence and the five2 stages of
change:
ambivalence is low during precontemplation (i.e., the earliest
stage); highest during the three intermediate stages, which
include
contemplation, preparation, and action; and low again during
maintenance (the last stage). Importantly, the TTM predicts that

for successful behavior change, pros and cons must first reach a
balance (i.e., resulting in high ambivalence) and then pros must
outweigh cons during the three middle stages. Take for example
the cessation-motivated smoker, who may feel no ambivalence
before contemplating quitting, but who must begin to simultane-
ously experience negativity and positivity toward smoking
before
committing to and ultimately achieving their goal. If
ambivalence
toward smoking cues results in behavioral immobilization, no
change may occur. If, however, our smoker can successfully
regulate their mixed feelings by focusing on the negative conse-
quences of smoking instead of the positive reinforcement of the
act, their cessation efforts may be more effective. Thus, the
current
study sought to investigate whether emotion regulation can be
effective in decreasing ambivalence, consequently reducing the
associated conflict, discomfort, and arousal, and providing clear
behavioral motivation.
Emotion Regulation
Research on emotion regulation has flourished in the past 30
years, due in part to thoughtful, thorough reviews of the
existing
literature (e.g., Gross, 1998); theoretical models that have
guided
empirical work (e.g., Gross, 2002; Ochsner, Silvers, & Buhle,
2012); and the development and refinement of paradigms and
1 It is worth noting that bipolar models of affect focus on
(hedonic)
valence as one of the descriptive features of core affect and
differentiate
between description and process (Barrett & Bliss-Moreau,
2009). In this

way, although valence may be seen as irreducible, and thus
feeling both
positive and negative at the same time may be theoretically
impossible,
bipolar models do allow that people may alternate quickly
between feelings
of positivity and negativity (Barrett & Bliss-Moreau, 2009).
Larsen and
McGraw (2011) have countered this argument by showing that
(a) partic-
ipants report simultaneously experienced mixed emotions using
continuous
measures with millisecond accuracy, and (b) participants
spontaneously
report mixed emotions even when not cued by measures
explicitly men-
tioning positive and negative affect. Thus, individuals report the
experience
of simultaneous positivity and negativity, regardless of the
underlying
process at work. The current study was not designed to directly
test
hypotheses regarding the process underlying ambivalence (i.e.,
simultane-
ity versus alternation) and instead focuses on the reported
experience of
mixed emotions (i.e., ambivalence).
2 The TTM has been modified by different researchers over the
past 50
years; some versions currently include as many as 7 stages of
change. We
focus on the core stages here.
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2 NORRIS AND WU
psychophysiological tools (e.g., functional MRI [fMRI], eye
track-
ing, event-related brain potentials [ERPs]) necessary for
investi-
gating the processes underlying regulation. An exhaustive
review
of the literature on regulation is beyond the scope of the current
paper; thus, we focus on research most relevant to our questions
of
interest.
Much research on emotion regulation has focused on reap-
praisal, in which individuals are instructed to (a) reappraise the
stimulus (e.g., movie, picture) in such a way as to feel nothing
(Gross, 1998), (b) attempt to feel less bad or negative about the
stimulus (Ochsner, Bunge, Gross, & Gabrieli, 2002), (c)
distance
themselves from the stimulus (Koenigsberg et al., 2010; McRae,
Hughes, et al., 2010), or (d) reinterpret the stimulus to feel
good or
positive (Doré et al., 2017). This focus on reappraisal is driven
by
classic work that has demonstrated its efficacy in the parallel

reductions of emotional experience, expression, and physiology
(Gross, 1998). In addition, reappraisal is antecedent-based,
mean-
ing that regulation occurs early in the emotion-generative
process,
and generally is implemented as an instructed strategy for
process-
ing subsequent emotional stimuli (Gross, 2002). Taken together,
these characteristics provide additional support for the efficacy
of
reappraisal, as it is implemented early on and can be trained
through instruction.
The majority of studies examining reappraisal as an emotion
regulation strategy have focused on the down-regulation of
nega-
tive feelings. Although this approach arguably has the strongest
external validity, as reduction of negativity may have
implications
for well-being and mental health, it represents only one
approach
to studying emotion regulation. A subset of studies has investi-
gated both the up- (increase) and down- (decrease) regulation of
emotional responses toward unpleasant stimuli (Ochsner et al.,
2004; Moser, Hajcak, Bukay, & Simons, 2006; van Reekum et
al.,
2007; Ray, McRae, Ochsner, & Gross, 2010). Their findings
have
begun to delineate mechanisms that are commonly shared versus
unique to increasing and decreasing (negative) feelings.
Additional
research has explored the up-regulation of positive feelings (cf.
Tugade & Fredrickson, 2004), with a particular focus on its
con-
sequences for well-being and resilience. Little to no research,
however, has addressed in a single study the up- and down-

regulation of both positive and negative affect, let alone investi-
gating the regulation of mixed emotions.
Measurement of Emotion Regulation
Different measures have been used to evaluate reappraisal suc-
cess. Often, participants are asked to self-report either their
nega-
tive affect during passive viewing and following reappraisal
(e.g.,
Ochsner et al., 2002; McRae, Ciesielski, & Gross, 2012) or their
success at implementing reappraisal (e.g., Eippert et al., 2007).
These measures are, however, prone to demand characteristics—
as
participants likely are aware that the experimenter expects them
to
feel less negative and to report successful regulation during in-
structed reappraisal of responses to unpleasant stimuli. Thus,
re-
searchers have turned to neural and psychophysiological
measures
to study reappraisal success. Ochsner and his colleagues have
conducted extensive research utilizing fMRI to examine the
neural
networks of emotion regulation (for reviews see Ochsner et al.,
2012; Buhle et al., 2014). Results have consistently shown that
instructed down-regulation (using reappraisal) is associated
with
decreased amygdala and increased prefrontal cortex (PFC;
dorso-
lateral and ventromedial regions, in particular) activation
(Ochsner
et al., 2002), whereas instructed up-regulation of negative affect
is
associated with increased amygdala and increased PFC

activation
(Ochsner et al., 2004). Together, Ochsner and his colleagues
(2002, 2004) suggest that these patterns indicate that the
amygdala,
which is involved in emotion processing, is affected
bidirectionally
by up- and down-regulation and that regions of the PFC are
implicated in the cognitive control required by reappraisal.
Other research has focused on ERPs as an indirect measure of
emotion regulation, given that emotional processes unfold over
time and that ERPs have superior temporal resolution compared
to
fMRI. These studies have focused on the late positive potential
(LPP), a positive-going component maximal over centro-parietal
midline sites occurring approximately 300 –1000 ms
poststimulus.
The LPP is larger to emotional than to neutral stimuli (e.g.,
arousing pictures; Hajcak & Olvet, 2008; Ito, Larsen, Smith, &
Cacioppo, 1998; Schupp et al., 2000) and is reduced during in-
structed emotion regulation (Hajcak & Nieuwenhuis, 2006; Haj-
cak, Dunning, & Foti, 2009; Foti & Hajcak, 2008; for a review
see
Hajcak, MacNamara, & Olvet, 2010). In sum, the inclusion of
an
indirect measure (e.g., ERPs) may thus be helpful in providing
convergent evidence (along with self-reported emotional re-
sponses) of the successful regulation of mixed feelings.
In the current study, we sought to examine the effects of
instructed regulation on emotional responses to mixed gamble
outcomes. Given the nature of mixed gamble outcomes, which
elicit both positive and negative affect, we were able to design a
single study to address the following three research questions:
Question 1a. Can instructed regulation decrease ambivalence

experienced when winning (i.e., disappointing win) or losing
(i.e.,
relieving loss) the lesser of two amounts? Previous research has
demonstrated that individuals reliably report mixed emotions
when, for example, winning $5 instead of winning $12 (Larsen
et
al., 2004; Larsen, Norris, McGraw, Hawkley, & Cacioppo,
2009).
We sought to determine whether instructions to focus on either
the
positive or negative aspect of such mixed gamble outcomes
would
reduce ambivalence.
Question 1b. Given the potential for experimental demands to
impact self-reports of emotional responses, we also included
ERPs
to provide an indirect and convergent measure of successful
reg-
ulation. We predicted that, if instructed regulation is effective
in
reducing ambivalence to mixed gamble outcomes, the LPP
would
reflect this reduction (Hajcak & Nieuwenhuis, 2006).
Question 2. If instructed regulation is effective in decreasing
ambivalence, what are the psychological mechanisms underlying
this reduction? Specifically, we examined how positive and neg-
ative affect (which combined produce ambivalence) are affected
by focusing on the positive versus negative aspects of a mixed
gamble outcome. Specifically, regulation involves a change in
emotion. Focusing on one aspect of a mixed gamble outcome
may
result in reciprocal activation of positive and negative affect
(i.e.,
an equivalent increase in one and decrease in the other),

uncoupled
activation (an increase/decrease in one with no change in the
other), or even asymmetrical patterns of activation (e.g., a
larger
increase in one and smaller decrease in the other).
Question 3. Given research demonstrating a negativity bias in
affective responses, such that negativity often outweighs
positiv-
ity, we investigated asymmetries in the regulation of
ambivalence.
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3REGULATION OF AMBIVALENCE
In particular, we examined the relative efficacy of (a) focusing
on
the positive versus negative aspects of mixed gamble outcomes
in
response to (b) wins versus losses, as measured by (c) positive
and
negative affect. Asymmetries may arise in any of these domains.
Method

Participants
Sixty-four (34 female) native English-speaking Swarthmore
College undergraduates between the ages of 18 and 21 (M �
19.12, SD � 1.02) were recruited through Sona (an online plat-
form), fliers, and word-of-mouth. Behavioral data were
excluded
from two participants (one did not follow instructions; one did
not
finish the study) and ERP data were not collected for four
partic-
ipants (due to experimenter error). Sample size was set based on
previous studies to allow for enough power to test predicted 3-
way
interactions, as well as moderation by individual difference
factors
(which are beyond the scope of the current study and will not be
discussed here). Arguably, the most relevant studies for
determin-
ing an appropriate sample size for the current come from the
literature on emotion regulation using ERPs. Using a design
very
similar to the current study, Hajcak and Nieuwenhuis (2006)
had
14 participants passively view pleasant, unpleasant, and neutral
IAPS images in an initial block and then use reappraisal during
a
subsequent regulation block consisting of only unpleasant IAPS
images and reported a reliable reduction of the LPP during reap-
praisal. Foti and Hajcak (2008) examined the LPP in response to
IAPS images that were preceded by a statement designed to
manipulate the interpretation of the emotional content. They re-
ported a reliable effect of condition (neutral vs. negative state-
ments preceding unpleasant pictures) from a sample size of 26
participants, with condition manipulated between participants.

Hajcak et al. (2009) had 32 participants view neutral and
unpleas-
ant IAPS images and directed their attention to a more or less
arousing portion of the picture; they reported a reliable
reduction
of the LPP when attention was directed to a less arousing
portion
of the picture. Notably, each of these studies used
approximately
the same number of trials per condition as the current study
(�20),
meaning that our LPP amplitudes should be equally reliable.
Thus,
we believe that our sample size (�60) is justified (and, in fact,
more than sufficient) by the previous literature examining
emotion
regulation via ERPs. The Swarthmore College Institutional Re-
view Board supervised the study, and participants received
either
course credit or $20 as compensation.
General Procedure
Upon arrival at the ERP Laboratory at Swarthmore College,
participants were given an overview of the study and provided
written and oral informed consent. Participants were seated in a
comfortable chair, and a 64-channel electrode net (Electrical
Geo-
desics, Inc., www.egi.com) was placed on their scalp.
Participants
performed two tasks, a card-choice gambling task and a picture-
viewing task, and then completed a series of personality surveys
and a demographics form.3 At the end of the session, which
lasted
approximately 2 hrs, participants answered a series of questions
and were debriefed.

Card-Choice Gambling Task
Participants were given a $10 endowment at the beginning of
the gambling task and were informed that the outcome of each
trial
would increase or decrease that amount (i.e., a loss of $1 on the
task would correspond to a loss of 10 cents from the real life
endowment). Participants played a series of gambles in which
they
chose one of two facedown cards; both cards either
corresponded
to wins or losses (i.e., a win and a loss were never possible
outcomes on the same trial), with each card containing a
different
amount. Four different gamble types, in which participants
either
won or lost a greater or lesser amount of money, were possible.
Winning or losing a greater amount was considered an outright
win (OW; e.g., winning $15 instead of $5) or outright loss (OL;
losing $15 instead of $5), respectively. Winning or losing a
lesser
amount was considered a disappointing win (DW; winning $5
instead of $15) or relieving loss (RL; losing $5 instead of $15),
respectively. Each gamble type (OW, DW, RL, OL) occurred for
five different levels of dollar amounts: $5/$15, $10/$30,
$15/$45,
$20/$60, and $25/$75. For example, the set of OW outcomes
included winning $15 instead of $5, $30 instead of $10, $45
instead of $15, $60 instead of $20, and $75 instead of $25. DW
outcomes included winning $5 instead of $15, $10 instead of
$30,
$15 instead of $45, $20 instead of $60, and $25 instead of $75.
The
sets of RL and OL outcomes were identical, but involved losses
instead of gains. Each gamble type and level was repeated four

times during each block, comprising a total of 80 trials per
block
(4 Gamble Types � 5 Levels � 4 Repetitions � 80). The order
of
gambles was prerandomized such that the tally of money won or
lost never exceeded $200 and that participants never
experienced
one gamble type more than three times in a row. Participants
were
randomly assigned to one of two prerandomized orders. Within
each prerandomized order, gamble sequences for each block
were
identical for all participants to ensure that they would all
experi-
ence the same emotional stimuli at the same time and in the
same
order.
After each gamble outcome, participants rated their emotional
responses using the evaluative space grid (ESG; Larsen et al.,
2009), a 5x5 grid that allows for independent assessments of
positivity (horizontal axis) and negativity (vertical axis).
Partici-
pants are instructed to move the mouse to one of the cells in the
grid to indicate their positive and negative feelings. The x- and
y-coordinates of responses provide scores on two 5-point
unipolar
scales, ranging from 0 (not at all positive/negative) to 4
(extremely
positive/negative), which reflect independent measures of
positiv-
ity and negativity (Larsen et al., 2009). In addition to these two
ratings, the ESG allows for a measure of ambivalence, which is
simply the minimum of the positivity and negativity ratings
(i.e.,
MIN[pos, neg]; Kaplan, 1972; Schimmack, 2001; Larsen et al.,

2004). Finally, we also calculated a measure of valence (i.e.,
positivity—negativity; Larsen et al., 2009) to allow for
compari-
sons between bipolar and bivariate predictions.
Each gamble began with a fixation cross for 500 ms, followed
by a blank screen for 250 ms, a screen indicating the gamble
stakes
(e.g., WIN $5, WIN $15) for 1500 ms, another blank screen for
3 Results from the picture-viewing task and the personality
surveys are
reported elsewhere, contact the first author for details.
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4 NORRIS AND WU
http://www.egi.com
250 ms, and finally the two cards appearing face down. At this
point, participants either clicked the left mouse button to choose
the left card or clicked the right mouse button to choose the
right
card. After their decision, the gamble outcome was displayed
for

1500 ms, and then participants rated their emotional responses
using the ESG (see Figure 1 for a trial schema). Before
beginning,
participants read written instructions for the card-choice
gambling
task and performed 8 practice trials.
All participants completed three blocks (80 trials each) of the
card-choice gambling task. Participants completed the first
block
with no emotion regulation instruction, responding naturally to
each outcome. Before the second and third blocks, participants
received written and verbal instructions to regulate their
emotions
by focusing on the positive or negative aspects of each trial,
with
the order of positive and negative regulation counterbalanced
across participants. For example, in the positive regulation
condi-
tion participants were told: “If you won $5 instead of $15, focus
on
the positive fact that you won money instead of the negative
fact
that you could have won more money.”
EEG Data Collection & Processing
Continuous electroencephalographic (EEG) data were collected
using an EGI standard amplifier and a Netstation system. Caps
contained 64 silver chloride electrodes fitted to an elastic cap in
compliance with the International 10 –20 system, and electrodes
were referenced to the average of the two (independent) mastoid
electrodes. Researchers adjusted electrodes to ensure none had
impedances above 40 k�. EEG was subjected to an online high-
pass filter at 0.1 Hz and was sampled at 500 Hz. In between
blocks,

researchers checked and fixed impedances.
Data preprocessing was performed using the EEGLAB package
(Delorme & Makeig, 2004) within MATLAB. Continuous EEG
was filtered through a high-pass filter of 0.5 Hz and a low-pass
filter of 30 Hz then downsampled to 256 Hz. Researchers
removed
portions of data collected during cap fitting and impedance
checks
by eye then interpolated bad channels. Ocular artifact was
removed
using independent components analysis (ICA), then data were
rereferenced to an average. Data were segmented into 1500 ms
epochs starting 500 ms before the outcome was revealed and
ending 1000 ms after and were baseline corrected. Segments
were
sorted and averaged into groups according to emotion regulation
instruction (none, negative focus, positive focus), outcome
(loss,
win), and comparison (negative, positive). Segments containing
data greater than 75 uV or less than �75 uV were rejected to
eliminate muscular or movement artifacts. Participants who had
fewer than 15 (of 20 possible) good trials for any of the 12
conditions (3 Regulation � 2 Outcome � 2 Comparison Design)
were dropped from ERP analyses. We set this as a strict
criterion
to ensure that there were no differences between conditions in
the
number of trials entered into averaged ERP waveforms. Out of a
possible 240 trials, the average number of bad trials for
excluded
participants was 107 (SD � 42); the average number of bad
trials
for included participants was 19 (SD � 15). Eight participants
were dropped due to having too many bad trials, six due to

software issues, and three due to data collection problems,
leaving
a sample size of 43 participants for ERP analyses.
Demographics & Debriefing
Finally, participants completed a short demographics question-
naire (to assess ethnicity, racial background, etc.) and answered
a
series of questions about the study, including how they
performed
the tasks and their perceptions about the study and our
hypotheses.
Afterward, the experimenter orally debriefed participants.
Results
The overall design of the study was a 3(regulation instruction:
none, negative focus, positive focus) � 2(outcome: loss, win) �
Figure 1. Mixed Gamble Trial Schema. The fixation screen and
stakes screen were followed by 250 ms of
black screen to allow for the collection of ERPs. The card
choice screen and Evaluative Space Grid (ESG) screen
remained up until a response was made. See the online article
for the color version of this figure.
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2(comparison: negative, positive)4 factorial, with all factors
ma-
nipulated within-participants. To streamline data reporting and
more directly provide tests of our hypotheses, we systematically
break down these analyses by (a) focusing on just the no
regulation
instruction condition to replicate past results and (b) focusing
on
regulation of mixed gamble outcomes (i.e., DW, RL). Full
results
from the omnibus model are presented in online supplementary
materials. A strict alpha level of 0.05 was used throughout, as
well
as the Bonferroni correction for multiple comparisons when ad-
dressing pairwise analyses, and exact p values are presented up
to
p � .001. Effect sizes (�p2) and 95% confidence intervals (CI
[low,
high]) are included; data are available through OSF
(osf.io/4a6k7).
Results from the No Regulation Condition
We sought to replicate past research (e.g., Larsen et al., 2004)
by
demonstrating that our gambling paradigm elicited expected
pat-
terns of emotional responses. We limited these analyses to re-
sponses from the first block of gambles, in which no emotion
regulation instruction was provided, and participants responded

naturally. We conducted 2(outcome: loss, win) � 2(comparison:
negative, positive) general linear models (GLMs) separately on
minimum scores (i.e., MIN[negativity, positivity]; Kaplan,
1972;
Larsen et al., 2004, 2009), negativity ratings, and positivity
ratings.
Minimum scores. The GLM conducted on minimum scores
(i.e., ambivalence) revealed only one significant effect, the out-
come x comparison interaction, F(1, 61) � 146.55, p � .001,
�p2 �
.71 (Figure 2a). As expected, pairwise tests indicated that RL
elicited more ambivalence (M � 0.66, SE � .05) than OL (M �
0.07, SE � .02), 95% CIs [0.49, 0.70], and OW (M � 0.07, SE
�
.02), 95% CIs [0.48, 0.70], both ps � .001. Similarly, DW
elicited
more ambivalence (M � 0.60, SE � .05) than did OW, 95% CIs
[0.44, 0.63], and OL, 95% CIs [0.44, 0.63], both ps � .001. This
finding shows that participants did indeed report the experience
of
mixed emotions in response to relieving losses and
disappointing
wins.
Negativity ratings. The main effect of outcome, F(1, 61) �
413.95, p � .001, �p2 � .87, indicated that losses were rated as
more negative (M � 1.87, SE � .06) than wins (M � 0.56, SE �
.04; Figure 2b), 95% CIs [1.18, 1.43]. Similarly, the main effect
of
comparison, F(1, 61) � 480.81, p � .001, �p2 � .89, indicated
that
negative comparisons were rated as more negative (M � 1.88,
SE � .06) than positive comparisons (M � 0.55, SE � .04),
95%
CIs [1.21, 1.45]. The outcome x comparison interaction, F(1,

61) � 75.32, p � .001, �p2 � .55, qualified both of the main
effects.
Pairwise comparisons showed that outright losses (OL) elicited
more negativity (M � 2.71, SE � .07) than did relieving losses
(RL; M � 1.02, SE � .07), p � .001, 95% CIs [1.54, 1.84], and
(DW; M � 1.05, SE � .07), p � .001, 95% CIs [1.50, 1.83].
Outright wins (OW; M � 0.08, SE � .02) elicited less
negativity
than did DW, 95% CIs [0.83, 1.11], and RL, 95% CIs [0.80,
1.09],
both ps � .001.
Positivity ratings. The main effect of outcome, F(1, 61) �
390.21, p � .001, �p2 � .87, indicated that wins were rated as
more
positive (M � 1.93, SE � .07) than losses (M � 0.72, SE � .05;
Figure 2c), 95% CIs [1.08, 1.33]. The main effect of
comparison,
F(1, 61) � 572.16, p � .001, �p2 � .90, indicated that positive
comparisons were rated as more positive (M � 2.02, SE � .07)
than negative comparisons (M � 0.63, SE � .04), 95% CIs
[1.28,
1.51]. The outcome x comparison interaction, F(1, 61) � 14.76,
p � .001, �p2 � .20, qualified both of the main effects.
Pairwise
comparisons showed that OW elicited more positivity (M �
2.71,
SE � .07) than did DW (M � 1.10, SE � .06), p � .001, 95%
CIs
[1.44, 1.68], and RL (M � 1.34, SE � .09), p � .001, 95% CIs
[1.22, 1.52]. OL (M � 0.11, SE � .02) elicited less positivity
than
did DW, 95% CIs [0.89, 1.19], and RL, 95% CIs [1.06, 1.40],
both
ps � .001.

In sum, all analyses replicated past research and indicated that
our gambling task reliably elicited ambivalence toward RL and
DW gamble outcomes.
The Effects of Regulation on Ambivalence: Self-Report
Measures
The first of our central research questions (Question 1a) con-
cerned the efficacy of instructed regulation in reducing ambiva-
lence to mixed gamble outcomes. To examine this question, we
focused solely on emotional responses to relieving losses (RL)
and
disappointing wins (DW) and subjected minimum scores (i.e.,
ambivalence) to a 3(regulation condition: no instruction,
negative
focus, positive focus) � 2(gamble outcome: RL, DW) GLMs.
Again, we included the order of regulation and the outcome
level
as variables of no interest.
Minimum scores. The gamble outcome main effect, F(1,
61) � 4.20, p � .045, �p2 � .06, indicated that RL elicited
more
ambivalence (M � 0.59, SE � .04) than did DW (M � 0.53, SE
�
.04), 95% CIs [0.001, 0.10]. Both the small effect size and the
fact
that this main effect collapses across the no regulation and
regu-
lation conditions make this pattern difficult to interpret. The
reg-
ulation main effect was not significant, p � .09, �p2 � .08.
More
importantly, the regulation x gamble outcome interaction, F(2,
60) � 13.54, p � .001, �p2 � .31, was significant (see Figure

3).
Pairwise tests revealed that a negative focus decreased ambiva-
lence for RL (M � 0.48, SE � .06) compared to no regulation
(M � 0.66, SE � .05), p � .028, 95% CIs [0.02, 0.34]; whereas
a
positive focus had no effect on ambivalence toward RL (M �
0.62,
SE � .06), p � 1.00, 95% CIs [�.12, .20]. Similarly, a positive
focus decreased ambivalence for DW (M � 0.38, SE � .05)
compared to no regulation (M � 0.60, SE � .05), p � .003, 95%
CIs [0.06, 0.38]; whereas a negative focus had no effect on
ambivalence toward DW (M � 0.62, SE � .06), p � 1.00, 95%
CIs [�.16, .14]. In sum, regulation did decrease ambivalence,
with
a negative focus impacting responses to RL and a positive focus
impacting responses to DW.
4 Note that including separate factors for outcomes (i.e., losses
and wins)
and comparisons (i.e., negative and positive) is both (a) critical
for our
question regarding the functioning of the negativity bias (i.e.,
asymmetries)
in regulation of ambivalence and (b) a typical approach in
studies of
emotional responses to mixed gamble outcomes (cf. Larsen et
al., 2004,
2009). Together, these two factors capture the four different
gamble
outcomes: a loss with a negative comparison is an outright loss,
a loss with
a positive comparison is a relieving loss, a win with a negative
comparison
is a disappointing win, and a win with a positive comparisons is
an outright
win.

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6 NORRIS AND WU
http://osf.io/4a6k7
The Effects of Regulation on Positive and Negative
Affect
Our second central research question (Question 2) concerned the
psychological mechanisms by which instructed regulation
reduced
ambivalence. Specifically, we sought to examine how a negative
versus a positive focus affected unipolar emotional responses
(i.e.,
negativity, positivity) to mixed gamble outcomes. In addition,
this
set of analyses allowed us to also investigate asymmetries in the
efficacy of a negative versus a positive focus on positive and
negative affect experienced in response to losses (RL) and wins
(DW; Question 3). We conducted 3(regulation) � 2(gamble out-
come) GLMs separately on negativity and positivity ratings to

address these questions.
Negativity ratings. The regulation main effect, F(2, 60) �
41.56, p � .001, �p2 � .58, showed that instructions to focus
on the
negative elicited more negativity (M � 1.31, SE � .07) than did
no
regulation (M � 1.04, SE � .05), 95% CIs [0.11, 0.43], which
in
turn elicited more negativity than instructions to focus on the
positive (M � 0.68, SE � .06), 95% CIs [0.20, 0.50], both ps �
.001 (Figure 4a). The gamble outcome main effect was not
signif-
icant, p � .18, �p2 � .04. The regulation x gamble outcome
interaction, F(2, 60) � 20.66, p � .001, �p2 � .41, however,
was
significant (Figure 4a). Pairwise tests revealed that a negative
focus increased negativity for RL (M � 1.31, SE � .08)
compared
to no regulation (M � 1.02, SE � .07), p � .001, 95% CIs
[0.13,
0.45]; whereas a positive focus had no effect on negativity
toward
RL (M � 0.89, SE � .08), p � .14, 95% CIs [�.03, .30].
Similarly,
a positive focus decreased negativity for DW (M � 0.48, SE �
.06) compared to no regulation (M � 1.05, SE � .07), p � .001,
95% CIs [0.38, 0.76]; whereas a negative focus had a marginal
effect on negativity toward DW (M � 1.30, SE � .09), p � .05,
95% CIs [.00, .51].
Positivity ratings. The regulation main effect, F(2, 60) �
42.23, p � .001, �p2 � .59, showed that instructions to focus
on the
positive elicited more positivity (M � 1.43, SE � .07) than did
no

regulation (M � 1.24, SE � .06), p � .008, 95% CIs [0.04,
0.33],
which in turn elicited more positivity than instructions to focus
on
the negative (M � 0.86, SE � .06), 95% CIs [0.23, 0.54], p �
.001. The gamble outcome main effect was not significant, p �
.67, �p2 � .003. The regulation x gamble outcome interaction,
F(2, 60) � 6.43, p � .003, �p2 � .18, however, was significant
a
b c
Figure 2. Results from the 2(outcome: loss, win) �
2(comparison: negative, positive) GLMs conducted on
minimum scores (i.e., ambivalence; a), negativity ratings (b),
and positivity ratings (c) in the no regulation
condition. Error bars represent 1 SE.
Figure 3. Results from the 3(regulation: no instructions,
negative focus,
positive focus) � 2(gamble outcome: relieving loss,
disappointing win)
interaction for minimum scores (i.e., ambivalence). A negative
focus
decreased ambivalence toward RL; a positive focus decreased
ambivalence
for DW.
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(Figure 4b). Pairwise tests revealed that a negative focus
decreased
positivity for RL (M � 0.77, SE � .09) compared to no
regulation
(M � 1.34, SE � .09), p � .001, 95% CIs [0.32, 0.81]; whereas
a
positive focus had no effect on positivity toward RL (M � 1.47,
SE � .10), p � .53, 95% CIs [�.11, .37]. A positive focus
increased positivity for DW (M � 1.39, SE � .07) compared to
no
regulation (M � 1.15, SE � .07), p � .001, 95% CIs [0.09,
0.38],
and a negative focus decreased positivity toward DW (M �
0.94,
SE � .08), p � .004, 95% CIs [.06, .37].
In sum, instructed regulation decreased ambivalence toward
mixed gamble outcomes. Instructions to focus on the negative
both
increased negativity and decreased positivity to RL; instructions
to
focus on the positive both decreased negativity and increased
positivity to DW. In addition, focusing on the negative also de-
creased positivity to DW, suggesting that a negative focus may
have a stronger impact than a positive focus and providing some
evidence for a negativity bias in regulation of emotional
responses
to mixed gamble outcomes.

Electrocortical Mechanisms of the Regulation of
Mixed Gamble Outcomes
In addition to self-report measures, we sought to use ERPs to
examine evidence for the regulation of ambivalence in response
to
mixed gamble outcomes (Question 1b). To examine this
question,
we focused on two ERP components. First, the feedback related
negativity (FRN) is a component that has previously been
shown
to be sensitive to both bad outcomes (e.g., losses; Hajcak,
Moser,
Holroyd, & Simons, 2006) and to unexpected negative outcomes
(Holroyd, Nieuwenhuis, Yeung, & Cohen, 2003). We
anticipated
that the FRN might be sensitive to negative comparisons, based
on
the fact that in the card choice gambling paradigm, participants
are
informed early on in a trial whether they have won or lost
money
and only find out later if they won or lost the greater or lesser
of
two amounts. If individuals anticipate or hope for the best
possible
outcome, a negative comparison (i.e., losing the greater or
winning
the lesser of two amounts) is an unexpected negative outcome.
We
measured the FRN as the peak negative amplitude at Fz between
200 and 400 ms after the outcome was revealed.
Second, the LPP has been shown to be (a) larger to emotional
versus neutral stimuli (e.g., arousing pictures; Hajcak & Olvet,

2008) and (b) reduced during instructed emotion regulation
(Haj-
cak & Nieuwenhuis, 2006). Thus, we anticipated that the LPP
would be reduced when participants were instructed to either
focus
on the negative or on the positive aspects of the gamble
outcomes.
We measured the LPP as the average amplitude at Pz between
300
and 750 ms after the outcome was revealed. Note that windows
for
both components were determined by examining average group
waveforms collapsed across all regulation conditions in order to
not allow our analyses to be biased by the effects of regulation
(Luck, 2012), and we limit analyses to sites of primary interest,
where the FRN (Fz) and LPP (Pz) are maximal, to prevent Type
I
error.
FRN peak amplitudes. FRN peak amplitudes were subjected
to a 3(regulation: no instruction, negative focus, positive focus)
�
2(outcome: loss, win) � 2(comparison: negative, positive)
GLM.
The main effect of comparison, F(1, 42) � 10.76, p � .002, �p2
�
.20, showed that, as predicted, negative comparisons elicited
larger
FRN amplitudes (M � �2.11, SE � .26) than did positive com-
parisons (M � �1.71, SE � .25), 95% CIs [.15, .65]. In
addition,
regulation had a significant linear effect, F(1, 42) � 4.41, p �
.042, �p2 � .10, such that FRN amplitudes were largest during
a
negative focus (M � �2.12, SE � .28), middling during no
regulation (M � �1.86, SE � .25), and smallest during a

positive
focus (M � �1.73, SE � .27). Although none of the pairwise
tests
were significant, the linear main effect indicates a significant
linear
trend across conditions.
FRN latencies. We subjected FRN latencies to a parallel
GLM. The comparison main effect, F(1, 42) � 17.85, p � .001,
�p
2 � .30, indicated that the FRN showed a later peak during a
negative (M � 319.60 ms, SE � 4.83) versus a positive (M �
299.98, SE � 6.17) comparison, 95% CIs [10.25, 29.00].
Regula-
tion had a significant quadratic effect on FRN latencies, F(1,
42) �
4.92, p � .033, �p2 � .11, such that the FRN was delayed
during
a negative focus (M � 316.86, SE � 6.03) as compared to no
regulation (M � 300.55, SE � 6.92), p � .05, 95% CIs [0.003,
32.61]. Neither the negative focus, p � 1.00, 95% CIs [�8.95,
18.76], nor the no regulation condition, p � .35, 95% CIs
[�6.36,
a
b
Figure 4. Results from the 3(regulation: no instructions,
negative focus,
positive focus) � 2(gamble outcome: relieving loss,
disappointing win)
interactions for (a) negativity and (b) positivity ratings.
Generally, a
negative focus affected responses to RL; a positive focus

affected re-
sponses to DW. In addition, a negative focus decreased positive
affect
toward DW.
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8 NORRIS AND WU
29.16], differed from the positive focus condition (M � 311.96,
SE � 5.75).
LPP area. LPP areas were subjected to a 3(regulation: no
instruction, negative focus, positive focus) � 2(outcome: loss,
win) � 2(comparison: negative, positive) GLM. As expected
(and
replicating past research; Hajcak & Nieuwenhuis, 2006), the
reg-
ulation main effect, F(2, 39) � 10.85, p � .001, �p2 � .36,
showed
that the LPP was larger during no regulation (M � 0.94, SE �
.08)
than during either the negative focus (M � 0.75, SE � .07), p �
.001, 95% CIs [.11, .27], or the positive focus (M � 0.79, SE �
.06), p � .007, 95% CIs [.04, .25] conditions. The negative and

positive focus conditions did not differ, p � .30, 95% CIs
[�.04,
.12]. The outcome main effect, F(1, 40) � 6.33, p � .016, �p2
�
.14, showed that the LPP was larger for wins (M � 0.86, SE �
.07)
than for losses (M � 0.80, SE � .07), 95% CIs [.01, .11]. This
was
qualified by the outcome x comparison interaction, F(1, 40) �
17.08, p � .001, �p2 � .30. Pairwise tests revealed that OW
elicited
larger LPP areas (M � 0.91, SE � .07) than RL (M � 0.74, SE
�
.07), p � .001, 95% CIs [.09, .24]; but there was no significant
difference between DW (M � 0.80, SE � .06) and OL (M �
0.85,
SE � .07), p � .18, 95% CIs [�.02, .11]. In addition, OL
elicited
larger LPP areas than did RL, p � .001, 95% CIs [.05, .17]; and
OW elicited alrger LPP areas than did DW, p � .006, 95% CIs
[.03, .17].
Finally, there was a significant effect for the regulation x
outcome
x comparison interaction (see Figure 5), F(1, 40) � 5.17, p �
.028,
�p
2 � .11. For OL, both a negative (M � 0.75, SE � .07), p �
.001,
95% CIs [.15, .37], and positive (M � 0.80, SE � .07), p �
.019,
95% CIs [.04, .38], focus decreased the LPP as compared to no
regulation (M � 1.00, SE � .09). For RL, only a negative focus
(M � 0.67, SE � .07), p � .021, 95% CIs [.02, .25], decreased

the
LPP compared to no regulation (M � 0.81, SE � .08); a positive
focus (M � 0.75, SE � .08) had no impact on LPP areas, p �
.47,
95% CIs [�.10, .22]. For DW, a negative focus (M � 0.75, SE
�
Figure 5. Group averaged waveforms at Pz for (a) RL and (c)
DW, as well as effects of instructed regulation
on LPP areas for (b) RL and (d) DW. A negative focus was
effective at reducing the LPP for both gamble
outcomes; a positive focus was only marginally effective at
reducing the LPP for just DW. � p � .05.
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.07), p � .045, 95% CIs [.003, .26], decreased the LPP
compared
to the no regulation condition (M � 0.88, SE � .07); a positive
focus (M � 0.79, SE � .06), one-tailed p � .07, 95% CIs [�.03,
.26], marginally decreased the LPP. For OW, both a negative
focus
(M � 0.84, SE � .07), p � .002, 95% CIs [.09, .35], and a

positive
focus (M � 0.83, SE � .07), p � .002, 95% CIs [.09, .37],
decreased the LPP compared to no regulation (M � 1.06, SE �
.10).
In sum, our hypotheses regarding both ERP components of
interest were supported. First, the FRN was larger to negative
versus positive comparisons, consistent with our prediction that
a
negative comparison (e.g., discovering that one had lost the
greater
or won the lesser of two amounts) functioned as negative
feedback.
Second, the LPP was reduced under instructed regulation versus
during no regulation, consistent with behavioral results
indicating
that participants were successful at reducing their emotional re-
sponses. There were, however, asymmetries in the efficacy of
regulation, such that a negative focus was effective at reducing
LPP areas for both RL and DW; whereas a positive focus was
only
marginally effective at reducing LPP areas for DW and had no
impact on LPP areas for RL.
Discussion
First, we examined whether instructed regulation could reduce
ambivalence experienced in response to mixed gamble
outcomes.
Relieving losses and disappointing wins reliably elicited
ambiva-
lence in our participants. More importantly, instructions to
focus
on either the positive or negative aspects of those mixed gamble
outcomes reduced ambivalence (Question 1a). In other words,
when individuals lost the lesser of two amounts, they reported

feeling both happy and sad. But when they were instructed to
focus
on either the negative (i.e., losing money) or the positive (i.e.,
the
fact that they could have lost more money) aspects of that out-
come, ambivalence was reduced. The reduction in ambivalence
was seen for both relieving losses and disappointing wins, and
when focusing on either the negative or positive aspects of the
gamble outcome. ERPs showed that, as expected, regulation de-
creased the magnitude of the LPP (Question 1b), consistent with
past research (Hajcak & Nieuwenhuis, 2006). In sum,
ambivalence
can be regulated and one potential electrocortical mechanism
im-
plicated in this reduction is the amplitude of the LPP.
We also sought to examine the psychological mechanisms by
which instructed regulation impacts ambivalence (Question 2).
Regulation instructions asked participants to focus on one
aspect
of their emotional experience: either positive or negative.
Different
theories of emotion predict different patterns of effects. Bipolar
models of emotion (e.g., the circumplex; Barrett, 2006; Barrett
&
Russell, 1999) that rely on a single continuum of valence
ranging
from unpleasant to pleasant predict that focusing on positive
aspects of a mixed gamble outcome would have equal and
oppos-
ing effects on positivity and negativity: as positivity increases,
negativity decreases (and vice versa for a negative focus).
Bivari-
ate models of emotion (e.g., the ESM; Cacioppo & Berntson,
1994; Cacioppo et al., 1997, 1999; Norris et al., 2010) that
allow

for independence of positive and negative affect predict that fo-
cusing on positive aspects of a mixed gamble outcome may
result
in either increased positivity, decreased negativity, or some
com-
bination of the two (and, again, vice versa for a negative focus).
Our results indicate support for both bipolar and bivariate
models:
First, instructed regulation did have opposing effects on
positivity
and negativity, supporting a bipolar perspective; but second,
those
effects were clearly not symmetrical or equivalent, supporting a
bivariate perspective. Instructions to focus on positive aspects
were more effective in reducing negativity (vs. increasing
positiv-
ity) to disappointing wins, and instructions to focus on negative
aspects were more effective in reducing positivity (vs.
increasing
negativity) to relieving losses. Thus, focusing on one aspect of
a
mixed gamble outcome (e.g., negativity) had a stronger impact
on
reducing the other aspect (positivity) — and this was
particularly
noticeable on gambles in which the dominant affect (dictated by
whether the outcome was a win or a loss) was consistent with
regulation instructions.
It is worth noting at this point that our emotion regulation
manipulation did not directly instruct participants to decrease
their
ambivalence; rather, we instructed individuals to focus on one
aspect (either positive or negative) of the outcome, with the as-
sumption that affecting the component affective responses

would
ultimately decrease ambivalence. We implemented regulation in
this way for the following reasons. First, asking participants to
decrease their mixed emotions could drive self-reports of
positive
and negative affect, as participants would know that we
expected
them to feel and report both. Second, much as asking
individuals
to “reappraise” their emotional responses may result in the use
of
multiple strategies that could differ across individuals and even
across trials, instructions to decrease mixed emotions may
produce
different strategies that would ultimately make results difficult
to
interpret. Third, and most importantly, we believe that the most
effective method of decreasing ambivalence in real life
scenarios
(e.g., quitting smoking) may be focusing on one aspect of the
behavior (the positive consequences of quitting), with the
ultimate
goal of allowing the pros to eventually outweigh the cons (per
TTM; Armitage et al., 2003). Thus, we aimed to implement the
most direct, effective, and clear method available to ultimately
decrease ambivalence. Finally, the use of instructions to focus
on
the positive versus negative aspects of a mixed gamble outcome
allowed for an examination of multiple asymmetries in affective
processing.
Furthermore, our instructions to focus on one aspect (either
positive or negative) of the outcome may arguably be more con-
sistent with methods of regulation that rely on attentional
deploy-
ment rather than reappraisal, as we did not explicitly instruct

participants to reappraise the gamble outcome in such a way as
to
feel nothing, feel less negative, or feel more positive; nor to
distance themselves from the outcome. In addition to
reappraisal,
researchers have studied the effects of attentional deployment
on
emotional responses (cf. Gross, 2002 for a model of emotion
regulation strategies). van Reekum and her colleagues (2007)
tracked eye movements while participants were instructed to re-
appraise their responses to affective images and found that (a)
patterns of gaze fixation indicated that individuals paid
attention to
different aspects of the pictures when instructed to up- and
down-
regulate their emotional responses as compared to a control con-
dition and that (b) gaze fixation patterns in these conditions ac-
counted for a significant portion of the variance in brain
activation
associated with reappraisal, including regions associated with
visu-
ospatial attention. In a partial replication of this study, Manera,
Samson, Pehrs, Lee, and Gross (2014) found that participants
spent
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10 NORRIS AND WU
more time looking at emotional regions in videos of faces when
asked to up-regulate their responses and less time when asked to
down-regulate their responses, but also found that reappraisal
and
attentional deployment are “. . . distinct components that
uniquely
influence negative emotions” (p. 833). Finally, in the absence of
any regulation instructions, Dunning and Hajcak (2009) directed
participants’ attention to either low- or high-arousing regions of
affective pictures and found that reduced LPP amplitudes when
attention was deployed to low-arousing areas. These few
examples
demonstrate that attentional deployment may be used spontane-
ously during reappraisal, may contribute to effective
reappraisal,
and — even in the absence of regulation instructions — may
affect
emotional responses. Given that we did not directly instruct our
participants to manipulate or control their emotional responses
to
gamble outcomes, but rather asked them to focus on one aspect
of
each outcome, our manipulation is more similar to methods of
attentional deployment than to cognitive reappraisal. Thus,
simply
changing one’s attentional focus when confronting a complex,
mixed emotional stimulus may be effective in reducing ambiva-
lence.
Although in the current study we have shown that focusing on
one or the other aspect of a mixed emotional response is
effective

in reducing experienced ambivalence, the effect of cognitive re-
appraisal on ambivalence remains unknown. Traditional reap-
praisal instructions might ask participants to decrease their
ambiv-
alence, to distance themselves from the stimulus, or to try to
feel
nothing. Each set of instructions might be effective in
decreasing
ambivalence, but their impact on the underlying mechanisms
(i.e.,
positive and negative affect) would be expected to greatly
differ.
Given that we did not directly ask participants to report on their
ambivalence (i.e., using a measure of subjective ambivalence;
“How mixed do you feel about this gamble outcome?”), a reduc-
tion in ambivalence would be observed via reductions in
positive
and/or negative affect. How individuals choose to regulate their
mixed emotional responses would therefore be informative
regard-
ing the relative ease or difficulty of regulating positivity versus
negativity, as well as shedding light on individual differences in
the recruitment of these processes. Even though we have
demon-
strated that instructed attentional deployment does impact
negative
affect, positive affect, and experienced ambivalence (i.e., their
combination), many questions remain regarding the underlying
mechanisms or the effects of traditional cognitive reappraisal on
the regulation of ambivalence. We look forward to exploring
these
questions in future research and further elucidating the
regulation
of complex, mixed emotional states.
Asymmetries in Instructed Regulation of Mixed

Gamble Outcomes
We investigated asymmetries in the efficacy of regulating re-
sponses to wins versus losses, changing positive versus negative
affect, and focusing on the positive versus negative aspects of a
gamble outcome (Question 3). Across all of our analyses, it is
clear
that focusing on the positive aspects of a relieving loss is
simply
not as effective as other forms of regulation: Ambivalence was
not
as reduced (Figure 3a), positivity and negativity were not as
impacted (Figure 4a, 4b), and the magnitude of the LPP was not
as
decreased (Figure 5a). This pattern could be due to an
asymmetry
in regulation efficacy (i.e., a positive focus is not as effective
as a
negative focus), an asymmetry to regulating losses versus wins
(i.e., losses are harder to regulate than wins), or some
combination
of the two. Addressing these possibilities, our results indicate
that
a positive focus is at least moderately effective in decreasing
ambivalence (via a reduction in negativity and in the LPP) for
disappointing wins. Thus, it appears likely that responses to
losses
are simply more difficult to regulate than responses to wins; or,
given our discussion above regarding attentional deployment, it
may be more difficult to shift attention away from the negative
aspect of a stimulus. Even so, our data suggest that both
positive
and negative affect can be regulated to equivalent degrees and
that
both a positive and a negative focus may be effective in

reducing
ambivalence, depending on the context. In sum, there is
evidence
for a negativity bias in the regulation of responses to gamble
outcomes, such that responses to losses are more difficult to
change than responses to wins; and although both negative and
positive regulation of negativity and positivity seem to be effec-
tive, a negative focus may be more so.
The FRN and Negative Comparisons
There is one additional ERP finding that is worth noting. We
hypothesized that the FRN, a component sensitive to both bad
outcomes (e.g., losses; Hajcak et al., 2006) and to unexpected
negative outcomes (Holroyd et al., 2003), would be larger for
negative comparisons (i.e., OL, DW). This hypothesis was
based
on the fact that participants discovered early in the trial whether
they had won or lost, and only later whether the final outcome
was
for better or worse. As predicted, we observed larger FRNs to
negative comparisons, which is consistent with the idea that
indi-
viduals anticipate or hope for the best possible outcome, and a
negative comparison is therefore both unexpected and
unpleasant.
However, it is worth noting that the observed pattern of larger
FRNs to negative versus positive comparisons is also consistent
with predictions made from rational choice theory (cf. Walsh &
Anderson, 2012 for an excellent review). Specifically, in the
current study, negative comparisons (i.e., outright losses and
dis-
appointing wins) always had a lower expected value (EV; which
is
calculated as the sum of the probabilities of each possible out-
come: e.g., for a win trial with possible $5 and $15 outcomes,

the
EV � $5 � 50%
$15 � 50% � $10) than positive comparisons
did. Rational choice theory would predict that the FRN would
be
larger for outcomes smaller than the EV (i.e., negative compari-
sons). Our results are clearly also consistent with this
hypothesis,
and future studies should consider addressing these competing
perspectives: Are FRNs larger to negative comparisons because
the obtained outcome is smaller than the expected value of the
gamble, or because individuals are, indeed, hoping for or antici-
pating the best possible outcome? In general, we acknowledge
that
the field of literature in economic decision-making is extremely
relevant for the current study and recognize that these different
perspectives may make contradictory predictions regarding our
results.
Limitations
There are a number of potential limitations to the current study,
including both theoretical interpretations and methodological
de-
tails. First, as mentioned above, the observed pattern of larger
FRN
T
hi
s
do
cu
m

y.
amplitudes to negative versus positive comparisons may be
inter-
preted in different ways: For example, the FRN may be
sensitive
to either unexpected negative feedback or to obtained outcomes
smaller than the expected value for a given gamble. Notably,
the
FRN was not sensitive to outcome (i.e., was not larger to losses
vs.
gains); this provides some evidence to suggest that — at least in
our dataset — the FRN does not seem to be solely sensitive to
EV
(as the EV for losses will always be lower than the EV for
wins).
However, the question remains open, and future studies should
consider these multiple perspectives.
In addition, the interpretation of the pattern of results for the
LPP may also be debatable, for two reasons. First, as P300 am-
plitudes (a component related to the LPP) are known to
habituate
over the course of many trials (Polich & Kok, 1995), it is
possible
that lower amplitudes during regulation blocks (2 and 3) than
during the no regulation block (1) could reflect habituation and
not
regulation. If the LPP does habituate over time, resulting in de-
creased amplitudes in later blocks, then we should see an
interac-
tion between regulation instruction and order of regulation

instruc-
tion, such that the LPP should be smaller during the last
regulation
condition (as order was counterbalanced across participants). To
examine the LPP data for evidence of this Order � Regulation
interaction, we conducted an analysis using 2(order: negative
first,
positive first) as an additional factor. None of the interactions
involving the order and regulation factors were significant:
Most
critically, results for the Order � Regulation interaction were:
F(2,
40) � 0.79, p � .46. These results suggest that the decreased
LPPs
observed during regulation were not due to habituation over
time.
Second, one could argue that a negative focus should only up-
regulate negative affect, therefore increasing (not decreasing)
the
magnitude of the LPP. Emotional responses to the gamble out-
comes argue against this interpretation, however, and for our
conclusion that decreased LPPs did, in fact, reflect regulation.
Specifically, a negative focus had an even stronger effect on
down-regulating positive affect than it did on up-regulating
neg-
ative affect, and these effects together ultimately decreased am-
bivalence. Thus, we argue that the overall summed effect of a
negative focus was to decrease emotional responses, given a
large
decrease in positive affect, a small increase in negative affect,
and
ultimately, a decrease in ambivalence (and the opposite is true
for
a positive focus).
Another potential limitation that concerns both the FRN and the

LPP is that, although we collected ERPs from 64 sites on the
scalp,
we only report results from one (i.e., Fz for the FRN and Pz for
the
LPP). We acknowledge that there is an ongoing argument in the
field of electrophysiology regarding the appropriate method of
reporting ERP results: Some researchers focus on a priori
compo-
nents and sites of interest (as in the current study), others
average
across sites to examine activity at regions and include
additional
factors (e.g., anterior/central/posterior and left/midline/right) in
analyses, yet others include multiple individual sites (e.g.,
midline;
Fz, Cz, Pz, Oz). Arguments can be made to support each of
these
approaches; we chose to limit analyses to sites of primary
interest,
where the FRN (Fz) and LPP (Pz) are maximal, to prevent Type
I
error (Luck, 2012). The primary limitation of this approach, of
course, is that we have not examined the spatial distributions of
the
FRN and LPP across the scalp. Future studies may want to con-
sider this approach.
Conclusion
The current study is unique in that it explores both positive and
negative regulation of both positive and negative affect (as well
as
ambivalence), whereas most studies of emotion regulation
concern
solely the down-regulation (i.e., reduction) of negative affect.
More often than not, emotion regulation is adaptive when used

to
down-regulate negativity, and this form of regulation is likely
the
most commonly used. Ambivalence, however, is a complicated
and unique emotional state that may benefit from both up- and
down-regulation of either positive or negative affect. First,
ambiv-
alence provides little to no behavioral guidance or motivation,
as
individuals feel both positive (appetitive) and negative
(avoidant)
at the same time. Second, ambivalence can be experienced
toward
things that are overall either good for us (e.g., establishing a
regular exercise regime) or bad for us (e.g., smoking cigarettes).
It
is therefore to our benefit to understand how focusing on the
positive or negative aspects of an outcome affect our
ambivalence,
our positive and negative affect, and ultimately our behavior.
Take,
for example, receiving a revise-and-resubmit review on the
initial
submission of a article. Most academics would see this as a
primarily positive event but would experience some ambiva-
lence— clearly, the editor and reviewers found enough
substance
worthy to consider a revision, but critical comments can be
plen-
tiful, time-consuming to address, and disheartening. Models of
ambivalence would predict that this combination of positivity
and
negativity might stall (i.e., produce equal approach and
avoidance
motivation) revisions necessary for publication. Our results ad-
dress a potential solution to this problem: for an overall positive

event (such as a revise-and-resubmit decision), the most
effective
manner of reducing ambivalence (and ultimately guiding
behavior
toward approaching the task of revising) is to focus on the
positive,
which ultimately should decrease negative affect (and avoidant
behavior) while increasing positive affect (and approach
behavior).
Such an approach should decrease ambivalence and focus moti-
vation on facing the reviews. A similar perspective may be
helpful
when encountering decisions regarding our health. Our results
suggest that ambivalence is not uniform across outcomes, and
considering the overall affective context, the method of
regulation
and the mechanisms behind it may ultimately make our
regulatory
efforts more effective, motivated, and productive.
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Received June 20, 2018
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14 NORRIS AND WU
http://dx.doi.org/10.1037/a0026351
http://dx.doi.org/10.1037/a0026351
http://dx.doi.org/10.1111/j.1469-8986.2006.00402.x
http://dx.doi.org/10.1111/j.1469-8986.2006.00402.x
http://dx.doi.org/10.4324/9780203496190
http://dx.doi.org/10.4324/9780203496190
http://dx.doi.org/10.1016/j.neuroimage.2004.06.030

http://dx.doi.org/10.1111/j.1749-6632.2012.06751.x
http://dx.doi.org/10.1111/j.1749-6632.2012.06751.x
http://dx.doi.org/10.1111/j.1471-6712.2007.00584.x
http://dx.doi.org/10.1016/0301-0511%2895%2905130-9
http://dx.doi.org/10.1016/0301-0511%2895%2905130-9
http://dx.doi.org/10.1037/h0088437
http://dx.doi.org/10.1037/a0019015
http://dx.doi.org/10.1207/S15327957PSPR0504_2
http://dx.doi.org/10.1037/0033-2909.125.1.3
http://dx.doi.org/10.1037/0033-2909.125.1.3
http://dx.doi.org/10.1080/0269993004200123
http://dx.doi.org/10.1080/02699930541000020
http://dx.doi.org/10.1017/S0048577200001530
http://dx.doi.org/10.1037/0033-2909.110.1.67
http://dx.doi.org/10.1037/10867-004
http://dx.doi.org/10.1037/10867-004
http://dx.doi.org/10.1037/0022-3514.86.2.320
http://dx.doi.org/10.1037/0022-3514.86.2.320
http://dx.doi.org/10.1016/j.neuroimage.2007.03.052Accentuate
the Positive, Eliminate the Negative: Reducing Ambivalence
Through Instructed Emotion ...A Brief Review of Theoretical
Perspectives on AmbivalenceEmotion RegulationMeasurement
of Emotion RegulationQuestion 1aQuestion 1bQuestion
2Question 3MethodParticipantsGeneral ProcedureCard-Choice
Gambling TaskEEG Data Collection & ProcessingDemographics
& DebriefingResultsResults from the No Regulation
ConditionMinimum scoresNegativity ratingsPositivity
ratingsThe Effects of Regulation on Ambivalence: Self-Report
MeasuresMinimum scoresThe Effects of Regulation on Positive
and Negative AffectNegativity ratingsPositivity
ratingsElectrocortical Mechanisms of the Regulation of Mixed
Gamble OutcomesFRN peak amplitudesFRN latenciesLPP
areaDiscussionAsymmetries in Instructed Regulation of Mixed
Gamble OutcomesThe FRN and Negative
ComparisonsLimitationsConclusionReferences

BRIEF REPORT
Motivation Drives Conflict Adaptation
David Dignath
University of Freiburg
Robert Wirth
University of Würzburg
Jan Kühnhausen and Caterina Gawrilow
University of Tübingen
Wilfried Kunde
University of Würzburg
Andrea Kiesel
University of Freiburg
The notion that impaired performance in the past increases
motivational vigor in the future is central to
many modern theories. The conflict monitoring model predicts
that conflict between incompatible
responses reduces susceptibility to upcoming conflict. Recently,
this conflict-adaptation effect has been
reframed in terms of motivational control: Conflict in the past
elicits negative affect, which can be
reduced by increasing control, thereby alleviating future
conflict and associated negative affect. While
evidence supports that conflict adaptation serves a motivational
purpose (e.g., affect regulation), it
remains unclear whether conflict adaptation is actually triggered
by motivational mechanisms. The

present research tested this hypothesis of a motivational
conflict-adaptation effect. We recorded contin-
uous finger movements toward target stimuli and away from
distractor stimuli. Motivational conflict was
instigated by assigning reward and penalty to stimuli that
functioned either as targets or distractors. To
index motivational vigor and increased precision due to
motivation, we measured initiation times and
spatial deviation from the shortest path to the target. Both a
reanalysis of published data and data from
a new replication study established a motivational conflict-
adaptation effect. Together, the results extend
a motivational control framework, showing that motivational
dynamics (i.e., motivational conflicts) can
be the driving force of control.
Keywords: conflict adaptation, motivation, conflict monitoring,
continuous movement
When facing difficulties, people have the remarkable ability to
persist and overcome obstacles. But how do we know when to
put
more effort in a task? In theory, it has been argued that the
difficulty of an action motivates additional effort investment
(Ach,
1935; Brehm & Self, 1989; Gendolla & Richter, 2010; Trope &
Fishbach, 2000). For instance, the conflict monitoring model
(Bot-
vinick, Braver, Barch, Carter, & Cohen, 2001) proposed that
recent
difficulty, as indexed by conflict between incompatible
responses,
is used as a teaching signal to upregulate control. Behaviorally,
the
so-called conflict-adaptation effect provides an index for this

in-
crease in control—when a conflicting stimulus was encountered
in
the previous trial, conflict has less detrimental consequences in
the
following trial (Gratton, Coles, & Donchin, 1992). Further
support
for a motivational account of the conflict-adaptation effect
comes
from studies showing that conflict triggers negative affect
(Dreisbach & Fischer, 2012; Fritz & Dreisbach, 2013) and in-
creases avoidance motivation (Dignath & Eder, 2015; Dignath,
Kiesel, & Eder, 2015), as well as from studies suggesting that
adaptation to conflict is modulated by affect and rewards
(Braem,
Verguts, Roggeman, & Notebaert, 2012; van Steenbergen, Band,
& Hommel, 2009; but see Dignath, Janczyk, & Eder, 2017).
While there is good reason to assume that conflict adaptation
serves a motivational purpose (Dreisbach & Fischer, 2015; In-
zlicht, Bartholow, & Hirsh, 2015), the role of motivation as a
trigger for conflict adaptation remains unclear. Here we tested
the
possibility that conflict adaptation is driven by motivational
con-
flict. To induce conflicting motivations, we presented stimuli
that
This article was published Online First April 11, 2019.
David Dignath, Department of Psychology, University of
Freiburg; Robert
Wirth, Department of Psychology, University of Würzburg; Jan
Kühnhausen
and Caterina Gawrilow, Department of Psychology, University
of Tübingen;

Wilfried Kunde, Department of Psychology, University of
Würzburg; Andrea
Kiesel, Department of Psychology, University of Freiburg.
This research was supported by a grant within the Priority
Program, SPP
1772 from the German Research Foundation (Deutsche
Forschungsge-
meinschaft, DFG), Grant DI 2126/1-1 and DI 2126/1-2 to DD.
Supplementary information containing additional analysis, data,
and
analysis scripts can be retrieved from https://osf.io/tdegq/.
David
Dignath, Department of Psychology, University of Freiburg,
Engelberger-
strasse 41, 79085 Freiburg, Germany. E-mail: [email protected]
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Motivation Science
1, 84 – 89
2333-8113/20/$12.00 http://dx.doi.org/10.1037/mot0000136
84

https://osf.io/tdegq/
http://dx.doi.org/10.1037/mot0000136
were associated with reward or penalty. To disentangle
cognitive
conflict from motivational conflict, we ruled out any differences
between targets and distractors in terms of overlearned behavior
(e.g., Stroop), automatic activation of responses (e.g., Simon
task),
or spatial proximity (e.g., flanker task) and selectively
manipulated
the value association of target and distractor. Following
previous
work, we hypothesized that value energizes corresponding
actions,
causing conflict if the distractor is motivational, but not the
target,
and causing no conflict if the target is motivational, but not the
distractor (Dignath, Pfister, Eder, Kiesel, & Kunde, 2014;
Wirth,
Dignath, Pfister, Kunde, & Eder, 2016).
To provide direct evidence for the assumption that motivational
conflict drives conflict adaptation, we measured continuous re-
cordings of finger movements toward targets and away from
distractors. Because recent research has shown that motivation
not
only increases speed but also reduces noise of motor signals
(Manohar et al., 2015), we considered both the speed of action
initiation (as an indicator of vigor; Niv, Daw, Joel, & Dayan,
2007)
and the spatial variability of movement execution (as an
indicator

of precision; Summerside, Shadmehr, & Ahmed, 2018).
Experiment
The present research used a finger-tracking paradigm to mea-
sure continuous movements. Participants moved toward a target
while a distractor was presented at the opposite location. Target
and distractor were defined at the beginning of each trial by a
spatial cue. To manipulate motivational valence, points were as-
signed according to the color of the target stimulus (the color
payoff regime from hereon): In trials with a gain-colored target,
participants received 5 points; in trials with a loss-colored
target,
they lost 5 points; and in trials with a neutral-colored target,
they
received zero points. Importantly, this payoff was independent
of
the actual performance. Therefore, participants could not avoid
a
penalty in trials with a loss target, for example, by selecting a
neutral distractor. Conversely, since points were only assigned
to
target stimuli, participants could not gain extra points, for
example,
by selecting a gain distractor. As a consequence, any influence
of
gain/loss distractors reflects value learning of color-incentive
as-
sociations during target trials. Independently from the color
payoff
regime, additional points were assigned according to the perfor-
mance in a trial: Errors (wrong responses and omissions) were
punished with �1 point and correct responses were rewarded
with �1 point.
In the motivational-target condition, a gain- or loss-colored

target was presented along with a neutral distractor; in the
motivational-distractor condition, a gain- or loss-colored
distractor
was presented along with a neutral target; and in the neutral-
condition, a neutral-colored target was presented along with a
neutral-colored distractor. In the present context, the term
motiva-
tional target/distractor refers to both reward and penalty,
because
research has shown that not only rewards but also penalties
have
an energizing effect on behavior and speed up responses toward
punishment (Eder, Dignath, Erle, & Wiemer, 2017; Neiss, 1988;
for additional analysis of the present data that considers the va-
lence of targets/distractors, see Supplement 3; Table 1 provides
an
overview of the online supplement).
Our hypothesis of a motivational conflict-adaptation effect pre-
dicts that motivational conflict in the previous trial mitigates
the
impact of motivational conflict in the current trial (see Figure 1,
lower panel for an illustration). We measured initiation times
(ITs)
of movements as an indicator of motivational vigor. To index
precision, we measured variability of movement trajectories in
each trial (see Figure 1, upper panel for an illustration of the
trial
sequence and the dependent variables [DVs]). To test this
hypoth-
esis, we first reanalyzed a data set previously reported in a
differ-
ent context (Wirth et al., 2016, Experiment 3) and then
replicated
this finding in a new sample. Both analyses returned similar

results, and for brevity, we present here only confirmatory tests
of
the replication study (reanalysis of the published data can be
found
in Supplement 1).
Method
The present experiment is a direct replication of Experiment 3
of
Wirth et al. (2016). For completeness, an analysis analog to the
one
reported in Experiment 3 in the original paper can be found in
Supplement 4.
Participants
Sixty-five participants were recruited (mean age � 21.3 years,
SD � 3.8, seven male, six left-handed) and received course
credit.
Given an effect size of d � .4 in the reanalysis of our published
data, this sample size allowed us to replicate conflict-adaptation
effects with a power of 88% (alpha level of .05, two-sided). All
participants gave informed consent, were naïve to the purpose
of
the experiment, and were debriefed after the session. Data of
one
participant were replaced by a new participant due to technical
difficulties during testing.
Apparatus and Stimuli
All materials and procedure were identical to Wirth et al.
(2016), Experiment 3. The experiment was run on an iPad
(move-
ments sampled at 100 Hz). The starting position (Ø 1 cm) was

located at the bottom center of the screen; target and distractor
Table 1
Index of Supplementary Materials (Available at
https://osf.io/tdegq/)
Section Pages
Supplement 1. Reanalysis of conflict-adaptation effects in
Experiment 3 of Wirth, Dignath, Pfister,
Kunde, and Eder (2016) 1–4
Supplement 2. Analysis of movement time and mean area under
the curve of the current data set 5–6
Supplement 3. Modulation by previous and current valence in
the reanalysis of Wirth et al.
(2016), Experiment 3 and the current data set 7–9
Supplement 4. Replication of the results of Wirth et al. (2016)
in the present data set 9–10
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85MOTIVATION DRIVES CONFLICT ADAPTATION
https://osf.io/tdegq/

(Ø 2 cm) were presented in the upper left and right corners of
the
display in blue, yellow (color-value mapping counterbalanced
across participants), or gray (neutral). Arrow cues prompted
move-
ments to the target (presented at the top of the screen). Each
combination of motivational target/neutral distractor and neutral
target/motivational distractor was presented 10 times per block.
Additionally, label trials (randomly intermixed four times per
block) were used to strengthen the color-value association
during
the experiment (not included in the analyses). In these trials,
gain
and loss colors were presented simultaneously, and a plus or
minus
sign indicated whether participants had to reach the gain or the
loss
stimulus.
Procedure
Participants touched the starting area with the index finger of
the
dominant hand to start a trial. Simultaneously, the two circles in
the upper half of the screen appeared, and after a dwell time of
200/300/400 ms (chosen randomly for each trial), an arrow indi-
cated the current target. Participants were then to execute a
smooth
and continuous movement to the target as quickly as possible.
Instructions emphasized accumulating as many points as
possible
(participant’s score was presented at the top). Points were
calcu-
lated based on the color payoff and the performance-contingent

payoff regime independently (see above for details). Overall,
par-
ticipants completed 10 blocks of 58 trials each.
Results
Preprocessing
We focused on two complementary DVs to assess motivational
drive and analyzed the average time to initiate movements
(ITmean)
and the average variability of the area under the curve (AUCSD)
Figure 1. Upper panel: Illustration of the trial sequence and
depend variables. Participants started a trial by
touching the start area with their index finger. After a variable
dwell interval, jittered between 200 and 400 ms,
the movement cue indicated the target. The initiation times
reflected the time from cue onset until the finger left
the start area and provides an index of how fast the movement
was started. The standard deviation of the area
under the curve reflected the spatial deviation between the
actual trajectory and a straight line from start to end
point and provides an index of variability of the movement.
Lower panel: Illustration of the hypothesized
motivational conflict adaptation effect: Motivational conflict
(i.e., moving toward a neutral target in the presence
of a motivational distractor [reward or penalty]) in the previous
trial reduces the impact of motivational conflict
in the current trial. See the online article for the color version
of this figure.
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86 DIGNATH ET AL.
between the actual trajectory and a straight line from start to
end
point. For completeness, similar analysis for movement time
and
mean AUC can be found in Supplement 2. Movements to the left
were mirrored at the vertical midline for all analyses. AUC was
computed from the time-normalized coordinate data of each trial
by using custom MATLAB scripts (The Mathworks, Inc.) as the
signed area relative to a straight line from start to end point of
the
movement (positive values indicating attraction toward the
distrac-
tor, negative values indicating attraction toward the edge of the
display).
Data Selection and Analyses
Participants gained 546 points on average (SD � 25.7). For the
following analyses, we removed error trials and response omis-
sions (2.9%). Trials were discarded as outliers if any of the DVs
deviated more than 2.5 SDs from the respective cell mean
(5.3%).
Overall Conflict Adaptation

In the present study, we were interested in whether motivational
conflict triggers conflict adaptation. To investigate this
question,
we first computed the conflict-adaptation effect for ITmean and
AUCSD. The difference between conflict (C) versus nonconflict
trials (I) after previously nonconflict (c) trials and conflict
versus
nonconflict trials after previously conflict (i) trials provides an
index of conflict adaptation � (cI – cC) – (iI – iC). Both
measures
produced significant conflict-adaptation effects, ITmean, t(64)
�
2.32, p � .024, d � 0.29 and AUCSD, t(64) � 4.32, p � .001, d
�
0.53, indicating that previous motivational conflict reduced the
impact of motivational conflict in the current trial. As shown in
Figure 2 (i.e., the difference between the green and red lines
depending on previous trial type), conflict in the previous trial
increased vigor and decreased variability in the next trial to
coun-
teract further conflict.
Reduced Distractor Susceptibility Versus Increased
Target Processing
Next, we investigated whether motivational conflict adaptation
was driven by enhanced target focus or increased shielding
against
distractors. Therefore, we included trials following the neutral
baseline condition (see Figure 2, right side of each plot) and
analyzed overall performance with the factors previous trial
type
(motivational target vs. motivational distractor vs. baseline) and
current trial type (motivational target vs. motivational
distractor).
Statistically, the interaction between both factors indicates a

pos-
sible difference in processing (target facilitation vs. distractor
shielding) following conflict.
For ITmean, the main effect previous trial, F(2, 63) � 4.75, p �
.012, �p2 � .13, showed slower response initiation after
motiva-
tional distractors (691 ms) than after motivational targets (686
ms)
or baseline (686 ms), ts � 2.49, ps � .015, ds � 0.31, and no
significant difference between the latter conditions, t � 1. The
main effect of current trial, F(1, 64) � 15.44, p � .001, �p2 �
.19,
indicated faster response initiation for motivational targets (684
ms) than for motivational distractors (692 ms). The interaction
was
not significant, F(2, 63) � 2.94, p � .060. Analysis of AUCSD
showed a main effect of previous trial, F(2, 63) � 6.14, p �
.004,
�p
2 � .16, with larger variability after motivational targets
(18,226
px2) than after motivational distractors (16,682 px2) or baseline
(17,052 px2), ts � 2.44, ps � .017, ds � 0.30, and no difference
between the latter conditions, t � 1. Further, there was a main
effect of the current trial, F(1, 64) � 83.47, p � .001, �p2 �
.56,
with higher spatial variability for motivational distractors
(20,248
px2) than for motivational targets (14,393 px2). The significant
interaction, F(2, 63) � 9.88, p � .001, �p2 � .24, indicated
smaller
differences between current motivational targets and
motivational
distractors after a motivational distractor (� � 4,118 px2) than

after a motivational target (� � 7,451 px2) or baseline trials (�
�
5,996 px2), ts � 2.07, ps � .043, ds � 0.26, and no difference
Figure 2. Mean initiation times (ITs; left) and standard
deviation of areas under the curve (AUCs; right) for
current motivational targets (red lines) and current motivational
distractors (green lines), separate for trials after
motivational targets, after motivational distractors, and after
neutral baseline trials. See the online article for the
color version of this figure.
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between the latter conditions, t(64) � 1.37, p � .176, d � 0.17.
In
other words, spatial variability of movements in motivational
conflict trials was reduced following conflict, indicating better
shielding against distraction, while target processing was not
fa-
cilitated by previous conflict.
Discussion

The present study tested whether previous motivational conflict
causes adaptation to conflict in the current trial. Results from a
reanalysis of a previous data set and a new replication provided
converging evidence for motivational conflict-adaptation
effect—
prior motivational conflict alleviates future conflict. This is in
line
with recent research on decision making (Heitmann & Deutsch,
2019), showing faster decisions following motivational conflict
compared to control trials. Moreover, it provides further support
for theoretical accounts that aim to explain why and how people
overcome obstacles during goal pursuit (e.g., Botvinick et al.,
2001; Inzlicht et al., 2015; see also Shenhav, Botvinick, &
Cohen,
2013). The present results extend this framework, showing that
control mechanisms not only serve motivational functions but
that
motivational conflict is a driving force of control in itself.
Closer inspection of the results (see Figure 2) suggests that
motivational conflict adaptation during movement execution
was
caused by an increased shielding against distraction. In contrast,
conflict-adaptation effects in movement initiation seemed to be
more likely due to increased target processing following
conflict,
although it should be noted that the critical comparison against
baseline failed to reach significance in the confirmatory study
(p �
.060). Why did the mechanisms underlying conflict adaptation
differ between DVs? One plausible explanation is that action
initiation and execution reflect distinct processes underlying
con-
trol (Song & Nakayama, 2009). For instance, for conflict-
adaptation effects in the Stroop task, it has been suggested that

action initiation describes a reactive change in control, while
action execution reflects monitoring of conflict (Erb & Marco-
vitch, 2018; Erb, Moher, Sobel, & Song, 2016). Furthermore,
neurophysiological work found that distinct neuronal population
code separately for action initiation and execution (Gaidica,
Hurst,
Cyr, & Leventhal, 2018). However, it is unclear how such a
sharp
distinction translates into continuous processing models,
suggest-
ing a constant flow of information from beginning until the end
of
an action (Miller, 1988; Pfister, Janczyk, Wirth, Dignath, &
Kunde, 2014; Spivey, Grosjean, & Knoblich, 2005). Clearly,
more
research is needed to determine when and how control is
instigated
during motivational conflicts.
In previous work (and also in this study; see Supplement 4), an
asymmetric influence of the current motivational distractor on
movement execution was observed. More specifically,
movement
execution showed an increased pull toward gain distractors,
while
targets and distractors associated with losses had no effect on
movement execution. Why did this valence asymmetry in the
current trial not influence conflict-adaptation effects? In theory,
the control signal is indexed by competing response activation.
Possibly, conflict monitoring is confined to the response
prepara-
tion stage and later conflict during response execution does not
feed into the control signal. This speculation fits with research
on
dorsal stream processing during movement execution, which is

often assumed to run encapsulated from other ongoing
processing
(e.g., Milner, 2017; but see Erb et al., 2016).
Summary
This experiment (and a reanalysis of a previous data set) tested
a key prediction of a motivational view of conflict adaptation,
suggesting that previous motivational conflict reduced
susceptibil-
ity to current motivational conflict. Using a simple measure of
motivational vigor and precision, we provided evidence that
sup-
ports this prediction by showing that previous motivational con-
flict drives control adaptation.
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Received October 25, 2018
Revision received February 5, 2019
Accepted March 6, 2019 �
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http://dx.doi.org/10.1037/mot0000037Motivation Drives
Conflict AdaptationExperimentMethodParticipantsApparatus
and StimuliProcedureResultsPreprocessingData Selection and
AnalysesOverall Conflict AdaptationReduced Distractor
Susceptibility Versus Increased Target

ProcessingDiscussionSummaryReferences
Functional Divergence of Two Threat-Induced Emotions:
Fear-Based Versus Anxiety-Based Cybersecurity Preferences
Violet Cheung-Blunden, Kiefer Cropper, Aleesa Panis, and
Kamilah Davis
University of San Francisco
Two threat-induced emotions and their respective ability to
sway cybersecurity preferences were
investigated after a cyberattack on financial institutions. Our
theoretical aim was to advance the
functionalist claim and differentiate between fear and anxiety
by their action tendencies. The emotions
were expected to have unique motivation power and thus show
mutually exclusive ties to the three types
of safety behaviors emerged in our study. Avoidance would be
uniquely embraced by fearful participants,
whereas surveillance and vigilance would uniquely appeal to
anxious participants. Study 1 (N � 199)
used a cross-sectional design and found full support for the
hypothesis regarding anxiety but only partial
support for the hypothesis regarding fear. Study 2 (N � 304), an
experiment of fearful, anxious, and
relaxed groups, did not yield significant results but did offer
methodological recommendations. The
quasi-experiments in Study 3 (N � 120) and Study 4 (N � 156)
supported the hypotheses on fear and
anxiety. Our results in the novel domain of cyber threat brought
new evidence to bear on the mixed
literature on fear and anxiety. A discussion is offered on the
methodological challenges of differentiating
two closely related emotions as well as implications for the

emerging debate of cybersecurity solutions.
Keywords: security motivation, action tendencies,
cybersecurity, hypervigilance, general anxiety disorder
Fear and anxiety are often synonymous in spoken English—for
example, when someone says “I fear for a future event” they
usually mean “I worry about a future event”—but the
functionalist
approach regards these two emotions as characteristically
distinct
(Barlow, 2000; Blakemore & Vuilleumier, 2017; Darwin, 1872;
Fontaine & Scherer, 2013; Frijda, 2007; Lazarus, 1999;
Levenson,
2011; Shuman, Clark-Polner, Meuleman, Sander, & Scherer,
2017). The functionalist approach aims to build a unique profile
for each emotion by connecting certain types of antecedent
events
to a set of phenomenology, a host of bodily sensations, and a
class
of behavioral outcomes. Past empirical studies have had
difficulty
differentiating the profiles of fear and anxiety because of their
well-documented comorbidity (Helbig-Lang & Petermann, 2010;
Perkins & Corr, 2006; Shen & Bigsby, 2010; van Uijen &
Toffolo,
2015). A concise understanding of two-closely related emotions
can benefit many fields, especially sentiment analysis where
every
nuance counts for its predictive value. Treating fear and anxiety
as
synonyms has resulted in many missed opportunities with regard
to appreciating the tangible ramifications of two most common
threat responses (Barlow, 2000; Beck, Emery, & Greenberg,
2005;
Mason, Stevenson, & Freedman, 2014; Perkins, Cooper,

Abdelall,
Smillie, & Corr, 2010; Sylvers, Lilienfeld, & LaPrairie, 2011;
Tovote, Fadok, & Lüthi, 2015).
The new domain of cyberinsecurity provides a valuable plat-
form to differentiate the functions of fear and anxiety. With cy-
berattacks on the rise, fearful and anxious sentiments have the
potential to spur public behavior en masse as well as influence
policy direction (Woody & Szechtman, 2013, 2016). This is not
the first time in history when technological advances have intro-
duced new risks and motivated new safety-seeking behaviors. If
the early 20th century was a dangerous time to drive on the road
(Loomis, 2015), then the early 21st century is a dangerous time
to
surf the Internet. Current newspaper headlines, such as
“Computer
Security is Broken From Top to Bottom” (Computer Security is
Broken, 2017) and “Cybersecurity: A Race Without a Finish
Line”
(DiPietro, 2016), are reincarnations of an earlier sense of
desper-
ation about automobile hazards. Desperation can spur actions
that
are not necessarily based on rational thinking but rather on an
emotional reaction (Cybersecurity National Action Plan, 2016;
Cyber Warfare, 2012; Macaskill & Dance, 2013; Preston, 2014;
Zetter, 2015). Functional differences between fear and anxiety
may be capitalized upon in order to more accurately predict
individual and collective action in a growing terrain of cyber
threats (Cohrs, Moschner, Maes, & Kielmann, 2005; Druckman
&
McDermott, 2008; Maitner, Mackie, & Smith, 2006; Mason et
al.,
2014; Sahar, 2008; Yzerbyt, Dumont, Wigboldus, & Gordijn,
2003).

Functionalist Claims of Fear and Anxiety
Ever since Darwin (1872), emotions have been seen as products
of evolutionary processes. Each emotion corresponds to a
distinc-
tive biological system that addresses a recurrent adaptive
problem
with a phylogenetically tested solution (Ekman, 1992; Frijda,
2007; Lazarus, 1999; Levenson, 2011; Tooby & Cosmides,
1990).
This article was published Online First November 26, 2018.
Violet Cheung-Blunden, Kiefer Cropper, Aleesa Panis, and
Kamilah
Davis, Department of Psychology, University of San Francisco.
Violet
Cheung-Blunden, Department of Psychology, University of San
Francisco,
2130 Fulton Street, San Francisco, CA 94117. E-mail:
[email protected]
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Emotion
8, 1353–1365
1528-3542/19/$12.00 http://dx.doi.org/10.1037/emo0000508

1353
Such a system must be able to detect situation cues, place the
cues
in the proper category, coordinate among biological circuitries
and
precipitate a specific action tendency. The coherence within
each
antecedent-behavior chain, nonredundant across discrete
emotions,
is at the center of the functionalist argument (Blakemore &
Vuil-
leumier, 2017; Shuman et al., 2017).
The functionalist tenet of distinctive emotion systems is partic-
ularly applicable to basic emotions (Ekman, 1992; Levenson,
2011). As a basic emotion, fear was described by Darwin (1872)
on several occasions—originating from the threat of physical
harm
and resulting in an uncontrollable urge to flee. Researchers have
since found that the typical triggers for fear are imminent
threats,
and the typical behavioral outcomes are avoiding, running away,
freezing, and hiding (Amir, Kuckertz, & Najmi, 2013; Frijda,
Kuipers, & ter Schure, 1989; Glotzbach, Ewald, Andreatta,
Pauli,
& Mühlberger, 2012; Roseman, Wiest, & Swartz, 1994). The
coherence between antecedents and behaviors is apparent in the
case of fear. As an age-old mechanism, the fear circuitry detects
immediate dangers and motivates a person to engage in
distance-
increasing strategies (Stein & Bouwer, 1997; Tooby &

Cosmides,
1990).
Barlow (2000) extended the functionalist argument from basic
emotions to a more complex emotion: anxiety. Anxiety is a
shadow of intelligence: Humans’ ability to plan for the future is
the
reason why members of our species worry about looming
threats.
This view of anxiety, which centers on how a species meets its
survival demands with a particular emotional faculty, is a func-
tionalist argument (Stein & Bouwer, 1997; Tooby & Cosmides,
1990). Barlow (2000) must have considered action tendency as
the
crux of the functionalist argument (Fontaine & Scherer, 2013)
and
defined anxiety as a prolonged hypervigilance in response to, or
in
anticipation of, a non-imminent threat. Basic research of
anxiety in
laboratories and applied research on voter anxiety found not
only
hyper-attentiveness to threats but also a tendency to proactively
search for threatening information (Gadarian & Albertson,
2014;
Ladd & Lenz, 2008; Marcus, MacKuen, & Neuman, 2011;
White,
Skokin, Carlos, & Weaver, 2016).
Woody and Szechtman (2011, 2013) conceptualized a security
motivation system and offered a detailed account of how the
anxiety system operates differently from the fear system. The
security system is a hardwired module, dedicated to the
assessment
and management of potential risks, whereas the fear system is
responsible for imminent threats. Therefore, anxiety is triggered

by
subtle signs of hidden risk, in the absence of the obvious signs
of
danger in the case of fear. The ambiguity of the risk calls for
information gathering, checking the environment for additional
cues, and taking precautions, rather than avoidance behaviors
typical of fear.
The motivation security system, as a product of our
evolutionary
past, is particularly relevant to today’s Internet-connected world
(Woody & Szechtman, 2013, 2016). The kind of antecedents
that
trigger anxiety are abundant on the Internet, with a nearly
limitless
supply of fragmented, uncertain threat cues ranging from
terrorist
attacks to cyber breaches. Consumers of this kind of
information
may find their security system activated and unable to
deactivate
the system because of a lack of technical know-how to
implement
effective countermeasures. The consumer is left with few
options
but to share their sense of insecurity online while
simultaneously
reading similar posts from peers in their social network. The
positive feedback loop offers a psychological insight into how
inaccurate news about potential threats spreads on the Internet.
Co-occurrence of Fear and Anxiety as a Barrier to
Functionalist Studies
A critique of many previous studies of fear and anxiety is that

they overlooked the tendency for the two emotions to co-occur
in
both state and trait forms. A state emotion arises primarily
because
of an instigating event in the outside world, whereas a trait
emotion
occurs primary because of a preexisting propensity within a
person
(Spielberger, 1983). For example, in their state forms, fearful
and
anxious responses to the threat of terrorism are treated synony-
mously by the extended parallel process model of fear
communi-
cation (Ruiter, Abraham, & Kok, 2001; Witte, 1992). Similarly,
in
an analysis of emotional communication using an automated
tool
to count emotion-related keywords, “anxiety,” “fear,” and
“scared”
were placed in the umbrella category of “fear” (Soroka, Young,
&
Balmas, 2015). In their trait forms, clinical research found
simul-
taneous experiences of exaggerated fear and anxiety among pa-
tients with a variety of disorders. The current edition of the
Diagnostic and Statistical Manual of Mental Disorders
(American
Psychiatric Association, 2013) has yet to successfully untangle
the
comorbidity of fear-based and anxiety-based disorders (Sharma,
Woolfson, & Hunter, 2013). The co-occurrence of fear and
anxiety
has even garnered theoretical attention. One claim is that any
differences between the two emotions are only microscopic or a
matter of degree (Kim et al., 2008; Öhman, 2008; Ruiter et al.,
2001).

The coexistence of fear and anxiety puts the result of many
functionalist studies in question, especially if a study only mea-
sured fear without anxiety, or vice versa (e.g., Cheung-Blunden
&
Blunden, 2008; Frijda et al., 1989; Marcus & MacKuen, 1993;
Roseman et al., 1994). In retrospect, an open question is
whether
the topic of these studies was in fact fear or anxiety. Strictly
speaking, any behavioral outcomes of these studies could be
equally attributed to either emotion. One way to address the
co-occurrence of fear and anxiety would be to look at
comparative
studies which are rare both in terms of review articles and
empir-
ical studies.
From Neural Networks to Behavioral Manifestations
Two review articles compared the neurobiology of fear and
anxiety with an explicit expectation that neurobiology lays the
foundation for the preparation, execution, and control of
voluntary
actions (Blakemore & Vuilleumier, 2017). Tovote et al. (2015)
concluded that the two emotions share overlapping neural sub-
strates but thought it promising to differentiate the
neurobiology of
fear and anxiety by investigating how long-range neural projec-
tions interact with local microcircuits. An earlier review article
by
Sylvers et al. (2011) acknowledged the neural similarities but
also
pointed to a key difference—fear is represented on a less
sophis-
ticated neural level, whereas anxiety is represented in much
wider

and more inclusive neural pathways (Davis, Walker, Miles, &
Grillon, 2010; de Wit, Kosaki, Balleine, & Dickinson, 2006;
Furmark, Fischer, Wik, Larsson, & Fredrikson, 1997; Gray,
1982;
Kalisch et al., 2004; McNaughton & Corr, 2004; Ruiz-Padial &
Vila, 2007).
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1354 CHEUNG-BLUNDEN, CROPPER, PANIS, AND DAVIS
One interpretation of the less complex fear module and the more
complex anxiety module is that they serve very different
functions
(Woody & Szechtman, 2013). Despite some shared neural sub-
strates, each module solves a specific type of adaptive problem
by
getting the body into a specific state of readiness. Although the
fear module engenders a “violent display of energy” by fully
mobilizing sympathetic activity, the anxiety system requires
atten-
tion to novelty, which in physiological terms is to attenuate
vagal
influences over viscera and remove the antagonistic parasympa-
thetic influence. Therefore, a shift in respiratory sinus
arrhythmia
(RSA) to lower variability ought to be a unique biomarker for
the

activation of the anxiety system (Gray, 1982; McNaughton &
Gray, 2000). In support of this notion, Hinds et al. (2010) docu-
mented the biomarker in an ambiguously threatening situation
(rather than an imminently threatening situation), which was
when
participants touched a low-intensity putative contagion (rather
than
a high-intensity stimulus). Of particular interest to the present
study is the precautionary behavior effective at deactivating the
anxiety system, which was when participants washed their
hands
in running water (rather than sham washing).
The unique biomarker no doubt allowed an in-depth look at the
operations of the anxiety system, but empirical comparisons of
emotional systems ought to offer additional insights.
McNaughton
and Corr (2004) hypothesized that the respective neural
networks
of fear and anxiety are meant to carry out behavioral functions
with markedly different “defensive directions.” While fear
causes
behaviors that increase the distance between the self and the
threat,
anxiety causes behaviors that decrease the distance. Empirical
studies on defensive direction, however, have not always sup-
ported the theoretical prediction. Perkins and colleagues (2010)
found consistent correlations between trait fear and the
tendency to
orient away from the threat, but they found inconsistent correla-
tions between trait anxiety and the tendency to orient toward the
threat (Perkins & Corr, 2006).
Clinical Behaviors of Fear and Anxiety
The largest body of comparative studies comes from the past

effort of clarifying the taxonomy of fear-based and anxiety-
based
disorders (Brown & Barlow, 2009; Brown, Chorpita, & Barlow,
1998; Gros, McCabe, & Antony, 2013; Gros, Simms, & Antony,
2011). Several notable factor analytical studies used behavioral
symptoms as the basis of classification but derived mixed
findings
(Kendler, Prescott, Myers, & Neale, 2003; Kotov, Perlman, Gá-
mez, & Watson, 2015; Slade & Watson, 2006; Waters, Bradley,
&
Mogg, 2014). On the one hand, Kendler et al. (2003) examined
the
diagnostic interview data of 5600 twins with 10 common
psychi-
atric and substance use disorders. Their analysis revealed two
lower-order factors. The anxiety-misery factor pertained to
major
depression and generalized anxiety disorder, and the other
factor
pertained to animal and situational phobia. On the other hand, a
longitudinal study by Kotov et al. (2015) showed mixed factors
with no clear boundaries between anxiety and fear. Their factors
were distress (depression, generalized anxiety, posttraumatic
stress,
irritability, and panic syndrome), fear (social anxiety,
agoraphobia,
specific phobias, and obsessive–compulsive disorders), and
bipolar
disorders (mania and obsessive-compulsive disorders).
An effort to classify psychopathologies based on safety behav-
iors alone was carried out by Helbig-Lang and Petermann
(2010).
Five subcategories of behaviors emerged: escape, reassurance-
seeking, distraction, compulsive behavior, and threat neutraliza-

tion. The authors did not tag the subcategories as fear-based or
anxiety-based. In theory, escape and threat neutralization seem
suitable behavioral strategies to address an immediate threat in
fearful episodes. Reassurance-seeking and compulsive behavior
seem to be aligned with a strategy of hypervigilance to address
looming threats in anxious episodes.
The Present Study
In sum, the functional difference between fear and anxiety can
be difficult to discern due to the comorbidity of the two
emotions.
Some neuroscientists argued that the smaller circuitry of fear
and
the larger circuitry of anxiety were supposed to prepare a
person to
carry out behaviors in opposite defensive directions. Their com-
parative studies were more successful at linking fear to “moving
away” behaviors than linking anxiety to “moving toward”
behav-
iors. We went beyond the question of directionality and cross
referenced an array of clinical literature to identify specific
behav-
ioral tendencies for the two emotions.
We hypothesized that participants who were gripped by fear or
anxiety would rally behind different types of cybersecurity
solu-
tions. We expected fearful participants to adopt avoidance or
escape strategies to address cyber threats; they would avoid
e-commerce, shun social media, and adopt air-gap defensive
mea-
sures. We expected anxious participants to gravitate toward hy-
pervigilance, reassurance-seeking, and compulsive checking to
counter cyberinsecurity; their solutions might range from
information-seeking to embracing mass surveillance policies.

Study 1
This cross-sectional study examined the common types of cy-
bersecurity strategies and uncovered their emotional appeals in
a
convenient Internet sample.
Method
Participants. The participants were 199 American adults
between 18 and 68 years of age (M � 35.5 years, SD � 12.1
years). The sample consisted of 62.3% males and 37.7% fe-
males. A few participants were students (15.1%), whereas the
rest were working adults. Most participants held a bachelor’s
degree (39.2%), followed by high school diploma (30.2%),
associate degree (15.1%), master’s degree (12.6%), doctoral
degree (1.5%), and some high school education (1.4%). The
ethnic composition was primarily Caucasian (62.8%), followed
by Indian (10.6%), other Asians (8.0%), African American
(6.0%), Latino (6.0%), Eastern European (2.5%), and a “mixed/
other” category (4.1%). Besides the 43.7% participants self-
identified as atheist or spiritual, the rest of the participants were
Christian (36.7%), Hindu (9.0%), Buddhist/Taoist (3.0%), Mus-
lim (3.0%), and “other” (4.6%). Political affiliation consisted of
38.2% Democrat, 31.2% independent, 17.6% Republican, 5.5%
Libertarian, 2.5% American Independent Party, 1.5% Green
Party, 1.5% Tea Party, and 2.0% “other.” All of the participants
were screened for U.S. citizenship, with a majority residing in
the United States at the time of the study (88.9%).
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1355TWO THREAT-INDUCED EMOTIONS
Procedure. The participants were recruited from an online
survey service. In light of the possible utility of the present
findings in democratic participation, the criteria were being an
American citizen at least 18 years old. After giving their
consent,
participants followed a web link and completed a multimedia
survey. The participants responded to demographic questions,
watched a news report of a cyberattack, responded to questions
about cybersecurity measures, and reported their current levels
of
state anxiety and state fear. Each participant received $1 upon
completion of the study. All procedures were approved by the
University of San Francisco Institutional Review Board (IRB)
and
were conducted according with American Psychological
Associa-
tion (APA) ethical conduct of research with human subjects.
Instruments.
Demographics. The participants reported their age, sex, edu-
cation, ethnicity, religion, political affiliation, citizenship, and
country of residence.
Multimedia news report. The hacking collective known as the
Anonymous Group retaliated against a decision by the State De-
partment and launched a Distributed Denial of Service attack on
MasterCard by posting 10,000 credit card numbers online. A
news

report by MSNBC was chosen and was edited for clarity. The
event details were delivered in approximately 550 words over
1.5
min.
State version of State–Trait Anxiety Inventory, Form Y (Spiel-
berger, 1983). The original State–Trait Anxiety Inventory
(STAI) provided 20 items to assess state anxiety. Kvaal, Laake,
and Engedal (2001) replicated the two-factor solution which
placed the negatively worded items in the “nervousness” factor
and the positively worded items in the “well-being” factor. The
present study chose the “nervousness” factor and further
excluded
the item “I feel frightened” because of its convergence with
fear.
The nine items from the STAI were administered with a four-
point
scale (1 � not at all to 4 � very much). Sample items include “I
feel nervous” and “I’m worried.” Kvaal et al. (2001) used seven
of
the negatively worded items and reported a Cronbach’s alpha of
.56. The nine-item scale in the present study reported an alpha
value of .94.
State Fear Inventory (Cheung-Blunden & Ju, 2016). The
nine-item State Fear Inventory gauges the degree of fearful ex-
pressions to a threat scenario. A four-point response scale was
used
(1 � not at all to 4 � very much). Sample items include “I feel
as
if I cannot move” and “I feel a cold sweat.” The alpha
coefficient
was .95 in Cheung-Blunden and Ju (2016) and .94 in the present
study.
Surveillance, vigilance, and avoidance cybersecurity measures.

On the basis of the news report of alleged and actually imple-
mented cybersecurity measures (Cybersecurity National Action
Plan, 2016; Macaskill & Dance, 2013; Preston, 2014; Zetter,
2015), we drafted 10 items to measure participants’ support for
surveillance policies (see Table 1). We also asked participants
in a pilot study to describe their own strategies for ensuring
cybersecurity (e.g., Mason et al., 2014). The popular answers
were captured by the eight items on vigilance behaviors and the
eight items on avoidance behaviors in Table 1. All cybersecu-
rity items were administered on a five-point scale (1 � not at
all to 5 � very much).
Results
An exploratory factor analysis was conducted on the cyberse-
curity measures using principle components analysis and
Varimax
rotation. A five-factor solution was recommended based on an
eigenvalue of one, accounting for about 74.9% of the variance
(see
Table 1). Factor I pertained to surveillance, with all of the
surveil-
lance items showing a minimum loading of .70 and an internal
consistency of .95. Factor II pertained to vigilance, with all
items
showing a minimum loading of .66 and an overall Cronbach’s
alpha of .94. Factors III, IV, and V pertained to various
avoidance
behaviors. Internal reliability was used to decide the best
compo-
sition of the avoidance scale, and the highest Cronbach’s alpha
of
.82 was achieved by including items 1, 2, 3, 4, 5, and 6.
Prior to hypothesis testing, we explored the effect of demo-
graphics on all three dependent variables. Results showed that

support for surveillance was higher among older participants,
r(194) � .28, p � .000, females, t(194) � 3.17, p � .002,
Christians or Hindus (simple comparisons: tChristian(194) �
2.54, p � .012; tHindu(194) � 5.88, p � .000), but was lower
among participants who resided outside the United States at the
time of study, t(194) � 3.88, p � .000, as well as those who
stated “spiritual” as their religion, t(194) � 3.47, p � .001. The
preference for vigilance measures was lower among Cauca-
sians, t(194) � 2.64, p � .009, and students, t(194) � 2.36, p �
.019; but higher among older participants, r(194) � .16, p �
.027, females, t(194) � 1.98, p � .049, Christians, t(194)
� 2.40, p � .017, Hindus, t(194) � 5.80, p � .000, Asians,
t(194) � 2.02, p � .045, as well as those who resided outside
the United States, t(194) � 6.24, p � .000. In terms of avoid-
ance strategies, the only demographic predictor was the positive
effect of residence outside the United States, t(194) � 2.40, p �
.017.
After establishing the significance of demographics in each
dependent variable, we added both fear and anxiety as
predictors
in the regression analysis for each cybersecurity measure. Our
hypothesis regarding anxiety gained support in the analyses of
surveillance and vigilance measures. Specifically, a significant
predictor for surveillance cyber policies, F(8, 185) � 12.97, p �
.000, R2 � 36.9%, was state anxiety (� � .27, p � .001) but
not
state fear (� � .12, p � .138). Similarly, a significant predictor
for
vigilance behaviors, F(10, 183) � 12.30, p � .000, R2 � 41.6%,
was state anxiety (� � .55, p � .000) but not state fear (� �
�.08,
p � .363). Although our anxiety hypothesis was fully supported,
our fear hypothesis was only partially supported. Results
showed
that avoidance behaviors, F(3, 184) � 17.28, p � .000, R2 �

22.3%, were significantly predicted by both state fear (� � .25,
p � .004) and state anxiety (� � .28, p � .001).
Study 2
Because relaxation is considered a state of mind opposite to a
myriad of negative emotions (e.g., Ergene, 2003; Spielberger,
1983), the present study used relaxation as a baseline to study
the
effects of fear and anxiety. We randomly assigned participants
to
a relaxed, fearful, or anxious group and then compared their
cybersecurity preferences.
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Method
Participants. Participants were 304 college students (M �
19.0 years, SD � 2.1 years) enrolled in various psychology
courses
in a university in the San Francisco Bay Area. The sample con-
sisted of 75.3% female students and 24.7% male students. More
than half of the sample were freshmen (68.1%), followed by
sophomores (16.4%), juniors (8.6%), and seniors (6.9%). The
ethnic composition was 31.6% Asian, 24.7% Caucasian, 21.7%

Latino, 4.3% African American, 3.6% Pacific Islander, 3.0%
Mid-
dle Eastern, and 11.1% various other ethnic origins. In terms of
common religious affiliations, 53.3% self-identified as
Christian,
21.1% were atheist, and 14.1% were spiritual. One-third of the
participants did not have a political affiliation (31.9%), whereas
the rest were primarily Democrat (54.9%), Republican (9.9%),
or Libertarian (1.6%). The sample consisted of mostly U.S.
citizens (91%) who were born in the United States (82.6%).
Procedure. The participants were recruited from psychology
classes on a voluntary basis. Participants reported their trait
fear
and trait anxiety in a preliminary questionnaire to make sure the
two groups did not have any a priori differences in the target
emotions. Regardless of their traits, they were randomly
assigned
into three groups where they were induced to feel fearful,
anxious,
and relaxed, respectively. Specifically, our mood induction was
conducted in three stages to maximize the effect of the
manipula-
tion. First, a 2-min video was used to prime relaxation (a sunset
on
the beach), fear (a clip from The Shining: Gross & Levenson,
1995), and anxiety (news report of natural disasters). Second,
behavioral tasks further enhanced relaxation (name 10 vacation
spots), fear (subtract 7 from 2,000 for 10 consecutive times),
and
anxiety (name 10 topics in an upcoming exam). Third,
participants
wrote a 200-word paragraph to describe their own emotional
experiences congruent with their group assignments (Lerner,
Gon-

zalez, Small, & Fischhoff, 2003). After the mood induction,
par-
ticipants watched a video of a cyberattack and then responded
to
questions regarding their cybersecurity preferences.
Before data analysis, two independent coders conducted com-
pliance checks and flagged problematic essays on a three-point
scale (0 � full compliance, 1 � slight incompliance, 2 �
obvious
incompliance). The specific coding for the fear/anxious essays
was
0 � descriptions of target emotion only, 1 � notable mentions
of
the other emotion, 2 � obvious mentions of the other emotion.
The
coding for the relaxed essays was 0 � descriptions of relaxation
only, 1 � relaxation was discussed in the context of stress man-
agement, 2 � mentions of unwillingness or inability to relax.
With
Table 1
Factor Structure of Cybersecurity Measures in Study 1
Factor
Items I II III IV V
Surveillance cybersecurity measure
1. Keep track of Internet traffic in order to locate cyber
attackers .82 .21 .10 .08 .04
2. Record the online activities of suspected cyber attackers .85
.23 .02 .18 .07
3. Demand Twitter to deny service to cyber attackers .84 .20 .02
.10 .24

4. Order YouTube to take down a video that serves as a tutorial
for cyber attackers .78 .17 .04 .16 .29
5. Ask Facebook to turn over the accounts of the suspected
cyber attackers .87 .16 .05 .18 .22
6. Monitor search engines to track users’ browsing habits .79
.27 .17 �.08 �.25
7. Ask software companies like Microsoft and Apple to grant
the government secret access to their
users’ machines through back doors .70 .22 .32 �.18 �.25
8. Offer Internet companies financial support for their
collaboration with the government .77 .26 .26 �.10 �.20
9. Use mass interception to detect immediate threats (in real–
time without storing any data) .80 .20 .09 .02 �.01
10. Store mass Internet communication data for future use .85
.14 .10 �.15 �.11
Vigilance cybersecurity measure
1. Keep a close eye on my credit card accounts .26 .80 .02 .30
.04
2. Search for news to stay informed about recent cyberattacks
.20 .85 .18 .00 .04
3. Check my bank statements every month .27 .72 �.01 .46
�.02
4. Safeguard my passwords to prevent them from falling into the
wrong hands .24 .66 .00 .53 .09
5. Conduct research on the level of security at my financial
institutions .17 .83 .12 .04 .08
6. Call credit card companies and make sure nothing is
happening to me .24 .79 .26 �.17 .11
7. Change the passwords on my online accounts at least once a
month .19 .84 .16 .07 .03
8. Keep 1–800 numbers handy in case I need to cancel my cards
quickly .29 .78 .10 .09 .07

Avoidance cybersecurity measure
1. Cancel some credit cards .19 .22 .75 �.04 �.03
2. Stop purchasing things online .17 .15 .87 .00 .01
3. Avoid using PayPal .00 �.05 .73 .31 �.11
4. Use cash as much as possible .02 .16 .54 .30 .43
5. Stop using online banking .19 .20 .71 .03 .30
6. Keep the number of credit cards to a minimum .07 .09 .45 .61
.28
7. Stop sharing personal information online .01 .12 .08 .03 .74
8. Refuse to open suspicious e–mails �.03 .19 .12 .81 �.02
Note. Values in boldface type indicate the highest loading
among the five factors.
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an intercoder reliability of .84, the flags from the two coders
were
added. The data from 11 participants were excluded from data
analysis as they received two or more flags from the coders. All
procedures were approved by the University of San Francisco
IRB
and were conducted according with APA ethical conduct of re-
search with human subjects.

Instruments.
Demographics, state emotions, and cybersecurity measures.
These were the same as in Study 1. The Cronbach’s alpha in the
present study was .92 for State Fear Inventory, .93 for state
anxiety
scale, .85 for avoidance scale, .90 for surveillance scale, and
.90
for vigilance scale.
Trait version of State–Trait Anxiety Inventory, Form Y (Spiel-
berger, 1983). The positively worded items on the scale were
not
used due to concerns of their content validity: a lack of
calmness
can imply a wide range of negative emotional states beyond
anxiety (Bieling, Antony, & Swinson, 1998). The negatively
worded items were used on a four-point scale (1 � not at all to
4 �
very much). Sample items include “I worry too much over
some-
thing that really doesn’t matter” and “I have disturbing
thoughts.”
The Cronbach’s alpha reported by Spielberger (1983) was .85
for
the entire scale, and the present study found an alpha value of
.87
for the subset used.
Fear Questionnaire (Marks & Mathews, 1979). Beck, Stan-
ley, and Zebb (1996) recommended three sets of five-item sub-
scales of the Fear Questionnaire (FQ) to tap into agoraphobia,
blood/injury phobia, and social phobia. Because the social
phobia
subscale has shown a comorbidity with social anxiety in the

past
(Kashdan, Elhai, & Breen, 2008; Oei, Moylan, & Evans, 1991),
we
only used the agoraphobia and blood/injury phobia subscales (0
�
would not avoid it to 8 � always avoid it). A sample item for
agoraphobia is “traveling alone by bus or coach,” and a sample
item for blood/injury is “going to the dentist.” Oei et al. (1991)
found a Cronbach’s alpha between .71 and .83 for the 15-item
scale, and the present study found a value of .76 for the 10-item
scale.
Results
The group assignments were conducted on a proper basis with-
out any a priori differences in trait fear, F(2, 292) � 0.04, p �
.964, or trait anxiety, F(2, 292) � 0.36, p � .695. However, the
manipulation resulted in no significant group differences in the
induced emotions (fear: F(2, 292) � 1.38, p � .253; anxiety:
F(2,
292) � 1.40, p � .248) or in the cybersecurity preferences
(avoid-
ance: F(2, 292) � 0.73, p � .481; surveillance: F(2, 292) �
0.36,
p � .696; vigilance: F(2, 292) � 0.19, p � .823). Post hoc
analyses were conducted to improve the research design for the
upcoming studies. To explore a matching state–trait
methodology
that has been used elsewhere (e.g., Reinholdt-Dunne, Mogg,
Esb-
jorn, & Bradley, 2012; Ruscio & Borkovec, 2004; White et al.,
2016), a two-way ANOVA examined how participants with dif-
ferent predispositions responded to the three types of mood
induc-
tions. Figure 1 illustrates the effects of mood inductions on par-
ticipants with or without a phobic tendency and the largest �p2

was
found when applying fearful mood induction on phobic partici-
pants. In a similar analysis of trait anxiety in Figure 2, the
largest
�p2 was found when applying anxious mood induction on
anxiety-
ridden participants. Most of all, the relaxed condition
effectively
reduced the preexisting trait differences and seemed a suitable
control condition in the upcoming studies.
Our upcoming studies would screen participants for trait sever-
ity and then administer a matching mood induction. A problem
with screening for fearful and anxious participants in a single
comparative study was that many participants would show
comor-
bid fearful and anxious traits and they could not be easily
assigned
to the fearful or the anxious group. We therefore opted for an
alternative approach of conducting two separate studies by con-
trasting fearful and relaxed participants in one study, and
compar-
ing anxious and relaxed participants in the other study. This
design
retained the ability to keep track of both fear and anxiety, and
ascertain not only the presence of the proper emotion-action
ten-
dency pair but also the absence of the improper emotion-action
tendency pair. The various �p2 in Figure 1 and Figure 2 were
used
to plan our sample size. With a numerator of �SSeffects �
SSmain
trait effect � SSmain mood effect � SSinteraction of matched
condition and a
denominator of SStotal � �SSeffects � SSerror, the effect sizes

to be
detected should be in the medium range (�fear2 � .050;
�anxiety2 �
.059). Cohen (1988) recommended a sample size of about 60
participants per group to detect such an effect with a power of
.80.
Study 3
This quasi-experiment contrasted the cybersecurity preferences
of participants from an anxious and a relaxed state of mind. The
groups were created by a matching state–trait method. Trait
anx-
iety scores were used as a basis to assign participants to receive
a
corresponding mood induction (e.g., Reinholdt-Dunne et al.,
2012;
Ruscio & Borkovec, 2004; White et al., 2016) and group differ-
ences were expected along the line of anxiety but not fear.
1.4
1.5
1.6
1.7
1.8
1.9
2.0
2.1

R e l a x a � o n
I n d u c � o n
F e a r
I n d u c � o n
A n x i e t y
I n d u c � o n
FE
A
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RE
PO
RT
ED
EXPERIMENTAL CONDITION
Low Trait Fear
High Trait Fear
Figure 1. Fearful emotion as a function of preexisting trait and
mood
induction in Study 2. The main effect of trait fear approached
significance,
F(1, 292) � 3.55, p � .061, �p2 � .012; the main effect of
mood induction
was not significant, F(2, 292) � 1.60, p � .203, �p2 � .011;
their interaction
approached significance, F(2, 292) � 2.62, p � .074. Simple
comparisons

found that participants with different fearful propensities
responded simi-
larly to the relaxed mood induction, F(1, 287) � 0.32, p � .572,
�p2 � .001,
and to the anxious mood induction, F(1, 287) � 1.63, p � .203,
�p2 � .006;
but they responded differently to the fearful mood induction,
F(1, 287) �
8.33, p � .004, �p2 � .028. See the online article for the color
version of this
figure.
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Method
Participants. The participants were 120 college students
(M � 18.8 years, SD � 1.2 years) enrolled in various psychol-
ogy courses in a university in the San Francisco Bay Area. The
sample consisted of 71.8% female students and 28.2% male
students. More than half of the sample were freshmen (58.2%),
followed by sophomores (23.6%), juniors (10.0%), and seniors
(8.2%). The ethnic composition was 36.4% Caucasian, 31.8%
Asian, 13.6% Latino, 3.6% Native American, 1.8% Eastern
European, 1.8% Middle Eastern, 11.0% various other ethnic
origins. A third of the participants were not religious (20.0%

atheists and 11.8% claiming spiritual), whereas the remaining
two thirds were mostly Christian (56.4%) followed by Buddhist
(4.5%). Nearly half of the participants claimed they had no
political affiliations (43.6%), and the rest were primarily Dem-
ocrat (40.9%), Republican (10.9%), and Libertarian (2.7%). All
participants were U.S. citizens, and a majority was born in the
United States (90.0%).
Procedure. The participants were recruited from psychol-
ogy classes on a voluntary basis. An anxious experimental
group and a relaxed control group were created by screening
participants for trait anxiety at Time 1. Depending on their
scores, a corresponding mood induction procedure was admin-
istered at Time 2. From their respective states of minds, the
experiment and control group watched the news report of the
cyberattack and then voiced their cybersecurity preferences.
Specifically, one week prior to the study session, each par-
ticipant received a web link. The questionnaire at Time 1
contained a consent form, questions on demographics, filler
questions, as well as questions concerning the two traits of our
interest: trait worry (PSWQ) and trait fear (FQ). The PSWQ
data were analyzed in a similar manner as in Ruscio and
Borkovec (2004). A total PSWQ score was calculated, and a
score of 54 was used to assign participants to either the anxious
condition (greater or equal to 54) or the relaxed condition (less
than 54). The FQ was also administered to check for divergent
validity/comorbidity between trait anxiety and trait fear. When
the participants arrived at the lab a week later, each received a
web link appropriate for their group assignment. The trait STAI
scale was administered at the start of Time 2 to verify group
assignment. Mood induction was carried out by following the
same threefold procedure as Study 2, before the participants
reported their cybersecurity preferences and their state emo-
tions. Two coders followed a similar procedure as Study 2 and

conducted compliance checks on the essays. With an intercoder
reliability of .75, the data from 10 participants were excluded.
All procedures were approved by the University of San Fran-
cisco IRB and were conducted according with APA ethical
conduct of research with human subjects.
Instruments.
Demographics, trait emotions, state emotions, and cybersecu-
rity measures. These were the same as Study 1 and 2. The
Cronbach’s alpha from the present study was .93 for State Fear
Inventory, .89 for state anxiety scale, .83 for avoidance scale,
.93
for surveillance scale, .91 for vigilance scale, .65 for trait
STAI,
and .78 for FQ.
Penn State Worry Questionnaire (Meyer, Miller, Metzger, &
Borkovec, 1990). The 16 items of PSWQ were designed to
measure the generality, excessiveness, and uncontrollability
char-
acteristics of pathological worry. Sample items include “My
wor-
ries overwhelm me” and “I’ve been a worrier all my life.” Each
item was administered on a five-point scale (1 � not at all to 5
�
very typical), and the total score ranged from 16 to 80. The
scale
had a Cronbach’s alpha between .90 to .95 in Meyer et al.
(1990),
and a value of .91 in the present study.
Results
Anxiety (but not fear) characterized the experimental group
according to the compliance checks in the first set of compar-

isons presented in Table 2. Specifically, significant group dif-
ferences were found in trait worry (PSWQ), trait anxiety (Trait
STAI), and state anxiety (State STAI), but not in trait fear (FQ)
or state fear (State STAI). In the second set of group compar-
isons presented in Table 2, our hypotheses regarding the three
dependent variables were supported. The anxious group signif-
icantly gravitated toward surveillance cybersecurity policies
and favored vigilance behaviors more than the relaxed group.
More importantly, the groups were not significantly different in
their affinity for avoidance.
Study 4
This quasi-experiment contrasted the cybersecurity preferences
from a fearful or a relaxed frame of mind. The study design was
similar to Study 3 except that trait fear scores were used as the
basis for group assignment.
1.9
2.0
2.1
2.2
2.3
2.4
2.5
2.6
2.7

2.8
2.9
R e l a x a � o n
I n d u c � o n
F e a r
I n d u c � o n
A n x i e t y
I n d u c � o n
A
N
XI
ET
Y
RE
PO
RT
ED
EXPERIMENTAL CONDITION
Low Trait Anxiety
High Trait Anxiety
Figure 2. Anxious emotion as a function of preexisting trait and
mood
induction in Study 2. The main effect of trait anxiety was

significant, F(1,
292) � 5.73, p � .017, �p2 � .020; the main effect of mood
induction was
not significant, F(2, 292) � 1.44, p � .239, �p2 � .010; their
interaction
effect approached significance, F(2, 292) � 2.81, p � .062.
Simple
comparisons found that participants with different anxious
propensities
responded similarly to the relaxed mood induction, F(1, 287) �
0.25, p �
.619. �p2 � .001; they responded somewhat differently, at a
near significant
level, to the fearful mood induction, F(1, 287) � 2.83, p � .051,
�p2 � .013,
and responded significantly differently to the anxious mood
induction, F(1,
287) � 9.47, p � .002, �p2 � .032. See the online article for
the color
T
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tio
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Method
Participants. The participants were 156 college students

(M � 19.97 years, SD � 1.6 years) enrolled in various psy-
chology courses in a university in the San Francisco Bay Area.
The sample consisted of 78.2% female students and 21.8% male
students. All undergraduate class levels were represented, with
27.6% freshmen, 26.30% juniors, 23.7% sophomores, and
21.8% seniors. The ethnic composition was 32.7% Asian,
30.1% Caucasian, 18.6% Latino, 4.5% African American, 2.6%
Native American, 1.9% Indian, 1.9% Middle Eastern, and 7.7%
various other ethnic origins. A third of the participants were not
religious (20.0% spiritual and 17.4% atheists), whereas the
remaining two thirds practiced Christianity (52.9%), Islam
(3.2%), Judaism (2.6%), Buddhism (1.9%), or other religions
(2.0%). Nearly half of the participants claimed they had no
political affiliations (42.9%), and the rest were Democrat
(41.7%), Republican (7.1%), Libertarian (1.9%), and “other”
(6.4%). A great majority of the participants were U.S. citizens
(88.0%) and/or were born in the United States. (85.3%).
Procedure. The participants were recruited from psychol-
ogy classes on a voluntary basis. In a preliminary questionnaire
administered approximately a week prior to the study, the 10
agoraphobia and blood/injury phobia FQ items were included
and a cutoff score of 33 was used to assign participants into
groups. Trait PSWQ was also administered at this time to check
for divergent validity in order to guard against the possibility
that the fearful group was also anxious. When the participants
arrived at the lab a week later, the fear subscale of Rothbart’s
(1986) Temperament Questionnaire was used to verify the
group assignment. Two independent coders flagged problematic
essays and reached an intercoder reliability of .75. The data
from 29 participants were excluded from analysis based on the
same criterion as in Study 3. All procedures were approved by
the University of San Francisco IRB and were conducted ac-
cording with APA ethical conduct of research with human
subjects.

Instruments.
Demographics, trait emotions, state emotions and cybersecu-
rity measures. These were the same as in Studies 1, 2, and 3.
The
Cronbach’s alpha was .80 for FQ, .93 for PSWQ, .91 for state
STAI, .92 for State Fear Inventory, .91 for surveillance scale,
.90
for vigilance scale, and .86 for avoidance scale.
Fear subscale of Adult Temperament Questionnaire (Roth-
bart, 1986). The seven-item scale measures trait fear on a scale
(1 � extremely untrue of you to 7 � extremely true of you).
Sample
items include “Sometimes, I feel a sense of panic or terror for
no
apparent reason” and “I become easily frightened.” The last
item
“When I try something new, I am rarely concerned about the
possibility of failing” was excluded because its face validity
was
judged to be more aligned with anxiety than with fear. The
Cronbach’s alpha was .76 in Rothbart (1986) and .58 in the
present
study.
Results
Our compliance checks showed that the experimental group
scored significantly higher on trait fear measures (both in terms
of
FQ and fear temperament) and on state fear measure than the
control group (see Table 3). However, the experimental group
also
showed greater signs of anxiety (in terms of trait PSWQ and

state
anxiety) than the control group. Among all the compliance
checks,
FQ had the largest effect size, suggesting that fear was a
notable
feature of the experimental group.
The results on the three dependent variables were less ambigu-
ous and clearly supported our hypotheses. The experimental
group
endorsed avoidance cybersecurity measures significantly more
than the control group. The effect size of avoidance measures is
particularly noteworthy: the popularity of avoidance measures
was
about half a standard deviation higher in the fearful group than
in
the relaxed group. Moreover, the rest of the results presented in
Table 2
Comparison Between Anxious Group and Relaxed Group in
Study 3
Measure
Anxious Relaxed 95% CI
M (SD) M (SD) t(108) LL UL Cohen’s d
Traits
PSWQ 60.82 (4.60) 42.10 (7.68) 15.21�� 16.28 21.16 2.93
FQ 3.61 (1.32) 3.40 (1.39) .82 �.30 .26 .16
Trait STAI 2.39 (.34) 2.13 (.35) 3.74�� .07 .39 .72
State emotions

State STAI 2.39 (.91) 1.91 (.70) 3.03�� .16 .78 .59
State Fear 1.25 (.47) 1.19 (.53) .69 �.13 .26 .13
Action tendencies
Surveillance 2.77 (1.01) 2.38 (.98) 2.01� .00 .77 .39
Vigilance 3.34 (.88) 2.89 (.97) 2.54� .10 .81 .49
Avoidance 2.08 (.73) 2.05 (.69) .25 �.30 .24 .05
Note. CI � confidence interval; LL � lower limit; UL � upper
limit; PSWQ � Penn State Worry
Questionnaire; FQ � Fear Questionnaire; STAI � State–Trait
Anxiety Inventory.
� p � .05. �� p � .01. �� p � .001.
T
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Table 3 support the uniqueness claim of functionalists with a
negative proof where the fearful group did not gravitate toward
surveillance or vigilance measures.
General Discussion
Little is known about how the public minimizes cyber risks at
a time when society is increasingly dependent on technology. If
history from the start of the 20th century is any indication

(Loomis, 2015), decades could go by before any democratic
society could settle on a set of safety measures. In the interim,
governments and private citizens experiment with new cyber
safety measures when their old physical safety measures seem
no longer germane. These new behaviors could offer fresh
evidence to bear on the functionalist approach to emotions,
especially in their ongoing bids to differentiate between fear
and anxiety.
Therefore, the first aim in our research was to find the
behavioral categories in cybersecurity solutions by surveying
the open-ended responses in a pilot study as well as the ongoing
congressional debates. Three types of strategies to counter
cyberinsecurity have emerged in Study 1. The first two behav-
ioral categories pertain to surveillance policies and vigilance
behaviors, respectively. The third behavioral category is con-
sidered to be avoidance tactics according to the scale’s
contents.
Two items (“not share personal information online” or “refuse
to open some e-mails”) were eliminated from the scale, as
participants did not see the coherence between these two items
and the prior six items. One explanation for this result is that
avoiding high-tech platforms is no longer a viable lifestyle, and
thus participants could not realistically endorse all items at
once. They may have to choose specific avoidance strategies
based on the nature of the threat and their life circumstances.
Although most of the behavioral strategies in our study are
reminiscent of the safety behaviors from Helbig-Lang and Pe-
termann (2010), we did not find threat neutralization or distrac-
tion. The average consumer may be unable to devise technical
threat neutralization strategies or enter into a state of denial
when cyberattacks are frequently reported in the news.
Our second research aim was to seek the emotional impetus
behind each type of cybersecurity strategy. The results of our

final
two studies found demarcations around avoidance and fear on
the one hand, and around surveillance, vigilance, and anxiety on
the other hand. It is noteworthy that we aimed to show not only
the
presence of the proper emotion–action tendency pairs (e.g.,
fear-
avoidance), but also the absence of the improper pairs (e.g.,
fear-vigilance). Although our final two studies verified the pres-
ence of the proper pairs, Study 3 was able to rule out the
improper
pairs with more confidence than Study 4. A reduction in sample
size should be considered in the future to avoid detecting the
smaller effects of the improper pairs in Study 4. We started with
156 participants and then excluded 29 essays, making our final
sample size 127. Future studies may consider smaller group size
than the recommended 60 participants per group.
Our results from the novel domain of cyber threats add to the
current bid to differentiate between fear and anxiety in neuro-
science and clinical research. These findings bring Barlow’s
(2000) theory of anxiety and Woody and Szechtman’s (2011,
2013) security motivation system into the fold of classic func-
tionalism, such that anxiety is not just a synonym or an exten-
sion of fear but rather a discrete emotion in its own right. The
distinct behavioral manifestations of fear and anxiety are given
concrete forms in the context of cyber threats. A fearful public
would disengage from e-commerce and other high-tech plat-
forms, not unlike the mass exodus from physical economic
activities post 9/11 when airports were shunned and shopping
malls were vacant. An anxiety-ridden public brings a different
outlook to America. The implication of expansive governmental
power could spell a loss of privacy or even curtailed freedom
(Preston, 2014). These projections may or may not materialize,
but they illustrate the unique transformational power of mass

Table 3
Comparison Between Fearful Group and Relaxed Group in
Study 4
Measure
Fearful Relaxed 95% CI
M (SD) M (SD) t(125) LL UL Cohen’s d
Traits
PSWQ 52.22 (11.42) 46.37 (13.95) 2.09� .26 9.44 .38
FQ 47.56 (9.60) 21.83 (6.78) 16.89�� 22.71 28.76 3.50
Fear Temp 4.13 (.72) 3.82 (.81) 2.24� .20 .83 .59
State emotions
State STAI 2.27 (.89) 1.87 (.72) 2.73�� .11 .70 .54
State Fear 1.67 (.73) 1.29 (.40) 3.24�� .10 .55 .74
Action tendencies
Surveillance 2.72 (.88) 2.51 (.89) 1.26 .12 .53 .23
Vigilance 3.31 (.95) 3.01 (.90) 1.81 .03 .63 .33
Avoidance 2.93 (1.01) 2.56 (.97) 2.05� .01 .73 .58
Note. CI � confidence interval; LL � lower limit; UL � upper
limit; PSWQ � Penn State Worry
Questionnaire; FQ � Fear Questionnaire; Fear Temp � Fear
subscale of the Adult Temperament Questionnaire;
STAI � State–Trait Anxiety Inventory.
� p � .05. �� p � .01. �� p � .001.
T
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lo
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A
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oc
ia
tio
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or
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of
its
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pu
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is
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.

T
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ar
tic
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in
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nd
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so
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ly
fo
r
th
e
pe
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on
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us
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oa
dl
y.
fear and mass anxiety, and give purpose to the quest to differ-
entiate between the two threat-induced emotions.
Methodological Challenges
We used a variety of study designs from cross-sectional to
experimental, but neither extreme was conducive to the
endeavor
of differentiating two closely related emotions. The findings
from
our cross-sectional study were similar to those of Perkins and
colleagues (2010), where fear showed a unique action tendency,
but anxiety did not (Perkins & Corr, 2006). A cross-sectional
design typically controls for one emotion in order to reveal the
uniqueness of the other emotion. Statistically carving out fear
from
anxiety is merely an approximation of the neural overlap
between
the two emotions and does not fully capture the complex neural
interaction (e.g., Tovote et al., 2015).
Our experimental design was also inconclusive due to a differ-
ent methodological challenge. The efficacy of eliciting target
emo-
tions was low, despite the threefold induction procedure
involving

a behavioral task, a film clip, and an essay writing segment.
Effective emotion induction relies on participants to transfer
their
moods from one situation to the next. This mechanism is
sensitive
to a variety of factors such as the emotional nature,
compatibility,
and placement of the situations. Göritz and Moser (2006) found
small effects if the mood induction procedures are conducted
online. Even though our mood induction was embedded in a
survey, participants completed the survey in supervised lab ses-
sions. Future methodological research is needed in order to
under-
stand the parameters around using films, vignettes, and imagery
to
induce emotions in participants at remote locations (Hernandez,
Vander Wal, & Spring, 2003; Rosenberg & Ekman, 2000; Stem-
mler, 2003; Västfjäll, 2001).
The optimal research strategy seems the middle ground be-
tween cross-sectional design and experimental design—a quasi-
experiment that combined trait screening and mood induction
(Reinholdt-Dunne et al., 2012; Ruscio & Borkovec, 2004; van
Uijen, van den Hout, & Engelhard, 2017; White et al., 2016).
According to the post hoc analysis from Study 2, trait screening
alone was a sufficient condition to precipitate a target emotional
response. Trait screening seems to be able to gauge a partici-
pant’s habitual appraisal style and promise the same emotional
experience to a new threat scenario. Although it was not abso-
lutely necessary to follow the trait screening with a mood
induction, the additional procedure enhanced the effect (or
divergent validity) that is necessary in a study of two closely
related emotions. The post hoc analysis in Study 2 verified that
a participant was best able to experience a target emotion if
they
had a predisposition for it and then underwent a corresponding

mood induction.
Conclusion
The current American congressional debate around cybersecu-
rity and the public uproar against the National Security
Agency’s
mass surveillance may be the harbinger of a protracted
discussion
of how to keep ourselves safe in cyberspace. Competing
cyberse-
curity solutions should be debated based on rational logic rather
than being hijacked by emotions. Both fear and anxiety are
com-
mon reactions to cyber threats. If fear were to take hold,
economic
participation would suffer and tarnish commercialism. If
anxiety
were to take the rein, surveillance policies would win out in the
public’s mind and decrease freedom. Materialism and freedom
are
the defining features of the American way of life. A future
Amer-
ica without materialism is very different from a future America
without freedom. The difference between the two visions is not
minute by any account; neither is the functional difference be-
tween fear and anxiety.
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Received February 2, 2018
Revision received July 10, 2018
Accepted July 18, 2018 �
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computer-simple-cell-phone/
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computer-simple-cell-phone/Functional Divergence of Two
Threat-Induced Emotions: Fear-Based Versus Anxiety-Based
Cybersecur ...Functionalist Claims of Fear and AnxietyCo-
occurrence of Fear and Anxiety as a Barrier to Functionalist
StudiesFrom Neural Networks to Behavioral
ManifestationsClinical Behaviors of Fear and AnxietyThe
Present StudyStudy
1MethodParticipantsProcedureInstrumentsDemographicsMultim
edia news reportState version of State–Trait Anxiety Inventory,
Form Y (Spielberger, 1983)State Fear Inventory (Cheung-
Blunden & Ju, 2016)Surveillance, vigilance, and avoidance
cybersecurity measuresResultsStudy
2MethodParticipantsProcedureInstrumentsDemographics, state
emotions, and cybersecurity measuresTrait version of State–
Trait Anxiety Inventory, Form Y (Spielberger, 1983)Fear
Questionnaire (Marks & Mathews, 1979)ResultsStudy
3MethodParticipantsProcedureInstrumentsDemographics, trait
emotions, state emotions, and cybersecurity measuresPenn State
Worry Questionnaire (Meyer, Miller, Metzger, & Borkovec,
1990)ResultsStudy
4MethodParticipantsProcedureInstrumentsDemographics, trait
emotions, state emotions and cybersecurity measuresFear
subscale of Adult Temperament Questionnaire (Rothbart,
1986)ResultsGeneral DiscussionMethodological
ChallengesConclusionReferences
Responding to the Emotions of Others: Age Differences in
Facial
Expressions and Age-Specific Associations With Relational
Connectedness
Sandy J. Lwi

University of California, Berkeley
Claudia M. Haase
Northwestern University
Michelle N. Shiota
Arizona State University
Scott L. Newton and Robert W. Levenson
University of California, Berkeley
Responding prosocially to the emotion of others may become
increasingly important in late life,
especially as partners and friends encounter a growing number
of losses, challenges, and declines. Facial
expressions are important avenues for communicating empathy
and concern, and for signaling that help
is forthcoming when needed. In a study of young, middle-aged,
and older adults, we measured emotional
responses (facial expressions, subjective experience, and
physiological activation) to a sad, distressing
film clip and a happy, uplifting film clip. Results revealed that,
relative to younger adults, older adults
showed more sadness and confusion/concern facial expressions
during the distressing film clip. More-
over, for older adults only, more sadness and fewer disgust
facial expressions during the distressing film
clip were associated with higher levels of relational
connectedness. These findings remained stable when
accounting for subjective emotional experience, physiological
activation, and trait empathy in response
to the film clip. When examining the uplifting film clip, older
adults showed more happiness facial
expressions relative to younger adults at trend levels. More
facial expressions of happiness were
associated with higher levels of relational connectedness, but

unlike the effect of sadness expressions,
this was not moderated by age. These findings underscore an
important adaptive social function of facial
expressions—particularly in response to the distress of others—
in late life.
Keywords: sadness, aging, relational connectedness, loneliness,
facial expressions
In social settings we are often faced with the emotions of
others, such as a child who is injured, a loved one who has
experienced a significant loss, or a friend who is celebrating a
positive event. These experiences may evoke strong emotional
responses of our own, which signal our concern for the other
person’s well-being. This, in turn, can lead to positive relation-
ship outcomes such as mutual feelings of interpersonal connec-
tion and closeness (Gray, Ishii, & Ambady, 2011). In the
present study we asked whether facial expressions in response
to others’ situations and emotions differ across age groups, and
whether these differences are related to our sense of connection
with other people.
The Social Functions of Facial Behaviors
Emotional facial expressions have high social visibility (Hager
& Ekman, 1979), making them a particularly effective way for
conspecifics to exchange emotional information (Darwin, 1998;
Schmidt & Cohn, 2001). When experiencing distress, displaying
negative emotion (e.g., sadness, fear) can communicate to
others
that assistance and support are needed (Campos, Campos, &
Barrett, 1989; Fischer & Manstead, 2008) and motivate them to
provide aid (Clark, Ouellette, Powell, & Milberg, 1987;

Graham,
Huang, Clark, & Helgeson, 2008). Smiling and laughter also
bring
others to our side, evoking liking (e.g., Johnston, Miles, &
Macrae,
This article was published Online First February 7, 2019.
Sandy J. Lwi, Department of Psychology, University of
California,
Berkeley; Claudia M. Haase, School of Education and Social
Policy,
Department of Psychology, and Institute for Policy Research,
Northwest-
ern University; Michelle N. Shiota, Department of Psychology,
Arizona
State University; Scott L. Newton and Robert W. Levenson,
Department of
Psychology, University of California, Berkeley.
The research was supported by National Institute on Aging
grants R01
AG041762 and P01 AG019724 to Robert W. Levenson, and
Bruce L.
Miller. We thank Michael D. Edge for his help with analyses
and for his
helpful comments on all versions of the article, Deepak Paul for
his help
with data processing, and Emily Ma and Michelle Pardo for
their assistance
with coding emotional facial expressions. Sandy J. Lwi, Claudia
M. Haase,
and Robert W. Levenson developed the study concept. Scott L.
Newton
performed the facial coding, Sandy J. Lwi processed the data,

and both
Sandy J. Lwi and Claudia M. Haase analyzed the data. Sandy J.
Lwi wrote
the first draft of the article, and all authors contributed to
revisions of the
article. Robert W. Levenson supervised all phases of the
project.
Robert
W. Levenson, Department of Psychology, University of
California, 3206
Berkeley Way West MC 1650, Berkeley, CA 94720. E-mail:
[email protected]
berkeley.edu
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Emotion
8, 1437–1449
1437

2010), perceptions of agreeableness and extroversion (e.g.,
Mehu,
Little, & Dunbar, 2008), and cooperation (e.g., Danvers &
Shiota,
2018; Scharlemann, Eckel, Kacelnik, & Wilson, 2001).
Emotional displays have longer-term social functions as well
(Shiota, Campos, Keltner, & Hertenstein, 2004). Using the face
to
mirror the emotions being experienced by another person, and to
display care and concern, can help build and strengthen
meaningful
interpersonal relationships (Gray et al., 2011). For individuals
observ-
ing others in a negative situation, emotional displays can signal
concern for that person’s well-being (Eisenberg et al., 1989;
Keltner,
2009; Keltner & Kring, 1998) and reduce their distress (Batson,
2011). When interacting with others in a positive situation,
smiling
and laughing communicate responsiveness, encouragement, and
re-
spect, and can lead to higher relationship satisfaction (Gable,
Gonzaga, & Strachman, 2006). These relationship-building
functions
may be particularly important in late life, when individuals
afford
heightened priority to interpersonal goals (Carstensen, 1993).
Prosocial Responding to Others’ Distress in Late Life
Responding to the distress of others can be a particularly prom-

inent feature of late life. Many older adults are involved in pro-
viding care for significant others, including spouses and grand-
children (Schulz & Eden, 2016), and are more likely than their
younger counterparts to aid strangers who are in need
(Midlarsky
& Hannah, 1989; Sze, Gyurak, Goodkind, & Levenson, 2012).
Older adults’ peers also tend to experience greater losses in a
number of life domains (Charles & Carstensen, 2010; Prohaska
et
al., 2006; Salthouse & Babcock, 1991; Steverink, Westerhof,
Bode, & Dittmann-Kohli, 2001), making it more common for
them
to encounter other people in distress.
Older adults’ increased exposure to loss and distress has been
theorized to strengthen their responsiveness to displays of these
emotions in others (Seider, Shiota, Whalen, & Levenson, 2011).
Extant research is consistent with this notion, as laboratory-
based
research has found that older adults report greater subjective
sadness experience (Kunzmann & Grühn, 2005; Seider et al.,
2011) and show greater autonomic reactivity (Sze et al., 2012)
in
response to films depicting the distress of others. In contrast,
two
prior studies comparing older adults with young or middle-aged
adults found no differences in facial responses to sad film clips
(Seider et al., 2011; Tsai, Levenson, & Carstensen, 2000). In
both
of these studies however, the sad stimulus (a film clip depicting
a
fictional boy’s response to his father’s death) evoked very low
levels of sadness expression across the sample; thus, a floor
effect
may have concealed age effects. Clearly more research is
needed

that examines age differences in expressive responses to high-
intensity, realistic depictions of others’ emotions (both distress
and
happiness), because this expressivity may play an important role
in
older adults’ social relationships.
Older individuals afford high priority to strengthening or
maintain-
ing meaningful social relationships (Carstensen, 1992;
Carstensen,
Isaacowitz, & Charles, 1999; Fredrickson & Carstensen, 1990),
but
simultaneously are vulnerable to shrinking social networks and
phys-
ical changes that can limit the frequency of their social
contacts. In
particular, older adults become less able to rely on social
obligations
commonly found in the lives of young and middle-aged adults
(e.g.,
living with family members and/or roommates, group leisure
activi-
ties, work-related interactions), reducing opportunities for
social con-
nection and potentially resulting in isolation and loneliness
(Lang,
Staudinger, & Carstensen, 1998; Pinquart & Sorensen, 2001).
The
ability to maintain close social connections may increasingly
rely on
signaling readiness for emotional engagement when
opportunities do
arise. Thus, older adults who can strongly signal concern and
care for

others’ experiences— especially their experiences of distress
and loss,
which are more common among late life peers—may be most
suc-
cessful at building and maintaining close relationships.
Thus far, little research has addressed whether empathetic sad-
ness reactivity is differentially associated with social outcomes
across age groups. In one study, we found that self-reported
sadness reactivity in response to an emotionally ambiguous film
clip was associated with greater well-being in older adults, but
not
in younger or middle-aged adults (Haase, Seider, Shiota, & Lev-
enson, 2012), suggesting that this aspect of sadness reactivity
can
be particularly beneficial in late life. This finding suggests that
more research is needed on the social implications of expressive
responding to others’ emotional experience, and the extent to
which this is moderated by age.
The Present Study
The present study used a community sample of young, middle-
aged, and older participants to examine: (a) age differences in
facial responses of sadness to others’ distress; (b) age
differences
in facial responses of happiness to others’ joy; and (c) age
differ-
ences in the association between these facial responses and
aspects
of social connectedness (i.e., relational connectedness). Partici-
pants watched two short film clips, one that depicted people
experiencing intense distress, and another more uplifting one
that
depicted people experiencing personal success and
empowerment.

Trained coders rated participants’ facial expressions of the
target
emotion for each clip (sadness for the distressing clip, and
happi-
ness for the uplifting clip), as well as expressions associated
with
other emotions (confusion/concern, disgust, and fear).
We tested two primary hypotheses. Hypothesis 1 was that older
adults would show more prosocial facial expressions than
middle-
aged and young adults in response to distressing and uplifting
film
clips depicting people in emotionally evocative situations.
Hypoth-
esis 2 was that more facial expressions of sadness, in particular,
would be associated with higher levels of relational
connectedness
in older adults, but not in young and middle-aged adults. We
focused on relational connectedness as the outcome of interest
because it captures a sense of deep connection with close others
(Hawkley, Browne, & Cacioppo, 2005), and aligns with the way
older adults are thought to invest in meaningful relationships
(Carstensen, 1992). The association between social
connectedness
and happiness facial expressions displayed during the uplifting
film was examined as well, but the case for expecting age to
moderate this effect is weaker than for sadness. Follow-up
analy-
ses addressed the specificity of the association between
prosocial
facial expressive responding and social connectedness, while
ac-
counting for the expression of alternative emotions, other
aspects
of emotional reactivity (subjective experience, physiological re-

sponding), and overall trait empathy.
This study utilized data from a larger research project from
which other findings have been reported previously.
Specifically,
using these data Sze et al. (2012) showed that older adults evi-
denced heightened personal distress, physiological activation,
and
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1438 LWI, HAASE, SHIOTA, NEWTON, AND LEVENSON
prosocial behavior (i.e., monetary donations) in response to the
films compared with young and middle-aged adults. Facial
expres-
sion data, which takes much longer to code and quantify than
other
aspects of emotional responding, were not yet available and
thus
has not been previously reported. Similarly, none of the self-
reported measures of social connection were reported
previously.
Method

Participants
Sixty-nine young participants (age range 20 –30 years, M �
23.14,
SD � 3.18), 67 middle-aged participants (age range 40 –50
years,
M � 44.36, SD � 2.91), and 66 older participants (age range 60
– 80
years, M � 66.36, SD � 5.49) were recruited using flyers and
online
postings in the local community and from a research participant
database administered by the University of California, Berkeley
(N �
202). Participants were recruited such that sex and ethnicity
were
stratified similarly across all three age groups. The sample
contained
66% women and 34% men. In terms of ethnicity, 71% were
European
American, 13% Asian American, 7% African American, 4%
Latinx
American, and 5% other. Participants were paid $50 for
completing a
questionnaire packet and participating in a 2.5-hr laboratory
study.
Table 1 presents sociodemographic characteristics of the
sample. For
a MANOVA with 202 participants across three groups, we had a
power of 0.96 at � � .05 to detect a main effect of age
assuming a
medium effect size (Cohen’s f 2 � .10), and a power of .99 at �
� .05
to detect a regression interaction effect assuming a medium
effect size
(Cohen’s f 2 � .15; Faul, Erdfelder, Buchner, & Lang, 2009).

All
variables were mean-centered for regression analyses.
Procedure
Prior to the laboratory session, participants completed a ques-
tionnaire packet at home. The questionnaires assessed a number
of
constructs including empathy, personality, and social
desirability,
some of which have been previously reported (Sze et al., 2012).
For the full list of questionnaires, see online supplementary
mate-
rials, Supplementary Table 2. The packet included measures of
loneliness/social connectedness (Russell, Peplau, & Cutrona,
1980) and empathy (Davis, 1983), which were included in the
present analyses. Approximately one week after completing the
questionnaires, participants came to the Berkeley
Psychophysiol-
ogy Laboratory for a laboratory session. Upon arrival, they were
informed that they would be participating in a study of emotion
and that their physiological, behavioral, and self-reported re-
sponses would be recorded and videotaped. Prior to the start of
the
session, participants had physiological sensors attached (see be-
low). Throughout the session, participants’ upper body and face
were filmed with a partially concealed video camera. The output
of
the camera was routed through video time-code generators that
added both computer-readable and visible timing information.
At
the end of the experiment, participants provided consent for
vary-
ing levels of usage of the video recording (e.g., research only,
public showings). The experimental protocol included a number

of
laboratory tasks that were designed to measure different aspects
of
emotional functioning. For the present study, we examined the
task
during which participants viewed two film clips, one best de-
scribed as distressing and the other as uplifting.
The “distressing” film clip began with a brief introduction to
the
political crisis in the Darfur region of Sudan, followed by
images
of the people of Darfur displaying distress at the loss of loved
ones,
suffering from starvation, and having serious wounds (117 s in
length). The “uplifting” film clip began with a brief
introduction to
childhood autism, followed by images of children with autism
successfully learning how to surf at a nonprofit camp called
Surfers Healing (116 s in length). The film clip pans to the
faces
of many young children and portrays the joy they feel as they
learn
how to surf, and the empowerment gained from their new skills.
The two films were shown in counterbalanced order. Both film
clips were preceded by a 1-min resting period, during which
participants were asked to clear their minds, relax, and focus on
an
X in the center of the video screen. Fifty-three seconds into the
resting period, a written message appeared above the X
indicating
that the film clip was about to start. After the film clip ended,
participants reported on their current emotional state using an
18-item emotion checklist (see below).
Measures

Facial expressions. Participants’ facial expressions were vid-
eotaped while they were watching the film clips, and were
subse-
quently coded by trained coders using the Emotional Expressive
Behavior coding system (Gross & Levenson, 1993). Coders
(blind
to the film clips being watched and the hypotheses) rated the
occurrence of 10 positive and negative facial expressions
(anger,
confusion/concern, contempt, disgust, embarrassment, fear,
happi-
ness, interest, sadness, and surprise). Sadness was coded when
participants displayed inner eyebrow raises, downturned lip cor-
ners (i.e., frowns), or crying. Happiness was coded when
partici-
Table 1
Sociodemographic Characteristics of Young, Middle-Aged, and
Older Adults
Young adults Middle-aged adults Older adults
(20 –30 years) (40 –50 years) (60 – 80 years)
n 69 67 66
Age (M [SD]) 23.14 (3.18) 44.36 (2.91) 66.36 (5.49)
Females (%) 70 64 64
European American (%) 72 66 74
Asian American (%) 16 13 11
African American (%) 3 12 6
Latinx American (%) 3 6 1
Other (%) 6 3 8
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1439RESPONDING TO THE EMOTIONS OF OTHERS
pants smiled, that is, turned the lip corners upward. For each
second that an expression was seen, its intensity was rated using
a
1 to 3 scale (e.g., 1 � slight downturned lip or inner eyebrow
raise
and 3 � strong inner eyebrow raise and downturned lip, or
crying). Overall interrater reliability across codes was high
(Cron-
bach’s alpha � .90), with reliability for individual codes
ranging
from Cronbach’s alpha � .87–.97. Anger expression was
removed
from analyses due to poor reliability (Cronbach’s alpha � .49),
and expressions of contempt, embarrassment, interest, and
surprise
were removed from analyses due to infrequent occurrence (�1%
in the sample). Final analyses included only the codes of confu-
sion/concern, disgust, fear, happiness, and sadness. Of note, the
Emotional Expressive Behavior coding manual defines
confusion
as a “brow furrow.” Prior research has linked brow furrowing to
sympathetic concern (Eisenberg et al., 1998) and concentration
(Rozin & Cohen, 2003), suggesting that this behavior may be

associated with different emotions and states. We thus referred
to
this code in this article as “confusion/concern.” For each facial
expression code, means were computed for each participant by
summing the intensity scores for every second in which the ex-
pression was present and dividing by the total number of
seconds
in the film clip. Thus, each participant ended up with five
scores
(one for each facial expression) for each film clip. Logarithmic
transformations were applied to the data to reduce skewness
(Co-
hen, Cohen, West, & Aiken, 1983).
Subjective experience. Upon arriving at the laboratory and
immediately after each film clip, participants rated their
experience of
18 positive and negative emotions (afraid, amused, angry,
ashamed,
calm, compassionate, disgusted, disturbed, embarrassed,
enthusiastic,
interested, moved, proud, sad, sympathetic, surprised, upset,
and
worried) using a 5-point Likert scale (1 � not at all to 5 �
extremely).
Reliabilities for both the presession emotion ratings and
postfilm
emotion rating were high (� � .82 and � � .90, respectively).
Physiological activation. While participants were watching the
film clips, continuous, second-by-second recordings of 10
physiolog-
ical measurements of autonomic nervous system activity were
mea-
sured using either a Grass Model 7 polygraph or a BIOPAC
poly-

graph and a computer equipped for processing multiple channels
of
analog information. Using a computer program written by
Robert W.
Levenson, physiology was monitored and averaged on a second-
by-
second basis for each of the following measures: (a) heart rate
(Beck-
man miniature electrodes with Redux paste or Vermed SilveRest
ECG pregelled electrodes were placed on opposite sides of the
par-
ticipant’s abdomen; the interbeat interval was calculated as the
num-
ber of milliseconds between successive R waves); (b) finger
pulse
amplitude (on the nondominant hand, a UFI
photoplethysmograph
attached to the tip of the ring finger recorded the volume of
blood in
the finger and the trough-to-peak amplitude of the finger pulse
was
determined); (c) finger pulse transmission time (the number of
milli-
seconds between the R wave of the electrocardiogram (ECG)
and the
upstroke of the peripheral pulse recorded by the
photoplethysmograph
on the finger was determined); (d) ear pulse transmission time
(a UFI
photoplethysmograph attached to the left earlobe recorded the
volume
of blood in the ear and the number of milliseconds between the
R
wave of the ECG and the upstroke of peripheral pulse at the ear
was
determined); (e) systolic blood pressure; (f) diastolic blood

pressure
(on the nondominant hand, a cuff was placed on the middle
finger and
blood pressure was measured on each heartbeat using an
Ohmeda
Finapress 2300); (g) skin conductance (on the ring and index
fingers
of the nondominant hand, a constant-voltage device passed a
small
voltage between two Beckman standard electrodes or BIOPAC
elec-
trodes filled with an electrolyte of sodium chloride in Unibase);
(h)
finger temperature (on the nondominant hand, a thermistor was
at-
tached to the top of the pinky finger); (i) respiration intercycle
interval
(a cloth belt wrapped around the participant’s chest compressed
an
inflated rubber bladder to provide a measure of chest wall
movement;
the number of milliseconds between the onset of inspiration of
each
respiration cycle was calculated); and (j) general activity (an
electro-
mechanical transducer attached to a platform under the
participant’s
chair generated an electrical signal proportional to the amount
of body
movement in any direction).
Reactivity scores were computed by subtracting the prefilm
base-
line average of each physiological measure from its average
level

during each film clip. To reduce the number of physiological
variables
and control for Type I error, a maximum-likelihood factor
analysis of
the 10 physiology channels was conducted, followed by varimax
rotation. Using parallel analysis (Horn, 1965; O’Connor, 2000)
three
factors were extracted. For each of the three rotated factors, we
computed an unweighted average of the standardized physiology
channels that had loadings larger than .30. Rotated factor
loadings are
shown in Table 2.
Relational connectedness. Relational connectedness was mea-
sured using the 20-item UCLA Loneliness Scale (Russell et al.,
1980).
Although originally conceptualized as a one-factor measure,
subse-
quent research has demonstrated that this scale actually has a
three-
factor structure (Hawkley et al., 2005), with five items
indicating
relational connectedness (e.g., “There are people who really
under-
stand me;” 1 � never to 4 � always), four items indicating
collective
connectedness (e.g., “I have a lot in common with the people
around
me;” 1 � never to 4 � always), and 10 items indicating
isolation
(e.g., “I lack companionship;” 1 � never to 4 � always). The
three-factor model of the UCLA Loneliness Scale has
subsequently
been used in a number of empirical studies (Cacioppo et al.,
2008;
Wildschut, Sedikides, Routledge, Arndt, & Cordaro, 2010). To

verify
this factor structure in our data, we conducted two confirmatory
factor
analyses (CFA) with the lavaan package in R using generalized
least
squares estimation. The first CFA loaded all of the items on a
single
factor, while the second CFA loaded all of the items on the
three
Table 2
Rotated Factor Loadings of Physiology Channels
Factor
1 2 3
Interbeat interval (heart rate) �.050 .129 �.440
Activity �.094 .112 .832
Intercycle interval (respiration) .005 �.002 �.428
Finger pulse transmission time .092 .989 �.113
Finger pulse amplitude �.172 �.326 �.038
Systolic blood pressure .779 .025 �.009
Diastolic blood pressure .991 �.048 .121
Ear pulse transmission time .132 .042 �.001
Skin conductance .061 �.077 .076
Temperature �.128 �.025 .008
Note. On the basis of parallel analysis (Horn, 1965; O’Connor,
2000)
three factors were extracted. For each of the three rotated
factors, we
computed an unweighted average of the standardized physiology
channels
that had a loading larger than .30.

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factors indicated by Hawkley and colleagues (2005; see
Appendix).
When comparing the differences between models, the likelihood
ratio
test rejected the one factor model in favor of the three-factor
model,
�2(3, N � 210) � 52.201, p � .001. Full scale reliability (� �
.93)
and subscale reliabilities were high in our sample (�s for
relational
connectedness � .86; collective connectedness � .72; isolation
�
.90). Means of the full scale and each subscale are shown in
Table 3.
For the present research questions, the relational connectedness
subscale is the most conceptually relevant. Whereas the
isolation
subscale emphasizes an extreme, negative sense of aloneness
and
withdrawal, and the collective connectedness subscale

emphasizes
feeling like part of a social group, the relational connectedness
scale
emphasizes the belief that one has multiple psychologically
intimate
relationships with other individuals, characterized by closeness,
un-
derstanding, and support. Consistent with this, the analysis
using the
relational connectedness subscale as the outcome provides the
best
test of Hypothesis 2. Nonetheless, we also examined the other
two
subscales as well as the full loneliness scale in separate
analyses.
Trait empathy. Trait empathy was assessed using four sub-
scales of the Interpersonal Reactivity Index (IRI; Davis, 1983):
em-
pathic concern (e.g., “When I see someone being taken
advantage of,
I feel kind of protective toward them”); personal distress (e.g.,
“In
emergency situations, I feel apprehensive and ill-at-ease”);
perspec-
tive taking (e.g., “When I am upset at someone, I usually try to
put
myself in his shoes for a while”); and fantasy (e.g., “I really get
involved with the feelings of the characters in a novel”). Full
scale
reliability (� � .78) and subscale reliabilities were adequate
(�s:
empathic concern � .78, personal distress � .79, perspective
tak-
ing � .83, fantasy � .79). Means of each subscale are shown in
Table

3, and correlations between these scales and facial expressions
can be
found in Supplemental Table 1.
Data Analysis
To examine our first hypothesis (i.e., older adults would show
more
prosocial facial expressions than young and middle-aged adults
in
response to the distressing and uplifting film clips), we
conducted
multivariate ANOVAs where the five individual facial
expressions
(i.e., confusion/concern, disgust, fear, happiness, and sadness)
were
treated as dependent variables, and age was included as a fixed
factor.
To examine our second hypothesis (i.e., more facial expressions
of
sadness would be associated with higher levels of relational
connect-
edness in older adults), we examined whether age was a
significant
moderator of the relationship between relational connectedness
and
sadness facial expressions in response to the distressing clip
using
Model 1 of the PROCESS macro with 50,000 bias-corrected
boot-
strapped samples (Hayes, 2008). This latter analysis was then
re-
peated for the uplifting positive film clip.
Three sets of post hoc follow-up analyses were conducted using

the
same PROCESS macro and 50,000 bias-corrected bootstrapped
sam-
ples, all assessing specificity of the link between sadness facial
ex-
pressions and relational connectedness in older adults. First, we
ex-
amined whether the association between sadness facial
expressions
and relational connectedness remained stable after accounting
for trait
empathy and the two other measured aspects of emotional
reactivity
in response to the film clip: (a) ratings of subjective emotional
experience and (b) our three factors of physiological activation.
Trait
empathy was found to be significantly associated with facial
expres-
sivity (see Supplementary Table 3), and thus was included as a
covariate in order for us to examine whether associations
between
facial expressions and social connection remained after
adjusting for
this variable. Second, to examine whether sadness facial
expressions
contributed unique effects beyond facial expressions of other
emo-
tions, we repeated our moderation analysis including all of the
other
facial responses to the distressing film (i.e., confusion/concern,
dis-
gust, fear, and happiness) and each expression’s interaction
with age.
Third, to examine whether associations with relational
connectedness
were specific to sadness facial expressions, we computed

separate
regression analyses entering each of the facial expressions other
than
sadness and their interaction with age, using data from both
films.
Results
Manipulation Checks
Distressing film clip. Dependent sample t tests comparing
postfilm ratings of subjective emotional experience with pre-
session ratings indicated that participants experienced the larg-
est increases in feeling sad and disturbed (M diffs � 2.89, p �
.001), while smaller increases (M diffs ranging from .38 to 2.66,
ps � .001) were observed for feeling moved, upset, disgusted,
sympathetic, angry, worried, compassionate, ashamed, embar-
rassed, afraid, and surprised. Decreases were revealed for in-
terest, pride, amusement, calm, and enthusiasm (M diffs ranging
from �.36 to �1.63, ps � .001).
Table 3
Self-Reported Loneliness and Empathy by Age
Young adults Middle-aged adults Older adults
M (SD) M (SD) M (SD)
UCLA Loneliness Scale
Relational connectedness 3.39 (.52) 3.20 (.60) 3.44 (.57)
Collective connectedness 3.21 (.54) 3.18 (.49) 3.32 (.55)
Isolation 2.11 (.56) 2.29 (.63) 2.04 (.64)
Full scale 38.26 (9.80) 40.94 (10.42) 36.89 (10.82)
Interpersonal Reactivity Index
Empathic concern 3.80 (.69) 4.01 (.65) 4.07 (.55)
Perspective taking 3.71 (.71) 3.66 (.74) 3.63 (.73)

Personal distress 2.57 (.73) 2.24 (.69) 2.22 (.71)
Fantasy 3.49 (.74) 3.09 (.84) 3.28 (.89)
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Uplifting film clip. Dependent sample t tests comparing post-
film ratings of subjective emotional experience with presession
ratings indicated that participants experienced the largest
increases
in feeling moved (M diff � 2.09, p � .001), sympathetic (M diff
�
1.23, p � .001), and compassionate (M diff � 1.17, p � .001).
Smaller increases (M diffs ranging from .18 to .66, ps � .05)
were
observed for surprise, sadness, pride, enthusiasm, feeling dis-
turbed, upset, and amused. Decreases in calm and worry (M
diffs
ranging from �.24 to �.15, ps � .02) were also observed, while
a decrease in embarrassment was trending (M diff � �.077, p �
.051). No changes were found for disgust, anger, interest,
shame,

or fear (M diffs ranging from � �.08 to .07, ps � .063).
Age Differences in Facial Expression Responses to
Distressing and Uplifting Film Clips
For the distressing film, the MANOVA revealed significant
main
effects of age on sadness facial expressions F(2, 199) � 5.06, p
�
.007, with post hoc analyses indicating that older adults
expressed
more sadness than younger adults (M diff � .55, p � .005) but
not
middle-aged adults (M diff � 0.28, p � .319; see Figure 1). Age
differences were not significant for any other facial expression
(ps �
.190) except for confusion/concern F(2, 199) � 4.71, p � .010,
with
post hoc analyses indicating that older adults expressed more
confu-
sion/concern than younger adults (M diff � .52, p � .009) but
not
middle-aged adults (M diff � 0.36, p � .112). As previously
reported
by Sze and colleagues (2012), older adults also responded with
greater
personal distress and physiological activation to the distressing
film
clip. These results can be viewed in Table 4.
For the uplifting film clip, results revealed a main effect of age
on
happiness facial expressions, F(2, 199) � 3.27, p � .040, with
post hoc
analyses indicating that older adults expressed more happiness

than
middle-aged and younger adults at trend levels (M diff � 0.38,
ps �
.077). The main effects of age on all other facial expressions
were not
significant (ps � .160). These results can be viewed in Figure 1.
Associations Between Facial Expressions and
Relational Connectedness
Primary analyses. For the distressing film, the regression
analysis revealed a significant interaction between sadness
facial
expression and age in predicting relational connectedness (B �
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
Confusion/
Concern
Disgust Fear Happiness Sadness
snoisserpxE laica F fo lev eL egarev
A

D
is
pa
ye
d
Pe
r
Se
co
nd
Facial Expressions
Facial Expressions In Response to the Upli�ing Film
Young
Middle-Aged
Older
-0.3
-0.2
-0.1
0
0.1

0.2
0.3
0.4
Confusion/
Concern
Disgust Fear Happiness Sadness
snoisserpxE laicaF fo leveL ega rev
A
D
is
pa
ye
d
Pe
r
Se
co
nd
Facial Expressions
Facial Expressions In Response to the Distressing Film
Young
Middle-Aged

Older
****
†
Figure 1. Age differences in facial expression responses when
viewing both films for young, middle-aged, and
older adults. † p � .08. �� p � .01.
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.03, SE(B) � .01, p � .009, 95% CI [.007, .046]). The Sadness
Facial Expression � Age interaction was not significant in pre-
dicting the full UCLA Loneliness scale or its subscales,
although
results were in the expected direction: full scale (B � �.28,
SE(B) � .18, p � .133, 95% CI [�.64, .09]), collective connect-
edness (B � .01, SE(B) � .01, p � .213, 95% CI [�.01, .03])
and
isolation (B � �.01, SE(B) � .01, p � .221, 95% CI [�.04,
.00]).

For confusion/concern facial expressions, no main effects or age
interaction effects were significant for any of the three
subscales
(ps � .638).
The Sadness Facial Expression � Age interaction predicting
relational connectedness was decomposed using simple slopes
analysis, with the effect of sadness facial expression estimated
at
three levels of age. Results showed that among older adults
(esti-
mated at mean age 1 SD, or 62.7 years), those who expressed
more sadness when viewing the film clip of others in distress
reported higher levels of relational connectedness (B � .64,
SE(B) � .22, p � .004, 95% CI [.21, 1.07]). This effect was not
significant for middle-aged (estimated at mean age, or 44.6
years;
B � .16, SE(B) � .19, p � .397, 95% CI [�.21, .54]) or young
adults (estimated at mean age �1 SD, or 26.4 years; B � �.32,
SE(B) � .30, p � .291, 95% CI [�.91, .28]). These results can
be
viewed in Figure 2. Exploratory simple slope analyses were also
conducted for the other subscales as well as the full UCLA
Loneliness Scale, to examine whether age slopes were in the
expected direction. In general, findings were that older adults
who
displayed more sadness facial expressions reported less total
lone-
liness (p � .005) and isolation (p � .115), and higher levels of
collective connectedness (p � .035). These associations were
largely nonsignificant for sadness facial expressions displayed
by
middle-aged and younger adults.1 Correlations between facial
ex-
pressions and these subscales can be viewed in Supplementary
Table 1.

For the uplifting film, the regression revealed a significant main
effect of happiness facial expressions in predicting relational
con-
nectedness (B � .43, SE(B) � .17, p � .014, 95% CI [.09, .77]);
this association was not significantly moderated by age (p �
.813).2
Follow-up analyses. To assess whether findings for sadness
facial expressions remained stable when accounting for trait
em-
pathy and the two other aspects of emotional responding (i.e.,
ratings of subjective emotional experience and physiological
acti-
vation), we repeated our moderation analysis while including
these
variables as additional predictors of relational connectedness.
The
Sadness Facial Expression � Age interaction remained robust
(B � .021, SE(B) � .011, p � .059, 95% CI [�.001, .04]).
To examine whether sadness facial expressions contributed
above and beyond other facial expressions, we repeated our
mod-
eration analysis while including all of the other facial
expressions
(i.e., confusion/concern, disgust, fear, and happiness) and each
expression’s interaction with age. The Sadness Facial Expres-
sion � Age interaction remained significant (B � .03, SE(B) �
.01, p � .008, 95% CI [.007, .05]).
To examine whether our findings were specific to sadness facial
expressions, we conducted separate hierarchical linear
regression
analyses examining facial expressions of confusion/concern,
dis-

gust, fear, and happiness in response to the distressing film clip,
and each of these variables’ interaction with age, as predictors
of
relational connectedness. No significant main effects of facial
expressions were found (ps � .078). When examining
interaction
effects between these facial expressions and age, the Disgust
Facial Expression � Age interaction was significant (B � �.07,
SE(B) � .03, p � .018, 95% CI [�.13, �.01]), with simple
slopes
indicating that older adults who expressed more disgust while
viewing the distressing film clip reported lower levels of
relational
connectedness (B � �1.83, SE(B) � .72, p � .012, 95% CI
[�3.26, �0.40]; see Figure 3). Simple slopes for middle-aged
and
young adults were not significant (ps � .329). The Disgust
Facial
Expression � Age interaction remained significant when
account-
ing for trait empathy and the two other aspects of emotional
responding (i.e., ratings of subjective emotional experience and
physiological activation), p � .009, and other facial behaviors
and
their interactions with age in the same model (p � .044). No
other
Facial Expression � Age interactions were significant
predictors
of relational connectedness (ps � .182).
Discussion
A central tenet of functionalist theories is that all emotions,
both
those typically considered to be “positive” (e.g., amusement,

joy) and
those typically considered to be “negative” (e.g., sadness,
anger), can
lead to adaptive outcomes for the individual (Gruber, Mauss, &
Tamir, 2011; Keltner & Kring, 1998). The ability to display
emotional
responses to other people’s situations adds another level of
function-
ality, demonstrating care and concern for others and helping
build
relational bonds (Keltner, Haidt, & Shiota, 2006; Shiota et al.,
2004). In the present study we found that, compared with
middle-aged and younger adults, older adults exhibited more
prosocial, situationally appropriate facial expressions in
response
to film clips depicting others in distressing and joyful
situations.
Moreover, more sadness and fewer disgust facial expressions in
response to others’ distress were associated with higher
reported
levels of relational connectedness in older adults, but not in
middle-
aged and younger adults. This association remained robust even
after
accounting for other emotional facial expressions, two other
aspects
1 Although the interaction effect between Sadness Facial
Expression �
Age was only significant for the relational connectedness
subscale, we
conducted exploratory analyses of simple slopes for the full
UCLA Lone-
liness Scale as well as for the isolation and collective
connectedness scales
to examine whether the direction of the results would be

consistent with
our hypotheses. Results indicated that older adults who
displayed more
sadness facial expressions reported significantly less full-scale
loneliness
(B � �11.40, SE(B) � 3.98, p � .005, 95% CI [�19.24,
�3.56]), trend
levels of less isolation (B � �.38, SE(B) � .24, p � .115, 95%
CI [�.84,
.09]), and significantly higher levels of collective
connectedness (B � .43,
SE(B) � .20, p � .035, 95% CI [.03, .83]). Middle-aged adults
who
expressed more sadness reported less full-scale loneliness at
trend levels
(B � �6.37, SE(B) � 3.48, p � .069, 95% CI [�13.23, .49]),
but
otherwise slopes for middle-aged and young adults were not
significant for
full-scale loneliness or any of the subscales (p � .219).
2 Although the interaction of Happiness Facial Expression �
Age was
not significant for the uplifting film, we also conducted
exploratory anal-
yses of simple slopes for the full UCLA Loneliness Scale as
well as for the
individual subscales. Results revealed that both middle-aged
and older
adults who expressed more happiness reported less full-scale
loneliness
(ps � .013) and isolation (ps � .034), and more relational (ps �
.031) and
collective connectedness (ps � .006). For young adults,
expressing hap-
piness was significantly associated with more collective

connectedness
(p � .024) and at trend levels for less full-scale loneliness (p �
.091). The
slopes for young adults’ relational connectedness and isolation
were not
significant (ps � .144).
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of emotional responding (subjective emotional experience and
phys-
iological activation), and trait empathy.
Heightened Prosocial Facial Expressions in Response
to Others’ Experience in Late Life
Viewing others in distress can be a powerful emotional stimulus
for people at all ages (Hoffman, 1975), but our findings indicate
that the distress of others may be particularly potent for older
adults, who displayed more sadness facial expressions. In this
context, sadness is an important interpersonal emotion that

signals
empathy (Eisenberg et al., 1989; Keltner, 2009), motivates
people
to provide support (Clark et al., 1987; Graham et al., 2008),
reduces distress in others (Batson, 2011), and promotes feelings
of
social connection (Gray et al., 2011). Our finding that older
people
respond more strongly to the distress of others with sadness is
consistent with theories that envision older adults as prioritizing
social relationships (Carstensen, 1992), as well as studies
demon-
strating heightened reactivity in subjective emotional
experience
and physiology (Seider et al., 2011; Sze et al., 2012) to themes
of
loss. Older adults also showed more “knit brow” or confusion/
concern expressions during this film clip, suggesting that
overall
attentiveness to and concern for others’ distress is heightened in
older adults. Expressions of disgust, fear, and happiness in re-
sponse to this clip did not differ across age groups.
Age was also associated with happiness expressions in response
to the uplifting film clip, with older adults displaying more hap-
piness than middle-aged and younger adults at trend levels. This
finding sheds some light on positive emotion expressions in
older
adults. Previous studies have found that older adults expressed
more positive emotions such as affection in some positive
contexts
(Carstensen, Gottman, & Levenson, 1995; Levenson,
Carstensen, &
Gottman, 1993) but not others (Gross et al., 1997; Levenson,
Carstensen, Friesen, & Ekman, 1991; Magai, Consedine, Kri-

voshekova, Kudadjie-Gyamfi, & McPherson, 2006; Tsai et al.,
2000).
It is possible that procedures invoking themes that align with
older
adults’ goals of investing in relationships and meaningful
connections
(e.g., dyadic interactions with loved ones) and age-relevant
stimuli
(Kunzmann & Grühn, 2005) are best able to uncover age
differences
in positive emotional expressions. Our uplifting stimulus
focused on
children, and may have been particularly salient to older adults
in their
roles as parents and grandparents.
Facial Expressions and Relational Connectedness in
Late Life
We also found that more sadness facial expressions in response
to a depiction of others’ suffering was distinctly associated with
heightened relational connectedness. As a response to one’s
own
loss, sadness has been linked to a number of positive
interpersonal
consequences including motivating others to provide support
(Clark et al., 1987; Graham et al., 2008) and promoting feelings
of
social connection (Gray et al., 2011). These positive social out-
comes may be particularly important in late life given
increasingly
Table 4
Facial Expressions, Self-Reported Emotions, and Physiological
Responses by Age

Distressing film clip Uplifting film clip
Young Middle-aged Older Young Middle-aged Older
M (SD) M (SD) M (SD) M (SD) M (SD) M (SD)
Facial expressions per second
Sadness .09 (.17) .15 (.22) .21 (.26) .00 (.01) .04 (.13) .03 (.11)
Disgust .02 (.06) .02 (.08) .02 (.07) 00 (.00) .00 (.00) .01 (.06)
Happiness .00 (.01) .00 (.01) .01 (.03) .16 (.21) .16 (.23) .25
(.27)
Confusion/concern .02 (.05) .04 (.09) .08 (.16) .02 (.07) .04
(.10) .04 (.13)
Fear .00 (.03) .01 (.03) .02 (.08) .00 (.01) .00 (.03) .00 (.01)
Self-reported emotions
Afraid 1.83 (1.01) 1.94 (1.16) 1.82 (1.16) 1.10 (.52) 1.08 (.36)
1.08 (.40)
Amused 1.17 (.73) 1.15 (.73) 1.00 (.00) 2.23 (1.07) 2.27 (1.12)
2.29 (1.22)
Angry 2.20 (1.20) 2.94 (1.30) 3.58 (1.39) 1.12 (.58) 1.11 (.53)
1.05 (.27)
Ashamed 2.17 (1.14) 2.64 (1.32) 3.33 (1.47) 1.19 (.73) 1.12
(.48) 1.06 (.49)
Calm 2.09 (1.10) 1.97 (1.11) 1.97 (1.18) 3.14 (1.19) 3.45 (1.23)
3.36 (1.33)
Compassionate 3.75 (1.16) 4.26 (1.04) 4.61 (.86) 3.17 (1.10)
3.77 (1.04) 4.24 (.91)
Disgusted 2.62 (1.10) 3.38 (1.48) 3.80 (1.46) 1.10 (.60) 1.12
(.60) 1.06 (.39)
Disturbed 3.55 (1.16) 4.06 (1.23) 4.42 (0.99) 1.22 (.80) 1.38
(.96) 1.36 (.82)
Embarrassed 1.72 (1.03) 2.29 (1.46) 2.79 (1.60) 1.10 (.55) 1.09
(.34) 1.09 (.38)
Enthusiastic 1.09 (.28) 1.35 (.89) 1.15 (.47) 2.59 (1.19) 3.06
(1.24) 3.86 (1.18)
Interested 2.94 (1.29) 3.71 (1.25) 3.79 (1.21) 3.25 (1.22) 3.70

(1.05) 4.26 (.88)
Moved 3.75 (1.29) 4.31 (1.06) 4.55 (.83) 2.91 (1.26) 3.77 (1.02)
4.21 (1.00)
Proud 1.07 (.36) 1.09 (.42) 1.11 (.40) 2.16 (1.18) 2.64 (1.36)
3.02 (1.59)
Sad 3.52 (1.27) 4.05 (1.29) 4.52 (.90) 1.41 (.89) 1.60 (.97) 1.92
(1.13)
Surprised 1.70 (1.00) 2.03 (1.25) 1.71 (1.08) 1.75 (1.13) 1.98
(1.21) 2.25 (1.34)
Sympathetic 3.68 (1.13) 4.26 (1.09) 4.42 (.98) 2.55 (1.05) 3.21
(1.26) 3.86 (1.14)
Upset 3.29 (1.41) 3.71 (1.36) 4.21 (1.16) 1.25 (.81) 1.29 (.76)
1.41 (.86)
Worried 2.72 (1.44) 2.89 (1.54) 3.53 (1.35) 1.22 (.70) 1.26 (.69)
1.27 (.62)
Physiological responses
Physio—blood pressure �.03 (.73) �.09 (.94) .07 (1.00) �.12
(.93) .08 (.85) .02 (.94)
Physio—peripheral .05 (.90) .07 (.79) �.02 (.86) .13 (.66) .05
(.66) �.17 (1.01)
Physio—cardiac and respiration .08 (.70) .01 (.65) �.10 (.87)
�.09 (.71) .03 (.68) .02 (.67)
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frequent losses, greater physical frailty and increased
dependence
on others (Charles & Carstensen, 2010; Prohaska et al., 2006;
Steverink et al., 2001), and strong associations between social
connectedness and health and well-being (Cacioppo, Hughes,
Waite, Hawkley, & Thisted, 2006; Hawkley, Masi, Berry, &
Cacioppo, 2006).
Our findings suggest that displays of sadness in response to
others’ distress may also be particularly useful in late life. Our
prior research showed that heightened sadness reactivity (i.e.,
greater subjective sadness in response to an emotionally am-
biguous film clip) is associated with greater well-being in late
life (Haase et al., 2012). The present study expands upon these
findings, linking more facial expressions of sadness in response
to others’ suffering (arguably the most socially visible aspect of
an emotional response) with greater relational connectedness in
older individuals. Older adults often have more physical limi-
tations relative to young and middle-aged adults, and fewer
social obligations that push them to maintain interpersonal
connections. Thus, it may become particularly important that
they signal active emotional engagement when opportunities for
connection do arise. Older adults who are more likely to signal
and express sadness for others may be best at maintaining this
sense of closeness (i.e., relational connectedness) with others in
their lives.
We found that more facial expressions of happiness in response
to others’ joy was associated with higher relational
connectedness
regardless of age. This is consistent with, and adds to, a body of
literature documenting the relationship-building effects of
shared

positivity in response to happy events (e.g., Gable et al., 2006;
Gable, Reis, Impett, & Asher, 2004). By sharing news about
one’s
own positive event with relationship partners, one opens the
door
to “capitalizing” on that event, amplifying and extending the
positive emotions associated with it (Gable et al., 2004). When
the
relationship partner engages actively and constructively in
talking
about the positive event, this facilitates capitalization and
boosts
relationship outcomes for both people (Gable et al., 2004,
2006).
The present findings suggest that this interpersonal upward
spiral
contributes in valuable ways to the social well-being of young,
middle-age, and older adults alike. Prior research has found that
people whose behaviors match those of the people they are
inter-
acting with tend to have more positive social interactions and
are
more empathic (Chartrand & Bargh, 1999; Iacoboni, 2009),
which
in turn can facilitate the close social relationships captured by
relational connectedness. Expressing emotions that are not con-
gruent with the context can be associated with social
disturbances,
which has been found in people with psychopathology (Keltner
&
Kring, 1998) and neurodegenerative disease (Chen et al., 2017).
Directions for Future Research
The present findings have implications for future research on

emotional aging and the functions of emotion. In terms of
research on emotional aging, early emotion research viewed
older adults as emotionally flat (Jung, 2001) or disengaged
(Cumming & Henry, 1961). The advent of newer theories in
social gerontology (Carstensen et al., 1999; Labouvie-Vief,
Diehl, Jain, & Zhang, 2007) spurred discovery of many ways in
which older adults respond with heightened positive emotions
(Carstensen et al., 1995; Levenson, Carstensen, & Gottman,
1993). The present study shows a more nuanced picture. Older
adults appear to be exquisitely attuned to social context—they
express more sadness when watching others in a distressing
Average Levels of Sadness Facial Expression
.30.20.10.00-.10-.20-.30
R
el
at
io
na
l C
on
ne
ct
ed
ne
ss
3.50
3.40

3.30
3.20
3.10
Older Adults
Middle-Aged Adults
Young Adults
B=.64, SE(B)=.22**
B=.16, SE(B)=.19
B=-.32, SE(B)=.30
Figure 2. Simple slopes for sadness facial expression as a
predictor of relational connectedness for young,
middle-aged, and older adults. Disgust facial expression plotted
at low (M � 1 SD) and high (M 1 SD) levels.
Age plotted at 62.7 years (older adults), 44.6 years (middle-
aged adults), and 26.4 years (younger adults).
Standardized regression coefficients and standard errors are
shown. �� p � .01.
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context as well as more happiness when watching others in an
uplifting context. Future research should continue to examine
age differences in specific emotions in specific contexts, in line
with a discrete emotions perspective on emotional aging (Kun-
zmann, Kappes, & Wrosch, 2014).
In terms of the functions of emotions, many studies have dem-
onstrated the adaptive functions of sadness (Fischer &
Manstead,
2008; Graham et al., 2008). Our work suggests that these
functions
become particularly adaptive for relational connectedness and
well-being (Haase et al., 2012) at specific ages, highlighting the
importance of examining how functions of emotions can differ
with age. Moreover, the sadness facial expressions we focused
on
in the present study were momentary responses of moderate in-
tensity to specific stimuli. Our findings that these expressions
are
associated with positive social outcomes do not mean that
chronic,
high intensity activation of sadness would be associated with
similar positive social outcomes. Indeed, there is clearly a point
beyond which greater sadness facial expressions no longer have
social benefits but instead have palpable social costs (e.g., when
chronic depression leads to fewer close relationships, impaired
social functioning, or less social support; Coyne, 1976;
Hirschfeld
et al., 2000; Keltner & Kring, 1998). Characterizing the
boundary
conditions and contexts that determine whether sadness facial

expressions are adaptive or maladaptive is an important area for
future research.
Beyond sadness, we also obtained evidence for the adaptive-
ness of a few other specific emotions. Displaying more facial
expressions of happiness in response to the uplifting film clip
was associated with greater relational connectedness across all
ages, in line with theories on the functions of positive emotions
(Fredrickson, 2000). Moreover, displaying fewer facial expres-
sions of disgust in response to the distressing film clip was
associated with greater relational connectedness in late life.
Theories on disgust suggest that the emotion serves the function
of avoiding disease (Rozin, Haidt, & McCauley, 1993), or
perhaps serves to express displeasure at unfairness (Chapman,
Kim, Susskind, & Anderson, 2009; Moretti & Di Pellegrino,
2010). Studies have yet to examine whether the expression and
function of disgust differs with age. For older adults who often
end up serving as the main caregivers for spouses and friends
receiving care at home (Schulz & Eden, 2016; Sneed & Schulz,
2017), avoiding situations that many may find disgusting (e.g.,
wounds from physical conditions, bedpans) or unfair (e.g., a
spouse contracts a terminal illness) could impede their ability to
serve as caregivers and have certain kinds of meaningful late-
life relationships. Further research is needed to replicate this
finding and explore the functions of disgust across the life span.
Strengths and Limitations
Strengths of this research include the use of a community
sample of male and female participants representing three
different
age groups and the multimethod assessment of emotional
reactiv-
ity using objectively coded facial expressions, subjective
experi-

ence, and peripheral physiological responding. Limitations in-
cluded the cross-sectional design, which raises the possibility
that
found age differences could reflect cohort rather than age. The
cross-sectional design also impacts our ability to determine the
directionality of found associations. For example, it is possible
that
older adults higher in relational connectedness are more likely
to
Average Levels of Disgust Facial Expression
.10.05.00-.05-.10
R
el
at
io
na
l C
on
ne
ct
ed
ne
ss
3.50
3.45
3.40

3.35
3.30
3.25
3.20
Older Adults
Middle-Aged Adults
Young Adults
B=.83, SE(B)=.85
B=-.50, SE(B)=.56
B=-1.83, SE(B)=.72*
Figure 3. Simple slopes for disgust facial expression as a
predictor of relational connectedness for young,
middle-aged, and older adults. Disgust facial expression plotted
at low (M � 1 SD) and high (M 1 SD) levels.
Age plotted at 62.7 years (older adults), 44.6 years (middle-
aged adults), and 26.4 years (younger adults).
Standardized regression coefficients and standard errors are
shown. � p � .05.
T
hi
s
do
cu
m

y.
express sadness facial expressions in response to the distress of
others.3 In addition, although we included both a sadness film
and
an uplifting film, we did not consider other contexts or films
with
other emotion-eliciting or distress-eliciting themes that might
have
revealed additional associations between facial expressions and
relational connectedness at other ages. Thus, although our
findings
suggest that sadness facial expressions are particularly
functional
in late life, it is also possible that in contexts not examined in
our
study, sadness facial expressions may be particularly functional
for
other age groups as well. Finally, our study also did not
consider
instances in which people respond with more than one emotion
(Chen et al., 2017; Ersner-Hershfield, Mikels, Sullivan, &
Carstensen, 2008), given the constraints of the coding protocol
used.
Conclusion
The present study found evidence that older adults who express
more sadness and less disgust in response to viewing others in
distress report greater relational connectedness. Thus, in late
life,

greater facial expression of sadness, an emotion generally
thought
to be negative, is associated with a positive social outcome.
This
finding underscores the drawback of viewing particular
emotions
as inherently “good” or “bad,” and supports the view that
different
emotions can be adaptive or maladaptive depending on the
inter-
action of particular goals, contexts, outcomes, and stages of de-
velopment. This also is a cautionary note for “one size fits all”
models of emotion (e.g., all positive emotions are always good
and
all negative emotions are always bad) and the interventions that
grow out of these models (e.g., individuals should always strive
to
increase positive emotion and decrease negative emotion). Just
as
positive emotions can have a darker side (Gruber et al., 2011),
negative emotions like sadness can have a brighter side as well.
3 Sadness facial expressions evidence an important social
bidirectional-
ity. When we are distressed, displaying sadness indicates that
we need help,
comfort, and support. When we see a person in distress,
displaying sadness
can convey that we care, are concerned, and might be willing to
help. In our
view, it is this bidirectionality, acting against the backdrop of
greater losses
typically experienced by older individuals, that underlies the
critical role
that sadness expressions play in strengthening social
relationships in late

life.
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ences and Social Sciences, 56, 364 –373.
geronb/56.6.P364
Sze, J. A., Gyurak, A., Goodkind, M. S., & Levenson, R. W.
(2012).
Greater emotional empathy and prosocial behavior in late life.
Emotion,
12, 1129 –1140. http://dx.doi.org/10.1037/a0025011
Tsai, J. L., Levenson, R. W., & Carstensen, L. L. (2000).
Autonomic,
subjective, and expressive responses to emotional films in older

and
younger Chinese Americans and European Americans.
Psychology and
Aging, 15, 684 – 693.
Wildschut, T., Sedikides, C., Routledge, C., Arndt, J., &
Cordaro, F.
(2010). Nostalgia as a repository of social connectedness: The
role of
attachment-related avoidance. Journal of Personality and Social
Psy-
Appendix
UCLA Loneliness Scale—Three Factor Subscales
Isolation Subscale
I lack companionship.
There is no one I can turn to.
I feel alone.
I am no longer close to anyone.
My interests and ideas are not shared by those around me.
I feel left out.
My social relationships are superficial.
No one really knows me well.
I feel isolated from others.
I am unhappy being so withdrawn.
Relational Connectedness
People are around me but not with me.
There are people I feel close to.
I can find companionship when I want it.

There are people who really understand me.
There are people I can talk to.
There are people I can turn to.
Collective Connectedness
I feel in tune with the people around me.
I feel part of a group of friends.
I have a lot in common with the people around me.
I am an outgoing person.
Received March 30, 2016
Revision received September 6, 2018
Accepted September 9, 2018 �
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http://dx.doi.org/10.1002/ajpa.20001
http://dx.doi.org/10.1002/ajpa.20001
http://dx.doi.org/10.1093/scan/nsq069
http://dx.doi.org/10.1177/0898264317733362
http://dx.doi.org/10.1177/0898264317733362
http://dx.doi.org/10.1093/geronb/56.6.P364
http://dx.doi.org/10.1093/geronb/56.6.P364

http://dx.doi.org/10.1037/a0025011
http://dx.doi.org/10.1037/a0017597Responding to the Emotions
of Others: Age Differences in Facial Expressions and Age-
Specific Ass ...The Social Functions of Facial
BehaviorsProsocial Responding to Others’ Distress in Late
LifeThe Present
StudyMethodParticipantsProcedureMeasuresFacial
expressionsSubjective experiencePhysiological
activationRelational connectednessTrait empathyData
AnalysisResultsManipulation ChecksDistressing film
clipUplifting film clipAge Differences in Facial Expression
Responses to Distressing and Uplifting Film ClipsAssociations
Between Facial Expressions and Relational
ConnectednessPrimary analysesFollow-up
analysesDiscussionHeightened Prosocial Facial Expressions in
Response to Others’ Experience in Late LifeFacial Expressions
and Relational Connectedness in Late LifeDirections for Future
ResearchStrengths and
LimitationsConclusionReferencesAppendix UCLA Loneliness
Scale—Three Factor SubscalesIsolation SubscaleRelational
ConnectednessCollective Connectedness
Group-Based Emotion Regulation: A Motivated Approach
Roni Porat
Princeton University and The Hebrew University of Jerusalem
Maya Tamir and Eran Halperin
The Hebrew University of Jerusalem
The regulation of group-based emotions has gained scholarly
attention only in recent years. In this article,
we review research on group-based emotion regulation, focusing
on the role of motivation and distinguishing

between different emotion regulation motives in the group
context. For that purpose, we first define
group-based emotions and their effects on both intragroup and
intergroup processes. We then review motives
for group-based emotion regulation, suggesting 3 classes of
group-based motives: (a) intragroup motives
pertaining to what I want to be in relation to the group (e.g.,
increase sense of belongingness), (b) intergroup
motives pertaining to what I want my group’s relationship with
other groups to be (e.g., preserve the status
quo), and (c) meta group motives pertaining to what I want my
group to be (e.g., perceive the ingroup more
positvely). We discuss the implications of these different
motives for group-based emotion regulation and how
they might inform scholars in the field.
Keywords: group-based emotion, emotion regulation,
motivation
If you are angry today, or if you have been angry for a while,
and
you’re wondering whether you’re allowed to be as angry as you
feel,
let me say: Yes. Yes you are allowed. You are, in fact,
compelled.
—Traister, 2018
Two days after Christine Blasey Ford testified in front of the
Senate Judiciary Committee, accusing Justice Brett Kavanaugh
of
sexually assaulting her years earlier, the writer Rebecca Traister
(2018) called upon American woman to get angry. In an op-ed

titled, “Fury Is a Political Weapon and Women Need to Wield
It,”
Traister argued that women need to upregulate their anger,
because
only anger will change political outcomes. Traister’s
observation
regarding the role anger can play in achieving group-level goals
is
consistent with research on group-based emotions and their role
in
shaping social behaviors (see Mackie & Smith, 2018), but it
also
highlights that their experience can be motivated.
Group-based emotions are emotions that individuals experience
as a result of their membership in, or identification with, a
group
(Mackie, Devos, & Smith, 2000). The experience of these emo-
tions can shape intragroup and intergroup processes (Mackie &
Smith, 2018). For example, with regard to the former, group-
based
emotions can determine how much group members identify with
their group (Kessler & Hollbach, 2005) and engage in affiliative
behaviors, such as displaying a national flag or voting (e.g.,
Smith,
Seger, & Mackie, 2007). With regard to the latter, group-based
emotions can mobilize societies in support of war or peace (e.g.,
Cohen-Chen, Halperin, Crisp, & Gross, 2014; Halperin, 2011).
Group-based emotions are often spontaneous reactions to group-
related events. However, they can be influenced by regulatory
processes (Goldenberg, Halperin, van Zomeren, & Gross, 2016).
Emotion regulation involves attempts to change an existing
emo-
tion into a desired emotion (e.g., Gross, 1998). Whereas the

majority of research on the regulation of group-based emotions
has
focused on how they may be regulated (Goldenberg et al.,
2016),
our focus herein is on why people try to regulate them. People
regulate their emotions in pursuit of different goals (e.g.,
Tamir,
2016). Tamir (2016) proposed a taxonomy that distinguishes be-
tween hedonic (i.e., maximizing immediate pleasure) and instru-
mental (i.e., pursuing other benefits of emotions) motives for
emotion regulation. With some adjustments, these ideas can be
extrapolated to the group level. Given that group-based
emotions
shape group-related outcomes, people may be motivated to
regu-
late group-based emotions to attain group-related goals. Group-
based emotions may be pursued for either hedonic benefits
(e.g.,
feeling proud of your group may feel good) or for instrumental
benefits (e.g., feeling proud of your group may help you feel
part
of the group). In this contribution, given space limitations, we
focus on instrumental benefits.
We identify three classes of motives that could drive the regu-
lation of group-based emotions: (a) motives pertaining to what I
want to be in relation to the group, (b) motives pertaining to
what
I want my group’s relationship with other groups to be, and (c)
motives pertaining to what I want my group to be. Each class
contains different group-level goals that may operate
individually
or interact with each other. Such motives can also influence
emotion generation to the extent that they shape how people
Roni Porat, Department of Psychology, Princeton University,

and De-
partments of Political Science and International Relations, The
Hebrew
University of Jerusalem; Maya Tamir and Eran Halperin,
Department of
Psychology, The Hebrew University of Jerusalem.
We thank Amit Goldenberg, Chelsey S. Clark, and the Coman
Lab for
their thoughtful comments.
Roni
Porat, Department of Psychology, Princeton University,
Peretsman Scully
Hall, Office 421, Princeton NJ, 08540. E-mail:
[email protected]
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Emotion
1, 16 –20
16

understand and react to their environment. However, in the
current
article, we focus on what people want to feel rather than how
they
are likely to feel. Given that this is a relatively new field of
research, there is still only limited empirical evidence directly
assessing motivation. As a result, we chose to review cases in
which motivation was directly measured as well as cases in
which
we believe it may have played a role, even if it was not
measured
directly. Finally, we discuss the implications for future
research.
Intragroup Motives: What I Want to Be in Relation to
the Group
People are inherently motivated to belong to social groups
(Baumeister & Leary, 1995; Tajfel & Turner, 1979) and are
therefore typically motivated to increase their sense of
belonging-
ness to the ingroup. However, sometimes people are motivated
to
decrease their sense of belongingness (Brewer, 1991). Given
that
emotions are instrumental for connecting individuals to their
group
(Kessler & Hollbach, 2005; Smith et al., 2007), we suggest that
people may be motivated to experience group-based emotions if
they believe these emotions can increase or decrease their sense
of
belongingness to their group.

One way of increasing sense of belongingness to the ingroup is
by experiencing an emotion that is perceived to be normative in
that group. Several studies support the notion that people
experi-
ence and actively regulate their emotions in a manner that con-
forms to their group’s emotion norm (Leonard, Moons, Mackie,
&
Smith, 2011; Lin, Qu, & Telzer, 2018; Nook, Ong, Morelli,
Mitch-
ell, & Zaki, 2016). With regard to the latter, Lin and colleagues
(2016) found that both American and Chinese participants
shifted
their emotional reactions when exposed to ingroup, but not out-
group, emotional responses. With regard to the former, studies
demonstrate that people mimic facial expressions of ingroup
mem-
bers but not necessarily outgroup members (Bourgeois & Hess,
2008; Weisbuch & Ambady, 2008). Experiencing normative
emo-
tions may contribute to one’s sense of belongingness via two
mechanisms. First, when experiencing normative emotions, one
may be judged positively by other group members (Shields,
2005).
For example, when looking at Twitter replies to two politically
charged events, tweets expressing normative emotions were re-
warded by more likes and shares (Goldenberg, Garcia, Halperin,
&
Gross, 2019). If experiencing normative emotions facilitates
social
acceptance, people may be motivated to experience them to in-
crease acceptance by others. Second, experiencing normative
emo-
tions may enhance one’s own sense of belongingness and
connec-
tion (Páez, Rimé, Basabe, Wlodarczyk, & Zumeta, 2015). With
regard to the latter, Porat, Halperin, Mannheim, and Tamir

(2016)
demonstrated this mechanism in the context of the Israeli
National
Memorial Day, a day when most Israelis experience sadness.
Individuals primed with the need to belong (vs. those who were
not) were more motivated to experience sadness. This
association
was mediated by the belief that sadness would help them
connect
to their group. If experiencing normative emotions facilitates a
sense of connection, people may be motivated to experience
them
to increase their sense of belonging to their group.
To strengthen their sense of belongingness to their group,
people
may be motivated to experience an emotion with the ingroup,
but
they might also be motivated to experience an emotion toward
the
ingroup or other ingroup members. For example, people tend to
spontaneously experience more empathy toward ingroup than
out-
group members (Cikara, Bruneau, & Saxe, 2011). A recent
study
demonstrated that people are also motivated to experience more
empathy toward their ingroup compared with their outgroup
(Has-
son, Tamir, Brahms, Cohrs, & Halperin, 2018).1 We suggest
that
the motivation to experience emotions, such as empathy, toward
one’s ingroup can be generalized to other emotions as long as
these
emotions can potentially promote social belongingness (e.g.,
pride

and love). Thus, people may want to experience certain
emotions
toward their ingroup if they believe such experiences can
enhance
their sense of belongingness.
Although people often want to increase their connection to their
group, they may sometimes be motivated to decrease it. For
example, people may seek to decrease belongingness when they
feel their group has acted inappropriately. This can be achieved
by
experiencing an outlaw emotion—namely, an emotion that di-
verges from the normative response (Jaggar, 1989). Hochschild
(1983) described such emotions as “misfitting feelings,” which
alert individuals to a discrepancy between their values and the
prevailing values of the group. For example, participants who
learned that the majority of their ingroup felt low levels of guilt
in
response to an immoral action committed toward an outgroup
member experienced greater levels of group-based guilt. Partici-
pants’ negative emotions toward the ingroup mediated this
effect
(Goldenberg, Saguy, & Halperin, 2014). Although the study did
not assess motivation directly, we propose that participants who
felt negatively toward their ingroup may have upregulated guilt
(i.e., the emotion that their group was not experiencing) to tem-
porarily decrease their sense of belongingness to the
misbehaving
ingroup. People might regulate their emotions to either increase
or
decrease their sense of belongingness to their ingroup.
Intergroup Motives: What I Want My Group’s
Relationship With Other Groups to Be
Group-based emotions may be experienced toward ingroup or

outgroup members. The emotions we experience toward other
groups, and the emotions that other groups experience toward
our
group, may shape the nature of the relationship between the
groups. Therefore, people may be motivated to regulate their
emotions toward other groups to promote desired relations with
such groups (i.e., intrapersonal regulation). People may also be
motivated to regulate emotions of outgroup members to attain
relational goals (i.e., interpersonal regulation).
People may regulate their emotions toward an outgroup if
they believe these emotional experiences might influence their
decision making. For example, Jewish Israelis actively down-
regulated anger or fear when they believed that the experience
of these emotions toward Palestinians might impair their polit-
ical decision making (Porat, Halperin, & Tamir, 2016). People
may also regulate their group-based emotions to preserve or
change the status quo. For example, Jewish-Israeli conserva-
tives who were motivated to preserve the nature of the asym-
metric relations between Israelis and Palestinians were more
motivated than liberals to experience anger toward Palestinians
(Porat, Halperin, et al., 2016). Similarly, Jewish-Israeli liberals
1 We refer to empathy as capturing empathic concern, involving
feelings
of sympathy and compassion (Batson & Shaw, 1991).
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17MOTIVATION FOR GROUP-BASED EMOTION
who were faced with existential threat (vs. those who were not)
were more motivated to experience collective angst. This might
be because collective angst can justify aggressive actions
against the outgroup that liberals typically do not endorse
(Porat, Tamir, Wohl, Gur, & Halperin, 2019).
Another way of influencing the ingroup’s relations with an
outgroup is by regulating emotions of outgroup members. Such
regulation has been studied at the interpersonal level (Netzer,
Van
Kleef, & Tamir, 2015). We propose that, at the group level,
people
may be motivated to regulate the emotions of outgroup members
to
attain group-related benefits. For example, participants who
were
motivated to deter the outgroup from attacking the ingroup
wanted
outgroup members to experience more fear and tried to shape
the
emotions of outgroup members accordingly. In contrast, partici-
pants who were motivated to negotiate and reconcile with the
outgroup wanted outgroup members to experience more
calmness
(Netzer, Halperin, & Tamir, 2019). Similarly, members of a dis-
advantaged group who were motivated to correct outgroup
injus-
tice without impairing relations with them wanted outgroup
mem-
bers to feel more regret, whereas those who were motivated to

punish the outgroup wanted outgroup members to feel more fear
(Hasan-Aslih et al., 2018). People may regulate their emotions
toward the outgroup or the emotions of outgroup members to
attain
relational goals.
Meta Group Motives: What I Want My Group to Be
Emotions may also be perceived as instrumental for shaping or
influencing one’s ingroup. We propose that emotions can be
reg-
ulated in pursuit of goals pertaining to what individuals want
the
ingroup to be. This includes goals related to how the ingroup is
perceived by the self and others and goals related to preserving
or
changing desirable values and policies.
Regarding the perception of the ingroup, people may seek to
experience (and sometimes avoid) group-based emotions that
help them perceive their ingroup more positively (Turner,
Hogg, Oakes, Reicher, & Wetherell, 1987). Such motives are
particularly salient among highly identified individuals
(Branscombe & Wann, 1994). For example, when learning
about an ingroup transgression, highly identified individuals
downregulated collective guilt (Sharvit, Brambilla, Babush, &
Colucci, 2015). Although the authors did not measure motiva-
tion directly, they speculated that highly identified individuals,
who are motivated to maintain a positive perception of their
ingroup, are likely motivated to decrease guilt to avoid perceiv-
ing their group as immoral. This is because guilt indicates that
the group is accountable for the wrongdoing. Thus, people may
regulate group-based emotions to promote a desirable percep-
tion of their ingroup (Haslam, Powell, & Turner, 2000).
People may also be motivated to experience group-based

emotions to influence the policies endorsed by other ingroup
members. For example, if one wishes to promote compensation
of the outgroup for ingroup wrongdoings, one may be motivated
to pursue an emotion that is perceived as instrumental for
achieving this goal. Sharvit and Valetzky (2019) demonstrated
this by manipulating the perceived instrumentality of guilt for
motivating corrective action among group members. Partici-
pants who learned that the experience of group-based guilt can
motivate corrective action among group members upregulated
group-based guilt.
People may also be motivated to regulate emotions to avoid
group-level costs. At the individual level, Cameron and Payne
(2011) showed that in the face of mass suffering, when one
recognizes that the cost of assisting others is too high, one
might
downregulate the emotion that triggers such costly behavior. We
suggest that a similar process may occur at the group level.
When
one realizes that experiencing an emotion may have costly
impli-
cations for the ingroup, one might be motivated to decrease that
emotion to avoid such cost. For example, one may downregulate
anger, an emotion that facilitates collective action (van
Zomeren,
Spears, Fischer, & Leach, 2004), if one fears it would have
negative consequences for the ingroup (i.e., violent repression
by
authorities).
Drawing from research on motivated reasoning (Kunda, 1990),
we suggest that people are sometimes motivated to experience
emotions that reinforce their group’s core values and beliefs.
For
example, liberals, who value change over tradition, were more

motivated than conservatives to experience hope, even in the
face
of intergroup violence (Pliskin, Nabet, Jost, Tamir, & Halperin,
2019). This association was mediated by liberals’ beliefs that
hope
would be instrumental in reinforcing their ideological values. In
contrast, conservatives were more motivated than liberals to ex-
perience fear, even when their group was facing no threat. This
association was mediated by conservatives’ beliefs that fear
would
be instrumental in reinforcing their ideological values. In sum-
mary, people may be motivated to regulate group-based
emotions
to influence their perceptions and beliefs about the ingroup as
well
as the policies the ingroup endorses.
Conclusions
We proposed three classes of motives that may drive instru-
mental regulation of group-based emotions. We argue that
individuals have social motives that go beyond individual goals
or a simple aggregate of such goals. These social motives are
unique as they reflect inherently social concerns, intergroup
relations, group identities, and power hierarchies. Indeed, such
group-level motives can sometimes conflict with individual-
level ones. For example, a person may want to upregulate
empathy toward refugees entering her country to show she is a
kind and moral person, and at the same time, she may want to
downregulate empathy to avoid the financial burden that inte-
grating them might place on her ingroup. Those interested in
group processes and in emotion regulation may benefit from
understanding such motives. First, group-based emotions have
traditionally been studied as spontaneous reactions to social
events. We propose that group-based emotions can also result
from motivated regulation, in pursuit of group-related goals.

Second, by understanding why and in which direction people
regulate their emotions in group contexts, we may be able to
design interventions that change group-based emotions. Al-
though many of the ideas we propose await empirical testing,
we believe testing them would lead to new insights into emotion
regulation and experience at the group level. A list of recom-
mended additional reading is provided in the online supplemen-
tal material.
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18 PORAT, TAMIR, AND HALPERIN
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instru-
mental emotion regulation. Journal of Experimental Social
Psychology,
58, 124 –135. http://dx.doi.org/10.1016/j.jesp.2015.01.006
Nook, E. C., Ong, D. C., Morelli, S. A., Mitchell, J. P., & Zaki,
J. (2016).
Prosocial conformity: Prosocial norms generalize across

behavior and
empathy. Personality and Social Psychology Bulletin, 42, 1045–
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Páez, D., Rimé, B., Basabe, N., Wlodarczyk, A., & Zumeta, L.
(2015).
Psychosocial effects of perceived emotional synchrony in
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Pliskin, R., Nabet, E., Jost, J. T., Tamir, M., & Halperin, E.
(2019). Infinite
hope (or fear): Using emotions to reinforce one’s ideology.
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Porat, R., Halperin, E., Mannheim, I., & Tamir, M. (2016).
Together we
cry: Social motives and preferences for group-based sadness.
Cognition
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Porat, R., Halperin, E., & Tamir, M. (2016). What we want is
what we get:
Group-based emotional preferences and conflict resolution.
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(2019).
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are
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faced with
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Sharvit, K., Brambilla, M., Babush, M., & Colucci, F. P. (2015).
To feel or
not to feel when my group harms others? The regulation of
collective
guilt as motivated reasoning. Personality and Social Psychology
Bulle-
tin, 41, 1223–1235.
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Sharvit, K., & Valetzky, S. (2019). Who wants to be
collectively guilty? A
causal role for motivation in the regulation of collective guilt.
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and Emotion, 43, 103–111. http://dx.doi.org/10.1007/s11031-
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Shields, S. A. (2005). The politics of emotion in everyday life:
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.1037/a0011993
Received December 4, 2018
Revision received May 13, 2019
Accepted May 14, 2019 �
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20 PORAT, TAMIR, AND HALPERIN
http://dx.doi.org/10.1037/0022-3514.93.3.431
http://dx.doi.org/10.1037/0022-3514.93.3.431
http://dx.doi.org/10.1177/1088868315586325
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http://dx.doi.org/10.1037/0022-3514.87.5.649
http://dx.doi.org/10.1037/a0011993
http://dx.doi.org/10.1037/a0011993Group-Based Emotion
Regulation: A Motivated ApproachIntragroup Motives: What I
Want to Be in Relation to the GroupIntergroup Motives: What I
Want My Group’s Relationship With Other Groups to BeMeta
Group Motives: What I Want My Group to
BeConclusionsReferences

PSY 1010, General Psychology 1
Course Learning Outcomes for Unit VII
Upon completion of this unit, students should be able to:
2. Recall research methodologies used in the field of
psychology.
3. Discuss social factors that influence human behaviors.
3.1 Identify social factors that influence people or groups to
conform to the actions of others.
3.2 Indicate how one’s behaviors and motivation are impacted
by the presence of others.
7. Identify biopsychology contributors to perception,
motivation, and consciousness.
7.1 Indicate the structures of the brain that are involved in
emotion and motivation.
8. Examine a scholarly, peer-reviewed psychology journal
article.
8.1 Examine an article’s generalizability to various areas of
psychology.

Course/Unit
Learning Outcomes
Learning Activity
2 Unit VII Article Critique
3.1
Unit Lesson
Chapter 12
Video: The Basics: Under the Influence of Others
Video: The Big Picture: The Social World
Video: Nature vs. Nurture and the Stanford Prison Experiment:
Phil Zimbardo
Unit VII Article Critique
3.2
Unit Lesson
Chapter 12
Video: The Basics: Under the Influence of Others
Video: The Big Picture: The Social World
Video: Nature vs. Nurture and the Stanford Prison Experiment:
Phil Zimbardo
7.1
Unit Lesson
Chapter 9
Video: The Big Picture: Motivation and Emotion
8.1 Unit VII Article Critique

Reading Assignment
Chapter 9: Motivation and Emotion
Chapter 12: Social Psychology
Links to Chapters 9 and 12 of the eTextbook are provided in the
Required Reading area of Unit VII in
Blackboard.
UNIT VII STUDY GUIDE
Motivation, Emotion, and Social Psychology
UNIT x STUDY GUIDE
Title
Additional Reading Assignments:
View the following four videos in MyPsychLab. You can access
the videos by clicking the links provided in the
Unit VII Required Reading area in Blackboard. (You must be
logged into Blackboard in order to access any
MyPsychLab features.)

Others
Zimbardo
Unit Lesson
Motivation and Emotion
Within this unit, Ciccarelli and White (2017) discuss that we
have various sources of motivation in our lives.
One particular universal source of motivation for eating is
related to hunger. However, did you realize that
hunger can be influenced by social and physiological factors?
When it comes to physiological drives, we need
our glucagons and insulin levels to remain a certain level to
effectively regulate the glucose in our blood.
Additionally, the hypothalamus is a large influencer over our
ability to regulate our eating habits.
Unfortunately, some people experience major complications in
these areas and thusly have a difficult time
maintaining a healthy weight. (We will examine this issue in
greater detail later.) Begin to think about how
hormonal imbalances could ultimately lead to obesity.
Speaking of motivation, did you realize that we are influenced
by intrinsic and extrinsic satisfaction in various
areas of our lives? As you explore this unit, you will learn more

about several innate drives that we possess
that influence our motivation in numerous areas including love,
sex, achievement, and eating. Ciccarelli and
White (2017) explain that instincts were initially utilized to
explore motivation, but this approach could only
focus on behaviors instead of truly explaining the reasons
behind them. New approaches eventually emerged
that helped researchers more effectively examine our needs and
drives. As you read this chapter, begin to
think about what drives you. Are you motivated by incentives?
Does it take a great deal to cajole you to do
something? How does Maslow’s hierarchy of needs relate to
your motivation? On the other hand, would you
say that the self-determination theory is a better way to explain
your motivation? In essence, proponents of
this theory purport that we must satisfy certain universal needs
to feel whole. Pay close attention to this
section, and examine your personal thoughts about how much
you seek to master challenges that arise in
your life (competence) while feeling in control (autonomy) and
possessing the ability to have secure
relationships (relatedness).
Do you think society positively shapes
women’s views of their bodies? Does
a woman’s outward size make a
difference in her acceptance by
society?
(Iqoncept, n.d.)

UNIT x STUDY GUIDE
Title
It is no surprise that some people view obesity as a turnoff.
However, did you know that some cultures view
large thighs and full hips as desirable characteristics? A study
was conducted by researchers at the University
of Arizona in which African American and Caucasian teen girls
were asked to describe what their idea was for
the perfect girl. According to Nichter (2001), the Caucasian
teens overwhelmingly described slender, blue-
eyed, tall girls who weighed no more than 110 pounds as their
goal. (Basically, they wanted to look like a real-
life Barbie.) On the other hand, the African American girls
initially chose to describe a girl’s personality, sense
of fashion or style, and her ability to think well as
characteristics that they valued most. When researchers
encouraged the Africa American girls to also include physical
aspects within their descriptions, they
commented that full hips and thighs were culturally acceptable
to them as well as a small waist. Sadly, 90% of
the Caucasian teens were unhappy with their weight. However,
70% of the African American girls were happy
with their looks, particularly their weight (Nichter, 2001). Do
you think the fact that Caucasian teens who were
more focused on their weight and physical appearance could
have experienced more stress as they longed to

have a socially acceptable body? Could you see how this
could lead to eating disorders? On the flip side of this coin,
what do you think about the initial lack of concern about the
weight of the African American girls? Could their relaxed
attitude about weight lead to a higher propensity for
hypertension later in adulthood? How could these sources of
motivation impact their eating habits?
While we are on the topic of eating, did you know that one’s
emotions can directly impact his or her hunger arousal as
well? Think about it. If you are having an extremely stressful
day at work, do you long to go home and eat a huge, veggie
laden salad while drinking a large, cold glass of ice water at
the end of your day? Or, does the local fast food chain that
you pass by on your way home seemingly call your name as
you tiredly travel your normal route? Does that large, juicy
cheeseburger with an order of crispy, slightly-salted fries and
an icy cold beverage seem like a quick fix for the end of your
long, hectic day? (Did reading this description make you either
hungry or repulsed?) We are indeed at times
driven by our emotions. In fact, Ciccarelli and White (2017)
explain that our emotions can be correlated to
numerous physiological reactions that we experience. Research
has discovered that the amygdala is critically
important to help regulate emotions. Further still, our emotions
can manifest to the world via facial
expressions, the manner in which we move our bodies, and
numerous other actions.
Since we are on the topic of emotions, has anyone ever told you
that you look worried? Do you show your
emotions on your face? Let’s explore this area a little more.
When you are experiencing an emotion, the
sympathetic nervous system is aroused. Have you ever noticed

that your breathing quickens, your pupils
possibly dilate, and your heart rate increases as you become
anxious? Ciccarelli and White (2017) explain
that the amygdala is associated with numerous emotions,
including pleasure and fear, while also influencing
the expressions on our face. You will be interested to know that
research conducted by Ekman et al. (1987)
discovered that people display different kinds of smiles for
multiple situations: the embarrassed smile, the
angry smile, and the compliant one, just to name a few. Think
about this for a moment. Have you ever noticed
the various smiles you share? What happens when you attempt
to look happy? Could there be a hidden
message behind your pearly whites? Additional perusal of this
unit will introduce you to early theories of
emotion. Which one resonates most with you?
Body image
(Drawlab19, n.d.)
UNIT x STUDY GUIDE
Title
Social Psychology

As we continue our exploration
of this unit, let’s begin to put the
puzzle pieces together. You do
not have to be a psychology
major to agree that numerous
social factors exist that influence
our behaviors on a daily basis.
In fact, think about your life.
How often do you embrace
social media? Do you check
your accounts daily? Are you
aware of just how many social
media platforms are out there
and being used to connect
people to each other? The
image to the right is just a
sampling of icons for different
social medial platforms. How
many do you use? Have you
even heard of any beyond
Facebook? Are you compelled
to like your friends’ pictures on
Facebook after you hear about their vacation? Further still, have
you heard teens and young adults talk about
Snapchat? Why do we feel pressure from various groups to
conform and join in the various social media
outlets to connect and share with people we know and even
people we have never met before?
Ciccarelli and White (2017) explain that social psychology
seeks to examine group influence on an
individual’s behaviors. On the other hand, cultural
psychologists want to ascertain the impact that those
cultural influences can have on our actions. As you progress
throughout this unit, pay close attention to the

information for conformity. Can you recall a period in your life
in which you desperately wanted to fit in so you
changed your behaviors to mirror that of the people around you?
Do you think conformity is especially more
prevalent with women? Why do you have the desire to be liked
by others? (Do you really need 100 people to
like your picture in Facebook to feel important?) If your friend
has more followers on Instagram than you, then
do you feel less accepted by others?
Most people will readily agree that their behaviors, albeit
minimally at times, are impacted by others. For
example, in groupthink, people tend to embrace the cohesion of
the group, even if the group’s opinions differ
from that of their own. This phenomenon can also escalate to
the point of group polarization. (This is when an
individual is willing to act out on extreme or risky behaviors
due to the influence of the group.) A group’s
influence on an individual’s performance can be so negative
that it becomes a social impairment. However,
not all group influences are viewed negatively. Sometimes
groups have a positive impact on an individual’s
performance, and this is known as social facilitation. Regardless
if the group has a negative or positive
influence in the individual’s life, he or she might experience
deindividuation; this takes place when a person
loses his or her sense of identity due to group associations.
Social media icons
(Rompikapo, n.d.)

UNIT x STUDY GUIDE
Title
Were you an obedient child? Did you behave as you were told
with no
questions asked? Did you often act-out?
Even as adults, we have numerous factors that influence why we
behave in
a certain manner. Stanley Milgram wanted to know why people
obeyed
authority figures so easily, even when the directives violated
the person’s
ethical beliefs. As you read this section, you will see that his
controversial
study revealed some alarming findings. What are your thoughts?
Would
you be willing to hurt someone if your superior instructed you
to do such?
What shocking truths might you discover about yourself?
As you conclude your readings for this unit, challenge yourself
to embrace
personal areas of growth. It is obvious that we all possess
various attitudes
in relation to numerous aspects of our lives. However, have you
ever
stopped to examine how yours were formed? Did you realize

that you can
formulate biases based on situations from your past and those
who have
influence in your life as well? If you have a negative attitude
towards
certain cultural groups, could you seek to avoid categorically
judging others based on your initial impressions?
Would you be bold enough to embrace self-reflection to
determine if you hold any prejudiced beliefs or
attitudes towards others while having the tendency to
discriminate? Are you willing to learn new ways in which
you could be inspired to help others?
References
Ciccarelli, S. K., & White, J. N. (2017). Psychology (5th ed.).
New York, NY: Pearson.
Drawlab19. (n.d.). Girl, mirror, body, distorted, weight concept,
ID 119507385 [Image]. Retrieved from
https://www.dreamstime.com/girl-mirror-body-distorted-weight-
concept-hand-drawn-unhappy-
teenage-girl-looks-mirror-distorted-body-image-concept-sketch-
image119507385
Ekman, P., Friesen, W. V., O’Sullivan, M., Chan, A.,
Diacoyanni-Tarlatzis, I., Heider, K., . . . Tzavaras, A.
(1987). Universals and cultural differences in the judgments of
facial expressions of emotion. Journal
of Personality and Social Psychology, 53(4), 712–717.

Iqoncept. (n.d.). What do you think survey poll question,
20602105 [Illustration]. Retrieved from
https://www.dreamstime.com/royalty-free-stock-photo-what-do-
you-think-survey-poll-question-
image20602105
Nichter, M. (2001). Fat talk: What girls and their parents say
about dieting. Cambridge, MA: Harvard
University.
Primovich-hrabar, O. (n.d.). Angry kid and sad mother, ID
114056451 [Illustration]. Retrieved from
https://www.dreamstime.com/angry-kid-sad-mother-angry-little-
boy-who-screams-stomp-legs-his-sad-
mother-image114056461
Rompikapo. (n.d.). Social media icons, ID 58260552 [Image].
Retrieved from
https://www.dreamstime.com/editorial-photography-social-
medis-icons-set-different-media-white-
background-image58260552
(Primovich-hrabar, n.d.)

UNIT x STUDY GUIDE
Title
Suggested Reading
In the Suggested Reading area of Unit VII in Blackboard, you
will find MyPsychLab links to the resources
below. The resources are interactive and allow you to further
explore concepts in this unit.
Food and Junk Food
Emotions?
For a review of this unit’s concepts, you are encouraged to view
the PowerPoint presentations for the chapter
readings by clicking on the links provided below.
Click here for the Chapter 9 PowerPoint Presentation. Click
here for a PDF of the presentation.
Click here for the Chapter 12 PowerPoint Presentation. Click
here for a PDF of the presentation.

Learning Activities (Nongraded)
Nongraded Learning Activities are provided to aid students in
their course of study. You do not have to submit
them. If you have questions, contact your instructor for further
guidance and information.
In the Nongraded Learning Activities area of Unit VII in
Blackboard, you will find MyPsychLab links to access
the following resources. They can help you to assess your
understanding of this unit’s concepts.
Yourself section. You can take the quiz to
assess your understanding of the chapter material.
–503 of the
eTextbook, there is a Test Yourself section. You
can take the quiz to assess your understanding of the chapter
material.
https://online.columbiasouthern.edu/bbcswebdav/xid-
111628496_1
111628495_1
111628498_1
111628497_1

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