Divergent thinking and stress dimensions

Thinking Skills and Creativity 17 (2015) 102–116

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Divergent thinking and stress dimensions Maria-Jose Sanchez-Ruiz a,∗ , Juan Carlos Pérez-González b , Manuela Romo c , Gerald Matthews d a b c d

Lebanese American University, P.O. Box 36, Byblos, Lebanon Universidad Nacional de Educación a Distancia (UNED), Spain Universidad Autónoma de Madrid, Spain University of Central Florida, USA

a r t i c l e

i n f o

Article history: Received 24 October 2014 Received in revised form 14 May 2015 Accepted 22 June 2015 Available online 29 June 2015 Keywords: Affect Creativity University majors Domain-specificity Stress Trait emotional intelligence

a b s t r a c t This study examines the role of the stress state dimensions of Engagement, Distress, and Worry before and during a divergent thinking (DT) task, while controlling for trait emotional intelligence (trait EI). The sample consisted of 175 university students in Technical and Natural Sciences, Social Sciences and Arts. Trait EI factors (Wellbeing, Emotionality, Sociability, and Self-control) correlated positively with Engagement (pre- and within-task), and negatively with Distress (pre- and within-task) and Worry (pre-task). Regression of DT scores showed incremental validity of post-task stress state dimensions over trait EI and pre-task stress state dimensions, whereby the individual predictors were Distress (negative) and Engagement (marginal and positive). Finally, ANOVAs revealed that within-task Distress scores were associated with high DT in the Arts group, but low DT in the other groups. From the results, a possible task-to-state as well as state-to-performance relationship is inferred, and the domain specificity of the affect-creativity relationship is discussed. Implications for the educational settings and the study and assessment of these two constructs are presented. © 2015 Elsevier Ltd. All rights reserved.

1. Introduction The construct of creativity is complex and multi-componential (Kim, 2006). To date, one widely accepted definition is that of creativity as a way of thinking that leads to novel and useful products (e.g., Mumford, 2003). Notwithstanding, there has been extensive empirical investigation of the cognitive elements of creative activity, but less research efforts have been dedicated to its affective (trait and state) components (Csikszentmihalyi, 1990; Russ, 1999; Shaw & Runco, 1994). In the educational context, these components are essential. Effective educational practices do not only focus on cognitive abilities, but also on emotional and motivational aspects (e.g., Runco, 2014). It is generally assumed that positive affect fosters creativity (e.g., Estrada, Isen, & Young, 1994; Lyubomirsky, King, & Diener, 2005), but the role of negative states such as stress remains controversial (e.g., George & Zhou, 2002). A meta-analysis conducted by Baas, De Dreu, & Nijstad (2008) suggests that, in fact, positive and negative mood both influence creativity, but in different ways, and through different routes. In the same line, Kaufmann (2003) argues that positive and negative moods may affect different dimensions of creativity. Teachers and counsellors can make use of this information and aim to

∗ Corresponding author. Fax: +961 9541 030. E-mail addresses: [email protected], [email protected] (M.-J. Sanchez-Ruiz). http://dx.doi.org/10.1016/j.tsc.2015.06.005 1871-1871/© 2015 Elsevier Ltd. All rights reserved.

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fulfil their students’ creative potential through an individualized teaching approach. For example, one way to enhance the creativity of students might be to facilitate opportunities to practice creative thinking under different conditions, including a variety of emotionally-charged situations. In the present study, we used divergent thinking (DT) as indicator of creativity. Guilford (1956) considered the 24 components of DT in its structure of intelligence as essential for the psychometric definition of creative thinking. DT is defined as the kind of thinking that results in several ideas, solutions or products, in contrast to convergent thinking, which leads to one correct answer, as in the case of traditional ability tests (Plucker & Renzulli, 1999). DT tests are among the most widely used tests to assess creativity, they measure idea-generation skills, and in particular fluency as well as other creativity dimensions such as flexibility, originality and elaborateness (e.g., Torrance, 1990). Many researchers consider DT as a necessary, but not sufficient, element of creativity, because creativity not only implies the generation of novel ideas, but also the ability to evaluate them so they are appropriate and valuable (e.g., Runco, 2008). In the same line, Zeng, Proctor and Salvendy (2011) stated that creativity cannot be reduced to original thinking. However, DT tests have shown certain predictive validity (e.g., Furnham, Batey, Anand, & Manfield, 2008) ad discriminant and convergent validity (e.g., Dollinger, Urban, & James, 2004), and, in sum, research has shown that DT is a valid indicator of creative potential in various contexts (e.g., Batey, Rawles, & Furnham, 2009).

2. Affect-related traits and states and their link to creativity Two main approaches to the creativity-affect link can be identified in the literature. The first one studies creativity in relation to affect-related personality traits and the second one does so in relation to affect-related subjective states. In engaging with this dual aspect of affect, it is important to be able to distinguish clearly between the two concepts of trait and state. The literature defines personality as “stabilities of behavior and beliefs about our enduring dispositions” (Matthews, Deary, & Whiteman, 2003), while emotional states are transient internal conditions (Eysenck & Eysenck, 1975) that are immediately accessible to the individual. Unlike states, which are directly experienced, traits are propensities for feelings, thoughts or behaviors. Both emotion-related traits and states potentially play a role in creativity, but more systematic empirical work is needed to elucidate the particular associations with creative performance, and DT in particular. Both strands of research are reflected in a theory developed by Russ (1999) in which general personality characteristics and affect-related states would influence cognitive processes associated with creativity, such as divergent thinking (DT). The present study seeks to build on this theoretical perspective extending it through the integration of emotion-related traits and states as two key sources of individual differences in emotionality. The novelty of this research is that it simultaneously examines effects on creativity of the well-established trait EI construct (Petrides, Furnham, & Mavroveli, 2007a), and a multidimensional subjective stress state involving the psychological domains (Hilgard, 1980) of cognition (e.g., appraisal), affect and volition (Matthews, Joyner, Gilliland, Campbell, Huggins, & Falconer, 1999; Matthews et al., 2002). As for the affect-related personality traits, research has addressed the issue indirectly using broad personality traits such as the Big Five or Giant Three personality dimensions (see Batey & Furnham, 2006; Kaufman, 2009). Only recently this relationship has been more specifically studied through the novel construct of trait emotional intelligence (trait EI or trait emotional self-efficacy) (e.g., Batastini, 2001; Guastello, Guastello, & Hanson, 2004; Wolfradt, Felfe, & Köster, 2002). Trait EI is considered the most prevalent model of EI used for research as well as educational and organizational purposes (Day, 2004; Mikolajczak, Menil, & Luminet, 2007b). Trait EI is a personality trait conceptualized as a constellation of affective dispositions. The construct provides a more comprehensive operationalization of the affect-related aspects of personality than general Big Five models (Petrides, 2011; Pérez-González & Sanchez-Ruiz, 2014; Vernon, Villani, Schermer, & Petrides, 2008) and lies wholly outside the taxonomy of human cognitive ability (Carroll, 1993). Trait EI can also be interpreted to some extent as the adult development of the “good temperament”; a collection of affective dispositions that are usually adaptive and can serve to reach social effectiveness and well-being (Pérez-González & Sanchez-Ruiz, 2014). Despite the scant research on this topic, some studies have been conducted in which trait EI correlated with indices of creative personality, creative production in literature, theater and apparel design (Guastello et al., 2004; Wolfradt et al., 2002). However, findings are inconclusive as regards creativity performance as in DT tasks. One of the few existent research on trait EI and DT showed a positive relationship between the two constructs (Batastini, 2001), while another one failed to demonstrate such relationship (Guastello et al., 2004). DT has been positively associated with emotional instability (e.g., Batey et al., 2009; Wuthrich & Bates, 2001) and affective disorders (Furnham, Batey, Anand & Manfield, 2008). However, evidence for the link between creativity and low self-control is inconsistent. For example, trait anxiety has shown to correlate negatively with DT (Wadia & Newell, 1963; White, 1968), originality (Dentler & Mackler, 1964), and innovativeness (Ganesan & Subramanian, 1982) while other studies have failed to find any relationship between trait anxiety and DT (Mijares-Colmenares, Masten, & Underwood, 1993; see Sanchez-Ruiz, 2011 for a review). A recent meta-analysis of 59 independent samples (Byron & Khazanchi, 2011) confirmed a modest negative association between trait anxiety and creativity test performance, but also found evidence for a number of moderator influences on the anxiety-creativity relationship. In sum, there is need to bring light into the relationship between creativity and those components of trait EI that relate to negative emotionality. One way to do so is to investigate the role of negative emotion at the state level, and not only at the trait level.

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The second approach to the creativity-affect link specifically addresses the role of affect-related subjective states in creative performance (Ashby, Isen, & Turken, 1999; De-Dreu, Baas, & Nijstad, 2008; George and Brief, 1996; Isen & Baron, 1991; Mumford, 2003). This strand of research has placed much attention on the valence dimension of states. However, the issue of whether “positive” and “negative” states have enhancing or detrimental effects on creative performance remains unclear (Clapham, 2001; Davis, 2008; Gasper, 2004; Kaufmann, 2003; Vosburg, 1998). Recent efforts have been made to elucidate the links between the stress state and creativity (Byron, Khazanchi, & Nazarian, 2010). Some researchers argue that stress has negative effects on creative performance (Okebukola, 1986; White, 1968), and positive moods are beneficial to it (Ashby et al., 1999). While Clapham (2001) found a positive association between DT performance and baseline pre-measures of trait and state anxiety, some studies have found no association between creativity-related process and anxiety (e.g., Mijares-Colmenares et al., 1993) or induced distress (Isen & Daubman, 1984). Indeed, the Byron and Khazanchi (2011) meta-analysis found no evidence for an association between state anxiety and creativity. In a study conducted by De Dreu, Baas, & Nijstad (2008), it was found that not only the valence, but the activation dimension of states may be relevant when studying them in relation to creativity. The authors found that negative moods enhance creative performance when mood states are activating, as in the case of anger, fear or stress, rather than deactivating, as in the case of sadness or depressive moods. 3. Key limitations of previous studies A number of potential limitations of previous work on the creativity-affect link can be identified. Firstly, the relationship between creativity and affect appears likely to have a domain-specific component (Kaufman & Baer, 2005), yet studies on creativity rarely include domain-specificity in their designs. Artists tend to experience more anxiety, higher affect intensity, and less ability to control emotional experiences than scientists (Feist, 1998, 1999) and than non-artists (Burch, Pavelis, Hensley & Corr, 2006). In addition to variation in trait EI factors across domains (e.g., Castejón, Cantero, & Pérez, 2008; Sanchez-Ruiz, Pérez-González, & Petrides, 2010), there is also some evidence of variation by domain in the relationships between those trait EI factors and performance in DT tasks (Sanchez-Ruiz, Hernández-Torrano, Pérez- González, Batey, & Petrides, 2011). In particular, within the Arts domain, but not within others, high levels of Emotionality and low levels of Self-control have been demonstrated to predict DT performance. This could be due to the fact that distress might be a motivator for artists who are used to work in a state of higher tension and be ¨in touch¨ with their emotions (e.g., Runco, 1999), which might not be the case in sciences, whereby objectivity and emotion regulation is preferred (e.g., Feist, 1998). In addition, the creative work in arts is more linked to affective processes (e.g., music and painting) than it is the scientific problem solving. Taken together, findings suggests that conflicting or ambiguous results in the literature on the creativity-affect link may therefore simply arise due to the use of differing domains or differently balanced samples. Secondly, the concentration of research on the valence of the states has raised some criticisms. For instance, negative valenced states have complex dimensional structures and can be very different from each other (Ellsworth & Smith, 1988), to the extent that they can have contradictory effects (Davis, 2008), as in the case of stress. Furthermore, De Dreu et al. (2008) argue that states and traits may not necessarily be associated with creativity via one unique pathway, but can operate multidimensionally, with different factors such as valence or activation enhancing one or other element of creativity. Some authors (e.g., Kaufmann, 2003) have pointed out the desirability of moving beyond a one-dimensional construct of state when studying it in relation to creativity tasks. Similarly, research on subjective states such as task-induced stress, whereby a multidimensional account appears necessary, refers not only to the negative valence of ‘mood’, “but also to disturbances of motivation (e.g., loss of task interest) and cognition (e.g., worry; Matthews et al., 2006p. 96). Thirdly, despite Russ’ (1999) efforts at developing a contrastable theory linking both states and personality traits to creativity, the majority of empirical studies so far have focused solely on one aspect or the other. This leaves open the possibility that results for states simply reflect a proxy of the effect of traits (or vice versa). Overall, the failure to develop trait-state models for affect-creativity links is potentially a major shortcoming. Lastly, an aspect neglected in some creativity-affect studies is the temporal ordering, which may lead to equivocal conclusions (Davis, 2008). To date, most empirical studies have assumed that states are an antecedent of creativity and have a degree of influence upon it. However, whilst prior states, as well as traits, may be predictive of creativity, Amabile, Barsade, Mueller, and Staw (2005) argue for an affect-creativity cycle in which affect can also be a consequence of creative thought events. Somewhat surprisingly, only a few studies have systematically studied this interrelationship (Akbari Chermahini & Hommel, 2011; Amabile et al., 2005; Feist, 1994). 4. Approach of the present study As noted previously, creativity-affect studies have often overlooked domain-specificities, and the exceptions have most often considered two archetypically ‘contrasting’ domains: Sciences and Arts (Carson, Peterson, & Higgins, 2005; Feist, 1994, 1999). In the present study, we broadened this range by investigating the domains of Arts, Technical & Natural (T&N) Sciences, and Social Sciences. This study also seeks to go beyond the conventional unidimensional approach (positive/negative tone; high/low activation) in examining the creativity-state link. To capture the diverse aspects of subjective experience in affect- related states,

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our analysis is based on previous work by Matthews et al. (2002), where the three psychological domains of cognition, affect and volition were sampled and resulted in three qualitatively different stress state dimensions through factor-analysis of the Dundee Stress State Questionnaire (DSSQ): Engagement, Distress, and Worry. The present study is the first using the DSSQ to examine its association with creative performance (i.e., a DT task). The distress scale is substantially correlated with scales for negative affect, including state anxiety and state depression (Matthews & Campbell, 2010), and so it provides a means for testing the association between DT and the negative moods that may be elicited by performing a cognitive task. The study of creativity in relation to multidimensional stress states (capturing emotional, cognitive, and motivational aspects) may also elucidate links between creativity and other personality traits such as trait EI. Another key element of this study involves the assessment of the creativity-state link in the context of the creativitytrait link. Since both elements are here designed to be explicitly affect-related, more insight may be gained into whether state-creativity effects are merely proxying for trait-creativity effects (trait EI has been previously related to stress state dimensions; Matthews et al., 2014). In particular, we seek to analyze the relationship between stress state and creativity, controlling for trait EI. Finally, we noted the limitations of the empirical literature in terms of addressing the temporal ordering of subjective states and creativity. There is some theoretical support for models of two-way relationships between states and creativity. For example, in Csikszentmihaly’s (1996) notion of the flow state, the individual experiences (affective) states of enthusiasm and enjoyment whilst engaged in creative activity. Amabile et al. (2005), developed an explanation of the relationship between creativity and affect, in which prior (intrinsic) motivation drives creative activity, which in turn feeds back into positively valenced states (i.e., enjoyment). The DSSQ permits temporal analysis of stress state dimensions by using an initial test of pre-task state, and a subsequent test of within-task state, which allows a measure of how stress states may vary during a creative performance task. For example, a previous study using the DSSQ demonstrated reciprocal associations between subjective states and performance of a stressful working memory task (Matthews & Campbell, 2010). We have analyzed the data so as to explore the relationships from state dimensions to DT, and from DT to state dimensions. 5. Objective and hypotheses The objective of the present study is to investigate the role of the stress state in DT performance, addressing the previously noted limitations: The neglect of domain-specificity in the creativity-affect link, the narrow state dimensionality, and the lack of effective trait-state and temporal modelling in research designs. 5.1. Stress state dimensions and trait EI factors Affect-related traits and states are closely linked, in the sense that traits can be defined as general propensities towards particular states (Matthews et al., 2003). Exploring the relationship between stress state dimensions and trait EI is the first step in examining their joint and individual relationship with creativity. In a study conducted by Matthews et al. (2006) using the DSSQ, ability EI was related to lower Distress and Worry before and after three cognitive tasks. Matthews et al. (2015), using the same measure in relation to different tasks, found an association between stress dimensions and several trait EI facets within the Emotionality factor (controlling for personality traits). In particular, emotion perception, empathy, and emotion expression were related to lower Distress and Worry, both pre- and within- task. Empathy and emotion expression facets were also positively related to pre-task Engagement. Furthermore, studies have demonstrated that trait EI is also a strong negative predictor of negative mood (Petrides, Pérez- González, & Furnham, 2007b) and a buffer for the negative impact of stressful events (e.g., Mikolajczak et al., 2007). On the basis of these findings, we stated the first set of hypotheses: H1a.

There will be positive correlations between the four trait EI factors and Engagement (pre- and within-task).

H1b.

There will be negative correlations between the four trait EI factors and Distress (pre- and within-task).

H1c.

There will be negative correlations between the four trait EI factors and Worry (pre- and within-task).

5.2. Stress state dimensions and DT As noted earlier, there is some evidence that (affective) states are associated with creativity and, in particular, activities based on idea generation such as DT tasks (Davis, 2008), although the findings are inconclusive as to the direction of such relationship. We aim to use the combination of multi-dimensional states and traits to assess this relationship in more detail. In the light of hypothesized correlations between subjective states and trait EI, it is appropriate to explore the incremental validity of the stress state dimensions over trait EI for predicting DT performance. As noted above, the creativity-affect link may vary depending on the temporal order of measurement (Amabile et al., 2005). Pre- and within-task stress state dimensions may both be predictive of DT task performance. However, DT may also reciprocally affect within-task state. For this reason, we tested the pre- and within-task state dimensions separately for their relationship with DT. Given the exploratory nature of this study and in order to acknowledge the temporal ordering of the measurements, we controlled for pre-task state dimensions when testing the incremental validity of within- task states. Hence we hypothesized that:

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H2.

Pre-task state dimensions will predict DT over trait EI factors.

H3.

Within-task state dimensions will predict DT over trait EI factors and pre-task state dimensions.

5.3. Stress state dimensions and DT across domains Some studies have found differences in trait EI factors and creativity across domains (Castejón et al., 2008; Sanchez-Ruiz et al., 2010). Furthermore, some personality traits (Big Five; specifically Neuroticism) and trait EI factors (specifically Selfcontrol) have shown domain-specific relationships with DT. Both, Neuroticism and Self-control are theoretically related to the stress state dimension of Distress. This suggests that the Distress-DT relationship may also be domain-specific. In particular, DT has been associated with low Self-control, along with high Neuroticism and high Emotionality, only within the Arts domain (Sanchez- Ruiz et al., 2011). Even though the previous rationale could apply to possible differences in pre-state dimensions, we were mainly interested in the within-state dimensions. This is because the nature of the task was not known to the participants at the pre-task situation, therefore only within-state dimensions could have captured a possible interaction between DT and stress state dimensions. We advanced the last hypotheses: H4. H4a.

The relationship between levels of DT and within-task Distress will vary by domain. Within the Arts domain, high DT scorers will have higher levels of within-task Distress than low DT scorers.

The stress state dimensions of Engagement and Worry may also have important links with creativity; therefore, we also set forth an additional exploratory aim to investigate their relationship with creativity across domains. 6. Method 6.1. Participants The sample consisted of 175 (82 male, 93 female) Spanish undergraduates and recent graduates whose mean age was 25.76 years (SD = 7.07). Participants were drawn from three academic domains: T&N Sciences (n = 64; 36 male, 28 female) including engineering and computer science, and chemistry and biology; Social Sciences (n = 69; 19 male, 46 female) including psychology, psycho-pedagogy, social work, and education; and Arts (n = 46; 27 male, 19 female) including drama, music, and visual arts. Prior to merging undergraduates and recent graduates, these two groups were compared on the key study variables and no significant differences were found between them. 6.2. Measures Divergent thinking: Torrance Test of Creative Thinking (TTCT-Figural Form B: Torrance, 1974). Spanish adaptation by Ferrando, Ferrándiz, Bermejo, Sanchez, Parra, & Prieto (2007). The TTCT- Figural Form is an instrument designed to measure DT through three figural tasks (each taking 10 min.). The test covers four DT dimensions: fluency (number of meaningful responses given), flexibility (number of changes of response category), originality (number of statistically infrequent responses), and elaborateness (number of items to embellish the ideas). In order to assess the stability of the TTCT scores, we estimated the inter- rater agreement between two raters using a two-way random intraclass correlation model (Shrout & Fleiss, 1979). The Intraclass Correlation Coefficients were ICC (3, 1) = .77, .94, .85, .48, and .79, respectively. In the present study, we incorporated only the composite score (total TTCT) or creativity index (CI; Torrance & Ball, 1984), used as an indicator of creative potential. Subjective states: Dundee Stress State Questionnaire (DSSQ short form; Matthews, Szalma, Panganiban, Neubauer, & Warm, 2013), Spanish adaptation by Pérez-González & Sanchez-Ruiz (2007). The DSSQ (Matthews et al., 1999, 2002) short form is a 22- item questionnaire derived from the long 96-item version. It assesses task-related aspects of the subjective state of stress, namely task Engagement (i.e., intrinsic motivation, success striving, energetic arousal, concentration), Distress (i.e., tense arousal, low self-confidence, low hedonic tone), and Worry (i.e., self-focus of attention, low self-esteem, task-relevant and task-irrelevant cognitive interferences). The DSSQ has been validated in relation to sensitivity to tasks, environment stressors, personality, cognitive stress processes and performance indicators (Matthews et al., 2013). The pre-test items relate to current feelings and thoughts about the forthcoming task (e.g., Engagement: “I am very much motivated to do the task”). The post-test asks the respondent to report states retrospectively, during the performance of the task - the previously described TTCT in the case of the present study—(e.g., Distress: “I felt tense”), so we label them ‘within-task states’ in the present study. On this sample, the Cronbach alphas were .82, .83 and .73 for pre-task Engagement, Distress and Worry respectively, and .82, .85 and .81 for the equivalent within-task states, respectively. Trait EI: Trait Emotional Intelligence Questionnaire (TEIQue—v. 1.50: Petrides, 2009), Spanish adaptation by PérezGonzález (2010) .

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Table 1 Intercorrelations, means, SDs, and internal consistencies of the key study variables in the total sample (N = 175). TTCT

1

1. TTCT global TEIQue 2. Wellbeing 3. Self-control 4. Emotionality 5. Sociability DSSQ 6. Pre-Engagement 7. Pre-Distress 8. Pre-Worry 9. Within-Engagement 10. Within-Distress 11. Within-Worry Mean SD ˛

–

* ** ***

−.07 −.10 .01 .13 −.02 −.04 −.03 .11 −.15* −.02 137.7 35.5 .83

2

3

4

5

– .42*** .51*** .53***

– .29*** .36***

– .55***

–

.34*** −.33*** −.07 .32*** −.28*** .04 5.0 0.80 .84

.25** −.40*** −.21** .24** −.38*** −.07 4.3 0.78 .80

.24** −.23** .08 .20** −.17* .01 4.8 0.65 .70

.18* −.29** .05 .18* −.20* .00 4.6 0.67 .74

6

7

8

9

10

11

– −.54*** −.00 .71*** −.27*** -.01 21.61 5.57 .82

– .20** −.39*** .49*** .14 9.86 5.85 .83

– .01 .10 .37*** 10.28 4.54 .73

– −.39*** .05 21.97 5.78 .82

– .03 13.80 6.49 .85

– 7.40 5.19 .81

p < .05. p < .01. p < .001.

The TEIQue is a 153-item questionnaire providing comprehensive coverage of the sampling domain of trait EI. Items are scored on a 7-point Likert scale and completion time is approximately 25 min. The TEIQue encompasses 15 facets and 4 factors (i.e., Wellbeing, Self-control, Emotionality and Sociability) as well as a global trait EI score. An example of item, from the emotionality factor, is “I am normally able to ‘get into someone’s shoes’ and experience their emotions”. The instrument has demonstrated excellent psychometric properties in a number of studies (e.g., Freudenthaler, Neubauer, Gabler, Scherl, & Rindermann, 2008; Gardner & Qualter, 2010; Martins, Ramalho, & Morin, 2010; Mikolajczak, Luminet, Leroy, & Roy, 2007a). On this sample, the internal consistencies on the factors and global scores were .84, .80, .70, .74, .95, respectively. Only factor scores are used in the present study. 6.3. Procedures Demographic information was gathered at the beginning of the survey pack, followed in sequence by the DSSQ pre-task, TTCT (DT tasks), DSSQ post-task, and the TEIQue. Participants completed the DSSQ pre-task previously to completing the TTCT (so they did not know the nature of the task ahead). After the TTCT, they filled out the DSSQ post-task. Testing sessions lasted 60 min approximately. All participants were non-paid volunteers, who were debriefed and received an individualized feedback report. 7. Results A series of t-tests were conducted to examine possible sex differences in the study variables, whereby no statistically significant differences were found. Two univariate ANOVAs were conducted with total TTCT and trait EI scores as dependent variables, respectively, and domain as the independent variable in both cases, yielding non-significant results. Similarly, results from a MANOVA carried out with the pre- and post-state dimensions as dependent variables and domain as the independent variable were non-significant. 7.1. Stress state dimensions and trait EI factors We first tested for differences between pre- and within-task stress state dimensions through a series of paired-samples ttests. DT tasks elicited the next profile of changes: Increased Engagement (change mean = .35; SD = 4.36; t(173) = −1.07; n.s.) and Distress (change mean = 3.94; SD = 6.23; t(173) = −8.34; p < .001; r = .54), and decreased Worry (change mean = −2.87; SD = 5.48; t(173) = 6.91; p < .001; r = .15). To test the first three hypotheses (H1a–H1c), and in line with our expectation that affect-related traits and states are related, the pre- and within-task stress state dimensions were correlated against the trait EI factors (Table 1). Pre- and withintask Engagement both positively correlated with all four trait EI factors; the highest correlations were found with Wellbeing (r = .34, p < .001 and r = .32, p < .001, for pre- and within-task, respectively). In contrast, pre- and within-task Distress were both negatively correlated with all trait EI factors; most strongly with Self-control (r = −.40, p < .001 and r = -.38, p < .001, for pre- and within-task, respectively). Pre- Worry correlated significantly—and negatively- only with Self-control (r = −.21, p < .01) while within-task Worry did not correlate significantly with any trait EI factor.

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Table 2 Hierarchical regression of divergent thinking on trait EI factors, and Pre- and Within-task stress state dimensions.

Step 1

Step 2

Step 3

† * **

Wellbeing Self-control Emotionality Sociability F (4, 166) Adj-R2 Wellbeing Self-control Emotionality Sociability Pre-task Engagement Pre-task Distress Pre-task Worry F (7, 163) Adj-R2  F (7, 163)/ R2 Wellbeing Self-control Emotionality Sociability Pre-task Engagement Pre-task Distress Pre-task Worry Within-task Engagement Within-task Distress Within-task Worry F (10, 160) Adj-R2  F (10, 160)/ R2

B

t

−.14 −.13 −.02 .26 2.44* .03 −.15 −.18 −.01 .26 −.03 −.08 −.07 1.66 .03 .61 / .01 −.19 −.22 .00 .26 −.15 −.01 −.08 .19 −.18 −.01 2.17* .08 3.19* /.05

1.46 1.55 .19 2.65**

1.52 1.93† .06 2.59* .28 .82 .91

1.86† 2.42* .01 2.68** 1.24 .06 .89 1.72† 2.00* .16

p < .10. p < .05. p < .01.

7.2. Stress state dimensions and DT Attending to the need for examining the independent role of stress state when controlling for emotion-related traits, a three-step hierarchical regression was carried out to establish the incremental validity of pre-task stress dimensions vis-àvis the trait EI factors in predicting DT (H2) and the subsequent incremental validity of within-task state dimensions (H3). These results are depicted in Table 2. At step one, the trait EI factors jointly predicted DT, F(4, 166) = 2.44, p < .05, R2 adj = .03. Individually, Sociability was a significant positive predictor of DT (ˇ = .26, t = 2.65, p < .01). With the addition of the three pre-task state dimensions at step two, the model was no longer significant overall, and the three pre-task state dimensions did not demonstrate incremental validity over the trait EI factors in predicting DT, F(7, 163) = 1.66, p > .10, R2 adj = .03. At step three, with the within-task state dimensions added to the equation, the model regained overall significance, F(10, 159) = 2.17, p < .05, R2 adj = .80. Withintask Distress was individually significant (ˇ = −.18, t = 2.00, p < .05), and within-task Engagement was close to significance (ˇ = .19, t = 1.72, p = .08). Sociability remained a significant positive predictor, (ˇ = .26, t = 2.68, p < .01) and Self-control became a significant negative predictor (ˇ = −.22, t = 2.42, p < .05), as well as Wellbeing (close to significance; ˇ = −.19, t = 1.86, p = .06). Quadratic specifications were also used to test for non-linear, specifically inverted U- shaped, relationships between each of the stress state dimensions and DT, but none were identified.

7.3. Stress state dimensions and DT across domains To address H4, H4a and our additional exploratory objective, we analyzed the data considering that domain-specificity is a crucial factor in the creativity-affect link, and that there may be a relationship from the DT task to the stress states. Thus, three factorial ANOVA analyses were carried out, with each of the within-task state dimensions (i.e., Engagement, Distress and Worry) as dependent variables and DT scores (high/low by median score), and domain as the between-subject variable. Independent sample t-tests were then conducted to test for significant differences in the within-task stress dimensions between the two DT groups, within each domain. These results are presented in Table 3, with descriptive statistics in Table 4. Figure 1 illustrates the high/low DT × domain interaction for each stress state dimensions. In the ANOVA with within-task Engagement as the dependent variable, neither domain DT nor their interaction were significant. Analyses (t-tests) revealed marginally significant differences in Engagement across DT levels of performance for

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Table 3 Analysis of variance results for Within-task state dimensions by high/low divergent thinking and domain. Source ANOVA

DV Within-task Engagement

Domain High/low DT Domain × high/low DT ANOVA Domain High/low DT Domain × high/low DT

Within-task Distress

ANOVA Domain High/low DT Domain × high/low DT

Within-task Worry

df Between s

F

2

p

2 1 2

2.06 2.40 1.33

.03 .02 .02

.13 .12 .27

Between s 2 1 2

1.88 1.22 3.70*

.03 .01 .05

.16 .27 .03

Between s 2 1 2

1.24 5.49* 2.43†

.02 .04 .03

.29 .02 .09

Note. DV: Divergent thinking. † p < .1. * p < .05. Table 4 Stress state dimensions by domain and high/low divergent thinking. Within-task Engagement Low DT T&N Sciences M 21.44 SD 5.67 Social sciences M 20.74 SD 6.72 Arts M 22.14 SD 4.58 Total M 21.32 SD 5.85

Within-task Distress

Within-task Worry

High DT

Total

Low DT

High DT

Total

Low DT

High DT

Total

20.83 6.09

21.13 5.85

15.40 6.02

12.03 6.28

13.71 6.33

6.50 5.25

7.03 5.08

6.76 5.13

22.95 5.30

21.76 6.16

16.11 7.19

13.36 6.77

14.84 7.08

8.77 4.89

5.96 3.92

7.47 4.65

24.62 4.95

23.46 4.89

10.90 4.42

13.80 6.29

12.44 5.63

9.95 6.80

6.70 4.73

8.22 5.95

22.63 5.67

21.97 5.78

14.61 6.49

12.98 6.43

13.80 6.49

8.22 5.63

6.56 4.57

7.40 5.19

Note. Subsample sizes: T&N (Technical & Natural) = 32 high, 32 low DT; Social Sciences = 30 high, 35 low DT; and Arts = 24 high, 22 low DT. DT: Divergent thinking.

artists, whereby high DT scorers (M = 24.62, SD = 4.95) scored lower than low DT scorers (M = 22.14, SD = 4.58; t (44) = 1.74, p < .10, r = .17). The ANOVA with within-task Distress as the dependent variable did not show a main effect of neither domain nor DT. However, the interaction between domain and DT was significant, F (2, 168) = 3.70, p < .05, 2 = .05. Specifically, as it can be seen in Fig. 1, the Distress-DT link was positive for the Arts group, but negative for T&N Sciences and Social Sciences groups. Independent sample t-tests showed that, within the Arts group, high DT scorers (M = 13.80, SD = 6.29) reported more within-task Distress than low DT scorers (M = 10.90, SD = 4.42; t (44) = 1.77, p < .05, d = .53), while for the T&N Sciences group, high DT scorers (M = 12.03, SD = 6.28) showed less within-task Distress than low DT scorers (M = 15.40, SD = 6.02; t (62) = 2.19, p < .05, d = .56). As can be seen in Fig. 1, the Distress-DT association in the Social Science group was similar to that for T&N Sciences group, although the difference in within-task Distress by DT level was non-significant. For within-task Worry, the ANOVA demonstrated a significant main effect of DT, F (1, 168) = 5.49, p < .05, 2 = .04. High DT scorers showed lower levels of Worry (M = 6.56, SD = 4.57) than low DT scorers (M = 8.22, SD = 5.63). There was no significant main effect of domain or the domain × DT interaction although the statistical trend is noteworthy, F (2, 168) = 2.43, p < .09, 2 = .03. Within the Social Sciences group, high DT scorers (M = 5.96, SD = 3.92) scored higher than low DT scorers (M = 8.77, SD = 4.89; t (63) = 2.52, p < .05, d = .64), and the same occurred within the Arts group high DT scorers, M = 6.70, SD = 4.73; and low DT scorers, M = 9.95, SD = 6.80; t (44) = 1.86, p < .05, d = .56. The T&N Sciences group did not exhibit this negative Worry-DT association. The same analyses were conducted using trait EI and pre-state dimensions as covariates yielding similar results. 8. Discussion 8.1. Summary of findings This study examined the relationship between stress state dimensions (pre- and within-task) and DT performance, controlling for trait EI factors, and explored the domain-specific aspect of such relationship.

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Fig. 1. Within-task Engagement, Within-task Distress and Within-task worry mean scores for high/low divergent thinking (TTCT) Scorers by Domain.

The four trait EI factors correlated with both the pre- and within-task dimensions of Engagement (positively) and Distress (negatively), but not with Worry. Pre-task dimensions of stress state did not show incremental validity in predicting DT over the trait EI factors. However, the within-task stress state dimensions showed joint incremental validity for DT over the combination of trait EI factors and pre-task dimensions. Within-task Distress was a significant individual (negative) predictor of DT for the total sample and, according to our expectations, the relationship between within-task Distress and DT varied across domains, with higher Distress being associated with a high level of DT performance in the Arts domain, but with a low level of DT performance in the other domains. Across the sample, higher within-task Worry was associated with a low level of DT performance, except in the T&N domain. 8.2. Stress state dimensions and trait EI factors Results provided support for the first two hypotheses (1a and 1b); the four trait EI factors were positively correlated with pre- and within-task Engagement (H1a), and negatively with pre- and within-task Distress (H1b). Pre-task Worry (but not within-task) correlated only with Self-control among the trait EI factors, thus partially supporting hypothesis 1c (H1c). The

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strongest association was found between Self-control and Distress (pre- and within-task), thus supporting the theorised inverse relationship between the two constructs. During the DT task, the Distress state of participants increased while their level of Worry decreased. Overall, trait EI factors were associated with high Engagement, low Distress, and low Worry. This pattern can be viewed as generally adaptive for task performance (e.g., Matthews & Campbell, 2010). Our results suggest that trait EI factors have a role in influencing stress state dimensions before and during tasks requiring concentration, in this case, a DT task. This finding is in line with Matthews et al. (2015) who found facets within the Emotionality trait EI factor to be associated with reduced Distress in a task of visual search of facial emotions. We replicated similar correlations regarding the Emotionality trait EI factor as a whole. Trait EI may work as a significant moderator of the effect of affective states on creativity, in particular, trait EI may moderate detrimental effects of negative stress states on creativity. 8.3. Stress state dimensions and DT The regression analysis showed that two trait EI factors (Sociability and Self-control) are positively associated with DT. Our second hypothesis (H2) was not supported by the results since the three pre-task state dimensions did not demonstrate incremental validity over the trait EI factors in predicting DT. We found non-significant correlations between the three pre-task state dimensions and DT (Table 1). This result is in contrast with findings from previous studies linking stress and DT (e.g., Amabile et al., 2005; Davis, 2008; Estrada, Isen, & Young, 1994). Some explanations for this lack of correlation emerge when considering the effect of within-task state dimensions. Within-task state dimensions displayed incremental validity over the combination of trait EI and pre-task stress dimensions in predicting DT, thus supporting H3. Indeed, trait EI facets and within-task state were independently related to DT in the regression analysis. Within-task Distress predicted poor DT performance. This is in line with research finding detrimental effects of Distress on creativity. Martindale (1989, 2007) processing model conceptualizes creativity in relation to a neural net containing many interconnected nodes. Creativity is facilitated when multiple nodes are activated, increasing the probability of novel associations between nodes. The same author argues that negative affect increases cortical arousal, thus leading to fewer active nodes, and consequently fewer unusual associations. Also, following this model, negative states such as Distress elicit less material in memory. Furthermore, the increased arousal associated with stress may create a preference for more predictable responses over unusual ones (Isen, 1984). Future research on the Distress-DT link will benefit from including memory tasks as well as neuroscience techniques (e.g., neuroimaging and cortisol assays) to test these hypotheses. In sum, while pre-task state dimensions did not predict DT, controlling for affect-related traits, within-task state dimensions did. The dispersion of scores was similar for pre- and the within-task state dimensions, ruling out a lack of variance in pre-task state dimensions as a reason for their null correlation with DT. The explanation then may be more connected with the (transactional) appraisal of the task. The main difference between pre-and within-task state dimensions is that only the latter embody a response to task. On this view, pre-task state dimensions may thus be unrelated to DT because they cannot be affected by performance. They cannot even be affected by reliable expectations of performance, based on perceived skill-to-task match, because the nature of the task was not revealed to participants before hand. This is consistent with the view of states as products of personality and situation and the recent findings supporting that creative thinking tasks might affect people’s mood (Akbari Chermahini & Hommel, 2011). 8.4. Stress state dimensions and DT across domains The relationship between within-task Distress and the level of DT was found to vary significantly by domain, supporting hypothesis H4a. For artists, the association was positive, thus supporting hypothesis H4b. The association was negative for the other two groups, although only significant for T&N Sciences. The consistency of these findings with those showing a differential relationship between Self-control trait EI and DT across domains (Sanchez-Ruiz et al., 2011) supports the idea that there is some correspondence between the trait and the state levels of this relationship. However, given that stress state dimensions had incremental validity over traits in the prediction of DT, a tentative state-level explanation for the association is also required. We suggest two possible explanations. First, whereas DT performance was associated with elevated Distress, it may relate to the Distress facets differently across the participant groups. The Distress state dimension loads onto a number of lower level facets, including tense arousal, as well as confidence and control, and hedonic tone. These facets may be differentially related to performance; Matthews and Campbell (1999) found that low confidence related more strongly to working memory impairment than either tension or hedonic tone. A figurative DT task may be unfamiliar to non-artists, thus undermining their confidence and control or performance self-efficacy (giving higher Distress scores), and, in turn, impairing concentration and DT performance. In terms of the appraisal theory of Lazarus (1991), a stressful task may induce “anxiety” in non-artists, for which the “core relational theme” is: “Uncertain, existential threat” (Lazarus, 1991p. 235). By contrast, for artists, the confidence and control facet of Distress may be less relevant due to the more domain- appropriate nature of the figurative DT task. Instead, the relationship with performance may be centred on another facet of Distress: tense arousal. This would be in line with studies reporting a relationship between activating stress and enhanced creativity (e.g., Russell & Barrett, 1999). It is possible that artists in particular learn to make use of tense arousal as an activating state in favour of creativity. Indeed,

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around half the individuals within the Arts domain were drama and music students who must manage the tension of live performance. A second, complementary hypothesis is that Distress has differing motivational effects in artists and non-artists. Martin’s (2001) mood- as-input hypothesis proposes that the influence of mood on performance operates through motivating effects that depend on context. For example, a negative mood is motivating if the context is one of trying to attain a performance target, but de-motivating if the context is one of performing to enjoy a task. Arts students, who are accustomed to stress in creative contexts, may interpret distress as a cue that motives productive creativity. Work on anxiety in sports has also suggested that anxiety may be used as a motivator for certain athletes (Raglin & Hanin, 2000). Non-arts students may interpret Distress to mean that the task is unpleasant and to be avoided, leading to reduced task-directed effort. It is worth noting that the interaction effect is partly determined by the group of Arts students scoring low on DT, and lower on distress than the two other groups. One hypothesis for this could be that a portion of the Arts students did not feel motivated enough because they probably found the DT task to be trivial. However, this hypothesis is not supported by the interactions found regarding Engagement. Results for Engagement showed that, although the interaction between high/low DT and domain did not reach significance, within-task Engagement was higher for artists, and the relationship between Engagement and DT was most positive in this group, consistent with the motivational hypothesis previously suggested. Higher DT performance was also related to lower Worry in the general sample. A tentative explanation for this is that when the resources and attention are dedicated to the task, there is less room for intrusive thoughts (the cognitive interference facet is loaded by Worry). Again, there is some variation by domain; individuals in Arts and Social Science who performed better on the DT test were less worried during the task, while for those in T&N Sciences, higher DT was linked to higher Worry. The latter group may be more reliant on use of introspection as a resource to support creativity than the Arts and Social Sciences groups. Divergent thoughts may also be perceived as intrusive to task by this group. This is linked to the idea that the educational training of participants within T&N Sciences is usually focused on convergent problems (closed-ended) versus divergent problems (open-ended) (Rugarcia, Felder, Woods, & Stice, 2000). In sum, Distress may vary at the facet level across different domains. It may also have special motivational properties for artists. It should be noted that these suggestions may help explain the apparently contradictory findings in the literature about the overall creativity-stress state link. Future studies could further explore these effects, including analyses of the stress dimensions at the facet level (e.g., by using the long version of the DSSQ). Further work to explore the respective roles of valence and activation in creativity performance would also be of value. 8.5. Reflections and implications for future research This study has shown that domain is an important moderator of the relationship between Distress and DT. Future studies should consider including this variable in their designs. The moderator effect of domain may help explain the weakness of the overall association between state anxiety and creativity in the Byron and Khazanchi (2011) meta-analysis. Also, further investigation could replicate these results and expand the samples used to allow analyses within domains. This study explored wide domains (e.g., Arts), but this can be extended to specific domains (e.g., music), and even micro-domains (e.g., percussion; Baer & Kaufman, 2005). Indeed, this is a long-standing recommendation within the literature (see Jarvin & Subotnik, 2006; Mumford, 2003; Sears, 1986). In addition, it is worth noting that the creativity-affect pattern within Social Science was similar to that of Sciences regarding Distress, and similar to that of Arts regarding Worry. These differential patterns could be further elucidated in future research focusing on the creativity-affect link in this particular domain and its sub-disciplines. The different creativity-affect links found in this study could be due to either dispositional characteristics or developmental components (Simonton, 2009), such as the training in a particular field. Within the person-environment fit approach to vocational and counselling psychology (Holland, 1997; Sanchez-Ruiz et al., 2010), academic and vocational choices are understood as expressions of personality dispositions. However, to rule out that it is the maturity within the discipline what results in the differential effects of emotions on DT performance across domains, students need to control for number of years of study, and use longitudinal designs to thoroughly investigate the developmental hypothesis. Future research can further investigate the type of creativity tasks performed by students in different domains as well as the amount of exposure to creative work in each domain as potential factors influencing the domain- specificity of the creativity-affect link. For example, while some researchers have claimed that there is only partial support for the idea of an Arts bias in creativity research (e.g., Cropley, 2014), others have argued that Arts bias exists indeed in the education of creativity, since creativity is represented in Arts subjects curriculum more than in other subjects (Wyse & Ferrari, 2014). Previous work on the relationship between creativity and subjective states mainly examined the valence dimension. The present study instead conceptualized stress as a multifaceted state, which has dimensions defined largely in terms of negative valence (except Engagement). These dimensions had distinctive associations with DT. Future research will benefit from also examining positively valenced states in a multidimensional way. De Dreu et al. (2008) have suggested that positive activating states enhance creativity through cognitive flexibility, but that the activating states with negative tone are also creativity enhancing in terms of perseverance. It would be useful to explore this and other possible pathways for the creativity-affect link. For a more refined examination of this relationship, when the research design allows, the DT subscales (i.e., fluency, flexibility, originality and elaborateness) could also be analyzed for possible differential relationships with affect.

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Also, findings, in particular the interaction effects found, need to be replicated in studies using not only figural DT tasks but also verbal ones, and tests other than the TTCT. Lastly, DT is only one aspect of creativity, as noted earlier, and has received some criticisms (see Batey & Furnham, 2006; Runco, 2008 for a review on strengths and limitations of DT tests). In sum, convergent thinking and other features of the construct would need to be explored in order to avoid the monooperational bias and have a bigger picture of the creativity-stress link. Measuring creativity through psychometric and alternative assessment methods is recommended given the complexity and broadness of the construct and the evidence supporting the method effect hypothesis (see Plucker, 2004), which refers to the idea that “different criteria of creativity will demonstrate different patterns of association with other psychological constructs” (Sanchez-Ruiz, 2011). In line with the previous comment, future studies could potentially explain more variance in creativity dimensions by using more “objective” measures such as sensors (Lehman, D’Mello, & Graesser, 2012) and leaning analytics (Artino & Jones Ii, 2012). This study has built upon previous correlational work by evaluating stress state prior to and within a creative task. Although the task-to-state relationship inferred here is the focus of a number of general studies using the DSSQ (e.g., Matthews et al., 2006), it has been largely overlooked by research on creativity and affect. Future research could explore possible causal pathways by refining temporal measurements of states and mood induction. Also, future experimental designs can manipulate the DT task by having some of the participants engage in a filler task (control group), instead of the TTCT task (experimental group), in order to test the hypothesis that DT causally predicts changes in the multidimensional state of stress. This would rule out the possibility that results are a function of participant’s perceptions of success or failure during their particular experience with the task. A methodological issue concerning this paper is that the within-task DSSQ measure applied is retrospective, potentially allowing recall bias (Frederickson & Kahneman, 1993; Rosenberg & Ekman, 1994). In addition, the 60-min test duration is a relatively long timespan during which subjective state might change. However, research has typically found that states measured by the DSSQ are fairly stable during a single test session (Matthews et al., 2002). By contrast, 6-month test-retest correlations for DSSQ scores are low, as required for a state measure. If within-task state could be assessed concurrently with processing, the bi-directionality of the DT-state could be further examined. In addition, we are aware that the sample size used in the present study is too small for the application of more sophisticated methods of data analysis allowing for a comprehensive examination of the proposed domain- specificity in the creativity-affect link.

9. Conclusion This study has investigated the links between subjective stress state dimensions (pre- and post- task) and an indicator of creativity performance (DT), controlling for trait EI factors. Some key limitations of previous research on the creativity-affect link have been identified and addressed, namely the lack of attention to domain-specificity, narrow affect-state dimensionality, lack of consideration for temporality in research designs, and neglect of trait-state models. Core results were the incremental validity of the three within-task stress state dimensions over trait EI factors in predicting DT and the domainspecificity of the relationship between the stress state dimension of Distress and DT. Our results have practical implications for teaching and learning practices and training on DT. First, our study helps demystify the idea that creativity is higher in artists than in non-artists (Arts bias), and points out at the potential benefits of developing emotional strengths in different fields of creativity. Being aware of how affect might moderate, boost or impair creativity is the first step for students to apply self-regulatory strategies and could help prevent failure in the pursuit of the ideal flow state when dealing with DT-demanding tasks. Even though this is a correlational study and thus causation cannot be inferred, our results invite to test the use of different emotion regulation strategies for each academic domain and for ˜ different emotional states, as well as training on emotional intelligence (e.g., Pena-Sarrionandia, Mikolajczak, & Gross, 2015) as possible ways to enhance creative performance. DT training programs should consider the emotion-related idiosyncrasies of a domain, use DT domain-specific tasks, and facilitate opportunities for students to creatively perform under proto-typical stress conditions in their particular field. In a similar vein, findings from this study can inform educators about the interactions between emotions (at the state and trait levels) and creativity in the learning environment (e.g., Newton, 2013). Teachers’ awareness of students’ emotional profiles and needs, as well as the emotional states prior and post creative tasks, can help support students in transforming emotions such as stress in order to enhance creativity and problem solving skills through teaching and learning and emotion management practices in the classroom. In addition, our results indicate that the emotional processes related to creative work might be different between Art students and others, which might require the exploration of different educational and counseling strategies. One of the research implications of this study is that findings supported the necessity of taking into account the multidimensional state of stress and emotion-related traits in understanding creative performance (DT). The study also showed evidence of the complexity of the relationship between DT performance and the stress state, which may be potentially bidirectional and domain-specific. Finally, it has been demonstrated that a trait-state model to explain this relationship creates a more complete picture of the relationships between creativity and affect.

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