Momentary Emotion Differentiation: The derivation and validation of a framework to study within-person fluctuations in emotion differentiation

Abstract

Emotion differentiation refers to the tendency to label emotions in a granular way. While differentiation is an important individual difference in the context of psychological well-being (Kashdan et al., 2015), it is unknown how it fluctuates within individuals. Such a within-person measure is important, since it would allow the study of how changes in differentiation predict subsequent levels of other variables of interest. Here, we present a framework to study emotion differentiation at the within-person level by introducing a momentary emotion differentiation index. This index is directly derived from the classical emotion differentiation index, the intra-class correlation. We first give a theoretical derivation of this index. Next, using data from two experience sampling studies, we show how this new momentary index is related to other momentary indicators of well-being, and take the first steps in building its nomological network. A better understanding of within-person fluctuations in emotion differentiation will allow us to identify the causes and consequences of these fluctuations, and search for ways to teach individuals to increase their level of emotion differentiation.

Emotional Granularity Increases With Intensive Ambulatory Assessment: Methodological and Individual Factors Influence How Much

“…Until fairly recently, emotional granularity has been operationalized as a trait, using a single aggregate estimate per person ( Tugade et al, 2004 ). The observation that emotional granularity changes over the course of experience sampling is consistent with other evidence that documents meaningful within-person variability in granularity over time ( Tomko et al, 2015 ; Grossmann et al, 2016 ; Erbas et al, 2018 , 2021 ). In particular, our finding of increases in emotional granularity as a function of study engagement supports the hypothesis that granularity is a skill that can be acquired and improved ( Kashdan et al, 2015 ).…”

“…As typically measured, emotional granularity represents the extent to which an individual distinguishes between like-valenced emotions (e.g., anger vs. sadness and excitement vs. pride) over time. Recent studies have shown that emotional granularity varies not only between but also within individuals over time ( Tomko et al, 2015 ; Erbas et al, 2018 , 2021 ), suggesting that it may be shaped and even improved. Moreover, accumulating evidence illustrates that higher emotional granularity is often associated with positive health outcomes in both clinical and non-clinical samples ( Kashdan et al, 2015 ; Smidt and Suvak, 2015 ; Barrett, 2017a ; O’Toole et al, 2020 ; Thompson et al, 2021 ).…”

“…More context-specific predictions – which come from more precise (emotion) concepts – are more efficient because they better anticipate probable actions and upcoming energy needs ( Hoemann et al, 2021 ). Functionalist ( Goldston et al, 1992 ; Shiota et al, 2014 ; Plonsker et al, 2017 ) and appraisal-based accounts ( Boden et al, 2013 ; Erbas et al, 2014 , 2018 , 2021 ; Thompson et al, 2021 ) of emotional granularity, in turn, hypothesize that differentiated emotional experiences are adaptive because they provide more specific or accurate information about the current situation, which enables individuals to react appropriately and engage in more effective emotion regulation ( Kalokerinos et al, 2019 ).…”

“…In practice, however, a participant’s emotional granularity is usually operationalized as the extent to which their intensity ratings for various emotion adjectives covary across all assessments [e.g., using an intra-class correlation (ICC; Shrout and Fleiss, 1979 )], thereby representing an aggregate (i.e., trait) estimate of granularity. Recently, researchers have begun to estimate granularity at the momentary and/or day level ( Tomko et al, 2015 ; Grossmann et al, 2016 ; Erbas et al, 2018 , 2021 ) and have found that lower emotional granularity within individuals is predictive of behavioral tendencies (e.g., self-reported impulsivity; Tomko et al, 2015 ) and predicted by current distress and negative affect ( Erbas et al, 2018 ). Furthermore, recent studies have leveraged this within-person variability to examine whether emotional granularity can be increased.…”

How to explore within-person and between-person measurement model differences in intensive longitudinal data with the R package lmfa

“…A research team conducts an ESM study to investigate between-subject differences regarding dynamics in affective well-being of employees in the travel sector during the Covid-19 pandemic. On the one hand, the underlying MM may differ across employees because people generally differ in their ability to label emotions in a granular way (Barrett, Gross, Christensen, & Benvenuto, 2001;Erbas, Kalokerinos, Kuppens, van Halem, & Ceulemans, 2020;Kashdan, Barrett, & McKnight, 2015).…”