Conference PaperPDF Available
HUMAN NEUROSCIENCE
HYPOTHESIS AND THEORY ARTICLE
published: 24 July 2014
doi: 10.3389/fnhum.2014.00540
Mind the gap: an attempt to bridge computational and
neuroscientific approaches to study creativity
Geraint A. Wiggins1and Joydeep Bhattacharya2*
1Computational Creativity Laboratory, School of Electronic Engineering and Computer Science, Queen Mary, University of London, London, UK
2Department of Psychology, Goldsmiths, University of London, London, UK
Edited by:
Zbigniew R. Struzik, The University
of Tokyo, Japan
Reviewed by:
Bernhard Hommel, Leiden
University, Netherlands
Armen Allahverdyan, Yerevan
Physics Institute, Armenia
*Correspondence:
Joydeep Bhattacharya, Department
of Psychology, Goldsmiths,
University of London, New Cross,
London SE14 6NW, UK
e-mail: j.bhattacharya@gold.ac.uk
Creativity is the hallmark of human cognition and is behind every innovation, scientific
discovery, piece of music, artwork, and idea that have shaped our lives, from ancient
times till today. Yet scientific understanding of creative processes is quite limited, mostly
due to the traditional belief that considers creativity as a mysterious puzzle, a paradox,
defying empirical enquiry. Recently, there has been an increasing interest in revealing
the neural correlates of human creativity. Though many of these studies, pioneering in
nature, help demystification of creativity, but the field is still dominated by popular beliefs
in associating creativity with “right brain thinking”, “divergent thinking”, “altered states”
and so on (Dietrich and Kanso, 2010). In this article, we discuss a computational framework
for creativity based on Baars’ Global Workspace Theory (GWT; Baars, 1988) enhanced with
mechanisms based on information theory. Next we propose a neurocognitive architecture
of creativity with a strong focus on various facets (i.e., unconscious thought theory, mind
wandering, spontaneous brain states) of un/pre-conscious brain responses. Our principal
argument is that pre-conscious creativity happens prior to conscious creativity and the
proposed computational model may provide a mechanism by which this transition is
managed. This integrative approach, albeit unconventional, will hopefully stimulate future
neuroscientific studies of the inscrutable phenomenon of creativity.
Keywords: creativity, neuroscience, psychology, computational modeling, methodology
INTRODUCTION
In recent years, the scientific study of creativity has burgeoned
(Vartanian et al., 2013). However, the problem has proven to
be inscrutable, and the various different approaches have not
made as much progress as initially expected. Part of the reason
for this is without doubt the as-yet-unclear specification of what
the key questions are; part is without doubt the sheer com-
plexity of the problem. But part, we claim, is also down to the
lack of a suitable integrated framework within which the var-
ious approaches (neuroscientific, psychological, computational,
philosophical, etc.) may collaborate. The aim of this paper is to
provide a hypothetical framework for research, built around a
key idea, which itself is a combination of ideas from cognitive
psychology and communications engineering. These ideas in their
own right are far from novel; their combination, however, affords
a mechanism for creativity which yields testable predictions of
creative and other behavior.
The argument is structured as follows: we begin by decon-
structing the social notion of creativity, to the point at which
it may be possible to study it in a reductionist context, and
give a review of earlier attempts to do so, with an emphasis on
the neuroscientific. Next, we describe a proposal for a cognitive
architecture that supports creative production, based on extant
models of efficient memory and information processing. We then
describe some potential neuroscientific approaches to testing the
model. Ultimately, we propose that our tripartite combination
of neuroscientific, behavioral and computational methods is an
example of the kind of relatively sophisticated methodology
required to address what is, after all, one of the fundamental
questions of humanity.
CREATIVITY—WHAT IS IT?
Creativity is notoriously difficult to study. Even the meaning of
the word itself is confounded by subjective value judgments, and
the problem of evaluating created artifacts rigorously is difficult
enough to deserve its own section. Therefore, it is appropriate
to begin with a deconstruction of this troublesome concept,
with a view to specifying what is and what is not amenable to
scientific enquiry. A key part of this endeavor is demystification:
our claim is that the perception of creativity in an action is
relative to creator, observer, and their social context. That rel-
ativity has, in the past, led to a significant Romanticization of
creativity in some societies, which, we believe, can obscure its true
nature.
We begin from the premise that creativity is fundamentally
a property of a process (as in “a creative act”). The exhibition
of that property may then, in common parlance, be transferred
to the organism or machine that is executing the process (as
in “a creative person”) or its product (as in “a creative novel”).
However, we eschew these less precise usages here.
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Wiggins and Bhattacharya Bridging computational and neuroscientific approaches to study creativity
We are not alone in focusing on process. Cognitive and
behavioral psychologists have been investigating the creative
process for almost a century. Wallas (1926) suggested that
the mental process of creativity with four observably distinct
stages: preparation, incubation, illumination and verification.
Guildford’s (1967) proposed the creative process is often charac-
terized by divergent thinking, a break from the previous, most
obvious type of thinking, followed by convergent thinking, a
narrowing process of choosing the “best” idea among the available
options. Getzels and Csikszentmihalyi (1976) described mental
states during occurrence of the process, the best known aspect
of which is the state of “flow” experienced by a creator dur-
ing a process of creativity. Koestler (1964) proposed a vague
but convincing process of “bisociation” of cognitive structures,
which has recently formed the basis of a successful computa-
tional research project (Dubitzky et al., 2012). Boden (2003)
gave a general cognitivist theory of creative conceptualization and
process, which has been formalized as a mathematical “Creative
Systems Framework” by Wiggins (2006a,b), with a view to com-
putational study. Finally, a significant series of work from the
past decade and a half, which remains somewhat unfamiliar to
psychologists and neuroscientists working on creativity, is that
of Jürgen Schmidhuber (2010), whose theory relates a notion of
intrinsic reward, derived from a rate of learning, to the discovery
of new patterns that are compressible. This theory relies on
ideas from Information Theory, to which we return in the later
section.
One advantage of taking the process view is that it begins
to demystify creativity itself, simply because value is normally
attributed to a product or artifact only after its achievement
(Boden, 1998); thus, by studying the creativity before the artifact
is judged, we can blow away some of the Romantic fog. Further,
the (perceived) value of a product is never constant, but is depen-
dent on society, places, groups, time (e.g., El Greco (1561–1614)
was ridiculed for his style of painting during his life-time, but now
is considered to be one of the foremost Renaissance painters).
So the process of valuation is very subjective, and constrained
by socio-cultural factors, therefore defying empirical objectivity
(Schaffer, 1994). Thus, success and the associated social pedestals
on which “great creators” are placed, become quite irrelevant: it is
the attempt not the product that counts in the first instance. Thus
we defuse, to some extent, the debate between what researchers
call “big-C creativity” and its lower case counterpart (Kaufman
and Sternberg, 2006). Having disposed of these highly subjective
and emotive judgments, we can better identify creativity in a
wide range of activities, and therefore be in a better position to
understand it as a whole.
Having settled on the process view of creativity, we must
consider what is executing the process. Since there is evidence
that some animals (e.g., corvids, pigeons) can exhibit complex
cognition including creative tool construction and insight prob-
lem solving (Epstein et al., 1984; Emery and Clayton, 2004; van
Horik et al., 2012; Jelbert et al., 2014), it would be inappropriate to
restrict study to humans (Kaufman and Kaufman, 2004). The new
research field of Computational Creativity has explicitly drawn
creativity into the purview of Artificial Intelligence (Colton and
Wiggins, 2012), and therefore it seems appropriate to consider
the possibility of non-biological organisms exhibiting creativity
also. There is, of course, a debate about whether computers can
be properly called creative (Cohen, 1999); however, to rule them
out a priori would be to prejudge its outcome. The benefit of
this inclusive approach is that it admits study by simulation,
in the same way that computational linguistics has done for
the study of linguistic cognition. Above all, the computational
approach forces us to formulate our theories to an extreme level
of detail, and allows us to test them to destruction in ways that
would be ethically questionable if applied to biological organisms,
yielding a very powerful method. Ultimately, it does not matter
if one believes that the computer is “really” creative, or if it
merely appears to be so (Turing, 1950): the methodological value
remains.
The next distinction to make is between what we term, on
one hand, spontaneous creativity and, on the other, creative
reasoning. The former corresponds with the moment of illumi-
nation in Wallas’ theory (Wallas, 1926), the “Aha!” moment. It
is often preceded by Guildford’s (1967) two searching phases. It
is the event that causes awareness of an unanticipated outcome
in a creator, where “outcome” is interpreted broadly: what enters
awareness need not be a solution to a problem, for example,
but might instead be the method by which to find one. This is
best understood in contrast with creative reasoning, which is the
deliberate, conscious application of reasoned construction steps
in producing an artifact that will ultimately be deemed creative.
An example of the latter is the jobbing song-writer, who has
been commissioned to write the theme tune for a radio program,
by a certain date. The song-writer cannot simply wait for spon-
taneous creativity to appear (or she will be soon in breach of
contract); rather, she must apply expert knowledge of her domain
to come up with something novel. As one learns in undergraduate
music courses, it is possible to do this by means of considered
application of musical rules, though merely generating musical
structures in this way does not guarantee a satisfactory musical
experience at the end; skill is required. Creative reasoning, then,
is the deliberate application of construction steps in the creative
process.
Having made this distinction, we note that many creative
processes will consist of a combination of both kinds of creativity.
Wolfgang Amadeus Mozart described his own creative process in
this way, as a cycle of spontaneous creativity and then creative
reasoning (in this case, selection):
“When I am, as it were, completely myself, entirely alone, and
of good cheer—say traveling in a carriage, or walking after a good
meal, or during the night when I cannot sleep; it is on such
occasions that my ideas flow best and most abundantly. Whence
and how they come, I know not; nor can I force them. Those ideas
that please me I retain in memory, and am accustomed, as I have
been told, to hum them to myself.
All this fires my soul, and provided I am not disturbed, my
subject enlarges itself, becomes methodized and defined, and the
whole, though it be long, stands almost completed and finished
in my mind, so that I can survey it, like a fine picture or a
beautiful statue, at a glance. Nor do I hear in my imagination the
parts successively, but I hear them, as it were, all at once. What
a delight this is I cannot tell! All this inventing, this producing
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Wiggins and Bhattacharya Bridging computational and neuroscientific approaches to study creativity
takes place in a pleasing lively dream. Still the actual hearing of the
toutensemble is after all the best. What has been thus produced I
do not easily forget, and this is perhaps the best gift I have my
Divine Maker to thank for” (Holmes, 2009, pp. 317–318).
Wiggins (2012a) expounds this argument in more detail. For
the current purpose, the distinction serves to highlight a differ-
ence between two modes of creativity, of which most humans
have experience, and which may or may not demand different
mechanisms and/or neural loci, thus entailing an interesting set
of research questions.
CREATIVITY—WHY IS IT THERE?
An important question in the scientific study of creativity is, “why
and how should such a faculty evolve?” This is not a question
that is open to empirical science, for obvious reasons. However, to
proceed without considering the evolution of such an important
cognitive faculty, and without proposing at least one pathway by
which mechanisms predicted by one’s theory might evolve, is to
admit the possibility of fiction. To answer this question requires
careful teasing apart of the various meanings of the C-word, and
of the value judgments associated with them, and also of the
different ways in which evolution can take place. First, let us
briefly distinguish between biological and social evolution.
In the biological case, one might argue for creativity as an
outcome of evolutionary processes themselves: the very existence
of humankind, for example, might be deemed an act of creativity.
However, it seems to us that attributing the property of creativity
to a process which has no definable agent1is less than meaningful.
Therefore, we draw a line between the process of evolution (which
is, perhaps, more serendipitous than creative, in the final analysis),
and the processes applied, one way or another, by agents or groups
of agents in the world. Thus, neither the scarab beetle “creating”
its ball of dung, nor the evolutionary chance sequence that gave
rise to it, falls within our definition of creativity.
In the social case, on the other hand, it is hard to deny
the existence of creative evolution. Indeed, it is impossible to
achieve a situation in which an individual can create a concept
that persists through arbitrary time, but is also unknown to all
parts of society. Given this basic truth, and that transmission of
information between biological organisms is imperfect, and that
no two biological individuals have exactly the same background
knowledge, we are left with a situation somewhat analogous to
evolution, which is the analogy amplified in the study of so-called
“memetics” (Dawkins, 1989; Blackmore, 2000). It is not necessary
to go to such extremes to see a process analogous to evolution
working in society: the development of Western music since the
13th century is a paradigmatic example.
The key question, though, is, “what evolutionary pressure
would favor the capacity for individual creativity?” In a later
section, after Wiggins (2012b), we propose that creativity results
from a general cognitive mechanism of adaptive prediction, which
allows organisms to manage the world more effectively by predict-
ing the immediate future, based on experience, rather than merely
reacting to events in the world. Other evidence for the importance
of anticipation in humans is given by Huron (2006). Thus, as
1At least, if we eschew descent into mysticism.
above, we are drawn to the suggestion that creativity is not a
special faculty in its own right, but a combination of properties
which contribute to evolutionary fitness. These properties may
contribute to fitness for creative or non-creative reasons in the
individual, or to sexual (or other social) selection, in the group,
or both.
CREATIVITY—HOW CAN WE MEASURE IT?
If creativity is hard to define, it is even harder to measure. There
is no single measure or method that can adequately capture the
multifaceted nature of creativity; in fact, well over a hundred mea-
sures have been developed and applied to this purpose (Plucker
and Makel, 2010). Here we briefly mention some of the most
commonly used ones.
The current empirical research on creativity was spearheaded
by Joy Paul Guildford who in his Presidential address in 1950 sug-
gested to the American Psychological Association that creativity,
though an elusive construct, could be psychometrically studied.
Over the following decade, Guilford and his team developed the
Divergent Thinking Test (DTT): for any problem, there exist
many possible solutions which may qualitatively differ from each
other. For example, in the “unusual uses” test, an example of
a DTT, participant is asked to list many different uses for a
familiar object (e.g., brick, paper-clip), and the responses are
coded by independent raters to judge: (i) fluency (the number of
responses: the more ideas a creative person can come up with, the
greater the chance that some of it will be useful); (ii) originality
(responses that are less-frequently reported by others: a creative
person produces ideas that are unique); (iii) flexibility (number
of responses falling into distinct categories: a creative person is
flexible, i.e., can break away from habitual mode of thinking);
and (iv) elaboration (the detailed nature of responses: a creative
person has a detailed plan). The concept of divergent think-
ing subsequently led to the Torrance tests of creative thinking
(Torrance, 1974), the most widely used paper-and-pencil test of
creativity.
Another influential test of creativity, the remote associate test
(RAT), is based on the concept of associations and convergence:
creativity involves combinations of two remotely associated con-
cepts in a novel and convergent way (Mednick, 1962). Here, a
participant is given a list of word triplets (e.g., paper, stone, fire),
and asked to produce a target word (wall) that makes three valid
compound words (wallpaper, stonewall, firewall). As each triplet
is likely to have a fixed solution, this test, unlike the DTT, does not
require any subjective judgment.
Finally, the Consensual Assessment Technique (CAT) was
developed specifically to assess creativity perceived in finished
artifacts (Amabile, 1996). A group of experts in the domain of
the artifacts are invited to make judgments about the creativity of
the artifact in question, including discursive remarks explaining
their judgment in detail. Statistical analysis is used to ensure that
only the judgments where the experts are unanimous are used,
thus reducing subjectivity. The experts’ remarks can be unified to
develop an account of their reasons for their judgments.
Further, creativity is often associated with cognitive insight,
the sudden understanding of the solution to a problem without
any conscious forewarning (the “Aha!” experience; Wallas, 1926).
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Wiggins and Bhattacharya Bridging computational and neuroscientific approaches to study creativity
Researchers either use classical insight problems to characterize
creativity (Metcalfe and Wiebe, 1987) or distinguish non-creative
performances from creative ones based on subjective experience
of insight (Jung-Beeman et al., 2004).
Though these kinds of test batteries are very useful to study
little-c creative processes in a laboratory setting, the possible
relationships between the scores on these tests and big-C creativity
are not conclusive (Sawyer, 2012). Yet it is increasingly accepted
that creative thinking, even at the highest level, is not mysterious,
but rather composed of standard cognitive processes, including
problem solving (Weisberg, 2006). Our approach, therefore, is to
consider mechanisms and evaluation methods that can account
for both big-C and little-c creative behavior, and anything in
between.
Computational cognitive modeling gives us a substantial
advantage in the context of evaluation, because theories that
are computational are no longer static objects that serve only
as thought experiments, but active ones capable of action on
information, or even, given a robot, in the physical world. That
is to say, a computational theory can be implemented, as a
program, and run, and its behavior studied, in comparison with
human behavior, or as a separate creative “organism” in its own
right. This approach is the keystone of the hierarchical modeling
strategy described by Wiggins (2011), which we propose to use
here. First, we offer a brief overview of the neuroscientific studies
of creativity and some possible limitations.
CREATIVITY—WHERE IN THE BRAIN?
NEUROSCIENTIFIC FINDINGS ON CREATIVITY
Over the last decade, a substantial body of research has been
published on neuroscience of creativity. The literature can broadly
be divided into three groups: (i) studies using divergence (DTT)
or convergence (RAT) tests; (ii) studies on “Aha!” insight; and (iii)
studies using professionally creative individuals like musicians
or artists. A diverse range of neuroimaging techniques, from
correlational methods like EEG, MEG and f-MRI to causal stim-
ulation methods like transcranial magnetic stimulation (TMS),
transcranial direct current stimulation (tDCS) have been used. In
addition to these functional neuronal correlates, researchers have
also been interested in establishing link between creativity and
specific brain structure(s) by studying patients with brain injury
(lesion studies) and by adopting latest structural neuroimaging
measures including diffusion tensor imaging (DTI) and variants
of magnetic resonance spectroscopy (MRS). It is beyond the scope
of this paper to provide a comprehensive review of neuroscientific
findings, and we refer the readers to some excellent recent review
articles (Arden et al., 2010; Dietrich and Kanso, 2010; Jung et al.,
2013). Nevertheless, we briefly mention here some of the key
findings.
For example, f-MRI studies of DTT suggest widespread acti-
vations over prefrontal cortex but without any clear hemispheric
lateralization (Howard-Jones et al., 2005; Fink et al., 2009a). EEG
studies do not indicate any consistent hemispheric lateralization
nor any consistent changes in terms of alpha power (8–12 Hz)
(Dietrich and Kanso, 2010); however, a recent review points
towards a systematic effect of alpha increase for the creative
generation of ideas (Fink and Benedek, 2012). Neuroimaging
studies of insight problem solving suggest that, during sudden
comprehension of the problem, multiple brain regions activate,
including the anterior cingulate, hippocampus, anterior superior
temporal gyrus, right prefrontal cortex (Dietrich and Kanso,
2010). EEG studies provide temporal dynamics of the neuronal
activations underlying cognitive insight during convergent think-
ing: enhanced gamma band power (>30 Hz) in the right frontal
cortex is observed 300 ms before the subjective “Aha!” moment
while solving remote associate problems (Jung-Beeman et al.,
2004; Sandkuhler and Bhattacharya, 2008), and this is interpreted
as the sudden conscious availability of the target solution words.
Remote EEG brain waves up to 8 s before the insightful solution
are also reported in participants while solving insight problems
(Sheth et al., 2009). Further, EEG alpha (8–12 Hz) power is
found to be associated with mental states prepared for insight
(Kounios et al., 2006). Brain stimulation studies suggest that
performance on RATs could be enhanced by applying electrical
stimulation to various brain regions, i.e., the dorsolateral pre-
frontal cortex (dLPFC; Cerruti and Schlaug, 2009), right anterior
temporal lobe (Chi and Snyder, 2011). Altogether the neuroscien-
tific studies have adopted the task paradigms (i–ii), of divergent
and convergent types, which are grouped together under little-c
creativity.
The neural correlates of creative processes associated with big-
C creativity could be investigated by studying brains of creative
professionals in action; so far only a few studies have investigated
creativity-related (like improvising music, composing an artwork)
brain activity in samples of performing artists like visual artists
(Bhattacharya and Petsche, 2005), musicians (Limb and Braun,
2008), dancers (Fink et al., 2009b). During mental composition
of drawings, professional artists showed greater long-distance
synchronization between multiple brain regions in low frequency
neuronal oscillations (<4 Hz), suggesting a more emphasized
top-down processing (Bhattacharya and Petsche, 2005); on the
other hand, non-artists showed more locally synchronized activ-
ities over prefrontal regions. Enhanced top-down control is also
observed in the professional dancers during mental imagery of
an improvised dance but not during a learned routine (i.e.,
classic waltz) (Fink et al., 2009a). On the other hand, dur-
ing spontaneous musical improvisation, trained musicians show
an activation pattern of a widespread neuronal network but
with a simultaneous deactivation of dLPFC, a region usually
involved in planning and conscious self-monitoring (Limb and
Braun, 2008). Similar wide spread activation of brain network
encompassing multitude of brain regions with a concomitant
deactivation of dLPFC is also reported during the sponta-
neous lyrical improvisation in freestyle rap artists (Liu et al.,
2012).
Altogether these studies suggest that creativity, either little-c
or Big-C, cannot be localized to one or a few brain regions, rather
they show that when humans are engaged with any sort of creative
process, a multitude of brain regions become active,2and these
2By “active”, we mean some sort of involvement or association of brain
regions in specific creative process(es), since brain areas are shown to be
both positively (activated in neuroimaging sense) and negatively (deactivated)
correlated with creativity.
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Wiggins and Bhattacharya Bridging computational and neuroscientific approaches to study creativity
are also the brain regions that become active in many “ordinary”
cognitive processes (e.g., working memory, attention, cognitive
control, performance monitoring) which are not usually thought
of as requiring creativity. Therefore, these studies help breaking
the classical dogma that creativity is too complex or too difficult
to study empirically, instead they suggest that creativity could be
considered as a product of complex interplay between ordinary
cognitive processes like memory, attention, executive function,
and even emotion (Ward et al., 1999; Weisberg, 2006). However,
the nature of interplay between the constituent cognitive (and
affective) processes may not be trivial and the creative output can
be considered as an emergent outcome of this intricate interplay,
so a pure reductionist approach would not offer an adequate
understanding of creative process(es).
REMARKS ON NEUROSCIENCE OF CREATIVITY
As stated earlier, neuroscientific studies have contributed greatly
to our understanding and subsequent demystification of cre-
ativity. However, one should also exercise caution while taking
these neuroscientific findings into account, as there are several
limitations and issues to consider. We do not provide here a
detailed treatment on the scopes and limitations on possible
interpretations of neuroimaging, but refer the interested readers
to some recent monographs (Uttal, 2003; Satel and Lilienfeld,
2013); we highlight only some of the issues that are especially
relevant for creativity research.
First, different neuroimaging techniques have different sets
of assumptions, different spatial and temporal resolutions (e.g.,
excellent temporal resolution of EEG vs. slow blood-oxygen-level-
dependent responses of f-MRI or poor localization of EEG due to
ill-posed inverse problem vs. much higher spatial resolution of f-
MRI), and therefore, tap different aspects of neuronal activities—
e.g., EEG is a direct indicator of electrical activities, whereas f-
MRI is an indirect indicator lacking a clear interpretation of
neuronal origin (Logothetis, 2008). Hence, it is not surprising
that a coherent picture of the neuroscience of creativity is yet to
emerge; for a review see Dietrich and Kanso (2010).
Second, findings based on neuroimaging studies provide only
correlational, but not causal, information, i.e., neuroscientific
findings show that certain brain regions are correlated with
creative tasks/measures under investigation but cannot prove
whether these brain regions will be causally linked to creative
task/measures. In this context, brain stimulation methods could
provide some causal information, but evidence is still scarce
(Cerruti and Schlaug, 2009; Chi and Snyder, 2011).
Third, brain responses related with a task are several magni-
tudes lower than those at rest, i.e., they are “noisy” and results
are obtained after averaging across many trials and participants,
so the neuroscientific findings are meaningful only in statistical
sense. Therefore, it would be premature to claim that a brain
region, location of which is obtained by statistical manipulations,
is the seat of creative process(es). In fact, any search for finding
an isolated seat of creativity in the brain is likely to be futile;
it is the complex interplay between multiple and distant brain
regions forming a network that is more likely to be associated with
creative cognition (Bressler and Menon, 2010). One interesting
proposal by Merker (2013) identifies not a locus of creativity (the
dorsal pulvinar) but a potential locus of the selection process
that is central to our model below. Further, we speculate that
this network may be dynamically evolving as a creative process
is not static, but a time-varying dynamic process (e.g., consider
musical improvisation, “Because it’s improvised, musicians don’t
know what they’ll play in advance; the notes emerge in the
moment, from the complex give-and-take among the members
of the ensemble.... Creativity takes place over time, and most of
the creativity occurs while doing the work” (Sawyer, 2012, p. 88),
yet little is known about its time profile.
Fourth, the ecological validity of the most of the reported
studies are necessarily compromised due to an overtly constrained
environment in the laboratory (e.g., inside the scanner) and
the impoverished nature of the adopted tasks, so the observed
findings might not be generalizable to real-life creativity (Hasson
and Honey, 2012). Further, most of these studies on creativity are
based on samples of university students, though a common prac-
tice in the broad field of behavioral science, their generalizability
may be quite limited (Henrich et al., 2010; Jones, 2010).
Finally, most of the neuroimaging studies on creativity have
applied the subtraction method, i.e., by contrasting two condi-
tions (i.e., creative vs. non-creative, insight vs. analysis, impro-
visation vs. memorization). A crucial assumption behind such
a method is that the two conditions differ from each other in
only one cognitive operation. This procedure is termed “pure
insertion” because it assumes that the condition of interest is
different from the other condition in the insertion of a sin-
gle cognitive operation, and therefore, any differences in brain
responses between the two conditions would be related to that
single inserted operation. Though this assumption works well
for perceptual or well-controlled cognitive task paradigms, it is
difficult to achieve in task involving creative cognition as dis-
cussed below. For example, researchers often compared insight
vs. analysis methods of problem solving based on a subjec-
tively perceived “Aha!” response (Jung-Beeman et al., 2004;
Kounios et al., 2006, 2008); yet the two methods differ on
multiple cognitive processes including impasse (a state in which
the solver is mentally stuck on an unsuitable construct of the
problem and fails to progress further), restructuring (a mech-
anism by which the solver breaks out of mental impasse, and
is a transition from an initial inappropriate and thus incorrect
representation of a problem and state of not knowing how
to proceed in solving a problem to a state of knowing how
to solve it), and deeper understanding (a form of deeper or
more appropriate understanding of the problem and its solu-
tion). Therefore, a straightforward comparison between insight
and analysis method would contain more than one cognitive
components, which implies that any neuroimaging finding on
the basis of such comparison would be inadequate to isolate
the unique neural component, if any, of insight. We believe
this is a very serious limitation that deserves to be properly
looked at before we can consider any claim of isolating neu-
ral correlates of creative process(es). Therefore, it is important
to have a well-controlled task design so that the compared
conditions in a neuroscientific study are as close as possi-
ble; see Weisberg (2013) for an elaborated discussion on this
issue.
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Wiggins and Bhattacharya Bridging computational and neuroscientific approaches to study creativity
A COMPUTATIONAL FRAMEWORK THAT ACCOUNTS
FOR CREATIVITY
We now propose an overarching, hypothetical, computational
theory of creativity, which is a candidate formulation to help
coordinate research in both psychology and neuroscience. It may
serve as such because it is more detailed and mechanistic than
most previous approaches, to the extent that it can be readily
implemented on a computer. Implementations are made possible
at multiple levels by application, in principle and practice, of
the hierarchical approach of descriptive and explanatory model-
ing (Wiggins, 2007). It would be possible to proceed from this
proposal either by attempting to falsify its various components
(Popper, 1959), or testing the relationships between them, or by
using it to identify particular areas of interest for future study
(which also, of course, might ultimately lead to falsification). The
theory is currently specified at a functional level considerably
more abstract than that of neural activity, leaving scope for further
theorizing at intermediate levels.
Our principal aim here is to propose an account of “spon-
taneous creativity”: the kind of creative cognitive events appear
to happen spontaneously without any conscious volition. This
kind of creativity is associated with the generation of language
(Plotkin, 1998), music (Limb and Braun, 2008; Wiggins, 2012a),
and along with others (Arieti, 1976; Loehle, 1994; Weisberg,
2006). We propose that it reflects spontaneous thinking and
therefore represents a mechanism for domain-general creativity.
The framework used is Baars’ Global Workspace Theory (GWT;
Baars, 1988), enhanced with mechanisms based on Shannon’s
Information Theory (Shannon and Weaver, 1949).
GLOBAL WORKSPACE THEORY (GWT)
Bernard Baars (1988) has formulated the GWT, a theory of con-
sciousness whose principal hypothesis is that conscious content
must be globally available as it allows the content to be broadcast
to many higher-level cognitive systems for possible motor control,
executive functions, verbal awareness, and so on; in short, accord-
ing to this theory, consciousness is equated to brain-wide infor-
mation sharing (Dehaene, 2014). The theory posits a framework
within which consciousness can take place, based around a multi-
agent cognitive architecture (Minsky, 1985) communicating via
something like an AI blackboard system (Corkill, 1991), but with
constraints, outlined below. We avoid Chalmers’ “hard” question
of “what is conscious?” (Chalmers, 1996) and instead ask “what
is it conscious of, and how?” This is appropriate, because the
nature of consciousness is not our central issue, but presentation
of information to it is.
Baars models the non-conscious mind as a large collection of
expert generators, like the multiple experts in Minsky’s (1985)
The Society of Mind, but with less clear hierarchy, process-
ing information in massive parallel, competing for access to a
Global Workspace via which (and only via which) information
is exchanged; information must cross a notional threshold of
importance before it is allowed in, and we return to this below.
The Global Workspace is visible to all generators, and contains
the information of which the organism is conscious at any
given time. It is capable of containing exactly one “thing” at
once, though the nature of that “thing” is underspecified. It
is highly contextualized, and meaning in it is context-sensitive
and structured; contexts can contain goals, desires, etc. Baars
mentions the possibility of creativity within this framework in
passing, implicitly equating entry of a generator’s output into
consciousness with the “Aha!” moment (Wallas, 1926), but does
not develop this idea further.
Baars proposes that information integration happens sequen-
tially, i.e., in stages, via something that one might (but he does
not) call local workspaces, that integrate information step by
step in a sequence, rather than all in one go as it arrives in the
Global Workspace. This information integration approach has
been extended later by Tononi and Edelman (1998), who propose
information-theoretic measures of information integration as a
measure of consciousness of an information-processing mecha-
nism. Baars has embraced the information-theoretic stance, too,
and the three authors have jointly proposed to begin implement-
ing a conscious machine (Edelman et al., 2011) based on their
ideas. The current work may contribute to this endeavor, though
probably at a level more abstract from neurophysiology than these
authors intend.
THE THRESHOLD PARADOX
Baars (1988, pp. 98–99) also addresses what he acknowledges is a
problem for his theory. He proposes a threshold for input access to
the Global Workspace, crossing of which is thought of in terms of
“recruiting” sufficient generators to produce information that is
somehow coordinated, or synchronized between them. However,
in terms of the Global Workspace alone, there is no means of
doing this: generators can only be coordinated (whatever that
means) via the Global Workspace, and so the generators are faced
with a classic Catch 22 situation. This form of the Workspace
is illustrated in Figure 1. Baars presents two possible solutions
to this paradox, but both are somewhat incomplete, therefore
causing a gap in the theory. Our approach presents a possible
solution, and simultaneously accounts for the “Aha!” moment.
PERCEPTION, ANTICIPATION, AND EVOLUTION
Reaction vs. anticipation
We now present a mechanism for managing the competition
between generators in Baars’ system, after Wiggins (2012b). The
key distinctions are: (a) between the information content and
entropy (defined below) of various stimuli; and (b) between
organisms that react and organisms that anticipate. Our model is
inspired by evolution after taking into consideration of the evolu-
tionary advantage conferred by the resulting behaviors. Thus, the
evolutionary argument presented here is part of the design, and
not merely an example.
In artificial intelligence, an agent (of which an creative agent
is presumably an instance) is defined as a program or robot
with a perception-action cycle: its action is purely shaped by its
perceptions of the environment or world. Lower organisms seem
to be modeled well by such simple agents, as their actions are
limited to reacting to environmental conditions, coping rather
poorly when their evolved reactive program is interrupted or
the environment is suddenly altered. However, to model higher
cognitive development, one could consider a more predictive
system as opposed to a reactive one; here an organism is in
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Wiggins and Bhattacharya Bridging computational and neuroscientific approaches to study creativity
FIGURE 1 | Illustration of Baars’ threshold paradox. Generators operate on
perceptual input and associative memory. In order to reach into the level of
consciousness, a coalition of generators needs to be formed and this is
possible only via the Global Workspace. However, before it can be made
possible, support from the generators that are to be recruited are needed,
and therein lies the paradox.
predicting mode perpetually as it forms the prediction from a
learned model of previous sensory data, what is likely to come
next, and compared this with current sensory input. A related
mechanism based on expectation and prediction was proposed
by Sokolov (1963) on orientation reflex, and there is currently
increasing interest in predictive coding in perception; Hohwy
(2014) gives an excellent philosophical introduction to predictive
coding. The principal advantage of such anticipation is a simple
mechanism for spotting what is unusual, what, therefore, consti-
tutes a potential new opportunity or threat, and what deserves
cognitive resource, or attention. Our proposal draws together
orientation reflex and predictive coding of perceptual input into
one process of information management and attention resource
deployment, and suggests how creativity can be a by-product of
these two related things.
The consequence of sequence: managing uncertainty
with expectation
The most important feature of an autonomous agent is that it can
predict what is to come next, and react, or prepare to react, in
advance. To predict usefully in a changing world, an organism
must learn. It must be able to learn not just categorizations (to
understand what something is), but also associations (to associate
co-occurrence of events with reward or threat), and, crucially
here, sequence.
However, a simple statistical learning mechanism is not sub-
tle enough (Huron, 2006). Evolutionary success entails that an
organism breeds, so learning only from potentially fatal con-
sequences will not do: if the experience kills the organism,
there is little benefit of that experience. The successful strategy
here is at a meta-level, above the learned body of experience:
if an organism is aware that it is in circumstances where its
predictions are uncertain, it must behave more cautiously, its
metabolism should be prepared for flight, and it should devote
extra attention to its surroundings. Huron (2006) suggests that
this effect serves as an exaptation or spandrel producing part
of the affect of music; but, here, the mere adaptation suffices:
self-evidently, uncertainty affects behavior in humans and other
animals, and doing so does not rely on explicit reasoning. Indeed,
the converse is true: we feel nervous in uncertain situations,
and the feeling makes us pay attention to appropriate sensory
inputs and prepare for flight. This mechanism, and the associated
affective response, is not the same as fear, but can lead there in
extremis.
Finally, any kind of learning of this nature must include
generalization—from both co-occurrence and sequence—that
similar consequences arise from similar events, encounters, etc.
Without this, mere tension cannot lead to fear at the sight of the
bared fangs of an unknown but large animal. This accords with
Gärdenfors’ (2000) proposal, that perceptual learning systems
are motivated to understand similarities and differences between
perceived entities in the world, and to place observations at the
appropriate point with respect to previous experience.
Prediction and selection
Given a world-model, categorized into types, situations, etc.,
a set of generators with recent and current perceptual inputs
matched against precursors of sequential associations, can make
stochastic predictions conditioned by prior observations. Making
predictions quickly, sequentially, would be valuable, but slow,
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Wiggins and Bhattacharya Bridging computational and neuroscientific approaches to study creativity
multiple predictions, in parallel, are a more likely candidate
for evolutionary success, and the more the better—as in Baars
proposal. But many predictions occurring simultaneously will be
chaotic and disorganized, so how will useful candidates for pre-
diction be selected? Baars’ solution is the problematic threshold,
described above.
The GWT is unclear about the precise notion of generators
“recruiting” each other. The desired effect is something like an
additive weight: the more generators “recruited”, the greater their
impact. We will avoid answering this question, by approximating
this effect probabilistically.
Our proposal is based on statistical, frequentist notions of
learning, and so our account is in terms of statistical models;
however, we do not claim that the reasoning is in principle
exclusive to such models. In this view of the system surrounding
the Global Workspace, there are many independent subsystems,
making multiple predictions from a predictive (statistical) model
of (assumedly) reasonable quality. We must assume imperfect
models: each generator can only have a partial view of its world
and its predictions, since modeling everything all the time would
be prohibitively expensive. It follows from the use of statistical
models that the more expected occurrences are the more likely
ones to be predicted. Conversely, extremely unlikely predictions
will rarely be “popular”.
In a model of prediction and action based only on observed
frequency, an organism will do the commonest thing, even if inap-
propriate, and so will fail: it will not predict unlikely or surprising
situations, and will not prepare itself against eventualities.
In Baars’ theory, the most likely outcome, corresponds with
multiple generators “in coalition” generating that outcome. The
likelihood of each generator predicting an outcome is propor-
tional to the popularity of that outcome across the set of gener-
ators. Thus, we can use the likelihood of the outcome, p, to model
its popularity.
In reality, though, we know well that an animal will not
just carry on as normal in unexpected circumstances: it will
experience the negative-affective response, described above, so we
need a mechanism for that response. The obvious choice is the
notion of entropy, formalized by Shannon (1948).MacKay (2003)
makes a finer distinction between information content,h, defined
as an estimate of the number of bits required to describe an event,
e, in a context, c, denoted by (c|e), or its unexpectedness:
h(e|c)= −log2p(e|c), (1)
and entropy, H, defined as an estimate of the uncertainty inherent
in the distribution of the set of events Efrom which that emight
be selected, given the context, c:
H(c)=X
eE
p(e|c)h(e|c)= X
eE
p(e|c)log2p(e|c). (2)
His maximized when all outcomes are equally likely, and mini-
mized when a single outcome is certain. Both hand Hare useful
to our hypothetical animal, but here we consider only h.
htis the unexpectedness of a partial model of the actual
on-going experience in a particular state, t. If the experi-
ence is likely (that is, if it is predicted as likely from what
has gone before), it is not unexpected, and therefore htis
low; on the other hand, if the experience is unlikely, it is
unexpected, and so htis high. In pure frequentist terms, a
completely new experience is maximally unlikely. To model
this, we propose that the selection process is sensitive to ht,
and decreases priority of generators when it is low. Thus,
the likelihood of models of the experience in which the new
scent is included being accepted in the Global Workspace
is positively related to its unexpectedness. We call this the
recognition-h case; it explains why unexpected events attract
attention.
Now, consider ht+1, the unexpectedness of a predicted sit-
uation: the prediction-h case. It is maximally unlikely that a
prediction will be made including a previously unencountered
scent, so we would expect ht+1to be high, causing alarm. Excess
of such predictions, or repeated occurrence of a single one, would
lead to a state of constant anxiety.3This explains why surprising
(or interesting) predictions are more likely to draw attention than
prosaic ones.
Of course, in a simplistic frequentist account, predictions
introducing new percepts or concepts cannot arise, because they
entail the creation of new symbols. This is why it is necessary
to include generalization and/or interpolation in the theory (see
above).
The problem of over-active prediction-his mitigated by the
mechanism supplied above, in which prediction is probabilistic
and (broadly) additive across predictors, modeled by p. There
are two opposing forces here, one of which changes inversely
relative to the other, and because they are co-occurrent, their
effects should (broadly) multiply. Therefore, the overall pop-
ular outcome among the generators in the global workspace
can be estimated by multiplying the probability, pof an event
(which estimates the likely number of generators predicting
it) by h(which estimates the volume at which they are pre-
dicting). The resulting likelihood is illustrated by the unit-
free diagram in Figure 2. It biases away from predictions
which are either very likely or least expected, reducing the
power of very unlikely or very obvious predictions to attract
attention. This may explain why unlikely possibilities do not
prevent action by overwhelming the acting organism with
choice.
Recognition-hand prediction-hare quite different in the
context of the Global Workspace. We propose that generators
may generate structures of either kind, and that the two hs
will compete for the resource of attention. In this way, present
danger or benefit outweighs predicted likelihoods, because the
distribution of potential predictions is over a much wider range
of possibilities than that over actual perceptions, and so proba-
bility mass is spread more thinly. Conversely, for example, likely
but unexpected predicted benefits can outweigh less seriously
dangerous present circumstances—thus, prioritizing an unusual
opportunity can be mechanistically explained as an emergent
behavior. As there are two kinds of generation, we must propose a
means of distinguishing between them: otherwise, consciousness
3Some root symptoms of clinical anxiety would be explicable in terms of a
breakdown of this mechanism.
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Wiggins and Bhattacharya Bridging computational and neuroscientific approaches to study creativity
FIGURE 2 | Illustration of the interaction between likelihood and
unexpectedness. The overall likelihood (solid) is formed by the
multiplication of two monotonic functions: the unexpectedness of a
generated item (dashed) and the number of generators likely to agree on it,
according to its likelihood (dotted).
could not distinguish between the perceived and the predicted
world.4
At any given moment, this “popularity” value, p×h, is used
in deciding which of the range of possible inputs, derived from
matching sensory input to statistical models in memory, enters
the Global Workspace. This is illustrated in Figure 3.
CREATIVITY FROM PREDICTION
The remaining question is now: how does this mechanism for
choosing access to consciousness help to understand and simulate
creative cognition? We propose that a surprising answer may
be found in the opening sentence of Mozart’s comment, above,
which might be paraphrased as “when I am not being bothered,
and when I have no worries and no particular goals”, which in
turn means “when I have no distractions” or “when I have no
information-rich input to consciousness from outside or within.
In this situation, the Global Workspace is occupied only by
weakly-informative ephemera, and its generators are receiving
little or no external stimulus.
Earlier we proposed that anticipatory animals base their
actions not on stimuli, but on the results of comparing stimuli
with predictions about the state of the world made from pre-
vious state(s). We now propose that those generators continue
to generate at all times. Generators can freewheel, within the
same statistical framework as above, but lacking the statistical
influence of an external stimulus. The outputs will be more diffuse
than when directly stimulated, but they can still enter the Global
Workspace, in the absence of competition. Given this, the diagram
4Coupled with a deficit in suppression of less likely outcomes, as above, this
situation might lead to some root symptoms of schizophrenia: hallucinations,
delusions, and cognitive distortions.
in Figure 2 can be seen as the Wundt curve (Wundt and Titchener,
1904; Margulis and Beatty, 2008), as it defines a sweet spot of
balance between dullness and over-complexity in information-
theoretic terms.
The mechanism above also accounts for why a stimulus of
one type may give rise to a creative production of another: the
perception conditions the sampling, and this affects the likely
outcomes, which are generated all the time. The ones with the
right statistical properties make it into the Global Workspace, and
can thence be further elaborated.
The mechanism works with any model from which mean-
ingful statistical likelihoods can be estimated. Therefore, it can
account for the generation of sentences, and possibly internal
speech, commonly equated with essential thought. Therefore the
current approach can account for general creative thought and
for the emergence of particular thoughts into consciousness as
intuition.
At this point, we have dispensed with an explanation of cre-
ativity as a special mechanism: in our approach, non-conscious
creativity is happening continually as a result of on-going antici-
pation in all sensory (and other) modalities. When conditions are
right, this essential survival mechanism gives rise to creativity as a
side effect.
RELATING NEUROPHYSIOLOGY TO THE COGNITIVE
ARCHITECTURE
The cognitive architecture presented in the previous section is
located at a very abstract level, distant from neural models. Never-
theless, some success has been achieved by neurophysiological and
behavioral methods, relating the information-theoretic signals
(i.e., the statistical values that change in time) with measurable
responses (Pearce, 2005; Pearce et al., 2010a; Hansen and Pearce,
2012; Egermann et al., 2013). With such empirical grounding,
the possibility of filling in the gaps in a methodical way becomes
available.
Here, we relate the proposal above with other cognitive and
neurophysiological theories of creativity. Recall the distinction
introduced earlier between spontaneous creativity and creative
reasoning. These, respectively, correspond with non-conscious
and conscious creative activity, the point of transition from the
former to the latter corresponding with Wallas (1926) Aha!”
moment, illumination. We briefly focus on three related aspects
here: unconscious thought, mind wandering, and spontaneous
intrinsic brain activities. We argue that non-conscious creativity
happens prior to conscious creativity and this distinction is exem-
plified in detail by Wiggins (2012a). In context of GWT, the “Aha!”
moment is the entry of an idea into the Global Workspace; ideas
must compete in relative terms, and the winners are the ones that
enter consciousness (Wiggins, 2012a).
UNCONSCIOUS/CONSCIOUS PROCESSING IN CREATIVITY
It has been widely accepted that creativity cannot solely be
explained by conscious processes alone; conscious thought has
limited processing capacity (Miller, 1956), yet unconscious
thought (i.e., being distracted yet still actively maintaining a task-
related goal in the background) could process a vast amount
of information (Dijksterhuis and Nordgren, 2006; Ham et al.,
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Wiggins and Bhattacharya Bridging computational and neuroscientific approaches to study creativity
FIGURE 3 | Schematic diagram of Wiggins (2012b) proposal for the Global Workspace. In this version, there is no need for a threshold of access. Instead,
the generators compete one against another and probability and information content determine the winner.
2009; Lerouge, 2009). Earlier spontaneous unconscious thought
is shown to benefit complex decisions requiring manipulation of
multiple attributes (Dijksterhuis et al., 2006). Here, participants
were given various alternatives with the aim to choose the most
attractive option; participants chose either immediately or after
a period of deliberate thinking or after performing a distracting
task. It was found that participants who performed a distracting
task actually made a better and more optimal decision than
those who performed conscious deliberation before taking the
final decision. Interestingly, this beneficial effect of unconscious
thought holds only for complex decisions, while conscious delib-
eration outperforms unconscious thought for simple decisions
(i.e., decisions involving only a few attributes) (Dijksterhuis and
Nordgren, 2006; Dijksterhuis et al., 2006). Since the selection of a
creative idea among many possible alternatives can be considered
a decision making process, unconscious thought may have a ben-
eficial effect on the idea selection phase of the creative process. If
this to be the case, it party explains why there are many anecdotal
evidence of unconscious processing in real life creativity focusing
on idea selection (Ghiselin, 1952), whereas the empirical studies
focusing on idea generation provide a weaker evidence (Ritter
et al., 2012).
In creativity, the time period during which the unconscious
mental processes are “active” is termed “incubation”(Wallas,
1926), and one direct way incubation helps problem solving
is by reducing mental fatigue (Kahneman, 1973). Further, the
mental set-shifting hypothesis suggests that putting an unsolved
problem aside for a while and then coming back to it would
help eliminating incorrect old representations leading earlier to
mental fixedness, and allowing the emergence of the most optimal
or correct representation (Schooler and Melcher, 1995). How-
ever, one could argue that this primarily demonstrates a passive
role for unconscious thought, on the other hand, unconscious
deliberation can also be assumed to be a proactive and goal-
driven process (Dijksterhuis and Nordgren, 2006; Bos et al., 2008),
and recent findings do provide evidence in favor of the latter
account. For example, in the study of Dijksterhuis and Meurs
(2006) participants had to list Dutch places names starting with
the letter “A” or letter “H”, and it was found that the participants
who were engaged with conscious deliberation listed more names
of large cities and towns, where as the participants who were
engaged with unconscious thought listed more names of small
villages. This demonstrates that unconscious thought facilitates
access to unconventional or non-dominant information in the
long term memory, thereby, potentially promoting remote cre-
ative association. This possibility is indeed supported by Zhong
et al. (2008) who found that participants engaged with uncon-
scious thought were faster to solve difficult RAT problems than
participants who were engaged with conscious thought. Inter-
estingly, the number of solved problems that were very difficult
did not differ significantly between the two thought conditions,
but conscious thought led to more solutions of easy problems
than unconscious thought. This facilitatory effect of unconscious
thought in solving complex tasks including creative problem
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Wiggins and Bhattacharya Bridging computational and neuroscientific approaches to study creativity
solving is explained by a two-stage processes (Zhong et al., 2008)
that is very similar to our earlier proposition (Wiggins, 2012b):
the first stage involves unconscious deliberation generating cre-
ative ideas by “deep cognitive activation” (Wegner and Smart,
1997) and the second phase is the transfer of output from the
unconscious to conscious awareness; an incomplete processing at
either step would impair the influence of unconscious thought on
creativity.
MIND WANDERING IN CREATIVITY
A closely related mental phenomenon is mind wandering, also
known as task-unrelated thought. Mind wandering, a sort of day-
dreaming, involves a shift of attention away from a primary task
to process some other, personal information, but in a way that
is not obviously goal-directed. The major characteristic of mind
wandering is that the attention is decoupled from the immediate
task context (Schooler et al., 2011), which allows the mind to
shift towards cognitions unrelated to the current demands of the
external environment (Singer, 1966). Mind wandering is a very
common process: experience sampling studies suggest that up
to 50 percent of our waking thought is stimulus/task unrelated
(Killingsworth and Gilbert, 2010). Mind wandering occurs with-
out intention or even awareness (Smallwood and Schooler, 2006),
and when mind wanders, people are often not aware of their
thoughts.
Although there are several costs of mind wandering including
a failure of cognitive control (see Table 1 of Mooneyham and
Schooler, 2013), several new lines of research suggest that mind
wandering could be linked to creative thinking. For example, indi-
viduals with attention-deficit/hyperactivity disorder (ADHD),
which is known to be associated with high level of mind wan-
dering, tend to score higher than individuals without ADHD
on laboratory measures of creativity (White and Shah, 2006).
Zhiyan and Singer (1997) reported that positive constructive
daydreaming, a style of daydreaming associated with playful
and constructive imagination, is correlated with openness to
experience, a personality trait correlates positively with creativity
(McCrae, 1987). Mind wandering is also assumed to be linked
to the periods of incubation and insight of the creative pro-
cess. A recent meta-analysis (Sio and Ormerod, 2009) suggests
that incubation is most effective when the incubation period
is filled with an undemanding task as compared to demand-
ing task or a no task at all. Although mind-wandering seems
to be spontaneous and a resource-free process, it is, in fact,
supposed to be a resource intensive process (Smallwood and
Schooler, 2006) (but see also McVay and Kane, 2010); therefore,
any demanding task that tax working memory decreases mind
wandering, and conversely, an undemanding task on working
memory promotes mind wandering. Recently, Baird et al. (2012)
combined these two lines of evidence by showing that the ben-
efits of incubation intervals are greater in divergent thinking
tasks when participants were distracted by an undemanding
task than when they were engaged with either a demanding
task or no task at all. As the mind wandering is more fre-
quent in undemanding tasks than the two other conditions, this
result shows that one feature that may characterize successful
incubation intervals could be the opportunity of the mind to
wander. It should be noted, however, that the reported ben-
eficial effect is only found for the previously presented tasks
but not for the new ones, suggesting that the relationship
between mind wandering and creativity cannot be easily gener-
alized, but could be mediated by several factors, like contents
and durations of mind wandering, working memory capacity,
personality traits, and dimensions of creativity (e.g., novelty,
appropriateness).
In a seminal study using f-MRI, Mason et al. (2007) showed
that periods of mind wandering correlate with activity in a con-
stellation of neural regions across the brain, known collectively as
the default network (Raichle et al., 2001). The default network is a
network of cortical and subcortical structures (including the ante-
rior and posterior cingulate cortices, precuneus region, the medial
prefrontal cortex and the posterior parietal lobule) (Raichle
et al., 2001; Buckner, 2012); this network is particularly activated
when participants are at rest and deactivated when engaged with
demanding tasks with high central executive demand. This further
supports the notion that mind wandering is inversely related with
cognitive demand and associated with a reduced cognitive control
and a broadening of attention (Antrobus et al., 1966; Antrobus,
1968). Interestingly, a subsequent study by Christoff et al. (2009)
found that in addition to the default network, mind wandering
is also associated with executive network (lateral PFC, inferior
parietal lobe), and the activations of these dual, apparently func-
tionally opposing, networks occur for those mind wandering
without metacognition. This finding is intriguing because the
executive network is usually antagonistic to the default network
(Fox et al., 2005): when one network is activated, the other is
deactivated; hence, mind wandering without awareness seems to
be a unique mental state that allows co-activations of these two
opposing brain networks.
Therefore, mind wandering can also be considered as a goal-
driven process despite the fact that it is not explicitly directed
towards an internal task (Smallwood and Schooler, 2006), and
has an access to the same global workspace during internally
generated thought (see Smallwood et al., 2012 for a review).
SPONTANEOUS NEURONAL ACTIVITY: CRADLE OF CREATIVITY?
The suggestion of ideas competing for entry to conscious aware-
ness entails the suggestion of multiple ideas being produced, and
some falling by the wayside. Such a situation is entirely alien
to conscious personal experience, but that is what we would
expect: one should not be conscious of information that fails
to enter one’s consciousness. But what evidence is there of such
activity?
As discussed earlier creative thinking may be spontaneous,
sudden and without any conscious forewarning; it can occur
with or without external input, and is often the product of long
labor of unconscious efforts preceded by a mental impasse out
of focused efforts. Insight is not necessarily complete at first, so
must be subsequently improved. Brilliant ideas must be worked
for, and worked after! Little is known about the underlying
spontaneous brain mechanisms of such creative processes; how-
ever, short periods of disengagement or rest may increase the
likelihood of a flash of insight, i.e., the “resting brain” could be
conducive for creative thinking. Earlier we have discussed the
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Wiggins and Bhattacharya Bridging computational and neuroscientific approaches to study creativity
roles of unconscious thought and mind wandering in creative
cognition. Here we suggest that the mechanism by which the
“best idea” enters into the realm of consciousness may be visible
in spontaneous neuronal activity, i.e., resting state brain activ-
ity (Raichle and Mintun, 2006). Although the neuroscientific
research on creativity is mostly concerned with brain responses
during the performance of a creative task, we suggest that it is
in the spontaneous and dynamic fluctuations of brain activity
patterns that the seed of creative cognition may reside.
However, the brain is never actually at rest: its energy con-
sumption in a spontaneous/resting state is much larger com-
pared to the additional energy consumption during cognitive
tasks (Raichle, 2010). Earlier we highlighted the default mode
network (Raichle et al., 2001) that becomes more activated during
the resting or inactive state than during the performance of a
task. Interestingly, the concept of such network was first put
forward by a noted creativity researcher Nancy Andreasen et al.
(1995) who noted that “free-ranging mental activity (random
episodic memory) produces large activations in association cor-
tex and may reflect both active retrieval of past experiences
and planning of future experiences”,; the “rest” is then appro-
priately rephrased by “Random Episodic Silent Thinking” and
reflects ongoing spontaneously occurring long-term memory
retrieval and encoding (Andreasen et al., 1995). Binder et al.
(1999) showed that for perceptual tasks the default network
gets attenuated, and for conceptual tasks, the default network
is activated much as in the resting state, and noted that default
networks are “active during conscious resting and are engaged
in such processes as retrieval or information from long-term
memory, information representation in conscious awareness in
the form of mental images and thoughts, and manipulation
of this information for problem-solving and planning” (Binder
et al., 1999, p. 86); this crucially links with our earlier dis-
cussion on mind wandering and internally generated thought
processes. Further, this ability of building alternative mental mod-
els and of simulating future scenarios as possibly mediated by
the default network, often processed at the subconscious level
(Buckner and Caroll, 2007), are considered to facilitate real-
life problem solving and creativity (Hogarth, 1987; Antonietti,
1991).
The default network is also of interest to creative processes for
various reasons. The default network regions (anterior cingulate
and posterior cingulate cortices) are found to be more active
immediately before the presentation of RAT problems solved with
subjectively reported insight as compared to those solved without
insight (Kounios et al., 2006). Further, some default network
regions are also activated during a creative story generation from
a list of unrelated words (Howard-Jones et al., 2005). Recently,
Ellamil et al. (2012) asked participants to design book cover
illustrations while allowing switching between the generative and
exploratory stages of the creative process, and they found more
activations in default network regions (medial PFC, posterior
cingulate, temporoparietal junction) during creative evaluation
process as compared to creative generation process. Takeuchi
et al. (2011) found an enhanced activation of the precuneus,
a core component of default network, with heightened creative
performance in an effortful working memory task. Recently the
resting state functional connectivity analysis showed that the
strength of correlation between the medial PFC and the pos-
terior cingulate, two core default regions, is positively associ-
ated with scores on DTT (Takeuchi et al., 2012). Further, the
resting-state network fluctuations differentiate personal styles
of problem solving, i.e., insight vs. analytic (Kounios et al.,
2008).
PREDICTION AND EMPIRICAL VALIDATION
The level of detail inherent in our proposed research program
will allow formulation of specific and testable hypotheses, and
more exploratory work, such as our search for neural correlates
of information content signals (Pearce et al., 2010a). What is
more, the possibility of computational implementation admits
more rigorous testing of the theory than is available with pencil-
and-paper models. Thus, a program such as that proposed above
may assist neuroscientific study by providing a hypothetical map
of the territory. Then, work may be focused in such a way as to
test structural hypotheses efficiently and quickly, either falsifying
the framework or allowing it to develop into a Lakatosian core
(Lakatos, 1978). What is more, computational and behavioral
methods can be applied to the same program, not only testing its
formulation further, but also uniting computational, behavioral
and neuroscientific thinking at multiple levels.
Our framework predicts certain specific perceptual events
in conscious experience—most obviously, the time-variant
information-theoretic signals that filter items into consciousness.
We have shown empirically that, in some circumstances, these
perceptual events correlate with particular measurable electro-
physiological events in the brain (Pearce et al., 2010a). Therefore,
the current proposal raises the possibility of multi-faceted empir-
ical attack on the problem of ideation by means of simulation,
prediction, and empirical validation/falsification. For example,
in our work on segmentation (Pearce et al., 2010b), we have
demonstrated that bottom-up information-theoretic predictions
are at least as reliable as top-down rules in simulating human
chunking of musical sequences. The framework proposed here
can in principle account for this, and related linguistic effects
(chunking and “garden-path” sentences) in terms of competition
between alternative hypothetical chunks, and we are currently
empirically testing of this account.
CONCLUSION
In this paper, we have argued for a tripartite research program for
the neuroscience of creativity, based around: (i) computational
modeling; and (ii) behavioral confirmation. We have suggested
that the many various attempts at mapping, of high quality
though they be, risk degeneration into a directionless activity
without overarching theories of the cognitive function that is
associated with them. We have proposed a computational mod-
eling paradigm of creativity by extending Baars’ GWT based on
the principles of information theory, and we have touched on
current work to examine the predictions of our approach. The
whole, we suggest, can only be empirically stronger than the sum
of its parts, and such strength is required to address the difficult
and fundamental question of creativity—which, after all, is part
of what it means to be human.
Frontiers in Human Neuroscience www.frontiersin.org July 2014 | Volume 8 | Article 540 |12
Wiggins and Bhattacharya Bridging computational and neuroscientific approaches to study creativity
ACKNOWLEDGMENTS
Geraint Wiggins gratefully acknowledges the influence of many
colleagues on our thinking, notably Marcus Pearce, Jamie Forth
and Murray Shanahan. Joydeep Bhattacharya dedicates this article
to Professor Hellmuth Petsche who introduced him to the neuro-
science of creativity.
Funding: Both authors’ were funded by EPSRC Research Grant
EP/H01294X, “Information and neural dynamics in the percep-
tion of musical structure”. The first author was further funded
by Lrn2Cre8 ConCreTe, and the second author was funded by
CREAM. The projects Lrn2Cre8 and ConCreTe acknowledge the
financial support of the Future and Emerging Technologies (FET)
program within the Seventh Framework Program for Research
of the European Commission, under FET grant numbers 610859
and 611733, respectively. The CREAM project has been funded
with support from the European Commission under Grant Agree-
ment no. 612022. This publication reflects the views only of the
authors, and the European Commission cannot be held responsi-
ble for any use which may be made of the information contained
therein.
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Conflict of Interest Statement: The authors declarethat the research was conducted
in the absence of any commercial or financial relationships that could be construed
as a potential conflict of interest.
Received: 20 May 2013; accepted: 02 July 2014; published online: 24 July 2014.
Citation: Wiggins GA and Bhattacharya J (2014) Mind the gap: an attempt to
bridge computational and neuroscientific approaches to study creativity. Front. Hum.
Neurosci. 8:540. doi: 10.3389/fnhum.2014.00540
This article was submitted to the journal Frontiers in Human Neuroscience.
Copyright © 2014 Wiggins and Bhattacharya. This is an open-access article distr ibuted
under the terms of the Creative Commons Attribution License (CC BY). The use, dis-
tribution or reproduction in other forums is permitted, provided the original author(s)
or licensor are credited and that the original publication in this journal is cited, in
accordance with accepted academic practice. No use, distribution or reproduction is
permitted which does not comply with these terms.
Frontiers in Human Neuroscience www.frontiersin.org July 2014 | Volume 8 | Article 540 |15
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