Does a Person Have to Be Intelligent to Be Creative?
The question brings us back to the main problem identified in our previous blog on creativity, namely how to measure it? Although both constructs, intelligence and creativity, lack a widely accepted definition, we have valid and reliable tests of intelligence, which on the other hand do not exist for creativity. This must be kept in mind when reviewing the intelligence – creativity relationship.
Theoretical background
The interpretation and explanation of the creativity-intelligence relationship mainly depends on the research area scholars are coming from. Intelligence researchers usually consider creative cognition simply as part of their intelligence model. Even Guilford, who is considered the initiator of modern creativity research, placed creativity within the broad range of subcomponents of intelligence. In the structure-of-intellect (SOI) morphological model of intelligence, divergent thinking is just one of the operations proposed (Guilford, 1967).
Guilford’s theory never had a major influence on intelligence research. But even in models that have emerged during the past decades and are regarded as the consensus psychometric-based models for understanding the structure of human intelligence, the Cattell–Horn Gf–Gc and the Carroll Three-Stratum model (McGrew, 2009), creativity is seen as one of the second and first stratum factors. Figure 1 shows the factors that contribute to creativity in the Cattell–Horn–Carroll (CHC) theory of cognitive abilities, which represents a broad umbrella term for a synthesis of the two models. The broad factors of fluid reasoning (Gf) and long-term storage and retrieval (Glr) are seen as main components of creative cognition. Gf is defined as the use of mental operations to solve novel problems that cannot be performed automatically. Drawing inferences, generating and testing hypothesis, problem solving, extrapolating, and transforming information, inductive and deductive reasoning are of central importance in this process. Glr (also dubbed TSR, Glm and Gr – broad retrieval ability) refers to the ability to store and consolidate new information in long-term memory and later fluently retrieve it. The most prominent narrow abilities related to creativity are: Ideational fluency (FI), Associational fluency (FA), Expressional fluency (FE), Word fluency (FW), Figural fluency (FF), Figural flexibility (FX), Sensitivity to problems (SP), Originality/creativity (FO).
Sternberg (2012) for example proposed that creativity is a habit that can be explained in a theoretical framework of investment. The idea is to buy low and sell high in the realm of ideas. Creative individuals have the ability to pick up ideas that are unknown or have been abandoned, but have the potential to grow, they transform them and make them attractive for a broad community – the consumers. In that way they acquire a creative habit. This process requires six distinct but interrelated resources: intellectual abilities, knowledge, styles of thinking, personality, motivation, and environment.
The most important intellectual skills are the analytic ability to see problems and to see which ideas are potentially worth pursuing and a practical ability to sell the ideas to the consumers. The theory is rather broad and it is difficult verify it using a psychometric approach.
Most often the intelligence-creativity relation was explained by the threshold hypothesis, which assumes that above-average intelligence represents a necessary condition for high-level creativity. Hence, stronger average associations between intelligence and creativity should be observed among less intelligent individuals than among more intelligent ones (Guilford, 1967). Creativity scholars perceived this relation as an argument for the distinctiveness of both constructs – intelligence and creativity (Karwowski et al., 2016).
However, such relationships (lower correlations at higher ability levels) are not new in intelligence research. They correspond with Spearman’s (1927) law of diminishing returns (SLODR), which predicts that the g factor will account for a smaller proportion of individual differences in cognitive tests scores at higher levels of ability because its structure is more differentiated and g is a weaker contributor to cognitive performance. However when applying contemporary methods (non-linear g-loadings, or skewed g assumptions) to test SLODR, the obtained results are not as straightforward as with more traditional (splitting) methods. The obtained findings call into question the empirical support for SLODR (Murray et al., 2013).
Recent research
The easiest way to show that the constructs of intelligence and creativity are independent is by correlating tests of intelligence with creativity tests. The review papers by Kim (2005) and by Batey and Furnham (2006) showed that the correlations are small and heterogeneous. Kim in his analysis included 447 correlation coefficients from 21 studies and 45,880 participants. The mean correlation coefficient was r = .174 (95% CI = .165 – .183). Another finding was that the correlation coefficients were extremely heterogeneous: Q(446) = 937.058, p < .0001. With respect to the threshold theory no significant differences in correlations between those with an IQ greater or lower than 120 points were observed (.201 versus .234). The analysis of moderator variables revealed significant differences related to the tests used to measure creativity and intelligence, creativity subscales (originality, fluency, flexibility and figural redefinition), whereas no differences were observed with respect to sex, verbal/nonverbal tests and IQ levels (IQ below 100, between 100 – 120, 120 –135 and above 135). The authors concluded that: “…the negligible relationship between creativity and IQ scores indicates that even students with low IQ scores can be creative” (Kim, 2005, p. 65). A similar conclusion, suggesting a dichotomy between creativity and intelligence, was also put forward in the review by Batey and Furnham (2006).
Recent research employing more sophisticated methodology and analysis but also different theoretical paradigms has tried to further elucidate the intelligence-creativity relation. Jauk et al. (2013) tested the threshold theory in a sample of 297 participants by means of segmented regression analysis. This method allows for the detection of a threshold in continuous data. Thresholds of creative potential varied as a function of criteria: for a more easy criterion of ideational originality (i.e., two original ideas) the threshold was around 100 IQ points, whereas a threshold of 120 IQ points emerged when the criterion was more demanding (i.e., many original ideas).
A threshold around 85 IQ points was found for a purely quantitative measure of creative potential (i.e., ideational fluency). The authors concluded that their study confirmed the threshold hypothesis for the creativity – intelligence relationship and further showed that this relation depends on the applied measure of creative potential. A similar finding was reported by Karwowski et al. (2016), who tested the threshold hypothesis with the necessary condition analysis. The author concluded that intelligence is necessary but not sufficient for creative cognition. Further support for the threshold theory of creativity was provided by a magnetic resonance spectroscopy study by Jung et al. (2009).
However, the results must be taken with caution given the diversity of findings reported in a meta-analyses of neuroimaging studies on creativity. The authors related the concentration of the N-acetyl-aspartate (NAA) neurometabolite with measures of creativity. Different patterns of correlations between NAA and a composite creativity index (ratings provided by independent judges) were found in higher verbal ability versus lower verbal ability participants. Another shortcoming of the study is that verbal ability can be regarded as a measure of crystalized intelligence but not fluid intelligence, which was used as the indicator of intelligence in the previously mentioned studies.
A different strand of research focused on the possibility to investigate components of executive functioning in relation to fluid intelligence and creativity. Nusbaum and Silvia (2011) for instance showed that the creativity-intelligence relationship was moderated by executive switching (how often people switched from one idea category to a new idea) and clustering (how many uses are in a category).
A latent variable analysis showed that intelligence significantly predicted switching – more intelligent individuals made significantly more category changes but they didn’t have more ideas per category. Furthermore, more intelligent individuals when provided with an effective strategy for an unusual uses task performed better than those of average intelligence. The authors concluded: “…that divergent thinking is more convergent than modern creativity theories presume” (Nusbaum and Silvia, 2011, p. 36).
In yet another study by Benedek et al. (2014), the executive abilities of updating, shifting, and inhibition were related to intelligence and creativity within a latent variable model approach. The main findings were that intelligence was predicted by updating, whereas creativity was predicted by updating and inhibition. The authors suggested that updating is the central mechanism underlying the correlation of intelligence and creativity.
It seems that recent research suggests that models of intelligence such as the CHC, which regard creativity as a subcomponent of intelligence, might be a more adequate approach to study creativity – at least from a psychometric perspective.
The authors of this article are Norbert JauĊĦovec and Anja Pahor. This article was originally published in their Increasing Intelligence Blogspot and the original blog can be read here.
If you found this article interesting, you may be interested in learning more from the authors’ new book, Increasing Intelligence. The book covers behavioral training and different electrical stimulation methods such as TMS, tDCS, tACS, and tRNS, along with alternative approaches ranging from neurofeedback to cognitive-enhancing drugs. It describes crucial brain features that underlie intelligent behavior and discusses theoretical and technical shortcomings of the reported studies, then goes on to suggest avenues for future research and inquiry.
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- The Mystery of Intelligence
- The Biological Background of Intelligence
- Raising Intelligence by Means of Behavioral Training
- Changing Brain Activity, Increasing Intelligence: Transcranial Electrical and Magnetic Stimulation
- Other Approaches: From Neurofeedback to Cognitive-Enhancing Drugs
- Once Upon a Time, When We Were on the Moon
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