cognitive psychology problem solving and creativity

22 August 2024: Due to technical disruption, we are experiencing some delays to publication. We are working to restore services and apologise for the inconvenience. For further updates please visit our website: https://www.cambridge.org/universitypress/about-us/news-and-blogs/cambridge-university-press-publishing-update-following-technical-disruption

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings .

Login Alert

• > Creativity and Reason in Cognitive Development
• > Attention, Cognitive Flexibility, and Creativity: Insights from the Brain

Book contents

• Frontmatter
• List of Contributors
• Acknowledgments
• 1 Creativity, Reason and Cognitive Development: Ten Years Later
• SECTION ONE CREATIVITY AND REASON IN CHILDHOOD AND THE SCHOOLS
• SECTION TWO CREATIVITY AND REASON IN COGNITION AND NEUROSCIENCE
• 8 The Role of Domain Knowledge in Creative Problem Solving
• 9 Processes, Strategies, and Knowledge in Creative Thought: Multiple Interacting Systems
• 10 Dynamic Processes within Associative Memory Stores: Piecing Together the Neural Basis of Creative Cognition
• 11 Creativity and Constraint: Friends, Not Foes
• 12 Creative Genius, Knowledge, and Reason: The Lives and Works of Eminent Creators
• 13 Attention, Cognitive Flexibility, and Creativity: Insights from the Brain
• SECTION THREE CREATIVITY AND REASON: INTERACTIONS AND RELATED CONSTRUCTS
• Author Index
• Subject Index

13 - Attention, Cognitive Flexibility, and Creativity: Insights from the Brain

from SECTION TWO - CREATIVITY AND REASON IN COGNITION AND NEUROSCIENCE

Published online by Cambridge University Press:  05 February 2016

As an emerging area of research, the neuroscience of creativity has made significant strides over the last decade by beginning to elucidate the brain bases of creative cognition (Vartanian, Bristol, & Kaufman, 2013). Among researchers and practitioners of creativity, the contributions of this new area are deemed valuable to the extent that they shed light on the key mechanisms and processes that underlie creativity. In this sense, rather than merely confirming what is already known about creativity using a new set of metrics, the true worth of neuroscientific data will involve the quality of the tools and ideas it offers for testing the “joints in the system” (Goel, 2005, p. 268; see also Shallice, 1988). In turn, as better mechanistic accounts of brain function in relation to creativity emerge, neuroscientifically informed interventions to facilitate this mode of thinking in educational, professional, and applied settings can be developed.

As noted by Goel (2005) in relation to another high-order process – reasoning – the contribution neuroscience can make to our understanding of cognition includes but also extends beyond elucidating mechanisms. Specifically, and more immediately, neuroscientific data have been a great source of knowledge regarding dissociation of cognitive functions. For example, Miller and Tippet (1996) administered Guilford's (1967) classic Match Problems task to patients with focal brain lesions and normal controls. Critically, two types of problems were administered to the participants: one set of problems involved straightforward match removal for solution, whereas another set of problems required “set shifting” to arrive at correct solutions. In the problem solving literature a set shift is defined as the ability to overcome the conceptual and/or perceptual constraints that define the problem space. Set shift problems are typically measured using perseverative errors in the Wisconsin Card Sorting Test (Grant & Berg, 1948), given that they demonstrate the participant's inability to revise the card sorting strategy following a rule change. Miller and Tippett (1996) reported that patients with focal right prefrontal cortex (PFC) lesions were impaired specifically on those match problems that required set shifts, but not otherwise. In addition, this selective impairment in performance on set-shift problems was especially apparent in patients with lesions to right ventral (as opposed to dorsal) PFC. Importantly, no such selective impairment on set-shift problems was observed in patients with temporal-occipital, parietal, central, or left frontal lesions.

Access options

Save book to kindle.

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle .

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service .

• Attention, Cognitive Flexibility, and Creativity: Insights from the Brain
• By Oshin Vartanian , University of Toronto Scarborough
• Edited by James C. Kaufman , University of Connecticut , John Baer , Rider University, New Jersey
• Book: Creativity and Reason in Cognitive Development
• Online publication: 05 February 2016
• Chapter DOI: https://doi.org/10.1017/CBO9781139941969.013

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox .

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive .

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Behav Sci (Basel)

The Relationships between Cognitive Styles and Creativity: The Role of Field Dependence-Independence on Visual Creative Production

Marco giancola.

1 Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy; [email protected] (M.P.); [email protected] (S.D.)

Massimiliano Palmiero

Laura piccardi.

2 Department of Psychology, Sapienza University of Rome, 00185 Rome, Italy; [email protected]

3 Cognitive and Motor Rehabilitation and Neuroimaging Unit, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy

Simonetta D’Amico

Associated data.

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to subject confidentiality.

Previous studies explored the relationships between field dependent-independent cognitive style (FDI) and creativity, providing misleading and unclear results. The present research explored this problematic interplay through the lens of the Geneplore model, employing a product-oriented task: the Visual Creative Synthesis Task (VCST). The latter requires creating objects belonging to pre-established categories, starting from triads of visual components and consists of two steps: the preinventive phase and the inventive phase. Following the Amabile’s consensual assessment technique, three independent judges evaluated preinventive structures in terms of originality and synthesis whereas inventions were evaluated in terms of originality and appropriateness. The Embedded Figure Test (EFT) was employed in order to measure the individual’s predisposition toward the field dependence or the field independence. Sixty undergraduate college students (31 females) took part in the experiment. Results revealed that field independent individuals outperformed field dependent ones in each of the four VCST scores, showing higher levels of creativity. Results were discussed in light of the better predisposition of field independent individuals in mental imagery, mental manipulation of abstract objects, as well as in using their knowledge during complex tasks that require creativity. Future research directions were also discussed.

1. Introduction

Creativity has been widely recognized as the key to success in contemporary society, affecting art, science, economy, and everyday problem solving [ 1 , 2 ]. Given its relevance in human activities, creativity has received growing attention since the second half of the 20th century, when Guilford proposed the multifactorial Structure of Intellect Model [ 3 ], in which creative thinking encompassed convergent thinking (CT) and divergent thinking (DT). Whereas CT is stated as the ability to converge on prevailing ways of thinking in order to find a single, right, and ready-made solution to a problem that other people could also reach, DT represents a spontaneous and free-flowing form of thought and exemplifies the ability to find many new solutions to an open-ended problem. Additionally, DT is widely recognized as one of the main indicators of people’s creative potential [ 4 ], informing about the likelihood that one can act “outside the box”. Although Guilford’s work has represented a milestone in the literature of creativity, researchers have suggested alternative frameworks [ 5 , 6 ], including the Geneplore model [ 7 ]. The latter represents one of the most influential developments in the tradition of cognitive psychology [ 8 ], and exemplifies a twofold framework in which real-world creative production involves a cyclic motion between generative and explorative phases. The generative phase plays a crucial role in producing pre-inventive structures that are internal prototypes of inventions characterized by different degrees of creative potential and originality [ 9 ]. Whereas this phase requires several cognitive resources, including memory retrieval, mental synthesis, mental transformation, and categorical reduction [ 10 ], the explorative phase, employing cognitive and meta-cognitive processes such as attribute finding, conceptual interpretation, functional inference, and hypothesis testing, drives the examination and practical interpretation of preinventive structures to generate a creative outcome [ 11 ]. According to the product perspective [ 12 , 13 ], two main criteria are necessary to evaluate creative inventions: originality and appropriateness. Whereas the former encompasses the degree of novelty and uncommonness of productions, the latter refers to the relevance and usefulness, exemplifying the individual ability to produce an outcome that fits the needs and constraints of a given situation and is well situated in a given context [ 14 ]. Notably, even though different attributes for evaluating creative inventions can be found in the literature of creative production (e.g., elegance, aesthetics, and surprise), originality and appropriateness are the most accurate criteria, which reflect the full standard definition of creativity [ 15 ].

Given the multifaceted nature of creativity, the impact of cognitive factors on creative accomplishment has been long discussed in the past [ 16 ]. Cognitive style, also known as thinking style, represents a pivotal factor unquestionably related to creativity [ 17 ]. The expression cognitive style refers to how people acquire, organize, and use information [ 18 ]. Cognitive styles are usually conceptualized as bipolar, pervasive, and relatively stable over time, representing a critical dimension of the individual functioning. Although the key role of cognitive styles in human cognition and behavior has been extensively acknowledged, their universal explanatory power has not consistently been demonstrated. However, amongst all cognitive styles, the universal explanatory power of the field dependent-independent cognitive style (FDI) has been widely recognized [ 19 ]. In their seminal work, Witkin and colleagues defined FDI as “the extent to which the person perceives part of a field as discrete from the surrounding field as a whole, rather than embedded in the field” [ 20 ] (pp. 6–7). This cognitive style describes a stable and habitual tendency [ 21 , 22 ] characterized by two different poles: field dependence and field independence. Unlike field dependent subjects (FDs), field independent individuals (FIs) usually show less difficulty in separating information from the surrounding context [ 23 ] and are generally more focused on relevant information, inhibiting attention to irrelevant information coming from the environment [ 24 ]. Although FIs are generally defined as more flexible, open-minded, and capable of breaking down the routine than FDs, empirical evidence on the role of FDI on creativity provided unclear results [ 21 ], probably because of the involvement of specific cognitive processes (e.g., fluid intelligence, inhibition, working memory, and flexibility [ 25 , 26 ]), socio-cultural factors (e.g., Western vs. Eastern [ 27 ]), as well as the discrepancy between the scoring methods (e.g., empirically-based and rater-based scoring methods [ 28 ]), tasks used (e.g., divergent and convergent tasks, real-world creative production tasks [ 29 ]), and sampling bias. For a systematic review on the role of FDI in creativity, see Giancola et al. [ 30 ].

Regarding DT, some studies revealed that FIs outperformed FDs [ 31 , 32 ] in generating ideas, whereas others found non-significant results [ 33 , 34 ]. For instance, Li and colleagues [ 19 ] revealed that FIs outperformed FDs in scientific and social brainstorming tasks in terms of fluency and novelty, confirming previous research [ 32 , 35 ]. In addition, Lei and colleagues [ 17 ] found that field independence was related to fluency and originality but not to flexibility, whereas Niaz and colleagues [ 34 ] found no significant effect of FDI. Regarding CT, some authors stressed that FIs attained significantly higher scores than FDs in convergent measures [ 36 , 37 ], others found no significant effect of FDI [ 38 ]. Finally, for real-world creative production, to the knowledge of the current research, only two studies explored the impact of FDI on creative production. Specifically, Miller’s study [ 39 ] showed that FIs reported higher creativity scores than FDs in the creative collage making task. Similarly, Giancola et al. [ 29 ] found that FIs outperformed FDs in the ability to generate real-world creative objects. Notably, even though some authors found positive correlations amongst FDI and self-rated artistic abilities and artistic competencies [ 40 , 41 ], research on the impact of FDI on creative production remains scattered to date.

Therefore, the present research aimed to shed further light on the issue, employing the logic of the Geneplore model, which involves the combined effect of generation and exploration of ideas to generate real-world visual creative objects. Compared to Giancola et al. [ 29 ], who used a one-step procedure, priming participants with object category names while combining the visual stimuli, in the present study a two-step procedure was used. Specifically, participants were firstly instructed to construct pre-inventive forms combining the visual stimuli, and then interpret them within a specific conceptual category. This led to a better understanding of the role of FDI in the creative process, which encompasses both a visuo-spatial generative phase of undefined ideas and a conceptual/inferential phase of refined ideas. In this vein, the generative phase requires mainly divergent thinking to generate preinventive structures, whereas the explorative phase requires mainly convergent thinking to define and evaluate actual inventions [ 42 ].

Based on previous studies, the first two hypotheses were formulated as follows:

FIs outperform FDs in the preinventive phase because FIs are more divergent, thus more able in generating ideas than FDs [ 31 , 32 ];

FIs outperform FDs in the inventive phase because FIs are also more convergent, thus more analytic in evaluating ideas than FDs [ 36 , 37 ];

FIs do not outperform FDs, regardless of the phase of the creative process, because previous studies found inconsistent relationships between FDI and both divergent and convergent measures of the creative process [ 33 , 34 , 38 ].

2. Materials and Methods

2.1. participants and procedure.

Sixty undergraduate college students attending different courses at The University of L’Aquila (L’Aquila, Italy) participated in the study (mean age = 22.30 ± 3.31; age range = 19–32). Twenty-nine of them were males (48.3%), and thirty-one were females (51.7%). After signing the written informed consent to participate in the study, all participants were asked to complete an anamnesis questionnaire assessing biographical and educational information, general health state, and background or formal achievement in art. No participant reported psychiatric or neurological disorders, drug or alcohol addictions, and no participants declared a background or formal achievement in art. The experimental protocol was administered individually to each participant in a quiet room of the Socio-Cognitive Processes in Life Span Laboratory at The University of L’Aquila (L’Aquila, Italy). The experiment lasted approximately 45 min. The Local Ethics Committee approved this experiment in accordance with the Declaration of Helsinki.

2.2. Measures

2.2.1. assessment of field dependent independent cognitive style.

The Embedded Figure Test—EFT [ 43 ] is usually employed to evaluate the individual’s predisposition toward the field dependent or the field independent cognitive style. It is a paper and pencil test in which participants were requested to find a simple black and white shape within a geometric colored complex figure. The test consists of 24 cards (12 cards with simple shapes and 12 cards with complex figures) 12.9 × 7.7 cm (see Figure 1 ).

An example of an item taken from the Embedded Figure Test (EFT). ( A ) The geometric colored complex figure. ( B ) The simple black and white simple shape. ( C ) The simple shape within the complex figure.

The experimenter presented the complex-colored figure one by one for 15 s and the participant had to describe the figure in a loud voice. Then, the experimenter removed the complex figure and presented the simple one; after 10 s, he took away the simple black and white shape and presented once again the complex-colored figure. After that, participants had to find the simple black and white shape embedded in the complex figure. They were instructed to inform the experimenter as soon as they found the figure and trace its outlines using a pencil. When the participants declared to have found the simple black and white shape within the complex figure, the experimenter annotated the elapsed time (timing). If the response (tracing of the outlines) was wrong, the experimenter continued to take the time until the participant provided the correct response or until 180 s had elapsed. The total time was divided by the number of items (12) in order to compute the average time (RTs) which was used as the measure of the individual’s cognitive style. A shorter time indicated a higher predisposition towards field independence, whereas a longer time indicated a higher predisposition towards field dependence.

2.2.2. Assessment of Creativity

The Visual Creative Synthesis Task—VCST [ 7 ] aimed to create objects belonging to pre-established categories, starting from triads of visual components. The task consists of two steps: the preinventive phase and the inventive phase. Following Palmiero and colleagues [ 44 ], three triads of components and three categories were used. Preinventive phase: participants, after a practical example, were asked to combine the components into a preinventive structure, one for each triad. They could be changed in position, rotation, and size but not in their general structure. The triads of components were presented along with their name on a paper sheet (see Figure 2 ). Participants had 15 s to fix and memorize the components and 2 min to think of the preinventive structure for each triad. Inventive phase: after creating the three preinventive structures, participants were presented with a category name for each of them (1 furniture, 1 weapon, and 1 sport goods) and were requested to think of their inventions. Participants had 3 min to describe the functioning of inventions (1 min for each invention), also providing their title (see Figure 3 ). The description of the objects was also defined in terms of length of responses (i.e., number of words). The order of the triads was randomized.

The three triads of components for the Visual Creative Synthesis Task (VCST): ( 1 ) cube, bracket, cone (sport goods); ( 2 ) parallelepiped, dy-pyramid, horn (furniture); ( 3 ) strip, trapezoid, cylinder (weapons).

An example of creative production based on the triad n.3 made up of one stripe, one trapezoid, and one cylinder. Category: Weapons; Title: Grenade; Description: This cylinder is a grenade. The cylinder contains the explosive, the trapezoid is the trigger mechanism, and the strip controls the whole grenade and has a safety function.

Based on Amabile’s consensual assessment technique [ 45 ], three independent judges, two females and one male (mean age = 25.33 ± 4.50), evaluated preinventive structures and inventions. The judges were three psychology students who attended training on creativity and its assessment for a total of 20 h. During the training sessions, the main models and descriptive frameworks of creativity were explained, including the SOI and the Geneplore Model. In addition, students were shown examples of creative productions already evaluated in the past by judges, and they were trained to evaluate creative productions in terms of creativity. After the training, the evaluation sessions began. Notably, three basic parameters were used: originality and appropriateness, which are the most accurate and used criteria, reflecting the full standard definition of creativity [ 15 ], as well as synthesis, which was used specifically to evaluate the participants’ ability to holistically associate the elements during the preinventive phase of the VCST. Therefore, the preinventive structures were evaluated by each judge along a 5-point Likert-type scale in terms of originality, defined as a form being new and not derived from something else (from 1 = very poor originality to 5 = very high originality) and synthesis, defined as the extent to which components were well assembled (from 1 = very poor synthesis to 5 = very high synthesis). The inter-rater correlation (intra-class correlation coefficient—absolute agreement) were significant for both originality ( α = 0.92; p < 0.01) and synthesis ( α = 0.93; p < 0.01). Inventions were evaluated by each judge along a 5-point Likert-type scale in terms of originality defined as a product being new and not derived from something else (from 1 = very poor originality to 5 = very high originality), and appropriateness, defined as an invention with a practical instead of a hypothetical use (from 1 = very poor appropriateness to 5 = very high appropriateness). The inter-rater correlation (intra-class correlation coefficient—absolute agreement) was significant for both originality ( α = 0.94; p < 0.01) and appropriateness ( α = 0.96; p < 0.01).

Statistical analyses were performed using IBM SPSS Statistics version 24 for Windows (IBM Corporation, Armonk, NY, USA). Data were tested for normality and all measures were normally distributed (Kolmogorov–Smirnov Test: Z VCST—Pre—Originality = 0.75, ns; Z VCST—Pre—Synthesis = 0.73, ns; Z VCST—Inv—Originality = 0.76, ns; Z VCST—Inv—Appropriateness = 0.31, ns; Z VCST — Description length = 0.84, ns) except for the EFT (RTs): (Kolmogorov–Smirnov Test: Z EFT (RTs) = 0.02, sig). Additionally, the analysis of the distribution of the EFT using the interquartile range method (IQR) revealed that only four FDs were ‘far out’ from the mean. This suggests that in the present sample data were mostly skewed toward field dependence (e.g., high RTs) rather than field independence (e.g., low RTs). Table 1 reported descriptive statistics divided for group (field dependence vs. field independence).

Descriptive statistics divided by FDI groups.

Note. FDs = Field Dependents; FIs = Field Independents; VCST = Visual Creative Synthesis Task; Pre = Preinventive phase; Inv = Inventive phase.

Correlational analysis was computed using Spearman’s Rho (see Table 2 ). Results revealed that the EFT (RTs) was negatively correlated with the VCST—Preinventive phase Originality (r = −0.50, p < 0.01), VCST—Preinventive phase Synthesis (r = −0.48, p < 0.01), VCST—Inventive phase Originality (r = −0.51, p < 0.01) VCST—Inventive phase Appropriateness (r = −0.52, p < 0.01), and VCST—Description length (r = −0.30, p < 0.05). Notably, VCST—Description length positively correlated to VCST—Inventive phase Originality (r = 0.31, p < 0.05) VCST—Inventive phase Appropriateness (r = 0.29, p < 0.05). In addition, gender also correlated negatively to EFT (RTs) (r = −0.44, p < 0.01) and positively to VCST—Preinventive phase Originality (r = 0.34, p < 0.01), VCST—Preinventive phase Synthesis (r = 0.35, p < 0.01), VCST—Inventive phase Originality (r = 0.27, p < 0.05) and VCST—Inventive phase Appropriateness (r = 0.29, p < 0.05), meaning that males showed higher field dependence and creativity scores than females.

Means, standard deviations, and Spearman’s Rho inter-correlations.

Note. N = 60, gender was dummy coded (F = 0, M = 1) * p < 0.05 (two tailed) ** p < 0.01 (two tailed) EFT = Embedded Figure Test RTs = Response Times: VCST = Visual Creative Synthesis Task Pre = Preinventive phase Inv = Inventive phase.

In order to obtain a more accurate picture of the relationship between EFT-RTs and the creativity scores, possible gender confounding effects were checked using the Spearman’s Rho partial correlations (see Table 3 ). The latter showed that controlling for gender had a little effect on the strength of the relationships between EFT-RTs and the creativity scores, and the significance level was not affected at all. Therefore, the variable ‘gender’ was not further considered when analyzing the comparison between FDs and FIs in terms of creativity.

Spearman’s Rho partial correlations.

Note. N = 60, * p < 0.05 (two tailed) ** p < 0.01 (two tailed) EFT = Embedded Figure Test RTs = Response Times: VCST = Visual Creative Synthesis Task Pre = Preinventive phase Inv = Inventive phase.

Following Tascón and colleagues [ 46 ], since the EFT does not have a scale to divide FIs and FDs and taking into consideration that the individual’s predisposition toward field dependence and field independence is along a continuum, the median-split technique was applied. This method has also been used not only in previous studies on the interplay between FDI and creativity [ 36 , 38 , 47 ] but also in other research areas, including perception [ 48 ], spatial cognition [ 46 ], and problem solving [ 49 ]. Participants were divided by the median-split of the EFT score (average solution times). Therefore, subjects with lower scores than the median (31.23) were classified as FIs ( n = 30), whereas participants with higher scores than the median were classified as FDs ( n = 30).

Four Mann–Whitney U tests were performed in order to measure the differences between FDs and FIs in both preinventive and inventive phases. The significant level of the Mann–Whitney U tests was set as 5 % (α = 0.05). Results revealed that there are significant differences between FDs and FIs in terms of VCST Preinventive Originality (FDs rank = 23.83, FIs rank = 37.17, U = 250.000, p = 0.003), VCST Preinventive Synthesis (FDs rank = 23.47, FIs rank = 37.53, U = 239.000, p = 0.002), VCST Inventive Originality (FDs rank = 23.13, FIs rank = 37.87, U = 229.000, p = 0.001), and VCST Inventive Appropriateness (FDs rank = 23.35, FIs rank = 37.65, U = 235.000, p = 0.001).

4. Discussion

Previous research on the relationships between FDI and creativity revealed unclear results, demonstrating a lack of consensus amongst researchers [ 21 ]: mixed findings have been found taking into account creative thinking in terms of convergent [ 37 , 38 ] and divergent productions [ 32 , 34 ], whereas little work has been done on creative production [ 39 ]. Given this scenario, the current research was aimed at investigating the extent to which the individual’s predisposition towards field dependence and field independence affects creativity. To this aim, we measured FDI using the Embedded Figure Test, whereas creativity was evaluated by the VCST according to the logic of the Geneplore model. The VCST is a product-oriented task, which relies on preinventive and inventive phases. For the first time in this research field, we adopted such a twofold model, assuming that creative production requires both ideas generation and ideas evaluation, underpinned by divergent and convergent mental processes, respectively [ 4 ]. Specifically, the VCST relies on a generative process in which individuals produce preinventive structures characterized by different degrees of creative potential and on an exploratory process in which such structures are evaluated by considering their potential fruition analytically [ 9 ].

Regarding the preinventive phase, results revealed that FI negatively correlated to originality and synthesis. This result was also confirmed by the ANOVA, showing that FIs provided significantly higher scores in originality and synthesis than FDs, meaning that the faster were participants in identifying the simple shape embedded in the complex figure (field independence), the more original and well-assembled were their preinventive structures. Thus, the H1 was confirmed. Given the nature of the task used, it is not surprising that mental imagery plays a key role during the preinventive phase of VCST: indeed, the task requires mentally transforming, combining, and synthesizing visual components in order to generate preinventive structures, that is, mental prototypes of inventions. The pivotal role of mental imagery in creative tasks such as the VCST has been widely recognized in the past [ 10 , 50 , 51 , 52 ]. More specifically, spatial imagery and mental manipulation of spatial forms seem to be crucial in tasks involving objects’ construction [ 53 ], including creative inventions. Indeed, the ability to mentally manipulate shapes was found positively related with the originality score of preinventive structures [ 54 ] and the ability to generate shapes that were well-assembled and synthesized [ 10 , 55 ]. The role of spatial manipulation in creative tasks is also consistent with those studies using the think-aloud method in order to reveal mental processes actively involved in creativity. For instance, Palmiero and Piccardi’s study [ 56 ] revealed that spatial thoughts—containing spatial information of size and rotation—generated during the preinventive phase positively predicted the originality of productions during the invention phase. Although we did not detect mental imagery directly, the assumptions reported above could represent a relevant point to explain our results. Indeed, FIs seem to be more skilled than FDs in spatial abilities implying mental imagery. Specifically, FIs showed higher performance than FDs in tasks required to process different objects’ features such as shape and orientation [ 57 ] and in tasks tapping visual-spatial information [ 58 ]. For instance, Boccia and colleagues [ 59 ], in a sample of 50 young adults, found that FIs outperformed FDs in mental rotation test and Li and colleagues [ 60 ] revealed similar results in 2D and 3D map mental rotation, underlining that regardless of map dimensionality, as the degree of the image rotation increased, the accuracy of the FIs’ performance increased. Although the more flexible mental imagery and the better predisposition to use visual stimuli of FIs could represent a pivotal factor in this phase of the Geneplore cycle, undoubtedly, different mechanisms could affect it, and further investigations are needed.

Regarding the inventive phase, results revealed that FDI negatively correlated with originality and appropriateness. This result was also confirmed by the ANOVA, showing that FIs provided significantly higher scores in originality and appropriateness than FDs, meaning that the faster participants were in identifying the simple shape embedded in the complex figure (field independence), the more creative (original and appropriate) were their inventions. Results confirmed H2 and align with previous studies on real-world creative production [ 29 , 39 ] as well as research on both divergent thinking [ 17 , 19 , 32 ] and convergent thinking [ 36 , 37 ]. Two main explanations can explain the better performance of FIs in the VCST than FDs. First, the assumption of the pivotal role of mental imagery in the preinventive phase can be also extended to the inventive phase. For instance, Roskos-ewoldsen and colleagues [ 54 ], in a sample composed of 41 young and 41 older adults, found a positive relationship between the Paper Folding Test and originality score of productions in the Creative Invention Task. Similar results were also found by Palmiero and colleagues’ study [ 51 ], in which the individual vividness of mental imagery was positively related to the practicality score of the invention in the Mental Synthesis Task. Therefore, the nature of the VCST used in this study and the better predisposition of FIs than FDs in mentally manipulating spatial shapes could represent a possible explanation of our results during the inventive phase. Second, it has been found that the better predisposition of FIs in using their own knowledge and in extracting it from memory, especially in complex tasks in which the solution is unclear, positively affects creative performance [ 19 ]. This assumption is consistent with the two-step form of the VCST used in this research. Indeed, unlike the one-step form [ 29 , 51 ] in which the category of creative inventions is specified before combining the visual components, the category is specified in the two-step form only after the assembly of components. This makes the creative process more complex because, at least, in the combination phase, the goal of creative production is not defined, and participants have to adapt what they have previously assembled to the category provided by the task. In other words, during the two-step form of VCST, participants have to reorganize their prior knowledge in order to generate the creative product. Given that FIs, compared with FDs, have a better capacity to extract their own knowledge, this individual predisposition could be helpful to them in reorganizing and updating the structure previously generated in order to generate the creative invention.

Taken together these results showed that field independence positively affected both phases (pre-inventive and inventive) of the creative process. Although one would be expected that FDI differently impacted on the two phases, it is important to acknowledge that, on the one hand, the preinventive phase relies on field independence based on divergent thinking [ 31 , 32 ]; and, on the other hand, the inventive phase relies on field independence based on convergent analytic thinking [ 36 , 37 ]. That is, FIs can encompass the creative process given their ability in shifting between generative/divergent and explorative/convergent phases. This means that real-world creative production based on the Geneplore Model might be fully supported by the field independent cognitive style, given that the latter loads on both the preinventive and inventive phases. Although this interpretation is intriguing, it should be supported by further scientific evidence.

Notably, the VCST—Description length correlated positively to the originality and appropriateness of the inventive scores, meaning that higher the number of words used to describe the objects the higher the originality and appropriateness. This result suggested that the description length may represent a characteristic of creativity [ 61 ], especially of the inventive phase of the creative process. Although in this study we did not used a written-narrative task to measure creativity, this result confirmed that when it is necessary to make causal inferences and category reductions, the number of words, reflecting the complexity of the construct [ 62 ], can play a key role. Of course, this idea needs also to be verified by future studies.

5. Conclusions

To conclude, this research provides empirical evidence on a problematic and complex relationship involving FDI and creativity, and results seem to support the hypothesis that FIs outperform FDs in creative performance. Despite these findings, the current research shows a critical limit concerning the small sample size. This suggests that studies with more subjects should be carried out in order to reach more reliable conclusions. In addition, the field dependent and field independent groups are unbalanced in terms of gender. However, the study provides interesting future directions. First, although the Geneplore model represents a domain-general framework [ 11 ], the research focused only on visual real-world creative production. Further studies should encompass different domains of creativity (e.g., verbal and motor domains) in order to define an exhaustive picture of the impact of FDI on creativity. Yet, even though the VCST is not a time-based creativity task, future studies could assess the times necessary to carry on the creative process and related it to the attributes of creativity, such as originality and appropriateness. Then, in the present research, we stressed the pivotal role of mental imagery during the generation and evaluation of preinventive structure as well as during the invention phase. Future investigations could further explore the relationships amongst FDI, creativity, and mental imagery, considering how the vividness of mental images may spur or inhibit this cognitive style’s influence on creative performance.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, M.G. and M.P.; methodology, M.G. and M.P.; formal analysis, M.G. and M.P.; investigation, M.G. and M.P.; resources, L.P. and S.D.; data curation, M.G.; writing—original draft preparation, M.G.; writing—review and editing, M.G., M.P., L.P. and S.D.; supervision, S.D. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethic Committee of the University of L’Aquila.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The science behind creativity

Psychologists and neuroscientists are exploring where creativity comes from and how to increase your own

Vol. 53 No. 3 Print version: page 40

• Neuropsychology
• Creativity and Innovation

Paul Seli, PhD, is falling asleep. As he nods off, a sleep-tracking glove called Dormio, developed by scientists at the Massachusetts Institute of Technology, detects his nascent sleep state and jars him awake. Pulled back from the brink, he jots down the artistic ideas that came to him during those semilucid moments.

Seli is an assistant professor of psychology and neuroscience at the Duke Institute for Brain Sciences and also an artist. He uses Dormio to tap into the world of hypnagogia, the transitional state that exists at the boundary between wakefulness and sleep. In a mini-experiment, he created a series of paintings inspired by ideas plucked from his hypnagogic state and another series from ideas that came to him during waking hours. Then he asked friends to rate how creative the paintings were, without telling them which were which. They judged the hypnagogic paintings as significantly more creative. “In dream states, we seem to be able to link things together that we normally wouldn’t connect,” Seli said. “It’s like there’s an artist in my brain that I get to know through hypnagogia.”

The experiment is one of many novel—and, yes, creative—ways that psychologists are studying the science of creativity. At an individual level, creativity can lead to personal fulfillment and positive academic and professional outcomes, and even be therapeutic. People take pleasure in creative thoughts, research suggests—even if they don’t think of themselves as especially creative. Beyond those individual benefits, creativity is an endeavor with implications for society, said Jonathan Schooler, PhD, a professor of psychological and brain sciences at the University of California, Santa Barbara. “Creativity is at the core of innovation. We rely on innovation for advancing humanity, as well as for pleasure and entertainment,” he said. “Creativity underlies so much of what humans value.”

In 1950, J. P. Guilford, PhD, then president of APA, laid out his vision for the psychological study of creativity ( American Psychologist , Vol. 5, No. 9, 1950). For half a century, researchers added to the scientific understanding of creativity incrementally, said John Kounios, PhD, an experimental psychologist who studies creativity and insight at Drexel University in Philadelphia. Much of that research focused on the personality traits linked to creativity and the cognitive aspects of the creative process.

But in the 21st century, the field has blossomed thanks to new advances in neuroimaging. “It’s become a tsunami of people studying creativity,” Kounios said. Psychologists and neuroscientists are uncovering new details about what it means to be creative and how to nurture that skill. “Creativity is of incredible real-world value,” Kounios said. “The ultimate goal is to figure out how to enhance it in a systematic way.”

Creativity in the brain

What, exactly, is creativity? The standard definition used by researchers characterizes creative ideas as those that are original and effective, as described by psychologist Mark A. Runco, PhD, director of creativity research and programming at Southern Oregon University ( Creativity Research Journal , Vol. 24, No. 1, 2012). But effectiveness, also called utility, is a slippery concept. Is a poem useful? What makes a sculpture effective? “Most researchers use some form of this definition, but most of us are also dissatisfied with it,” Kounios said.

Runco is working on an updated definition and has considered at least a dozen suggestions from colleagues for new components to consider. One frequently suggested feature is authenticity. “Creativity involves an honest expression,” he said.

Meanwhile, scientists are also struggling with the best way to measure the concept. As a marker of creativity, researchers often measure divergent thinking—the ability to generate a lot of possible solutions to a problem or question. The standard test of divergent thinking came from Guilford himself. Known as the alternate-uses test, the task asks participants to come up with novel uses for a common object such as a brick. But measures of divergent thinking haven’t been found to correlate well with real-world creativity. Does coming up with new uses for a brick imply a person will be good at abstract art or composing music or devising new methods for studying the brain? “It strikes me as using way too broad a brush,” Seli said. “I don’t think we measure creativity in the standard way that people think about creativity. As researchers, we need to be very clear about what we mean.”

One way to do that may be to move away from defining creativity based on a person’s creative output and focus instead on what’s going on in the brain, said Adam Green, PhD, a cognitive neuroscientist at Georgetown University and founder of the Society for the Neuroscience of Creativity . “The standard definition, that creativity is novel and useful, is a description of a product,” he noted. “By looking inward, we can see the process in action and start to identify the characteristics of creative thought. Neuroimaging is helping to shift the focus from creative product to creative process.”

That process seems to involve the coupling of disparate brain regions. Specifically, creativity often involves coordination between the cognitive control network, which is involved in executive functions such as planning and problem-solving, and the default mode network, which is most active during mind-wandering or daydreaming (Beaty, R. E., et al., Cerebral Cortex , Vol. 31, No. 10, 2021). The cooperation of those networks may be a unique feature of creativity, Green said. “These two systems are usually antagonistic. They rarely work together, but creativity seems to be one instance where they do.”

Green has also found evidence that an area called the frontopolar cortex, in the brain’s frontal lobes, is associated with creative thinking. And stimulating the area seems to boost creative abilities. He and his colleagues used transcranial direct current stimulation (tDCS) to stimulate the frontopolar cortex of participants as they tried to come up with novel analogies. Stimulating the area led participants to make analogies that were more semantically distant from one another—in other words, more creative ( Cerebral Cortex , Vol. 27, No. 4, 2017).

Green’s work suggests that targeting specific areas in the brain, either with neuromodulation or cognitive interventions, could enhance creativity. Yet no one is suggesting that a single brain region, or even a single neural network, is responsible for creative thought. “Creativity is not one system but many different mechanisms that, under ideal circumstances, work together in a seamless way,” Kounios said.

In search of the eureka moment

Creativity looks different from person to person. And even within one brain, there are different routes to a creative spark, Kounios explained. One involves what cognitive scientists call “System 1” (also called “Type 1”) processes: quick, unconscious thoughts—aha moments—that burst into consciousness. A second route involves “System 2” processes: thinking that is slow, deliberate, and conscious. “Creativity can use one or the other or a combination of the two,” he said. “You might use Type 1 thinking to generate ideas and Type 2 to critique and refine them.”

Which pathway a person uses might depend, in part, on their expertise. Kounios and his colleagues used electroencephalography (EEG) to examine what was happening in jazz musicians’ brains as they improvised on the piano. Then skilled jazz instructors rated those improvisations for creativity, and the researchers compared each musician’s most creative compositions. They found that for highly experienced musicians, the mechanisms used to generate creative ideas were largely automatic and unconscious, and they came from the left posterior part of the brain. Less-experienced pianists drew on more analytical, deliberative brain processes in the right frontal region to devise creative melodies, as Kounios and colleagues described in a special issue of NeuroImage on the neuroscience of creativity (Vol. 213, 2020). “It seems there are at least two pathways to get from where you are to a creative idea,” he said.

Coming up with an idea is only one part of the creative process. A painter needs to translate their vision to canvas. An inventor has to tinker with their concept to make a prototype that actually works. Still, the aha moment is an undeniably important component of the creative process. And science is beginning to illuminate those “lightbulb moments.”

Kounios examined the relationship between creative insight and the brain’s reward system by asking participants to solve anagrams in the lab. In people who were highly sensitive to rewards, a creative insight led to a burst of brain activity in the orbitofrontal cortex, the area of the brain that responds to basic pleasures like delicious food or addictive drugs ( NeuroImage , Vol. 214, 2020). That neural reward may explain, from an evolutionary standpoint, why humans seem driven to create, he said. “We seem wired to take pleasure in creative thoughts. There are neural rewards for thinking in a creative fashion, and that may be adaptive for our species.”

The rush you get from an aha moment might also signal that you’re onto something good, Schooler said. He and his colleagues studied these flashes of insight among creative writers and physicists. They surveyed the participants daily for two weeks, asking them to note their creative ideas and when they occurred. Participants reported that about a fifth of the most important ideas of the day happened when they were mind-wandering and not working on a task at hand ( Psychological Science , Vol. 30, No. 3, 2019). “These solutions were more likely to be associated with an aha moment and often overcoming an impasse of some sort,” Schooler said.

Six months later, the participants revisited those ideas and rated them for creative importance. This time, they rated their previous ideas as creative, but less important than they’d initially thought. That suggests that the spark of a eureka moment may not be a reliable clue that an idea has legs. “It seems like the aha experience may be a visceral marker of an important idea. But the aha experience can also inflate the meaningfulness of an idea that doesn’t have merit,” Schooler said. “We have to be careful of false ahas.”

Boosting your creativity

Much of the research in this realm has focused on creativity as a trait. Indeed, some people are naturally more creative than others. Creative individuals are more likely than others to possess the personality trait of openness. “Across different age groups, the best predictor of creativity is openness to new experiences,” said Anna Abraham, PhD, the E. Paul Torrance Professor and director of the Torrance Center for Creativity and Talent Development at the University of Georgia. “Creative people have the kind of curiosity that draws them toward learning new things and experiencing the world in new ways,” she said.

We can’t all be Thomas Edison or Maya Angelou. But creativity is also a state, and anyone can push themselves to be more creative. “Creativity is human capacity, and there’s always room for growth,” Runco said. A tolerant environment is often a necessary ingredient, he added. “Tolerant societies allow individuals to express themselves and explore new things. And as a parent or a teacher, you can model that creativity is valued and be open-minded when your child gives an answer you didn’t expect.”

One way to let your own creativity flow may be by tapping into your untethered mind. Seli is attempting to do so through his studies on hypnagogia. After pilot testing the idea on himself, he’s now working on a study that uses the sleep-tracking glove to explore creativity in a group of Duke undergrads. “In dream states, there seems to be connectivity between disparate ideas. You tend to link things together you normally wouldn’t, and this should lead to novel outcomes,” he said. “Neurally speaking, the idea is to increase connectivity between different areas of the brain.”

You don’t have to be asleep to forge those creative connections. Mind-wandering can also let the ideas flow. “Letting yourself daydream with a purpose, on a regular basis, might allow brain networks that don’t usually cooperate to literally form stronger connections,” Green said.

However, not all types of daydreams will get you there. Schooler found that people who engage in more personally meaningful daydreams (such as fantasizing about a future vacation or career change) report greater artistic achievement and more daily inspiration. People who are prone to fantastical daydreaming (such as inventing alternate realities or imaginary worlds) produced higher-quality creative writing in the lab and reported more daily creative behavior. But daydreams devoted to planning or problem-solving were not associated with creative behaviors ( Psychology of Aesthetics, Creativity, and the Arts , Vol. 15, No. 4, 2021).

It’s not just what you think about when you daydream, but where you are when you do it. Some research suggests spending time in nature can enhance creativity. That may be because of the natural world’s ability to restore attention, or perhaps it’s due to the tendency to let your mind wander when you’re in the great outdoors (Williams, K. J. H., et al., Journal of Environmental Psychology , Vol. 59, 2018). “A lot of creative figures go on walks in big, expansive environments. In a large space, your perceptual attention expands and your scope of thought also expands,” Kounios said. “That’s why working in a cubicle is bad for creativity. But working near a window can help.”

Wherever you choose to do it, fostering creativity requires time and effort. “People want the booster shot for creativity. But creativity isn’t something that comes magically. It’s a skill, and as with any new skill, the more you practice, the better you get,” Abraham said. In a not-yet-published study, she found three factors predicted peak originality in teenagers: openness to experience, intelligence, and, importantly, time spent engaged in creative hobbies. That is, taking the time to work on creative pursuits makes a difference. And the same is true for adults, she said. “Carve out time for yourself, figure out the conditions that are conducive to your creativity, and recognize that you need to keep pushing yourself. You won’t get to where you want to go if you don’t try.”

Those efforts can benefit your own sense of creative fulfillment and perhaps lead to rewards on an even grander scale. “I think everyday creativity is the most important kind,” Runco said. “If we can support the creativity of each and every individual, we’ll change the world.”

How to become more creative

1. Put in the work: People often think of creativity as a bolt of inspiration, like a lightbulb clicking on. But being creative in a particular domain—whether in the arts, in your work, or in your day-to-day life—is a skill. Carve out time to learn and practice.

2. Let your mind wander: Experts recommend “daydreaming with purpose.” Make opportunities to let your daydreams flow, while gently nudging them toward the creative challenge at hand. Some research suggests meditation may help people develop the habit of purposeful daydreaming.

3. Practice remote associations: Brainstorm ideas, jotting down whatever thoughts or notions come to you, no matter how wild. You can always edit later.

4. Go outside: Spending time in nature and wide-open spaces can expand your attention, enhance beneficial mind-wandering, and boost creativity.

5. Revisit your creative ideas: Aha moments can give you a high—but that rush might make you overestimate the merit of a creative idea. Don’t be afraid to revisit ideas to critique and tweak them later.

Further reading

Creativity: An introduction Kaufman, J. C., and Sternberg, R. J. (Eds.), Cambridge University Press, 2021

The eureka factor: Aha moments, creative insight, and the brain Kounios, J., & Beeman, M., Random House, 2015

Creativity anxiety: Evidence for anxiety that is specific to creative thinking, from STEM to the arts Daker, R. J., et al., Journal of Experimental Psychology: General , 2020

Predictors of creativity in young people: Using frequentist and Bayesian approaches in estimating the importance of individual and contextual factors Asquith, S. L., et al., Psychology of Aesthetics, Creativity, and the Arts , 2020

Recommended Reading

Contact apa, you may also like.

REVIEW article

The link between creativity, cognition, and creative drives and underlying neural mechanisms.

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

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

Introduction

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

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

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

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

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

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

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

Table 1 . Potential candidate genes for creativity.

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

Creative Cognition Is Rooted in Executive Functions (EFs)

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

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

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

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

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

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

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

How do mood states influence creativity.

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

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

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

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

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

Does Reward Matter in the Case of Creativity?

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

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

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

Where Bright Ideas Are Produced in Our Brains

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

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

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

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

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

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

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

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

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

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

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

How the Neuromodulatory Systems Are Involved in Creative Performance

The dopaminergic (da) system and creativity.

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

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

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

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

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

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

Artistic Style Shifts, Dopamine (DA), and Creativity

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

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

Genetic Research Reveals a Strong Association Between DA Activity and Creativity

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

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

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

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

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

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

Other Neuromodulatory Systems and Creativity

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

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

Noradrenaline (NE) System, and Creativity

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

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

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

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

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

Serotonergic (5-HT) System and Creativity

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

Brain Illness and Creativity

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

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

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

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

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

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

Open Questions and Future Directions

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

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

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

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

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

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

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

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

Author Contributions

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

Conflict of Interest Statement

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

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.

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

PubMed Abstract | CrossRef Full Text | Google Scholar

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

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

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

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

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

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

PubMed Abstract | Google Scholar

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

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

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

CrossRef Full Text | Google Scholar

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Google Scholar

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewed by:

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

*Correspondence: Radwa Khalil, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Enhancement of Creative Thinking Skills Using a Cognitive-Based Creativity Training

• Original Article
• Open access
• Published: 07 October 2016
• Volume 1 , pages 243–253, ( 2017 )

Cite this article

You have full access to this open access article

• Simone M. Ritter 1 &
• Nel Mostert 2  

47k Accesses

117 Citations

15 Altmetric

Explore all metrics

Creative thinking skills can be considered one of the key competencies for the twenty-first century—they allow us to remain flexible and provide us with the capacity to deal with the opportunities and challenges that are part of our complex and fast-changing world. The increased focus on innovation combined with recent reports of decrements in creative performance brings attention to the need to develop creative thinking skills at both the educational and business levels. The main objective of the current project was to develop and scientifically test a brief, domain-unspecific creativity training. Undergraduate university students ( N  = 32) participated in the creativity training, which was a single session of 1.5 h and employed a cognitive approach (i.e., participants were shown how to apply creative thinking techniques in a systematic fashion). The effectiveness of the training was tested by means of a pre- and post-training comparison employing creativity measures that relied on divergent thinking, convergent thinking, and creative problem solving skills. To control for a possible instrumentation threat, two versions of each task were created and counterbalanced between the pre- and post-measure across participants. Following the creativity training, improvements were observed across a variety of creative performance measures. Importantly, the creativity level of the ideas generated during the divergent thinking task improved post-training. Moreover, the findings of the current study shed light on a possible underlying mechanism for these improvements in creativity, that is, cognitive flexibility. In addition to these divergent thinking skills, the training also improved convergent thinking and produced marginal improvements in creative problem solving skills. The current findings have important implications for educational and organizational settings, as they suggest that this brief creativity training (or one employing similar cognitive techniques) could be implemented to facilitate creative thinking skills.

Similar content being viewed by others

Online Creativity Training: Examining the Effectiveness of a Comprehensive Training Approach

Fostering creative minds: what predicts and boosts design competence in the classroom?

A meta-analytical review of the impact of mindfulness on creativity: Framing current lines of research and defining moderator variables

Explore related subjects.

• Artificial Intelligence

Avoid common mistakes on your manuscript.

Introduction

Creative thinking can be considered one of the key competencies for the twenty-first century, and its effects are widespread. It allows us to fly to the moon, create art, develop computers, and cure illnesses. Creativity has not only been recognized in the sciences and the arts (Feist and Gorman 1998 ; MacKinnon 1962 ; Sternberg and Lubart 1996 ) but has also been shown to play an important role in everyday problem solving (Cropley 1990 ; Mumford et al. 1991 ; Runco 1994 ; Torrance 1971 ; Wallas 1926 ). The word creativity has its roots in the Latin term creō , which means “to create, to make,” and commonly refers to the ability to generate ideas or problem solutions that are original (i.e., novel) and useful (i.e., effective) (for example, Amabile 1983 ; Mumford 2003 ; Sternberg and Lubart 1999 ). In addition to its function of problem solving, creativity allows us to remain flexible. Cognitive flexibility provides us with the capacity to deal with the opportunities and changes that are part of our complex and fast-changing world (Cropley 1990 ; Reiter-Palmon et al. 1998 ). Due to its crucial role in innovation, the creation of new ideas and problem solutions has become a key concern for most organizations and businesses (Runco 2004 ), and some scholars refer to today’s economy as a creative economy (Florida 2002 ; Hawkins 2001 ). Supporting this trend, the US Council on Competitiveness has announced “innovation will be the single most important factor in determining […] success through the twenty-first century” (Wince-Smith 2006 ).

To meet the needs of the twenty-first century, academics, business leaders, and policy makers around the world have placed creativity high on their agenda. For example, 2009 was announced “the Year of Creativity and Innovation” to facilitate creative thinking skills among the entire population (European Commission 2008 ). Creativity is a skill that should be fostered in all disciplines and across all intellectual and social areas (UNESCO International Bureau of Education 2014 ). Initiatives to facilitate creativity are especially important since a creativity crisis has been identified, revealing significant decrements in creativity since the 1990s (Kim 2011 ; Kimbell 2000 ; Newton and Newton 2010 ). Both the heightened focus on creativity and innovation and the overall decline in creative performance bring attention to the need to develop creative thinking skills at both the educational and business levels. Creativity was long considered a topic not open to scientific research (Sternberg and Lubart 1999 ; Treffinger 2009 )—perhaps due to traditional beliefs that creativity has mystical origins—but in recent years, increasing insights have been gained into how creative ideas arise in the brain (e.g., see the review by Sawyer 2011 ). For example, it is now understood that creative thinking depends on fundamental cognitive processes, such as working memory, the ability to create new mental categories, and the ability to mentally manipulate objects (Ward et al. 1999 ). Creative thinking skills are thus inherent to normative cognitive functioning rather than an innate talent available to only a few genius minds. Importantly, research supports the idea that creative thinking can be trained (for a meta-analysis, see Scott et al. 2004a ).

Despite the urgent need for creativity, few curriculums devote much time or attention to developing creative thinking skills; in fact, the education system often discourages it (Edwards et al. 2006 ). This means that often, we are trained to consume knowledge but are not taught how to produce creative ideas and solutions. This is particularly problematic when graduates enter the workforce, as they have to be prepared for the needs of our creative economy (Florida 2002 ; Hawkins 2001 ). Similarly, those already established in work during adult life need to deal with twenty-first century problems but are not taught the creative thinking skills required to solve them. As such, people of all age groups could benefit from a training that enhances creative performance. Developing, evaluating, and implementing new content into the educational curriculum, such as creative thinking skills, take significant time. A valid alternative during this transition period might be to offer a short, well-developed, and scientifically tested creativity training—one that can be implemented easily in schools and business settings.

Previous creativity training approaches have been reported to differ across four main types of variables, including the cognitive processes targeted by the training, the techniques used in the training, the media used to deliver the training, and the types of exercises used during the training (for a more thorough discussion about these categories, see the meta-analysis of creativity training types by Scott et al. 2004b ). Of importance for the current study, the cluster analysis of creativity training techniques by Scott et al. ( 2004b ), revealed four broad themes: imagery training ( N  = 43, 27.6 %), idea production training ( N  = 83, 53.2 %), cognitive training ( N  = 17, 10.9 %), and thinking skills training ( N  = 13, 8.3 %). Thus, cognitive training approaches were found to be relatively uncommon. Although less common than idea production training, some cognitive training approaches (e.g., conceptual combination training) were found to have larger effects and higher success rates than did idea production training. These findings, plus the meta-analysis of training effectiveness by Scott et al. ( 2004a ), suggest that cognitive approaches will be effective, providing there is a focus on how to apply the technique (see Scott et al. 2004b ). One noted disadvantage of cognitive approaches, however, is that these techniques tend to be lengthy (see Scott et al. 2004b ). Thus, the length of cognitive training approaches could be a factor that limits the implementation of such creativity trainings in educational and business settings.

The aim of the current study was to develop and scientifically test a creativity training that anticipates these needs, and several requirements were specified for the training. First, it had to be domain unspecific ; that is, the training could be applied in various contexts irrespective of the trainee’s educational background. Second, the training had to employ a cognitive approach , as training programs that incorporate cognitive-oriented techniques have been shown to be effective (see Scott et al. 2004a ). Third, the training had to be brief (a single session, not exceeding 1.5 h) so that it could be implemented within an existing education program. Fourth, the current creativity training was developed by a scientist who holds a PhD in creativity and works as a creativity researcher, university teacher, and consultant and by a practitioner who has facilitated more than 900 creativity sessions with more than 14,000 participants worldwide. Thus, scientific insights and practical knowledge were combined when designing the training, which may strengthen the internal validity of the training (see Scott et al. 2004a ). Finally, the effectiveness of the training had to be scientifically tested by means of an extensive pre- and post-training assessment of participants’ creativity. We hypothesized that improvements in creative performance would be observed following the creativity training.

Materials and Methods

Participants.

A total of 32 (20 females) participants between the ages of 18 and 34 years old ( M  = 23.13, SD = 5.76) gave written informed consent to participate in the study, which was conducted according to the principles of the institutional review board (Ethics Committee Faculty of Social Sciences, Radboud University, the Netherlands) and the principles expressed in the Declarations of Helsinki. All the participants were Dutch and recruited for voluntary participation via the online research participation system (Sona) of Radboud University. The participants were from varied educational backgrounds, including MBO (EQ National Diploma or Vocational training; n  = 1), HAVO/VWO (EQ High School Diploma; n  = 2), HBO (EQ Applied Bachelor’s degree; n  = 2), and WO (EQ University Bachelor’s degree; n  = 27). Participants were given a choice of earning course credit (2.5 points) or €15 (approximately $16.70 USD) for their participation. Finally, the creativity training took place on March 30, 2015 at the laboratory of the Behavioural Science Institute, Radboud University, the Netherlands. Participants were subdivided across three training sessions (09:00–11:30, 10 participants; 11:45–14:15, 13 participants; 14:30–17:00, 9 participants). The same procedures were used during all sessions, which were conducted by the same experimenter and creativity trainer.

The overall effectiveness of the training was examined using a within-subjects design, with creative performance (pre, post) as the dependent variable. The techniques that were applied in the creativity training are described in the “ Training Techniques ” section, the measurement of creative performance is described in the “ Measures of Creative Performance ” section, and the procedure is described in the “ Procedure ” section.

Training Techniques

The training lasted 1.5 h. Based on the requirements outlined in the introduction, the following techniques were incorporated in the training: Silence , lines of evolution , random connections , and SCAMPER . Each of these techniques is described in detail below.

Technique 1: Silence

The participants were first provided with an explanation of the benefits of brainstorming individually and in silence. In particular, they were informed that brainstorming alone and in silence is beneficial for the creative process as it allows one to generate ideas without any restrictions, guidelines, or distractions. In addition, personal expertise and background knowledge can be used and individuals are not influenced by the ideas generated by other people. Moreover, during an individual brainstorming session, the creative thought process is not influenced by group processes (e.g., fear of criticism), idea loss due to turn-taking, and the dominance of certain group members (Nijstad and Stroebe 2006 ). If these group processes are at play at the beginning of a brainstorming session, the group may focus on a narrow range of idea directions—the ones mentioned by the participants who take the lead—and individual brainpower and expertise may be lost. After being introduced to the silent brainstorming technique, the participants generated ideas individually and in silence for 5 min.

Technique 2: Lines of Evolution

This technique relies on the findings of a Russian engineer, Genrikh Altshuller, who studied thousands of patents. He noticed that the evolution of breakthrough ideas—especially in the domain of technical innovation—follows universal principles. For example, a line of evolution could include changes in the form of an object using the following pattern: from solid, to powder or pieces, to liquid, to foam, to gel, to mechanics, to electronics, to spheres. A possible line of evolution for real-world inventions could be that what was once a chocolate bar can become mini chocolates or a chocolate drink, and what was once a solid $1 coin can become a virtual bit coin. This technique may facilitate the generation of creative ideas and solutions by examining how the current form of an idea or product can be changed into the next evolutionary form, that is, by “digging deeper.”

Technique 3: Random Connections

Creative ideas often come from making connections between seemingly unrelated concepts or objects. Accordingly, in some situations, creative thinking may not benefit from digging deeper, but instead from “digging elsewhere.” By digging elsewhere, one allows creative ideas to emerge from associative processes. The underlying approach of this technique is that one uses a random stimulus—for example, an object in the room or a picture in a newspaper—and tries to generate as many associations related to this stimulus as possible. Next, one can connect these associations to the problem that needs to be solved. To illustrate this process, imagine the following example: the problem at hand is “generate a new sun cream,” and the random object chosen is a “ballpoint pen.” Associations can be generated from the ballpoint pen, such as writing, color, and roller. By connecting these associations to the sun cream problem, one might generate the idea of colored sun cream (i.e., the sun cream is colored during application, which disappears once absorbed), a roll-on sun cream, or a roll-on sun cream containing colored sun cream. Thus, by facilitating the generation of random connections, this technique helps to create an environment that allows and encourages the generation of ideas that would very likely not emerge intentionally—a process which is called serendipitous creativity. The notion of serendipity is common throughout the history of creativity and scientific innovation, reportedly being involved in discoveries such as penicillin, the microwave, and the Post-it note.

Technique 4: Scamper

During the creative process, novel solutions may emerge when forced to think of possible changes to an existing idea or product. Hereby, a list of suggestions for possible changes can be helpful. A list with seven possible thinking techniques was provided using SCAMPER (Osborn 1953 ; Eberle 1971 ), and the participants could use any or all of the suggested approaches: substitute (remove some part of the accepted situation, thing, or concept and replace it with something else), combine (join, affiliate, or force together two or more elements of your subject matter and consider ways that such a combination might move you toward a solution), adapt (change some part of your problem so that it works where it did not before), modify (consider many of the attributes and change them if necessary; attributes can include size, shape, texture, color, attitude, position), purpose (put the product to some other use), eliminate (remove any or all elements of your subject, simplify it, or reduce it to its core functionality), reverse (change the direction or orientation; turn it upside-down, inside-out, or make it go backwards/against the direction it was intended to move or be used), and rearrange (modify the order of operations or any other hierarchy involved in the product). While applying these techniques, the participants have to remember the principle of force fitting; that is, if they cannot think of anything in response to the SCAMPER prompt they are using, they have to force a response (i.e., regardless of how ridiculous it seems) and then to think of ways to make any illogical responses work.

Measures of Creative Performance

Divergent thinking: the aut.

One of the creative skills to be developed by the current training program was divergent thinking, which is the capacity to generate multiple alternatives and solutions. There is a multitude of evidence suggesting that divergent thinking represents a distinct ability necessary for many forms of creative performance (Bachelor and Michael 1997 ; Mumford et al. 1998 ; Plucker and Renzulli 1999 ; Scott et al. 2004a ; Scratchley and Hakstian 2001 ; Sternberg and O’Hara 1999 ; Vincent et al. 2002 ). Divergent thinking tests can be considered the most widely used creativity test (Cropley 2000 ; Davis 2003 ), and they are applied in approximately 40 % of all creativity studies with college students and adults (Torrance and Presbury 1984 ). Divergent thinking can be assessed using open-ended tests, and several studies have documented its test-retest reliability (for example, see Yamamoto 1963a , 1963b ). Moreover, divergent thinking tests have been recommended as tests of effectiveness for creativity trainings (DeHaan 2011 ).

One of the most frequently used and well-validated divergent thinking test is the Alternative Uses task (AUT, Guilford 1967 ). During the AUT, the participants are asked to list as many different uses for a common object as possible and to make sure that the ideas they come up with are not too common and not completely impossible. The objects used in the current study were a brick and a newspaper and they were counterbalanced between the pre- and post-measure across the participants. The participants were given 3 min to perform the AUT and were instructed to list their ideas in the space provided. By coding the listed ideas, the participants’ creativity —the ability to generate ideas that are both novel and useful (for example, Amabile 1983 ; Mumford 2003 ; Sternberg and Lubart 1999 )—was examined. Moreover, the participant’s cognitive flexibility —the flexible switching among approaches—was assessed. Cognitive flexibility is characterized by global (as opposed to local) processing of information (for example, Ashby et al. 1999 ; Murray et al. 1990 ) and by the use of flat (as opposed to steep) associative hierarchies (for example, Mednick 1962 ). In other words, cognitive flexibility involves the ability to break cognitive patterns, to overcome functional fixedness, and to avoid a reliance on conventional ideas or solutions (Guilford 1967 ). Additionally, participant’s fluency —the total number of ideas generated by a participant—was measured. A more detailed description of the three measures is provided below.

Each idea was assigned a creativity score, ranging from not at all creative (=1) to very much creative (=5). Hereby, the two essential criteria of a creative idea—novelty and usefulness (for example, Amabile 1983 ; Mumford 2003 ; Sternberg and Lubart 1999 )—were taken into consideration. Two raters performed the creativity scoring. One rater assigned a creativity score to all of the ideas, and the other rater assigned creativity scores to 50 % of the ideas (50 % of the ideas generated for a brick and 50 % of those generated for a newspaper). The interrater reliability of the ratings was calculated using a two-way random intraclass correlation coefficient (ICC) analysis for consistency and can be considered substantial (ICC BothTasks  = 0.71, ICC Krant  = 0.65, ICC Baksteen  = 0.75). For each participant, across the ideas generated, a creativity sum score was calculated. The creativity sum score can be correlated with fluency (i.e., the total number of ideas generated by a participant). To control for the possibility that quantity confounds quality (e.g., that many less original and less useful ideas get a higher score than a few highly original and highly useful ideas) mean scores were calculated for each participant by dividing their creativity sum score by their fluency score.

Cognitive Flexibility

Cognitive flexibility can be quantified by the number of distinct idea categories used: each idea generated by a participant is assigned to a category from a predefined list of idea categories, and the total number of distinct idea categories is then calculated. For example, when asked to list possible uses for a brick, the ideas “build a house” and “build a bridge” would lead to a cognitive flexibility score of 1, as all ideas can be assigned to the category “building something.” On the other hand, the ideas build a house and “break a window” would lead to a score of 2, as the ideas can be assigned to two different idea categories (i.e., building something, and “destroying something”). For the flexibility scoring, a list of predefined idea categories was developed by two trained raters for each of the common objects (i.e., the brick and the newspaper). One of the raters assigned all of the ideas to the predefined idea categories, while the other rater did so for 50 % of the ideas (for 50 % of the ideas generated for a brick and for 50 % of those generated for a newspaper). The interrater reliability of the ratings was calculated using a two-way random ICC analysis for consistency and can be considered excellent (ICC BothTasks  = 0.97, ICC Krant  = 0.98, ICC Baksteen  = 0.95).

To calculate a participant’s fluency score, the number of complete and non-redundant ideas produced was counted.

Convergent Thinking: the RAT

Although important, divergent thinking is only one component of creative thinking. Many scholars emphasize the need for an additional cognitive ability, convergent thinking; that is, the cognitive process of deriving the single best, or most correct, answer to a problem or question (Fasko 2001 ; Guilford 1967 ; Nickerson 1999 ; Treffinger 1995 ). This component of creative thought was assessed using the Remote Associates Test (RAT), which was originally developed by Mednick ( 1962 ). In the RAT, the participants are presented with three-word combinations and are required to generate a fourth word that connects the three seemingly unrelated words (e.g., bar–dress–glass, fourth word: cocktail; cocktail bar, cocktail dress, cocktail glass). The structure of the RAT—finding a highly constrained, single solution—fits well with the concept of convergent thinking. As the English RAT version is rather difficult for non-native speakers of English (e.g., Estrada et al. 1994 ), in the current study, the Dutch version of the RAT (adapted from Chermahini et al. 2012 ) was used. The participants were presented with a list of ten three-word combinations. Two versions of the RAT were provided and counterbalanced between the pre- and post-measure across participants.

Creative Problem Solving

A creative activity that requires the interplay of divergent and convergent thinking is creative problem solving — the cognitive process of searching for a novel and inconspicuous solution to a problem. Creative problem solving can be blocked by fixations—a persistent impasse in problem solving in which unwarranted assumptions, typical thinking, or recent experiences block awareness of the solution. Two common forms are perceptual and functional fixations. The participants’ ability to overcome perceptual fixation was measured by a pattern perception task and the nine-dot-problem; the ability to overcome functional fixation was measured by insight tasks. Two different versions of the tasks were used and counterbalanced between the pre- and post-measure across participants.

In the pattern perception task, participants are presented with a picture consisting of various black patches on a white background and they have to indicate which pattern is presented in the picture. In the nine-dot-problem, nine dots are arranged in a square pattern. The task is to join the dots using four straight lines. Although there are no borders surrounding the task, people often feel constrained by the assumption that they must only draw within the square boundary formed by the dots. In fact, the task can only be solved if one draws outside of the square.

The insight tasks used in the current study were the two-string problem, the ball problem, the candle problem, and the switch problem. To solve these tasks, one has to use a displayed object in an unfamiliar manner (i.e., in the two-string and candle problems) or one has to complete the task in a manner which is different from prior experience or expectations (i.e., in the ball and switch problems). For example, in the two-string problem, participants are required to tie together two strings hanging from the ceiling. However, the strings are arranged so far apart that they cannot be reached at the same time. The solution requires the use of one of the objects available in the room so that one string can be set in motion as a pendulum. This swinging string can then be caught, while holding the other string, and thus can then be tied together.

Demographics

In addition to the various measures of creative performance, participants completed several demographic questions, determining the gender, age, nationality, and educational background of the participants.

Participants were welcomed individually at the BSI entrance. Once all of the participants who were scheduled for the training session had arrived, they were accompanied to the room in which the training was held. In the training room, the experimenter briefly introduced herself and the creativity trainer and informed the participants of how the 2.5-h session would be conducted.

During the first 20 min of the session, participants’ creative performance was measured (the pre-training, i.e., baseline measure) using several well-known creativity tasks (for information about the creativity tasks, see the “ Measures of creative performance ” section). Following the pre-training measure of creative performance, the participants received the creativity training for 1.5 h (for information about the training techniques, see the “ Training techniques ” section). The training itself started with a short word of welcome by the trainer as well as an explanation of the real-world problem that would be used for all brainstorming sessions during the training. The real-world problem required generating ideas for what the next generation sponge might look like (i.e., Hoe ziet de volgende generatie spons eruit? ). For each of the four techniques, the participants completed two procedures. First, the cognitive mechanism underlying the technique and how the technique can be applied were explained to them by the trainer. Second, the participants practiced and applied the technique to the real-world problem; first alone and then in a small group (the question whether brainstorming in groups has any benefit over-and-above brainstorming individually will be addressed in a separate paper). After the training, the post-measure of creative performance was administered. The post-measure lasted 20 min and employed equivalent versions of the tasks used in the creativity pre-measure (i.e., the versions did not differ in the types of questions nor in level of difficulty). To control for a possible instrumentation threat (i.e., the risk that an observed change from pre- to post-measure is due to the test that was used, rather than the training), two versions of each task were created and counterbalanced between the pre- and post-measure across participants. This meant that half of the participants performed one version as the pre-measure and the other version as the post-measure; the remaining half of the participants completed these versions in the reverse order. Finally, the participants ended the study by completing the demographic questions (for information about these questions, see the “ Demographics ” section). All questionnaires and training materials were provided on paper.

Impact of the Training on Creative Performance

The effectiveness of the training was scientifically tested by means of a pre- and post-test, employing creativity measures that relied on divergent thinking (the “ Divergent Thinking: the AUT ” section), convergent thinking (the “ Convergent Thinking: the RAT ” section), and creative problem solving skills (the “ Creative Problem Solving ” section).

An ANOVA was performed on the mean creativity rating of ideas generated during the AUT with training ( pre , post ) as the within-subjects variable and task order ( brick – newspaper , newspaper – brick ) as the between-subjects variable. The mean creativity level of ideas produced did not differ significantly across task order group ( F (1, 30) = 0.092, p  = .764), indicating that one group was not significantly more creative than the other, nor was a significant interaction effect found between task order and training ( F (1, 30) = 0.428, p  = .518). Importantly, a significant main effect for training was observed ( F (1, 30) = 5.709, p  = .023), suggesting that the mean creativity of the ideas generated following creativity training ( M  = 2.59, SD = 0.45) was significantly higher than that of the ideas generated prior to training ( M  = 2.36, SD = 0.41) (see Fig.  1 ).

Mean creativity of the ideas generated pre- and post-creativity training

Given that a significant improvement in creative performance was found following training, it is interesting to examine the possible mechanism for the observed change. Cognitive flexibility was examined as a possible mechanism, as the training employed a cognitive approach. That is, the increase in creativity after training could be partly explained by participants diversifying the categories of their given responses (Ritter et al. 2012 , 2014 ). As such, a 2 × 2 mixed ANOVA was performed on the number of distinct idea categories generated for the AUT ( cognitive flexibility ), with training (pre, post) as the within-subjects variable and task order (brick–newspaper, newspaper–brick) as the between-subjects variable. The analysis revealed that the cognitive flexibility of the participants in the different task order groups did not significantly differ ( F (1, 30) = 1.009, p  = .323), indicating that one group did not score higher on cognitive flexibility than the other. Importantly, a main effect of the training approached significance ( F (1, 30) = 3.788, p  = .061), suggesting that the mean number of idea categories generated on the AUT task could improve by approximately one distinct category from pre-training ( M  = 5.41, SD = 2.67) to post-training ( M  = 6.34, SD = 2.52), see Fig.  2 .

Cognitive flexibility pre- and post-creativity training

Finally, an interaction effect was found between training and task order ( F (1, 30) = 31.128, p  < .001) (see Fig.  2 ). Post hoc analyses revealed significant differences between the two tasks before and after training, such that the number of idea categories was higher for the newspaper task both prior to training ( p  < .001) and following training ( p  = .018). These results suggest that generating distinct ideas might be easier for the newspaper task overall, and this was confirmed by follow-up tests—the newspaper produced a larger number of distinct idea categories ( M  = 7.22, SD = 2.73) compared with the brick ( M  = 4.53, SD = 1.67; t (31) = 5.344, p  < .001). Importantly, follow up tests revealed that performance on the more difficult task (i.e., the brick) was significantly improved from pre-training ( M  = 3.75, SD = 1.18) to post-training ( M  = 7.06, SD = 2.74; t (30) = 2.970, p  = .006).

To examine whether the creativity training had any impact on divergent thinking, the participants’ number of correctly solved RAT word pairs prior to training were compared with that following creativity training. As no participants reported prior knowledge of the RAT word pairs used, all the participant responses were included in the analysis. Initially, a mixed ANOVA was performed to include an examination of task order. However, as no significant effects involving task order were found, a within-subjects t test was performed on the effect of creativity training on RAT scores (pre, post). The training appeared to have a significant impact on RAT task performance: on average, the participants solved approximately one more RAT word pair following creativity training ( M  = 4.73, SD = 2.32) compared with pre-training performance ( M  = 3.97, SD = 2.27; t (31) = 2.342, p  = .026) (see Fig.  3 ).

Performance on the RAT pre- and post-creativity training

To examine whether creativity training had any impact on creative problem solving skills, the problem solving performance scores prior to and following creativity training were calculated by adding the participants’ scores on the picture tasks, the dot problem task, and the two insight problems. Correct responses were excluded where participants reported prior knowledge of the task(s). Given the exclusion of scores for participants who reported prior knowledge of the tasks, mean problem solving scores were also calculated (i.e., an average score for the unknown tasks completed) and examined. As the overall findings did not differ for mean or sum scores, sum scores were retained in the analysis for improved ease of interpretation. A 2 × 2 mixed ANOVA was performed on the problem solving score with task order as the between-subjects variable. No significant main effect was found for task order ( F (1, 30) = 0.375, p  = .545), indicating that one group was not significantly better at solving the tasks than the other. Importantly, a main effect for training approached significance ( F (1, 30) = 3.695, p  = .064), such that performance on these tasks was higher following creativity training ( M  = 0.97, SD = 0.80) compared with performance prior to the training ( M  = 0.66, SD = 0.70).

In addition, the analyses revealed a significant interaction effect between training and task order ( F (1, 30) = 5.320, p  = .028). Post hoc tests indicated that prior to training, participants who completed the problem solving task set that included the ball and rope insight tasks performed significantly better than those who completed the set containing the candle and switch tasks ( p  = .041). Interestingly, no task order effect was observed post-training ( p  = .387). Moreover, participants who completed the set of problem solving tasks including the candle and switch insight tasks prior to training showed a significant improvement in task performance post-training ( p  = .006), while such a difference was not observed for the group who completed the problem solving tasks in the reverse order ( p  = .788). Taken together, these results suggest that the task set containing the candle and switch tasks were harder to solve than that containing the ball and rope problems and that the training increased performance for the more difficult tasks (Fig.  4 ).

Creative problem solving performance pre- and post-creativity training

Summary of Research Aims and Findings

Creativity has a crucial role in innovation, and the creation of new ideas and problem solutions has become a key concern for most organizations and businesses (Runco 2004 ). This goal is further supported by findings showing that creativity plays an important role in everyday problem solving (Cropley 1990 ; Mumford et al. 1991 ; Runco 1994 ; Torrance 1971 ; Wallas 1926 ) and in emotional health and well-being (Runco 2004 ; Simonton 2000 ). Given the importance of creativity and that creative thinking skills can be trained (Scott et al. 2004a ), the goal should be to train creative skills throughout the entire population. As such, there is a strong need for a well-developed, domain-unspecific creativity training that has been scientifically tested. In addition, such creativity training would be relatively easier to implement in educational and organizational settings if it was a single, brief session. Thus, the main objectives of the current research were to develop a brief creativity training that meets these requirements and to establish whether this training can enhance creative performance.

The findings of the current study demonstrate that a short training (i.e., a single training session of just 1.5 h), which develops cognitive skills necessary for creativity, can have an impact on creative performance. Following the creativity training session, improvements were observed across a variety of creative performance measures. Importantly, the creativity level of the ideas generated during the divergent thinking task improved post-training. In addition, the findings of the current study shed light on a possible underlying mechanism for these improvements in creativity, that is, cognitive flexibility. This is evidenced by a marginal improvement in the number of distinct idea categories generated post-training. Next to these divergent thinking skills, the training also improved convergent thinking, as improved performance on the RAT was observed post-training. Finally, the training provided marginal improvements in creative problem solving skills by reducing perceptual and functional fixations and mental blocks. Interestingly, it seems that the training benefitted the more difficult versions of some tasks, as demonstrated by the interaction effects for the AUT and the problem solving tasks.

The current findings provide support to the creative cognition model of creativity (for example, Ward et al. 1999 ), which states that individual differences in creativity can be explained by variations in the efficiency of cognitive processes underlying creativity (for example, Ward et al. 1999 ), and to the idea that creative thinking can be trained (Scott et al. 2004a ). Moreover, the current findings have important implications for educational and organizational settings. If the goal is to train creative skills among the entire population, effective creativity training programs need to be successfully implemented—this is particularly important if we want to meet the needs of the twenty-first century. The increases in creative performance reported here are impressive and promising since the training was only short (1.5 h), and the effects were demonstrated across a variety of well-validated measures.

Strengths and Contributions

Previous research has shown that creativity trainings with a focus on developing cognitive skills contribute to effectiveness (Scott et al. 2004a ). However, cognitive approaches tend to take longer to explain and implement and appear to be relatively less common (see Scott et al. 2004b ). The current training employed a cognitive approach, with the techniques used targeting multiple divergent, convergent, and problem-solving processes (i.e., not just idea generation) (see Scott et al. 2004a , b ). As such, the current creativity training makes a distinct contribution by employing a cognitive training approach in a brief, single-session, creativity training. Importantly, the exercises used during the creativity training differed from those used to evaluate the effectiveness of the training; that is, participants were not trained to the criterion (see Scott et al. 2004a ). Given that significant improvements were found following the current training employing a cognitive approach, this demonstrates a transfer of cognitive skills required for creative performance—and further supports the domain-unspecific nature of the training. In line with variables thought to strengthen training quality and efficacy (see Scott et al. 2004a ), the current creativity training did not include prizes, overt praise, or external motivation for creative performance.

Limitations and Suggestions for Future Research

While the current study provides evidence that the combined effects of various cognitive skills training methods work, there are some limitations of the study that should be addressed in future research. The current study included a within-subjects design with pre- and post-test creativity measures. Given the nature of the tasks included in the study, it is unlikely that the observed increase in creative performance on the post-measure was due to practice or learned effects (e.g., different objects were used in the AUT versions and different problems were presented in the insight task versions). Moreover, interaction effects were observed for some of the creativity measures (i.e., the training benefitted the more difficult task versions), suggesting that these effects would not be improved by practice alone. However, to eliminate any practice or learned effects on creative performance with certainty, a future study could employ a between-subjects design, or a mixed design, employing a control group. In future research, it could also be interesting to investigate whether the training is particularly effective for specific creativity domains. Importantly, the current study does not allow any conclusions to be made about the long-term effects of the training. In future research, a follow-up measure could be included to gain information about the maintained effects of creativity training.

The four techniques employed in the current study were carefully selected by the authors, and it was assumed that their combined effects would have a greater impact on creative performance. It remains unclear whether just one of the training methods would be necessary to obtain these observed effects or whether their combined effects were necessary to observe significant improvements in creative performance. Future research could answer this question by examining the impact of each of these techniques on creative performance in isolation. Such a test may, moreover, provide valuable information to further improve the form of the techniques applied during the training.

Finally, the western participant sample had a high education level and a relatively high proportion of females, which could limit the ecological validity of this study. On the other hand, findings of a meta-analysis by Scott et al. ( 2004a ) suggest that creativity training may be more effective in organizational than academic settings and may have greater effects on men than on women. Considering that this study relied on a population and setting for which the a priori chance of finding a training effect was not high, the ecological validity and generalizability of the current findings may be enhanced. However, it is still unknown what impact such training would have on eastern participants and on other age groups, for example, school-aged children and elderly people. Future research could include examining how this or a similar training can be adapted in eastern cultures and for other age groups.

Conclusions

Creative thinking can be considered one of the key competencies for the twenty-first century and is viewed as being essential for entrepreneurial activities and long-term economic growth (Amabile 1997 ; Wise 1992 ). If a goal is to train creative thinking skills, effective creativity training programs need to be developed and successfully implemented. The current study provided further evidence that creative potential is inherent to cognitive functioning and can be facilitated with training. Impressively, following a short (a single session lasting 1.5 h) domain-unspecific training, which develops cognitive skills necessary for creativity, improved creative performance on a variety of well-validated measures. These findings have important implications for educational and organizational settings, as they suggest that the present brief creativity training (or one employing similar cognitive techniques) could be implemented to facilitate creative thinking skills among the entire population.

Amabile, T. M. (1983). The social psychology of creativity: a componential conceptualization. Journal of Personality and Social Psychology, 45 (2), 357–376. doi: 10.1037/0022-3514.45.2.357 .

Article   Google Scholar  

Amabile, T. M. (1997). Motivating creativity in organizations: on doing what you love and loving what you do. California Management Review, 40 (1), 39–58. doi: 10.2307/41165921 .

Ashby, F. G., Isen, A. M., & Turken, A. U. (1999). A neuropsychological theory of positive affect and its influence on cognition. Psychological Review, 106 (3), 529–550. doi: 10.1037/0033-295X.106.3.529 .

Article   PubMed   Google Scholar  

Bachelor, P. A., & Michael, W. B. (1997). The structure of intellect model revisited. In M. A. Runco (Ed.), The creativity research handbook: volume 1 (pp. 155–182). New York: Hampton Press.

Google Scholar  

Chermahini, S. A., Hickendorff, M., & Hommel, B. (2012). Development and validity of a Dutch version of the remote associates task: an item-response theory approach. Thinking Skills and Creativity, 7 (3), 177–186. doi: 10.1016/j.tsc.2012.02.003 .

Cropley, A. J. (1990). Creativity and mental health in everyday life. Creativity Research Journal, 3 (3), 167–178. doi: 10.1080/10400419009534351 .

Cropley, A. J. (2000). Defining and measuring creativity: are creativity tests worth using? Roeper Review, 23 (2), 72–79. doi: 10.1080/02783190009554069 .

Davis, G. A. (2003). Identifying creative students, teaching for creative growth. In N. Colangelo & G. A. Davis (Eds.), Handbook of gifted education (pp. 311–324). Boston: Allyn and Bacon.

DeHaan, R. L. (2011). Teaching creative science thinking. Science, 334 (6062), 1499–1500. doi: 10.1126/science.1207918 .

Eberle, R. (1971). SCAMPER games for imagination development . New York: DOK Publications.

Edwards, M., McGoldrick, C., & Oliver, M. (2006). Creativity and curricula in higher education: academics’ perspectives. In N. Jackson, M. Oliver, M. Shaw, & J. Wisdom (Eds.), Developing creativity in higher education: an imaginative curriculum (pp. 59–73). London: Routledge.

Estrada, C. A., Isen, A. M., & Young, M. J. (1994). Positive affect improves creative problem solving and influences reported source of practice satisfaction in physicians. Motivation and Emotion, 18 , 285–299.

European Commission. (2008). 2009 , European year of creativity and innovation . Retrieved from http://www.create2009.europa.eu

Fasko, D. (2001). Education and creativity. Creativity Research Journal, 13 (3-4), 317–327. doi: 10.1207/S15326934CRJ1334_09 .

Feist, G. J., & Gorman, M. E. (1998). The psychology of science: review and integration of a nascent discipline. Review of General Psychology, 2 (1), 3–47. doi: 10.1037/1089-2680.2.1.3 .

Florida, R. (2002). The raise of the creative class: and how it’s transforming work, leisure, community and everyday life . New York: Basic Books.

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

Hawkins, J. (2001). The creative economy: how people make money from ideas . London: Allen Lane.

Kim, K. H. (2011). The creativity crisis: the decrease in creative thinking scores on the torrance tests of creative thinking. Creativity Research Journal, 23 (4), 285–295. doi: 10.1080/10400419.2011.627805 .

Kimbell, R. (2000). Creativity in crisis. Journal of Design and Technology Education, 5 (3), 206–211.

MacKinnon, D. W. (1962). The nature and nurture of creative talent. American Psychologist, 17 (7), 484–495. doi: 10.1037/h0046541 .

Mednick, S. (1962). The associative basis of the creative process. Psychological Review, 69 (3), 220–232. doi: 10.1037/h0048850 .

Mumford, M. D. (2003). Where have we been, where are we going? Taking stock in creativity research. Creativity Research Journal, 15 (2-3), 107–120. doi: 10.1080/10400419.2003.9651403 .

Mumford, M. D., Mobley, M. I., Reiter‐Palmon, R., Uhlman, C. E., & Doares, L. M. (1991). Process analytic models of creative capacities. Creativity Research Journal, 4 (2), 91–122. doi: 10.1080/10400419109534380 .

Mumford, M. D., Marks, M. A., Connelly, M. S., Zaccaro, S. J., & Johnson, J. F. (1998). Domain-based scoring in divergent-thinking tests: validation evidence in an occupational sample. Creativity Research Journal, 11 (2), 151–163. doi: 10.1207/s15326934crj1102_5 .

Murray, N., Sujan, H., Hirt, E. R., & Sujan, M. (1990). The influence of mood on categorization: a cognitive flexibility interpretation. Journal of Personality and Social Psychology, 59 (3), 411–425. doi: 10.1037/0022-3514.59.3.411 .

Newton, L. D., & Newton, D. P. (2010). What teachers see as creative incidents in elementary science lessons. International Journal of Science Education, 32 (15), 1989–2005. doi: 10.1080/09500690903233249 .

Nickerson, R. S. (1999). Enhancing creativity. In R. J. Sternberg (Ed.), Handbook of creativity (pp. 392–430). Cambridge: Cambridge University Press.

Nijstad, B. A., & Stroebe, W. (2006). How the group affects the mind: a cognitive model of idea generation in groups. Personality and Social Psychology Review, 10 (3), 186–213. doi: 10.1207/s15327957pspr1003_1 .

Osborn, A. F. (1953). Applied imagination: principles and procedures of creative problem-solving . New York: Scribners.

Plucker, J. A., & Renzulli, J. S. (1999). Psychometric approaches to the study of human creativity. In R. J. Sternberg (Ed.), Handbook of creativity (pp. 35–61). Cambridge: Cambridge University Press.

Reiter-Palmon, R., Mumford, M. D., & Threlfall, K. V. (1998). Solving everyday problems creatively: the role of problem construction and personality type. Creativity Research Journal, 11 (3), 187–197. doi: 10.1207/s15326934crj1103_1 .

Ritter, S. M., Damian, R. I., Simonton, D. K., van Baaren, R. B., Strick, M., Derks, J., et al. (2012). Diversifying experiences enhance cognitive flexibility. Journal of Experimental Social Psychology, 48 (4), 961–964. doi: 10.1016/j.jesp.2012.02.009 .

Ritter, S. M., Kühn, S., Müller, B. C., Van Baaren, R. B., Brass, M., & Dijksterhuis, A. (2014). The creative brain: corepresenting schema violations enhances TPJ activity and boosts cognitive flexibility. Creativity Research Journal, 26 (2), 144–150. doi: 10.1080/10400419.2014.901061 .

Runco, M. A. (1994). Problem finding, problem solving, and creativity . California: Greenwood Publishing Group.

Runco, M. A. (2004). Everyone has creative potential. In R. J. Sternberg & E. L. Grigorenko (Eds.), Creativity: from potential to realization (pp. 21–30). Washington, D.C.: American Psychological Association.

Chapter   Google Scholar  

Sawyer, K. (2011). The cognitive neuroscience of creativity: a critical review. Creativity Research Journal, 23 (2), 137–154. doi: 10.1080/10400419.2011.571191 .

Scott, G., Leritz, L. E., & Mumford, M. D. (2004a). The effectiveness of creativity training: a quantitative review. Creativity Research Journal, 16 (4), 361–388. doi: 10.1080/10400410409534549 .

Scott, G., Leritz, L. E., & Mumford, M. D. (2004b). Types of creativity training: approaches and their effectiveness. Journal of Creative Behavior, 38 (3), 149–179. doi: 10.1002/j.2162-6057.2004.tb01238.x .

Scratchley, L. S., & Hakstian, A. R. (2001). The measurement and prediction of managerial creativity. Creativity Research Journal, 13 (3-4), 367–384. doi: 10.1207/S15326934CRJ1334_14 .

Simonton, D. K. (2000). Methodological and theoretical orientation and the long-term disciplinary impact of 54 eminent psychologists. Review of General Psychology, 4 (1), 13–24. doi: 10.1037/1089-2680.4.1.13 .

Sternberg, R. J., & Lubart, T. I. (1996). Investing in creativity. American Psychologist, 51 (7), 677–688. doi: 10.1037/0003-066X.51.7.677 .

Sternberg, R. J., & Lubart, T. I. (1999). The concept of creativity: prospects and paradigms. In R. J. Sternberg (Ed.), Handbook of creativity (pp. 3–15). Cambridge: Cambridge University Press.

Sternberg, R. J., & O’Hara, L. A. (1999). Creativity and intelligence. In R. J. Sternberg (Ed.), Handbook of creativity (pp. 251–272). Cambridge: Cambridge University Press.

Torrance, E. P. (1971). Are the torrance tests of creative thinking biased against or in favor of “disadvantaged” groups? Gifted Child Quarterly, 15 (2), 75–80. doi: 10.1177/001698627101500201 .

Torrance, E. P., & Presbury, J. (1984). The criteria of success used in 242 recent experimental studies of creativity. Creative Child & Adult Quarterly, 9 (4), 238–242.

Treffinger, D. J. (1995). Creative problem solving: overview and educational implications. Educational Psychology Review, 7 (3), 301–312. doi: 10.1007/BF02213375 .

Treffinger, D. J. (2009). Myth 5: creativity is too difficult to measure. Gifted Child Quarterly . doi: 10.1177/0016986209346829 .

UNESCO International Bureau of Education (2014). Creativity. In Guiding Principles for Learning in the Twenty First Century (p. 15 – 18) . Retrieved from www.ecolint.ch/file/621/download?token=zhQHu7qg .

Vincent, A. S., Decker, B. P., & Mumford, M. D. (2002). Divergent thinking, intelligence, and expertise: a test of alternative models. Creativity Research Journal, 14 (2), 163–178. doi: 10.1207/S15326934CRJ1402_4 .

Wallas, G. (1926). The art of thought . New York: Harcourt Brace.

Ward, T. B., Smith, S. M., & Finke, R. A. (1999). Creative cognition. In R. J. Sternberg (Ed.), Handbook of creativity (pp. 189–212). Cambridge: Cambridge University Press.

Wince-Smith, D. L. (2006). The creativity imperative: a national perspective. Peer Review, 8 (2), 12.

Wise, G. (1992). Inventor and corporations in the maturing electrical industry. In R. J. Weber & D. N. Perkins (Eds.), Inventive minds: creativity in technology (pp. 291–310). Oxford: Oxford University Press.

Yamamoto, K. (1963a). Creative writing and school achievement. School and Society, 91 , 307–308.

Yamamoto, K. (1963b). Relationships between creative thinking abilities of teachers and achievement and adjustment of pupils. The Journal of Experimental Education, 32 (1), 3–25. doi: 10.1080/00220973.1963.11010803 .

Download references

Acknowledgments

Financial support was provided by a Netherlands Organization for Scientific Research (NWO) Veni grant awarded to Simone M. Ritter (016.155.049. Veni 2014. Division Social Sciences).

We would like to thank Bernice Plant for her help with the data analysis and the writing of the paper.

Author information

Authors and affiliations.

Department of Social and Cultural Psychology, Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands

Simone M. Ritter

Mostert Consultancy for Creativity and Innovation Management, Naaldwijk, The Netherlands

Nel Mostert

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Simone M. Ritter .

Ethics declarations

A total of 32 (20 females) participants between the ages of 18 and 34 years old ( M  = 23.13, SD = 5.76) gave written informed consent to participate in the study, which was conducted according to the principles of the institutional review board (Ethics Committee Faculty of Social Sciences, Radboud University, the Netherlands) and the principles expressed in the Declarations of Helsinki.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and permissions

About this article

Ritter, S.M., Mostert, N. Enhancement of Creative Thinking Skills Using a Cognitive-Based Creativity Training. J Cogn Enhanc 1 , 243–253 (2017). https://doi.org/10.1007/s41465-016-0002-3

Download citation

Received : 08 June 2016

Accepted : 18 August 2016

Published : 07 October 2016

Issue Date : September 2017

DOI : https://doi.org/10.1007/s41465-016-0002-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Creativity training
• Creative thinking techniques
• Divergent thinking
• Convergent thinking
• Problem solving
• Find a journal
• Publish with us
• Track your research

International Journal of Learning, Teaching and Educational Research

• Announcements
• Editorial Board
• Submit a Paper
• Publication Ethics
• ##PAPER TEMPLATE##
• ##Retraction Policy##

Project-Based Learning in Fostering Creative Thinking and Mathematical Problem-Solving Skills: Evidence from Primary Education in Indonesia

The interdependence between the Project-Based Learning (PjBL) Model and the growth and enhancement of Creative Thinking and Mathematical Problem Solving Skills in Elementary Schools is unquestionable nowadays. Prior studies have yet to discover concrete evidence regarding the interdependence being discussed. This study highlighted cognitive abilities related to creative thinking and mathematics problem-solving by implementing the Project-Based Learning Model. This research was a quasi-experiment with a pretest-posttest control group design involving 43 students in the sixth grade of two elementary schools; data was collected through test and classroom observation, and then the data was analyzed using Multivariate Analysis of Variance (MANOVA). Conversely, students exposed to project-based learning models exhibit higher skill levels in creative thinking and problem-solving than those instructed using conventional learning models. The project-based learning model significantly impacted elementary school children’s creative thinking and mathematics problem-solving skills. These findings suggest that the Project-Based Learning Model is acceptable for instructors seeking to foster creativity in teaching mathematics at the primary school level in Indonesia or other countries with comparable settings.

https://doi.org/10.26803/ijlter.23.8.15

Adijaya, M. A., Widiana, I. W., Parwata, I. G. L. A., & Antara, I. G. W. S. (2023). Bloom’s taxonomy revision-oriented learning activities to improve procedural capabilities and learning outcomes. International Journal of Educational Methodology, 9(1), 261–270. https://doi.org/10.12973/ijem.9.1.261

Albab, U., Budiyono, & Indriati, D. (2020). Metacognition skills and higher order thinking skills (HOTS) in mathematics. Journal of Physics: Conference Series, 1613(1), Article 012017. https://doi.org/10.1088/1742-6596/1613/1/012017

Almulla, M. A. (2020). The effectiveness of the project-based learning (PBL) approach as a way to engage students in learning. SAGE Open, 10(3), 1–15. https://doi.org/10.1177/2158244020938702

Amerstorfer, C. M., & Münster-Kistner, C. F. V. (2021). Student perceptions of academic engagement and student-teacher relationships in problem-based learning. Frontiers in Psychology, 12, 1–18. https://doi.org/10.3389/fpsyg.2021.713057

Arends, R. I. (2013). Learning to teach (9th ed.). McGraw-Hill.

Arici, F. (2023). An examination of the effectiveness of problem-based learning method supported by augmented reality in science education. Journal of Computer Assisted Learning, 39(2), 446–476. https://doi.org/10.1111/jcal.12752

Astuti, P., Qohar, A., & Hidayanto, E. (2019). Proses berpikir siswa dalam menyelesaikan soal higher order thinking skills berdasarkan pemahaman konseptual dan prosedural [Students’ thinking process in solving higher order thinking skills questions based on conceptual and procedural understanding]. Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan, 4(1), 117. https://doi.org/10.17977/jptpp.v4i1.11910

Astutik, S., Mahardika, I. K., Indrawati, Sudarti, & Supeno. (2020). HOTS student worksheet to identification of scientific creativity skill, critical thinking skill and creative thinking skill in physics learning. Journal of Physics: Conference Series, 1465(1), Article 012075. https://doi.org/10.1088/1742-6596/1465/1/012075

Aziz, A. A. M. A. (2021). The development of the HOTS mathematical problem-solving framework using the Bar model strategy: A need analysis. Review of International Geographical Education Online, 11(4), 972–981. https://doi.org/10.33403/rigeo.8006811

Babakr, Z. H., Mohamedamin, P., & Kakamad, K. (2019). Piaget’s cognitive developmental theory: Critical review. Education Quarterly Reviews, 2(3), 517–524. https://doi.org/10.31014/aior.1993.02.03.84

Benraghda, A. (2022). Self-assessment as a self-regulated learning approach in English oral presentations: College students’ choices and perceptions. Cogent Education, 9(1), Article 2123472. https://doi.org/10.1080/2331186X.2022.2123472

Cammies, C., Cunningham, J. A., & Pike, R. K. (2022). Not all Bloom and gloom: Assessing constructive alignment, higher order cognitive skills, and their influence on students’ perceived learning within the practical components of an undergraduate biology course. Journal of Biological Education, 58(3), 588–608. https://doi.org/10.1080/00219266.2022.2092191

Chen, J., Kolmos, A., & Du, X. (2021). Forms of implementation and challenges of PBL in engineering education: A review of literature. European Journal of Engineering Education, 46(1), 90–115. https://doi.org/10.1080/03043797.2020.1718615

Cheng, L., Wang, M., Chen, Y., Niu, W., Hong, M., & Zhu, Y. (2022). Design my music instrument: A project-based science, technology, engineering, arts, and mathematics program on the development of creativity. Frontiers in Psychology, 12, 1–8. https://doi.org/10.3389/fpsyg.2021.763948

C?r?t, D. K., & Aydemir, S. (2023). Online scratch activities during the COVID-19 pandemic: Computational and creative thinking. International Journal of Evaluation and Research in Education, 12(4), 2111–2120. https://doi.org/10.11591/ijere.v12i4.24938

Cook, S. C. (2020). Schema-based instruction for mathematical word problem solving: An evidence-based review for students with learning disabilities. Learning Disability Quarterly, 43(2), 75–87. https://doi.org/10.1177/0731948718823080

Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). SAGE Publication.

Cruz, S., Lencastre, J. A., & Viseu, F. (2023). Heuristics and usability testing of a project-based learning online course: A case study with structural mathematical concepts. International Journal of Instruction, 16(3), 465–488. https://doi.org/10.29333/iji.2023.16325a

Diego-Mantecon, J. M., Prodromou, T., Lavicza, Z., Blanco, T. F., & Ortiz-Laso, Z. (2021). An attempt to evaluate STEAM project-based instruction from a school mathematics perspective. ZDM – Mathematics Education, 53(5), 1137–1148. https://doi.org/10.1007/s11858-021-01303-9

Edwar, E., Putri, R. I. I., Zulkardi, Z., & Darmawijoyo, D. (2023). Developing a workshop model for high school mathematics teachers constructing HOTS questions through the Pendidikan Matematika Realistik Indonesia approach. Journal on Mathematics Education, 14(4), 603–626. https://doi.org/10.22342/jme.v14i4.pp603-626

Gunawardena, M. (2021). Scaffolding students’ critical thinking: A process not an end game. Thinking Skills and Creativity, 41, Article 100848. https://doi.org/10.1016/j.tsc.2021.100848

Guo, P., Saab, N., Post, L. S., & Admiraal, W. (2020). A review of project-based learning in higher education: Student outcomes and measures. International Journal of Educational Research, 102, Article 101586. https://doi.org/10.1016/j.ijer.2020.101586

Handayani, A. D., & Iswantiningtyas, V. (2020). Javanese traditional games as a teaching and learning media to socialize and introduce mathematics since early age. Journal of Physics: Conference Series, 1521(3), 1–7. https://doi.org/10.1088/1742-6596/1521/3/032008

Hawari, A. D. M., & Noor, A. I. M. (2020). Project based learning pedagogical design in STEAM art education. Asian Journal of University Education, 16(3), 102–111. https://doi.org/10.24191/ajue.v16i3.11072

Hidayati, Y. M., Ngalim, A., Sutama, Arifin, Z., Abidin, Z., & Rahmawati, E. (2020). Level of combinatorial thinking in solving mathematical problems. Journal for the Education of Gifted Young Scientists, 8(3), 1231–1243. https://doi.org/10.17478/JEGYS.751038

Hujjatusnaini, N. (2020). The effect of blended project-based learning integrated with 21st-century skills on pre-service biology teachers’ higher-order thinking skills. Jurnal Pendidikan IPA Indonesia, 11(1), 104–118. https://doi.org/10.15294/jpii.v11i1.27148

Irdalisa, I., Zulherman, Z., Elvianasti, M., Widodo, W. S., & Hanum, E. (2024). Effectiveness of project-based learning on STEAM-based student’s worksheet analysis with ecoprint technique. International Journal of Educational Methodology, 10(1), 123–135. https://doi.org/10.12973/ijem.10.1.923

Jiang, B., & Li, Z. (2021). Effect of Scratch on computational thinking skills of Chinese primary school students. Journal of Computers in Education, 8(4), 505–525. https://doi.org/10.1007/s40692-021-00190-z

Jiang, Y., Xu, N., Xu, S., & Wang, S. (2022). The enlightenment of Piaget’s theory to Chinese primary school education. Advances in Social Science, Education and Humanities Research, 670, 878–882. https://doi.org/10.2991/assehr.k.220704.158

Junianto, J., & Wijaya, A. (2019). Developing students’ mathematical literacy through problem based learning. Journal of Physics: Conference Series, 1320(1), Article 012035. IOP Publishing. https://doi.org/10.1088/1742-6596/1320/1/012035

Karan, E. (2022). Enhancing students’ problem-solving skills through project-based learning. Journal of Problem Based Learning in Higher Education, 10(1), 74–87. https://doi.org/10.54337/ojs.jpblhe.v10i1.6887

Kemendikbudristek. (2023). Laporan PISA Kemendikbudristek [PISA report of the Ministry of Education and Culture]. PISA 2022 Dan Pemulihan Pembelajaran Indonesia (pp. 1–25). https://balaibahasariau.kemdikbud.go.id/wpcontent/uploads/2023/12/LAPORAN-PISA-KEMENDIKBUDRISTEK.pdf

Khalid, M., Saad, S., Hamid, S. R. A., Abdullah, M. R., Ibrahim, H., & Shahrill, M. (2020). Enhancing creativity and problem solving skills through creative problem solving in teaching mathematics. Creativity Studies, 13(2), 270–291. https://doi.org/10.3846/cs.2020.11027

Kharisma, F. N., Susilowati, S. M. E., & Ridlo, S. (2019). The effective learning models in developing problem-solving skills. KnE Social Sciences, 3(18), 595–604 https://doi.org/10.18502/kss.v3i18.4750

Kim, H. W., & Kim, M. K. (2021). A case study of children’s interaction types and learning motivation in small group project-based learning activities in a mathematics classroom. Eurasia Journal of Mathematics, Science and Technology Education, 17(12), em2051. https://doi.org/10.29333/ejmste/11415

Kurniawan, E. S., Mundilarto, M., & Istiyono, E. (2024). Improving student higher order thinking skills using Synectic-HOTS-oriented learning model. International Journal of Evaluation and Research in Education, 13(2), 1132–1140. https://doi.org/10.11591/ijere.v13i2.25002

Lafmejani, A. Q. (2022). Cognitive evolution of the “Human” concept and its adaptation to Piaget’s theory. Caspian Journal of Neurological Sciences, 8(4), 222–233. https://doi.org/10.32598/CJNS.4.31.355.1

Lazi?, B. D., Kneževi?, J. B., & Mari?i?, S. M. (2021). The influence of project-based learning on student achievement in elementary mathematics education. South African Journal of Education, 41(3), Article 1909. https://doi.org/10.15700/saje.v41n3a1909

Leasa, M. (2020). The effect of learning styles on the critical thinking skills in natural science learning of elementary school students. Elementary Education Online, 19(4), 2086–2097. https://doi.org/10.17051/ilkonline.2020.763449

Lee, J. S., & Galindo, E. (2021). Examining project-based learning successes and challenges of mathematics preservice teachers in a teacher residency program: Learning by doing. Interdisciplinary Journal of Problem-Based Learning, 15(1), 1–20. https://doi.org/10.14434/ijpbl.v15i1.28786

Li, Z. (2022). Assessment of scientific thinking and creativity in an electronic educational environment. International Journal of Science Education, 44(3), 463–486. https://doi.org/10.1080/09500693.2022.2032863

Lin, X. (2021). Investigating the unique predictors of word-problem solving using meta-analytic structural equation modeling. Educational Psychology Review, 33(3), 1097?1124. https://doi.org/10.1007/s10648-020-09554-w

Liu, M. (2023). Bayesian optimization and ensemble learning algorithm combined method for deformation prediction of concrete dam. Structures, 54, 981–993. https://doi.org/10.1016/j.istruc.2023.05.136

Lu, X., & Kaiser, G. (2022). Creativity in students’ modelling competencies: Conceptualisation and measurement. Educational Studies in Mathematics, 109(2), 287–311. https://doi.org/10.1007/s10649-021-10055-y

MacLeod, M., & Veen, J. T. V. D. (2020). Scaffolding interdisciplinary project-based learning: A case study. European Journal of Engineering Education, 45(3), 363–377. https://doi.org/10.1080/03043797.2019.1646210

Marbán, J. M., Radwan, E., Radwan, A., & Radwan, W. (2021). Primary and secondary students’ usage of digital platforms for mathematics learning during the COVID19 outbreak: The case of the Gaza strip. Mathematics, 9(2), Article 110. https://doi.org/10.3390/math9020110

Menggo, S., Pramesti, P. D. M. Y., & Krismayani, N. W. (2023). Integrating project-based learning in preparing students’ interpersonal communication skills on speaking courses in Indonesia. International Journal of Learning, Teaching and Educational Research, 22(9), 219–240. https://doi.org/10.26803/ijlter.22.9.12

Meyer, M. W., & Norman, D. (2020). Changing design education for the 21st century. The Journal of Design, Economics, and Innovation, 6(1), 13–49. https://doi.org/10.1016/j.sheji.2019.12.002

Michalsky, T. (2024). Metacognitive scaffolding for preservice teachers’ self-regulated design of higher order thinking tasks. Heliyon, 10(2), e2480. https://doi.org/10.1016/j.heliyon.2024.e24280

Moma, L. (2015). Pengambangan instrumen kemampuan berpikir kreatif matematis untuk siswa SMP [Development of mathematical creative thinking instruments for junior high school students]. Delta-Pi: Jurnal Matematika Dan Pendidikan Matematika, 4(1), 27–41. https://doi.org/10.33387/dpi.v4i1.142

Morteza, T., & Moghaddam, M. Y. (2017). On the plausibility of Bloom’s higher order thinking strategies on learner autonomy: The paradigm shift. Asian-Pacific Journal of Second and Foreign Language Education, 2, Article 14. https://doi.org/10.1186/s40862-017-0037-8

Munar, A., Winarti, W., Nai’mah, N., Rezieka, D. G., & Aulia, A. (2022). Improving higher order thinking skill (HOTs) in early children using picture story book. AL-ISHLAH: Jurnal Pendidikan, 14(3), 4611–4618. https://doi.org/10.35445/alishlah.v14i3.2224

Muttaqin, H., Susanto, Hobri, & Tohir, M. (2021). Students’ creative thinking skills in solving mathematics higher order thinking skills (HOTs) problems based on online trading arithmetic. Journal of Physics: Conference Series, 1832(1), Article 012036. https://doi.org/10.1088/1742-6596/1832/1/012036

Ndiung, S., Sariyasa, Jehadus, E., & Apsari, R. A. (2021). The effect of treffinger creative learning model with the use RME principles on creative thinking skill and mathematics learning outcome. International Journal of Instruction, 14(2), 873–888. https://doi.org/10.29333/iji.2021.14249a

Newton, K. J. (2020). Mathematical flexibility: Aspects of a continuum and the role of prior knowledge. Journal of Experimental Education, 88(4), 503–515. https://doi.org/10.1080/00220973.2019.1586629

Nizaruddin, N., & Kusmaryono, I. (2023). Transforming students’ pseudo-thinking into real thinking in mathematical problem solving. International Journal of Educational Methodology, 9(3), 477–491. https://doi.org/10.12973/ijem.9.3.477

Nuryadin, A., Karlimah, K., Lidinillah, D. A. M., & Apriani, I. F. (2023). Blended learning after the pandemic: The flipped classroom as an alternative learning model for elementary classrooms. Participatory Educational Research, 10(3), 209–225. https://doi.org/10.17275/per.23.52.10.3

OECD (Organisation for Economic Co-operation and Development). (2024). PISA results 2022 (Volume III) – Factsheets: Indonesia. OECD. https://www.oecd.org/en/publications/pisa-results-2022-volume-iii-factsheets_041a90f1-en/indonesia_a7090b49-en.html

Pakpahan, F. H., & Saragih, M. (2022). Theory of cognitive development by Jean Piaget. Journal of Applied Linguistics, 2(2), 55–60. https://doi.org/10.52622/joal.v2i2.79

Palinussa, A. L. (2013). Students’ critical mathematical thinking skills and character: Experiments for junior high school students through realistic mathematics education culture-based. Journal on Mathematics Education, 4(1), 75–94. https://doi.org/10.22342/jme.4.1.566.75-94

Piaget, J. (1936). Origins of intelligence in the child. Routledge & Kegan Paul.

PISA. (2023). PISA 2022 results (Volume I and II) – Country notes: Indonesia. https://www.oecd.org/en/publications/pisa-2022-results-volume-i-and-ii-country-notes_ed6fbcc5-en/indonesia_c2e1ae0e-en.html

Prastiti, T. D., Tresnaningsih, S., Mairing, J. P., & Azkarahman, A. R. (2020). HOTS problem on function and probability: Does it impact to students’ mathematical literacy in Universitas Terbuka? Journal of Physics: Conference Series, 1613(1), Article 012003. https://doi.org/10.1088/1742-6596/1613/1/012003

Puccio, G., Lohiser, A., & Seemiller, C. (2021). Understanding convergent thinking: Developing effective critical thinking. In Creative problem solving: A 21st century workplace skill. SAGE Publications. https://doi.org/10.4135/9781071865637

Purwaningsih, E., Sari, A. M., Yuliati, L., Masjkur, K., Kurniawan, B. R., & Zahiri, M. A. (2020). Improving the problem-solving skills through the development of teaching materials with STEM-PjBL (science, technology, engineering, and mathematics-project based learning) model integrated with TPACK (technological pedagogical content knowledge). Journal of Physics: Conference Series, 1481(1), Article 012133. https://doi.org/10.1088/1742-6596/1481/1/012133

Putri, N., Rusdiana, D., & Suwarma, I. R. (2020). Enhanching physics students’ creative thinking skills using CBL model implemented in STEM in vocational school. Journal of Physics: Conference Series, 1521, Article 042045. https://doi.org/10.1088/1742-6596/1521/4/042045

Rahman, M. S., Juniati, D., & Manuharawati, M. (2023). The quality of mathematical proficiency in solving geometry problem: Difference cognitive independence and motivation. Pegem Journal of Education and Instruction, 13(3), 255–266. https://doi.org/10.47750/pegegog.13.03.27

Rehman, N., Zhang, W., Mahmood, A., Fareed, M. Z., & Batool, S. (2023). Fostering twenty-first century skills among primary school students through math project-based learning. Humanities and Social Sciences Communications, 10(1), Article 424. https://doi.org/10.1057/s41599-023-01914-5

Roble, D. B., Lomibao, L. S., & Luna, C. A. (2021). Developing students’ creative constructs in mathematics with problem-based (PB) and problem posing (PP) tasks. Canadian Journal of Family and Youth, 13(2), 82–94. https://doi.org/10.29173/cjfy29672

Rosidin, U., Suyatna, A., & Abdurrahman, A. (2019). A combined HOTS-based assessment/STEM learning model to improve secondary students’ thinking skills: A development and evaluation study. Journal for the Education of Gifted Young Scientists, 7(3), 435–448. https://doi.org/10.17478/jegys.518464

Rusilowati, A., Negoro, R. A., Aji, A. P., & Subali, B. (2023). Development of waves critical thinking test: Physics essay test for high school student. European Journal of Educational Research, 12(4), 1781–1794. https://doi.org/10.12973/eu-jer.12.4.1781

Safie, N., & Zakaria, S. (2023). Examining the effectiveness of thinking maps usage by analysing students’ achievement in mathematics subject. Journal of Advanced Research in Applied Sciences and Engineering Technology, 31(1), 197–209. https://doi.org/10.37934/ARASET.31.1.197209

Sahudra, T. M., Ary, K. K., Ramadhani, D., Asnawi, A., & Handrianto, C. (2021). The impact of project-based flipped learning model on the technological pedagogical content knowledge skill of prospective teachers. Sustainability (Switzerland), 13(5), Article 2606. https://doi.org/10.3390/su13052606

Saldo, I. J. P., & Walag, A. M. P. (2020). Utilizing problem-based and project-based learning in developing students’ communication and collaboration skills in physics. American Journal of Educational Research, 8(5), 232–237. https://doi.org/10.12691/education-8-5-1

Samsudin, M. A., Jamali, S. M., Zain, A. N. M., & Ebrahim, N. A. (2020). The effect of STEM project based learning on self-efficacy among high-school physics students. Journal of Turkish Science Education, 17(1), 94–108. https://doi.org/10.36681/tused.2020.15

Schindler, M., & Bakker, A. (2020). Affective field during collaborative problem posing and problem solving: A case study. Educational Studies in Mathematics, 105(3), 303?324. https://doi.org/10.1007/s10649-020-09973-0

Schleicher, A. (2018). PISA 2018 insights and interpretations. OECD. Available at https://www.oecd.org/content/dam/oecd/en/about/programmes/edu/pisa/publications/national-reports/pisa-2018/PISA%202018%20Insights%20and%20Interpretations%20FINAL%20PDF.pdf

Serin, H. (2023). Teaching mathematics: The role of project-based learning. International Journal of Social Sciences & Educational Studies, 10(2), Article 28. https://doi.org/10.23918/ijsses.v10i2p378

Setianingsih, R., Sa’dijah, C., As’ari, A. R., & Muksar, M. (2017). Investigating fifth-grade students’ construction of mathematical knowledge through classroom discussion. International Electronic Journal of Mathematics Education, 12(4), 383–396. https://doi.org/10.29333/iejme/619

Shidqi, L., Trisniawati, T., & Rhosyida, N. (2023). The development of kobatar learning media for learning mathematics in elementary school. Advances in Mobile Learning Educational Research, 3(2), 886–892. https://doi.org/10.25082/amler.2023.02.015

Simonton, K. L. (2021). Project-based learning and its potential in physical education: An instructional model inquiry. Curriculum Studies in Health and Physical Education, 12(1), 36–52. https://doi.org/10.1080/25742981.2020.1862683

Sofiyan, S., Amalia, R., & Suwardi, A. B. (2020). Development of mathematical teaching materials based on project-based learning to improve students’ HOTS and character. Journal of Physics: Conference Series, 1460(1), Article 012006. https://doi.org/10.1088/1742-6596/1460/1/012006

Son, A. L., Darhim, D., & Fatimah, S. (2020). Students’ mathematical problem-solving ability based on teaching models intervention and cognitive style. Journal on Mathematics Education, 11(2), 209–222. https://doi.org/10.22342/jme.11.2.10744.209-222

Suanto, E., Maat, S. M., & Zakaria, E. (2023). The effectiveness of the implementation of three dimensions geometry KARA module on higher order thinking skills (HOTS) and motivation. International Journal of Instruction, 16(3), 95–116. https://doi.org/10.29333/iji.2023.1636a

Sukkeewan, P., Songkram, N., & Nasongkhla, J. (2024). Development and validation of a reliable and valid assessment tool for measuring innovative thinking in vocational students. International Journal of Educational Methodology, 10(1), 35–44. https://doi.org/10.12973/ijem.10.1.835

Suratno, S., Wahono, B., Chang, C. Y., Retnowati, A., & Yushardi, Y. (2020). Exploring a direct relationship between students’ problem-solving abilities and academic achievement: A STEM education at a coffee plantation area. Journal of Turkish Science Education, 17(2), 211–224. https://doi.org/10.36681/tused.2020.22

Syahriridani, M., Susilo, H., & Ibrohim, I. (2022). Developing problem based learning through lesson study. Journal of Learning Improvement and Lesson Study, 1(2), 15–22. https://doi.org/10.24036/jlils.v1i2.20

Syamsuddin, A., Juniati, D., & Siswono, T. Y. E. (2020). Understanding the problem solving strategy based on cognitive style as a tool to investigate reflective thinking process of prospective teacher. Universal Journal of Educational Research, 8(6), 2614?2620. https://doi.org/10.13189/ujer.2020.080644

Tamur, M., Ndiung, S., Nurjaman, A., & Jerito, P. (2020). Do differences in measured mathematical abilities moderate the effectiveness of the realistic mathematics education approach? Meta-analysis studies. Jurnal Math Educator Nusantara, 7(1), 13–26. https://doi.org/10.29407/jmen.v7i1.15736

Tanjung, H. S., Nababan, S. A., Sa’dijah, C., & Subanji, S. (2020). Development of assessment tools of critical thinking in mathematics in the context of HOTS. Advances in Mathematics: Scientific Journal, 9(10), 8659–8667. https://doi.org/10.37418/amsj.9.10.91

Tanujaya, B., Prahmana, R. C. I., & Mumu, J. (2021). Mathematics instruction to promote mathematics higher-order thinking skills of students in Indonesia: Moving forward. TEM Journal, 10(4), 1945–1954. https://doi.org/10.18421/TEM104-60

Vink, I. C. D., Hornstra, L., & Kroesbergen, E. H. (2023). Latent profile analysis of working memory: Relations with creativity and academic achievement. Creativity Research Journal, 1–17. https://doi.org/10.1080/10400419.2023.2183323

Wang, X. M. (2023). An online progressive peer assessment approach to project-based learning: A constructivist perspective. Educational Technology Research and Development, 71(5), 2073–2101. https://doi.org/10.1007/s11423-023-10257-6

Widiatsih, A., Wardani, D. A. R., Royhana, U., Djamali, F., & Septory, B. J. (2020). The development of mathematical problems based on higher order thinking skills (HOTS) on comparative material by implementing PBL and its effect on the teacher’s creative thinking skill. Journal of Physics: Conference Series, 1538(1), Article 012110. https://doi.org/10.1088/1742-6596/1538/1/012110

Widyaningsih, S. W., & Yusuf, I. (2019). The project-based learning model is based on simple teaching tools and critical thinking skills. Kasuari: Physics Education Journal (KPEJ), 1(1), 12–21. https://doi.org/10.37891/kpej.v1i1.33

Wijaya, T. T., Zhou, Y., Ware, A., & Hermita, N. (2021). Improving the creative thinking skills of the next generation of mathematics teachers using dynamic mathematics software. International Journal of Emerging Technologies in Learning, 16(13), 212–226. https://doi.org/10.3991/ijet.v16i13.21535

Zubaidah, S., Fuad, N. M., Mahanal, S., & Suarsini, E. (2017). Improving creative thinking skills of students through differentiated science inquiry integrated with a mind map. Journal of Turkish Science Education, 14(4), 77–91. https://doi.org/10.12973/tused.10214a

Zulyusri, Z., Elfira, I., Lufri, L., & Santosa, T. A. (2023). Literature study: Utilization of the PjBL model in science education to improve creativity and critical thinking skills. Jurnal Penelitian Pendidikan IPA, 9(1), 133–143. https://doi.org/10.29303/jppipa.v9i1.2555

• There are currently no refbacks.

e-ISSN: 1694-2116

p-ISSN: 1694-2493