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A computational model for flexibility in emotion regulation



Emotion regulation is a vital psychological process that allows people to manage their own emotional states. Recent psychological research has highlighted the importance of flexibility in emotion regulation, such that people can alternative between different emotion regulation strategies a strategy is chosen depending upon the demands of the situation. This means that healthy emotion regulation is context-sensitive. This paper presents a computational model which models this form of flexible adaptation in emotion regulation in a simplified scenario in which the person has to switch between expressive suppression and attention modulation in managing anger in different work situations, . Simulation results are reported that illustrate the capacity of the model to display adaptivity in emotion regulation across different contexts.
A Computational Model for Flexibility
in Emotion Regulation
Nimat Ullah1, Jan Treur1 and Sander L. Koole2
1Behavioural Informatics Group, Department of Computer Science,
Vrije Universiteit Amsterdam, The Netherlands.
2Amsterdam Emotion Regulation Lab, Department of Clinical Psychology,
Vrije Universiteit Amsterdam, The Netherlands.,,
Emotion regulation is a vital psychological process that allows people to manage their own emotional
states. Recent psychological research has highlighted the importance of flexibility in emotion regulation,
such that people can alternative between different emotion regulation strategies. A strategy is chosen
depending upon the demands of the situation. This means that healthy emotion regulation is context-
sensitive. This paper presents a computational model which models this form of flexible adaptation in
emotion regulation in a simplified scenario in which the person has to switch between expressive
suppression and attention modulation in managing anger in different work situations. Simulation results
are reported that illustrate the capacity of the model to display adaptivity in emotion regulation across
different contexts.
Keywords: Emotion regulation, Strategies, Context, Flexibility, Adaptivity, Psychopathology
1 Introduction
Emotion regulation refers to the process whereby people control their emotions to achieve some
long or short-term desirable goals. Because emotions are pervasive in everyday life, emotion regulation
is a vital capacity for psychological health and wellbeing (Koole, 2009). The dysregulation of emotion
disrupts people’s thoughts, actions, and goal achievements, and impairs social functioning (Zeman et
al., 2006). Indeed, emotional dysregulation is associated with a wide array of psychological problems,
including substance abuse, eating pathology, depression and anxiety (Aldao et al., 2010; Gross, 2012).
It is well-documented that people use many different strategies in emotion regulation (Gross, 1998;
Koole, 2009; Parkinson & Totterdell, 1999). Some examples are distraction (diverting attention away
from the negative emotion triggering entity), suppression (preventing the expression of emotion),
venting (freely expressing emotions), and cognitive reappraisal (reappraising belief about the emotion
triggering stimuli). Each of these strategies may be useful under a specific set of conditions, Thus, an
important question becomes how people are able to flexibly adapt their use of emotion regulation
strategies to shifting situational demands (see also Aldao, Gross, & Sheppes, 2015).
This paper addresses this question and proposes a computational model of flexibility in emotion
regulation. The rest of the paper is organized as follows. Section 2 of the paper discusses background
knowledge and the example strategies used later in simulations, Section 3 presents the computational
model, Section 4 elaborates the scenarios and the simulation results and finally Section 5 concludes the
2 Background
Different studies in the field have shown that the types of emotion regulation strategies chosen have
different types of consequences on the personality of an individual (Gross, 1998, 2001, 2014; Koole,
2009; Tamir, 2009, 2011; Webb, Miles, & Sheeran, 2012). Effects of these strategies can show up
immediate as well as over a longer time span. Based on the varying results of the studies conducted in
(Richards & Gross, 2000; Butler et al., 2003; Gross & Thompson, 2007; Sheppes & Meiran, 2007)
wherein various emotion regulation strategies have been compared for the possible outcomes in different
situations, Suri et al. (2018) conclude that “these differences suggest that decisions about which emotion
regulation strategies to use in a particular situation can be profoundly consequential, and it is therefore
important to understand the drivers of these decisions in different emotional contexts”.
Similarly, (Aldao et al., 2014) qualifies rigid emotion regulation responses as maladaptive due to
the varied environmental situations, thereby suggesting contextual and adaptive emotion regulation
instead. Koole and Veenstra, (2015) also acknowledge (Aldao et al., 2014) by criticizing traditional
models of emotion regulation as being too much based on decontextualized mental representations. The
latter authors propose a new theoretical approach in which emotion regulation is continually and flexibly
adjusted to the contingencies of the situation.
Various studies support the notion that good mental health tends to go hand in hand with emotion
regulation flexibility (Kashdan and Rottenberg 2010; Hollenstein et al. 2013; Bonanno and Burton
2014). This literature has emphasized the importance of emotion regulation flexibility as it is considered
crucial to be properly understood for better treatment of a variety of mental disorders (Aldao, 2013;
Bonanno and Burton, 2014). To gain more depth in the analysis of this phenomenon by applying
computational techniques, this paper presents a computational model for context-dependent adaptive
emotion regulation. It will be shown how it can adaptively switch from one strategy to another and vice
versa as the context changes over time. It will be illustrated for two important and widely studied
regulation strategies: expressive suppression and attention deployment.
As a subcategory of response modulation, expressive suppression refers to the act of suppressing
emotional expression, specifically, facial expression experienced as a result of an emotion eliciting
stimulus. Initial work suggested that expressive suppression is generally an ineffective emotion
regulation strategy (Gross, 2001) with negative social consequences (Butler et al., 2003). However,
subsequent work indicated that the expressive suppression may have fewer negative consequences in
non-Western cultures (Butler, Lee, & Gross, 2007). Moreover, some authors have suggested that
expressive suppression may also be adaptive in some specific contexts (Tamir, 2009).
Attention deployment (Gross, 1998) can further be classified into distraction (Sheppes and Gross, 2011),
rumination, worry and thought suppression (Campbell-Sills & Barlow, 2007). The most widely studied
form of attention deployment is distraction, in which attention is diverted away from the emotion-
triggering stimulus, for example, by performing a cognitive task (Van Dillen & Koole, 2007). However,
there are also more subtle forms of attention deployment, for instance, when people focusing on
remembering what is being said rather than who said it.
3 The Computational Model
This section discusses the proposed computational model. As discussed earlier, context and
adaptivity play an important role in emotion regulation and through this model anger is adaptively
regulated in different contexts. First the Network-Oriented Modeling approach used from (Treur, 2016)
is briefly explained. For this modelling perspective, networks are used in which the nodes are interpreted
as states (or state variables) that vary over time, and the connections are interpreted as causal relations
that define how each state can affect other states over time. This type of network has been called a
temporal-causal network. A conceptual representation of a temporal-causal network model as a labelled
graph provides a fundamental basis, and includes the following labels for such a graph:
a notion of strength of a connection is used as a label for connections, to distinguish between
different types of connections (connection weight X,Y)
some way to aggregate multiple causal impacts on a state is used, to distinguish between the
differences in how different causal impacts work together (combination function cY(..))
a notion of speed of change of a state is used for timing of the processes (speed factor Y)
These three notions connection weight X,Y, combination function cY(..), and speed factor Y, define the
network structure of a temporal-causal network model by a conceptual representation; see the upper part
(first 5 rows) of Table 1.
conceptual representation
States and
X, Y, XY
Describes the nodes and links of a network structure
(e.g., in graphical or matrix format)
Connection weight
The connection weight X,Y [-1, 1] represents the
strength of the causal impact of state X on state Y
through connection XY
multiple impacts on
a state
For each state Y (a reference to) a combination function
cY(..) is chosen to combine the causal impacts of other
states on state Y
Timing of the effect
of causal impact
For each state Y a speed factor Y 0 is used to
represent how fast a state is changing upon causal
numerical representation
State values over
time t
At each time point t each state Y in the model has a real
number value in [0, 1]
Single causal impact
= X,Y X(t)
At t state X with a connection to state Y has an impact
on Y, using connection weight X,Y
multiple causal
= cY(impactX1,Y(t),…, impactXk,Y(t))
= cY(X1,YX1(t), …, Xk,YXk(t))
The aggregated causal impact of multiple states Xi on Y
at t, is determind using combination function cY(..)
Timing of the causal
Y(t+t) = Y(t) + Y [aggimpactY(t) - Y(t)] t
= Y(t) + Y [cY(X1,YX1(t), …, Xk,YXk(t)) - Y(t)] t
The causal impact on Y is exerted over time gradually,
using speed factor Y; here the Xi are all states with
outgoing connections to state Y
Table 1. Conceptual and numerical representations of a temporal-causal network model
There are many different approaches possible to address the issue of combining multiple impacts. To
provide sufficient flexibility, for each state its combination function can be chosen that specifies how
multiple causal impacts on this state are aggregated. For this, a library with a number of standard
combination functions are available as options, but also own-defined functions can be added.
In the lower part of Table 1, the numerical representation describing the dynamics of a temporal-
causal network is displayed, and it is shown how it is defined based on the characteristics (labels) of the
conceptual network structure specification. In the lower part of Table 1, this interpretation is expressed
in a formal-numerical way, thus associating semantics to any temporal-causal network specification in
a detailed numerical-mathematically defined manner; see also (Treur, 2016b), Ch. 2. The obtained
difference equations in the last row in Table 2 constituting the numerical representation of the network
dynamics are just defined in terms of the network structure parameters X,Y, cY(..), and Y.
For the specific temporal-causal network model introduced here the conceptual representation is
given in Fig. 1. In Table 2 the different states are explained.
Figure 1. Conceptual representation of the computational model as a temporal-causal network
Informal Name
World state for stimulus s
Sensory representation state for
stimulus s
Sensory representation state for bp
Feeling state for body state b
Preparation for body state b
Preparation state for attention
Control state for suppression
Control state for attention deployment
Execution state for body state b
Execution state for attention
Table 2. Overview of the states of the proposed model (in connection to Fig. 1)
The computational model presented above, regulates emotion over time. The positive and
negative links of srsbp shows the presence and absence of a specific context factor (in the example
simulations the presence of the boss) which has some effect on the way emotions are regulated. In case
of presence of this context factor, expressive suppression is employed while in case of absence of it
attentional deployment is chosen for downregulating negative emotions. In both contexts, the primary
stimulus srss triggering anger is present. Moreover, when the context changes over time, the emotion
regulation strategies also change, as emphasized by various scholars; this model shows indeed a change
in the choice of strategy over time; this will be demonstrated in the next section through graphs of
simulation results. In the model in Fig. 1 it can been seen the control state cssup for suppression reduces
fsb, psb and esb by the negative connections to them. On the other hand, in attentional deployment, the
attention is moved away from the emotional eliciting situation. It can be seen in Fig. 1 that csad works
via psad and esad on srss to reduce attention for the stimulus.
4 Scenarios and Simulation Results
Using the computational model introduced above, the following scenario has been simulated, which
is loosely based on an example discussed by Aldoa et al. (2015):
“An employee feels quite angry every time when a particularly obnoxious coworker starts
speaking. This anger might be accompanied by an intense urge to roll his eyes.
Fortunately, the employee can prevent his anger by deploying his attention and focusing
on remembering what was said in the meeting rather than who said it. However, when the
employee’s manager is present, the employee becomes concerned that she will still pick
up on some of his hostile feelings toward the coworker. Therefore, when the manager is
present, he attempts to hide his facial expressions of anger, and therefore switches from
attention deployment to expressive suppression.
To simplify the scene, let’s say person A gets angry as person B talks. Four different contexts
are possible with this situation. The first is the boss is present and B talks. Person A would go for
suppression of his expression. If the boss is not present and B talks, A would go for attention deployment
and focus on remembering to what B says. It is also possible that the boss is initially present and B talks.
A goes for suppression but in the meanwhile boss leaves the room. So, A switches to attention
deployment from expressive suppression and vice versa. In this way this model adaptively models
emotion dependent on the context which changes over time.
In the simulations presented here for all states for the combination function the advanced logistic sum
combination function alogisticσ,τ (…) is used (Treur, 2016); here σ is a steepness parameter and τ a
threshold parameter.
cY(V1, …Vk) = alogistic,(V1, …, Vk) = (
 -
 ) (   )
The parameter values used are shown in Table 3 and 4.
Table 3. Values of threshold and steepness parameters for the logistic sum combination functions and the
speed factors used
wss, srss
fsb, cssup
srsbp, csad
fsb, psb
srsbp, cssup
psad, csad
srss, csad
psad, esad
srss, cssup
psb, csad
srss, psb
psb, cssup
csad, psad
psb, fsb
cssup, fsb
psb, esb
cssup, psb
esad, srss
cssup, esb
esb, cssup
fsb, csad
Table 4. Values of connection weights used
Figure 2. Simulation results when the boss is not present (Attentional Deployment is applied)
Figure 3. Simulation results when the boss is initially not present but joins later on (switch from Attentional
Deployment to Expressive Suppression)
Figure 4. Simulation results when the boss is present (Suppression of Expression is applied)
Figure 5. Simulation results when the boss is present initially but leaves later on (switch from Expressive
Suppression to Attentional Deployment)
Fig. 2 illustrates attentional deployment; the boss has not joined the meeting yet and B talks.
So srss goes up, triggering anger of A, but it starts decreasing as csad starts going up, i.e., the attention
deployment strategy gets activated and hence the anger comes down. A case in which the boss still joins
the meeting at a later point in time is depicted in Fig. 3. A has already lost his temper due to B and is
busy with attention deployment when the boss joins. After the boss has joined A switches from attention
deployment to expressive suppression. This transition in strategy occurs with the change in context,
which is referred to as adaptivity in terms of strategies: expressive suppression gets activated and
attentional deployment goes down as per demand of the context.
Fig. 4, represents expressive suppression, where A gets angry as B talks but as the boss is
present right from the start, A suppresses the emotion. It can be seen that the stimulus representation
srss still remains high but suppression gets higher with time and doesn’t let A express the anger.
In another scenario, as soon as A is about to get some control over his expression, the boss gets a call
and leaves the meeting. Fig. 5 demonstrates this context dynamics where initially the boss is present and
A gets teased as B talks. A suppresses the emotion but in the meantime, the boss leaves the room. It can
be observed that when the boss was present, suppression is active but as soon as the boss leaves, attention
deployment gets activated and the stimulus representation srss also falls down, due the attention
deployment. Note that it can also be seen in the graphs srss remains up in case of expressive suppression,
depicted in the respective graphs.
5 Conclusion
The computational model introduced here was inspired by literature regarding the adaptive
contextual choice of emotion regulation strategies. As elaborated by various researchers in the area,
emotion regulation strategies cannot be termed adaptive or maladaptive without taking, into account, a
specific context. These strategies can only be termed adaptive or maladaptive pertaining to a specific
context. The model described in this paper elaborates this computationally and highlights the adaptivity
of the strategies in different contexts. The results of the simulation experiments for the different
scenarios are in accordance with what is described in this literature.
In future work, as an extension, a more complex model may be designed for simulating the
adaptivity of more emotion regulation strategies in various contexts relative to each other. In the current
model each context triggers a strategy in a reactive manner. Another extension of this work may be
obtained by incorporating a more explicit decision-making process for the choice of strategy, for
example, by including elements from (Manzoor, Abro, and Treur, 2017).
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... 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 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 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. ...
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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. Flexibility, therefore, plays a key role in the use of these emotion regulation strategies. 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.
... Empirical research on emotion regulation flexibility has so far been limited. This is one of the reasons why previous work (Sheppes et al., 2011) and our own previous computational model of emotion regulation flexibility (Ullah et al., 2018) that was mainly based on that, only focused on the choice between attention deployment and reappraisal. Going beyond this work, however, this section of the chapter illustrates flexibility by a simulated scenario that involves flexibility among four emotion regulation strategies as per demand of the context. ...
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... [12] considers the sensitivity level in the choice of ER strategies. Similarly, [13] presents a model for contextual emotion regulation. This paper is specifically unique in the sense that it for the first time considers age and gender differences in the choice of ER strategies. ...
... The stationary point equation derived from the difference equation in a temporal causal network model is aggimpact Xi (t) = X i (t). From Table 6 we can see that except for bs + (13), all other values of aggimpact Xi (t) -X i (t) are within the range of [−0.002, 0.002]. The deviation in bs + (13) is a bit higher because the inflection in the curve of bs + is sharp (and Dt = 0.5). ...
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... Various strategies can be used for the regulation of emotions and desires, however the adaptivity of these strategies is purely dependent on the context in which they are employed [17,18]. In the model developed in this paper (explained in Section 5), reappraisal, situation modification, and problem solving have been used for the regulation of the negative emotions because of the way they deal with negative emotions and also because these strategies are more adaptive as compared to other similar strategies. ...
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... This strategy is response focused maladaptive strategy [15]. For how suppression can be modeled, please refer to [16,17]. Rumination is also referred to as worry and in the above scenario, if the employee keeps thinking about what had happened and keeps reiterating over what he has said, will be referred to as rumination. ...
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... This strategy is response focused maladaptive strategy [15]. For how suppression can be modeled, please refer to [16,17]. Rumination is also referred to as worry and in the above scenario, if the employee keeps thinking about what had happened and keeps reiterating over what he has said, will be referred to as rumination. ...
Conference Paper
Emotion regulation plays a major role in everyday life, as it enables individuals to modulate their emotions. Several strategies, for regulating emotions, can be used individually or simultaneously, such as suppression, rumination, acceptance, problem-solving, self-criticism, and experiential avoidance. This paper presents a temporal causal network model that simulates the employment of these seven emotion regulation strategies by a person experiencing varying intensity of anxiety. Simulation results are reported for both, the high and low, emotional intensity where the level of activation of these strategies varies with the intensity of negative emotions.
... The multi-level adaptive network model in this paper has been developed and simulated using adaptive casual network-oriented modeling approach and its supportive environment from (Treur 2019(Treur , 2020a(Treur , 2020b. This approach has already proven well-applicable for various adaptive as well as non-adaptive network models, for instance (Ullah et al. 2018(Ullah et al. , 2020Gao et al. 2019;Ullah and Treur 2019). Moreover, this paper extends a conference paper in COMPLEX NETWORKS 2019 (Ullah and Treur 2019a). ...
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The choice of which emotion regulation strategy to use, changes as per context which within Psychology is referred to as 'flexibility'. Besides that, choices of emotion regulation strategies are prone to various other factors, ranging from culture to gender, expectations of their effect, to age. This paper considers the phenomenon where choices of emotion regulation strategies change adaptively with age. In addition, the choices within specific age frames are driven by some kind of reward that affects in an adaptive manner the learning of a specific emotion regulation strategy. These adaptive phenomena involve plasticity of metaplasticity of different orders. They have been modeled by a fourth-order adaptive mental network model where the choice of emotion regulation strategies is motivated by reward prediction and different age phases have their own adaptive influences. Simulation results are discussed for evaluation of the adaptive network model. The fourth-order adaptive network model presented here extends a second-order adaptive network model previously addressed in a paper at the conference COMPLEX NETWORKS 2019. This paper will appear in the journal Applied Network Science.
... (Manzoor, Abro, & Treur, 2017) considers the sensitivity level in the choice of ER strategies. Similarly, (Ullah, Treur, & Koole, 2018) presents a model for contextual emotion regulation. Most close to the model presented here is the model presented in (Ullah & Treur, 2020) which also is adaptive and demonstrates the role of age in choice of emotion regulation strategies but does not consider the role of gender in choice of emotion regulation strategies. ...
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Emotion being an essential part of one’s life, its regulation is of utmost importance. The choice of strategies that one employs for regulating their emotions, varies at various stages of their lives and with contexts. Similarly, gender also has an influence on the choice of emotion regulation strategies. This paper presents a second-order adaptive network model for this phenomenon where the choice of strategies varies with age and gender in an adaptive manner. Simulation results for both the genders (male and female) have been provided where for both the genders, the choice of emotion regulation strategies changes as age increases. In: Journal of Information and Telecommunication, 2020. The second-order adaptive network model presented here extends a non-adaptive network model previously introduced at ICCCI’19. Published in: Journal of Information and Telecommunication 4(2) (2020) 213-228
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As an essential protein interaction, self-interacting proteins (SIPs) plays a vital role in biological processes. Identifying and confirming SIPs is of great significance for the exploration of new gene functions, protein function research and proteomics research. Although a large number of SIPs have been confirmed with the rapid development of high-throughput technology, the biological experimental method is still limited by blindness and high cost, and has a high false-positive rate. Therefore, the use of computational techniques to accurately and efficiently predict SIPs has become an urgent need. In this study, a novel SIPs prediction method GCNSP based on Graph Convolutional Networks (GCN) is proposed. Firstly, the evolution information of protein is described by Position-Specific Scoring Matrix (PSSM). Then the feature information is extracted by GCN, and finally fed into Random Forest (RF) classifier for accurate classification. In the five-fold cross-validation on Human and Yeast data sets, GCNSP achieved 93.65% and 90.69% prediction accuracy with 99.64% and 99.08% specificity, respectively. In comparison with different classifier models and other existing methods, GCNSP shows strong competitiveness. The excellent results show that the proposed method is very suitable for SIPs prediction and can provide highly reliable candidates for biological experiments.
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Which emotion regulation strategy one uses in a given context can have profound affective, cognitive, and social consequences. It is therefore important to understand the determinants of emotion regulation choice. Many prior studies have examined person-specific, internal determinants of emotion regulation choice. Recently, it has become clear that external variables that are properties of the stimulus can also influence emotion regulation choice. In the present research, we consider whether reappraisal affordances, defined as the opportunities for re-interpretation of a stimulus that are inherent in that stimulus, can shape individuals’ emotion regulation choices. We show that reappraisal affordances have stability across people and across time (Study 1), and are confounded with emotional intensity for a standardised set of picture stimuli (Study 2). Since emotional intensity has been shown to drive emotion regulation choice, we construct a context in which emotional intensity is separable from reappraisal affordances (Study 3) and use this context to show that reappraisal affordances powerfully influence emotion regulation choice even when emotional intensity and discrete emotions are taken into account (Study 4).
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This book has been written with a multidisciplinary audience in mind without assuming much prior knowledge. In principle, the detailed presentation in the book makes that it can be used as an introduction in Network-Oriented Modelling for multidisciplinary Master and Ph.D. students. In particular, this implies that, although also some more technical mathematical and formal logical aspects have been addressed within the book, they have been kept minimal, and are presented in a concentrated and easily avoidable manner in Part IV. Much of the material in this book has been and is being used in teaching multidisciplinary undergraduate and graduate students, and based on these experiences the presentation has been improved much. Sometimes some overlap between chapters can be found in order to make it easier to read each chapter separately. Lecturers can contact me for additional material such as slides, assignments, and software Springer full-text download:
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People respond to stressful events in different ways, depending on the event and on the regulatory strategies they choose. Coping and emotion regulation theorists have proposed dynamic models in which these two factors, the person and the situation, interact over time to inform adaptation. In practice, however, researchers have tended to assume that particular regulatory strategies are consistently beneficial or maladaptive. We label this assumption the fallacy of uniform efficacy and contrast it with findings from a number of related literatures that have suggested the emergence of a broader but as yet poorly defined construct that we refer to as regulatory flexibility. In this review, we articulate this broader construct and define both its features and limitations. Specifically, we propose a heuristic individual differences framework and review research on three sequential components of flexibility for which propensities and abilities vary: sensitivity to context, availability of a diverse repertoire of regulatory strategies, and responsiveness to feedback. We consider the methodological limitations of research on each component, review questions that future research on flexibility might address, and consider how the components might relate to each other and to broader conceptualizations about stability and change across persons and situations. © The Author(s) 2013.
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The construct of flexibility has been a focus for research and theory for over 100 years. However, flexibility has not been consistently or adequately defined, leading to obstacles in the interpretation of past research and progress toward enhanced theory. We present a model of socioemotional flexibility—and its counterpart rigidity—at three time scales using dynamic systems modeling. At the real-time scale (micro), moment-to-moment fluctuations in affect are identified as dynamic flexibility. At the next higher meso-time scale, adaptive adjustments to changes in context are characterized as reactive flexibility. At the macro scale is flexibility that occurs across months or years, reflecting flexibility due to developmental or life transitions. Implications of the model and suggestions for future research are discussed.
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How do people flexibly regulate their emotions in order to manage the diverse demands of varying situations? This question assumes particular importance given the central role that emotion regulation (ER) deficits play in many forms of psychopathology. In this review, we propose a translational framework for the study of ER flexibility that is relevant to normative and clinical populations. We also offer a set of computational tools that are useful for work on ER flexibility. We specify how such tools can be used in a variety of settings, such as basic research, experimental psychopathology, and clinical practice. Our goal is to encourage the theoretical and methodological precision that is needed in order to facilitate progress in this important area.
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There has been a increasing interest in understanding emotion regulation deficits in social anxiety disorder (SAD; e.g., Hofmann, Sawyer, Fang, & Asnaani, 2012). However, much remains to be understood about the patterns of associations among regulation strategies in the repertoire. Doing so is important in light of the growing recognition that people's ability to flexibly implement strategies is associated with better mental health (e.g., Kashdan et al., 2014). Based on previous work (Aldao & Nolen-Hoeksema, 2012), we examined whether putatively adaptive and maladaptive emotion regulation strategies interacted with each other in the prediction of social anxiety symptoms in a sample of 71 participants undergoing CBT for SAD. We found that strategies interacted with each other and that this interaction was qualified by a three-way interaction with a contextual factor, namely treatment study phase. Consequently, these findings underscore the importance of modeling contextual factors when seeking to understand emotion regulation deficits in SAD.
In “Emotion Regulation: Current Status and Prospects”, Gross (in press) reviews the state of the art in modern emotion regulation research and presents a new model of emotion regulation. We applaud the extended process model (Gross, in press), as part of a more general push towards more dynamic conceptions of emotion regulation. At the same time, we feel that the field still has a long way to go before it can provide a satisfactory account of people’s emotion-regulatory dynamics. The extended process model and its conceptual cousins maintain that emotion regulation is driven by mental representations like goals and “valuation systems” (Gross, in press). In our view, such static representations do not adequately explain the dynamic nature of emotion regulation. To tackle this problem, we propose a situated cognition approach, which treats emotion regulation as an activity that emerges dynamically from people’s interactions with their environment.