The Moderating Effect of Depression Symptoms on the Concordance between the Experience and Verbal Disclosure of Emotion

Kahn, Jeffrey H.; Lawell, Kyle J.; Allen, Alyssa; Henning, Julia; Heidenreich, Byron A.; Cox, Daniel W.; Williams, Elizabeth L.; Ladd, Kendall; Samlow, Brandee N.; Hamlet, Alison N.; Woodrum, Jennifer L.

doi:10.1007/s10862-020-09851-2

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…For NETWORK MODEL AND EMOTION DURATION 10 extraversion, no research linked it to emotion coherence of positive emotions, yet it predicted higher thresholds for components associated with many positive emotions such as higher control, being assertive, higher power, and being active (Soto & John, 2017) as well as being motivated to approach (Smits & Boeck, 2006). Depression predicted less coherence of various components for both positive and negative emotions (Bendezú et al, 2022;Brown et al, 2020;Kahn et al, 2021;Mauss et al, 2005;Sommerfeldt et al, 2019) and predicted higher thresholds for components associated with many negative emotions such as inactivity or loss of interest (American Psychiatric Association, 2000) as well as a cognitive style predisposing to experience negative emotions (Hankin et al, 2005). For rumination, no research linked it to emotion coherence of negative emotions, yet it predicted higher thresholds for components associated with many negative emotions such as a maladaptive cognitive style and lower motivation to act (Nolen-Hoeksma et al, 2008) as well as appraisals of intrusive thoughts about the likelihood of challenging relevant situations (Watkins, 2004).…”

Section: Predictors Of Emotion Duration Network Connectivity and Thre...mentioning

confidence: 99%

Embedding research in emotion duration in a network model

Lange¹

2023

Preprint

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Contrary to early theorizing, emotions often last for longer periods of time. Variability in people’s emotion duration contributes to psychopathologies. Therefore, emotion theories need to account for this variability. So far, reviews only list predictors of emotion duration without integrating them in a theoretical framework. Mechanisms explaining why these predictors relate to emotion duration remain unknown. I propose to embed research on emotion duration in a network model of emotions and illustrate the central ideas with simulations using a formal network model. In the network model, the components of an emotion have direct causal effects on each other. According to the model, emotions last longer (a) when the components are more strongly connected or (b) when the components have higher thresholds (i.e., they are more easily activated). High connectivity prolongs emotions because components are constantly reactivated. Higher thresholds prolong emotions because components are more easily reactivated even when connectivity is lower. Indirect evidence from research on emotion coherence and research on the relationship of predictors of emotion duration with components outside of emotional episodes supports the usefulness of the network model. I further argue and show in simulations that a common cause model, in which a latent emotion causes changes in emotion components, cannot account for research on emotion duration. Finally, I describe future directions for research on emotion duration and emotion dynamics from a network perspective.

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