Looking at Emotion Regulation Through the Window of Emotion Dynamics

Kuppens, Peter; Verduyn, Philippe

doi:10.1080/1047840x.2015.960505

Cited by 137 publications

(153 citation statements)

References 43 publications

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“…In formulating this model, we also use this opportunity to incorporate recent advances in the broader emotion regulation field (Gross, 2015a) into alexithymia theory, because we believe these advances might provide a useful theoretical framework for conceptualising alexithymia. We specifically use Gross's (2015a) recently introduced extended process model of emotion regulation as a framework for our model of alexithymia, because many authors consider it to be at the forefront of emotion regulation theory and it has been successfully applied to a multitude of emotional phenomena (e.g., Aldao & Christensen, 2015;Diaz & Eisenberg, 2015;Giuliani & Berkman, 2015;Kuppens & Verduyn, 2015;Schmader & Mendes, 2015). Within this framework, we also seek to integrate Lane and Schwartz's (1987) cognitive-developmental theory of levels of emotional awareness.…”

Section: And the Toronto Structuredmentioning

confidence: 99%

Establishing the theoretical components of alexithymia via factor analysis: Introduction and validation of the attention-appraisal model of alexithymia

Preece

Becerra

Allan

et al. 2017

Personality and Individual Differences

168

185

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Alexithymia is an important mental health construct, but there is continuing debate regarding its definition and measurement. We attempt to resolve this definitional uncertainty in two ways. Firstly, we trace the development of the alexithymia construct, focusing particularly on what we call the Toronto and Amsterdam models, and examine a body of empirical research that shows strong support for the hypothesis that alexithymia consists of three components (difficulty identifying feelings, difficulty describing feelings, and externally orientated thinking). Based on these components, we formulate an alternate theoretical model of alexithymia, the attention-appraisal model of alexithymia, that aligns alexithymia theory with recent advances in the broader emotion regulation field. Secondly, we examine the construct\u27s latent structure by factor analysing data from multiple psychometric measures administered to a community sample (N = 368). Our results suggest statistical support for our model, rather than the Toronto or Amsterdam models. We end by discussing how our model accounts for several unresolved issues within the alexithymia field, including the construct\u27s relation to imaginal capacities and emotional reactivity, whether alexithymia is a deficit or a defence, how it might be addressed in psychiatric treatment, and the discordance that has existed between alexithymia theory and alexithymia measurement. Ethical statement Ethics approval for this project was granted by the Edith Cowan University Human Research Ethics Committee. The guidelines of this committee were followed. All participants provided informed consent for their data to be used

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Section: And the Toronto Structuredmentioning

confidence: 99%

Establishing the theoretical components of alexithymia via factor analysis: Introduction and validation of the attention-appraisal model of alexithymia

Preece

Becerra

Allan

et al. 2017

Personality and Individual Differences

168

185

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“…The aim of these applications is to get at the processes that structure change in daily affective experiences, among them emotion regulation (Boker, 2002;Kuppens, Oravecz, & Tuerlinckx, 2010;Kuppens & Verduyn, 2015;Ram & Gerstorf, 2009), and specific model parameters are interpreted as parameters of regulatory processes (e.g., auto-regressive strength as "emotional inertia"; Brose, Schmiedek, Koval, & Kuppens, 2015;Koval, Kuppens, Allen, & Sheeber, 2012;Suls, Green, & Hillis, 1998).…”

Section: General Rationalementioning

confidence: 99%

“…Behavioral affect research increasingly relies on these intensive longitudinal assessment protocols to target emotion regulation processes. The underlying rationale is that such processes may be identifiable from the temporal patterns contained in the observed affect trajectories (Boker, 2002;Kuppens & Verduyn, 2015;Ram & Gerstorf, 2009). Formal dynamic models that parameterize temporal regularities in short-term changes are used to cast these dynamics of emotional functioning.…”

Section: Introductionmentioning

confidence: 99%

Capturing Context-Related Change in Emotional Dynamics via Fixed Moderated Time Series Analysis

Adolf

Voelkle

Brose

et al. 2017

Multivariate Behavioral Research

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Much of recent affect research relies on intensive longitudinal studies to assess daily emotional experiences. The resulting data are analyzed with dynamic models to capture regulatory processes involved in emotional functioning. Daily contexts, however, are commonly ignored. This may not only result in biased parameter estimates and wrong conclusions, but also ignores the opportunity to investigate contextual effects on emotional dynamics. With fixed moderated time series analysis, we present an approach that resolves this problem by estimating context-dependent change in dynamic parameters in single-subject time series models. The approach examines parameter changes of known shape and thus addresses the problem of observed intra-individual heterogeneity (e.g., changes in emotional dynamics due to observed changes in daily stress). In comparison to existing approaches to unobserved heterogeneity, model estimation is facilitated and different forms of change can readily be accommodated. We demonstrate the approach's viability given relatively short time series by means of a simulation study. In addition, we present an empirical application, targeting the joint dynamics of affect and stress and how these co-vary with daily events. We discuss potentials and limitations of the approach and close with an outlook on the broader implications for understanding emotional adaption and development.

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“…After about 20 years, Davidson has reemphasized their crucial role in understanding emotions (Davidson, 2015), in a special issue of the journal Emotion Review on advancing research into affect dynamics (Kuppens, 2015). Additionally, it has been shown in the literature that emotion transitions carry a great deal of valuable information for social interactions (Filipowicz et al, 2011;Mesquita and Boiger, 2014;Hareli et al, 2015), marital relationships, emotional intelligence (Gross, 2001;Kuppens and Verduyn, 2015) and psychological well-being (Kuppens et al, 2010;Choi et al, 2015;Houben et al, 2015). For instance, emotional transitions during conversations have a great impact on conversational outcomes (Filipowicz et al, 2011) and the final impression/perception of how dominant a person is (Hareli et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…A psychological sociodynamic model of emotions in context was proposed, and many interesting aspects of emotions were discussed, one of which is that emotions do not occur or change simply in response to social events, but are also an integral part of determining how social interactions proceed (Mesquita and Boiger, 2014). Recently, emotion regulation and emotion dynamics were associated using several parameters describing trajectories of emotion changes (Kuppens and Verduyn, 2015). Regulating emotions is more often than not associated with knowing the timings of emotional transitions, so that people react to change in the course of emotions (Gross, 2001).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Emotion Change From Speech

Huang

Epps

2018

Front. ICT

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The fact that emotions are dynamic in nature and evolve across time has been explored relatively less often in automatic emotion recognition systems to date. Although withinutterance information about emotion changes recently has received some attention, there remain open questions unresolved, such as how to approach delta emotion ground truth, how to predict the extent of emotion change from speech, and how well change can be predicted relative to absolute emotion ratings. In this article, we investigate speech-based automatic systems for continuous prediction of the extent of emotion changes in arousal/valence. We propose the use of regression (smoothed) deltas as ground truth for emotion change, which yielded considerably higher inter-rater reliability than first-order deltas, a commonly used approach in previous research, and represent a more appropriate approach to derive annotations for emotion change research, findings which are applicable beyond speech-based systems. In addition, the first system design for continuous emotion change prediction from speech is explored. Experimental results under the Output-Associative Relevance Vector Machine framework interestingly show that changes in emotion ratings may be better predicted than absolute emotion ratings on the RECOLA database, achieving 0.74 vs. 0.71 for arousal and 0.41 vs. 0.37 for valence in concordance correlation coefficients. However, further work is needed to achieve effective emotion change prediction performances on the SEMAINE database, due to the large number of non-change frames in the absolute emotion ratings.

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Looking at Emotion Regulation Through the Window of Emotion Dynamics

Cited by 137 publications

References 43 publications

Establishing the theoretical components of alexithymia via factor analysis: Introduction and validation of the attention-appraisal model of alexithymia

Establishing the theoretical components of alexithymia via factor analysis: Introduction and validation of the attention-appraisal model of alexithymia

Capturing Context-Related Change in Emotional Dynamics via Fixed Moderated Time Series Analysis

Prediction of Emotion Change From Speech

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