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. 2024 Nov 14;6(2):224–235. doi: 10.1007/s42761-024-00284-8

Links Between Emotion Word, Usage, Understanding, Accuracy, and Emotion Dysregulation: An Integrative Analysis

Jennifer M B Fugate 1,✉, Maria Gendron 2, Katie Hoemann 3,4
PMCID: PMC12209092  PMID: 40605951

Abstract

People vary in the precision with which they experience and report on their emotions, known as emotional granularity, and this precision predicts their ability to regulate their emotions. It is not yet known, however, whether links between emotional granularity and emotion regulation are due to variation in knowledge of emotion words—specifically, individuals’ reported usage, understanding, and ability to accurately define emotion words. In the present report, we combined data from six studies to address this gap in the literature using an integrative data analysis. Participants across the studies reported on how often they used and how well they understood a list of precise emotion words, and were tested on whether they could correctly pick a definition for each. They also completed questionnaire measures of self-reported emotional granularity (differentiation) and emotion dysregulation. Emotion word accuracy and understanding were highly correlated, so individual models were tested each separately to predict emotion dysregulation. In the model including usage and understanding, we observed a main effect of understanding, such that participants with greater self-reported understanding of emotion words reported less difficulty regulating their emotions. Similar effects were found for the model including usage and accuracy, such that individuals with higher emotion word accuracy had less difficulty regulating their emotions. Critically, these findings held when accounting for self-reported granularity (differentiation), suggesting that measures of emotion word knowledge have value for predicting emotion regulatory outcomes. Future work should examine whether individuals’ emotion word knowledge is also linked to mental health outcomes.

Keywords: Emotional granularity, Emotion word knowledge, Emotional dysregulation

When people are asked to report on their emotional experiences, they vary in their ability to make distinctions among related concepts. This variation is used to estimate emotional granularity (also known as emotion differentiation; Barrett et al., 2001). Emotional granularity is an individual’s ability to create experiences of emotion that are nuanced and context-specific (Barrett, 1997). If, when evaluating their experiences, someone does not distinguish between neighboring emotion words (e.g., “angry” and “sad,” which both refer to negative emotions), it implies that these words are being used less precisely and to refer to a broader affective state (e.g., “bad”). This, in turn, might suggest that this person is unable to flexibly construct emotions that are finely tuned to the current set of circumstances. Thus, emotional granularity is lower when two or more emotion words are endorsed similarly over time, but higher when words are endorsed distinctively.

Extensive prior research reveals that emotional granularity is related to mental health (for reviews see Dunning et al., 2022; Nook, 2021; Tan et al., 2022). One explanation of why these links emerge is that precisely representing emotions is a precursor to their healthy and efficient emotion regulation (e.g., Barrett et al., 2001; Kalokerinos et al., 2019; Tugade et al., 2004). Specifically, early evidence suggested that participants with higher emotional granularity used more emotion regulation strategies (identified retrospectively over two weeks) to address their negative emotions in comparison to individuals with lower emotional granularity (Barrett et al., 2001). In turn, higher granularity for positive emotions was found to be a predictor of effective coping and therefore a factor that influences individual differences in resilience (Tugade et al., 2004). More recent evidence suggests that lower emotional granularity, in particular, is associated with ineffective use of emotion regulation strategies in the moment (Kalokerinos et al., 2019), but not the selection of regulation techniques.

How might emotional granularity be linked to emotion regulation? One reason might be that individual differences in emotional granularity are supported by corresponding differences in emotion word knowledge—specifically, in the usage and understanding of emotion words, as well as in the ability to accurately define emotion words (Kang & Shaver, 2004; Vedernikova et al., 2021). Indirect evidence from the study of alexithymia supports this link. Alexithymia was initially conceptualized as a limited ability to use words as symbols for emotional states (Nemiah & Sifneos, 1970; Ruesch, 1948), but is now more often assumed to be a disorder of affect regulation (Lane et al., 1996; Taylor et al., 1997). Prior work has found that people higher in alexithymia produce fewer types of emotion words and fewer synonyms for target emotion words than those who are lower in alexithymia (Wotschack & Klann-Delius, 2013) and that self-reported alexithymia is negatively correlated with self-reported emotional granularity (r = − .37) (Kang & Shaver, 2004). This link is further substantiated by a recent meta-analysis documenting a significant (albeit weak) negative relationship (r = − .10) between alexithymia and various measures of emotional granularity (Lee et al., 2022; see also Hoemann et al., 2021).

Emotional granularity, along with emotional awareness, has also been shown to mediate the relationship between alexithymia and emotion regulation (da Silva et al., 2017), suggesting that emotional granularity supports healthy emotion regulation strategies because it entails an enhanced ability to assign meaning to emotional experiences, increasing the subtle distinctions between them. Taken together, the literature on alexithymia suggests that there is a modest relationship between the ability to represent emotions with words and the tendency to experience emotions in a granular manner, and it has an impact on emotion regulation.

Research that links emotion language more broadly to emotion regulation is still in its infancy and has produced some conflicting findings. Initial work on the topic suggested that emotion word usage—i.e., labeling one’s own emotions—may benefit emotion regulation (e.g., Kircanski et al., 2012), through a phenomenon referred to as affect labeling. There are several potential mechanisms proposed for how affect labeling affects emotion regulation (Torre & Lieberman, 2018). These include (1) by serving as an intrinsic form of emotion regulation and (2) by bringing online emotion concept knowledge that can guide emotion regulation (for consistent neural evidence, see Brooks et al., 2017). More recent evidence, however, suggests that labeling one’s emotions may not consistently serve these functions. For example, when participants applied a label to their own negative emotions, this did not lead to decreases in negative affect, as would be predicted by an intrinsic regulatory account (Vlasenko et al., 2021). Further, when participants labeled their own emotions before being asked to reappraise their emotions, labeling hindered reappraisal efficacy (Nook et al., 2021). This suggests that, at least for some regulation strategies that relate to changing one’s representation of the emotional situation, labeling one’s emotions can backfire (for review and discussion, see Hoemann, 2024).

Another account of how emotion language, granularity, and regulation may be linked is that emotion labels function to build a robust and flexible set of category representations or concepts. For example, emotion labels learned during development might serve to cohere sets of emotion instances that vary widely in their physical, mental, and situational properties (Hoemann et al., 2020). Labels may direct attention to commonalities across these disparate instances, in the process organizing concepts for emotion that can then be generalized to new instances (Shablack & Lindquist, 2019). Indeed, specific labels (e.g., “disgusted”) rather than broad (“bad”) or irrelevant labels (“sitting down”) help young children to link photos of emotional faces to corresponding scenarios (Ogren & Sandhofer, 2022). These findings are consistent with a constructionist approach to emotion, which proposes that emotion words serve as a critical tool for learning emotions as abstract concepts (Hoemann et al., 2019, 2020; see also Nook & Somerville, 2019). Finally, there is evidence linking early emotion vocabulary and the development of emotion regulation, such that emotion regulation ability may be built on a child’s emerging emotional understanding (Cole et al., 2010; Pons et al., 2003; for review, Atzil & Gendron, 2017).

What is not yet known, however, is whether individual differences in emotion word knowledge are related to emotion regulation in adulthood and whether they account for said variation above and beyond emotional granularity (differentiation). In the present report, we combined data from six studies to address the relationship between emotion word knowledge variables and emotional granularity (differentiation), as they affect emotion regulation. We operationalized emotional granularity (differentiation) through participants’ answers on the differentiation subscale of the Range and Differentiation of Emotional Experiences Scale (RDEES; Kang & Shaver, 2004). We operationalized “emotion word knowledge” with three variables: how often people report using a set of precise emotion words (i.e., usage); how well they report understanding each of these words (i.e., understanding); and how often they pick the correct definition of these words from related emotion-word definitions (i.e., accuracy). Finally, we operationalized emotion dysregulation through participants’ answers on the Difficulties in Emotion Regulation Scale-16 (DERS-16, Bjureberg et al., 2016). This version has two fewer questions than the original DERS-18 (Victor & Klonsky, 2016).

We tested these relationships using an integrative data analysis (IDA; Curran & Hussong, 2009), a means of pooling individual subject data from independent studies that allowed us to examine whether emotion word knowledge was associated with emotion regulation difficulty across studies despite slight variation in study parameters (e.g., number of included emotion words) while accounting for between-study heterogeneity.

Method

Emotional Granularity (Differentiation)

Emotional granularity (differentiation) was measured using the 7-item differentiation subscale of the Range and Differentiation of Emotional Experience Scale (RDEES) (Kang & Shaver, 2004). The full scale was designed to assess individual differences in the range of emotional experiences, including how attentive people are to their feelings, how open they are to experience, their ability to understand others’ feelings, and their ability to adjust socially. Participants respond to items such as “I usually experience a wide range of emotions” or “I am aware of the subtle differences between feelings I have” on a 5-point Likert scale, with 1 indicating “does not describe me very well” and 5 indicating “describes me very well.” Four of these items are reverse-coded. Scores are averaged across all items within a given subscale. For present purposes, higher scores on the differentiation subscale represent higher self-reported emotional granularity (differentiation), whereas lower scores indicate lower self-reported emotional granularity (differentiation). In its initial validation by Kang and Shaver (2004), the differentiation subscale was found to be significantly positively correlated with the clarity and repair of emotion subscales of the Trait Meta-Mood Scales (TMMS). In addition, the differentiation scale was more strongly correlated with performance in an emotion card sorting task and with self- and peer-reported interpersonal relationship quality compared to the range subscale. Thus, the differentiation subscale has good construct validity, such that self-reported differentiation of emotional experience is related to fine-grained emotion categorization. It also has predictive validity for outcomes relevant to emotion regulation.

Emotion Dysregulation

Emotion dysregulation was measured using the 16-item version of the Difficulties in Emotion Regulation Scale (DERS-16, Bjureberg et al., 2016). The DERS-16 is based on a clinically-useful conceptualization of emotion regulation “that was developed to be applicable to a wide variety of psychological difficulties and relevant to clinical applications and treatment development” (in Bjureberg et al., 2016, p. 284). Specifically, this conceptual definition of emotion regulation emphasizes the functionality of emotions and focuses on adaptive ways of responding to emotional distress, including the (a) awareness, understanding, and acceptance of emotions; (b) ability to control behaviors when experiencing negative emotions; (c) flexible use of situationally-appropriate strategies to modulate the intensity and/or duration of emotional responses, rather than to eliminate emotions entirely; and (d) willingness to experience negative emotions as part of pursuing meaningful activities in life. Participants respond to items such as “When I am upset, I have difficulty controlling my behaviors” or “When I am upset, I have difficulty focusing on other things” on a 5-point Likert scale, with 1 indicating “almost never” to 5 indicating “almost always.” Three of the items are reverse-coded. The minimum overall score is 16, and the maximum score is 80. Higher scores indicate greater difficulties with emotion regulation.

Emotion Word Knowledge

An initial set of 40 emotion words was selected from the ANEW list (Bradley & Lang, 1999), Baron-Cohen et al., (2010), and from a pdf of emotion terms (Byron Katie International, Inc., 2018, thework.com) to maximize coverage of the affective circumplex (Russell, 1980) while also balancing for word length and frequency of use (Brysbaert et al., 2018). A subset of these words was rated by participants in up to three separate pilot studies (see Table 1). Participants selected one of seven emotion categories (sad, calm, happy, fear, disgust, angry, and surprised) or “none of the above” to represent the best emotion category to which it belonged. Participants also rated how well they understood and used each emotion word. Finally, participants in some pilots also were asked to choose the correct definition of the emotion word from among four distractor definitions. From the original 40 words, 23 emotion words (see Table 1) were selected based on similarities in usage, understanding, and accuracy. Two emotion words were chosen for each of the seven emotion categories (listed previously). The remaining words were distributed across positive valence/low arousal, positive valence/high arousal, negative valence/low arousal, and negative valence/high arousal. Studies (datasets) 1–4 used the same 20 emotion words; Studies (datasets) 5–6 used 14 emotion words (some of which were not in Studies 1–4) due to the demographic of the studies being graduate students (see details below).

Table 1.

Words used in each study and rating information from previous pilots

Word Study 1 Study 2 Study 3 Study 4 Study 5 Study 6 BRM valence BRM arousal Pilot usage Pilot understand Pilot accuracy Pilot Q sort Source Frequency English Emotion category agreed
Argumentative x x x x x x 3.20 (1.8) 5.61 (2.1) 2.62 (0.8) 1.27 (0.5) 0.94 (0.2) Anger 67.4% The work 2.883 Anger
Furious x x x x x x 2.57 (1.6) 6.09 (2.2) 2.07 (0.8) 1.04 (0.2) 0.94 (0.2) Anger 98.0% ANEW 3.779 Anger
Accepting(ance) x x x x x x 6.84 (1.3) 4.30 (2.8) 1.98 (0.8) 1.08 (0.3) 0.97 (0.2)** Calm 54.5% ANEW 3.501 Calm
Settle(d) x x x x x x 5.62 (1.8) 3.40 (1.9) 3.49 (1.6) 1.33 (0.6) 0.79 (0.5) Calm 67.1% Baron-Cohen 4.288 Calm
Displeased x x x x x x 3.41 (1.8)

4.22

(2.6)

 2.86 (0.7) 1.16 (0.4)

0.91

(0.3)

 Disgust 55.6% ANEW 2.894 Disgust
Disdainful x x 3.13 (1.5) 4.70 (2.4) Disgust 61.0% ANEW 1.894 Disgust
Horrified(ying) x x 2.68 (1.7) 6.29 (2.7)  2.21 (1.0)  1.11 (0.5) The Work 3.155 Fear
Terrified x x x x x x 2.51 (1.6) 6.10 (2.6) 2.43 (0.7) 1.05 (0.3) 0.99 (0.1) Fear 99.3% ANEW 3.981 Fear
Overjoyed x x x x x x 7.17 (1.5) 5.57 (3.0) 2.75 (0.8) 1.10 (0.3) 0.92 (0.3) Happy 99.3% The work 2.872 Happy
Thrill(ed) x x x x x x 7.37 (1.5) 7.19 (2.0) 2.44 (0.7) 1.06 (0.2) 0.95 (0.2) Happy 89.1% ANEW 4.044 Happy
Anguished x x 2.78 (1.5) 4.79 (2.5) 3.17 (1.2)* 1.95 (0.8)* – Sad 82.0% ANEW 2.438 Sad
Useless x x x x x x 2.80 (1.4) 4.39 (2.4) 2.13 (0.9) 1.01 (0.1) 0.95 (0.2) Sad 59.9% ANEW 4.300 Sad
Impressed(ive) x x x x x x 7.05 (1.6) 4.10 (2.4) 1.87 (0.7) 1.02 (0.1) 0.97 (0.2) Surprise 61.9% ANEW 4.229 Surprise
Shock(ed) x x x x x x 3.90(2.05) 5.95 (2.9) 1.84 (0.8) 1.04 (0.2) 0.97 (0.2)** Surprise 86.3% ANEW 4.459 Surprise
Disagreeable x x x x 3.29 (2.0) 4.26 (2.6) 2.88 (0.8) 1.16 (0.4) 0.70 (0.4)** None 63.3% Baron-Cohen 2.991
Disturb(ed) x x x x 3.45 (1.6) 4.98 (2.5) 2.48 (0.8) 1.07 (0.3) 0.78 (0.4) Fear 30.3% ANEW 3.994
Forgiving x x x x 6.74 (1.7) 3.95 (2.4) The work 3.399
Frustrated x x x x 2.55 (1.0) 5.40 (2.3) ANEW 3.700
Grateful x x x x 7.50 (1.3) 4.29 (2.5) ANEW 4.424
Involve(d) x x x x 5.44 (1.9) 3.60 (2.2) The work 4.832
Irritate(d) x x x x 3.19 (1.7) 5.85 (2.2) ANEW 2.982
Puzzled x x x x 4.60 (0.9) 4.43 (2.2) 2.78 (0.9) 1.06 (0.3) 1.00 (0.0)** None 72.4% Baron-Cohen 3.143
Unsure x x x x 3.37 (1.5) 4.55 (2.1) The work 3.016
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Words were initially sourced from the following: Baron-Cohen et al. (2010), Byron Katie International, Inc., 2018 (thework.com), and the ANEW (Bradley & Lang, 1999). BRM ratings were taken from Warriner et al. (2013). Valence ratings are reported as Mn (SD) on a 1–9 scale (9 = happy). BRM arousal ratings are similarly reported as Mn (SD) on a 1–9 scale (1 = calm). The overall average BRM valence rating across words was Mn (SD) = 4.40 (1.6). The overall average BRM arousal rating across words was Mn (SD) = 4.96 (2.4). The rating for usage, understanding, and accuracy were based on a sample size n = 147–166, otherwise noted as *n = 19–35 or **n = 19. Usage was based on participants rating each word on a scale of 1–5 (1 = regularly used). The overall average pilot usage was Mn (SD) = 2.52 (0.9). Understanding was based on participants rating each word on a scale of 1–5 (1 = I know what this word means). The overall average pilot understanding was Mn (SD) = 1.16 (0.3). Accuracy was the percentage of time participants chose the correct definition (among four) for each emotion word. The overall average pilot accuracy was Mn (SD) = 0.91 (0.2). Pilot Q sort category was based on participants (n = 147–166) choosing one of seven (or “none of the above”) emotion categories to which each word best belonged. Frequency ratings are from Brysbaert et al. (2018) in English (FreqZIP US). The overall average frequency of use was Mn (SD) = 3.53 (0.7). Emotion category was the category to which each emotion word best fit based on the Pilot Q sort (horrified was agreed upon by researcher consensus).

Participants and Specific Procedures by Study

Dataset 1

Nine undergraduate students from the University of Massachusetts–Dartmouth and 30 participants from the general population (advertised via social media) between the ages of 18–25 years (n = 32 females, n = 4 males, 2 = prefer not to say, 1 = did not answer) completed an online Qualtrics survey, which assessed their understanding (1 = I know what this word means; 4 = I do not know what this word means), usage (1 = I use this word often; 4 = I do not use this word often), and accuracy of 20 pre-normed precise emotion words. The usage and accuracy questions for an individual word were not asked if participants indicated a “3” or a “4” on the understanding question, on the logic that people would not claim to use a word or be able to select its definition if they did not know what it meant. For the accuracy questions, the incorrect answers were the correct answers from other words, randomly assigned and equally repeated throughout the list. In all datasets, understanding and usage were reverse-coded before analysis, yielding variables that scaled intuitively from 1 (no understanding/usage) to 4 (high understanding/usage), and were then averaged across all emotion words with a response. Accuracy was calculated as the proportion of correct responses over all possible trials (regardless of the number of attempted trials). Participants also completed the RDEES and DERS-16 measures. This survey was part of a larger study that also collected information on participants’ online social media usage and emotional health, and included the Mental Health Continuum (MHC). Participants were entered into a raffle for the chance to win one $50 gift card at the end of the study for their participation. Data were collected in 2019 under IRB #18.084 at the University of Massachusetts-Dartmouth.

Datasets 2 and 3

One hundred fourteen undergraduate students from the University of Massachusetts–Dartmouth completed a Qualtrics survey similar to that described for Dataset 1. Participants were required to be between 18 and 25 years of age to participate. No further demographic data were collected. Due to a survey error, the data from one participant was excluded from analysis, for a final sample size of 113. The study included all the measures described for Dataset 1, the only difference being that participants were also asked to rate each presented emotion word on valence, arousal, and embodiment. Participants additionally completed an emotional face perceptual discrimination task, in which they viewed individual morphed faces created from combining (using morphing software) faces between each of two emotions. Participants were asked to judge whether two presented ambiguous faces matched or did not match using a computer keyboard. Participants received a $10 gift card or one research credit for participation if they were enrolled in Psychology 101 as part of the course requirement. Data were collected in 2019 under IRB #18.084 at the University of Massachusetts–Dartmouth.

Dataset 4

One hundred twenty-five undergraduate students from the University of Massachusetts-Dartmouth completed a Qualtrics survey similar to that described for Dataset 1, except the MHC and gender questions did not function correctly and did not record data. The usage question for one of the emotion words also malfunctioned, resulting in a total usage score that represented 19 rather than 20 words. Two participants were excluded from analysis due to responding to all questions with a “4” for understanding. The final sample size was 123. Participants were entered into a raffle for the chance to win one $50 gift card at the end of the study for their participation. Data were collected in 2020 under IRB #18.084 at the University of Massachusetts-Dartmouth.

Datasets 5 and 6

Thirty-six (Dataset 5) and 38 (Dataset 6) graduate students from the Kansas City University completed a Qualtrics survey similar to that described for Dataset 1, with the addition of six other measures in Dataset 5 (Schutte Suite Emotional Intelligence Scale (SSEI), Emotion Regulation Questionnaire (ERQ), Satisfaction with Life Scale (SWL), MHC, TMMS, and TAS-20), and these same scales minus the SSEI in Dataset 6. Instead of 20 emotion words, only 14 emotion words were presented, which included three new words which replaced words that had a consistently high (> 98%) emotion accuracy. In these datasets, participants were first asked for their usage of each emotion word, followed by understanding and accuracy. The understanding and accuracy questions only appeared if participants did not report a “4” for usage. In addition, participants completed two emotion perception tasks (see Datasets 2 and 3) in the first PI’s laboratory. Participants received a $10 gift card to Amazon for contributing to Dataset 5. This study was approved through IRB #1942774 at Kansas City University. Dataset 6 included additional training as part of a long-term intervention of emotion word acquisition and experience sampling, so these participants received additional compensation (up to $100 over 3 weeks). This study was approved through IRB #1906743 at Kansas City University.

Results

Main Analysis

We conducted a fixed-effects IDA in which we treated study membership as a property of each participant’s data (Curran & Hussong, 2009) while testing the effect of emotion word knowledge on emotion dysregulation using a linear model across all datasets (N = 349). To examine the effect of emotion word knowledge above and beyond self-reported emotional granularity, in a first step, we fitted a model predicting emotion dysregulation (DERS-16) from study (1–6), emotional granularity (RDEES differentiation subscale), and the interaction between the two. Then, in a second step, we added the three measures of emotion word knowledge (understanding, usage, and accuracy) and their interactions with each study. Emotion word knowledge and self-report variables were standardized by dataset. We used deviation coding to compare each level of the study factor to the grand mean without setting any particular dataset as reference (Brehm & Alday, 2022). As a consequence, the last level of the study factor was not represented in the initial regression results. We accounted for this by manually resetting the contrast and re-running the models to get coefficients for Dataset 6. Descriptive statistics by study are presented in Table 2.

Table 2.

Descriptive statistics (means, standard deviations) by a dataset

Variable Dataset 1 Dataset 2 Dataset 3 Dataset 4 Dataset 5 Dataset 6
N 39 40 73 123 36 38
Emotion dysregulation (DERS-16) 40.31 (11.80) 40.88 (11.87) 38.25 (12.40) 43.09 (11.76) 34.39 (10.09) 36.89 (9.41)
Emotional granularity (RDEES differentiation) 2.29 (0.74) 3.32 (0.80) 3.68 (0.60) 2.49 (0.72) 3.74 (0.66) 3.75 (0.72)
Usage 2.93 (0.37) 2.86 (0.36) 2.67 (0.43) 2.73 (0.41) 2.98 (0.38) 2.95 (0.44)
Understanding 3.82 (0.50) 3.91 (0.15) 3.80 (0.41) 3.83 (0.35) 3.85 (0.18) 3.89 (0.14)
Accuracy 0.90 (0.17) 0.92 (0.12) 0.91 (0.14) 0.91 (0.14) 0.89 (0.11) 0.86 (0.12)
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Regression results are presented in Table 3. Analyses were run in R using the lme4 and lmerTest packages (Bates et al., 2015; Kuznetsova et al., 2017). All tests of significance are two-tailed at ɑ = .05.

Table 3.

Integrative data analysis regression results

Parameter β SE t 95% CI
Step 1: Dysregulation ~ Study + Granularity (differentiation) + Study: Granularity (differentiation)
Intercept .001 .06 0.01  − 0.11, 0.12
Dataset 1  − .01 .14  − 0.05  − 0.28, 0.27
Dataset 2  − .0001 .14  − 0.0001  − 0.28, 0.27
Dataset 3 .01 .11 0.07  − 0.21, 0.23
Dataset 4 .0002 .09 .002  − 0.18, 0.18
Dataset 5  − .0002 .15  − 0.002  − 0.29, 0.29
Dataset 6  − .001 .14  − 0.01  − 0.28, 0.28
Granularity (differentiation)  − .08 .06  − 1.31  − 0.19, 0.04
Dataset 1 × Granularity (differentiation) .47 .14 3.26** 0.18, 0.75
Dataset 2 × Granularity (differentiation)  − .11 .14  − 0.81  − 0.39, 0.16
Dataset 3 × Granularity (differentiation) .19 .11 1.74†  − 0.02, 0.41
Dataset 4 × Granularity (differentiation)  − .05 .09  − 0.57  − 0.24, 0.13
Dataset 5 × Granularity (differentiation)  − .23 .15  − 1.52  − 0.52, 0.07
Dataset 6 × Granularity (differentiation)  − .27 .14  − 1.85†  − 0.55, 0.02
Step 2: Dysregulation ~ Study + Granularity (differentiation) + Usage + Understanding + Accuracy + Study: Granularity (differentiation) + Study: Usage + Study: Understanding + Study: Accuracy
Intercept .003 .06 0.05  − 0.11, 0.11
Dataset 1  − .01 .14  − 0.06  − 0.28, 0.26
Dataset 2  − .003 .14  − 0.02  − 0.27, 0.27
Dataset 3 .01 .11 0.05  − 0.21, 0.22
Dataset 4 .01 .09 0.12  − 0.17, 0.19
Dataset 5  − .003 .14  − 0.02  − 0.28, 0.28
Dataset 6  − .003 .14  − 0.02  − 0.28, 0.27
Granularity (differentiation)  − .07 .06  − 1.09  − 0.18, 0.05
Usage .05 .06 0.74  − 0.08, 0.17
Understanding  − .10 .09  − 1.09  − 0.29, 0.08
Accuracy  − .09 .10  − 0.94  − 0.28, 0.10
Dataset 1 × Granularity (differentiation) .44 .15 2.97** 0.15, 0.7
Dataset 2 × Granularity (differentiation)  − .11 .14  − 0.75  − 0.39, 0.18
Dataset 3 × Granularity (differentiation) .15 .11 1.33  − 0.07, 0.36
Dataset 4 × Granularity (differentiation)  − .04 .09  − 0.46  − 0.23, 0.14
Dataset 5 × Granularity (differentiation)  − .21 .15  − 1.41  − 0.51, 0.08
Dataset 6 × Granularity (differentiation)  − .23 .15  − 1.55  − 0.52, 0.06
Dataset 1 × Usage .004 .15 0.03  − 0.28, 0.29
Dataset 2 × Usage  − .15 .14  − 1.05  − 0.44, 0.13
Dataset 3 × Usage .27 .11 2.39** 0.05, 0.49
Dataset 4 × Usage .14 .09 1.44  − 0.05, 0.32
Dataset 5 × Usage  − .09 .15  − 0.57  − 0.39, 0.21
Dataset 6 × Usage  − .17 .17  − 1.00  − 0.50, 0.16
Dataset 1 × Understanding .43 .31 1.37  − 0.19, 1.04
Dataset 2 × Understanding  − .31 .16  − 1.92†  − 0.63, 0.01
Dataset 3 × Understanding  − .10 .24  − 0.41  − 0.57, 0.37
Dataset 4 × Understanding  − .17 .19  − 0.89  − 0.53, 0.20
Dataset 5 × Understanding .11 .17 0.67  − 0.21, 0.44
Dataset 6 × Understanding .04 .17 0.24  − 0.29, 0.37
Dataset 1 × Accuracy  − .29 .31  − 0.92  − 0.91, 0.33
Dataset 2 × Accuracy .25 .17 1.49  − 0.08, 0.57
Dataset 3 × Accuracy .02 .24 0.07  − 0.45, 0.49
Dataset 4 × Accuracy .05 .17 0.30  − 0.28, 0.37
Dataset 5 × Accuracy  − .02 .17  − 0.10  − 0.35, 0.32
Dataset 6 × Accuracy  − .01 .19 1.37  − 0.19, 1.04
Step 2A: Dysregulation ~ Study + Granularity (differentiation) + Usage + Understanding + Study: Granularity (differentiation) + Study: Usage + Study: Understanding
Intercept .003 .06 0.06  − 0.11, 0.11
Dataset 1  − .01 .14  − 0.07  − 0.28, 0.26
Dataset 2  − .003 .14  − 0.02  − 0.27, 0.26
Dataset 3 .01 .11 0.05  − 0.21, 0.22
Dataset 4 .01 .09 0.14  − 0.16, 0.19
Dataset 5  − .003 .14  − 0.02  − 0.28, 0.28
Dataset 6  − .004 .14  − 0.02  − 0.28, 0.27
Granularity (differentiation)  − .07 .06  − 1.20  − 0.19, 0.05
Usage .03 .06 0.59  − 0.08, 0.15
Understanding  − .17 .06  − 2.86**  − 0.29, − 0.05
Dataset 1 × Granularity (differentiation) .47 .15 3.18** 0.18, 0.76
Dataset 2 × Granularity (differentiation)  − .14 .14  − 0.96  − 0.41, 0.14
Dataset 3 × Granularity (differentiation) .15 .11 1.38  − 0.06, 0.37
Dataset 4 × Granularity (differentiation)  − .04 .09  − 0.39  − 0.22, 0.15
Dataset 5 × Granularity (differentiation)  − .22 .15  − 1.48  − 0.51, 0.07
Dataset 6 × Granularity (differentiation)  − .23 .15  − 1.60  − 0.52, 0.05
Dataset 1 × Usage .01 .14 0.07  − 0.27, 0.29
Dataset 2 × Usage  − .12 .14  − 0.85  − 0.40, 0.16
Dataset 3 × Usage .28 .11 2.52* 0.06, 0.49
Dataset 4 × Usage .15 .09 1.61  − 0.03, 0.33
Dataset 5 × Usage  − .10 .15  − 0.69  − 0.39, 0.19
Dataset 6 × Usage  − .21 .14  − 1.47  − 0.49, 0.07
Dataset 1 × Understanding .16 .14 1.10  − 0.12, 0.44
Dataset 2 × Understanding  − .20 .14  − 1.46  − 0.48, 0.07
Dataset 3 × Understanding  − .10 .11  − 0.89  − 0.31, 0.12
Dataset 4 × Understanding  − .14 .10  − 1.38  − 0.34, 0.06
Dataset 5 × Understanding .19 .15 1.33  − 0.09, 0.48
Dataset 6 × Understanding .09 .15 0.63  − 0.20, 0.38
Step 2B: Dysregulation ~ Study + Granularity (differentiation) + Usage + Accuracy + Study: Granularity (differentiation) + Study: Usage + Study: Accuracy
Intercept .001 .06 0.01  − 0.11, 0.11
Dataset 1  − .01 .14  − 0.05  − 0.28, 0.27
Dataset 2  − .0003 .14  − 0.002  − 0.27, 0.27
Dataset 3 .01 .11 0.07  − 0.21, 0.22
Dataset 4 .0001 .09 0.001  − 0.18, 0.18
Dataset 5  − .0003 .14  − 0.002  − 0.28, 0.27
Dataset 6  − .001 .14  − 0.01  − 0.28, 0.05
Granularity (differentiation)  − .06 .06  − 1.08  − 0.18, 0.05
Usage .04 .06 0.66  − 0.08, 0.16
Accuracy  − .13 .06  − 2.02*  − 0.25, − 0.003
Dataset 1 × Granularity (differentiation) .44 .15 2.96** 0.15, 0.74
Dataset 2 × Granularity (differentiation)  − .09 .15  − 0.62  − 0.38, 0.20
Dataset 3 × Granularity (differentiation) .15 .11 1.36  − 0.07, 0.37
Dataset 4 × Granularity (differentiation)  − .05 .09  − 0.57  − 0.24, 0.13
Dataset 5 × Granularity (differentiation)  − .21 .15  − 1.41  − 0.51, 0.08
Dataset 6 × Granularity (differentiation)  − .24 .14  − 1.67  − 0.53, 0.04
Dataset 1 × Usage .01 .15 0.04  − 0.28, 0.30
Dataset 2 × Usage  − .18 .14  − 1.23  − 0.46, 0.11
Dataset 3 × Usage .28 .11 2.52* 0.06, 0.50
Dataset 4 × Usage .14 .09 1.45  − 0.05, 0.32
Dataset 5 × Usage  − .08 .15  − 0.53  − 0.38, 0.22
Dataset 6 × Usage  − .16 .17  − 0.97  − 0.50, 0.17
Dataset 1 × Accuracy .04 .15 0.29  − 0.25, 0.33
Dataset 2 × Accuracy .16 .14 1.12  − 0.12, 0.45
Dataset 3 × Accuracy  − .13 .11  − 1.15  − 0.35, 0.09
Dataset 4 × Accuracy  − .11 .09  − 1.13  − 0.29, 0.08
Dataset 5 × Accuracy .02 .15 0.11  − 0.28, 0.32
Dataset 6 × Accuracy .01 .17 0.09  − 0.32, 0.35
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Significant parameters are highlighted in bold

**p < .01; *p < .05; †p < .10

An initial model including study, emotional granularity (differentiation), and their interaction revealed no main effect of either predictor on emotion dysregulation. There was a significant interaction between study level 1 and granularity/differentiation (β = .47, t(337) = 3.26, p = .001). Separate models per study showed that granularity/differentiation was a significant positive predictor in Dataset 1, such that participants who reported being better able to distinguish between emotions also reported more difficulty regulating them. This relationship was nonsignificant or negative in all other studies, suggesting limited generalizability of this effect.

A subsequent model including study, emotional granularity (differentiation), usage, understanding, and accuracy, as well as the respective interactions with study, also revealed no main effects. In addition to the interaction between study level 1 and granularity/differentiation (β = .44, t(319) = 2.97, p = .003), there was an interaction between study level 3 and usage (β = .29, t(319) = 2.39, p = .02). Separate models per study showed that usage was a significant positive predictor in Datasets 3 and 4, such that participants who reported more frequent use of the emotion words also reported more difficulty regulating their emotions. This relationship was strongest in Dataset 3, and we did not find statistically significant evidence for the effect in Dataset 1, 2, 5, or 6.

To follow up on these results, we examined the partial correlations between the raw (unstandardized) emotion word knowledge variables, accounting for dataset. We found that understanding and accuracy were highly correlated with each other (r = .75), but less so with usage (understanding and usage: r = .06; accuracy and usage: r = .10).

With these relationships in mind, we ran two separate versions of our second-step model: one with study, emotional granularity (differentiation), usage, and understanding; the other with study, emotional granularity (differentiation), usage, and accuracy.

In the model including usage and understanding, we observed a main effect of understanding (β = − .17, t(325) = − 2.86, p = .01), such that participants with a greater self-assessed understanding of emotion words also reported less difficulty regulating their emotions. We observed the same interactions between study level 1 and granularity/differentiation (β = .47, t(325) = 3.18, p = .002) and between study level 3 and usage (β = .28, t(325) = 2.52, p = .01).

In the model including usage and accuracy, we observed a main effect of accuracy (β = − .13, t(325) = − 2.02, p = .04), such that participants with higher performance on the emotion word definition task also reported less difficulty regulating their emotions. We once again observed the interactions between study level 1 and granularity/differentiation (β = .44, t(325) = 2.96, p = .003) and between study level 3 and usage (β = .28, t(325) = 2.52, p = .01).

Supplementary Analysis

In an exploratory analysis, we examined the potential role of emotional prototypicality in participants’ ratings of usage, understanding, and accuracy to define the emotion words included across the datasets. Words like “grateful” and “irritated” could be regarded as more typical exemplars of the category emotion than words like “involved” and “useless,” and this might have influenced our results—especially if participants in each dataset were presented with sets of words that differed significantly on this dimension. To investigate this possibility, we operationalized emotional prototypicality in three different ways: as arousal and valence extremity (as proxies for affective intensity; using ratings from Warriner et al., 2013) and as frequency of use (as a proxy for accessibility or familiarity; using data from Brysbaert et al., 2018). We were not able to use direct emotion prototypicality ratings because available norms (e.g., Shaver et al., 1987) do not include ratings for all the words used in the present datasets. When aggregating responses for usage, understanding, and accuracy at the word level (across datasets), we found, not surprisingly, that frequency of use had a strong positive correlation with both usage (r = .60, p = .002) and understanding (r = .50, p = .01), but that the affective intensity variables were not associated with emotion word knowledge. Critically, frequency of use did not differ as a function of dataset (one-way ANOVA F(5,102) = 0.39, p = .85).

Discussion

The present report investigated how emotion word knowledge relates to emotional granularity and emotion dysregulation. Specifically, we examined how self-assessed usage and understanding of emotion words, as well as demonstrated definitional accuracy of these words, related to self-reported difficulties in managing emotions, while accounting for self-reported differentiation of emotional experience. We examined these relationships across six studies using an integrative analysis approach. We found that individuals with a greater self-assessed understanding of emotion words reported less emotion dysregulation (i.e., fewer difficulties regulating their emotions). We also found that individuals who more accurately defined emotion words reported less emotion dysregulation. Critically, these findings held when accounting for emotional granularity, suggesting that measures of emotion word knowledge have value for predicting emotion regulatory outcomes. These findings speak to the theoretically important issue of whether emotion knowledge matters for having well-regulated, functional emotions rather than dysregulated emotional experiences. Our findings are consistent with the constructionist proposal that robust knowledge of emotion can serve as a basis for constructing emotions that are more functional within the contexts of everyday life. A practical implication is that improving emotional knowledge may help reduce dysfunctional emotional episodes, and interventions directly aimed at improving emotion word knowledge will be critical for supporting this claim.

The findings reported here are not without some complexity. Understanding and accuracy only emerged as significant predictors of emotion dysregulation when the variables were examined in separate models, suggesting that these effects are not fully independent of one another. The effect sizes were also similar, which might suggest that the simpler measure of asking participants to report their understanding of emotion words may be sufficient to capture emotion word knowledge and predict meaningful outcomes. At the same time, the relationship between self-assessed emotion word understanding and self-reported emotion dysregulation might be partially attributable to methods variance. Emotion word accuracy, as a performance-based measure, avoids this potential confound. The strong positive correlation between understanding and accuracy, coupled with the weak correlation between understanding and usage (also self-assessed), suggests however that method variance may not be a factor in the present results. Equally, it should be noted that accuracy was not directly assessed for all words. Trials in which participants indicated they did not understand (and/or use) the word were treated as incorrect responses (i.e., wrong, even though not presented). This decision maintained a larger range in the accuracy variable, but might also explain why it did not account for emotion regulation difficulties above and beyond understanding. Other assessments of accuracy may provide added value. For example, it may be that the accuracy of self-generated definitions for emotion labels (e.g., Nook et al., 2020) will better predict emotion dysregulation than the current method of having participants pick the correct definition.

We did not observe reliable effects of emotion word usage in predicting emotion dysregulation. This is somewhat inconsistent with recent findings suggesting that the breadth of one’s active vocabulary for negative emotions is linked to distress (Vine et al., 2019) and that this breadth is evident in individuals with bipolar disorder (Entwistle et al., 2023). The present usage measure was based on self-report and therefore may diverge from the active emotion vocabularies assessed in natural language. Consistent with this idea, recent work has shown that when emotion word fluency is measured in a decontextualized manner (i.e., adapting a classic semantic fluency paradigm), individual differences do not predict emotion regulation, or depressive symptoms (Hegefeld et al., 2023). Together, these findings suggest that usage measures likely have complex and contextually bounded relationships with emotion regulation and mental health outcomes.

We also did not observe that our measures of emotion word knowledge were related to self-reported emotional granularity (differentiation), nor did we observe robust relationships between emotional granularity (differentiation) and emotion dysregulation. Importantly, word measures predicted emotion dysregulation, even when accounting for emotional granularity (differentiation). One possible reason that the granularity (differentiation) measure has less predictive utility in this context is that individuals have limited insight into their level of this skill, indicating what they believe to be true rather than what might be observed from their behavior (Dang et al., 2020; Robinson & Clore, 2002). Indeed, it is unclear from the literature whether global self-report measures, such as the RDEES, share sufficient empirical overlap with emotional granularity (differentiation) as it is typically assessed from emotion ratings or labels (Ottenstein and Lischetzke, 2020; Thompson et al., 2021). A second possible reason for these null findings is because emotional granularity (differentiation), as a skill, is thought to manifest in how emotion concepts are applied (via emotion words) to complex, often ambiguous events. Assessing knowledge of word meanings in a decontextualized manner, as in the present studies, does not capture this variation in contextual usage (for discussion, see Hegefeld et al., 2023). For these reasons, an important future direction would be to compare the present emotion word knowledge measures against estimates of emotional granularity (differentiation) derived from reports of momentary lived experiences.

Future work should likewise examine whether individuals’ emotion word knowledge also relates to mental health outcomes. Lower emotional granularity is linked to clinically significant forms of behavioral dysregulation, including eating disorders (Mikhail et al., 2020; Rommel et al., 2013) and substance abuse disorders (Emery et al., 2014; Kashdan et al., 2010), whereas higher emotional granularity appears to be protective against non-suicidal self-injury in individuals with borderline personality disorder (Zaki et al., 2013). Further, lower emotional granularity predicts the severity of depressive symptoms in both adolescence and adulthood (Starr et al., 2020; Tomko et al., 2015; Willroth et al., 2020). Given the bidirectional associations between mental health outcomes and difficulties with emotion regulation (Dunning et al., 2022; Nook, 2021; Tan et al., 2022), it is critical to directly examine the role that emotion word knowledge plays in these relationships.

Acknowledgements

The third author was supported by the Research Foundation – Flanders (12A3923N).

Additional information

Funding

K.H. was supported by the Research Foundation–Flanders (12A3923N).

Competing Interests

The authors declare no competing interests.

Data Availability

Data may be accessed by emailing the corresponding author.

Code Availability

Not applicable.

Author Contribution

J.F. collected data and conceived of the idea. K.H. analyzed the data and contributed to the conception of the model. M.G. contributed to the data analysis and also contributed to the conception of the idea. All authors co-wrote the manuscript and approve the final and revised version.

Ethics Approval

Studies 1–4 were approved by the IRB at University of MA–Dartmouth, #18.084. Study 5 was approved through the IRB at Kansas City University, #1942774. Study 6 was approved through the IRB at Kansas City University, #1906743. Data were collected anonymously, with the exception of information pertaining to payment/remuneration, collected at the end of the surveys electronically, and stored separately from responses.

Informed Consent

All participants signed either electronic or paper consent forms stating their voluntary willingness and informed consent to participate.

Consent for publication

Not applicable.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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