When Feelings Lack Precision: Low Positive and Negative Emotion Differentiation and Depressive Symptoms in Daily Life

Abstract

Research suggests the ability to differentiate discrete emotions protects against psychopathology. Little is known about daily processes through which negative and positive emotion differentiation (NED, PED) influence depressive symptomatology. We examined NED and PED as moderators of associations between daily processes (negative/positive experiences, brooding, and savoring) and daily depressive symptoms. Hypotheses were tested using intensive longitudinal techniques in two samples oversampled for depression: 157 young adults (Study 1) and 50 veterans recruited from VA primary care (Study 2). In Study 1, low NED predicted stronger associations between daily brooding and depressive symptoms. In Study 2, low NED predicted stronger reactivity to daily negative events. In both studies, low PED strengthened salutary effects of positive experiences and savoring on symptoms. Largely consistent across demographically divergent samples, results suggest both low NED and PED enhance effects of daily events and pers...

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Running Head: EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
1
When Feelings Lack Precision: Low Positive and Negative Emotion Differentiation and
Depressive Symptoms in Daily Life
Lisa R. Starr1
Rachel Hershenberg2
Y. Irina Li1
Zoey A. Shaw1
1University of Rochester
2Emory University
To appear in Clinical Psychological Science. Accepted for publication January 26, 2017.
Published Online First on April 30, 2017. For the publisher’s version of this manuscript, please
see http://journals.sagepub.com/doi/full/10.1177/2167702617694657
Correspondence can be directed to Lisa R. Starr, Ph.D., Department of Clinical and Social
Sciences in Psychology, University of Rochester, 491 Meliora Hall, P.O. Box 270266,
Rochester, NY 14627-0266. Telephone: (585) 276-6862, Fax: (585) 273-1100, Email:
lisa.starr@rochester.edu
KEY WORDS: negative emotion differentiation, positive emotion differentiation, depression,
brooding, savoring, ecological momentary assessment, daily diary

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
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Abstract
Research suggests the ability to differentiate discrete emotions protects against
psychopathology. Little is known about daily processes through which negative and positive
emotion differentiation (NED, PED) influence depressive symptomatology. We examined NED
and PED as moderators of associations between daily processes (negative/ positive experiences,
brooding, and savoring) and daily depressive symptoms. Hypotheses were tested using intensive
longitudinal techniques in two samples oversampled for depression: 157 young adults (Study 1)
and 50 Veterans recruited from VA primary care (Study 2). In Study 1, low NED predicted
stronger associations between daily brooding and depressive symptoms. In Study 2, low NED
predicted stronger reactivity to daily negative events. In both studies, low PED strengthened
salutary effects of positive experiences and savoring on symptoms. Largely consistent across
demographically divergent samples, results suggest both low NED and PED enhance effects of
daily events and perseverative self-focus on fluctuations in depressive symptoms.

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
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When Feelings Lack Precision: Low Positive and Negative Emotion Differentiation and
Depressive Symptoms in Daily Life
Emotion differentiation (ED) refers to the ability to identify and precisely label discrete
emotional states (Barrett, Gross, Christensen, & Benvenuto, 2001; Kashdan, Barrett, &
McKnight, 2015). Those who are low on this ability tend to report their emotions in broad terms
of valence (“I feel good,” or “I feel upset”) rather than pinpointing concrete emotions (“I feel
excited,” or “I feel afraid”). Increasing research suggests that ED has consequences for
psychopathology and well-being. Emotions communicate critical information about the need to
employ attentional and behavioral resources. Thus, when people are better able to discriminate
between discrete emotional states they are more prepared to extract relevant information about
the causes and consequences of their emotions, such as the eliciting context, cognitive and
physiological correlates, and behavioral urges. This awareness also provides information needed
to effectively select and deploy appropriate emotion regulation strategies (Barrett et al., 2001).
Supporting this theoretical model, ED is associated with less impulsive emotional responding
and more effective use of emotion regulation strategies (Barrett et al., 2001; Kashdan et al.,
2015; Tugade, Fredrickson, & Barrett, 2004).
Given that appropriately experiencing and flexibly expressing emotions is central to well-
being and represents a transdiagnostic process in psychopathology (Kring & Sloan, 2011), it is
not surprising that difficulty discriminating between concrete emotional states – that is, low
ED—has been linked to critical processes and behaviors across a wide range of disorders,
including substance abuse, eating disorders, borderline personality disorder, autism spectrum
disorder, and social anxiety disorder (Dixon-Gordon, Chapman, Weiss, & Rosenthal, 2014;
Erbas, Ceulemans, Boonen, Noens, & Kuppens, 2013; Kashdan & Farmer, 2014; Kashdan,

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
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Ferssizidis, Collins, & Muraven, 2010; O’Toole, Jensen, Fentz, Zachariae, & Hougaard, 2014;
Selby et al., 2013; Zaki, Coifman, Rafaeli, Berenson, & Downey, 2013). Some evidence also
specifically links poor differentiation of negative emotions [NEs] (referred to as Negative
Emotion Differentiation [NED]) to depression, including self-reported depressive symptoms,
major depressive disorder (MDD), and symptom severity within depressive episodes (Demiralp
et al., 2012; Erbas, Ceulemans, Lee Pe, Koval, & Kuppens, 2014; Golston, Gara, & Woolfolk,
1992). This growing body of work suggests that NED could be an important contributor to
depression’s etiology and maintenance. However, research on ED and depression remains
limited, and no research has teased apart specific daily processes through which ED influences
depressive symptoms.
Most research on ED has focused on NED. A much smaller set of studies has explored
positive emotion differentiation (PED), perhaps because the limited existing research suggests
that compared to NED, PED is less consistently linked to emotion regulation deficits and well-
being (Barrett et al., 2001; Demiralp et al., 2012; Kashdan & Farmer, 2014; Pond et al., 2012).
NE and positive emotion (PE) serve different purposes; whereas PEs build long-term resources
and broaden one’s response repertoire (Fredrickson, 1998, 2001), NEs primarily function to
allocate resources to avoid or mitigate immediate threats (Parrott, 2002). Thus, failure to regulate
PEs may be much less costly than failure to regulate NEs (Barrett et al., 2001; Quigley & Barrett,
1999). That said, several studies have suggested that PED does play a role in psychopathological
processes and coping behaviors (Dixon-Gordon et al., 2014; Selby et al., 2013; Tugade et al.,
2004). However, PED has rarely been explored in the context of depression, despite the fact that
there has been a sharpening focus on the role of PEs and rewarding experiences within the
depression literature (Treadway & Zald, 2011). Indeed, the absence of PEs and failure to

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
5
anticipate, seek, and benefit from positive experiences are increasingly considered a central and
perhaps defining feature of depression (Kovacs et al., 2016; Pizzagalli et al., 2009; Rottenberg,
2007; Treadway & Zald, 2011; Watson & Naragon-Gainey, 2010; Weinberg, Liu, Hajcak, &
Shankman, 2015). Although one prior study failed to document a basic association between PED
and MDD (Demiralp et al., 2012), PED may still influence critical daily processes that influence
depressive symptoms.
The limited previous research has assumed that, like low NED, low PED would confer
risk for maladaptive outcomes (Dixon-Gordon et al., 2014; Hill & Updegraff, 2012; Selby et al.,
2013; Tugade et al., 2004). This assumption is based on the logic that the ability to perceive
emotions in a more sophisticated, granular manner is adaptive, regardless of whether the
emotions are positive (high PED) or negative (high NED). However, it is also possible that
excessively differentiating between positive emotional states may lead to a more constrained,
narrower experience of positive emotions. As a result, high PED may make some behaviors and
experiences less emotionally rewarding. On the one hand, this may reduce risk for maladaptive
behaviors that are typically reinforced by emotional rewards; supporting this notion, high PED
has been associated with a reduced rate of self-destructive behaviors such as disordered eating
and self-injurious behavior (Dixon-Gordon et al., 2014; Selby et al., 2013). On the other hand,
PED may also constrain the emotional benefits of adaptive experiences, such as everyday uplifts
and positive experiences. Notably, a core feature of depression is the absence of PE and
reactivity to rewarding experiences. Thus, high PED could contribute to anhedonia and
depressive symptom risk by reducing the antidepressant effects of positive activities.
In the current research, we first focus on the impact of ED on levels of depressive
symptoms when positive and negative events happen in everyday life. In addition, we examine

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
6
the role of ED in amplifying or decreasing depressive symptoms when employing two common
emotion regulation strategies: brooding (on NE) and savoring (PE).
NED and Daily Negative Experiences. We expect that low NED will predict a stronger
association between everyday negative experiences and daily depressive symptoms. As noted
previously, those with poor NED have more difficulty selecting and implementing effective
emotion regulation strategies (Barrett et al., 2001; Kashdan et al., 2015) and are more likely to
resort to destructive behaviors when confronted with NE (Kashdan et al., 2010; Pond et al.,
2012; Zaki et al., 2013). Lacking appropriate coping skills, these individuals may be more
vulnerable to exacerbation of depressive symptoms when negative experiences occur. Moreover,
a wide body of evidence suggests that the act of affect labeling may be regulatory in itself. Affect
labelling has been linked to diminished emotional reactivity (as measured by self-report, neural
activation, and autonomic response) following exposure to negative stimuli (Kircanski,
Lieberman, & Craske, 2012; Lieberman et al., 2007; Lieberman, Inagaki, Tabibnia, & Crockett,
2011). As NED suggests a greater propensity toward precise labeling of emotions, those with
higher NED may be protected against depressive responses following negative everyday
experiences.
To our knowledge no studies have examined whether NED confers reactivity to daily
stressors. However, Kashdan et al. (2014) did find that, among those with low self-esteem, low
NED predicted greater neural reactivity in response to social rejection, consistent with the
hypothesis that poor NED serves as a vulnerability factor that exacerbates the consequences of
stressors or other kinds of negative environmental events. We thus hypothesized that lower NED
would predict stronger associations between daily negative experiences and daily depressive
symptoms.

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
7
PED and Daily Positive Experiences. As a corollary, we also examined whether PED
would impact the association between everyday positive experiences and daily depressive
symptoms. Although the depression literature has traditionally focused far more on stressors,
positive experiences have long been considered important agents in reducing depressive
symptoms (Lewinsohn & Graf, 1973; Lewinsohn, Sullivan, & Grosscup, 1980), and reactivity to
positive experiences is being increasingly explored in the context of depression, both in daily life
and within the laboratory (Bylsma, Morris, & Rottenberg, 2008; Bylsma, Taylor-Clift, &
Rottenberg, 2011; Peeters, Nicolson, Berkhof, Delespaul, & deVries, 2003; Starr & Hershenberg,
in press; Thompson et al., 2012). As previously explained, low PED may amplify salutatory
effects of positive events on depressive symptoms by allowing for a more diffuse, less
constrained positive emotional experience when “good things” happen. Moreover, research
suggests that positive affect labeling is associated with diminished self-reported pleasure
(Lieberman et al., 2011). Therefore, individuals with low PED, who are not prone to labeling
positive emotions, may be more reactive to daily positive experiences. We thus hypothesized that
lower PED would predict stronger associations between daily positive experiences and lower
levels of daily depressive symptoms.
Emotion Differentiation and Positive and Negative Rumination. Next, we considered
how NED and PED might interact with, respectively, negative rumination (brooding) and
positive rumination (savoring). In a wide body of research that includes longitudinal, daily diary,
and experimental evidence, depressive rumination robustly predicts the onset and maintenance of
negative mood and depressive episodes (Nolen-Hoeksema, Wisco, & Lyubomirsky, 2008).
Brooding, or passive focus on negative consequences of symptoms, has been identified as the
most depressogenic component of rumination (Treynor, Gonzalez, & Nolen-Hoeksema, 2003).

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
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Low NED may exacerbate the tendency to engage in, and the negative consequences of,
brooding. Indeed, those with difficulty understanding their NEs may feel more compelled to
dwell on them, and low NED may amplify the negative effects of brooding on daily depressive
symptoms. For example, having a more diffuse, generalized perception of negative emotions
may mean that intense focus on one negative feeling (e.g., I feel disappointed) may quickly
spread to other NEs (I feel sad, I feel guilty, I feel anxious). In turn, brooding about a wider
range of NEs may provoke correspondingly broad negative inferences about the self, world, and
future, triggering or exacerbating depressive symptoms. Little previous work has examined the
effects of NED and rumination, though one study showed that high NED protects against the
effects of trait rumination on non-suicidal self-injury in borderline personality disorder (Zaki et
al., 2013). We thus hypothesized that low NED would be associated with trait rumination, and
that low NED would strengthen the association between daily brooding and depressive
symptoms.
Although rumination is typically shorthand for brooding and other forms of perseverative
focus on NE, rumination can also be in response to PE. For example, savoring refers to PE-
focused cognitive responses that serve to increase or maintain one’s PEs (Martin & Tesser, 1996;
Quoidbach, Berry, Hansenne, & Mikolajczak, 2010; Wood, Heimpel, & Michela, 2003).
Although less widely studied than depressive rumination, growing research suggests that
savoring may be protective against depression. Higher savoring beliefs are negatively correlated
with depression, and savoring predicts decreased daily depressive symptoms (Bryant, 2003;
Hurley & Kwon, 2012; Li, Starr, & Hershenberg, 2016). Analogous to our predictions for NED
and brooding, we expect that the association between savoring and lower depressive symptoms
will be amplified among those with low PED. For example, for those with a less differentiated

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
9
perception of PE, savoring one PE (e.g., I feel cheerful) may intensify a broad range of PEs (I
feel enthusiastic, I feel confident, etc.), which may translate into reduced depressive symptoms.
Thus, we hypothesized that the association between savoring and lower depressive symptoms
will be amplified among those with low PED.
The Present Research. We tested these hypotheses across two studies, one in a sample
of young adults and another in a sample of Veterans recruited from a primary care cohort being
evaluated for behavioral health symptoms. Both were oversampled for depressive symptoms to
allow for significant variation in daily depressive symptoms over the course of the study. Both
studies relied on intensive longitudinal techniques for calculation of PED/ NED and testing of
hypotheses. These techniques produce real-time data collected in naturalistic settings,
minimizing the need for retrospective recall and increasing generalizability. Further, objectively
calculating ED from momentary affect ratings, rather than asking participants to self-report on
their perceived ability to differentiate emotions, reduces reliance on introspection and self-
awareness. In Study 1, ecological momentary assessment (EMA) and daily diary surveys were
both administered over overlapping time periods. EMA data were used to calculate ED, as EMA
is better suited to capture discrete emotional states, and daily diary data were used to test study
hypotheses (because daily diary allows for longer surveys and more careful assessment of
hassles, uplifts, savoring, and brooding). Study 2 relied exclusively on EMA data, allowing for
replication of findings across intensive longitudinal approaches.
After examining basic associations between ED and baseline depression and rumination,
we tested the following hypotheses: Hypothesis 1) Low NED will predict stronger associations
between daily negative experiences and daily depressive symptoms. Hypothesis 2) Low PED
will predict stronger associations between daily positive experiences and reductions in daily

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
10
depressive symptoms. Hypothesis 3) Low NED will predict stronger associations between daily
brooding and daily depressive symptoms. Hypothesis 4) Low PED will predict stronger
associations between daily savoring and reduced daily depressive symptoms. With the exception
of Hypothesis 3, which was examined only in Study 1, all hypotheses were tested in both
samples, allowing for direct replication across two samples that varied considerably in
depression risk, demographic characteristics, and experience sampling methodology.
Study 1
Method
Participants. We recruited 160 undergraduate psychology students. Although they
should not be considered representative of clinical populations, college students are vulnerable to
depressive symptoms (e.g., Garlow et al., 2008), and research suggests that findings generated in
undergraduate samples generalize to clinical samples (Vredenburg, Flett, & Krames, 1993).
Eligibility criteria for participation were minimum age of 18 years, access to Internet and a
personal cell phone, and no English comprehension difficulties. To ensure we recruited a sample
with a broad range of depressive symptoms, we conducted a screening study where potential
participants completed a self-report depression measure, the Quick Inventory of Depressive
Symptomatology (QIDS; Rush et al., 2003). Participants were then preferentially recruited to
achieve approximately equal distribution across three categories (based on published clinical
thresholds, Rush et al., 2003): no symptoms (QIDS < 6, 31% of sample), mild symptoms (QIDS
score of 6-10, 33% of sample) and moderate to severe symptoms (QIDS > 10, 36% of sample).
Students received extra credit and were entered into raffles based on diary compliance. Table 1
reports sample characteristics. Of the participants recruited, one did not complete diaries, five
provided too few valid EMA surveys for ED calculation, and two were excluded from analyses

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
11
after failing inattention checks (described below), resulting in a final sample of 152 participants.
This research was approved by the University of Rochester’s Research Subjects Review Board.
Procedure. Participants completed an initial baseline visit and once-a-day daily diaries
for 14 consecutive days, beginning the evening of the baseline visit. Diaries were completed as
close to bedtime as convenient. Participants completed 10.97 (78.3%) diaries on average.
Number of missed diaries was not significantly related to baseline depressive symptoms, NED,
or PED.
In addition to the daily diary, participants also completed a brief ecological momentary
assessment protocol by completing short, telephone-based surveys five times per day for five
days. In the current study, EMA data was used for the computation of NED/ PED only; all
hypotheses were tested using daily diary data. EMA surveys were administered using the
telephone-based platform telEMA (Fernandez, Johnson, & Rodebaugh, 2013). Participants
designated time-of-day windows (typically 12-hours) in which they were available to complete
surveys. The daily window was divided into five equal intervals, and one call was placed at a
random time within each of the five intervals, with the additional constraint that no two calls
could be less than 1.5 hours apart (to minimize burden). Participants were able to designate
multiple phone numbers and specify the number of repeat calls they would receive if they missed
a call. Participants had up to 30 minutes to call a designated number to complete a survey if they
missed a call. EMAs were typically started the day after baseline participation (thus, EMA and
daily diary periods overlapped) and were always timed so that the five-day period included three
weekdays and two weekend days. Item order was randomized within blocks. During the data
cleaning process, EMA data were inspected for evidence of invalid response patterns (e.g.,
repeatedly entering identical numbers), and suspicious data were excluded. Participants

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
12
completed an average of 19.44 EMAs (78%), and 80% of the sample completed at least 18/25
EMAs. Four participants completed three or fewer valid EMAs and were excluded.
In line with Maniaci and Rogge’s (2014) recommendation, we included six inattention
items in the baseline measure to identify inattentive respondents. Exclusion of inattentive
respondents improves statistical power (Maniaci & Rogge, 2014). Two participants failed
inattention checks consistently and were excluded from all analyses.
Measures. Baseline. Baseline depressive symptom severity was measured using the
QIDS (Rush et al., 2003), a 16-item self-report questionnaire assessing the nine criterion
symptom domains of MDD according to the Diagnostic and Statistical Manual of Mental
Disorders—4th edition (American Psychiatric Association, 1994). The QIDS was administered in
the screening study for recruitment purposes and then re-administered at baseline and utilized as
a continuous score. Previous research has supported the psychometric properties of the QIDS
(Rush et al., 2006; Rush et al., 2003). For example, the QIDS has demonstrated construct validity
through correlations with depressive symptoms and diagnoses, as well as sensitivity to symptom
change (Gonzalez, Boals, Jenkins, Schuler, & Taylor, 2013; Trivedi et al., 2004). Cronbach’s
alpha in this study was .84. Baseline rumination was assessed using the Ruminative Response
Scale (RRS; Nolen-Hoeksema & Morrow, 1991), a widely used 22-item scale prompting
respondents to rate the frequency of 22 ruminative thoughts or behaviors. The RRS has excellent
internal consistency and external validity (Butler & Nolen-Hoeksema, 1994; Nolen-Hoeksema &
Morrow, 1991); in this sample, Cronbach’s alpha= .94.
Diary Items. Daily Negative and Positive Experiences. Daily negative (“hassles”) and
positive (“uplifts”) experiences were assessed based on methods of Totenhagen and colleagues
(2012). Participants were given a list of items and asked to indicate how much of a hassle and an

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
13
uplift each item was on that day on a scale of zero (none) to three (a great deal). Items ranged
across 15 general life domains: 1) parents and family members, 2) romantic life, 3) close friends,
4) other peers, 5) social events, 6) career, 7) finances, 8) exercise, 9) health, 10), chores, 11)
hobbies, 12) extra-curricular activities, 13) recreation, 14) online activities, and 15) other. An
average total score was then computed for hassles and uplifts, respectively. Internal consistency
for these and other daily measures was computed by separately computing Cronbach’s alphas for
each of the fourteen days and then calculating the mean. Mean internal consistency for hassles
and uplifts were .83 and .84, respectively. Hassles and uplifts scales in this sample were
significantly associated with concurrent daily depressive symptoms, PE, and NE in expected
directions, supporting construct validity (see also Li et al., 2016; Starr & Hershenberg, in press).
Daily Depressive Symptoms. We assessed daily depressive symptoms using a modified
version of the seven-item depression subscale of the Depression Anxiety Stress Scale (DASS;
Antony, Bieling, Cox, Enns, & Swinson, 1998). Items are rated on a Likert-type scale from zero
to three. The DASS has demonstrated strong psychometric properties (Brown, Chorpita,
Korotitsch, & Barlow, 1997; Clara, Cox, & Enns, 2001). The original items were modified so
that the time frame indicates the current day (e.g. “Indicate how much the statement applied to
you today”). Supporting validity, aggregated mean of daily DASS depressive symptoms was
significantly correlated with baseline depressive symptoms (r= .61, p < .001). Mean internal
consistency across individual days was .93.
Daily Brooding and Savoring. Daily brooding was assessed using the 5-item brooding
subscale of the RRS (Treynor et al., 2003), with instructions modified to cover brooding over the
course of the current day. Each item was rated on a 4-point Likert-type scale. This scale, which
has shown excellent psychometric properties in between-persons studies (Miranda & Nolen-

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
14
Hoeksema, 2007 ; Moberly & Watkins, 2008; Treynor et al., 2003), has been previously adapted
to assess daily brooding in daily diary research (Starr, 2015). Reports of daily brooding using
this measure have been associated with baseline rumination measures and concurrent depressed
mood (Li, Hershenberg, & Starr, 2016; Starr, 2015), supporting construct validity of the daily
measure. Savoring was assessed using a shortened version of the Response to Positive Affect
scale (RPA; Feldman, Joormann, & Johnson, 2008), with instructions prompting participants to
consider how they have responded to feeling “happy, excited, or enthused” modified to apply to
the current day only. Consistent with previous research suggesting that emotion-focused (EF)
and self-focused (SF) positive rumination comprise a single factor (Nelis et al., 2016), two items
with the highest factor loadings were taken from the EF and SF subscales respectively to create a
4-item daily savoring scale (e.g., “Think about how happy you feel”), each rated on a 4-point
Likert-type scale. The full RPA scale has shown adequate internal consistency as well as
convergent and incremental validity (Feldman et al., 2008; Raes, Daems, Feldman, Johnson, &
Van Gucht, 2010). In support of the validity of the daily application of the RPA, Li, Starr, and
Hershenberg (2016) showed that baseline savoring was significantly associated with aggregated
mean ratings of daily savoring (r = .50, p < .01). Mean daily Cronbach’s alpha for brooding and
savoring were .86 and .83, respectively.
Positive and Negative Emotion Differentiation. In the EMA survey, we calculated
current PE and NE based on ratings on standard mood adjectives used in emotion reactivity
research (replicating Byslma et al., 2011; also see Hershenberg, Mavandadi, Wright, & Thase,
2017). Seven positive mood ratings (talkative, enthusiastic, confident, cheerful, energetic,
satisfied, and happy) and seven negative mood ratings (tense, anxious, distracted, restless,
irritated, depressed, guilty) were assessed on each call. To calculate ED, average intraclass

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
15
correlation coefficients (ICCs) for either PE or NE items were calculated for each participant
across all assessments (Shrout & Fleiss, 1979; Tugade et al., 2004). For ease of interpretation,
ICCs were subtracted from 1.0 to reverse the score, so that higher NED/ PED scores reflect
greater emotion differentiation ability and lower scores reflected lower differentiation of discrete
emotions. This established, well-validated method of calculating ED has been used in multiple
previous studies (e.g., Hill & Updegraff, 2012; Selby et al., 2013; Tugade & Fredrickson, 2007).
Mean emotion intensity (used as a covariate) was calculated by taking the mean levels of NE and
PE for each participant across all EMA observations.
Data Analytic Approach
We used multilevel modeling (MLM) using SPSS 23.0 MIXED. MLM is a powerful
statistical approach that accounts for the nested, non-independent nature of intensive longitudinal
data. Repeated measures were nested within participants. Most hypotheses were tested using
cross-level interactions (known as slopes-as-outcomes models) between a level-one (within-
subjects) predictor (daily hassles, uplifts, brooding, or savoring) and a level-two (between-
subjects) moderator (NED or PED). MLM copes well with missing data and has greater
statistical power compared to traditional analytic approaches.
All predictors were entered as fixed effects, with level-one variables and the intercept
also modeled as random effects. Level-two predictors were mean centered. In addition, following
the recent recommendations of Bolger and Laurenceau (2013), we partitioned each level-one
predictor into two orthogonal components: a between-subjects component, represented by the
person’s grand-mean-centered aggregated mean score over the course of the full diary period
(i.e., a means component, 
), and a within-subjects component, represented by the person-
mean-centered score (i.e., the daily deviation from the means component, 
  
). Both

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
16
within- and between-subjects components were included for all level-one predictors, both as
main effects and in cross-level interactions. The inclusion of the between-subjects components of
level-one variables enhances the interpretability of models by ensuring that results are not
artifacts of individual differences in the average level of the variable (e.g., the tendency for some
people to brood more than others). However, results for the between-subjects variables are not
themselves considered interpretable (Bolger & Laurenceau, 2013). Thus, although results for
between-subjects variables are included in Table 1 for the sake of comprehensiveness, readers
should focus primarily on results for the “within” variables (e.g., broodingwithin); for simplicity,
“between” variables are not described in the Results section text. Importantly, this caveat only
applies to the between-subjects component of level-one variables; level two variables (e.g.,
NED/ PED) are inherently between-subjects and are interpretable. Note that use of this relatively
new analytic approach did not substantially change results, as compared to a more traditional
approach where between-subjects components of level-one variables were not included.
We also controlled for time in all models to ensure that effects were not artifacts of
temporal change. We applied a first-order autoregressive (AR[1]) model to correct for
autocorrelation of residuals and an unstructured covariance matrix for random effects. Daily
depressive symptoms (concurrent to predictors) were entered as the outcome in all MLM models.
Following the notation of Bolger and Laurenceau (2013), moderation models
(Hypotheses 1-4) can be described with the following equations:
Level 1:

  
  
     (1)
Level 2:
    
 
  

  (2)

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
17
    
  (3)
   (4)
These equations can be simplified into the following equation, in which the first seven
terms denote fixed effects and the last three terms represent random effects (see Bolger &
Laurenceau, 2013):

   
 
  

  
  
  

  
  
 
  
   (5)
For example, in the model constructed to test Hypothesis 3, 
 represents NED (the level-2
moderator), 
 and 
  
 respectively represent main effects for broodingbetween and
broodingwithin (the level-1 predictor), and 

 and 

  
 respectively denote NED 
broodingbetween and NED  broodingwithin interactions. In all moderation models, of primary
interest is the significance of the sixth term in equation 5, 

  
, the interaction between
ED and the within-subjects component of the level-1 variable. Significant interactions were
probed using simple slope tests (Aiken & West, 1991; Preacher, Curran, & Bauer, 2006) at one
standard deviation above and below the moderator’s mean.
Finally, because individuals prone to more extreme (i.e., higher or lower) levels of
negative and positive affect will have a restricted range of emotion ratings, we also controlled for
mean emotion intensity (NE intensity in Hypotheses 1 and 3, PE intensity in Hypotheses 2 and
4). Doing so was a conservative approach to rule out any concerns related to a restricted range.
Note that results remained unchanged when controlling for mean emotion intensity; thus, for
parsimony, we do not present the models including this additional covariate.
Results

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
18
The Study 1 EMA dataset (used for the computation of ED) included 3,029 valid
observations. Average ICC levels were .17 for NE and .34 for PE, resulting in mean NED and
PED (1-ICC) of .83 and .66 respectively.
The Study 1 daily diary dataset included 1,723 valid observations. Descriptive data for
study variables are reported in Table 1.
As a preliminary step, we examined bivariate correlations between NED, PED, baseline
depressive symptoms (QIDS), and baseline rumination (RRS). We found a marginally
significant, negative association between NED and QIDS (r= -.16, p = .056) but no correlation
between PED and QIDS (p > .05). NED was significantly correlated with baseline RRS (r= .19,
p= .017). NED and PED were also significantly correlated with each other (r = .19, p = .017).
Hypothesis 1. Next, we examined whether NED moderated the association between
fluctuations in daily hassles and daily depressive symptoms. As described in the Data Analytic
Approach section, a model was constructed that included main effects for daily hassles and NED
as well as their interaction and the effects of time. Full results are reported in Table 2. The
interaction between daily hassles and NED were not significant (p > .05). Although non-
significant, the interaction is illustrated in Figure 1a-i to facilitate comparison with Study 2.
Hypothesis 2. Next, we tested whether low PED moderated the association between
uplift fluctuations and daily depressive symptoms. Following the approach described above, we
constructed a multilevel model with daily uplifts, NED, and their interaction, as well as time,
with daily depressive symptoms entered as the outcome. Full results are reported in Table 2. As
shown there, the main effect for uplifts (but not PED) was significant, p < .001. Of note, the
interaction term was significant, p= .004. The significant interaction was decomposed using a
simple slope tests (Aiken & West, 1991; Preacher et al., 2006). Supporting Hypothesis 2, as

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
19
illustrated in Figure 2a-i, the negative association between daily uplifts and daily depressive
symptoms was stronger when PED was low (M -1 SD), b= -.16, SE= .03, p < .001; in contrast,
uplifts did not significantly predict reductions in depressive symptoms when PED was high (M +
1 SD), b= -.03, SE= .03, p= .403.
Hypothesis 3. We next examined whether NED predicted stronger positive associations
between daily brooding and daily depressive symptoms. NED and daily brooding were entered
into a multilevel model as both main effects and an interactive effect, along with time, with daily
depressive symptoms as the outcome. Full results are displayed in Table 2. The main effect for
brooding was significant, p< .001, but not for NED. Supporting Hypothesis 3, the interaction
between daily brooding fluctuations and NED was significant, p= .028. Decomposition is
illustrated in Figure 1b. As expected, reports of daily brooding were more predictive of same-day
depressive symptoms at low levels of NED, b= .56, SE= .06, t(136.65)= 9.21, p< .001, as
compared to high levels, b= .37, SE = .06, p< .001.
Hypothesis 4. Finally, we examined whether PED likewise predicted stronger
associations between daily savoring and reduced depressed mood. Analogous to the above
models, we constructed a model that included PED, daily savoring, PED  daily savoring, and
time. As shown in Table 2, the main effect for savoring (but not for PED) was significant, p <
.001. Moreover, supporting Hypothesis 4, the interaction was significant, p= .001 and is
presented in Figure 2b-i. As predicted, daily savoring more strongly predicted lower depressive
symptoms for those with low PED, b= -2.26, SE= .30, p< .001, compared to those with high
PED, b= -.81, SE= .30, p = .008.
Conclusions and Limitations

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
20
These results provide broad support for the majority of hypotheses (with the notable
exception of Hypothesis 1; we found no support for NED conferring stress reactivity in this
sample). Results should be interpreted in the context of study limitations. First, this study utilized
an undergraduate sample. On one hand, young adults are at high risk for first onset of depression
(Kessler, Berglund, Demler, Jin, & Walters, 2005), and research suggests that undergraduate
samples generate comparable findings to clinical samples (e.g., Vredenburg et al., 1993). That
said, some have criticized the overreliance on college samples to study clinical phenomena
(Coyne, 1994). A higher risk sample could arguably produce findings more applicable to clinical
depression. From a demographic standpoint, as with most studies recruiting from psychology
courses, our sample had a limited age range and was predominantly female. This is potentially
problematic because ED increases with age (Carstensen, Pasupathi, Mayr, & Nesselroade, 2000),
and depression risk (and rumination) varies considerably by gender (Nolen-Hoeksema, Larson,
& Grayson, 1999). Replicating findings in a sample with markedly different demographics and
higher clinical risk would provide greater confidence in the generalizability of results.
Further, all Study 1 models reflect concurrent associations (i.e., the level-one predictor
variable was reported at the same time as the outcome variable. The daily diary design (with a
full-day interval between surveys), while offering numerous benefits, was not well-suited for
testing lagged effects. The effects of daily events tend to quickly dissipate and are sometimes
countered by mood rebound effects, making lagged findings often elusive when using daily diary
designs (Bolger, DeLongis, Kessler, & Schilling, 1989; Stone, Neale, & Shiffman, 1993).
Because we only tested concurrent models in Study 1, it is unclear if predictor variables preceded
outcomes; the reverse direction of causality remains a possibility (for example, those with low
NED may be more prone to generating negative experiences when depressed).

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
21
To address these limitations, we tested hypotheses in a second study, which included a
sample of older, largely male Veterans recruited from a primary care cohort being evaluated for
behavioral health symptoms. Veterans are at substantially higher risk for mental health problems
compared to the general population (Dohrenwend et al., 2006; Seal, Bertenthal, Miner, Sen, &
Marmar, 2007), thus, depressive symptoms reported in this sample may be more reflective of
clinically significant pathology. This sample used an experience sampling/ ecological
momentary assessment (EMA) approach in which participants were signaled multiple times per
day to complete surveys in naturalistic settings at random intervals. We tested the same
hypotheses as in Study 1, with the exception of Hypothesis 3 (NED  brooding), for which data
were not available. In addition, because the EMA approach allowed for shorter intervals between
surveys, we were able to explore lagged models (predictor variables temporally preceding
outcomes) in addition to testing concurrent models, analogous to the diary design.
Study 2
Method
Participants and procedure. We recruited Veterans with a range of depression severity
based on scores on the Patient Health Questionnaire-9 (PHQ-9; Kroenke, Spitzer, & Williams,
2001) from a primary care cohort being evaluated for behavioral health symptoms at a
northeastern Veteran Affairs Medical Center. The Behavioral Health Laboratory (BHL) within
the VAMC collects screening data on new Veteran patient referrals on an ongoing basis.
Potentially eligible Veterans were identified by their responses to the PHQ-9 included in the
BHL assessment; those eligible to participate and interested in hearing more about the study
were contacted by study staff. Veterans were selectively recruited to achieve an approximate
distribution of one-third no depressive symptoms, one-third minor depression, and one-third

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
22
major depression, according to PHQ-9 cut-offs (Kroenke et al., 2001). Exclusion criteria
included psychotic disorders and current mania. Although sampling methods specifically
targeted an over-selection of depression, the sample was psychiatrically and medically
heterogeneous; one participant (2%) had a probable past manic episode, 25 participants (50%)
received a probable diagnosis of Posttraumatic Stress Disorder, 34 participants (68%) indicated
significant interference from pain, and four participants (8%) were considered at risk for alcohol
problems.
Interested Veterans were invited to a laboratory session, during which they provided a
baseline measure of depressive symptoms with repeated administration of the PHQ-9 and
received instructions on the EMA portion of the study, which began the following day. We
collected EMA data using Interactive Voice Recording, a phone-based system for collecting data
via keypad press. Participants were called six times per day for seven days. Calls occurred on a
random basis within a 12-hour block of participant designated time (e.g., 9am to 9pm). If they
missed the call, participants were given 25 minutes to call back a toll-free number to complete
the survey. We worked closely with participants during the baseline assessment to make sure that
they understood the items being asked of them on the phone surveys. Adherence to the protocol
was monitored, and we reached out to participants to troubleshoot noncompliance. Participants
received $35 for the lab session and $2.50 per EMA call (max payment $140). Participants
completed an average of 69% of calls (M = 29.10, sd = 10; modal number of calls 39 out of 42),
which is comparable to other EMA studies (Byslma et al., 2011). Rate of missed surveys was
unrelated to baseline depression, NED, or PED. Demographic information is displayed in Table
1; as shown, Veterans were predominantly male and racially diverse. The majority (72%) were

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
23
above 50 years of age. All study procedures were approved by the Corporal Michael J. Crescenz
VAMC Institutional Review Board.
Baseline. Depressive Symptoms. Participants rated the frequency over the past two
weeks with which they experienced each of nine symptoms of depression using the PHQ-9
(Kroenke et al., 2001). Psychometric properties of the PHQ-9 are well-established (Spitzer,
Kroenke, Williams, & Group, 1999; Spitzer, Williams, Kroenke, Homyak, & McMurray, 2000),
and Cronbach’s alpha in this study was .83. Baseline rumination was measured with the RRS
(see Study 1).
Momentary Positive and Negative Experiences. At the time of each phone call,
participants were asked to report “how you were spending your time before you took a break to
take this survey”. To rate the valence of their current activity, participants used a face-valid,
continuous scale, ranging from 1 (most unpleasant) to 5 (most pleasant). We refer to this scale as
“pleasant activities.” To facilitate comparisons with Study 1, for Hypothesis 1 only we reverse-
coded the scale, so that greater scores reflect more unpleasant recent experiences (which we refer
to as “unpleasant activities”).
NED and PED. On each phone call, after rating the pleasantness of their current
activity, participants were asked to “Keep thinking about how you felt before you took a break to
take this survey. Use a 1 to 5 scale, where 1 is ‘I didn’t feel this way at all’ and 5 is ‘I felt this
way a great deal.’” We used the same adjectives as in Study 1, and PED and NED were
computed using identical procedures as in Study 1.
Momentary Depressed Mood. Depressed mood was assessed using the rating for the
single item, “I felt depressed,” on the 1-5 continuous scale described above. The use of single-
item indicators of mood is relatively common in EMA/ diary research (as brevity is critical for

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
24
survey compliance) and is psychometrically justifiable for non-complex constructs (Burisch,
1997; Laurenceau, Barrett, & Rovine, 2005; Starr, 2015; Starr & Davila, 2012a).
Savoring. Similar to Study 1, savoring items were adapted from Feldman, Joorman, &
Johnson’s (2008) RPA scale, although in this study, all items were taken from the EF scale of the
RPA (no items from the SF scale were administered; we still refer to this scale as savoring for
consistency with Study 1, but note that in this study it reflects the somewhat narrower construct
of emotion-focused savoring). Participants were asked, “How you have responded to these
feelings?” (i.e., their emotion ratings) using a 1 (not at all) to 5 (a great deal) scale. We
administered three emotion-focused items: “I started to think about how happy I feel”; “I started
to think about how strong I feel”; and “I savored this moment,” and took their average for a total
savoring score. Mean Cronbach’s alpha (computed separately for each prompt, and then
averaged) was .77.
Data Analytic Approach
Prior to data analysis, EMA data were inspected and suspicious response patterns (e.g.,
large numbers of identical numeric responses) were flagged for exclusion (see McCabe, Mack, &
Fleeson, 2012). The analytic approach for Study 2 was similar to that in Study 1, with the
following changes to accommodate the EMA design. A continuous variable representing time
passed since the first completed survey was used as the repeated measures variable and was
entered as a covariate to account for temporal artifacts. To control for possible effects of diurnal
mood variation, time of day was also included in all concurrent models (as shown in Table 2, this
variable was not significant in any models and was dropped from lagged models to for
parsimony). As in Study 1, we ran additional models including mean emotion intensity as a
covariate in tests of all hypotheses; findings were not substantially impacted, and results

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
25
presented here exclude this covariate for simplicity. Depressed mood was entered as the outcome
variable in all MLM analyses.
We tested both concurrent and lagged models in this sample. To create lagged variables,
data for each signal were shifted in our dataset, so that depressed mood at each signal t could be
predicted by variables at signal t-1. To prevent overnight lags, the first signal of each day was
excluded as outcomes; because this reduced the amount of data available for analysis, power was
correspondingly reduced in lagged analyses. Moreover, effects are generally weaker in lagged
analyses (because effects of within-day events are typically short-lived; Marco & Suls, 1993).
Consequently, we consider lagged models exploratory, as they may be somewhat underpowered
and should be interpreted in conjunction with concurrent models. Time lag between observations
was included as a covariate in lagged models to account for non-equal intervals.
Results
The Study 2 dataset included 1,455 valid observations. Table 1 displays descriptive data
for major study variables. In this dataset, mean NE ICC was .26 (M NED = 1-ICC = .74, SD=
.20) and mean PE ICC was .44 (M PED= .56, SD= .44). Baseline depressive symptoms were
significantly, negatively correlated with NED (r= -.29, p= .040), but not with PED (r= -.23, p =
.116). NED was significantly, negatively correlated with baseline rumination (r= -.31, p= .033).
NED and PED were also significantly correlated with each other (r= .40, p= .004).
Hypothesis 1. We next examined whether low NED predicted stronger associations
between unpleasantness ratings of recent activities and depressed mood. Following the
prescribed data analytic plan, we tested a model that included unpleasant activities, NED, and
their interaction, as well as total elapsed time, time of day, and between-subjects components of
unpleasant activities as covariates. Full results appear in Table 2. As shown, there was a main

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
26
effect for negative experiences, but not for NED. Importantly, the NED  unpleasant experiences
interaction was significant (p < .001). Decomposition revealed that, in line with expectations, at
low levels of NED, unpleasant activities were strongly related to depressed mood, b= .35, SE=
.04, p < .001, whereas at high levels of NED, unpleasant activities were not significantly
predictive of depressed mood, b= .07, SE= .05, p= .150 (see Figure 1a-ii for illustration).
We also tested this hypothesis using lagged data, where NED was examined as a
moderator of the lagged association between unpleasant activities at the previous signal and
current depressed mood.
1
The interaction was again significant, b= -.50, SE= .22, p= .031. At
low levels of NED, negative experiences predicted non-significant increases in depressed mood,
b= .08, SE= .06, p= .171, whereas at high levels, negative experiences actually predicted
marginal decreases in depressed mood, b= -.12, SE= .07, p= .096.
Hypothesis 2. We next tested PED as a moderator of the association between
pleasantness ratings of recent activities and depressed mood, by testing a model that included
pleasant activities, PED, PED  pleasant activities, and time and between-subjects component
covariates. As shown in Table 2, the main effect for positive experiences (but not PED) was
significant. Notably, the focal interaction term was significant. We probed the significant
interaction, as shown in Figure 2a-ii, and found that, consistent with hypotheses and Study 1
1
For lagged analyses, we did not control for depressed mood at signal t1 because including
lagged dependent variables as predictors in multilevel models introduces severe bias (see
Allison, 2015). To allow for a more conservative approach that is appropriate within a multilevel
framework, we also examined whether the lagged model would remain significant after
controlling for the concurrent model (see Starr & Davila, 2012b for an example of this
approach). To do so, we included as predictors (in addition to the NED main effect, random
effects, time, and other covariates) a) unpleasant activities at t1, b) NED  unpleasant activities
at t1, c) unpleasant activities at t and d) NED  unpleasant activities at t. Terms a) and b)
represent the lagged model, and terms c) and d) represent the concurrent model. The interaction
terms for the concurrent model and the lagged model were both significant (concurrent p < .001,
lagged p = .029). Although this approach may be unnecessarily conservative, the significance of
the lagged model provides greater confidence in the robustness of results.

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
27
results, pleasant activities sharply predicted reduced depressed mood at low levels of PED, b =-
.33, SE= .05, p < .001 but only marginally predicted lower depressed mood at high levels of
PED, b= -.10, SE= .05 p= .071. Testing the same hypothesis using lagged data, we again found a
significant interaction, b= .58, SE= .20, p= .020, with a similar pattern of results, with positive
experiences predicting decreased later depressed mood only at low levels of PED.
2
Hypothesis 4. Finally, we examined whether PED predicts strengthened associations
between daily savoring and depressed mood. We constructed a multilevel model where daily
savoring, PED, and their interaction predicted concurrent depressed mood, with total elapsed
time, time of day, and between-subjects savoring components as covariates. Full results are listed
in Table 2; as displayed there, the main effect for savoring was significant. The interaction
between PED and savoring was marginally significant, p = .072 (interestingly, this interaction
was significant in supplemental analyses controlling for the covariate of mean emotional
intensity, p= .046). Aligning with predictions and with Study 1 findings, daily savoring predicted
significantly decreased concurrent depressed mood among those with low PED, b= -.27, SE=
.07, p= .001, but not among those with high PED, b= -.06, SE= .08, p= .459 (see Figure 2b-ii).
When substituting lagged depressed mood as the outcome, the interaction was non-significant
(p= .113) though trended similarly such that momentary savoring was predictive of decreases in
depressed mood among those with lower levels of PED.
General Discussion
Drawing from two intensive longitudinal studies with complementary designs and
samples, our findings provide evidence for roles of both NED and PED in modulating within-
2
As with the NED  unpleasant activities model, we again re-ran lagged analyses using the very
conservative approach of controlling for concurrent main and interactive effects (see Footnote 1).
The PED  pleasant activities interaction term was significant for both the concurrent (p < .001)
and lagged (p= .018) pleasant activity variables.

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
28
person variations in depressive symptoms in daily life. Results largely replicated across both
studies, despite substantial differences in sample demographics; although both samples were
oversampled for depressive symptoms, Study 1 featured undergraduate, predominately female
young adults and Study 2 included Veterans recruited from a primary care cohort being
evaluated for behavioral health symptoms who were predominantly males over 50. The
consistency of findings across such divergent samples supports the robustness and
generalizability of our pattern of results.
First, we found evidence that NED (but not PED) was correlated with depressive
symptoms, aligning with other studies that have linked low NED to depressive symptoms and
disorders (Demiralp et al., 2012; Erbas et al., 2014; Golston et al., 1992). This finding is
consistent with research linking depression to alexithymia, a personality trait marked in part by
blunted ability to identify and describe one’s emotions (Honkalampi, Hintikka, Tanskanen,
Lehtonen, & Viinamäki, 2000). Individuals with depression show a broad range of deficits in
cognitive functioning, including overgeneralized autobiographical memory, negative attentional
biases, reduced prospective imagery, and impaired executive functioning (for a review see
Joormann & Arditte, 2014), which may cause depressed people to perceive negative emotion in a
blunt, overgeneralized manner. Discrete emotional states provide a wealth of information about
the nature of the emotional situation, its potential consequences, and effective regulation
strategies (Parrott, 2002); decreased ability to discern nuance in negative emotional experiences
puts depressed individuals at an inherent disadvantage for managing these emotions. Further, the
act of affect labeling may be implicitly regulatory (e.g., Kircanski et al., 2012; Lieberman et al.,
2011). That said, our data (as with other existing studies) cannot distinguish between two
important possibilities: that low NED is a pre-existing risk factor for the emergence of

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
29
depression, or that low NED is a feature of depression that emerges with onset and improves
with remission. Future research using long-term longitudinal design can help to resolve this
important question.
The current research does provide evidence that ED (both negative and positive)
influences the ebb and flow of depressive symptoms. First, the present study is the first to show
that poor NED predicts a stronger association between daily negative experiences and daily
depressive symptoms. This suggests that the effects of negative emotion differentiation may be
more salient in the context of negative experiences. This finding is consistent with the affect-as-
information theory, which posits that specific, differentiated negative emotional experiences are
more adaptive than global affective states because they are subject to fewer misattribution errors
(Schwarz & Clore, 1996). When people have the ability to discriminate between discrete
emotional states elicited by a negative event, they are better able to identify the cause of the
emotional experience and generate an adaptive response, whereas those who experience
undifferentiated, global affective states cannot (Russell & Barrett, 1999). This capacity may be
particularly relevant to depressive symptoms, since the meaning attributed to adverse events
(e.g., interpreting them as internal, global, and stable) is often associated with the etiology and
maintenance of symptoms (Abramson, Metalsky, & Alloy, 1989; Hankin, Fraley, & Abela, 2005;
Weiner, 1974). Moreover, greater negative emotional differentiation is correlated with the
selection and implementation of effective emotion regulation strategies (Barrett et al., 2001;
Kashdan et al., 2015), and the act of labeling emotions may itself help regulate emotions
(Kircanski et al., 2012; Lieberman et al., 2007; Lieberman et al., 2011). Accordingly, decreased
differentiation may lead to more difficulties coping with negative experiences and their resulting
emotions and increased daily depressive symptoms.

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
30
Moreover, NED moderated the associations between daily negative events and depressive
symptoms in our Veteran sample (Study 2), but not in our sample of young adults (Study 1). This
discrepant finding may be associated with the demographic differences of the two samples.
Previous research suggests that emotion differentiation improves as adults age, so that poor
differentiation of negative emotions may confer more risk for depressive symptoms in older
adults (Carstensen et al., 2000). This explanation is speculative, and future work will help to
address if this difference reflects the different demographics of the study samples.
Low NED also predicted stronger associations between daily brooding and concurrent
depressive symptoms. Coupled with the negative correlation between NED and trait rumination,
findings suggest that people with low NED suffer a rumination “double whammy”, with a
greater tendency towards rumination as well as greater depressive reactivity to it. A number of
studies have suggested that low NED is related to the selection and deployment of ineffective or
destructive coping strategies. The current study is the first to show that maladaptive emotion
regulation techniques also have more deleterious affective consequences among those with low
NED. Findings align with those of Zaki et al. (2013), who showed that trait rumination was more
predictive of non-suicidal self-injury in borderline personality disorder for those with low NED.
People who have trouble understanding their emotions may be more likely to get especially
“stuck” when brooding about their NE, which may lead the brooding to have more of a
deleterious impact on effective problem solving. In contrast, it could be the case that those with
high NED may be more effective at identifying the source of their emotions, which may make
ruminative self-focus more productive and less impactful on depressive symptoms.
Paralleling our NED findings, we also showed that, across both samples, low PED
enhanced the ameliorative effects of both positive experiences and positive rumination

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
31
(savoring) on depressive symptoms. Most of the limited previous research on PED has focused
on the role of low PED in predicting problematic behaviors (e.g., Dixon-Gordon et al., 2014;
Selby et al., 2013); we showed that low PED might be associated with adaptive outcomes in the
context of depressive symptoms. Indeed, the extent to which low PED is harmful versus helpful
may largely depend on the outcome being considered. Among those with low PED, positive
experiences may trigger a wealth of PEs, rather than a specific, constrained emotion. In some
contexts, these broad, undifferentiated PEs may motivate and reinforce destructive behaviors
(e.g., Dixon-Gordon et al., 2014; Selby et al., 2013). However, in the context of depression, these
PEs may motivate more behavioral activation, culminating in reduced depressive symptoms.
Thus, PED may have trade-offs; it may be protective in some clinical contexts while adding to
vulnerabilities in others. That said, research on PED remains very limited, and more
investigation is decidedly needed.
Theoretical Considerations, Future Directions, and Conclusions
Can emotion differentiation be differentiated? Notably, although research has
typically assumed that low ED scores reflect an inability to cognitively discriminate between
emotions, they may also reflect a genuine propensity towards experiencing multiple emotions in
clusters rather than individually. If so, perseverative focus on one NE (through brooding) or PE
(through savoring) may activate a network of related emotions, amplifying the affective
experience and influencing depressive symptoms; likewise, positive and negative events may
trigger a broader (and perhaps more intense) affective response in individuals labeled as low
emotion differentiators. This may be one reason why the literature on PED is somewhat mixed;
low PED may actually be comprised of two counteractive components, one potentially beneficial
(the propensity to experience multiple concurrent positive emotions) and one potentially

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
32
maladaptive (deficient ability to discern discrete emotional states). In contrast, low NED may be
more consistently maladaptive because both the tendency to experience large clusters of NEs and
the inability to differentiate these NEs are likely to have negative consequences (and may
therefore have additive effects). Although our approach to assessing NED and PED (using EMAs
to calculate intraclass correlations among momentary emotions) is a widely accepted method
(Selby et al., 2013; Shrout & Fleiss, 1979; Tugade et al., 2004), future researchers should
develop techniques that better discriminate between the experience and the discernment of
multiple concurrent emotions.
Clinical implications. Our findings on how NED and PED influence fluctuations of
depressive symptoms in daily life may be helpful in increasing understanding of the dynamics of
emotional experience in depression and inform the application of effective treatment targets.
Evidence has pointed toward the efficacy of interventions that help individuals expand their
emotion vocabulary to better identify and precisely label discrete emotions (Cameron, Payne, &
Doris, 2013; Kircanski et al., 2012). This approach, known as affect labeling, suggests that better
recognizing and naming discrete emotional states reduces emotional reactivity and maladaptive
emotion regulation strategies (Lieberman et al., 2007). This, in turn, may facilitate the ability to
manage one’s behavior or distress in response to negative experiences, thereby reducing
depressive symptoms. Moreover, while working to improve NED, clinicians might help patients
harness the effects of low PED as they work to increase patients’ awareness of positive
emotional states and increased sensitivity to rewards, though future empirical work is needed to
see if this could be a useful application of our basic research findings.
Study limitations. When considered in isolation, each study has several limitations.
Study 1 utilized an undergraduate sample; although participants were oversampled for depressive

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
33
symptoms, the pathology captured is likely a non-ideal proxy for clinical depression. Study 1
also used a daily diary design that relied on recall of experiences, perseverative thought, mood,
and symptoms over the course of the day, and all analyses tested concurrent associations. Study
2 relied on single-item measures and a relatively small sample. However, these limitations were
well-balanced across the two studies. Study 2 included a sample of Veterans (a higher-risk
population with a wider and older age range) and shorter interval EMA sampling which allowed
lagged analyses; Study 1 included more detailed daily measures, a larger sample, and a greater
representation of women. Thus, the replication of results across the two studies provides greater
confidence in findings. That said, several limitations remain across our study as a whole.
Although both studies oversampled for self-reported depressive symptoms, neither utilized
clinical interviews to assess clinical depression. Likewise, as Study 1 utilized a college sample
and Study 2 included a psychiatrically heterogeneous group of Veterans, neither sample can be
considered exclusively representative of major depression, and future research can help to
establish if results generalize. Moreover, as explained previously, our operationalization of
emotion differentiation cannot tease apart if low ED reflects an inability to discriminate between
emotions or a genuine propensity to experience multiple emotions concurrently. In addition, both
studies relied on the same emotion adjectives (derived from Bylsma et al., 2011), which were not
explicitly designed to assess ED and may capture non-discrete emotional states. Recent evidence
underscores the importance of careful selection of emotion adjectives in momentary research,
and this should be an important consideration in future ED research. As noted previously, our
design cannot determine whether NED contributes to risk for depression or simply emerges
concomitant with symptoms. Finally, our measures of daily preservative self-focus, particularly
savoring, were brief and may have conceptual overlap with other related constructs (e.g.,

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
34
mindfulness), suggesting that future research will benefit from testing the specificity of these
established associations.
Despite these limitations, the current study adds to a growing literature highlighting the
importance of ED to emotional health. Indeed, evidence points to low NED as a transdiagnostic
factor that contributes to a range of psychopathological conditions and behaviors including
depression, eating disorders, social anxiety disorder, alcohol abuse, and aggression. In contrast,
low PED may have trade-offs for mental health, depending on the clinical context. Future
research is needed to better understand the nature and consequences of these intriguing
constructs.
AUTHORSHIP. L.R.S. and R.H. conceptualized the manuscript collaboratively. L.R.S. was
principal investigator of Study 1 and R.H. was principal investigator of Study 2. L.R.S.
conducted all analyses and led manuscript preparation. R.H., Y.I.L., and Z.A.S. made substantial
contributions to manuscript preparation. All authors approved the final version of the paper for
submission.
ACKNOWLEDGEMENTS. Study 1 was completed with funds provided by the University of
Rochester. We thank the members of our research team, particularly Fanny Mlawer and
Chistopher Anzalone, who managed data collection for this study, as well as the participants who
generously contributed their time. Study 2 was completed with funds provided by the VISN 4
Mental Illness Research, Education, and Clinical Center (MIRECC Director: D. Oslin) Pilot
Project Funds, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA.
The views expressed in the article are those of the authors and do not necessarily reflect the

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
35
position or policy of the Department of Veterans Affairs or the United States government. We
thank Erin Wright and Sara Mooar for help with data collection, Dorothy McDougall and Joan
Havey for administrative support, Christopher Petro for technical support, and the Veterans who
generously participated in this research.

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
36
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49
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EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
50
Table 1
Demographic Information and Descriptive Data for Study 1 and Study 2
Study 1
Study 2
N
157
50
Age
20.10 (1.23)
56.48 (14.06)
% Female
81%
12%
Race
% Caucasian
% African American
% Asian
% Other
57%
6%
29%
7%
44%
52%
0%
6%?
Baseline Depressive Symptoms
8.81 (5.19)
12.92 (6.00)
Baseline Rumination
56.56 (15.03)
53.90 (15.58)
NED
.83 (.13)
.74 (.20)
PED
.66 (.18)
.56 (.21)
Aggregated Daily/ Momentary Variables
Depressive Sx/ Depressed Mood
4.00 (4.29)
1.85 (.81)
Negative Experiences/ Hassles
7.83 (5.45)
2.41 (.70)
Positive Experiences/ Uplifts
10.71 (6.29)
3.59(.70)
Brooding
9.41 (3.18)
---
Savoring
1.97 (.56)
2.53(.87)
Notes. All variables (except rumination) were assessed using different measures in Study 1 and
Study 2, and therefore descriptive data should not be directly compared across studies. Baseline
depressive symptoms assessed with QIDS (Rush et al., 2003) in Study 1 and PHQ-9 (Kroenke et
al., 2001) in Study 2. NED and PED were computed using different indicators of NEs and PEs
and may also not be directly comparable. See Method sections for Study 1 and Study 2 for more
detail.

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
51
Table 2
Results of Multilevel Models Predicting Daily Depressive Symptoms from Daily Variables, as
Moderated by Negative and Positive Emotion Differentiation
Study 1
Study 2
Hypothesis 1
b (SE)
p
95% CI
b (SE)
p
95% CI
Intercept
4.32(0.35)
< .001
[3.63, 5.00]
1.77(.15)
< .001
[1.48, 2.07]
Neg. Experienceswithin
.20(0.03)
< .001
[.14, .25]
.21(.03)
< .001
[.14, .28]
NED
-4.16(2.48)
.096
[-9.06, .74]
-.49(.51)
.357
[-1.49, .56]
NED  Neg. Experienceswithin
-.16(0.22)
.468
[-.60, .28]
-.69(.16)
<.001
[-1.02, -.41]
Neg. Experiencesbetween
.30(0.06)
< .001
[.18, .41]
.70(.19)
.001
[.32, 1.07]
NED  Neg. Experiencesbetween
.85(0.38)
.027
[.10, 1.60]
-2.12(1.31)
.112
[-4.76, 0.52]
Time
-.06(0.02)
.006
[3.63, 5.00]
.00(.00)
.027
[.00, .00]
Time of Daya
n/a
.00(.00)
.902
[.00, .00]
Hypothesis 2
Intercept
4.81(.38)
< .001
[4.07, 5.55]
1.81(.15)
< .001
[1.52, 2.10]
Pos. Experienceswithin
-.10(.02)
< .001
[-.14, -.05]
-.22(.04)
< .001
[-.29, -.14]
PED
1.99(1.97)
.313
[-1.89, 5.88]
-.07(.50)
.883
[-1.08, .94]
PED  Pos. Experienceswithin
.38(.13)
.004
[.12, .64]
.56(.17)
.002
[.21, .90]
Pos. Experiencesbetween
-.03(.05)
.552
[-.14, .07]
-.52(.17)
.003
[-.86, -.19]
PED  Pos. Experiencesbetween
.07(.27)
.803
[-.46, .59]
-.29(.65)
.657
[-1.58, 1.01]
Time
-.14(.02)
< .001
[-.19, -.09]
.00(.00)
.029
[.00, .00]
Time of Daya
n/a
.00(.00)
.778
[.00, .00]
Hypothesis 3b
Intercept
4.24(.28)
< .001
[3.69, 4.79]
n/a
Daily Broodingwithin
.46(.04)
< .001
[.38, .55]
NED
-1.80(1.95)
.357
[-5.65, 2.05]
NED  Broodingwithin
-.73(.33)
.028
[-1.38, -.08]
Daily Broodingbetween
.80(.07)
< .001
[.65, .94]
NED  Broodingbetween
-.45(.50)
.368
[-1.43, .54]
Time
-.07(.02)
< .001
[-.11, -.04]
Hypothesis 4
Intercept
4.73(.35)
< .001
[4.04, 5.42]
1.94(.15)
<.001
[1.64, 2.24]
Daily Savoringwithin
-1.54(.22)
< .001
[-1.97, -1.11]
-.16(.05)
.004
[-.27, -.05]
PED
2.74(1.84)
.139
[-0.9, 6.39]
-.09(.52)
.860
[-1.15, .96]
PED  Daily Savoringwithin
4.15(1.19)
.001
[1.79, 6.51]
.48(.26)
.072
[-.05, 1.01]
Daily Savoringbetween
-2.28(.58)
< .001
[-3.43, -1.12]
-.08(.13)
.541
[-.19, .35]
PED  Daily Savoringbetween
-1.21(.26)
.672
[-7.50, 4.85]
-1.21(.58)
.029
[-2.30, .-.13]
Time
-.13(0.02)
< .001
[-0.17, -0.08]
.00(.00)
.016
[.00, .00]
Time of Daya
n/a
.00(.00)
.279
[.00, .00]
aStudy 2 only. bHypothesis 3 was not evaluated in Study 2.
Notes. Focal moderation findings are bolded. Daily depressive symptoms (depressed mood in Study
2) were the outcome variable in all models. Positive and negative experiences defined respectively
as uplifts and hassles in Study 1 and pleasantness/ unpleasantness of recent activities in Study 2.

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
52
1
1.2
1.4
1.6
1.8
2
2.2
2.4
Low (M -1 SD) High (M +1 SD)
Momentary Depressed Mood
Negative Experiences
2
2.5
3
3.5
4
4.5
5
5.5
6
6.5
Low (M -1 SD) High (M +1 SD)
Daily Depressive Symptoms
Negative Experiences
2
2.5
3
3.5
4
4.5
5
5.5
6
6.5
Low (M -1 SD) High (M +1 SD)
Daily Depressive Symptoms
Daily Brooding
Study 1 Study 2
a) i. ii.
(note: non-significant interaction)
b)
Figure 1
Daily depressive symptoms as a function of a) recent negative experiences (Hypothesis 1, with results from i) Study 1 and ii) Study 2)
and b) daily brooding (Hypothesis 3, Study 1 only), as moderated by negative emotion differentiation

EMOTION DIFFERENTIATION AND DEPRESSIVE SYMPTOMS
53
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2
Low (M -1 SD) High (M +1 SD)
Momentary Depressed Mood
Positive Experiences
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2
2.1
2.2
Low (M -1 SD) High (M +1 SD)
Momentary Depressed Mood
Momentary Savoring
3
3.5
4
4.5
5
5.5
6
Low (M -1 SD) High (M +1 SD)
Daily Depressive Symptoms
Positive Experiences
3
3.5
4
4.5
5
5.5
6
Low (M -1 SD) High (M +1 SD)
Daily Depressive Symptoms
Daily Savoring
Study 1 Study 2
a) i. ii.
b) i. ii.
Figure 2
Findings from i) Study 1 and ii) Study 2 showing daily depressive symptoms as a function of a) recent positive experiences
(Hypothesis 2) and b) recent savoring (Hypothesis 4), as moderated by positive emotion differentiation