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When Chatting About Negative Experiences Helps-and When It Hurts: Distinguishing Adaptive Versus Maladaptive Social Support in Computer-Mediated Communication

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Does talking to others about negative experiences improve the way people feel? Although some work suggests that the answer to this question is “yes,” other work reveals the opposite. Here we attempt to shed light on this puzzle by examining how people can talk to others about their negative experiences constructively via computer-mediated communication, a platform that people increasingly use to provide and receive social support. Drawing from prior research on meaning-making and self-reflection, we predicted that cueing participants to reconstrue their experience in ways that lead them to focus on it from a broader perspective during a conversation would buffer them against negative affect and enhance their sense of closure compared with cueing them to recount the emotionally arousing details concerning what happened. Results supported this prediction. Content analyses additionally revealed that participants in the reconstrue condition used the word “you” generically (e.g., you cannot always get what you want) more than participants in the recount condition, identifying a linguistic mechanism that supports reconstrual. These findings highlight the psychological processes that distinguish adaptive versus maladaptive ways of talking about negative experiences, particularly in the context of computer-mediated support interactions.
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Emotion
When Chatting About Negative Experiences Helps—and
When It Hurts: Distinguishing Adaptive Versus
Maladaptive Social Support in Computer-Mediated
Communication
David S. Lee, Ariana Orvell, Julia Briskin, Taylor Shrapnell, Susan A. Gelman, Ozlem Ayduk, Oscar
Ybarra, and Ethan Kross
Online First Publication, January 10, 2019. http://dx.doi.org/10.1037/emo0000555
CITATION
Lee, D. S., Orvell, A., Briskin, J., Shrapnell, T., Gelman, S. A., Ayduk, O., Ybarra, O., & Kross, E.
(2019, January 10). When Chatting About Negative Experiences Helps—and When It Hurts:
Distinguishing Adaptive Versus Maladaptive Social Support in Computer-Mediated
Communication. Emotion. Advance online publication. http://dx.doi.org/10.1037/emo0000555
When Chatting About Negative Experiences Helps—and When It Hurts:
Distinguishing Adaptive Versus Maladaptive Social Support in
Computer-Mediated Communication
David S. Lee
University of Michigan, Ann Arbor, and The Ohio State
University, Columbus
Ariana Orvell, Julia Briskin, Taylor Shrapnell,
and Susan A. Gelman
University of Michigan, Ann Arbor
Ozlem Ayduk
University of California, Berkeley Oscar Ybarra and Ethan Kross
University of Michigan, Ann Arbor
Does talking to others about negative experiences improve the way people feel? Although some work
suggests that the answer to this question is “yes,” other work reveals the opposite. Here we attempt to
shed light on this puzzle by examining how people can talk to others about their negative experiences
constructively via computer-mediated communication, a platform that people increasingly use to provide
and receive social support. Drawing from prior research on meaning-making and self-reflection, we
predicted that cueing participants to reconstrue their experience in ways that lead them to focus on it from
a broader perspective during a conversation would buffer them against negative affect and enhance their
sense of closure compared with cueing them to recount the emotionally arousing details concerning what
happened. Results supported this prediction. Content analyses additionally revealed that participants in
the reconstrue condition used the word “you” generically (e.g., you cannot always get what you want)
more than participants in the recount condition, identifying a linguistic mechanism that supports
reconstrual. These findings highlight the psychological processes that distinguish adaptive versus
maladaptive ways of talking about negative experiences, particularly in the context of computer-mediated
support interactions.
Keywords: emotion regulation, coping, meaning-making, social support, computer-mediated communi-
cation
Supplemental materials: http://dx.doi.org/10.1037/emo0000555.supp
The advent and proliferation of computer-mediated communi-
cation technologies (e.g., text messaging, chat rooms, social me-
dia) has rapidly changed the way people interact. According to a
Pew Research Center survey published in 2018, 68% of all U.S.
adults use at least one social media platform to interact with others,
with about three-quarters of them using it on a daily basis (Smith
& Anderson, 2018). Moreover, teens now report that texting is the
most common way they communicate with their friends (Lenhart,
Smith, Anderson, Duggan, & Perrin, 2015).
Emerging evidence indicates that much of these computer-
mediated communications involve providing and receiving social
support (e.g., Ellison, Steinfield, & Lampe, 2007; Kross et al.,
2013; Oh, Ozkaya, & LaRose, 2014; Park et al., 2016; Valenzuela,
Park, & Kee, 2009; Wright, 2016). For example, one study indi-
cated that 68% of teens receive support from friends through social
media during tough times (Lenhart et al., 2015). Data from the Pew
Internet and American Life Project (Fox, 2011) revealed that
almost one in five adult Internet users in the United States reported
having received support online the last time they had a health
issue.
Despite the frequency with which people exchange social sup-
port with others via computer-mediated communication methods,
no studies that we are aware of have examined whether certain
ways of chatting with others via these technologies are more
David S. Lee, Department of Psychology, University of Michigan, Ann
Arbor, and Department of Psychology, The Ohio State University, Colum-
bus; Ariana Orvell, Julia Briskin, Taylor Shrapnell, and Susan A. Gelman,
Department of Psychology, University of Michigan, Ann Arbor; Ozlem
Ayduk, Department of Psychology, University of California, Berkeley;
Oscar Ybarra and Ethan Kross, Department of Psychology, University of
Michigan, Ann Arbor.
David S. Lee, Julia Briskin, Ozlem Ayduk, Oscar Ybarra, and Ethan
Kross conceived and designed the study. David S. Lee, Julia Briskin, and
Taylor Shrapnell performed the study. David S. Lee, Oscar Ybarra, Ethan
Kross, and Ariana Orvell analyzed the data. David S. Lee, Oscar Ybarra,
Ethan Kross, and Ariana Orvell wrote the article. All authors provided
feedback on the final draft.
Correspondence concerning this article should be addressed to David S.
Lee, Department of Psychology, The Ohio State University, Columbus,
1827 Neil Avenue, Columbus, OH 43210. E-mail: lee.4152@osu.edu
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.
Emotion
© 2019 American Psychological Association 2019, Vol. 1, No. 999, 000
1528-3542/19/$12.00 http://dx.doi.org/10.1037/emo0000555
1
effective at facilitating emotion regulation than others. Addressing
this issue is important, because prior research suggests that it is
possible for people to reflect on negative experiences in different
ways that have direct implications for how they think, feel and
behave (e.g., Gross, 1998; Kross & Ayduk, 2011, 2017; Nolen-
Hoeksema, Wisco, & Lyubomirsky, 2008; Wilson & Gilbert,
2008). In the current research, we build on this work to examine
the processes that facilitate adaptive social support via computer-
mediated communication.
Processes Distinguishing Adaptive Versus Maladaptive
Social Support
Psychologists have long been interested in identifying the pro-
cesses that distinguish adaptive versus maladaptive forms of self-
reflection (e.g., Kross, Ayduk, & Mischel, 2005; Nolen-Hoeksema
et al., 2008; Ochsner & Gross, 2008; Wilson & Gilbert, 2008).
According to one line of work within this research tradition (see
Kross & Ayduk, 2017 for a review), one mechanism that deter-
mines whether self-reflection leads people to feel better or worse
is whether they focus on recounting the emotionally arousing
features of their negative experience or reconstruing their experi-
ence by thinking about it in a broader context that promotes insight
and closure (Kross & Ayduk, 2011, 2017; Rude, Mazzetti, Pal, &
Stauble, 2011; Schartau, Dalgleish, & Dunn, 2009). Specifically,
whereas recounting has been consistently linked with negative
outcomes such as increased negative emotional and physiological
reactivity (Bushman, 2002; Glynn, Christenfeld, & Gerin, 2002;
Kross & Ayduk, 2008), reconstruing has been consistently asso-
ciated with more beneficial outcomes such as improved emotional
and physiological reactivity and enhanced sense of closure (Ayduk
& Kross, 2010; Gross, 1998; Gruber, Harvey, & Johnson, 2009;
Rude et al., 2011; Schartau et al., 2009).
1
Drawing from this
research, we hypothesized that whether talking to others about
negative experiences is helpful or harmful should depend critically
on whether people recount or reconstrue their negative experi-
ences during their conversations.
Indirect evidence supporting this prediction comes from two
domains. First, research on corumination and the social sharing of
emotion literatures indicate that focusing excessively on discuss-
ing the negative content of one’s experiences (i.e., what happened
and what one felt) perpetuates negative emotional responses (e.g.,
Rimé, 2009; Rose, 2002; Rose, Carlson, & Waller, 2007). How-
ever, this prior work is less clear on how talking to others about
one’s distressing experience can be beneficial. For instance, re-
search on corumination has not examined the ways in which
people can adaptively discuss their negative experience with oth-
ers. Although Nils and Rimé (2012) showed that prompting people
to positively reframe their negative feelings during conversations
is beneficial, the participants discussed their reactions to watching
distressing film clips rather than highly stressful autobiographical
experiences. Thus, whether the benefits of engaging in a
perspective-broadening reconstrual also explain how people can
talk to others adaptively about their negative experiences without
becoming overwhelmed by negative affect has not been explored.
Second, some research has examined the role that cognitive
reappraisal strategies play in interpersonal contexts (e.g., Butler et
al., 2003; Richards, Butler, & Gross, 2003). However, most of
these studies focused on how trying to remain calm and dispas-
sionate or thinking about positive aspects of one’s relationship
compare with expressive suppression or an uninstructed control
condition in terms of its implications for rapport with one’s con-
versation partner (Butler et al., 2003) and what people remember
from their conversations (Richards et al., 2003). Although these
studies have revealed positive effects of cognitive reappraisal
strategies (e.g., willingness to affiliate with conversation partner),
they focus on different reappraisal operations, a different compar-
ison strategy (e.g., expressive suppression), and different outcome
variables from the current work.
Linguistic Trace of Reconstrual
Our second goal was to examine whether we could identify a
linguistic trace of reconstrual in people’s conversations about their
negative experiences. Several recent studies indicate that when
people try to make meaning out of negative events through writing,
they use the word “you” generically to situate their experience in
a context that extends beyond the self and describe it as a more
normative phenomenon that others share (e.g., “In life, you don’t
always get what you want”; Orvell, & Kross, & Gelman, 2017a,
2017b). For example, in one study, Orvell and colleagues (2017a)
found that participants who were instructed to make meaning out
of their negative experience (vs. relive it) used generic-you sig-
nificantly more in their essays, which in turn led them to report
feeling more psychologically distant from their event (Orvell et al.,
2017a). Thus, to the extent that reconstruing negative experiences
involves thinking broadly about one’s experiences from a less
egocentric perspective (i.e., more psychologically distant), we
predicted that cueing participants to reconstrue (vs. recount) their
experience should also lead them to use generic-you more during
their conversations.
Overview of Research
The present research examined how people can talk to others
about their negative experiences constructively in computer-
mediated communication. To do this, we randomly assigned par-
ticipants who recently experienced a negative interpersonal event
to discuss their experience via instant messenger with a confeder-
ate who cued them to either recount or reconstrue their experience.
Afterward, we assessed participants’ negative affect and closure
levels, and content analyzed transcripts of their conversations for
generic-you usage. Based on prior research on self-reflection (e.g.,
Kross & Ayduk, 2011, 2017), we predicted that cueing participants
to reconstrue their experience by focusing on it from a broader
perspective during a conversation would buffer them against neg-
ative affect and enhance their sense of closure compared with
cueing them to recount the emotionally arousing details regarding
what happened. We also hypothesized that cueing participants to
1
We note that the terms reconstrual, as we define in the current research,
and reappraisal are largely synonymous. Both refer to the concept of
changing the way one thinks about a stimulus, which can be done in
potentially infinite ways. Whereas reconstrual is used more frequently in
social psychology, growing out of the tradition of work on the importance
of “mental construal” (e.g., Kelly, 1955; Mischel, 1973; Mischel & Shoda,
1995; Ross, 1989; Trope & Liberman, 2003, 2010), reappraisal is more
commonly used in the coping and emotion regulation literature (Gross,
1998; Lazarus & Alfert, 1964; Lazarus & Folkman, 1984).
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2LEE ET AL.
reconstrue (vs. recount) their experience would lead them to use
generic-you more during their conversations.
Method
Data Collection Overview and Participants
Data collection occurred across two phases (N
Sample 1a
64;
N
Sample 1b
119). In Sample 1a, we sought to collect as much data
as we could in one semester, with the aim of collecting at least 30
participants per condition. After completing data collection for
Sample 1a, we analyzed the data and determined that more power
was needed to robustly test our predictions. Note that data collec-
tion for these studies occurred during a time of rapidly increasing
power recommendations (e.g., Simmons, Nelson, & Simonsohn,
2011). Thus, despite the fact that we observed significant findings
that were consistent with our predictions in Sample 1a, we erred on
the side of collecting more data with Sample 1b to ensure that we
were not capitalizing on error in a smaller sample. In Sample 1b,
we decided to roughly double data collection during the next
semester (see Murayama, Pekrun, & Fiedler, 2014 for a discussion
advocating this approach). The combined sample included 183
participants (150 females; M
age
22.39, SD
age
8.35; 49%
Caucasian, 26% Asian, 14% other, 9% African American, 2%
Hispanic).
Because our main goal was to examine the processes involved in
support conversations that promote closure and meaning-making,
it was critical that we only recruit participants who were upset
about an ongoing source of distress. Thus, participants had to (a)
be in the midst of experiencing an ongoing conflict with another
person, (b) still be upset about it, and (c) be willing to talk about
it to qualify for inclusion in the study. Participants were compen-
sated $10. The University of Michigan Institutional Review Board
approved this study.
Procedure and Materials
Overview of procedure. One experimenter (the Facilitator),
blind to participants’ condition and the study hypotheses, guided
participants through the experiment. A second experimenter (the
Support-provider), blind to the study hypotheses, talked to partic-
ipants about their experience via an online instant messenger.
Phase 1: Baseline affect. After providing consent, partici-
pants responded to the following question, “How do you feel right
now?” usinga0(very bad) to 100 (very good) scale (M67.54,
SD 18.87).
Phase 2: Getting-acquainted session. Next, the Support-
provider initiated an “ice-breaking” conversation for 5 min (Yba-
rra et al., 2008) to help participants feel comfortable. The Support-
provider asked scripted questions (e.g., “How is your summer
going?”) and provided standardized responses (e.g., “That sounds
interesting!”).
Phase 3: Manipulation. After the icebreaker, the Support-
provider transitioned to talking about the participant’s experience.
Participants were told: “Could you briefly tell me about [the
experience]? What happened? Who did it involve?” Pilot work
indicated that these “warm-up” questions were necessary to facil-
itate natural exchanges. In additon, two judges coded the response
to confirm that the emotional intensity of participants’ events (␣⫽
.93) did not differ across conditions usinga1(a little)to3(very)
scale.
After asking the above questions, the experimental manipulation
was administered. In the recount condition (N92), the Support-
provider asked five standardized questions that prompted partici-
pants to talk about what happened to them and what they felt
during their experience. In the reconstrue condition (N91),
participants were asked five standardized questions that prompted
them to focus on their experience from a broader perspective (see
Appendix for verbatim questions). The Support-provider followed
a standardized script to ensure that participants in each condition
were asked the same questions. These questions were theoretically
derived based on prior research that has carefully operationalized
these constructs (e.g., Kross & Ayduk, 2017). When asking these
questions, the Support-provider was instructed to acknowledge
participants’ situation with a standardized response (e.g., “I’m
sorry to hear that”) but not to provide any further feedback to
ensure that participants within each group received the same
manipulation.
To ensure that participants could respond to all questions, no
time limit was imposed (M
minutes
30.58, SD
minutes
10.36).
Conversation length did not differ by condition (p.57). At the
end of the conversation, the Support-provider sent participants a
Web link to our dependent measures.
Phase 4: DVs.
Postconversation affect. Immediately following their conver-
sation, participants once again rated how they felt in that moment
by answering the question, “How do you feel right now?” using a
0(very bad) to 100 (very good) scale. We reverse-scored this item
(M38.52, SD 20.93) and standardized it along with the
negative affect items to create a single negative affect index (␣⫽
.61). Participants also rated how upset they felt (M4.84, SD
1.46) and how intense their emotions and physical reactions were
during the conversation (M4.27, SD 1.63), ona1(strongly
disagree)to7(strongly agree) scale.
Closure. Next, participants responded to “As I was talking to
the research assistant about the event, I had a realization that led
me to experience a sense of closure,” ona1(strongly disagree)to
7(strongly agree) scale (M4.11, SD 1.66).
Content analyses. We coded participant’s conversations for
three types of information.
First, three judges coded participants’ portion of the conversa-
tion on the extent to which it contained recounting- (␣⫽.77; M
1.94, SD .73) and reconstruing (␣⫽.83; M.53, SD .59)
statements usinga0(not at all)to3(very much) scale, following
protocols used in prior research (Ayduk & Kross, 2010).
Second, two judges counted the number of times participants
used the word “you” generically following Orvell and colleagues
(2017a, 2017b). Discrepancies were resolved by a third coder (␣⫽
.99); generic-you tallies were converted to percent scores out of the
total word count.
Finally, to rule out the possibility that Support-providers were
more supportive to participants in one condition than the other, two
judges coded Support-provider’s utterances on the extent to which
they reflected supportiveness (␣⫽.94) ona0(not at all)to2
(very) scale.
Covariates. Because how much participants like the support-
provider can influence support outcomes (e.g., therapeutic alli-
ance; Martin, Garske, & Davis, 2000), we asked participants to
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3
SOCIAL SUPPORT AND COPING
rate “how much they liked” and “how close they felt” to the
Support-provider using 1 (not at all)to7(very much) scales (␣⫽
.78; M4.83, SD 1.28).
Results
Analyses Overview
The two samples we collected had identical aims and core
methods.
2
In addition, sample did not moderate any results (all
ps.34), and controlling for it did not substantively alter any
results. Analyzing the data as separate versus combined samples
yielded the same pattern of results. Thus, we combined them to
enhance power and parsimony. Table 1 provides statistics for each
subsample analyses, the combined analysis, and an internal meta-
analysis of data from the two samples (see Braver, Thoemmes, &
Rosenthal, 2014; Maner, 2014).
We excluded seven participants (four reconstrue participants)
because of a computer malfunction (one), scheduling conflicts
(three), prior relationship with the Facilitator (one), low English
proficiency (one), and not following instructions (one). This left
176 participants. Including these participants did not substantively
alter the results. Table 1 presents all descriptive and inferential
statistics.
Preliminary Analyses
Coders’ ratings of the emotional intensity of participants’ events
did not differ by condition, F(1, 173) .22, p.64. Moreover,
across conditions participants rated their Support-providers as
equally likable, F(1, 174) .42, p.52 and Support-providers’
utterances were rated by coders as equally supportive, F(1, 173)
.83, p.36. None of these variables moderated any of the results
(ps.51), and controlling for them did not substantively alter any
of the findings. Table 2 presents partial correlations among all key
variables (controlling for baseline affect).
Manipulation Check
Content analyses confirmed that our manipulation was effective:
Participants in the recount (vs. reconstrue) condition recounted
more (Ms2.73 vs. 1.98, SDs.38 vs. .70), F(1, 170) 75.51,
p.0001, and reconstrued less (Ms.25 vs. 1.39, SDs.41 vs.
.77), F(1, 170) 89.91, p.0001 during the conversation.
Primary Analyses
A 2 (Condition: Recount vs. Reconstrue) 2 (Time of State
Affect Assessment (i.e., “How do you feel right now?”) repeated-
measures analysis of variance (ANOVA) revealed a significant
Condition Time interaction, F(1, 173) 14.39, p.001, ␩␳
2
.077, indicating that participants in the recount condition felt
significantly worse after talking about their experience compared
with how they felt before the conversation, t(173) 4.91, p
.001, 95% confidence interval (CI) [5.63, 13.19], d.48. In
contrast, participants in the reconstrue condition were buffered
against experiencing increased negative affect after talking about
their experience, t(173) .47, p.64 (Figure 1).
Next, we performed a series of analyses of covariance (ANCO-
VAs; controlling for baseline affect) on the closure and additional
negative affect items. The results revealed that participants in the
reconstrue condition scored lower on the negative affect index and
higher on closure.
3
Finally, as expected, participants in the reconstrue condition
used generic-you more than participants in the recount condition.
Generic-you use was not significantly correlated with any of the
affect measures or closure, ps.25.
Discussion
The present research examined how people can talk to others
about their negative experiences constructively during computer-
mediated supportive interactions. It generated two key findings.
First, participants who recounted their negative experience while
chatting with a confederate on instant messenger experienced a
significant increase in negative affect compared with baseline. In
contrast, participants who reconstrued their experience during their
instant messenger conversations were buffered against this in-
crease in negative affect despite spending just as much time talking
about their negative experience as recounting participants. They
also reported having more closure. These findings are consistent
with research indicating that venting negative experiences during
an offline social interaction exacerbates distress (Nils & Rimé,
2012; Rose, 2002) whereas reconstruing negative events during
self-refection facilitates successful emotion regulation (Kross &
Ayduk, 2017). Broadly, they suggest that a common set of mech-
anisms may underlie how people self-reflect on negative experi-
ences and how they talk about them with others in computer-
mediated communication.
Second, we found that reconstruing participants spontaneously
used the word “you” generically more in their conversations than
recounting participants. This is noteworthy because prior research
indicates that generic-you serves as a linguistic marker for making
meaning (Orvell et al., 2017a, 2017b). Thus, these findings provide
converging evidence across an additional level of analysis indicat-
ing that reconstruing one’s experience during conversations helps
people make meaning out of their negative experiences. To our
knowledge, this is the first study to demonstrate how generic-you
functions in an interpersonal context, and thus may have important
implications for therapy. For example, clinicians and support-
providers could attend to people’s generic-you usage during con-
versations as a signal for whether they are effectively creating
meaning from a negative experience.
2
The two samples differed only in their exploratory scope. In Sample
1a, we collected exploratory data to examine how people felt one day after
the experiment. Because there were no reliable effects, we did not collect
these data in Sample 1b. In Sample 1b, we assessed participants’ preferred
coping strategy one week before the experiment to explore its impact on
coping (see online supplemental material for all exploratory measures and
analyses).
3
We computed a negative affect composite index on a priori grounds
following prior work (Ayduk & Kross, 2010). However, because of its
moderate reliability (␣⫽.61), we also analyzed each item of this index
individually. These analyses revealed that the effects of condition were
significant on post-conversation affect (p.001), marginally significant
on upset feelings (p.07), and nonsignificant on affect intensity (p
.45).
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4LEE ET AL.
Importantly, the present research provides insight into how
effective social support can occur in nonface to face contexts—a
rapidly increasing context in which people give and receive sup-
port (Kross, 2017; Morris, Schueller, & Picard, 2015; Park et al.,
2016; Wright, 2016). To our knowledge, this is the first study to
examine how people can guide others to adaptively make sense of
their negative experience during online conversations.
Although support interactions that occur in computer-mediated
communications are devoid of nonverbal cues that can contribute
to successful support outcomes (e.g., touch; Jakubiak & Feeney,
2017), a growing number of studies corroborate the effectiveness
of remote interactions, including cognitive behavior therapy online
(e.g., Kessler et al., 2009) and online peer-to-peer platform inter-
actions (e.g., Morris et al., 2015). Given how frequently people
interact with others online and the various advantages that online
interactions can offer (e.g., increased accessibility to a wider range
of people across time and space, privacy, buffer against potential
stigma), we encourage scholars to further investigate this emerging
topic in future research. Such investigation seems timely given the
rapidly growing popularity of online therapy, and discussions
surrounding its effectiveness (e.g., Cohen & Kerr, 1999; Kessler et
al., 2009; Reynolds, Stiles, Bailer, & Hughes, 2013; Wagner,
Horn, & Maercker, 2014).
Our findings importantly also have implications for offline
social support interactions. Although people often talk to others
about their negative experiences (Rimé, 2009), whether this actu-
Table 1
Means, SDs, F Values (or Mann-Whitney U), Significant Levels for Univariate Analyses of Covariance (ANCOVAs)
Sample
M(SD)Inferential
statistics Internal
meta-analysis
Recount Reconstrue F(or U)df p
295% CI Z95% CI
Recounting statements
Combined 2.73 (.38) 1.98 (.70) 75.51
ⴱⴱⴱ
170 .31 [.58, .92] 7.83
ⴱⴱⴱ
[1.00, 1.67]
Sample 1a 2.72 (.30) 2.43 (.52) 6.64
57 .10 [.06, .51]
Sample 1b 2.74 (.42) 1.73 (.67) 89.91
ⴱⴱⴱ
110 .45 [.79, 1.21]
Reconstruing statements
Combined .25 (.41) 1.39 (.77) 149.27
ⴱⴱⴱ
170 .47 [1.33, .96] 10.30
ⴱⴱⴱ
[1.54, 2.26]
Sample 1a .25 (.47) 1.22 (.82) 29.50
ⴱⴱⴱ
57 .34 [1.31, .61]
Sample 1b .25 (.39) 1.48 (.74) 134.82
ⴱⴱⴱ
110 .55 [1.48, 1.05]
Change in negative affect (postconversation
minus baseline)
Combined 9.41 (17.68) .90 (18.26) 15.84
ⴱⴱⴱ
172 .08 [5.00, 14.83] 3.97
ⴱⴱⴱ
[.31, .92]
Sample 1a 7.57 (19.32) .94 (16.06) 4.47
58 .07 [.46, 17.10]
Sample 1b 10.36 (16.87) 1.91 (19.43) 11.58
ⴱⴱⴱ
111 .09 [4.51, 17.08]
Negative affect composite
Combined .10 (.71) .10 (.73) 8.26
ⴱⴱ
172 .05 [.06, .34] 7.18
ⴱⴱⴱ
[.20, .35]
Sample 1a .11 (.67) .13 (.67) 4.26
58 .07 [.01, .47]
Sample 1b .10 (.73) .08 (.76) 4.13
111 .04 [.004, .35]
Closure
Combined 3.77 (1.71) 4.46 (1.51) 8.68
ⴱⴱ
172 .05 [1.18, .23] 3.06
ⴱⴱ
[.17, .77]
Sample 1a 3.53 (1.63) 4.48 (1.46) 5.26
58 .08 [1.73, .12]
Sample 1b 3.90 (1.75) 4.45 (1.55) 4.53
111 .04 [1.24, .04]
Generic-you usage (percentage)
Combined .06 (.20) .33 (.63) U2926
ⴱⴱⴱ
170 .08 [.004, .001] 3.83
ⴱⴱⴱ
[.29, .90]
Sample 1a .02 (.07) .37 (.78) U313
ⴱⴱ
57 .09 [.006, .001]
Sample 1b .09 (.24) .31 (.53) U1299.50
110 .30 [.004, .001]
Note.CIconfidence interval. Degrees of freedom vary because conversation data of two participants did not get recorded and because of one missing
value in the baseline mood measure.
p.05.
ⴱⴱ
p.01.
ⴱⴱⴱ
p.001.
Table 2
Partial Correlations (Controlling for Baseline Affect) for All Key Variables
Variables 12345678
1. Recounting statements
2. Reconstruing statements .40
ⴱⴱⴱ
3. Negative affect composite index .11 .21
ⴱⴱ
4. Change in negative affect over time .14
.35
ⴱⴱⴱ
.71
ⴱⴱⴱ
5. Upset feelings .01 .25
ⴱⴱ
.59
ⴱⴱⴱ
.30
ⴱⴱⴱ
6. Intense feelings .04 .01 .81
ⴱⴱⴱ
.17
.58
ⴱⴱⴱ
7. Closure .09 .29
ⴱⴱⴱ
.28
ⴱⴱⴱ
.42
ⴱⴱⴱ
.22
ⴱⴱ
.04 —
8. Generic-you usage (percentage) .16
.28
ⴱⴱⴱ
.04 .10 .08 .03 .08 —
p.10.
p.05.
ⴱⴱ
p.01.
ⴱⴱⴱ
p.0001.
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5
SOCIAL SUPPORT AND COPING
ally helps them feel better remains unclear: Some studies suggest
talking to others to be beneficial (Frattaroli, 2006; Thoits, 1986)
while others have found the opposite (Nils & Rimé, 2012; Rose,
2002). Surely, discussing “what happened” is natural in support
conversations and may even be necessary to establish a context for
support provision to take place. However, our results suggest that
once this context is established, support providers should shift to
cueing support recipients to reconstrue their experience to prevent
negative affect from escalating (Rose, 2002). Thus, the current
findings also extend work on corumination by identifying a po-
tential way in which conversations can be structured to prevent
rumination and foster meaning-making.
Finally, the current findings contribute to research on emotion
regulation. Although cognitive reappraisal is largely considered to
be one of the most effective emotion regulation strategies (Gross,
2015), there are infinite ways in which people can engage in
reappraisal (e.g., positive reinterpretation, incremental mindset). A
growing number of scholars in recent years have proposed the
need to distinguish the different types of reappraisal processes
(e.g., Gross, 2015; Kross, 2015; Moser, Hartwig, Moran, Jen-
drusina, & Kross, 2014; Shiota & Levenson, 2009). The current
work addresses this issue by demonstrating how a specific type of
cognitive reappraisal process (i.e., perspective broadening) influ-
ences people’s capacity to make sense of their negative personal
experiences in an ecologically valid interpersonal context.
It is important to acknowledge that our study used confederates
to cue participants to recount or reconstrue their negative experi-
ence. This approach allowed us to reduce several sources of noise
that could have influenced the conversation outcomes—for exam-
ple, relationship-specific factors (e.g., closeness, expectations),
nonverbal feedback (e.g., tone of voice, nodding). However, given
that support outcomes are shaped by the dynamic interchange
between support recipients and support providers, future research
should examine how our findings generalize to spontaneous sup-
port interactions among dyads in daily life. In this vein, one
interesting direction for future research is to examine the implica-
tion of facilitating reconstrual for the support provider (Doré,
Morris, Burr, Picard, & Ochsner, 2017).
It is important to comment on the lack of correlations between
generic-you usage with negative affect and closure in this study.
These nonsignificant correlations are consistent with findings from
prior research, which likewise failed to find direct effects of
generic-you on negative affect and closure. Instead, prior work
revealed an indirect effect of generic-you usage on reduced neg-
ative affect and enhanced closure—generic-you predicted in-
creased levels of psychological distance which in turn predicted
less negative affect and higher levels of closure (Orvell et al.,
2017a). Although researchers agree that direct effects are not
required to establish indirect effects (Hayes, 2009; Rucker,
Preacher, Tormala, & Petty, 2011; Shrout & Bolger, 2002; Zhao,
Lynch, & Chen, 2010), an important question for future research is
to identify why generic-you does not influence affect and closure
directly.
Finally, given the low number of male participants in this study,
future research should seek to test whether our findings generalize
to diverse samples of individuals (McRae, Ochsner, Mauss, Ga-
brieli, & Gross, 2008). Future research should also examine po-
tential boundary conditions (e.g., self-esteem, habitual emotion
regulation patterns) to determine when and to whom cueing re-
construal is beneficial.
Conclusion
People often talk to others about their negative experiences, and
increasingly more so using computer-mediated communication
technologies. However, does talking to others in these modalities
help them make sense of their negative experiences? The present
research suggests that it depends on whether the conversation
allows people to reconstrue or recount their experience, and that
the same processes that help people to adaptively self-reflect on
their negative experience may also apply to when they talk to
others about it via computer mediated communication.
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Appendix
List of Questions Asked by Confederate While Discussing Distressing Personal Experience
Condition Questions
Recount 1. Can you tell me about what happened—what happened and what did you feel—from start to finish?
2. What went through your mind during the exact moment?
3. What stuck out the most at that moment?
4. What did (he/she/they) say and do?
5. How did this make you feel at that moment?
Reconstrue 1. Looking at the situation, could you tell me why this event was stressful to you?
2. Why do you think you reacted to (the event/the person) that way?
3. Why do you think (the other person in your experience) react that way?
4. Have you learned anything from this experience, and if so, would you mind sharing it with me?
5. In the grand scheme of things, if you look at the “big picture,” does that help you make sense of this experience? Why or why not?
Received August 28, 2017
Revision received October 2, 2018
Accepted October 19, 2018
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8LEE ET AL.
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One of the fastest growing areas within psychology is the field of emotion regulation. However, enthusiasm for this topic continues to outstrip conceptual clarity, and there remains considerable uncertainty as to what is even meant by “emotion regulation.” The goal of this review is to examine the current status and future prospects of this rapidly growing field. In the first section, I define emotion and emotion regulation and distinguish both from related constructs. In the second section, I use the process model of emotion regulation to selectively review evidence that different regulation strategies have different consequences. In the third section, I introduce the extended process model of emotion regulation; this model considers emotion regulation to be one type of valuation, and distinguishes three emotion regulation stages (identification, selection, implementation). In the final section, I consider five key growth points for the field of emotion regulation.