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PAPER

The development of creative cognition across adolescence:distinct trajectories for insight and divergent thinking

Sietske W. Kleibeuker,1 Carsten K.W. De Dreu2 and Eveline A. Crone1,2

1. Institute for Psychological Research, Leiden University, The Netherlands2. Department of Psychology, University of Amsterdam, The Netherlands

Abstract

We examined developmental trajectories of creative cognition across adolescence. Participants (N = 98), divided into four agegroups (12 ⁄ 13 yrs, 15 ⁄ 16 yrs, 18 ⁄ 19 yrs, and 25–30 yrs), were subjected to a battery of tasks gauging creative insight (visual;verbal) and divergent thinking (verbal; visuo-spatial). The two older age groups outperformed the two younger age groups oninsight tasks. The 25–30-year-olds outperformed the two youngest age groups on the originality measure of verbal divergentthinking. No age-group differences were observed for verbal divergent thinking fluency and flexibility. On divergent thinking inthe visuo-spatial domain, however, only 15 ⁄ 16-year-olds outperformed 12 ⁄ 13-year-olds; a model with peak performance for15 ⁄ 16-years-old showed the best fit. The results for the different creativity processes are discussed in relation to cognitive andrelated neurobiological models. We conclude that mid-adolescence is a period of not only immaturities but also of creativepotentials in the visuo-spatial domain, possibly related to developing control functions and explorative behavior.

Introduction

Creativity is considered a cornerstone of human society.Creativity is defined as the ability to generate ideas andproblem solutions that are both novel and appropriate(Amabile, 1996; Sternberg & Lubart, 1996) and is aprerequisite for human survival and prosperity (Runco,2004). For example, creative insights solve daily problems(Runco, 2004), artistic creativity promotes mate attrac-tion (Griskevicius, Cialdini & Kenrick, 2006; Miller,2000), and creative strategizing allows one to win com-petitions and conflicts (De Dreu & Nijstad, 2008). Theseand related insights suggest that creativity provides fit-ness and functionality, in that individuals with creativeability have a higher probability of surviving and pros-pering than those lacking creative abilities.

Across human development, adolescence is a periodcharacterized by transformations towards life indepen-dency (Collins, Gleason & Sesma, 1997; Hill & Holmbeck,1986), and is a crucial phase for the development ofmany cognitive abilities (see e.g. Casey, Jones & Hare,2008; Steinberg, 2005). It has been argued that creativeproblem solving abilities are necessary and importantskills facilitating the advancement toward mature adultfunctioning (Jaquish & Ripple, 1980). Hence, adoles-cence is expected to involve important changes in crea-tive abilities. The aim of the current study was therefore

to examine creative abilities across this transitional per-iod measuring two cognitive functions that representcreative potential: insight and divergent thinking.

Creative insight and divergent thinking

Insight tasks are commonly used to understand perfor-mance in creative problem solving situations (e.g. DeDreu, Baas & Nijstad, 2008; Friedman & Fçrster, 2001;Harkins, 2006; Kounios & Beeman, 2009). Insight taskstypically require establishing associations among previ-ously unrelated or weakly related information, andmentally restructuring the problem space; processeswhich have a central role in creative cognition (Fçrster,Friedman & Liberman, 2004; Smith & Kounios, 1996).There is widespread agreement that insight solutionsdiffer from non-insight solutions in that: (1) solversexperience their solutions as sudden and obviously cor-rect; (2) prior to producing an insight, solution solverssometimes come to an impasse, no longer progressingtowards a solution; (3) solvers usually cannot report theprocessing that enables them to overcome an impasseand reach a solution.

The second type of task, divergent thinking, is com-monly used to verify creative potential and captures theextent to which individuals create novelty (Torrance,1966). Divergent thinking tasks generally require

Address for correspondence: Sietske W. Kleibeuker, Brain and Development Lab, Department of Developmental Psychology, Wassenaarseweg 52,2333AK Leiden, The Netherlands; e-mail: [email protected]

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Developmental Science 16:1 (2013), pp 2–12 DOI: 10.1111/j.1467-7687.2012.01176.x

participants to generate multiple solutions to an open-ended problem (Guilford, 1967). The rationale behindthese tasks is that creative success is assumed to berelated to: (a) one’s ability to generate many responses(fluency), under the assumption that quantity breedsquality; (b) the ability to generate responses in manydifferent conceptual categories (flexibility); and (c) theability to generate unusual or infrequently generatedresponses (originality) (Guilford, 1950, 1967). Insightand divergent thinking are both associated with theability to be creative, yet represent different aspects of thecreative process.

It is important to distinguish divergent thinking fromconvergent thinking, as both have been associated withcreative cognition (Cropley, 2006; Guilford 1950, 1967;DeYoung, Flanders & Peterson, 2008). As summarizedabove, divergent thinking refers to the ability to generatemultiple associations to an idea in a random, unorga-nized way (Baas, De Dreu & Nijstad, 2008; Friedman &Fçrster, 2000; Isen & Daubman, 1984; Martindale,Hines, Mitchell & Covello, 1984). Convergent thinking,however, refers to an analytical and evaluative thinkingmode, associated with discovering relations amonginformation, and represents the capacity to quickly focuson the one best solution to a problem (Guilford, 1967;Gaborra, 2010; Runco, 2004). Consequently, convergentthinking has previously been related to cognitive controlfunctioning and general intelligence (De Haan, 2009;Runco, 2004).

Prior research on creativity development has mainlyfocused on divergent thinking, but most authors docu-mented divergent thinking only in elementary grades,and few have investigated development beyond age 12.For example Claxton, Pannels and Rhoads (2005) per-formed a longitudinal study on figural divergent think-ing, examining participants from 4th, 6th and 9th grade.Few age differences were apparent, although theyobserved a slump in originality for 6th graders. Jaquishand Ripple (1980) applied a verbal task in which par-ticipants had to respond to presented sounds. Compari-sons between pre-adolescents (Mage = 10.8 yrs) andadolescents (Mage = 16.4 yrs) showed increased fluencyand flexibility for the adolescents, but no changes inoriginality. Runco and Bahleda (1986) used a differentand extensive set of divergent thinking tasks across 5th to8th graders. Performance on the measures of verbaldivergent thinking changed as a linear function of age.Lau and Cheung (2010) applied a similar set of tests to4th to 9th graders, but showed that changes may be non-linear. They observed increased performances from 4thto 5th grade, a decrease to 7th graders, and then anincrease in divergent thinking in 9th graders. Studiesincluding comparisons between adolescents and adultsare scarce. In one study, where participants had to comeup with unusual uses for a common object, comparing6th graders with university students, revealed no age-related changes for verbal divergent thinking (Wu,Cheng, Ip & McBride-Chang, 2005). This set of findings

indicates that developmental changes differ betweenstudy methods, even within one type of creative cognitiontask such as divergent thinking. Moreover, this review offindings sets out the lack of research on development ofcreativity throughout the period of adolescence. In all, itleads to the question how creativity develops betweenlate childhood, adolescence and adulthood for thebroader domain of creativity, assessing divergent think-ing and creative insight within the same individuals. Thebroader assessment in the same individuals is needed tounravel whether there are different developmental pat-terns for different aspects of creative cognition, and tounderstand how these aspects are related. To ourknowledge the current study is the first to assess bothinsight and divergent thinking capacities from adoles-cence to adulthood.

The current study

A set of creativity tasks was administered to early(12 ⁄ 13 yrs), middle (15 ⁄ 16 yrs) and late adolescents(18 ⁄ 19 yrs) as well as adults (25–30 yrs). The batteryincluded three creative insight tasks: Gestalt CompletionTest (GCT; De Dreu et al., 2008; Eckstrom, French,Harman & Dermen, 1976), Snowy Picture Test (SPT;Baas, De Dreu & Nijstad, 2011; Friedman & Fçrster,2000, 2001; Eckstrom et al., 1976) and Remote Associ-ates Test (RAT; Mednick 1962), and two tasks gaugingdivergent thinking: the Alternate Uses Test (AUT; Tor-rance, 1966) and the Creatieve Aanleg Test (CreativeAbility Test; CAT; Van Dam & Van Wesel, 2006).

The three insight tasks all tap into insight but for dif-ferent domains; visual or verbal. The GCT consists offragments of pictures, the SPT consists of pictures blur-red through complex patterns of visual noise, and theRAT consists of triads of words and requires participantsto find a fourth related word. All insight tasks involverestructuring and unifying complex or remotely associ-ated information to find a single optimum solution thatis retrieved from memory. As such, these tasks requiredivergent but also convergent thinking (Guilford, 1950)as well as general knowledge, for which we expected tofind age-related increases.

The two divergent thinking tasks also focused on dif-ferent domains, verbal and visuo- spatial. The AlternateUses Test (AUT; Torrance, 1966) requires participants tospecify as many original uses for a well-known object (e.g.brick) as they can; these are assessed in terms of fluency,flexibility and originality. Based on prior research usingsimilar tests (Wu et al., 2005), we did not expect perfor-mance differences between the younger and older agegroups. The second test, the CAT, is a visuo-spatial taskin which participants are instructed to find as manymatching figures as possible according to prespecifiedrules. To find correct solutions to the CAT, relationsamong objects have to be retrieved based on corre-sponding features. The CAT requires some degree ofconvergent thinking as participants need to incorporate

Creative cognition across adolescence 3

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the rules provided. It shows some similarities with rela-tional reasoning tasks and tasks that test for frontal lobefunctioning in terms of cognitive flexibility, e.g. theWisconcin Card-Sorting Test (WCST; Miyake, Fried-man, Emerson, Witzki, Howerter & Wager, 2000). Thenature of the CAT is, however, divergent rather thanconvergent, as rules are not provided about where solu-tions might be found or what solutions might look like.As such, the task differs from relational reasoning andcognitive flexibility tasks. We expected performanceincreases across the younger age groups, but we did notexpect this task to be related to insight tasks.

Methods

Participants

Ninety-eight participants were divided into four agegroups: 25 12 ⁄ 13-year-olds (M = 13.10 years, SD = .47,13 male), 30 15 ⁄ 16-year-olds (M = 16.07 years, SD = .48,13 male), 25 18 ⁄ 19-year-olds (M = 19.12 years, SD = .50,8 male), and 18 25–30-year-olds (M = 27.03, SD = 1.81,eight male). Gender distributions did not significantlydiffer across age groups (v2 (3, N = 98) = 2.13, p = .55).Participants were recruited from local schools (early andmiddle adolescents), from Leiden University (late ado-lescents and adults), and through local advertisements.All participants provided informed consent. In the caseof minors, consent was also obtained from primarycaregivers. To screen for behavioral problems, partici-pants from all age groups filled out the self-report ver-sion of the Strengths and Difficulties Questionnaire(SDQ; Goodman, 1997). Total scores for the two youn-gest age groups fell in the non-clinical range. No stan-dard scores were available for ages > 18 years, but scoresfrom the older age groups did not differ significantlyfrom the younger age groups. Depending on age andtesting location, participants received a fixed payment,course credits, or a present.

Cognitive and behavioral assessment

Creativity has been associated with intelligence(Furnham & Bachtiar, 2008; Batey, Furnham & Safiul-lina, 2010), working memory (De Dreu, Nijstad, Baas,Wolsink & Roskes, 2012; Oberauer, S�, Wilhelm &Wittmann, 2008; Vandervert, Schimpf & Liu, 2007), andverbal fluency (Gilhooly, Fioratou, Anthony & Wynn,2007). To control for age effects related to these concepts,standard scores were obtained from the WISC or WAISsubtests Similarities (verbal IQ) and Digit Span (DS;working memory) (Wechsler, 1991, 1997), and theGroninger Intelligence Test subtest Verbal Fluency (VF)(GIT; Luteijn & van der Ploeg, 1983). Eighty-four par-ticipants completed the Similarities subtest, 82 partici-pants completed the DS subtest, and 87 participantscompleted the VF subtest. One-way ANOVAs performed

on these three measures revealed no significant age groupeffects (Similarities: F(3, 83) = 1.08, p = .36; DS: F(3,81) = 1.83, p = .15; VF: F(3, 86) = .750, p = .53). Thisrenders alternative explanations for age-related differ-ences in terms of verbal intelligence, working memory orverbal fluency unlikely.

Materials

Insight tasks

Three insight tasks were included: the Gestalt CompletionTest, the Snowy Picture Test and the Remote AssociatesTest. Both the Gestalt Completion Test (GCT; Eckstromet al., 1976) and the Snowy Picture Test (SPT; Eckstromet al., 1976) measure visual insight (Fçrster et al., 2004;Eckstrom et al., 1976). In the GCT, participants view aseries of fragmented pictures of familiar objects andindicate what they see. Successful identification of theobjects requires processing relations among elements andintegrating the fragments into a coherent ‘Gestalt’. Thisprocess of restructuring a stimulus set is commonlyconsidered a basic process of creative cognition (Schooler& Melcher, 1995). In the current study, we used a com-puterized version comprising 10 fragmented pictures (DeDreu et al., 2008). The test items were preceded by twoexamples. Participants could type their answers or skipthe question. By clicking a button the participantsproceeded to the next picture. Response time was notrestricted. Responses were coded as correct or incorrect(including skipped items).

In the SPT participants are presented with a series ofimages of familiar objects hidden within visual noise. Toidentify the hidden objects participants need to disregardmisleading interpretations rendered by the context. Thecomputerized version, used in this study, contained thefirst 12 pictures from the original SPT set (Baas et al.,2011). Test items were presented in random order andwere preceded by two examples. Answers could be typedduring an unlimited response time, and responses werecoded as correct or incorrect (including skipped items).The next picture was presented upon a button click.

The Remote Associates Test (RAT; Mednick, 1962) isa measure of verbal insight. Participants are presentedwith three words (e.g. envy, golf, beans) and areinstructed to generate the one word that relates to all ofthese three words (i.e. green). To come up with the cor-rect solution, participants need to identify relationsamong the three stimulus words. These relations aregenerally not the most obvious. In the current study, weused a computerized version of the RAT comprising 30triads of Dutch words (De Dreu, Nijstad & Baas, 2011;Baas et al., 2011). Test items were presented in randomorder, preceded by two example items. Participants wereinstructed to type their answers or skip if no solution wasfound and click a button to go to the next item. Theresponse time was unlimited. Responses were coded ascorrect or incorrect (including skipped items).

4 Sietske W. Kleibeuker et al.


Divergent thinking tasks

To gauge divergent thinking, we used the Alternate UsesTest (AUT; Guilford, 1967) and the Creative Ability Test(CAT; Van Dam & Van Wesel, 2006). The AUT measuresdivergent thinking in the verbal domain in terms of flu-ency, flexibility, and originality. Participants are given thename of an object and asked to generate as many alter-native uses for the object as possible. In the currentcomputerized version, participants were instructed togenerate alternative uses for a brick (e.g. Baas et al.,2011; Friedman & Fçrster, 2001). Solutions can beunusual but must be appropriate. Answers could betyped for a fixed length of 4 minutes. Participants wereinstructed to press ENTER after each answer typed tosubmit the answer. Concurrently, the response field wascleared and the next answer could be given. Fluencyscores were computed by counting the number of correctsolutions provided. Originality was determined as fol-lows. For each solution, frequency of occurrence acrossthe total of solutions (provided by all participants) wasdetermined. Since frequency distributions were positivelyskewed, frequency scores were log-transformed beforeaverages were computed (similar effects were found withnon-log-transformed frequency scores). Then, averagefrequency scores were computed for each participant.Flexibility was measured by the number of solution-cat-egories. A trained rater assigned each solution to one of35 solution-categories (e.g. building aspect; load; toy).Then, the number of applied solution-categories wascounted for each participant individually.

The CAT measures fluency and originality of divergentthinking in the visuo-spatial domain. The test problemconsists of nine squares including figures. Participantsare asked to compose triads of squares based on theproperties (e.g. number, position) of the figures included.These squares must be similar concerning a particularfigure property (or properties) and therefore differ fromthe other six squares. Fluency sores were computed bycounting participants’ number of correct answers. Orig-inality was computed by summing the uniqueness scores(1 = ‘common’ to 5 = ‘very unique’) of correct answers.Uniqueness scores were based on occurrence of solutionsin previous validity studies (Van Dam & Van Wesel,2006). Higher scores corresponded to lower frequency ofoccurrence. Participants were instructed to write down asmany triads as they could. Response time was limited to10 minutes. Prior to the test, participants were presentedwith an example emphasizing the variety of possiblesolutions (Van Wesel, 2006).

Procedure

Participants were invited to participate in a study aboutgeneral problem solving. They were tested individuallyeither in a classroom or in a separate room at LeidenUniversity. Test administration was divided into threeparts: (1) computerized versions of the GCT, SPT, RAT

and AUT, for which participants were seated in front of a15-inch laptop; (2) Similarities, DS and VF, which wereadministered orally; and (3) a paper and pencil version ofthe CAT. Duration of the three test parts was approxi-mately 30, 15 and 15 minutes, respectively, includinginstructions. Participants were encouraged to ask forhelp if any ambiguity concerning a test remained afterreading the instructions. In between tasks participantswere given a break, and upon completion of the entireexperiment, participants were debriefed and receivedtheir compensation.

Due to practical limitations, only 89 of the total of 98participants completed all the computerized tests(N12 ⁄ 13 yrs = 25; N15 ⁄ 16 yrs = 23; N18 ⁄ 19 yrs = 25;N25–30 yrs = 16), and 95 participants completed the CAT(N12 ⁄ 13 yrs = 24; N15 ⁄ 16 yrs = 30; N18 ⁄ 19 yrs = 20;N25–30 yrs = 17).

Results

First, we tested for age differences on insight anddivergent thinking measures using analyses of variance(ANOVAs) with age as between-subjects factor. Signifi-cance thresholds were set to p < .05. All significanteffects survived Greenhouse-Geisser correction. For allANOVAs, Levene’s test of homogeneity of variances wasapplied. Tukey HSD tests when variances were homo-geneous, or Games-Howell tests when variances werenon-homogeneous, were applied for post-hoc analysis ofbetween-group comparisons. Means and standard devi-ations of insight and divergent thinking measures arepresented in Table 1. In addition, we conducted PrincipalComponent Analyses (PCA) and correlations amongtask performances to identify relations between the dif-ferent types of creativity measures.

Insight tasks

Performances for the insight tests were examined interms of accuracy (quantified as the number of correctsolutions).

Visual insight: GCT and SPT

Participants’ number of correct insights performanceswere submitted to a multivariate ANOVA with age group(12 ⁄ 13 yrs, 15 ⁄ 16 yrs, 18 ⁄ 19 yrs, 25–30 yrs) as between-subjects variable. A multivariate main effect of age group(F(6, 170) = 5.36; p < .001; pg2 = .25) was found. Uni-variate ANOVAs were used to further examine the data.Both tasks revealed univariate main effects of age group.On the GCT, F(3, 85) = 9.58; p < .001; pg2 = .25, post-hoc Games-Howell comparisons (Levene’s test F(3, 85)= 3.17; p = .03) showed that participants from the twoolder age groups out-performed participants from the twoyounger age groups (12 ⁄ 13 yrs < 18 ⁄ 19 yrs, p = .001;15 ⁄ 16 yrs < 18 ⁄ 19 yrs, p = .002; 12 ⁄ 13 yrs < 25–30 yrs,



p = .004 and 15 ⁄ 16 yrs < 25–30 yrs, p = .006; see Fig-ure 1a). The univariate ANOVA for the SPT also resultedin a main effect of age, (F(3, 85) = 5.80; p = .002;pg2 = .16), and again, the age group effect was driven bysignificant differences between participants of the twoyounger age groups on the one hand and participants fromthe two older age groups on the other hand; better per-formance was achieved by older participants. Post-hocTukey HSD (Levene’s test: p > .5) revealed significanteffects for contrasts 12 ⁄ 13 yrs < 18 ⁄ 19 yrs, p = .02;15 ⁄ 16 yrs < 18 ⁄ 19 yrs, p = .04; 12 ⁄ 13 yrs < 25–30 yrs,p = .02; and 15 ⁄ 16 yrs < 25–30 yrs, p = .04 (see Fig-ure 1b).

Verbal insight: RAT

Performance scores were entered into an ANOVA withage group (12 ⁄ 13 yrs, 15 ⁄ 16 yrs, 18 ⁄ 19 yrs, and 25–30 yrs) as between-subjects variable. Performance dif-fered across age groups F(3, 85) = 14.68; p < .001;pg2 = .34. Tukey HSD post-hoc analysis (Levene’s test:p > .5) showed that accuracy for participants from thetwo older age groups was significantly better comparedto participants from the two younger age groups;12 ⁄ 13 yrs < 18 ⁄ 19 yrs, p < .001; 15 ⁄ 16 yrs < 18 ⁄ 19 yrs,p = .016; 12 ⁄ 13 yrs < 25–30 yrs, p < .001 and 15 ⁄ 16 yrs< 25–30 yrs, p = .008 (see Figure 1c).

In all, the developmental trajectories of the creativeinsight tasks seemed to follow stepwise rather than linearor curvilinear patterns, generally observed for develop-ment of cognitive control (e.g. Huizinga, Dolan & vander Molen, 2006). Post-hoc model analyses using linearregression analyses confirmed that SPT and GCT resultsfitted stepwise patterns (contrast: )1, )1, 1, 1; for12 ⁄ 13 yrs, 15 ⁄ 16 yrs, 18 ⁄ 19 yrs, 25–30 yrs age groups)better than linear (modeling results as a linear functionof age) or curvilinear models (modeling results aslog-transformed or quadratic functions of age),SPTstepwise: F(1, 87) = 16.56, R = .37, p < .001;GCTstepwise: F(1, 87) = 27.15, R = .49, p < .001. RATresults revealed best fit with the log-transformed curvi-linear model: F(1, 87) = 39.30, R = .56, p < .001.

Divergent thinking tasks

Verbal divergent thinking: AUT

Performance was measured in terms of fluency, flexibilityand originality. Univariate ANOVAs for the fluency andflexibility measures with age group (12 ⁄ 13 yrs, 15 ⁄ 16 yrs,18 ⁄ 19 yrs, 25–30 yrs) as between-subjects variableshowed no age group differences (p > .05). A univariateANOVA on originality, represented by the mean log-transformed frequency of solutions, yielded a significantage group effect (F(3, 85) = 3.79, p = .01; pg2 = .12).Post-hoc Tukey HSD analyses (Levene’s test: p > .5)showed that the two younger age groups had higherfrequency scores (i.e. were less original) compared to theoldest age group (12 ⁄ 13 yrs < 25–30 yrs, p = .03 and15 ⁄ 16 yrs < 25–30 yrs, p = .02). The 18 ⁄ 19 yrs grouptook an intermediate position and did not significantlydiffer from the other three groups. Accordingly, agegroup-related changes of AUT originality, displayed inFigure 2, were best described by a linear model, F(1, 87)= 11.37, R = .34, p = .001.

Response times (time between entering consecutivesolutions) showed large intra-subject variability, renderingit unlikely that participants’ typing speed, rather than thetime for generating correct solutions, was the main factorresponsible for the observed inter-subject differences.

Visuo-spatial divergent thinking: CAT

Performance was measured in terms of fluency andoriginality. Univariate ANOVAs with age group(12 ⁄ 13 yrs, 15 ⁄ 16 yrs, 18 ⁄ 19 yrs, 25–30 yrs) as between-subjects variable resulted in significant age group effectsfor fluency (F(3, 91) = 2.71, p < .05; pg2 = .08) but notfor originality (F(3, 91) = 1.39, p = .25; pg2 = .04). Post-hoc Tukey HSD analyses (Levene’s test of equality:p > .5) showed that 15 ⁄ 16 years olds gave significantlymore correct answers than 12 ⁄ 13 yrs olds (p = .03) . Thetwo adult groups did not significantly differ from eachother or from the two younger age groups (all ps > .05).As such, the results, presented in Figure 3, show a

Table 1 Means and standard deviations for performance parameter for insight and divergent thinking tasks by age group

12 ⁄ 13 yrs 15 ⁄ 16 yrs 18 ⁄ 19 yrs 25–30 yrs

M SD M SD M SD M SD

InsightVisual GCTa 5.36 1.87 5.65 1.43 6.92 1.04 7.44 1.36

SPTa 5.89 1.83 6.00 2.20 7.56 2.04 7.82 1.74Verbal RATa 7.32 3.24 9.57 3.26 12.44 3.49 13.06 2.95

Original IdeationVerbal AUTfluencya 10.76 5.72 9.70 4.88 11.12 5.92 10.13 4.59

AUTflexibilitya 6.80 3.08 5.96 2.44 7.36 2.69 7.19 3.06AUToriginalitya 0.85 0.13 0.67 0.19 0.73 0.10 0.81 0.12

Visuo-spatial CATfluencyb 7.17 3.19 9.17 2.39 8.35 2.29 8.28 2.22CAToriginalityb 10.58 6.79 13.53 5.26 11.48 4.71 12.22 4.80

Note: a 12 ⁄ 13 yrs, n = 25; 15 ⁄ 16 yrs, n = 23; 18 ⁄ 19 yrs, n = 25; 25–30 yrs, n = 16; b 12 ⁄ 13 yrs, n = 24; 15 ⁄ 16 yrs, n = 30; 18 ⁄ 19 yrs, n = 20; 25–30 yrs, n = 17;GCT = Gestalt Completion Test; SPT = Snowy Picture Test; RAT = Remote Association Test; AUT = Alternate Uses Test; CAT = Creative Ability Test.



complex pattern that did not fit significantly with linear,stepwise, or curvilinear models. Alternative model anal-yses regarding peak performance for middle adolescents(contrast: )1 3 )1 )1; for 12 ⁄ 13 yrs, 15 ⁄ 16 yrs, 18 ⁄ 9 yrs,and 25–30 yrs, respectively) revealed significant fit (F(3,91) = 5.01, p = .028).

Principal component analyses

Principal component analyses were performed on GCT,SPT and RAT performances, AUT fluency, AUT

flexibility, AUT originality, CAT fluency and CAT orig-inality scores (N12 ⁄ 13 yrs = 25; N15 ⁄ 16 yrs = 23; N18 ⁄ 19

yrs = 25; N25–30 yrs = 16) followed by varimax rotations.This approach was chosen to maximize the distinctionsamong aspects. The threshold for retained eigenvalueswas set to at 1.0 and only variables with loadings of atleast .5 are interpreted as significant. The analysisrevealed three factors, which accounted for 70% of thecommon variance. Table 2 shows that these factorsreflect the trichotomy of developmental patternsobserved with previous analyses. The first factor loadedwith performances on the insight tasks and, marginallysignificantly, with AUT originality. The second factorloaded with AUT fluency, and AUT flexibility, and thethird factor with CAT performances.

Correlations

Bivariate correlations

Table 3 shows the correlations between creative task per-formances over all participants. As predicted, significant

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Figure 1 Insight performances. Mean ± 1 standard error(SEM) of the number of correct solutions for each group for (a)the Gestalt Completion Test, (b) Snowy Picture Test, and (c)Remote Association Test.

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Figure 2 Verbal divergent thinking originality. Mean ± 1standard error (SEM) of average log-transformed frequencies ofideas generated in the Alternate Uses Test for each age group.

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Figure 3 Visuo-spatial divergent thinking fluency. Mean ± 1standard error (SEM) of the number of correct solutions on theCreative Ability Test for each age group.



correlations are observed between measures of insight,between measures of verbal divergent thinking, andbetween measures of visuo-spatial divergent thinking.Additional correlations are observed between AU origi-nality and insight performance, but only the correlationwith GCT was statistically significant. Correlationsbetween AU originality and SPT and RAT, respectively,were only marginally significant. Notice that AU origi-nality scores are based on frequency scores, so that neg-ative correlations indicate positive relations betweenoriginality and insight. In all, these correlations are incongruence with the extracted factors in the PCA.

Partial correlations

Only correlations between GCT and RAT (r = .24,p = .026), AU fluency and AU flexibility (r = .79,p < .001), and CAT fluency and CAT originality(r = .87, p < .001) remained significant after controllingfor age group means. The disappearances and strongdecreases of correlations between insight and divergentthinking after correction for age group effects indicatethat the initial bivariate correlations between these con-structs are likely driven by developmental differences.

Discussion

The aim of this study was to augment understanding ofthe development of creativity across adolescence. Using a

range of creativity tests, we found distinct developmentalpatterns indicating that: (1) creative insight and thequalitative measure of verbal divergent thinking, origi-nality, continue to develop into late adolescence; (2) thetwo quantitative measures of verbal divergent thinking,fluency and flexibility, reach adult level early in adoles-cence; and (3) visuo-spatial divergent thinking shows anon-linear developmental pattern with best performanceat age 15–16. This trichotomy was supported by factorreduction and correlation analyses. In all, these datasupport the distinctiveness of creativity aspects. Mecha-nisms that may underlie these trajectories are discussedin the following paragraphs.

Creative insight

Creative insight showed increased performance with agecontinuing into late adolescence on both visual andverbal problems. Our results therefore indicate that theability to successfully restructure and unify complex orremotely associated information is not fully developeduntil late adolescence.

A first factor that might have contributed to thesedevelopmental differences is the increasing amount ofknowledge and experience gained with increasing age;both forms of insight require retrieval of stored knowl-edge and associations. Second, age-related increasesmight be related to development of cognition controlfunctioning. Creative insight performance has beenshown to benefit from deliberate, focused, and structured

Table 2 Varimax rotated exporatory factor model

Factor 1 Factor 2 Factor 3

GCT .77 .08 ).01SPT .68 ).13 ).06RAT .76 ).10 ).01AUTfluency ).03 .94 ).00AUTflexibility .03 .93 .03AUToriginality ).49 ).26 ).10CATfluency .04 ).01 .96CAToriginality ).04 .04 .97

Note: N = 86; GCT = Gestalt Completion Test; SPT = Snowy Picture Test; RAT = Remote Association Test; AUT = Alternate Uses Test; CAT = Creative Ability Test.Loadings ‡.50 are in given in boldface.

Table 3 Bivariate correlations for performance parameters for Insight and Divergent Thinking tasks

Insight Divergent Tinking

SPT RAT AUTfluency AUTflexibility AUToriginality CATfluency CAToriginality

GCT .320* .453** .064 .078 ).275* ).002 ).003SPT .334** ).073 ).056 ).201 .019 ).099RAT ).115 .001 ).183 .063 ).072AUTfluency .791** ).152 ).002 .041AUTflexibility .142 .044 .046AUToriginality ).014 ).105CATfluency .870**

Note: N = 89 except for following correlations: Insight-CPT (N = 86); AUT-CPT (N = 86); CPT-CPT (N = 95).GCT = Gestalt Completion Test; SPT = Snowy Picture Test; RAT = Remote Association Test; AUT = Alternate Uses Test; CAT = Creative Ability Test.*p < .01, two-tailed; **p £ .001, two-tailed.



exploration of cognitive categories or perspectives (DeDreu et al., 2008; Finke, 1996; Schooler, Ohlsson &Brooks, 1993; Simonton, 1999) and incremental searchprocesses (Boden, 1998; Newell & Simon, 1972). Inaddition, neuro-imaging studies indicate the importanceof the prefrontal cortex for successful creative insight(Kounios, Frymiare, Bowden, Fleck, Subramaniam,Parrish & Jung-Beeman, 2006; Razumnikova, 2007). It isnow well documented that cognitive control functions,and associated prefrontal cortex areas, develop acrosschildhood and adolescence (see e.g. Casey et al., 2008;Crone, 2009). Specifically, cognitive control functionssuch as working memory, inhibition, monitoring, andmental switching, show protracted developmental tra-jectories throughout adolescence (Huizinga et al., 2006;Luna, Garver, Urban, Lazar & Sweeny, 2004). Accord-ingly, immature cognitive control functioning for the twoyoungest age groups in our study likely explains, at leastto some degree, the observed age-related differences.

Visual insight

The developmental pattern for visual insight was bestdescribed by a model that tested for a performance stepbetween middle and late adolescence, rather than a linearage change. Non-linear developmental patterns withrelatively poor performance in middle adolescence arenot uncommon in literature on visual cognition. Forexample, Uhlhaas and colleagues (2009) found suddeninterruptions in Gestalt perception development duringadolescence. Neurophysiologic measures indicate thatthese developmental interruptions are related to reorga-nizations of functional neural networks, which is com-patible with functional and structural non-lineartrajectories of cortical networks during this period asseen in imaging studies (Ashtari et al., 2007; Gogtayet al., 2004; Luna et al., 2001). Accordingly, we mightexpect functional changes towards more adult-likecoordination of visual information processing to inducedevelopmental stagnation of visual insight performance,as observed in our study.

Verbal divergent thinking

For verbal divergent thinking we found different devel-opmental patterns for the three divergent thinking mea-sures. Fluency and flexibility performances did notchange throughout the age range. These results are incongruence with previous findings on verbal ideation for6th graders and university students (Wu et al., 2005) andindicate that the capacity to generate numerous ideasfrom different categories is already fully developed inearly adolescence. For the third measure of ideation,originality, which was not reported separately in the Wuet al. (2005) study, marked increases were found aftermiddle adolescence. Thus, although adolescents as youngas 12 years old are able to produce adult-level numbersof solutions, the quality of solutions still develops.

Common to insight development, a first factor thatmight (partially) account for the developmental changesin originality performance concerns knowledge andexperiences (Weisberg, 1999). Individual lifestyles ofadults and late adolescents generally involve larger inter-individual variance of experiences and knowledge com-pared to younger groups. Consequently, older age groupsmight create relatively infrequent associations and ideas.A second possible explanation for the age group differ-ences concerns developmental changes in other cognitiveprocesses. Successful creative thinking is associated withflexible coordination between analytic and associativeprocessing (Christoff, Gordon & Smith, 2009a, 2009b;Martindale, 1999; Martindale & Hasenfus, 1978). Bothassociative and analytic processing is believed to lead tonumerous ideas (De Dreu et al., 2012, Nijstad, De Dreu,Rietzschel & Baas, 2010). However, the quality of gen-erated ideas seems related to the coordination betweenthem (e.g. Martindale, 1999), an ability that is associatedwith functioning of late developing prefrontal brainregions (Kerns, 2006; Kerns, Cohen, MacDonald, Cho,Stenger & Carter, 2004). Thus, for early and middleadolescents the ability to successfully shift between thetwo types of processing might not be fully developed(Smolucha & Smolucha, 1986; see also Runco, 2007). Totest these hypotheses on the combined influence ofassociative and analytical processing, future researchmight relate creative thinking across age groups to acti-vation in prefrontal cortex (e.g. see Fink, Grabner,Benedek, Reishofer, Hauswirth, Fally, Neuper, Ebner &Neubauer, 2009).

Visuo-spatial divergent thinking

The participants’ performance on the CAT showedmarked increases from early to middle adolescence,whereas early adolescents’ performance did not differfrom late adolescents’ or adults’. These results suggest anadvantage for middle adolescents on this aspect of cre-ativity. It should be noted that the middle adolescents didnot differ significantly from the older age groups inperformance, but the model that tested for a middleadolescent peak provided significant fit.

Success on this type of task is relatively independent ofknowledge but requires shifting between representationsof the visual information provided, applying a set ofrules, and monitoring behavior; cognitive functions thatare still developing during young adolescence (Huizingaet al., 2006). It seems logical that the above-mentionedfactors relate to the increase for middle compared toearly adolescents. However, as mentioned, the youngestgroup did not differ significantly from the oldest two agegroups, suggesting a relative advantage for the middleadolescents. The divergent character of the task intro-duces benefits for widely focused exploration of theexternally presented information. Both animal andhuman studies indicate that explorative behavior ischaracteristic for adolescents (Dahl, 2011; Johnson &



Wilbrecht, 2011) and that this behavior is likely associ-ated with increased levels of dopamine in prefrontalcortex during this age period (see Casey et al., 2008;Spear, 2000). For middle adolescents, the required cog-nitive control functions are expected to be sufficientlydeveloped. Combining these prerequisites with broad-ened attention towards externally presented stimuli (e.g.Gray, Buhusi & Schmajuk, 1997) provides an explana-tion for the observed developmental pattern withadvantages for middle adolescents. This hypothesis needsto be tested more elaborately in future research.

To summarize, several cognitive and related neurobi-ological aspects are likely to contribute to the distinctdevelopmental patterns for insight and divergent think-ing, including knowledge and experience, coordinationbetween information processes, reorganizations of func-tional networks, and widely focused explorative behaviorduring adolescence. There are, however, some limitationsof the current study that should be taken into accountwhen drawing conclusions about developmental chan-ges: (a) the current study included a relatively smallsample size (n = 19 to n = 30 per age group) andtherefore future studies should replicate results to vali-date our findings; (b) the study design was cross-sectional rather than longitudinal, which limits thereliability of the revealed age differences; (c) the gapsbetween age groups may hide short-term changes inperformance; and (d) because of practical limitations,there were some differences between the younger andolder participants in test administration (see Methodssection). However, a lack of significant age group dif-ferences with regard to norm scores on measures ofgeneral cognitive abilities render it unlikely that theobserved developmental patterns were merely the con-sequence of cohort effects. Thus, we believe the currentstudy provides meaningful insights into the creativeabilities and constraints across adolescent developmentand provides interesting hypotheses that need to betested in future research.

Conclusions

To our knowledge, the current study is the first toexamine the development of diverse aspects of creativityacross adolescent development. The findings demon-strated distinct developmental trajectories with markeddiscrepancies between divergent thinking and creativeinsight. These results could be related to age-related dif-ferences of knowledge and experiences only to somedegree, and are likely related to the protracted develop-ment of cognitive control functioning and the relativelywide and explorative focusing style characteristic formiddle adolescents. Similar conclusions have beensuggested by animal research showing that adolescentmice show relatively greater flexibility for learning thanmore mature mice (Johnson & Wilbrecht, 2011). Wehypothesize that these developmental changes are related

to reorganizations of functional networks, which is in linewith the developmental theory of interactive specializa-tion (Johnson, 2011).

In future studies, it will be of interest to relate creativethinking to activation in the prefrontal cortex, toexamine the combined influence of associative, auto-matic, and analytical, deliberate types of processing (e.g.see Fink et al., 2009). As individuals enter adolescence,this confronts them with multiple possibilities forlearning and adaptation, possibly guided by increasedcapacity for widely focused processing, giving themopportunities for exploration. In sum, the current resultsindicate that adolescent development is not only a timeof immaturity but also of flexibility and potential forcreativity.

Acknowledgements

This research was supported by a grant from NWO(open competition program; grant no. 400-08-023)awarded to E.A. Crone. We thank Dietsje Jolles andClaire Stevenson for their helpful comments on a priorversion of this article.

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Received: 4 October 2011Accepted: 2 May 2012



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