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. Author manuscript; available in PMC: 2026 Feb 1.
Published in final edited form as: Emotion. 2024 Sep 26;25(1):144–157. doi: 10.1037/emo0001424

Examining the association among adolescents’ emotional clarity, emotion differentiation and the regulation of negative and positive affect using a daily diary approach

Nicola Hohensee 1,2, Jutta Joormann 4, Reuma Gadassi-Polack 3,4
PMCID: PMC11920256  NIHMSID: NIHMS2056396  PMID: 39325397

Abstract

Emotional clarity and emotion differentiation are two core aspects of the application of emotional knowledge. During adolescence, novel emotional experiences result in temporary decreases of differentiation and clarity. These temporary difficulties might profoundly impact choices of regulatory strategies. And indeed, prior research has shown that lower emotional clarity and emotion differentiation are each associated with higher use of putatively maladaptive emotion regulation strategies in youth. The two constructs, however, are rarely examined together and it remains unclear how they are associated in daily life, particularly in children and adolescents. In addition, previous studies have focused on the regulation of negative but not positive affect. To address these gaps, the current study used an intensive longitudinal design in youth. Between June 2021 and March 2022, 172 children and adolescents (M = 12.99 years) completed a 28-day diary (>3500 entries in total) reporting daily affect, emotional clarity, and use of five emotion regulation strategies in response to negative and positive affect (i.e., rumination, dampening, behavioral avoidance, negative and positive suppression). As predicted, on both between- and within-person levels, higher emotional clarity was associated with decreased use of all maladaptive emotion regulation strategies after adjusting for mean affect intensity. Results for emotion differentiation were mostly non-significant. Only higher daily positive emotion differentiation was associated with decreased rumination. In sum, this innovative study explores multiple aspects of emotional knowledge usage and regulation during a critical developmental stage and emphasizes the role of emotional clarity in the regulation of negative and positive affect.

Keywords: emotion regulation, emotional clarity, emotion differentiation or granularity, adolescence, daily diary

Understanding our affective experiences has been repeatedly linked with higher well-being and less psychopathology (Dunning et al., 2022; Nook, 2021; Nyquist & Luebbe, 2020; Sendzik et al., 2017). It remains unclear, however, why poor understanding of one’s own emotions can have such detrimental effects on well-being. In previous work, researchers posited that lack of knowledge about affective experiences might lead to the compensatory use of more putatively maladaptive emotion regulation strategies1 (Lischetzke & Eid, 2017). In addition, when individuals have less emotional knowledge, they might select less situationally-suitable emotion regulation strategies, which could, in turn, decrease well-being and facilitate the development of emotional disorders (Barrett & Gross, 2001; Barrett et al., 2001; Gratz & Roemer, 2004; Mennin et al., 2007; Nook, 2021).

Emotional clarity and emotion differentiation

Research on the role of knowledge of affective experiences, and especially its application to emotion self-reports, has mostly focused on two related constructs: emotional clarity and emotion differentiation. The concept of emotional clarity is defined as an individual’s meta-knowledge of their affective experience and can involve their subjective feeling regarding their ability to identify, discriminate, and understand the type as well as the source of affect (Bodena & Berenbaum, 2011; Coffey et al., 2003; Gohm & Clore, 2000, 2002). Emotional clarity is considered a core feature of alexithymia and emotional intelligence (e.g., Gardner, 2011; Salovey et al., 1995; Taylor, 2004). It is typically conceptualized as a trait and assessed via self-reports. For example, high ratings on the item “I am confused about how I am feeling.” (Gratz & Roemer, 2004) indicate low clarity about the felt type of emotions. Thus, emotional clarity captures the subjective certainty about the emotion an individual is experiencing. Individuals reach this broader certainty when they reflect on changes, for example, in behaviors or bodily sensations. Therefore, emotional clarity relies to a certain degree on the level of insight.

Emotion differentiation is defined as the degree of complexity with which individuals identify, label, and represent their affective experiences (Barrett et al., 2001), and to what degree they feel a specific emotion vs. another. It is usually assessed by indirect, more objective measures like intraclass correlation coefficients (ICC) between repeated self-reported negative or positive in-the-moment emotion ratings (referred to as negative or positive emotion differentiation) using either an ecological momentary assessment (e.g., Kalokerinos et al., 2019) or laboratory emotion ratings after the presentation of emotional pictures (e.g., Nook et al., 2021). Higher ICCs indicate less variability between emotion ratings at the same time point relative to the variability within emotions across time, i.e., lower emotion differentiation (e.g., Boden et al., 2013; Lennarz et al., 2018; Robinson & Clore, 2002; Sendzik et al., 2017; Thompson et al., 2021). More precisely, an individual who has low emotion differentiation among negative emotions will frequently report equal (or similar) intensity of emotion ratings, i.e., equally angry, sad, and guilty. In contrast, someone with high negative emotion differentiation will rate negative affect in a more fine-grained, differentiated manner, e.g., feeling a little bit sad and guilty but very angry at one time and feeling very sad, moderately guilty, and a little bit angry at another time.

Both emotional clarity and emotion differentiation tap into the understanding and recognition of one’s own emotions (Park et al., 2022). Theoretically, the ability to make more fine-grained distinctions between emotions (i.e., higher emotion differentiation) should be associated with higher emotional clarity, i.e., the subjective feeling of a better emotional understanding (Boden et al., 2013). However, despite their conceptual overlap, only a few researchers have tried to integrate both constructs in a joint theoretical framework (e.g., Boden et al., 2013; Hoemann et al., 2021). Correlations between clarity and differentiation are either non-significant or modestly positive (Boden et al., 2013; Edwards & Wupperman, 2017; Erbas et al., 2014; Hoemann et al., 2021; Mankus et al., 2016; O’Toole et al., 2014; Erbas et al., 2019). A recent meta-analysis aggregating two studies examining the association between emotion clarity and emotion differentiation (Boden et al., 2013; Edwards & Wupperman, 2017) found no significant correlations among emotional clarity, negative emotion differentiation, and positive emotion differentiation (Park et al., 2022).

These findings suggest that the two constructs tap into different aspects of knowledge about emotional experiences and there might be several reasons for the low correlations between clarity and differentiation. First, despite their overlap these constructs differ conceptually: The more objective ability to differentially describe how one is experiencing emotions may be independent from the subjective clarity one can feel about these differentiations. To carry the previous example forward, one can rate negative affect items very similarly all the time (i.e., low emotion differentiation) and can feel very certain about the accuracy of these ratings (i.e., high emotional clarity). Another time, one might rate negative affect items in a very fine-grained manner (i.e., high emotion differentiation) but feel very unsure about the accuracy of these more “differentiated” ratings (i.e., low emotional clarity). In addition, emotional clarity reflects the ability to identify what emotion one is feeling, whereas emotion differentiation reflects more the ability to describe how one is feeling their emotions (e.g., reflected by intensity ratings as conventionally measured).2 Second, emotional clarity and emotion differentiation were typically measured on different time scales: While emotional clarity is usually assessed on a global trait-level with self-reports (i.e., “I usually have a good sense of what emotions I am feeling”), emotion differentiation indices are derived from specific in-the-moment affect ratings (i.e., individuals can report how they are feeling right now in a manner that differentiates between the different emotions one can experience; Boden et al., 2013).

Emotional clarity, emotion differentiation, and emotion regulation

According to the theory of constructed emotion (Barrett, 2017), emotions are actively constructed based on sensory input from our body and our surroundings in a specific moment. These constructions of emotions help to decide how to respond to a specific situation. Low emotion differentiation as well as lower emotional clarity may facilitate habitual misinterpretation of external and internal signals. Thus, deficits in emotional knowledge usage might produce inappropriate and inflexible constructions of emotions, making it challenging to implement appropriate regulatory strategies (Barrett, 2017). Similarly, the feeling-as-information theory (Schwarz, 2012) posits that individuals use their feelings as a source of information about the current situation. Deficits in emotional knowledge might facilitate misleading judgements and in turn inappropriate decisions such as choice of maladaptive regulatory strategies (Schwarz, 2012). In conclusion, higher emotion differentiation as well as higher emotional clarity are expected to facilitate more adaptive responses to affective experiences and in turn, lead to improved psychosocial functioning (Barrett et al., 2001; Gratz & Roemer, 2004; Nook, 2021).

Supporting these theoretical claims, lower emotional clarity and negative as well as positive emotion differentiation have been linked with different forms of psychopathology, decreased well-being and psychosocial adjustment across different age groups (Dunning et al., 2022; Nook, 2021; Seah & Coifman, 2022; Smidt & Suvak, 2015; Vine & Aldao, 2014). In adults, both higher emotional clarity and negative emotion differentiation were associated with fewer self-reported emotion regulation difficulties (Jones & Herr, 2018; Vine & Aldao, 2014). More specifically, higher clarity and more negative differentiation were associated with less use of repetitive negative thinking, less use of putatively maladaptive emotion regulation strategies, and more effective implementation of regulatory behavior (Brown et al., 2021; Cabello et al., 2013; Gross & John, 2003; Kalokerinos et al., 2019; Starr et al., 2017; Vine & Aldao, 2014; Vine & Marroquín, 2018). However, all of these studies focused either on the associations of emotional clarity or emotion differentiation with emotion regulation. The few studies that examined both constructs simultaneously show that they contribute to affect intensity and variability as well as self-reported emotion regulation difficulties above and beyond each other (Boden et al., 2013; Edwards & Wupperman, 2017).

The majority of studies on emotion differentiation focused on the differentiation of negative affect (e.g., O’Toole et al., 2020). A handful of studies examined the differentiation of positive affect: Some of them report no significant findings for positive (vs. negative) emotion differentiation (e.g., Jones & Herr, 2018). Other studies point towards beneficial effects of positive emotion differentiation on adaptive regulatory behavior, sometimes to a greater extent than negative emotion differentiation (Dixon-Gordon et al., 2014; Edwards & Wupperman, 2017).

In children and adolescents, individuals with higher emotional clarity tend to report less repetitive negative thinking and maladaptive internal and external regulatory behavior in cross-sectional studies (Eastabrook et al., 2014; Hatzenbuehler et al., 2008; Rieffe et al., 2008; Rieffe et al., 2007; Rueth et al., 2019). Longitudinally, higher emotional clarity in children and adolescents was associated with lower maladaptive stress responses and higher adaptive emotion regulation behavior two years later (Flynn & Rudolph, 2010, 2014; Riley et al., 2019).

However, to our knowledge, the direct association between emotion differentiation and emotion regulation has not been examined in samples of children and adolescents yet. Three studies on developmental populations provide indirect evidence for a link between reduced negative and positive emotion differentiation and more maladaptive regulatory behavior: Starr et al. (2020) showed that increased negative emotion differentiation buffered the concurrent, within-person association between daily hassles and depressive symptoms (above and beyond positive emotion differentiation). In the long-term, higher negative emotion differentiation buffered the between- and within-person association between the severity of stressful life events and depression (Starr et al., 2020). Similarly, Nook et al. (2021) found that high positive and negative emotion differentiation attenuated the within-person association between momentarily perceived stress and depressive symptoms. In addition, higher negative emotion differentiation attenuated within-person associations between monthly severity of stressful life events and depression as well as anxiety symptoms (Nook et al., 2021). Lastly, Lennarz et al. (2018) showed that adolescents with higher negative (but not positive) emotion differentiation across several in-the-moment emotion ratings reported better emotional well-being in daily life.

Studies using intensive longitudinal designs in daily life

Recent studies have shown that emotional clarity and emotion differentiation vary considerably over time and across situations, i.e., within hours and days (Erbas et al., 2018; Erbas et al., 2022; Thompson & Boden, 2019). To account for these variations and to further align the time scales of the two constructs, it is important to investigate emotional clarity and emotion differentiation using an intensive longitudinal design (e.g., daily diaries or ecological momentary assessment).

Few studies, all conducted on adults, have assessed within-person associations among momentary emotional clarity, emotion differentiation, and emotion regulation using intensive longitudinal designs. These studies have shown that higher emotional clarity (at the within- and between-person level) was associated with emotion regulation success in patients with internalizing disorders, and with less repetitive negative thinking in college students (Eckland & Berenbaum, 2021; Park & Naragon-Gainey, 2019).

Similarly, studies on emotion differentiation using intensive longitudinal designs showed that momentary differentiation of positive and negative emotions was associated with higher self-perceived success of emotional coping at the within-person level (Erbas et al., 2022). Erbas et al. (2022) reported significant, albeit contrasting, associations among differentiation and rumination as well: Higher momentary emotion differentiation was associated with higher rumination in one study and with lower momentary rumination in another. In addition, O’Toole et al. (2014) found negative associations between the daily use of expressive suppression and daily positive emotion differentiation, but only in individuals low (vs. high) in social anxiety. Daily negative emotion differentiation was not significantly associated with expressive suppression (O’Toole et al., 2014). In sum, studies in adults point towards within-person associations between momentary emotional clarity and better regulatory behavior as well as momentary emotion differentiation and emotion regulation.

Adolescence as critical phase for the maturation of emotional clarity and differentiation

Adolescence is a time of profound biological (e.g., physical sexual maturation), neural (e.g., changes in connectivity of brain regions involved in emotional processing; Vink et al., 2014), and social (e.g., first romantic relationships) changes. These changes lead to more extreme and complex emotions while abilities to regulate these new emotional experiences are still maturing (Young et al., 2019; Zimmermann & Iwanski, 2014). More precisely, studies indicate that adolescents begin to conceptualize emotions in a more multi-dimensional way and start to experience different emotions simultaneously (see Nook & Somerville, 2019 for an overview). These novel emotional experiences seem to result in a temporary decrease of emotion differentiation and self-perceived emotional clarity during adolescence (Nook et al., 2018; Salguero et al., 2010). Following leading theoretical models (Barrett, 2017; Schwarz, 2012), these temporary difficulties in the application of emotional knowledge might profoundly impact situational judgements and choices of regulatory strategies. Importantly, the choice of maladaptive emotion regulation strategies might have especially detrimental consequences in adolescence, a time during which many emotional disorders have their typical onsets (Kessler et al., 2005). Therefore, it is crucial to enhance understanding of associations between emotional knowledge usage and emotion regulation strategies in this vulnerable developmental period.

The present study

Several questions remain in our understanding of emotional clarity and differentiation. First, no study to date has examined the relative importance of emotional clarity and emotion differentiation for the prediction of emotion regulation strategies in the same study, in adolescents or in adults. Second, to our knowledge, no study investigated the association among momentary emotional clarity, emotion differentiation, and emotion regulation in children and adolescence using an intensive longitudinal design, and only a handful of studies have examined these associations in adults (Eckland & Berenbaum, 2021; Erbas et al., 2022; O’Toole et al., 2014; Park & Naragon-Gainey, 2019). Intensive longitudinal designs have several advantages. They consider both, differences between and within individuals and account for situational variations (Russell & Gajos, 2020). The effects of these situational variations are especially important to investigate in adolescence, a developmental period that is characterized by fast and drastic changes in emotional experiences in daily life (Reitsema et al., 2022). In addition, the study design allows us to align the time scale of measures for emotional clarity and emotion differentiation.

Third, most studies on the association between the application of emotional knowledge and the use of emotion regulation strategies have focused on regulation of stress or negative affect, thereby neglecting the role of positive affect. Positive affect is central for one’s overall well-being and can facilitate social learning and exploration, which are key markers in adolescence (Gilbert, 2012; McCormick & Telzer, 2017). In addition, reduced positive affect is a central aspect of many psychological disorders that often start during adolescence, such as depression (Gilbert, 2012; Young et al., 2019). The current study will address these gaps by using a daily diary approach to assess emotional clarity, emotion differentiation, and the use of regulation strategies for negative and positive affect in children and adolescence (9 to 18 years old) over the course of 28 days.

Hypotheses

We tested the following hypotheses (pre-registered hypotheses are indicated with a *):

  • H1. We expect a significant positive association between emotional clarity and emotion differentiation.

  • H2*. Lower emotional clarity and lower negative emotion differentiation will be associated with more use of putatively maladaptive emotion regulation strategies in response to negative affect, i.e., negative rumination, negative suppression, and avoidance.

  • eH3*. Due to a lack of prior work, we explore the association between clarity and negative differentiation and the use of putatively maladaptive emotion regulation strategies in response to positive affect (i.e., dampening and positive suppression).

  • eH4. In addition, because of scarce and conflicting findings in prior research, we explore the association between positive emotion differentiation and the use of putatively maladaptive emotion regulation strategies in response to negative and positive affect.

All hypotheses (H2 to eH4) were examined on both the between-person and the within-person level. Considering known age and gender differences in emotional knowledge usage, we examined moderating effects of age and gender as well (e.g., Gonçalves et al., 2019; Nook et al., 2018; Rueth et al., 2019; Sendzik et al., 2017).

Method

The present study is part of a larger project examining emotion regulation and social interactions in children and adolescents; only relevant measures are described. The parent study included three data waves (Gadassi-Polack et al., 2024), however the present study includes only data from the third data wave, as it is the only one that included the assessment of emotion clarity.

Transparency and openness

The third data wave of the parent study (registration ID https://doi.org/10.17605/OSF.IO/KJQ6G) and the present project (registration ID https://doi.org/10.17605/OSF.IO/JD4XZ) were preregistered on Open Science Framework. We report how we determined our sample size, all data exclusions, all manipulations, and all measures in the study. All necessary analysis code, and study materials are available at https://doi.org/10.17605/OSF.IO/ASXJC. Data is available by request from the last author.

Participants and Procedure

We have complied with APA ethical standards in the treatment of our sample and all procedures were approved by the Yale Institutional Review Board. Children and adolescents were recruited by contacting participants who completed previous waves of a daily diary study (e.g., Gadassi-Polack et al., 2024), and additional participants were recruited via social media ads to compensate for attrition. The inclusion criteria were being between 9 to 18 years old and having access to a device with internet access (i.e., smart phone, computer, or tablet); participants who completed previous data waves could participate if they still resided in the US at the third data wave; new participants had to live in Connecticut at the time of the study. Data collection took place from June 2021 to March 2022.

During an initial zoom session with their parents, participants received a detailed briefing about the daily diaries where a research assistant went over all the survey items to ensure understanding and gave concrete examples for emotion regulation strategies. Then, participants gave informed consent and assent, and answered demographic questionnaires and other tasks and questionnaires not described here (see registration ID https://doi.org/10.17605/OSF.IO/KJQ6G for further details). Then, youths completed daily diaries on a secure website (Qualtrics) over the period of 28 days. Links to the surveys were sent via e-mail to the children directly at either 7 or 9 pm (whichever was closest to the participant’s bedtime) and expired after 16 hours. Participant’s compensation depended on their completion rates: they received $10 if they completed less than 60% of the surveys, $50 if they completed at least 60%, and $70 if they completed at least 90% of the daily diaries.

In total, N = 195 children and adolescents participated in the third wave of the present project. We excluded from analyses n = 23 participants for the following reasons: (1) n = 8 youths completed less than 13 diaries; (2) n = 15 participants had low variance in their affect ratings, making it impossible to compute emotion differentiation indices or had negative ICC scores which are uninterpretable (e.g., Erbas et al., 2018; Erbas et al., 2022; Giraudeau, 1996). The final sample included N = 172 children and adolescents. On average, participants were M = 12.99 years old (SD = 2.58, age range = 9–18 years), 55.23% of the sample was female. Ninety-one participants identified as female (52.91%), 78 identified as male (45.35%), and three identified as non-binary (1.74%). The reported race was White (72.09%), Asian (5.23%), Black (2.33%), and other (20.35%). We did not have information about socioeconomic status and clinical diagnoses. Youths completed on average 25.27 of 28 diary entries (SD = 3.51). Older (vs. younger) participants completed significantly less diaries (b = −0.95; p <.001). Completion rate did not differ by sex or ethnicity (all p > .05).

Power Analysis

Sample size was determined based on power calculations conducted for the original study (see Gadassi Polack, Everaert, et al., 2021 for more details). Based on a sample size of N = 172 participants, N = 28 daily assessments, and an intraclass correlation coefficient (ICC) score of 0.46 (guided by the lowest ICC score for emotion regulation strategy), we should have been able to detect medium effects with a likelihood of 100% and small effects with a likelihood of around 40% (based on power curves from the R package “EMAtools”; Kleiman, 2017).

Measures

We used the Automated Readability Index (http://www.readabilityformulas.com) to ensure that the reading level of all used diary items did not exceed 4th grade level. ICC scores as well as between-person and within-person reliabilities (calculated following Shrout & Lane, 2012) for all measures are reported in Table 1.

Table 1.

Descriptive statistics for daily diary measures

M SD between SD within ICC Rbetween Rwithin
Negative Affect 1.60 0.56 0.44 0.56 0.85 0.76
Positive Affect 2.68 0.79 0.64 0.56 0.83 0.72
Emotional Clarity 4.22 0.90 0.55 0.64 0.86 0.71
Between-person level NED 0.64 0.22 NA NA NA NA
Between-person level PED 0.64 0.22 NA NA NA NA
Within-person level NED 2.48 0.94 5.11 NA NA NA
Within-person level PED 2.21 0.72 3.30 NA NA NA
Rumination 0.71 0.79 0.49 0.65 0.83 0.63
Negative Suppression 1.31 1.03 0.82 0.55 0.83 0.71
Avoidance 1.16 0.94 0.90 0.46 NA NA
Dampening 0.87 0.80 0.48 0.68 0.82 0.51
Positive Suppression 1.35 1.00 0.69 0.62 0.83 0.65
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Note. To aid in interpretability of means, we included the raw emotion differentiation indices (i.e., prior to Fisher’s z transformation and before multiplication with −1). It was not possible to estimate between-person and within-person reliability for avoidance, because this construct was only measured via one item. NED = negative emotion differentiation; PED = positive emotion differentiation; ICC = intraclass correlation; Rbetween = between-person reliability; Rwithin = within-person reliability.

Affect

Affect was assessed via 12 items: Seven items captured current negative affect (i.e., sad, upset, stressed, mad, miserable, guilty, and nervous) and five items assessed current positive affect (i.e., happy, excited, relaxed, proud, and joyful) on a 5-point Likert scale from 1 (“very slightly or not at all”) to 5 (“extremely”). Items were based on the Positive and Negative Affect Schedule for Children (PANAS-C; Laurent et al., 1999).

Emotional clarity

Current emotional clarity was assessed via two items from the ‘Lack of emotional clarity’ subscale of the Difficulties in Emotion Regulation Scale (DERS; Gratz & Roemer, 2004). The two items were chosen because they had the highest loading on the emotion clarity subscale in an adolescent sample (Neumann et al., 2010). Participants rated the statements “I have difficulty making sense out of my feelings.” And “I am confused about how I am feeling.” for the current moment using a 5-point Likert scale from 1 (“very slightly or not at all”) to 5 (“extremely”).

Emotion regulation

Rumination.

Rumination was assessed via three items from the ‘Rumination’ subscale of the Children’s Response Style Questionnaire (CRSQ; Abela et al., 2000; e.g., “I thought about other times I have felt bad”). Instructions and items of the CRSQ were adapted for the use in a daily diary. Thus, instead of asking youths about the general use of rumination without a specific time frame, they were asked about their endorsement of this emotion regulation strategy in response to bad mood between last night and right now (i.e., in the last 24 hours). In addition, the category “not at all” was added to the response scale to enable the participants to report if they did not use rumination in the last 24 hours at all, so that all items were rated on a 5-point Likert scale from 0 (“not at all”) to 4 (“almost all of the time”).

Negative suppression.

Suppression of negative emotions was assessed via two items from the ‘Suppression’ subscale of the Emotion Regulation Questionnaire for Children and Adolescents (ERQ–CA; Gullone & Taffe, 2012; e.g., “I controlled my feelings by not showing them.”). Adaptations to instructions and the response scale were identical to the ones made for items assessing rumination (see above).

Avoidance.

Behavioral avoidance was assessed via the item “I tried to avoid being around the people or situation that was bothering me.” Based on previous work from Silk et al. (2003). Again, instructions were adapted for daily diary use and the item was answered on a 5-point Likert scale (see rumination).

Dampening.

Dampening was assessed using three items from the ‘Dampening’ subscale of the Responses to Positive Affect Questionnaire for Children (RPA-C; Bijttebier et al., 2012; e.g., “Thought “I don’t deserve this”.”). Instructions and items of the RPA-C were adapted for the use in a daily diary. Thus, youths were asked about their endorsement of different emotion regulation strategies in response to good mood between last night and right now using a 5-point Likert scale from 0 (“not at all”) to 4 (“almost all of the time”).

Positive suppression.

The suppression of positive emotions was assessed via two items from the ‘Suppression’ subscale of the Emotion Regulation Questionnaire for Children and Adolescents (ERQ–CA; Gullone & Taffe, 2012). Adaptations to instructions and the response scale were identical to the ones made for items assessing dampening (see above).

Data analysis

Data were analyzed using the statistic software R version 4.2.0 (R Core Team, 2022).

Emotion differentiation

The between-person level emotion differentiation scores were computed separately for negative and positive affect items by calculating the ICC scores, measuring average consistency between affect ratings across all 28 days for each individual (e.g., Demiralp et al., 2012; Erbas et al., 2022). A higher ICC represents higher levels of covariation between affect ratings and therefore index lower levels of differentiation. To ease interpretation, ICC indices were multiplied with −1 and Fisher z-transformed, so that higher values represent higher levels of emotion differentiation. The within-person level emotion differentiation scores were based on the procedure described by Erbas et al. (2022), following these steps separately for negative and positive emotions: (1) we person-mean centered each emotion, (2) computed the means of the centered emotions for each day, (3) multiplied this by the number of emotions (i.e., seven for negative affect and five for positive affect) and squared the product. This was the numerator of the equation. Next, (4) we computed the variance for each of the centered emotions, and (5) summed the variances of all emotions. This was the denominator of the equation. Again, within-person level emotion differentiation scores were inversed by multiplication with −1 and Fisher z-transformed.

Hypotheses testing

Between-person level analyses.

To assess the association between emotional clarity and differentiation, we calculated Pearson correlations and partial Pearson correlations, controlled for affect. To assess all other hypotheses on the between-person level, we averaged variables of interest across the diary period and ran five general linear regression models using the R package “nlme” version 3.1-160 (Pinheiro & Bates, 2000). Each emotion regulation strategy (i.e., rumination, negative suppression, avoidance, dampening, positive suppression) was predicted from emotional clarity and negative emotion differentiation scores. Because heightened negative affect is associated with more regulation effort and less differentiation, we included mean negative affect (averaged across all diary days as well) as a control variable, because we were interested in the relationship independent of these effects and to reduce confound (e.g., Kalokerinos et al., 2019). For a clearer interpretation of the results, all predictor variables (except Fisher z-transformed emotion differentiation indices) were grand-mean centered. Analyses were repeated with emotional clarity and positive emotion differentiation score as predictor variables. Instead of negative affect, we included mean positive affect (averaged across all diary days) as a control variable. The remaining components of the regression models were identical to the previous analyses.

Because 27% of our sample were siblings, we compared the goodness of fit of two different models using likelihood ratio tests for each outcome variable: One general linear regression model and a multilevel regression model with a random intercept where participants were partly nested within families. Most likelihood ratio tests (i.e., seven out of ten) were not significant, indicating that the consideration of a nested structure of participants within families did not significantly improve the goodness of fit (all p > .05). Thus, we decided to use the more parsimonious models in these cases. For the remaining three models, revealing significant likelihood ratio tests (p < .05), results of multilevel regression models with a random intercept for families were compared with results of general linear regression models. Because results did not differ, model estimates for general linear regression models will be reported in the result section for consistency reasons (see Table S1 in supplementary material for estimates of multilevel regression models with random intercept for families).

Within-person level analyses.

To assess hypotheses on the within-person level, we calculated five multilevel regression models using the R package “lme4” version 1.1-31 (Bates et al., 2015). Daily assessments (level 1) were nested within participants (level 2) and multilevel models account for this hierarchical data structure. We included random intercepts and random slopes for daily emotional clarity as well as daily negative emotion differentiation indices. Daily endorsement of each emotion regulation strategy was predicted from daily person-mean centered emotional clarity and negative emotion differentiation scores. Next to daily person-mean centered negative affect and day of assessment, we included other control variables for our within-person level models: (1) the person-mean centered score of the outcome variable from the previous diary day and (2) the grand-mean centered score of the outcome variable averaged across the whole diary period. Thus, we transformed our outcome variable into a residualized change score and so that observed effects for emotional clarity and emotion differentiation are not driven by variance due to yesterday’s endorsement of an emotion regulation strategy. In order to separate between-person and within-person level effects, we also included the grand-mean centered score of emotional clarity and negative emotion differentiation (averaged across all diary days) in our models (Bolger & Laurenceau, 2013). As described for the between-person level models, analyses were repeated with daily emotional clarity and positive emotion differentiation score as predictor variables. Instead of negative affect, we included daily person-mean centered positive affect as a control variable. The remaining components of the regression models were identical to the previous analyses.

As with between-person level analyses, we compared the goodness of fit between two multilevel models with likelihood ratio tests for each outcome variable: One model only included a random statement for participants and one model included crossed random effects for participants and siblings. Because likelihood ratio tests revealed that goodness of fit never significantly improve when including an additional random statement for siblings (all p > .05), we decided to use the more parsimonious models (only including a random intercept for participants) for our final analyses.

Gender and age.

We ran additional between-person and within-person level models including either age or gender as moderator variables to assess whether these variables affect the relation between emotional clarity, negative and positive emotion differentiation, and the endorsement of different emotion regulation strategies. Because the effects of gender and age did not form a consistent pattern, we present them only in the supplementary materials (see Table S6 to S13).

Multiple comparisons correction.

Due to the high number of conducted statistical models, we corrected our analyses for multiple comparisons in order to reduce type 1 error. All reported results are adjusted using false discovery rate correction (Benjamini & Hochberg, 1995).

Additional analyses.

In addition to the five putatively maladaptive regulatory strategies (i.e., rumination, negative suppression, avoidance, dampening, positive suppression), we examined whether emotion differentiation and clarity predicted putatively adaptive emotion regulation strategies (i.e., problem solving, reappraisal, positive rumination, acceptance; see pre-registration https://doi.org/10.17605/OSF.IO/JD4XZ). However, as there were no consistent result patterns for putatively adaptive regulatory strategies as outcome variable, for brevity sake these results are not presented in this manuscript.

Results

For a detailed descriptive analysis of daily diary measures and a correlation matrix, see Tables 1 and 2.

Table 2.

Pearson correlation matrix

Variable 1 2 3 4 5 6 7 8 9 10
1. Negative Affect -- −.42** −.31** −.53** −.16** .31** .13** .14** .12** .04
2. Positive Affect −.15 -- .18** .25** <.01 −.15** −.07** −.05* .03 −.01
3. Emotional Clarity −.66** .19* -- .11** .03 −.20** −.09** −.11** −.14** −.07**
4. Negative ED −.32** −.08 .22** -- .30** −.18** −.07** −.09** −.05* .02
5. Positive ED .04 −.05 −.01 .29** -- −.08** −.02 −.04 −.08** <.01
6. Rumination .65** −.08 −.63** −.23** .06 -- .32** .34** .22** .07**
7. Negative Suppression .50** .02 −.57** −.11 .07 .67** -- .30** .09** .19**
8. Avoidance .47** .07 −.52** −.12 .03 .69** .83** -- .08** .05*
9. Dampening .56** .08 −.53** −.18* .01 .77** .61** .64** -- .17**
10. Positive Suppression .38** −.01 −.45** −.13 −.01 .42** .65** .44** .48** --
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Note. Within-person correlations are shown above the diagonal and between-person correlations are shown below the diagonal.

*

p < .05;

**

p < .01.

Regarding our data distribution, mean scores of emotion regulation measures and negative affect appeared to be positively skewed, as can be seen in Table 1. However, the skewness and excess kurtosis statistics for most of these variables (except for rumination and negative affect) were less than 1.0, and statistics for all variables were below 2.0. Mean score of emotional clarity appeared to be negatively skewed, but skewness and excess kurtosis statistics were again below 2.0. Skewness and excess kurtosis are assumed to be acceptable between ± 2.0 (George & Mallery, 2019). However, it should be noted that in order to meet assumptions of multilevel modeling, the distribution of the model residuals (and not the data itself) should be considered (e.g., Snijders & Bosker, 2012). Visual analyses of the residual’s distribution for statistical models of main interest pointed towards underdispersion, i.e., the observed variability in the data was less than what was expected based on the assumed models. However, we performed simulation-based tests for underdispersion using the R package “DHARMa” (version 0.4.6; Hartig, 2020). The variance of the observed raw residuals was compared against the variance of simulated residuals and a significant ratio smaller than one would have indicated underdispersion. However, all tests were non-significant (all p > .05), implying that model assumptions should still be sufficiently met.

Between-person level analyses

See Table 3 for an overview of results from hypothesis 2 to 4 and Tables S2 and S3 in supplementary material for all estimates of between-person level regression models. Overall, R2 for between-person level models ranged from 0.20 to 0.49. These values indicate that a moderate to large amount of variance in the average use of putatively maladaptive emotion regulation strategies from 20 to 49% was explained by the independent variables in our models.

Table 3.

Overview of the main results of interest

Between-person level Within-person level
RU
b (pfdr)		 NS
b (pfdr)		 AV
b (pfdr)		 DA
b (pfdr)		 PS
b (pfdr)		 RU
b (pfdr)		 NS
b (pfdr)		 AV
b (pfdr)		 DA
b (pfdr)		 PS
b (pfdr)
EC −0.276 (<.001) −0.436 (<.001) −0.344 (<.001) −0.227 (.002) −0.338 (.001) −0.078 (<.001) −0.080 (.037) −0.100 (.005) −0.048 (.038) −0.061 (.067)a
NED −0.035 (.863) 0.136 (.610) 0.083 (.771) 0.011 (.974) −0.003 (.996) −0.003 (.466) 0.001 (.914) −0.011 (.068) 0.002 (.594) 0.005 (.061)
PED 0.102 (.587) 0.210 (.337) 0.071 (.798) 0.031 (.909) −0.029 (.952) −0.009 (.029) −0.005 (.465) −0.011 (.101) −0.006 (.086) 0.003 (.497)
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Note. For simplicity, we only show estimates for emotional clarity which are controlled for negative affect and negative emotion differentiation.

a

negative association between daily emotional clarity and daily suppression of positive emotions was significant when controlling for positive affect and positive emotion differentiation.

RU = rumination; NS = negative suppression; AV = avoidance; DA = dampening; PS = positive suppression; EC = emotional clarity; NED = negative emotion differentiation; PED = positive emotion differentiation; fdr = false discovery rate correction. Significant estimates are written in bold font.

Association among emotional clarity, negative, and positive emotion differentiation (H1).

Emotional clarity and negative emotion differentiation were significantly and positively correlated (r = .22, p < .01). However, the correlation was not significant after controlling for negative affect (p > .05). Emotional clarity and positive emotion differentiation were not significantly correlated (r = −.01, p > .05).

Association among emotional clarity, negative emotion differentiation, and emotion regulation strategies in response to negative affect (H2*).

In line with our hypothesis, participants with higher (vs. lower) emotional clarity reported less use of maladaptive emotion regulation strategies in response to negative affect, i.e., rumination (b = −0.28; pfdr <.001; 95% CI [−0.39, −0.16]; R2 = 0.49), negative suppression (b = −0.44; pfdr <.001; 95% CI [−0.60, −0.27]; R2 = 0.36), and avoidance (b = −0.34; pfdr <.001; 95% CI [−0.50, −0.19]; R2 = 0.30), when adjusting for negative emotion differentiation and mean negative affect. Contrary to our hypothesis, between-person level negative emotion differentiation was not significantly associated with the endorsement of any maladaptive emotion regulation strategies in response to negative affect (all pfdr > .05).3

Association among emotional clarity, negative emotion differentiation, and emotion regulation strategies in response to positive affect (eH3*).

Participants who had higher (vs. lower) emotional clarity had significantly lower maladaptive emotion regulation strategies in response to positive affect, i.e., dampening (b = −0.23; pfdr = .002; 95% CI [−0.36, −0.10]; R2 = 0.35) and positive suppression (b = −0.34; pfdr = .001; 95% CI [−0.52, −0.16]; R2 = 0.21), when adjusting for negative emotion differentiation and mean negative affect. Between-person level negative emotion differentiation was not significantly associated with the use of maladaptive emotion regulation strategies in response to positive affect (all pfdr > .05).

Association among emotional clarity, positive emotion differentiation, and emotion regulation strategies in response to negative and positive affect (eH4).

Participants with higher (vs. lower) emotional clarity reported less use of rumination (b = −0.50; pfdr <.001; 95% CI [−0.59, −0.40]; R2 = 0.40), negative suppression (b = −0.60; pfdr <.001; 95% CI [−0.73, −0.47]; R2 = 0.34), avoidance (b = −0.51; pfdr <.001; 95% CI [−0.63, −0.39]; R2 = 0.30), dampening (b = −0.45; pfdr <.001; 95% CI [−0.55, −0.35]; R2 = 0.32), and positive suppression (b = −0.45; pfdr <.001; 95% CI [−0.59, −0.32]; R2 = 0.20), when adjusting for positive emotion differentiation and mean positive affect. Between-person level positive emotion differentiation was not significantly associated with the endorsement of any maladaptive emotion regulation strategies in response to negative and positive affect (all pfdr > .05).

Within-person level analyses

See Table 3 for an overview of results from hypothesis 2 to 4 and Table S4 and S5 in supplementary material for all estimates of within-person level multilevel regression models. Since we were mainly interested in fixed effects, we report marginal R2 indicating the amount of variance explained by all fixed effects of a multilevel model. In linear mixed-effects models, marginal R2 is contrasted with conditional R2 indicating the amount of variance explained by all fixed and random effects (Nakagawa & Schielzeth, 2013). Overall, marginal R2 ranged between 0.50 and 0.73 for our multilevel models, which means that a large amount of variance in the daily use of emotion regulation strategies from 50 to 73% was explained by all fixed effects.

Association among emotional clarity, negative emotion differentiation, and emotion regulation strategies in response to negative affect (H2*).

As predicted, on days in which youths experienced higher emotional clarity compared to what they usually do, they engaged in less maladaptive emotion regulation strategies in response to negative affect, i.e., rumination (b = −0.078; pfdr <.001; 95% CI [−0.115, −0.042]; Marginal R2 = 0.71), negative suppression (b = −0.080; pfdr = .037; 95% CI [−0.145, −0.016]; Marginal R2 = 0.58), and avoidance (b = −0.100; pfdr = .005; 95% CI [−0.154, −0.038]; Marginal R2 = 0.50), when adjusting for daily negative emotion differentiation and daily negative affect.

The hypotheses that higher daily negative emotion differentiation would be associated with less daily use of maladaptive emotion regulation strategies in response to negative affect was not confirmed: Effects on rumination, negative suppression, and avoidance were non-significant (all pfdr > .05).

Association among emotional clarity, negative emotion differentiation, and emotion regulation strategies in response to positive affect (eH3*).

On days in which youth experienced higher emotional clarity compared to what they usually do, they engaged in less dampening (b = −0.048; pfdr = .038; 95% CI [−0.086, −0.009]; Marginal R2 = 0.73) in response to positive affect, when adjusting for daily negative emotion differentiation and daily negative affect. Results for suppression of positive emotions were non-significant (pfdr > .05). Effects for negative emotion differentiation on dampening and suppression in response to positive affect were non-significant (all pfdr > .05).

Association among emotional clarity, positive emotion differentiation, and emotion regulation strategies in response to negative and positive affect (eH4).

Within-person level emotional clarity was still significant for rumination (b = −0.128; pfdr <.001; 95% CI [−0.170, −0.087]; Marginal R2 = 0.69), negative suppression (b = −0.103; pfdr = .010; 95% CI [−0.170, −0.035]; Marginal R2 = 0.58), avoidance (b = −0.139; pfdr <.001; 95% CI [−0.201, −0.077]; Marginal R2 = 0.50), dampening (b = −0.072; pfdr <.001; 95% CI [−0.109, −0.036]; Marginal R2 = 0.73), and positive suppression (b = −0.066; pfdr = .039; 95% CI [−0.120, −0.013]; Marginal R2 = 0.67) when adjusting for daily positive emotion differentiation and daily positive affect. On days in which participants had higher positive emotion differentiation compared to what they usually, they engaged in less daily rumination (b = −0.009; pfdr = .029; 95% CI [−0.016, −0.002]; Marginal R2 = 0.69). Results for all other emotion regulation strategies were non-significant (all pfdr > .05).

Discussion

The present study is one of the first to use a daily diary design with over 3500 observations to examine the associations among youth’s application of emotional knowledge and the use of putatively maladaptive emotion regulation strategies. Extending prior work, this approach allowed us to investigate the specific contribution of two constructs tapping into the application of emotional knowledge to emotion self-reports (i.e., emotional clarity and emotion differentiation). Our results show that though they are conceptually related, emotional clarity and emotion differentiation are distinct constructs which are differently associated with increased use of maladaptive emotion regulation strategies. Three major findings emerged from our analyses.

Higher levels of emotional clarity are associated with lower levels of putatively maladaptive emotion regulation

Supporting our hypotheses, the results show that low emotional clarity was associated with higher use of all putatively maladaptive emotion regulation strategies of both negative and positive affect, i.e., rumination, behavioral avoidance, dampening, negative, and positive suppression. These findings were robust and consistent on the between-person and the within-person level. On the between-person level, youth with lower (vs. higher) levels of emotional clarity used maladaptive emotion regulation strategies more frequently. On the within-person level, on days in which youth experience lower (vs. higher) levels of emotional clarity compared to what they usually do, they used more maladaptive strategies (except from positive suppression when controlling for negative emotion differentiation) to regulate their mood. These findings are in line with previous studies on emotional clarity (e.g., Eastabrook et al., 2014; Eckland & Berenbaum, 2021; Flynn & Rudolph, 2010, 2014; Hatzenbuehler et al., 2008; Rieffe et al., 2008; Riley et al., 2019; Rueth et al., 2019). However, our study extends previous research in several ways.

First, we focused on several putatively maladaptive emotion regulation strategies in response to negative mood, whereas other studies investigated a single strategy (e.g., Hatzenbuehler et al., 2008) or more general measures of emotion regulation or coping (e.g., Riley et al., 2019). Second, past research focused on regulation of negative affect, whereas we extended findings to regulation of positive affect (i.e., dampening and positive suppression). In contrast to regulation of negative affect, regulation of positive affect remains largely understudied (Young et al., 2019). However, past research has shown that maladaptive strategies used to regulate positive affect are negatively associated with adolescents’ emotional well-being (Gomez-Baya et al., 2018). Therefore, it is crucial to consider the role of positive affect in youth, and further investigate factors which contribute to maladaptive regulation of positive affect (Gilbert, 2012; Young et al., 2019). Third, to our knowledge, our study is one of the first to assess emotional clarity, emotion differentiation, and emotion regulation using a daily diary approach. Using an intensive longitudinal design has several advantages. We demonstrated consistent associations between emotional clarity and regulatory behavior on a between-person and within-person level. In addition, self-report measures derived from daily diaries are closer to youth’s everyday experiences and reduce recall biases (Russell & Gajos, 2020). Fourth, we extended prior findings on adults to children and adolescents, a population that is especially vulnerable to the onset of emotion dysregulation and psychopathology (Rapee et al., 2019). Taken together, our results show that the subjective experience of emotional clarity plays a crucial role in youth’s emotion regulation behavior in response to negative and positive affect.

In line with leading theoretical models (Barrett, 2017; Schwarz, 2012), it seems that the subjective certainty about an emotional experience (but not the more objective ability to differentiate between emotions) is more significant for implementation of emotion regulation strategies. It seems that individuals who are unsure about their feelings make “bad” and habitual choices and choose putatively maladaptive emotion regulation strategies. However, future studies using experimental designs are needed to examine this more closely.

Emotion differentiation is mostly unrelated to levels of maladaptive emotion regulation

Surprisingly, the vast majority of our analyses for negative and positive emotion differentiation were non-significant. Regarding positive emotion differentiation, youth reported decreased use of rumination on days with higher-than-usual emotion differentiation. This finding is in line with past work emphasizing the protective role of positive emotion differentiation in emotion regulation across different age groups (e.g., Dixon-Gordon et al., 2014; Nook et al., 2021; Starr et al., 2017). In addition, it further extends prior research on youth and thereby emphasizes the important role of positive affect in this specific population (Gilbert, 2012). Our finding highlights the importance of understanding positive (vs. negative) affect during the transition to adolescence. Indeed, adolescence is a time of increased positive affect (vs. adulthood; Gilbert, 2012). Accordingly, neuroimaging studies show that the transition to adolescence is characterized by increased reactivity in reward circuitry that supports the experience of positive affect (e.g., Casey, 2015; Gadassi Polack et al., 2023; McCormick & Telzer, 2017). These changes are thought to be adaptive and support exploration and social interest, but they are also related to increased vulnerability to psychopathology and risk-taking behaviors (Casey, 2015; McCormick & Telzer, 2017). Our findings suggest that understanding one’s own positive affect facilitates adaptive positive emotion regulation, which may present a protective factor in youth.

Regarding negative emotion differentiation, against our expectations, we did not find any significant associations with putatively maladaptive emotion regulation strategies in response to negative and positive affect. One possible explanation for null findings might be our sample selection. Using a community sample of children and adolescents with relatively low intensity and variability in negative affect might have contributed to these results. Replications with clinical samples are needed. Another possible explanation is that emotion differentiation may be related to the effective implementation (vs. mere use) of emotion regulation strategies (see Kalokerinos et al., 2019; O’Toole et al., 2021 for prior work in adults). In this regard, future studies should additionally investigate associations between emotional knowledge usage and strategy effectiveness.

Emotional clarity and emotion differentiation are distinct constructs

Contrary to our expectations, emotional clarity and differentiation of negative and positive emotions were not correlated after controlling for mean affect intensity. Despite the clear theoretical overlap between the constructs, previous studies have also failed to find a significant associations between emotional clarity and differentiation (e.g., Boden et al., 2013; Edwards & Wupperman, 2017; O’Toole et al., 2014; Park et al., 2022). To our knowledge, the present study is one of the first to examine the two constructs on a similar timescale which we thought would help detect correlations. Because we cannot speculate about null findings, future work is needed to investigate the relation between emotional clarity and differentiation by conducting more extensive meta-analyses including within- and between-person assessments for both constructs or using indirect measures (instead of self-reports) for emotional clarity (e.g., response times to affect items; Arndt et al., 2018).

Strengths and limitations

The present study is, to our knowledge, the first to examine two constructs tapping into the application of emotional knowledge and their association with youth’s emotion regulation behavior using a daily diary design. Daily diaries reduce recall biases, increase ecological validity, and allow us to differentiate between-person and within-person level associations. Additionally, we are the first to examine a wide range of emotion regulation strategies used in response to negative and positive affect. This approach is in line with the theory of emotion poly-regulation (Ford et al., 2019). Assessing different regulation strategies enabled us to uncover differential associations of emotional clarity and differentiation with regulatory behavior. Additionally, we investigated associations between emotional knowledge usage and regulatory behavior in adolescents. Adolescence is an especially vulnerable developmental period, where emotional abilities are maturing and many emotional disorders have their typical onsets (Kessler et al., 2005; Nook & Somerville, 2019). With N = 172 children and adolescents, we included a large number of participants and were able to conduct statistical analyses with over 3500 observations in total. Importantly, our within-person level models controlled for reverse causality and reduced third-variable explanations by including average outcome and average affect in the models. Considering these control variables, correction for multiple comparisons, and the similarity in results across between-person and within-person levels, our results seem very robust. As the effect sizes of our models indicated, we were able to explain a large amount of variance in the use of regulation strategies with our independent variables. Meta-analytic effect sizes for the association between emotion regulation strategies and psychopathology are medium to large (e.g., Aldao et al., 2010), thus, we speculate that person- and within-person level differences in application of emotional knowledge do indeed have a significant impact on psychopathology via regulatory behavior. Future studies with clinical samples or including measures of psychopathology are needed.

The present study has, however, some limitations that need to be acknowledged. First, the low temporal resolution (i.e., once a day), may have limited our ability to detect effects. Additional studies using a higher temporal resolution (e.g., measurement every few hours) are needed to capture dynamics between emotional knowledge usage and emotion regulation behavior within one day. Second, we observed underdispersed residuals in our statistical models which could have biased our results. Simulation-based tests for underdispersion, however, were non-significant, indicating that model assumptions were still sufficiently met. Third, we assessed emotion regulation of positive and negative affect separately even though recent evidence shows that regulating positive and negative affect is related (Gadassi Polack, Everaert, et al., 2021). Fully discussing the issue of emotion poly-regulation (Ford et al., 2019; Wen et al., 2021), however, is beyond the scope of the current investigation. Future research should investigate associations of emotional clarity and emotion differentiation with novel measures combining usage of different regulatory strategies (e.g., the emotion regulation diversity index, Wen et al., 2021; maladaptive emotion regulation ratio, Gadassi-Polack et al., 2024).

Constraints on generality

Our design, though longitudinal, is not experimental, precluding us from making any claims regarding causation. Future studies are needed to assess causal links between emotional clarity and use of emotion regulation strategies as well as psychopathology, e.g., by experimentally manipulating emotional clarity in laboratory settings. Additionally, the current sample was a community sample, resulting in comparatively low intensity and variability of negative affect. Relatedly, we have little information about the socioeconomic status of our participants, which may restrict the generalizability of our findings.

Conclusions

The present study is the first to examine whether the subjective construct of emotional clarity and the objective construct of positive emotion differentiation contribute to daily decreases in putatively maladaptive regulatory behavior in children and adolescents. For emotional clarity, we found strong evidence for between-person level differences in use of maladaptive emotion regulation strategies as well. To our knowledge, the examination of these associations in an intensive longitudinal design and a youth sample are innovative and unique contributions. Overall, our findings have important implications for studying emotional knowledge in general and in children and adolescents in particular. Future research and practice should focus on emotional clarity and positive emotion differentiation, because these two facets of the application of emotional knowledge seem to protect youth from using maladaptive emotion regulation strategies, thus potentially contributing to youth mental health.

Supplementary Material

supplemental material

Funding:

This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 786064 and the National Institute of Mental Health Translational Developmental Neuroscience Training Grant (T32 #MH18268) awarded to Dr. Gadassi-Polack, the National Institute of Mental Health R21 MH119552 awarded to Dr. Joormann, and Fulbright Germany to Nicola Hohensee.

Footnotes

We have no conflicts of interest to disclose. The study was preregistered at https://doi.org/10.17605/OSF.IO/JD4XZ and all necessary analysis code, and study materials are available at https://doi.org/10.17605/OSF.IO/ASXJC. Data is available by request from the last author.

1

For simplicity, we classify some emotion regulation strategies we focus on as putatively maladaptive. We acknowledge that although they are more consistently linked to detrimental emotional outcome and psychopathology in the long run (e.g., Aldao et al., 2010), they may not always lead to bad outcomes. Indeed, the result of emotion regulation depends on multiple factors; therefore, it is possible that putatively maladaptive regulatory strategies may be “good” under specific circumstances as well (Sheppes, 2020).

2

We thank the anonymous reviewer for their valuable suggestions which have substantially improved the quality of the current paragraph.

3

The vast majority of reported result patterns did not change when models were calculated separately for emotional clarity and emotion differentiation. Analysis code for separate models is available at https://osf.io/asxjc/.

We additionally checked whether the inclusion of an interaction effect between emotional clarity and emotion differentiation changed any result patterns. Only 4 out of 20 interaction effects were significant and no consistent pattern emerged (without false discovery rate detection). Further details regarding estimates of all statistical models including an interaction effect can be found in Tables S14 to S17 in the supplemental material. See Figures 1 to 4 in the supplements for plots of significant interaction effects.

Upon the request of an anonymous reviewer, statistical models where daily emotional clarity and emotion differentiation predict the use of emotion regulation strategies at the subsequent day can be found in Tables S18 and S19 of the supplements. Associations among emotional clarity, emotion differentiation, and strategy use at the subsequent day were mostly non-significant.

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