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Review article, the link between creativity, cognition, and creative drives and underlying neural mechanisms.

creative problem solving cognitive load

  • 1 Department of Psychology and Methods, Jacobs University Bremen, Bremen, Germany
  • 2 Department of Psychiatry and Psychotherapy, University Clinic Tübingen, Tübingen, Germany
  • 3 Department of Health Psychology and Neurorehabilitation, SRH Mobile University, Riedlingen, Germany

Having a creative mind is one of the gateways for achieving fabulous success and remarkable progress in professional, personal and social life. Therefore, a better understanding of the neural correlates and the underlying neural mechanisms related to creative ideation is crucial and valuable. However, the current literature on neural systems and circuits underlying creative cognition, and on how creative drives such as motivation, mood states, and reward could shape our creative mind through the associated neuromodulatory systems [i.e., the dopaminergic (DA), the noradrenergic (NE) and the serotonergic (5-HT) system] seems to be insufficient to explain the creative ideation and production process. One reason might be that the mentioned systems and processes are usually investigated in isolation and independent of each other. Through this review, we aim at advancing the current state of knowledge by providing an integrative view on the interactions between neural systems underlying the creative cognition and the creative drive and associated neuromodulatory systems (see Figure 1 ).


Creativity and innovative thinking have been a vast construct of questioning to scholars, psychologists, therapists and, more lately, neuroscientists ( Jung et al., 2010 ). Creativity appears in various diverse models, tones, and shades ( Feist, 2010 ; Perlovsky and Levine, 2012 ). The creative contributions of extraordinary artists, designers, inventors, and scientists attract our greatest consideration as they express the foundations of their culture and provide breakthroughs influencing cultural development and progress. Therefore, creativity is a crucial operator of human progress. Nevertheless, not every person who is an artist, inventor or scientist is similarly creative, nor are all creative (innovative) individual artists, inventors or scientists. Some are innovative in business, in communication with other individuals, or just in living.

Consequently, creativity is a multidimensional domain that could be executed in the arts, science, stage performance, the commercial enterprise and business innovation ( Sawyer, 2006 ). Following Baas et al. (2015) who defined the roots of creative cognition in the arts and sciences, creativity is not just a cultural or social construct. Instead, it is an essential psychological and cognitive process as well ( Csikszentmihalyi, 1999 ; Sawyer, 2006 ; Kaufman, 2009 ; Gaut, 2010 ; Perlovsky and Levine, 2012 ). Even so, many experimental investigations on creativity have reported various findings that often seem to be inconsistent and scattered. One of the principal reasons for that could be due to the wide variety of the experimental approaches in the domain of creativity research and the immense diversity in measuring and interpreting creative performance ( Fink et al., 2007 , 2014 ; Abraham, 2013 ; Zhu et al., 2013 ). In this review article we will discuss the relation between creative cognition, creative drives and their underlying neuromodulatory circuits (see Figures 1 , 5 and Table 2 ). We will first elaborate on how different cognitive functions support creativity and on their neural basis as revealed by structural and functional brain imaging studies. Second, we will detail the link between mood and motivation as drives for creative performance and the role of dopamin (DA), noradrenaline (NE) and serotonin (5 HT) as key neuromodulatory systems. Next, we will discuss studies on pathological brain conditions which provide further evidence on the role of the neuromodulatory systems. Finally, based on this integrative view, we will list some open questions and provide suggestions for future research directions.


Figure 1 . A schematic overview of the neurobiology of creativity as outlined in this review. It symbolizes the brain systems and neuromodulatory pathways underlying and modulating creative cognition and creative drive in health and disease. The creative cognition is based on various cognitive functions such as cognitive flexibility, inhibitory control, working memory (WM) updating, fluency, originality, and insights. The creative drive includes several factors that influence creativity such as emotion motivation, reward and other factors such as mood states, regulatory focus, and social interaction. The neuromodulatory pathways include the noradrenergic (NE), the dopaminergic (DA) and the serotonergic (5-HT) pathways.


Figure 2 . A schematic overview of the link between creativity and different mood states (after Baas et al., 2008 , 2013 ; De Dreu et al., 2008 ). It illustrates how activating and deactivating mood states (i.e., valences, motivational state), and regulatory focus influence creativity. A “ >” symbolizes a higher influence in the condition left as compared to the right of the symbol. Symbols ± symbolize positive and negative influences, while an “X” symbolizes no influence revealed.


Figure 3 . A schematic overview of the different networks in the brain involved in three dimensions of creativity (after Boccia et al., 2015 ): musical (red colored symbols), verbal (blue colored symbols), and visuospatial (green colored symbols). Filled symbols represent left hemispheric brain regions, open symbols represent right hemispheric regions. For simplicity, several separate foci within brain regions are represented by one single symbol. Brain regions are abbreviated as follows: PFC, prefrontal cortex; PCC, posterior cingulate cortex; IPL, intraparietal lobule; TC, temporal cortex; OCC, occipital cortex; Th, thalamus; CeC, cerebellar cortex; and CS, central sulcus. Black arrows symbolize the interaction between the executive control (EC) network and the default mode network (DMN) according to Beaty et al. (2017) .


Figure 4 . A schematic overview of the neurobiology of different facets of creativity as proposed from animal studies (after Kaufman et al., 2011 ). The creative animal model consists of three levels with increasing cognitive complexity: novelty, observational learning, and innovative behavior. The first level comprises of both the cognitive ability to recognize novelty, which is linked to hippocampal (HPC) function, and the seeking out of novelty, which is associated with the mesolimbic DA system. The second level refers to observational learning, which could range in complexity from imitation to the cultural transmission of creative behavior. Observational learning might critically depend on the cerebellum and the PFC. The third level is represented in the innovative behavior, which relates to specific recognition of a particular object characterized by novelty. This innovative behavior may be reliant upon PFC.


Figure 5 . A schematic overview of the effects of the two DA pathways (the nigrostriatal and mesocortical DA) on the creative drives and the creative cognitions [i.e., executive functions (EFs)]. Both pathways influence creativity via the dual process model, which is composed of a resistance and cognitive flexibility. The prediction of creativity through EFs (i.e., shifting, inhibition and WM) requires an optimal balance between deliberate (controlled) processing and spontaneous processing. On the other hand, there is a link between reward (i.e., promises, training, and intrinsic interest) and creativity through the action effect binding. Moderating effects of mindset (cooperative and competitive) and cognitive resources on creative drives (i.e., mood, motivation, and emotion) is also illustrated. Numbers refer to references as indicated in Table 2 .


Table 1 . Potential candidate genes for creativity.


Table 2 . References related to corresponding numbers in Figure 5 .

Creative Cognition Is Rooted in Executive Functions (EFs)

The field of creative cognition deals with the understanding of the cognitive processes underlying creative performance. A pioneering study by Mednick (1962) linked creativity to associative thinking. This interpretation was not directed to any specific field of application such as art or science. Instead, it was attempted to define processes that underlie all creative thought. Rossmann and Fink (2010) extended Mednick’s theory by investigating the relationship between individual differences in processing associative information and various aspects of creativity.

Along with a variety of creative psychometric tasks, these authors provided a slightly modified variant of Gianotti et al.’s (2001) list of word pairs and asked the participants (university students) to rank the semantic associative distance between the words of a given pair. This list comprised pairs of indirectly related (e.g., cat—cheese) and unrelated word pairs (e.g., subject—marriage). In comparison to the less creative group, the more creative group reported smaller distances between unrelated word pairs, which can be interpreted as that they found creative associations between usually unrelated words.

Recently, Benedek et al. (2012) proposed a close connection between associative processes and divergent thinking (DT) as measured, for example, by the Alternate Uses Task (AUT, Guilford, 1967 ). Accordingly, the notion of creative cognition can be conceptualized within an evolutionary framework, namely Blind Variation and Selective Retention (BVSR; Jung et al., 2013 ). From a behavioral perspective, one could link the “blind variation” component to idea generation as measured by DT tasks. In contrast, the “selective retention” component could be represented by convergent thinking (CT), as represented by measures of remote associates (e.g., Remote Associates Test; RAT). Radel et al. (2015) revealed that inhibition influences certain kinds of creative processes selectively. Exposure to a Flanker or Simon task and thus exhausting inhibitory resources led to enhanced fluency and originality in a following AUT (i.e., DT) task. For a RAT (i.e., CT) task, no such effect was found ( Radel et al., 2015 ). Therefore, a lack of resources for inhibition might lead to the facilitation of the frequency and the novelty (i.e., originality) of thoughts (i.e., ideas). Accordingly, one could claim that particularly idea generation processes profit from a depletion of the resources for inhibition.

Within a latent variable model approach, Benedek et al. (2014) explained the association between fluid intelligence and creative cognition through a general executive component. According to Benedek et al. (2014) , creativity was predicted by working memory (WM) updating and inhibition, but not by mental set shifting. Further, WM updating and the personality factor openness represented a related factor of the shared variance between creativity and fluid intelligence ( Benedek et al., 2014 ). Fleming et al. (2016) described associations between another personality trait, i.e., conscientiousness and mental set shifting, but not response inhibition nor WM updating. Level of conscientiousness influences whether people set and maintain long-range goals, deliberate over preferences (i.e., choices) or behave impulsively, and take obligations to others critically. It was associated with cognitive competencies which are related to rigid (i.e., inflexible) control over impulses (i.e., inhibition), and therefore might inhibit creativity. Mok (2014) highlighted the possibility for creative cognition to be originated from an optimal balance between spontaneous and controlled processes. It was hypothesized by Dietrich (2004) that the principal distinction between spontaneous and deliberate (i.e., controlled) modes of processing is the approach utilized to depict the unconscious novel information in WM. For example, the spontaneous process happens when the attentional system does not actively choose (decide or select) the content to become conscious, enabling unconscious thoughts that are relatively further random, unfiltered, and unusual to be represented in WM. On the other hand, deliberate insights are prompted by circuits in the prefrontal cortex (PFC) and therefore tend to be structured, rational (logical), and corresponding to internalized values and belief systems. A delicate balance between further spontaneous processing vs. more controlled processing may likely enhance creative cognition to the extent that default activity does not become suppressed due to the substantial need for controlled processing ( Mok, 2012 ).

Cassotti et al. (2016) discussed how a dual-process model of creativity could expand our knowledge concerning the creative-cognitive associations. This dual-process model resembles the proposed model to account for reasoning and decision making ( Evans et al., 1993 ). According to the dual pathway of creativity model ( Nijstad et al., 2010 ), there are two qualitatively peculiar pathways to creative performance: the flexibility pathway and the persistence pathway. The flexibility pathway suggests stimulating creativity through a flexible switching between categories, approaches, and sets while the persistence pathway leads to creativity through hard work, systematic and effortful exploration of possibilities, and in-depth exploration of just a few categories ( Nijstad et al., 2010 ). Lu et al. (2017) also revealed that cognitive flexibility could enhance two critical forms of creativity (DT and CT) by reducing the cognitive fixation, which, however, at the same time reduces the creative benefits of cognitive persistence. Combined, during the process of task switching, there is often an implicit tradeoff between flexibility and persistence ( Nijstad et al., 2010 ). When task switching strengthens flexibility, it reduces persistence and vice versa ( Lu et al., 2017 ). Also, supported and directed effort can further improve creative performance (e.g., Lucas and Nordgren, 2015 ).

Concerning inhibitory control, it is acknowledged that this executive function (EF) might be a core process involved in creative problem solving and idea generations ( Cassotti et al., 2016 ). During generating creative thoughts, individuals of all ages (i.e., children, adolescents, and adults) tend to follow the path of least resistance. In the meantime, proposed solutions are constructed based on the most common and accessible information within a distinct specialty, which leads to a fixation effect. Given these points, the ability to think about the novel (original) ideas necessitates: (1) inhibiting spontaneous solutions, that cross to mind rapidly and unconsciously; and (2) exploring original (novel) ideas using a generative type of reasoning.

The Link Between Mood States, Motivation, Reward, and Creativity

How do mood states influence creativity.

Creativity is a multifaceted construct, in which different moods influence distinct components of creative thoughts ( Kaufmann, 2003 ). A remarkable study by Baas et al. (2008) explained how creativity is enhanced most by the positive mood states (see Figure 2 ); see also Bittner et al. (2016) . Baas et al. (2008) pointed out that positive-activating moods with an approach motivation and promotion focus (e.g., happiness) activated creativity. On the contrary, negative-activating moods with avoidance motivation and a prevention focus (e.g., fear, anxiety, or even relaxation) correlated with lower creativity. Surprisingly, negative-deactivating moods together with approach motivation and a promotion focus (e.g., sadness) did not link with creativity.

Consequently, mood shifts are crucial in scaling creativity. Along the same line, De Dreu et al. (2008) argued that activating moods (e.g., anger, fear, happiness, elation) induce more creative fluency (i.e., number of ideas or insights) and originality (i.e., novelty) than deactivating moods such as sadness, depression, relaxation, and sereneness do ( Figure 2 ; see also, Yang and Hung, 2015 ). According to De Dreu et al. (2008) , activating moods could affect creative fluency and originality through enhancing cognitive flexibility when the tone is positive while enhancing persistence when the tone is negative (see also, To et al., 2015 ). Despite the previous findings, which related decreased creativity to an avoidance motivation and prevention focus when in a negative mood ( Baas et al., 2008 , 2013 ), an intriguing investigation by Roskes et al. (2012) explicated the contrary. For instance, they indicated that avoidance motivation could stimulate creativity through cognitive effort. However, this finding is incompatible with the dual process model of creativity ( Nijstad et al., 2010 ), which suggests that both flexible and persistent processing styles could construct a creative output. In other words, avoidance motivation has often been related to decreased creativity since it elicits a relatively inflexible processing style ( Baas et al., 2008 , 2013 ). Adjusting these disagreements, Roskes et al. (2012) viewed that people with an avoidance-motivated behavior are not incapable of being creative; instead, they have to compensate for their inflexible processing style by a demanding and constrained processing. Therefore, it is a matter of compensation. Noteworthy, Roskes et al. (2012) reported that whether the individuals are avoidance motivated or approach motivated, their creativity could be enhanced under certain circumstances. These circumstances necessitate their creativity to be directed to a role for goal achievement, which motivates them to exert an additional effort of high-cost cognitive function.

Focusing on anxiety as another mood state that affects creativity, Byron and Khazanchi (2011) provided a meta-analytical study on the association between anxiety and creative performance (i.e., figural and verbal tasks). Anxiety was significantly and negatively related to figural and verbal creative performance. Using fMRI, Gawda and Szepietowska (2016) revealed that trait anxiety could slightly modulate neural activation during the creative verbal performance, notably, in the more complicated tasks. Additionally, there were significant variations in brain activation during the performance of more complex tasks between individuals with low anxiety and those with high anxiety. Also, Lin et al. (2014) reported how emotions shape different creative achievements (CAs). In their study, the positive emotional states reduced switch costs while enhancing the performance in DT and problem solving (i.e., performance in an open-ended DT test and a closed-ended insight problem-solving task).

Moreover, cognitive flexibility (as measured by a switching task) could have a mediating impact on the association between the positive emotion and the insight problem solving, but not between the positive emotion and DT. Bledow et al. (2013) revealed a strong influence of the dynamic interaction of positive and negative mood on creativity. Extraordinary creativity, for example, necessitates that a person should experience an episode of negative affect. This episode should be followed by a reduction in negative affect and an increment in positive affect. This process is termed “an affective shift.”

Concerning mindset, regulatory focus and creativity, Bittner and Heidemeier (2013) observed that mindsets have no direct control over creativity while prevention focus decreases subsequent creativity. They explicated that a cooperative mindset activates a promotion focus while a competitive mindset activates a prevention focus. Thus, prevention focus provides the indirect negative effect of competitive mindsets on creativity ( Bittner and Heidemeier, 2013 ; Bittner et al., 2016 ).

Does Reward Matter in the Case of Creativity?

A number of researchers highlighted the strong connection between reward and creativity ( Eisenberger and Selbst, 1994 ; Eisenberger and Cameron, 1998 ; Eisenberger et al., 1998 , 1999 ; Eisenberger and Rhoades, 2001 ; Baer et al., 2003 ; Chen et al., 2012 ; Muhle-Karbe and Krebs, 2012 ; Volf and Tarasova, 2013 ; see, Figure 5 and Table 2 ). In the following subsection, we will detail this relationship. Muhle-Karbe and Krebs (2012) highlighted the impact of reward on the action-effect binding, which underlies the ideomotor theory. They defined this theory as the formation of anticipatory representations about the perceptual outcomes of an action, i.e., action-effect (A-E) binding, thus, presenting the functional basis of voluntary action control.

A startling study proposed that reward training could improve generalized creativity ( Maltzman, 1960 ; Eisenberger and Selbst, 1994 ; Figure 5 and Table 2 ). This enhancement requires the presence of a high degree of divergent thought and a reward. Eisenberger et al. (1998) argued that the assured reward improves creativity if there is an explicit positive relationship between creativity and reward (either currently or previously, i.e., it does not matter when). Besides, Eisenberger and Cameron (1998) focused on reward, intrinsic interest, and creativity. Herewith, the contribution of behavioral processes and cognitive-induced motivation represented possible determinants of the reward effects, which were crucial factors for enhancing creativity. Progressing in reward and creativity, Eisenberger et al. (1999) depicted the consequences of earlier experiences of a promised reward for creativity. They investigated how creativity (measured by a DT task) could be boosted by the distinction of a positive association between reward and creative novel performance. The demand for such novel performance in one task (whether associated with reward or not) established the promise of reward as a cue for creative performance. Herewith, the reward could either increase or reduce creativity depending on how it was supervised. As for the incremental effects of reward on creativity, Eisenberger and Rhoades (2001) questioned whether two-ways reward could enhance creativity. Based on their study, the reward required a contingent relation to creativity. This relation strengthened the extrinsic motivation. Hence, the expected reward for exceptional performance could boost creativity by enhancing the perceived self-determination and, consequently, the intrinsic interest. Later on, Chen et al. (2012) highlighted the interactive influences of the level and form of reward system design on group creativity, and how this interplay could assist in mastering the identified obstacles in the prior research.

Lastly, Volf and Tarasova (2013) argued about the impact of reward on the performance of creative verbal tasks. The promise of the monetary reward was favorable for creative thinking and original solutions. Interestingly, monetary reward-induced changes in brain oscillations, as measured with EEG, were characteristic of men but not women (i.e., a promise of a cash reward were correlated with EEG changes in men but not in women). For instance, in response to the monetary reward, men expressed an increase in both the θ2-rhythm asymmetry and the power of α rhythm. This finding reveals that women might refer to a tendency for a different effective strategy for processing verbal information to create a more original solution in the verbal task to receive a cash reward; thus, the promise of monetary reward is favorable for creative thinking and original solutions.

Where Bright Ideas Are Produced in Our Brains

Concerning the neural correlates of creative cognition, a number of studies referred to the PFC as one of the chief brain areas for new idea generation and inhibition of prevalent solutions ( Carlsson et al., 2000 ; Flaherty, 2005 , 2011 ; Karim et al., 2010 ; Krippl and Karim, 2011 ; Mok, 2014 ; Cassotti et al., 2016 ). The prefrontal brain regions are known as components of a deliberate control brain network and inhibition controller, which is considered to be a central process for problem-solving and idea generation from adolescence to adulthood ( Cassotti et al., 2016 ).

Dietrich and Kanso (2010) pointed out that creative thinking does not critically depend on a particular single mental process or specific brain region, and it is not mainly associated with right brains, defocused attention, low arousal, or alpha synchronization, as it also has often been hypothesized. Rather, Dietrich and Kanso (2010) proposed further subdividing creativity into different subtypes to make it traceable in the brain. In the same vein, a meta-analysis of 45 fMRI studies by Boccia et al. (2015) , suggested that creativity depends on multi-component neural networks and that creative performance in three different cognitive domains (musical, verbal, and visuospatial; see Figure 3 ) rely on diverse brain regions and networks. Using general activation likelihood estimation (ALE) analyses, these authors revealed creativity-related clusters of activations in all four cortical lobes while the maximum activation of the individual ALE expressed distinct neural networks in each creative cognition domain as follows:

1. Musical creativity expressed activation in a bilateral network consisting of the bilateral medial frontal gyrus (MeFG) and posterior cingulate cortex (PCC), left middle frontal gyrus (MFG) and inferior parietal lobule (IPL), and the right postcentral gyrus (PoCG) and fusiform gyrus (FG), as well as bilaterally the cerebellum.

2. The network for verbal creativity was left-hemispheric dominated and comprised of several activation foci in the left MFG, inferior parietal lobule (IPL), SMG, middle occipital gyrus (MOG), and middle and superior temporal gyrus (MTG and STG), and the bilateral inferior frontal gyrus (IFG) and insula, and the right lingual gyrus (LG) and cerebellum.

3. Visuospatial creativity relied on a slightly right-hemispheric dominated network including activation foci in the right MFG and IFG, the left precentral gyrus (PrCG), and the bilateral thalamus.

Concerning underlying brain networks, Mok (2014) further pointed out that EEG data related to creative cognition often inferred widespread alpha synchronization (synchronized brain waves that occur at 8–12 cycles per second), particularly in posterior regions. Controlled processing may co-occur with spontaneous cognition—mediated by a subset of the default mode networks (DMNs; e.g., the angular gyrus (AnG) in the posterior parietal cortex (PPC), which has been frequently implicated in creative cognition; Mok, 2014 ). Subsequently, when the demand for controlled processing is substantially increased, the DMN may be suppressed. There is preliminary evidence suggesting an association between alpha synchronization and default-mode processing. Also, Andrews-Hanna et al. (2014) highlighted the interplay between the DMN, with the systems of executive control (EC) while regulating components of internal thought. Importantly, response inhibition (which underlies creative thought) demands dynamic interactions of large-scale brain systems ( Beaty et al., 2016 , 2017 ). Herewith the default mode and EC networks, which usually show an antagonistic relationship, tend to cooperate in enhancing creative cognition and thus artistic performance.

Regarding WM, Takeuchi et al. (2011) explored the association between brain activity during the N-back task as widely used WM paradigm ( Jaeggi et al., 2010 ) and a psychometric measure of creativity (with a DT test). Through multiple regression analysis, Takeuchi et al. (2011) reported a significant and positive correlation between individual creativity and brain activity in the precuneus (a part of the superior parietal lobule in front of the cuneus in the occipital lobe) during a 2-back WM task but not during the non-WM 0-back task. This finding was coupled with task-induced deactivation (TID) in the precuneus (as part of the DMN, i.e., the brain network that is functional during the resting state), and correlated with higher DT. Using resting-state functional connectivity (RSFC) measures, Takeuchi et al. (2012) further showed an association between the medial PFC (mPFC) and PCC as the key nodes of the DMN during DT.

Another study revealed that DT was positively correlated with the strength of the RSFC between the mPFC and the MTG ( Wei et al., 2014 ). Further, cognitive stimulation through creativity training significantly increased the RSFC between the mPFC and the MTG. Besides, cognitive stimulation successfully enhanced cognitive performance in a novelty (originality) creativity task ( Wei et al., 2014 ).

An exciting study linked psychometric measurements of creativity [both DT and CA to cortical thickness in various brain regions in healthy young adults ( Jung et al., 2010 )]. In detail, these authors suggested the following: (1) higher CA was positively correlated with volume of the lower left lateral orbitofrontal cortex (lOFC) and cortical thickness in the right AnG; and (2) a composite creativity index (CCI) was negatively correlated with cortical thickness in the LG while positively correlated with cortical thickness in the right PCC.

Concerning the relation between hemispheric brain lateralization and creative thinking (i.e., formulating and producing novel ideas), a meta-analytic evaluation by Mihov et al. (2010) implied relative dominance of the right hemisphere (RH) during creative thinking. However, moderator analyses revealed no difference in predominant RH activation for many creative tasks (verbal, figural, holistic, analytical, context-dependent and context-independent). Carlsson et al. (2000) also analyzed the connection between creativity and hemispheric asymmetry, by measuring regional cerebral blood flow (rCBF) during rest and different creative verbal tasks. Highly creative subjects expressed bilateral frontal activation in the Brick task, a task in which participants were required to name potential uses of an object, while low creative subjects had unilateral activation. Importantly, in a word fluency test and the Brick test, the highly creative group expressed either an increase or unchanged CBF activity in the frontal region, while the low creative group showed a decrease in CBF instead.

Only a few animal studies also provided valuable insights into the link between brain and creative cognition. For example, a framework developed by Kaufman et al. (2011) suggested a three-level model of creativity (novelty, observational learning, and innovative behavior; see Figure 4 ). First, regarding novelty, the cognitive ability to recognize was proposed to be linked to hippocampal (HPC) function while seeking out for novelty could be connected to the mesolimbic DA system. Second, observational learning, which could range in complexity from imitation to the cultural transmission of creative behavior, was supposed to rely significantly, besides frontal brain regions, on the cerebellum. Third, innovative behavior such as creating a tool or exhibiting a behavior with the specific recognition that it is novel and different was described as being especially reliant upon the PFC and the balance between left-and-right hemispheric functions.

How the Neuromodulatory Systems Are Involved in Creative Performance

The dopaminergic (da) system and creativity.

The DA system is involved in various aspects of cognitive functions related to reward, addiction, attention, compulsions, and others. Recent studies imply that the DA system may act to coordinate the integration of information through selective potentiation of circuits and pathways ( Grace, 2010 ). Several lines of evidence support the crucial role of DA neurotransmission in human creative thought and behavior ( Flaherty, 2005 ; Reuter et al., 2006 ; Kulisevsky et al., 2009 ; Chermahini and Hommel, 2010 ; de Manzano et al., 2010 ; Inzelberg, 2013 ; van Schouwenburg et al., 2013 ; Lhommée et al., 2014 ; Surmeier et al., 2014 ; Zhang et al., 2014a , b , 2015 ; Zabelina et al., 2016 ; Boot et al., 2017 ; Kleinmintz et al., 2018 ), nevertheless, these studies remain sparse.

For example, Flaherty (2005) reported that novelty seeking and creative drive are influenced by mesolimbic DA. Colzato et al. (2009) measured spontaneous eye-blink rates (EBR) as a marker of central DA functioning in a stop signal task. They found that EBR predicted the efficiency in inhibiting tendencies to undesired action in this task. As these findings were obtained from patient and drug studies, the authors constrained their conclusions on a positive effect of DA stimulants on response inhibition to cases of suboptimal inhibitory functioning ( Colzato et al., 2009 ). Later, Chermahini and Hommel (2010) revealed that EBR predicted flexibility in both kinds of thinking (DT and CT) but in different ways. Notably, there was a positive correlation between CT and intelligence, but a negative correlation with EBR, proposing a correlation between CT impairment and higher levels of DA.

Furthermore, Zhang et al. (2015) investigated the relation between EBR and many EFs (i.e., mental set shifting, response inhibition, and WM updating). Their study revealed a correlation between increasing EBR (which refers to increasing DA) with a better mental set shifting and response inhibition, but poorer WM updating. The increment in EBR levels was associated with an increase in the accuracy in both mental set shifting and response inhibition related tasks; however, a reduction in the cost of mental set shifting and response inhibition was associated with a decrease in the accuracy in WM updating tasks. These findings indicate a diverse role of the central DA system in mental set shifting and response inhibition as compared to updating ( Figure 5 ; see also Zhang et al., 2017 ).

Recently, Boot et al. (2017) provided an integrative review on creative cognition and DA modulation in frontostriatal networks (see, Figure 5 and Table 2 ). Integrating results from different experimental tasks (i.e., creative ideation, DT, or creative problem-solving) and various study approaches (such as looking at polymorphisms in DA receptor genes, measuring indirect markers of DA activity, manipulating the DA system, or investigating clinical populations with dysregulated DA activity) proposed the followings: (i) creative cognition benefits from both flexible and persistent processing; (ii) an association between striatal DA, the integrity of the nigrostriatal-DA pathways, and flexible processing; and (iii) an association between prefrontal DA, the integrity of the mesocortical-DA pathway and persistent processing ( Figure 5 and Table 2 ). Altogether, while the literature indicates a functional differentiation between the striatal and prefrontal DA, it seems that the functional level of DA has to be moderate for both striatal DA and prefrontal DA to benefit creative cognition by facilitating flexible processing and enable persistence-driven creativity, respectively ( Boot et al., 2017 ).

Regional Gray Matter Volume (rGMV) of The Dopaminergic (DA) System and Creativity

Despite the existence of a consistent number of functional imaging studies on creativity, the relationship between individual creativity and volumetric morphological changes in the regional gray matter (rGMV) within the DA system has not been explored adequately until recently. Salgado-Pineda et al. (2003) reported increased rGMV in parts of the mesencephalic DA system (thalamic, inferior-parietal, and frontal cortical regions) following the treatment with of levodopa (i.e., DA replacement therapy). Moreover, different studies on patients with Tourette’s Syndrome (which is another disease associated with an excessive function of the mesencephalic DA system) described related increases of rGMV in these regions ( Shapiro et al., 1989 ; Singer et al., 2002 ; Albin et al., 2003 ). These investigations imply that the morphology of the mesencephalic DA system and associated DA function are correlated with creativity. This assumption is further supported by Takeuchi et al. (2010) who revealed a positive correlation between individual creativity (as measured by a DT task) and rGMV in particular parts of the mesencephalic DA system [i.e., the right dorsolateral PFC (rDLPFC), bilateral striata and anatomical clusters in the Substantia Nigra (STN), the ventral tegmental area (VTA) and periaqueductal gray (PAG)]. These findings resonate the core link between individual creativity and rGMV of the mesencephalic DA system. Accordingly, there is an agreement with the opinion that associates DA physiological mechanisms and individual creativity.

Artistic Style Shifts, Dopamine (DA), and Creativity

An exciting study by Kulisevsky et al. (2009) described the relationship between mental shifts and the artistic style in Parkinson’s disease (PD) focusing on the link between creativity and DA. They provided a case study with a PD patient, which reported changes in the creative artistic performance. These changes appeared to be correlated with the DA imbalance in the limbic system. When this patient was supplied with DA agonists, then, hidden creativity had been awaked. This awake led to progressive improvement in painting productivity. Then, the rebirth of artistic creativity in PD relied on sustaining DA level (see also Inzelberg, 2013 ). However, it is yet unclear whether the enhancement of the creative drive was due to the physiological regulation of DA because the underlying mechanisms remain speculative ( Inzelberg, 2013 ). It is well known that neurodegenerative diseases are characterized by reduced flexibility, conceptualization, and visuospatial abilities ( Asaadi et al., 2016 ). Although these features are essential elements for creativity, case studies revealed the evolution of creativity during PD.

Along with the same line, Lhommée et al. (2014) explained the possibility of inducing creativity through DA treatments in PD; however, this effect feasibility slowly disappeared after withdrawal of DA agonists, and only one of eleven patients remained creative after the surgery. Also, the reduction of DA agonist was significantly correlated to the decrease in creativity in the whole study population. Consequently, there is a strong link between creativity in PD and DA agonist therapy.

Genetic Research Reveals a Strong Association Between DA Activity and Creativity

One critical step towards a better understanding of creativity is to unveil its underlying genetic architectures. Many studies reported the first candidate genes for creativity ( Reuter et al., 2006 ; Runco et al., 2011 ; Zhang et al., 2014a , b ; Zabelina et al., 2016 ; Grigorenko, 2017 ; see Table 1 ).

On describing the genetic basis of creativity and ideational fluency, Runco et al. (2011) referred to Reuter et al. (2006) who defined what they called the first candidate gene for creativity. Runco et al. (2011) replicated and extended the investigation of Reuter et al. (2006) for further accurate analysis of five candidate genes, which are: DA transporter (DAT), catechol-O-methyl-transferase (COMT), Dopamine Receptor D4 (DRD4), D2 Dopamine Receptor (DRD2), and Tryptophan Hydroxylase 1 (TPH1). In the study by Runco et al. (2011) , participants received a battery of tests related to creativity. Multivariate analyses of variance indicated a significant association between the ideational fluency scores and several genes (DAT, COMT, DRD4, and TPH1). Therefore, in contrast to initial studies, the offered conclusion by Runco et al. (2011) suggested a clear genetic basis for ideational fluency. However, fluency, alone, is not sufficient to predict and guarantee creative performance.

Mayseless et al. (2013) reported an association between DT and DRD4 (7R polymorphism in the DRD4 gene). DT abilities were associated with DA activity while impaired DT has been reported in populations with DA dysfunctions. The authors concluded that individuals carrying the DRD4–7R allele scored significantly lower in DT (particularly on the flexibility dimension) compared to non-carriers of this allele.

Zabelina et al. (2016) observed that performance in two tests of creativity (i.e., the Torrance test and the real-world CA index) could be predicted by specific genetic polymorphisms that are related to the frontal (COMT gene) and striatal (DAT gene) DA pathways. High performance at the Torrance test was related to DA polymorphisms associated with higher cognitive flexibility and low to medium top-down control (9/9 or 9/10 DAT and Met/Val or Val/Val COMT genotypes, respectively), or, particularly for the originality component of the DT, with weak cognitive flexibility and strong top-down control (10/10 DAT and Met/Met COMT genotypes, respectively). Weak cognitive flexibility (10/10 DAT genotype) and weak cognitive control (Val/Val COMT genotype) were associated with high real-world CA.

An additional exploratory study on DA gene DRD2 and the creative potential (DT test) was provided by Zhang et al. (2014a) . This study systematically explored the associations between DRD2 genetic polymorphisms and DT in 543 unrelated healthy Chinese undergraduate students. There were significant associations between specific single-nucleotide polymorphisms (SNPs), fluency (verbal and figural), verbal originality and figural flexibility. Extending on these findings, Zhang et al. (2014b) thoroughly examined the relationship between COMT, creative potential and the interaction between COMT and DRD2. Their study provided a shred of evidence for the implication of COMT in creative potential, which suggests that DA-related genes may act in coordination to contribute to creativity.

Based on these findings, one can conclude that human creativity principally relies on the interplay among frontal and striatal DA pathways. The dynamical interaction between these two pathways might assist to explain the inconsistencies due to the independent evaluation in measuring genes and creativity during the past decade.

Other Neuromodulatory Systems and Creativity

According to Flaherty (2011) , the induction of creativity could rely on the goal-driven approach motivation from the midbrain DA system; however, fear-driven avoidance motivation could have an insignificant influence on creativity. Therefore, one could argue about the role of other neuromodulators in addition to DA regarding their influences on motivational behavior and creativity.

Researchers observed that when 5-HT and NE lower motivation and flexibility, they can inhibit creativity. For example, antidepressants (ADs) that inhibit fear-driven motivation (i.e., selective serotonin reuptake inhibitors) could inhibit goal-oriented motivation as well. On the other hand, ADs that boost goal-directed motivation (i.e., bupropion) may remediate this effect. As for benzodiazepines and alcohol, they might have a counterproductive effect. Although DA agonists might stimulate creativity, their actions may inappropriately disinhibit this creative behavior through suppressing its motivational drive. Moreover, it was suggested that the presence of NE induces fluctuations in levels of other catecholamines, such as DA, which has been extensively discussed in the schizophrenia literature.

Noradrenaline (NE) System, and Creativity

The link between the noradrenergic (NE) system, arousal and the creative process has been examined either through the direct pharmacological manipulation of the NE system, or by investigating the influences of endogenous changes in the NE system (i.e., sleep and waking states) on behavior and cognition ( Folley et al., 2003 ). Also, situational stressors correlate with particular physiological responses, including an increase in the activity of the NE system ( Ward et al., 1983 ; Kvetňanský et al., 1997 ).

Experimental evidence proposed a central role of the NE system in modulating cognitive flexibility ( Beversdorf et al., 1999 , 2002 ; Folley et al., 2003 ; Heilman et al., 2003 ; Heilman, 2016 ; de Rooij et al., 2018 ). Beversdorf et al. (1999 , 2002) investigated the influence of NE modulation on the performance in various problem-solving tasks during pharmacological treatments that either increased or decreased noradrenergic activity. The authors reported better performance in the anagram task (one of the problem-solving tasks that demand cognitive flexibility), following the uptake of propranolol (peripheral and central β-adrenergic antagonist) than after ephedrine (β-adrenergic agonist). Comparing the effects of central and peripheral NE antagonists, Beversdorf et al. (2002) further revealed that NE modulation of cognitive flexibility, in particular in problem-solving tasks, occurs by a central feedback mechanism. This is in agreement with an earlier reported influence of arousal on cognitive flexibility during creative tasks through the regulation of the central NE system ( Martindale and Greenough, 1973 ). Martindale and Hasenfus (1978) provided physiological evidence about enhancing creative innovation through maintaining a low level of arousal (i.e., the significant development of alpha activity in the EEG in the highly creative group during the innovative stage). Also, the reported central modulatory effect of NE on cognitive flexibility may relate to changes in the signal-to-noise ratio of neuronal activity within the cortex by suppressing the intrinsic excitatory synaptic potentials relative to the evoked potentials by external direct afferent input ( Hasselmo et al., 1997 ; Usher et al., 1999 ).

In light of the findings described previously ( Hasselmo et al., 1997 ; Beversdorf et al., 1999 , 2002 ; Usher et al., 1999 ), one could evaluate the dependency of problem-solving on the regulation states of the NE system. The first state refers to situations up-regulating the NE system, which diminishes cognitive flexibility while the second state relates to situations down-regulating NE system, which enhances cognitive flexibility.

For example, NE upregulation by increased situational stress could weaken cognitive flexibility and thus creativity ( Beversdorf et al., 1999 , 2002 ) while people seem to be highly creative during relaxation as compared to when they are stressed ( Faigel, 1991 ).

Recently, de Rooij et al. (2018) explored the function of the LC-NA system in creativity using pupillometry. LC is a brain area which contains noradrenergic (NE) neurons that project to the frontal lobe modulating the frontal lobe’s activity ( Arnsten and Goldman-Rakic, 1984 ). Accordingly, elevation in LC activity is correlated with increasing levels of cortical NE. de Rooij et al. (2018) now examined whether tonic pupil dilation and phasic pupil dilation (as proxies for measuring tonic and phasic LC-NA activity, respectively) could predict performance on divergent and CT using both psychometric and real-world creativity tasks. During DT, the tonic pupil dilation predicted the generation of original ideas in both creativity tasks while phasic pupil dilation predicted the generation of useful ideas only in the real-world creativity task. Nevertheless, during CT, tonic and phasic pupil dilation did not predict creative task performance in both creativity tasks. Hence, tonic and phasic LC-NA activity differentially predicted the generation of original and useful ideas during creative tasks that require DT.

Serotonergic (5-HT) System and Creativity

The neurotransmitter serotonin [5-hydroxytryptamine (5-HT); Walther et al., 2003 ] is causally involved in multiple central nervous facets of mood control and in regulating sleep, anxiety, alcoholism, drug abuse, food intake, and sexual behavior ( Veenstra-VanderWeele et al., 2000 ). Volf et al. (2009) provided one of the earliest reports on a significant association between the polymorphism in the human serotonin transporter gene [i.e., serotonin-transporter-linked polymorphic region (5-HTTLPR)] and CAs (i.e., figural and verbal). Up to now, however, there has not been sufficient evidence to conclude on a direct connection between 5-HT and creativity, but there has been between 5-HT and reward. Kranz et al. (2010) presented an argument regarding 5-HT as an essential mediator of emotional, motivational and cognitive elements of reward representation. Consequently, one could claim that 5-HT is of a similar value to DA for reward processing; nevertheless, it is mostly ignored in the studies related to creativity.

Brain Illness and Creativity

Accumulated evidence suggests a strong connection between developing the drive of creativity and a number of brain illnesses (i.e., depression, bipolar disorder, psychosis, PD, temporal lobe epilepsy (TLE), frontotemporal dementia (FTD), and autism spectrum disorders (ASDs); see Flaherty, 2011 , see also Flaherty, 2005 ; Carson, 2011 ; Abraham et al., 2012 ; Mula et al., 2016 ), other studies questioned the relation between madness and genius ( Kyaga, 2014 ).

Flaherty (2005) tested a wide range of subjects from normal to several pathological states and proposed a three-factor model to predict idea generation and creative drive. This model focused on the interactions between temporal lobes, frontal lobes, and the limbic system, in which the frontotemporal and DA control represents the source for idea generation and creative drive. The author summarized her findings as follows. First, the generation of the progressive idea (sometimes at the expense of its quality) is associated with alterations in the activity of the temporal lobe (i.e., hypergraphia). Second, deficits in the frontal lobe might diminish idea generation due to the rigid judgments about the value of the idea. These observations were most visible in verbal creativity, and approximately resemble the constrained communication of temporal lobe epilepsy (TLE), mania, and Wernicke’s aphasia, rather than the sparse speech and cognitive inflexibility of depression, Broca’s aphasia, and other frontal lobe lesions. Third, patients with FTD expressed an enhancement in non-linguistic creativity. Lastly, the mutual inhibitory cortico-cortical interactions mediated the proper balance between temporal and frontal activity ( Flaherty, 2005 ).

Abraham et al. (2012) examined distinct facets of creative thinking in many neurological populations as compared to matched healthy control participants. They reported a dissociation between patient groups with frontal, temporoparietal, and basal ganglia (BG) lesions for diverse aspects of creativity. The temporoparietal and frontolateral groups expressed lower overall creative performance while the temporoparietal group demonstrated reduced fluency in the AUT and a creative imagery task. On the other hand, the frontolateral group was less proficient at producing original responses. In contrast, BG and frontopolar groups showed remarkable performance in the ability to overcome the constraints demand by salient semantic distractors during generating creative responses.

Consequently, the lesion area posed selective obstacles to the ability to generate novel (original) responses in distinctive contexts, but not on the ability to generate relevant responses (which was compromised in most patient groups). Thereby, Mula et al. (2016) discussed FTD and bipolar cyclothymic mood disorder as clinical conditions that are assisting to unravel the underlying neuroanatomy and neurochemistry of human creativity. They described the emergence of artistic talent in a subset of patients with dementia who developed incipient and impassioned abilities in visual arts. Earlier, Miller and Miller (2013) stated that in addition to the emergence of visual artistry in such patients, new onset creativity occasionally extends to obsessions with word punning and poetry. These recently compelling artistic and creative behaviors have been noticed initially in non-Alzheimer’s dementia, specifically, those with primary progressive aphasia (PPA), a particular form of FTD ( Wu et al., 2015 ; Mula et al., 2016 ). Furthermore, de Souza et al. (2014) reported a series of clinical observations about patients with neurodegenerative diseases affecting PFC (i.e., FTD) and the facilitation of artistic production.

On the link between creativity and bipolarity, researchers aimed at dissecting principal components of mania showing that feeling creative is usually told by patients with bipolar disorders ( Cassano et al., 2009 ; Mula et al., 2016 ). These patients often express themselves as very artistic and creative with bursts of inspiration or creativity and mentally very sharp, brilliant and talented. Remarkably, specialized studies that focus exclusively at creativity in patients with mood disturbances explicated that even when using quite a broad definition of creativity, no more than 8% of patients with bipolar or unipolar disorders could be considered creative ( Akiskal et al., 1998 ; Mula et al., 2016 ).

On the association between creativity and psychopathology, Carson (2011) provided an advanced model of a shared vulnerability to intensify creative ideation. This model suggested an interaction between the biological determinants, presenting the risk for psychopathology, and the protective cognitive factors. The elements of shared vulnerability included the following: (1) cognitive disinhibition (it brings more stimuli into conscious awareness); (2) an attentional style (which is driven by novelty salience); and (3) a neural hyperconnectivity (which may increase associations between diverse stimuli). These vulnerabilities interact with superior meta-cognitive protective factors (i.e., high IQ, increased WM capacity, and enhanced cognitive flexibility) to maximize the range and the depth of stimuli. Hence, stimuli, which are acquirable in conscious mindfulness, could be manipulated and integrated to form novel (original) ideas.

Open Questions and Future Directions

The PFC, which is considered to play a critical role in creativity, has been extensively involved in the cognitive control of emotion; however, the cortico-subcortical interactions that mediate this capability remain elusive, in particular when it is related to creativity. Previously, Wager et al. (2008) declared that prefrontal-subcortical pathways mediate effective emotion regulation. This regulation was associated with the activity of the right ventrolateral prefrontal area (vlPFC) as a response to diminished negative emotional experience during cognitive reappraisal of aversive (i.e., unpleasant) images. Following this initial finding, researchers implemented a unique pathway-mapping approach to map subcortical mediators of the association between vlPFC activity and reappraisal achievement (i.e., a decrease in the expressed emotion). Their data proposed two distinct pathways that collectively defined half of the revealed variance in self-stated emotion. The first pathway [which was through nucleus accumbens (NAc)] anticipated more reappraisal achievement while the second pathway (through ventral amygdala) anticipated reduced reappraisal achievement. Here, one could ask whether the interaction between emotion and creative cognition could be predicted through similar pathways.

Regarding providing an overarching experimental model for creative performances, one should consider the interactions between the factors described in this review (cognition, emotion, mood state, reward, and neuromodulators) and whether such interactions could mark creative signatures of individuals. In other words, getting more insight into the creative thinking and ideation necessitates the ability to identify: (1) the core cognitive, motivational, and emotional processes underlying creative thought; and (2) the brain circuitries and neuromodulators underlying the creative ideation.

Prospective research should further specify the neural mechanisms by which the neuromodulator systems influence the creative process. Particularly their modulatory effect on the creative cognition and the creative drive in pathological conditions such as depression, bipolar disorders, PD and schizophrenia remains elusive. DA requires additional exploration regarding the interplay between frontal and striatal DA pathways, the underlying genetic architecture and CAs in healthy and pathological conditions. On the other hand, research on creativity and the noradrenergic (NE) system is implicated in the stress-related modulation of cognitive flexibility in problem-solving, however there is a prominent demand to determine the range of cognitive tasks modulated by the NE system more precisely. Also, studies on the relation between the fluctuations in the level of NE, the level of arousal and its modulation signature on the creative process before and after treatment in pathological conditions such as depression, bipolar disorders, and schizophrenia remain dispersed and isolated. Concerning 5-HT, there is an ultimate need for elaborative research on the relationship between 5-HT and CAs since it is a fundamental mediator of emotional, motivational and cognitive elements of reward processing and representation.

In summary, advancing the research on creativity demands providing an integrative framework assembling the neural, cognitive, motivational, and emotional correlates of creativity. Furthermore, computational approaches such as neural network models could assist to provide a predictive perspective for this integrative framework for creativity ( Perlovsky and Levine, 2012 ). Although these models are not likely to be achieved merely, computational approaches to particular emotional processing could be both plausible and useful to develop the integrative framework model. For instance, Levine and Perlovsky (2011) proposed a dual-system approach to integrating emotional and rational decision making while Perlovsky and Levine, 2012 suggested a model of DA influences on creative processes. Thus, extending these computational models would be beneficial as a predictive approach to our proposed integrative framework for creativity.

In this review, we outlined how three factors crucially shape the creative mind: (1) creative cognition and the associated neural systems in human and animal models; (2) creative drives such as mood states, emotion, motivation and regulatory focus and how their interactions could shape the creative performance; and (3) the impacts of three central neuromodulator systems, i.e., DA, NE, and 5-HT, on the interplay between creative cognition and creative drives.

Specifically, we detailed how according to the dual pathway model ( Nijstad et al., 2010 ; Boot et al., 2017 ; Lu et al., 2017 ) the nigrostriatal and mesocortical DA pathways, influence creative drives ( Baas et al., 2008 , 2013 ; De Dreu et al., 2008 ) and creative cognition, see Figure 5 and Table 2 . As implicated by the dual process model, both pathways affect creativity via their influence on resistance and cognitive flexibility ( Cassotti et al., 2016 ). The prediction of creativity through EFs (i.e., shifting, inhibition and WM; Benedek et al., 2014 ; Radel et al., 2015 ; Zhang et al., 2015 ; Fleming et al., 2016 ) demands an optimal balance between deliberate (controlled) processing and spontaneous processing ( Mok, 2014 ). On the other hand, there is a link between reward (i.e., promises, training, and intrinsic interest; Maltzman, 1960 ; Eisenberger and Selbst, 1994 ; Eisenberger and Cameron, 1998 ; Eisenberger et al., 1998 , 1999 ; Eisenberger and Rhoades, 2001 ; Baer et al., 2003 ; Chen et al., 2012 ; Volf and Tarasova, 2013 ) and creativity through the action effect binding ( Muhle-Karbe and Krebs, 2012 ). Both mindset (cooperative and competitive; Bittner and Heidemeier, 2013 ; Bittner et al., 2016 ) and cognitive resources ( Roskes et al., 2012 ) have moderating effects on creative drives (i.e., mood, motivation, and emotion). Moreover, we discussed potential candidate genes for creativity.

Herewith we presented our perspective to advance our knowledge about creativity research through evaluating an overarching model of the interactions between creative cognition (i.e., cognitive flexibility, inhibitory control, WM updating, fluency, originality, and insights) and creative drive (i.e., emotion motivation, reward and other factors such as mood states, regulatory focus, social interaction), and the underlying neuromodulator mechanisms ( Figure 1 ).

Lastly, we highlighted the possibility of implementing a neural network model as a predictive tool for the suggested integrated framework of creativity. For more insights on the computational model of creativity and emotion, see Perlovsky and Levine (2012) and Levine and Perlovsky (2011) , respectively.

Author Contributions

RK and BG outlined the structure of the review and wrote the manuscript. AK participated in the conceptualization of the manuscript and the final editing.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We acknowledge the support by Deutsche Forschungsgemeinschaft and Open Access Publishing Fund of the University of Tübingen. This study was partly funded by the Deutsche Forschungsgemeinschaft (D.27.14841).


5-HT, serotonin; ADs, antidepressants; ALE, activation likelihood estimation; BG, basal ganglia; BVSR, blind variation and selective retention; CAQ, Creative Achievement Questionnaire; CCI, composite creativity index; COMT, catechol-O-methyl-transferase; DA, dopamine; DAT, Dopamine Transporter; DMN, default mode network; DRD2, D2 Dopamine Receptor; DRD4, D4 Dopamine Receptor; DT, divergent thinking; EBR, spontaneous eye-blink rates; EFs, executive functions; FTD, frontotemporal dementia; mPFC, medial prefrontal cortex; mTG, middle temporal gyrus; NAc, nucleus accumbens; NE, noradrenaline; PCC, posterior cingulate cortex; PD, Parkinson’s disease; PFC, prefrontal cortex; RSFC, resting-state functional connectivity; STN, Substantia Nigra; TID, task-induced deactivation; TPH1, Tryptophan Hydroxylase; vlPFC, right ventrolateral prefrontal region; VTA, tegmental ventral area; WM, working memory.

Abraham, A. (2013). The promises and perils of the neuroscience of creativity. Front. Hum. Neurosci. 7:246. doi: 10.3389/fnhum.2013.00246

PubMed Abstract | CrossRef Full Text | Google Scholar

Abraham, A., Beudt, S., Ott, D. V., and Yves Von Cramon, D. (2012). Creative cognition and the brain: dissociations between frontal, parietal-temporal and basal ganglia groups. Brain Res. 1482, 55–70. doi: 10.1016/j.brainres.2012.09.007

Akiskal, H. S., Placidi, G. F., Maremmani, I., Signoretta, S., Liguori, A., Gervasi, R., et al. (1998). TEMPS-I: delineating the most discriminant traits of the cyclothymic, depressive, hyperthymic and irritable temperaments in a nonpatient population. J. Affect. Disord. 51, 7–19. doi: 10.1016/s0165-0327(98)00152-9

Albin, R. L., Koeppe, R. A., Bohnen, N. I., Nichols, T. E., Meyer, P., Wernette, K., et al. (2003). Increased ventral striatal monoaminergic innervation in Tourette syndrome. Neurology 61, 310–315. doi: 10.1212/01.wnl.0000076181.39162.fc

Andrews-Hanna, J. R., Smallwood, J., and Spreng, R. N. (2014). The default network and self-generated thought: component processes, dynamic control, and clinical relevance. Ann. N Y Acad. Sci. 1316, 29–52. doi: 10.1111/nyas.12360

Arnsten, A. F. T., and Goldman-Rakic, P. S. (1984). Selective prefrontal cortical projections to the region of the locus coeruleus and raphe nuclei in the rhesus monkey. Brain Res. 306, 9–18. doi: 10.1016/0006-8993(84)90351-2

Asaadi, S., Ashrafi, F., Omidbeigi, M., Nasiri, Z., Pakdaman, H., and Amini-Harandi, A. (2016). Persian version of frontal assessment battery: correlations with formal measures of executive functioning and providing normative data for Persian population. Iran. J. Neurol. 15, 16–22.

PubMed Abstract | Google Scholar

Baas, M., De Dreu, C. K. W., and Nijstad, B. A. (2008). A meta-analysis of 25 years of mood-creativity research: hedonic tone, activation, or regulatory focus? Neuroepidemiology 134, 779–806. doi: 10.1037/a0012815

Baas, M., Nijstad, B. A., and De Dreu, C. K. W. (2015). Editorial: “The cognitive, emotional and neural correlates of creativity.” Front. Hum. Neurosci. 9:275. doi: 10.3389/fnhum.2015.00275

Baas, M., Roskes, M., Sligte, D., Nijstad, B. A., and De Dreu, C. K. W. (2013). Personality and creativity: the dual pathway to creativity model and a research agenda. Soc. Personal. Psychol. Compass 7, 732–748. doi: 10.1111/spc3.12062

CrossRef Full Text | Google Scholar

Baer, M., Oldham, G. R., and Cummings, A. (2003). Rewarding creativity: when does it matter? Leadersh. Q. 14, 569–586. doi: 10.1016/s1048-9843(03)00052-3

Beaty, R. E., Benedek, M., Silvia, P. J., and Schacter, D. L. (2016). Creative cognition and brain network dynamics. Trends Cogn. Sci. 20, 87–95. doi: 10.1016/j.tics.2015.10.004

Beaty, R. E., Christensen, A. P., Benedek, M., Silvia, P. J., and Schacter, D. L. (2017). Creative constraints: brain activity and network dynamics underlying semantic interference during idea production. Neuroimage 148, 189–196. doi: 10.1016/j.neuroimage.2017.01.012

Benedek, M., Jauk, E., Sommer, M., Arendasy, M., and Neubauer, A. C. (2014). Intelligence, creativity, and cognitive control: the common and differential involvement of executive functions in intelligence and creativity. Intelligence 46, 73–83. doi: 10.1016/j.intell.2014.05.007

Benedek, M., Könen, T., and Neubauer, A. C. (2012). Associative abilities underlying creativity. Psychol. Aesthetics Creat. Arts 6, 273–281. doi: 10.1037/a0027059

Beversdorf, D. Q., Hughes, J. D., Steinberg, B. A., Lewis, L. D., and Heilman, K. M. (1999). Noradrenergic modulation of cognitive flexibility in problem solving. Neuroreport 10, 2763–2767. doi: 10.1097/00001756-199909090-00012

Beversdorf, D. Q., White, D. M., Chever, D. C., Hughes, J. D., and Bornstein, R. A. (2002). Central β-adrenergic modulation of cognitive flexibility. Neuroreport 13, 2505–2507. doi: 10.1097/00001756-200212200-00025

Bittner, J. V., Bruena, M., and Rietzschel, E. F. (2016). Cooperation goals, regulatory focus, and their combined effects on creativity. Think. Ski. Creat. 19, 260–268. doi: 10.1016/j.tsc.2015.12.002

Bittner, J. V., and Heidemeier, H. (2013). Competitive mindsets, creativity, and the role of regulatory focus. Think. Ski. Creat. 9, 59–68. doi: 10.1016/j.tsc.2013.03.003

Bledow, R., Rosing, K., and Frese, M. (2013). A dynamic perspective on affect and creativity. Acad. Manag. J. 56, 432–450. doi: 10.5465/amj.2010.0894

Boccia, M., Piccardi, L., Palermo, L., Nori, R., and Palmiero, M. (2015). Where do bright ideas occur in our brain? Meta-analytic evidence from neuroimaging studies of domain-specific creativity. Front. Psychol. 6:1195. doi: 10.3389/fpsyg.2015.01195

Boot, N., Baas, M., van Gaal, S., Cools, R., and De Dreu, C. K. W. (2017). Creative cognition and dopaminergic modulation of fronto-striatal networks: integrative review and research agenda. Neurosci. Biobehav. Rev. 78, 13–23. doi: 10.1016/j.neubiorev.2017.04.007

Byron, K., and Khazanchi, S. (2011). A meta-analytic investigation of the relationship of state and trait anxiety to performance on figural and verbal creative tasks. Personal. Soc. Psychol. Bull. 37, 269–283. doi: 10.1177/0146167210392788

Carlsson, I., Wendt, P. E., and Risberg, J. (2000). On the neurobiology of creativity. Differences in frontal activity between high and low creative subjects. Neuropsychologia 38, 873–885. doi: 10.1016/s0028-3932(99)00128-1

Carson, S. H. (2011). Creativity and psychopathology: a shared vulnerability model. Can. J. Psychiatry 56, 144–153. doi: 10.1177/070674371105600304

Cassano, G. B., Mula, M., Rucci, P., Miniati, M., Frank, E., Kupfer, D. J., et al. (2009). The structure of lifetime manic-hypomanic spectrum. J. Affect. Disord. 112, 59–70. doi: 10.1016/j.jad.2008.04.019

Cassotti, M., Agogué, M., Camarda, A., Houdé, O., and Borst, G. (2016). Inhibitory control as a core process of creative problem solving and idea generation from childhood to adulthood. New Dir. Child Adolesc. Dev. 2016, 61–72. doi: 10.1002/cad.20153

Chen, C. X., Williamson, M. G., and Zhou, F. H. (2012). Reward system design and group creativity: an experimental investigation. Account. Rev. 87, 1885–1911. doi: 10.2308/accr-50232

Chermahini, S. A., and Hommel, B. (2010). The (b)link between creativity and dopamine: spontaneous eye blink rates predict and dissociate divergent and convergent thinking. Cognition 115, 458–465. doi: 10.1016/j.cognition.2010.03.007

Colzato, L. S., van den Wildenberg, W. P. M., van Wouwe, N. C., Pannebakker, M. M., and Hommel, B. (2009). Dopamine and inhibitory action control: evidence from spontaneous eye blink rates. Exp. Brain Res. 196, 467–474. doi: 10.1007/s00221-009-1862-x

Csikszentmihalyi, M. (1999). “Implications of a systems perspective for the study of creativity,” in Handbook of Creativity , ed. R. J. Sternberg (New York, NY: Cambridge University Press), 313–335.

Google Scholar

De Dreu, C. K. W., Baas, M., and Nijstad, B. A. (2008). Hedonic tone and activation level in the mood-creativity link: toward a dual pathway to creativity model. J. Pers. Soc. Psychol. 94, 739–756. doi: 10.1037/0022-3514.94.5.739

de Manzano, Ö., Cervenka, S., Karabanov, A., Farde, L., and Ullén, F. (2010). Thinking outside a less intact box: thalamic dopamine D2 receptor densities are negatively related to psychometric creativity in healthy individuals. PLoS One 5:e10670. doi: 10.1371/journal.pone.0010670

de Rooij, A., Vromans, R., and Dekker, M. (2018). Noradrenergic modulation of creativity: evidence from pupillometry. Creat. Res. J. 20, 339–351. doi: 10.1080/10400419.2018.1530533

de Souza, L. C., Guimarães, H. C., Teixeira, A. L., Caramelli, P., Levy, R., Dubois, B., et al. (2014). Frontal lobe neurology and the creative mind. Front. Psychol. 5:761. doi: 10.3389/fpsyg.2014.00761

Dietrich, A. (2004). The cognitive neuroscience of creativity. Psychon. Bull. Rev. 11, 1011–1026. doi: 10.3758/BF03196731

Dietrich, A., and Kanso, R. (2010). A review of EEG, ERP, and neuroimaging studies of creativity and insight. Psychol. Bull. 136, 822–848. doi: 10.1037/a0019749

Eisenberger, R., Armeli, S., and Pretz, J. (1998). Can the promise of reward increase creativity? J. Pers. Soc. Psychol. 74, 704–714. doi: 10.1037/0022-3514.74.3.704

Eisenberger, R., and Cameron, J. (1998). Reward, intrinsic interest, and creativity: new findings. Am. Psychol. 53, 676–679. doi: 10.1037/0003-066x.53.6.676

Eisenberger, R., Haskins, F., and Gambelton, P. (1999). Promised reward and creativity: effects of prior experience. J. Exp. Soc. Psychol. 35, 308–325. doi: 10.1006/jesp.1999.1381

Eisenberger, R., and Rhoades, L. (2001). Incremental effects of reward on creativity. J. Pers. Soc. Psychol. 81, 728–741. doi: 10.1037/0022-3514.81.4.728

Eisenberger, R., and Selbst, M. (1994). “Does reward increase or decrease creativity?”: correction to eisenberger and selbst. J. Pers. Soc. Psychol. 67, 125–125. doi: 10.1037/h0090356

Evans, J. S. B. T., Over, D. E., and Manktelow, K. I. (1993). Reasoning, decision making and rationality. Cognition 49, 165–187. doi: 10.1016/0010-0277(93)90039-x

Faigel, H. C. (1991). The effect of beta blockade on stress-induced cognitive dysfunction in adolescents. Clin. Pediatr. 30, 441–445. doi: 10.1177/000992289103000706

Feist, G. J. (2010). “The function of personality in creativity: the nature and nurture of the creative personality,” in The Cambridge Handbook of Creativity , eds J. C. Kaufman and R. J. Sternberg (New York, NY: Cambridge University Press), 113–130.

Fink, A., Benedek, M., Grabner, R. H., Staudt, B., and Neubauer, A. C. (2007). Creativity meets neuroscience: experimental tasks for the neuroscientific study of creative thinking. Methods 42, 68–76. doi: 10.1016/j.ymeth.2006.12.001

Fink, A., Weber, B., Koschutnig, K., Benedek, M., Reishofer, G., Ebner, F., et al. (2014). Creativity and schizotypy from the neuroscience perspective. Cogn. Affect. Behav. Neurosci. 14, 378–387. doi: 10.3758/s13415-013-0210-6

Flaherty, A. W. (2005). Frontotemporal and dopaminergic control of idea generation and creative drive. J. Comp. Neurol. 493, 147–153. doi: 10.1002/cne.20768

Flaherty, A. W. (2011). Brain illness and creativity: mechanisms and treatment risks. Can. J. Psychiatry 56, 132–143. doi: 10.1177/070674371105600303

Fleming, K. A., Heintzelman, S. J., and Bartholow, B. D. (2016). Specifying associations between conscientiousness and executive functioning: mental set shifting, not prepotent response inhibition or working memory updating. Front. Behav. Neurosci. 84, 348–360. doi: 10.1111/jopy.12163

Folley, B. S., Doop, M. L., and Park, S. (2003). Psychoses and creativity: is the missing link a biological mechanism related to phospholipids turnover? Prostaglandins Leukot. Essent. Fatty Acids 69, 467–476. doi: 10.1016/j.plefa.2003.08.019

Gaut, B. (2010). The philosophy of creativity. Philos. Compass 5, 1034–1046. doi: 10.1111/j.1747-9991.2010.00351.x

Gawda, B., and Szepietowska, E. (2016). Trait anxiety modulates brain activity during performance of verbal fluency tasks. Front. Behav. Neurosci. 10:10. doi: 10.3389/fnbeh.2016.00010

Gianotti, L. R. R., Mohr, C., Pizzagalli, D., Lehmann, D., and Brugger, P. (2001). Associative processing and paranormal belief. Psychiatry Clin. Neurosci. 55, 595–603. doi: 10.1046/j.1440-1819.2001.00911.x

Grace, A. A. (2010). “Dopamine modulation of forebrain pathways and the pathophysiology of psychiatric disorders,” in Dopamine Handbook , eds L. Iversen, S. Iversen, S. Dunnett and A. Bjorklund (New York, NY: Cambridge University Press), 590–598.

Grigorenko, E. L. (2017). Creativity and the genome: the state of affairs. J. Creat. Behav. 51, 327–329. doi: 10.1002/jocb.201

Guilford, J. P. (1967). Creativity: yesterday, today and tomorrow. J. Creat. Behav. 1, 3–14. doi: 10.1002/j.2162-6057.1967.tb00002.x

Hasselmo, M. E., Linster, C., Patil, M., Ma, D., and Cekic, M. (1997). Noradrenergic suppression of synaptic transmission may influence cortical signal-to-noise ratio. J. Neurophysiol. 77, 3326–3339. doi: 10.1152/jn.1997.77.6.3326

Heilman, K. M. (2016). Possible brain mechanisms of creativity. Arch. Clin. Neuropsychol. 31, 285–296. doi: 10.1093/arclin/acw009

Heilman, K. M., Nadeau, S. E., and Beversdorf, D. O. (2003). Creative innovation: possible brain mechanisms. Neurocase 9, 369–379. doi: 10.1076/neur.9.5.369.16553

Inzelberg, R. (2013). The awakening of artistic creativity and Parkinson’s disease. Behav. Neurosci. 127, 256–261. doi: 10.1037/a0031052

Jaeggi, S. M., Buschkuehl, M., Perrig, W. J., and Meier, B. (2010). The concurrent validity of the N-back task as a working memory measure. Memory 18, 394–412. doi: 10.1080/09658211003702171

Jung, R. E., Mead, B. S., Carrasco, J., and Flores, R. A. (2013). The structure of creative cognition in the human brain. Front. Hum. Neurosci. 7:330. doi: 10.3389/fnhum.2013.00330

Jung, R. E., Segall, J. M., Bockholt, H. J., Flores, R. A., Smith, S. M., Chavez, R. S., et al. (2010). Neuroanatomy of creativity. Hum. Brain Mapp. 31, 398–409. doi: 10.1002/hbm.20874

Karim, A. A., Schneider, M., Lotze, M., Veit, R., Sauseng, P., Braun, C., et al. (2010). The truth about lying: inhibition of the anterior prefrontal cortex improves deceptive behavior. Cereb. Cortex 20, 205–213. doi: 10.1093/cercor/bhp090

Kaufman, A. B., Butt, A. E., Kaufman, J. C., and Colbert-White, E. N. (2011). Towards a neurobiology of creativity in nonhuman animals. J. Comp. Psychol. 125, 255–272. doi: 10.1037/a0023147

Kaufman, J. C. (2009). The Psych 101 Series. Creativity 101. New York, NY: Springer Publishing Co.

Kaufmann, G. (2003). Expanding the mood—creativity equation. Creat. Res. J. 15, 131–135. doi: 10.1080/10400419.2003.9651405

Kleinmintz, O. M., Abecasis, D., Tauber, A., Geva, A., Chistyakov, A. V., Kreinin, I., et al. (2018). Participation of the left inferior frontal gyrus in human originality. Brain Struct. Funct. 223, 329–341. doi: 10.1007/s00429-017-1500-5

Kranz, G. S., Kasper, S., and Lanzenberger, R. (2010). Reward and the serotonergic system. Neuroscience 166, 1023–1035. doi: 10.1016/j.neuroscience.2010.01.036

Krippl, M., and Karim, A. A. (2011). [“Theory of mind” and its neuronal correlates in forensically relevant disorders]. Nervenarzt 82, 843–852. doi: 10.1007/s00115-010-3073-x

Kulisevsky, J., Pagonabarraga, J., and Martinez-Corral, M. (2009). Changes in artistic style and behavior in Parkinson’s disease: dopamine and creativity. J. Neurol. 256, 816–819. doi: 10.1007/s00415-009-5001-1

Kvetňanský, R., Pacak, K., Sabban, E. L., Kopin, I. J., and Goldstein, D. S. (1997). Stressor specificity of peripheral catecholaminergic activation. Adv. Pharmacol. , 42, 556–560. doi: 10.1016/S1054-3589(08)60811-X

Kyaga, S. (2014). Creativity and Mental Illness: The Mad Genius in Question. New York, NY: Palgrave Macmillan

Levine, D. S., and Perlovsky, L. I. (2011). Emotion in the pursuit of understanding. Int. J. Synth. Emot. 1, 1–11. doi: 10.4018/jse.2010070101

Lhommée, E., Batir, A., Quesada, J. L., Ardouin, C., Fraix, V., Seigneuret, E., et al. (2014). Dopamine and the biology of creativity: lessons from Parkinson’s disease. Front. Endocrinol. 5:55. doi: 10.3389/fneur.2014.00055

Lin, W.-L., Tsai, P., Lin, H.-Y., and Chen, H. (2014). How does emotion influence different creative performances? The mediating role of cognitive flexibility. Cogn. Emot. 28, 834–844. doi: 10.1080/02699931.2013.854195

Lu, J. G., Akinola, M., and Mason, M. F. (2017). “Switching On” creativity: task switching can increase creativity by reducing cognitive fixation. Organ. Behav. Hum. Decis. Process. 139, 63–75. doi: 10.1016/j.obhdp.2017.01.005

Lucas, B. J., and Nordgren, L. F. (2015). People underestimate the value of persistence for creative performance. J. Pers. Soc. Psychol. 109, 232–243. doi: 10.1037/pspa0000030

Maltzman, I. (1960). On the training of originality. Psychol. Rev. 67, 229–242. doi: 10.1037/h0046364

Martindale, C., and Greenough, J. (1973). The differential effect of increased arousal on creative and intellectual performance. J. Genet. Psychol. 123, 329–335. doi: 10.1080/00221325.1973.10532692

Martindale, C., and Hasenfus, N. (1978). EEG differences as a function of creativity, stage of the creative process, and effort to be original. Biol. Psychol. 6, 157–167. doi: 10.1016/0301-0511(78)90018-2

Mayseless, N., Uzefovsky, F., Shalev, I., Ebstein, R. P., and Shamay-Tsoory, S. G. (2013). The association between creativity and 7R polymorphism in the dopamine receptor D4 gene (DRD4). Front. Hum. Neurosci. 7:502. doi: 10.3389/fnhum.2013.00502

Mednick, S. (1962). The associative basis of the creative process. Psychol. Rev. 69, 220–232. doi: 10.1037/h0048850

Mihov, K. M., Denzler, M., and Förster, J. (2010). Hemispheric specialization and creative thinking: a meta-analytic review of lateralization of creativity. Brain Cogn. 72, 442–448. doi: 10.1016/j.bandc.2009.12.007

Miller, Z. A., and Miller, B. L. (2013). Artistic creativity and dementia. Prog. Brain Res. 204, 99–112. doi: 10.1016/B978-0-444-63287-6.00005-1

Mok, L. W. (2012). Short-term retrospective versus prospective memory processing as emergent properties of the mind and brain: human fMRI evidence. Neuroscience 226, 236–252. doi: 10.1016/j.neuroscience.2012.09.005

Mok, L. W. (2014). The interplay between spontaneous and controlled processing in creative cognition. Front. Hum. Neurosci. 8:663. doi: 10.3389/fnhum.2014.00663

Muhle-Karbe, P. S., and Krebs, R. M. (2012). On the influence of reward on action-effect binding. Front. Psychol. 3:450. doi: 10.3389/fpsyg.2012.00450

Mula, M., Hermann, B., and Trimble, M. R. (2016). Neuropsychiatry of creativity. Epilepsy Behav. 57, 225–229. doi: 10.1016/j.yebeh.2015.12.050

Nijstad, B. A., De Dreu, C. K. W., Rietzschel, E. F., and Baas, M. (2010). The dual pathway to creativity model: creative ideation as a function of flexibility and persistence. Eur. Rev. Soc. Psychol. 21, 34–77. doi: 10.1080/10463281003765323

Perlovsky, L. I., and Levine, D. S. (2012). The drive for creativity and the escape from creativity: neurocognitive mechanisms. Cognit. Comput. 4, 292–305. doi: 10.1007/s12559-012-9154-3

Radel, R., Davranche, K., Fournier, M., and Dietrich, A. (2015). The role of (dis)inhibition in creativity: decreased inhibition improves idea generation. Cognition 134, 110–120. doi: 10.1016/j.cognition.2014.09.001

Reuter, M., Roth, S., Holve, K., and Hennig, J. (2006). Identification of first candidate genes for creativity: a pilot study. Brain Res. 1069, 190–197. doi: 10.1016/j.brainres.2005.11.046

Roskes, M., De Dreu, C. K. W., and Nijstad, B. A. (2012). Necessity is the mother of invention: avoidance motivation stimulates creativity through cognitive effort. J. Pers. Soc. Psychol. 103, 242–256. doi: 10.1037/a0028442

Rossmann, E., and Fink, A. (2010). Do creative people use shorter associative pathways? Pers. Individ. Dif. 49, 891–895. doi: 10.1016/j.paid.2010.07.025

Runco, M. A., Noble, E. P., Reiter-Palmon, R., Acar, S., Ritchie, T., and Yurkovich, J. M. (2011). The genetic basis of creativity and ideational fluency. Creat. Res. J. 23, 376–380. doi: 10.1080/10400419.2011.621859

Salgado-Pineda, P., Baeza, I., Pérez-Gómez, M., Vendrell, P., Junqué, C., Bargalló, N., et al. (2003). Sustained attention impairment correlates to gray matter decreases in first episode neuroleptic-naive schizophrenic patients. Neuroimage 19, 365–375. doi: 10.1016/s1053-8119(03)00094-6

Sawyer, K. R. (2006). Explaining Creativity—The Science If Human Innovation. New York, NY: Oxford Press. 130–169.

Shapiro, E., Shapiro, A. K., Fulop, G., Hubbard, M., Mandeli, J., Nordlie, J., et al. (1989). Controlled study of haloperidol, pimozide and placebo for the treatment of Gilles de la Tourette’s syndrome. Arch. Gen. Psychiatry 46, 722–730. doi: 10.1001/archpsyc.1989.01810080052006

Singer, H. S., Szymanski, S., Giuliano, J., Yokoi, F., Dogan, A. S., Brasic, J. R., et al. (2002). Elevated intrasynaptic dopamine release in Tourette’s syndrome measured by PET. Am. J. Psychiatry 159, 1329–1336. doi: 10.1176/appi.ajp.159.8.1329

Surmeier, D. J., Graves, S. M., and Shen, W. (2014). Dopaminergic modulation of striatal networks in health and Parkinson’s disease. Curr. Opin. Neurobiol. 29, 109–117. doi: 10.1016/j.conb.2014.07.008

Takeuchi, H., Taki, Y., Hashizume, H., Sassa, Y., Nagase, T., Nouchi, R., et al. (2011). Failing to deactivate: the association between brain activity during a working memory task and creativity. Neuroimage 55, 681–687. doi: 10.1016/j.neuroimage.2010.11.052

Takeuchi, H., Taki, Y., Hashizume, H., Sassa, Y., Nagase, T., Nouchi, R., et al. (2012). The association between resting functional connectivity and creativity. Cereb. Cortex 22, 2921–2929. doi: 10.1093/cercor/bhr371

Takeuchi, H., Taki, Y., Sassa, Y., Hashizume, H., Sekiguchi, A., Fukushima, A., et al. (2010). Regional gray matter volume of dopaminergic system associate with creativity: evidence from voxel-based morphometry. Neuroimage 51, 578–585. doi: 10.1016/j.neuroimage.2010.02.078

To, M. L., Fisher, C. D., and Ashkanasy, N. M. (2015). Unleashing angst: negative mood, learning goal orientation, psychological empowerment and creative behavior. Hum. Relat. 68, 1601–1622. doi: 10.1177/0018726714562235

Usher, M., Cohen, J. D., Servan-Schreiber, D., Rajkowski, J., and Aston-Jones, G. (1999). The role of locus coeruleus in the regulation of cognitive performance. Science 283, 549–554. doi: 10.1126/science.283.5401.549

van Schouwenburg, M. R., Zwiers, M. P., van der Schaaf, M. E., Geurts, D. E. M., Schellekens, A. F. A., Buitelaar, J. K., et al. (2013). Anatomical connection strength predicts dopaminergic drug effects on fronto-striatal function. Psychopharmacology 227, 521–531. doi: 10.1007/s00213-013-3000-5

Veenstra-VanderWeele, J., Anderson, G. M., and Cook, E. H. Jr. (2000). Pharmacogenetics and the serotonin system: initial studies and future directions. Eur. J. Pharmacol. 410, 165–181. doi: 10.1016/s0014-2999(00)00814-1

Volf, N. V., Kulikov, A. V., Bortsov, C. U., and Popova, N. K. (2009). Association of verbal and figural creative achievement with polymorphism in the human serotonin transporter gene. Neurosci. Lett. 463, 154–157. doi: 10.1016/j.neulet.2009.07.070

Volf, N. V., and Tarasova, I. B. (2013). The influence of reward on the performance of verbal creative tasks: behavioral and EEG effects. Hum. Physiol. 39, 302–308. doi: 10.1134/s0362119713020187

Wager, T. D., Davidson, M. L., Hughes, B. L., Lindquist, M. A., and Ochsner, K. N. (2008). Prefrontal-subcortical pathways mediating successful emotion regulation. Neuron 59, 1037–1050. doi: 10.1016/j.neuron.2008.09.006

Walther, D. J., Peter, J. U., Bashammakh, S., Hörtnagl, H., Voits, M., Fink, H., et al. (2003). Synthesis of serotonin by a second tryptophan hydroxylase isoform. Science 299:76. doi: 10.1126/science.1078197

Ward, M. M., Mefford, I. N., Parker, S. D., Chesney, M. A., Taylor, B. C., Keegan, D. L., et al. (1983). Epinephrine and norepinephrine responses in continuously collected human plasma to a series of stressors. Psychosom. Med. 45, 471–486. doi: 10.1097/00006842-198312000-00002

Wei, D., Yang, J., Li, W., Wang, K., Zhang, Q., and Qiu, J. (2014). Increased resting functional connectivity of the medial prefrontal cortex in creativity by means of cognitive stimulation. Cortex 51, 92–102. doi: 10.1016/j.cortex.2013.09.004

Wu, T. Q., Miller, Z. A., Adhimoolam, B., Zackey, D. D., Khan, B. K., Ketelle, R., et al. (2015). Verbal creativity in semantic variant primary progressive aphasia. Neurocase 21, 73–78. doi: 10.1080/13554794.2013.860179

Yang, J. S., and Hung, H. V. (2015). Emotions as constraining and facilitating factors for creativity: companionate love and anger. Creat. Innovation Manag. 24, 217–230. doi: 10.1111/caim.12089

Zabelina, D. L., Colzato, L., Beeman, M., and Hommel, B. (2016). Dopamine and the creative mind: Individual differences in creativity are predicted by interactions between dopamine genes DAT and COMT. PLoS One 11:e0146768. doi: 10.1371/journal.pone.0146768

Zhang, S., Zhang, M., and Zhang, J. (2014a). An exploratory study on DRD2 and creative potential. Creat. Res. J. 26, 115–123. doi: 10.1080/10400419.2014.874267

Zhang, S., Zhang, M., and Zhang, J. (2014b). Association of COMT and COMT-DRD2 interaction with creative potential. Front. Hum. Neurosci. 8:216. doi: 10.3389/fnhum.2014.00216

Zhang, T., Mou, D., Wang, C., Tan, F., Jiang, Y., Lijun, Z., et al. (2015). Dopamine and executive function: increased spontaneous eye blink rates correlate with better set-shifting and inhibition, but poorer updating. Int. J. Psychophysiol. 96, 155–161. doi: 10.1016/j.ijpsycho.2015.04.010

Zhang, T., Zhang, Q., Wang, C., and Chen, A. (2017). The developmental relationship between central dopaminergic level and response inhibition from late childhood to young adulthood. Int. J. Psychophysiol. 116, 53–59. doi: 10.1016/j.ijpsycho.2017.02.009

Zhu, F., Zhang, Q., and Qiu, J. (2013). Relating inter-individual differences in verbal creative thinking to cerebral structures: an optimal voxel-based morphometry study. PLoS One 8:e79272. doi: 10.1371/journal.pone.0079272

Keywords: creativity, cognitive flexibility, persistence, artistic shifts, emotion, reward, brain illness, neuromodulators

Citation: Khalil R, Godde B and Karim AA (2019) The Link Between Creativity, Cognition, and Creative Drives and Underlying Neural Mechanisms. Front. Neural Circuits 13:18. doi: 10.3389/fncir.2019.00018

Received: 04 June 2018; Accepted: 04 March 2019; Published: 22 March 2019.

Reviewed by:

Copyright © 2019 Khalil, Godde and Karim. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Radwa Khalil, [email protected]

This article is part of the Research Topic

Neuromodulation of Circuits in Brain Health and Disease


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Creative Problem Solving

Finding innovative solutions to challenges.

By the Mind Tools Content Team

creative problem solving cognitive load

Imagine that you're vacuuming your house in a hurry because you've got friends coming over. Frustratingly, you're working hard but you're not getting very far. You kneel down, open up the vacuum cleaner, and pull out the bag. In a cloud of dust, you realize that it's full... again. Coughing, you empty it and wonder why vacuum cleaners with bags still exist!

James Dyson, inventor and founder of Dyson® vacuum cleaners, had exactly the same problem, and he used creative problem solving to find the answer. While many companies focused on developing a better vacuum cleaner filter, he realized that he had to think differently and find a more creative solution. So, he devised a revolutionary way to separate the dirt from the air, and invented the world's first bagless vacuum cleaner. [1]

Creative problem solving (CPS) is a way of solving problems or identifying opportunities when conventional thinking has failed. It encourages you to find fresh perspectives and come up with innovative solutions, so that you can formulate a plan to overcome obstacles and reach your goals.

In this article, we'll explore what CPS is, and we'll look at its key principles. We'll also provide a model that you can use to generate creative solutions.

About Creative Problem Solving

Alex Osborn, founder of the Creative Education Foundation, first developed creative problem solving in the 1940s, along with the term "brainstorming." And, together with Sid Parnes, he developed the Osborn-Parnes Creative Problem Solving Process. Despite its age, this model remains a valuable approach to problem solving. [2]

The early Osborn-Parnes model inspired a number of other tools. One of these is the 2011 CPS Learner's Model, also from the Creative Education Foundation, developed by Dr Gerard J. Puccio, Marie Mance, and co-workers. In this article, we'll use this modern four-step model to explore how you can use CPS to generate innovative, effective solutions.

Why Use Creative Problem Solving?

Dealing with obstacles and challenges is a regular part of working life, and overcoming them isn't always easy. To improve your products, services, communications, and interpersonal skills, and for you and your organization to excel, you need to encourage creative thinking and find innovative solutions that work.

CPS asks you to separate your "divergent" and "convergent" thinking as a way to do this. Divergent thinking is the process of generating lots of potential solutions and possibilities, otherwise known as brainstorming. And convergent thinking involves evaluating those options and choosing the most promising one. Often, we use a combination of the two to develop new ideas or solutions. However, using them simultaneously can result in unbalanced or biased decisions, and can stifle idea generation.

For more on divergent and convergent thinking, and for a useful diagram, see the book "Facilitator's Guide to Participatory Decision-Making." [3]

Core Principles of Creative Problem Solving

CPS has four core principles. Let's explore each one in more detail:

  • Divergent and convergent thinking must be balanced. The key to creativity is learning how to identify and balance divergent and convergent thinking (done separately), and knowing when to practice each one.
  • Ask problems as questions. When you rephrase problems and challenges as open-ended questions with multiple possibilities, it's easier to come up with solutions. Asking these types of questions generates lots of rich information, while asking closed questions tends to elicit short answers, such as confirmations or disagreements. Problem statements tend to generate limited responses, or none at all.
  • Defer or suspend judgment. As Alex Osborn learned from his work on brainstorming, judging solutions early on tends to shut down idea generation. Instead, there's an appropriate and necessary time to judge ideas during the convergence stage.
  • Focus on "Yes, and," rather than "No, but." Language matters when you're generating information and ideas. "Yes, and" encourages people to expand their thoughts, which is necessary during certain stages of CPS. Using the word "but" – preceded by "yes" or "no" – ends conversation, and often negates what's come before it.

How to Use the Tool

Let's explore how you can use each of the four steps of the CPS Learner's Model (shown in figure 1, below) to generate innovative ideas and solutions.

Figure 1 – CPS Learner's Model

creative problem solving cognitive load

Explore the Vision

Identify your goal, desire or challenge. This is a crucial first step because it's easy to assume, incorrectly, that you know what the problem is. However, you may have missed something or have failed to understand the issue fully, and defining your objective can provide clarity. Read our article, 5 Whys , for more on getting to the root of a problem quickly.

Gather Data

Once you've identified and understood the problem, you can collect information about it and develop a clear understanding of it. Make a note of details such as who and what is involved, all the relevant facts, and everyone's feelings and opinions.

Formulate Questions

When you've increased your awareness of the challenge or problem you've identified, ask questions that will generate solutions. Think about the obstacles you might face and the opportunities they could present.

Explore Ideas

Generate ideas that answer the challenge questions you identified in step 1. It can be tempting to consider solutions that you've tried before, as our minds tend to return to habitual thinking patterns that stop us from producing new ideas. However, this is a chance to use your creativity .

Brainstorming and Mind Maps are great ways to explore ideas during this divergent stage of CPS. And our articles, Encouraging Team Creativity , Problem Solving , Rolestorming , Hurson's Productive Thinking Model , and The Four-Step Innovation Process , can also help boost your creativity.

See our Brainstorming resources within our Creativity section for more on this.

Formulate Solutions

This is the convergent stage of CPS, where you begin to focus on evaluating all of your possible options and come up with solutions. Analyze whether potential solutions meet your needs and criteria, and decide whether you can implement them successfully. Next, consider how you can strengthen them and determine which ones are the best "fit." Our articles, Critical Thinking and ORAPAPA , are useful here.

4. Implement

Formulate a plan.

Once you've chosen the best solution, it's time to develop a plan of action. Start by identifying resources and actions that will allow you to implement your chosen solution. Next, communicate your plan and make sure that everyone involved understands and accepts it.

There have been many adaptations of CPS since its inception, because nobody owns the idea.

For example, Scott Isaksen and Donald Treffinger formed The Creative Problem Solving Group Inc . and the Center for Creative Learning , and their model has evolved over many versions. Blair Miller, Jonathan Vehar and Roger L. Firestien also created their own version, and Dr Gerard J. Puccio, Mary C. Murdock, and Marie Mance developed CPS: The Thinking Skills Model. [4] Tim Hurson created The Productive Thinking Model , and Paul Reali developed CPS: Competencies Model. [5]

Sid Parnes continued to adapt the CPS model by adding concepts such as imagery and visualization , and he founded the Creative Studies Project to teach CPS. For more information on the evolution and development of the CPS process, see Creative Problem Solving Version 6.1 by Donald J. Treffinger, Scott G. Isaksen, and K. Brian Dorval. [6]

Creative Problem Solving (CPS) Infographic

See our infographic on Creative Problem Solving .

creative problem solving cognitive load

Creative problem solving (CPS) is a way of using your creativity to develop new ideas and solutions to problems. The process is based on separating divergent and convergent thinking styles, so that you can focus your mind on creating at the first stage, and then evaluating at the second stage.

There have been many adaptations of the original Osborn-Parnes model, but they all involve a clear structure of identifying the problem, generating new ideas, evaluating the options, and then formulating a plan for successful implementation.

[1] Entrepreneur (2012). James Dyson on Using Failure to Drive Success [online]. Available here . [Accessed May 27, 2022.]

[2] Creative Education Foundation (2015). The CPS Process [online]. Available here . [Accessed May 26, 2022.]

[3] Kaner, S. et al. (2014). 'Facilitator′s Guide to Participatory Decision–Making,' San Francisco: Jossey-Bass.

[4] Puccio, G., Mance, M., and Murdock, M. (2011). 'Creative Leadership: Skils That Drive Change' (2nd Ed.), Thousand Oaks, CA: Sage.

[5] OmniSkills (2013). Creative Problem Solving [online]. Available here . [Accessed May 26, 2022].

[6] Treffinger, G., Isaksen, S., and Dorval, B. (2010). Creative Problem Solving (CPS Version 6.1). Center for Creative Learning, Inc. & Creative Problem Solving Group, Inc. Available here .

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Cognitive load and creativity of knowledge workers: a diary study

  • Published: 11 November 2023

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  • Weina Yu 1 ,
  • Xue Qin 1 ,
  • Min Li 1 &
  • Xian Xue 2  

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By integrating cognitive offloading theory and unconscious thought process theory, this study aimed to examine the impact of knowledge workers’ daily cognitive load on their creativity. This study employed the experience sample method, utilizing 1016 daily observations collected by 102 full-time researchers. The findings indicated that: there was a positive correlation between cognitive load and cyberloafing, and a curvilinear relationship between cognitive load and problem-oriented mind wandering mediated by cyberloafing; cognitive load had an inverted U-shaped relationship with daily creativity mediated by cyberloafing and problem-oriented mind wandering; the relationship between cyberloafing and problem-oriented mind wandering, as well as between cognitive load and daily creativity, was moderated by role breadth self-efficacy. The conclusions elucidated the influence mechanism of cognitive load on daily creativity, thereby offering essential theoretical guidance for organizations to effectively steer and intervene in cyberloafing.

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The datasets analyzed during the current study are available from the corresponding author on reasonable request.

RMSEA = Root-Mean-Square Error of Approximation, CFI = Comparative Fit Index, TLI = Tucker-Lewis Index.

Lin et al. ( 2017 ). Why is underemployment related to creativity and OCB? A task-crafting explanation of the curvilinear moderated relations. Academy of Management Journal , 60(1), 156–177.

Ambrosetti, J., Macheret, L., Folliet, A., Wullschleger, A., Amerio, A., Aguglia, A., ... & Costanza, A. (2021). Impact of the COVID-19 pandemic on psychiatric admissions to a large Swiss emergency department: an observational study. International Journal of Environmental Research and Public Health, 18 (3), 1174.

Amerio, A., Lugo, A., Stival, C., Fanucchi, T., Gorini, G., Pacifici, R., ... & Gallus, S. (2021). COVID-19 lockdown impact on mental health in a large representative sample of Italian adults. Journal of Affective Disorders, 292, 398–404.

Ashforth, B. E., Kreiner, G. E., & Fugate, M. (2000). All in a day’s work: Boundaries and micro role transitions. Academy of Management Review, 25 (3), 472–491.

Article   Google Scholar  

Baer, M., Dane, E., & Madrid, H. P. (2021). Zoning out or breaking through? Linking daydreaming to creativity in the workplace. Academy of Management Journal, 64 (5), 1553–1577.

Buysse, D. J., Reynolds, C. F., III., Monk, T. H., Berman, S. R., & Kupfer, D. J. (1989). The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Research, 28 (2), 193–213.

Article   PubMed   Google Scholar  

Cai, Z., Teng, X., Wang, Q., Qian, J., & Shi, W. (2023). Would people persist in proactive work behavior? Comparing the motivation and resource-depletion pathways. Current Psychology, 42 (29), 25755–25772.

Chu, F., Liu, S., Guo, M., & Zhang, Q. (2021). I am the top talent: Perceived overqualification, role breadth self-efficacy, and safety participation of high-speed railway operators in China. Safety Science, 144 , 105476.

Dijksterhuis, A., & Meurs, T. (2006). Where creativity resides: The generative power of unconscious thought. Consciousness and Cognition, 15 (1), 135–146.

Fan, T., Khan, J., Khassawneh, O., & Mohammad, T. (2023). Examining toxic leadership nexus with employee cyberloafing behavior via mediating role of emotional exhaustion. Journal of Organizational and End User Computing (JOEUC), 35 (1), 1–23.

Fox, K. C., & Beaty, R. E. (2019). Mind-wandering as creative thinking: Neural, psychological, and theoretical considerations. Current Opinion in Behavioral Sciences, 27 , 123–130.

Gable, S. L., Hopper, E. A., & Schooler, J. W. (2019). When the muses strike: Creative ideas of physicists and writers routinely occur during mind wandering. Psychological Science, 30 (3), 396–404.

Güngör, H., & Ustabulut, M. Y. (2023). An investigation of cyberloafing behaviors in learners of Turkish as a foreign language. Current Psychology , 1–12. Retrieved from https://doi.org/10.1007/s12144-023-04491-7 .

Haans, R. F., Pieters, C., & He, Z. L. (2016). Thinking about U: Theorizing and testing U-and inverted U-shaped relationships in strategy research. Strategic Management Journal, 37 (7), 1177–1195.

Hayes, A. F., & Preacher, K. J. (2010). Quantifying and testing indirect effects in simple mediation models when the constituent paths are nonlinear. Multivariate Behavioral Research, 45 (4), 627–660.

Hoever, I. J., Betancourt, N. E., Chen, G., & Zhou, J. (2023). How others light the creative spark: Low power accentuates the benefits of diversity for individual inspiration and creativity. Organizational Behavior and Human Decision Processes, 176 , 104248.

Hwang, G. J., Yang, L. H., & Wang, S. Y. (2013). A concept map-embedded educational computer game for improving students’ learning performance in natural science courses. Computers & Education, 69 , 121–130.

Kang, E. (2023). Easily accessible but easily forgettable: How ease of access to information online affects cognitive miserliness. Journal of Experimental Psychology: Applied, 29 (3), 620.

PubMed   Google Scholar  

Kim, M., & Beehr, T. A. (2023). Employees’ entrepreneurial behavior within their organizations: Empowering leadership and employees’ resources help. International Journal of Entrepreneurial Behavior & Research, 29 (4), 986–1006.

Korzynski, P., Paniagua, J., & Rodriguez-Montemayor, E. (2019). Employee creativity in a digital era: The mediating role of social media. Management Decision, 58 (6), 1100–1117.

Li, G., Li, L., Xie, L., & Lopez, O. S. (2023). The effects of ethical leadership on creativity: A conservation of resources perspective. Current Psychology , 1–11. Retrieved from https://doi.org/10.1007/s12144-023-04703-0 .

Lin, B., Law, K. S., & Zhou, J. (2017). Why is underemployment related to creativity and OCB? A task-crafting explanation of the curvilinear moderated relations. Academy of Management Journal, 60 (1), 156–177.

Lu, Z., Ding, Y., & Nie, Y. (2023). How does family socioeconomic status affect creativity? The role of creative self-efficacy and critical thinking disposition. Current Psychology , 1–8. Retrieved from https://doi.org/10.1007/s12144-023-04768-x .

Lua, E., Liu, D., & Shalley, C. E. (2023). Multilevel outcomes of creativity in organizations: An integrative review and agenda for future research. Journal of Organizational Behavior . Retrieved from https://doi.org/10.1002/job.2690 .

McShane, B. B., & Böckenholt, U. (2022). Meta-analysis of studies with multiple contrasts and differences in measurement scales. Journal of Consumer Psychology, 32 (1), 23–40.

Metin-Orta, I., & Demirtepe-Saygılı, D. (2023). Cyberloafing behaviors among university students: Their relationships with positive and negative affect. Current Psychology, 42 (13), 11101–11114.

Moody, G. D., & Siponen, M. (2013). Using the theory of interpersonal behavior to explain non-work-related personal use of the Internet at work. Information & Management, 50 (6), 322–335.

Nobari, H., Fashi, M., Eskandari, A., Villafaina, S., Murillo-Garcia, Á., & Pérez-Gómez, J. (2021). Effect of COVID-19 on health-related quality of life in adolescents and children: A systematic review. International Journal of Environmental Research and Public Health, 18 (9), 4563.

Article   PubMed   PubMed Central   Google Scholar  

Ou, Y. C., & Verhoef, P. C. (2017). The impact of positive and negative emotions on loyalty intentions and their interactions with customer equity drivers. Journal of Business Research, 80 , 106–115.

Parker, S. K., Williams, H. M., & Turner, N. (2006). Modeling the antecedents of proactive behavior at work. Journal of Applied Psychology, 91 (3), 636.

Pattusamy, M., & Jacob, J. (2017). The mediating role of family-to-work conflict and work-family balance in the relationship between family support and family satisfaction: A three path mediation approach. Current Psychology, 36 , 812–822.

Pindek, S., Krajcevska, A., & Spector, P. E. (2018). Cyberloafing as a coping mechanism: Dealing with workplace boredom. Computers in Human Behavior, 86 , 147–152.

Podsakoff, N. P., Spoelma, T. M., Chawla, N., & Gabriel, A. S. (2019). What predicts within-person variance in applied psychology constructs? An empirical examination. Journal of Applied Psychology, 104 (6), 727–754.

Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010). A general multilevel sem framework for assessing multilevel mediation. Psychological Methods, 15 (3), 209–233.

Reizer, A., Galperin, B. L., Chavan, M., Behl, A., & Pereira, V. (2022). Examining the relationship between fear of COVID-19, intolerance for uncertainty, and cyberloafing: A mediational model. Journal of Business Research, 145 , 660–670.

Ren, S., Hu, J., Tang, G., & Chadee, D. (2023). Digital connectivity for work after hours: Its curvilinear relationship with employee job performance. Personnel Psychology, 76 (3), 731–757.

Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20 (9), 676–688.

Scuotto, V., Tzanidis, T., Usai, A., & Quaglia, R. (2023). The digital humanism era triggered by individual creativity. Journal of Business Research, 158 , 113709.

Shrout, E. P., & Lane, S. P. (2012). Psychometrics. In M. R. Mehl & T. S. Conner (Eds.), Handbook of research methods for studying daily life (pp. 302–320). New York: Guilford Press.

Google Scholar  

Steindorf, L., Hammerton, H. A., & Rummel, J. (2021). Mind wandering outside the box—About the role of off-task thoughts and their assessment during creative incubation. Psychology of Aesthetics, Creativity, and the Arts, 15 (4), 584.

Stothart, C., Mitchum, A., & Yehnert, C. (2015). The attentional cost of receiving a cell phone notification. Journal of Experimental Psychology: Human Perception and Performance, 41 (4), 893.

Sun, S., Wang, N., Zhu, J., & Song, Z. (2020). Crafting job demands and employee creativity: A diary study. Human Resource Management, 59 (6), 569–583.

Swar, B., Hameed, T., & Reychav, I. (2017). Information overload, psychological ill-being, and behavioral intention to continue online healthcare information search. Computers in Human Behavior, 70 , 416–425.

Sweller, J. (2011). Cognitive load theory. In J. P. Mestre & B. H. Ross (Eds.), The psychology of learning and motivation (pp. 37–76). Elsevier Academic Press.

Tan, T., Zou, H., Chen, C., & Luo, J. (2015). Mind wandering and the incubation effect in insight problem solving. Creativity Research Journal, 27 (4), 375–382.

Tsai, H. Y. (2023). Do you feel like being proactive day? How daily cyberloafing influences creativity and proactive behavior: The moderating roles of work environment. Computers in Human Behavior, 138 , 107470.

Wong, G. Y. L., Kwok, R. C. W., Zhang, S., Lai, G. C. H., & Cheung, J. C. F. (2023). Mutually complementary effects of cyberloafing and cyber-life-interruption on employee exhaustion. Information & Management, 60 (2), 103752.

Wu, J., Mei, W., Ugrin, J., Liu, L., & Wang, F. (2021). Curvilinear performance effects of social cyberloafing out of class: The mediating role as a recovery experience. Information Technology & People, 34 (2), 581–598.

Yang, T., & Wu, G. (2022). Spontaneous or deliberate: The dual influence of mind wandering on creative incubation. The Journal of Creative Behavior, 56 (4), 584–600.

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This study is supported by the National Natural Science Foundation of China (71802118) & National Social Science Foundation of China (22FGLB096).

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The calculation process of Hypothesis 6

To investigate the indirect effects of cognitive load on creativity via cyberloafing and problem-oriented mind wandering ( Hypothesis 6 ), we adopted the methodology employed by Lin et al. ( 2017 ), Footnote 2 and the calculation process is as follows:

Firstly, we used the following equation to test the moderating effect of role breadth self-efficacy (RBSE):

In Eq. ( 1 ), significance for β 5 would indicate that the inverted U-shaped relationship between cyberloafing (CY) and problem-oriented mind wandering (POMW) would vary as a function of role breadth self-efficacy (RBSE).

Secondly, when there is a nonlinear relationship in the influence path of independent variable (X) on dependent variable (Y) through the mediating variable (M), the indirect rate of change of Y due to changes in X causing changes in M is represented by θ . θ can be calculated as the product of the first-order partial derivative of M with respect to X and the first-order partial derivative of Y with respect to M:

So to calculate the instantaneous indirect effect in this study, this study substituted cyberloafing for X, problem-oriented mind wandering for M, and daily creativity for Y. According to theorization of the study, daily creativity (DC) is linearly related to problem-oriented mind wandering (POMW):

Next, this study derived the partial derivative of problem-oriented mind wandering with respect to cyberloafing from Eq. ( 1 ) and the partial derivative of daily creativity with respect to tproblem-oriented mind wandering from Eq. ( 2 ):

Finally, according to the formula ( 3 ) and \(\frac{\partial Y(X,M)}{{\partial M}} = \mathop \beta \nolimits_{{7}}\) , the instantaneous indirect effect of cyberloafing—role breadth self-efficacy on daily creativity through problem-oriented mind wandering is:

Thus, it is observed that the mediating effect ( θ ) exhibits a non-linear relationship, indicating that θ is not a constant but rather a linear relationship of the interactive term (cyberloafing × role breadth self-efficacy) and role breadth self-efficacy.

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Yu, W., Qin, X., Li, M. et al. Cognitive load and creativity of knowledge workers: a diary study. Curr Psychol (2023). https://doi.org/10.1007/s12144-023-05395-2

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Intelligence and Creativity in Problem Solving: The Importance of Test Features in Cognition Research

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This paper discusses the importance of three features of psychometric tests for cognition research: construct definition, problem space, and knowledge domain. Definition of constructs, e.g., intelligence or creativity, forms the theoretical basis for test construction. Problem space, being well or ill-defined, is determined by the cognitive abilities considered to belong to the constructs, e.g., convergent thinking to intelligence, divergent thinking to creativity. Knowledge domain and the possibilities it offers cognition are reflected in test results. We argue that (a) comparing results of tests with different problem spaces is more informative when cognition operates in both tests on an identical knowledge domain, and (b) intertwining of abilities related to both constructs can only be expected in tests developed to instigate such a process. Test features should guarantee that abilities can contribute to self-generated and goal-directed processes bringing forth solutions that are both new and applicable. We propose and discuss a test example that was developed to address these issues.

The definition of the construct a test is to measure is most important in test construction and application, because cognitive processes reflect the possibilities a task offers. For instance, a test constructed to assess intelligence will operationalize the definition of this construct, being, in short, finding the correct answer. Also, the definition of a construct becomes important when selecting tests for the confirmation of a specific hypothesis. One can only find confirmation for a hypothesis if the chosen task instigates the necessary cognitive operations. For instance, in trying to confirm the assumed intertwining of certain cognitive abilities (e.g., convergent thinking and divergent thinking), tasks should be applied that have shown to yield the necessary cognitive process.

The second test feature, problem space , determines the degrees of freedom cognition has to its disposal in solving a problem. For instance, cognition will go through a wider search path when problem constraints are less well defined and, consequently, data will differ accordingly.

The third test feature, knowledge domain , is important when comparing results from two different tests. When tests differ in problem space, it is not advisable they should differ in knowledge domain. For instance, when studying the differences in cognitive abilities between tests constructed to asses convergent thinking (mostly defined problem space) and divergent thinking (mostly ill-defined problem space), in general test practice, both tests also differ in knowledge domain. Hence, data will reflect cognition operating not only in different problem spaces, but also operating on different knowledge domains, which makes the interpretation of results ambiguous.

The proposed approach for test development and test application holds the promise of, firstly, studying cognitive abilities in different problem spaces while operating on an identical knowledge domain. Although cognitions’ operations have been studied extensively and superbly in both contexts separately, they have rarely been studied in test situations where one or the other test feature is controlled for. The proposed approach also presents a unique method for studying thinking processes in which cognitive abilities intertwine. On the basis of defined abilities, tasks can be developed that have a higher probability of yielding the hypothesized results.

The construct of intelligence is defined as the ability to produce the single best (or correct) answer to a clearly defined question, such as a proof to a theorem ( Simon, 1973 ). It may also be seen as a domain-general ability ( g -factor; Spearman, 1904 ; Cattell, 1967 ) that has much in common with meta cognitive functions, such as metacognitive knowledge, metacognitive monitoring, and metacognitive control ( Saraç et al., 2014 ).

The construct of creativity, in contrast, is defined as the ability to innovate and move beyond what is already known ( Wertheimer , 1945/1968 ; Ghiselin , 1952/1985 ; Vernon, 1970 ). In other words, it emphasizes the aspect of innovation. This involves the ability to consider things from an uncommon perspective, transcend the old order ( Ghiselin , 1952/1985 ; Chi, 1997 ; Ward, 2007 ), and explore loosely associated ideas ( Guilford, 1950 ; Mednick, 1962 ; Koestler, 1964 ; Gentner, 1983 ; Boden, 1990 ; Christensen, 2007 ). Creativity could also be defined as the ability to generate a solution to problems with ill-defined problem spaces ( Wertheimer , 1945/1968 ; Getzels and Csikszentmihalyi, 1976 ). In this sense it involves the ability to identify problematic aspects of a given situation ( Ghiselin , 1952/1985 ) and, in a wider sense, the ability to define completely new problems ( Getzels, 1975 , 1987 ).

Guilford (1956) introduced the constructs of convergent thinking and divergent thinking abilities. Both thinking abilities are important because they allow us insights in human problem solving. On the basis of their definitions convergent and divergent thinking help us to structurally study human cognitive operations in different situations and over different developmental stages. Convergent thinking is defined as the ability to apply conventional and logical search, recognition, and decision-making strategies to stored information in order to produce an already known answer ( Cropley, 2006 ). Divergent thinking, by contrast, is defined as the ability to produce new approaches and original ideas by forming unexpected combinations from available information and by applying such abilities as semantic flexibility, and fluency of association, ideation, and transformation ( Guilford, 1959 , as cited in Cropley, 2006 , p. 1). Divergent thinking brings forth answers that may never have existed before and are often novel, unusual, or surprising ( Cropley, 2006 ).

Guilford (1967) introduced convergent and divergent thinking as part of a set of five operations that apply in his Structure of Intellect model (SOI model) on six products and four kinds of content, to produce 120 different factors of cognitive abilities. With the SOI model Guilford wanted to give the construct of intelligence a comprehensive model. He wanted the model to include all aspects of intelligence, many of which had been seriously neglected in traditional intelligence testing because of a persistent adherence to the belief in Spearman’s g ( Guilford, 1967 , p. vii). Hence, Guilford envisaged cognition to embrace, among other abilities, both convergent and divergent thinking abilities. After these new constructs were introduced and defined, tests for convergent and divergent thinking emerged. Despite the fact that Guilford reported significant loadings of tests for divergent production on tests constructed to measure convergent production ( Guilford, 1967 , p. 155), over the years, both modes of thinking were considered as separate identities where convergent thinking tests associated with intelligence and divergent thinking tests with creativity ( Cropley, 2006 ; Shye and Yuhas, 2004 ). Even intelligence tests that assess aspects of intelligence that supposedly reflect creative abilities do not actually measure creativity ( Kaufman, 2015 ).

The idea that both convergent and divergent thinking are important for solving problems, and that intelligence helps in the creative process, is not really new. In literature we find models of the creative process that define certain stages to convergent and divergent thinking; the stages of purposeful preparation at the start and those of critical verification at the end of the process, respectively ( Wallas, 1926 ; Webb Young , 1939/2003 ). In this view, divergent thinking enables the generation of new ideas whereas the exploratory activities of convergent thinking enable the conversion of ideas into something new and appropriate ( Cropley and Cropley, 2008 ).

We argue that studying the abilities of divergent and convergent thinking in isolation does not suffice to give us complete insight of all possible aspects of human problem solving, its constituent abilities and the structure of its processes. Processes that in a sequence of thoughts and actions lead to novel and adaptive productions ( Lubart, 2001 ) are more demanding of cognition for understanding the situation at hand and planning a path to a possible solution, than abilities involved in less complex situations ( Jaušovec, 1999 ). Processes that yield self-generated and goal-directed thought are the most complex cognitive processes that can be studied ( Beaty et al., 2016 ). Creative cognition literature is moving toward the view that especially in those processes that yield original and appropriate solutions within a specific context, convergent and divergent abilities intertwine ( Cropley, 2006 ; Ward, 2007 ; Gabora, 2010 ).

The approach of intertwining cognitive abilities is also developed within cognitive neuroscience by focusing on the intertwining of brain networks ( Beaty et al., 2016 ). In this approach divergent thinking relates to the default brain network. This network operates in defocused or associative mode of thought yielding spontaneous and self-generated cognition ( Beaty et al., 2015 ). Convergent thinking relates to the executive control network operating in focused or analytic modes of thought, yielding updating, shifting, and inhibition ( Benedek et al., 2014 ). Defocused attention theory ( Mendelssohn, 1976 ) states that less creative individuals operate with a more focused attention than do creative individuals. This theory argues that e.g., attending to two things at the same time, might result in one analogy, while attending to four things might yield six analogies ( Martindale, 1999 ).

In the process of shifting back and forth along the spectrum between associative and analytic modes of thinking, the fruits of associative thought become ingredients for analytic thought processes, and vice versa ( Gabora, 2010 ). In this process, mental imagery is involved as one sensory aspect of the human ability to gather and process information ( Jung and Haier, 2013 ). Mental imagery is fed by scenes in the environment that provide crucial visual clues for creative problem solving and actuates the need for sketching ( Verstijnen et al., 2001 ).

Creative problem solving processes often involve an interactive relationship between imagining, sketching, and evaluating the result of the sketch ( van Leeuwen et al., 1999 ). This interactive process evolves within a type of imagery called “visual reasoning” where forms and shapes are manipulated in order to specify the configurations and properties of the design entities ( Goldschmidt, 2013 ). The originality of inventions is predicted by the application of visualization, whereas their practicality is predicted by the vividness of imagery ( Palmiero et al., 2015 ). Imaginative thought processes emerge from our conceptual knowledge of the world that is represented in our semantic memory system. In constrained divergent thinking, the neural correlates of this semantic memory system partially overlap with those of the creative cognition system ( Abraham and Bubic, 2015 ).

Studies of convergent and divergent thinking abilities have yielded innumerable valuable insights on the cognitive and neurological aspects involved, e.g., reaction times, strategies, brain areas involved, mental representations, and short and long time memory components. Studies on the relationship between both constructs suggest that it is unlikely that individuals employ similar cognitive strategies when solving more convergent than more divergent thinking tasks ( Jaušovec, 2000 ). However, to arrive at a quality formulation the creative process cannot do without the application of both, convergent and divergent thinking abilities (e.g., Kaufmann, 2003 ; Runco, 2003 ; Sternberg, 2005 ; Dietrich, 2007 ; Cropley and Cropley, 2008 ; Silvia et al., 2013 ; Jung, 2014 ).

When it is our aim to study the networks addressed by the intertwining of convergent and divergent thinking processes that are considered to operate when new, original, and yet appropriate solutions are generated, then traditional thinking tests like intelligence tests and creativity tests are not appropriate; they yield processes related to the definition of one or the other type of construct.

Creative Reasoning Task

According to the new insights gained in cognition research, we need tasks that are developed with the aim to instigate precisely the kind of thinking processes we are looking for. Tasks should also provide a method of scoring independently the contribution of convergent and divergent thinking. As one possible solution for such tasks we present the Creative Reasoning Task (CRT; Jaarsveld, 2007 ; Jaarsveld et al., 2010 , 2012 , 2013 ).

The CRT presents participants with an empty 3 × 3 matrix and asks them to fill it out, as original and complex as possible, by creating components and the relationships that connect them. The created matrix can, in principle, be solved by another person. The creation of components is entirely free, as is the generation of the relationships that connects them into a completed pattern. Created matrices are scored with two sub scores; Relations , which scores the logical complexity of a matrix and is, therefore, considered a measure for convergent thinking, and Components and Specifications , which scores the originality, fluency, and flexibility and, therefore, is considered an indication for divergent thinking (for a more detailed description of the score method, see Appendix 1 in Supplementary Material).

Psychometric studies with the CRT showed, firstly, that convergent and divergent thinking abilities apply within this task and can be assessed independently. The CRT sub score Relations correlated with the Standard Progressive Matrices test (SPM) and the CRT sub score Components and Specifications correlated with a standard creativity test (TCT–DP, Test of Creative Thinking–Drawing Production; Urban and Jellen, 1995 ; Jaarsveld et al., 2010 , 2012 , 2013 ). Studies further showed that, although a correlation was observed for the intelligence and creativity test scores, no correlation was observed between the CRT sub scores relating to intelligent and creative performances ( Jaarsveld et al., 2012 , 2013 ; for further details about the CRT’s objectivity, validity, and reliability, see Appendix 2 in Supplementary Material).

Reasoning in creative thinking can be defined as the involvement of executive/convergent abilities in the inhibition of ideas and the updating of information ( Benedek et al., 2014 ). Jung (2014) describes a dichotomy for cognitive abilities with at one end the dedicated system that relies on explicit and conscious knowledge and at the other end the improvisational system that relies more upon implicit or unconscious knowledge systems. The link between explicit and implicit systems can actually be traced back to Kris’ psychoanalytic approach to creativity dating from the 1950s. The implicit system refers to Kris’ primary process of adaptive regression, where unmodulated thoughts intrude into consciousness; the explicit system refers to the secondary process, where the reworking and transformation of primary process material takes place through reality-oriented and ego-controlled thinking ( Sternberg and Lubart, 1999 ). The interaction between explicit and implicit systems can be seen to form the basis of creative reasoning, i.e., the cognitive ability to solve problems in an effective and adaptive way. This interaction evolved as a cognitive mechanism when human survival depended on finding effective solutions to both common and novel problem situations ( Gabora and Kaufman, 2010 ). Creative reasoning solves that minority of problems that are unforeseen and yet of high adaptability ( Jung, 2014 ).

Hence, common tests are insufficient when it comes to solving problems that are unforeseen and yet of high adaptability, because they present problems that are either unforeseen and measure certain abilities contained in the construct of creativity or they address adaptability and measure certain abilities contained in the construct of intelligence. The CRT presents participants with a problem that they could not have foreseen; the form is blank and offers no stimuli. All tests, even creativity tests, present participants with some kind of stimuli. The CRT addresses adaptability; to invent from scratch a coherent structure that can be solved by another person, like creating a crossword puzzle. Problems, that are unforeseen and of high adaptability, are solved by the application of abilities from both constructs.

Neuroscience of Creative Cognition

Studies in neuroscience showed that cognition operating in ill-defined problem space not only applies divergent thinking but also benefits from additional convergent operations ( Gabora, 2010 ; Jung, 2014 ). Understanding creative cognition may be advanced when we study the flow of information among brain areas ( Jung et al., 2010 ).

In a cognitive neuroscience study with the CRT we focused on the cognitive process evolving within this task. Participants performed the CRT while EEG alpha activity was registered. EEG alpha synchronization in frontal areas is understood as an indication of top-down control ( Cooper et al., 2003 ). When observed in frontal areas, for divergent and convergent thinking tasks, it may not reflect a brain state that is specific for creative cognition but could be attributed to the high processing demands typically involved in creative thinking ( Benedek et al., 2011 ). Top-down control, relates to volitionally focusing attention to task demands ( Buschman and Miller, 2007 ). That this control plays a role in tasks with an ill-defined problem space showed when electroencephalography (EEG) alpha synchronization was stronger for individuals engaged in creative ideation tasks compared to an intelligence related tasks ( Fink et al., 2007 , 2009 ; Fink and Benedek, 2014 ). This activation was also found for the CRT; task related alpha synchronization showed that convergent thinking was integrated in the divergent thinking processes. Analyzes of the stages in the CRT process showed that this alpha synchronization was especially visible at the start of the creative process at prefrontal and frontal sites when information processing was most demanding, i.e., due to multiplicity of ideas, and it was visible at the end of the process, due to narrowing down of alternatives ( Jaarsveld et al., 2015 ).

A functional magnetic resonance imaging (fMRI) study ( Beaty et al., 2015 ) with a creativity task in which cognition had to meet specific constraints, showed the networks involved. The default mode network which drives toward abstraction and metaphorical thinking and the executive control network driving toward certainty ( Jung, 2014 ). Control involves not only maintenance of patterns of activity that represent goals and the means to achieve those ( Miller and Cohen, 2001 ), but also their voluntary suppression when no longer needed, as well as the flexible shift between different goals and mental sets ( Abraham and Windmann, 2007 ). Attention can be focused volitionally by top-down signals derived from task demands and automatically by bottom-up signals from salient stimuli ( Buschman and Miller, 2007 ). Intertwining between top-down and bottom-up attention processes in creative cognition ensures a broadening of attention in free associative thinking ( Abraham and Windmann, 2007 ).

These studies support and enhance the findings of creative cognition research in showing that the generation of original and applicable ideas involves an intertwining between different abilities, networks, and attention processes.

Problem Space

A problem space is an abstract representation, in the mind of the problem solver, of the encountered problem and of the asked for solution ( Simon and Newell, 1971 ; Simon, 1973 ; Hayes and Flowers, 1986 ; Kulkarni and Simon, 1988 ; Runco, 2007 ). The space that comes with a certain problem can, according to the constraints that are formulated for the solution, be labeled well-defined or ill-defined ( Simon and Newell, 1971 ). Consequently, the original problems are labeled closed and open problems, respectively ( Jaušovec, 2000 ).

A problem space contains all possible states that are accessible to the problem solver from the initial state , through iterative application of transformation rules , to the goal state ( Newell and Simon, 1972 ; Anderson, 1983 ). The initial state presents the problem solver with a task description that defines which requirements a solution has to answer. The goal state represents the solution. The proposed solution is a product of the application of transformation rules (algorithms and heuristics) on a series of successive intermediate solutions. The proposed solution is also a product of the iterative evaluations of preceding solutions and decisions based upon these evaluations ( Boden, 1990 ; Gabora, 2002 ; Jaarsveld and van Leeuwen, 2005 ; Goldschmidt, 2014 ). Whether all possible states need to be passed through depends on the problem space being well or ill-defined and this, in turn, depends on the character of the task descriptions.

When task descriptions clearly state which requirements a solution has to answer then the inferences made will show little idiosyncratic aspects and will adhere to the task constraints. As a result, fewer options for alternative paths are open to the problem solver and search for a solution evolves in a well-defined space. Vice versa, when task or problem descriptions are fuzzy and under specified, the problem solver’s inferences are more idiosyncratic; the resulting process will evolve within an ill-defined space and will contain more generative-evaluative cycles in which new goals are set, and the cycle is repeated ( Dennett, 1978 , as cited in Gabora, 2002 , p. 126).

Tasks that evolve in defined problem space are, e.g., traditional intelligence tests (e.g., Wechsler Adult Intelligence Scale, WAIS; and SPM, Raven , 1938/1998 ). The above tests consist of different types of questions, each testing a different component of intelligence. They are used in test practice to assess reasoning abilities in diverse domains, such as, abstract, logical, spatial, verbal, numerical, and mathematical domains. These tests have clearly stated task descriptions and each item has one and only one correct solution that has to be generated from memory or chosen from a set of alternatives, like in multiple choice formats. Tests can be constructed to assess crystallized or fluid intelligence. Crystallized intelligence represents abilities acquired through learning, practice, and exposure to education, while fluid intelligence represents a more basic capacity that is valuable to reasoning and problem solving in contexts not necessarily related to school education ( Carroll, 1982 ).

Tasks that evolve in ill-defined problem space are, e.g., standard creativity tests. These types of test ask for a multitude of ideas to be generated in association with a given item or situation (e.g., “think of as many titles for this story”). Therefore, they are also labeled as divergent thinking test. Although they assess originality, fluency, flexibility of responses, and elaboration, they are not constructed, however, to score appropriateness or applicability. Divergent thinking tests assess one limited aspect of what makes an individual creative. Creativity depends also on variables like affect and intuition; therefore, divergent thinking can only be considered an indication of an individual’s creative potential ( Runco, 2008 ). More precisely, divergent thinking explains just under half of the variance in adult creative potential, which is more than three times that of the contribution of intelligence ( Plucker, 1999 , p. 103). Creative achievement , by contrast, is commonly assessed by means of self-reports such as biographical questionnaires in which participants indicate their achievement across various domains (e.g., literature, music, or theater).

Studies with the CRT showed that problem space differently affects processing of and comprehension of relationships between components. Problem space did not affect the ability to process complex information. This ability showed equal performance in well and ill-defined problem spaces ( Jaarsveld et al., 2012 , 2013 ). However, problem space did affect the comprehension of relationships, which showed in the different frequencies of relationships solved and created ( Jaarsveld et al., 2010 , 2012 ). Problem space also affected the neurological activity as displayed when individuals solve open or closed problems ( Jaušovec, 2000 ).

Problem space further affected trends over grade levels of primary school children for relationships solved in well-defined and applied in ill-defined problem space. Only one of the 12 relationships defined in the CRT, namely Combination, showed an increase with grade for both types of problem spaces ( Jaarsveld et al., 2013 ). In the same study, cognitive development in the CRT showed in the shifts of preference for a certain relationship. These shifts seem to correspond to Piaget’s developmental stages ( Piaget et al., 1977 ; Siegler, 1998 ) which are in evidence in the CRT, but not in the SPM ( Jaarsveld et al., 2013 ).

Design Problems

A sub category of problems with an ill-defined problem space are represented by design problems. In contrast to divergent thinking tasks that ask for the generation of a multitude of ideas, in design tasks interim ideas are nurtured and incrementally developed until they are appropriate for the task. Ideas are rarely discarded and replaced with new ideas ( Goel and Pirolli, 1992 ). The CRT could be considered a design problem because it yields (a) one possible solution and (b) an iterative thinking process that involves the realization of a vague initial idea. In the CRT a created matrix, which is a closed problem, is created within an ill-defined problem space. Design problems can be found, e.g., in engineering, industrial design, advertising, software design, and architecture ( Sakar and Chakrabarti, 2013 ), however, they can also be found in the arts, e.g., poetry, sculpting, and dance geography.

These complex problems are partly determined by unalterable needs, requirements and intentions but the major part of the design problem is undetermined ( Dorst, 2004 ). This author points out that besides containing an original and a functional value, these types of problems contain an aesthetic value. He further states that the interpretation of the design problem and the creation and selection of possible suitable solutions can only be decided during the design process on the basis of proposals made by the designer.

In design problems the generation stage may be considered a divergent thinking process. However, not in the sense that it moves in multiple directions or generates multiple possibilities as in a divergent thinking tests, but in the sense that it unrolls by considering an initially vague idea from different perspectives until it comes into focus and requires further processing to become viable. These processes can be characterized by a set of invariant features ( Goel and Pirolli, 1992 ), e.g., structuring. iteration , and coherence .

Structuring of the initial situation is required in design processes before solving can commence. The problem contains little structured and clear information about its initial state and about the requirements of its solution. Therefore, design problems allow or even require re-interpretation of transformation rules; for instance, rearranging the location of furniture in a room according to a set of desirable outcomes. Here one uncovers implicit requirements that introduce a set of new transformations and/or eliminate existing ones ( Barsalou, 1992 ; Goel and Pirolli, 1992 ) or, when conflicting requirements arise, one creates alternatives and/or introduces new trade-offs between the conflicting constraints ( Yamamoto et al., 2000 ; Dorst, 2011 ).

A second aspect of design processes is their iterative character. After structuring and planning a vague idea emerges, which is the result of the merging of memory items. A vague idea is a cognitive structure that, halfway the creative process is still ill defined and, therefore, can be said to exist in a state of potentiality ( Gabora and Saab, 2011 ). Design processes unroll in an iterative way by the inspection and adjustment of the generated ideas ( Goldschmidt, 2014 ). New meanings are created and realized while the creative mind imposes its own order and meaning on the sensory data and through creative production furthers its own understanding of the world ( Arnheim , 1962/1974 , as cited in Grube and Davis, 1988 , pp. 263–264).

A third aspect of design processes is coherence. Coherence theories characterize coherence in, for instance, philosophical problems and psychological processes, in terms of maximal satisfaction of multiple constraints and compute coherence by using, a.o., connectionist algorithms ( Thagard and Verbeurgt, 1998 ). Another measure of coherence is characterized as continuity in design processes. This measure was developed for a design task ( Jaarsveld and van Leeuwen, 2005 ) and calculated by the occurrence of a given pair of objects in a sketch, expressed as a percentage of all the sketches of a series. In a series of sketches participants designed a logo for a new soft drink. Design series strong in coherence also received a high score for their final design, as assessed by professionals in various domains. Indicating that participants with a high score for the creative quality of their final sketch seemed better in assessing their design activity in relation to the continuity in the process and, thereby, seemed better in navigating the ill-defined space of a design problem ( Jaarsveld and van Leeuwen, 2005 ). In design problems the quality of cognitive production depends, in part, on the abilities to reflect on one’s own creative behavior ( Boden, 1996 ) and to monitor how far along in the process one is in solving it ( Gabora, 2002 ). Hence, design problems are especially suited to study more complex problem solving processes.

Knowledge Domain

Knowledge domain represents disciplines or fields of study organized by general principles, e.g., domains of various arts and sciences. It contains accumulated knowledge that can be divided in diverse content domains, and the relevant algorithms and heuristics. We also speak of knowledge domains when referring to, e.g., visuo-spatial and verbal domains. This latter differentiation may refer to the method by which performance in a certain knowledge domain is assessed, e.g., a visuo-spatial physics task that assesses the content domain of the workings of mass and weights of objects.

In comparing tests results, we should keep in mind that apart from reflecting cognitive processes evolving in different problem spaces, the results also arise from cognition operating on different knowledge domains. We argue that, the still contradictory and inconclusive discussion about the relationship between intelligence and creativity ( Silvia, 2008 ), should involve the issue of knowledge domain.

Intelligence tests contain items that pertain to, e.g., verbal, abstract, mechanical and spatial reasoning abilities, while their content mostly operates on knowledge domains that are related to contents contained in school curricula. Items of creativity tests, by contrast, pertain to more idiosyncratic knowledge domains, their contents relating to associations between stored personal experiences ( Karmiloff-Smith, 1992 ). The influence of knowledge domain on the relationships between different test scores was already mentioned by Guilford (1956 , p. 169). This author expected a higher correlation between scores from a typical intelligence test and a divergent thinking test than between scores from two divergent thinking tests because the former pair operated on identical information and the latter pair on different information.

Studies with the CRT showed that when knowledge domain is controlled for, the development of intelligence operating in ill-defined problem space does not compare to that of traditional intelligence but develops more similarly to the development of creativity ( Welter et al., in press ).

Relationship Intelligence and Creativity

The Threshold theory ( Guilford, 1967 ) predicts a relationship between intelligence and creativity up to approximately an intelligence quotient (IQ) level of 120 but not beyond ( Lubart, 2003 ; Runco, 2007 ). Threshold theory was corroborated when creative potential was found to be related to intelligence up to certain IQ levels; however, the theory was refuted, when focusing on achievement in creative domains; it showed that creative achievement benefited from higher intelligence even at fairly high levels of intellectual ability ( Jauk et al., 2013 ).

Distinguishing between subtypes of general intelligence known as fluent and crystallized intelligence ( Cattell, 1967 ), Sligh et al. (2005) observed an inverse threshold effect with fluid IQ: a correlation with creativity test scores in the high IQ group but not in the average IQ group. Also creative achievement showed to be affected by fluid intelligence ( Beaty et al., 2014 ). Intelligence, defined as fluid IQ, verbal fluency, and strategic abilities, showed a higher correlation with creativity scores ( Silvia, 2008 ) than when defined as crystallized intelligence. Creativity tests, which involved convergent thinking (e.g., Remote Association Test; Mednick, 1962 ) showed higher correlations with intelligence than ones that involved only divergent thinking (e.g., the Alternate Uses Test; Guilford et al., 1978 ).

That the Remote Association test also involves convergent thinking follows from the instructions; one is asked, when presented with a stimulus word (e.g., table) to produce the first word one thinks of (e.g., chair). The word pair table–chair is a common association, more remote is the pair table–plate, and quite remote is table–shark. According to Mednick’s theory (a) all cognitive work is done essentially by combining or associating ideas and (b) individuals with more commonplace associations have an advantage in well-defined problem spaces, because the class of relevant associations is already implicit in the statement of the problem ( Eysenck, 2003 ).

To circumvent the problem of tests differing in knowledge domain, one can develop out of one task a more divergent and a more convergent thinking task by asking, on the one hand, for the generation of original responses, and by asking, on the other hand, for more common responses ( Jauk et al., 2012 ). By changing the instruction of a task, from convergent to divergent, one changes the constraints the solution has to answer and, thereby, one changes for cognition its freedom of operation ( Razumnikova et al., 2009 ; Limb, 2010 ; Jauk et al., 2012 ). However, asking for more common responses is still a divergent thinking task because it instigates a generative and ideational process.

Indeed, studying the relationship between intelligence and creativity with knowledge domain controlled for yielded different results as defined in the Threshold theory. A study in which knowledge domain was controlled for showed, firstly, that intelligence is no predictor for the development of creativity ( Welter et al., 2016 ). Secondly, that the relationship between scores of intelligence and creativity tests as defined under the Threshold theory was only observed in a small subset of primary school children, namely, female children in Grade 4 ( Welter et al., 2016 ). We state that relating results of operations yielded by cognitive abilities performing in defined and in ill-defined problem spaces can only be informative when it is ensured that cognitive processes also operate on an identical knowledge domain.

Intertwining of Cognitive Abilities

Eysenck (2003) observed that there is little justification for considering the constructs of divergent and convergent thinking in categorical terms in which one construct excludes the other. In processes that yield original and appropriate solutions convergent and divergent thinking both operate on the same large knowledge base and the underlying cognitive processes are not entirely dissimilar ( Eysenck, 2003 , p. 110–111).

Divergent thinking is especially effective when it is coupled with convergent thinking ( Runco, 2003 ; Gabora and Ranjan, 2013 ). A design problem study ( Jaarsveld and van Leeuwen, 2005 ) showed that divergent production was active throughout the design, as new meanings are continuously added to the evolving structure ( Akin, 1986 ), and that convergent production was increasingly important toward the end of the process, as earlier productions are wrapped up and integrated in the final design. These findings are in line with the assumptions of Wertheimer (1945/1968) who stated that thinking within ill-defined problem space is characterized by two points of focus; one is to work on the parts, the other to make the central idea clearer.

Parallel to the discussion about the intertwining of convergent and divergent thinking abilities in processes that evolve in ill-defined problem space we find the discussion about how intelligence may facilitate creative thought. This showed when top-down cognitive control advanced divergent processing in the generation of original ideas and a certain measure of cognitive inhibition advanced the fluency of idea generation ( Nusbaum and Silvia, 2011 ). Fluid intelligence and broad retrieval considered as intelligence factors in a structural equation study contributed both to the production of creative ideas in a metaphor generation task ( Beaty and Silvia, 2013 ). The notion that creative thought involves top-down, executive processes showed in a latent variable analysis where inhibition primarily promoted the fluency of ideas, and intelligence promoted their originality ( Benedek et al., 2012 ).

Definitions of the Constructs Intelligence and Creativity

The various definitions of the constructs of intelligence and creativity show a problematic overlap. This overlap stems from the enormous endeavor to unanimously agree on valid descriptions for each construct. Spearman (1927) , after having attended many symposia that aimed at defining intelligence, stated that “in truth, ‘intelligence’ has become a mere vocal sound, a word with so many meanings that finally it has none” (p. 14).

Intelligence is expressed in terms of adaptive, goal-directed behavior; and the subset of such behavior that is labeled “intelligent” seems to be determined in large part by cultural or societal norms ( Sternberg and Salter, 1982 ). The development of the IQ measure is discussed by Carroll (1982) : “Binet (around 1905) realized that intelligent behavior or mental ability can be ranged along a scale. Not much later, Stern (around 1912) noticed that, as chronological age increased, variation in mental age changes proportionally. He developed the IQ ratio, whose standard deviation would be approximately constant over chronological age if mental age was divided by chronological age. With the development of multiple-factor-analyses (Thurstone, around 1935) it could be shown that intelligence is not a simple unitary trait because at least seven somewhat independent factors of mental ability were identified.”

Creativity is defined as a combined manifestation of novelty and usefulness ( Jung et al., 2010 ). Although it is identified with divergent thinking, and performance on divergent thinking tasks predicts, e.g., quantity of creative achievements ( Torrance, 1988 , as cited in Beaty et al., 2014 ) and quality of creative performance ( Beaty et al., 2013 ), it cannot be identified uniquely with divergent thinking.

Divergent thinking often leads to highly original ideas that are honed to appropriate ideas by evaluative processes of critical thinking, and valuative and appreciative considerations ( Runco, 2008 ). Divergent thinking tests should be more considered as estimates of creative problem solving potential rather than of actual creativity ( Runco, 1991 ). Divergent thinking is not specific enough to help us understand what, exactly, are the mental processes—or the cognitive abilities—that yield creative thoughts ( Dietrich, 2007 ).

Although current definitions of intelligence and creativity try to determine for each separate construct a unique set of cognitive abilities, analyses show that definitions vary in the degree to which each includes abilities that are generally considered to belong to the other construct ( Runco, 2003 ; Jaarsveld et al., 2012 ). Abilities considered belonging to the construct of intelligence such as hypothesis testing, inhibition of alternative responses, and creating mental images of new actions or plans are also considered to be involved in creative thinking ( Fuster, 1997 , as cited in Colom et al., 2009 , p. 215). The ability, for instance, to evaluate , which is considered to belong to the construct of intelligence and assesses the match between a proposed solution and task constraints, has long been considered to play a role in creative processes that goes beyond the mere generation of a series of ideas as in creativity tasks ( Wallas, 1926 , as cited in Gabora, 2002 , p. 1; Boden, 1990 ).

The Geneplore model ( Finke et al., 1992 ) explicitly models this idea; after stages in which objects are merely generated, follow phases in which an object’s utility is explored and estimated. The generation phase brings forth pre inventive objects, imaginary objects that are generated without any constraints in mind. In exploration, these objects are evaluated for their possible functionalities. In anticipating the functional characteristics of generated ideas, convergent thinking is needed to apprehend the situation, make evaluations ( Kozbelt, 2008 ), and consider the consequences of a chosen solution ( Goel and Pirolli, 1992 ). Convergent reasoning in creativity tasks invokes criteria of functionality and appropriateness ( Halpern, 2003 ; Kaufmann, 2003 ), goal directedness and adaptive behavior ( Sternberg, 1982 ), as well as the abilities of planning and attention. Convergent thinking stages may even require divergent thinking sub processes to identify restrictions on proposed new ideas and suggest requisite revision strategies ( Mumford et al., 2007 ). Hence, evaluation, which is considered to belong to the construct of intelligence, is also functional in creative processes.

In contrast, the ability of flexibility , which is considered to belong to the construct of creativity and denotes an openness of mind that ensures the generation of ideas from different domains, showed, as a factor component for latent divergent thinking, a relationship with intelligence ( Silvia, 2008 ). Flexibility was also found to play an important role in intelligent behavior where it enables us to do novel things smartly in new situations ( Colunga and Smith, 2008 ). These authors studied children’s generalizations of novel nouns and concluded that if we are to understand human intelligence, we must understand the processes that make inventiveness. They propose to include the construct of flexibility within that of intelligence. Therefore, definitions of the constructs we are to measure affect test construction and the resulting data. However, an overlap between definitions, as discussed, yields a test diversity that makes it impossible to interpret the different findings across studies with any confidence ( Arden et al., 2010 ). Also Kim (2005) concluded that because of differences in tests and administration methods, the observed correlation between intelligence and creativity was negligible. As the various definitions of the constructs of intelligence and creativity show problematic overlap, we propose to circumvent the discussion about which cognitive abilities are assessed by which construct, and to consider both constructs as being involved in one design process. This approach allows us to study the contribution to this process of the various defined abilities, without one construct excluding the other.

Reasoning Abilities

The CRT is a psychometrical tool constructed on the basis of an alternative construct of human cognitive functioning that considers creative reasoning as a thinking process understood as the cooperation between cognitive abilities related to intelligent and creative thinking.

In generating relationships for a matrix, reasoning and more specifically the ability of rule invention is applied. The ability of rule invention could be considered as an extension of the sequence of abilities of rule learning, rule inference, and rule application, implying that creativity is an extension of intelligence ( Shye and Goldzweig, 1999 ). According to this model, we could expect different results between a task assessing abilities of rule learning and rule inference, and a task assessing abilities of rule application. In two studies rule learning and rule inference was assessed with the RPM and rule application was assessed with the CRT. Results showed that from Grades 1 to 4, the frequencies of relationships applied did not correlate with those solved ( Jaarsveld et al., 2010 , 2012 ). Results showed that performance in the CRT allows an insight of cognitive abilities operating on relationships among components that differs from the insight based on performance within the same knowledge domain in a matrix solving task. Hence, reasoning abilities lead to different performances when applied in solving closed as to open problems.

We assume that reasoning abilities are more clearly reflected when one formulates a matrix from scratch; in the process of thinking and drawing one has, so to speak, to solve one’s own matrix. In doing so one explains to oneself the relationship(s) realized so far and what one would like to attain. Drawing is thinking aloud a problem and aids the designer’s thinking processes in providing some “talk-back” ( Cross and Clayburn Cross, 1996 ). Explanatory activity enhances learning through increased depth of processing ( Siegler, 2005 ). Analyzing explanations of examples given with physics problems showed that they clarify and specify the conditions and consequences of actions, and that they explicate tacit knowledge; thereby enhancing and completing an individual’s understanding of principles relevant to the task ( Chi and VanLehn, 1991 ). Constraint of the CRT is that the matrix, in principle, can be solved by another person. Therefore, in a kind of inner explanatory discussion, the designer makes observations of progress, and uses evaluations and decisions to answer this constraint. Because of this, open problems where certain constraints have to be met, constitute a powerful mechanism for promoting understanding and conceptual advancement ( Chi and VanLehn, 1991 ; Mestre, 2002 ; Siegler, 2005 ).

Convergent and divergent thinking processes have been studied with a variety of intelligence and creativity tests, respectively. Relationships between performances on these tests have been demonstrated and a large number of research questions have been addressed. However, the fact that intelligence and creativity tests vary in the definition of their construct, in their problem space, and in their knowledge domain, poses methodological problems regarding the validity of comparisons of test results. When we want to focus on one cognitive process, e.g., intelligent thinking, and on its different performances in well or ill-defined problem situations, we need pairs of tasks that are constructed along identical definitions of the construct to be assessed, that differ, however, in the description of their constraints but are identical regarding their knowledge domain.

One such possible pair, the Progressive Matrices Test and the CRT was suggested here. The CRT was developed on the basis of creative reasoning , a construct that assumes the intertwining of intelligent and creativity related abilities when looking for original and applicable solutions. Matched with the Matrices test, results indicated that, besides similarities, intelligent thinking also yielded considerable differences for both problem spaces. Hence, with knowledge domain controlled, and only differences in problem space remaining, comparison of data yielded new results on intelligence’s operations. Data gathered from intelligence and creativity tests, whether they are performance scores or physiological measurements on the basis of, e.g., EEG, and fMRI methods, are reflections of cognitive processes performing on a certain test that was constructed on the basis of a certain definition of the construct it was meant to measure. Data are also reflections of the processes evolving within a certain problem space and of cognitive abilities operating on a certain knowledge domain.

Data can unhide brain networks that are involved in the performance of certain tasks, e.g., traditional intelligence and creativity tests, but data will always be related to the characteristics of the task. The characteristics of the task, such as problem space and knowledge domain originated at the construction of the task, and the construction, on its turn, is affected by the definition of the construct the task is meant to measure.

Here we present the CRT as one possible solution for the described problems in cognition research. However, for research on relationships among test scores other pairs of tests are imaginable, e.g., pairs of tasks operating on the same domain where one task has a defined problem space and the other one an ill-defined space. It is conceivable that pairs of test could operate, besides on the domain of mathematics, on content of e.g., visuo-spatial, verbal, and musical domains. Pairs of test have been constructed by changing the instruction of a task; instructions instigated a more convergent or a more a divergent mode of response ( Razumnikova et al., 2009 ; Limb, 2010 ; Jauk et al., 2012 ; Beaty et al., 2013 ).

The CRT involves the creation of components and their relationships for a 3 × 3 matrix. Hence, matrices created in the CRT are original in the sense that they all bear individual markers and they are applicable in the sense, that they can, in principle, be solved by another person. We showed that the CRT instigates a real design process; creators’ cognitive abilities are wrapped up in a process that should produce a closed problem within an ill-defined problem space.

For research on the relationship among convergent and divergent thinking, we need pairs of test that differ in the problem spaces related to each test but are identical in the knowledge domain on which cognition operates. The test pair of RPM and CRT provides such a pair. For research on the intertwining of convergent and divergent thinking, we need tasks that measure more than tests assessing each construct alone. We need tasks that are developed on the definition of intertwining cognitive abilities; the CRT is one such test.

Hence, we hope to have sufficiently discussed and demonstrated the importance of the three test features, construct definition, problem space, and knowledge domain, for research questions in creative cognition research.

Author Contributions

All authors listed, have made substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpsyg.2017.00134/full#supplementary-material

  • Abraham A., Bubic A. (2015). Semantic memory as the root of imagination. Front. Psychol. 6 : 325 10.3389/fpsyg.2015.00325 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Abraham A., Windmann S. (2007). Creative cognition: the diverse operations and the prospect of applying a cognitive neuroscience perspective. Methods 42 38–48. 10.1016/j.ymeth.2006.12.007 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Akin O. (1986). Psychology of Architectural Design London: Pion. [ Google Scholar ]
  • Anderson J. R. (1983). The Architecture of Cognition Cambridge, MA: Harvard University Press. [ Google Scholar ]
  • Arden R., Chavez R. S., Grazioplene R., Jung R. E. (2010). Neuroimaging creativity: a psychometric view. Behav. Brain Res. 214 143–156. 10.1016/j.bbr.2010.05.015 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Arnheim R. (1962/1974). Picasso’s Guernica Berkeley: University of California Press. [ Google Scholar ]
  • Barsalou L. W. (1992). Cognitive Psychology: An Overview for Cognitive Scientists Hillsdale, NJ: LEA. [ Google Scholar ]
  • Beaty R. E., Benedek M., Silvia P. J., Schacter D. L. (2016). Creative cognition and brain network dynamics. Trends Cogn. Sci. 20 87–95. 10.1016/j.tics.2015.10.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Beaty R. E., Kaufman S. B., Benedek M., Jung R. E., Kenett Y. N., Jauk E., et al. (2015). Personality and complex brain networks: the role of openness to experience in default network efficiency. Hum. Brain Mapp. 37 773–777. 10.1002/hbm.23065 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Beaty R. E., Nusbaum E. C., Silvia P. J. (2014). Does insight problem solving predict real-world creativity? Psychol. Aesthet. Creat. Arts 8 287–292. 10.1037/a0035727 [ CrossRef ] [ Google Scholar ]
  • Beaty R. E., Silvia R. E. (2013). Metaphorically speaking: cognitive abilities and the production of figurative language. Mem. Cognit. 41 255–267. 10.3758/s13421-012-0258-5 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Beaty R. E., Smeekens B. A., Silvia P. J., Hodges D. A., Kane M. J. (2013). A first look at the role of domain-general cognitive and creative abilities in jazz improvisation. Psychomusicology 23 262–268. 10.1037/a0034968 [ CrossRef ] [ Google Scholar ]
  • Benedek M., Bergner S., Konen T., Fink A., Neubauer A. C. (2011). EEG alpha synchronization is related to top-down processing in convergent and divergent thinking. Neuropsychologia 49 3505–3511. 10.1016/j.neuropsychologia.2011.09.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Benedek M., Franz F., Heene M., Neubauer A. C. (2012). Differential effects of cognitive inhibition and intelligence on creativity. Pers. Individ. Dif. 53 480–485. 10.1016/j.paid.2012.04.014 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Benedek M., Jauk E., Sommer M., Arendasy M., Neubauer A. C. (2014). Intelligence, creativity, and cognitive control: the common and differential involvement of executive functions in intelligence and creativity. Intelligence 46 73–83. 10.1016/j.intell.2014.05.007 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Boden M. A. (1990). The Creative Mind: Myths and Mechanisms London: Abacus. [ Google Scholar ]
  • Boden M. A. (1996). Artificial Intelligence New York, NY: Academic. [ Google Scholar ]
  • Buschman T. J., Miller E. K. (2007). Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science 315 1860–1862. 10.1126/science.1138071 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Carroll J. B. (1982). “The measurement of Intelligence,” in Handbook of Human Intelligence , ed. Sternberg R. J. (New York, NY: Cambridge University Press; ), 29–120. [ Google Scholar ]
  • Cattell R. B. (1967). The theory of fluid and crystallized general intelligence checked at the 5-6 year-old level. Br. J. Educ. Psychol. 37 209–224. 10.1111/j.2044-8279.1967.tb01930.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Chi M. T. H. (1997). “ Creativity: Shifting across ontological categories flexibly ,” in Creative Thought: An Investigation of Conceptual Structures and Processes , eds Ward T., Smith S., Vaid J. (Washington, DC: American Psychological Association; ), 209–234. [ Google Scholar ]
  • Chi M. T. H., VanLehn K. A. (1991). The content of physics self-explanations. J. Learn. Sci. 1 69–105. 10.1207/s15327809jls0101_4 [ CrossRef ] [ Google Scholar ]
  • Christensen B. T. (2007). The relationship of analogical distance to analogical function and preinventive structure: the case of engineering design. Mem. Cogn. 35 29–38. 10.3758/BF03195939 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Colom R., Haier R. J., Head K., Álvarez-Linera J., Quiroga M. A., Shih P. C., et al. (2009). Gray matter correlates of fluid, crystallized, and spatial intelligence: testing the P-FIT model. Intelligence 37 124–135. 10.1016/j.intell.2008.07.007 [ CrossRef ] [ Google Scholar ]
  • Colunga E., Smith L. B. (2008). Flexibility and variability: essential to human cognition and the study of human cognition. New Ideas Psychol. 26 158–192. 10.1016/j.newideapsych.2007.07.012 [ CrossRef ] [ Google Scholar ]
  • Cooper N. R., Croft R. J., Dominey S. J. J., Burgess A. P., Gruzelier J. H. (2003). Paradox lost? Exploring the role of alpha oscillations during externally vs. internally directed attention and the implications for idling and inhibition hypotheses. Int. J. Psychophysiol. 47 65–74. 10.1016/S0167-8760(02)00107-1 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Cropley A. (2006). In praise of convergent thinking. Creat. Res. J. 18 391–404. 10.1207/s15326934crj1803_13 [ CrossRef ] [ Google Scholar ]
  • Cropley A., Cropley D. (2008). Resolving the paradoxes of creativity: an extended phase model. Camb. J. Educ. 38 355–373. 10.1080/03057640802286871 [ CrossRef ] [ Google Scholar ]
  • Cross N., Clayburn Cross A. (1996). Winning by design: the methods of Gordon Murray, racing car designer. Des. Stud. 17 91–107. 10.1016/0142-694X(95)00027-O [ CrossRef ] [ Google Scholar ]
  • Dennett D. (1978). Brainstorms: Philosophical Essays on Mind and Psychology Montgomery, VT: Bradford Books. [ Google Scholar ]
  • Dietrich A. (2007). Who’s afraid of a cognitive neuroscience of creativity? Methods 42 22–27. 10.1016/j.ymeth.2006.12.009 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Dorst K. (2004). The problem of design problems: Problem solving and design expertise. J. Design Res. 4 10.1504/JDR.2004.009841 [ CrossRef ] [ Google Scholar ]
  • Dorst K. (2011). The core of ‘design thinking’ and its application. Des. Stud. 32 521–532. 10.1016/j.destud.2011.07.006 [ CrossRef ] [ Google Scholar ]
  • Eysenck H. J. (2003). “Creativity, personality and the convergent-divergent continuum,” in Critical Creative Processes , ed. Runco M. A. (Cresskill, NJ: Hampton Press; ), 95–114. [ Google Scholar ]
  • Fink A., Benedek M. (2014). EEG alpha power and creative ideation. Neurosci. Biobehav. Rev. 44 111–123. 10.1016/j.neubiorev.2012.12.002 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Fink A., Benedek M., Grabner R. H., Staudt B., Neubauer A. C. (2007). Creativity meets neuroscience: experimental tasks for the neuroscientific study of creative thinking. Methods 42 68–76. 10.1016/j.ymeth.2006.12.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Fink A., Grabner R. H., Benedek M., Reishofer G., Hauswirth V., Fally M., et al. (2009). The creative brain: investigation of brain activity during creative problem solving by means of EEG and FMRI. Hum. Brain Mapp. 30 734–748. 10.1002/hbm.20538 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Finke R. A., Ward T. B., Smith S. M. (1992). Creative Cognition: Theory, Research, and Applications Cambridge, MA: MIT Press. [ Google Scholar ]
  • Fuster J. M. (1997). Network memory. Trends Neurosci. 20 451–459. 10.1016/S0166-2236(97)01128-4 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Gabora L. (2002). “Cognitive mechanisms underlying the creative process,” in Proceedings of the Fourth International Conference on Creativity and Cognition , eds Hewett T., Kavanagh T. (Loughborough: Loughborough University; ), 126–133. [ Google Scholar ]
  • Gabora L. (2010). Revenge of the ‘neurds’: Characterizing creative thought in terms of the structure and dynamics of human memory. Creat. Res. J. 22 1–13. 10.1080/10400410903579494 [ CrossRef ] [ Google Scholar ]
  • Gabora L., Kaufman S. B. (2010). “Evolutionary approaches to creativity,” in The Cambridge Handbook of Creativity , eds Kaufman J. S., Sternberg R. J. (Cambridge: Cambridge University Press; ), 279–300. [ Google Scholar ]
  • Gabora L., Ranjan A. (2013). “How insight emerges in a distributed, content-addressable memory,” in The Neuroscience of Creativity , eds Bristol A., Vartanian O., Kaufman J. (Cambridge: MIT Press; ), 19–43. [ Google Scholar ]
  • Gabora L., Saab A. (2011). “Creative inference and states of potentiality in analogy problem solving,” in Proceedings of the Annual Meeting of the Cognitive Science Society , Boston, MA, 3506–3511. [ Google Scholar ]
  • Gentner D. (1983). Structure mapping: a theoretical framework for analogy. Cogn. Sci. 7 155–170. 10.1207/s15516709cog0702_3 [ CrossRef ] [ Google Scholar ]
  • Getzels J. W. (1975). Problem finding and the inventiveness of solutions. J. Creat. Behav. 9 12–18. 10.1002/j.2162-6057.1975.tb00552.x [ CrossRef ] [ Google Scholar ]
  • Getzels J. W. (1987). “Creativity, intelligence, and problem finding: retrospect and prospect,” in Frontiers of Creativity Research: Beyond the Basics , ed. Isaksen S. G. (Buffalo, NY: Bearly Limited; ), 88–102. [ Google Scholar ]
  • Getzels J. W., Csikszentmihalyi M. (1976). The Creative Vision: A Longitudinal Study of Problem Finding in Art New York, NY: Wiley. [ Google Scholar ]
  • Ghiselin B. (ed.) (1952/1985). The Creative Process Los Angeles: University of California. [ Google Scholar ]
  • Goel V., Pirolli P. (1992). The structure of design problem spaces. Cogn. Sci. 16 395–429. 10.1207/s15516709cog1603_3 [ CrossRef ] [ Google Scholar ]
  • Goldschmidt G. (2013). “A micro view of design reasoning: two-way shifts between embodiment and rationale,” in Creativity and Rationale: Enhancing Human Experience by Design, Human-Computer Interaction Series , ed. Carroll J. M. (London: Springer Verlag; ). 10.1007/978-1-4471-2_3 [ CrossRef ] [ Google Scholar ]
  • Goldschmidt G. (2014). Linkography: Unfolding the Design Process Cambridge, MA: MIT Press. [ Google Scholar ]
  • Grube H. E., Davis S. N. (1988). “Inching our way up mount Olympus: The evolving-systems approach to creative thinking,” in The Nature of Creativity , ed. Sternberg R. J. (New York, NY: Cambridge University Press; ), 243–270. [ Google Scholar ]
  • Guilford J. P. (1950). Creativity. Am. Psychol. 5 444–454. 10.1037/h0063487 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Guilford J. P. (1956). The structure of intellect model. Psychol. Bull. 53 267–293. 10.1037/h0040755 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Guilford J. P. (1959). “Traits of creativity,” in Creativity and its Cultivation , ed. Anderson H. H. (New York: Harper; ), 142–161. [ Google Scholar ]
  • Guilford J. P. (1967). The Nature of Human Intelligence New York, NY: McGraw-Hill, Inc. [ Google Scholar ]
  • Guilford J. P., Christensen P. R., Merrifield P. R., Wilson R. C. (1978). Alternate Uses: Manual of Instructions and Interpretation Orange, CA: Sheridan Psychological Services. [ Google Scholar ]
  • Halpern D. F. (2003). “Thinking critically about creative thinking,” in Critical Creative Processes , ed. Runco M. A. (Cresskill, NJ: Hampton Press; ), 189–208. [ Google Scholar ]
  • Hayes J. R., Flowers L. S. (1986). Writing research and the writer. Am. Psychol. 41 1106–1113. 10.1037/0003-066X.41.10.1106 [ CrossRef ] [ Google Scholar ]
  • Jaarsveld S. (2007). Creative Cognition: New Perspectives on Creative Thinking Kaiserslautern: University of Kaiserslautern Press. [ Google Scholar ]
  • Jaarsveld S., Fink A., Rinner M., Schwab D., Benedek M., Lachmann T. (2015). Intelligence in creative processes; an EEG study. Intelligence 49 171–178. 10.1016/j.ijpsycho.2012.02.012 [ CrossRef ] [ Google Scholar ]
  • Jaarsveld S., Lachmann T., Hamel R., van Leeuwen C. (2010). Solving and creating Raven Progressive Matrices: reasoning in well and ill defined problem spaces. Creat. Res. J. 22 304–319. 10.1080/10400419.2010.503541 [ CrossRef ] [ Google Scholar ]
  • Jaarsveld S., Lachmann T., van Leeuwen C. (2012). Creative reasoning across developmental levels: convergence and divergence in problem creation. Intelligence 40 172–188. 10.1016/j.intell.2012.01.002 [ CrossRef ] [ Google Scholar ]
  • Jaarsveld S., Lachmann T., van Leeuwen C. (2013). “The impact of problem space on reasoning: Solving versus creating matrices,” in Proceedings of the 35th Annual Conference of the Cognitive Science Society , eds Knauff M., Pauen M., Sebanz N., Wachsmuth I. (Austin, TX: Cognitive Science Society; ), 2632–2638. [ Google Scholar ]
  • Jaarsveld S., van Leeuwen C. (2005). Sketches from a design process: creative cognition inferred from intermediate products. Cogn. Sci. 29 79–101. 10.1207/s15516709cog2901_4 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Jauk E., Benedek M., Dunst B., Neubauer A. C. (2013). The relationship between intelligence and creativity: new support for the threshold hypothesis by means of empirical breakpoint detection. Intelligence 41 212–221. 10.1016/j.intell.2013.03.003 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Jauk E., Benedek M., Neubauer A. C. (2012). Tackling creativity at its roots: evidence for different patterns of EEG alpha activity related to convergent and divergent modes of task processing. Int. J. Psychophysiol. 84 219–225. 10.1016/j.ijpsycho.2012.02.012 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Jaušovec N. (1999). “Brain biology and brain functioning,” in Encyclopedia of Creativity , eds Runco M. A., Pritzker S. R. (San Diego, CA: Academic Press; ), 203–212. [ Google Scholar ]
  • Jaušovec N. (2000). Differences in cognitive processes between gifted, intelligent, creative, and average individuals while solving complex problems: an EEG Study. Intelligence 28 213–237. 10.1016/S0160-2896(00)00037-4 [ CrossRef ] [ Google Scholar ]
  • Jung R. E. (2014). Evolution, creativity, intelligence, and madness: “here be dragons”. Front. Psychol 5 : 784 10.3389/fpsyg.2014.00784 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Jung R. E., Haier R. J. (2013). “Creativity and intelligence,” in Neuroscience of Creativity , eds Vartanian O., Bristol A. S., Kaufman J. C. (Cambridge, MA: MIT Press; ), 233–254. [ Google Scholar ]
  • Jung R. E., Segall J. M., Bockholt H. J., Flores R. A., Smith S. M., Chavez R. S., et al. (2010). Neuroanatomy of creativity. Hum. Brain Mapp. 31 398–409. 10.1002/hbm.20874 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Karmiloff-Smith A. (1992). Beyond Modularity: A Developmental Perspective on Cognitive Science Cambridge, MA: MIT Press. [ Google Scholar ]
  • Kaufman J. C. (2015). Why creativity isn’t in IQ tests, why it matters, and why it won’t change anytime soon probably. Intelligence 3 59–72. 10.3390/jintelligence303005 [ CrossRef ] [ Google Scholar ]
  • Kaufmann G. (2003). What to measure? A new look at the concept of creativity. Scand. J. Educ. Res. 47 235–251. 10.1080/00313830308604 [ CrossRef ] [ Google Scholar ]
  • Kim K. H. (2005). Can only intelligent people be creative? J. Second. Gift. Educ. 16 57–66. [ Google Scholar ]
  • Koestler A. (1964). The Act of Creation London: Penguin. [ Google Scholar ]
  • Kozbelt A. (2008). Hierarchical linear modeling of creative artists’ problem solving behaviors. J. Creat. Behav. 42 181–200. 10.1002/j.2162-6057.2008.tb01294.x [ CrossRef ] [ Google Scholar ]
  • Kulkarni D., Simon H. A. (1988). The processes of scientific discovery: the strategy of experimentation. Cogn. Sci. 12 139–175. 10.1016/j.coph.2009.08.004 [ CrossRef ] [ Google Scholar ]
  • Limb C. J. (2010). Your Brain on Improve Available at: http://www.ted.com/talks/charles_limb_your_brain_on_improv [ Google Scholar ]
  • Lubart T. I. (2001). Models of the creative process: past, present and future. Creat. Res. J. 13 295–308. 10.1207/S15326934CRJ1334_07 [ CrossRef ] [ Google Scholar ]
  • Lubart T. I. (2003). Psychologie de la Créativité. Cursus. Psychologie Paris: Armand Colin. [ Google Scholar ]
  • Martindale C. (1999). “Biological basis of creativity,” in Handbook of Creativity , ed. Sternberg R. J. (New York, NY: Cambridge University Press; ), 137–152. [ Google Scholar ]
  • Mednick S. A. (1962). The associative basis of the creative process. Psychol. Rev. 69 220–232. 10.1037/h0048850 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Mendelssohn G. A. (1976). Associational and attentional processes in creative performance. J. Pers. 44 341–369. 10.1111/j.1467-6494.1976.tb00127.x [ CrossRef ] [ Google Scholar ]
  • Mestre J. P. (2002). Probing adults’ conceptual understanding and transfer of learning via problem posing. Appl. Dev. Psychol. 23 9–50. 10.1016/S0193-3973(01)00101-0 [ CrossRef ] [ Google Scholar ]
  • Miller E. K., Cohen J. D. (2001). An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24 167–202. 10.1146/annurev.neuro.24.1.167 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Mumford M. D., Hunter S. T., Eubanks D. L., Bedell K. E., Murphy S. T. (2007). Developing leaders for creative efforts: a domain-based approach to leadership development. Hum. Res. Manag. Rev. 17 402–417. 10.1016/j.hrmr.2007.08.002 [ CrossRef ] [ Google Scholar ]
  • Newell A., Simon H. A. (1972). “The theory of human problem solving,” in Human Problem Solving , eds Newell A., Simon H. (Englewood Cliffs, NJ: Prentice Hall; ), 787–868. [ Google Scholar ]
  • Nusbaum E. C., Silvia P. J. (2011). Are intelligence and creativity really so different? Intelligence 39 36–40. 10.1016/j.intell.2010.11.002 [ CrossRef ] [ Google Scholar ]
  • Palmiero M., Nori R., Aloisi V., Ferrara M., Piccardi L. (2015). Domain-specificity of creativity: a study on the relationship between visual creativity and visual mental imagery. Front. Psychol. 6 : 1870 10.3389/fpsyg.2015.01870 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Piaget J., Montangero J., Billeter J. (1977). “La formation des correlats,” in Recherches sur L’abstraction Reflechissante I , ed. Piaget J. (Paris: Presse Universitaires de France; ), 115–129. [ Google Scholar ]
  • Plucker J. (1999). Is the proof in the pudding? Reanalyses of torrance’s (1958 to present) longitudinal study data. Creat. Res. J. 12 103–114. 10.1207/s15326934crj1202_3 [ CrossRef ] [ Google Scholar ]
  • Raven J. C. (1938/1998). Standard Progressive Matrices, Sets A, B, C, D & E Oxford: Oxford Psychologists Press. [ Google Scholar ]
  • Razumnikova O. M., Volf N. V., Tarasova I. V. (2009). Strategy and results: sex differences in electrographic correlates of verbal and figural creativity. Hum. Physiol. 35 285–294. 10.1134/S0362119709030049 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Runco M. A. (1991). The evaluative, valuative, and divergent thinking of children. J. Creat. Behav. 25 311–319. 10.1177/1073858414568317 [ CrossRef ] [ Google Scholar ]
  • Runco M. A. (2003). “Idea evaluation, divergent thinking, and creativity,” in Critical Creative Processes , ed. Runco M. A. (Cresskill, NJ: Hampton Press; ), 69–94. [ Google Scholar ]
  • Runco M. A. (2007). Creativity, Theories and Themes: Research, Development, and Practice New York, NY: Elsevier. [ Google Scholar ]
  • Runco M. A. (2008). Commentary: divergent thinking is not synonymous with creativity. Psychol. Aesthet. Creat. Arts 2 93–96. 10.1037/1931-3896.2.2.93 [ CrossRef ] [ Google Scholar ]
  • Sakar P., Chakrabarti A. (2013). Support for protocol analyses in design research. Des. Issues 29 70–81. 10.1162/DESI_a_00231 [ CrossRef ] [ Google Scholar ]
  • Saraç S., Önder A., Karakelle S. (2014). The relations among general intelligence, metacognition and text learning performance. Educ. Sci. 39 40–53. [ Google Scholar ]
  • Shye S., Goldzweig G. (1999). Creativity as an extension of intelligence: Faceted definition and structural hypotheses. Megamot 40 31–53. [ Google Scholar ]
  • Shye S., Yuhas I. (2004). Creativity in problem solving. Tech. Rep. 10.13140/2.1.1940.0643 [ CrossRef ] [ Google Scholar ]
  • Siegler R. S. (1998). Children’s Thinking , 3rd Edn Upper Saddle River, NJ: Prentice Hall, 28–50. [ Google Scholar ]
  • Siegler R. S. (2005). Children’s learning. Am. Psychol. 60 769–778. 10.1037/0003-066X.60.8.769 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Silvia P. J. (2008). Creativity and intelligence revisited: a reanalysis of Wallach and Kogan (1965). Creat. Res. J. 20 34–39. 10.1080/10400410701841807 [ CrossRef ] [ Google Scholar ]
  • Silvia P. J., Beaty R. E., Nussbaum E. C. (2013). Verbal fluency and creativity: general and specific contributions of broad retrieval ability (Gr) factors to divergent thinking. Intelligence 41 328–340. 10.1016/j.intell.2013.05.004 [ CrossRef ] [ Google Scholar ]
  • Simon H. A. (1973). The structure of ill structured problems. Artif. Intell. 4 1012–1021. 10.1016/0004-3702(73)90011-8 [ CrossRef ] [ Google Scholar ]
  • Simon H. A., Newell A. (1971). Human problem solving: state of theory in 1970. Am. Psychol. 26 145–159. 10.1037/h0030806 [ CrossRef ] [ Google Scholar ]
  • Sligh A. C., Conners F. A., Roskos-Ewoldsen B. (2005). Relation of creativity to fluid and crystallized intelligence. J. Creat. Behav. 39 123–136. 10.1002/j.2162-6057.2005.tb01254.x [ CrossRef ] [ Google Scholar ]
  • Spearman C. (1904). ‘General intelligence,’ objectively determined and measured. Am. J. Psychol. 15 201–293. 10.2307/1412107 [ CrossRef ] [ Google Scholar ]
  • Spearman C. (1927). The Abilities of Man London: Macmillan. [ Google Scholar ]
  • Sternberg R. J. (1982). “Conceptions of intelligence,” in Handbook of Human Intelligence , ed. Sternberg R. J. (New York, NY: Cambridge University Press; ), 3–28. [ Google Scholar ]
  • Sternberg R. J. (2005). “The WICS model of giftedness,” in Conceptions of Giftedness , 2nd Edn, eds Sternberg R. J., Davidson J. E. (New York, NY: Cambridge University Press; ), 237–243. [ Google Scholar ]
  • Sternberg R. J., Lubart T. I. (1999). “The concept of creativity: Prospects and paradigms,” in Handbook of Creativity , ed. Sternberg R. J. (New York, NY: Cambridge University Press; ), 3–15. [ Google Scholar ]
  • Sternberg R. J., Salter W. (1982). “The nature of intelligence and its measurements,” in Handbook of Human Intelligence , ed. Sternberg R. J. (New York, NY: Cambridge University Press; ), 3–24. [ Google Scholar ]
  • Thagard P., Verbeurgt K. (1998). Coherence as constraint satisfaction. Cogn. Sci. 22 l–24. 10.1207/s15516709cog2201_1 [ CrossRef ] [ Google Scholar ]
  • Torrance E. P. (1988). “The nature of creativity as manifest in its testing,” in The Nature of Creativity: Contemporary Psychological Perspectives , ed. Sternberg R. J. (New York, NY: Cambridge University Press; ), 43–75. [ Google Scholar ]
  • Urban K. K., Jellen H. G. (1995). Test of Creative Thinking – Drawing Production Frankfurt: Swets Test Services. [ Google Scholar ]
  • van Leeuwen C., Verstijnen I. M., Hekkert P. (1999). “Common unconscious dynamics underlie uncommon conscious effect: a case study in the iterative nature of perception and creation,” in Modeling Consciousness Across the Disciplines , ed. Jordan J. S. (Lanham, MD: University Press of America; ), 179–218. [ Google Scholar ]
  • Vernon P. E. (ed.) (1970). Creativity London: Penguin. [ Google Scholar ]
  • Verstijnen I. M., Heylighen A., Wagemans J., Neuckermans H. (2001). “Sketching, analogies, and creativity,” in Visual and Spatial Reasoning in Design, II. Key Centre of Design Computing and Cognition , eds Gero J. S., Tversky B., Purcell T. (Sydney, NSW: University of Sydney; ). [ Google Scholar ]
  • Wallas G. (1926). The Art of Thought New York, NY: Harcourt, Brace & World. [ Google Scholar ]
  • Ward T. B. (2007). Creative cognition as a window on creativity. Methods 42 28–37. 10.1016/j.ymeth.2006.12.002 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Webb Young J. (1939/2003). A Technique for Producing Ideas New York, NY: McGraw-Hill. [ Google Scholar ]
  • Welter M. M., Jaarsveld S., Lachmann T. Problem space matters: development of creativity and intelligence in primary school children. Creat. Res. J. (in press) [ Google Scholar ]
  • Welter M. M., Jaarsveld S., van Leeuwen C., Lachmann T. (2016). Intelligence and creativity; over the threshold together? Creat. Res. J. 28 212–218. 10.1080/10400419.2016.1162564 [ CrossRef ] [ Google Scholar ]
  • Wertheimer M. (1945/1968). Productive Thinking (Enlarged Edition) London: Tavistock. [ Google Scholar ]
  • Yamamoto Y., Nakakoji K., Takada S. (2000). Hand on representations in two dimensional spaces for early stages of design. Knowl. Based Syst. 13 357–384. 10.1016/S0950-7051(00)00078-2 [ CrossRef ] [ Google Scholar ]

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Teaching Creativity and Inventive Problem Solving in Science

  • Robert L. DeHaan

Division of Educational Studies, Emory University, Atlanta, GA 30322

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Engaging learners in the excitement of science, helping them discover the value of evidence-based reasoning and higher-order cognitive skills, and teaching them to become creative problem solvers have long been goals of science education reformers. But the means to achieve these goals, especially methods to promote creative thinking in scientific problem solving, have not become widely known or used. In this essay, I review the evidence that creativity is not a single hard-to-measure property. The creative process can be explained by reference to increasingly well-understood cognitive skills such as cognitive flexibility and inhibitory control that are widely distributed in the population. I explore the relationship between creativity and the higher-order cognitive skills, review assessment methods, and describe several instructional strategies for enhancing creative problem solving in the college classroom. Evidence suggests that instruction to support the development of creativity requires inquiry-based teaching that includes explicit strategies to promote cognitive flexibility. Students need to be repeatedly reminded and shown how to be creative, to integrate material across subject areas, to question their own assumptions, and to imagine other viewpoints and possibilities. Further research is required to determine whether college students' learning will be enhanced by these measures.


Dr. Dunne paces in front of his section of first-year college students, today not as their Bio 110 teacher but in the role of facilitator in their monthly “invention session.” For this meeting, the topic is stem cell therapy in heart disease. Members of each team of four students have primed themselves on the topic by reading selected articles from accessible sources such as Science, Nature, and Scientific American, and searching the World Wide Web, triangulating for up-to-date, accurate, background information. Each team knows that their first goal is to define a set of problems or limitations to overcome within the topic and to begin to think of possible solutions. Dr. Dunne starts the conversation by reminding the group of the few ground rules: one speaker at a time, listen carefully and have respect for others' ideas, question your own and others' assumptions, focus on alternative paths or solutions, maintain an atmosphere of collaboration and mutual support. He then sparks the discussion by asking one of the teams to describe a problem in need of solution.

Science in the United States is widely credited as a major source of discovery and economic development. According to the 2005 TAP Report produced by a prominent group of corporate leaders, “To maintain our country's competitiveness in the twenty-first century, we must cultivate the skilled scientists and engineers needed to create tomorrow's innovations.” ( www.tap2015.org/about/TAP_report2.pdf ). A panel of scientists, engineers, educators, and policy makers convened by the National Research Council (NRC) concurred with this view, reporting that the vitality of the nation “is derived in large part from the productivity of well-trained people and the steady stream of scientific and technical innovations they produce” ( NRC, 2007 ).

For many decades, science education reformers have promoted the idea that learners should be engaged in the excitement of science; they should be helped to discover the value of evidence-based reasoning and higher-order cognitive skills, and be taught to become innovative problem solvers (for reviews, see DeHaan, 2005 ; Hake, 2005 ; Nelson, 2008 ; Perkins and Wieman, 2008 ). But the means to achieve these goals, especially methods to promote creative thinking in scientific problem solving, are not widely known or used. An invention session such as that led by the fictional Dr. Dunne, described above, may seem fanciful as a means of teaching students to think about science as something more than a body of facts and terms to memorize. In recent years, however, models for promoting creative problem solving were developed for classroom use, as detailed by Treffinger and Isaksen (2005) , and such techniques are often used in the real world of high technology. To promote imaginative thinking, the advertising executive Alex F. Osborn invented brainstorming ( Osborn, 1948 , 1979 ), a technique that has since been successful in stimulating inventiveness among engineers and scientists. Could such strategies be transferred to a class for college students? Could they serve as a supplement to a high-quality, scientific teaching curriculum that helps students learn the facts and conceptual frameworks of science and make progress along the novice–expert continuum? Could brainstorming or other instructional strategies that are specifically designed to promote creativity teach students to be more adaptive in their growing expertise, more innovative in their problem-solving abilities? To begin to answer those questions, we first need to understand what is meant by “creativity.”

What Is Creativity? Big-C versus Mini-C Creativity

How to define creativity is an age-old question. Justice Potter Stewart's famous dictum regarding obscenity “I know it when I see it” has also long been an accepted test of creativity. But this is not an adequate criterion for developing an instructional approach. A scientist colleague of mine recently noted that “Many of us [in the scientific community] rarely give the creative process a second thought, imagining one either ‘has it’ or doesn't.” We often think of inventiveness or creativity in scientific fields as the kind of gift associated with a Michelangelo or Einstein. This is what Kaufman and Beghetto (2008) call big-C creativity, borrowing the term that earlier workers applied to the talents of experts in various fields who were identified as particularly creative by their expert colleagues ( MacKinnon, 1978 ). In this sense, creativity is seen as the ability of individuals to generate new ideas that contribute substantially to an intellectual domain. Howard Gardner defined such a creative person as one who “regularly solves problems, fashions products, or defines new questions in a domain in a way that is initially considered novel but that ultimately comes to be accepted in a particular cultural setting” ( Gardner, 1993 , p. 35).

But there is another level of inventiveness termed by various authors as “little-c” ( Craft, 2000 ) or “mini-c” ( Kaufman and Beghetto, 2008 ) creativity that is widespread among all populations. This would be consistent with the workplace definition of creativity offered by Amabile and her coworkers: “coming up with fresh ideas for changing products, services and processes so as to better achieve the organization's goals” ( Amabile et al. , 2005 ). Mini-c creativity is based on what Craft calls “possibility thinking” ( Craft, 2000 , pp. 3–4), as experienced when a worker suddenly has the insight to visualize a new, improved way to accomplish a task; it is represented by the “aha” moment when a student first sees two previously disparate concepts or facts in a new relationship, an example of what Arthur Koestler identified as bisociation: “perceiving a situation or event in two habitually incompatible associative contexts” ( Koestler, 1964 , p. 95).

In this essay, I maintain that mini-c creativity is not a mysterious, innate endowment of rare individuals. Instead, I argue that creative thinking is a multicomponent process, mediated through social interactions, that can be explained by reference to increasingly well-understood mental abilities such as cognitive flexibility and cognitive control that are widely distributed in the population. Moreover, I explore some of the recent research evidence (though with no effort at a comprehensive literature review) showing that these mental abilities are teachable; like other higher-order cognitive skills (HOCS), they can be enhanced by explicit instruction.

Creativity Is a Multicomponent Process

Efforts to define creativity in psychological terms go back to J. P. Guilford ( Guilford, 1950 ) and E. P. Torrance ( Torrance, 1974 ), both of whom recognized that underlying the construct were other cognitive variables such as ideational fluency, originality of ideas, and sensitivity to missing elements. Many authors since then have extended the argument that a creative act is not a singular event but a process, an interplay among several interactive cognitive and affective elements. In this view, the creative act has two phases, a generative and an exploratory or evaluative phase ( Finke et al. , 1996 ). During the generative process, the creative mind pictures a set of novel mental models as potential solutions to a problem. In the exploratory phase, we evaluate the multiple options and select the best one. Early scholars of creativity, such as J. P. Guilford, characterized the two phases as divergent thinking and convergent thinking ( Guilford, 1950 ). Guilford defined divergent thinking as the ability to produce a broad range of associations to a given stimulus or to arrive at many solutions to a problem (for overviews of the field from different perspectives, see Amabile, 1996 ; Banaji et al. , 2006 ; Sawyer, 2006 ). In neurocognitive terms, divergent thinking is referred to as associative richness ( Gabora, 2002 ; Simonton, 2004 ), which is often measured experimentally by comparing the number of words that an individual generates from memory in response to stimulus words on a word association test. In contrast, convergent thinking refers to the capacity to quickly focus on the one best solution to a problem.

The idea that there are two stages to the creative process is consistent with results from cognition research indicating that there are two distinct modes of thought, associative and analytical ( Neisser, 1963 ; Sloman, 1996 ). In the associative mode, thinking is defocused, suggestive, and intuitive, revealing remote or subtle connections between items that may be correlated, or may not, and are usually not causally related ( Burton, 2008 ). In the analytical mode, thought is focused and evaluative, more conducive to analyzing relationships of cause and effect (for a review of other cognitive aspects of creativity, see Runco, 2004 ). Science educators associate the analytical mode with the upper levels (analysis, synthesis, and evaluation) of Bloom's taxonomy (e.g., Crowe et al. , 2008 ), or with “critical thinking,” the process that underlies the “purposeful, self-regulatory judgment that drives problem-solving and decision-making” ( Quitadamo et al. , 2008 , p. 328). These modes of thinking are under cognitive control through the executive functions of the brain. The core executive functions, which are thought to underlie all planning, problem solving, and reasoning, are defined ( Blair and Razza, 2007 ) as working memory control (mentally holding and retrieving information), cognitive flexibility (considering multiple ideas and seeing different perspectives), and inhibitory control (resisting several thoughts or actions to focus on one). Readers wishing to delve further into the neuroscience of the creative process can refer to the cerebrocerebellar theory of creativity ( Vandervert et al. , 2007 ) in which these mental activities are described neurophysiologically as arising through interactions among different parts of the brain.

The main point from all of these works is that creativity is not some single hard-to-measure property or act. There is ample evidence that the creative process requires both divergent and convergent thinking and that it can be explained by reference to increasingly well-understood underlying mental abilities ( Haring-Smith, 2006 ; Kim, 2006 ; Sawyer, 2006 ; Kaufman and Sternberg, 2007 ) and cognitive processes ( Simonton, 2004 ; Diamond et al. , 2007 ; Vandervert et al. , 2007 ).

Creativity Is Widely Distributed and Occurs in a Social Context

Although it is understandable to speak of an aha moment as a creative act by the person who experiences it, authorities in the field have long recognized (e.g., Simonton, 1975 ) that creative thinking is not so much an individual trait but rather a social phenomenon involving interactions among people within their specific group or cultural settings. “Creativity isn't just a property of individuals, it is also a property of social groups” ( Sawyer, 2006 , p. 305). Indeed, Osborn introduced his brainstorming method because he was convinced that group creativity is always superior to individual creativity. He drew evidence for this conclusion from activities that demand collaborative output, for example, the improvisations of a jazz ensemble. Although each musician is individually creative during a performance, the novelty and inventiveness of each performer's playing is clearly influenced, and often enhanced, by “social and interactional processes” among the musicians ( Sawyer, 2006 , p. 120). Recently, Brophy (2006) offered evidence that for problem solving, the situation may be more nuanced. He confirmed that groups of interacting individuals were better at solving complex, multipart problems than single individuals. However, when dealing with certain kinds of single-issue problems, individual problem solvers produced a greater number of solutions than interacting groups, and those solutions were judged to be more original and useful.

Consistent with the findings of Brophy (2006) , many scholars acknowledge that creative discoveries in the real world such as solving the problems of cutting-edge science—which are usually complex and multipart—are influenced or even stimulated by social interaction among experts. The common image of the lone scientist in the laboratory experiencing a flash of creative inspiration is probably a myth from earlier days. As a case in point, the science historian Mara Beller analyzed the social processes that underlay some of the major discoveries of early twentieth-century quantum physics. Close examination of successive drafts of publications by members of the Copenhagen group revealed a remarkable degree of influence and collaboration among 10 or more colleagues, although many of these papers were published under the name of a single author ( Beller, 1999 ). Sociologists Bruno Latour and Steve Woolgar's study ( Latour and Woolgar, 1986 ) of a neuroendocrinology laboratory at the Salk Institute for Biological Studies make the related point that social interactions among the participating scientists determined to a remarkable degree what discoveries were made and how they were interpreted. In the laboratory, researchers studied the chemical structure of substances released by the brain. By analysis of the Salk scientists' verbalizations of concepts, theories, formulas, and results of their investigations, Latour and Woolgar showed that the structures and interpretations that were agreed upon, that is, the discoveries announced by the laboratory, were mediated by social interactions and power relationships among members of the laboratory group. By studying the discovery process in other fields of the natural sciences, sociologists and anthropologists have provided more cases that further illustrate how social and cultural dimensions affect scientific insights (for a thoughtful review, see Knorr Cetina, 1995 ).

In sum, when an individual experiences an aha moment that feels like a singular creative act, it may rather have resulted from a multicomponent process, under the influence of group interactions and social context. The process that led up to what may be sensed as a sudden insight will probably have included at least three diverse, but testable elements: 1) divergent thinking, including ideational fluency or cognitive flexibility, which is the cognitive executive function that underlies the ability to visualize and accept many ideas related to a problem; 2) convergent thinking or the application of inhibitory control to focus and mentally evaluate ideas; and 3) analogical thinking, the ability to understand a novel idea in terms of one that is already familiar.


What do we know about how to teach creativity.

The possibility of teaching for creative problem solving gained credence in the 1960s with the studies of Jerome Bruner, who argued that children should be encouraged to “treat a task as a problem for which one invents an answer, rather than finding one out there in a book or on the blackboard” ( Bruner, 1965 , pp. 1013–1014). Since that time, educators and psychologists have devised programs of instruction designed to promote creativity and inventiveness in virtually every student population: pre–K, elementary, high school, and college, as well as in disadvantaged students, athletes, and students in a variety of specific disciplines (for review, see Scott et al. , 2004 ). Smith (1998) identified 172 instructional approaches that have been applied at one time or another to develop divergent thinking skills.

Some of the most convincing evidence that elements of creativity can be enhanced by instruction comes from work with young children. Bodrova and Leong (2001) developed the Tools of the Mind (Tools) curriculum to improve all of the three core mental executive functions involved in creative problem solving: cognitive flexibility, working memory, and inhibitory control. In a year-long randomized study of 5-yr-olds from low-income families in 21 preschool classrooms, half of the teachers applied the districts' balanced literacy curriculum (literacy), whereas the experimenters trained the other half to teach the same academic content by using the Tools curriculum ( Diamond et al. , 2007 ). At the end of the year, when the children were tested with a battery of neurocognitive tests including a test for cognitive flexibility ( Durston et al. , 2003 ; Davidson et al. , 2006 ), those exposed to the Tools curriculum outperformed the literacy children by as much as 25% ( Diamond et al. , 2007 ). Although the Tools curriculum and literacy program were similar in academic content and in many other ways, they differed primarily in that Tools teachers spent 80% of their time explicitly reminding the children to think of alternative ways to solve a problem and building their executive function skills.

Teaching older students to be innovative also demands instruction that explicitly promotes creativity but is rigorously content-rich as well. A large body of research on the differences between novice and expert cognition indicates that creative thinking requires at least a minimal level of expertise and fluency within a knowledge domain ( Bransford et al. , 2000 ; Crawford and Brophy, 2006 ). What distinguishes experts from novices, in addition to their deeper knowledge of the subject, is their recognition of patterns in information, their ability to see relationships among disparate facts and concepts, and their capacity for organizing content into conceptual frameworks or schemata ( Bransford et al. , 2000 ; Sawyer, 2005 ).

Such expertise is often lacking in the traditional classroom. For students attempting to grapple with new subject matter, many kinds of problems that are presented in high school or college courses or that arise in the real world can be solved merely by applying newly learned algorithms or procedural knowledge. With practice, problem solving of this kind can become routine and is often considered to represent mastery of a subject, producing what Sternberg refers to as “pseudoexperts” ( Sternberg, 2003 ). But beyond such routine use of content knowledge the instructor's goal must be to produce students who have gained the HOCS needed to apply, analyze, synthesize, and evaluate knowledge ( Crowe et al. , 2008 ). The aim is to produce students who know enough about a field to grasp meaningful patterns of information, who can readily retrieve relevant knowledge from memory, and who can apply such knowledge effectively to novel problems. This condition is referred to as adaptive expertise ( Hatano and Ouro, 2003 ; Schwartz et al. , 2005 ). Instead of applying already mastered procedures, adaptive experts are able to draw on their knowledge to invent or adapt strategies for solving unique or novel problems within a knowledge domain. They are also able, ideally, to transfer conceptual frameworks and schemata from one domain to another (e.g., Schwartz et al. , 2005 ). Such flexible, innovative application of knowledge is what results in inventive or creative solutions to problems ( Crawford and Brophy, 2006 ; Crawford, 2007 ).

Promoting Creative Problem Solving in the College Classroom

In most college courses, instructors teach science primarily through lectures and textbooks that are dominated by facts and algorithmic processing rather than by concepts, principles, and evidence-based ways of thinking. This is despite ample evidence that many students gain little new knowledge from traditional lectures ( Hrepic et al. , 2007 ). Moreover, it is well documented that these methods engender passive learning rather than active engagement, boredom instead of intellectual excitement, and linear thinking rather than cognitive flexibility (e.g., Halpern and Hakel, 2003 ; Nelson, 2008 ; Perkins and Wieman, 2008 ). Cognitive flexibility, as noted, is one of the three core mental executive functions involved in creative problem solving ( Ausubel, 1963 , 2000 ). The capacity to apply ideas creatively in new contexts, referred to as the ability to “transfer” knowledge (see Mestre, 2005 ), requires that learners have opportunities to actively develop their own representations of information to convert it to a usable form. Especially when a knowledge domain is complex and fraught with ill-structured information, as in a typical introductory college biology course, instruction that emphasizes active-learning strategies is demonstrably more effective than traditional linear teaching in reducing failure rates and in promoting learning and transfer (e.g., Freeman et al. , 2007 ). Furthermore, there is already some evidence that inclusion of creativity training as part of a college curriculum can have positive effects. Hunsaker (2005) has reviewed a number of such studies. He cites work by McGregor (2001) , for example, showing that various creativity training programs including brainstorming and creative problem solving increase student scores on tests of creative-thinking abilities.

Model creativity—students develop creativity when instructors model creative thinking and inventiveness.

Repeatedly encourage idea generation—students need to be reminded to generate their own ideas and solutions in an environment free of criticism.

Cross-fertilize ideas—where possible, avoid teaching in subject-area boxes: a math box, a social studies box, etc; students' creative ideas and insights often result from learning to integrate material across subject areas.

Build self-efficacy—all students have the capacity to create and to experience the joy of having new ideas, but they must be helped to believe in their own capacity to be creative.

Constantly question assumptions—make questioning a part of the daily classroom exchange; it is more important for students to learn what questions to ask and how to ask them than to learn the answers.

Imagine other viewpoints—students broaden their perspectives by learning to reflect upon ideas and concepts from different points of view.

How Is Creativity Related to Critical Thinking and the Higher-Order Cognitive Skills?

It is not uncommon to associate creativity and ingenuity with scientific reasoning ( Sawyer, 2005 ; 2006 ). When instructors apply scientific teaching strategies ( Handelsman et al. , 2004 ; DeHaan, 2005 ; Wood, 2009 ) by using instructional methods based on learning research, according to Ebert-May and Hodder ( 2008 ), “we see students actively engaged in the thinking, creativity, rigor, and experimentation we associate with the practice of science—in much the same way we see students learn in the field and in laboratories” (p. 2). Perkins and Wieman (2008) note that “To be successful innovators in science and engineering, students must develop a deep conceptual understanding of the underlying science ideas, an ability to apply these ideas and concepts broadly in different contexts, and a vision to see their relevance and usefulness in real-world applications … An innovator is able to perceive and realize potential connections and opportunities better than others” (pp. 181–182). The results of Scott et al. (2004) suggest that nontraditional courses in science that are based on constructivist principles and that use strategies of scientific teaching to promote the HOCS and enhance content mastery and dexterity in scientific thinking ( Handelsman et al. , 2007 ; Nelson, 2008 ) also should be effective in promoting creativity and cognitive flexibility if students are explicitly guided to learn these skills.

Creativity is an essential element of problem solving ( Mumford et al. , 1991 ; Runco, 2004 ) and of critical thinking ( Abrami et al. , 2008 ). As such, it is common to think of applications of creativity such as inventiveness and ingenuity among the HOCS as defined in Bloom's taxonomy ( Crowe et al. , 2008 ). Thus, it should come as no surprise that creativity, like other elements of the HOCS, can be taught most effectively through inquiry-based instruction, informed by constructivist theory ( Ausubel, 1963 , 2000 ; Duch et al. , 2001 ; Nelson, 2008 ). In a survey of 103 instructors who taught college courses that included creativity instruction, Bull et al. (1995) asked respondents to rate the importance of various course characteristics for enhancing student creativity. Items ranking high on the list were: providing a social climate in which students feels safe, an open classroom environment that promotes tolerance for ambiguity and independence, the use of humor, metaphorical thinking, and problem defining. Many of the responses emphasized the same strategies as those advanced to promote creative problem solving (e.g., Mumford et al. , 1991 ; McFadzean, 2002 ; Treffinger and Isaksen, 2005 ) and critical thinking ( Abrami et al. , 2008 ).

In a careful meta-analysis, Scott et al. (2004) examined 70 instructional interventions designed to enhance and measure creative performance. The results were striking. Courses that stressed techniques such as critical thinking, convergent thinking, and constraint identification produced the largest positive effect sizes. More open techniques that provided less guidance in strategic approaches had less impact on the instructional outcomes. A striking finding was the effectiveness of being explicit; approaches that clearly informed students about the nature of creativity and offered clear strategies for creative thinking were most effective. Approaches such as social modeling, cooperative learning, and case-based (project-based) techniques that required the application of newly acquired knowledge were found to be positively correlated to high effect sizes. The most clear-cut result to emerge from the Scott et al. (2004) study was simply to confirm that creativity instruction can be highly successful in enhancing divergent thinking, problem solving, and imaginative performance. Most importantly, of the various cognitive processes examined, those linked to the generation of new ideas such as problem finding, conceptual combination, and idea generation showed the greatest improvement. The success of creativity instruction, the authors concluded, can be attributed to “developing and providing guidance concerning the application of requisite cognitive capacities … [and] a set of heuristics or strategies for working with already available knowledge” (p. 382).

Many of the scientific teaching practices that have been shown by research to foster content mastery and HOCS, and that are coming more widely into use, also would be consistent with promoting creativity. Wood (2009) has recently reviewed examples of such practices and how to apply them. These include relatively small modifications of the traditional lecture to engender more active learning, such as the use of concept tests and peer instruction ( Mazur, 1996 ), Just-in-Time-Teaching techniques ( Novak et al. , 1999 ), and student response systems known as “clickers” ( Knight and Wood, 2005 ; Crossgrove and Curran, 2008 ), all designed to allow the instructor to frequently and effortlessly elicit and respond to student thinking. Other strategies can transform the lecture hall into a workshop or studio classroom ( Gaffney et al. , 2008 ) where the teaching curriculum may emphasize problem-based (also known as project-based or case-based) learning strategies ( Duch et al. , 2001 ; Ebert-May and Hodder, 2008 ) or “community-based inquiry” in which students engage in research that enhances their critical-thinking skills ( Quitadamo et al. , 2008 ).

Another important approach that could readily subserve explicit creativity instruction is the use of computer-based interactive simulations, or “sims” ( Perkins and Wieman, 2008 ) to facilitate inquiry learning and effective, easy self-assessment. An example in the biological sciences would be Neurons in Action ( http://neuronsinaction.com/home/main ). In such educational environments, students gain conceptual understanding of scientific ideas through interactive engagement with materials (real or virtual), with each other, and with instructors. Following the tenets of scientific teaching, students are encouraged to pose and answer their own questions, to make sense of the materials, and to construct their own understanding. The question I pose here is whether an additional focus—guiding students to meet these challenges in a context that explicitly promotes creativity—would enhance learning and advance students' progress toward adaptive expertise?

Assessment of Creativity

To teach creativity, there must be measurable indicators to judge how much students have gained from instruction. Educational programs intended to teach creativity became popular after the Torrance Tests of Creative Thinking (TTCT) was introduced in the 1960s ( Torrance, 1974 ). But it soon became apparent that there were major problems in devising tests for creativity, both because of the difficulty of defining the construct and because of the number and complexity of elements that underlie it. Tests of intelligence and other personality characteristics on creative individuals revealed a host of related traits such as verbal fluency, metaphorical thinking, flexible decision making, tolerance of ambiguity, willingness to take risks, autonomy, divergent thinking, self-confidence, problem finding, ideational fluency, and belief in oneself as being “creative” ( Barron and Harrington, 1981 ; Tardif and Sternberg, 1988 ; Runco and Nemiro, 1994 ; Snyder et al. , 2004 ). Many of these traits have been the focus of extensive research of recent decades, but, as noted above, creativity is not defined by any one trait; there is now reason to believe that it is the interplay among the cognitive and affective processes that underlie inventiveness and the ability to find novel solutions to a problem.

Although the early creativity researchers recognized that assessing divergent thinking as a measure of creativity required tests for other underlying capacities ( Guilford, 1950 ; Torrance, 1974 ), these workers and their colleagues nonetheless believed that a high score for divergent thinking alone would correlate with real creative output. Unfortunately, no such correlation was shown ( Barron and Harrington, 1981 ). Results produced by many of the instruments initially designed to measure various aspects of creative thinking proved to be highly dependent on the test itself. A review of several hundred early studies showed that an individual's creativity score could be affected by simple test variables, for example, how the verbal pretest instructions were worded ( Barron and Harrington, 1981 , pp. 442–443). Most scholars now agree that divergent thinking, as originally defined, was not an adequate measure of creativity. The process of creative thinking requires a complex combination of elements that include cognitive flexibility, memory control, inhibitory control, and analogical thinking, enabling the mind to free-range and analogize, as well as to focus and test.

More recently, numerous psychometric measures have been developed and empirically tested (see Plucker and Renzulli, 1999 ) that allow more reliable and valid assessment of specific aspects of creativity. For example, the creativity quotient devised by Snyder et al. (2004) tests the ability of individuals to link different ideas and different categories of ideas into a novel synthesis. The Wallach–Kogan creativity test ( Wallach and Kogan, 1965 ) explores the uniqueness of ideas associated with a stimulus. For a more complete list and discussion, see the Creativity Tests website ( www.indiana.edu/∼bobweb/Handout/cretv_6.html ).

The most widely used measure of creativity is the TTCT, which has been modified four times since its original version in 1966 to take into account subsequent research. The TTCT-Verbal and the TTCT-Figural are two versions ( Torrance, 1998 ; see http://ststesting.com/2005giftttct.html ). The TTCT-Verbal consists of five tasks; the “stimulus” for each task is a picture to which the test-taker responds briefly in writing. A sample task that can be viewed from the TTCT Demonstrator website asks, “Suppose that people could transport themselves from place to place with just a wink of the eye or a twitch of the nose. What might be some things that would happen as a result? You have 3 min.” ( www.indiana.edu/∼bobweb/Handout/d3.ttct.htm ).

In the TTCT-Figural, participants are asked to construct a picture from a stimulus in the form of a partial line drawing given on the test sheet (see example below; Figure 1 ). Specific instructions are to “Add lines to the incomplete figures below to make pictures out of them. Try to tell complete stories with your pictures. Give your pictures titles. You have 3 min.” In the introductory materials, test-takers are urged to “… think of a picture or object that no one else will think of. Try to make it tell as complete and as interesting a story as you can …” ( Torrance et al. , 2008 , p. 2).

Figure 1.

Figure 1. Sample figural test item from the TTCT Demonstrator website ( www.indiana.edu/∼bobweb/Handout/d3.ttct.htm ).

How would an instructor in a biology course judge the creativity of students' responses to such an item? To assist in this task, the TTCT has scoring and norming guides ( Torrance, 1998 ; Torrance et al. , 2008 ) with numerous samples and responses representing different levels of creativity. The guides show sample evaluations based upon specific indicators such as fluency, originality, elaboration (or complexity), unusual visualization, extending or breaking boundaries, humor, and imagery. These examples are easy to use and provide a high degree of validity and generalizability to the tests. The TTCT has been more intensively researched and analyzed than any other creativity instrument, and the norming samples have longitudinal validations and high predictive validity over a wide age range. In addition to global creativity scores, the TTCT is designed to provide outcome measures in various domains and thematic areas to allow for more insightful analysis ( Kaufman and Baer, 2006 ). Kim (2006) has examined the characteristics of the TTCT, including norms, reliability, and validity, and concludes that the test is an accurate measure of creativity. When properly used, it has been shown to be fair in terms of gender, race, community status, and language background. According to Kim (2006) and other authorities in the field ( McIntyre et al. , 2003 ; Scott et al. , 2004 ), Torrance's research and the development of the TTCT have provided groundwork for the idea that creative levels can be measured and then increased through instruction and practice.


How could creativity instruction be integrated into scientific teaching.

Guidelines for designing specific course units that emphasize HOCS by using strategies of scientific teaching are now available from the current literature. As an example, Karen Cloud-Hansen and colleagues ( Cloud-Hansen et al. , 2008 ) describe a course titled, “Ciprofloxacin Resistance in Neisseria gonorrhoeae .” They developed this undergraduate seminar to introduce college freshmen to important concepts in biology within a real-world context and to increase their content knowledge and critical-thinking skills. The centerpiece of the unit is a case study in which teams of students are challenged to take the role of a director of a local public health clinic. One of the county commissioners overseeing the clinic is an epidemiologist who wants to know “how you plan to address the emergence of ciprofloxacin resistance in Neisseria gonorrhoeae ” (p. 304). State budget cuts limit availability of expensive antibiotics and some laboratory tests to patients. Student teams are challenged to 1) develop a plan to address the medical, economic, and political questions such a clinic director would face in dealing with ciprofloxacin-resistant N. gonorrhoeae ; 2) provide scientific data to support their conclusions; and 3) describe their clinic plan in a one- to two-page referenced written report.

Throughout the 3-wk unit, in accordance with the principles of problem-based instruction ( Duch et al. , 2001 ), course instructors encourage students to seek, interpret, and synthesize their own information to the extent possible. Students have access to a variety of instructional formats, and active-learning experiences are incorporated throughout the unit. These activities are interspersed among minilectures and give the students opportunities to apply new information to their existing base of knowledge. The active-learning activities emphasize the key concepts of the minilectures and directly confront common misconceptions about antibiotic resistance, gene expression, and evolution. Weekly classes include question/answer/discussion sessions to address student misconceptions and 20-min minilectures on such topics as antibiotic resistance, evolution, and the central dogma of molecular biology. Students gather information about antibiotic resistance in N. gonorrhoeae , epidemiology of gonorrhea, and treatment options for the disease, and each team is expected to formulate a plan to address ciprofloxacin resistance in N. gonorrhoeae .

In this project, the authors assessed student gains in terms of content knowledge regarding topics covered such as the role of evolution in antibiotic resistance, mechanisms of gene expression, and the role of oncogenes in human disease. They also measured HOCS as gains in problem solving, according to a rubric that assessed self-reported abilities to communicate ideas logically, solve difficult problems about microbiology, propose hypotheses, analyze data, and draw conclusions. Comparing the pre- and posttests, students reported significant learning of scientific content. Among the thinking skill categories, students demonstrated measurable gains in their ability to solve problems about microbiology but the unit seemed to have little impact on their more general perceived problem-solving skills ( Cloud-Hansen et al. , 2008 ).

What would such a class look like with the addition of explicit creativity-promoting approaches? Would the gains in problem-solving abilities have been greater if during the minilectures and other activities, students had been introduced explicitly to elements of creative thinking from the Sternberg and Williams (1998) list described above? Would the students have reported greater gains if their instructors had encouraged idea generation with weekly brainstorming sessions; if they had reminded students to cross-fertilize ideas by integrating material across subject areas; built self-efficacy by helping students believe in their own capacity to be creative; helped students question their own assumptions; and encouraged students to imagine other viewpoints and possibilities? Of most relevance, could the authors have been more explicit in assessing the originality of the student plans? In an experiment that required college students to develop plans of a different, but comparable, type, Osborn and Mumford (2006) created an originality rubric ( Figure 2 ) that could apply equally to assist instructors in judging student plans in any course. With such modifications, would student gains in problem-solving abilities or other HOCS have been greater? Would their plans have been measurably more imaginative?

Figure 2.

Figure 2. Originality rubric (adapted from Osburn and Mumford, 2006 , p. 183).

Answers to these questions can only be obtained when a course like that described by Cloud-Hansen et al. (2008) is taught with explicit instruction in creativity of the type I described above. But, such answers could be based upon more than subjective impressions of the course instructors. For example, students could be pretested with items from the TTCT-Verbal or TTCT-Figural like those shown. If, during minilectures and at every contact with instructors, students were repeatedly reminded and shown how to be as creative as possible, to integrate material across subject areas, to question their own assumptions and imagine other viewpoints and possibilities, would their scores on TTCT posttest items improve? Would the plans they formulated to address ciprofloxacin resistance become more imaginative?

Recall that in their meta-analysis, Scott et al. (2004) found that explicitly informing students about the nature of creativity and offering strategies for creative thinking were the most effective components of instruction. From their careful examination of 70 experimental studies, they concluded that approaches such as social modeling, cooperative learning, and case-based (project-based) techniques that required the application of newly acquired knowledge were positively correlated with high effect sizes. The study was clear in confirming that explicit creativity instruction can be successful in enhancing divergent thinking and problem solving. Would the same strategies work for courses in ecology and environmental biology, as detailed by Ebert-May and Hodder (2008) , or for a unit elaborated by Knight and Wood (2005) that applies classroom response clickers?

Finally, I return to my opening question with the fictional Dr. Dunne. Could a weekly brainstorming “invention session” included in a course like those described here serve as the site where students are introduced to concepts and strategies of creative problem solving? As frequently applied in schools of engineering ( Paulus and Nijstad, 2003 ), brainstorming provides an opportunity for the instructor to pose a problem and to ask the students to suggest as many solutions as possible in a brief period, thus enhancing ideational fluency. Here, students can be encouraged explicitly to build on the ideas of others and to think flexibly. Would brainstorming enhance students' divergent thinking or creative abilities as measured by TTCT items or an originality rubric? Many studies have demonstrated that group interactions such as brainstorming, under the right conditions, can indeed enhance creativity ( Paulus and Nijstad, 2003 ; Scott et al. , 2004 ), but there is little information from an undergraduate science classroom setting. Intellectual Ventures, a firm founded by Nathan Myhrvold, the creator of Microsoft's Research Division, has gathered groups of engineers and scientists around a table for day-long sessions to brainstorm about a prearranged topic. Here, the method seems to work. Since it was founded in 2000, Intellectual Ventures has filed hundreds of patent applications in more than 30 technology areas, applying the “invention session” strategy ( Gladwell, 2008 ). Currently, the company ranks among the top 50 worldwide in number of patent applications filed annually. Whether such a technique could be applied successfully in a college science course will only be revealed by future research.

  • Abrami P. C., Bernard R. M., Borokhovski E., Wadem A., Surkes M. A., Tamim R., Zhang D. ( 2008 ). Instructional interventions affecting critical thinking skills and dispositions: a stage 1 meta-analysis . Rev. Educ. Res 78 , 1102-1134. Google Scholar
  • Amabile T. M. ( 1996 ). Creativity in Context , Boulder, CO: Westview Press. Google Scholar
  • Amabile T. M., Barsade S. G., Mueller J. S., Staw B. M. ( 2005 ). Affect and creativity at work . Admin. Sci. Q 50 , 367-403. Google Scholar
  • Ausubel D. ( 1963 ). The Psychology of Meaningful Verbal Learning , New York: Grune and Stratton. Google Scholar
  • Ausubel B. ( 2000 ). The Acquisition and Retention of Knowledge: A Cognitive View , Boston, MA: Kluwer Academic Publishers. Google Scholar
  • Banaji S., Burn A., Buckingham D. ( 2006 ). The Rhetorics of Creativity: A Review of the Literature , accessed 29 December 2008 London: Centre for the Study of Children, Youth and Media, www.creativepartnerships.com/data/files/rhetorics-of-creativity-12.pdf . Google Scholar
  • Barron F., Harrington D. M. ( 1981 ). Creativity, intelligence and personality . Ann. Rev. Psychol 32 , 439-476. Google Scholar
  • Beller M. ( 1999 ). Quantum Dialogue: The Making of a Revolution , Chicago, IL: University of Chicago Press. Google Scholar
  • Blair C., Razza R. P. ( 2007 ). Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten . Child Dev 78 , 647-663. Medline ,  Google Scholar
  • Bodrova E., Leong D. J. ( 2001 ). The Tool of the Mind: a case study of implementing the Vygotskian approach In: American Early Childhood and Primary Classrooms , Geneva, Switzerland: UNESCO International Bureau of Education. Google Scholar
  • Bransford J. D.Brown A. L.Cocking R. R. ( 2000 ). How People Learn: Brain, Mind, Experience, and School , Washington, DC: National Academies Press. Google Scholar
  • Brophy D. R. ( 2006 ). A comparison of individual and group efforts to creatively solve contrasting types of problems . Creativity Res. J 18 , 293-315. Google Scholar
  • Bruner J. ( 1965 ). The growth of mind . Am. Psychol 20 , 1007-1017. Medline ,  Google Scholar
  • Bull K. S., Montgomery D., Baloche L. ( 1995 ). Teaching creativity at the college level: a synthesis of curricular components perceived as important by instructors . Creativity Res. J 8 , 83-90. Google Scholar
  • Burton R. ( 2008 ). On Being Certain: Believing You Are Right Even When You're Not , New York: St. Martin's Press. Google Scholar
  • Cloud-Hanson K. A., Kuehner J. N., Tong L., Miller S., Handelsman J. ( 2008 ). Money, sex and drugs: a case study to teach the genetics of antibiotic resistance . CBE Life Sci. Educ 7 , 302-309. Medline ,  Google Scholar
  • Craft A. ( 2000 ). Teaching Creativity: Philosophy and Practice , New York: Routledge. Google Scholar
  • Crawford V. M. ( 2007 ). Adaptive expertise as knowledge building in science teachers' problem solving accessed 1 July 2008 Proceedings of the Second European Cognitive Science Conference Delphi, Greece http://ctl.sri.com/publications/downloads/Crawford_EuroCogSci07Proceedings.pdf . Google Scholar
  • Crawford V. M., Brophy S. ( 2006 ). Adaptive Expertise: Theory, Methods, Findings, and Emerging Issues; September 2006 In: accessed 1 July 2008 Menlo Park, CA: SRI International, http://ctl.sri.com/publications/downloads/AESymposiumReportOct06.pdf . Google Scholar
  • Crossgrove K., Curran K. L. ( 2008 ). Using clickers in nonmajors- and majors-level biology courses: student opinion, learning, and long-term retention of course material . CBE Life Sci. Educ 7 , 146-154. Link ,  Google Scholar
  • Crowe A., Dirks C., Wenderoth M. P. ( 2008 ). Biology in bloom: implementing Bloom's taxonomy to enhance student learning in biology . CBE Life Sci. Educ 7 , 368-381. Link ,  Google Scholar
  • Davidson M. C., Amso D., Anderson L. C., Diamond A. ( 2006 ). Development of cognitive control and executive functions from 4–13 years: evidence from manipulations of memory, inhibition, and task switching . Neuropsychologia 44 , 2037-2078. Medline ,  Google Scholar
  • DeHaan R. L. ( 2005 ). The impending revolution in undergraduate science education . J. Sci. Educ. Technol 14 , 253-270. Google Scholar
  • Diamond A., Barnett W. S., Thomas J., Munro S. ( 2007 ). Preschool program improves cognitive control . Science 318 , 1387-1388. Medline ,  Google Scholar
  • Duch B. J., Groh S. E., Allen D. E. ( 2001 ). The Power of Problem-based Learning , Sterling, VA: Stylus Publishers. Google Scholar
  • Durston S., Davidson M. C., Thomas K. M., Worden M. S., Tottenham N., Martinez A., Watts R., Ulug A. M., Caseya B. J. ( 2003 ). Parametric manipulation of conflict and response competition using rapid mixed-trial event-related fMRI . Neuroimage 20 , 2135-2141. Medline ,  Google Scholar
  • Ebert-May D., Hodder J. ( 2008 ). Pathways to Scientific Teaching , Sunderland, MA: Sinauer. Google Scholar
  • Finke R. A., Ward T. B., Smith S. M. ( 1996 ). Creative Cognition: Theory, Research and Applications , Boston, MA: MIT Press. Google Scholar
  • Freeman S., O'Connor E., Parks J. W., Cunningham M., Hurley D., Haak D., Dirks C., Wenderoth M. P. ( 2007 ). Prescribed active learning increases performance in introductory biology . CBE Life Sci. Educ 6 , 132-139. Link ,  Google Scholar
  • Gabora L. ( 2002 ). Hewett T.Kavanagh E. Cognitive mechanisms underlying the creative process Proceedings of the Fourth International Conference on Creativity and Cognition 2002 October 13–16 Loughborough University, United Kingdom 126-133. Google Scholar
  • Gaffney J.D.H., Richards E., Kustusch M. B., Ding L., Beichner R. ( 2008 ). Scaling up education reform . J. Coll. Sci. Teach 37 , 48-53. Google Scholar
  • Gardner H. ( 1993 ). Creating Minds: An Anatomy of Creativity Seen through the Lives of Freud, Einstein, Picasso, Stravinsky, Eliot, Graham, and Ghandi In: New York: Harper Collins. Google Scholar
  • Gladwell M. ( 2008 ). In the air; who says big ideas are rare? The New Yorker accessed 19 May 2008 www.newyorker.com/reporting/2008/05/12/080512fa_fact_gladwell . Google Scholar
  • Guilford J. P. ( 1950 ). Creativity . Am. Psychol 5 , 444-454. Medline ,  Google Scholar
  • Hake R. ( 2005 ). The physics education reform effort: a possible model for higher education . Natl. Teach. Learn. Forum 15 , 1-6. Google Scholar
  • Halpern D. E., Hakel M. D. ( 2003 ). Applying the science of learning to the university and beyond . Change 35 , 36-42. Google Scholar
  • Handelsman J. ( 2004 ). Scientific teaching . Science 304 , 521-522. Medline ,  Google Scholar
  • Handelsman J, Miller S., Pfund C. ( 2007 ). Scientific Teaching , New York: W. H. Freeman and Co. Google Scholar
  • Haring-Smith T. ( 2006 ). Creativity research review: some lessons for higher education. Association of American Colleges and Universities . Peer Rev 8 , 23-27. Google Scholar
  • Hatano G., Ouro Y. ( 2003 ). Commentary: reconceptualizing school learning using insight from expertise research . Educ. Res 32 , 26-29. Google Scholar
  • Hrepic Z., Zollman D. A., Rebello N. S. ( 2007 ). Comparing students' and experts' understanding of the content of a lecture . J. Sci. Educ. Technol 16 , 213-224. Google Scholar
  • Hunsaker S. L. ( 2005 ). Outcomes of creativity training programs . Gifted Child Q 49 , 292-298. Google Scholar
  • Kaufman J. C., Baer J. ( 2006 ). Intelligent testing with Torrance . Creativity Res. J 18 , 99-102. Google Scholar
  • Kaufman J. C., Beghetto R. A. ( 2008 , Ed. R. L. DeHaanK.M.V. Narayan , Exploring mini-C: creativity across cultures In: Education for Innovation: Implications for India, China and America , Rotterdam, The Netherlands: Sense Publishers, 165-180. Google Scholar
  • Kaufman J. C., Sternberg R. J. ( 2007 ). Creativity . Change 39 , 55-58. Google Scholar
  • Kim K. H. ( 2006 ). Can we trust creativity tests: a review of the Torrance Tests of Creative Thinking (TTCT) . Creativity Res. J 18 , 3-14. Google Scholar
  • Knight J. K., Wood W. B. ( 2005 ). Teaching more by lecturing less . Cell Biol. Educ 4 , 298-310. Link ,  Google Scholar
  • Cetina Knorr K. ( 1995 , Ed. S. JasanoffG. MarkleJ. PetersenT. Pinch , Laboratory studies: the cultural approach to the study of science In: Handbook of Science and Technology Studies , Thousand Oaks, CA: Sage Publications, 140-166. Google Scholar
  • Koestler A. ( 1964 ). The Act of Creation , New York: Macmillan. Google Scholar
  • Latour B., Woolgar S. ( 1986 ). Laboratory Life: The Construction of Scientific Facts , Princeton, NJ: Princeton University Press. Google Scholar
  • MacKinnon D. W. ( 1978 , Ed. D. W. MacKinnon , What makes a person creative? In: In Search of Human Effectiveness , New York: Universe Books, 178-186. Google Scholar
  • Martindale C. ( 1999 , Ed. R. J. Sternberg , Biological basis of creativity In: Handbook of Creativity , Cambridge, United Kingdom: Cambridge University Press, 137-152. Google Scholar
  • Mazur E. ( 1996 ). Peer Instruction: A User's Manual , Upper Saddle River, NJ: Prentice Hall. Google Scholar
  • McFadzean E. ( 2002 ). Developing and supporting creative problem-solving teams: Part 1—a conceptual model . Manage. Decis 40 , 463-475. Google Scholar
  • McGregor G. D. ( 2001 ). Creative thinking instruction for a college study skills program: a case study. . Dissert Abstr. Intl 62 , 3293A UMI No. AAT 3027933. Google Scholar
  • McIntyre F. S., Hite R. E., Rickard M. K. ( 2003 ). Individual characteristics and creativity in the marketing classroom: exploratory insights . J. Mark. Educ 25 , 143-149. Google Scholar
  • Mestre J. P. ( 2005 ). Transfer of Learning: From a Modern Multidisciplinary Perspective , Greenwich, CT: Information Age Publishing. Google Scholar
  • Mumford M. D., Mobley M. I., Uhlman C. E., Reiter-Palmon R., Doares L. M. ( 1991 ). Process analytic models of creative capacities . Creativity Res. J 4 , 91-122. Google Scholar
  • National Research Council ( 2007 ). Rising Above the Gathering Storm: Energizing and Employing America for a Brighter Economic Future, Committee on Science, Engineering and Public Policy In: Washington, DC: National Academies Press. Google Scholar
  • Neisser U. ( 1963 ). The multiplicity of thought . Br. J. Psychol 54 , 1-14. Medline ,  Google Scholar
  • Nelson C. E. ( 2008 ). Teaching evolution (and all of biology) more effectively: strategies for engagement, critical reasoning, and confronting misconceptions Integrative and Comparative Biology Advance Access accessed 15 September 2008 http://icb.oxfordjournals.org/cgi/reprint/icn027v1.pdf . Google Scholar
  • Novak G, Gavrin A., Christian W, Patterson E. ( 1999 ). Just-in-Time Teaching: Blending Active Learning with Web Technology , San Francisco, CA: Pearson Benjamin Cummings. Google Scholar
  • Osborn A. F. ( 1948 ). Your Creative Power , New York: Scribner. Google Scholar
  • Osborn A. F. ( 1979 ). Applied Imagination , New York: Scribner. Google Scholar
  • Osburn H. K., Mumford M. D. ( 2006 ). Creativity and planning: training interventions to develop creative problem-solving skills . Creativity Res. J 18 , 173-190. Google Scholar
  • Paulus P. B., Nijstad B. A. ( 2003 ). Group Creativity: Innovation through Collaboration , New York: Oxford University Press. Google Scholar
  • Perkins K. K., Wieman C. E. ( 2008 , Ed. R. L. DeHaanK.M.V. Narayan , Innovative teaching to promote innovative thinking In: Education for Innovation: Implications for India, China and America , Rotterdam, The Netherlands: Sense Publishers, 181-210. Google Scholar
  • Plucker J. A., Renzulli J. S. ( 1999 , Ed. R. J. Sternberg , Psychometric approaches to the study of human creativity In: Handbook of Creativity , Cambridge, United Kingdom: Cambridge University Press, 35-61. Google Scholar
  • Quitadamo I. J., Faiola C. L., Johnson J. E., Kurtz M. J. ( 2008 ). Community-based inquiry improves critical thinking in general education biology . CBE Life Sci. Educ 7 , 327-337. Link ,  Google Scholar
  • Runco M. A. ( 2004 ). Creativity . Annu. Rev. Psychol 55 , 657-687. Medline ,  Google Scholar
  • Runco M. A., Nemiro J. ( 1994 ). Problem finding, creativity, and giftedness . Roeper Rev 16 , 235-241. Google Scholar
  • Sawyer R. K. ( 2005 ). Educating for Innovation Thinking Skills Creativity accessed 13 August 2008 1 41-48 www.artsci.wustl.edu/∼ksawyer/PDFs/Thinkjournal.pdf . Google Scholar
  • Sawyer R. K. ( 2006 ). Explaining Creativity: The Science of Human Innovation , New York: Oxford University Press. Google Scholar
  • Schwartz D. L., Bransford J. D., Sears D. ( 2005 , Ed. J. P. Mestre , Efficiency and innovation in transfer In: Transfer of Learning from a Modern Multidisciplinary Perspective , Greenwich, CT: Information Age Publishing, 1-51. Google Scholar
  • Scott G., Leritz L. E., Mumford M. D. ( 2004 ). The effectiveness of creativity training: a quantitative review . Creativity Res. J 16 , 361-388. Google Scholar
  • Simonton D. K. ( 1975 ). Sociocultural context of individual creativity: a transhistorical time-series analysis . J. Pers. Soc. Psychol 32 , 1119-1133. Medline ,  Google Scholar
  • Simonton D. K. ( 2004 ). Creativity in Science: Chance, Logic, Genius, and Zeitgeist , Oxford, United Kingdom: Cambridge University Press. Google Scholar
  • Sloman S. ( 1996 ). The empirical case for two systems of reasoning . Psychol. Bull 9 , 3-22. Google Scholar
  • Smith G. F. ( 1998 ). Idea generation techniques: a formulary of active ingredients . J. Creative Behav 32 , 107-134. Google Scholar
  • Snyder A., Mitchell J., Bossomaier T., Pallier G. ( 2004 ). The creativity quotient: an objective scoring of ideational fluency . Creativity Res. J 16 , 415-420. Google Scholar
  • Sternberg R. J. ( 2003 ). What is an “expert student?” . Educ. Res. 32 , 5-9. Google Scholar
  • Sternberg R., Williams W. M. ( 1998 ). Teaching for creativity: two dozen tips accessed 25 March 2008 www.cdl.org/resource-library/articles/teaching_creativity.php . Google Scholar
  • Tardif T. Z., Sternberg R. J. ( 1988 , Ed. R. J. Sternberg , What do we know about creativity? In: The Nature of Creativity , New York: Cambridge University Press, 429-440. Google Scholar
  • Torrance E. P. ( 1974 ). Norms and Technical Manual for the Torrance Tests of Creative Thinking , Bensenville, IL: Scholastic Testing Service. Google Scholar
  • Torrance E. P. ( 1998 ). The Torrance Tests of Creative Thinking Norms—Technical Manual Figural (Streamlined) Forms A and B , Bensenville, IL: Scholastic Testing Service. Google Scholar
  • Torrance E. P., Ball O. E., Safter H. T. ( 2008 ). Torrance Tests of Creative Thinking: Streamlined Scoring Guide for Figural Forms A and B , Bensenville, IL: Scholastic Testing Service. Google Scholar
  • Treffinger D. J., Isaksen S. G. ( 2005 ). Creative problem solving: the history, development, and implications for gifted education and talent development . Gifted Child Q 49 , 342-357. Google Scholar
  • Vandervert L. R., Schimpf P. H., Liu H. ( 2007 ). How working memory and the cerebellum collaborate to produce creativity and innovation . Creativity Res. J 9 , 1-18. Google Scholar
  • Wallach M. A., Kogan N. ( 1965 ). Modes of Thinking in Young Children: A Study of the Creativity-Intelligence Distinction , New York: Holt, Rinehart and Winston. Google Scholar
  • Wood W. B. ( 2009 ). Innovations in undergraduate biology teaching and why we need them . Annu. Rev. Cell Dev. Biol in press. Medline ,  Google Scholar
  • Fostering creativity in low-engagement students through socratic dialogue: An experiment in an operations class The International Journal of Management Education, Vol. 22, No. 1
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  • Entrepreneurial competencies of undergraduate students: The case of universities in Nigeria The International Journal of Management Education, Vol. 19, No. 1
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  • Curriculum Differentiation’s Capacity to Extend Gifted Students in Secondary Mixed-ability Science Classes 27 June 2020 | Talent, Vol. 10, No. 1
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  • Integrating Entrepreneurship and Art to Improve Creative Problem Solving in Fisheries Education 27 February 2020 | Fisheries, Vol. 45, No. 2
  • Concepts Re-imagined: Relational Signs Beyond Definitional Rigidity 15 August 2020
  • Promoting Student Creativity and Inventiveness in Science and Engineering 24 October 2020
  • Teaching for Leadership, Innovation, and Creativity
  • Problem Çözme Becerileri Eğitim Programının Çocukların Karar Verme Becerileri Üzerindeki Etkisi 30 December 2019 | Erzincan Üniversitesi Eğitim Fakültesi Dergisi, Vol. 21, No. 3
  • Mento’s change model in teaching competency-based medical education 27 December 2019 | BMC Medical Education, Vol. 19, No. 1
  • The Effects of Individual Preparations on Group Creativity 21 January 2020
  • The Value of Creativity for Enhancing Translational Ecologies, Insights, and Discoveries 9 July 2019 | Frontiers in Psychology, Vol. 10
  • Using the International Classification of Functioning, Disability, and Health to Guide Students' Clinical Approach to Aging With Pathology Topics in Geriatric Rehabilitation, Vol. 35, No. 3
  • Impact of Brainstorming Strategy in Dealing With Knowledge Retention Skill: An Insight Into Special Learners' Needs In Saudi Arabia 1 January 2021 | MIER Journal of Educational Studies Trends & Practices
  • The potential of students’ creative disposition as a perspective to develop creative teaching and learning for senior high school biological science 12 March 2019 | Journal of Physics: Conference Series, Vol. 1157
  • Evaluating Remote Experiment from a Divergent Thinking Point of View 25 July 2018
  • Exploring Creative Education Practices and Implications: A Case study of National Chengchi University, Taiwan 4 April 2022 | Journal of Business and Economic Analysis, Vol. 02, No. 02
  • Exploring Creative Education Practices and Implications: A Case study of National Chengchi University, Taiwan 1 January 2020 | Journal of Business and Economic Analysis, Vol. 02, No. 02
  • Diverging from the Dogma: A Call to Train Creative Thinkers in Science 14 September 2018 | The Bulletin of the Ecological Society of America, Vol. 100, No. 1
  • Comparison of German and Japanese student teachers’ views on creativity in chemistry class 16 May 2018 | Asia-Pacific Science Education, Vol. 4, No. 1
  • The use of humour during a collaborative inquiry 27 August 2018 | International Journal of Science Education, Vol. 40, No. 14
  • Connecting creative coursework exposure and college student engagement across academic disciplines 29 August 2019 | Gifted and Talented International, Vol. 33, No. 1-2
  • Views of German chemistry teachers on creativity in chemistry classes and in general 1 January 2018 | Chemistry Education Research and Practice, Vol. 19, No. 3
  • Embedding Critical and Creative Thinking in Chemical Engineering Practice
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  • Teachers’ learning on the workshop of STS approach as a way of enhancing inventive thinking skills
  • Creativity Development Through Inquiry-Based Learning in Biomedical Sciences
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  • The influential factors and hierarchical structure of college students’ creative capabilities—An empirical study in Taiwan Thinking Skills and Creativity, Vol. 26
  • Teacher perceptions of professional role and innovative teaching at elementary schools in Taiwan 10 November 2017 | Educational Research and Reviews, Vol. 12, No. 21
  • Evaluation of creative problem-solving abilities in undergraduate structural engineers through interdisciplinary problem-based learning 28 July 2016 | European Journal of Engineering Education, Vol. 42, No. 6
  • What Shall I Write Next? 19 September 2017
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  • Kyle J. Frantz ,
  • Melissa K. Demetrikopoulos ,
  • Shari L. Britner ,
  • Laura L. Carruth ,
  • Brian A. Williams ,
  • John L. Pecore ,
  • Robert L. DeHaan , and
  • Christopher T. Goode
  • Elizabeth Ambos, Monitoring Editor
  • A present absence: undergraduate course outlines and the development of student creativity across disciplines 3 October 2016 | Teaching in Higher Education, Vol. 22, No. 2
  • Exploring differences in creativity across academic majors for high-ability college students 16 February 2018 | Gifted and Talented International, Vol. 32, No. 1
  • Creativity in chemistry class and in general – German student teachers’ views 1 January 2017 | Chemistry Education Research and Practice, Vol. 18, No. 2
  • Accessing the Finest Minds
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  • Possibilities and limitations of integrating peer instruction into technical creativity education 6 September 2016 | Instructional Science, Vol. 44, No. 6
  • Creative Cognitive Processes in Higher Education 20 November 2014 | The Journal of Creative Behavior, Vol. 50, No. 4
  • An Evidence-Based Review of Creative Problem Solving Tools 6 April 2016 | Human Resource Development Review, Vol. 15, No. 2
  • Case-based exams for learning and assessment: Experiences in an information systems course
  • Case exams for assessing higher order learning: A comparative social media analytics usage exam
  • Beyond belief: Structured techniques prove more effective than a placebo intervention in a problem construction task Thinking Skills and Creativity, Vol. 19
  • A Belief System at the Core of Learning Science
  • Student Research Work and Modeled Situations in Order to Bridge the Gap between Basic Science Concepts and Those from Preventive and Clinical Practice. Meaningful Learning and Informed beneficience Creative Education, Vol. 07, No. 07
  • FOSTERING FIFTH GRADERS’ SCIENTIFIC CREATIVITY THROUGH PROBLEM-BASED LEARNING 25 October 2015 | Journal of Baltic Science Education, Vol. 14, No. 5
  • Scaffolding for Creative Product Possibilities in a Design-Based STEM Activity 16 November 2014 | Research in Science Education, Vol. 45, No. 5
  • Intuition and insight: two concepts that illuminate the tacit in science education 18 June 2015 | Studies in Science Education, Vol. 51, No. 2
  • Arts and crafts as adjuncts to STEM education to foster creativity in gifted and talented students 28 March 2015 | Asia Pacific Education Review, Vol. 16, No. 2
  • Initiatives Towards an Education for Creativity Procedia - Social and Behavioral Sciences, Vol. 180
  • Brian A. Couch ,
  • Tanya L. Brown ,
  • Tyler J. Schelpat ,
  • Mark J. Graham , and
  • Jennifer K. Knight
  • Michèle Shuster, Monitoring Editor
  • Kim Quillin , and
  • Stephen Thomas
  • Mary Lee Ledbetter, Monitoring Editor
  • The Design of IdeaWorks: Applying Social Learning Networks to Support Tertiary Education 21 July 2015
  • Video Games and Malevolent Creativity
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  • “Development of Thinking Skills” Course: Teaching TRIZ in Academic Setting Procedia Engineering, Vol. 131
  • Leadership in the Future Experts’ Creativity Development with Scientific Research Activities 4 November 2014
  • Developing Deaf Children's Conceptual Understanding and Scientific Argumentation Skills: A Literature Review 3 January 2014 | Deafness & Education International, Vol. 16, No. 3
  • Leslie M. Stevens , and
  • Sally G. Hoskins
  • Nancy Pelaez, Monitoring Editor
  • Cortex, Vol. 51
  • A Sociotechnological Theory of Discursive Change and Entrepreneurial Capacity: Novelty and Networks SSRN Electronic Journal, Vol. 3
  • GEOverse: An Undergraduate Research Journal: Research Dissemination Within and Beyond the Curriculum 1 August 2013
  • Learning by Practice, High-Pressure Student Ateliers 2 August 2013
  • Relating Inter-Individual Differences in Verbal Creative Thinking to Cerebral Structures: An Optimal Voxel-Based Morphometry Study 5 November 2013 | PLoS ONE, Vol. 8, No. 11
  • 21st Century Biology: An Interdisciplinary Approach of Biology, Technology, Engineering and Mathematics Education Procedia - Social and Behavioral Sciences, Vol. 102
  • Reclaiming creativity in the era of impact: exploring ideas about creative research in science and engineering Studies in Higher Education, Vol. 38, No. 9
  • An Evaluation of Alternative Ways of Computing the Creativity Quotient in a Design School Sample Creativity Research Journal, Vol. 25, No. 3
  • A.-M. Hoskinson ,
  • M. D. Caballero , and
  • J. K. Knight
  • Eric Brewe, Monitoring Editor
  • Understanding, attitude and environment International Journal for Researcher Development, Vol. 4, No. 1
  • Promoting Student Creativity and Inventiveness in Science and Engineering
  • Building creative thinking in the classroom: From research to practice International Journal of Educational Research, Vol. 62
  • A Demonstration of a Mastery Goal Driven Learning Environment to Foster Creativity in Engineering Design SSRN Electronic Journal, Vol. 111
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  • Evaluation of fostering students' creativity in preparing aided recalls for revision courses using electronic revision and recapitulation tools 2.0 Behaviour & Information Technology, Vol. 31, No. 8
  • Scientific Creativity: The Missing Ingredient in Slovenian Science Education 15 April 2012 | European Journal of Educational Research, Vol. 1, No. 2
  • Could the ‘evolution’ from biology to life sciences prevent ‘extinction’ of the subject field? 6 March 2012 | Suid-Afrikaanse Tydskrif vir Natuurwetenskap en Tegnologie, Vol. 31, No. 1
  • Mobile innovations, executive functions, and educational developments in conflict zones: a case study from Palestine 1 October 2011 | Educational Technology Research and Development, Vol. 60, No. 1
  • Informing Pedagogy Through the Brain-Targeted Teaching Model Journal of Microbiology & Biology Education, Vol. 13, No. 1
  • Teaching Creative Science Thinking Science, Vol. 334, No. 6062
  • Sally G. Hoskins ,
  • David Lopatto , and
  • Leslie M. Stevens
  • Diane K. O'Dowd, Monitoring Editor
  • Embedding Research-Based Learning Early in the Undergraduate Geography Curriculum Journal of Geography in Higher Education, Vol. 35, No. 3
  • Jared L. Taylor ,
  • Karen M. Smith ,
  • Adrian P. van Stolk , and
  • George B. Spiegelman
  • Debra Tomanek, Monitoring Editor
  • IFAC Proceedings Volumes, Vol. 43, No. 17
  • Critical and Creative Thinking Activities for Engaged Learning in Graphics and Visualization Course
  • Creativity Development through Inquiry-Based Learning in Biomedical Sciences

Submitted: 31 December 2008 Revised: 14 May 2009 Accepted: 28 May 2009

© 2009 by The American Society for Cell Biology


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Don’t Let Gen AI Limit Your Team’s Creativity

creative problem solving cognitive load

Treat it as a partner in a structured conversation.

No one doubts ChatGPT’s ability to generate lots of ideas. But are those ideas any good? A recent real-world experiment showed that teams engaged in a creative problem-solving task saw only modest gains from AI assistance for the most part—and some underperformed. Surveys conducted before and after the exercise showed that the teams using AI gained far more confidence in their problem-solving abilities than the others did, but that much of their confidence was misplaced.

But don’t blame the technology, says Kian Gohar, CEO of the leadership-development firm GeoLab and one of the study’s authors. “Brainstorming with generative AI requires rethinking your ideation workflow and learning new skills,” Gohar says. This article offers guidance for approaching the exercise as a structured, ongoing conversation, opening up a staggering capacity to develop better and more-creative ideas faster.

No one doubts ChatGPT’s ability to generate lots of ideas. But are those ideas any good? In a recent real-world experiment, teams engaged in a creative problem-solving task saw modest gains from AI assistance for the most part—and some underperformed. Don’t blame the technology, says Kian Gohar, CEO of the leadership-development firm GeoLab and one of the study’s authors. Common misconceptions about generative AI, problem-solving, and the creative process are causing workers and their managers to use the tools improperly, sometimes leaving them worse off than if they’d proceeded without AI input.

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  1. Creative Problem Solving

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  2. What Is Creative Problem-Solving and How to Master It with These 8

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  3. Problem-Solving Strategies: Definition and 5 Techniques to Try

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  4. (PDF) Creative Problem-Solving Process Styles, Cognitive Work Demands

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  6. Creative Problem Solving Process

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  1. Reduce Cognitive Load for Better Creative Problem Solving

    Creative problem solving (CPS) is a skill that involves generating and implementing novel and effective solutions to complex challenges. However, CPS can be hindered by cognitive load,...

  2. A Cognitive Trick for Solving Problems Creatively

    May 04, 2016 Post Post Many experts argue that creative thinking requires people to challenge their preconceptions and assumptions about the way the world works. One common claim, for example, is...

  3. Implicit Theories, Working Memory, and Cognitive Load: Impacts on

    tive ability, long known to affect problem-solving and per-formance on other complex cognitive tasks (Conway & Engle, 1996; Seyler, Kirk, & Ashcraft, 2003), have been ... impact of working memory (WM) and cognitive load on creative thinking. Cognitive load fully mediated the relationship between implicit theories and creative thinking, with ...

  4. Cognitive-Load Theory: Methods to Manage Working Memory Load in the

    Cognitive-load researchers attempt to engineer the instructional control of cognitive load by designing methods that substitute productive for unproductive cognitive load.

  5. Self-efficacy and performance feedback: Impacts on cognitive load

    Given the known impact of self-efficacy on academic learning (Choi, 2005; Schunk, 1996) and problem-solving (Pajares & Miller, 1994), as well as the impact of cognitive load on traditional learning outcomes (Sweller, 2011) and more recently, creativity (Redifer et al., 2019), it is important to examine the relationship between creative self ...

  6. Team creativity: Cognitive processes underlying problem solving

    Abstract. Creative cognition is a critical aspect of creative problem solving for both teams and individuals, but the cognitive processes underlying creativity have received more attention at the ...

  7. Cognitive Load During Problem Solving: Effects on Learning

    It is suggested that a major reason for the ineffectiveness of problem solving as a learning device, is that the cognitive processes required by the two activities overlap insufficiently, and that conventional problem solving in the form of means-ends analysis requires a relatively large amount of cognitive processing capacity which is ...

  8. The Link Between Creativity, Cognition, and Creative Drives and

    Figure 1. A schematic overview of the neurobiology of creativity as outlined in this review. It symbolizes the brain systems and neuromodulatory pathways underlying and modulating creative cognition and creative drive in health and disease.

  9. Does cognitive load affect creativity? An experiment using a divergent

    Does cognitive load affect creativity? An experiment using a divergent thinking task☆ Cortney S. Rodet Add to Mendeley https://doi.org/10.1016/j.econlet.2022.110849 Get rights and content • Dual-task experiment featuring divergent thinking task and number memorization. • Introducing cognitive load significantly reduces the number of creative ideas.

  10. Facilitating Flexible Problem Solving: A Cognitive Load Perspective

    The development of flexible, transferable problem-solving skills is an important aim of contemporary educational systems. Since processing limitations of our mind represent a major factor influencing any meaningful learning, the acquisition of flexible problem-solving skills needs to be based on known characteristics of our cognitive architecture in order to be effective and efficient. This ...

  11. Creative Problem Solving

    Creative problem solving (CPS) is a way of solving problems or identifying opportunities when conventional thinking has failed. It encourages you to find fresh perspectives and come up with innovative solutions, so that you can formulate a plan to overcome obstacles and reach your goals.

  12. Implicit Theories, Working Memory, and Cognitive Load: Impacts on

    Although the impacts of WM and cognitive load on complex tasks such as mathematical problem solving (Ayres, 2006a; Beilock & DeCaro, 2007) and retrieval fluency (Rosen & Engle, 1997; Schelble, Therriault, & Miller, 2012) have been studied extensively, their relationship with creative thinking is less clear, particularly in the case of cognitive ...

  13. A discussion of the cognitive load in collaborative problem-solving

    This paper presents results on cognitive load related to problem-solving in the decision-making phase, in which alternative solutions or options are evaluated and the group engages in, inter alia, consensus building, negotiation or commitment building to agree on a course of action.

  14. Effect of cognitive load and working memory capacity on the ...

    Many studies on creative problem solving and strategy selection demonstrate that cognitive load for facilitates the discovery of alternatives. However, findings are inconsistent regarding Einstellung situations.

  15. Does cognitive load affect creativity? An experiment using a divergent

    Results indicate that introducing cognitive load reduces the quantity and variety of creative ideas. Therefore, results suggest that organizations seeking to spark creativity ought to carefully consider work design (e.g., work load, division of labor, and timing of deadlines) to mitigate the negative effects of cognitive load on creativity.

  16. PDF Cognitive Load in Solving Mathematics Problems: Validating the ...

    Cognitive Load in Solving Mathematics Problems: Validating the Role of Motivation and the Interaction Among Prior Knowledge, Worked Examples, and Task Difficulty. European Journal of STEM Education, 5(1), 05. https://doi.org/10.20897/ejsteme/9252 Published: November 24, 2020 ABSTRACT

  17. Principles of Creative Problem Solving in AI Systems

    In the second part, which comprises chapter 5 th to 8 th, the author develops a cognitive framework to explore how a diverse set of creative problem solving tasks can be solved computationally using a unified set of principles. To facilitate the understanding of insight and creative problem solving, Dr. Oltețeanu puts forward a metaphor, in ...

  18. Cognitive load and creativity of knowledge workers: a diary study

    The findings indicated that: there was a positive correlation between cognitive load and cyberloafing, and a curvilinear relationship between cognitive load and problem-oriented mind wandering mediated by cyberloafing; cognitive load had an inverted U-shaped relationship with daily creativity mediated by cyberloafing and problem-oriented mind wa...

  19. Intelligence and Creativity in Problem Solving: The Importance of Test

    Divergent thinking tests should be more considered as estimates of creative problem solving potential rather than of actual creativity (Runco, 1991). Divergent thinking is not specific enough to help us understand what, exactly, are the mental processes—or the cognitive abilities—that yield creative thoughts (Dietrich, 2007).

  20. Teaching Creativity and Inventive Problem Solving in Science

    Engaging learners in the excitement of science, helping them discover the value of evidence-based reasoning and higher-order cognitive skills, and teaching them to become creative problem solvers have long been goals of science education reformers. But the means to achieve these goals, especially methods to promote creative thinking in scientific problem solving, have not become widely known ...

  21. Don't Let Gen AI Limit Your Team's Creativity

    In a recent real-world experiment, teams engaged in a creative problem-solving task saw modest gains from AI assistance for the most part—and some underperformed. Don't blame the technology ...