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Differentiating Emotions Across Contexts: Comparing Adults With and Without Social Anxiety Disorder Using Random, Social Interaction, and Daily Experience Sampling


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The ability to recognize and label emotional experiences has been associated with well-being and adaptive functioning. This skill is particularly important in social situations, as emotions provide information about the state of relationships and help guide interpersonal decisions, such as whether to disclose personal information. Given the interpersonal difficulties linked to social anxiety disorder (SAD), deficient negative emotion differentiation may contribute to impairment in this population. We hypothesized that people with SAD would exhibit less negative emotion differentiation in daily life, and these differences would translate to impairment in social functioning. We recruited 43 people diagnosed with generalized SAD and 43 healthy adults to describe the emotions they experienced over 14 days. Participants received palmtop computers for responding to random prompts and describing naturalistic social interactions; to complete end-of-day diary entries, they used a secure online website. We calculated intraclass correlation coefficients to capture the degree of differentiation of negative and positive emotions for each context (random moments, face-to-face social interactions, and end-of-day reflections). Compared to healthy controls, the SAD group exhibited less negative (but not positive) emotion differentiation during random prompts, social interactions, and (at trend level) end-of-day assessments. These differences could not be explained by emotion intensity or variability over the 14 days, or to comorbid depression or anxiety disorders. Our findings suggest that people with generalized SAD have deficits in clarifying specific negative emotions felt at a given point of time. These deficits may contribute to difficulties with effective emotion regulation and healthy social relationship functioning. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
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Differentiating Emotions Across Contexts: Comparing Adults
with and without Social Anxiety Disorder Using Random, Social
Interaction, and Daily Experience Sampling
Todd B. Kashdan and
Department of Psychology, George Mason University
Antonina S. Farmer
Department of Psychology, George Mason University
The ability to recognize and label emotional experiences has been associated with well-being and
adaptive functioning. This skill is particularly important in social situations, as emotions provide
information about the state of relationships and help guide interpersonal decisions, such as
whether to disclose personal information. Given the interpersonal difficulties linked to social
anxiety disorder (SAD), deficient negative emotion differentiation may contribute to impairment
in this population. We hypothesized that people with SAD would exhibit less negative emotion
differentiation in daily life, and these differences would translate to impairment in social
functioning. We recruited 43 people diagnosed with generalized SAD and 43 healthy adults to
describe the emotions they experienced over 14 days. Participants received palmtop computers for
responding to random prompts and describing naturalistic social interactions; to complete end-of-
day diary entries, they used a secure online website. We calculated intraclass correlation
coefficients to capture the degree of differentiation of negative and positive emotions for each
context (random moments, face-to-face social interactions, and end-of-day reflections). Compared
to healthy controls, the SAD group exhibited less negative (but not positive) emotion
differentiation during random prompts, social interactions, and (at trend level) end-of-day
assessments. These differences could not be explained by emotion intensity or variability over the
14 days, or to comorbid depression or anxiety disorders. Our findings suggest that people with
generalized SAD have deficits in clarifying specific negative emotions felt at a given point of
time. These deficits may contribute to difficulties with effective emotion regulation and healthy
social relationship functioning.
social anxiety disorder; negative emotions; ecological momentary assessment; experience
Correspondence concerning this article should be addressed to Todd B. Kashdan, Department of Psychology, MS 3F5, George Mason
University, Fairfax, VA 22030.
T. B. Kashdan and A. S. Farmer contributed equally to this work.
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Emotion. Author manuscript; available in PMC 2015 June 01.
Published in final edited form as:
Emotion. 2014 June ; 14(3): 629–638. doi:10.1037/a0035796.
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People with the generalized subtype of social anxiety disorder (SAD) experience an intense,
persistent fear of drawing attention to themselves in social situations, believing that their
flaws will be exposed and that they, in turn, will be evaluated negatively and rejected
(Morrison & Heimberg, 2013). As one of the most common psychiatric disorders in the
United States, epidemiological studies have demonstrated a lifetime prevalence rate as high
as 12 to 16% (Kessler et al., 2005; Magee, Eaton, Wittchen, McGonagle, & Kessler, 1996).
This disorder is associated with significant impairment in social, occupational, and daily
functioning (Schneier et al., 1994). In this study, we investigated a new mechanism that may
play a role in the development or maintenance of excessive, impairing social anxiety. In
particular, we explored the relationship between SAD and the degree to which people felt
emotional experiences in the context of everyday life events.
People experience a constant stream of information—internal and external—throughout
their daily lives that help them navigate the social world. Emotional experiences are one
such source of information (Keltner & Kring, 1998). At times these experiences are distinct
(e.g., intense anger with a clear cause), but at other times, emotions are diffuse and even
muddled (e.g., a general sense of discomfort, dread, and anger, with no particular
distinguishing features). Emotion differentiation, also referred to as emotional granularity, is
the degree to which a person is able to classify felt experiences into discrete emotion
categories (Barrett, Gross, Christensen, & Benvenuto, 2001; Tugade, Fredrickson, & Barrett,
2004). People vary in their ability to differentiate positive (Tugade et al., 2004) and negative
emotions (e.g., Barrett, 2004; Kashdan, Ferssizidis, Collins, & Muraven, 2010). People low
in emotion differentiation are less attentive to and less able to describe how they feel at any
given time; thus, their descriptions of emotional states tend to be limited to broader, non-
specific terms such as “good” for people low in positive emotion differentiation or “bad” for
people low in negative emotion differentiation.
The ability to recognize and label distinct emotional experiences with great specificity has
been shown to buffer people from maladaptive behaviors and other adverse outcomes that
often arise in response to stress. In general, the more differentiated people’s emotions are,
the better able they are to use that emotion as a source of information to calibrate their
behavioral responses to match the demands of a given situation (Barrett et al., 2001). High
differentiators of negative emotions recover faster from induced negative mood states
(Salovey, Mayer, Goldman, Turvey, & Palfai, 1995), employ a broader range of emotion
regulation strategies (Barrett et al., 2001), and are less likely to use avoidant or impulsive
coping strategies during stressful situations (Tugade et al., 2004; Zaki, Coifman, Rafaeli,
Berenson, & Downey, in press). The ability to differentiate emotions provides people access
to information about whether their goals are being met and—if not—about the particular
strategies that would best help them pursue these goals (Carver & Scheier, 1990). Negative
emotion differentiation may be particularly important in social situations, as emotions
provide important information about the state of a relationship and help guide decisions to
resolve conflict, communicate needs, enhance social bonds, or protect oneself from
Emotion differentiation is conceptually related to the broader construct of alexithymia—a
deficit in insight into emotions, including difficulty identifying and labeling emotions,
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verbally describing emotional states, and differentiating the subjective from the
physiological arousal aspect of emotion. Alexithymia has been assessed with global self-
report questionnaires, with occasional corroboration with observer-ratings in clinical settings
(Taylor, Bagby, & Luminet, 2000). In a study comparing patients with SAD to patients with
generalized anxiety disorder and a healthy comparison group (Turk, Heimberg, Luterek,
Mennin, & Fresco, 2005), researchers found that patients with SAD endorsed greater
difficulty identifying and describing their emotions than healthy controls, and greater
difficulty describing their emotions than patients with generalized anxiety disorder. In
another study, people with SAD could be distinguished from people without SAD based on
their endorsement of global survey items indicating difficulty understanding emotions
(Mennin, McLaughlin, & Flanagan, 2009).
While single occasion, self-report questionnaire studies hint at a relationship between SAD
and negative emotion differentiation, the methodology of this line of research is limited by
its reliance on people’s ability to accurately recall and aggregate their emotional
experiences. Such global self-report measures (e.g., Taylor, Ryan, & Bagby, 1985) require
people to describe their metacognitive abilities without a specific time frame . For example,
they may ask participants to indicate their agreement with items such as “I am usually very
clear about my feelings” on a 1 (strongly disagree) to 5 (strongly agree) scale. Thus, the
validity of this approach might be compromised when people have poor insight into their
internal, felt experiences (as implied by anybody who has alexithymia).
To capture negative emotion differentiation more directly and in a naturalistic context,
researchers have sought to observe how people actually distinguish among emotion
categories over multiple assessments in their daily lives (Barrett et al., 2001; Demiralp et al.,
2012; Pond et al., 2011; Zaki et al., in press). This methodology is not only grounded in
people’s actual experiences over time (i.e., more ecologically valid) but also minimizes bias
due to social desirability, since questions about emotion and mood are time-limited. By
calculating the associations between the emotion adjectives people endorse at any
measurement instance, researchers capture the degree to which people use emotion
categories to represent distinct felt experiences (e.g., Tugade et al., 2004). This measurement
approach is consistent with literature on emotional complexity, which argues that people
gain complexity in their understanding of emotional experiences as they learn to associate
the activation of particular emotion circuits with a category tied to environmental conditions
(Lindquist & Barrett, 2008).
In the only empirical investigation of global questionnaires of emotional clarity and/or
alexithymia with the intensive repeated measure approach to assess emotion differentiation,
there was virtually no association between these constructs (rs < .10; Boden, Thompson,
Dizén, Berenbaum, & Baker, 2013). This fits with the notion that emotions are short-lived
experiences and best measured in the moment as opposed to a context-free retrospective
assessment across time (“in general”). To date, the only two studies on constructs related to
emotion differentiation relied on surveys asking people with SAD to reflect on how effective
they are at paying attention to and clarifying their exact, specific emotions (Mennin et al.,
2009; Turk et al., 2005). Instead of using measures that reflect an individual’s perception of
his/her skill in differentiating clearly among emotions categories, we measured people’s
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skills directly in multiple real-world social and non-social settings across a two-week
assessment period. This novel methodological approach allows for a fine-grained analysis of
whether people with SAD show emotion-related deficits across multiple contexts with an
ability-based measure.
Negative emotion differentiation may be particularly important when stressors induce
distress, creating a particular need for healthy emotion regulation strategies that increase the
likelihood of desirable intrapersonal and social outcomes (Barrett et al., 2001; Gohm, 2003).
For people with SAD, most social interactions tend to be stressful and anxiety-provoking.
Thus, it is likely that they have difficulty identifying primary emotions such as sadness, fear,
and anger, and instead experience their emotions as undifferentiated, confusing, and
overwhelming. Turk et al. (2005) found that participants with SAD reported paying less
attention to emotions (positive and negative) compared to healthy controls or participants
with generalized anxiety disorder. Paying attention to proprioceptive cues during emotional
arousal can help people make meaning from these sensations, aid encoding, and relate them
to future situations, resulting in more differentiated experiences. Taken together, prior
research suggests that people with SAD may fail to take advantage of the adaptive
information that their emotions provide about social interactions and relationships.
Existing theoretical models of SAD offer insight into the potential relevance of negative
emotion differentiation. When socially anxious people are hyper-focused on making a good
impression on others, they often fear that they are deficient in some important way
(Moscovitch, 2009) or doubt their ability to be successful in social situations (Schlenker &
Leary, 1982). It appears that people with SAD experience primarily two kinds of negative
emotions as they orient to preventing the public exposure of self-attributed flaws in social
situations: (a) anxiety about the potential that such attributes would be exposed; and (b)
embarrassment if exposure of flaws or failure to make a desired impression is perceived
(Moscovitch, 2009). To reduce the possibility of being evaluated and rejected by other
people, people devote considerable cognitive resources to anticipating, avoiding, and
controlling anxiety-related thoughts, feelings, and behaviors. This includes engaging in
safety behaviors, such as excessive rehearsal, self-censorship, and deflection of attention to
minimize the possibility of feared consequences (Clark & Wells, 1995). Recurrent, intense
efforts to control anxiety (i.e., acts of experiential avoidance) are essentially a mode of
prevention where the avoidance of threat and failure dominates.
To the extent that people with SAD are oriented toward fearing and avoiding social
situations, the range of possible negative emotions they tend to experience would necessarily
be quite limited. Unfortunately, the prevention mode inherent to SAD disrupts the ability to
pay attention to situational and proprioceptive cues when emotionally aroused (see Morrison
& Heimberg, 2013). Excessive self-focused attention during anxiety-provoking situations
combined with hypervigilance to threat and reliance on safety behaviors may serve to reduce
the ability of people with SAD to attend to stimuli that may differentiate negative emotions.
That is, with their information processing biases and reduced attentional control (e.g.,
Heimberg, Brozovich, & Rapee, 2010), people with SAD may be more likely to describe
their emotional experience in a crude manner, focusing on the degree of distress instead of
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the discrete types of emotions that are distressing (e.g., high anxiety, moderate anger, no
Prior research has found that people with SAD show overall elevations in negative emotions
and deficits in positive emotions, as well as a tendency to view positive events as
threatening, which may further contribute to the co-occurrence of (minimal) positive and
(excessive) negative emotions (Kashdan, Weeks, & Savostyanova, 2011). Extending prior
theorizing on SAD and emotions, we suspect that people with SAD will also show evidence
of low differentiation of negative emotions. Because people with SAD generally experience
fewer and less intense positive emotions, the presence of positive emotional experiences is
expected to be fairly context-specific; thus, there is no reason to expect low positive emotion
differentiation to be part of the phenomenology of SAD.
Lack of skill in discriminating between unpleasant states may contribute to people with SAD
viewing emotions as upsetting and uncomfortable and, consequently, making efforts to
suppress or avoid them. A growing body of evidence suggests that people with SAD possess
a skill deficit in using healthy emotion regulation strategies such as cognitive reappraisal
effectively (Werner, Goldin, Ball, Heimberg, & Gross, 2011). In one functional
neuroimaging study, participants with SAD not only reported more difficulty employing
cognitive reappraisal to reduce negative emotions, but also displayed less activation in brain
regions related to this strategy (Goldin, Manber-Ball, Werner, Heimberg, & Gross, 2009).
Additionally, people with SAD are more likely to view emotions, even positive emotions, as
threatening compared to healthy controls (Turk et al., 2005). The combination of lack of
skills for managing emotions and negative reactions to emotions suggest that people with
SAD would likely make efforts to quash emotional experiences. Consistent with this,
researchers have found people with SAD to be more likely to use emotion regulation
strategies of avoidance and suppression, which tend to be ineffective or counterproductive
for diminishing negative experiences (e.g., Farmer & Kashdan, 2012; Kashdan, Morina, &
Priebe, 2009; Kashdan & Steger, 2006). Since negative emotions are most likely to be
targeted in emotion regulation efforts, having difficulty distinguishing among discrete
negative emotions may contribute to people with SAD failing to respond to emotional
experiences in an appropriate manner given the environmental context.
Only a handful of published studies exist on the real-world emotions of people with SAD.
Each of these studies has focused exclusively on intensity aggregated pleasant and
unpleasant emotions, with the one exception that targeted the tendency of people with SAD
to fear and avoid anxiety (Kashdan et al., 2013). However, other dimensions of emotion may
also be relevant to the phenomenology of SAD. In particular, intraindividual variability in
emotions over time and the degree to which people experience emotions in a discrete and
differentiated manner may also be related to SAD symptomology. By gathering reports of
emotions close in time in naturally occurring contexts, researchers increase the ecological
validity and generalizability of findings by reducing the potential for retrospective bias.
These issues are relevant to the basic understanding of emotion and especially important for
the study of people with SAD, who are prone to negatively biased information processing
(Clark & McManus, 2002). In fact, the only way to determine the relevance of emotion
differentiation to emotional difficulties and functional impairment (such as SAD) is to
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measure actual differentiation in naturalistic setting over time and compare this ability with
other dimensions of emotion in the same study (i.e., simultaneous investigation of within-
person variability and between-person differences in intensity of emotions).
The present study extends prior research by using an experience sampling design to directly
assess emotion differentiation skills in people with SAD and a demographically matched
group of healthy adults. The availability of a disordered and healthy sample allowed us to
examine the importance of dynamic intra-individual emotion-related processes to the
presence of SAD. Using portable electronic devices, we investigated the extent to which
people discriminate among emotion categories in three contexts: during random prompts,
during face-to-face social interactions (reported on immediately afterwards), and during end-
of-day reports (i.e., reflecting on the whole day). Prior to addressing the primary research
questions, we explored the relationship between three different indices of emotion structure:
intensity, variability, and differentiation. We hypothesized that people who were better able
to discriminate among specific emotions would derive greater benefits, defined as less
intense and variable negative emotions and more intense and less variable positive emotions;
fitting with prior research (e.g., Boden et al., 2013). Results consistent with our hypotheses
would provide additional evidence for emotion differentiation being linked to psychological
health. With regard to the primary research questions, we hypothesized that: (a) participants
with SAD would display less differentiation of negative emotions than healthy controls
across random, social, and end-of-day contexts; (b) these effects would not be attributed to
group differences in average emotion intensity and variability over the 14-day assessment
period; and (c) negative emotion differentiation deficits would be linked specifically to
social anxiety severity, controlling for comorbid conditions—an important test of construct
specificity given recent evidence of emotion differentiation difficulties in people with major
depressive disorder (Demiralp et al., 2012). Given that people with SAD tend to experience
broad deficits in positive emotionality (Kashdan et al., 2011), rare positive experiences are
likely to be context-specific; thus, we expected group differences to be restricted to the
ability to differentiate negative (and not positive) emotions.
We recruited 86 adults from the Northern Virginia community, of whom 43 were diagnosed
with Social Anxiety Disorder (SAD), generalized subtype, and 43 were a healthy control
(HC) group with no psychological difficulties. All participants spoke English fluently and
were familiar with using computers. During initial screening procedures, participants were
excluded from the HC group if they endorsed symptoms consistent with any psychological
diagnosis, and participants were excluded from the SAD group if they presented with
symptoms of psychosis, substance misuse, or suicidality. We excluded one participant for
not providing more than three entries for any of the data collection methods after the initial
screening. This led to a final sample of 43 participants with SAD (26 women) and 42 HC
participants (27 women), with an average age of 28.5 years (SD = 8.6). Of our sample, 46
identified themselves as White/Caucasian, 17 as Black/African-American, 9 as Hispanic/
Latino, 4 as Asian/Asian-American, 1 as Middle Eastern, and 8 as “other”. As for
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relationship status, 52 participants were single, 14 were married, 10 were cohabitating, 4
were divorced or separated, and 4 listed another relationship status. As for education level, 6
individuals had completed high school or less, 28 had finished some college, 6 completed an
Associate’s degree or professional school, 26 held a Bachelor’s degree, and 18 had
completed at least some graduate study. Notably, one participant in the HC group omitted
questions on relationship and education status.
We used a semi-structured clinical interview to evaluate participants for the presence of
Axis I psychological diagnoses. Of the SAD group, 19 people met criteria for a comorbid
anxiety disorder (44%) and eight (19%) met criteria for a current major depressive disorder
(MDD) episode or dysthymic disorder, and one participant met criteria for bipolar disorder.
The average age of onset for social anxiety disorder was 12.5 years (SD = 4.3). Notably,
42% of participants in the SAD group had no comorbid diagnoses, and only 23% were
receiving pharmacological treatment.
Potential participants called our laboratory in response to online advertisements and bulletin
board flyers in the community. Following a brief verbal informed consent procedure, trained
research assistants conducted phone screens to assess for social anxiety, generalized anxiety,
and depression symptoms, as well as functional impairment, suicidality, and psychotic
symptoms. If potential participants endorsed social fears beyond public performance
situations (or no clinically significant psychological difficulties for the HC group), the
research assistant scheduled an initial face-to-face appointment. During these sessions
(conducted with 122 potentials), participants provided informed consent, completed self-
report measures, and underwent clinical interviews by doctoral-level students in clinical
psychology. The Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I; First,
Spitzer, Gibbon, & Williams, 2002) was used to assess for anxiety, mood, substance use,
eating, and psychotic disorders. In addition, the SAD module of the Anxiety Disorders
Interview Schedule for DSM–IV: Lifetime Version (Di Nardo, Brown, & Barlow, 1994)
provided additional information to determine SAD subtype. To be eligible for the SAD
group, generalized SAD had to be the primary or most severe diagnosis if other comorbid
psychiatric conditions were present. Inter-rater reliability (based on 45 randomly chosen
recorded interviews) suggested acceptable agreement for SAD diagnoses (Cohen’s κ = .87).
After diagnostic interviews, participants received palmtop devices and a 1.5-hour session to
instruct them on how to provide self-initiated recording of daily social interactions, respond
to random prompts, and provide online end-of-day records for the following 14 days. To
maximize compliance, we used an incentive structure, such that participants received a
minimum payment of $165 and could earn up to an additional $50 for prompt and consistent
reporting. Researchers sent multiple reminder e-mails each week to remind participants
about instructions, data coding details, and confidentiality. Furthermore, we kept
experience-sampling measures brief to maintain participant motivation and to minimize
missing data (see Nezlek, 2012). At the end of the experience sampling data collection,
participants were debriefed and data were downloaded from their palmtop devices.
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Global Self-Report Measures
Social anxiety severity—The Social Interaction Anxiety Scale (SIAS; Mattick & Clarke,
1998) is a self-report measure of tendencies to fear and avoid social interactions due to
concerns about being scrutinized by other people. Participants rated 20 statements using a 5-
point Likert scale ranging from 0 (not at all characteristic of me) to 4 (extremely
characteristic of me). Higher total scores represent greater social anxiety. This scale has
demonstrated good test-retest reliability, convergent validity, and discriminant validity
across clinical and community samples (Brown et al., 1997; Heimberg, Mueller, Holt, Hope,
& Liebowitz, 1993; Mattick & Clarke, 1998). Prior work has shown that removing the three
reverse scored items improves reliability and validity (Rodebaugh et al., 2011; Rodebaugh,
Woods, & Heimberg, 2007). Thus, we used the 17-item SIAS-Straightforward (SIAS-S)
scores for analyses for a more reliable and valid measure, although the 17- and 20-item
versions in our sample had identical internal consistency (α = .971) and correlated at .993, p
< .001.
Experience Sampling Measures
Momentary emotion ratings—Five times per day, participants received a prompt to
record their emotional experiences using their PDA devices. These prompts occurred three
to five random times during the waking hours (minus blackout times provided by the
participant, e.g., when driving to work). Participants were asked to rate their positive and
negative affective experiences at that moment using a 5-point Likert scale from 1 (very
slightly or not at all) to 5 (extremely). The four positive emotion items were content,
relaxed, enthusiastic, and joyful. The four negative emotion items were anxious/nervous,
angry, sad, and sluggish. These adjectives spanned both high and low energy quadrants of
positive and negative emotions in the circumplex model of emotion (Barrett, 1998).
Social interaction emotion ratings—Social interaction recordings were participant-
initiated recordings (via PDAs) of social interactions lasting at least 10 minutes (as soon as
possible after the interaction). The positive and negative emotion items were identical to
those used for random prompts, with the same 5-point Likert response scale. We used time
stamps of social interaction entries to ensure that participants did not enter multiple
interactions in one sitting. All but two entries (99.7%) were at least 25 minutes apart from
each other.
End-of-day emotion ratings—Each evening during the data collection period,
participants used a de-identified code to log on to a dedicated encrypted website to answer
questions about that particular day. Participants rated their positive and negative affect
experiences on that particular day using a 5-point Likert scale from 1 (very slightly/not at
all) to 5 (extremely). The six positive affect items were content, relaxed, enthusiastic, joyful,
proud, and interested. The six negative affect items were anxious, angry, sluggish, sad,
irritable, and distressed. To minimize retrospective bias, participants were instructed to
enter data between 6:00 P.M. on the day in question up to 11:59 A.M. of the following day.
Entries submitted outside the requested times were excluded from analyses (checked via
date-and-time stamps).
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Calculating Emotion Differentiation, Intensity, and Variability
We computed indices of positive and negative emotion differentiation by calculating the
average intraclass correlations (ICCs) with absolute agreement between the positive and
negative adjectives, respectively, across all assessment points for each participant (see
Kashdan et al., 2010; Tugade et al., 2004). Larger ICCs indicate greater relation between
emotion categories (i.e., lower level of emotion differentiation), while lower ICCs indicate
less relation between emotion categories (i.e., higher degree of emotion differentiation).
Although it is possible that a person experiences both anger and anxiety on a particular
occasion, a high differentiator should be less likely to report experiencing multiple negative
emotions consistently. Notably, ICCs were not calculated when participants reported no
variance in emotions (six participants gave lowest scores on all negative emotions in social
interactions and one for random prompts); for similar procedures, see Demiralp et al. (2012).
The average ICC for emotions in random prompts (positive: M = .61, SD = .22; negative: M
= .24, SD = .25), social interactions (positive: M = .64, SD = .27; negative: M = .29, SD = .
29), and end-of-day reports (positive: M = .60, SD = .24; negative: M = .45, SD = .29)
suggested an acceptable level of variability for analyses. We multiplied scores by −1 to
improve interpretation with larger scores reflecting greater emotion differentiation, and we
performed Fisher r-to-z transformation prior to subsequent analyses (Corey, Dunlap, &
Burke, 1998).
To answer questions as to whether emotion differentiation provides incremental information
beyond other elements of emotion, we also calculated the average intensity and variability
(standard deviation) of negative and positive emotions. We first created an average score of
the positive and negative emotion ratings (separately) at each measurement. Then we
averaged positive and negative scores, separately for random prompt, social interaction, and
end-of-day entries for each participant. To capture variability of positive and negative
emotions, we similarly aggregated the standard deviations of positive and negative emotion
ratings within each context.
Preliminary Analyses
Compliance—Overall 85% of participants completed at least one week of their end-of-day
entries. After excluding six participants for providing less than three days of entries, 79
remaining participants provided an average of 12.2 end-of-day entries (SD = 3.67; range = 4
– 23). Overall, 77% of participants responded to at least ¾ of random prompts. After
excluding 10 participants who provided less than three random prompt entries, we analyzed
76 participants who provided an average of 53.7 prompt responses (SD = 11.0; range = 19 –
81). After excluding 14 participants who provided fewer than three social interaction entries,
72 participants provided an average of 10.0 social interaction entries (SD = 4.5; range = 3 –
31). There were no significant differences between the SAD and HC groups in the number
of entries provided for any of the contexts (ps > .5). Furthermore, groups did not differ in the
percentage of random prompts to which participants responded (t = 0.49, p = .63) or time
between responses (t = −1.38, p = .17). Proportion of missing data was unrelated to outcome
variables (ps > .15). Groups also did not differ in age (t = 0.46, p = .65), gender, (χ2 = 0.13,
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p = .72), ethnicity, (χ2 = 2.63, p = .76), relationship status, (χ2 = 5.50, p = .24), or level of
education, (χ2 = 4.24, p = .38).
Social anxiety severity—The SIAS-S scores of our SAD group (M = 43.81, SD = 8.72)
were commensurate with clients in treatment for SAD (Rodebaugh et al., 2011), and
significantly higher than the healthy control group (M = 8.89, SD = 6.53), t = 20.86, p < .
001, d = 4.60. In addition, 100% of the SAD sample scored above the optimal cut-off score
of 34 on the 20-item SIAS for distinguishing people with SAD from a healthy community
comparison group (Brown et al., 1997). SAD participants also scored significantly higher on
the BDI (M = 17.44, SD = 10.88) than healthy controls (M = 3.19, SD = 3.50), t = 8.17, p < .
001, d = 1.79.
Emotion intensity and variability—Table 1 displays group means in positive and
negative emotion intensity and variability from each data collection method. Overall, the
SAD group reported greater intensity of negative emotions (ds = 1.03 to 1.19) and less
intensity of positive emotions (ds = 1.39 to 1.81). Additionally, the SAD group
demonstrated greater variability of negative (ds = 1.07 to 1.28) but not positive (ds = 0.0 to
0.13) emotions.
Emotion differentiation measures—Prior to testing hypotheses, we examined the
relationships of the emotion differentiation indices to rule out multicollinearity. Negative
emotion differentiation indices were significantly correlated across all three contexts (rs = .
403 – .519, ps ≤ .001). Positive emotion differentiation during random prompts was
positively correlated with positive emotion differentiation during social interactions (r = .
358, p = .002) and end-of-day (r = .333, p = .005); social interaction and end-of-day positive
emotion differentiation were not significantly correlated (r = .205, p = .096). These results
suggest that, although related, the emotion differentiation indices are sufficiently
independent to analyze as separate dependent variables when examining the effects of SAD
diagnostic status.
Additionally, we examined the relationships between emotion differentiation and other
indices of emotional structure within each context (see correlations in Table 2.). The small
to moderate correlations of emotion differentiation indices with emotion intensity and
variability indicate that these reflect different constructs. People who were better able to
differentiate negative emotions tended to experiencing less intense negative emotions and
more intense positive emotions on average, but this index was not related to emotion
variability. In contrast, positive emotion differentiation was significantly related to greater
variability in positive emotions, but not other aspects of emotional experiences. These
results suggest that people whose positive experiences are more variable tend to be better at
differentiating among discrete positive emotions, while those whose emotions are generally
less negative (more positive) are better able to discriminate among specific negative
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Are People with SAD Worse at Differentiating Emotions?
To minimize multiple comparisons, we conducted a three-way ANOVA of Group (SAD vs.
HC) × Valence (positive vs. negative) × Context (random prompts vs. social interaction vs.
end-of-day). This analysis yielded significant main effects of SAD group, F(1, 60) = 6.84, p
= .011, ηp2 = .102, valence, F(1, 60) = 143.26, p < .001, ηp2 = .705, Group × Valence, F(1,
60) = 4.66, p = .035, ηp2 = .072, context, F(2,59) = 3.99, p = .021, ηp2 = .062, and Valence ×
Context, F(2, 59) = 11.30, p < .001, ηp2 = .158, but not Group × Context (p = .376, ηp2 = .
016) or Group × Context × Valence (p = .795, ηp2 = .004). Post-hoc tests with Bonferroni
correction revealed that participants exhibited worse emotion differentiation at end-of-day
compared to random prompts (p = .014) but not social interactions (p = .230), and emotion
differentiation for random prompts and social interactions did not differ (p = .940).
Participants were generally better at differentiating negative than positive emotions;
however, while positive emotion differentiation was similar across contexts (p = .22, ηp2 = .
050), negative emotion differentiation differed across contexts (p < .001, ηp2 = .291).
Participants were worse at differentiating negative emotions at the end of the day compared
to random prompts and social interactions (ps < .001), fitting with the idea that
distinguishing emotions is easier when referring to brief time frames.
In terms of group differences, we found a main effect of poorer emotion differentiation in
the SAD group. However, the significant interaction showed that participants with SAD
demonstrated poorer negative emotion differentiation (p = .001, ηp2 = .158); there was no
significant difference in positive emotion differentiation (p = .290, ηp2 = .019). The presence
of SAD predicted less negative emotion differentiation during random prompts (β = −.30, p
= .009, R2 = .09) and in social interactions (β = −.29, p = .019, R2 = .081), but only
marginally for end-of-day reflections (β = −.20, p = .084, R2 = .038). See Table 1. for group
We ran additional construct specificity tests to determine whether effects of SAD could be
found over and above the variance attributable to comorbid emotional disorders. In the first
model, we added comorbid depressive and anxiety disorders as predictors (main and
interactive effects). The SAD group and Group × Valence effects remained significant (p = .
011 and .049, respectively). Comorbid depression predicted better emotion differentiation,
F(1, 58) = 4.45, p = .039, ηp2 = .071, and slightly more so for positive than negative
emotions, F(1, 58) = 3.07, p = .085, ηp2 = .050; there were no other significant effects with
comorbid disorders (ps > .15). In the second model, we excluded the eight participants in the
SAD group who also met criteria for comorbid depressive disorders. With this smaller
sample, the SAD group effect remained significant and, in fact, increased in magnitude, F(1,
53) = 10.22, p = .002, ηp2 = .16; the SAD Group x Valence interaction was no longer
significant (p = .24).
In subsequent analyses, we explored whether SAD effects remain over and above the
variance attributable to age and sex. Upon adding these demographic variables as predictors,
age was not a significant predictor, F(1, 60) = 0.19, p = .67, ηp2 = .003, but women were
worse at differentiating emotions than men, F(1, 60) = 4.88, p = .031, ηp2 = .075. When
statistically controlling for these demographic variables, both the SAD Group and Group x
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Valence effects remained significant (p = .008 and .036, respectively), suggesting the
robustness of SAD group effects.
Are These Differences Due to Emotion Intensity or Variability?
Given the group differences in intensity and variability (see Table 1.)Table 1., it was
possible that these constructs accounted for observed differences in emotion differentiation.
We used hierarchical regression models to predict negative emotion differentiation
(separately for each context) after controlling for average intensity and variability in the first
step. Notably negative emotion intensity and variability were both significant predictors in
each context (ps < .001), together explaining a significant amount of variance in negative
emotion differentiation during random prompts (R2 = .443), social interactions (R2 = .245),
and at end-of-day (R2 = .360). SAD group status significantly predicted negative emotion
differentiation over and above intensity and frequency in random prompts (β = −.23, t =
−2.36, p = .021, ΔR2 = .041) and social interactions (β = −.27, t = −2.26, p = .027, ΔR2 = .
057); the end-of-day effect became non-significant (β = −.07, t = −0.66, p = .51, ΔR2 = .
004). Thus, between-group differences for negative emotion differentiation (in-the-moment)
could not be attributed to differences in intensity and variability.
We sampled the emotional experiences of people with SAD over the course of two weeks
using three different methods—random momentary prompts, self-initiated online recording
of face-to-face social interactions, and end-of-day reflections. By calculating the degree of
association between ratings of emotion adjectives, we determined that adults with
generalized SAD displayed less differentiated negative emotions compared to healthy adults.
This difference remained significant when controlling for comorbid depressive disorders and
comorbid anxiety disorders. Furthermore, we tested whether the SAD group displayed
poorer negative emotion differentiation after accounting for the greater average intensity and
variability of negative emotions in people with SAD. Results demonstrated that negative
emotions during random prompts and in social interactions were less differentiated in adults
with SAD above and beyond the effects of other aspects of emotion structure (i.e., emotion
intensity and variability) or individual variables (e.g., comorbid diagnoses, demographic
variables). Our work offers novel contributions to understanding the phenomenology of
SAD. Adding to prior research showing that low frequency/intensity of positive emotions
and avoidance of anxious feelings distinguish people with SAD from healthy adults
(Kashdan et al., 2013), we found that difficulty clearly differentiating what emotions one
feels at a given moment in daily life is also linked to the presence of disorder.
This study was the first to investigate emotion differentiation in people with SAD with a
demographically matched control group, finding deficits in the ability of participants with
SAD to discriminate among negative emotions compared to healthy controls. Being able to
identify discrete negative emotions helps people to make inferences about a problematic
situation (Ekman, 1992) and to initiate behavioral or coping strategies to alter the situation
(Barrett et al., 2001). Thus, poor differentiators of negative emotions are likely to suffer
interpersonal costs, as well as detriments to self-regulatory resources due to inadequate
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management of energy in stressful situations. Consistent with this idea, researchers have
found people with self-reported difficulty identifying emotions (high in alexithymia) to use
more distraction to cope with negative emotions compared with healthy cognitive
reappraisal strategies to manage distress (Parker, Taylor, & Bagby, 1998). Our finding of
reduced ability to differentiate negative emotions in people with SAD provides insights into
a growing body of literature on the emotion regulation deficits in this population (e.g.,
Goldin, Manber, Hakimi, Canli, & Gross, 2009). An inability to classify negative emotions
may make these experiences seem more threatening and uncomfortable, increasing the
likelihood that people with SAD would use maladaptive emotion strategies such as
avoidance and suppression—strategies shown to maintain and even exacerbate anxiety
symptoms (Kashdan et al., 2011; Morrison & Heimberg, 2013).
It is worth noting that prior research has identified deficits in negative emotion
differentiation in other populations, including one study that found such deficits in people
with major depressive disorder (Demiralp et al., 2012). In our sample, neither controlling for
comorbid depression and comorbid anxiety disorders nor removing the eight participants
with both SAD and depression significantly impacted our findings. This suggests that
deficient negative emotion differentiation is not specific to depression; instead, it may be a
transdiagnostic factor associated with both SAD and depression. In contrast, we failed to
find positive emotion differentiation to be relevant to SAD, whereas Demiralp et al. found
depression to relate to less positive emotion differentiation; this suggest that positive
emotion differentiation ability may help distinguish these two conditions. Future research
using multiple relevant clinical comparison groups (e.g., SAD and major depressive
disorder) in the same study is necessary to determine the potential transdiagnostic nature of
these meta-cognitive processes.
While we expected to find significant differences in negative emotion differentiation in all
three contexts, it is important to highlight that effects were only at trend level for end-of-day
emotion reports and disappeared when we controlled for intensity and variability of negative
emotions. One possible reason for this finding is that time-inclusive reports require some
degree of retrospection, and thus may be influenced by some of the recall biases encountered
in global self-report research (e.g., Fredrickson, 2000). A shorter duration of time
retrospection is more likely to access experiential knowledge vs. semantic or belief-based
knowledge (Robinson & Clore, 2002). In effect, random prompts for momentary emotional
experiences and reports immediately following social interaction reports are more likely to
reflect felt emotions than questions asking participants to aggregate their experiences over
an entire day. With more time to ruminate and ponder on felt experiences, people with SAD
may be able to better classify their emotions into specific categories. Notably, our results are
consistent with research suggesting people with SAD have more difficulty forming on-line
inferences about situations (e.g., Hirsch & Mathews, 2000).
Consistent with expectations, we did not find differences in positive emotion differentiation
between participants with SAD and healthy controls. People with SAD have broad positivity
deficits (see Kashdan et al., 2011), which was confirmed by our observation of lower
intensity of positive emotions in the SAD group. A noteworthy finding was that participants
with more variable positive emotions (i.e., a broader range of positive emotion intensity over
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the sampling period) were better at differentiating those emotions. One way to interpret this
association is that when people experience fluctuations, this might help people pay attention
to and learn to associate response patterns with specific positive emotion categories.
Since prior research on emotion differentiation in SAD was limited to cross-sectional global
self-report designs (e.g., Turk et al., 2005), our study’s use of intense repeated assessments
via experience sampling adds a novel approach to understanding the phenomenology of
SAD. Feldman-Barrett et al. (2001) paved the way for studying emotion differentiation as a
process in daily life, i.e., as a process derived from daily diary accounts of experiences as
opposed to single-occasion questionnaires. This approach is advantageous for capturing
experiences in the context they naturally occur instead of requiring participants to
contemplate their emotion skills and aggregate across large time periods retrospectively.
Scientists would never find it acceptable to measure skills such as analytical reasoning or
creativity by asking people to self-report the strength of their abilities; in this vein, it makes
little sense to assess meta-cognitive skills outside of the context in which they are used.
Although our use of the intraclass correlation coefficient to capture the degree of relatedness
of emotion categories is appropriate for studying individual differences in emotion
discrimination, our findings are limited by our use of aggregate measures.
It is important to note that different methods for operationalizing emotion differentiation
may have different correlates depending on the approach used (Grühn, Lumley, Diehl, &
Labouvie-Vief, 2013). Despite our comprehensive examination of random prompts, face-to-
face social interactions, and end-of-day records to understand emotional differentiation,
future researchers need to also explore multiple operationalization strategies for each of
these contexts. Our reliance on variability scores and granularity across all emotion
adjectives reflected an attempt to match the analytic approach to existing conceptual models
of SAD (e.g., Heimberg et al., 2010). Future investigations can use multilevel modeling to
examine the temporal dependency and contextual importance of differentiated and
undifferentiated emotional responses to study the mechanisms by which the ability to
discriminate emotions confers resiliency, recovery, and well-being.
As the first study providing evidence of actual skill deficits in negative emotion
differentiation in people with SAD (not just perceptions; Turk et al., 2005), this research has
important clinical implications. Specifically, current treatment methods fail to help people
with SAD achieve levels of functioning comparable to healthy adults and improvements in
well-being often fail to maintain long-term (e.g., Eng, Coles, Heimberg, & Safren, 2001).
Our study suggests that training people to be better at identifying and discriminating among
negative emotions may be an important adjunct to existing therapeutic approaches, as this
skill may lead to improvements in reacting to and repairing negative mood states (Kashdan
et al., 2010; Zaki et al., in press). Mindfulness training has particular potential, as recent
evidence suggests that greater mindfulness predicts better emotion differentiation and less
difficulty using healthy emotion regulation strategies (Hill & Updegraff, 2012). In fact, these
authors found emotion differentiation to mediate the relationship between mindfulness and
emotion lability, confirming the importance of being able to discriminate among emotions as
a precursor to the healthy management of emotions. It will be important for future SAD
treatment research to include emotion differentiation not only as a target, but also as a
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treatment outcome measure to clarify the role of this deficit in the trajectory of social
anxiety difficulties over time. Beyond theory, research, and treatment of SAD, it will be
important to continue research to determine whether emotion differentiation difficulty is a
cause, correlate, and/or consequence of psychopathology.
This research was supported by a grant from the National Institute of Mental Health (R21-MH073937) and the
Center for the Advancement of Well-Being at George Mason University to TBK and the National Institute of Drug
Abuse (F31-DA029390) to ASF.
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Figure 1. Effect of Diagnostic Group X Context on Negative and Positive Emotion
Notes. These bar graphs demonstrate group differences in positive and negative emotion
differentiation by context (i.e., random prompt, social interaction emotion reports, or end-of-
day reports). Emotion differentiation values are scaled such that higher values represent
better differentiation. Bars represent standard error of means. *p < .05, **p < .01.
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Table 1
Emotion Differentiation Group Differences
Variable SAD mean (SD) HC mean (SD) t-ratio d
Positive emotion intensity
Random prompts 2.33 (0.57) 3.19 (0.68) 5.96*** 1.39
End-of-day 2.33 (0.66) 3.28 (0.55) 7.00*** 1.60
Social interactions 2.32 (0.54) 3.38 (0.64) 7.70*** 1.81
Negative emotion intensity
Random prompts 1.74 (0.35) 1.36 (0.39) −4.44*** 1.03
End-of-day 1.97 (0.46) 1.43 (0.37) −5.83*** 1.33
Social interactions 1.80 (0.45) 1.31 (0.37) −5.07*** 1.19
Positive emotion variability
Random prompts 0.76 (0.25) 0.76 (0.34) −0.04 0.01
End-of-day 0.78 (0.25) 0.75 (0.23) −0.57 0.13
Social interactions 0.69 (0.26) 0.69 (0.34) −0.01 0.00
Negative emotion variability
Random prompts 0.85 (0.31) 0.47 (0.41) −4.59*** 1.07
End-of-day 0.85 (0.26) 0.50 (0.30) −5.65*** 1.28
Social interactions 0.83 (0.36) 0.42 (0.38) −4.78*** 1.13
Positive emotion differentiation
Random prompts −0.80 (0.31) −0.79 (0.46) 0.15 0.03
End-of-day −0.81 (0.40) −0.77 (0.40) 0.53 0.06
Social interactions −0.98 (0.44) −0.84 (0.56) 1.24 0.30
Negative emotion differentiation
Random prompts −0.36 (0.33) −0.17 (0.24) 2.69** 0.63
End-of-day −0.66 (0.43) −0.49 (0.44) 1.75 0.40
Social interactions −0.45 (0.41) −0.22 (0.35) 2.40*0.60
*p < .05,
**p < .01,
***p < .001. All tests two-tailed. HC = healthy control; SAD = social anxiety disorder; d refers to Cohen’s (1988) effect size, where large effects
> .80. Range of n for SAD group = 38–40; range of n for HC group = 36–39.
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Table 2
Correlations of Emotion Differentiation with Emotion Intensity and Variability
Positive Emotion Differentiation Negative Emotion Differentiation
Random Social End-of-Day Random Social End-of-Day
Positive .108 .088 −.127 .292*.239.257*
Negative .211 −.066 −.164 −.449*** −.247*−.372**
Positive .445*** .464*** .368** .071 .157 .172
Negative −.204 .070 .038 −.088 .008 −.064
p < .06,
*p < .05,
**p < .01,
***p < .001.
Emotion. Author manuscript; available in PMC 2015 June 01.
... 4 It must be noted that the positive association between sensibility scores and emotional granularity was exclusively observed for negative words. Although we did not predict a valence-specific effect, this finding converges with previous studies showing stronger associations between the granularity for negative words and external indicators (Barrett et al., 2001;Demiralp et al., 2012;Kashdan and Farmer, 2014;Kalokerinos et al., 2019). One potential reason for the divergence between the granularity for positive and negative words may be related to the fact that, at least in the current sample, granularity for positive words did not reflect the differentiation between emotional experiences to the same extent as granularity for negative words. ...
... Correspondingly, the higher emotional granularity for negative words has been positively associated with healthy and adaptive behaviors such as the use and efficacy (Barrett et al., 2001;Kalokerinos et al., 2019) of emotional regulation strategies. Also, emotional granularity has been negatively related to depressive and social anxiety symptomatology, and it has been suggested as a correlate of resilience against the development of psychological disorders Kashdan et al., 2010;Demiralp et al., 2012; see also Erbas et al., 2014;Kashdan and Farmer, 2014). Here, we observed a positive association between Sensibility scores and well-being and adaptability scores, thereby providing further evidence for the association between correlates of conceptualization and wellbeing and adaptability. ...
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The theory of constructed emotions suggests that different psychological components, including core affect (mental and neural representations of bodily changes), and conceptualization (meaning-making based on prior experiences and semantic knowledge), are involved in the formation of emotions. However, little is known about their role in experiencing emotions. In the current study, we investigated how individual differences in interoceptive sensibility and emotional conceptualization (as potential correlates of these components) interact to moderate three important aspects of emotional experiences: emotional intensity (strength of emotion felt), arousal (degree of activation), and granularity (ability to differentiate emotions with precision). To this end, participants completed a series of questionnaires assessing interoceptive sensibility and emotional conceptualization and underwent two emotion experience tasks, which included standardized material (emotion differentiation task; ED task) and self-experienced episodes (day reconstruction method; DRM). Correlational analysis showed that individual differences in interoceptive sensibility and emotional conceptualization were related to each other. Principal Component Analysis (PCA) revealed two independent factors that were referred to as sensibility and monitoring. The Sensibility factor, interpreted as beliefs about the accuracy of an individual in detecting internal physiological and emotional states, predicted higher granularity for negative words. The Monitoring factor, interpreted as the tendency to focus on the internal states of an individual, was negatively related to emotional granularity and intensity. Additionally, Sensibility scores were more strongly associated with greater well-being and adaptability measures than Monitoring scores. Our results indicate that independent processes underlying individual differences in interoceptive sensibility and emotional conceptualization contribute to emotion experiencing.
... Across three EMA studies, between 42% and 76% of the variance in NA/ dysphoria symptoms was attributable to within-person changes over time. Similarly, 36%-66% of the variance in PA/high energy was due to within-person change (Forkmann et al., 2018;Kashdan & Farmer, 2014;Naragon-Gainey, 2019). These findings highlight the importance of measuring symptoms related to dysphoria and well-being as momentary states, as well as the need for valid EMA measures that are sensitive to change over time. ...
Assessment of internalizing symptoms has generally relied on cross-sectional and retrospective self-reports, but ecological momentary assessment (EMA) is increasingly used to capture quick fluctuations in symptoms, enhance ecological validity, and improve recall accuracy. However, there are very few measures of internalizing symptoms that have been validated for use in EMA designs. In Study 1, we chose candidate items for EMA short forms of the Dysphoria and Well-Being scales from the Inventory of Depression and Anxiety Symptoms (IDAS), based on principal factor analyses and internal consistency analyses conducted on aggregated cross-sectional datasets (total N = 8,876). In Study 2, we tested the items using an EMA design in a sample of college students (N = 279) oversampled for elevated neuroticism. Scale structure, reliability, and convergent and discriminant validity (regarding baseline IDAS scales, baseline affect, and EMA affect) were evaluated at the within- and between-person levels using multilevel structural equation modeling. Exploratory and confirmatory factor analyses in separate subsamples revealed the expected two-factor structure, yielding a four-item Well-Being scale and a five-item Dysphoria scale. Both scales showed acceptable to good internal consistency, strong convergent validity, and generally adequate discriminant validity. However, some associations of the new scales with EMA affect (i.e., Dysphoria with negative affect; Well-Being with positive affect) were very strong at the between-person level, such that they were not empirically distinct. Overall, this study provides an initial validation of brief EMA-IDAS Dysphoria and Well-Being scales that can be used in research or clinical settings, with particular utility for capturing within-person, dynamic effects. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
... In addition, technology-based communication allows for greater controllability over emotions compared to real-life situations(Buckner et al., 2012;Hollett & Harris, 2019). Therefore, emotion dysregulation may strengthen individuals" beliefs about the usefulness of communication applications(Buckner et al., 2012;Buyukcolpan, 2019; Casale et al., 2016;Hollett & Harris, 2019;Kashdan & Farmer, 2014; and the MP. For such reasons, the fear of MP absence may increase the risk of nomophobia in these individuals. ...
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In the modern world, the mobile phone has become an indispensable part of modern life. On the one hand, the mobile phone allows maintaining interpersonal contacts and fulfilling work or school duties regardless of time and location. It enables individuals to plan their daily routines and their free times. On the other hand, a mobile phone is a tool that can cause several psychological and physical problems. Nomophobia, which is considered the phobia of the modern era, is only one of these problems. In the simplest terms, nomophobia is the fear of being without a mobile phone and the intense anxiety and distress experienced in the absence of a mobile phone. Although technological addictions such as smartphone addiction and internet addiction have been studied extensively in the psychology literature, it is striking that nomophobia is a neglected psychological problem. However, nomophobia is emerging as a common phenomenon among young adults, as most young adults use the mobile phone for about 5 hours a day. Some users define the mobile phone as a friend and the meaning of life. More importantly, prevalence studies have revealed that about half of young adults suffer from nomophobia. Since nomophobia causes many serious consequences such as physical pain, social problems and a decrease in academic achievement, nomophobia studies are important and beneficial especially for the younger generation. This book has been written to emphasize the importance of nomophobia and to provide detailed information about the diagnosis, treatment, prevalence, predictors and symptoms of nomophobia. In addition, this book aimed to conceptualize nomophobia theoretically. Also, based on the theoretical conceptualization, psychological structures that can cause nomophobia have been identified. The theoretical conceptualization has been tested and validated using scientific methods. This book, which contains a comprehensive literature review and scientific research, can shed light on researchers for future nomophobia studies. I also believe that this book will make valuable contributions to the clinical field by providing a better understanding of the factors that should be considered in prevention programs and treatment interventions developed for nomophobia. I hope that scholars, clinicians, and students from a variety of disciplines will find my efforts helpful.
... In addition to coping style, emotion regulation may also play an important role in PD among undergraduates, serving as a mediator between stress and psychological problems (Kashdan and Farmer, 2014). Effective emotion regulation was shown to help protect perfectionists from experiencing PD (Beblo et al., 2012). ...
Background : When COVID-19 emerged in China in late 2019, most citizens were home-quarantined to prevent the spread of the SARS-CoV-2 virus. Extended periods of isolation have detrimental effects on an individual's mental health. Therefore, the impact of the COVID-19 pandemic should include assessment of psychological distress and its known risk factors, including coping style and emotional regulation. Methods : This cross-sectional study surveyed 6,027 Chinese university students recruited from May 25, 2020 to June 10, 2020. In addition to sociodemographic information, participant data were collected using online versions of the 10-item Kessler Psychological Distress Scale (K10), Simplified Coping Style Questionnaire (SCSQ), and Emotion Regulation Questionnaire (ERQ). Results : The incidence of psychological distress was found to be 35.34%. Negative coping style and expressing panic about COVID-19 on social media were the most important predictors of psychological distress. In addition, being male, being a “left-behind child” or having a monthly household income lower than 5000 CNY or higher than 20,000 CNY were associated with higher psychological distress. Conclusion : The psychological consequences of the COVID-19 pandemic could be serious. Psychological interventions that reduce nervousness and negative coping style need to be made available to home-quarantined university students, especially those who are male, are “left-behind”, have a monthly household income lower than 5000 CNY or higher than 20,000 CNY, or express panic on social media.
Social anxiety disorder (SAD) is a prevalent condition negatively affecting one’s sense of self and interpersonal functioning. Relying on cognitive but integrating interpersonal and evolutionary models of SAD as our theoretical base, we review basic processes contributing to the maintenance of this condition (e.g., self-focused attention, imagery, avoidance), as well as the treatment techniques geared to modify such processes (e.g., exposure, attention modification, imagery rescripting). We discuss cognitive-behavioral treatments (CBT) as combining multiple treatment techniques into intervention “packages.” Next, we review the existing empirical evidence on the effectiveness of CBT. Although CBT has accumulated the most support as superior to other credible interventions, we suggest that many treatment challenges remain. We conclude by discussing the ways to enhance the efficacy of CBT for SAD. Specifically, we highlight the need to (a) elucidate the complex relationship between basic processes and techniques, (b) advance personalized interventions, and (c) include a more diverse and comprehensive array of outcome measures.
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The main objective of the present research was to study psychological distress (depression, anxiety, stress) and its relationship with emotional regulation in patients with substance-related disorders. Emotional regulation and psychological distress are directly linked as psychological distress takes place when an individual has difficulties with regulating their emotions. It was a correlational study that followed a cross-sectional research design. G power analysis with a medium effect size suggested a 153-sample size. A sample of 155 men in the age range of 18 to 55(M=30.39, SD=7.97) was drawn from government hospitals admitted for the treatment of the substance-related disorders through purposive sampling. Demographic information sheet, Drug History Performa, Depression, Anxiety, Stress Scale-42 (DASS 42), and Emotional Regulation scale were used to collect data. Descriptive analysis, for demographic and social variables, depicted most of the participants belonged to a lower socioeconomic class. The mean duration of drug addiction was 12 years (SD =2.45). Most of the participants were multiple drug abusers. Peer pressure, failure in love affairs, and stress were reasons for drug addiction while reasons for relapse were peer pressure along with withdrawal symptoms and cessation of treatment. Descriptive analysis revealed that expressive-suppression emotional regulation strategies were common in study participants. Pearson product-moment correlational analysis demonstrates a significant positive relationship between psychological distress and expressive-suppression emotional regulation. In the present population, both social (peer pressure and bad company of friends) and psychological (stress, loneliness, curiosity and failure in love affair) factors are proven related to drug addiction problems. It is important to consider them for assessment as well as therapeutic intervention plans for patients with substance-related disorders.
Daily life demands that we differentiate between a multitude of emotional facial expressions (EFEs). The mirror neuron system (MNS) is becoming increasingly implicated as a neural network involved with understanding emotional body expressions. However, the specificity of the MNS’s involvement in emotion recognition has remained largely unexplored. This study investigated whether six basic dynamic EFEs (anger, disgust, fear, happiness, sadness, and surprise) would be differentiated through event-related desynchronisation (ERD) of sensorimotor alpha and beta oscillatory activity, which indexes sensorimotor MNS activity. We found that beta ERD differentiated happy, fearful, and sad dynamic EFEs at the central region of interest, but not at occipital regions. Happy EFEs elicited significantly greater central beta ERD relative to fearful and sad EFEs within 800 - 2,000 ms after EFE onset. These differences were source-localised to the primary somatosensory cortex, which suggests they are likely to reflect differential sensorimotor simulation rather than differential attentional engagement. Furthermore, individuals with higher trait anxiety showed less beta ERD differentiation between happy and sad faces. Similarly, individuals with higher trait autism showed less beta ERD differentiation between happy and fearful faces. These findings suggest that the differential simulation of specific affective states is attenuated in individuals with higher trait anxiety and autism. In summary, the MNS appears to support the skills needed for emotion processing in daily life, which may be influenced by certain individual differences. This provides novel evidence for the notion that simulation-based emotional skills may underlie the emotional difficulties that accompany affective disorders, such as anxiety.
Background Alexithymia can lead to problematic mobile phone use (PMPU). However, the underlying mechanisms remain unclear. Methods Drawing on the Interaction of Person-Affect-Cognition-Execution model, the present study tests the mediating effects (parallel and serial) of social interaction anxiety (SIA) and core self-evaluations (CSE) on the relationship between alexithymia and PMPU. We obtained 1267 valid responses from adolescent students (mean age, 20.36, standard deviation, 0.97) from southeast China who completed the Mobile Phone Addiction Index, Toronto Alexithymia-20 Scale, Social Interaction Anxiousness Scale, and Core Self-Evaluation Inventory. Results After controlling for demographic variables (i.e., gender) as covariates, the results revealed that: (1) alexithymia had a positive predictive effect on PMPU in adolescent students; (2) SIA and CSE mediated the association between alexithymia and PMPU; and (3) a series of indirect pathways (i.e., from alexithymia to PMPU via SIA and CSE) were detected. Thus, alexithymia can directly affect (parallel mediation) PMPU by increasing SIA and lowering CSE simultaneously. However, alexithymia can also indirectly affect (serial mediation) PMPU by increasing the level of SIA by decreasing CSE. Limitations Data were collected by participant self-report. This method may lead to recall bias. Further, we adopted a cross-sectional rather than an experimental design, thus precluding causal conclusions. Lastly, it would be useful to validate our findings with other age groups outside southeast China. Conclusions The current study findings are conducive to understanding the relationship between alexithymia and PMPU and inspire the prevention and intervention of PMPU.
Эмоциональная дифференцированность (ЭД) выражает дробность эмоционального опыта человека, то есть степень различения человеком своих эмоций. Показано, что ЭД не сводится лишь к семантической структуре эмоционального языка, которой располагает человек. ЭД обнаруживает положительную связь с эмоциональной регуляцией и разными компонентами психологического благополучия. Люди с высокой ЭД применяют более широкий круг стратегий эмоциональной регуляции, в меньшей степени склонны к употреблению алкоголя в тяжелых жизненных ситуациях, реже проявляют агрессивное поведение в ситуации злости.
To better define the boundaries of conceptually overlapping constructs of intrapersonal emotion knowledge (EK), we examined meta-analytic correlations among five intrapersonal EK-related constructs (affect labelling, alexithymia, emotional awareness, emotional clarity, emotion differentiation) and attention to emotion. Affect labelling, alexithymia, and emotional clarity were strongly associated, and they were moderately associated with attention to emotion. Alexithymia and emotional awareness were weakly associated, and emotion differentiation was unrelated with emotional clarity. Sample characteristics and measures moderated some of the associations. Publication bias was not found, except for the alexithymia-emotional awareness association. This study helped to clarify the extent to which similarly defined constructs overlap or are distinct, which can inform our decision to adequately label important constructs and employ corresponding measures.
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Despite the increased attention that researchers have paid to social anxiety disorder (SAD), compared with other anxiety and mood disorders, relatively little is known about the emotional and social factors that distinguish individuals who meet diagnostic criteria from those who do not. In this study, participants with and without a diagnosis of SAD (generalized subtype) described their daily face-to-face social interactions for 2 weeks using handheld computers. We hypothesized that, compared with healthy controls, individuals diagnosed with SAD would experience fewer positive emotions, rely more on experiential avoidance (of anxiety), and have greater self-control depletion (feeling mentally and physically exhausted after socializing), after accounting for social anxiety, negative emotions, and feelings of belonging during social interactions. We found that compared with healthy controls, individuals with SAD experienced weaker positive emotions and greater experiential avoidance, but there were no differences in self-control depletion between groups. Moreover, the differences we found could not be attributed to comorbid anxiety or depressive disorders. Our results suggest that negative emotions alone do not fully distinguish normal from pathological social anxiety, and that assessing social anxiety disorder should include impairments in positive emotional experiences and dysfunctional emotion regulation (in the form of experiential avoidance) in social situations. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
This study examined the hypothesis that emotion is a psychological event constructed from the more basic elements of core affect and conceptual knowledge. Participants were primed with conceptual knowledge of fear, conceptual knowledge of anger, or a neutral prime and then proceeded through an affect-induction procedure designed to induce unpleasant, high-arousal affect or a neutral affective state. As predicted, only those individuals for whom conceptual knowledge of fear had been primed experienced unpleasant core affect as evidence that the world was threatening. This study provides the first experimental support for the hypothesis that people experience world-focused emotion when they conceptualize their core affective state using accessible knowledge about emotion.
The question of how affect arises and what affect indicates is examined from a feedback-based viewpoint on self-regulation. Using the analogy of action control as the attempt to diminish distance to a goal, a second feedback system is postulated that senses and regulates the rate at which the action-guiding system is functioning. This second system is seen as responsible for affect. Implications of these assertions and issues that arise from them are addressed in the remainder of the article. Several issues relate to the emotion model itself; others concern the relation between negative emotion and disengagement from goals. Relations to 3 other emotion theories are also addressed. The authors conclude that this view on affect is a useful supplement to other theories and that the concept of emotion is easily assimilated to feedback models of self-regulation.
This article reviews existing empirical research on the peak-and-end rule. This rule states that people's global evaluations of past affective episodes can be well predicted by the affect experienced during just two moments: the moment of peak affect intensity and the ending. One consequence of the peak-and-end rule is that the duration of affective episodes is largely neglected. Evidence supporting the peak-and-end rule is robust, but qualified. New directions for future work in this emerging area of study are outlined. In particular, the personal meanings associated with specific moments and with specific emotions should be assessed. It is hypothesised that moments rich with self-relevant information will dominate people's global evaluations of past affective episodes. The article concludes with a discussion of ways to measure and optimise objective happiness.
Errors in Byline, Author Affiliations, and Acknowledgment. In the Original Article titled “Lifetime Prevalence and Age-of-Onset Distributions of DSM-IV Disorders in the National Comorbidity Survey Replication,” published in the June issue of the ARCHIVES (2005;62:593-602), an author’s name was inadvertently omitted from the byline and author affiliations footnote on page 592, and another author’s affiliation was listed incorrectly. The byline should have appeared as follows: “Ronald C. Kessler, PhD; Patricia Berglund, MBA; Olga Demler, MA, MS; Robert Jin, MA; Kathleen R. Merikangas, PhD; Ellen E. Walters, MS.” The author affiliations footnote should have appeared as follows: “Author Affiliations: Department of Health Care Policy, Harvard Medical School, Boston, Mass (Dr Kessler; Mss Demler and Walters; and Mr Jin); Institute for Social Research, University of Michigan, Ann Arbor (Ms Berglund); and Section on Developmental Genetic Epidemiology, National Institute of Mental Health, Rockville, Md (Dr Merikangas).” On page 601, the first sentence of the acknowledgment should have appeared as follows: “The authors appreciate the helpful comments of William Eaton, PhD, and Michael Von Korff, ScD.” Online versions of this article on the Archives of General Psychiatry Web site were corrected on June 10, 2005.
This article reviews existing empirical research on the peak-and-end rule. This rule states that people's global evaluations of past affective episodes can be well predicted by the affect experienced during just two moments: the moment of peak affect intensity and the ending. One consequence of the peak-and-end rule is that the duration of affective episodes is largely neglected. Evidence supporting the peak-and-end rule is robust, but qualified. New directions for future work in this emerging area of study are outlined. In particular, the personal meanings associated with specific moments and with specific emotions should be assessed. It is hypothesised that moments rich with self-relevant information will dominate people's global evaluations of past affective episodes. The article concludes with a discussion of ways to measure and optimise objective happiness.