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Running Head: INTELLIGENCE, CREATIVITY, AND THE BRAIN
Intelligence and Creativity Share a Common Cognitive and Neural Basis
Emily Frith
1
, Daniel B. Elbich
2
, Alexander P. Christensen
3
, Monica D. Rosenberg
4
,
Qunlin Chen
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, Michael J. Kane
3
, Paul J. Silvia
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, Paul Seli
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, & Roger E. Beaty
2
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Department of Psychology, University of Mississippi, USA
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Department of Psychology, Pennsylvania State University, USA
3
Department of Psychology, University of North Carolina at Greensboro, USA
4
Department of Psychology, University of Chicago, IL, USA
5
School of Psychology, Southwest University, China
6
Department of Psychology and Neuroscience, Duke University, USA
Word Count: 18,482
Author Note
R.E.B. is supported by a grant from the National Science Foundation [DRL-1920653]. This
research was supported by grant RFP-15-12 to R.E.B, M.J.K, and P.J.S. from the Imagination Institute
(www.imagination-institute.org), funded by the John Templeton Foundation. The opinions expressed in
this publication are those of the authors and do not necessarily reflect the view of the Imagination Institute
or the John Templeton Foundation.
Correspondence should be addressed to Roger Beaty, Department of Psychology, 140 Moore
Building, University Park, PA, 16801, USA; rebeaty@psu.edu.
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Abstract
Are intelligence and creativity distinct abilities, or do they rely on the same cognitive and neural systems?
We sought to quantify the extent to which intelligence and creative cognition overlap in brain and behavior
by combining machine learning of fMRI data and latent variable modeling of cognitive ability data in a
sample of young adults (N = 186) who completed a battery of intelligence and creative thinking tasks. The
study had three analytic goals: (a) to assess contributions of specific facets of intelligence (e.g., fluid and
crystallized intelligence) and general intelligence to creative ability (i.e., divergent thinking originality), (b)
to model whole-brain functional connectivity networks that predict intelligence facets and creative ability,
and (c) to quantify the degree to which these predictive networks overlap in the brain. Using structural
equation modeling, we found moderate to large correlations between intelligence facets and creative ability,
as well as a large correlation between general intelligence and creative ability (r = .63). Using connectome-
based predictive modeling, we found that functional brain networks that predict intelligence facets overlap
to varying degrees with a network that predicts creative ability, particularly within the prefrontal cortex of
the executive control network. Notably, a network that predicted general intelligence shared 46% of its
functional connections with a network that predicted creative ability—including connections linking
executive control and salience/ventral attention networks—suggesting that intelligence and creative
thinking rely on similar neural and cognitive systems.
Keywords: creativity, connectome-based predictive modeling, divergent thinking, intelligence, structural
equation modeling
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Intelligence and Creativity Share a Common Cognitive and Neural Basis
Intelligence is a mental faculty of undeniable importance. To successfully reason, people must draw
from previous experiences to adapt to novel circumstances. In 1937, Bingham argued that the meaning of
intelligence should be interpreted as the ability to “solve new problems.” Other theorists suggest that
intelligence permits imagination, abstraction from rote experiences, and manipulation of ambiguity to make
sense of the world (Feurestein et al., 1979, 2002; Guilford, 1967; Jensen, 1998; Terman, 1922)—all of
which are cognitive processes that have been historically associated with creative ability (Abraham, 2018).
The apparent overlap between intelligence and creative cognition has motivated decades of psychometric
research aiming to characterize this relationship, with more recent evidence pointing to a considerable
overlap between these two cognitive abilities (Silvia, 2015).
Several questions remain, however, about the nature of the intelligence-creativity relationship,
including whether intelligence influences creative thinking through general or specific abilities (e.g.,
visuospatial reasoning, verbal fluency) and whether intelligence and creative thinking rely on a similar
neural architecture in the brain. In the present research, we aimed to address these questions by combining
structural equation modeling of multiple intelligence facets with machine learning of functional brain data
obtained during creative task performance. This approach allowed us to quantify the extent to which
creative cognition and intelligence overlap in brain and behavior.
Intelligence and Creative Cognition
Broadly, creative thinking encompasses the ability to generate novel ideas and solutions that are
task- and context-appropriate and effective (Diedrich et al., 2015; Runco & Jaegar, 2012). According to the
controlled-attention theory of creative cognition, goal-directed idea generation is governed by top-down
control of mental processes that promote the strategic search for task-relevant responses (Beaty & Silvia,
2012, 2013; Benedek, Franz, Heene, & Neubauer, 2012; Jauk, Benedek, & Neubauer, 2014; Silvia & Beaty,
2012). The quality of creative ideas is thought to depend largely upon individual differences in executive
function, a collection of cognitive processes involved in strategic control over thought and action. In
contrast, the associative theory of creative cognition posits that novel ideas emerge from automatic,
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combinatory processes that are contingent upon underlying semantic knowledge structures, and that
individual differences in creative thinking ability reflect variation in the organization of, and access to,
concepts within semantic networks (Mednick, 1962; Kenett & Faust, 2019). More recently, arguments for
a dual-process perspective were raised to account for the complementary interaction between executive and
associative processes in creative cognition. According to this integrated framework, controlled executive
abilities are needed to access associative elements and further inhibit (in the case of salient but unoriginal
elements), adapt (transform old experiences into new ideas), and combine (link disparate concepts into
novel responses) knowledge into high-quality ideas (Beaty et al., 2014b; Benedek & Neubauer, 2013).
One way to measure creative cognition is with divergent thinking assessments, which require
people to generate original ideas based on an open-ended prompt, such as generating uncommon uses for
common objects (Guilford, 1967; Silvia, Martin, & Nusbaum, 2009). Laboratory-based creativity research
often uses divergent thinking tasks as indications of broader creative potential (Runco, & Acar, 2012), as
they moderately predict the frequency of real-world creative behaviors (Jauk, Benedek, & Neubauer, 2014;
Beaty et al., 2013). Unlike convergent thinking tasks, which are evaluated in terms of speed and accuracy
(Cropley, 2006), divergent thinking tasks encourage a variety of novel responses to stimuli that are
inherently unexpected and tend to vary across individuals (Dygert & Jarosz, 2019). In this context,
divergent thinking can be viewed as a cognitive ability that is supported both by associative and executive
processes, which work together to activate diffuse semantic knowledge and override salient (but unoriginal)
mental representations to guide the generation of novel and task-appropriate solutions (Beaty et al., 2014;
Silvia, Nusbaum, & Beaty, 2017).
Notably, however, performance on laboratory-assessed divergent thinking tasks does not always
predict creative accomplishment (Barron & Harrington, 1981; Cropley, 2000; Plucker, 1999; Runco &
Acar, 2012; Sternberg & Lubart, 1996; Zeng, Proctor, & Salvendy, 2011). One reason for this lack of
consensus between measurement and real-world achievement is that creativity—as assessed with common
verbal tasks of divergent thinking—is not exclusively domain-general. For example, a musician may use
divergent thinking primarily in an auditory domain, meaning that a divergent thinking assessment in the
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verbal domain may not adequately capture her potential for domain-specific musical creativity (Barron &
Harrington, 1981). Taken together, although domain-general divergent thinking measures serve as viable
indices of creative potential, such assessments do not comprehensively capture creativity as a singular entity
because these measures alone cannot account for the multitude of factors that influence broader creative
abilities and accomplishments (Silvia, Winterstein, & Willse, 2008), including personality, motivation,
social context, domain experience, and intelligence (Barron & Harrington, 1981; Sternberg & Lubart,
1991).
Intelligence is a hierarchical construct reflecting multiple, correlated general abilities that hinge on
executing goal-directed behavior (Gottfredson, 1997; Jensen, 1998). Mental operations, including
reasoning, planning, problem-solving, and environmental adaptation, interact to reflect broader concepts of
intelligence (Goldstein et al., 2015; Gottfredson, 1997; Neisser et al., 1996). Rather than merely questioning
whether intelligence and creative cognition are associated, then, modern scientific inquiry is increasingly
focused on delineating mechanisms to explain the nature of this often-observed relationship (Plucker et al.,
2015; Plucker & Renzulli, 1999; Silvia, 2015). Although a wealth of research has investigated relationships
between intelligence and creative thinking, a lack of empirical resolution remains, perhaps because
creativity and intelligence are complex constructs that have been subjected to a wide range of
conceptualizations. However, despite considerable variability in the operationalization of these constructs,
modern research efforts continue to focus on clarifying the creativity-intelligence relationship by
identifying cognitive and neural operations that may play a role in intelligent and creative behavior (Jung
& Vartanian, 2018; Sternberg & Kaufman, 2011).
Sternberg and O’Hara (1999) proposed three interpretations of the intelligence-creativity relation:
(a) creative thinking is an element of human intelligence, such that various cognitive factors including
divergent thinking, memory, and complex reasoning are integral to intelligent thinking (Guilford, 1967);
(b) intelligence is an element of creative thinking, meaning that higher-order cognitions, such as cognitive
flexibility, inhibition, and goal-directed problem-solving, as well as the capacity for knowledge acquisition,
comprehension, and retention, play essential roles in directing creative cognition (Sternberg, 1996), or (c)
