University of Southern California Dissertations and Theses

The cost of a poker-face: consequences of self-regulation on emotion recognition and interpersonal perception

(USC Thesis Other)

The cost of a poker-face: consequences of self-regulation on emotion recognition and interpersonal perception

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content

THE COST OF A POKER-FACE: CONSEQUENCES OF SELF-REGULATION ON
EMOTION RECOGNITION AND INTERPERSONAL PERCEPTION

by

Adam Wood

___________________________________________________________________________

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BUSINESS ADMINISTRATION)

Copyright 2013 Adam Wood
ii

ACKNOWLEDGEMENTS

Thank you to my family, who kept me in touch with what is truly important;

to my friends, who kept me sane; and,

to my committee, who kept me focused.

You already know who you are.
iii

TABLE OF CONTENTS

LIST OF TABLES

v
LIST OF FIGURES

vi
ABSTRACT

vii
CHAPTER 1: INTRODUCTION 1
Decision Making and Emotions 3
Emotion Recognition 5
Emotion Regulation 6
Consequences of Emotion Regulation and Emotion Experience 7
Overview of Studies

11
CHAPTER 2: STUDY 1
Effects of Emotion Regulation and Experienced Emotion on Emotion
Recognition Accuracy
12
Method 12
Participants and Experimental Design
Procedure
Dependent Variables
Results 16
Discussion

27
CHAPTER 3: STUDY 2
Effects of Emotion Regulation and Experienced Emotion on Emotion
Recognition Accuracy Extended
29
Method 30
Participants and Experimental Design
Procedure
Dependent Variables
Results 31
Discussion

33
CHAPTER 4: STUDY 3
Effects of Experienced Emotion and Emotion Regulation on Non-Verbal
Perception
35
Method 35
Participants and Experimental Design
Procedure
Dependent Variables
Results 37
Discussion

38

iv

CHAPTER 5: GENERAL DISCUSSION 41
Theoretical Implications 42
Practical Implications 43
Limitations and Future Directions 44

CHAPTER 6: CONCLUSION

46
REFERENCES

48
APPENDICES
Appendix A: Emotion Regulation Manipulations 58
Appendix B: Profile of Non-Verbal Sensitivity Scenes for Video-40 59

v

LIST OF TABLES

Table 1. Number of Observations, Means, and Standard Deviations for Self-
Report Emotion Induction in Study 1

17
Table 2. Number of Observations, Means, and Standard Deviations for Observed
Expressivity in Study 1

21
Table 3. Number of Observations, Means, and Standard Deviations for Observed
Eye Blinks and Facial Movement in Study 1

21
Table 4. Rank Ordering of Observed Emotion by Emotion Regulation Method in
Study 1

22
Table 5. Number of Observations, Means, and Standard Deviations for Accuracy
Rates in Study 1

24
Table 6. Number of Observations, Means, and Standard Deviations for Accuracy
Rates in Study 2

33

vi

LIST OF FIGURES

Figure 1. Conceptual Causal Model Diagram

8
Figure 2. Emotion Recognition Accuracy Rates (All Pictures) by Emotion
Induction and Emotion Regulation Conditions, including No Regulation
in Study 1

26
Figure 3. Emotion Recognition Accuracy Rates by Self-Regulation Method in
Study 2

32
Figure 4. Non-Verbal Perception Accuracy Rates in Study 3

38

vii

ABSTRACT
This paper examines how emotion suppression and emotion reappraisal may
differentially affect people’s ability to recognize emotions in others. I hypothesized that the
cost of suppressing one's own emotion, compared to reappraising one's own emotion, results
in decreased rates of emotion recognition in others. Study 1 supported the hypothesis for the
experienced emotions of anger, happiness, and sadness in that emotion reappraisal resulted in
higher rates of recognition accuracy than emotion suppression. This study also demonstrated
that people who are angry or happy are better able to detect others’ emotions than are people
who are fearful or sad. Study 2 replicated and extended some of these findings using a
separate static photographic set. Finally, Study 3 demonstrated that the regulation strategies
of suppression and reappraisal both contributed to decreased interpersonal sensitivity as
measured through a test of non-verbal perception. Overall, these results provide evidence
that suppressing one’s emotions impairs emotion recognition more severely than does
reappraising one’s emotion state. The findings from the current investigation extend research
in emotional and social intelligence by identifying potential conditions under which
emotional cues are likely to be more accurately perceived and interpreted.

CHAPTER 1: INTRODUCTION
Poker players often try to conceal their true emotions in order to prevent other players
from using their emotions against them. This display of such “poker faces” is not limited to
the game of cards. Professionals across a range of industries are often called upon to regulate
their emotions in the workplace so that others perceive them in a specific light regardless of
their actual emotional states (Grove & Fisk, 1989; Ashforth & Humphrey, 1993). Many
organizational settings prescribe that employees be viewed by others as consistently pleasant,
while controlling, suppressing, or exaggerating their real feelings (Hochschild, 1983/2003;
Brotheridge & Grandey, 2002). For example, although a store owner may be internally
enraged by a customer demand to return an obviously used or abused item, he is likely to
recognize and suppress his own outrage while forcing a smile.
Although concealing emotions may be performed with the best intentions, it is not
without potentially unforeseen costs. Extensive work on emotional labor has shown that
obscuring or feigning outward emotion displays often yields negative consequences. This
wide range of consequences includes costs to personal health, such as burnout and physical
ailments, as well as to interpersonal functioning (see Bono & Vey, 2005). In addition to
increased stress (Grandey, Fisk, & Steiner, 2005), asking employees to exhibit “fake”
emotions that do not match their true feelings can paradoxically result in increased negative
states (Brotheridge & Grandey, 2002; Scott & Barnes, 2011). Such forced emotion may lead
to a lack of social and information awareness, potentially degrading subtle communication
cues between people (Gross & John, 2003). However, no research has to-date examined
whether these consequences include a diminished ability to recognize emotions in others.
2

Just as the capacity to regulate one’s emotions can be useful in social situations, so
too is the ability to accurately perceive others’ emotions. Emotion recognition, generally
defined as the ability to “read” another’s emotional state, can provide key advantages in
knowing how to respond to another, what and how much to offer in a negotiation, or how to
better manage others (Salovey & Mayer, 1990; Thompson, Medvec, Seiden, Kopelman,
2004; Elfenbein, Foo, White, Tan, & Aik, 2007; Van Dijk, Van Kleef, Steinel, & Van Beest,
2008; Sanchez-Burks & Huy, 2009). As one’s emotions reveal both judgment about an
object or event and potential intentionality about future action (Elfenbein, 2008), recognizing
and remaining aware of the emotional state of one’s interaction partners can facilitate a
transaction, foster a deal, or transform initially unhappy customers into repeat clients.
In an ideal world, especially gifted or trained managers and negotiators could regulate
their own emotions while simultaneously and correctly evaluating the emotions of the other
person. Those adroit at both of these capacities could simultaneously portray the emotion
that is most beneficial to the overall outcome and accurately discern the affective states of
others. However, concurrently regulating one’s own emotions and accurately recognizing
others’ emotions may be more difficult than it sounds, as doing so may yield unintended and
deleterious consequences. Because each process requires high cognitive effort, engaging in
one of these processes may leave little cognitive effort available for the other process. As a
further complication, it is likely that the effects of emotion regulation and emotion
recognition differ depending on the specific emotions being experienced and displayed. The
present research seeks to address a lacuna in our understanding of emotions and self-
regulation strategies by examining how emotion regulation and emotion recognition
processes interact to shape the effectiveness of individual perception and social interactions.
3

Decision Making and Emotions
For many years, the role of emotion in the decision-making process was ignored or
assumed to have little role (Loewenstein & Lerner, 2003). However, work in this area has
begun to investigate how the experience of emotion impacts the specific choices and
judgments we make. Research has shown that emotion can influence decision making both
positively and negatively, resulting in higher or poorer quality decisions. For instance,
positive mood inductions have been shown to lead doctors to arrive at correct diagnoses more
quickly (Estrada, Isen, & Young, 1997), although other research has demonstrated that happy
affect increased fundamental attribution error rates (Forgas, 1998a). While research in
emotions continues to investigate specific outcomes, models that incorporate emotions into
the decision-making process tend to result in higher explanatory power (Mellers, Schwartz,
Ho, & Ritov, 1997; Lopes & Oden, 1999).
Over the years, much of this research has divided and categorized emotions in
different ways, such as by positive versus negative valence, by motivation in approach versus
avoidance orientation, and by specific appraisal theme. Early research adopted a valence-
based approach, in which outcomes were primarily ascribed to whether the emotion
experience was either negative or positive (see Han, Lerner, & Keltner, 2007). Meanwhile,
other research produced an approach–avoidance distinction, which focused on the tendency
to move toward or away from a stimulus (Arnold, 1960; Frijda, 1986; Lang, 1984; Lazarus,
1991). This approach–avoidance division has a long history in the study of human behavior
likely because it is a fundamental element in the evolutionary adaptation of all organisms
(Tooby & Cosmides, 1990).
4

More recently, researchers have begun to unpack the impact of specific emotions on
choice. Using an Appraisal Tendency Framework (ATF), they have found that even
similarly valenced emotions, such as anger and fear, often result in opposing choices, while
differently valenced emotions may produce more similar decisions (Lerner & Keltner, 2000,
2001; Lerner & Tiedens, 2006). In this sense, the fundamental appraisal theme, rather than
simply valence, plays a strong role in influencing one’s decision. The ATF uses the six
cognitive dimensions identified by Smith and Ellsworth (1985) to distinguish these appraisal
patterns, which form different emotions including certainty, pleasantness, attentional activity,
control, anticipated effort, and responsibility.
While some work in this area has focused on how decision-makers’ emotions affect
their choices in social contexts (Forgas, 1998b; Van Kleef, De Dreu, & Manstead, 2004;
Kopelman, Rosette, & Thompson, 2006; Van Kleef & Coté, 2007), there is a lack of inquiry
into how their emotions and attempts to control them influence their ability to detect other
people’s emotions. This approach essentially ignores any contribution of the observer’s
regulatory process, as well as the other person’s emotion state. However, reactions to others’
emotions have a direct influence on people’s behavior. This has been demonstrated through
research showing other’s portrayal of emotions, such as smiling, leads to increased tipping
levels and customer satisfaction (Tidd & Lockard, 1978; Brown & Sulzer-Azaroff, 1994). Of
course, the emotional cues or messages sent by a person or group are ineffective in the social
interaction unless they are received and correctly interpreted by another. When emotional
expressions are misunderstood, or simply never recognized as meaningful, the exchange is
likely to be less productive than it would be otherwise.
5

In this way, both the portrayal of an emotion state by one person and the accurate
recognition of that emotion by another facilitate the communicative process. For instance, it
has been demonstrated that those who naturally display higher levels of emotion expressivity
also tend to be more cooperative during a negotiation (Schuga, Matsumoto, Horita,
Yamagishi, & Bonnet, 2010). Further, Elfenbein et al. (2007) found that higher levels of
recognition of facial expressions were predictive of better outcomes in a buyer-seller
negotiation task both for creating and claiming value. Such outcomes add to the argument
for the contributory value of emotion recognition in negotiation, as well as in other dyadic
and organizational interactions. Accordingly, this research involves an examination of the
conditions that impact the expedient and accurate interpretation of emotions.
Emotion Recognition
Human beings are born with some natural capability to recognize facial activity, pitch
of speech, and muscle movements associated with emotions in others. This capacity forms a
crucial element of interpersonal sensitivity. Interpreting another’s emotional state has been
an important element of the emotional intelligence model (see Bar-On, 1997; Gowing, 2001;
Mayer, Salovey, & Caruso, 2002; Wong, Law, & Wong, 2004). Fundamentally, emotional
recognition is a component of affective social competence (Matthews, Zeidner, & Roberts,
2002) that falls under the larger umbrella construct of emotional intelligence (Salovey &
Mayer, 1990). Emotional recognition ability (ERA) captures how capably individuals
recognize the emotional states of other people. It is considered one of the four key elements
of emotion intelligence (Mayer, Salovey, Caruso, & Sitarenios, 2001).
Recently, there has been increased interest in the processes of emotion recognition,
even spawning nascent development of human affect sensing technology (Zeng, Pantic,
6

Roisman, & Huang, 2009). Predicting or creating the conditions under which the recognition
of others’ emotions are likely to be impaired or enhanced is a potential path to more
successful social interactions. However, most of the existing research in this area assumes
that emotion recognition is a fixed measure without considering the variables and
circumstances that may impact the rate of recognition. One recent exception reveals that
paralyzing expressive facial muscles using neurotoxins interferes with process of emotion
perception (Neal & Chartrand, in press), lending additional evidence to the notion that
suppression may indeed be deleterious to emotion recognition accuracy.
Emotion Regulation
Emotion regulation describes the processes through which people influence the
emotions that they are experiencing near to, or at, the time they are experiencing them, and
the manner in which the emotions are experienced and expressed (Gross, 1998a). Regulated
emotion denotes the changes that take place in the activated emotion, which may include
variations in valence, time, and intensity (Thompson, 1994). The emotional labor regulation
framework proposed by Hochschild (1979, 1983) distinguishes between “surface acting,”
whereby individuals change only their outward emotion expressions without any
corresponding internal changes, and “deep acting” in which the emotions that are displayed
actually reflect what the person is truly feeling. Similarly, the process model of emotion
regulation (described below) suggested by Gross (2001) stipulates two primary ways that we
regulate emotions.
The ego-depletion model of emotion regulation focuses on self-control strength to
posit that regulating affect necessitates consumption of a finite, internal resource, which is
expended and eventually exhausted (Muraven & Baumeister, 2000). Similar to a muscle,
7

enabling emotion self-control uses resources and weakens over time. Ensuing efforts at
emotion regulation are thus more likely to attenuate and eventually fail. However, the
question remains whether this depletion also overlaps or induces similar weakening of other
systems, such as emotion awareness or social perception.
While our behaviors may be driven by our emotions, we are not blindly forced to
follow their consonant responses. Instead, we have the ability to influence and control our
own emotions, to varying extents. We can exert this regulation by means of constraining,
repressing, enhancing, and otherwise managing our emotions (Gross, 2002). So, sad news
doesn’t necessarily result in tears and, conversely, a smile on our face doesn’t necessarily
mean we are happy. Two methods to achieve this self-regulation over our emotions are
either through suppression or reappraisal (Gross, 1998b).
Suppression, usually involved later in the regulatory process, simply changes the
external expression of the emotion experience. In this way, suppression is unlikely to alter
the experience of the emotion itself and it does not typically impair the memory of that
experience (Richards & Gross, 2000). Reappraisal, which tends to occur earlier in the
regulatory process, involves reframing how a situation is viewed, such that it changes one’s
emotional reaction to it.
Consequences of Emotion Regulation and Emotion Experience
Suppression as a method of emotion regulation necessitates both self-monitoring of
one’s external expression and constant self-adjustment to make the appropriate changes.
These consistent observations and correctives deplete cognitive resources, potentially leaving
fewer cognitive resources available for other tasks, such as recognizing emotions in others.
Reappraisal, on the other hand, does not tend to necessitate this constant vigilance and
8

corrective actions. Thus, the cognitive cost is lower and more cognitive resources are
available for people to notice and process others’ emotions (Richards & Gross, 2000). Figure
1 (below) displays the proposed conceptual causal model.

Figure 1. Conceptual Causal Model Diagram

In the current investigation, I strive to clarify the impact of experiencing and
regulating intrapersonal emotion on interpersonal perception. Specifically, I aim to examine
how different methods of self-regulation and detection of emotions in others interact. Using
the ego-depletion model (Baumeister, Bratslavsky, Muraven, & Tice, 1998), I examine how
the use of emotion suppression and emotion reappraisal affect people’s ability to recognize
emotions in others. The results of this research will add to the literature on emotion
expression, recognition, and regulation.
There are numerous benefits to emotion regulation in a myriad of circumstances.
Emotional regulation influences the effectiveness of organizational leadership, job
performance and satisfaction, as well as group processes and individual health (Cropanzano,
Weiss, Suckow, & Grandey, 2000; John & Gross, 2004). In addition, the processes involved
in emotion regulation have been shown to help leaders make decisions (George, 2000).

Emotion
Emotion
Regulation
Approach-
Avoidance
Emotion
Recognition
9

However, many of the benefits derived from emotion regulation point to the effortful
processes necessary to invoke control over one’s emotion.
While acknowledging the advantages to regulating one’s emotions, there are likely
also costs associated with the labor involved in both recognizing one’s own emotions and
applying the necessary intrapersonal interventions required to contradict one’s natural
responses (see Richards & Gross, 2000). Therefore, I predict that these efforts are likely to
decrease emotion recognition through the proposed mechanisms of the ego-depletion model
(Baumeister et al., 1998) compared to other self-regulatory methods, including both emotion
reappraisal and no regulation.
Conversely, emotion regulation through reappraisal does not necessarily deplete one’s
internal resources. Unlike suppressing an emotion, which can necessitate a more effortful
process aimed at changing the external expression of that emotion, reappraisal can be seen as
changing the emotion experience itself from an internal perspective (Gross & John, 2003).
Therefore, I predict that self-regulation using suppression is likely to inhibit emotion
recognition relative to emotion reappraisal or no regulation.

H1. Emotion recognition accuracy is lower under conditions of emotion suppression
than emotion reappraisal.

H2. Emotion recognition accuracy is lower under conditions of emotion suppression
than no regulation.

At a fundamental level, people’s behaviors are also driven by one of two opposing
action tendencies, namely, approach or avoidance, which similarly influences our judgments
and actions (Miller, 1944; Elliot, 1999). On this basic level, such a differentiation can be
viewed as a key motivator that directs and energizes behavior (Elliot, 2008). For example,
10

we are drawn toward events that bring us joy or happiness, such as playing with a puppy.
Conversely, we are repelled from situations that make us fearful, such as suddenly
discovering a snake in our path. Each of the basic emotions falls along this dimension and
gives us an indication of the likely resultant behavior.
In this way, our emotions direct our attention toward or away from objects of interest.
So, in an approach situation our attention is focused toward the object, while in an avoidance
situation our attention is diverted away from the object and re-focused in another direction,
such as toward an escape. This directional attention is likely to impact our ability to
recognize detailed facets of an object. Correspondingly, if the object is another person, an
approach emotion would focus interest and lead to higher levels of accurately identifying
others’ emotional states. Conversely, an avoidance emotion would re-direct interest away
from that person and lead to lower levels of accurately identifying their state. Using the
approach-avoidance orientation framework, I predict that the emotions which induce an
approach tendency will be more likely to direct one’s focus onto the external stimulus, while
those emotions which include an avoidance tendency will more likely to direct attention
away from an external stimulus. This leads to the following hypothesis:
H3. People recognize emotions more accurately when experiencing approach
emotions (e.g., happiness and anger) than when experiencing avoidance
emotions (e.g., fear and sadness).

11

Overview of Studies
The goal of this research is to examine the experience of emotions and emotion
regulation on emotion recognition and interpersonal perception. These studies seek to
demonstrate potential differences between the strategies of suppression versus reappraisal in
their effect on the perception of others. Across the three studies, emotion experiences were
achieved by having participants view movie segments that have been shown to reliably evoke
specific emotions. For each self-regulatory method, some participants were asked to
suppress their emotion experience, others were asked to reappraise their emotion experience,
and others were not given any such instructions.
Study 1 examines the baseline rates for emotion recognition accuracy of participants
who are experiencing the approach emotions of anger and happiness as compared to the
avoidance emotions of fear and sadness. It also tests whether those who suppress their
experience of anger, happiness, fearfulness, or sadness differ in their ability to accurately
recognize emotion in others compared to those who reappraise their emotion experience.
Further, it introduces these self-regulatory strategies to demonstrate the effect of suppression
versus reappraisal on each experience of emotion. Tests of emotion recognition are
measured using a well-researched and validated static set of photographs. Study 2 follows a
similar design in order to replicate and extend the findings from the first study by using a
different set of static photographs. Finally, Study 3 examines whether regulatory approaches
contribute to decreased general interpersonal sensitivity as measured through a test of non-
verbal perception. Overall, these results examine how suppression and reappraisal affect
emotion recognition.
12

CHAPTER 2: STUDY 1
Effects of Emotion Regulation and Experienced Emotion
on Emotion Recognition Accuracy

Study1 examines both the effect of emotion regulation and experienced emotion on
the ability to recognize emotions in others. It explores whether the self-control depletion
associated with regulating one’s own experienced emotion affects one’s ability to recognize
emotions accurately in others. In this study, I hypothesize that the suppression mode of
emotion self-regulation decreases emotion recognition accuracy, while the reappraisal mode
of emotion self-regulation will not interfere with emotion recognition. Because emotion
regulation is an effortful process, it is likely to decrease the cognitive resources that would
otherwise be available to the process of emotion recognition. Reappraisal, on the other hand,
should not lead to a similar depletion of cognitive resources allowing for the increased
emotion recognition.
Further, following from the approach-avoidance orientation framework, I predict that
the emotions that induce an approach tendency are more likely to direct one’s focus onto the
external stimulus, while those emotions which include an avoidance tendency are more likely
to direct attention away from the external stimulus. Thus, the emotion experience of anger or
happiness serves to orient focus onto the presented stimuli, leading to higher rates of emotion
recognition accuracy. Conversely, the emotion experiences of fear or sadness serve to orient
focus away from the presented stimuli, resulting in lower rates of emotion recognition
accuracy.
Method
Participants and Experimental Design. Three hundred and fifty-two students (59%
male; Mage = 21.0 years, SD = 2.7 years) participated in this study at a large, private, West
13

Coast university for partial fulfillment of their course requirement. The design of this study
was 3 (emotion regulation: suppression, reappraisal, no regulation) x 5 (emotion induction:
anger, happiness, fear, sadness, neutral). A manipulated emotion induction technique was
used to evoke a specific, discrete emotion in the participant, after which he or she completed
the Diagnostic Analysis of Nonverbal Accuracy (DANVA-2; Pitterman & Nowicki, 2004)
test of emotion recognition designed to measure his or her ability to distinguish specific,
discrete emotions in others.
Procedure and Materials. This study used a computer-based experimental lab
design. All participants sat in front of a separate, dedicated computer and monitor where
they first watched a movie while wearing headphones to hear the corresponding movie audio
track. Using film clips that past research has shown reliably elicit specific emotion states,
participants first viewed his or her respective single movie that had been edited to detailed
timed segments as described in previous work (see Gross & Levenson, 1995; Hewig,
Hagemann, Seifert, Gollwitzer, Naumann, & Bartussek, 2005). These included the movies
“Cry Freedom” (2:38 minutes) / “My Bodyguard” (4:06 minutes) in the anger condition,
“When Harry met Sally” (2:39 minutes) / “Bill Cosby, Himself” (2:01 minutes) in the
happiness condition, “The Shining” (1:22 minutes) / “Silence of the Lambs” (3:29 minutes)
in the fear condition, “The Lion King” (2:12 minutes) / “The Champ” (2:51 minutes) in the
sadness condition, and “Alaska’s Denali” (5:02 minutes) / “Alaska’s Wild Denali” (5:00
minutes) in the neutral condition. The neutral condition film was used as a control condition
as it has been shown not to elicit any specific emotion, but participants could still be asked to
self-regulate. The scenes and timings used for each movie clip were the same as in previous
research.
14

For emotion regulation, the study included written instructions for participants to
either suppress or reappraise their emotional reaction (adapted from Richards & Gross,
2000). Two conditions of emotion regulation were used involving either suppression or
reappraisal instructions. This was achieved by informing participants immediately prior to
viewing the videos that a goal of the experiment is to see what happens when they change
either their expressions or their viewpoint.

Participants in the suppression condition read the following instructions:
SPECIAL INSTRUCTIONS: PLEASE FOLLOW CAREFULLY!
We will show you the video in just a moment. Please watch it carefully and
listen to the accompanying sound through the headphones. In addition, we
would like to see how well you can control your facial expressions.
Therefore, it is very important to us that you try your best to adopt a neutral
facial expression as you watch the film. To do this, we would like for you to
keep your facial muscles from moving. In other words, as you watch the
video, try to keep a straight face by keeping the muscles around your neck,
your chin, your lips, your cheeks, your eyes, and your forehead very still. So,
watch the film clip carefully, but please try to keep your facial muscles still
so that you don't make any expressions at all.

Participants in the reappraisal condition read the following instructions:
SPECIAL INSTRUCTIONS: PLEASE FOLLOW CAREFULLY!
We will show you a movie clip in just a moment. Please view the video
carefully and listen to the accompanying sound through the headphones. In
addition, we would like to see how well you can control the way you view
things. Therefore, it is very important to us that you try your best to adopt a
neutral attitude as you watch the video. To do this, we would like for you to
view this film clip with the detached interest of a movie critic (e.g.,
observing the setting, the costumes and clothing, the lighting, and the overall
cinematography in general). In other words, as you watch the video, try to
think about it objectively and analytically rather than as personally, or in any
way, emotionally relevant to you. So, watch the film clip carefully, but please
try to think about what you are seeing in such a way that you don't feel
anything at all.

15

Immediately following viewing of the film clip, participants were administered the
DANVA-2 assessment and asked to rate the emotions of 24 faces displayed in the measure.
While participants could take as much time as they needed for their responses, the faces are
only displayed on the screen for four seconds, which is the standard time for the DANVA-2.
Participants then completed an online questionnaire developed using Qualtrics, which
assessed their emotional state using the Positive-Negative Affect Scale (PANAS; Watson &
Tellegen, 1985; Watson, Clark, & Tellegen, 1988), as well as their current preferences for
emotion regulation using the Emotion Regulation Questionnaire (ERQ; Gross & John,
2001/2003).
Dependent Measures
Emotion Recognition Accuracy. The primary dependent variable of interest in this
study was the accuracy rate of emotion recognition as assessed by correctly identifying facial
expressions contained in the DANVA-2. This set of 24 photographs includes posed facial
expressions by both men and women in a range of emotional states, which differ in intensity.
This set of static emotion displays provides a time-limited, reliable test of ability to
accurately identify discrete emotions in others, including the emotions of anger, happiness,
fear, and sadness. The DANVA-2 measure has been shown to have both high construct
validity and reliability ratings (Nowicki & Carton, 1993). Performance on the measure has
also been shown to have a high correlation with overall social competence, psychosocial
adjustment, academic achievement, locus of control, and socio-metric status (Nowicki &
Duke, 2001).
Results from each participant’s DANVA-2 responses were compared to the emotional
expression designated in each picture. Based on the number of correct answers, a combined
16

accuracy score (out of 100%) was calculated for each participant
1
. This ability to recognize
the emotions of others was calculated as a ratio of the correctly identified emotion to actual
emotion expressed out of 24 static photographs of the DANVA-2. These picture accuracy
scores were then used as the primary dependent variable.
Results
Emotion Induction Manipulation Check (Self-Report). Following completion of the
study, participants reported their emotional states on five discrete emotions corresponding to
the five experimental conditions ranging from (1) Not at All to (5) Extremely. Results from
this manipulation check show that the targeted emotion for each condition tended to be
highest in the respective emotion induction condition, except for the sadness condition, in
which it was the second highest.
Participants in the anger induction condition reported feeling angrier (M = 1.86, SD =
.89) than participants in all other conditions (M = 1.35, SD = .65), F(1, 115) = 9.76, p < .01.
Participants in the fearful induction condition reported feeling more fearful (M = 1.74, SD =
.96) than participants in all other conditions (M = 1.34, SD = .67), F(1, 115) = 5.64, p = .02.
Participant self-ratings of happiness and sadness were also higher than in their respective
conditions, but were not significantly different. Potential reasons for these differences are
discussed below. See Table 1 for number of observations, means, and standard deviations.

1
One common way to account for biased hit rates that can occur during these types of categorical judgment
studies is to use Wagner’s (1993) formula. This may happen because percentage scores could include
participant bias (Rosenthal, 1987; Banse & Scherer, 1996). Wagner’s unbiased hit rate uses the product of the
relative accuracy multiplied by one minus the number of false alarms and then transforms using the arcsine
function to normalize, resulting in values that range from zero (for no correct responses) to 1.57 (for a perfect
accuracy rate with no false hits) (Elfenbein et al., 2007). While all judgment accuracy statistics in the following
studies were analyzed using this formula, the results were not significantly different from those presented, so
the raw scores were kept instead.

Table 1

Number of Observations, Means, and Standard Deviations for Self-Report Emotion Induction in Study 1

Reported Emotion Angry Happy Fearful Sad
Emotion Induction / Regulation N M SD N M SD N M SD N M SD

Anger
No Regulation 22 1.86 0.89

22 2.55 0.86

22 1.59 0.85

22 2.45 1.10
Suppression 23 1.91 1.35

23 3.26 0.86

23 1.61 0.99

23 2.09 1.16

Reappraisal 22 1.64 0.95

22 2.77 1.11

22 1.64 0.90

22 1.91 1.11

Happiness
No Regulation 23 1.22 0.42

23 3.09 0.85

23 1.26 0.54

23 1.52 0.79

Suppression 25 1.32 0.75

25 3.20 0.87

25 1.40 0.96

25 1.56 0.87

Reappraisal 24 1.29 0.55

24 3.00 0.83

24 1.21 0.66

24 1.54 0.78

Fear
No Regulation 23 1.52 0.85 23 2.96 0.93 23 1.74 0.96 23 1.83 0.98

Suppression 23 1.52 0.79

23 2.91 0.95

23 1.35 0.65

23 1.65 0.83

Reappraisal 24 1.42 0.78

24 2.88 0.80

24 1.92 1.18

24 2.21 1.10

Sadness
No Regulation 24 1.42 0.58 24 2.88 0.90 24 1.42 0.78 24 1.88 1.03

Suppression 22 1.41 0.73

22 3.05 0.72

22 1.23 0.53

22 1.82 0.96

Reappraisal 22 1.23 0.53

22 2.91 0.97

22 1.27 0.55

22 1.68 1.09

Neutral
No Regulation 25 1.24 0.66

25 2.92 0.86

25 1.08 0.40

25 1.60 0.71

Suppression 24 1.67 1.09

24 2.96 0.86

24 1.33 0.92

24 1.63 0.82

Reappraisal 26 1.58 1.03

26 3.04 1.00

26 1.19 0.63

26 1.73 0.96

Total
No Regulation 117 1.44 0.72 117 2.88 0.88 117 1.41 0.76 117 1.85 0.97

Suppression 117 1.56 0.98

117 3.08 0.85

117 1.38 0.83

117 1.74 0.94

Reappraisal 118 1.43 0.80 118 2.92 0.94 118 1.44 0.85 118 1.81 1.02

Emotion Regulation Manipulation Check (Self-Report). In the post-study
questionnaire, participants were asked whether they received any instructions to follow while
viewing the film. They were given the choice between three possibilities, which summarized
all experimental conditions. The suppression condition described whether their instructions
had asked them to “control your facial expressions; adopt a neutral facial expression as you
watch the film; keep your facial muscles from moving; and, try to keep your facial muscles
still so that you don't make any expressions at all.” The reappraisal condition described
whether their instructions had asked them to “try to adopt a neutral attitude as you watch the
video; view the film clip with the detached interest of a movie critic; try to think about the
movie objectively and analytically; and, think about what you are seeing in such a way that
you don't feel anything at all.” Finally, participants could choose to answer that they hadn’t
received any instructions prior to the start of the film. While 93.4% of participants passed
this manipulation check correctly, those who failed this check were distributed across all
conditions and excluded from analysis.
Emotion Induction and Regulation Manipulation Checks (Observer-Report)
2
. By
recording the participants during the study, it was possible to track their facial expressions.
Comparisons between participants’ self-reports and observer-reports indicate that students in
the suppression condition effectively masked their emotions, while those in the reappraisal
condition did not conceal their expressivity. Two female raters, external and hypothesis-
blind to the study, were asked to independently score observed participant emotions,
expressions, behaviors (following procedures outlined in Gross and Levenson, 1993) that
occurred during the final 1:22 minutes of the emotion induction and emotion regulation

2
Extensive self-report and observer-report manipulation checks were conducted and analyzed in this study as
all emotion inductions remained the same throughout the three studies and occurred prior to any subsequent
experimental manipulations. In this way, verification of emotion inductions was comprehensive of all studies.
19

manipulations. This time segment was selected because it was the length of time of the
shortest emotion induction film clip. All video segments watched by the raters were cut to
equal time lengths. In this way, raters would be unbiased by the observation time, as well as
unable to detect any condition patterns due to differing time segments.
To verify that participants actually suppressed their own expressions, ratings for
participant behaviors were scored for the same specific behaviors used in Gross and
Levenson (1993)
3
, including the specific observed emotion, overall expressivity, and
individual measures of expressivity, including eye blinks and facial movements. Reliability
ratings showed high levels of agreement between the raters in the identification of
participants’ expressions, behaviors, and emotions. These included scoring for rates of
blinking (α = .97), facial movement (α= .76), and overall levels of expressivity (α= .72).
Across all emotion induction conditions, including the neutral control condition, participants
in the suppression condition who were instructed to restrain their facial expressions were
rated as significantly less expressive (M = 1.87, SD = .95) compared to those who received
reappraisal instructions (M = 2.38, SD = 1.10), F(1, 174) = 10.50, p = .001. See Table 2 for
number of observations, means, and standard deviations by condition. Participants in the
suppression condition blinked their eyes significantly less (M = 20.78, SD = 14.39) and had
less facial movement (M = 1.60, SD = .71) than those in the reappraisal condition (M =
26.87, SD = 15.71; F(1, 174) = 7.18, p < .01) and (M = 2.13, SD = 1.03), F(1, 174) = 15.73, p
< .001. See Table 3 for number of observations, means, and standard deviations.
Raters also judged the emotion being induced in the participants on a five-point scale
from (1) Not At All to (5) Extremely. Emotion ratings were included for anger, happiness,

3
While Gross and Levenson (1993) also included facial touching and body movement, those measures were
not included in this analysis because they weren’t pertinent and typically the whole body was not viewable in
the video frame, which focused instead on participants’ faces.
20

fearful, and sadness. When rank ordered, all reappraised emotions were rated highest for
their respective condition when compared to suppression. Moreover, each specific emotion
was observed at higher rates for reappraisal when compared to suppression for each of the
emotion conditions. See Table 4 for rank ordering and means by condition
4
.

4
Fundamental and common challenges in emotion research include both inducing the correct emotion and for
the necessary amount of time. Emotions not clearly positively or negatively valenced present even greater
difficulties. This is potentially why the preponderance of early emotion research focused solely on positive
and negative affect (see Isen, Clark, & Schwartz, 1976; Staw, Bell, & Clausen, 1986). While the studies in this
research used an accepted and customary method of emotion induction, namely movie clips, self-reports of
specific emotion experience doesn’t necessarily yield perfectly corresponding results. Moreover,
supplementing emotion induction with emotion self-regulation adds further complexity. For example, past
studies have shown that suppression effects may result in decreases to both positive and negative emotion-
expressive behavior. On the other hand, using reappraisal tends to lead to decreases in negative emotion
experience, but to have little effect on positive emotion experience (Gross, 2002). Moreover, the effects of
the emotion induction could fade toward the end of the study. These issues may help to explain the results of
the emotion manipulation checks in this study. To address these challenges both self-report and observer-
report data should be considered.
21

Table 2

Number of Observations, Means, and Standard Deviations by Condition for
Observed Expressivity in Study 1

Expressivity

Emotion Induction Emotion Regulation N M SD Sig.

Anger
Suppression 17 1.94 0.88 a

Reappraisal 16 2.72 1.15 b

Happiness
Suppression 18 2.39 1.35 a
Reappraisal 16 3.66 0.96 b
Fear
Suppression 19 1.55 0.81 a
Reappraisal 17 2.38 0.89 b
Sadness
Suppression 16 1.75 0.68 ab

Reappraisal 18 1.94 0.73 ab

Neutral
Suppression 17 1.74 0.69 ab
Reappraisal 22 1.55 0.49 ab
Total
Suppression 87 1.87 0.95 a
Reappraisal 89 2.38 1.1 b

Means designated by "a" significantly different from "b" at p < .05.
Means designated by "ab" do not significantly differ from means designated by "a" or "b" at p > .10.

Table 3

Number of Observations, Means, and Standard Deviations by Emotion
Regulation Method for Observed Eye Blinks and Facial Movement in Study 1

Blinks Facial Movement
Emotion Regulation N M SD Sig. N M SD Sig.

Suppression 87 20.78 14.39 a 87 1.60 0.71 a
Reappraisal 89 26.87 15.71 b 89 2.13 1.03 b

Means designated by "a" significantly different from "b" at p < .05.
Means designated by "ab" do not significantly differ from means designated by "a" or "b" at p > .10.

Table 4

Rank Ordering of Observed Emotion by Emotion Regulation Method in Study 1

Observed Emotion: Angry Happy Fearful Sad
Reappraisal
Sig.

Sig.

Sig.

Sig.

Anger 2.13
a
Happiness 3.50
a
Fear 1.53
a
Sadness 1.56
ab

Fear 1.85
b
Anger 1.50
b
Sadness 1.36
ab
Fear 1.56
ab

Sadness 1.50
b
Fear 1.44
b
Anger 1.34
ab
Neutral 1.43
ab

Neutral 1.48
b
Sadness 1.25
b
Happiness 1.19
b
Anger 1.38
ab

Happiness 1.41
b
Neutral 1.23
b
Neutral 1.18
b
Happiness 1.31
ab

Observed Emotion: Angry Happy Fearful Sad
Suppression
Neutral 1.38
ab
Happiness 2.31
a
Anger 1.38
ab
Neutral 1.44
Anger 1.38
ab
Sadness 1.56
b
Neutral 1.24
ab
Anger 1.32
ab

Happiness 1.25
ab
Anger 1.41
b
Sadness 1.19
ab
Sadness 1.25
ab

Sadness 1.22
ab
Fear 1.39
b
Fear 1.18
ab
Fear 1.24
ab

Fear 1.21
ab
Neutral 1.06
b
Happiness 1.17
ab
Happiness 1.19

Means designated by "a" significantly different from "b" at p < .05.
Means designated by "ab" do not significantly differ from means designated by "a" or "b" at p > .10.

Emotion Recognition Accuracy. The ability to recognize the emotions of others was
calculated as a ratio of the correctly identified emotion to actual emotion expressed out of 24
static photographs of the DANVA-2. Across all emotion conditions, results revealed an
overall marginally significant main effect for reappraisal (M = 80.8%, SD = .10) versus
suppression (M = 78.4%, SD = .10), F(1, 183) = 2.71, p = .10. Examining the specific
conditions in which self-regulation was used, participants in the anger, happiness, and
sadness condition all accurately recognized emotions at a higher rate when reappraising than
when suppressing, supporting Hypothesis 1 for those emotions. Contrary to this hypothesis,
however, the pattern was reversed in the fearful condition.
In the anger condition, participants who reappraised resulted in higher rates of
accuracy identification (M = 83.3%, SD = .06) than those who suppressed their emotions (M
= 78.6%, SD = .12), F(1, 43) = 2.85, p < .10. Similarly, in the happiness condition,
participants who reappraised resulted in significantly higher rates of accuracy identification
(M = 82.8%, SD = .07) than those who suppressed (M = 76.5%, SD = .09), F(1, 47) = 7.40, p
< .01. In the sadness condition those who reappraised performed better at emotional
identification (M = 83.7%, SD = .08) than did those who suppressed (M = 78.0%, SD = .11),
F(1, 42) = 7.40, p = .06. These results for the conditions of anger, happiness, and sadness
provide some support for H1, in which I predicted that emotion reappraisal would lead to
increased emotion recognition accuracy.
In the fearful condition, however, this pattern inverted such that those in the
reappraisal condition performed significantly worse (M = 74.0%, SD = .13) than did those
who suppressed (M = 80.6%, SD = .10), F(1, 45) = 3.95, p = .05. As expected, there were no
significant differences between reappraisal (M = 82.4%, SD = .09) and suppression (M =
24

80.8%, SD = .12) accuracy rates in the neutral condition, F(1, 48) = .33, p = .57. See Figure
2 and Table 5 for number of observations, means, and standard deviations.

Table 5
Number of Observations, Means, and Standard Deviations for Accuracy
Rates by Condition in Study 1

Accuracy Rate
Emotion Induction Emotion Regulation N M SD Sig.

Anger
Suppression 23 0.79 0.12

Reappraisal 22 0.83 0.06

Happiness
Suppression 25 0.76 0.09 a
Reappraisal 24 0.83 0.07 b
Fear
Suppression 23 0.81 0.10 a
Reappraisal 24 0.74 0.13 b
Sadness
Suppression 22 0.78 0.11

Reappraisal 22 0.84 0.08

Neutral
Suppression 24 0.82 0.09 ab
Reappraisal 26 0.81 0.12 ab
Total
Suppression 117 0.79 0.10 ab
Reappraisal 118 0.81 0.10 ab

Means designated by "a" significantly different from "b" at p < .05.
Means designated by "ab" do not significantly differ from means designated by "a" or "b" at p > .10.

Analysis also revealed some support for H2 in which accuracy rates were lower when
suppressing compared to when not self-regulating. In the anger condition, rates of
identification between reappraisal (M = 83.3%, SD = .06) and no regulation were nearly the
same (M = 83.9%, SD = .09). Whereas those in the no regulation condition recognized
emotions more accurately than did those in the suppression condition (M = 78.6%, SD = .12),
t(64) = 2.07, p < .05. Similarly, in the happiness condition, accuracy rates were essentially
25

the same between reappraisal (M = 82.8%, SD = .07) and no regulation (M = 83.0%, SD =
.09), but significantly higher than in suppression (M = 76.5%, SD = .09), t(69) = 3.05, p <
.05. Thus, the conditions of anger and happiness provide some support for H2.
In the fearful condition, however, H2 was not supported, as rates for no regulation (M
= 77.5%, SD = .10) fell between reappraisal (M = 80.6%, SD = .10) and suppression (M =
74.0%, SD = .13), which were not significantly different, F(2, 67) = 2.19, p = .12. Finally, in
the sadness condition, accuracy rates for no regulation (M = 75.7%, SD = .10) were closer to
those using suppression (M = 78.0%, SD = .11) and significantly lower than those in the
reappraisal condition (M = 78.0%, SD = .08), F(2, 65) = 3.82, p < .05, which again does not
fully support H2. As expected, identification accuracy rates in the neutral condition did not
significantly change between no regulation (M = 84.0%, SD = .09), reappraisal (M = 80.8%,
SD = .12), and suppression (M = 82.4, SD = .09), F(2, 72) = .63, p = .54, as shown in Figure
2.
As predicted in Hypothesis 3, people in the anger condition accurately recognized
emotions at a higher rate (M = 83.9%, SD = .09) compared with people in both the fearful (M
= 77.5%, SD = .10), F(4, 112) = 3.80, p = .03 and sadness conditions (M = 75.7%, SD = .11),
F(4, 112) = 3.80, p < .01. Similarly, people in the happiness condition accurately recognized
emotions at a higher rate (M = 83.0%, SD = .09) than did people both the fearful (M = 77.5%,
SD = .10), F(4, 112) = 3.80, p = .06 and sadness conditions (M = 75.7%, SD = .11), F(4, 112)
= 3.80, p = .01. Compared to the neutral condition (M = 84.0%, SD = .10), those in both
fearful (M = 77.5%, SD = .10), F(4, 112) = 3.80, p = .02 and sadness conditions (M = 75.7%,
SD = .11), F(4, 112) = 3.80, p < .01 did not identify emotions as accurately. The accuracy
26

rates between those in the anger and happiness conditions compared to the neutral condition
did not significantly differ from one another (p’s > .72).
When combined, those in the approach conditions of anger and happiness more
accurately recognized emotions (M = 83.4%, SD = .09) compared to those in the avoidance
conditions of fear and sadness (M = 80.8%, SD = .10), F(2, 114) = 2.93, p = .02. These
results provide support for H3 in that participants who experienced the approach emotions of
anger and happiness had higher emotion recognition accuracy rates than those who
experienced the avoidance emotions of fear and sadness, as shown in Figure 2.

Figure 2. Emotion Recognition Accuracy Rates (All Pictures) by Emotion Induction and
Emotion Regulation, including No Regulation, F(14, 337) = 2.73, p = .001.

70.0%
75.0%
80.0%
85.0%
Anger Happiness Fear Sadness Neutral
Emotion Recognition Accuracy Rates
(All Pictures)
Suppression
Reappraisal
No Regulation
27

Emotion Recognition Accuracy Gender Differences. Overall, women identified
emotions more accurately (M = 83.7%, SD = .08) than men did (M = 79.0%, SD = .10), F(1,
138) = 8.51, p < .01.
Discussion
Study 1 demonstrated some support for the prediction that emotion reappraisal results
in higher rates of recognition accuracy than emotion suppression. Notably, while
experiencing the emotions of anger, happiness, and sadness, the self-regulatory process of
emotion reappraisal led to better rates of emotion identification in others than did emotion
suppression. Unexpectedly, the emotion experience of fear reversed this pattern, such that
emotion reappraisal resulted in lower accuracy rates than emotion suppression. These results
provide some evidence that there is a cost to emotion recognition in attempting to suppress
one’s emotions, which does not exist when employing a reappraisal regulation process.
Additional support for interference as depicted in the ego-depletion model of emotion
suppression was revealed in the results that showed lower emotion recognition accuracy rates
from those using suppression compared to no regulation. This prediction was supported
under the conditions of anger and happiness, but not of fear and sadness.
The study also presented evidence that one’s emotion states influence people’s ability
to identify the emotion state of others accurately. A manipulated emotion induction
technique was used to examine the circumstances that led to increased and decreased
emotion recognition. Specifically, people who were angry or happy were better able to
detect others’ emotions than those who were fearful or sad. These results show support for
the predicted hypothesis that the affective experience of approach emotions, including both
28

anger and happiness, improve emotion recognition accuracy compared to the affective
experiences of avoidance emotions, including both fear and sadness.

29

CHAPTER 3: STUDY 2
Effects of Emotion Regulation and Experienced Emotion
on Emotion Recognition Accuracy Extended

Study 2 was designed to replicate the main effects and extend findings from Study 1
with several key differences, namely, the use of static emotion photographs from two
separate sets, including Ekman and Friesen’s (1975) Japanese and Caucasian Facial
Expressions of Emotion (JACFEE) and Spilich’s (2012) Duchenne versus Social Smiles
(DSS). The study used a combination of JACFEE and DSS photographs because the Ekman
and Friesen set is a foundational measure that has been used in numerous research studies,
and Spilich’s DSS photographic set is more recent and differentiates between Duchenne and
social smiles, which could have an impact on emotion recognition.
Duchenne smiles (1892/1990) are considered to be genuine portrayals of happiness,
while social smiles are viewed as deceptive portrayals used for a specific purpose, such as to
increase affiliative opportunity (Bernstein, Sacco, Brown, Young, & Claypool, 2010). The
motivation behind using the DSS set was to determine whether reappraisal might also be
more beneficial than suppression at detecting such genuine smiles versus social smiles.
Other differences in this study were the inclusion of “no emotion” faces, as well as removal
of the time limit in which photographs were viewable by participants. The aim of this study
was to test the results of the first study under these disparate circumstances.
The JACFEE complete set consists of 56 photographs, including eight photos each of
anger, contempt, disgust, fear, happiness, sadness, and surprise, as well as four photographs
of each person (2 males, 2 females) displaying a neutral expression that were used as the
measure of “no emotion” in this study. The DSS consists of 20 photographs of both
Duchenne (i.e., genuine) and social (i.e., faking) smiles.
30

Method
Participants and Experimental Design. Three hundred and fifty-nine students
(57.4% male; Mage = 20.9 years, SD = 2.5 years) participated in this study at a large, private,
West Coast university for partial fulfillment of their course requirement. The design of the
study was 3 (emotion regulation: suppression, reappraisal, no regulation) x 4 (emotion
induction: anger, happiness, sadness, neutral). The manipulated emotion induction and self-
regulation methods remained the same as in the previous study, after which participants rated
the emotions of faces in the static photographs by choosing between mutually exclusive
options of sadness, genuine happiness, faking happiness, or no emotion. There was no time
limit on how long participants were allowed to answer the questions, since the photographs
remained on the screen until participants advanced them.
Procedure and Materials. This study used a computer-based experimental lab
design as before. The materials consisted of three photographs from each emotion category
(3 x sad, 3 x genuine smile, 3 x faking smile, and 3 x no emotion) for a total of 12
photographs combined from the JACFEE and DSS. After reading the respective self-
regulation strategy instructions and watching the movie clip, participants were then shown
and rated the 12 photographs in randomized order (see Appendix A for self-regulation
manipulation instructions). Finally, participants completed a post-study questionnaire.
Dependent Measures.
Emotion Recognition Accuracy. As in the previous study, the primary dependent
variable of interest in this study was the rate of emotion recognition. This accuracy rate was
determined by matching respondents’ answers against known results for the 12 photographs
presented from the JACFEE and DSS sets. Possible answers included a mutually exclusive
31

selection that the photographs were portraying sadness, genuine happiness, faking happiness,
or no emotion. Each respondent answer was coded into a dichotomous score of “1” for the
correct match or “0” for any other match. Based on the number of correct answers, a
combined accuracy score (out of 100%) was calculated for each participant. Emotion
recognition ability was calculated as a ratio of the correctly identified emotion to actual
emotion expressed in the 12 photographs. These combined picture accuracy scores were then
used as the primary dependent variable.
Results
Emotion Regulation Manipulation Check. At the end of the study, participants
completed a questionnaire that asked whether they received any instructions to follow. To
ascertain their regulation strategy, they chose between the options of descriptions for
suppression, reappraisal, or no instructions. Approximately 94.0% of participants passed this
manipulation check correctly. Those who failed this check were distributed across all
conditions and were excluded from the analysis.
Emotion Recognition Accuracy Rates. Emotion recognition accuracy rates were
examined to determine the effect of suppression, reappraisal, and no regulation. As predicted
in Hypotheses 1 and 2, participants in the suppression condition had lower accuracy rates (M
= 73.8%, SD = .16) than did both those in the reappraisal (M = 76.5%, SD = .12) and no
regulation conditions (M = 77.0%, SD = .12), t(356) = -1.96, p = .05. Results revealed a
significant effect for suppression across all conditions (by dummy coding the emotion
conditions of angry, happy and sad to 0 and suppression self-regulation to 0 in a regression),
β = -.03, SE = .01, p = .04, as shown in Figure 3. While patterns for specific emotions
generally mirrored those in Study 1, individual contrasts between specific emotions by
32

regulation strategy were not significantly different from each other. See Table 6 for number
of observations, means, and standard deviations
5
. Contrary to predictions, emotion
regulation condition did not significantly affect accuracy of recognizing Duchenne and social
smiles, p’s > .17.

Figure 3. Emotion Recognition Accuracy Rates by Self-Regulation Method.

5
Hypothesis 3 was not tested in this study because the avoidance emotion of fear was not included as an
independent variable.
70.0%
71.0%
72.0%
73.0%
74.0%
75.0%
76.0%
77.0%
78.0%
79.0%
Suppression Reappraisal No Regulation
Emotion Recognition Accuracy Rates
33

Table 6
Number of Observations, Means, and Standard Deviations for
Accuracy Rates by Condition in Study 2

Accuracy Rate
Emotion Induction Emotion Regulation N M SD

Anger
Suppression 36 0.76 0.12
Reappraisal

28 0.78 0.12
No Regulation 29 0.77 0.13
Happiness
Suppression 35 0.73 0.22
Reappraisal

25 0.79 0.12
No Regulation 30 0.78 0.12
Sadness
Suppression 29 0.73 0.13
Reappraisal

29 0.72 0.12
No Regulation 32 0.77 0.14
Neutral
Suppression 26 0.73 0.16
Reappraisal

30 0.77 0.10
No Regulation 30 0.76 0.10
Total
Suppression 126 0.74 0.16
Reappraisal

112 0.76 0.12
No Regulation 121 0.77 0.12

Emotion Recognition Accuracy Gender Differences. As in the first study, women
identified emotions more accurately overall (M = 77.6%, SD = .13) than did men (M =
74.4%, SD = .14), F(1, 355) = 4.98, p = .03.
Discussion
Using a combination of the JACFEE and DSS measures, Study 2 provided additional
support for the predicted hypotheses that the method of regulating one’s emotional response
to an affective experience influences one’s subsequent ability to recognize emotions in
others. This study was designed to replicate the main effects and extend findings from Study
1. While using the same emotion induction and regulation strategies as the first study, the
34

key differences included the use of a different set of static emotion photographs, the
inclusion of “no emotion” faces, and the removal of a time limit for participant responses.
Applying the ego-depletion model of emotion suppression to emotion recognition, the
study results again indicate support for Hypothesis 1 that emotion recognition accuracy was
higher under conditions of emotion reappraisal than emotion suppression. Further support
was shown for Hypothesis 2 that emotion recognition accuracy is lower under conditions of
emotion suppression compared to no regulation. Finally, the study results revealed that these
identification rates were not significantly different for those employing emotion reappraisal
and those using no regulation to control their emotions. A key reason for including the
Duchenne and social smiles photographs was to investigate any potential differences between
participants’ ability to distinguish between Duchenne and social smiles. However, no such
differences emerged in the data analysis.
The added measure of “no emotion” faces in this study revealed the same pattern of
support for suppression, reappraisal, and no regulation conditions. Moreover, providing
participants with additional time in which to assess the emotions being portrayed to them in
the photographic sets did not appear to provide any benefit to those suppressing their
emotions. Once participants made their decisions about the photographic stimuli, they
decided when to advance to the next screen, implying that they were making the best
decision with the feeling that more time would not be advantageous. Overall, these results
provide further evidence of the cost in attempting to suppress one’s emotions over
reappraisal or no regulation strategies. Again, this cost only existed for those who used the
suppression method, impacting their ability to identify the emotional state of others
accurately.
35

CHAPTER 4: STUDY 3
Effects of Experienced Emotion and Emotion Regulation
on Non-Verbal Perception

Study 3 examined the effects of self-regulation using a test of interpersonal sensitivity
consisting of non-verbal perception. Having demonstrated the self-regulation effects on
emotions in determining emotion recognition accuracy using several different photograph
sets in the previous studies, the aim of this study was to examine the effects of self-regulation
beyond emotion recognition into non-emotion scenarios. This was achieved by using the
Profile of Non-Verbal Sensitivity (PONS) as a measure (Hall, DiMatteo, Rogers, & Archer,
1979). Adding a non-emotion assessment intended to achieve two goals: (1) to test whether
the effects of self-regulation used in the previous studies were due to cognitive load; and, (2)
to test whether the effects of regulation seen in the previous studies extended beyond emotion
recognition.
This study focused on a limited subset of emotions for anger and happiness (versus
four used in the previous studies), in addition to the neutral condition for baseline
comparison purposes. The emotions of anger and happiness were included in this study
because they are common opposing emotions that people in organizational life are often
called upon to regulate.
Method
Participants and Experimental Design. Two hundred and fifty-eight students
(59.7% male; Mage = 20.9 years, SD = 2.7 years) participated in this study at a large, private,
West Coast university for partial fulfillment of their course requirement. The design of this
study was 3 (emotion regulation: suppression, reappraisal, no regulation) x 3 (emotion
induction: anger, happiness, neutral). The manipulated emotion induction and self-
36

regulation techniques remained the same as in earlier studies after which participants
completed the Profile of Non-Verbal Sensitivity (PONS).
Procedure and Materials. This study used a computer-based experimental lab
design as in the previous studies. Each participant sat in front of a separate, dedicated
computer and monitor where he or she first watched a movie while wearing headphones to
listen to the corresponding movie audio track. Using the same film clips described in the
previous studies, participants first viewed their respective single movie clip.
Immediately following viewing of the film clip, participants were administered the
PONS measure and asked to decide between two mutually exclusive choices presented for
each of the non-verbal scenes included in the measure. The subset PONS Video-40 measure
used in this study consisted of 40 representative behaviors from the full 220 video set. The
moving image clips display a 24 year-old Caucasian female who was instructed to enact
specific behaviors. None of the videos in this subset include any verbal information or audio
tracks. Participants then completed an online questionnaire that assessed their emotional
state using the Positive-Negative Affect Scale (PANAS), their current preferences for
emotion regulation using the Emotion Regulation Questionnaire (ERQ), and finally,
manipulation check questions.
Dependent Measures
Non-Verbal Perception Accuracy. The primary dependent variable of interest in this
study was the accuracy rate of non-verbal perception as assessed by correctly identifying the
40 behaviors shown in the PONS Video-40 set. Examples of the behaviors displayed in this
set include: asking forgiveness, expressing gratitude, leaving on a trip, helping a customer,
37

nagging a child, and expressing motherly love. No specific emotions, discrete or otherwise,
are included (see Appendix B for the full set of behaviors).
Results
Emotion Regulation Manipulation Check (Self-Report). As in the previous studies,
participants completed a post-study questionnaire in which they were asked about any
instructions they had received prior to viewing the film. Possible answers included
instructions regarding suppression, reappraisal, or none. A total of 96.6% of participants
passed this manipulation check correctly. Those who failed this check were distributed
across all conditions and excluded from analysis.
Non-Verbal Accuracy Rates. Emotion recognition accuracy rates were examined to
determine the effect of suppression, reappraisal, and no regulation. Providing support for
Hypothesis 2 across all measured emotions, but not for Hypothesis 1, analysis by self-
regulation strategy reveals similarly low non-verbal accuracy rates for both suppression (M =
72.5%, SD = .06) and reappraisal (M = 72.8%, SD = .07) when compared to no regulation (M
= 76.7%, SD = .09), F(2, 255) = 8.15, p < .001. Further examination by discrete emotions
reveals that participants in the anger condition who suppressed (M = 73.9%, SD = .07, t(83) =
-2.32, p = .02) had similar accuracy rates compared with those who reappraised (M = 72.1%,
SD = .08, t(83) = -3.13, p < .01). Both had significantly lower non-verbal recognition
accuracy rates compared to those who did not follow any regulation strategy (M = 78.3%, SD
= .07), F(2, 83) = 5.22, p < .01.
In the happiness condition, the same pattern existed in which participants who either
suppressed (M = 71.1%, SD = .07, t(85) = -2.11, p = .04) or reappraised (M = 72.1%, SD =
.07, t(85) = -1.74, p = .08) had significantly and marginally significant lower non-verbal
38

recognition accuracy rates compared to no regulation, (M = 76.0%, SD = .11). As shown in
Figure 4, the additional instructions of either suppressing or reappraising led participants in
the no emotion condition to recognize emotions less accurately than did people in the no
regulation condition (M = 72.1%, SD = .05 for suppression, M = 74.4%, SD = .06 for
reappraisal vs. M = 75.8%, SD = .08 for neutral), F(2, 81) = 2.39, p = .10.

Figure 4. Non-Verbal Perception Accuracy Rates, F(8, 248) = 3.47, p = .001.

Emotion Recognition Accuracy Gender Differences. Contrary to Study 1 and Study
2, non-verbal accuracy rates were not significantly different between women (M = 74.1%, SD
= .07) and men in this study (M = 73.9%, SD = .08), F(1, 256) = .06, p > .81.
Discussion
In contrast to the previous studies that focused on emotion recognition, Study 3
examined the effects of suppression and reappraisal regulation methods on overall non-verbal
perception. This approach was used to determine how the cognitive load associated with
66.0%
68.0%
70.0%
72.0%
74.0%
76.0%
78.0%
80.0%
Anger Happiness Neutral
Non-Verbal Perception Accuracy Rates
Suppression
Reappraisal
No Regulation
39

suppression that led to lower emotion recognition accuracy rates would impact interpersonal
perception, which does not involve a strictly emotional aspect. One possibility was that the
same pattern as in the first two studies would emerge, whereby accuracy rates would be
greater for reappraisal and no regulation in comparison to suppression. The ego-depletion
model would predict that the cognitive cost to recognition would be high while suppressing
one’s emotion, but low while reappraising. However, the results for this third study indicate
that there is a cognitive cost for either suppressing or reappraising one’s emotion.
Like the previous assessment measures for emotion recognition accuracy, the PONS
also tests for individual ability to differentiate between a range of messages. However,
because the PONS does not focus on emotion specifically, or on the facial areas of the
encoder in particular, it is considered a more generalized test of overall interpersonal
communication. One potential criticism of the PONS could be the use of a single, white
female encoder who is featured in all of the video clips. However, a previous meta-analysis
(Hall, 1978, 1984) that looked at multiple measures of non-verbal perception did not find any
differences whether the posing person was either female or male.
Using this measure of non-verbal perception, the PONS differed from the assessments
presented in the previous two studies in that it lacks an emotion component. Instead, the
PONS tests for interpersonal perception by presenting non-verbal messages conveyed
through the hands, body, posture, and movement, rather than focusing on emotions portrayed
in the face of the encoder. The results of this study reveal the same pattern for participants in
the anger as in the happiness condition using suppression and reappraisal, as seen in Figure 4.
These regulation methods resulted in similarly lower levels of accurately identifying the
messages contained in the PONS compared to those who were not instructed to self-regulate.
40

This same trend appeared even in the neutral condition although it was not nearly as
pronounced as in the emotion induction conditions.
Overall, the results of Study 3 provide further evidence that the cognitive demand of
suppression shown in the previous two studies applies both to emotion recognition and non-
verbal perception. However, this study also indicates that there is a similar consequence to
reappraisal, specifically for the observation effort necessary to distinguish these types of non-
verbal messages.

41

CHAPTER 5: GENERAL DISCUSSION
The studies presented here demonstrate that self-regulation strategies and emotional
experiences can inhibit the accurate recognition of emotions. Study 1 demonstrated the
moderating effects of two different self-regulation methods, including the beneficial results
of reappraisal over the deleterious outcomes of suppression for emotion recognition accuracy
when experiencing anger, happiness, or sadness. Further, the study differentiated the effects
of approach versus avoidance emotions on the ability to accurately recognize and identify
emotions. This included the approach emotions of anger and happiness in contrast to the
avoidance emotions of sadness and fear. Specifically, angry and happy participants were
able to determine others’ emotions more accurately than were sad and fearful participants.
Study 2 replicated the main self-regulation findings of Study 1 using a different measure of
emotion recognition. Lastly, Study 3 demonstrated that the cognitive cost of suppression
evidenced in the first two studies also may apply to reappraisal when evaluating non-verbal
perception.
Taken together, these studies highlight the influence of our emotion state and our
response to those emotions in regard to our perceptual abilities and interactions with others.
While it is not surprising that our feelings play a role in our judgments (Forgas, 1995), this
research also shows that these affective consequences are moderated by our finite ability to
respond and manage various emotion states. Extending the ego-depletion model of emotion
regulation posited by Baumeister et al. (1998) indicates that there is a cognitive cost of self-
regulation strategy on emotion recognition.
Regulating affect necessitates consumption of a finite, internal capacity. Existing
research in emotion has demonstrated the impact of self-regulatory processes on cognitive
42

resources. These include a decline in physical stamina, early quitting of unsolvable
anagrams, and eventual weakening of control of one’s own mood while completing a variety
of cognitively taxing exercises (Wegner, Erber, & Zanakos, 1993; Baumeister et al., 1998).
What has not yet been illuminated is whether this depletion impacts the recognition of
emotions in others. The results of the studies presented here provide evidence for the cost of
emotion regulation in terms of emotion recognition.
Theoretical Implications
There are multiple theoretical implications from the research presented here for the
ego-depletion model of self-regulation, the construct of emotional intelligence, and the
approach-avoidance distinction in decision making. First, the results of these studies extend
the ego-depletion model of self-regulation into the arena of emotion recognition, affirming
that managing one’s affect also exacts a toll on one’s limited regulatory resources in terms of
recognizing emotions in others. As such, the system involved in emotion recognition is
clearly susceptible to the efforts of actively regulating one’s own emotional experiences.
Building on the ego-depletion model of emotion regulation, these studies demonstrate the
potential trade-offs of using one self-regulation strategy over another and their impact on
emotion recognition.
Second, the basis of this research was to examine whether emotion recognition
accuracy was subject to enhancement or impairment based on the state of an individual.
Extending the construct of emotional intelligence, this research uncovered a way in which the
emotion recognition component of emotional intelligence could be influenced. As such,
these findings suggest an avenue toward expansion of the emotion intelligence model.
43

Finally, examination of emotion recognition rates from differently valenced emotions
contributes to the approach-avoidance distinction model for evaluating other’s emotions.
The perceiver’s emotion state during the process of emotion recognition has an impact on
these accuracy rates based on whether the emotion experience is one of approach versus one
of avoidance. While approach emotions are seemingly incongruous (e.g., happiness versus
anger), they are comparable in their unifying effect of orientation, producing similar rates of
emotion recognition. Consequently, those in an approach state are likely to experience a
benefit to emotion recognition accuracy due to their focus toward the external stimuli, while
those in an avoidance state (e.g., sadness and fear) do not derive the same accuracy benefits
due to their focus away from the external stimuli.
Practical Implications
The results presented here have implications for multiple areas of social interchange,
including customer service, employee interaction, and negotiation. Many employees may be
tempted, or even instructed, to mask or suppress their unwelcome emotions. However, these
studies suggest that suppressing emotion reduces people’s ability to recognize emotions
accurately. Because decreased emotion recognition ability can impair people’s capacity to
negotiate effectively (Elfenbein et al., 2007) and pick-up on subtle communication cues
(Gross & John, 2003), managers may be well advised to find different ways to regulate their
emotions.
Rather than imposing a blanket prescription to “always smile” regardless of the
customer encounter, employees may actually create a more positive exchange by considering
the dilemma or concentrating on the informational content of the customer’s complaint. In
this way, they could free up their own cognitive resources to understand and re-evaluate the
44

issue at hand. By suppressing their own expressivity, managers may unwittingly dampen the
natural synchrony movements (Bernieri, 1988) and non-verbal mimicry (Lakin & Chartrand,
2003) of social exchange that is likely to create rapport. Instead, increased information
sharing by focusing on providing and soliciting information is likely to yield better outcomes
(Thompson, 1991).
Limitations and Future Directions
As with all research, there are limitations to these studies. While the studies here
used multiple different photographic collections, there are dozens of emotion recognition
measures that may differ in subtle ways and influence the results. Of course, social
interaction involves more than just static displays of emotion. Although it is a starting point,
there is an acknowledgment of the limitations of static versus dynamic, and even live in-
person interactions between individuals, dyads, and groups. In addition, it is difficult to
determine the exact contribution of accurate emotion recognition in advancing social
concord.
The present research opens numerous paths for future investigation, both in terms of
expanding areas of emotion recognition and emotion regulation. As proposed by Ambady,
Bernieri, & Richeson (2000), emotion perception of a group versus a single individual is an
important distinction that also contributes to social interactions. Another “thin-slice”
measure of emotion recognition could include the use of moving emotion displays in addition
to static encoders, such as presented in the Spontaneous Expressions Recognition Test (Kang,
2012) that portrays discrete emotions thorough recorded video clips. Other current work that
warrants further investigation involves using neurotoxins to paralyze expressive facial
muscles. This induced interference plays a role in the interpretation of facial feedback
45

signals and mirroring behaviors that act in processing of emotion perception (Neal &
Chartrand, in press).
Other research indicates that people from different cultures may process emotion
recognition in different ways by focusing on separate parts of the face. From an
anthropological perspective, these cultural differences in emotion display and perception
facilities may be impacted by disparate self-regulation strategies. Recent work has indicated
that Eastern and Western inhabitants tend to focus on specific sections of the face for
interpreting emotion (Jack, Blais, Scheepers, Schyns, & Caldara, 2009). Using video
analysis of fractional micro-expressions from participants’ faces, this research has revealed
that Eastern observers tend to concentrate much more on the eyes of their counterparts, while
Westerners tend to distribute their gaze across the entire face, leading to varying rates of
emotion detection. Such differences point to cultural influences, also adding to the
discussion on individualistic versus collectivist modalities (Markus & Kitayama, 1994).
Further work in this area could investigate how the self-regulation strategies presented in
these studies impact emotion recognition across cultures.
In addition, future research may productively explore whether one can train and
improve this faculty as it remains an open question. Finally, another promising area of
research might examine how self-regulation impacts other similar forms of interpersonal
sensitivity, such as empathy, using the empathy accuracy paradigm developed by Ickes,
Bissonnette, Garcia, and Stinson (1990). Clearer answers to these lines of research may help
contribute to improved positive interpersonal relationships.

46

CHAPTER 6: CONCLUSION
The purpose of these studies was to develop a better understanding of the underlying
process of interpersonal interactions under multiple intrapersonal self-regulation strategies
and emotions. People who suppressed emotions recognized emotions in others less
accurately than did people who reappraised their emotions or those who did not regulate their
emotions. Further, outside of an exclusively emotional context, those who either suppressed
or reappraised experienced lower rates of interpersonal perception. There are clearly costs to
self-regulation in terms of emotion recognition and overall perceptive ability.
Extending beyond the limits of personal traits, identifying potential conditions under
which emotional cues are likely to be more accurately perceived and interpreted can provide
fundamental benefits to enhancing interpersonal communication. In this way, the emotion
recognition element could be augmented and leveraged under specific circumstances. The
possibility of increasing emotion recognition ability in others calls for additional
investigation of the emotional intelligence model.
Although exerting effort to maintain a “poker face” may provide certain advantages
in a number of contexts, the price of such suppression should be carefully considered. The
studies here reveal that people who suppress emotions recognized static emotions less
accurately than did people who reappraise their emotions or people who did not regulate their
emotions. Because emotion recognition brings benefits across the domains of customer
interactions, organizational dynamics, and negotiation, the present studies suggest that
emotion regulation strategies may impair communication in ways not previously understood.
Consequently, identifying conditions under which emotion cues are likely to be perceived
47

and interpreted accurately can provide fundamental benefits to effective communication and
lead to improved social relations.

48

REFERENCES
Ambady, N., Bernieri, F. J., & Richeson, J. A. (2000). Toward a histology of social behavior:
Judgmental accuracy from thin slices of the behavioral stream. Advances in
Experimental Social Psychology, 32, 201–271.
Arnold, M. (1960). Emotion and Personality. New York: Columbia University Press.
Ashforth, B. E., & Humphrey, R. H. (1993). Emotional labor in service roles: The influence
of identity. Academy of Management Review, 18, 88–115.
Banse, R., & Scherer, K. R. (1996). Acoustic profiles in vocal emotion expression. Journal of
Personality and Social Psychology, 70, 614–636.
Bar-On, R. (1997). Bar-On emotional quotient inventory: Technical manual. Toronto: Multi-
Health Systems.
Baumeister, R. F., Bratslavsky, E., Muraven, M., & Tice, D. M. (1998). Ego depletion: Is the
active self a limited resource? Journal of Personality and Social Psychology, 74(5),
1252–1265.
Bernieri, F. J. (1988). Coordinated movement and rapport in teacher-student interactions.
Journal of Nonverbal behavior, 12(2), 120-138.
Bernstein, M. J., Sacco, D. F., Brown, C. M., Young, S. G., & Claypool, H. M. (2010). A
preference for genuine smiles following social exclusion. Journal of Experimental
Social Psychology, 46, 196–199.
Bono, J. E., & Vey, M. A. (2005). Toward understanding emotional management at work: A
quantitative review of emotional labor research. Emotions in Organizational
Behavior, 213-233.
49

Brotheridge, C. M., & Grandey, A. A. (2002). Emotional labor and burnout: Comparing two
perspectives of “people work.” Journal of Vocational Behavior, 60(1), 17-39.
Brown, C. S. & Sulzer–Azaroff, B. (1994). An assessment of the relationship between
customer satisfaction and service friendliness. Journal of Organizational Behavior
Management, 14, 55–75.
Cropanzano, R., Weiss, H. M., Suckow, K. J., & Grandey, A. A. (2000). Doing justice to
workplace emotion. In N. M. Ashkanasy, C. E. J. Hartel, & W. J. Zerbe (Eds.),
Emotions in the workplace: Research, theory, and practice. Westport, CT: Quorum
Books.
Duchenne de Bologne, G. B. (1892/1990). The mechanism of human facial expression or an
electro-physiological analysis of the expression of emotions (R. A. Cutherbertson, Ed.
& Trans.). New York: Cambridge University Press. (Original work published 1862).
Ekman, P., & Friesen, W. V. (1975). Unmasking the face. Englewood Cliffs, NJ: Prentice-
Hall.
Elfenbein, H. A. (2008). Emotion in organizations. Academy of Management Annals, 1(1).
Elfenbein, H. A., Der Foo, M., White, J., Tan, H. H., & Aik, V. C. (2007). Reading your
counterpart: The benefit of emotion recognition accuracy for effectiveness in
negotiation. Journal of Nonverbal Behavior, 31, 205–223.
Elliot, A. (1999). Approach and avoidance motivation and achievement goals. Educational
Psychologist, 34, 169–189.
Elliot, A. J. (2008). Approach and avoidance motivation. New York: Psychology Press.
50

Estrada, C.A., Isen, A.M., & Young, M. J. (1997). Positive affect facilitates integration of
information and decreases anchoring in reasoning among physicians. Organizational
Behavior and Human Decision Processes, 72(1), 117–135.
Forgas, J. P. (1995). Mood and judgment: The affect infusion model (AIM). Psychological
Bulletin, 117, 39–66.
Forgas, J. P. (1998a). On being happy and mistaken: mood effects on the fundamental
attribution error. Journal of Personality and Social Psychology, 75(2), 318.
Forgas, J. P. (1998b). On feeling good and getting your way: Mood effects on negotiator
cognition and bargaining strategies. Journal of Personality and Social Psychology,
74, 565–577.
Frijda, N. H. (1986). The emotions. Cambridge University Press.
George, J. M. (2000). Emotions and leadership: The role of emotional intelligence. Human
Relations, 53(8), 1027–1055.
Gowing, M. K. (2001). Measurement of emotional competence. In C. Cherniss & D.
Goldman (Eds.), The emotionally intelligent workplace (pp. 83–131). San Francisco,
CA: Jossey-Bass.
Grandey, A. A., Fisk, G. M., & Steiner, D. D. (2005). Must "service with a smile" be
stressful? The moderating role of personal control for American and French
employees. Journal of Applied Psychology, 90(5), 893–904.
Gross, J. J. (1998a). The emerging field of emotion regulation: An integrative review. Review
of General Psychology, 2(3), 271–299.
51

Gross, J. J. (1998b). Antecedent- and response-focused emotion regulation: Divergent
consequences for experience, expression, and physiology. Journal of Personality and
Social Psychology, 74, 224–237.
Gross, J. J. (2001). Emotion regulation adulthood: Timing is everything. Current Directions
in Psychological Science, 10(6), 214–219.
Gross, J. J. (2002). Emotion regulation: Affective, cognitive, and social consequences.
Psychophysiology, 39, 281–291.
Gross, J. J., & John, O. P. (2003). Individual differences in two emotion regulation processes:
implications for affect, relationships, and well-being. Journal of Personality and
Social Psychology, 85(2), 348.
Gross, J. J., & Levenson, R. W. (1993). Emotional suppression: physiology, self-report, and
expressive behavior. Journal of Personality and Social Psychology, 64(6), 970.
Gross, J. J. & Levenson, R. W. (1995). Emotion elicitation using films. Cognition and
Emotion, 9(1), 87–108.
Grove, S. J., & Fisk, R. P. (1989). Impression management in services marketing: A
dramaturgical perspective. In R. A. Giacalone & P. Rosenfeld (Eds.), Impression
management in the organization (pp. 427–438). Hillsdale, NJ: Erlbaum.
Hall, J. A. (1978). Gender effects in decoding nonverbal cues. Psychological Bulletin, 85,
845–857.
Hall, J. A. (1984). Nonverbal sex differences: Accuracy of communication and expressive
style. Baltimore: Johns Hopkins University Press.
Hall, J. A., DiMatteo, M. R., Rogers, P. L., & Archer, D. (1979). Sensitivity to nonverbal
communication: The PONS test. Baltimore: Johns Hopkins University Press.
52

Han, S., Lerner, J. S., & Keltner, D. (2007). Feelings and consumer decision making: The
appraisal-tendency framework. Journal of Consumer Psychology, 17(3), 158–168.
Hewig, J., Hagemann, D., Seifert, J., Gollwitzer, M., Naumann, E., & Bartussek, D. (2005).
A revised film set for the induction of basic emotions. Cognition and Emotion, 19,
1095–1109.
Hochschild, A. R. (1979). Emotion work, feeling rules and social structure. American
Journal of Sociology, 85, 551–575.
Hochschild, A. R. (1983/2003). The managed heart: Commercialization of human feeling.
Berkeley, CA: University of California Press.
Ickes, W., Bissonnette, V., Garcia, S., & Stinson, L. (1990). Implementing and using the
dyadic interaction paradigm. In C. Hendrick & M. Clark (Eds.), Review of personality
and social psychology: Research methods in personality and social psychology (Vol.
11, pp. 16–44). Newbury Park, CA: Sage.
Isen, A. M., Clark, M., & Schwartz, M. F. (1976). Duration of the effect of good mood on
helping:" Footprints on the sands of time.". Journal of Personality and Social
Psychology, 34(3), 385.
Jack, R. E., Blais, C., Scheepers, C., Schyns, P. G., & Caldara, R. (2009). Cultural confusions
show that facial expressions are not universal. Current Biology, 19(18), 1543–1548.
John, O. P., & Gross, J. J. (2004). Healthy and unhealthy emotion regulation: Personality
processes, individual differences, and life span development. Journal of Personality,
72(6), 1301–1334.
53

Kang, S. M. (2012). Individual differences in recognizing spontaneous emotional
expressions: Their implications for positive interpersonal relationships. Psychology,
3(12A), 1183–1188.
Kopelman, S., Rosette, A. S., & Thompson, L. (2006). The three faces of Eve: Strategic
displays of positive, negative, and neutral emotions in negotiations. Organizational
Behavior and Human Decision Processes, 99(1), 81–101.
Lakin, J., & Chartrand, T. L. (2003). Using nonconscious behavioral mimicry to create
affiliation and rapport. Psychological Science, 14, 334–339.
Lang, P. (1984). Cognition in emotion: Concept and action. In Izard, C., Kagan, J., and
Zajonc, R. (Eds.), Emotion, Cognition, and Behavior (pp. 196–226). New York:
Cambridge University Press.
Lazarus, R. S. (1991). Emotion and adaptation. New York: Oxford University Press.
Lerner, J. S., & Keltner, D. (2000). Beyond valence: Toward a model of emotion-specific
influences on judgment and choice. Cognition and Emotion, 14(4), 473–493.
Lerner, J. S., & Keltner, D. (2001). Fear, anger, and risk. Journal of Personality and Social
Psychology, 81, 146–159.
Lerner, J. S., & Tiedens, L. Z. (2006). Portrait of the angry decision maker: How appraisal
tendencies shape anger’s influence on cognition. Journal of Behavioral Decision
Making. Special Issue: The Role of Affect in Decision Making, 19(2), 115–137.
Loewenstein, G., & Lerner, J. S. (2003). The role of affect in decision making. In R.
Davidson, K. Scherer, & H. Goldsmith (Eds.), Handbook of affective science (pp.
619–642). New York: Oxford University Press.
54

Lopes, L. L., & Oden, G. C. (1999). The role of aspiration level in risky choice: A
comparison of cumulative prospect theory and SP/A theory. Journal of Mathematical
Psychology, 43(2), 286–313.
Markus, H. R., & Kitayama, S. (1994). The cultural construction of self and emotion:
Implications for social behavior. In S. Kitayama & H. R. Markus (Eds.), Emotion and
culture: Empirical studies of mutual influence (pp. 89–132). Washington, DC:
American Psychological Association.
Matthews, G., Zeidner, M., & Roberts, R. D. (2002). Emotional intelligence: science and
myth. Cambridge, MA: MIT Press.
Mayer, J. D., Salovey, P., & Caruso, D. R. (2002). Mayer-Salovey-Caruso emotional
intelligence test (MSCEIT) user's manual. Toronto: Multi-Health Systems.
Mayer, J. D., Salovey, P., Caruso, D. R., & Sitarenios, G. (2001). Emotional intelligence as a
standard intelligence. Emotion, 1, 232–242.
Mellers, B. A., Schwartz, A., Ho, K., & Ritov, I. (1997). Decision affect theory: Emotional
reactions to the outcomes of risky options. Psychological Science, 423–429.
Miller, N. E. (1944). Experimental studies of conflict. In J. M. Hunt (Ed.), Personality and
the behavior disorders: A handbook based on experimental and clinical research
(Vol. 1, pp. 431–465). New York: Ronald Press.
Muraven, M., & Baumeister, R. F. (2000). Self-regulation and depletion of limited resources:
Does self-control resemble a muscle?, Psychological Bulletin, 126(2), 247–259.
Neal, D. T., & Chartrand, T. L. (in press). Embodied emotion perception: Amplifying and
dampening facial feedback modulates emotion perception accuracy. Social
Psychological and Personality Science.
55

Nowicki, S., Jr., & Carton, J. S. (1993). Measuring emotional intensity from facial
expressions: The DANVA FACES 2. Journal of Social Psychology, 133, 749–750.
Nowicki, S., & Duke, M. P. (2001). Nonverbal receptivity: The diagnostic analysis of
nonverbal accuracy (DANVA).
Pitterman, H., & Nowicki, S. (2004). A test of ability to identify emotion in human standing
and sitting postures: The diagnostic analysis of nonverbal accuracy-2 posture test
(DANVA2-POS). Genetic, Social, and General Psychology Monographs, 130(2),
146–162.
Richards, J. M., & Gross, J. J. (2000). Emotion regulation and memory: the cognitive costs of
keeping one's cool. Journal of Personality and Social Psychology, 79(3), 410.
Rosenthal, R. (1987). Judgment studies: Design, analysis, and meta-analysis. Cambridge,
England: Cambridge University Press.
Sanchez-Burks, J., & Huy, Q. N. (2009). Emotional aperture and strategic change: The
accurate recognition of collective emotions. Organization Science, 20(1), 22–34.
Salovey, P., & Mayer, J. D. (1990). Emotional intelligence. Imagination, Cognition and
Personality, 9(3), 185–211.
Scott, B. A., & Barnes, C. M. (2011). A multilevel field investigation of emotional labor,
affect, work withdrawal, and gender. Academy of Management Journal, 54(1), 116-
136.
Schuga, J., Matsumoto, D., Horita, Y., Yamagishi, T., & Bonnet, K. (2010). Emotional
expressivity as a signal of cooperation. Evolution and Human Behavior, 31, 87–94.
Smith, C. A., & Ellsworth, P. C. (1985). Patterns of cognitive appraisal in emotion. Journal
of Personality and Social Psychology, 48(4), 813.
56

Spilich, G. (2012). Duchenne and social smiles photographic set. Chestertown, MD:
Washington College.
Staw, B. M., Bell, N. E., & Clausen, J. A. (1986). The dispositional approach to job attitudes:
A lifetime longitudinal test. Administrative Science Quarterly, 56–77.
Thompson, L. L. (1991). Information exchange in negotiation. Journal of Experimental
Social Psychology, 27(2), 161-179.
Thompson, R. A. (1994). Emotion regulation: A theme in search of definition. In N. A. Fox
(Ed.), The development of emotion regulation: Biological and behavioral
considerations, Monographs of the Society for Research in Child Development, (Vol.
59, pp. 25–52).
Thompson, L., Medvec, V. H., Seiden, V., & Kopelman, S. (2004). Poker face, smiley face,
and rant 'n' rave: Myths and realities about emotion in negotiation. Malden:
Blackwell Publishing.
Tooby, J., & Cosmides, L. (1990). The past explains the present: Emotional adaptations and
the structure of ancestral environments. Ethology and Sociobiology, 11(4), 375–424.
Tidd, K. L., & Lockard, J. S. (1978). Monetary significance of the affiliative smile: A case
for reciprocal altruism. Bulletin of the Psychonomic Society, 11, 344–346.
Van Kleef, G. A., & Coté, S. (2007). Expressing anger in conflict: When it helps and when it
hurts. Journal of Applied Psychology, 92(6), 1557–1569.
Van Kleef, G. A., De Dreu, C. K. W., & Manstead, A. S. R. (2004). The interpersonal effects
of emotions in negotiations: A motivated information processing approach. Journal of
Personality and Social Psychology, 87(4), 510–528.
57

Van Dijk, E., Van Kleef, G. A., Steinel, W., & Van Beest, I. (2008). A social functional
approach to emotions in bargaining: When communicating anger pays and when it
backfires. Journal of Personality and Social Psychology, 94(4), 600.
Wagner, H. L. (1993). On measuring performance in category judgment studies of nonverbal
behavior. Journal of Nonverbal Behavior, 17(1), 3–28.
Watson, D., & Tellegen, A. (1985). Toward a consensual structure of mood. Psychological
Bulletin, 98, 219–235.
Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief
measures of positive and negative affect: The PANAS scales. Journal of Personality
and Social Psychology, 54, 1063–1070.
Wegner, D. M., Erber, R., & Zanakos, S. (1993). Ironic processes in the mental control of
mood and mood-related thought. Journal of Personality and Social Psychology,
65(6), 1093.
Wong, C., Law, K., & Wong, P. (2004). Development and validation of a forced choice
emotional intelligence measure for Chinese respondents in Hong Kong. Asia Pacific
Journal of Management, 21(4), 535–559.
Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2009). A survey of affect recognition
methods: Audio, visual, and spontaneous expressions. Pattern Analysis and Machine
Intelligence, IEEE Transactions, 31(1), 39–58.
58

Appendix A: Self-Regulation Manipulations

Suppression (SP):
SPECIAL INSTRUCTIONS: PLEASE FOLLOW CAREFULLY!

We will show you the video in just a moment. Please watch it carefully and listen to the
accompanying sound through the headphones. In addition, we would like to see how well you can
control your facial expressions. Therefore, it is very important to us that you try your best to adopt
a neutral facial expression as you watch the film. To do this, we would like for you to keep your
facial muscles from moving. In other words, as you watch the video, try to keep a straight face by
keeping the muscles around your neck, your chin, your lips, your cheeks, your eyes, and your
forehead very still. So, watch the film clip carefully, but please try to keep your facial muscles still
so that you don't make any expressions at all.

Reappraisal (RA):
SPECIAL INSTRUCTIONS: PLEASE FOLLOW CAREFULLY!

We will show you a movie clip in just a moment. Please view the video carefully and listen to the
accompanying sound through the headphones. In addition, we would like to see how well you can
control the way you view things. Therefore, it is very important to us that you try your best to
adopt a neutral attitude as you watch the video. To do this, we would like for you to view this film
clip with the detached interest of a movie critic (e.g., observing the setting, the costumes and
clothing, the lighting, and the overall cinematography in general). In other words, as you watch the
video, try to think about it objectively and analytically rather than as personally, or in any way,
emotionally relevant to you. So, watch the film clip carefully, but please try to think about what
you are seeing in such a way that you don't feel anything at all.

59

Appendix B: Profile of Non-Verbal Sensitivity – Video 40 Scenes

Scene 1
 Admiring nature
 Saying a prayer
Scene 21
 Leaving on a trip
 Nagging a child
Scene 2
 Admiring nature
 Helping a customer
Scene 22
 Criticizing someone for being late
 Expressing gratitude
Scene 3
 Leaving on a trip
 Expressing deep affection
Scene 23
 Saying a prayer
 Talking about one’s wedding
Scene 4
 Trying to seduce someone
 Talking to a lost child
Scene24
 Asking forgiveness
 Nagging a child
Scene 5
 Talking about one’s wedding
 Talking about one’s divorce
Scene 25
 Expressing deep affection
 Admiring nature
Scene 6
 Expressing strong dislike
 Helping a customer
Scene 26
 Helping a customer
 Asking forgiveness
Scene 7
 Nagging a child
 Criticizing someone for being late
Scene 27
 Expressing jealous anger
 Threatening someone
Scene 8
 Saying a prayer
 Threatening someone
Scene 28
 Talking about one’s divorce
 Leaving on a trip
Scene 9
 Ordering food in a restaurant
 Threatening someone
Scene 29
 Talking about one’s divorce
 Trying to seduce someone
Scene 10
 Leaving on a trip
 Trying to seduce someone
Scene 30
 Talking about one’s divorce
 Returning a faulty item to the store
Scene 11
 Talking about the death of a friend
 Expressing jealous anger
Scene 31
 Expressing strong dislike
 Expressing deep affection
Scene 12
 Returning a faulty item to a store
 Helping a customer
Scene 32
 Ordering food in a restaurant
 Expressing jealous anger
Scene 13
 Expressing jealous anger
 Nagging a child
Scene 33
 Returning a faulty item to the store
 Expressing strong dislike
Scene 14
 Returning a faulty item to a store
 Talking about the death of a friend
Scene 34
 Nagging a child
 Talking to a lost child
Scene 15
 Talking about one’s divorce
 Asking forgiveness
Scene 35
 Expressing motherly love
 Threatening someone
Scene 16
 Expressing motherly love
 Talking to a lost child
Scene 36
 Expressing deep affection
 Nagging a child
Scene 17
 Expressing mother’s love
 Asking for forgiveness
Scene 37
 Talking about one’s wedding
 Talking about the death of a friend
Scene 18
 Expressing strong dislike
 Ordering food in a restaurant
Scene 38
 Criticizing someone for being late
 Expressing gratitude
Scene 19
 Admiring nature
 Expressing motherly love
Scene 39
 Saying a prayer
 Nagging a child
Scene 20
 Talking about one’s wedding
 Expressing gratitude
Scene 40
 Threatening someone
 Expressing strong dislike

Abstract (if available)

Abstract This paper examines how emotion suppression and emotion reappraisal may differentially affect people's ability to recognize emotions in others. I hypothesized that the cost of suppressing one's own emotion, compared to reappraising one's own emotion, results in decreased rates of emotion recognition in others. Study 1 supported the hypothesis for the experienced emotions of anger, happiness, and sadness in that emotion reappraisal resulted in higher rates of recognition accuracy than emotion suppression. This study also demonstrated that people who are angry or happy are better able to detect others' emotions than are people who are fearful or sad. Study 2 replicated and extended some of these findings using a separate static photographic set. Overall, these results provide evidence that suppressing one's emotions impairs emotion recognition more severely than does reappraising one's emotion state. The findings from the current investigation extend research in emotional and social intelligence by identifying potential conditions under which emotional cues are likely to be more accurately perceived and interpreted.

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

The curse of loyalty: interdependent self construal and support for corruption

PDF

Better now than later: the cost of victims' delayed accusations

PDF

The interpersonal effect of emotion in decision-making and social dilemmas

PDF

Power, status, and organizational citizenship behavior

PDF

Social class determines which purchases bring happiness

PDF

The development of object recognition in the newborn brain

PDF

The balanced scorecard and long-term financial performance: evidence from publicly traded companies

PDF

The morality of technology

PDF

Green strategies: producers' competition and cooperation in sustainability

PDF

Black sheep or devil protection?: the effects of cooperative and competitive intergroup goal structures on group members reactions to an offensive act when committed by an ingroup or an outgroup ...

PDF

The effects of curiosity-evoking events on consumption enjoyment

PDF

Essays on bounded rationality and revenue management

PDF

The effects of a customer's comparative processing with positive and negative information on product choice

PDF

Increasing capabilities or decreasing cost: Fairness perceptions of job displacement due to automation and outsourcing

PDF

The influence of organizational ethics on job attitudes and government performance

PDF

Essays on commercial media and advertising

PDF

Technology, behavior tracking, and the future of work

PDF

Effect of mindfulness training on attention, emotion-regulation and risk-taking in adolescence

PDF

Two essays on the mutual fund industry and an application of the optimal risk allocation model in the real estate market

PDF

For the love of the game? ownership and control in the NBA

Asset Metadata

Creator Wood, Adam (author)

Core Title The cost of a poker-face: consequences of self-regulation on emotion recognition and interpersonal perception

School Marshall School of Business

Degree Doctor of Philosophy

Degree Program Business Administration

Publication Date 11/15/2015

Defense Date 10/23/2013

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag OAI-PMH Harvest,organizational behavior

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Wiltermuth, Scott (committee chair), Cummings, Thomas (committee member), Read, Stephen (committee member)

Creator Email adamw@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-346235

Unique identifier UC11296288

Identifier etd-WoodAdam-2152.pdf (filename),usctheses-c3-346235 (legacy record id)

Legacy Identifier etd-WoodAdam-2152.pdf

Dmrecord 346235

Document Type Dissertation

Format application/pdf (imt)

Rights Wood, Adam

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA