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Emotion
Does Negative Emotion Differentiation Influence How People Choose to
Regulate Their Distress After Stressful Events? A Four-Year Daily Diary
Study
Bradley A. Brown, Fallon R. Goodman, David J. Disabato, Todd B. Kashdan, Stephen Armeli, and Howard Tennen
Online First Publication, April 8, 2021. http://dx.doi.org/10.1037/emo0000969
CITATION
Brown, B. A., Goodman, F. R., Disabato, D. J., Kashdan, T. B., Armeli, S., & Tennen, H. (2021, April 8). Does Negative
Emotion Differentiation Influence How People Choose to Regulate Their Distress After Stressful Events? A Four-Year Daily
Diary Study. Emotion. Advance online publication. http://dx.doi.org/10.1037/emo0000969
Does Negative Emotion Differentiation Influence How People Choose to
Regulate Their Distress After Stressful Events? A Four-Year Daily
Diary Study
Bradley A. Brown
1
, Fallon R. Goodman
1
, David J. Disabato
2
, Todd B. Kashdan
3
, Stephen Armeli
4
,
and Howard Tennen
5
1
Department of Psychology, University of South Florida
2
Department of Psychological Sciences, Kent State University
3
Department of Psychology, George Mason University
4
Department of Psychology, Fairleigh Dickinson University
5
Department of Public Health Sciences, University of Connecticut School of Medicine
Much is known about the types of strategies people use to regulate emotions. Less is known about indi-
vidual differences that influence emotion regulation strategy selection. In this study, we tested the mod-
erating role of negative emotion differentiation (NED; i.e., the ability to label and describe subtle
differences among negative emotions) on the relationship between the intensity of stressful daily events
and the strategies used to regulate distress arising from these events. Prior research shows that NED is
associated with low endorsement of disengagement emotion regulation (e.g., substance use), but less is
known about the link to engagement regulation (e.g., problem-solving, seeking social support).
Participants were college students (N= 502) completing a 30-day daily diary survey for each of four
college years. We preregistered hypotheses that 1) the intensity of each day’s most stressful event would
be associated with greater use of disengagement and engagement regulation strategies, and 2) people
higher in NED would be less likely to use disengagement and more likely to use engagement strategies
when highly stressed. Results suggest that higher stress intensity is associated with greater use of all reg-
ulation strategies. Greater NED is associated with less use of disengagement regulation strategies,
whereas NED was unrelated to engagement regulation strategies and did not moderate the relationship
between stress and engagement strategies. The majority of hypothesized moderation effects of NED
were nonsignificant, prompting a reconsideration of whether, when, and how NED plays a role in stress
responding.
Keywords: emotion differentiation, emotion regulation, stress, coping
Supplemental materials: https://doi.org/10.1037/emo0000969.supp
When stressful events occur, there is a desire to regain a sense
of equanimity. When people experience stress in their day-to-day
lives, they deploy certain emotion regulation strategies to meet the
demands of stressors and regulate emotions that may otherwise
overwhelm them (e.g., Kashdan & Rottenberg, 2010). Emotion
regulation strategies are used to manage negative emotions, most
commonly to decrease their frequency or duration (Gross, 1998).
Prior research shows that individual differences in the strategies
people use influence psychological functioning. For example, the
use of cognitive reappraisal, which involves the reframing of emo-
tional events to alter their emotional impact (Gross, 2015), is
linked to greater well-being (Gross & John, 2003;John & Gross,
2004), whereas suppressing emotions is linked to more adverse
outcomes (e.g., psychopathology, increase in negative emotions;
Gross & John, 2003;Srivastava et al., 2009). Although different
types of emotion regulation strategies and their outcomes receive
ample scientific attention, less understood are the individual differ-
ences that influence strategies people select to regulate distress.
Recent work suggests that certain situational factors influence
strategy selection. For example, experimental studies show that
the intensity of emotions (e.g., Sheppes et al., 2011), the complex-
ity of a regulation strategy (e.g., Sheppes et al., 2014), and a per-
son’s regulation goals (e.g., English et al., 2017;Tamir, 2009)
Fallon R. Goodman https://orcid.org/0000-0002-1115-1467
David J. Disabato https://orcid.org/0000-0001-7094-4996
Todd B. Kashdan https://orcid.org/0000-0001-6438-0485
Bradley A. Brown, Fallon R. Goodman, and David J. Disabato
contributed equally to this work. This research was supported by the
National Institute on Alcohol Abuse and Alcoholism (NIAAA) Grant
5P60-AA003510. There are no conflicts of interest by any of the authors.
Correspondence concerning this article should be addressed to Todd B.
Kashdan, Department of Psychology, George Mason University, David
King Hall, Room 2048, 4400 University Drive, Fairfax, VA 22030-4422,
United States. Email: tkashdan@gmu.edu
1
Emotion
©2021 American Psychological Association
ISSN: 1528-3542 https://doi.org/10.1037/emo0000969
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
influence strategy selection. The source of stress also influences
strategy selection. One study shows that the strategies people
choose to regulate daily anger are influenced by what or who trig-
gered the anger (Kashdan et al., 2016). Taken together, research
suggests that strategy selection to regulate distress is not a uniform
process and is influenced by contextual (e.g., Aldao, 2013) and
individual differences (Doré et al., 2016). Research examining
how individual differences (e.g., how one processes emotional in-
formation) interact with contextual factors of distress (e.g., inten-
sity of the distress) to influence strategy selection in daily life can
provide a nuanced view of the distress regulation process. In a
four-year daily diary study, we examined how individual differen-
ces in the ability to label and describe discrete negative felt emo-
tions moderated the relationship between the intensity of daily
events and the strategies chosen to regulate distress arising from
these events.
Disengagement and Engagement Emotion Regulation
People use numerous strategies to regulate distress. Since early
research on coping (e.g., Carver et al., 1989) and emotion regula-
tion (e.g., Gross, 1998), researchers attempted to classify and
organize a seemingly infinite number of regulatory strategies. In
modern affective science, arguably the most commonly used
framework categorized strategies as either “adaptive”or “malad-
aptive.”Classifications typically match Aldao et al.’s (2010) meta-
analytic findings suggesting strong positive correlations between
psychopathological symptoms and use of strategies such as avoid-
ance and strong negative correlations between psychopathological
symptoms and use of strategies such as problem-solving. Nonethe-
less, correlations with psychopathology do not necessarily provide
information about adaptiveness; this classification creates a false
dichotomy that implies strategies are uniformly helpful or
unhelpful.
In an updated framework, Naragon-Gainey et al. (2017) con-
ducted a meta-analysis to identify how common emotion regula-
tion strategies co-occur and interact. She failed to find support for
the adaptive–maladaptive distinction and instead found three
classes of strategies based on a person’s engagement with the
emotion or source of stress: engagement (characterized by engage-
ment with the stress, primarily by problem-solving), disengage-
ment (characterized by efforts to escape the distressing emotions
and thoughts, such as through avoidance or distraction), and aver-
sive cognitive perseveration (characterized by overengagement
with negative cognitions and failed attempts to avoid negative
emotions and thoughts, such as through rumination). Of note, sup-
port for this factor structure does not imply that strategies within
each class are uniform. Naragon-Gaineyet et al. suggest that strat-
egies within each class might still be examined independently.
They write,
However, the meta-analyses of correlations among strategies revealed
only small to moderate associations. It is striking that, even in these
analyses that were based on concurrent measurement and a single
method (i.e., self-report), the emotion regulation strategies showed
reasonable discriminant validity with one another and did not suggest
empirical redundancy. (p. 410)
Accordingly, we view the categorical schemes of adaptive and
maladaptive strategies as heterogeneous. In our research program,
we examined two primary disengagement strategies (avoidance
and distraction) and the primary engagement strategy (problem-
solving).
1
In addition to these cognitive-emotion strategies, we
also examined two behavioral strategies (see Aldao & Dixon-
Gordon, 2014) that are pertinent to college students: substance use
and social support seeking (e.g., Friedlander et al., 2007;O’Malley
& Johnston, 2002;Staff et al., 2010;Wilcox et al., 2005). Neither
of these strategies were included in Naragon-Gainey et al.’s meta-
analysis, so we relied on existing empirical research for classifica-
tion purposes. Substance use is often used to escape or avoid dis-
tress (Khantzian, 1997). For instance, substance use is common
among people who have difficulties tolerating distress (i.e., dis-
tress intolerance) or who have tendencies toward impulsive behav-
iors when experiencing distress (i.e., negative urgency; e.g.,
Cyders et al., 2009;Kaiser et al., 2012); suggesting that reducing
or escaping distress is a common motive for substance use
(Cooper et al., 1995). One longitudinal study of 418 college stu-
dents transitioning to college showed that participant’s motiva-
tions to use substances to avoid distress accounted for a positive
relationship between their tendency toward impulsive actions
when distressed (i.e., negative urgency) and the amount of alcohol
consumed during the first year of college (Settles et al., 2010).
Thus, we classified alcohol use as a disengagement strategy. In
contrast, social support seeking, including instrumental support
seeking (seeking social help to problem-solve) or emotional sup-
port seeking (e.g., receiving moral support), has been conceptual-
ized as actively engaging with the stressor or subsequent distress
(Carver et al., 1989). Seeking and receiving social support has
been associated with actively coping with stressors in cancer
patients (e.g., Manne et al., 1999;Schulz & Schwarzer, 2004).
Therefore, we classified social support seeking as an engagement
strategy.
Emotion Differentiation as an Individual Difference in
How People Regulate Distress
People experience and process emotions in different ways, and
these individual differences, in part, influence how distress is regu-
lated (e.g., Gohm & Clore, 2000;Smith & Kirby, 2011;Zeidener
et al., 2006). One individual difference that appears particularly
relevant to stress responses is negative emotion differentiation
(NED), defined as the ability to label and describe subtle differen-
ces among emotions of a similar valence (negative, e.g., anxiety
vs. guilt vs. fear; Barrett et al., 2001; for a review, see Kashdan et
al., 2015). People low on NED characterize their emotional experi-
ence in more global terms (e.g., “I feel bad”), whereas people high
1
In the meta-analysis (Naragon-Gainey et al., 2017), Disengagement
and Aversive Cognitive Perseveration were highly correlated (r= .67) and
conceptually similar in that they both involve avoidance. Experiential
avoidance measures demonstrate high cross-loadings, and numerous
measurement concerns have been raised about experiential avoidance
measures (e.g., Doorley et al., 2020). Aversive cognitive perseveration is
primarily characterized by difficulty disengaging, specifically rumination
and worry, two strategies we did not measure in this study. Therefore, we
only examined disengagement strategies, but recognize that “avoidance”
could reasonably be considered either a disengagement or aversive
cognitive perseveration strategy.
2BROWN ET AL.
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on NED identify and label their emotions in a more granular fash-
ion (e.g., “I feel guilty and a little frustrated, but I do not feel
ashamed or angry”). People low in NED, for example, may have
difficulty separating feelings of anxiety, anger, and fear, leading
them to rate all the discrete emotions at a similar intensity and rep-
resent their emotional state in more broad terms such as feeling
“bad”or “unpleasant”––limiting emotion-specific information.
Emotion theorists suggest that discrete emotion states are more
adaptive than global affective states because they provide more in-
formation and are less vulnerable to misattribution errors (Kehner
et al., 1993;Schwarz, 2011). Distinct emotions give meaning to
our daily lives and influence how we appraise and react to specific
situations (e.g., Barrett et al., 2007;Scherer & Moors, 2019). Peo-
ple higher in NED may be better suited to identify the sources
behind specific emotions, providing them with information about
how to actively engage with a stressor to regulate distress (e.g.,
Barrett, 2006). For example, someone labeling their affect as anger
might be assertive with others, whereas someone labeling their
affect as guilt might make amends.
Deficits in the ability to recognize and identify emotions likely
hinder a person's ability to regulate their distress adaptively, possibly
by limiting contextual information needed to accurately appraise a
stressor and respond effectively. Indeed, compared to healthy coun-
terparts, research suggests that NED is dampened among individuals
with psychological maladies associated with difficulties in regulating
distress and a tendency toward disengagement strategies (e.g.,
depression; Demiralp et al., 2012;Yoon & Rottenberg, 2020; bor-
derline personality disorder; Linehan, 2018;Suvak et al., 2011;
social anxiety disorder; Kashdan & Farmer, 2014;Seah et al., 2020).
For example, one study of 38 people with borderline personality dis-
order found that those lower in NED were more likely to use the
maladaptive disengagement strategy of self-harming than people
higher in NED (Zaki et al., 2013).
Considerable evidence suggests that people with high NED are
less likely to use disengagement strategies to cope with stressful
situations than people with low NED. For example, people higher
in NED were less likely than lower NED peers to drink to excess
when experiencing distress (Kashdan et al., 2010) or act aggres-
sively when angered (Pond et al., 2012). NED also buffers the
effects of stressful events on depressed mood (e.g., Nook et al.,
2020;Starr et al., 2017;2020). In a 2020 EMA study of adolescent
participants, the adverse effects of daily stressors on depressed
mood were attenuated for people with higher compared with lower
NED (Starr et al., 2020); however, it is unclear how NED influ-
enced regulatory strategies to deal with distress.
In a similar vein, findings in college student samples have
shown that a person’s ability to identify discrete emotions was
positively associated with engagement strategies and other impor-
tant facets of well-being (Barrett et al., 2001;Gohm & Clore,
2002); however, the majority of studies examining the effect of
NED on emotion regulation explored disengagement strategies
(e.g., substance use, nonsuicidal self-injury). As such, it is impor-
tant to examine NED as both a protective factor against disengage-
ment emotion regulation as well as an individual difference that
might promote the use of engagement regulation strategies.
To our knowledge, only two studies have evaluated the effect of
NED on engagement regulation strategies. Barrett and colleagues
(2001) found that NED was associated with more frequent use of
strategies; however, these findings were limited to retrospective
reports of emotion regulation (how often participants used regula-
tion strategies over the prior two weeks). Additionally, researchers
used a sum score for regulation strategies in this study, so it is
unclear if NED was associated with specific regulation strategies
(e.g., engagement vs disengagement).
Expanding upon this work, Kalokerinos and colleagues (2019)
conducted two experience-sampling studies to examine the effects
of NED on strategy selection and strategy effectiveness (defined
as decreases in negative emotions after strategy use). They exam-
ined three engagement strategies—acceptance, reappraisal, and
social sharing. In contrast to the findings of Barrett and colleagues
(2001), NED was negatively associated with the selection of cog-
nitive reappraisal in Study 1 and unrelated in Study 2, negatively
associated with the selection of social sharing, and unrelated to the
selection of acceptance. NED was associated with strategy effec-
tiveness, such that people with lower NED experienced increases
in negative affect after using cognitive reappraisal or social
sharing (suggesting ineffective use for those lower in NED), a
relationship also present, but attenuated, for those higher in NED.
Interestingly, acceptance was associated with reduced negative
affect (indicating strategy effectiveness) for those lower in NED in
this study. The present study aims to extend this work by examin-
ing NED and both disengagement (avoidance, substance use,
distraction) and engagement (problem-solving, seeking social sup-
port) regulation strategies in the context of stress and coping over
the course of four years.
Studying Emotion and Stress in Daily Life
Self-reports of how one generally experiences emotions (e.g.,
retrospective trait measures of emotion) and self-reports of emo-
tions in the moment likely represent two distinct psychological
processes (Robinson & Clore, 2002). This is consistent with a
prior study showing negligible relationships between trait emotion
clarity (assessing general experiences of emotion) and state NED
(in-the-moment reports of emotion; Boden et al., 2013). Meas-
uring emotions as they occur decreases the likelihood of recall
error and bias and other difficulties in memory retrieval of emo-
tional experiences (Kihlstrom et al., 1999;Trull & Ebner-Priemer,
2009). Emotions, stress, and emotional reactivity to stress vary
over time within a single person (Howland et al., 2017;Sliwinski
et al., 2009). Accurate examination of these constructs requires
assessment of processes close to real time.
With recent developments in methodological tools to measure
psychological processes in real time (e.g., daily diary studies),
researchers are better able to observe how people experience emo-
tions as they occur (Barrett, 1997;Shiffman et al., 2008;Wilhelm
& Grossman, 2010). In particular, experience-sampling methodol-
ogies (e.g., daily diary, ecological momentary assessment) can
account for meaningful fluctuations across consecutive days. In
this study, participants completed up to four 30-day diary periods,
each one year apart, which allowed us to account for changes in
stress patterns over the course of several years during a significant
development period (i.e., collegiate years).
The Present Study
The present study used a four-wave daily diary design to exam-
ine how individual differences in NED moderated the relationship
NEGATIVE EMOTION DIFFERENTIATION 3
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between stress and the use of emotion regulation strategies. We
hypothesized that the stress intensity of each day's most stressful
event would be positively associated with greater endorsement of
all regulation strategies. For disengagement strategies (i.e., avoid-
ance, substance use, distraction), we hypothesized that the positive
stress-regulation strategy relationship would be weaker for people
higher in NED compared to people lower in NED. For engagement
strategies (i.e., problem-solving and social support seeking), we
hypothesized that the positive stress-regulation strategy relation-
ship would be stronger for people higher in NED compared to peo-
ple lower in NED. We preregistered hypotheses and the analytic
plan with the Open Science Foundation (https://osf.io/2yn4z).
2
Method
Participants and Procedures
College students (N= 574) were recruited from several intro-
ductory psychology courses. Potential participants were asked to
be part of a four-year longitudinal study examining the daily
behavior of college students. The mean age of participants (86.3%
White; 52.8% women) at Wave 1 was 18.77 (SD = 1.08 years). A
majority of participants began the study in their freshman (57%)
or sophomore (34%) year at the university. Data were collected
using a daily diary design, where participants answered question-
naires examining personality traits and demographic information
in a trait assessment and then completed a 30-day daily diary, be-
ginning two to three weeks after the trait assessment. Participants
completed the trait assessment and 30-day diary each year for up
to four years. Participants began in either the fall or spring semes-
ter; 61% of participants began the study one month into the fall se-
mester, and 39% began one month into the spring semester. Those
who began in a certain semester completed their daily diary study
in that same semester each year.
Participants completed the daily diary assessment using a secure
website once per day between 2:30 p.m. and 7:00 p.m. Participants
received up to $220 in monetary compensation per year, with each
participant receiving $20 for the completion of their baseline sur-
veys and up to $100 depending on daily survey adherence. As an
additional incentive, participants who completed all 30 days were
entered into a lottery for an added prize of $100. The current study
is limited to secondary analyses of the broader study (see Armeli
et al., 2015 for additional details on original data collection).
For our final analyses, data for each wave were only retained
for participants who completed at least 15 out of 30 days of daily
diary data per wave. Of the initial 574 participants, 502 partici-
pants in Wave 1 met the inclusion criteria for the current study for
study days. We used the same inclusionary criteria for each wave:
Wave 2 (N= 449), Wave 3 (N= 406), and Wave 4 (N= 362). Ta-
ble 1 details the demographic information of participants. The ad-
herence rate for daily diary reporting across all years in this
sample was 81.8% (24.5 days out of 30 days).
We deviated in several ways from preregistered inclusion and
exclusion criteria (responding to consultation with other research-
ers, including the senior author and peer reviewers). First, at the
suggestion of the senior author who collected the original data, we
changed our exclusion to retain only participants with at least 15
days of data (as opposed to the 10-day criteria outlined in our
preregistration). Second, per reviewer suggestions, we included
participants who had intraclass correlation (ICC) scores of zero for
the negative emotion items across the daily surveys at each wave
(as opposed to only including participants with a positive ICC
score). Analyses that match the preregistration exclusion criteria
are reported in the online supplemental materials.
Measures
Stress Intensity of Daily Event
Stress intensity was measured using a one-item daily measure.
Participants were asked to think about their most negative event of
the day. They rated how stressful the event was (“How stressful
was this event?”) using a 7-point Likert scale ranging from 1 =
(Not at all stressful)to7=(Extremely stressful).
Daily Emotion Regulation Strategies
Daily use of regulation strategies was assessed using a single-
item measure for each of the six listed emotion regulation strat-
egies. When participants were asked to think of their most nega-
tive event of the day (see above), they were then asked to rate how
much they had been engaging in each of six different emotion reg-
ulation strategies in response to the negative daily event: avoid-
ance (“Avoided dealing with the situation”), alcohol use (“Used
alcohol to help get through it”), drug use (“Used drugs to help get
through it”), distraction (“Tried distracting myself to keep my
mind off the problem”), problem-solving (“Thought about what
needed to be done and did it”), and seeking social support
(“Sought out individuals who could help me or make me feel bet-
ter”). Participants rated how much they had used each strategy to
Table 1
Participant Demographics
Characteristics N= 502 (%)
Sex
Male 237 (47.2%)
Female 265 (52.8%)
Year
Freshman 281 (56.0%)
Sophomore 168 (33.5%)
Junior 39 (7.8%)
Senior 10 (2.0%)
Fifth year or beyond 2 (0.4%)
Missing 2 (0.4%)
Race
White/Caucasian 433 (86.3%)
Black/African American 17 (3.4%)
Latino/Hispanic 15 (3.0%)
Asian/Pacific Islander 33 (6.6%)
Other 4 (0.8%)
2
Hypotheses and analyses were registered after data were collected but
before data analyses. Before preregistering this manuscript, a NED index
was created and analyses were conducted using this dataset for two
separate projects that led to a conference talk (https://osf.io/a9wm5/) and
poster (https://osf.io/s8kn4). The hypotheses for these projects differed
from the hypotheses in the present research. After these projects, we
produced research questions and then preregistered a plan with the current
study hypotheses and data analyses.
4BROWN ET AL.
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cope with the most negative event of their day on a 4-point Likert
scale from 0 = (I haven’t been doing this at all)to3=(I’ve been
doing this a lot). The two items referring to alcohol use and drug
use were summed together and conceptualized as regulating emo-
tions through substance use, resulting in a total of five emotion
regulation strategy items. The correlation between the two sub-
stance use items was moderate (ranging from .5 to .6 at each
wave), empirically supporting our decision to aggregate them.
Avoidance, substance use, and distraction fit conceptually under
the domain of disengagement regulation strategies, whereas prob-
lem-solving and seeking social support represent two engagement
regulation strategies (Carver et al., 1989;Naragon-Gainey et al.,
2017).
Negative Emotional Intensity
Daily mood was captured using emotion items from the Positive
and Negative Affect Schedule (Watson et al., 1988) and Larsen
and Diener’s circumplex model of emotion (Larsen & Diener,
1992), resulting in 15 emotion terms capturing negative and posi-
tive emotions (6 positive emotion terms and 9 negative emotion
terms). Participants rated each emotion using a 5-point Likert scale
from 1 = (Not at all)to5=(Extremely) to describe their emotions
at the time of the prompt. For the purposes of the current study,
eight of the nine negative emotion items were used (sad, dejected,
nervous, jittery, angry, hostile, guilty, ashamed). “Bored”was not
included in analyses because of poor factorial validity in the mood
literature (Goetz et al., 2014). Participants’total emotional inten-
sity scores for each day were calculated by averaging their reports
of the eight negative emotion items. We conducted multilevel reli-
ability for emotion intensity per recommendations for assessing
within-person and between-person reliability using daily diary
data (Cranford et al., 2006). Both within-person reliability (a=
.80) and between-person reliability (a= .99) were acceptable.
Negative Emotion Differentiation
NED was measured with the ICC (Shrout & Fleiss, 1979) across
the eight negative emotion items used in the momentary assess-
ments of mood for each participant. The ICC can be interpreted as
how similarly negative emotion ratings vary over time. Someone
with a high ICC would give very similar ratings for the emotions
at any given time point (e.g., rating of 2 for almost all negative
emotions), while someone with a low ICC would give very differ-
ent responses for the emotions (e.g., rating of a 1 for some emo-
tions, 2 for others, 3 for some, etc.). We deviated from our
preregistration by using the ICC(2,k), rather than the ICC(1,1), to
provide a better measure of NED. The ICC(2,k) refers to negative
emotions as a whole, rather than a single negative emotion, and
incorporates mean differences across items, rather than only their
correlations. This type of ICC is consistent with the literature on
emotion differentiation, specifically when capturing NED using
experience sampling methodology (e.g., Kashdan et al., 2010;
Tugade et al., 2004). We calculated the ICC separately for each
person and wave/year of assessment. The ICCs at each wave were
estimated using the “psych”R package via linear mixed-effects
modeling to better allow for missing data (compared with
ANOVA based ICC estimation; Revelle, 2019). No participants
had negative ICCs, and participants with zero ICCs were retained.
NED scores at each wave were defined as 1–ICC, such that higher
scores indicated higher NED, and then averaged across the waves
to obtain final NED scores.
Data Analytic Plan
Generalized linear mixed-effects models were applied to test the
study hypotheses using the “lme4”package (Bates et al., 2015).
We ran five statistical models—one for each of the five emotion
regulation strategy outcomes. The distribution of the disengage-
ment regulation strategy scores was very positively skewed (and
integer-only), thus informing the use of a Poisson error distribu-
tion with a natural log link function (Afifi et al., 2007). The models
had three levels with day at level 1, wave at level 2, and person at
level 3. There were no predictors at level 2, but unconditional like-
lihood ratio tests of each outcome indicated three-level random
structure fit the data better than a two-level random structure that
ignored between-wave differences (ps,.05). No random slopes
were included in the models.
The only level-1 predictor was daily stress intensity, which was
person-centered (i.e., group-mean centered) to reflect within-per-
son variance. Average levels of daily stress intensity across the
study for each person was a level-3 predictor reflecting the
between-person variance and was grand-mean centered. The two
other level-3 predictors were NED and negative emotion intensity,
both grand-mean centered. In line with recent NED work (e.g.,
Starr et al., 2017,2020) and Bolger and Laurenceau’s (2013) rec-
ommendation for daily diary research, we partitioned our level-1
predictor (i.e., stress intensity) into two independent components:
between-person and within-person components. We included both
the main effects and interactions for both within-person and
between-person components in our model (see Table 4). However,
as our hypotheses are only concerned with the within-person
effects of stress intensity, we do not describe the effects of
between-person stress intensity in the Results section. To clarify
this, we have bolded the row in the tables corresponding to the
cross-level interaction of within-person stress intensity and NED.
Sensitivity analyses revealed that the addition of the between-per-
son component of stress intensity did not change the results when
compared to analyses excluding this between-subjects component.
The four product terms in the statistical models are 1) within-
person stress intensity * NED, 2) between-person stress intensity *
NED, 3) within-person stress intensity * negative emotion inten-
sity, and 4) between-person stress intensity * negative emotion in-
tensity. While the focal product terms were those for NED, we
controlled for participant’s mean negative emotion intensity to
ensure that we captured the effects of NED beyond differences in
negative emotion intensity (e.g., Demiralp et al., 2012;Erbas et
al., 2018). Simple slopes were calculated to illustrate significant
moderation effects at high NED (1 SD above the mean) and low
NED (1 SD below the mean).
We deviated from our preregistered data analytic plan following
consultation with other researchers, including peer reviewers (see
online supplemental materials for results from the original prereg-
istered analyses). First, we applied Poisson mixed-effects model-
ing rather than linear mixed-effects modeling to account for the
distribution of the disengagement regulation strategies. Second,
we combined the four waves together into a three-level model,
rather than apply separate two-level models at each wave, to
reduce the total number of hypothesis tests and study-wise type I
NEGATIVE EMOTION DIFFERENTIATION 5
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error. Third, we included the average level of stress intensity to
test for the NED interaction at both the within-person and
between-person level.
To correct for the 10 statistical significance tests conducted, we
used the Benjamini-Hochberg (B-H) correction (Benjamini &
Hochberg, 1995) for any statistically significant interaction effects
that were found. The B-H correction is an approach to control for
the false discovery rate, which ensures that the likelihood of incor-
rectly rejecting the null hypothesis (i.e., type I error) for statisti-
cally significant findings is 5%.
Results
Descriptive Statistics and Preliminary Analyses
Descriptive statistics are summarized in Table 2. NED scores
were comparable (M= .47, SD = .22) to past research on NED
(Erbas et al., 2014;Kashdan et al., 2010). Given the nested struc-
ture of the data, correlations were decomposed to illustrate rela-
tionships for both between-person and within-person variables.
Correlations are presented in Table 3. Analyses revealed signifi-
cant, positive within-person correlations between stress intensity
and each regulation strategy at the .01 level. The correlation
between our level-3 variables (NED and emotion intensity) fell
under the threshold of problematic multicollinearity (r=.46).
Primary Analyses
We examined how NED moderated the relationship between
stress intensity and each of the five emotion regulation strategies.
A statistical summary of moderation analyses is reported in Table
4. We controlled for the moderation effect of emotion intensity.
Some of the emotion intensity moderation effects are significant,
however, these effects were not probed as this was not the focal
point of our analyses.
Avoidance as an Outcome: Within-Person Stress Intensity
as a Predictor
Within-person stress intensity was positively associated with
the use of avoidance (b= .09, z= 18.50, p,.001, 95% CI [.08,
.10]). NED was negatively associated with the use of avoidance
(b=–1.00, z=–4.47, p,.001, 95% CI [–1.43, –.56]). Negative
emotion intensity was positively associated with the use of avoid-
ance (b= .89, z= 6.98, p,.01, 95% CI [.64, 1.15]). Moderation
analyses did not yield a significant moderation effect of NED on
the relationship between within-person stress intensity and the use
of avoidance (p= .16). No moderation effect was found of nega-
tive emotion intensity on the relationship between within-person
stress intensity and the use of avoidance (p= .08).
Substance Use as an Outcome: Within-Person Stress
Intensity as a Predictor
Within-person stress intensity was positively associated with
substance use (b= .14, z= 11.80, p,.001, 95% CI [.11, .16]).
NED was negatively associated with substance use (b=2.04, z=
–4.61, p,.001, 95% CI [–2.90, –1.17]). Negative emotion inten-
sity was positively associated with substance use (b= 2.13, z=
8.92, p,.001, 95% CI [1.66, 2.60]). Moderation analyses did not
yield a significant moderation effect of NED on the relationship
between within-person stress intensity and substance use (p= .29).
A significant moderation effect was found of negative emotion in-
tensity on the relationship between within-person stress intensity
and substance use (b=–.09, z=–4.77, p,.001, 95% CI [–.12,
–.05]).
Distraction as an Outcome: Within-Person Stress Intensity
as a Predictor
Within-person stress intensity was positively associated with
the use of distraction (b= .11, z= 29.23, p,.001, 95% CI [.10,
.12]). NED was negatively associated with the use of distraction
(b=–.73, z=–3.96, p,.001, 95% CI [–1.10, -.37]). Negative
emotion intensity was positively associated with distraction (p=
.47, z= 4.39, p,.001, 95% CI [.26, .68]). Moderation analyses
yielded a significant moderation effect of NED on the relationship
between within-person stress intensity and the use of distraction
(b=–.06, z=–2.70, p,.01, 95% CI [–.10, –.02]). This modera-
tion effect remained significant after applying the B-H correction.
Simple slopes analysis revealed a weaker positive effect at high lev-
els of NED (b=.09,z=15.36,p,.001, 95% CI [.08, .11]) com-
pared with low NED (b=.12,z=21.95,p,.001, 95% CI [.11,
.13]). This moderation effect is illustrated in Figure 1. A significant
moderation effect was found of negative emotion intensity on the
relationship between within-person stress intensity and the use of
distraction (b=-.04,z=3.85, p,.001, 95% CI [–.06, –.02]).
Problem-Solving as an Outcome: Within-Person Stress
Intensity as a Predictor
Within-person stress intensity was positively associated with the use
of problem-solving (b=.12,z=44.69,p,.001, 95% CI [.11, .12]).
NED was unrelated to problem-solving (p= .12). Negative emotion in-
tensity was unrelated to the use of problem-solving (p= .28). Modera-
tion analyses did not yield a significant moderation effect of NED on
the relationship between within-person stress intensity and the use of
problem-solving (p=.14).Asignificant moderation effect was found
of negative emotion intensity on the relationship between within-per-
son stress intensity and the use of problem-solving (b=–.05, z=
–5.14, p,.001, 95% CI [–.06, –.03]).
Social Support as an Outcome: Within-Person Stress
Intensity as a Predictor
Within-person stress intensity was positively associated with
the use of social support (b= .19, z= 52.84, p,.001, 95% CI
Table 2
Descriptive Statistics for Variables
Variable Range MSDSkewness
Stress intensity 17 4.17 1.88 .20
Avoidance 03 .47 .82 1.73
Substance use 03 .12 .50 4.49
Distraction 03 .75 .96 1.02
Problem-solving 03 1.44 1.12 .07
Social support 03 .82 1.00 .89
Emotion intensity 15 1.36 .37 2.01
NED 01 .47 .22 .22
Note. SD = standard deviation; NED = negative emotion differentiation.
6BROWN ET AL.
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[.18, .19]). NED was unrelated to the use of social support (p=
.14). Negative emotion intensity was positively associated with the
use of social support (b= .37, z= 3.51, p,.001, 95% CI [.16,
.57]). Moderation analyses yielded a marginally significant moder-
ation effect of NED on the relationship between within-person
stress intensity and social support (b=–.04, z=–1.92, p= .05,
95% CI [–.08, .001]), although it bordered on the threshold of sig-
nificance (p,.05). After applying the B-H correction, this effect
was no longer approaching statistical significance. A significant
moderation effect was found of negative emotion intensity on the
relationship between within-person stress intensity and the use of
social support (b=–.09, z=7.79, p,.001, 95% CI [–.11,
–.06]).
Discussion
The purpose of this study was to examine how individual differ-
ences in the ability to label and describe distinct emotional experi-
ences (i.e., NED) moderate the effect of stress intensity on the
selection of emotion regulation strategies. As predicted, people
used emotion regulation strategies more often when they perceived
events as highly stressful. Consistent with our hypotheses, people
who described their emotions in a more granular fashion (i.e., high
NED) were less likely to use disengagement strategies than people
lower in NED. We found limited support for the hypothesis that
higher NED would protect against the use of disengagement
regulation strategies to cope with highly stressful situations.
Higher NED protected against the use of distraction in high-stress
situations, but not for substance use or avoidance.
NED was unrelated to engagement strategies (i.e., problem-
solving and social support). Contrary to predictions, people higher
in NED did not more frequently use engagement strategies, and
NED did not moderate the relationship between stress intensity
and engagement strategy use. A lack of empirical support for mod-
eration hypotheses prompts a reconsideration of the role that NED
plays in regulating distress and highlights potential problems with
dichotomizing regulation strategies. This reconsideration is espe-
cially important considering the methodological differences in
how NED is operationalized.
These results slightly differ from what we discovered from our
preregistered analyses (see online supplemental materials for pre-
registered Methods and Results). In our preregistered analyses, we
found a significant moderating effect of NED on the relationship
between stress intensity and each of the five regulation strategies
at Wave 2. These moderation effects, however, did not replicate
for any outcome at any other wave (i.e., Waves 1, 3, and 4), with
the exception of distraction, which was significant at Wave 1 and
Wave 2. Notably, the stress–distraction relationship is the only
relationship in the present analyses to be significantly moderated
by NED (see Table 4). These discrepancies may stem from a dif-
fering number of significance tests conducted. Our preregistered
analyses—which were conducted at the wave level (i.e., five
Table 3
Correlations for Between-Person and Within-Person Variables
Variable Stress intensity Avoidance Substance use Distraction Problem-solving Social support NED Emotion intensity
Stress intensity —.32*** .02 .43*** .40*** .42*** .10* .19***
Avoidance .10*** —.39*** .80*** .13*** .38*** .31*** .47***
Substance use .05*** .18*** —.23*** .09* .17*** .28*** .60***
Distraction .16*** .49*** .14*** —.33*** .59*** .27*** .37***
Problem-solving .29*** .07*** .01* .03*** —.63*** .06 .01
Social support .28*** .08*** .10*** .23*** .36*** —.16*** .25***
NED ———— — ——.46***
Emotion intensity ———— — —.02*** —
Note. Upper triangle represents between-subject correlations and lower triangle represents within-subject correlations. NED = negative emotion differen-
tiation.
* Correlation is significant at the .05 level (2-tailed). *** Correlation is significant at the .001 level (2-tailed).
Table 4
The Moderating Effect of NED on the Relationship Between Stress Intensity and Strategy Selection
Variable Avoidance Substance use Distraction Problem-solving Social support
bzbzbzbzbz
Intercept 1.34 31.90*** 4.04 42.19*** .70 20.10*** .18 8.38*** .61 18.09***
Within-person stress .09 18.50*** .14 11.80*** .11 29.23*** .12 44.69*** .19 52.84***
NED 1.00 4.47*** 2.04 4.61*** .73 3.96*** .17 1.57 .26 1.46
Emotion intensity .89 6.98*** 2.13 8.92*** .47 4.39*** .07 1.07 .37 3.51***
Between-person stress .40 8.18*** .01 .11 .45 11.12*** .28 11.40*** .42 10.82***
Within-Person Stress 3NED 2.04 21.42 2.05 21.05 2.06 22.70*** 2.02 21.48 2.04 21.92
Within-Person Stress 3Emotion Intensity .02 1.77 .09 4.77*** .04 3.85*** .05 5.14*** .09 7.79***
Between-Person Stress 3NED .36 1.54 .46 .98 .07 .35 .03 .22 .08 .40
Between-Person Stress 3Emotion Intensity .19 1.36 .50 1.94 .09 .78 .03 .39 .16 1.42
Note. Within-person effects are bolded. Significant NED interaction effects are bolded and italicized. NED = negative emotion differentiation.
*** p,.001.
NEGATIVE EMOTION DIFFERENTIATION 7
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separate 2-level models at each wave)—resulted in 25 significance
tests, whereas the final analyses, which combined all four waves
into one three-level model, resulted in 10 significance tests for
which a B-H correction was applied. Thus, once correcting for sig-
nificance testing, the evidence for a moderating effect of NED on
the stress-regulation strategy relationship in this sample is weak.
NED as a Protective Factor Against the Use of
Disengagement Strategies
NED predicted the use of disengagement strategies. For all dis-
engagement strategies tested, people higher in NED were less
likely to use disengagement strategies to cope with daily stressors.
These findings are consistent with prior findings that people higher
in NED are less likely to use substances (e.g., Anand et al., 2017;
Emery et al., 2014;Kashdan et al., 2010) and avoidance (Seah et
al., 2020) to cope with stress. Moreover, our findings that people
higher in NED less frequently use distraction are consistent with
research demonstrating that people may use distraction when they
feel overwhelmed and confused by emotional experiences (e.g.,
Sheppes et al., 2011;2014). Thus, rather than try to decipher their
emotional experiences, they may instead disengage from them.
This framework is consistent with a recent review of experiential,
behavioral, and neuroimaging research that suggests that labeling
emotions can serve as a form of implicit regulation (Torre & Lie-
berman, 2018). Thus, because people higher in NED identify and
label discrete emotions, they are less likely to use strategies aimed
at escaping negative emotions and are better able to down-regulate
their emotions. Increased emotional awareness may facilitate a
sense of control over emotions, thus reducing regulatory strategies
that often function as an escape from aversive emotions (e.g., sub-
stance use). Indeed, compared to people lower in NED, people
higher in NED report a greater sense of control over emotions
(Pond et al., 2012) and a lower proclivity to act rashly and impul-
sively in response to negative emotions (Emery et al., 2014).
Thus, identifying and labeling discrete emotional experiences may
facilitate greater coping self-efficacy, making it less likely for a
person to use disengagement strategies for a short-term reprieve.
Additionally, greater attention and awareness to a person’semo-
tional experience may prevent people from becoming “entangled”or
“fused”with their emotional experiences (Gillanders et al., 2014;Van
der Gucht et al., 2019). When people become more “entangled”in an
emotional experience, they may be more likely to experience second-
ary emotions or appraisals (i.e., “emotions about emotions”), which of-
ten manifest in a cascade of negative emotions linked to the inflexible
use of disengagement strategies (Gratz & Roemer, 2004;Selby et al.,
2009). Indeed, recent evidence suggests that people with lower NED
report greater cognitive fusion (Plonsker et al., 2017). Future work
exploring mechanisms that might underlie NED will likely help to
inform preventative efforts against the use of problematic disengage-
ment strategies such as using substances to cope with distress.
Contrary to predictions, NED did not moderate the relationship
between stress intensity and four of the five measured regulatory
strategies (distraction being the exception). Thus, we largely failed
to find support that people with different levels of NED regulate
differently in response to varying levels of stress.
Figure 1
The Moderating Effect of NED on the Relationship Between Within-Person Stress and Distraction
Note. þ1SD = one standard deviation above the mean; –1SD = one standard deviation below the mean.
8BROWN ET AL.
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These findings are inconsistent with prior research suggesting
that NED protects against the use of disengagement strategies to
regulate intense negative emotions (Kashdan et al., 2010;Seah et
al., 2020;Zaki et al., 2013). A primary benefit of NED is the abil-
ity to access distinct information about certain affective states
(e.g., physiological sensations, thoughts, urges) to effectively reg-
ulate distress (Kashdan et al., 2015). In this way, NED might be
more beneficial in situations where the source of negative emotion
is ambiguous or diffuse (e.g., a day consisting of low mood or
intense negative emotions) than for a more specific, unambiguous
stressful situation (e.g., “I am stressed and I do not know why I
woke up feeling bad”vs. “I am stressed because I missed my
flight”). Regulatory efforts tied to a specific event may not benefit
as much from NED because already knowing the source of the
negative emotion may mitigate a core mechanism of NED. Future
work examining other factors associated with a specific stressor (e.
g., perceived controllability) within the relationship of NED and
emotion regulation is indicated.
Another explanation for null findings may be due to methodo-
logical differences. A sizable number of NED studies examined
negative emotion intensity, calculated using a wide range of nega-
tive emotion adjectives (e.g., Barrett et al., 2001;Kashdan et al.,
2010) or maladaptive cognitive styles (i.e., rumination; Seah et al.,
2020;Zaki et al., 2013) as predictors of disengagement regulation
strategies. In the current study, the event that engendered regula-
tory efforts were subject to change (different events on different
days), and it may be that in some stressful situations, low NED is
more beneficial or protective to elicit a quick, adaptive response.
For example, in the event that a fire alarm goes off and a quick
evacuation is needed, a granular view of specific emotions may be
unhelpful. An interesting avenue of research would be to detail if
people high in NED demonstrate a sense of flexibility in how they
differentiate between emotions, perhaps becoming less likely to
identify specific emotions when quicker responses are needed.
Null Results for NED and Engagement Strategies and
the Dichotomy of Adaptive and Maladaptive Regulation
Strategies
NED was unrelated to use of engagement strategies (i.e., prob-
lem-solving and social support) and did not moderate stress-regu-
lation strategy relationships. These findings were contrary to
prediction, and caution is warranted against overinterpreting null
effects. Nonetheless, we offer a few possible explanations for
these findings.
In the present study, we classified strategies as involving either
engagement or disengagement with the distress or its source. Lack
of associations between NED and engagement regulation strat-
egies may be an artifact of the categorical splitting of emotion reg-
ulation strategies into adaptive and maladaptive strategies. Our
hypotheses for moderation effects were informed by research sug-
gesting that disengagement strategies tend to be maladaptive and
engagement strategies tend to be adaptive (Aldao et al., 2010) and
that NED is associated with adaptive emotion regulation (Barrett
et al., 2001;Kashdan et al., 2015). However, a growing body of
research suggests that dichotomizing strategies into putatively
adaptive and maladaptive categories makes the erroneous assump-
tion that strategies are uniformly effective (e.g., Aldao et al., 2015;
Bonanno & Burton, 2013;Ford & Troy, 2019). Affective scientists
have called for more emotion regulation research addressing con-
textual factors (Aldao, 2013), such as flexibility of strategy use
(e.g., Kashdan et al., 2020), situational goals (e.g., hedonic goals
vs. instrumental goals; e.g., Tamir et al., 2008), and situational
demands (e.g., Kobyli
nska & Kusev, 2019) that influence emotion
regulation effectiveness.
In this study, it is possible that problem-solving and social sup-
port are less relevant to NED than avoidance, substance use, and
distraction. For example, engagement strategies may not have
been indicated or effective in regulating distress for a particular
stressful event. Prior research found that the use of more complex
strategies (e.g., problem-solving) are more effective at lower stress
intensity (e.g., Ortner et al., 2016). It may be that people who iden-
tify and label instances of emotion in a granular fashion are better
suited to select strategies that fit best for a particular situation
rather than using certain “adaptive”strategies more often.
Another possibility for the lack of support for an association
between NED and engagement regulation strategies was that we
did not assess engagement strategies germane to NED. For exam-
ple, research found that people tend to use cognitive reappraisal in
lower intensity situations because it requires more effortful proc-
essing of stimuli than disengagement strategies like distraction
(e.g., trying to find the silver lining of a failure vs. trying not to
think about the failure; Sheppes et al., 2014). It is possible that
NED moderates this relationship, where people higher in NED use
cognitive reappraisal in high-intensity situations because they can
appropriately sort through their emotions (and potentially even
draw causal explanations for them) and therefore be more
equipped to reinterpret the situation (cf. Kalokerinos et al., 2019).
As another example, mindfulness involves harnessing present
moment awareness (e.g., Bishop et al., 2004). Although there is
debate concerning whether mindfulness is a trait or state/skill
(e.g., Lau et al., 2006;Thompson & Waltz, 2007), people higher
in NED might be better able to direct their momentary experien-
ces, particularly emotional experiences. Thus, we might expect
people higher in NED to use mindfulness more often, even in
stressful situations. Future research on NED will benefit from
including a variety of engagement regulation strategies (e.g.,
mindfulness, acceptance, cognitive reappraisal).
Limitations and Future Directions
Several study limitations warrant mention. Daily diary method-
ology allows examination of how people differentiate between
emotions and regulate their distress over time. Nonetheless, our
analyses are correlational, which prevents conclusion about cau-
sality. Our NED scores represent participants’average level of
NED across the study, and thus we cannot determine how partici-
pants labeled and identified emotions in the immediate aftermath
of daily stressors. Future research can assess temporal relation-
ships between stress and NED, such as by examining fluctuations
of NED during stressful moments as they unfold using ecological
momentary assessment (e.g., Erbas et al., 2018) or immediately af-
ter using event-contingent recording (cf. Tennen et al., 2006).
Although our approach of quantifying NED (e.g., 1–ICC) is
common, it does not separate people who truly experience mixed
negative emotions at the same time (e.g., sadness and guilt) from
low NED. This may be problematic because the most emotionally
aware individuals are likely able to accurately identify mixed
NEGATIVE EMOTION DIFFERENTIATION 9
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emotional experiences. Open-response formats may be a better al-
ternative to capture mixed emotional experiences (Ottenstein &
Lischetzke, 2020).
We measured a fixed set of emotion regulation strategies. While
they represent a diverse range of commonly used strategies, they
are not comprehensive. Future studies can allow open responses
for regulation strategies rather than relying on a fixed list. We also
examined each regulatory strategy separately, which precludes
us from examining how strategies work in tandem. Emerging
research suggests that people commonly use multiple regulatory
strategies within a single emotional episode and/or in response to a
single stressor (for a review, see Ford et al., 2019). Daily reports
may be suboptimal for assessing polyregulation because they do
not allow for assessments of temporality —when someone
switches strategies over time (i.e., sequential polyregulation) dur-
ing the course of an emotion episode (e.g., Kalokerinos et al.,
2017). Future experience-sampling research may use assessment
approaches with shorter time intervals (e.g., ecological momentary
assessment) to capture short-term changes in strategy use. We also
did not assess the effectiveness of each strategy. A recent study
found that NED was associated with relatively successful emotion
regulation across a range of engagement and disengagement strat-
egies (Kalokerinos et al., 2019). Similar to our findings, this study
also found inconsistent relationships between NED and strategy
selection.
Lastly, we did not assess other individual differences pertinent
to emotion regulation. While NED provides information about the
degree to which people distinguish their experience of negative
emotion, it does not provide metaemotional information (i.e., a
person’s beliefs about various aspects of their emotions). The
beliefs and values people hold about their emotions are associated
with the strategies they select to regulate emotions (e.g., Ford &
Gross, 2019). For example, people who believe emotions are rela-
tively malleable tend to use more engagement strategies (for a
review, see Kneeland et al., 2016). People also differ in their valu-
ations of emotion (i.e., how helpful/acceptable vs. unhelpful/unac-
ceptable emotions are; see Ford & Gross, 2019), especially for
negative emotions (Luong et al., 2016). Individual differences in
how people value emotions influence how people effectively regu-
late distress. One study found that participants who placed a higher
value on emotional control (e.g., “people should control their emo-
tions more”) used suppression more frequently to regulate emo-
tions than those who placed a lower value on emotional control
(Goodman et al., 2020). Additionally, when people appraise their
experience of stress as adaptive or functional, it can optimize their
response to stress (e.g., Crum et al., 2020;Jamieson et al., 2018).
People who appraise negative emotions as useful or meaningful
may be more inclined to identify the nuances across negative
states (i.e., higher NED).
Conclusion
The strategies people use to regulate distress predict important
psychological and physiological outcomes. The present study
examined how individual differences in NED moderated the rela-
tionship between the stress intensity of the days’most stressful
event and strategy use. Although people higher in NED less fre-
quently used disengagement strategies, NED was largely unrelated
to strategy selection across varying levels of stress, suggesting that
NED’s protective effect may function differently in response to
specific stressful events. NED was also unrelated to use of engage-
ment strategies, suggesting that possible benefits of NED on emo-
tion regulation processes are not specific to strategy selection. In
sum, these findings support the notion that NED is protective
against the use of certain disengagement strategies (e.g., substance
use) and raise a set of important future directions, including identi-
fying when NED is most optimal.
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