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Creativity, Cognition, Creative Drive & Neural Mechanisms

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Creativity, Cognition, Creative Drive & Neural Mechanisms

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Department of Psychology and Methods, Jacobs University Bremen, Bremen, Germany

Department of Psychiatry and Psychotherapy, University Clinic Tübingen, Tübingen, Germany

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).

Introduction

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.

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?

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.

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."

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).

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.

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.

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

Artistic Style Shifts, Dopamine (DA), and Creativity

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

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).

Serotonergic (5-HT) System and 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.

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.

Conclusion

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.

Acknowledgments

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).

Abbreviations

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.

References

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The Link Between Creativity, Cognition, and Creative Drives and Underlying Neural Mechanisms

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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.

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Learning Objectives

By the end of this section, you will be able to:

Describe cognition

Distinguish concepts and prototypes

Explain the difference between natural and artificial concepts

Imagine all of your thoughts as if they were physical entities, swirling rapidly inside your mind. How is it possible that the brain is able to move from one thought to the next in an organized, orderly fashion? The brain is endlessly perceiving, processing, planning, organizing, and remembering-it is always active. Yet, you don't notice most of your brain's activity as you move throughout your daily routine. The infinite amount of sub-routines we organize every day to make up larger behaviors such as driving, operating machinery, participating in sports or even holding conversations (all relatively new behaviors in terms of the evolution of a species) go unnoticed but together allow us to navigate our environment safely and efficiently. There are facets to the multitude of complex processes involved in human cognition and what we understand about animal thought processes. Simply put, cognition is thinking, and it encompasses the processes associated with perception, knowledge, problem-solving, judgment, language, and memory. Scientists who study cognition are searching for ways to understand how we integrate, organize, and utilize our conscious cognitive experiences without being aware of all of the unconscious work that our brains are doing (for example, Kahneman, 2011).

COGNITIVE PSYCHOLOGY - A BRIEF HISTORY

Although discussions and descriptions of thought processes date back millennia to societies such as the ancient Greeks, Egyptians and Maya, the formal scientific study of cognition is relatively new, growing out of philosophical debates including Rene Descartes' 16th century argument suggesting humans are born with innate knowledge and the that the mind and body reflect two different entities. This theory was known as substance dualism. From Descartes' theories in the 16th century, major debates formed on whether human thought is created solely through the stimulation of our sense organs (empiricism) or whether we are born with innate knowledge which allows us to form language and maintain conscious experience (nativism). Supporters of empiricist views included philosophers such as George Berkeley, an Irish bishop who denied the existence of material substance, suggesting instead that the objects we interact with are only ideas in the minds of the perceivers, and John Locke, an English philosopher who founded the study of theory of mind which lead to modern conceptions of identity and the self. Supporters of nativism included Immanuel Kant, a German philosopher who argued that the human mind creates the structure of human experience and that the world (as it is) is independent of humanity's perception of it. These arguments in philosophy would later lead to important advancements in the 19th century by Paul Broca and Carl Wernicke. Paul Broca, a French physician, anatomist, and anthropologist treated a patient now known as "Tan", who with the exception of some curse words, could only create the utterance "tan" when he tried to speak. After the patient died, Dr. Broca inspected his brain and discovered that a specific area of the lateral frontal cortex (now known as " Broca's area") was damaged. He concluded that Broca's areas was an important processing center for language production. Shortly after Broca's publication documenting language deficits related to damage in the lateral frontal cortex, the German physician, psychiatrist, and anatomist, Carl Wernicke, noticed that not all language deficits were related to damage to Broca's area. Wernicke found that damage to the left posterior and superior temporal gyrus resulted in deficits in language comprehension as opposed to language production. This area of the brain is what we now refer to as Wernicke's area and these two findings together provided important evidence for theories related to functional localization within the brain, a theory separate from previous ideas related to the study of phrenology.

Around the turn of the 20th century, experimental research conducted in the experimental labs of Wilhelm Wundt and Ernst Weber in German, and Charles Bell in Britain, lead to the experimental study of behavior. Edward Thorndike's Law of Effect (1898) described how behavior can be shaped by conditions and patterns of reinforcement. Theories in behaviorism were popular until the 1920s, when Jean Piaget began studying thoughts, language, and intelligence as well as how these capabilities change over the course of human development and aging. While WWII was taking the lives of millions of humans across the globe, psychology searched for new and innovative ways of studying human performance in order to address questions such as how to best train soldiers to use new technology, and how attention might be affected by stress. This research eventually lead to Claude Shannon's developments in information theory in 1948, which described the quantification, storage, and communication of information. Developments in computer science soon led to parallels between human thought processes and computer information processing. Newell and Simon's development of artificial intelligence (AI) described both advanced capabilities in computing and descriptive models of cognitive processes. In responses to behaviorists' criticisms of analyzing and modeling thought processes, Noam Chompsky argued against B.F. Skinner's views that language is learned through reinforcement, suggesting that Skinner ignored the human creativity found in linguistics. Within the same decade George Miller published research describing humans' ability to maintain information while performing secondary tasks (Miller, 1956) and founded the Harvard Center for Cognitive Studies. Soon after, the first Cognitive Psychology textbook was published in 1967 by Ulrich Neisser (1967), a former student of George Miller. Neisser was influenced by Gestalt psychologists, Wolfgang Kohler and Hans Wallach, as well as MIT computer scientist Oliver Selfridge. Neisser's definition of the new term "cognition" illustrates the then progressive concept of cognitive processes as:

"all processes by which the sensory input is transformed, reduced, elaborated, stored, recovered, and used. It is concerned with these processes even when they operate in the absence of relevant stimulation, as in images and hallucinations. . . Given such a sweeping definition, it is apparent that cognition is involved in everything a human being might possibly do; that every psychological phenomenon is a cognitive phenomenon. But although cognitive psychology is concerned with all human activity rather than some fraction of it, the concern is from a particular point of view. Other viewpoints are equally legitimate and necessary. Dynamic psychology, which begins with motives rather than with sensory input, is a case in point. Instead of asking how a man's actions and experiences result from what he saw, remembered, or believed, the dynamic psychologist asks how they follow from the subject's goals, needs, or instincts." (page 4 of Neisser's 1967 publication of Cognitive Psychology)

COGNITION

Upon waking each morning, you begin thinking-contemplating the tasks that you must complete that day. In what order should you run your errands? Should you go to the bank, the cleaners, or the grocery store first? Can you get these things done before you head to class or will they need to wait until school is done? These thoughts are one example of cognition at work. Exceptionally complex, cognition is an essential feature of human consciousness, yet not all aspects of cognition are consciously experienced. For example, many decisions we make about choosing to do something or refraining from doing something involve cognitive processes related to weighing options and making comparisons to other events in memory. However, cognition has been argued to not be involved in all the actions we make, such as reflexes that recoil your hand after touching an extremely hot surface, which operate on automatic feedback loops between the effector and spinal cord. Cognition is described in the Oxford dictionary as the mental actions or processes involved in acquiring, maintaining and understanding knowledge through thought, experience and the senses (definition of Cognition from the English Oxford Dictionary, 2018), and is described by Licht, Hull and Ballantyne (2014) as the mental activity associated with obtaining, converting and using knowledge. It is important to recognize that although the term Cognition is an umbrella term that encompasses many different mental processes, similarities exist between how groups define cognition by defining it as a variety of mental processes that allow us to maintain, understand and use information to create knowledge and reflect upon it. Within the pieces that make up cognition, a main component is what is commonly referred to as thinking, which Matlin (2009) has defined as coming to a decision, reaching a solution, forming a belief, or developing an attitude. Again, we see that even a subcomponent of cognition, such as thinking, still represents somewhat of an umbrella term which can be broken up into groups of processes and procedures that make up our thinking. Definitions are not universally accepted, and some groups within psychology consider cognition and thinking as the same group of processes. However, we will use the definitions provided above for the sake of simplicity.

Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem solving, in addition to other cognitive processes. Cognitive psychologists strive to determine and measure different types of intelligence, why some people are better at problem solving than others, and how emotional intelligence affects success in the workplace, among countless other topics. They also sometimes focus on how we organize thoughts and information gathered from our environments into meaningful categories of thought, which will be discussed later. Basically, cognitive scientists work to define the smallest components of what make up broader topics in cognition in order to continue improving working definitions of how we conceptualize human cognition. Many techniques have been discovered that allow psychologists to selectively evaluate and compare different components of cognition. Modern advancements in technology have allowed psychologist to use these methods to collect various forms of cognitive data such as basic measurements of reaction times and response accuracies to more advance techniques of physiological responses, such as eye tracking, electromyography (EMG), electroencephalography (EEG), functional magnetic resonance imaging (fMRI), magnet encephalography (MEG), and positron emission tomography. Cognitive scientists work to create experimental designs using these methods, generate new findings, publish their work and add to the world-wide discussion of how various cognitive processes work and what makes our life experience similar or different from other species.

CONCEPTS AND PROTOTYPES

The human nervous system is capable of handling endless streams of information, as emphasized in the sensation and perception chapter. The senses serve as the interface between the mind and the external environment, receiving stimuli and translating it into nervous impulses that are transmitted to the brain. The brain then processes this information and uses the relevant pieces, which are held in working memory, later expressed through language, or stored in memory for future use. To make this process more complex, the brain does not gather information from external environments only. When thoughts are formed, the brain pulls information from emotions and memories (figure below). Emotion and memory are powerful influences on both our thoughts and behaviors.

In order to organize this staggering amount of information, the brain has developed a file cabinet of sorts in the mind. The different files stored in the file cabinet are called concepts. Concepts are categories or groupings of linguistic information, images, ideas, or memories, such as life experiences. Concepts are, in many ways, big ideas that are generated by observing details, and categorizing and combining these details into cognitive structures. You use concepts to see the relationships among the different elements of your experiences and to keep the information in your mind organized and accessible.

Concepts are informed by our semantic memory (you will learn more about semantic memory in a later chapter) and are present in every aspect of our lives; however, one of the easiest places to notice concepts is inside a classroom, where they are discussed explicitly. When you study United States history, for example, you learn about more than just individual events that have happened in America's past. You absorb a large quantity of information by listening to and participating in discussions, examining maps, and reading first-hand accounts of people's lives. Your brain analyzes these details and develops an overall understanding of American history. In the process, your brain gathers details that inform and refine your understanding of related concepts like democracy, power, and freedom.

Concepts can be complex and abstract, like justice, or more concrete, like types of birds. In psychology, for example, Piaget's stages of development are abstract concepts. Some concepts, like tolerance, are agreed upon by many people, because they have been used in various ways over many years. Other concepts, like the characteristics of your ideal friend or your family's birthday traditions, are personal and individualized. In this way, concepts touch every aspect of our lives, from our many daily routines to the guiding principles behind the way governments function.

HIERARCHIES OF CONCEPTS

Concepts can be understood by considering how they can be organized into hierarchies. At the top are superordinate concepts. This is the broadest category which encompasses all the objects belonging to a concept. The subordinate concept of "furniture" covers everything from couches to nightstands. If we were to narrow our focus to include only couches, we are considering the midlevel or basic level of the hierarchy. This is still a fairly broad category, but not quite as broad as the superordinate concept of furniture. The midlevel category is what we use most often in everyday life to identify objects. Sub-ordinate concepts are even narrower, referring to specific types. To continue with our example, this would include loveseat, a La-Z-Boy, or sectional.

Another technique used by your brain to organize information is the identification of prototypes for the concepts you have developed. A prototype is the best example or representation of a concept. For example, for the category of civil disobedience, your prototype could be Rosa Parks. Her peaceful resistance to segregation on a city bus in Montgomery, Alabama, is a recognizable example of civil disobedience. Or your prototype could be Mohandas Gandhi, sometimes called Mahatma Gandhi ("Mahatma" is an honorific title).

Mohandas Gandhi served as a nonviolent force for independence for India while simultaneously demanding that Buddhist, Hindu, Muslim, and Christian leaders-both Indian and British-collaborate peacefully. Although he was not always successful in preventing violence around him, his life provides a steadfast example of the civil disobedience prototype (Constitutional Rights Foundation, 2013). Just as concepts can be abstract or concrete, we can make a distinction between concepts that are functions of our direct experience with the world and those that are more artificial in nature.

NATURAL AND ARTIFICIAL CONCEPTS

In psychology, concepts can be divided into two categories, natural and artificial. Natural concepts are created "naturally" through your experiences and can be developed from either direct or indirect experiences. For example, if you live in Essex Junction, Vermont, you have probably had a lot of direct experience with snow. You've watched it fall from the sky, you've seen lightly falling snow that barely covers the windshield of your car, and you've shoveled out 18 inches of fluffy white snow as you've thought, "This is perfect for skiing." You've thrown snowballs at your best friend and gone sledding down the steepest hill in town. In short, you know snow. You know what it looks like, smells like, tastes like, and feels like. If, however, you've lived your whole life on the island of Saint Vincent in the Caribbean, you may never have actually seen snow, much less tasted, smelled, or touched it. You know snow from the indirect experience of seeing pictures of falling snow-or from watching films that feature snow as part of the setting. Either way, snow is a natural concept because you can construct an understanding of it through direct observations or experiences of snow.

An artificial concept, on the other hand, is a concept that is defined by a specific set of characteristics. Various properties of geometric shapes, like squares and triangles, serve as useful examples of artificial concepts. A triangle always has three angles and three sides. A square always has four equal sides and four right angles. Mathematical formulas, like the equation for area (length × width) are artificial concepts defined by specific sets of characteristics that are always the same. Artificial concepts can enhance the understanding of a topic by building on one another. For example, before learning the concept of "area of a square" (and the formula to find it), you must understand what a square is. Once the concept of "area of a square" is understood, an understanding of area for other geometric shapes can be built upon the original understanding of area. The use of artificial concepts to define an idea is crucial to communicating with others and engaging in complex thought. According to Goldstone and Kersten (2003), concepts act as building blocks and can be connected in countless combinations to create complex thoughts.

SCHEMATA

A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.

There are several types of schemata. A role schema makes assumptions about how individuals in certain roles will behave (Callero, 1994). For example, imagine you meet someone who introduces himself as a firefighter. When this happens, your brain automatically activates the "firefighter schema" and begins making assumptions that this person is brave, selfless, and community-oriented. Despite not knowing this person, already you have unknowingly made judgments about him. Schemata also help you fill in gaps in the information you receive from the world around you. While schemata allow for more efficient information processing, there can be problems with schemata, regardless of whether they are accurate: Perhaps this particular firefighter is not brave, he just works as a firefighter to pay the bills while studying to become a children's librarian.

An event schema, also known as a cognitive script, is a set of behaviors that can feel like a routine. Think about what you do when you walk into an elevator. First, the doors open and you wait to let exiting passengers leave the elevator car. Then, you step into the elevator and turn around to face the doors, looking for the correct button to push. You never face the back of the elevator, do you? And when you're riding in a crowded elevator and you can't face the front, it feels uncomfortable, doesn't it? Interestingly, event schemata can vary widely among different cultures and countries. For example, while it is quite common for people to greet one another with a handshake in the United States, in Tibet, you greet someone by sticking your tongue out at them, and in Belize, you bump fists (Cairns Regional Council, n.d.)

Because event schemata are automatic, they can be difficult to change. Imagine that you are driving home from work or school. This event schema involves getting in the car, shutting the door, and buckling your seatbelt before putting the key in the ignition. You might perform this script two or three times each day. As you drive home, you hear your phone's ring tone. Typically, the event schema that occurs when you hear your phone ringing involves locating the phone and answering it or responding to your latest text message. So without thinking, you reach for your phone, which could be in your pocket, in your bag, or on the passenger seat of the car. This powerful event schema is informed by your pattern of behavior and the pleasurable stimulation that a phone call or text message gives your brain. Because it is a schema, it is extremely challenging for us to stop reaching for the phone, even though we know that we endanger our own lives and the lives of others while we do it (Neyfakh, 2013).

Remember the elevator? It feels almost impossible to walk in and not face the door. Our powerful event schema dictates our behavior in the elevator, and it is no different with our phones. Current research suggests that it is the habit, or event schema, of checking our phones in many different situations that makes refraining from checking them while driving especially difficult (Bayer & Campbell, 2012). Because texting and driving has become a dangerous epidemic in recent years, psychologists are looking at ways to help people interrupt the "phone schema" while driving. Event schemata like these are the reason why many habits are difficult to break once they have been acquired. As we continue to examine thinking, keep in mind how powerful the forces of concepts and schemata are to our understanding of the world.

SUMMARY

In this section, you were introduced to cognitive psychology, which is the study of cognition, or the brain's ability to think, perceive, plan, analyze, and remember. Concepts and their corresponding prototypes help us quickly organize our thinking by creating categories into which we can sort new information. We also develop schemata, which are clusters of related concepts. Some schemata involve routines of thought and behavior, and these help us function properly in various situations without having to "think twice" about them. Schemata show up in social situations and routines of daily behavior.
References:
Openstax Psychology text by Kathryn Dumper, William Jenkins, Arlene Lacombe, Marilyn Lovett and Marion Perlmutter licensed under CC BY v4.0. https://openstax.org/details/books/psychology

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By the end of this section, you will be able to: Describe cognition Distinguish concepts and prototypes Explain the difference between natural and artificial concepts Imagine all of your thoughts as if they were physical entities, swirling rapidly inside your mind.

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Metacognitive Components in Creative Thinking

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Metacognitive Components in Creative Thinking

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Abstract

Metacognition refers to the knowledge and regulation of one's own cognitive processes, which has been regarded as a critical component of creative thinking. However, the current literature on the association between metacognition and creative thinking remains controversial, and the underlying role of metacognition in the creative process appears to be insufficiently explored and explained. This review focuses on the roles of three aspects of metacognition (i.e., metacognitive knowledge, metacognitive experience, and metacognitive monitoring and control) in creative thinking and offers a primary summary of the neurocognitive mechanisms that support metacognition during creative thinking. Future research is needed to explore the interactive effects of the metacognitive components on creative thinking and to elucidate the function of metacognition during different stages of the creative process.

Keywords: creative thinking, metacognitive knowledge, metacognitive experience, metacognitive monitoring and control, creative process

Introduction

Metacognition is viewed as the ability to think about one's current cognitive processes ( Flavell, 1976). It is also called "cognition about cognition," which plays a top-down regulation role in various cognitive processes, such as learning, memory, decision-making, and other high-level cognition ( Son and Metcalfe, 2000; Metcalfe, 2002; Ariel et al., 2009). Creativity, a unique ability of human beings, refers to generating original and useful ideas or developing novel solutions to problems under a given context ( Runco, 2010; Runco and Acar, 2012; Abraham, 2013). In the past decade, researchers have hypothesized that creative thinking may rely on metacognition components ( Davidson and Sternberg, 1998; Berkowitz and Ansari, 2008; Lizarraga and Baquedano, 2013; Erbas and Bas, 2015; Preiss et al., 2016). We believe that a relevant review and discussion of this topic can not only enrich the current theories of creative thinking but also provide a new direction for the cultivation of creativity.

Investigations of the processing mechanism that underlies creative thinking have typically considered metacognition as a single cognitive component, such as self-regulation during representational change and metacognitive self-monitoring or self-confidence when outputting the answer ( Hong et al., 2016; Rudolph et al., 2017). Although researchers in the creativity field have emphasized the special role of metacognition, to the best of our knowledge, very few theoretical or empirical studies have clarified how metacognition affects creative thinking. Early on, researchers emphasized creative thinking as a self-regulated metacognitive process ( Pesut, 1990). For example, some researchers have advanced the concept of "creative metacognition," which is a combination of self-knowledge (e.g., knowing one's own creative advantages and disadvantages in a certain field) and contextual knowledge (e.g., knowing when/where/how/why to be creative, Feldhusen and Goh, 1995; Davidson and Sternberg, 1998; Kaufman and Beghetto, 2013). In addition, a few empirical studies have examined the relationship between metacognition and creative thinking in terms of the following three aspects: (1) exploring the positive/negative correlation between metacognition and creative thinking via behavioral investigation ( Lizarraga and Baquedano, 2013; Erbas and Bas, 2015; Hong et al., 2016; Preiss et al., 2016); (2) understanding the function of brain regions activated in creative thinking from the metacognition perspective, for example, the anterior cingulate gyrus (ACC) and dorsolateral prefrontal cortex (DLPFC, Geake and Hansen, 2005; Berkowitz and Ansari, 2008; Kounios et al., 2008); and (3) enhancing individual creative thinking by metacognitive training ( Hargrove, 2013; Abdivarmazan et al., 2014; Hargrove and Nietfeld, 2015). Although theoretical and empirical studies have indicated that metacognition may be critically involved in creative thinking, the conclusion regarding whether metacognition has a positive or negative effect on creative thinking and how it engages in the creative process remains controversial. Therefore, this article systematically disentangles the roles of the three components of metacognition in creative thinking and discusses several central issues in the current literature to guide future research.

The Construct of Metacognition

In general, metacognition refers to individuals' ability to have knowledge, awareness, and control of their cognitive activities ( Nelson, 1990). The concept of metacognition is regarded as being fuzzy with indistinct boundaries, as researchers have often classified it into the three interconnected components of metacognitive knowledge, metacognitive experience, and metacognitive monitoring and control ( Flavell, 1979). Specifically, metacognitive knowledge, which refers to the declarative knowledge of cognitive processes and products ( Dowson and Mcinerney, 2004; Efklides, 2011), has generally been divided into personal knowledge (e.g., hobbies, memory characteristics, ways of thinking, and ability limitations); task knowledge (e.g., task structures, task goals); and strategic knowledge (e.g., advantages or disadvantages and the applicability of each strategy). Metacognitive experience, the cognitive or emotional experience that accompanies cognitive activity, can occur in the early, middle, and late stages of cognitive activity ( Flavell, 1979). Metacognitive experience is not a cognitive operation itself but an individual's subjective perception of the ease or difficulty of certain cognitive operations ( Rummer et al., 2016). In addition, metacognitive monitoring and control refers to an individual's self-conscious supervision and regulation of the cognitive processes. Specifically, metacognitive monitoring includes individuals ability to plan, monitor, and evaluate their cognitive activities, followed by subsequent metacognitive control that allows individuals to regulate their cognitive processes, such as adjusting task goals, distributing study time, and selecting cognitive strategies ( Flavell, 1979).

The Construct of Creative Thinking

A standard definition of creativity has lacked consensus, as the construct of creativity is complex and different disciplines have distinct focuses. Early researchers were more likely to consider creativity as a personal trait, such as personality ( Guilford, 1950; Eysenck, 1993). With the development of experimental technology in the field of psychology, especially neuroimaging methods, a clear operational definition may benefit from investigations into the nature of creativity; thus, most researchers have viewed creativity as a problem-solving ability, namely, the ability to imagine novel or useful ideas or products in a given context ( Sternberg and Lubart, 1999; Runco, 2010). In addition, some comprehensive frameworks have attempted to describe a profile of creativity. Batey (2012) proposed an integrative perspective of the 4-P model of creativity by emphasizing the following four dominant factors of creativity: person-individual traits or characteristics; process-thought process involved in the creation of ideas; press-environmental influences; and product-output from creative activity. These four factors are highly interrelated, as a product is created by a series of cognitive processes that a person uses in a specific environment. Furthermore, it can be recognized that the lack of a consensual definition of creativity has led to a multitude of measurement approaches. A review of research methods in creativity studies (2003-2012) revealed that researchers have relied heavily on divergent thinking tests, problem-solving tasks or products to assess creativity ( Long, 2014).

Eysenck (1993) framed divergent-convergent interactions as important to conceptualizations of creativity. That is, creativity could be described as a constant oscillation between divergent and convergent thinking ( Finke et al., 1992; Bink and Marsh, 2000). Specifically, divergent thinking refers to the expansive generation of novel ideas for an open-ended problem, whereas convergent thinking emphasizes producing a single response from all possible answers to a given problem ( Guilford, 1967). The differences between these two types of creative thinking lead to distinct measurement approaches. Generally, divergent thinking can be assessed by a diverse set of tasks, such as the classic Alternative Uses Task (AUT, Guilford, 1967), Torrance Test of Creative Thinking ( Wallach and Torrance, 1968), and Multiple Choice Test ( Auzmendi et al., 1996). The degree of divergent thinking (i.e., scoring) mainly depends on the sum of fluency, flexibility, and originality of ideas. In contrast, convergent thinking is typically assessed by the Remote Associations Test (RAT, Mednick, 1962), insight problem-solving tasks ( Luo and Knoblich, 2007), and creative analogical reasoning ( Zhang et al., 2014). Although these two prominent measures do not guarantee actual creative thinking performance, compelling evidence well supports the construct validity of the two psychometric tasks for creative thinking.

The Intersection of Metacognition and Creative Thinking

Creative thinking can be regarded as a metacognitive process in which the combination of individual's cognitive knowledge and action evaluation results in creation. Specifically, creative thinking involves a series of cognitive processes, such as the acquisition of knowledge and skills, the transformation of knowledge into new forms, and the verification of products from internal and external standards ( Amabile, 1983). It seems to be appropriate to involve metacognition in these stages due to its crucial role in high-level cognition. For example, for any creative action to be successful, relevant prior knowledge must be consciously selected, and a work plan must be implemented. Moreover, the strategies must be flexibly adjusted, and the originality and utility of products must be evaluated. In fact, all of these functions are metacognitive in nature, and their use would likely enhance creativity ( Armbruster, 1989). Accordingly, we systemically review the role of the three components of metacognition in creative thinking.

Metacognitive Knowledge and Creative Thinking

Metacognitive knowledge guides individuals to select, evaluate, and correct cognitive strategies, which are important for creative thinking. Empirically, several works have shown that individual's metacognitive knowledge contributes to domain-specific creativity. For example, Lizarraga and Baquedano (2013) found a moderate correlation between metacognitive knowledge and visual-spatial creativity (e.g., drawing and titling four drawings from provided lines), and similar findings were reported on mathematic creativity ( Erbas and Bas, 2015). Fayenatawil et al. (2011) adopted a protocol analysis to examine both artists and non-artists during the creation of original drawings. The results revealed that artists who possess much more metacognitive knowledge of plans, goals, and descriptions performed better than non-artists in an artistic creation task. Additionally, Zeng et al. (2011) constructed a conceptual model of the IT creativity of studying and designing computer hardware or software and found that the metacognitive knowledge about explicit problem analysis, remote association, abstraction, and domain-specific knowledge played important roles in the analysis, ideation, evaluation, and implementation of IT creativity, respectively.

Several intervention studies have found that the training of metacognitive knowledge promotes creative problem solving. For instance, Abdivarmazan et al. (2014) used a pretest-posttest design to examine the effect of training metacognitive knowledge for problem solving. The subjects were divided into an experimental group and a control group. The experimental group received metacognitive strategy knowledge training a total of eight times (50 min each time), while the control group did not receive any intervention. The results showed that metacognitive knowledge training can significantly improve creative problem solving. This intervention effect is consistent with previous findings ( Hargrove, 2013).

Nevertheless, Preiss et al. (2016) found no correlation between individual metacognitive knowledge and creative thinking. In their study, the AUT and the compound word association task were used to measure creative thinking, and the self-reporting scale was used to evaluate individual declarative strategic knowledge about planning, monitoring, and regulating ( Dowson and Mcinerney, 2004). The results showed that metacognitive knowledge did not significantly predict the performance in either of the two creative thinking tasks after controlling for fluid intelligence and reading difficulties.

Not all empirical studies have found a positive correlation between metacognitive knowledge and creative thinking, and several limitations should be considered. First, an individual's metacognition knowledge assessed through a self-report approach ( Antonietti et al., 2000; Hargrove and Nietfeld, 2015; Preiss et al., 2016) has been debated due to potential problems with its reliability and validity. Previous studies have suggested that unskilled individuals always exaggerate their self-assessment because they have poor analytical ability ( Kruger and Dunning, 1999) and are overly interested in motivations and intentions ( Kruger and Gilovich, 2004; Pronin, 2008). Similarly, Preiss et al. (2016) suggested that the self-report method may not accurately reflect metacognitive knowledge-especially the metacognitive strategic knowledge of planning, monitoring, and regulation-for individuals who have difficulty in recognizing their abilities. Second, there is a dissociation between self-report metacognitive knowledge and its application to specific tasks, and self-report metacognitive knowledge may not directly affect task performance ( Scherer and Tiemann, 2012; Hargrove and Nietfeld, 2015). Third, existing studies have mainly focused on the role of metacognitive strategic knowledge in creative thinking, whereas an examination of the other two variables (personal knowledge and task knowledge) is disregarded. For example, creative mindsets, a type of metacognitive knowledge that refers to individuals' incremental or entity-mindset view of creativity (e.g., Creative mindsets, O'Connor et al., 2013), may influence their creative performance ( O'Connor et al., 2013; Karwowski, 2014). That is, individuals with different types of creativity mindsets have different cognitive processing characteristics, such as having different ways of learning, orienting toward a target, making strategic choices, and cognitive persistence ( Dweck and Leggett, 1988; De Dreu et al., 2008; Baas, 2010; Benedek et al., 2011; Roskes et al., 2012), which are regarded as critical aspects of creativity. It is inferred that creative mindsets may indirectly influence creativity through other cognitive variables. Therefore, more empirical studies are needed to uncover the mechanism of the different components of metacognitive knowledge in creative thinking.

Metacognitive Experience and Creative Thinking

Numerous empirical studies have confirmed that metacognitive experience can be indicated by the metacognitive cue of processing fluency ( Koriat et al., 2004; Oppenheimer, 2008; Alter and Oppenheimer, 2009b; Jia et al., 2016). Processing fluency, the subjective feeling of the ease of information processing ( Koriat et al., 2004), influences a variety of cognitive tasks, such as factual preferences, aesthetic appreciation, brand assessment, and reading comprehension ( Alter and Oppenheimer, 2009a; Miele and Molden, 2010). For the relationship between processing fluency and creative thinking, previous research has revealed that processing fluency affects a series of cognitive activities involved in creative thinking ( Gilhooly et al., 2007) such as goal setting ( Storbeck and Clore, 2007), work efforts ( Miele and Molden, 2010), strategy choice ( Lucas and Nordgren, 2015), and processing styles ( Alter et al., 2007).

Mehta et al. (2012) asked 95 participants to complete the AUT and RAT with different levels of background noise. Meanwhile, a 7-point scale that contained three questions was used to assess the subjective level of processing disfluency. The results showed that a moderate (vs. low) level of noise induced higher processing disfluency and consequently enhanced creative thinking performance. Alter and Oppenheimer (2009a) suggested that processing disfluency could induce individual's higher construal thinking and less attention-focused, which were beneficial to creative thinking.

Moreover, processing fluency could also influence creative thinking by inducing different types of processing styles. Alter et al. (2007) argued that processing fluency can induce different degrees of intuition and analytical processing. That is, if information processing is perceived as easy and fluent, much more intuitive processing will be activated; conversely, if information processing is perceived as difficult and disfluent, a much greater analytical processing style will be activated ( Kuhl et al., 2014). Mehta et al. (2012) found that the disfluent processing experience allows individuals to use more analytical processing, which, in turn, promotes creative thinking performance as measured by both the AUT and RAT. In addition, neurophysiological evidence has revealed that processing disfluency induces the activation of the anterior cingulate cortex ( Boksman et al., 2005) and the prefrontal cortex (PFC), which allows people to think thoughtfully and use analytical processing to complete creative tasks ( Goel et al., 2000; Botvinick et al., 2001; Lieberman et al., 2002). Taken together, these results suggest that processing disfluency could promote creative thinking by activating a much higher level of analytical processing.

Nevertheless, the notion that overly analytical processing induced by processing disfluency impedes convergent thinking has been supported by some studies ( Friedman and Forster, 2005; Aiello et al., 2012). For example, in the study by Aiello et al. (2012) in which both bilingual and monolingual participants completed the RAT before or after an artificial grammar task with or without the "use your gut" instruction (just go with your "gut feeling" to make a decision), the results showed that the completion of an artificial grammar task with the "use your gut" instruction before enhanced the RAT performance, suggesting the beneficial role of a less analytic approach in the RAT performance. Similarly, another effective indicator of convergent thinking-insight problem solving, which involves an "aha!" experience that the solution could occur in a sudden and unpredictable manner with little or no conscious processing, has been confirmed to be inhibited much more by analytical processing ( Metcalfe and Wiebe, 1987; Qiu and Zhang, 2008).

Whether the metacognitive experience reflected by processing fluency promotes or inhibits creative thinking is controversial. There are several reasons for this controversy. First, different types of creative thinking, such as divergent and convergent thinking, may have different relationships with processing fluency. According to Benedek et al. (2011), different types of creative thinking have significant differences in processing mechanisms. Specifically, divergent thinking tasks involve analytical processing ( Unsworth et al., 2011), whereas too much analytical processing may inhibit convergent thinking tasks as a requirement of a novel representation for problems and the search for remote connections to memory ( Metcalfe and Wiebe, 1987). Therefore, the differentiated roles of processing fluency in divergent and convergent thinking should be considered. Second, the problem of the classification and operation of the metacognitive experience may be partly responsible for the controversial results. Previous studies, however, have paid less attention to exploring this issue. To be more specific, processing fluency, an indicator of the metacognitive experience which has always been used in previous studies, could be divided into perceptual fluency, encoding fluency, and retrieval fluency, whereas these distinct types of processing fluency may have different effects on different types of creative thinking ( Koriat et al., 2004). For example, the AUT, which requires individuals to generate as many novel ideas as possible, was relied on the fast and effective strategic memory retrieval ability ( Forthmann et al., 2019). That is, the retrieval fluency could play a key role in the creative ideas production. Conversely, the RAT requires individuals to generate a target word from a set of cue words, which means that perceptual and encoding fluency may influence the results. Third, the indirect ways of manipulating processing fluency, such as pre-experiment tasks or noise activation, are greatly affected by additional factors beyond the experiment ( Mehta et al., 2012). Therefore, the direct ways of disrupting subjective feelings of fluency, such as font style manipulation ( Alter and Oppenheimer, 2009b; Jia et al., 2016), semantic priming ( Winkielman and Cacioppo, 2001), and statement-background color contrast ( Hansen et al., 2008), should be investigated in future studies.

Metacognitive Monitoring and Control and Creative Thinking

It is worth mentioning that metacognition can be divided into the "knowledge of cognition" and the "regulation of cognition" by using a dichotomy ( Brown, 1978). The regulation of the cognition component includes individual's planning, examining, monitoring, testing, and evaluating cognitive activities, which corresponds to "metacognitive monitoring and control." Thus, we now comprehensively introduce the relationship between "metacognitive monitoring and control" and the "regulation of cognition" and creativity.

Sternberg (1985) argued that the process of creative thinking involved "self-monitoring" by monitoring other components through metacognition. Evidence from cognitive neuroscience studies reveals that the brain regions responsible for creative thinking overlap with the activated brain regions in metacognition monitoring and control, which mainly involve the dorsolateral prefrontal and ventrolateral prefrontal cortexes ( Carlsson et al., 2000; Zysset et al., 2001). Empirically, Zhang and Xiao (1996) asked participants to complete the Mutilated Chickboard problem, which requires people to change the representation from the space of all possible coverings to the "meta-level" space to find the correct problem representation. Their results showed that successful problem solvers were better at monitoring, transforming, and adjusting their search strategies according to changeable problem conditions, suggesting the positive effect of metacognitive monitoring and control on creative problem solving. Similarly, Xing and Chen (2009) further revealed that individuals with higher metacognitive monitoring and control abilities showed better performance at solving a Chinese logogriph task (i.e., a type of creative problem-solving task in which participants respond to puzzles) than individuals with a lower ability. The process monitoring theory proposed by Macgregor et al. (2001) explains that metacognitive monitoring and control ability can constantly monitor the gap between the existing state and the target state and then adjust cognitive strategies to access creative problem solving.

Moreover, intervention studies have shown that metacognitive skills training could promote creative thinking ( Atman et al., 2005). For example, Hargrove (2013) divided participants into an intervention group and a control group by counterbalancing their professional categories and genders. The participants in the intervention group received 1-2 semesters (17 h/semester) of metacognitive skills training to learn how to plan and implement thinking strategies, how to monitor and evaluate the quality of thinking, and how to amend incorrect thinking, whereas the participants in the control group received only professional courses every semester. The participants in the intervention group showed a significantly higher level of creative thinking as measured by the RAT and an art design task. A similar effect was also reported by Hargrove and Nietfeld (2015).

Since Kaufman and Beghetto (2013) proposed the concept of creative metacognition, a growing number of studies have attempted to examine creative metacognitive monitoring accuracy, which can be assessed by comparing a general self-external assessment. Silvia et al. (2008) required participants to complete the AUT and then asked them and external raters to indicate the most creative responses from the reaction pool. The results showed that when the level of creative ability was higher, the participants more accurately monitored their responses. Beghetto et al. (2011) further examined this issue by asking primary school students to assess their creative ability in mathematics and science and observed that creative metacognitive monitoring accuracy (one type of metacognitive knowledge) can significantly explain the instructor's assessment of creative ability. Priest (2006) and Kaufman et al. (2010), however, did not find significant correlations between creative thinking and metacognitive monitoring and control in the art, writing, and musical fields.

The lack of a correlation between creative thinking and metacognitive monitoring and control can be found in other empirical studies. Metcalfe (1986) asked participants to make feeling-of-knowing judgments, an index of metacognitive monitoring ( Maclaverty and Hertzog, 2009), for creative problems and then give corresponding answers within 5 min. If the participants realized that the correct answer was closer, the value of the feeling-of-judgment would be higher. However, the results showed that the value of the feeling-of-judgment did not relate to the probability of producing the correct answer. Hong et al. (2016) asked participants to complete a divergent thinking task of creating a new cultural environment and to answer eight questions, such as "I always monitor my job completion process," in order to measure their metacognitive plans and monitoring. The results showed that individuals' metacognitive monitoring had no significant effect on their divergent thinking performance.

Overall, the conclusion that a positive correlation exists between metacognitive monitoring and control and creative thinking may not be as stable as we expected. In fact, metacognitive monitoring and control includes a set of subcomponents, such as goal setting, planning execution, strategy selection, and cognitive assessment ( Flavell, 1976). Many previous studies have either focused on either one or some subcomponents of metacognitive monitoring and control. For example, Hong et al. (2016) explored only the effects of planning and monitoring subcomponents on divergent thinking but summarized the results at the overall level. This method is likely to result in biased or conflicting evidence. In addition, according to Kelemen et al. (2000), there are both "trait" and "situational" metacognitive monitoring and control, which have different concepts and measurements ( Veenman et al., 2004; Preiss et al., 2016). Hong et al. (2016) found that individuals' situational metacognitive monitoring and control have no significant effect on creative thinking while controlling for the variable of an individual's trait metacognitive monitoring and control. Therefore, their confusion in related studies could at least partly account for the inconsistency in the related results. Generally, all of the above problems should be considered to obtain a better understanding of the relationship between creative thinking and metacognitive monitoring and control.

Neurophysiological Evidence of Metacognition and Creative Thinking

The general framework of metacognition is characterized by the interplay of meta-level and object-level information ( Nelson, 1990). The object-level refers to one's current cognitive processes (e.g., perception, attention, and decision making), which are monitored or controlled at the meta-level. Previous cognitive neuroscience evidence suggests that the PFC plays a central role in the processing of meta-level top-to-bottom adjustment of the object-level ( Fernandezduque et al., 2000). Specifically, the PFC regulates the posterior cortical circuit involvement in object-level processing through a filtering mechanism. In recent years, there has been increasing interest in identifying the regions in the PFC involved in metacognition, including the lateral prefrontal cortex (LPFC), medial prefrontal cortex (mPFC), and DLPFC. These brain regions are responsible for different functions in metacognition ( Christoff et al., 2003; Fleming et al., 2010; Fleming and Dolan, 2012). For example, a metacognitive assessment of cognitive tasks (e.g., working memory, episodic memory retrieval, and abstract thinking) induces greater activation of the lateral PFC ( Braver and Bongiolatti, 2002; Christoff et al., 2009), whereas metacognitive judgment generally activates the rostral medial prefrontal cortex (RMPFC, prospective judgment), the rostral lateral prefrontal cortex (RLPFC, retrospective judgment, Fleming and Dolan, 2012) and the DLPFC. An fMRI study found that the DLPFC and VLPFC were activated when tasks involved the metacognitive inhibition of sensory information, whereas the DMPFC and DLPFC were activated when tasks (e.g., the Stroop task) involved metacognitive control for concurrent conflicts ( Zysset et al., 2001).

Recently, some noninvasive brain stimulation and lesion studies have suggested that a disabled PFC can affect metacognitive monitoring in perceptual decision making ( Cul et al., 2009; Rounis et al., 2010; Ham et al., 2014). Despite this, the neural mechanism that underlies individual metacognition remains controversial. One core component of this controversy is whether functional segregation exists in the prefrontal system that is specific to metacognition. Qiu et al. (2017) used a novel decision-redecision paradigm, in which participants make an initial decision on perceptual and rule-based decision-making tasks (decision phase) followed by another decision on the same tasks (redecision phase), to examine the underlying neural substrates of metacognition on decision making. The results revealed that the dACC is responsible for decision uncertainty monitoring, while the FPC is responsible for the metacognitive control of decision adjustment, suggesting a disconnected role in the PFC and a distinct role in metacognition.

Interestingly, these studies of metacognition show brain recruitment (e.g., ACC, IFG, mPFC, and DLPFC) similar to that in creative thinking ( Dietrich and Kanso, 2010; Fink et al., 2012; Fox and Christoff, 2014). Specifically, the LPFC (including the IFG and DLPFC) is essential to various creativity ( Aziz-Zadeh et al., 2010). Similarly, the ACC, which plays a role in the solutions monitoring, was also confirmed to be activated during the creative process ( Geake and Hansen, 2005; Berkowitz and Ansari, 2008; Kounios et al., 2008). A study regarding musical improvisation found that pianists exhibited stronger activation in the ACC under conditions of rhythmic and melodic freedom, suggesting the positive effect of metacognition for monitoring the conflicts among different melodies or rhythms on the creative process ( Berkowitz and Ansari, 2008).

In addition, several fMRI studies have reported that metacognition is associated with the anterior insula, which is responsible for promoting the individual consciousness of emotional and physical states ( Craig, 2009), and for delivering this information to PFC areas ( Fleming and Dolan, 2012). For example, people who have had mindfulness training are more likely to perceive their thoughts, emotions, and physical state; in addition, they show stronger activation in the insula and the lateral prefrontal cortex ( McCaig et al., 2011). Similarly, the mPFC and the anterior insula can also be activated in the generation stage in multiple creative tasks ( Geake and Hansen, 2005; Howard-Jones et al., 2005; Limb and Braun, 2008). According to the two-stage theory of the creative process ( Ellamil et al., 2016), metacognition might play different roles in idea generation and idea evaluation. A low level of metacognitive control can make more diverse pieces of information appear to enter the mind to construct more novel ideas at the stage of idea generation, whereas at the stage of idea evaluation, the activation of the metacognitive system can contribute to evaluations of the novelty and utility of the spontaneous ideas generated during the previous stage. Based on this framework, the latter process may possibly be associated with cognitive activation and positive emotion, which further guides the ensuing idea generation. This neurophysiological evidence from the aforementioned studies reveals that the prefrontal regions related to metacognition are involved not only in monitoring but also in value evaluation and emotion in the creative process.

Concluding Remarks and Future Directions

The present study primarily focuses on the intersection between metacognition and creative thinking. Although increasing research points out that metacognition may play an important role in creative thinking, the empirical studies reviewed in the present study have not reached a consensus. Some obvious limitations remain. First, previous studies have mainly applied correlational approaches to investigate the intersection between metacognition and creative thinking and have neglected to reveal the cause-and-effect between the two constructs. Future research is particularly essential to explore the internal mechanism of metacognition that affects creative thinking. Second, the reliability and validity of metacognition measurements are controversial. Specifically, self-reporting is greatly influenced by subjective expectations, whereas a think-aloud protocol is time consuming, and discourse analysis is subject to the quality of the interpersonal interaction among groups ( Desoete, 2008). To avoid unexpected factors generated by these methods, objective measurement indexes such as prospective monitoring, retrospective monitoring, and the judgment of confidence ( Bjork et al., 2013) could be promising ways to assess metacognition. Third, the three components of metacognition are independent but closely interrelated ( Dowson and Mcinerney, 2004; Efklides, 2011). Previous research has always focused on metacognition as a whole or a single component, which has led to the lack of an interaction effect of the three metacognitive subcomponents on creativity. Fourth, the differentiated effects of metacognition on different types of creative thinking have not yet been described. Accordingly, we discuss two important directions in future research as follows.

Exploring the Role of Metacognition in the Creative Process

According to the aforementioned 4-P model of creativity ( Batey, 2012), it should be acknowledged that most of the previous studies have tended to explore the relationship between metacognition and creativity outcomes (e.g., responses in the AUT) but have neglected to discuss the role of metacognition during the dynamic creative process. The creative process, namely, the sequence of thoughts and actions that leads to novel, adaptive productions ( Lubart, 2001), has been identified as the combination of a series of cognitive processes. According to the classic four-stage model proposed by Guilford (1950), the creative process can be divided into the following four stages: reparation-consciously define and establish the problem; incubation-no conscious mental work on the problem; illumination-the promising idea breaks through to conscious awareness; and verification-evaluate and refine ideas. Whether metacognition plays a different role in different stages of the creative process remains an open question. Armbruster (1989) suggested that the role of metacognition in incubation may be unconscious, whereas it is conscious in verification. Similarly, the geneplore model of the creative process (i.e., idea generation, namely, operating on unstructured, illogical thoughts to produce ideational materials, and idea evaluation, namely, controlling, evaluating, and selecting the best ideas) suggests that the idea generation stage requires no participation of metacognition to produce many more ideas, whereas the idea evaluation stage needs the participation of metacognition to assess the originality and usefulness of ideas ( Fox and Christoff, 2014). In addition, Shen et al. (2013) demonstrated that P2 in processing creative problems, as a stimulus-driven frontal metacognitive mechanism, reflects preconscious awareness of the mental impasse at a relatively early rather than the late stage of creative problem solving.

Nevertheless, in investigations of the current issue, regarding metacognition as a whole remains controversial due to its complex construct. Perhaps different components of metacognition have different effects on the creative process. In a recent study, for example, Jankowska et al. (2018) integrated the psychometric approach, eye-tracking methodology, and thinking-aloud protocols and found that the three categories of metacognition play different roles in the creative process. Specifically, one category of exploratory activities was demonstrated to be essential in the initial phase of the creative process, while another two categories, decision-making and control activities and affective-evaluation activities, were involved in the entire creative process. From this independent point of view, we propose that the effect of metacognitive monitoring can be separated from metacognitive control on the creative process. According to the monitoring-affect-control hypothesis ( Nelson and Leonesio, 1988), metacognitive control may be the result of prior metacognitive monitoring ( Metcalfe and Finn, 2008). For instance, individuals can adjust the strategy selection (an indicator of metacognitive control, Beaty and Silvia, 2012) during the generation of the next idea according to self-assessment of previous ideas originality (an indicator of metacognitive monitoring, Silvia et al., 2008). In this case, we believe that separating the two subcomponents leads to a better understanding of the dynamic monitoring-affect-control process in creative thinking.

Although the three key components of metacognition have been discussed separately, these components are not independent as an interactive system ( Efklides, 2011). That is, metacognitive monitoring and control could be activated by relying on metacognitive knowledge and the information provided by metacognitive experiences about the flow of cognitive processing. Accordingly, how these three factors interact in the process of creative thinking remains unclear. Here, we take a creative metacognitive monitoring accuracy, processing fluency (an index of metacognitive experience), and metacognitive monitoring accuracy as examples. Previous studies have demonstrated that individuals with different types of creative mindsets exhibited significant differences in their interpretation of the experience of process disfluency ( Miele et al., 2011) and in their level of metacognitive monitoring accuracy ( Blackwell et al., 2007). When completing a creative thinking task, individuals with an incremental creative mindset could interpret their processing disfluency as lacking in effort and would show much greater cognitive persistence, whereas individuals with an entity creative mindset could interpret it as an ability deficiency and would give up on further cognitive persistence. Meanwhile, individuals with an incremental creative mindset showed better performance in the metacognitive monitoring of the selecting and evaluating strategies than individuals with an entity creative mindset.

Under the framework of the dynamical creative process, the effect of metacognition components and their interaction on the creative process could be helpful for understanding the current work. Future research could examine the independence and interaction effect of the metacognitive components on the creative process using multiple methods.

Cultivating Creativity From the Perspective of Metacognition

The practical implication that should be considered is how to foster individual creative thinking from the perspective of metacognition. Apart from teaching individuals' metacognitive skills ( Scott et al., 2004; Hargrove, 2013; Hargrove and Nietfeld, 2015), a new training perspective based on metacognition knowledge could prove to be a novel avenue for creativity cultivation in future studies. This promising example of a metacognition training is creative mindset intervention. A creative mindset, metacognitive knowledge that refers to individuals' domain-specific implicit theories of creativity aforementioned, could have independent and interactive effects on creativity ( Blackwell et al., 2007; Miele et al., 2011). The main idea is that an incremental creative mindset (viewing creativity as malleable and changeable) is beneficial to creativity compared with an entity creative mindset (viewing creativity as stable and unchangeable). More importantly, similar to the idea that it is possible to successfully intervene in a general mindset ( Hong et al., 1999; Blackwell et al., 2007; Paunesku et al., 2015), it is possible to intervene in a creative mindset.

A general mindset intervention has been a popular topic in many disciplines such as learning, writing, anxiety, and musicality ( Donohoe et al., 2012; Müllensiefen et al., 2015; Paunesku et al., 2015; Schleider and Weisz, 2018), and improving creativity through creative mindset intervention shows promise. The general mindset intervention methods that aim to encourage an incremental mindset could be transferred to and borrowed by the creativity field. For example, Hong et al. (1999) asked students to read articles in popular magazines to emphasize the importance of environmental factors rather than genetic components to mindset development. Blackwell et al. (2007) succeeded in altering the mindsets of middle school students over the course of eight intensive sessions that focused on the study strategies of brain plasticity and ways that their mindset changes over time. Other researchers have also used a similar design in their intervention methods ( Aronson et al., 2002; Yeager et al., 2013). Bostwick (2015) summarized that the key commonalities of these intervention designs included three factors, namely, the "saying is believing aspect" (the article was read), "students formalized it in their own words" (the article was understood), and the "interventional time point of students' most susceptible to the intervention." Future research could attempt to create a series of standardized creative mindset interventions to contribute to creativity cultivation.

Author Contributions

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

Conflict of Interest

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.

Footnotes

Funding. This work was supported by the Social Science Planning Project of Chongqing (2018BS93) and the Fundamental Research Funds for the Central Universities (SWU1809709).

References

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Bostwick K. C. P. (2015). The effectiveness of a malleable mindset intervention in an introductory psychology course.

Brown A. L. (1978). Knowing when, where, and how to remember: a problem of metacognition. Technical Report No. 47. Cognitive Processes, 152.

Fink A., Koschutnig K., Benedek M., Reishofer G., Ischebeck A., Weiss E. M., et al.. (2012). Stimulating creativity [[via the exposure to other people's ideas. Hum. Brain Mapp. 33, 2603-2610. 10.1002/hbm.21387, PMID:PubMed] [PMC free article] [CrossRef] Google Scholar]

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The Role of Metacognitive Components in Creative Thinking

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Metacognition refers to the knowledge and regulation of one's own cognitive processes, which has been regarded as a critical component of creative thinking. However, the current literature on the association between metacognition and creative ...

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Summary: A new study involving Philadelphia-area jazz guitarists, has explored the brain processes that enable creative flow. The research reveals that achieving flow requires a solid foundation of expertise, after which one must learn to relax conscious control to allow creativity to flourish.

By measuring brain activity and performance quality during improvisation, the study shows that experienced musicians entering flow exhibit less frontal lobe activity, which is associated with executive functions, and more in sensory processing areas. These findings suggest that mastering and then mentally releasing one's craft is key to achieving the high creativity and productivity associated with flow states.
Key Facts:

1. The study demonstrated that high levels of expertise and the ability to reduce cognitive control are crucial for achieving flow.

2. Experienced musicians reported higher levels of flow and creativity, correlating with specific patterns of brain activity that minimize executive control while enhancing sensory processing.

3. The findings underscore the importance of both intensive practice and the ability to "let go" to reach the optimal creative state known as flow.

Source: The Conversation
Flow, or being "in the zone," is a state of amped-up creativity, enhanced productivity and blissful consciousness that, some psychologists believe, is also the secret to happiness. It's considered the brain's fast track to success in business, the arts or any other field.
But in order to achieve flow, a person must first develop a strong foundation of expertise in their craft. That's according to a new neuroimaging study from Drexel University's Creativity Research Lab, which recruited Philly-area jazz guitarists to better understand the key brain processes that underlie flow. Once expertise is attained, the study found, this knowledge must be unleashed and not overthought in order for flow to be reached.

As a cognitive neuroscientist who is senior author of this study, and a university writing instructor, we are a husband-and-wife team who collaborated on a book about the science of creative insight. We believe that this new neuroscience research reveals practical strategies for enhancing, as well as elucidating, innovative thinking.

Jazz musicians in flow

The concept of flow has fascinated creative people ever since pioneering psychological scientist Mihály Csíkszentmihályi began investigating the phenomenon in the 1970s.

Yet, a half-century of behavioral research has not answered many basic questions about the brain mechanisms associated with the feeling of effortless attention that exemplifies flow.

The Drexel experiment pitted two conflicting theories of flow against each other to see which better reflects what happens in people's brains when they generate ideas. One theory proposes that flow is a state of intensive hyperfocus on a task. The other theory hypothesizes that flow involves relaxing one's focus or conscious control.

The team recruited 32 jazz guitarists from the Philadelphia area. Their level of experience ranged from novice to veteran, as quantified by the number of public performances they had given. The researchers placed electrode caps on their heads to record their EEG brain waves while they improvised to chord sequences and rhythms that were provided to them.

Jazz improvisation is a favorite vehicle for cognitive psychologists and neuroscientists who study creativity because it is a measurable real-world task that allows for divergent thinking - the generation of multiple ideas over time.

The musicians themselves rated the degree of flow that they experienced during each performance, and those recordings were later played for expert judges who rated them for creativity.

Train intensively, then surrender

As jazz great Charlie Parker is said to have advised, "You've got to learn your instrument, then, you practice, practice, practice. And then, when you finally get up there on the bandstand, forget all that and just wail."

This sentiment aligns with the Drexel study findings. The performances that the musicians self-rated as high in flow were also judged by the outside experts as more creative. Furthermore, the most experienced musicians rated themselves as being in flow more than the novices, suggesting that experience is a precondition for flow. Their brain activity revealed why.

The musicians who were experiencing flow while performing showed reduced activity in parts of their frontal lobes known to be involved in executive function or cognitive control. In other words, flow was associated with relaxing conscious control or supervision over other parts of the brain.

And when the most experienced musicians performed while in a state of flow, their brains showed greater activity in areas known to be involved in hearing and vision, which makes sense given that they were improvising while reading the chord progressions and listening to rhythms provided to them.

In contrast, the least experienced musicians showed very little flow-related brain activity.

Flow vs. nonflow creativity

We were surprised to learn that flow-state creativity is very different from nonflow creativity.

Previous neuroimaging studies suggested that ideas are usually produced by the default-mode network, a group of brain areas involved in introspection, daydreaming and imagining the future. The default-mode network spews ideas like an unattended garden hose spouts water, without direction.

The aim is provided by the executive-control network, residing primarily in the brain's frontal lobe, which acts like a gardener who points the hose to direct the water where it is needed.

Creative flow is different: no hose, no gardener. The default-mode and executive-control networks are tamped down so that they cannot interfere with the separate brain network that highly experienced people have built up for producing ideas in their field of expertise.

For example, knowledgeable but relatively inexperienced computer programmers may have to reason their way through every line of code. Veteran coders, however, tapping their specialized brain network for computer programming, may just start writing code fluently without overthinking it until they complete - perhaps in one sitting - a first-draft program.

Coaching can be a help or hindrance

The findings that expertise and the ability to surrender cognitive control are key to reaching flow are supported by a 2019 study from the Creativity Research Lab. For that study, jazz musicians were asked to play "more creatively." Given that direction, the nonexpert musicians were indeed able to improvise more creatively.

That is apparently because their improvisation was largely under conscious control and could therefore be adjusted to meet the demand. For example, during debriefing, one of the novice performers said, "I wouldn't use these techniques instinctively, so I had to actively choose to play more creatively."

On the other hand, the expert musicians, whose creative process was baked in through decades of experience, were not able to perform more creatively after being asked to do so. As one of the experts put it, "I felt boxed-in, and trying to think more creatively was a hindrance."

The takeaway for musicians, writers, designers, inventors and other creatives who want to tap into flow is that training should involve intensive practice followed by learning to step back and let one's skill take over. Future research may develop possible methods for releasing control once sufficient expertise has been achieved.

About this creativity and neuroscience research news

Author: John Kounios and Yvette Kounios
Source: The Conversation
Contact: John Kounios and Yvette Kounios - The Conversation
Image: The image is credited to Neuroscience News

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Unlocking Flow: The Neuroscience of Creative Bliss - Neuroscience News

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A new study involving Philadelphia-area jazz guitarists, has explored the brain processes that enable creative flow.

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How your brain predictions interfere with what you see | Georg Keller | TEDxBasel

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Transformative Learning in the Workplace

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Transformative Learning in the Workplace

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Learning and development (L&D) professionals are confronted with the challenge of balancing transactional learning elements, like engagement data and reach, with transformational elements, like behavior changes and learning application. L&D measurement experts, Dr. David Vance and Peggy Parskey, call these elements efficiency and effectiveness measures, respectively. While transactional and efficiency data are generally simple to collect using a good learning management system (LMS), transformational and effectiveness data are not as simple because they can require reporting subjective observations from learners and their managers.

No organizational leader wants to expend resources on learning without understanding outcomes. So how can leaders better understand what learning is and how to improve it in the course of daily work? You can view learning collectively: The more knowledge and skills within an organization, the higher performing it tends to be. Many academic and journal articles bear that out.

But for L&D pros, learning starts at the personal level. For this reason, understanding learning theory may help you better design learning products and resources. Three of the best-known adult learning theories are Knowles' andragogy approach, Kolb's experiential learning cycle, and Mezirow's transformative learning. Before you trudge through dense academic theory on the internet, let's drop anchor on Mezirow's transformative learning theory.

Jack Mezirow's transformative learning theory is a method for changing frames of reference, which are defined as the sum of a person's experiences, values, opinions, and understandings. Frames of reference have two components:

Habits of mind. These are broad, often abstract, codes of thinking, usually based on assumptions, that encompass psychological, social, political, and cultural beliefs. Habits of mind are stable and typically enduring.

Points of view. These are conceptions that shift when new information is available or new conclusions are formed.

Mezirow ultimately theorized that transformative learning occurs in 11 phases. In 2002, Gregory Henderson consolidated the 11 phases into four:

1. Disorienting dilemma
2. Critical reflection
3. Development of new perspectives
4. Integration

While reviewing the full list of Mezirow's phases is well worth a few hours of reading, the four phases here provide a gist of the theory. More importantly, they provide valuable insight into learning product and resource design. Let's begin.

Disorienting Dilemma

The phrase "disorienting dilemma" conveys a sense of distress in the first stage of learning. Many learners have emotional realizations when they lack the expertise needed to move into a desired job or career. Some learners even experience imposter syndrome when their colleagues seem more advanced in one skill or another. However, some people experience aha or eureka moments in this phase.

This phase is important for L&D pros. People often experience triggers at the beginning of a learning program that challenge their frames of reference or clarify the necessity to learn. In cases where learners are "voluntold" to participate in a learning program, learning designers can simulate disorienting dilemmas in learning activities through role play, realistic case studies, or real-world stories, for example, to generate the necessity.

Critical Reflection

When learners start to engage with material, it should enable critical reflection. In this phase, the learner begins to question and evaluate the assumptions embedded in their frames of reference. Critical thinking happens when a person sees the visible and invisible connections between current, past, and future phenomena. Critical reflection is similar: We reflect on what we know, how we know it, and how the learning material relates to all of it.

One way that learning pros can build critical reflection into learning materials is to promote discussion. Each learner has unique frames of reference, based on their lived experiences, so discussing material between unique learners is a great incubator of critical reflection, and may produce rich insights.

Development of New Perspectives

If we think of "perspective" in photographic terms, it means "from where you are standing." If you're looking at an object and you move closer or step to the right or left, your perspective changes; you see additional detail and your frame of reference may change. Critical reflection causes a learner to shift perspective and develop new insights, or learn. That is what L&D pros want, that learners take away new information or shift perspective.

Of this phase, Mezirow was quite hopeful. He theorized that once new perspectives develop, learners are very unlikely to revert to previous perspectives. So learning professionals should build in elements that allow learners to annotate their new perspectives as well as those of other learners. They can continue to use these notes for reference as they move into the next phases.

Integration

In this phase, learners put their new frames of reference into action. This is the most difficult phase for organization leaders to measure, or even observe. When technical or hard skills are involved one can observe a learner performing learned skills. However, when human or soft skills are learned, it's challenging for leaders to tell if they are being integrated into practice.

For example, did the associate who participated in a communications program become a better communicator? Did they do so consistently? What does better communication look like? The answers really come down to subjective frames of reference. Transformative learning theory doesn't provide instructions about how to improve the process of shifting frames of reference, just guidance on how they typically change. This doesn't mean that there aren't pragmatic applications of the theory; knowledge of the consolidated phases described here can provide a starting point for stimulating transformation.

Conclusion

Learning professionals should consider creating or improving strategies to measure transformational and effectiveness data as well as transactional and efficiency data. Doing so may make a fuller measure of learning in an organization possible. Studying Mezirow's theory is a good place to start.

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Role of Transformative Learning Theory in Workplace Learning | ATD

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Learning professionals should consider creating or improving strategies to measure transformational and effectiveness data as well as transactional and efficiency data. Doing so may make a fuller measure of learning in an organization possible. Studying Mezirow's theory is a good place to start.

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Arts, Imagination & Creativity in Transformative Learning

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Arts, Imagination & Creativity in Transformative Learning

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A Tired Brain Could Actually Be More Creative

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Insight--you know the feeling. It's that amazing idea, the solution that hits you like a bolt of lightning. It can come to you while you're mulling over a problem, or days later, when you're making a sandwich or mowing the lawn. And John Kounios has seen what your brain is doing when it has that "aha moment."

With support from the National Science Foundation's (NSF) Social, Behavioral and Economic Sciences Directorate, the Creativity Research Lab that Kounios heads at Drexel University uses electroencephalography (EEG) and other technology to record brain activity. The lab has been looking for something very specific: What goes on in the brain when a flash of insight hits.

What Kounios has found has overturned some long-held beliefs in cognitive science about how quickly the brain can snap from a state of not knowing something to knowing it completely. He's also made discoveries that shed light on exactly how ideas emerge--including how your brain briefly blocks out visual information to focus on finding an answer to a tough question.

And his work could yield benefits that extend past the academic realm. In the book that he and his research collaborator, Northwestern University psychology professor Mark Beeman, published earlier this year, "The Eureka Factor: Aha Moments, Creative Insight and the Brain," Kounios notes that moments of insight have resulted in major economic benefits. For example, inventors including Clarence Birdseye, who came up with a method for flash-freezing food, and Philo Farnsworth, the father of television, turned bursts of outside-the-box thinking into entire industries. Understanding how creative insight happens in the brain can lead to strategies for generating breakthrough innovations, he says.

Kounios discussed his work--and where it might be headed in the future--with NSF:
Q. What drew you to study insight?
A. Back in graduate school, I researched cognitive processes that changed suddenly versus gradually. Back in the 1980s, the view seemed to be that mental states changed only gradually--you don't snap from one state to another. Rather, these states flow continuously and overlap with each other in time. The cognitive science community seemed to assume that there were no exceptions to this view. What I thought at the time was that there didn't seem to be any evidence that this was always the case. A few years later my first graduate student, Roderick Smith, and I started looking for an extreme case of sudden-state changes. We figured that if there were a case of one mental state snapping to another, it would be insight, the "aha" moment.

In behavioral studies that we did in the 1990s, we found that was the case for insight. You go from a state of having no idea what a problem's solution is to a state of knowing exactly what the answer is with high certainty. There's nothing in between. Insight during problem-solving was the only type of cognitive task that we could find in which that was the case.

Later, around the year 2000, I met Mark Beeman--we were both at the University of Pennsylvania at the time. He did functional magnetic resonance imaging (fMRI) research and I was using high-density EEG. We decided to team up and do the first neuroimaging study that was able to isolate and study insight.
Q. Most people have heard the term "insight" and have some understanding of it, but what's the scientific definition?
A. The term "insight" has a general, conversational definition that's a bit different from the scientific definition. In popular use, the term insight means any deep understanding or realization. The scientific definition of insight is a new idea or perspective that is novel to the person and pops into awareness suddenly. The suddenness is an integral part of the definition. It can't be gradual.
Q. So it really is that distinct a shift.
A. Yes. The discrete, all-or-nothing change happens when you start working on a problem and don't know what the solution is. Later, at a specific point in time, when you have an insight, you go directly from not knowing anything consciously about the solution to completely knowing what the solution is.
Q. How does that work? Is your brain working things out even before you know the answer?
A. Yes. Before you have a conscious awareness of what the solution is, there is unconscious processing leading up to the eventual realization. And an insight doesn't have to be related to a particular problem that you were thinking about. In real life, people have spontaneous insights all the time that have nothing to do with any problem that you've been working on. You could be walking down the street and all of a sudden an idea pops into your head that solves a problem that you didn't even know you had. In that case, your brain may have been unconsciously processing ideas--for seconds, or for years. This is called incubation.

In insight problem-solving, the work of solving goes on unconsciously and the final product gets dumped into the consciousness. Even if you're not aware of it, your brain chugs along and can come up with that solution.
Q. How do you monitor a person for insight?
A. We recorded people's brain activity with EEG and fMRI while they solved little verbal puzzles. For each one that they were able to solve, we asked them whether the solution had just popped into their awareness or whether they had worked it out in a deliberate, conscious way. Afterwards, we went back and sorted the EEG recordings into two groups--those that involved a flash of insight and those that were solved more methodically. Then we separately analyzed the neuroimaging data corresponding to these two types of solutions. The most striking finding was that when participants had a flash of insight, there was a burst of high-frequency EEG activity in the right temporal lobe.
Q. What's happening in that area of the brain?
A. We believe that's the solution popping into awareness. The timing of that flash is just about when they reach the solution. The specific area where this is happening, the right anterior superior temporal gyrus, connects to other areas of the brain. People who have damage in that area, due to strokes or other reasons, have trouble connecting disparate ideas to understand jokes or metaphors. Getting a joke often involves a sudden insight. A joke often leads you down a path, and then you get to the punchline and it goes somewhere else--that's what makes it funny.

There was another key finding. A few hundred milliseconds before that flash was a burst of a different kind of activity in the back of the brain--a burst of EEG alpha waves. Those are lower-frequency waves. Alpha waves have been studied for about 80 years, and what we know about them is that they're a measure of inhibition of the visual system. They are the opposite of neural activity--when alpha is high, neural activity is low.

What we found is that just before an insight, a person's visual system is inhibited for a few hundred milliseconds. Think about it this way: when you ask people a difficult question, to concentrate on it, they often look away. They may look down at their feet or up at the sky, or even close their eyes to think, to focus. The brain does the exact same thing. When you present a difficult question, the solution may already be activated at an unconscious level. But there is a lot of distracting noise, so you can't easily retrieve it into awareness. The brain briefly blocks out visual information. That boosts the signal of the solution, allowing it to pop into awareness.
Q. What is your latest NSF-supported work showing?
A. My current NSF-supported project examines differences among people in the extent to which they use insight or a deliberate analytical strategy to solve problems. This work shows that these differences are pervasive. We compared the EEG activity of people who tend to be more insightful to those who tend to be more analytical and found that the brain activity of these two groups of people is different even when they are in a resting state with no task to perform.

At rest, the more analytical people have greater left frontal activity, suggesting greater control, focus and organization of thought. The more insightful people have more right parietal activity, suggesting broader attention and less focus. Right now, we're working on manipulating this resting-state brain activity by giving different types of thought exercises to our participants. We're finding that this can alter their cognitive style to rely more on insightful or analytical thought.
Q. Does your research suggest practical strategies for enhancing creative thinking?
Yes. We've explained the practical implications and strategies at length in non-technical language in our book "The Eureka Factor." Because insights are generated by unconscious processes that eventually deposit their results into awareness, conscious thought strategies are not a very effective way to enhance insight.

Insights are more likely to occur when a person's brain is in a particular state. This state is characterized by a positive mood and expansive, de-focused attention. In this state, there is more activity in a part of the brain called the anterior cingulate, which monitors other brain areas for conflicting ideas or tendencies. When the anterior cingulate is activated, then it can detect a weak, unconscious, alternative idea and signal other brain areas to switch attention to it, resulting in a sudden insight. But when the anterior cingulate is less active, then alternative ideas aren't detected and a person's scope of thought is limited to the most obvious, straightforward idea. This is like mental tunnel vision.

Research by us and by others shows that, in addition to a positive mood, there are ways to manipulate the environment to encourage the kind of expansive, de-focused attention that promotes insight. For example, large rooms with high ceilings allow visual attention to broaden. This effect is magnified further in the great outdoors. This, in turn, broadens conceptual attention to expand a person's scope of thought to include a wide variety of ideas and associations that can form the basis for an insight. In contrast, striking objects tend to grab and focus attention, thereby promoting analytical thought at the expense of creative insight. We discuss many other tools and strategies in detail in the book.
Q. You've gotten interesting results so far. You're working in an area with some clear societal importance, given the economic activity that can result from sudden insights, as we've seen before with inventors like Birdseye and Farnsworth. Where do you go next with this?
A. We're hoping to take this more into the real world. You might have great insights but be ineffective at turning them into tangible creative achievements. To do that, you need not only the insights but also the analytical skills to refine, test, develop and implement your ideas. Without both insight and analytical thought, you won't have a real-world product. We plan to look at how insightful and analytical thought combine to yield creative accomplishments.

Another project involves examining the emotional effects of insights. When you have an "aha" moment, there is a subjective emotional rush, which is probably why people like to solve puzzles, read murder mysteries and do research. There is extensive neuroscience research on what are called reward signals in the brain, such as occur when you gamble and win. Two of my doctoral students--Brian Erickson and Monica Truelove-Hill--and I have started to analyze our EEG recordings to look for such a reward signal, and the preliminary results are very promising.

What's really interesting about this, if it pans out, is that reward signals aren't limited to pleasurable outcomes of thought or behavior. It seems to be the case that particular ways of thinking are more pleasurable than other ways of thinking, even when the outcome--the solution to the problem--is the same. We're very excited about this new line of research.

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Insight--you know the feeling. It's that amazing idea, the solution that hits you like a bolt of lightning. It can come to you while you're mulling over a...

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John Kounios - The Eureka Factor: Aha Moments, Creative Insight and the Brain

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