Abstract
Creativity is a vast construct, seemingly intractable to scientific inquiry—perhaps due to the vague concepts applied to the field of research. One attempt to limit the purview of creative cognition formulates the construct in terms of evolutionary constraints, namely that of blind variation and selective retention (BVSR). Behaviorally, one can limit the “blind variation” component to idea generation tests as manifested by measures of divergent thinking. The “selective retention” component can be represented by measures of convergent thinking, as represented by measures of remote associates. We summarize results from measures of creative cognition, correlated with structural neuroimaging measures including structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and proton magnetic resonance spectroscopy (1H-MRS). We also review lesion studies, considered to be the “gold standard” of brain-behavioral studies. What emerges is a picture consistent with theories of disinhibitory brain features subserving creative cognition, as described previously (Martindale, 1981). We provide a perspective, involving aspects of the default mode network (DMN), which might provide a “first approximation” regarding how creative cognition might map on to the human brain.
Definitions
Creativity is a complex and vast construct that has been vital to the progress of human civilization and very likely the development of human reasoning processes. Indeed, the immense array of creative endeavors encompasses the works of such disparate activities as those undertaken by painters, sculptors, nuclear engineers, landscape architects, graphic designers, and software developers: how do we imagine to capture such a broad construct? At the onset there should be noted two major potential pitfalls for creativity/neuroimaging research: the singular focus on the iconic genius—known as Big “C”—at the expense of the vast majority of creative endeavors undertaken by the other 99% of the distribution of creative endeavors—known as little “c” (Stein, 1953), and undue focus on an encompassing definition around which largely unedifying academic arguments often ensue (e.g., “gene” has no commonly accepted definition although research in this area progresses apace; the same can be said for cognitive constructs such as “intelligence” and “creativity”) (Arden et al., ). To be sure, much of value can be learned from the historiometric assessment of great giants of creative history (think Mozart, Einstein, Van Gogh—the list goes on), divining how they might have lived, what formative experiences they might have had, what their neurological makeup might have looked like (Simonton, 1984); unfortunately, these individuals and their magnificent brains are (save Einstein) lost to history. Just as fortunate, individuals who make up the vast underbelly of the “c” portion of the distribution avail themselves to us to this day, indeed offer themselves readily to most neuroimaging experiments (in exchange only for some nominal compensation, a.k.a. “beer money”).
With these and several other well articulated caveats in mind (Dietrich, ), any truly plausible definition of creativity, intelligence, or other broad behavioral construct must be applicable not just to humans, and not just to exceptionally talented humans (i.e., “genius”), but also to other species and across evolutionary time. Thus, for the purposes of this neuroscience of creativity discussion, we adopt a broadly accepted definition of creativity, which refers to the production of something both novel and useful (Stein, 1953; Martindale, 1999; Runco and Jaeger, 2012). This definition is plausible, is broadly applicable, and would appear to hold true across much of evolutionary time. As such, it also refers to the workings of the brain.
Creativity as blind variation and selective retention
While the varieties of creative expression are many (i.e., domain specific), the cognitive processes critical to its manifestation (i.e., domain general) are likely to be relatively few; thus, in order to make the problem tractable, researchers have attempted to identify cognitive processes central to creative cognition. In 1960, Donald Campbell attempted to explain the development of creative thought with a theory of “blind variation and selective retention” (BVSR). Campbell presents the process of “achieving innovation” as the next step in the evolutionary progression from blind floundering to an intelligent knowledge process (Campbell, ). Campbell notes similarities between “trial-and-error” problem solving and natural selection in evolution, namely “a mechanism for introducing variation, a consistent selection process, and a mechanism for preserving and reproducing the selected variations (p. 381).” The emphasis on “blind” as opposed to “random” is important, as the variations are seen to be independent of the environmental conditions from which they might have sprung. This simple law states that “the greater the heterogeneity and volume of trials the greater the chance of a productive innovation (p. 395).” This law has been codified by Dean Keith Simonton, who provides extensive and compelling support for such a BVSR system underlying creative cognition (Simonton, 1999). Critiques to the notion of BVSR underlying creative cognition have also been raised (Gabora, ). The blind-variation component reflects elements of divergent thinking measures (e.g., tell me as many ways you can think of to use a brick) insofar as it hinges on the ability to generate a large number of novel ideas. Simultaneously, as scores of exploratory thought trials are filtered through the mind, the selection criteria are eventually met and the innovative process is terminated. This ability to eliminate the absurd and frivolous from the meaningful and appropriate makes up the “selective retention” component of Campbell's theory, and is also measured by the usefulness or appropriateness of the new use for the common item (e.g., BRICK = to grind corn into meal) (Campbell, ). Criticism of the BVSR theory rely on its potential lack of falsifiability (Simonton, 2010), although it stands as a compelling model for cognitive processes underlying creativity.
Neuroimaging studies of creativity
The majority of psychometric research studies in creativity have emerged in the latter half of the 20th century (Guilford, ; Torrance, 1974; Amabile, ), but little progress has been made regarding brain correlates of this construct prior to the advent of modern neuroimaging techniques. Whereas, neuroimaging studies of intelligence have a 20-year history and span dozens of studies (Jung and Haier, ), similar studies of creativity are relatively few although spanning roughly the same period of time. Neuroimaging of the creative process can be undertaken to assess brain traits [structural magnetic resonance imaging (sMRI); diffusion tensor imaging (DTI); proton magnetic resonance spectroscopy] and brain states (functional Magnetic Resonance Imaging; Magnetoencephalography; Electroencephalography) associated with task performance. Both the behavioral and neuroimaging approaches can be combined to select people as high and low on trait measures of creativity and then compare the state of their brain functioning as they perform creative tasks. For example, imaging studies of intelligence have identified a network of areas where intelligence test scores correlate to brain features; these areas are distributed throughout the brain but most prominent in parietal and frontal areas (Haier and Jung, ; Jung and Haier, ). Another approach is to image the state of brain function as it fluctuates in people performing creative tasks. Of course, the field is not sufficiently well developed to have focused specifically upon subcomponents of creative cognition, although some studies do distinguish between measures of convergent vs. divergent reasoning (Fink and Neubauer, ), insight (Jung-Beeman et al., 2004), implicit thought processes (Haider, ; Kaufman et al., 2010), and other promising candidates, most recently those of “conceptual expansion” and “constraints of examples” (analogous to “BV” and “SR” respectively of BVSR) (Abraham and Windmann, ).
Methodologically, it is likely impossible to capture someone being truly creative in a laboratory setting; rather, we describe ways by which to measure this cognitive construct by capturing particular elements found to be important to the creative process including, “insight,” “convergent,” and “divergent” cognitive processes. Divergent tasks are characterized by having many possible answers as opposed to having one correct answer (i.e., “convergent thinking”) characteristic of most measures of intelligence and reasoning. We also describe a “Consensual Assessment Technique” (Amabile, ) by which independent judges might rank the creative products of each subject, with high inter-rater reliability, from which a “composite creativity score” can be compiled. Following intelligence studies, one approach to creativity research is to use neuroimaging to identify brain features (structural and functional) which differ between individuals deemed as being high or low on a trait of creativity as assessed by various measures (e.g., psychometric tests, peer evaluations). We focus on structural measures below.
Why structural studies?
One of the tasks facing research in the field of creativity is the difficulty in measuring such a complex entity. Proxy measures such as divergent thinking tasks have been heavily relied upon in the laboratory, though they are at best a measure of creative potential and cannot assess lifetime creative output or impact of creative products (Piffer, 2012). For this reason, the neurosciences have begun to look for highly reliable and valid ways to measure creative cognition. It is essential to any scientific endeavor that reproducible results are obtained so that new information can be effectively shared with the scientific community, thus to build upon the foundation of scientific knowledge. Without reliable results, unmeasured error can be incorporated into the data set and ultimately hinder the progress of scientific knowledge (Bennett and Miller, ). While there are a growing number of neuroimaging techniques available for research on creativity, this paper limits its scope to highly reliable and reproducible test methods and results. The essence of this review is to summarize the best results this field has to offer from sMRI, DTI, and proton magnetic resonance imaging (1H-MRS). In addition, the results of lesion studies, considered the “gold standard” of brain-behavioral studies, are discussed.
First, morphometric measures were used to analyze correlations between cortical thickness and creative achievement. Wonderlick et al. showed that surface maps of cortical thickness were highly reproducible with Intra-class correlation analyses ≥0.95. More recently, side-by-side comparisons of the three volumetric segmentation algorithms (Voxel Base Morphometry, FreeSurfer, and FAST) found extremely high reliability for the first two algorithms (≥0.99), with FAST being ≥0.90, with all segmentation techniques tending to underestimate gray matter volume (Eggert et al., ). Second, spectroscopic studies are presented to demonstrate the relationship between laboratory measures of creativity and the concentrations of N-acetyl-aspartate (NAA), a biomarker for neuronal integrity. In a study conducted by Gasparovic et al. to assess the test–retest reliability and reproducibility of 1H magnetic resonance spectroscopic imaging (1H-MRSI), the tissue-specific estimates of NAA metabolite were obtained with high reliability and reproducibility with interclass correlation coefficient (ICC) values ≥0.90 (Gasparovic et al., ). DTI was utilized to assess whether white matter integrity, and structural connectivity, measured using fractional anisotrophy (FA), was related to composite creativity scores (Jung et al., ). Danielian et al. showed that fiber tracking measurement has excellent inter-rater reliability and test–retest precision demonstrating ICCs ≥0.77 for all evaluated tracts (Danielian et al., ). The results from these studies indicate that the test measures discussed in this review are highly accurate and impactful.
Where do we begin looking in the brain for sources of creative cognition?
Neurological inquiries regarding creativity have tended to focus upon whether the frontal lobes are engaged or whether more posterior brain regions (Heilman et al., ) or subcortical structures such as the basal ganglia are more predominant (Dietrich, ; Flaherty, ). From these myriad perspectives have emerged several attempts designed to capture the neuroscience of creativity, based largely on data gleaned from neurological and psychiatric patients and largely confined to artistic expression (Pollack et al., 2007). Indeed, de novo artistic expression have been associated with left fronto-temporal (Finkelstein et al., ) and right temporal lobe epilepsy (Mendez, 2005), several case studies of fronto-temporal lobe dementia (FTLD) (Miller et al., 1998, 2000; Thomas Anterion et al., 2002), a case of Parkinson's disease treated with dopaminergic agonists (Schrag and Trimble, 2001), and a single case of subarachnoid hemorrhage (Lythgoe et al., 2005). Miller postulates that the selective atrophy of the anterior temporal and basal frontal lobes that accompanies FTD may reduce inhibition of the more posteriorly located visual systems, resulting in the patients' heightened interest in artistic works (Miller et al., 1998). Similarly, in a patient with primary progressive aphasia, a profound increase in artistic interest and ability coincided with significant atrophy of the left inferolateral frontal cortex (Seeley et al., 2008). However, subsequent systematic study of artistic ability associated with the various dementias found no general increase in creativity to be linked with fronto-temporal dementia (or semantic or dementia of the Alzheimer's type), with the authors noting that “despite the existence of these isolated patients with increased artistic production, however, apathy leading to diminished creativity is more clinically typical of patients with FTLD, suggesting that these case studies may be the exception rather than the rule (Rankin et al., 2007).
In contrast to fronto-temporal degenerative facilitation of artistic creativity, other lesion studies have indicated that certain parietal lesions can lead to reduced creative ability, at least within the visual arts. Lewy Body Dementia is a disease that is characterized by progressive degeneration of visuo-spatial skills and constructional abilities. In a case study presented by Drago et al., a 78-year-old visual artist experienced gradual reduction in his ability to express his artistic subject matter. This loss of expression was attributed to cellular deterioration of the parietal lobes. Throughout the progression of his disease, the artist preserved the ability to create novel works of art, which is proposed to coincide with preserved frontal lobe function. This case study seems to provide support for the importance of visuospatial cortical networks in artistic creation and ultimately the parietal lobes (Drago et al., ).
What these disparate lesions have to say about the creative process, particularly as related to creative cognition, is hard to say other than to speculate regarding the likely disinhibitory nature of lesions located within an eloquent network producing increased behavioral output. For example, Flaherty notes that the temporal lobes modulate creative drive, but notes also that changes to the temporal lobes characterize other neurological syndromes including hypergraphia, pressured speech, hypomania, and even hallucinations (p. 149, Table 1). She further states that: “to a first approximation” the corticocortical connections between frontal and temporal lobes are “mutually inhibitory” (p. 149, Figure 1) (Flaherty, ). We interpret this to imply that, with regard to a possible neurological framework underlying creativity, we must look not only to increased neural tissue in key brain regions, but perhaps also to some mismatch between mutually excitatory and inhibitory brain regions that form a network subserving such complex human behaviors as preparation, incubation, illumination, and verification components of creative cognition. This notion of a delicate interplay of both increases and decreases in neural mass, white matter organization, biochemical composition, and even functional activations within and between brain lobes and hemispheres is an important notion, critical to a full understanding of the neurological underpinnings of creative cognition.
Table 1
| Author (date) | N | Proxy test measures | Higher brain integrity-higher creativity | Lower brain integrity-higher creativity |
|---|---|---|---|---|
| MORPHOMETRY STUDIES | ||||
| Jung et al. (,,c) | 61 | Three divergent thinking tasks: design fluency test, four line condition of the DFT, uses of objects test | R. posterior cingulate | L. lingual gyrus, R. fusiform, R. cuneus, R. angular gyrus, R. vertices form inferior parietal, superior parietal and lateral occipital |
| Creative achievement test | R. angular gyrus | L. lateral orbitofrontal | ||
| Takeuchi et al. (2010a,b) | 55 | S-A creativity test | Regional gray matter volume: R. dorsolateral prefrontal cortex, bilateral striate, a cluster that includes the dorsal midbrain, the reticular formation, the periaqueductal gray, the ventral midbrain (substantia nigra and ventral tegmental area), and regions in the precuneus | |
| Raven's advanced progressive matrix | ||||
| Gansler et al. () | 18 | Torrance test of creative thinking | R. parietal lobe | Corpus callosum area (splenium region) |
| SPECTROSCOPY STUDIES | ||||
| Jung et al. (,) | 56 | Three divergent thinking tasks: design fluency test, four line condition of the DFT, uses of objects test | L. anterior gray matter NAA | R. anterior gray matter NAA |
| Controlled oral word association test (COWAT) | ||||
| Wechsler abbreviated scale of intelligence (WASI) | ||||
| NEO factor five inventory: neuroticism, extraversion, openness, agreeableness, and conscientiousness | ||||
| DIFFUSION TENSOR IMAGING | ||||
| Jung et al. (,,c) | 72 | Four divergent thinking tasks: verbal and drawing creativity tests, four line condition of the DFT, uses of objects test, and generation of captions to a New Yorker Magazine cartoon | FA within predominantly left inferior frontal white matter (i.e., regions overlapping the uncinate fasciculus and anterior thalamic radiation) | |
| Wechsler abbreviated scale of intelligence (WASI) | ||||
| NEO factor five inventory: neuroticism, extraversion, openness, agreeableness, and conscientiousness | FA within the right frontal white matter (i.e., regions overlapping the uncinate fasciculus and anterior thalamic radiation) | |||
| Takeuchi et al. (2010a,b) | 55 | S-A creativity test | Frontal lobe, anterior cingulate cortex bilaterally extending into the body of the corpus callosum, white matter regions adjacent to the anterior part of the bilateral inferior parietal lobe and a white matter region extending into the right temporo-parietal junction from the frontal lobe (arcuate fasciculus) and the right occipital lobe | |
| Raven's advanced progressive matrix | ||||
| LESION STUDIES | ||||
| Shamay-Tsoory et al. (2011) | Medial prefrontal cortex lesion (mPFC) N = 12 | Neuropsychological assessment, torrance test of creative thinking, alternate uses test | R. mPFC lesions were associated with impaired originality | |
| Inferior frontal gyrus lesion (IFG) N = 7 | L. IFG lesions exhibited high originality scores | |||
| mPFC and IFG lesions N = 6 | ||||
| Posterior lesions (PC) involving damage in the temporoparietal, inferior parietal, or superior parietal lobule N = 15 | Positive correlation between lesions in the left PC and originality scores | |||
| Abraham et al. (2012b) | Frontal lobe: frontal lobe extensive (FL-EXT), frontal lobe lateral (FL-LAT), frontal lobe polor and/or orbital (FL-ORB). N = 29 | Torrance test of creative thinking, alternate uses test | Poor performance on fluency, originality and creative imagery | FL-POL performed better on constraints of examples tests |
| Basal ganglia (BG) N = 16 | Poor performance on originality, practicality and incremental problem solving | Superior performance on the constraints of examples test | ||
| Parietal-temporal lobe (PTL) N = 11 | Poor Performance on fluency, practicality and constraints of examples | |||
Structural studies reviewed.
R., right hemisphere; L., left hemisphere; DFT, design fluency test; NAA, N-acetylaspartate; COWAT, controlled oral word association test; WASI, Wechsler abbreviated scale of intelligence; FA, fractional anisotropy; mPFC, medial prefrontal cortex; IFG, inferior frontal gyrus; PC, posterior cortex; FL-EXT, frontal lobe - extensive; FL-LAT, frontal lobe - lateral; FL-ORB, frontal lobe - orbital; FL-POL, frontal lobe - polar; BG, basal ganglia; PTL, parietal-temporal lobe.
Figure 1
Structural magnetic resonance imaging (sMRI)
The brain is not easily parceled into segmented regions in spite of the elegant cellular organization articulated by Brodmann (
sMRI was utilized to hypothesize a link between the results of divergent thinking and creative achievement test and cortical thickness (Jung et al., 2010c). Subjects participating in this study were administered the creative achievement questionnaire (CAQ)1, an objective and reliable measure of creative productivity (Carson et al.,
Takeuchi et al. set out to study the relationship between regional gray matter volume (rGMV) in subcortical regions and individual creativity. All subjects (42M, 13F) were administered the S-A creativity test, designed to evaluate creativity using three DT tasks, and assigned a total creativity score (Takeuchi et al., 2010a). Moreover, the Raven's Advanced Progressive Matrix, a psychometric test of general intelligence that is well correlated with general IQ test results (Raven, 1994), was used to measure each subjects intellectual capacity. These results were compared with morphometric data collected via MRI and revealed significant, positive correlation with creativity scores in the following regions: right dorsolateral pre-frontal cortex (DLPFC), bilateral striatum, the dorsal midbrain, the reticular formation, the periaqueductal gray (PAG), the ventral midbrain (substantia nigra and ventral tegmental area) and regions in the precuneus. The authors interpret their findings of increased fGMV in the dopaminergic systems of the brain to correspond with the notion that the complex construct of creativity requires diverse cognitive abilities, such as working memory, sustained attention, cognitive flexibility and fluency in the generation of ideas. It should be noted that these results are in contrast to those reported by Jung et al., in the same year: How could this be? One explanation could be the very high predominance of male subjects in the Takeuchi sample (3M:1F) as compared to the Jung sample (~1M:1F). Given numerous previous studies showing sex differences associated with brain-behavior relationships (Yurgelun-Todd et al., 2002; Haier et al.,
Finally, Gansler et al. hypothesized that the torrance test of creative thinking (TTCT), one of the most commonly accepted methods to measure visual and verbal DT production tasks (Torrance, 1974) should be linked to cortical volume in specialized areas (Gansler et al.,
What these three morphological studies of creativity show is rather striking. Unlike studies of most cognitive capacities, including intelligence, where greater ability is associated with increased cortical thickness and/or volume (e.g., Draganski et al.,
Magnetic resonance spectroscopy study
Proton magnetic resonance spectroscopy (1H-MRS) is an imaging technique that allows for neurochemistry of a research subject to be assessed in vivo. N-acetylaspartate (NAA) is a metabolite that is frequently used as a marker of neuronal integrity (Moffett et al., 2007). Studies have shown that high concentration of NAA in the brain is associated with higher cognitive function (Ross and Sachdev, 2004) and intelligence in normal subjects (Jung et al.,
In support of the threshold effect, this study found differential metabolic profiles supporting performance on the CCI at verbal IQ's above and below a cutoff of 116: higher NAA within the left anterior cingulate predicted higher CCI performance in subjects with VIQ >116, while lower NAA with the right anterior cingulate predicted better CCI performance in subjects with VIQ ≤116. Previous behavioral studies have noted that below an IQ of 120, creativity and intelligence are weakly (~0.30) correlated, while above 120, the correlation approaches zero—the so-called “threshold hypothesis” (Guilford,
Diffusion tensor imaging studies
The relative contribution of white matter to higher cognitive functioning has remained relatively understudied compared to gray matter research linking particular cortical regions to performance. However, several lines of inquiry would suggest that the integrity of myelinated axons plays a critical role in intellectual attainment (Miller, 1994). For example, myelin thickness is correlated to axonal size (Bishop and Smith,
In effort to explore the relationship between creativity and the microstructure of the brain's white matter, Takeuchi et al. used DTI to determine whether white matter integrity is related to proxy measures of creativity. Similar to the morphometry study conducted by Takeuchi et al. subjects were administered the S-A creativity test, assigned a total creativity score, and given the Raven's Advanced Progressive Matrix intelligence test (Raven, 1994). Results showed that increased structural integrity and connectivity involving the frontal lobe and corpus collosum was positively and significantly correlated with higher creativity scores. Additionally, positive correlation was measured in the white matter of the bilateral striatum, the right temporal-parietal junction, the anterior part of the bilateral inferior parietal lobes and the right occipital lobe. This data indicates that white matter pathways facilitate creative thinking through “efficient integration of information” and “diverse high-level cognitive function” (Takeuchi et al., 2010b). The frontal lobe is responsible for many functions that are associated with creativity. Diverse cognitive abilities, regulated by the frontal lobe, such as working memory, sustained attention, idea generation and cognitive flexibility are vital to breaking old conventions and developing new patterns of thinking. Positive correlation between FA and the corpus callosum support the theory that interhemispheric connectivity is essential for information integration and the expansion of creative thought (Carlsson et al.,
Jung et al. also utilized DTI to evaluate white matter contribution to creative cognition (Jung et al.,
Lesion studies
While neuroimaging studies have become increasingly vital to the analysis of neural networks involved in creativity, lesion studies have always been viewed as the “gold standard” of brain-behavioral studies in that they have the capacity to directly demonstrate which areas of the brain are central to cognitive functioning (Harlow,
Abraham et al. studied neurological patients having suffered various strokes to determine the impact of lesion location on creative performance (Abraham et al.,
What does it all mean?
There has been a relatively long effort (~50 years) to localize creative processes within the human brain. Early work by Sperry and Gazzaniga with split brain patients (patients undergoing surgery to section the corpus callosum and commissures) demonstrated that the left and right hemispheres of the brain functioned independently from one another, and that “interaction through the commissures may have some particular importance for artistic drawing in the normal brain” (p. 136)—one of the many progenitors, no doubt, for the erroneous “right brain” locus of creativity (Gazzaniga and Sperry,
The brain does appear, however, to function in a manner consistent with the notion of “networks” or hubs (Buckner et al.,
In his delightful book “The Art of Thought” Graham Wallace (p. 42) heralds a critical notion of creative cognition in the following passage: “The cortex of the upper brain may, for instance, of its own initiative, to satisfy its own need of activity, and to carry out its own function in the organism as a whole, start the process of thought without waiting for the primitive stimulus of sensation” (Wallace, 1931). The importance and role of “task unrelated thoughts” (Giambra,
But how well does the data fit the hypothesis? We have rendered Figure 1 to summarize the results from the structural and lesion studies reviewed above: what emerges is a figure that begins to resemble large scale cortical networks, particularly the DMN and associated hubs (Buckner et al.,
There is a relatively long and consistent effort to link arousal with creative cognition, with early studies showing decreased creative ideation associated with increased arousal induced by stress (Krop et al., 1969), brainstorming (i.e., social stress) (Lindgren and Lindgren, 1965a,b), and intense white noise (Martindale and Greenough, 1973). This general observation, led to the hypothesis that decreased cortical arousal was associated with increased creative cognition, a notion supported by numerous electroencephalographic (EEG) studies (Fink and Benedek,
Raichle described the discovery of this “default mode of brain function” as a “problem” to the neurosciences in that activity decreases were associated with cognition within a discrete network of brain regions (p. 1085) (Raichle and Snyder, 2007). A similar “problem” exists within the cognitive neuroscience of creativity: namely how to account for the growing number of studies showing decreased cortical thickness/volume or white matter integrity associated with increased human cognitive ability. More neurons and/or dendritic arborization or thicker myelin corresponding to higher levels of cognitive capacity makes intuitive sense. Decreased brain integrity (as commonly understood) associated with higher levels of ability, requires a mechanistic framework by which such relationships can make sense. We shall lay out such a mechanistic framework in a future paper. Suffice it to say that the current results support a disinhibitory bias within a network of brain regions that normally stand in excitatory and inhibitory balance, corresponding to those commonly observed within the DMN (including the anterior-inferior frontal cortex, inferior parietal cortex, and anterior temporal cortex). This DMN normally serves to “instantiates the maintenance of information for interpreting, responding to and even predicting environmental demands (p. 1087).” It is described as a “Bayesian inference engine” designed to make predictions about the future. It can also be viewed here as serving blind variation in creative cognition.
What other evidence do we have supporting network notions of creative cognition? While our purpose was to highlight structural imaging studies, two recent functional imaging studies bears mentioning. Limb and Braun studied six full-time musicians using functional Magnetic Resonance Imaging (fMRI) while they either performed overlearned or improvised piano pieces (Limb and Braun, 2008). They found that spontaneous improvisation was associated with widespread deactivation of the lateral prefrontal cortex along with simultaneous activation of medial frontal cortex, and describe this finding as “intrinsic to the creative process,” being “innovative, internally motivated production of novel material” (p. e1679). In a replication and extension of this work, researchers had rap musicians either perform overlearned lyrics (repeated condition) in the scanner or to create rap on the fly (improvised condition) (Liu et al., 2012). Twelve male freestyle artists were studied. Again, they found a dissociation of activity between brain regions: increased activation within the mPFC and simultaneous decreases within the dorsolateral prefrontal cortex, with subsequent analyses showing concurrent activation within the anterior cingulate gyrus and cingulate motor area. Connectivity analyses found that the mPFC activation was correlated with activations across a broad network including the amygdala, inferior frontal gyrus, and inferior parietal gyrus (p. 5). Thus, this functional study of rap musicians appears to show a back and forth between large brain networks, with improvisation resulting in increased activation of the DMN (and decreased activation within the CCN), as well as modulation of the interplay between these two networks by the salience network (i.e., anterior cingulate, insula, etc.). We hypothesize that the back and forth between these two networks (default, cognitive control) likely corresponds to the BVSR components of creative cognition respectively. While speculative, it would be of interest to determine whether these rap musicians had decreased cortical thickness, as compared to age, sex, and IQ matched controls, particularly within inferior parietal and anterior-inferior frontal regions identified in Figure 1.
As others have stated far more adamantly (Dietrich,
Conclusions
An appropriate, well-accepted, operational definition exists for creative cognition: the production of something both novel and useful.
Such a definition can be conceptualized within an evolutionary framework, particularly notions of BVSR.
Structural imaging techniques provide a reliable framework within which the field can begin to discuss brain traits associated with creative cognitive abilities.
Both increased and decreased brain “fidelity” measures are associated with creative cognitive abilities across a wide array of brain regions.
Regions of decreased brain fidelity associated with increased creative cognitive ability tend to correspond to the so-called DMN as demonstrated by case studies, imaging studies, and lesion studies.
Focus on cortical hubs within the DMN represents a research opportunity to further refine the manifestation of creative cognition in the brain.
The results indicate a dynamic interplay between inhibitory and excitatory networks corresponding to cortical hubs likely corresponding to BVSR components to creative cognition respectively.
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.
Statements
Acknowledgments
Research funded by a grant to Rex Jung from the John Templeton Foundation entitled “The Neuroscience of Scientific Creativity.”
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Footnotes
1.^Although see Nusbaum and Silvia (2011) for relationship of CAQ to Openness/Intellect. Also note that CAQ is heavily weighted toward artistic creativity, especially in college-aged sample.
References
1
AboitizF. (1992). The origin of the mammalian brain as a case of evolutionary irreversibility. Med. Hypotheses38, 301–304. 10.1016/0306-9877(92)90021-4
2
AbrahamA.BeudtS.OttD. V.Yves von CramonD. (2012a). Creative cognition and the brain: dissociations between frontal, parietal-temporal and basal ganglia groups. Brain Res. 1482, 55–70. 10.1016/j.brainres.2012.09.007
3
AbrahamA.PieritzK.ThybuschK.RutterB.KrögerS.SchweckendiekJ.et al. (2012b). Creativity and the brain: uncovering the neural signature of conceptual expansion. Neuropsychologia50, 1906–1917. 10.1016/j.neuropsychologia.2012.04.015
4
AbrahamA.WindmannS. (2007). Creative cognition: the diverse operations and the prospect of applying a cognitive neuroscience perspective. Methods42, 38–48. 10.1016/j.ymeth.2006.12.007
5
AbrahamA.WindmannS.McKennaP.GunturkunO. (2007). Creative thinking in schizophrenia: the role of executive dysfunction and symptom severity. Cogn. Neuropsychiatry12, 235–258. 10.1080/13546800601046714
6
AmabileT. M. (1982). Social psychology of creativity: a consensual assessment technique. J. Pers. Soc. Psychol. 43, 997–1013. 10.1037/0022-3514.43.5.997
7
ArdenR.ChavezR. S.GraziopleneR.JungR. E. (2010). Neuroimaging creativity: a psychometric view. Behav. Brain Res. 214, 143–156. 10.1016/j.bbr.2010.05.015
8
AronA. R.DurstonS.EagleD. M.LoganG. D.StinearC. M.StuphornV. (2007). Converging evidence for a fronto-basal-ganglia network for inhibitory control of action and cognition. J. Neurosci. 27, 11860–11864. 10.1523/JNEUROSCI.3644-07.2007
9
AshburnerJ.FristonK. J. (2000). Voxel-based morphometry–the methods. Neuroimage11, 805–821. 10.1006/nimg.2000.0582
10
AtchleyR. A.KeeneyM.BurgessC. (1999). Cerebral hemispheric mechanisms linking ambiguous word meaning retrieval and creativity. Brain Cogn. 40, 479–499. 10.1006/brcg.1999.1080
11
BassettD. S.BullmoreE. (2006). Small-world brain networks. Neuroscientist12, 512–523. 10.1177/1073858406293182
12
BennettC. M.MillerM. B. (2010). How reliable are the results from functional magnetic resonance imaging?Ann. N.Y. Acad. Sci. 1191, 133–155. 10.1111/j.1749-6632.2010.05446.x
13
BishopG. H.SmithJ. M. (1964). The size of nerve fibers supplying cerebral cortex. Exp. Neurol. 9, 483–501. 10.1016/0014-4886(64)90056-1
14
BresslerS. L.MenonV. (2010). Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn. Sci. 14, 277–290. 10.1016/j.tics.2010.04.004
15
BrocaM. P. (1861). Remarques sur le siege de la faculte du langage articule suivies d'une observation d'aphemie. Bull. Soc. Anat. Paris36, 330–357.
16
BrodmannK. (1905). Beiträge zur histologischen lokalisation der grosshirnrinde: dritte mitteilung: die rindenfelder der niederen affen. J. Psychol. Neurol. 4, 177–226.
17
BucknerR. L.Andrews-HannaJ. R.SchacterD. L. (2008). The brain's default network: anatomy, function, and relevance to disease. Ann. N.Y. Acad. Sci. 1124, 1–38. 10.1196/annals.1440.011
18
BucknerR. L.SepulcreJ.TalukdarT.KrienenF. M.LiuH.HeddenT.et al. (2009). Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. J. Neurosci. 29, 1860–1873. 10.1523/JNEUROSCI.5062-08.2009
19
CampbellD. T. (1960). Blind variation and selective retention in creative thought as in other knowledge processes. Psychol. Rev. 67, 380–400. 10.1037/h0040373
20
CarlssonG.UvebrantP.HugdahlK.ArvidssonJ.WiklundL. M.von WendtL. (1994). Verbal and non-verbal function of children with right- versus left-hemiplegic cerebral palsy of pre- and perinatal origin. Dev. Med. Child Neurol. 36, 503–512. 10.1111/j.1469-8749.1994.tb11880.x
21
CarsonS. H.PetersonJ. B.HigginsD. M. (2003). Decreased latent inhibition is associated with increased creative achievement in high-functioning individuals. J. Pers. Soc. Psychol. 85, 499–506. 10.1037/0022-3514.85.3.499
22
CarsonS. H.PetersonJ. B.HigginsD. M. (2005). Reliability, validity, and factor structure of the creative achievement questionnaire. Creat. Res. J. 17, 37–50. 10.1207/s15326934crj1701_4
23
DanielianL. E.IwataN. K.ThomassonD. M.FloeterM. K. (2010). Reliability of fiber tracking measurements in diffusion tensor imaging for longitudinal study. Neuroimage49, 1572–1580. 10.1016/j.neuroimage.2009.08.062
24
DevinskyO.MorrellM. J.VogtB. A. (1995). Contributions of anterior cingulate cortex to behaviour. Brain118, 279–306. 10.1093/brain/118.1.279
25
DietrichA. (2004). The cognitive neuroscience of creativity. Psychon. Bull. Rev. 11, 1011–1026. 10.3758/BF03196731
26
DietrichA. (2007). Who's afraid of a cognitive neuroscience of creativity?Methods42, 22–27. 10.1016/j.ymeth.2006.12.009
27
DietrichA.KansoR. (2010). A review of EEG, ERP, and neuroimaging studies of creativity and insight. Psychol. Bull. 136, 822–848. 10.1037/a0019749
28
DraganskiB.GaserC.BuschV.SchuiererG.BogdahnU.MayA. (2004). Changes in grey matter induced by training. Nature427, 311–312. 10.1038/427311a
29
DragoV.CrucianG. P.FosterP. S.CheongJ.FinneyG. R.PisaniF.et al. (2006). Lewy body dementia and creativity: case report. Neuropsychologia44, 3011–3015. 10.1016/j.neuropsychologia.2006.05.030
30
EggertL. D.SommerJ.JansenA.KircherT.KonradC. (2012). Accuracy and reliability of automated gray matter segmentation pathways on real and simulated structural magnetic resonance images of the human brain. PLoS ONE7:e45081. 10.1371/journal.pone.0045081
31
FinkA.BenedekM. (2012). EEG alpha power and creative ideation. Neurosci. Biobehav. Rev. pii: S0149-7634(12)00211-4. 10.1016/j.neubiorev.2012.12.002
32
FinkA.NeubauerA. C. (2006). EEG alpha oscillations during the performance of verbal creativity tasks: differential effects of sex and verbal intelligence. Int. J. Psychophysiol. 62, 46–53. 10.1016/j.ijpsycho.2006.01.001
33
FinkeR. A.WardT. B.SmithS. M. (1996). Creative Cognition: Theory, Research, and Applications. Cambridge, MA: The MIT Press.
34
FinkelsteinY.VardiJ.HodI. (1991). Impulsive artistic creativity as a presentation of transient cognitive alterations. Behav. Med. 17, 91–94. 10.1080/08964289.1991.9935164
35
FlahertyA. W. (2005). Frontotemporal and dopaminergic control of idea generation and creative drive. J. Comp. Neurol. 493, 147–153. 10.1002/cne.20768
36
FlahertyA. W. (2011). Brain illness and creativity: mechanisms and treatment risks. Can. J. Psychiatry56, 132–143.
37
FriedeR. L.SamorajskiT. (1967). Relation between the number of myelin lamellae and axon circumference in fibers of vagus and sciatic nerves of mice. J. Comp. Neurol. 130, 223–231. 10.1002/cne.901300304
38
GaboraL. (2011). An analysis of the ‘blind variation and selective retention’ theory of creativity. Creat. Res. J. 23, 155–165. 10.1080/10400419.2011.571187
39
GanslerD. A.MooreD. W.SusmarasT. M.JerramM. W.SousaJ.HeilmanK. M. (2011). Cortical morphology of visual creativity. Neuropsychologia49, 2527–2532. 10.1016/j.neuropsychologia.2011.05.001
40
GasparovicC.BedrickE. J.MayerA. R.YeoR. A.ChenH.DamarajuE.et al. (2011). Test-retest reliability and reproducibility of short-echo-time spectroscopic imaging of human brain at 3T. Magn. Reson. Med. 66, 324–332. 10.1002/mrm.22858
41
GazzanigaM. S.SperryR. W. (1967). Language after section of cerebral commissures. Brain90, 131. 10.1093/brain/90.1.131
42
GiambraL. M. (1989). Task-unrelated-thought frequency as a function of age: a laboratory study. Psychol. Aging4, 136–143. 10.1037/0882-7974.4.2.136
43
GuilfordJ. P. (1967). The Nature of Human Intelligence. New York, NY: McGraw-Hill.
44
GuilfordJ. P. (1968). Creativity, Intelligence, and Their Educational Implications. San Diego, CA: EDITS/Knapp.
45
HaiderH. (1992). Implicit knowledge and learning - an artifact. Z. Exp. Angew. Psychol. 39, 68–100.
46
HaierR. J.JungR. E. (2007). Beautiful minds (i.e., brains) and the neural basis of intelligence. Behav. Brain Sci. 30, 174–178. 10.1017/S0140525X07001380
47
HaierR. J.JungR. E.YeoR. A.HeadK.AlkireM. T. (2005). The neuroanatomy of general intelligence: sex matters. Neuroimage25, 320–327. 10.1016/j.neuroimage.2004.11.019
48
HarlowJ. M. (1848). Passage of an iron rod through the head. Boston Med. Surg. J. 39, 389–393. 10.1056/NEJM184812130392001
49
HaznedarM. M.RoversiF.PallantiS.Baldini-RossiN.SchnurD. B.LicalziE. M.et al. (2005). Fronto-thalamo-striatal gray and white matter volumes and anisotropy of their connections in bipolar spectrum illnesses. Biol. Psychiatry57, 733–742. 10.1016/j.biopsych.2005.01.002
50
HeilmanK. M.NadeauS. E.BeversdorfD. O. (2003). Creative innovation: possible brain mechanisms. Neurocase9, 369–379. 10.1076/neur.9.5.369.16553
51
JungR. E.BrooksW. M.YeoR. A.ChiulliS. J.WeersD. C.SibbittW. L.Jr. (1999). Biochemical markers of intelligence: a proton MR spectroscopy study of normal human brain. Proc. Biol. Sci. 266, 1375–1379. 10.1098/rspb.1999.0790
52
JungR. E.GasparovicC.ChavezR. S.CaprihanA.BarrowR.YeoR. A. (2009a). Imaging intelligence with proton magnetic resonance spectroscopy. Intelligence37, 192–198. 10.1016/j.intell.2008.10.009
53
JungR. E.GasparovicC.ChavezR. S.FloresR. A.SmithS. M.CaprihanA.et al. (2009b). Biochemical support for the “threshold” theory of creativity: a magnetic resonance spectroscopy study. J. Neurosci. 29, 5319–5325. 10.1523/JNEUROSCI.0588-09.2009
54
JungR. E.GraziopleneR.CaprihanA.ChavezR. S.HaierR. J. (2010a). White matter integrity, creativity, and psychopathology: disentangling constructs with diffusion tensor imaging. PLoS ONE5:e9818. 10.1371/journal.pone.0009818
55
JungR. E.SegallJ. M.GraziopleneR. G.QuallsC.SibbittW. L.RoldanC. A. (2010b). Cortical thickness and subcortical gray matter reductions in neuropsychiatric systemic lupus erythematosus. PLoS ONE5:e9302. 10.1371/journal.pone.0009302
56
JungR. E.SegallJ. M.Jeremy BockholtH.FloresR. A.SmithS. M.ChavezR. S.et al. (2010c). Neuroanatomy of creativity. Hum. Brain Mapp. 31, 398–409.
57
JungR. E.HaierR. J. (2007). The parieto-frontal integration theory (P-FIT) of intelligence: converging neuroimaging evidence. Behav. Brain Sci. 30, 135–154. 10.1017/S0140525X07001185
58
JungR. E.HaierR. J.YeoR. A.RowlandL. M.PetropoulosH.LevineA. S.et al. (2005). Sex differences in N-acetylaspartate correlates of general intelligence: an 1H-MRS study of normal human brain. Neuroimage26, 965–972. 10.1016/j.neuroimage.2005.02.039
59
Jung-BeemanM.BowdenE. M.HabermanJ.FrymiareJ. L.Arambel-LiuS.GreenblattR.et al. (2004). Neural activity when people solve verbal problems with insight. PLoS Biol. 2:e97. 10.1371/journal.pbio.0020097
60
KaufmanS. B. (2009). Faith in intuition is associated with decreased latent inhibition in a sample of high-achieving adolescents. Psychol. Aesth. Creativity Arts3, 28–34. 10.1037/a0014822
61
KaufmanS. B.DeyoungC. G.GrayJ. R.JimenezL.BrownJ.MackintoshN. (2010). Implicit learning as an ability. Cognition116, 321–340. 10.1016/j.cognition.2010.05.011
62
KrogerS.RutterB.StarkR.WindmannS.HermannC.AbrahamA. (2012). Using a shoe as a plant pot: neural correlates of passive conceptual expansion. Brain Res. 1430, 52–61. 10.1016/j.brainres.2011.10.031
63
KropH. D.AlegreC. E.WilliamsC. D. (1969). Effect of induced stress on convergent and divergent thinking. Psychol. Rep. 24, 895–898. 10.2466/pr0.1969.24.3.895
64
LauE. F.PhillipsC.PoeppelD. (2008). A cortical network for semantics: (de)constructing the N400. Nat. Rev. Neurosci. 9, 920–933. 10.1038/nrn2532
65
LimbC. J.BraunA. R. (2008). Neural substrates of spontaneous musical performance: an FMRI study of jazz improvisation. PLoS ONE3:e1679. 10.1371/journal.pone.0001679
66
LindgrenH. C.LindgrenF. (1965a). Brainstorming and orneriness as facilitators of creativity. Psychol. Rep. 16, 577–583. 10.2466/pr0.1965.16.2.577
67
LindgrenH. C.LindgrenF. (1965b). Creativity, brainstorming, and orneriness: a cross-cultural study. J. Soc. Psychol. 67, 23–30. 10.1080/00224545.1965.9922254
68
LiuS.ChowH. M.XuY.ErkkinenM. G.SwettK. E.EagleM. W.et al. (2012). Neural correlates of lyrical improvisation: an FMRI study of freestyle rap. Sci. Rep. 2:834. 10.1038/srep00834
69
LythgoeM. F.PollakT. A.KalmusM.de HaanM.ChongW. K. (2005). Obsessive, prolific artistic output following subarachnoid hemorrhage. Neurology64, 397–398. 10.1212/01.WNL.0000150526.09499.3E
70
MacDonaldA. W.3rd.CohenJ. D.StengerV. A.CarterC. S. (2000). Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science288, 1835–1838. 10.1126/science.288.5472.1835
71
MacKayD. G.StewartR.BurkeD. M. (1998). H.M. revisited: relations between language comprehension, memory, and the hippocampal system. J. Cogn. Neurosci. 10, 377–394. 10.1162/089892998562807
72
MartindaleC. (1971). Degeneration, disinhibition, and genius. J. Hist. Behav. Sci. 7, 177–182.
73
MartindaleC. (1981). Creativity and primary process thinking. Contemp. Psychol. 26, 568.
74
MartindaleC. (1989). Personality, situation, and creativity, in Handbook of Creativity, eds GloverR. R. R. J. A.ReynoldsC. R. (New York, NY: Plenum), 221–228. 10.1007/978-1-4757-5356-1_13
75
MartindaleC. (1999). Biological bases of creativity, in Handbook of Creativity, ed SternbergR. J. (Cambridge: Cambridge University Press), 137–152.
76
MartindaleC.GreenoughJ. (1973). The differential effect of increased arousal on creative and intellectual performance. J. Genet. Psychol. 123, 329–335. 10.1080/00221325.1973.10532692
77
MartindaleC.HinesD. (1975). Creativity and cortical activation during creative, intellectual and EEG feedback tasks. Biol. Psychol. 3, 91–100. 10.1016/0301-0511(75)90011-3
78
McGuireP. K.PaulesuE.FrackowiakR. S.FrithC. D. (1996). Brain activity during stimulus independent thought. Neuroreport7, 2095–2099.
79
MendezM. F. (2005). Hypergraphia for poetry in an epileptic patient. J. Neuropsychiatry Clin. Neurosci. 17, 560–561. 10.1176/appi.neuropsych.17.4.560
80
MesulamM. M. (1998). From sensation to cognition. Brain121, 1013–1052. 10.1093/brain/121.6.1013
81
MillerB. L.BooneK.CummingsJ. L.ReadS. L.MishkinF. (2000). Functional correlates of musical and visual ability in frontotemporal dementia. Br. J. Psychiatry176, 458–463. 10.1192/bjp.176.5.458
82
MillerB. L.CummingsJ.MishkinF.BooneK.PrinceF.PontonM.et al. (1998). Emergence of artistic talent in frontotemporal dementia. Neurology51, 978–982. 10.1212/WNL.51.4.978
83
MillerE. M. (1994). Intelligence and brain myelination - A hypothesis. Pers. Individ. Dif. 17, 803–832. 10.1016/0191-8869(94)90049-3
84
MillerG. F.TalI. R. (2007). Schizotypy versus openness and intelligence as predictors of creativity. Schizophr. Res. 93, 317–324. 10.1016/j.schres.2007.02.007
85
MoffettJ. R.RossB.ArunP.MadhavaraoC. N.NamboodiriA. M. (2007). N-Acetylaspartate in the CNS: from neurodiagnostics to neurobiology. Prog. Neurobiol. 81, 89–131. 10.1016/j.pneurobio.2006.12.003
86
MoriS.van ZijlP. C. (2002). Fiber tracking: principles and strategies – a technical review. NMR Biomed. 15, 468–480. 10.1002/nbm.781
87
NettleD.CleggH. (2006). Schizotypy, creativity and mating success in humans. Proc. R. Soc.B Biol. Sci. 273, 611–615. 10.1098/rspb.2005.3349
88
NusbaumE. C.SilviaP. J. (2011). Are openness and intellect distinct aspects of openness to experience? A test of the O/I model. Pers. Indivd. Diff. 51, 571–574.
89
PierpaoliC.BasserP. J. (1996). Toward a quantitative assessment of diffusion anisotropy. Magn. Reson. Med. 36, 893–906. 10.1002/mrm.1910360612
90
PierpaoliC.JezzardP.BasserP. J.BarnettA.Di ChiroG. (1996). Diffusion tensor MR imaging of the human brain. Radiology201, 637–648.
91
PifferD. (2012). Can creativity be measured? An attempt to clarify the notion of creativity and general directions for future research. Thinking Skills Creativity7, 258–264. 10.1016/j.tsc.2012.04.009
92
PollackT. A.MulvennaC. M.LythgoeM. F. (2007). De novo Artistic behaviour following brain injury, in Neurological Disorders in Famous Artists - Part 2, Vol. 22, eds BogousslavskyJ.HennericiM. G. (Basel: Karger), 75–88.
93
RaichleM. E.SnyderA. Z. (2007). A default mode of brain function: a brief history of an evolving idea. Neuroimage37, 1083–1090. 10.1016/j.neuroimage.2007.02.041
94
RankinK. P.LiuA. L. A.HowardS.SlamaH.HouC. E.ShusterK.et al. (2007). A case-controlled study of altered visual art production in Alzheimer's and FTLD. Cogn. Behav. Neurol. 20, 48–61. 10.1097/WNN.0b013e31803141dd
95
RavenJ. C. (1994). Advanced Progressive Matrices. Manual Sections 1 and 4 (Sets I, II). Oxford: Oxford Psychologists Press.
96
RossA. J.SachdevP. S. (2004). Magnetic resonance spectroscopy in cognitive research. Brain Res. Brain Res. Rev. 44, 83–102. 10.1016/j.brainresrev.2003.11.001
97
RuncoM. A.JaegerG. J. (2012). The standard definition of creativity. Creat. Res. J. 24, 92–96. 10.1080/10400419.2012.650092
98
SchmithorstV. J. (2009). Developmental sex differences in the relation of neuroanatomical connectivity to intelligence. Intelligence37, 164–173. 10.1016/j.intell.2008.07.001
99
SchragA.TrimbleM. (2001). Poetic talent unmasked by treatment of Parkinson's disease. Move. Disord. 16, 1175–1176. 10.1002/mds.1239
100
SeeleyW. W.MatthewsB. R.CrawfordR. K.Gorno-TempiniM. L.FotiD.MackenzieI. R.et al. (2008). Unravelling Bolero: progressive aphasia, transmodal creativity and the right posterior neocortex. Brain131(Pt 1), 39–49.
101
Shamay-TsooryS. G.AdlerN.Aharon-PeretzJ.PerryD.MayselessN. (2011). The origins of originality: the neural bases of creative thinking and originality. Neuropsychologia29, 178–185. 10.1016/j.neuropsychologia.2010.11.020
102
SimontonD. K. (1984). Genius, Creativity, and Leadership: Historio-metric Inquiries. Cambridge: Harvard University Press.
103
SimontonD. K. (1999). Creativity as blind variation and selective retention: is the creative process Darwinian?Psychol. Inq. 10, 309–328.
104
SimontonD. K. (2010). Creative thought as blind-variation and selective-retention: combinatorial models of exceptional creativity. Phys. Life Rev. 7, 156–179. 10.1016/j.plrev.2010.02.002
105
SimontonD. K. (2013). Creative problem solving as sequential BVSR: exploration (total ignorance) versus elimination (informed guess). Thinking Skills Creativity8, 1–10. 10.1016/j.tsc.2012.12.001
106
SpornsO.HoneyC. J.KotterR. (2007). Identification and classification of hubs in brain networks. PLoS ONE2:e1049. 10.1371/journal.pone.0001049
107
SteinM. I. (1953). Creativity and culture. J. Psychol. 36, 311–322. 10.1080/00223980.1953.9712897
108
SussmannJ. E.LymerG. K.McKirdyJ.MoorheadT. W.ManiegaS. M.JobD.et al. (2009). White matter abnormalities in bipolar disorder and schizophrenia detected using diffusion tensor magnetic resonance imaging. Bipolar Disord. 11, 11–18. 10.1111/j.1399-5618.2008.00646.x
109
TakeuchiH.TakiY.SassaY.HashizumeH.SekiguchiA.FukushimaA.et al. (2010a). Regional gray matter volume of dopaminergic system associate with creativity: evidence from voxel-based morphometry. Neuroimage51, 578–585. 10.1016/j.neuroimage.2010.02.078
110
TakeuchiH.TakiY.SassaY.HashizumeH.SekiguchiA.FukushimaA.et al. (2010b). White matter structures associated with creativity: evidence from diffusion tensor imaging. Neuroimage51, 11–18. 10.1016/j.neuroimage.2010.02.035
111
Thomas AnterionC.Honore-MassonS.DirsonS.LaurentB. (2002). Lonely cowboy's thoughts. Neurology59, 1812–1813.
112
TorranceE. P. (1974). Torrance Tests of Creative Thinking: Norms-Technical Manual. Princeton, NJ: Personnel Press/Ginn.
113
WallaceG. (1931). The Art of Thought, Vol. 24. London: Jonathan Cape.
114
WangY.AdamsonC.YuanW.AltayeM.RajagopalA.ByarsA. W.et al. (2012). Sex differences in white matter development during adolescence: a DTI study. Brain Res. 1478, 1–15. 10.1016/j.brainres.2012.08.038
115
Yurgelun-ToddD. A.KillgoreW. D.YoungA. D. (2002). Sex differences in cerebral tissue volume and cognitive performance during adolescence. Psychol. Rep. 91(3 Pt 1), 743–757.
Summary
Keywords
creativity, default mode network, blind variation, divergent thinking, structural neuroimaging, magnetic resonance spectroscopy, diffusion tensor imaging
Citation
Jung RE, Mead BS, Carrasco J and Flores RA (2013) The structure of creative cognition in the human brain. Front. Hum. Neurosci. 7:330. doi: 10.3389/fnhum.2013.00330
Received
01 May 2013
Accepted
12 June 2013
Published
08 July 2013
Volume
7 - 2013
Edited by
Zbigniew R. Struzik, The University of Tokyo, Japan
Reviewed by
Anna Abraham, Kuwait University, Kuwait; Scott B. Kaufman, New York University, USA
Copyright
© 2013 Jung, Mead, Carrasco and Flores.
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
*Correspondence: Rex E. Jung, Department of Neurosurgery, University of New Mexico, 801 University SE, Suite 202, Albuquerque, 87106 NM, USA e-mail: rex.jung@gmail.com
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