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Creativity and Cognitive Skills among Millennials: Thinking Too Much and Creating Too Little

Creativity and Cognitive Skills among Millennials: Thinking Too Much and Creating Too Little ORIGINAL RESEARCH published: 25 October 2016 doi: 10.3389/fpsyg.2016.01626 Creativity and Cognitive Skills among Millennials: Thinking Too Much and Creating Too Little 1 2, 3 3, 4 Brice Corgnet , Antonio M. Espín * and Roberto Hernán-González 1 2 EMLYON Business School, Univ Lyon, GATE L-SE UMR 5824, Ecully, France, Economics Department, Middlesex University Business School, London, UK, Granada Lab of Behavioral Economics, Universidad de Granada, Granada, Spain, Business School, University of Nottingham, Nottingham, UK Organizations crucially need the creative talent of millennials but are reluctant to hire them because of their supposed lack of diligence. Recent studies have shown that hiring diligent millennials requires selecting those who score high on the Cognitive Reflection Test (CRT) and thus rely on effortful thinking rather than intuition. A central question is to assess whether the push for recruiting diligent millennials using criteria such as cognitive reflection can ultimately hamper the recruitment of creative workers. To answer this question, we study the relationship between millennials’ creativity and their performance on fluid intelligence (Raven) and cognitive reflection (CRT) tests. The good news for recruiters is that we report, in line with previous research, evidence of a positive relationship of fluid intelligence, and to a lesser extent cognitive reflection, with convergent creative thinking. In addition, we observe a positive effect of fluid intelligence on originality Edited by: and elaboration measures of divergent creative thinking. The bad news for recruiters is Nikolaos Georgantzis, University of Reading, UK the inverted U-shape relationship between cognitive reflection and fluency and flexibility Reviewed by: measures of divergent creative thinking. This suggests that thinking too much may hinder Noelia Sánchez-Pérez, important dimensions of creative thinking. Diligent and creative workers may thus be a University of Murcia, Spain Conny Ernst-Peter Wollbrant, rare find. University of Gothenburg, Sweden Keywords: creativity, cognitive reflection, intelligence, cognition, intuition *Correspondence: Antonio M. Espín a.espin@mdx.ac.uk INTRODUCTION Specialty section: Evidence from a recent survey reports that managers are three times more likely to hire a mature This article was submitted to worker than to hire a millennial (born between 1980 and 2000; Rainer and Rainer, 2011) despite Personality and Social Psychology, desperately needing their creative talent . Mature workers are appealing to recruiters because they a section of the journal are seen as more reliable and more committed than millennials. The dilemma for managers is thus Frontiers in Psychology to hire millennials that are both diligent and creative. Received: 20 July 2016 Recent studies have shown that firms can secure the hiring of diligent millennials by relying on Accepted: 05 October 2016 measures of cognitive skills. For example, intelligence has been found to be the main predictor of Published: 25 October 2016 overall work performance in a wide variety of occupations and across age and gender (e.g., Hunter Citation: and Hunter, 1984; Olea and Ree, 1994; see Schmidt, 2009 for a review). Standard measures of Corgnet B, Espín AM and cognitive ability have been found to correlate positively with task performance (Schmidt et al., 1986; Hernán-González R (2016) Creativity and Cognitive Skills among Millennials: Murphy, 1989) and negatively with counterproductive work behaviors such as theft or absenteeism Thinking Too Much and Creating Too Little. Front. Psychol. 7:1626. See the following press release: http://www.forbes.com/sites/susanadams/2012/09/24/older-workers-theres-hope-study- doi: 10.3389/fpsyg.2016.01626 finds-employers-like-you-better-than-millennials/#1f5799cb4aa6 (accessed September 21, 2016). Frontiers in Psychology | www.frontiersin.org 1 October 2016 | Volume 7 | Article 1626 Corgnet et al. Creativity and Cognitive Skills among Millenials (Dilchert et al., 2007). Moreover, the results of a recent study a disposition-based definition (“cognitive styles”, reflective suggest that these effects may be mediated by individuals’ vs. intuitive) and is not adequately measured by standard cognitive styles (Corgnet et al., 2015b). In particular, Corgnet intelligence tests (which assess “cognitive ability”) but by tasks et al. (2015b) find that millennials characterized by a more of cognitive reflection like the Cognitive Reflection Test (CRT; reflective style (as measured by the Cognitive Reflection Test; Frederick, 2005). Individuals characterized by a more reflective Frederick, 2005) are more diligent, displaying higher levels of mind tend to show higher levels of self-control and lower task performance and lower levels of counterproductive work levels of “cognitive impulsivity” (Frederick, 2005; Kahneman and behaviors . A crucial caveat is whether hiring millennials based Frederick, 2007; Cokely and Kelley, 2009; Oechssler et al., 2009; on cognitive measures may ultimately select less creative workers. Toplak et al., 2011; Brañas-Garza et al., 2012). To address this point we need to assess the relationship between From this perspective, one can conjecture that cognitive cognitive skills and creativity. reflection may relate negatively to creativity. This is the case Traditionally, intelligence, and creativity have been because a number of studies suggest that the capacity to control considered to be unrelated (Getzels and Jackson, 1962; Wallach one’s attention and behavior may even be detrimental for creative and Kogan, 1965; Batey and Furnham, 2006; Sawyer, 2006; thinking (for a review, see Wiley and Jarosz, 2012a). For example, Weisberg, 2006; Runco, 2007; Kaufman, 2009; Kim et al., 2010). creative problem solving has been shown to relate positively In a meta-analysis, Kim (2005) finds that the correlation between to moderate alcohol intoxication (Jarosz et al., 2012), which is creativity test scores and IQ varies widely and is, on average, known to impair inhibition and attentional control (Peterson small (r = 0.174). et al., 1990; Kovacevic et al., 2012; Marinkovic et al., 2012). However, a growing consensus has emerged in recent Similarly, an “experiential” thinking style (which maps onto Type research stressing a close relationship between intelligence and 1 processing) has been found to correlate positively with creative creative performance (see Silvia, 2015, for a review). This performance (Norris and Epstein, 2011). emerging consensus heavily relies on recent studies that have As mentioned, past literature arrived at conflicting employed more sophisticated statistical techniques and more conclusions regarding whether executive cognition favors robust assessment methods than prior research on the topic. (e.g., Nusbaum and Silvia, 2011; Beaty and Silvia, 2012; Silvia, For example, the use of latent variable models has allowed 2015) or hampers (e.g., Eysenck, 1993; Kim et al., 2007; Ricks researchers to uncover a positive and significant relationship et al., 2007; Norris and Epstein, 2011; Jarosz et al., 2012; Wiley between creativity and intelligence using data from previous and Jarosz, 2012b) creative thinking. Dual-process theory can studies that reported non-significant correlations (Silvia, 2008b). reconcile these apparently conflicting findings by positing that The recent wave of research on intelligence and creativity has creativity may be generated by a mix of Type 1 and Type 2 also improved upon traditional assessment of creativity that processes (Allen and Thomas, 2011; Ball et al., 2015; Barr et al., exclusively relied on scoring methods based on the originality 2015; see Sowden et al., 2015, for a review). It follows that the and uniqueness of responses in creative tasks (such as finding dual-process approach lays out a promising research agenda unusual uses for an object). These traditional scoring methods based on assessing the exact mix of Type 1 and Type 2 processes are imprecise because they confound several factors, such as that bolsters creativity as well as analyzing separately the effect fluency and sample size (Hocevar, 1979; Silvia et al., 2008), and of algorithmic and reflective Type 2 processes on creative can thus lead to inaccurate estimates of the relationship between thinking. intelligence and creativity (Silvia, 2008a; Nusbaum and Silvia, Following a dual-process approach, Barr et al. (2015) find 2011). The results of this new wave of research on creativity and experimental evidence of an important effect of controlled Type intelligence have been taken as evidence that executive cognition 2 analytic processes on both convergent and divergent (Guilford, is undoubtedly beneficial to creative thinking (Silvia, 2015). 1967) creative thinking. In particular, they find that both Yet, although there is an obvious link between intelligence cognitive ability (measured as the combination of numeracy and and executive cognition, from the point of view of modern verbal skills) and reflective cognitive style (average of scores in dual-process theory (Evans, 2008, 2009; Stanovich, 2009, 2010; the CRT and base-rate problem tasks) covary positively with one’s Evans and Stanovich, 2013), one should distinguish between capacity to make remote associations, that is, with convergent algorithmic and reflective cognitive processes. Algorithmic creative thinking. Regarding divergent creative thinking, Barr processes are typically associated with computational efficiency et al. (2015) show that cognitive ability but not cognitive and are measured by standard intelligence tests whereas reflective reflection predicts higher originality scores in an alternate uses processing is associated with a disposition to employ the task. Fluency in the latter task, however, was not correlated with resources of the algorithmic mind, that is, to switch from either cognitive measure. autonomous “Type 1” thought to analytic “Type 2” (working In this paper, we use a similar approach to Barr et al. memory-dependent) thought. The reflective mind thus has (2015) and investigate how both types of cognitive processes affect creativity. In particular, we analyze how cognitive abilities Positive effects of cognitive reflection on people’s willingness to choose socially- (measured using Raven as a test of fluid intelligence) and efficient resource allocations (Lohse, 2016; Capraro et al., 2016) as well as to trust cognitive styles (intuitive vs. reflective; as measured by the strangers (Corgnet et al., 2016) suggest other possible channels through which CRT) relate to convergent and divergent creative thinking. organizations may benefit from hiring individuals with a more reflective cognitive We extend Barr et al. (2015) by analyzing other measures style. Cognitive reflection has also been found to play a key role in moral judgment (e.g., Paxton et al., 2012; Pennycook et al., 2014). of divergent thinking such as flexibility and elaboration and Frontiers in Psychology | www.frontiersin.org 2 October 2016 | Volume 7 | Article 1626 Corgnet et al. Creativity and Cognitive Skills among Millenials by exploring possible non-linearities between creativity and Cognitive Style Assessment cognitive measures. We measured the participants’ tendency to rely on intuition vs. Given the conflicting results regarding whether executive reflection using the CRT introduced by Frederick (2005). The cognition is beneficial or detrimental for creative thinking, test is characterized by the existence of an incorrect response we conjecture that there might exist a non-linear relationship which automatically comes to mind but has to be overridden in between different measures of creativity and cognition. order to find the correct solution. To the original CRT questions, Specifically, it might be that a minimum level of executive we added four questions recently developed by Toplak et al. cognition is necessary for creative performance but, beyond some (2014). This extended task (see Text S1) will allow us to uncover level, the relationship disappears or even turns negative. This potentially non-linear relationships that would be hard to observe might explain why previous findings seem to be inconsistent. using the classical three-item task (Frederick, 2005). In Table S1, A related line of reasoning has been proposed in the so- we display the proportion of subjects answering each question called “threshold hypothesis” of the relationship between correctly, split by gender. As expected, males performed better IQ and creativity (Guilford, 1967; Jauk et al., 2013). The in the test than females (Frederick, 2005; Bosch-Domènech et al., threshold hypothesis states that intelligence is positively related 2014). Our measure of cognitive reflection is given by the total to creative thinking for low IQ levels but the relationship number of correct answers (from 0 to 7). The full distribution of blurs for high IQ levels. Similar arguments arise in recent correct answers by males (mean± SD= 4.09± 2.31) and females accounts of the “mad genius hypothesis”: moderate levels of (mean ± SD = 2.89 ± 2.03) is provided in Figure S1. inhibitory or top-down control dysfunction, characteristic Convergent Creative Thinking of subclinical psychiatric populations (e.g., mild ADHD and schizophrenia disorders), can spur creativity under some We used a subset of the Remote Associate Test (RAT; Mednick, 1962) to measure subjects’ ability to make remote associations. conditions whereas clinical-severe levels typically lead to impoverished creative thinking (Schuldberg, 2005; Abraham In particular, subjects were shown 13 sets of three words (e.g., widow-bite-monkey) and asked to find a word which relates to et al., 2007; Jaracz et al., 2012; Acar and Sen, 2013; Abraham, all the three words provided (in this example the solution is 2014). “spider”). Our measure of convergent thinking is the number of problems correctly solved (from 0 to 13). METHODS Divergent Creative Thinking Participants and General Protocol We measured divergent thinking using a variant of the Alternate Participants were 150 students (46.67% female; age: mean ± SD Uses Task (AUT; Guilford, 1967). Participants were instructed = 20.23 ± 1.96) from Chapman University in the U.S. These to provide as many unusual uses of a pen as possible during 6 participants were recruited from a database of more than 2000 min. We construct four different measures of divergent thinking: students. Invitations to participate in the current study were sent fluency, originality, flexibility, and elaboration. We measured to a random subset of the whole database. This study is part of fluency as the total number of answers provided by a participant. a larger research program on cognition and economic decision Three raters were presented with a random list of answers and making. The local Institutional Review Board approved of this asked to score the degree of originality of each entry using research. All participants provided written informed consent a 1 (not at all) to 5 (very much) Likert scale. We computed prior to participating. We conducted a total of 12 sessions, nine originality as the sum of the average score of the three raters had 12 participants and three had 14 participants. On average, for all the entries provided by a participant, divided by the total sessions lasted for 45 min. All subjects completed the same tasks number of answers. Following Troyer and Moscovitch (2006) in the following order: (1) CRT, (2) Raven test, (3) Remote and Gilhooly et al. (2007), all the answers were classified in associates task, (4) Alternate uses task. Subjects had 6 min to broad differentiated categories (e.g., uses of the pen as cloth or complete each task and a 2-min break after completing the Raven hair accessories). Then, flexibility was measured as the number test. of different categories provided by each participant. Finally, elaboration refers to the average amount of detail (from 0 to 2) provided by each participant. Measures Cognitive Ability Assessment Participants completed a subset of Raven progressive matrices Statistical Analysis test (Raven, 1936). Specifically, we used the odd number of the For the data analysis, we start by showing the descriptive statistics last three series of matrices (Jaeggi et al., 2010; Corgnet et al., of all the measures used and their zero-order correlations. To 2015a). The number of matrices correctly solved in the Raven test further assess the relationships between creativity and cognitive (in our sample, ranging from 9 to 18, mean ± SD = 14.40 ± 2.42 measures, we first provide a graphical representation using for males and 14.47± 2.16 for females) is a conventional measure LOWESS smoothing (Cleveland, 1979; Cleveland and McGill, of cognitive ability. This test captures an important aspect of 1985). We then run ordinary least squares regressions which cognitive processing which is referred to as fluid intelligence allow us to test the statistical significance of the linear and non- and is closely related to algorithmic thinking (Stanovich, 2009, linear relationships which were shown in the LOWESS graphs. 2010). All the analyses were performed using Stata 14.0. Frontiers in Psychology | www.frontiersin.org 3 October 2016 | Volume 7 | Article 1626 Corgnet et al. Creativity and Cognitive Skills among Millenials RESULTS regressions in which both predictors (linear and quadratic terms) are included simultaneously (columns [5] and [6] in Tables Descriptive Statistics and Correlations S2–S6) in order to test for possible mediation or confounding Means, standard deviations, and correlations are shown in effects. The interaction between CRT and Raven scores is Table 1. Unsurprisingly, we find moderate positive correlation never significant in predicting creativity (all p’s > 0.3) and is between the number of correct answers in the CRT and Raven thus not reported in the tables for the sake of brevity. The tests (r = 0.26, p < 0.01) which suggests that CRT and Raven results remain qualitatively similar if we also control for gender are not entirely measuring the same cognitive skills (Frederick, and age. 2005; Stanovich, 2009, 2010). Similarly, the different measures The models with the best fit (Table 2) report a positive linear of divergent thinking (AUT) are significantly correlated (all relationship of convergent thinking (RAT) with both Raven p’s < 0.01), except for originality and flexibility (p = 0.28). (p < 0.01) and CRT scores (p = 0.03), which is consistent with Regarding our cognitive measures, we find that both Raven the positive and significant correlations reported in the previous (p < 0.01) and CRT scores (p = 0.03) are positively correlated section. Effect sizes are substantial: in both cases, one SD increase with convergent thinking (RAT). However, the relationship in the predictor is associated with about 20% of one SD increase between cognitive skills and divergent thinking is more in RAT (0.22 and 0.17 for Raven and CRT, respectively; see complicated. High levels of cognitive ability (Raven) relate coefficients in Table 2). Interestingly, the effect of Raven on RAT positively with originality (p = 0.01) and elaboration (p < 0.01), remains significant (p = 0.02) if we include both Raven and CRT but negatively with the number of answers provided (fluency; p = scores as predictors (see column [5] in Table S2) whereas the 0.04) and non-correlated with flexibility (p= 0.20). Finally, we do effect of CRT becomes non-significant (p = 0.15). This result not find a significant correlation between cognitive styles (CRT suggests that the significant effect of CRT scores on convergent scores) and any measure of divergent thinking (all p’s > 0.26). thinking is driven more by cognitive ability (basic computational skills are also necessary for solving the CRT correctly) rather than Non-linear Effects and Regression Analysis by reflectiveness. The relationship between our cognitive measures and We now turn to the study of possible non-linear relationships between our measures of cognition and creativity. Figure 1 divergent thinking is more complex. The models with the best fit report a linear and significant relationship between cognitive displays all the relationships under study using LOWESS (bandwidth = 0.8; Cleveland, 1979; Cleveland and McGill, 1985). ability and all the measures of divergent thinking (all p’s < 0.03), except for flexibility (p = 0.22; see Table 2). Subjects with a LOWESS is a model-free smoothing technique based on locally- higher Raven score tend to generate less uses (lower fluency), weighted regressions which can detect both linear and non- although these are more elaborated and original. Again, for these linear relationships. In order to compare the effect sizes, we three creativity measures, one SD increase in Raven produces a standardize all measures (standard deviations from the mean). variation in the dependent variable of about 20% of one SD. The We also ran ordinary least squares regressions to assess the effect of Raven on flexibility appears to be slightly U-shaped in statistical significance of the observed relationships. In Tables Figure 1 but the regressions do not report any significant linear S2–S6, we present the results of a series of regressions in or quadratic relationship (all p’s > 0.22; see columns [1] and which we estimated both linear and quadratic effects of each of [2] in Table S5). As shown in columns [5] and [6] of Tables the predictors (Raven and CRT) separately on each creativity S3–S6, the effect of Raven on the divergent thinking measures measure (columns [1] to [4]). From these regressions, we selected the models with the best fit, either linear or quadratic in remains virtually identical when controlling for CRT, which indicates that cognitive reflection does not mediate any of these each case, using the Akaike Information Criterion (AIC) and report them in summary Table 2. In addition, we ran similar relationships. TABLE 1 | Descriptive statistics and Pearson correlations. Mean Std Dev [1] [2] [3] [4a] [4b] [4c] [4d] COGNITIVE MEASURES 1. Raven 14.43 2.30 – 2. CRT 3.53 2.26 0.26** – CREATIVITY 3. RAT 3.69 2.97 0.23** 0.17* – 4. AUT 4.a. Originality 1.33 0.54 0.20* 0.09 0.14 – 4.b. Fluency 16.47 8.90 −0.17* −0.06 −0.06 −0.25** – 4.c. Flexibility 11.17 4.22 −0.10 −0.01 −0.02 −0.09 0.85*** − 4.d. Elaboration 0.23 0.29 0.26** 0.06 0.10 0.37*** −0.36*** −0.31*** − N = 150, *p < 0.05, **p < 0.01, ***p < 0.001. Frontiers in Psychology | www.frontiersin.org 4 October 2016 | Volume 7 | Article 1626 Corgnet et al. Creativity and Cognitive Skills among Millenials FIGURE 1 | Relationship between cognitive measures and creative thinking. The relationships are represented using locally weighted smoothing (LOWESS) techniques. All variables are standardized. Frontiers in Psychology | www.frontiersin.org 5 October 2016 | Volume 7 | Article 1626 Corgnet et al. Creativity and Cognitive Skills among Millenials TABLE 2 | The effect of cognitive abilities and cognitive styles on creativity (best fitting models). RAT AUT Originality AUT Fluency AUT Flexibility AUT Elaboration [Raven] [CRT] [Raven] [CRT] [Raven] [CRT] [Raven] [CRT] [Raven] [CRT] Raven 0.219*** 0.195*** −0.162** −0.099 0.246*** std (0.076) (0.071) (0.074) (0.081) (0.067) Raven std CRT 0.169** 0.089 −0.043 0.011 0.053 std (0.076) (0.082) (0.066) (0.073) (0.082) CRT −0.207*** −0.194** std (0.076) (0.079) Constant 0.001 −0.010 0.001 −0.005 −0.001 0.224 −0.001 0.206 0.001 −0.003 (0.080) (0.080) (0.080) (0.081) (0.081) (0.136) (0.082) (0.127) (0.079) (0.082) F 8.229 4.891 7.562 1.179 4.732 4.255 1.490 2.990 13.302 0.420 prob > F 0.005 0.029 0.007 0.279 0.031 0.016 0.224 0.053 0.000 0.518 R 0.053 0.030 0.042 0.008 0.029 0.039 0.011 0.031 0.067 0.003 Ll −208.277 −210.018 −209.129 −211.706 −210.139 −209.359 −211.522 −210.000 −207.174 −212.112 AIC 420.555 424.036 422.258 427.411 424.278 424.717 427.044 426.000 418.348 428.225 OLS estimates. N = 150. All variables are standardized. Robust standard errors are shown in parentheses. See Tables S2–S6 for alternative specifications. *p < 0.05, **p <0.01, ***p <0.001. Contrary to the results observed with Raven, we do not find Sowden et al., 2015). We contribute to this literature by any significant linear relationship between cognitive styles and differentiating between the algorithmic and reflective minds divergent thinking (all p’s > 0.28; see column [3] in Tables S3– (Evans and Stanovich, 2013), and by analyzing their separate S6). These results hold when we control for Raven (all p’s > 0.63; effects on convergent thinking and four different dimensions see column [5] in Tables S3–S6). However, we find a significant of divergent thinking. We partially replicate the results of inverted U-shape relationship of CRT with both fluency and Barr et al. (2015) by finding that individuals’ ability to make flexibility, as reported in Table 2 (p < 0.01 and p = 0.02, remote associations correlates positively with cognitive ability respectively). Subjects with an average level of cognitive reflection and cognitive reflection. However, we find that this effect tend to produce more answers and use more categories than on convergent thinking is mainly driven by cognitive ability. those subjects characterized by either a more intuitive or a more Similarly to Barr et al. (2015), we also find that higher levels of reflective cognitive style. Moreover, the fact that the coefficient cognitive ability are related with higher originality scores and of the linear term in the quadratic regression specification is not lower fluency scores in divergent thinking. Unlike Barr et al. significantly different from zero in either case (p = 0.52 and p = (2015), we also analyze non-linear effects and find an inverted U- 0.88, respectively) indicates that the maximum levels of fluency shape relationship between cognitive reflection and our measures and flexibility are observed at the mean CRT score, as suggested of flexibility and fluency on the divergent thinking task. These by Figure 1. Effect sizes are comparable to those reported above new results suggest that individuals who are highly deliberative insofar as, in both cases, moving one SD either above or below may have a disadvantage in producing a large number of new and the mean CRT is associated with a decrease of about 20% of creative ideas. one SD in the dependent variable. Yet, the effects are larger for Dual-process models of creativity suggest that both generative more extreme CRT values. Note that half of the observations and evaluative processes interact during the creative process fall outside the range mean ± one SD (see also Figure S1). (Finke et al., 1992; Basadur, 1995; Howard-Jones, 2002; Gabora, Controlling for Raven does not alter these relationships (p = 0.01 2005; Nijstad et al., 2010; Gabora and Ranjan, 2013). Although and p = 0.02, respectively; see column [6] in Tables S4, S5), which these models do not have a straightforward mapping onto dual- again indicates an absence of mediation effects. process models of cognition, the interaction between Type 1 and Type 2 cognitive processes may play a different role in different phases of the creative process. In this line, Sowden et al. (2015) call for future research “... to investigate the extent to which DISCUSSION creativity is determined by the ability to shift between Type 1 and The dual-process approach of cognition has been recently Type 2 thinking processes as a function of the circumstances and suggested to reconcile previous conflictive findings on the the stage of the creative processes” (p. 55). Our results suggest that relationship between creativity and executive cognition (Allen cognitive reflection, that is the disposition to override automatic and Thomas, 2011; Ball et al., 2015; Barr et al., 2015; responses related to Type 1 processing and engage in Type 2 Frontiers in Psychology | www.frontiersin.org 6 October 2016 | Volume 7 | Article 1626 Corgnet et al. Creativity and Cognitive Skills among Millenials controlled thought, has a complex effect on divergent thinking. income range. Although this was a methodological choice To some extent, cognitive reflection may be necessary to shift that allowed us to study the workforce of the future, further between the generative and evaluative processes involved in the studies may assess the robustness of our findings to different production of new ideas. However, individuals characterized by populations. Regarding our creativity measures, future research high levels of reflection may be less able to rely on their intuitive, may attempt to extend our analysis to the case of practical autonomous mind which can also be needed for unleashing one’s creative tasks that are commonly encountered, for example, at the creative power (e.g., Dorfman et al., 1996; Norris and Epstein, workplace. To that end, future research may embed the study of 2011; Jarosz et al., 2012). creativity in an organizational setting that allows for studying the The finding of an inverted U-shape relationship between relationship between workplace problem solving and cognitive cognitive reflection (and, analogously, intuitive processing) and skills. creativity is consistent with recent advances on the “mad genius On a methodological note, we used a fixed ordering of hypothesis”: mild levels of top-down control dysfunction may which may have influenced the results as, among other factors, be beneficial for creativity but severe impairment leads to poor fatigue may interfere with test results. While the 2-min break creative performance (for a review, see Abraham, 2014). in the middle of the experiment might have mitigated spillover Relatedly, neuropsychological research has shown an effects between the first and the second part of the experiment, inverted-U shape relationship between spontaneous eye concerns still remain. We encourage future research to explore blink rates and flexibility in divergent creative thinking tasks possible ordering effects. In addition, future research focusing (Chermahini and Hommel, 2010). To the extent that eye blink on state-level analyses of the role of intuition vs. reflection rates reflect dopaminergic activity (Karson, 1983), which is in in creative performance is necessary to assess the robustness turn linked to inhibitory control (Cohen and Servan-Schreiber, (and causality) of our trait-level findings as well as deepen 1992), our results are in line with the finding of Chermahini and our understanding of the cognitive basis of creativity. Along Hommel (2010). these lines, it would be interesting for future research to test Beyond its connection to basic cognitive research, our findings the effect of cognitive manipulations such as cognitive load, offer insights to managers in search for the creative talent ego depletion, priming, or time pressure/delay on creative of millennials. One essential implication of our study is that performance. Our findings suggest that future research on the thinking too much may hamper important aspects of divergent topic should attempt to capture potentially non-linear effects creative thinking. This result is of primary relevance to hiring thus elaborating experimental designs that allow such effects to managers who may want to rely on cognitive reflection as the materialize. This can be done, for example, by considering at least main criterion to recruit diligent (Corgnet et al., 2015b) and three levels per treatment condition. creative millennials. Our findings suggest that the cognitive tests used to recruit workers have to be adapted to the nature of the AUTHOR CONTRIBUTIONS job offered. For example, recruiting for jobs that fundamentally require finding well-defined solutions to problems (such as All authors listed, have made substantial, direct and intellectual accounting or actuarial jobs) can rely on a mix of cognitive ability contribution to the work, and approved it for publication. and reflection tests which are good predictors of convergent creative thinking and diligence. However, recruiting for jobs that FUNDING mainly require divergent creative thinking (such as marketing, industrial design, or psychology jobs) should not solely rely The authors acknowledge financial support from the on cognitive measures. Recruiting based on cognitive reflection International Foundation for Research in Experimental Economics, the Argyros School of Business and Economics skills may actually prevent the hire of highly creative workers. These recommendations are becoming increasingly relevant as a at Chapman University, the Spanish Ministry of Education growing number of jobs in modern economies require divergent [Grant 2012/00103/001], Ministry of Economy and Competence creative thinking (Pink, 2005). [2016/00122/001], Spanish Plan Nacional I+D MCI [ECO2013- The current research has some necessary limitations that 44879-R], 2014-17, and Proyectos de Excelencia de la Junta future research might remedy. To keep focus our study uses Andalucía [P12.SEJ.1436], 2014-18. only one measure of fluid intelligence (Raven) and a single measure of cognitive style (CRT). Future research may assess SUPPLEMENTARY MATERIAL the robustness of our findings to other measures of fluid The Supplementary Material for this article can be found intelligence and cognitive style, possibly extending the analysis to include crystallized intelligence. 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Creativity and Cognitive Skills among Millennials: Thinking Too Much and Creating Too Little

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ORIGINAL RESEARCH published: 25 October 2016 doi: 10.3389/fpsyg.2016.01626 Creativity and Cognitive Skills among Millennials: Thinking Too Much and Creating Too Little 1 2, 3 3, 4 Brice Corgnet , Antonio M. Espín * and Roberto Hernán-González 1 2 EMLYON Business School, Univ Lyon, GATE L-SE UMR 5824, Ecully, France, Economics Department, Middlesex University Business School, London, UK, Granada Lab of Behavioral Economics, Universidad de Granada, Granada, Spain, Business School, University of Nottingham, Nottingham, UK Organizations crucially need the creative talent of millennials but are reluctant to hire them because of their supposed lack of diligence. Recent studies have shown that hiring diligent millennials requires selecting those who score high on the Cognitive Reflection Test (CRT) and thus rely on effortful thinking rather than intuition. A central question is to assess whether the push for recruiting diligent millennials using criteria such as cognitive reflection can ultimately hamper the recruitment of creative workers. To answer this question, we study the relationship between millennials’ creativity and their performance on fluid intelligence (Raven) and cognitive reflection (CRT) tests. The good news for recruiters is that we report, in line with previous research, evidence of a positive relationship of fluid intelligence, and to a lesser extent cognitive reflection, with convergent creative thinking. In addition, we observe a positive effect of fluid intelligence on originality Edited by: and elaboration measures of divergent creative thinking. The bad news for recruiters is Nikolaos Georgantzis, University of Reading, UK the inverted U-shape relationship between cognitive reflection and fluency and flexibility Reviewed by: measures of divergent creative thinking. This suggests that thinking too much may hinder Noelia Sánchez-Pérez, important dimensions of creative thinking. Diligent and creative workers may thus be a University of Murcia, Spain Conny Ernst-Peter Wollbrant, rare find. University of Gothenburg, Sweden Keywords: creativity, cognitive reflection, intelligence, cognition, intuition *Correspondence: Antonio M. Espín a.espin@mdx.ac.uk INTRODUCTION Specialty section: Evidence from a recent survey reports that managers are three times more likely to hire a mature This article was submitted to worker than to hire a millennial (born between 1980 and 2000; Rainer and Rainer, 2011) despite Personality and Social Psychology, desperately needing their creative talent . Mature workers are appealing to recruiters because they a section of the journal are seen as more reliable and more committed than millennials. The dilemma for managers is thus Frontiers in Psychology to hire millennials that are both diligent and creative. Received: 20 July 2016 Recent studies have shown that firms can secure the hiring of diligent millennials by relying on Accepted: 05 October 2016 measures of cognitive skills. For example, intelligence has been found to be the main predictor of Published: 25 October 2016 overall work performance in a wide variety of occupations and across age and gender (e.g., Hunter Citation: and Hunter, 1984; Olea and Ree, 1994; see Schmidt, 2009 for a review). Standard measures of Corgnet B, Espín AM and cognitive ability have been found to correlate positively with task performance (Schmidt et al., 1986; Hernán-González R (2016) Creativity and Cognitive Skills among Millennials: Murphy, 1989) and negatively with counterproductive work behaviors such as theft or absenteeism Thinking Too Much and Creating Too Little. Front. Psychol. 7:1626. See the following press release: http://www.forbes.com/sites/susanadams/2012/09/24/older-workers-theres-hope-study- doi: 10.3389/fpsyg.2016.01626 finds-employers-like-you-better-than-millennials/#1f5799cb4aa6 (accessed September 21, 2016). Frontiers in Psychology | www.frontiersin.org 1 October 2016 | Volume 7 | Article 1626 Corgnet et al. Creativity and Cognitive Skills among Millenials (Dilchert et al., 2007). Moreover, the results of a recent study a disposition-based definition (“cognitive styles”, reflective suggest that these effects may be mediated by individuals’ vs. intuitive) and is not adequately measured by standard cognitive styles (Corgnet et al., 2015b). In particular, Corgnet intelligence tests (which assess “cognitive ability”) but by tasks et al. (2015b) find that millennials characterized by a more of cognitive reflection like the Cognitive Reflection Test (CRT; reflective style (as measured by the Cognitive Reflection Test; Frederick, 2005). Individuals characterized by a more reflective Frederick, 2005) are more diligent, displaying higher levels of mind tend to show higher levels of self-control and lower task performance and lower levels of counterproductive work levels of “cognitive impulsivity” (Frederick, 2005; Kahneman and behaviors . A crucial caveat is whether hiring millennials based Frederick, 2007; Cokely and Kelley, 2009; Oechssler et al., 2009; on cognitive measures may ultimately select less creative workers. Toplak et al., 2011; Brañas-Garza et al., 2012). To address this point we need to assess the relationship between From this perspective, one can conjecture that cognitive cognitive skills and creativity. reflection may relate negatively to creativity. This is the case Traditionally, intelligence, and creativity have been because a number of studies suggest that the capacity to control considered to be unrelated (Getzels and Jackson, 1962; Wallach one’s attention and behavior may even be detrimental for creative and Kogan, 1965; Batey and Furnham, 2006; Sawyer, 2006; thinking (for a review, see Wiley and Jarosz, 2012a). For example, Weisberg, 2006; Runco, 2007; Kaufman, 2009; Kim et al., 2010). creative problem solving has been shown to relate positively In a meta-analysis, Kim (2005) finds that the correlation between to moderate alcohol intoxication (Jarosz et al., 2012), which is creativity test scores and IQ varies widely and is, on average, known to impair inhibition and attentional control (Peterson small (r = 0.174). et al., 1990; Kovacevic et al., 2012; Marinkovic et al., 2012). However, a growing consensus has emerged in recent Similarly, an “experiential” thinking style (which maps onto Type research stressing a close relationship between intelligence and 1 processing) has been found to correlate positively with creative creative performance (see Silvia, 2015, for a review). This performance (Norris and Epstein, 2011). emerging consensus heavily relies on recent studies that have As mentioned, past literature arrived at conflicting employed more sophisticated statistical techniques and more conclusions regarding whether executive cognition favors robust assessment methods than prior research on the topic. (e.g., Nusbaum and Silvia, 2011; Beaty and Silvia, 2012; Silvia, For example, the use of latent variable models has allowed 2015) or hampers (e.g., Eysenck, 1993; Kim et al., 2007; Ricks researchers to uncover a positive and significant relationship et al., 2007; Norris and Epstein, 2011; Jarosz et al., 2012; Wiley between creativity and intelligence using data from previous and Jarosz, 2012b) creative thinking. Dual-process theory can studies that reported non-significant correlations (Silvia, 2008b). reconcile these apparently conflicting findings by positing that The recent wave of research on intelligence and creativity has creativity may be generated by a mix of Type 1 and Type 2 also improved upon traditional assessment of creativity that processes (Allen and Thomas, 2011; Ball et al., 2015; Barr et al., exclusively relied on scoring methods based on the originality 2015; see Sowden et al., 2015, for a review). It follows that the and uniqueness of responses in creative tasks (such as finding dual-process approach lays out a promising research agenda unusual uses for an object). These traditional scoring methods based on assessing the exact mix of Type 1 and Type 2 processes are imprecise because they confound several factors, such as that bolsters creativity as well as analyzing separately the effect fluency and sample size (Hocevar, 1979; Silvia et al., 2008), and of algorithmic and reflective Type 2 processes on creative can thus lead to inaccurate estimates of the relationship between thinking. intelligence and creativity (Silvia, 2008a; Nusbaum and Silvia, Following a dual-process approach, Barr et al. (2015) find 2011). The results of this new wave of research on creativity and experimental evidence of an important effect of controlled Type intelligence have been taken as evidence that executive cognition 2 analytic processes on both convergent and divergent (Guilford, is undoubtedly beneficial to creative thinking (Silvia, 2015). 1967) creative thinking. In particular, they find that both Yet, although there is an obvious link between intelligence cognitive ability (measured as the combination of numeracy and and executive cognition, from the point of view of modern verbal skills) and reflective cognitive style (average of scores in dual-process theory (Evans, 2008, 2009; Stanovich, 2009, 2010; the CRT and base-rate problem tasks) covary positively with one’s Evans and Stanovich, 2013), one should distinguish between capacity to make remote associations, that is, with convergent algorithmic and reflective cognitive processes. Algorithmic creative thinking. Regarding divergent creative thinking, Barr processes are typically associated with computational efficiency et al. (2015) show that cognitive ability but not cognitive and are measured by standard intelligence tests whereas reflective reflection predicts higher originality scores in an alternate uses processing is associated with a disposition to employ the task. Fluency in the latter task, however, was not correlated with resources of the algorithmic mind, that is, to switch from either cognitive measure. autonomous “Type 1” thought to analytic “Type 2” (working In this paper, we use a similar approach to Barr et al. memory-dependent) thought. The reflective mind thus has (2015) and investigate how both types of cognitive processes affect creativity. In particular, we analyze how cognitive abilities Positive effects of cognitive reflection on people’s willingness to choose socially- (measured using Raven as a test of fluid intelligence) and efficient resource allocations (Lohse, 2016; Capraro et al., 2016) as well as to trust cognitive styles (intuitive vs. reflective; as measured by the strangers (Corgnet et al., 2016) suggest other possible channels through which CRT) relate to convergent and divergent creative thinking. organizations may benefit from hiring individuals with a more reflective cognitive We extend Barr et al. (2015) by analyzing other measures style. Cognitive reflection has also been found to play a key role in moral judgment (e.g., Paxton et al., 2012; Pennycook et al., 2014). of divergent thinking such as flexibility and elaboration and Frontiers in Psychology | www.frontiersin.org 2 October 2016 | Volume 7 | Article 1626 Corgnet et al. Creativity and Cognitive Skills among Millenials by exploring possible non-linearities between creativity and Cognitive Style Assessment cognitive measures. We measured the participants’ tendency to rely on intuition vs. Given the conflicting results regarding whether executive reflection using the CRT introduced by Frederick (2005). The cognition is beneficial or detrimental for creative thinking, test is characterized by the existence of an incorrect response we conjecture that there might exist a non-linear relationship which automatically comes to mind but has to be overridden in between different measures of creativity and cognition. order to find the correct solution. To the original CRT questions, Specifically, it might be that a minimum level of executive we added four questions recently developed by Toplak et al. cognition is necessary for creative performance but, beyond some (2014). This extended task (see Text S1) will allow us to uncover level, the relationship disappears or even turns negative. This potentially non-linear relationships that would be hard to observe might explain why previous findings seem to be inconsistent. using the classical three-item task (Frederick, 2005). In Table S1, A related line of reasoning has been proposed in the so- we display the proportion of subjects answering each question called “threshold hypothesis” of the relationship between correctly, split by gender. As expected, males performed better IQ and creativity (Guilford, 1967; Jauk et al., 2013). The in the test than females (Frederick, 2005; Bosch-Domènech et al., threshold hypothesis states that intelligence is positively related 2014). Our measure of cognitive reflection is given by the total to creative thinking for low IQ levels but the relationship number of correct answers (from 0 to 7). The full distribution of blurs for high IQ levels. Similar arguments arise in recent correct answers by males (mean± SD= 4.09± 2.31) and females accounts of the “mad genius hypothesis”: moderate levels of (mean ± SD = 2.89 ± 2.03) is provided in Figure S1. inhibitory or top-down control dysfunction, characteristic Convergent Creative Thinking of subclinical psychiatric populations (e.g., mild ADHD and schizophrenia disorders), can spur creativity under some We used a subset of the Remote Associate Test (RAT; Mednick, 1962) to measure subjects’ ability to make remote associations. conditions whereas clinical-severe levels typically lead to impoverished creative thinking (Schuldberg, 2005; Abraham In particular, subjects were shown 13 sets of three words (e.g., widow-bite-monkey) and asked to find a word which relates to et al., 2007; Jaracz et al., 2012; Acar and Sen, 2013; Abraham, all the three words provided (in this example the solution is 2014). “spider”). Our measure of convergent thinking is the number of problems correctly solved (from 0 to 13). METHODS Divergent Creative Thinking Participants and General Protocol We measured divergent thinking using a variant of the Alternate Participants were 150 students (46.67% female; age: mean ± SD Uses Task (AUT; Guilford, 1967). Participants were instructed = 20.23 ± 1.96) from Chapman University in the U.S. These to provide as many unusual uses of a pen as possible during 6 participants were recruited from a database of more than 2000 min. We construct four different measures of divergent thinking: students. Invitations to participate in the current study were sent fluency, originality, flexibility, and elaboration. We measured to a random subset of the whole database. This study is part of fluency as the total number of answers provided by a participant. a larger research program on cognition and economic decision Three raters were presented with a random list of answers and making. The local Institutional Review Board approved of this asked to score the degree of originality of each entry using research. All participants provided written informed consent a 1 (not at all) to 5 (very much) Likert scale. We computed prior to participating. We conducted a total of 12 sessions, nine originality as the sum of the average score of the three raters had 12 participants and three had 14 participants. On average, for all the entries provided by a participant, divided by the total sessions lasted for 45 min. All subjects completed the same tasks number of answers. Following Troyer and Moscovitch (2006) in the following order: (1) CRT, (2) Raven test, (3) Remote and Gilhooly et al. (2007), all the answers were classified in associates task, (4) Alternate uses task. Subjects had 6 min to broad differentiated categories (e.g., uses of the pen as cloth or complete each task and a 2-min break after completing the Raven hair accessories). Then, flexibility was measured as the number test. of different categories provided by each participant. Finally, elaboration refers to the average amount of detail (from 0 to 2) provided by each participant. Measures Cognitive Ability Assessment Participants completed a subset of Raven progressive matrices Statistical Analysis test (Raven, 1936). Specifically, we used the odd number of the For the data analysis, we start by showing the descriptive statistics last three series of matrices (Jaeggi et al., 2010; Corgnet et al., of all the measures used and their zero-order correlations. To 2015a). The number of matrices correctly solved in the Raven test further assess the relationships between creativity and cognitive (in our sample, ranging from 9 to 18, mean ± SD = 14.40 ± 2.42 measures, we first provide a graphical representation using for males and 14.47± 2.16 for females) is a conventional measure LOWESS smoothing (Cleveland, 1979; Cleveland and McGill, of cognitive ability. This test captures an important aspect of 1985). We then run ordinary least squares regressions which cognitive processing which is referred to as fluid intelligence allow us to test the statistical significance of the linear and non- and is closely related to algorithmic thinking (Stanovich, 2009, linear relationships which were shown in the LOWESS graphs. 2010). All the analyses were performed using Stata 14.0. Frontiers in Psychology | www.frontiersin.org 3 October 2016 | Volume 7 | Article 1626 Corgnet et al. Creativity and Cognitive Skills among Millenials RESULTS regressions in which both predictors (linear and quadratic terms) are included simultaneously (columns [5] and [6] in Tables Descriptive Statistics and Correlations S2–S6) in order to test for possible mediation or confounding Means, standard deviations, and correlations are shown in effects. The interaction between CRT and Raven scores is Table 1. Unsurprisingly, we find moderate positive correlation never significant in predicting creativity (all p’s > 0.3) and is between the number of correct answers in the CRT and Raven thus not reported in the tables for the sake of brevity. The tests (r = 0.26, p < 0.01) which suggests that CRT and Raven results remain qualitatively similar if we also control for gender are not entirely measuring the same cognitive skills (Frederick, and age. 2005; Stanovich, 2009, 2010). Similarly, the different measures The models with the best fit (Table 2) report a positive linear of divergent thinking (AUT) are significantly correlated (all relationship of convergent thinking (RAT) with both Raven p’s < 0.01), except for originality and flexibility (p = 0.28). (p < 0.01) and CRT scores (p = 0.03), which is consistent with Regarding our cognitive measures, we find that both Raven the positive and significant correlations reported in the previous (p < 0.01) and CRT scores (p = 0.03) are positively correlated section. Effect sizes are substantial: in both cases, one SD increase with convergent thinking (RAT). However, the relationship in the predictor is associated with about 20% of one SD increase between cognitive skills and divergent thinking is more in RAT (0.22 and 0.17 for Raven and CRT, respectively; see complicated. High levels of cognitive ability (Raven) relate coefficients in Table 2). Interestingly, the effect of Raven on RAT positively with originality (p = 0.01) and elaboration (p < 0.01), remains significant (p = 0.02) if we include both Raven and CRT but negatively with the number of answers provided (fluency; p = scores as predictors (see column [5] in Table S2) whereas the 0.04) and non-correlated with flexibility (p= 0.20). Finally, we do effect of CRT becomes non-significant (p = 0.15). This result not find a significant correlation between cognitive styles (CRT suggests that the significant effect of CRT scores on convergent scores) and any measure of divergent thinking (all p’s > 0.26). thinking is driven more by cognitive ability (basic computational skills are also necessary for solving the CRT correctly) rather than Non-linear Effects and Regression Analysis by reflectiveness. The relationship between our cognitive measures and We now turn to the study of possible non-linear relationships between our measures of cognition and creativity. Figure 1 divergent thinking is more complex. The models with the best fit report a linear and significant relationship between cognitive displays all the relationships under study using LOWESS (bandwidth = 0.8; Cleveland, 1979; Cleveland and McGill, 1985). ability and all the measures of divergent thinking (all p’s < 0.03), except for flexibility (p = 0.22; see Table 2). Subjects with a LOWESS is a model-free smoothing technique based on locally- higher Raven score tend to generate less uses (lower fluency), weighted regressions which can detect both linear and non- although these are more elaborated and original. Again, for these linear relationships. In order to compare the effect sizes, we three creativity measures, one SD increase in Raven produces a standardize all measures (standard deviations from the mean). variation in the dependent variable of about 20% of one SD. The We also ran ordinary least squares regressions to assess the effect of Raven on flexibility appears to be slightly U-shaped in statistical significance of the observed relationships. In Tables Figure 1 but the regressions do not report any significant linear S2–S6, we present the results of a series of regressions in or quadratic relationship (all p’s > 0.22; see columns [1] and which we estimated both linear and quadratic effects of each of [2] in Table S5). As shown in columns [5] and [6] of Tables the predictors (Raven and CRT) separately on each creativity S3–S6, the effect of Raven on the divergent thinking measures measure (columns [1] to [4]). From these regressions, we selected the models with the best fit, either linear or quadratic in remains virtually identical when controlling for CRT, which indicates that cognitive reflection does not mediate any of these each case, using the Akaike Information Criterion (AIC) and report them in summary Table 2. In addition, we ran similar relationships. TABLE 1 | Descriptive statistics and Pearson correlations. Mean Std Dev [1] [2] [3] [4a] [4b] [4c] [4d] COGNITIVE MEASURES 1. Raven 14.43 2.30 – 2. CRT 3.53 2.26 0.26** – CREATIVITY 3. RAT 3.69 2.97 0.23** 0.17* – 4. AUT 4.a. Originality 1.33 0.54 0.20* 0.09 0.14 – 4.b. Fluency 16.47 8.90 −0.17* −0.06 −0.06 −0.25** – 4.c. Flexibility 11.17 4.22 −0.10 −0.01 −0.02 −0.09 0.85*** − 4.d. Elaboration 0.23 0.29 0.26** 0.06 0.10 0.37*** −0.36*** −0.31*** − N = 150, *p < 0.05, **p < 0.01, ***p < 0.001. Frontiers in Psychology | www.frontiersin.org 4 October 2016 | Volume 7 | Article 1626 Corgnet et al. Creativity and Cognitive Skills among Millenials FIGURE 1 | Relationship between cognitive measures and creative thinking. The relationships are represented using locally weighted smoothing (LOWESS) techniques. All variables are standardized. Frontiers in Psychology | www.frontiersin.org 5 October 2016 | Volume 7 | Article 1626 Corgnet et al. Creativity and Cognitive Skills among Millenials TABLE 2 | The effect of cognitive abilities and cognitive styles on creativity (best fitting models). RAT AUT Originality AUT Fluency AUT Flexibility AUT Elaboration [Raven] [CRT] [Raven] [CRT] [Raven] [CRT] [Raven] [CRT] [Raven] [CRT] Raven 0.219*** 0.195*** −0.162** −0.099 0.246*** std (0.076) (0.071) (0.074) (0.081) (0.067) Raven std CRT 0.169** 0.089 −0.043 0.011 0.053 std (0.076) (0.082) (0.066) (0.073) (0.082) CRT −0.207*** −0.194** std (0.076) (0.079) Constant 0.001 −0.010 0.001 −0.005 −0.001 0.224 −0.001 0.206 0.001 −0.003 (0.080) (0.080) (0.080) (0.081) (0.081) (0.136) (0.082) (0.127) (0.079) (0.082) F 8.229 4.891 7.562 1.179 4.732 4.255 1.490 2.990 13.302 0.420 prob > F 0.005 0.029 0.007 0.279 0.031 0.016 0.224 0.053 0.000 0.518 R 0.053 0.030 0.042 0.008 0.029 0.039 0.011 0.031 0.067 0.003 Ll −208.277 −210.018 −209.129 −211.706 −210.139 −209.359 −211.522 −210.000 −207.174 −212.112 AIC 420.555 424.036 422.258 427.411 424.278 424.717 427.044 426.000 418.348 428.225 OLS estimates. N = 150. All variables are standardized. Robust standard errors are shown in parentheses. See Tables S2–S6 for alternative specifications. *p < 0.05, **p <0.01, ***p <0.001. Contrary to the results observed with Raven, we do not find Sowden et al., 2015). We contribute to this literature by any significant linear relationship between cognitive styles and differentiating between the algorithmic and reflective minds divergent thinking (all p’s > 0.28; see column [3] in Tables S3– (Evans and Stanovich, 2013), and by analyzing their separate S6). These results hold when we control for Raven (all p’s > 0.63; effects on convergent thinking and four different dimensions see column [5] in Tables S3–S6). However, we find a significant of divergent thinking. We partially replicate the results of inverted U-shape relationship of CRT with both fluency and Barr et al. (2015) by finding that individuals’ ability to make flexibility, as reported in Table 2 (p < 0.01 and p = 0.02, remote associations correlates positively with cognitive ability respectively). Subjects with an average level of cognitive reflection and cognitive reflection. However, we find that this effect tend to produce more answers and use more categories than on convergent thinking is mainly driven by cognitive ability. those subjects characterized by either a more intuitive or a more Similarly to Barr et al. (2015), we also find that higher levels of reflective cognitive style. Moreover, the fact that the coefficient cognitive ability are related with higher originality scores and of the linear term in the quadratic regression specification is not lower fluency scores in divergent thinking. Unlike Barr et al. significantly different from zero in either case (p = 0.52 and p = (2015), we also analyze non-linear effects and find an inverted U- 0.88, respectively) indicates that the maximum levels of fluency shape relationship between cognitive reflection and our measures and flexibility are observed at the mean CRT score, as suggested of flexibility and fluency on the divergent thinking task. These by Figure 1. Effect sizes are comparable to those reported above new results suggest that individuals who are highly deliberative insofar as, in both cases, moving one SD either above or below may have a disadvantage in producing a large number of new and the mean CRT is associated with a decrease of about 20% of creative ideas. one SD in the dependent variable. Yet, the effects are larger for Dual-process models of creativity suggest that both generative more extreme CRT values. Note that half of the observations and evaluative processes interact during the creative process fall outside the range mean ± one SD (see also Figure S1). (Finke et al., 1992; Basadur, 1995; Howard-Jones, 2002; Gabora, Controlling for Raven does not alter these relationships (p = 0.01 2005; Nijstad et al., 2010; Gabora and Ranjan, 2013). Although and p = 0.02, respectively; see column [6] in Tables S4, S5), which these models do not have a straightforward mapping onto dual- again indicates an absence of mediation effects. process models of cognition, the interaction between Type 1 and Type 2 cognitive processes may play a different role in different phases of the creative process. In this line, Sowden et al. (2015) call for future research “... to investigate the extent to which DISCUSSION creativity is determined by the ability to shift between Type 1 and The dual-process approach of cognition has been recently Type 2 thinking processes as a function of the circumstances and suggested to reconcile previous conflictive findings on the the stage of the creative processes” (p. 55). Our results suggest that relationship between creativity and executive cognition (Allen cognitive reflection, that is the disposition to override automatic and Thomas, 2011; Ball et al., 2015; Barr et al., 2015; responses related to Type 1 processing and engage in Type 2 Frontiers in Psychology | www.frontiersin.org 6 October 2016 | Volume 7 | Article 1626 Corgnet et al. Creativity and Cognitive Skills among Millenials controlled thought, has a complex effect on divergent thinking. income range. Although this was a methodological choice To some extent, cognitive reflection may be necessary to shift that allowed us to study the workforce of the future, further between the generative and evaluative processes involved in the studies may assess the robustness of our findings to different production of new ideas. However, individuals characterized by populations. Regarding our creativity measures, future research high levels of reflection may be less able to rely on their intuitive, may attempt to extend our analysis to the case of practical autonomous mind which can also be needed for unleashing one’s creative tasks that are commonly encountered, for example, at the creative power (e.g., Dorfman et al., 1996; Norris and Epstein, workplace. To that end, future research may embed the study of 2011; Jarosz et al., 2012). creativity in an organizational setting that allows for studying the The finding of an inverted U-shape relationship between relationship between workplace problem solving and cognitive cognitive reflection (and, analogously, intuitive processing) and skills. creativity is consistent with recent advances on the “mad genius On a methodological note, we used a fixed ordering of hypothesis”: mild levels of top-down control dysfunction may which may have influenced the results as, among other factors, be beneficial for creativity but severe impairment leads to poor fatigue may interfere with test results. While the 2-min break creative performance (for a review, see Abraham, 2014). in the middle of the experiment might have mitigated spillover Relatedly, neuropsychological research has shown an effects between the first and the second part of the experiment, inverted-U shape relationship between spontaneous eye concerns still remain. We encourage future research to explore blink rates and flexibility in divergent creative thinking tasks possible ordering effects. In addition, future research focusing (Chermahini and Hommel, 2010). To the extent that eye blink on state-level analyses of the role of intuition vs. reflection rates reflect dopaminergic activity (Karson, 1983), which is in in creative performance is necessary to assess the robustness turn linked to inhibitory control (Cohen and Servan-Schreiber, (and causality) of our trait-level findings as well as deepen 1992), our results are in line with the finding of Chermahini and our understanding of the cognitive basis of creativity. Along Hommel (2010). these lines, it would be interesting for future research to test Beyond its connection to basic cognitive research, our findings the effect of cognitive manipulations such as cognitive load, offer insights to managers in search for the creative talent ego depletion, priming, or time pressure/delay on creative of millennials. One essential implication of our study is that performance. Our findings suggest that future research on the thinking too much may hamper important aspects of divergent topic should attempt to capture potentially non-linear effects creative thinking. This result is of primary relevance to hiring thus elaborating experimental designs that allow such effects to managers who may want to rely on cognitive reflection as the materialize. This can be done, for example, by considering at least main criterion to recruit diligent (Corgnet et al., 2015b) and three levels per treatment condition. creative millennials. Our findings suggest that the cognitive tests used to recruit workers have to be adapted to the nature of the AUTHOR CONTRIBUTIONS job offered. For example, recruiting for jobs that fundamentally require finding well-defined solutions to problems (such as All authors listed, have made substantial, direct and intellectual accounting or actuarial jobs) can rely on a mix of cognitive ability contribution to the work, and approved it for publication. and reflection tests which are good predictors of convergent creative thinking and diligence. However, recruiting for jobs that FUNDING mainly require divergent creative thinking (such as marketing, industrial design, or psychology jobs) should not solely rely The authors acknowledge financial support from the on cognitive measures. Recruiting based on cognitive reflection International Foundation for Research in Experimental Economics, the Argyros School of Business and Economics skills may actually prevent the hire of highly creative workers. These recommendations are becoming increasingly relevant as a at Chapman University, the Spanish Ministry of Education growing number of jobs in modern economies require divergent [Grant 2012/00103/001], Ministry of Economy and Competence creative thinking (Pink, 2005). [2016/00122/001], Spanish Plan Nacional I+D MCI [ECO2013- The current research has some necessary limitations that 44879-R], 2014-17, and Proyectos de Excelencia de la Junta future research might remedy. To keep focus our study uses Andalucía [P12.SEJ.1436], 2014-18. only one measure of fluid intelligence (Raven) and a single measure of cognitive style (CRT). Future research may assess SUPPLEMENTARY MATERIAL the robustness of our findings to other measures of fluid The Supplementary Material for this article can be found intelligence and cognitive style, possibly extending the analysis to include crystallized intelligence. 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Impact of job experience and ability on job knowledge, work sample performance, Copyright © 2016 Corgnet, Espín and Hernán-González. This is an open-access and supervisory ratings of job performance. J. Appl. Psychol. 71:432. doi: article distributed under the terms of the Creative Commons Attribution License (CC 10.1037/0021-9010.71.3.432 BY). The use, distribution or reproduction in other forums is permitted, provided the Schuldberg, D. (2005). Eysenck Personality Questionnaire scales and paper- original author(s) or licensor are credited and that the original publication in this and-pencil tests related to creativity. Psychol. Rep. 97, 180–182. doi: journal is cited, in accordance with accepted academic practice. No use, distribution 10.2466/pr0.97.5.180-182 or reproduction is permitted which does not comply with these terms. Frontiers in Psychology | www.frontiersin.org 9 October 2016 | Volume 7 | Article 1626

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