How does emotional intelligence predict happiness, optimism, and pessimism in adolescence? Investigating the relationship from the bifactor model

Emotional intelligence (EI) plays a key role in the adjustment of adolescents during this transitional life period. The accumulated evidences suggest that EI is associated with happiness, considered the affective component of subjective well-being and optimism and pessimism, considered cognitive mechanisms to expect either a brighter or darker future. In spite of the relevance of the relationship between EI, happiness, optimism, and pessimism, the majority of the research falls behind findings with adult samples, accumulating little knowledge in the context of adolescence. Furthermore, the measurement of EI has been recently challenged by the introduction of the bifactor model into the study of EI. The goal of the current study was to explore the association of EI with happiness, optimism, and pessimism in adolescence by introducing the bifactor EI model. The participants were 493 Spanish high-school students ranging from 14 to 18 years old (52.7% females) who completed self-report questionnaires. The results demonstrated that the bifactor EI model with an e-factor (general EI factor) and three emotional dimensions (emotional attention, emotional clarity, and emotional regulation) also represented the best well-fitted structure in adolescence. Most remarkably, results suggested that general EI and emotional regulation predicted positively happiness and optimism, while emotional attention predicted positively pessimism and negatively happiness. These results highlight the importance of the measurement of EI in the study of associated outcomes that are considered relevant during the period of adolescence. Hence, the specific role of the EI dimensions are important when explaining the relationship of EI with happiness, optimism, and pessimism.


Introduction
The study of emotional intelligence (EI) in adolescence has generated considerable interest over the years, especially in the field of well-being (Gascó et al. 2018;Sánchez-Álvarez et al. 2016). The period of adolescence entails developmental life challenges (Cejudo et al. 2018), wherein different affective (e.g., EI) and cognitive (e.g., optimism and pessimism) resources may serve to adaptively cope with these environmental demands and improve adolescents' well-being. One of the main difficulties in the field is that the relationship between these affective and cognitive variables with subjective wellbeing (SWB) falls behind adult samples, since little research has addressed this topic in the context of adolescence. In addition, the measurement of EI has been recently challenged for providing inaccurate results, and recent research suggested to introduce the bifactor model to study how EI relates to other outcomes (Blasco-Belled et al. 2019). Therefore, the current study attempts to replicate the bifactor EI structure and investigate the relationship of EI with happiness, optimism, and pessimism in adolescents.

The Study of EI in Adolescence
EI is traditionally conceived as "the ability to perceive emotions, to access and generate emotions so as to assist thought, to understand emotions and emotional knowledge and to reflectively regulate emotions so as to promote emotional and intellectual growth" (Mayer and Salovey 1997, p. 5). In the course of adolescence, many developmental changes occur along with demanding life adjustments, especially in the school and family environments (Cejudo et al. 2018). Adolescents have to deal with a great variety of biological, psychological, and social changes (Burger and Samuel 2017;Castillo-Gualda et al. 2017), where EI plays an essential role to improve adolescents' social skills and psychological adjustment (Mavroveli et al. 2007). Accordingly, EI enhances the personal and developmental growth of adolescents (Resurrección et al. 2014;Rey et al. 2011) and it serves as a vehicle to cope with the onset of common mental illness during this stage, specifically depression and disruptive behavior (Davis and Humphrey 2012). One major theoretical issue that has dominated the field for many years concerns the understanding of EI as a trait or as an ability. Although it falls beyond the scope of this study, we acknowledge the importance of understanding these two approaches. The trait model refers to the combination of behavioural dispositions that guide people in the process of adaptive coping and include different aspects of personality (Petrides et al. 2004). In contrast, the ability model refers to one's cognitiveemotional abilities to process information related to emotions (Mayer et al. 2016;Mestre et al. 2016).
Differences in the definition of EI can lead to misconceptions and misleading comparisons across studies. This distinction extends to the different instruments used for assessing aspects related to this construct (Abdollahi et al. 2019). For example, EI reports a greater relationship with health when measured as a trait than as an ability (Martins et al. 2010;Schutte et al. 2007). Trait measures have been usually preferred over ability measures throughout the literature for several reasons: they imply less time and economic resources than abilitymodel measures (Extremera et al. 2007) and its operationalization is appreciably easier than the abilitymodel (Spain et al. 2000). According to this, within the current study EI was evaluated through a trait-related measure and defined by three dimensions: emotional attentionthe degree of attention that individuals dedicate to their own and others feelings; emotional clarity the understanding of emotions; and emotional regulationthe capability to regulate emotions and to cope with situations that require an adjustment of the feelings.

Current Challenges in the Study of EI in Adolescence
Being a construct widely investigated in psychology, EI has accumulated few inconsistencies concerning structure measurement and the influence of EI dimensions on different outcomes. First, the study of EI in adolescents faces the challenge of scarce investigation about its relationship with happiness, optimism, and pessimism. Few studies evaluated EI with SWB (Abdollahi et al. 2019;Lim et al. 2015;Sánchez-Álvarez et al. 2015;Serrano and Andreu 2016); among them, only three examined specifically the three EI dimensions rather than the general construct of EI in relation to SWB (Extremera et al. 2007;Gascó et al. 2018;Serrano and Andreu 2016), only one focused on the relationship of EI with happiness (Serrano and Andreu 2016), and only one investigated EI with optimism and pessimism (Extremera et al. 2007). Second, methodological limitations in the measurement of EI hindered drawing accurate conclusions about its relationship with well-being outcomes, more particularly related to emotional attention. The literature reported contradictory results about the role of this facet on SWB indicators (Augusto-Landa et al. 2011;Sánchez-Álvarez et al. 2015) and it remained less studied than emotional clarity and emotional regulation. As these inconsistencies may respond to measurement limitations, a recent study suggested the introduction of the bifactor model as a plausible solution to assess EI more reliably (Blasco-Belled et al. 2019) The Bifactor Model in the Study of EI Most studies in the field have overlooked the fact that EI is not an unidimensional construct (e.g., Gutiérrez-Cobo et al. 2017;Koydemir et al. 2013;Szczygieł and Mikolajczak 2017). As a mean to understand the plausible implications of the bifactor EI model, Blasco-Belled et al. (2019) compared EI with general intelligence in terms of their structural organization. Akin to the g (eneral)-factor of intelligence, the e(motional)-factor of EI can explain the general ability to reason about emotions, while the specific dimensions of emotional attention, clarity, and regulation (compared with intelligence's specific dimensions) can describe independent abilities of EI (Carroll 1996;Wechsler 1997). In a similar vein, EI can also be resembled with the configuration of personality, in which a general factor of personality (GFP) along with specific personality traits (i.e., openness, extraversion, conscientiousness, agreeableness and emotional stability) can explain broad and specific implications of this construct (Peabody and Goldberg 1989), with individuals high in GFP reporting higher EI ( Van der Linden et al. 2017). To sum, the bifactor EI model allows to distinguish the unique contribution of each specific EI facet (emotional attention, clarity, and regulation) to the outcomes of interest while they all have the commonality of sharing emotional information (e-factor). The bifactor model has been used to investigate different psychological phenomena in the context of adolescence, such as the study of school engagement (Stefansson et al. 2016), strengths (Caci et al. 2015) or the structure of psychopathology (Laceulle et al. 2015). Despite the preference for the bifactor model over traditional EI models in previous research (Blasco-Belled et al. 2019), this structure has not been examined in adolescents yet.

The Relationship of EI with Happiness
Research accumulated evidences about the existing relationship between EI and SWB (Abdollahi et al. 2015(Abdollahi et al. , 2019Chamorro-Premuzic et al. 2007;Gascó et al. 2018), which is defined by a cognitive component (i.e., life satisfaction) and an affective component (i.e., happiness) (Tomlinson et al. 2017). Studies investigating happiness and its relationship with EI to promote greater psychological development are considered valuable, especially during adolescence (Abdollahi et al. 2019). In their longitudinal study, Sánchez-Álvarez et al. (2015) showed that EI was positively associated to SWB over time. EI may act as a promoting and protective mechanism to boost happiness (Abdollahi et al. 2019), and buffer depressive and stressful symptoms (Abdollahi et al. 2015). A systematic review of the literature found that lower levels of EI were related to increased depression, anxiety, substance abuse or aggression during adolescence, which influenced negatively on the promotion of happiness (Resurrección et al. 2014). Besides, research showed that the acknowledgment of emotions contribute to adolescents' happiness (Lischetzke et al. 2012). As a consequence, happiness may act as a protective factor against mental and physical disorders (Huang and Humphreys 2012).
Generally, a meta-analytic investigation (Sánchez-Álvarez et al. 2016) indicates that individuals with high EI are well acquainted with their emotions, can cope better with life situations and ultimately enhance their happiness. One study in adolescence further demonstrated that EI dimensions held an indirect effect over SWB through resilience (Ramos-Díaz et al. 2018). Concerning the three EI dimensions, the existing relationship between EI and SWB in adolescents suggested that emotional clarity and emotional regulation were positively associated with the cognitive component of SWB (i.e., life satisfaction) (Extremera et al. 2007;Gascó et al. 2018;Serrano and Andreu 2016). By contrast, disagreements exist concerning the role of emotional attention: while there seems to be a (although sometimes contradictory) link between EI and life satisfaction (Extremera et al. 2007;Serrano and Andreu 2016), the question regarding how EI relates to the affective component of SWB in adolescents remains underexplored.

The Relationship of EI with Optimism and Pessimism
Optimism refers to the general tendency to expect positive events in the future, while pessimism refers to the tendency to believe that unfavourable events will happen (Carver and Scheier 1995). These constructs were originally conceptualized as being unidimensional (Scheier and Carver 1985), but a considerable amount of literature has verified a two-dimensional structure (Appaneal 2012;Herzberg et al. 2006;Hinz et al. 2017), suggesting that they could be treated as separate dimensions. Optimism and pessimism influence how people cope with stressful events and can thus account for indicators of individual differences (Chang 2002). For example, pessimism was associated to depressive symptoms and hassles in adolescents under conditions of stress, whereas optimism was associated to less depressive symptoms, less hopelessness, and better psychological adjustment under similar conditions (Chang and Sanna 2003). Research on the relationship of EI with optimism and pessimism is rather limited, especially for adolescents. Nevertheless, some authors reported that emotional clarity and emotional regulation were associated positively with optimism, while pessimism was associated negatively to emotional regulation (Extremera et al. 2007).

The Present Study
The main purpose of the present study was to examine the relationship of EI with happiness, optimism, and pessimism in adolescence. As mentioned earlier, this line of inquiry has been barely investigated with adolescents. In light of previous research, the current study is the first to introduce the bifactor EI model in adolescence, which might help to better understand the contribution of each EI dimension on happiness, optimism, and pessimism. Based on previous research, we hypothesize that: 1) the introduction of the bifactor model (composed of the general e-factor, and the three specific dimensions of EI) will better represent the structure of EI compared to traditional approaches (e.g., one-, three-factor, and higher-order factor); 2) happiness and optimism will be negatively related to emotional attention, while positively related to the e-factor, emotional clarity, and emotional regulation, and; 3) pessimism will be positively related to emotional attention, while negatively related to the e-factor, emotional clarity, and emotional regulation.

Participants and Procedure
A sample of N = 493 (260 females and 233 males) Spanish adolescents ranging from 14 to 18 years old (M = 15.33; SD = .56) were recruited from three urban high schools who participated voluntarily in a program offered to all high schools from the city of Lleida. Adolescents from 4th grade of the three high schools engaged in a program consisting in the identification and promotion of individual traits (e.g., EI, optimism, and pessimism) with the aim to enhance their wellbeing. The identification of individual traits was assessed by means of self-report measures. The participants received an individualised report with their results after completing the measures, which was afterwards commented with a psychologist in order to explain the role of these traits in the promotion of happiness. An inform consent was obtained from the parents or a legal tutor for adolescents to participate in the study, and only those who returned the inform consent signed were accepted in the program.

Instruments
The Trait Meta-Mood Scale (TMMS-24; Salovey et al. 1995;Spanish adaptation of Fernandez-Berrocal et al. 2004) is a 24item self-report measure that evaluates three dimensions of EI (emotional attention, emotional clarity, and emotional regulation) in a 7-point Likert scale (1 = Strongly disagree to 7 = Strongly agree). The Cronbach's α (and McDonald's ω) reliability estimates of the TMMS-24 for the present study were .89 (.93) for the e-factor, .89 (.90) for emotional attention, .87 (.88) for emotional clarity, and .85 (.87) for emotional repair. A sample item for each dimension would be: "I can never tell how I feel" for emotional attention, "I pay a lot of attention to how I feel" for emotional clarity, and "If I find myself getting mad, I try to calm myself down" for emotional regulation.
The Subjective Happiness Scale (SHS; Lyubomirsky and Lepper 1999; Spanish adaptation of Extremera and Fernández-Berrocal 2014) is a 4-item self-report measure of happiness in which participants evaluate to what extent they agree with different happiness statements using absolute ratings, relative peer ratings and brief descriptions of happiness. The Cronbach's α (and McDonald's ω) reliability estimates of the SHS for the present study were .75 (.76). The instrument uses a 7-point Likert scale and they vary depending on the form of the question; an example of an item is "In general I consider my-self… (1 = not a very happy person, 7 = a very happy person)".
The Revised Life Orientation-Test (LOT-R; Scheier et al. 1994; Spanish adaptation of Ferrando et al. 2002) consists of 10 items (4 of them are fillers) measuring dispositional optimism and pessimism on a 5-point Likert scale (0 = Strongly disagree to 4 = Strongly agree), and evaluates individual differences in generalized optimism versus pessimism. The Cronbach's α (and McDonald's ω) reliability estimates of the LOT-R for the present study were .71 (.72) for optimism and .61 (.58) for pessimism. A sample item for optimism is "In uncertain times, I usually expect the best" and for pessimism is "If something can go wrong for me, it will".

Data Analysis
We used maximum likelihood estimation with robust standard errors due to lack of multivariate normality, and no correlations between residuals were allowed to analyze the measurement models.
In order to test the first hypothesis, we compared four different measurement models of EI: the one-factor, three-factor, higher-order, and bifactor models. Reliability estimates were calculated using McDonald's ω coefficient (although α was also included) due to the suitability to overcome some α reliability's limitations (see Sijtsma 2009). We analyzed the explained common variance (ECV) to evaluate factor saturation in the bifactor model (Zinbarg et al. 2005). If the general factor (e-factor) explains less than the 70%, the construct can be regarded as multidimensional. In respect to the measurement model of SHS, we tested the one-factor model, whereas for the measurement model of the LOT-R we tested two competitive models: the one-factor and two-factor models since previous disagreements regarding the LOT-R structure were reported in the literature (Hinz et al. 2017;Monzani et al. 2014). To assess the measurement models, we used the following goodness of fit indices and cut-offs: the Comparative Fit Index (CFI) and the Tucker-Lewis Index (TLI) should be above .90, the Root Mean Square of Approximation (RMSEA) should be below .05 and the Standardized Root Mean Residual (SRMR) should be below .08. In order to compare competitive models, we relied on the Bayesian Information Criterion (BIC) and the Akaike Information Criterion (AIC), wherein lower values would indicate better model fit (Byrne 1995;Kline 2011). Following Greene et al. (2019), the selection of models should rely not only on fit indices, but also on theoretically substantive grounds (Hershberger and Marcoulides 2006), which has been previously discussed.
To test the second and third hypothesis, we analyzed a Structural Equation Model (SEM), in which the e-factor and the three specific EI dimensions predicted happiness, optimism, and pessimism. All of the analyses were carried out in Mplus v. 7.2 (Muthén and Muthén 2012) except for ECV, which was tested in R software (R Development Core Team 2016), and all of the data and syntaxes necessary to replicate the results are available in an open repository at: https://osf.io/ cxg4r/ Table 1 shows the descriptive statistics and reliability estimates of the measured variables. Table 2 presents the results obtained from the assessment of the measurement models. Comparing the four TMMS-24 measurement models, only the bifactor model was well fitted to the data (χ 2 (228) = 628.71, p < 0.001; CFI = 0.915, TLI = .897, RMSEA = 0.060, 90% CI [054-.065], SRMR = .076), which confirmed our first hypothesis. The measurement model of SHS was well fitted to the data. The two-factor model of the LOT-R was better fitted to the data compared to the one-factor model. Factor loadings for each measurement model are presented in the "Appendix".

The Relationship of EI with Happiness, Optimism and Pessimism
The standardized regression coefficients of the SEM are presented in Fig. 1. For the sake of readability, we only present the results of the structural part. The analyzed model showed an acceptable fit to the data (χ 2 (488) = 1005.88; p < .000; CFI = .919; TLI = .907; RMSEA = .046 [.042-.050]; SRMR = .070). The e-factor was a positive predictor of happiness and optimism. Regarding the specific emotional dimensions, emotional attention predicted positively pessimism and negatively happiness. Emotional regulation was a positive predictor of happiness and optimism, whereas emotional clarity appeared as non-significant in relation to all variables. Taking into account these results, our second and third hypothesis were partially confirmed given the lack of significance of emotional clarity.

Discussion
Identifying how affective and cognitive mechanisms influence adolescent's well-being may serve to understand the mechanisms thorough which they can adaptively cope with the environmental demands. In this effort, this study examined the relationship of EI with happiness, optimism, and pessimism in adolescence introducing the bifactor EI model to do so (Blasco-Belled et al. 2019). By this means, we were able to control for the shared variance of the EI dimensions, and thus analyze their unique contribution on the studied variables. Overall, results indicated that the bifactor model was the most suitable to assess EI, extending previous findings (Blasco-Belled et al. 2019) to a sample of adolescents. SEM analysis suggested that the e-factor was a positive predictor of happiness and optimism. Regarding the EI dimensions, we found a distinctive pattern of relationships among the studied variables. Remarkably, emotional attention was a negative predictor of happiness and a positive predictor of pessimism, emotional regulation was a positive predictor of happiness and optimism, and emotional clarity was non-significant.

EI and Happiness in Adolescence
An issue emerging from these findings relates essentially to emotional attention, since much uncertainty still exists about the role of this EI facet, especially in adolescence. To date, the reported literature has been unable to reach a conclusion about the effect that emotional attention exerts on happiness. The present study found results which are consistent with a notion suggesting that the mechanisms responsible for attending to affective information can be detrimental to happiness if not handled properly. Some researchers have proposed that fussing over affective information could foster excessive monitoring of self-and other-emotional reactionsthis may lead to a sensitivity to perceive more stress and symptomatology (Goldman et al. 1996), and hinder engagement in efficient regulatory strategies (Boden and Thompson 2015).
These results could have indirect implications for the other two EI dimensions. Over-attending to emotional reactions may result in a higher probability of dwelling on negative thoughts that, in turn, might interfere with the proper functioning of the mechanisms responsible for understanding and regulating emotions (Gascó et al. 2018). One might argue that paying too much attention to emotional stimuli can actually prevent one from availing oneself of information that is fundamental to decoding the perceived stimuli and understanding them. Ultimately, the lack of emotional comprehension might translate into engaging in ineffective regulatory strategies that, according to the results, are detrimental to happiness. Even though this upward spiral may not necessarily occur in the given sequence, the resulting effects could nevertheless be expected. Indeed, differences in experiencing and understanding emotions have consequences for emotional management and for everyday judgments and decisions (Gohm 2003). Despite emotional clarity was non-significant, the results suggested that a greater adjustment of feelings on a demanding situation (emotional regulation) may help adolescents to use greater coping strategies. This adds knowledge to a growing body of research interested in understanding the affective and cognitive mechanisms that cause and maintain happiness (Lyubomirsky 2001), wherein EI has been proposed as a factor that can explain individual differences in the experiences of SWB, including happiness (Extremera and Fernández-Berrocal 2014;Mayer and Stevens 1994). Periods with major biological and social transitions, like adolescence, involve crucial variations in levels of happiness (González-Carrasco et al. 2017;Montserrat et al. 2015). Besides being one of the key predictors of enhanced SWB, emotional abilities play an important part in adolescents' classroom social climate, academic success, and self-concept (Ros Morente et al. 2017;Martínez-Monteagudo et al. 2019). Altogether, it makes sense that emotional attention and emotional regulation could be great contributors to providing and maintaining durable levels of happiness across young populations.

EI, Optimism, and Pessimism in Adolescence
The results of the present study showed that emotional mechanisms are connected to future expectancies in adolescence. Two main conclusions can be drawn from these findings. First, emotional attention and emotional regulation had a unique contribution beyond the general EI, suggesting that these dimensions play an important role in the process of future envisioning in adolescencebeing able to generally reason about emotions (e-factor) and regulate one's own and other's emotional states (emotional regulation) contributed to optimism, while overly dwelling on emotions (emotional attention) contributed to pessimism. Second, optimism and pessimism can be understood as two separated dimensions in adolescence, which helps to disentangle previous disagreements about the conceptualization of these two as independent constructs.
Functional perspectives have established that the interplay between emotions, thoughts, and behaviors have evolved to meet environmental demands (Lench et al. 2013). Evidence from this perspective showed that emotional processes play a  Fig. 1 Structural model of the e-factor, emotional attention, emotional clarity, and emotional regulation predicting happiness, optimism, and pessimism. The analyzed structural equation model includes both measurement and structural part of the model, but for the sake of readability, only the structural part is presented role in executive functioning tasks, such as future planning (Sass et al. 2013). The present findings seem to be consistent with other research which found that mental simulations of the future (i.e., imagining how a specific personal situation will unfold) have much to offer to emotion regulation (Taylor and Schneider 1989). Mental simulations may be beneficial in that they serve as a means of distracting one's focus on troublesome present situation (e.g., simulating that a friendship argument will be solved), but this sense of relief will only evoke momentary gains (e.g., the rehearsal effect of thinking about a happy end may dissipate over time) (Oettingen and Mayer 2002;Szpunar 2010).
However, a positive expectation of the future could also depend on the experience of the current affective state. Tamir (2016) proposed that people seek to experience emotions that confirm their current affective state, and having maladaptive or unhealthy goals (e.g., seeking negative emotions) can lead to maladaptive or unhealthy behaviors (e.g., seeking situations that prompt negative emotions) (Millgram et al. 2015). On this basis, adolescents overly attentive to emotional information may be more prone to expect and look for darker future scenarios. In fact, other researchers reported a negative relationship between rumination, considered a maladaptive form of self-attention, and optimism (Tucker et al. 2013).
Emotional regulation builds upon an extensive body of research studying how individuals engage, select, and appraise regulatory strategies associated with long-term psychological and physical gains (McRae and Gross 2020). Scholars recently proposed that successful emotional regulation involves cognitive controlselecting goal-relevant information while inhibiting goal-irrelevant information in affective contexts - (Schweizer et al. 2020) which was associated with mental health (Schweizer et al. 2019). This link between affective and cognitive processes might help to understand our findings. Affective mechanisms could serve as a personal resource to envision and pursue a brighter future, and the appropriate functioning of EI, particularly of emotional attention and regulation, may help adolescents to imagine, plan, and perform behaviors to accomplish the expected positive future. Taken together, our findings may indicate that fussing over emotional information, and therefore ruminating about affective states, could hinder the cognitive process of positive expectation, whereas being able to manage emotional information may facilitate that.
Our findings also have important implications that connect directly to the ongoing academic debate about the structure of optimism. Despite some researchers claimed an unidimensional continuum of this trait (Segerstrom et al. 2017), others supported a two-dimensional structure (Herzberg et al. 2006;Hinz et al. 2017). According to our results, optimism and pessimism can be seen as separate traits that can group together to explain individual affective and cognitive differences.

Limitations
There are several limitations of the present study. First, to increase the generalizability of the results, future studies should include larger samples and diverse cultural background, so the results can be more valuable (Bastian et al. 2014). Second, while the use of the TMMS-24 showed higher reliability in our study than in previous adolescent studies (e.g., Castela et al. 2013;Extremera et al. 2007;Pedrosa et al. 2014;Salguero et al. 2015), the LOT-R subscales were acceptable, being the α coefficients .71 for optimism and .61 for pessimism (and lower for the ω estimates). Third, the interpretation of the results cannot draw any cause-effect conclusion as a cross-sectional design was used for the study. Longitudinal studies would be required to further corroborate the results of the current study. Finally, the data of the study was obtained through self-report measures, which can be a drawback in adolescents since some of their responses might not have been sincere.

Conclusions
The present study adds several noteworthy contributions to the research on EI in adolescents. The bifactor model explained better the structure of EI. The refinement of EI measurement allowed us to explore the unique contribution of each EI facet on happiness, optimism, and pessimism. Emotional attention may be detrimental to happiness and optimism if not managed properly. By contrast, emotional regulation may help to promote these outcomes, and offer adolescents with strategies to improve future expectancy and a sense of optimal functioning. As plausible implications, the present findings emphasize the need to provide adolescents with effective strategies to handle emotions so as to face adversity. In this effort, we specifically propose the facilitation of techniques aimed at properly attending to their emotions and self-regulating their emotional responses.
Data Availability The data that supports the findings of this study are openly available in a repository.

Compliance with Ethical Standards
Conflict of Interest No potential conflict of interest was reported by the authors.
Ethical Approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee.
Research Involving Human Participants and Informed Consent Informed consent was obtained from all individual participants included in the study.