Creativity

One could say: “Creativity, yet another ill-defined construct – why bother?” In psychology, new concepts are often created to explain old ones, although for none of them accepted definitions exist. Such concepts are just  “cheap talk”, as dismissed by Deary (2000). However, creativity is a fundamental human ability that has enriched our lives. Furthermore, it is related to intelligence, therefore it deserves to be included in this blog. In our next posts we are going to discuss the relationship between creativity and intelligence, the process of illumination – insight, and the possibility of increasing creativity by means of interventions.  

Modern creativity research began with Guilford’s farewell address as president of the American Psychological Association in 1950. In his article, Guilford (1950) called for the study of creativity introducing the concept of divergent thinking – defined as the ability to generate multiple solutions to an open-ended problem (Guilford, 1967). The theoretical frame was provided by Guilford’s structure-of-intellect morphological model which, in the last version, comprises 180 different intellectual factors organized along three dimensions: operations (cognition, memory recording, memory retention, divergent production, convergent production and evaluation), content (visual, auditory, symbolic, semantic and behavioral), and products (units, relations, systems, transformations and implications). This model allows the study of 30 different facets of divergent production (6 content characteristics x  5 product types). The task Make a Figure (factor loading 0.61 on divergent production of figural units – DFU) requires individuals to combine 2 line elements into different figures in a great variety of ways (see Figure 1).

Figure 1. Make a Figure test: Given two line elements, the individual has to combine them in a great variety of ways to make figures.

An example for the DMS factor (divergent production of semantic systems) was the Four-Word Combinations FL test (DMS factor loading 0.59), which required the examinee to use 4 words with the same initial letters in a number of different meaningful sentences (no word to be repeated):

Task:         W________ C________ E________N________

Possible answers: “We can eat nuts”, “Who colored Emma’s nose?”, “Why cannot elephants navigate?” “What caused Eve’s nuisance?”

At that time also the first tests measuring divergent production appeared. Among the most established were the Torrance tests of creative thinking (TTCT), Guilford’s Alternate uses test, and Mednick’s test of remote associates. The common characteristic of all tests of divergent thinking was that they allowed for multiple answers (factor of fluency). The answers were then assessed according to originality or novelty and flexibility – the number of different types or categories of ideas. The main critique was that divergent thinking is not a prerequisite for creativity, because creativity can be also the result of convergent thinking, as demonstrated by the cases of Edison and his nearly algorithmic approach to inventing, or Bach’s methodical way of composing hundreds of cantatas. On the other hand, divergent thinking does not necessary yield creative products. It is further unclear whether or not creativity is psychometrically unitary as is the case with the g factor in intelligence. Hence, it is not surprising that neuroscience was not as interested in studying creativity as it was in other constructs such as intelligence or working memory. Nevertheless, in the last 15 years the number of studies investigating the neurobiological underpinnings of creativity has increased. A Web of Science search for the terms creativity and brain revealed 8 review papers or meta-analyses (Abraham, 2013; Arden, et al., 2010; Dietrich and Kanso, 2010; Gonen-Yaacovi, et al., 2013; Heilman, 2016; Pidgeon et al., 2016; Sawyer, 2011; Vartanian, 2012).

The first three meta-analyses on the neural underpinnings of creative behavior showed a rather devastating picture with respect to conclusions and answers provided. Dietrich and Kanso (2010) analyzed a total of 72 experiments, which were reported in 63 papers. They broadly classified them into 3 categories: divergent thinking, artistic creativity, and insight. The authors concluded (Dietrich and Kanso, 2010, p. 822): “Taken together, creative thinking does not appear to critically depend on any single mental process or brain region, and it is not especially associated with right brains, defocused attention, low arousal, or alpha synchronization, as sometimes hypothesized.” The only brain area that was most often mentioned in relation to creative cognition was the prefrontal cortex. However, the relation was complex. EEG studies revealed both increases and decreases in power measures as well as in measures of synchrony. Similar were the findings for neuroimaging studies based on the hemodynamic principle, reporting both activations and deactivations.

The same conclusion was put forward by Arden and colleagues (2010), who included 45 papers in their meta-analysis. The authors were critical about the fact that nearly as many tests and measures of creative cognition as the number of studies included in the analysis were used. Thus, it is difficult to conclude if the differences in brain activity should be attributed to the process of divergent thinking, or to differences in tasks and neuroimaging techniques employed. The suggestion put forward was that the focus should move to basic psychometric work in developing more valid and reliable measures and tests of creative cognition.

Sawyer (2011) in his review concluded that the entire brain is active while individuals are engaged in creative thinking. Identified were over twenty different brain regions related to the creative process. The areas involved were the same as with other routinely accomplished every-day tasks requiring no creativity. The critique was again directed toward the inconsistent operationalization of creativity and the wide range of tasks used for its determination. Abraham (2013) therefore suggested three main areas that should be improved in future neuroimaging research of creative cognition:

1. Optimizing neuroimaging paradigms, which includes several methodological improvements of the neuroimaging techniques and tasks used, as well as the assessment of creative answers, the time allowed for answering, etc.
2. Operationalizing the difference between creative cognition and normative cognition. It should be further clarified how are these differences instantiated in the brain.
3. Distinguishing between types of creativity. The authors suggested a division between creativity involved in problem solving (metaphor, analogy) and expression (art, verbal, music, design, architecture and dance).

Several recent meta-analyses have to some extent tried to follow these suggested directions. Vartanian’s (2012) meta-analysis, for instance, only included studies using functional magnetic resonance imaging (fMRI) and only those papers that focused on problem solving by analogy (10 papers) and metaphor (10 papers). The analysis for analogy revealed two brain areas which were reported in all studies to be activated – the dorsolateral and rostrolateral prefrontal cortex. The analysis for metaphor revealed three activated brain areas – the dorsolateral prefrontal cortex, temporal pole and the cingulate gyrus. The main conclusion was that the results of the meta-analysis do not support a unitary system or module for creativity in the brain. It was additionally suggested that future meta-analyses should focus on specific cognitive processes and be limited to a single neuroimaging modality.

Similar conclusions were put forward by Gonen-Yaacovi et al. (2013), who in their meta-analysis included 34 functional neuroimaging studies based on the cardiovascular brain response (PET, fMRI and MRI). The brain areas involved in creative cognition were predominantly located in the left hemisphere, involving the caudal lateral prefrontal cortex, the medial and lateral rostral prefrontal cortex, and the inferior parietal and posterior temporal cortices. Combination tasks (e.g., Remote Associates Task) activated to a greater extent anterior areas of the prefrontal cortex, whereas unusual generation tasks (e.g., involving an explicit request to freely generate an unusual response) activated caudal parts of the prefrontal cortex.

The recent meta-analysis by Pidgeon et al. (2016) was directed to the analysis of visual creativity including 19 EEG and 7 fMRI studies. The main findings were decreased alpha power reported in the EEG studies and significant clusters in thalamus, left fusiform gyrus, and in the right middle and inferior frontal gyrus observed in the fMRI studies. The authors concluded that the results of the meta-analysis are consistent with suggested contributions to visual creativity by means of prefrontal mediated inhibition, evaluation, and working memory, as well as visual imagery processes.

A somewhat different approach was put forward by  Heilman’s (2016) literature review. He analyzed 3 stages involved in creative cognition – preparation, innovation and creative production. The diversity of processes included in the meta-analysis did not allow for a detailed picture of the brain-creativity relationship and resembles the findings of prior meta-analyses coming close to Sawyer’s (2011) conclusion: the entire brain is active while individuals are engaged in creative cognition.

A more refined approach to study the brain-creativity relation was suggested by Benedek et al. (2014). They tried to distinguish between original answers to an alternate uses task, by asking subjects to classify their answers whether they have retrieved them from long term memory or created them at that very moment. For example two original answers to the request to generate creative uses for a car tire might be “using it for building a swing” or “using it as a picture frame”. The respondent would then for instance indicate that the first answer was recalled, while the latter was a new idea. The main findings of the study were that new ideas occurred more frequently at later stages in the ideation process. Divergent thinking was associated with activation of the left ventrolateral and dorsolateral prefrontal cortices. The generation of new ideas resulted in higher activation in the anterior part of the left inferior parietal cortex including parts of the left supramarginal gyrus as compared to retrieved answers.

This brief review demonstrates that recently there have been some attempts to improve the design of correlational studies investigating the brain-creativity relation. However, in a recent theoretical review by Dietrich and Haider (2015, p. 897), it was concluded that “Despite a flurry of activity in cognitive neuroscience, recent reviews have shown that there is no coherent picture emerging from the neuroimaging work”. To overcome the shortcomings of previous studies the authors proposed an evolutionary framework for the study of the neurobiological underpinnings of creativity in which (partial) blind variation and selective retention are central in a Darwinian adaptation process. Creativity is a variational system that involves the partial coupling of variation to selection subserved by internal representations of the emulated future in that way providing foresight and purpose to human creativity. In contrast, Gabora and Kauffman (2016, p. 638) objected the Darwinian approach, suggesting that “shifting from a Darwinian to a Lamarckian framework could be a step toward realizing their [Dietrich and Haider's] vision of grounding creativity in an evolutionary prediction framework.”

Gabora and Kauffman (2016) vividly explained the difference between what Dietrich and Haider suggested and their viewpoint of an evolutionary approach to study creativity:

Let’s say you have three ideas A, B, and C that you want to test for their effectiveness. You expose them (10 ideas of each type) to selection criteria (evolution).  In the next generation there are 15 ideas of type A, 5 of type B, and 10 of type C. At the end of the evolutionary process you have just variants of idea A. This example demonstrates how Darwinian evolution works. However, we do not decide for idea A because of the number of progenies it creates, but because we think that A is the best choice. They suggested an alternative evolutionary model for the study of creative cognition based on Lamarckian[1] evolutionary principles of formal concept combination.

From a pragmatic viewpoint the main obstacle for substantial research in the domain is the unsolved issue of how creative cognition should be assessed. At our lab we were among the first who investigated neurobiological underpinnings of creativity (Jaušovec, 1996). Later we abandoned this research strand mainly because of the lack of valid and reliable tests of creative cognition.

References

Abraham, A. (2013). The promises and perils of the neuroscience of creativity. Frontiers in Human Neuroscience, 7. https://doi.org/10.3389/fnhum.2013.00246

Arden, R., Chavez, R. S., Grazioplene, R., & Jung, R. E. (2010). Neuroimaging creativity: A psychometric view. Behavioural Brain Research, 214(2), 143–156. https://doi.org/10.1016/j.bbr.2010.05.015

Benedek, M., Jauk, E., Fink, A., Koschutnig, K., Reishofer, G., Ebner, F., & Neubauer, A. C. (2014). To create or to recall? Neural mechanisms underlying the generation of creative new ideas. NeuroImage, 88, 125–133. https://doi.org/10.1016/j.neuroimage.2013.11.021

Deary, I. J. (2000). Looking down on human intelligence: from psychometrics to the brain. Oxford ; New York: Oxford University Press.

Dietrich, A., & Haider, H. (2015). Human creativity, evolutionary algorithms, and predictive representations: The mechanics of thought trials. Psychonomic Bulletin & Review, 22(4), 897–915. https://doi.org/10.3758/s13423-014-0743-x

Dietrich, A., & Kanso, R. (2010). A review of EEG, ERP, and neuroimaging studies of creativity and insight. Psychological Bulletin, 136(5), 822–848. https://doi.org/10.1037/a0019749

Gabora, L., & Kauffman, S. (2016). Toward an evolutionary-predictive foundation for creativity: Commentary on “Human creativity, evolutionary algorithms, and predictive representations: The mechanics of thought trials” by Arne Dietrich and Hilde Haider, 2014 (Accepted pending minor revisions for publication in Psychonomic Bulletin & Review). Psychonomic Bulletin & Review, 23(2), 632–639. https://doi.org/10.3758/s13423-015-0925-1

Guilford, J. P. (1950). Creativity. American Psychologist, 5, 444–454. doi:10.1037/h0063487

Guilford, J. P. (1967). The nature of human intelligence. New York, NY: McGraw-Hill.

Gonen-Yaacovi, G., de Souza, L. C., Levy, R., Urbanski, M., Josse, G., & Volle, E. (2013). Rostral and caudal prefrontal contribution to creativity: a meta-analysis of functional imaging data. Frontiers in Human Neuroscience, 7. https://doi.org/10.3389/fnhum.2013.00465

Heilman, K. M. (2016). Possible Brain Mechanisms of Creativity. Archives of Clinical Neuropsychology, 31(4), 285–296. https://doi.org/10.1093/arclin/acw009

Jaušovec, N. (1996). Differences in EEG alpha activity related to giftedness. Intelligence, 23(3), 159–173. https://doi.org/10.1016/S0160-2896(96)90001-X

Pidgeon, L. M., Grealy, M., Duffy, A. H. B., Hay, L., McTeague, C., Vuletic, T., … Gilbert, S. J. (2016). Functional neuroimaging of visual creativity: a systematic review and meta-analysis. Brain and Behavior, 6(10), e00540. https://doi.org/10.1002/brb3.540

Sawyer, K. (2011). The Cognitive Neuroscience of Creativity: A Critical Review. Creativity Research Journal, 23(2), 137–154. https://doi.org/10.1080/10400419.2011.571191

Vartanian, O. (2012). Dissociable neural systems for analogy and metaphor: Implications for the neuroscience of creativity: Brain and creativity. British Journal of Psychology, 103(3), 302–316. https://doi.org/10.1111/j.2044-8295.2011.02073.x

---

[1] Lamarckism is understood to refer to an evolutionary process in which traits acquired during an organism’s lifetime can be transmitted to offspring without the mechanism of genetics.