Creativity in gifted classroom-

REVIEW ARTICLE

Intelligence and Creativity Are Pretty Similar After All

Paul J. Silvia

Published online: 20 February 2015 # Springer Science+Business Media New York 2015

Abstract This article reviews the history of thought on how intelligence and creativity, two individual differences important to teaching and learning, are connected. For decades, intelligence and creativity have been seen as essentially unrelated abilities. Recently, however, new theories, assessment methods, and statistical tools have caused a shift in the field’s consensus. New lines of work on creative thinking strategies, executive cognitive processes and abilities, and cognitive neuroscience have revealed that intelligence and creativity are much more closely linked than the field has thought. The deep connections between these concepts offer opportunities for a more fertile conception of both intelligence and creativity, one that emphasizes similarities between solving problems with right answers and thinking flexibly, critically, and playfully.

Keywords Creativity. Intelligence . Divergent thinking . Assessment . Executive control

Anyone who teaches encounters individual differences: students are so variable in so many ways. Understanding what these ways are, how they work, and how they can be assessed has been one of the major projects of educational theory and research during the past century. This article considers intelligence and creativity, two ways people differ that are deeply embedded in teaching and learning. Until recently, intelligence and creativity have been seen as essentially unrelated abilities, as distinct strengths that students bring to the classroom. This view is founded on some good evidence—including landmark studies and a high-quality meta-analysis—so it is not a mere myth. Recently, however, some new theories, assessment methods, and statistical tools have sparked interest in an alternative view: intelligence and creativity are probably much more alike than we have thought. The past decade has seen a reconsideration of past work, and the consensus is thus shifting. We will consider some of the new sources of evidence and some implications of viewing intelligence and creativity as similar cognitive strengths.

A Brief History

Intelligence and creativity have traveled on different tracks. For the most part, they are studied by different scholarly communities that seek to influence different audiences. At a few points,

Educ Psychol Rev (2015) 27:599–606 DOI 10.1007/s10648-015-9299-1

P. J. Silvia (*) Department of Psychology, University of North Carolina at Greensboro, P. O. Box 26170, Greensboro, NC 27402-6170, USA e-mail: [email protected]

however, these tracks have converged, and the most significant convergence by far is Guilford’s (1967) Structure of Intellect (SoI) model of intelligence. Guilford was interested in both creativity and intelligence, and his model of intelligence was unique in integrating them. His SoI model is largely of historical interest in modern intelligence research (Carroll 1993), but it continues to cast a long shadow over modern creativity research. Guilford set the stage for later work by casting some mental processes as convergent (processes that narrow thought and lead to correct answers) and others as divergent (processes that widen thought and lead to many responses). Since then, the convergent processes are what we see as prototypical markers of intelligence, and the divergent processes are what we see as markers of creativity.

The relationship between convergent and divergent processes was an obvious question implied by Guilford’s model, and it represents the modern start of research on the links between intelligence and creativity. He and his colleagues developed a wide range of tasks and generated an enormous amount of data (e.g., Guilford 1957, 1967; Wilson et al. 1953). Historically, the most influential aspect of Guilford’s work was (1) couching the problem in terms of convergent and divergent labels and (2) developing and popularizing tasks for assessing divergent thinking.

Other researchers soon started examining how intelligence and creativity were associated. Getzels and Jackson (1962), in an influential book, argued that the two concepts were distinct. They measured both intelligence and creativity in a sample of 132 middle-school and high school children, and they carved the sample into groups that were high in one trait but low in the other. This yielded a high IQ/low creativity group and a low IQ/high creativity group. In addition to its awkward emphasis on the diagonal elements of the four cells formed by intelligence and creativity, this study was criticized for the poor discriminant validity of the creativity tasks. When viewed as a multitrait/multimethod matrix, the study’s tasks show poor validity: the creativity tasks tended to correlate just as highly with the intelligence tasks as they did with each other. Nevertheless, the Getzels and Jackson book established the framing of the problem as Bintelligence versus creativity.^

Not long after, Wallach and Kogan (1965) published their touchstone work, a book that remains influential 50 years later. Motivated by similar questions, they assessed intelligence and creativity in a sample of 151 children. Their work is best remembered for the creativity assessment approach that they developed. Wallach and Kogan argued that creative responses were unique responses—responses that no one else in a sample gave. They had students complete divergent thinking tasks and then scored them for two things: fluency (the total number of responses) and uniqueness (the number of unique responses). People received one point for each response that they gave that no one else in the sample gave and zero points for each response given by anyone else. Guilford and his group had used similar systems, usually complex schemes that weighted each response by its prevalence or that constrained the number of responses (e.g., Wilson et al. 1953). Wallach and Kogan, however, greatly simplified how divergent thinking was measured: they whittled down Guilford’s many tasks and scoring systems into a small battery of tasks that was easy to administer and score. Wallach and Kogan found good validity for their tasks: the intelligence tasks correlated much more highly with each other than with the creativity tasks and vice versa. Overall, the correlation between intelligence and creativity was a meager r=0.09 (95 % confidence interval (CI) [−0.07, 0.25]).

It is easy to see why the work of Wallach and Kogan (1965) was so influential: it had a large sample of schoolchildren, clear findings, and some novel assessment tools that seemed to clarify and solve some of creativity’s more vexing aspects. The uniqueness scoring method turned out to be particularly popular. Because the scoring approach was straightforward and the uniqueness index could be scored objectively, researchers in the following decades primarily used the divergent thinking methods developed by Wallach and Kogan or a similar family of tasks from the Torrance Tests of Creative Thinking (TTCT; Torrance 2008).

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As researchers consistently observed small correlations between divergent thinking and intelligence, a consensus emerged. When Kim (2005) synthesized the large literature on creativity and intelligence using meta-analysis, she found a weighted average correlation of r=0.17 (95 % CI [0.16, 0.18]), which is modest at most. All the textbooks on creativity and its assessment from this period concluded that intelligence and creativity were largely independent cognitive abilities (e.g., Kaufman et al. 2008; Runco 2007; Sawyer 2006; Weisberg 2006).

A Contemporary Reevaluation

It seems like the story should end here. Nevertheless, several creativity researchers, at about the same time, began to suggest that the literature to date probably understated the case. Since Kim’s (2005) meta-analysis, the tide has shifted the other way: creativity researchers increas- ingly see creativity and intelligence as similar.

Statistical Advances

One simple reason to think that the relation between intelligence and creativity is larger comes from recent statistical models for studying cognitive abilities. Most studies have examined correlations between observed variables—and understandably so, for latent variable models have only recently become widespread. Latent variable models allow researchers to model a construct’s true score and error separately (Skrondal and Rabe-Hesketh 2004). As a result, they allow researchers to separate the variance due to an underlying trait (such as Bcreativity^) from variance due to task-specific and rater-specific factors. By distinguishing true trait variance from error, latent variable models give more accurate estimates of effect sizes. In most cases, the effects will be somewhat larger. One example comes from the classic study by Wallach and Kogan (1965). When the data are reanalyzed using structural equation models, the correlation between creativity and intelligence rises from r=0.09 to around r=0.20 (Silvia 2008b), thus illustrating how observed correlations can deflate relationships.

Assessment Advances

Uniqueness scoring became firmly entrenched. Between the Wallach and Kogan approach and the similar TTCT approach, most divergent thinking research from the 1960s to the present has used some form of it. But many researchers over the years have leveled serious criticisms at uniqueness scoring, and in hindsight, it is seriously flawed.

The first flaw is the confounding of fluency and uniqueness. As people generate more responses, their probability of having a unique response goes up. Researchers pointed out this problem long ago (e.g., Clark and Mirels 1970; Hocevar 1979a, b; Hocevar and Michael 1979). In even modest sample sizes, the correlation between fluency and uniqueness becomes very large. In the reanalysis of Wallach and Kogan’s data, the correlation was r=0.89 (Silvia 2008b). In the TTCT’s national norm samples (Torrance 2008), it is r=0.88. Clearly, there is little unique variance to be found in uniqueness scores—they are basically the same as fluency. A creativity task that assesses only the quantity of ideas, not their quality, seems impractical.

The second serious flaw is the confounding of uniqueness with sample size. People receive one point if their response was unique within their sample. As a result, the likelihood that any particular response is unique declines as the sample size increases. As a result, the sample’s

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mean uniqueness score will also decline as the sample size increases. People thus appear less creative when you assess more of them. Stated differently, the creativity task becomes Bharder^—it takes even higher levels of novelty to get a point—when the sample size is larger. Theoretically, a very large sample could have no unique responses. This flaw is fatal. There is something amiss about a test that performs more poorly as the sample size increases.

In recent years, researchers have revisited some tools from Guilford’s early work, most notably the value of subjective ratings. Guilford had developed many creativity tasks, such as coming up with clever titles for short stories, that asked raters to score the responses on dimensions like cleverness or remoteness, and he found evidence for the validity of such ratings (e.g., Wilson et al. 1953). This general approach was developed further by Amabile (1982) in her consensual assessment technique (CAT) for judging creative products. The CAT has been extensively applied and evaluated in creativity research (e.g., Kaufman et al. 2013).

Subjective ratings have turned out to be unusually useful for divergent thinking tasks (Benedek et al. 2013; Silvia et al. 2008, 2009). Researchers can have trained raters score each response (or some subset, like the best 2 or 3) on a rating scale. The raters’ scores can be averaged, used as indicators in a latent variable model to account for rater-specific variance, or scaled in Many-Facet Rasch models that estimate creativity scores in light of the difficulty of the tasks and raters (Primi 2014). Many studies show that subjective ratings resolve the problems of uniqueness scoring: (1) rated creativity correlates only minimally and usually negatively, with fluency, and (2) rated creativity is not so sample dependent (Benedek et al. 2013). And, as we will see later, their correlations with intelligence are much stronger.

The Executive Era

Since the 1960s, psychology became increasingly interested in executive processes. Huge literatures have developed around self-regulatory concepts in different areas of psychology, from motivation to social psychology to health behaviors to cognitive psychology. In partic- ular, cognitive psychology sowed the seeds of the new look at creativity and intelligence, with its sophisticated models of how executive abilities (e.g., working memory capacity, inhibition, and fluid intelligence) and executive processes (e.g., interference management and strategy use) affect reasoning and problem solving.

Several researchers began to suggest that divergent thinking tasks should involve abilities associated with intelligence. Gilhooly et al. (2007) examined people’s spontaneous strategies when confronted with a divergent thinking task. People were asked to come up with unusual uses for a common object and to Bthink aloud^ while doing so. The verbal protocols were coded and distilled into a core set of strategies. Many of the findings suggested a strong role for executive processes. First, and simply, people who came up with better responses were using abstract strategies, indicating that the ideation process was at least somewhat controlled. Second, the strategies that predicted better responses (e.g., mentally disassembling an object and using its parts) were more abstract and harder to deploy than strategies that did not (e.g., simply repeating the name of the object to oneself).

Other researchers conducted latent-variable studies of how different executive abilities predicted divergent thinking. This body of work has generally anchored itself in the Cattell- Horn-Carroll (CHC) approach to cognitive abilities (Carroll 1993; McGrew 2005), which distinguishes between a higher-level general intelligence (g), a middle level of cognitive abilities, such as fluid, crystallized, and visuospatial intelligences, and a lower level of narrow abilities (e.g., inductive reasoning as a facet of fluid intelligence). By differentiating intelli- gence into components, the CHC model offers a useful framework for thinking about how creativity relates to the many cognitive abilities studied in modern intelligence research.

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In our first studies, we focused on fluid intelligence, which captures a range of processes associated with reasoning and executive control (McGrew 2009). A latent fluid intelligence variable strongly predicted the creativity of response to unusual uses tasks (β=0.43; Silvia 2008a), scored using subjective ratings. These results were replicated elsewhere in a study that found large effects of fluid intelligence on idea originality (β=0.51; Benedek et al. 2012a). In a later analysis, we found that the effect of fluid intelligence on creativity was mediated by markers of Bexecutive switching,^ the ability to shift idea categories during the task (Nusbaum and Silvia 2011, study 1) and that people higher in fluid intelligence were better at using a good creativity strategy when given one (Nusbaum and Silvia 2011, study 2). Other studies have unpacked the executive abilities most strongly linked to creativity (Benedek et al. 2014a, b, c) and examined the likely nonlinear nature of the relationship (Jauk et al. 2013; Karwowski and Gralewski 2013).

In addition to fluid intelligence, research has explored other CHC abilities. Broad retrieval ability (Gr)—also known as long-term storage and retrieval and verbal fluency—primarily reflects the ability to retrieve knowledge from memory selectively and strategically. Several studies have shown large effects of broad retrieval ability on divergent thinking (e.g., Benedek et al. 2012a, b; Lee and Therriault 2013) and unpacked the unique contributions of its lower- level facets, such as ideational fluency (Silvia et al. 2013) and encoding ability (Avitia and Kaufman 2014). Broad retrieval ability has effects on divergent thinking that are distinct from the effects of fluid and crystallized intelligence (Avitia and Kaufman 2014; Benedek et al. 2012a, b), thus illustrating the value of a differentiated CHC approach.

Creative thought is much broader than divergent thinking, and recent research has explored how intelligence influences creative ideas on other tasks. Several studies have examined how people generate metaphors, which are a salient example of creativity in everyday language (Gibbs 1994). Some studies have asked people to generate creative metaphors to describe an experience—such as what it is like to sit in a boring class or eat disgusting food—and fluid intelligence strongly predicts the creativity of the metaphors (β=0.49; Silvia and Beaty 2012). When other CHC abilities like crystallized intelligence and broad retrieval ability are included, around half the variance in metaphor creativity can be accounted for (Beaty and Silvia 2013). Other studies have used a newly developed metaphor completion task (De Barros et al. 2010), which involves completing a metaphor stem (e.g., Camels are the _____ of the desert) with a creative entry. Fluid intelligence strongly predicts metaphor creativity for that task, too (β= 0.51; Primi 2014).

Like metaphor, humor is a common example of creativity in everyday life. Several researchers have considered whether intelligence is important to humor production, the ability to generate funny material when prompted (Earleywine 2010). Although this is a small area, several studies have found notable effects of fluid and crystallized intelligence on humor production (e.g., Greengross and Miller 2011; Howrigan and MacDonald 2008), which is measured by asking people to write captions for cartoons, complete jokes with funny endings, or draw silly pictures.

Finally, a flourishing cognitive neuroscience of creativity has supported the view that intelligence and creativity are strongly linked. This complex literature cannot be summarized here, but many EEG and fMRI studies have found strong support for a role of top-down executive processes in the generation of creative ideas. In general, these studies give people creativity tasks (usually divergent thinking, metaphor production, or musical improvisation) and then assess neurological markers of controlled, executive thought, such as the activation of specific regions known to be important in executive control and the activation of brain networks involved in top-down, controlled cognition (Beaty 2015; Beaty et al. 2014; Benedek and Beaty et al. 2014; Benedek et al. 2014a, b, c; Vartanian et al. 2014). Because the

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neuroscience of executive control is well understood, the cognitive neuroscience of creativity has been able to establish an important role for executive neurological systems when people perform creativity tasks.

Practical Implications

The modern look at intelligence and creativity sees them as closely linked: people who do better on prototypically Bintellectual^ tasks (e.g., fluid reasoning) also do much better on typically Bcreative^ tasks (e.g., divergent thinking). Thus, these seem to be similar—but certainly not identical—strengths. What are some implications of the field changing its mind about this issue?

One implication is for how we define and think about creativity. Many theorists have pointed out that viewing intelligence and creativity as distinct abilities did a disservice to both (Kaufman 2013). Carving out creativity yields a sterile view of intelligence that emphasizes how people get right answers over how they think flexibly, critically, and playfully. And carving out intelligence yields a view of creativity that seems capricious and uncontrolled instead of something that can be directed and nurtured. Talking about intelligence and creativity as noun-like things—instead of as families of processes and functions that the mind can do—obscures the bigger conceptual picture (Billig 2013). The mind can do a lot of things, and the processes that it uses for solving closed-ended problems are similar to those used for making, judging, and playing with ideas.

Another implication is for models of the creative process. Several classic theories of creative thought explain individual differences in terms of crystallized knowledge. Mednick (1962), for example, argued that creative people have flatter associative hierarchies (e.g., less tightly structured semantic networks). Likewise, Weisberg (2006) has argued that creativity is largely determined by how much people know. Research clearly shows that the amount (Weisberg 2006) and the organization (Kenett et al. 2014) of knowledge are important to creativity, but how people access, manage, and control their knowledge has been overlooked. The emerging body of work on fluid and executive abilities shows that creativity is not just a matter of what we know but how well we use our knowledge: how we access, manipulate, combine, and transform what we know in the service of creative goals. As a result, the renewed emphasis on intelligence is congenial to models that view creative thought in terms of problem solving, judgment, and decision making (e.g., Finke et al. 1992; Gilhooly et al. 2007; Sternberg and Lubart 1991).

A more practical implication concerns how and why we should assess creativity. Divergent thinking tests are widely used in educational contexts, often for assessments related to giftedness programs. Historically, adding creativity tests has been justified by creativity’s apparently small correlation with intelligence. As our understanding of intelligence and creativity shifts, this argument seems less compelling. There are, however, other and better reasons for using creativity assessments in educational contexts. Some evidence suggests that creativity assessments are less biased (Kaufman 2010), and flexible, productive thinking is a cardinal twenty-first century skill. As Kaufman (2013) argues, an expanded view of intelli- gence should include both sides of the convergent/divergent coin along with measures of key motivational factors, like interest and openness to experience.

At the same time, the field should take a new look at current commercial creativity tests and decide how comfortable it is with using them for high-stakes decisions, such as admitting a child into a gifted track. The most popular forms of these tests, such as the TTCT, are essentially fluency tests, and a view of creativity as mere idea quantity seems impoverished. On the bright side, an evolving sense of what both intelligence and creativity are like (Kaufman 2013) presents interesting opportunities for new models and measurement tools for assessing creative potential.

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