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Take It or Leave It:
A Computational Model for Flexibility in Decision-
Making in Downregulating Negative Emotions
Nimat Ullah
1(&)
, Sander L. Koole
2
, and Jan Treur
1
1
Social AI Group, Department of Computer Science, Vrije Universiteit
Amsterdam, Amsterdam, The Netherlands
nimatullah09@gmail.com, j.treur@vu.nl
2
Amsterdam Emotion Regulation Lab, Department of Clinical Psychology,
Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
s.l.koole@vu.nl
Abstract. Flexibility in emotion regulation strategies is one of the properties
associated to healthy minds. Emotion regulation strategies are context dependent
and the adaptivity of those strategies is solely subjected to the context. Flexi-
bility, therefore, plays a key role in the use of these emotion regulation strate-
gies. The computational model presented in this paper, models flexibility in
emotion regulation strategies that are dependent on context. Simulation results
of the model are presented which provides insight of four emotion regulation
strategies and highlights the role of context which activates them respectively.
Keywords: Emotion regulation Strategies Context Flexibility
Adaptivity Psychopathology
1 Introduction
Imagine that you are an office worker who gets upset when your colleague criticizes
you unfairly. You can choose to walk away, distract yourself with chores, hide your
negative reaction, or mentally distance yourself from your colleague. What would you
do? Which of these strategies is optimal, depends on the situation. For instance, if your
colleague is from a different department, you may find it easy to walk away. However,
if the colleague is your boss, walking away may not be of the question, so you may be
forced to distract yourself with chores. This simple example illustrates how different
emotion-regulation strategies tend to yield different outcomes in different situations [1].
It thus follows that people should be able to choose flexibly between different emotion-
regulation strategies in the face of different situational demands. The latter capacity is
known as emotion regulation flexibility [2,3].
Within the area of emotion regulation which is our long-term research focus, we
recently proposed a computational model of emotion regulation flexibility which
models a person who can switch between two strategies- expressive suppression and
attention modulation - in managing anger in different work situations [4]. Building on
and extending this work, the present model is inspired by the vast body of literature on
©Springer Nature Switzerland AG 2020
D.-S. Huang and K.-H. Jo (Eds.): ICIC 2020, LNCS 12464, pp. 175–187, 2020.
https://doi.org/10.1007/978-3-030-60802-6_16
flexibility in emotion regulation strategies and, therefore, addresses the challenge to
design a computational model of emotion regulation flexibility in which a person can
switch between four different emotion-regulation strategies: situation modification,
attention modulation, expressive suppression, and cognitive reappraisal.
In what follows, in Sect. 2, we discuss the theoretical background of our work,
which is grounded in the psychological literature on emotion regulation [5–7]. Next, in
Sect. 3, we present the computational model. In Sect. 4, we present the results of
simulations of the model using four different scenarios. Finally, in Sect. 5, we review
the main conclusions and implications of the present work.
2 Background
Emotion regulation has been defined as the set of processes whereby people control and
redirect the spontaneous flow of their emotions [8]. A large body of research has
implicated difficulties in emotion regulation as a transdiagnostic factor that is central to
the development and maintenance of psychopathology [9,10]. Accordingly, clinical
psychologists are increasingly moving toward unified treatments that target emotion
regulation for individuals with multiple disorders [11]. In addition, emotion regulation
is seen as a vital positive contributor to psychological health and wellbeing [12].
The influential process model of emotion regulation [13–15] has distinguished five
families of emotion regulation strategies. The first family of strategies is situation
selection, and consists of taking steps to influence which situation one will be exposed
to. The second family of strategies is situation modification, and consists of changing
one or more relevant aspects of the situation. The third family of strategies is attentional
deployment, and consists of influencing which portions of the situation are attended to.
The fourth family of strategies is cognitive change, and consists of altering the way the
situation is cognitively represented. Finally, the fifth family of strategies is response
modulation, and consists of directly modifying emotion-related actions.
Originally, the process model [14,16] proposed that some emotion-regulation
strategies are inherently more effective than other emotion-regulation strategies. The
main evidence for this notion came from studies showing that cognitive reappraisal–a
prototypical cognitive change strategy- is less effortful and more effective than emo-
tional suppression–a prototypical response modulation strategy [16]. Subsequent
research, however, has shown that the effectiveness of emotion-regulation strategies is
highly dependent on situational context. For instance, there are situations in which
cognitive reappraisal is ineffective, or may even backfire [17–19]. Conversely,
expressive suppression may be less problematic when this strategy can be flexibly
applied [20].
Evidence for context-dependent effects of emotion-regulation strategies has
inspired a new generation of theories that emphasize emotion regulation flexibility
[1,3,7,15,21]. Although these theories differ in their particulars, they converge on the
notion that, in healthy emotion regulation, strategies have to be adjusted to the demands
of the situation. Consequently, emotion regulation flexibility is key to successful
emotion regulation.
176 N. Ullah et al.
Empirical research on emotion regulation flexibility has so far been limited. The
available research to date has focused on switching between two strategies. This two-
strategy approach is presumably derived from the limitations of experimentally
examining alternations between a greater number of emotion regulation strategies. For
instance, Sheppes and colleagues [22] have studied the choice between distraction (an
attentional deployment strategy) and reappraisal (a cognitive change strategy). Like-
wise, Bonanno and colleagues have operationalized emotion-regulatory flexibility as
the ability to both up-regulate and down-regulate negative emotion [23].
In line with the two-strategy approach, we recently proposed a computational
model of emotion regulation flexibility in which the person switches between
expressive suppression and attention modulation in managing anger in different work
situations [4]. Simulation results illustrated the capacity of the model to display
adaptivity in emotion regulation across different contexts.
An important strength of our computational approach is that it can afford insight
into the interplay between a large number of variables simultaneously, larger than it is
practical to study experimentally. In the present work, we therefore sought to extend
our earlier computational model to a model in which we could examine flexible,
context-dependent switching between four emotion-regulation strategies.
3 The Computational Model
The computational model, presented in this paper, is hereby thoroughly elaborated.
This model simulates four emotion regulation strategies as per criteria presented in
Table 3. The basic concepts of the modeling approach used for this model, called
Network-Oriented modeling approach, can be consulted in Table 1from [24]. In
Network-Oriented modeling approach, a phenomenon is represented in a network form
consisting of nodes that varies over time. The nodes are interpreted as states and the
connections between these states are interpreted as relations and it defines the impact of
one state on another state over time. This type of network is, therefore, referred to as
temporal- network. Conceptually, a model in temporal–network can be represented as
labelled graph in which:
•Each connection carries some connection weight from one state to another called
impact represent by x
X,Y.
•There’s some way to aggregate multiple impacts on a state (combination function
c
Y
(..)).
•There’s a notion of speed of change of each state to define how faster a state
changes because of the incoming impact (speed factor η
Y
).
A temporal–network is defined by these three notions, see Table 1for more
explanation of the terms and for the numerical representation of the concepts.
Take It or Leave It 177
For aggregation of multiple incoming impacts, Network-Oriented modeling
approach provides a library of over 35 combination functions. Besides that, own-
defined functions can also be added for better flexibility.
Conceptual representation of the model presented in this paper is given in Fig. 1,
and the nomenclature of the states is provided in Table 2.
The model presented in Fig. 1, presents various courses of action of one person in
different contexts. It switches among four different emotion regulation strategies
depending on the context in which emotion has been felt and on the level of emotions felt.
Table 1. Basics of Network oriented modeling approach.
Concept Conceptual
Representation
Explanation
States and
connections
X, Y, X !YDescribes the nodes and links of a network
structure (e.g., in graphical or matrix form)
Connection weight x
X,Y
The connection weight x
X,Y
usually in [−1, 1]
represents the strength of the causal impact of
state Xon state Ythrough connection X!Y
Aggregating
multiple impacts on
a state
c
Y
(..) For each state Yacombination function c
Y
(..) is
chosen to combine the causal impacts of other
states on state Y
Timing of the effect
of causal impact
η
Y
For each state Yaspeed factor η
Y
0 is used
to represent how fast a state is changing upon
causal impact
Concept Numerical
representation
Explanation
State values over
time t
Y(t) At each time point teach state Yin the model has
a real number value, usually in [0, 1]
Single causal
impact
impact
X,Y
(t)=x
X,Y
X(t)
At tstate Xwith a connection to state Yhas
impact on Y, using connection weight x
X,Y
Aggregating
multiple causal
impacts
aggimpact
Y
(t)
¼c
Y
(impact
X1,Y
(t),…,
impact
Xk,Y
(t))
¼c
Y
(x
X1,Y
X
1
(t),…,
x
Xk,Y
X
k
(t))
The aggregated causal impact of multiple states
X
i
on Yat t, is determind using combination
function c
Y
(..)
Timing of the causal
effect
Y(t +Dt) =Y(t) +
η
Y
[aggimpact
Y
(t)−
Y(t)]Dt¼Y(t) +
η
Y
[c
Y
(x
X1,Y
X
1
(t), …,
x
Xk,Y
X
k
(t))−Y(t)]Dt
The causal impact on Yis exerted over time
gradually, using speed factor η
Y
; here the X
i
are
all states with outgoing connections to state Y
178 N. Ullah et al.
Fig. 1. Conceptual representation of the computational model as a temporal-causal network
Table 2. Nomenclature of the states of the proposed model (in connection to Fig. 1)
States Informal Name Description
ws
s
World state for stimulus sThe situation in the real world that triggers
emotion
ws
c.p
World state for context
pressure
A real-world situation which decides expression
of emotion
ss
s
Sensor state for stimulus sSensor state for the stimulus sin the real world
ss
c.p
Sensor state for context
pressure
Senses state for context pressure
ss
b
Sensor state for body Sensor state for body
srs
s
Sensory representation state
for stimulus s
Internal representation of the emotion triggering
situation
srs
c.p
Sensory rep: state for context
pressure
Internal representation of the context pressure in
the real world
srs
b
Sensory representation state
for body
Internal body representation state
bs
-
Negative believe state The negative believe that the person has about
something/someone
bs
+
Positive believe state The positive believe that the person has about
something/someone
(continued)
Take It or Leave It 179
The emotions can either be of high or low intensity and about the Context Pressure
(CP), belief can either be positive or negative. Belief about CP refers to the person’s
belief about presence or absence of any environmental factor in which expression of
emotion matters or doesn’t matter. In other words, the belief refers to one’s prediction
Table 2. (continued)
States Informal Name Description
ms
1
Monitoring state for low
emotion level
Monitors for low emotions
ms
2
Monitoring state for high
emotion level
Monitors for high emotions
bs
(+)c.p
Belief state for context
pressure
Believing that expression of emotion will matter
in the environment
bs
(-)c.p
Belief state for context
pressure
Believing that expression of emotion won’t
matter in the environment
cs
reapp
Control state for reappraisal Controlling negative beliefs about
something/someone
cs
s, a.d
Control state for attention
deployment
Control state for Attention Deployment
cs
s,s.m
Control state for situation
modification
Control state for situation modification as a result
of context
cs
sup
Control state for suppression Control state for Suppression of Expression
fs
b
Feeling state for body state bFeeling associated to body state b
ps
a
Preparation state for action a Preparing for action a
ps
b
Preparation for body state bPreparation state for body state b
ps
ad
Preparation state for attention
deployment
Preparation for the Attention deployment action
es
a
Execution state for action aExecution station for action a
es
b
Execution state for body
state b
Execution state for body state b
es
ad
Execution state for attention
deployment
Execution state for the Attentional Deployment
action
Table 3. Choice of strategies under high/low intensity of emotions and +/−belief about context
pressure
Flexibility Parameters Repertoire of Strategies
Emotion
Strength
Context
Pressure
(CP)
Situation
Modification
Attention
Deployment
Reappraisal Expressive
Suppression
++ ✓
+–✓
–+✓
–– ✓
180 N. Ullah et al.
of such factor. Selection of each strategy is subjected to two conditions i.e. the intensity
of emotion and CP. The ‘+’symbol represents high intensity under emotion strength
and positive belief/prediction about presence of an environment/factor where expres-
sion of emotion will matter. For instance, if a person feels high intensity of negative
emotions and he’s expecting a factor i.e. boss etc. due to which he believes his
expression of emotions can have bad consequences for him, he would prefer situation
modification. There are four combinations of the primary and secondary stimulus as
described in Table 1, which leads into four different emotion regulation strategies
interpreted below:
1. High intensity of emotion (+) and (+) belief about CP leads to situation modification
“cs
sm
”.
2. High intensity of emotion (+) and (−) belief about CP activates attention deploy-
ment “cs
a.d
”.
3. Low intensity of emotion (−) and (+) belief about CP triggers reappraisal “cs
reapp
”.
4. Low intensity of emotion (−) and (−) belief about CP initiates expressive sup-
pression “cs
sup
”.
4 Scenarios and Simulation Results
The computational model presented in this paper is loosely based on an example
discussed by [2] with flexibility in emotion regulation strategies as modeled in [4] and
decision making among various emotion regulation strategies as [25]. It’s worth
mentioning here that [25] is the only model, so far, considering decision making for
three ER strategies up till now, which the current model extends to a repertoire of four
strategies.
“An employee A feels angry every time a particular obnoxious coworker B starts talking. Next
week the organization has monthly review meeting where presence of all the employees is
mandatory unless emergency. Employee A doesn’t want anyone, especially his boss to come to
know about his attitude towards employee B. Employee A has four options to handle the
situation, all depending upon the combination of his intensity of emotions and the chances of
presences or absence of their boss in the meeting as shown in Table 3.”
The parameter values given in Tables 4and 5, in the absence of availability of
quantitative data, qualitatively validates the proposed model against the findings from
social sciences and psychology that serve as qualitative evaluation indicators. These
parameter values make the model reproducible; they give the simulation results as
shown in Figs. 2,3,4and 5. In Table 4each state can either have value of scaling
factor (k) for which scale sum function has been used or it can have values for
steepness (r) and threshold (s) for which alogistic combination function has been used.
Take It or Leave It 181
All the simulation results, given below, only have the most essential states for the
explanation of the results.
Figure 2depicts a context with low intensity of emotions and positive belief about
CP. This means that the person knows that his emotional expression will have con-
sequences for him and the intensity of negative emotions that he/she is feeling is of
moderate/low level. So, as the person already anticipates presence of the CP, therefore,
he’s reappraising his belief about the stimuli. In the figure, it can be seen that initially
negative belief bs
-
is quite high but it decreases as control state for reappraisal cs
reapp
gets activated. As a result, the positive belief bs
+
increases and the feeling state fs
b
also
gets lower as the negative belief gets weaker and the positive belief gets stronger.
Table 4. Values used for alogistic, scaled-sum combination functions and speed factor
State ksrηState srη
ws
s
0.94 0 0 0.1 ms
2
0.5 50 0.5
ss
s
0 0 0 0.5 bs
(-)c.p
0.1 50 0.5
ss
b
0 0 0 0.5 bs
(+)c.p
0.5 17 0.5
srs
s
1 0 0 0.5 cs
reapp
0.5 8 0.15
srs
b
1.4 0 0 0.5 cs
a.d
0.85 12 0.2
bs
-
0.91 0 0 0.5 cs
s.m
0.85 12 0.3
bs
+
0 0.1 10 0.5 cs
sup
0.5 6 0.15
ps
b
1.8 0 0 0.5 ps
a
0.6 5 0.5
es
b
0.98 0 0 0.5 ps
a.d
0 0 0.3
fs
b
1 0 0 0.5 es
a
0.5 3 0.5
ms
1
0 0.1 5 0.5 es
a.d
0 0 0.3
Table 5. Values used for connection weights
Connection Weight Connection Weight Connection Weight Connection Weight
x
wss,wss
0.95 x
bs+, bs-
−0.4 x
csreapp,css.m
−1x
fsb,ms1
0.5
x
wss,sss
1x
ms1,csreapp
0.2 x
csreapp,cssup
−1x
fsb,ms2
0.8
x
sss, srss
1x
ms1,cssup
0.4 x
csa.d,psa.d
1x
fsb, bs(-)c.p
0.5
x
ssb, srsb
0.7 x
ms2,ms1
−1x
csa.d,css.m
−1x
fsb, bs(+)c.p
0.5
x
srss, bsc-
0.9 x
ms2,csa.d
0.35 x
csa.d,cssup
−1x
fsb,psb
0.9
x
srss, bsc+
0.4 x
ms2,css.m
0.5 x
css.m,psa
0.8 x
psa,esa
0.5
x
srss,psa
0.3 x
bs(-)c.p, bs(+)c.p
−1x
css.m,esa
0.8 x
psb, srsb
0.75
x
srsc.p,bs(-)c.p
−1x
bs(-)c.p,cssup
0.3 x
css.m,csreapp
−1x
psb,esb
1
x
srsc.p,bs(+)c.p
1x
bs(-)c.p,csa.d
0.6 x
css.m,csa.d
−1x
psa.d,esa.d
1
x
srsb,fsb
1x
bs(+)c.p,bs(-)c.p
−1x
cssup,psb
−1x
esa,wss
−0.5
x
bs-, bs+
−0.4 x
bs(+)c.p,css.m
0.5 x
cssup, esb
−0.2 x
esb,ssb
1
x
bs-, csreapp
0.05 x
bs(+)c.p,csreapp
0.33 x
cssup, csreapp
−1x
esa.d, srss
0.63
x
bs-, psb
1x
csreapp, bs-
−0.35 x
cssup, csa.d
−1
182 N. Ullah et al.
Figure 3depicts the activation of situation modification as a strategy, which gets
activated when the person is feeling high intensity of emotions while having positive
belief about the CP (i.e. he is predicting an environment where he can’t afford if his
emotions are observed). In the figure it can be seen that initially control state for
reappraisal cs
reapp
gets activated. It’s because the person tries to reappraise initially
when the intensity of his emotions is not yet high. Later on, as the intensity of negative
emotions increases, control state for situation modification cs
s.m
gets activated. As
situation modification means that the person is leaving/changing the situation, there-
fore, the world state ws
s
where the emotional event is/will take place, gets decreased as
Fig. 2. Cognitive Reappraisal: low intensity of emotions and positive belief/prediction about the
context pressure
Fig. 3. Situation Modification: as a result of high intensity of emotions and positive
belief/prediction about the context pressure
Take It or Leave It 183
soon as preparation state for action ‘a’ps
a
and execution state for action ‘a’es
a
gets
increasing, representing some physical action in the real world. As a result, the exe-
cution of action decreases the intensity of (negative) feelings fs
b
.
Similarly, once again when the intensity of emotions is low enough after
leaving/changing the situation, control state for reappraisal cs
reapp
gets activated. The
result is as expected.
As highlighted in Table 2, low intensity of emotions in combination with negative
belief about CP activates control state for suppression cs
sup
. Figure 4shades light on
this situation where initially feeling state fs
b
is increasing with the increase of bs
-
but it
stops as soon as cs
sup
gets activated. It can be seen that cs
sup
suppresses expression of
emotions but the sensor representation state srs
s
and negative belief bs
-
still remains
high, that’s why expressive suppression is often regarded as maladaptive emotion
regulation strategy.
Just as Fig. 4, in Fig. 5too, as the person’s emotional intensity is increasing, it
activates two strategies. Initially, when the intensity of negative emotions is yet low
and as belief about the CP is already negative (i.e. he is predicting an environment
where he can afford if his expression of emotions is observed), control state for sup-
pression cs
sup
gets activated. Its affect can also be seen in fs
b
.
Later on, as the intensity of emotions gets higher and as the belief about CP is
already negative, control state for attention deployment cs
a.d
gets activated. Activation
of cs
a.d
decreases intensity of the stimuli and therefore, the bs
-
also decreases, resulting
in decrease of fs
b
.
The results obtained from the model in Fig. 1are in line with the literature from
psychology and social sciences and best describe the working of emotion regulation
strategies as described in the aforementioned literature.
Fig. 4. Expressive Suppression: Low intensity of emotions and negative belief/prediction about
the context pressure
184 N. Ullah et al.
5 Conclusion
This network-oriented temporal-causal network model models four different emotion
regulation strategies with four different contexts. Each strategy depends on a specific
context which activates it. The model not only acknowledges the ongoing debate about
impact of context on various emotion regulation strategies, it rather efficiently and with
computational clarity highlights the flexibility of the emotion regulation strategies
dependent on a specific context.
Context plays a very profound impact in selection of a strategy. A strategy may not
be as efficient in one context as it could be in another context. Therefore, a strategy
can’t be termed as maladaptive just because it’s not adaptive in one context. Similarly,
flexibility is referred to as a practice of healthy minds [3,26] which this model has
highlighted by being able to switch between different strategies as per demand of the
context. Moreover, this model also gives hint to the possibility of modeling simulta-
neous activation of multiple strategies as found by as described in [27] For instance, in
case of situation modification or attention deployment, it’s possible that reappraisal or
suppression is also activated at the same time, respectively. This phenomenon can be
considered for further study.
This model, apart from giving insight into the phenomenon of flexibility of emotion
regulation strategies, also acknowledges the strength of network oriented temporal-
causal modeling [24,28] of being able to effectively model such problems and give a
clear insight into its working mechanisms.
To carry on with flexibility, in future, some other emotion regulation strategies with
explicit decision-making ability maybe modeled to give insight into their working
mechanism. Moreover, simultaneous activation of multiple emotion regulation strate-
gies, from a broader repertoire, can also be considered as future project.
Fig. 5. Attention Deployment: high intensity of emotions and negative belief/prediction about
the context pressure
Take It or Leave It 185
References
1. Aldao, A.: The future of emotion regulation research. Perspect. Psychol. Sci. 8(2), 155–172
(2013). https://doi.org/10.1177/1745691612459518
2. Aldao, A., Sheppes, G., Gross, J.J.: Emotion regulation flexibility. Cognit. Ther. Res. 39(3),
263–278 (2014). https://doi.org/10.1007/s10608-014-9662-4
3. Bonanno, G.A., Burton, C.L.: Regulatory flexibility. Perspect. Psychol. Sci. 8(6), 591–612
(2013). https://doi.org/10.1177/1745691613504116
4. Ullah, N., Treur, J., Koole, S.L.: A computational model for flexibility in emotion regulation.
Procedia Compu. Sci. 145, 572–580 (2018). https://doi.org/10.1016/j.procs.2018.11.100
5. Gross, J.J., et al.: Emotion regulation: current status and future prospects. Psychol. Inquiry
26(1), 1–26 (2015). https://doi.org/10.1080/1047840x.2014.940781
6. Koole, S.L., Aldao, A.: The self-regulation of emotion: theoretical and empirical advances.
In: Vohs, K.D., Baumeister, R.F. (eds.) Handbook of Self-Regulation, pp. 1–36. Guilford
Press, New York (2015)
7. Webb, T.L., Miles, E., Sheeran, P.: Dealing with feeling: a meta-analysis of the effectiveness
of strategies derived from the process model of emotion regulation. Psychol. Bull. 138(4),
775–808 (2012). https://doi.org/10.1037/a0027600
8. Koole, S.L.: The psychology of emotion regulation: An integrative review. Cognit. Emotion
23(1), 4–41 (2009). https://doi.org/10.1080/02699930802619031
9. Aldao, A., Nolen-Hoeksema, S.: When are adaptive strategies most predictive of
psychopathology? J. Abnorm. Psychol. 121(1), 276–281 (2012). https://doi.org/10.1037/
a0023598
10. Sloan, E., Hall, K., Moulding, R., Bryce, S., Mildred, H., Staiger, P.K.: Emotion regulation
as a transdiagnostic treatment construct across anxiety, depression, substance, eating and
borderline personality disorders: a systematic review. Clinical Psychol. Rev. 57, 141–163
(2017). https://doi.org/10.1016/j.cpr.2017.09.002
11. Moses, E.B., Barlow, D.H.: A new unified treatment approach for emotional disorders based
on emotion science. Curr. Dir. Psychol. Sci. 15(3), 146–150 (2006). https://doi.org/10.1111/
j.0963-7214.2006.00425.x
12. Buruck, G., Dorfel, D., Kugler, J., Brom, S.S.: Enhancing well-being at work: The role of
emotion regulation skills as personal resources. J. Occup. Health Psychol. 21(4), 480–493
(2016). https://doi.org/10.1037/ocp0000023
13. Gross, J.J.: The emerging field of emotion regulation: an integrative review. Rev. Gen.
Psychol. 2(3), 271–299 (1988). https://doi.org/10.1037/1089-2680.2.3.271
14. Richards, J.M., Gross, J.J.: Emotion regulation and memory: The cognitive costs of keeping
one’s cool. J. Pers. Soc. Psychol. 79(3), 410–424 (2000). https://doi.org/10.1037/0022-3514.
79.3.410
15. Gross, J.J.: The extended process model of emotion regulation: elaborations, applications,
and future directions. Psychol. Inquiry 26(1), 130–137 (2015). https://doi.org/10.1080/
1047840x.2015.989751
16. Gross, J.J.: Antecedent- and response-focused emotion regulation: Divergent consequences
for experience, expression, and physiology. J. Pers. Soc. Psychol. 74(1), 224–237 (1998).
https://doi.org/10.1037/0022-3514.74.1.224
17. Ford, B.Q., Karnilowicz, H.R., Mauss, I.B.: Understanding reappraisal as a multicomponent
process: the psychological health benefits of attempting to use reappraisal depend on
reappraisal success. Emotion 17(6), 905–911 (2017). https://doi.org/10.1037/emo0000310
186 N. Ullah et al.
18. Troy, A.S., Shallcross, A.J., Mauss, I.B.: A person-by-situation approach to emotion
regulation: cognitive reappraisal can either help or hurt, depending on the context. Psychol.
Sci. 24(12), 2505–2514 (2013). https://doi.org/10.1177/0956797613496434
19. Veenstra, L., Schneider, I.K., Koole, S.L.: Embodied mood regulation: the impact of body
posture on mood recovery, negative thoughts, and mood-congruent recall. Cognit. Emotion
31(7), 1361–1376 (2017). https://doi.org/10.1080/02699931.2016.1225003
20. Dworkin, J.D., Zimmerman, V., Waldinger, R.J., Schulz, M.S.: Capturing naturally
occurring emotional suppression as it unfolds in couple interactions. Emotion 19(7), 1224–
1235 (2019). https://doi.org/10.1037/emo0000524
21. Sheppes, G.: Emotion regulation choice: theory and findings. In: Handbook of Emotion
Regulation, 2nd edn., pp. 126–139. Guilford Press, New York (2014)
22. Sheppes, G., Scheibe, S., Suri, G., Gross, J.J.: Emotion-regulation choice. Psychol. Sci.
22(11), 1391–1396 (2011). https://doi.org/10.1177/0956797611418350
23. Bonanno, G.A., Wortman, C.B., Nesse, R.M.: Prospective patterns of resilience and
maladjustment during widowhood. Psychol. Aging 19(2), 260–271 (2004). https://doi.org/
10.1037/0882-7974.19.2.260
24. Treur, J.: Network-oriented modeling and its conceptual foundations. Network-Oriented
Modeling. UCS, pp. 3–33. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-
45213-5_1
25. Manzoor, A., Abro, A.H., Treur, J.: Monitoring the impact of negative events and deciding
about emotion regulation strategies. In: Criado Pacheco, N., Carrascosa, C., Osman, N.,
Julián Inglada, V. (eds.) EUMAS/AT -2016. LNCS (LNAI), vol. 10207, pp. 350–363.
Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59294-7_30
26. Kashdan, T.B., Rottenberg, J.: Psychological flexibility as a fundamental aspect of health.
Clinical Psychol. Rev. 30(7), 865–878 (2010). https://doi.org/10.1016/j.cpr.2010.03.001
27. Dixon-Gordon, K.L., Aldao, A., De Los Reyes, A.: Emotion regulation in context:
examining the spontaneous use of strategies across emotional intensity and type of emotion.
Pers. Individ. Differ. 86, 271–276 (2015). https://doi.org/10.1016/j.paid.2015.06.011
28. Treur, J.: Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order
Adaptive Biological, Mental and Social Network Models. SSDC, vol. 251. Springer, Cham
(2020). https://doi.org/10.1007/978-3-030-31445-3
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