Quantity yields quality when it comes to creativity: a brain and behavioral test of the equal-odds rule

Abstract

The creativity research community is in search of a viable cognitive measure providing support for behavioral observations that higher ideational output is often associated with higher creativity (known as the equal-odds rule). One such measure has included divergent thinking: the production of many examples or uses for a common or single object or image. We sought to test the equal-odds rule using a measure of divergent thinking, and applied the consensual assessment technique to determine creative responses as opposed to merely original responses. We also sought to determine structural brain correlates of both ideational fluency and ideational creativity. Two-hundred forty-six subjects were subjected to a broad battery of behavioral measures, including a core measure of divergent thinking (Foresight), and measures of intelligence, creative achievement, and personality (i.e., Openness to Experience). Cortical thickness and subcortical volumes (e.g., thalamus) were measured using automated techniques (FreeSurfer). We found that higher number of responses on the divergent thinking task was significantly associated with higher creativity (r = 0.73) as independently assessed by three judges. Moreover, we found that creativity was predicted by cortical thickness in regions including the left frontal pole and left parahippocampal gyrus. These results support the equal-odds rule, and provide neuronal evidence implicating brain regions involved with “thinking about the future” and “extracting future prospects.”

Full text

ORIGINAL RESEARCH
published: 25 June 2015
doi: 10.3389/fpsyg.2015.00864
Edited by:
Cecilia Guariglia,
Sapienza University of Rome, Italy
Reviewed by:
Gregoire Borst,
Université Paris Descartes, France
Dean Keith Simonton,
University of California, Davis, USA
*Correspondence:
Rex E. Jung,
Department of Neurosurgery,
University of New Mexico,
Two Woodward Center,
700 Lomas Boulevard NE, Suite 102 ,
Albuquerque, NM 87101, USA
rex.jung@gmail.com
Specialty section:
This article was submitted to
Cognition,
a section of the journal
Frontiers in Psychology
Received: 13 April 2015
Accepted: 12 June 2015
Published: 25 June 2015
Citation:
Jung RE, Wertz CJ, Meadows CA,
RymanSG,VakhtinAAandFloresRA
(2015) Quantity yields quality when it
comes to creativity: a brain
and behavioral test of the equal-odds
rule.
Front. Psychol. 6:864.
doi: 10.3389/fpsyg.2015.00864
Quantity yields quality when it comes
to creativity: a brain and behavioral
test of the equal-odds rule
Rex E. Jung1,2*,ChristopherJ.Wertz
2, Christine A. Meadows2,Sephira G.Ryman
1,
Andrei A. Vakhtin1and Ranee A. Flores2
1Department of Psychology, University of New Mexico, Albuquerque, NM, USA, 2Department of Neurosurgery, University of
New Mexico, Albuquerque, NM, USA
The creativity research community is in search of a viable cognitive measure providing
support for behavioral observations that higher ideational output is often associated
with higher creativity (known as the equal-odds rule). One such measure has included
divergent thinking: the production of many examples or uses for a common or single
object or image. We sought to test the equal-odds rule using a measure of divergent
thinking, and applied the consensual assessment technique to determine creative
responses as opposed to merely original responses. We also sought to determine
structural brain correlates of both ideational fluency and ideational creativity. Two-
hundred forty-six subjects were subjected to a broad battery of behavioral measures,
including a core measure of divergent thinking (Foresight), and measures of intelligence,
creative achievement, and personality (i.e., Openness to Experience). Cortical thickness
and subcortical volumes (e.g., thalamus) were measured using automated techniques
(FreeSurfer). We found that higher number of responses on the divergent thinking task
was significantly associated with higher creativity (r=0.73) as independently assessed
by three judges. Moreover, we found that creativity was predicted by cortical thickness
in regions including the left frontal pole and left parahippocampal gyrus. These results
support the equal-odds rule, and provide neuronal evidence implicating brain regions
involved with “thinking about the future” and “extracting future prospects.”
Keywords: creativity, creative cognition, divergent thinking, imagination, cortical volume, neuroimaging
(anatomic and functional), magnetic resonance imaging
Introduction
There is a long history, within the creativity literature, noting an association between idea fluency
(the number of ideas generated) and the associated quality, originality, and/or creativity of the
ideas that are produced on divergent thinking tasks (Wallach and Kogan, 1965). This notion has
since been conceptualized as the “equal-odds rule” by Simonton (1997), which states that “the
relationship between the number of hits (i.e., creative successes) and the total number of works
produced in a given time period is positive, linear, stochastic, and stable.” This principle has great
appeal in that it conforms broadly to evolutionary principles (i.e., there is a variation/selection
process; Campbell, 1960), it is parsimonious (Simonton, 1984b), and it conforms to excitatory
and inhibitory neuronal processes familiar to the neurosciences (Logothetis, 2008). However, this
concept of productivity leading to originality is rarely exploited within either psychometric or
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Jung et al. Quantity yields quality
neuroimaging studies of creative cognition, with most studies
focused on rather convolved and/or abstruse psychometric
aspects of creativity including (but not limited to) fluency,
cognitive control, latent inhibition, improvisation, remote
associates, divergent thinking, and the like (Arden et al., 2010).
As we have noted previously (Jung et al., 2013), the varieties
of cognitive processes critical to creative cognition are likely
to be relatively few when deconvolved from more general
functions such as attention, memory, language, visual,
spatial, and executive processes subserving most aspects of
higher cognitive functioning. Variation/selection mechanisms
facilitated by ideational fluency/quality presents a viable
candidate for such a core cognitive and neuronal mechanism.
Substantial support has been generated through historiometric
analyses of Big C creative individuals, with the vast majority
of individuals studied conforming to the equal-odds rule
(Simonton, 1977, 1984a, 1985, 1988, 2010). The relationship
is not universally observed, however, with one recent study
of eminent composers finding a linear relationship between
“hits” and age, which should be independent if conforming
to “Darwinian” processes of variation/selection as opposed to
accumulated experience (Kozbelt, 2008). Kozbelt and Ostrofsky
(2013) further hypothesize an interaction of “domain specific
knowledge, which is acquired through intensive training” with
such variation/selection processes.
Poincare (1913) hypothesized five stages of his own creative
process: preparation, incubation, intimation, illumination, and
verification. The stages of preparation and verification are largely
obscure to scientific research as they are carried out over
periods of time that extend beyond the times allotted to most
experimental protocols (e.g., hours). It is our contention that
the cognitive processes between preparation and verification
is populated by a blind-variation selective-retention (BVSR),
characterized by ideational fluency and originality. This process
is accessible to scientific examination, and has been recently
examined in a large cohort of normal adult subjects, utilizing a
scoring methodology that purports to disentangle fluency from
creativity (Silvia et al., 2013). We have further noted the possible
roles of excitatory and inhibitory neural processes in modulating
this selection–retention process (Jung et al., 2013;Jung, 2014).
The interacting role of excitatory and inhibitory neural
processes in creative cognition has been described previously
(Heilman et al., 2003;Flaherty, 2005;Abraham and Windmann,
2007). Heilman et al. (2003)wasthefirsttonotethe
importance of frontal lobe interactions with “polymodal and
supramodal regions of the temporal and parietal lobes” in
divergent thinking, noting that “perhaps these connections
are important for inhibiting the activated networks that store
semantically similar information while exciting or activating
the semantic conceptual networks that have been only weakly
activated or not activated at all. Activation of these remote
networks might be important in developing the alternative
solutions so important in divergent thinking (page 373).”
Flaherty (2005), in her seminal theoretical article regarding
neural origins of innovation and creative drive, notes “the
appropriate balance between frontal and temporal activity is
mediated by mutually inhibitory corticocortical interactions.”
Finally, Abraham and Windmann (2007, p. 45) state “The mutual
inhibition between frontal language production and temporal
language reception has a parallel in the mutually inhibitory effects
of idea generation and of assessing what one has produced.”
It appears that the field is converging around a bifurcated
process involved in producing creative ideas: one involving
variation (i.e., cognitive expansion, divergent thinking), and the
other involving selection (i.e., constraint of example, usefulness;
Abraham and Windmann, 2007;Benedek et al., 2011;Mok,
2014).
We sought to support the equal-odds principle of creative
cognition as measured by the fluency–creativity association.
Moreover, we sought to link such associations with relevant
excitatory (i.e., fluency) and inhibitory (i.e., originality) brain
networks as hypothesized previously (Heilman et al., 2003;
Flaherty, 2005;Abraham and Windmann, 2007;Jung et al., 2013).
We hypothesize that fluency would be associated with creativity
as assessed on a measure of divergent thinking, and that fronto-
subcortical brain networks would constrain such relationships.
Materials and Methods
Sample
This study was conducted according to the principles expressed
in the Declaration of Helsinki. The study was approved by the
Institutional Review Board of the University of New Mexico
(IRB#11-531). All subjects provided written informed consent
before collection of samples and subsequent data analysis. Two-
hundred and forty-six subjects (127 males; 119 females) between
the ages of 16 and 31 (Mean =21.8; SD =3.5) were recruited
from the University of New Mexico. Subjects were screened by
questionnaire to exclude major neurological injury or disease
(e.g., traumatic brain injury) and psychiatric disorder (e.g., major
depression). All subjects were administered a 4 hours battery of
measures including tests of intelligence, personality, and aptitude,
and received $100 compensation for their time.
Behavioral Measures
All subjects were administered a broad battery of tests;
here we focus on the relationship between measures of
divergent thinking (Foresight) and other measures of intelligence
(Wechsler Abbreviated Scale of Intelligence II, WASI-II),
creativity (Creative Achievement Questionnaire, CAQ), and
personality (Big Five Aspect Scale, BFAS) relevant to our
hypotheses (Wechsler, 1999;Acton and Schroeder, 2001;Carson
et al., 2005;DeYoung et al., 2007). Foresight (reliability =0.96)
measures subjects’ creative thinking ability and comes from
the Johnson O’Connor battery of tests of aptitude1. Here,
subjects are presented with a design and asked to write as
many things that the design “makes you think of, looks like,
reminds you of, or suggests to you,” in 45 s, over six different
designs (Figure 1). This measure of divergent thinking over a
period of seconds (as opposed to several minutes) is analogous
1http://www.jocrf.org
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Jung et al. Quantity yields quality
FIGURE 1 | Example of Foresight figure used to elicit responses from
subjects. Subjects are asked to describe what the figure “makes you think of,
looks like, reminds you of, or suggests to you.”
and appropriately comparable to those administered within
functional neuroimaging settings.
We used the Consensual Assessment Technique (CAT,
Amabile, 1982) to rate subject responses on a scale from 1 to 5
with 1 being least creative and 5 being most creative. Importantly,
we used Silvia’s method of “snapshot scoring” wherein all six
subject responses were given a single holistic score by three judges
(Silvia et al., 2009). This method allows for the extraction of
“creative” responses as opposed to merely “unique” responses
as is customary in scores of divergent thinking ability (Silvia
et al., 2013). The WASI is a standardized measure of intelligence
consisting of measures of word knowledge (Vocabulary), verbal
associations (Similarities), design construction (Block Design),
and non-verbal problem solving (Matrix Reasoning). We
did not administer the Vocabulary section of this measure
and the Full Scale Intelligence Quotient (FSIQ) was derived
from the remaining three subtests. The Creative Achievement
Questionnaire (CAQ), has demonstrated adequate reliability and
validity as a measure of creative productivity across ten domains
including visual arts, music, creative writing, dance, drama,
architecture, humor, scientific discovery, invention, and culinary
arts (Carson et al., 2005).Ithasbeendescribedas“themost
promising” measure of creativity, spanning domain, ability, and
conforming to BVSR processes (Simonton, 2012). The BFAS was
used to assess personality (DeYoung et al., 2007), particularly
the subscale of Openness, which has been consistently related
to both divergent thinking and creative cognition (McCrae and
Ingraham, 1987).
Neuroimaging
Structural imaging was obtained using a 3 Tesla Siemens scanner
using a 32 channel head coil. We obtained a T1 five echo sagittal
MPRAGE sequence (TE =16.4 ms; 3.5 ms; 5.36 ms; 7.22 ms;
9.08 ms; TR =2530 ms; voxel size =1.0 mm ×1.0 ×mm 1.0 mm;
slices =192; acquisition time =6:03). Methods for cortical
reconstruction and volumetric segmentation were performed
with the FreeSurfer image analysis suite2and are described
2http://surfer.nmr.mgh.harvard.edu/
in detail elsewhere (Fischl et al., 2002, 2004;Han and Fischl,
2007). Briefly, this process includes motion correction and
averaging of volumetric T1 weighted images, removal of non-
brain tissue, automated Talairach transformation, segmentation
of the subcortical white matter and deep gray matter volumetric
structures, intensity normalization, tessellation of the gray
matter, white matter boundary, automated topology correction,
and surface deformation following intensity gradients to
optimally place the gray matter/white matter boundary and
gray matter/cerebrospinal fluid borders (also known as the
pial surface). Thickness measurements were obtained by
reconstructing representations of the Gray Matter/White Matter
boundary and the pial surface and then calculating the distance
between those surfaces at each point across the cortical mantle
(Dale et al., 1999). The results of the automatic segmentations
were quality controlled and any errors were manually corrected.
The cortical thickness parcellation yields 33 measures per
hemisphere (i.e., 66 across the surface of the brain) as well as
seven subcortical volumes per hemisphere (i.e., fourteen across
the brain) including bilateral caudate, putamen, globus pallidus,
nucleus accumbens, thalamus, amygdala, and hippocampus
(Fischl et al., 2002).
Analysis
We used bivariate correlation to determine relationships between
behavioral measures. Linear regression, controlling for age, sex,
handedness, and FSIQ, was used to determine the relationship
between measures of fluency (Foresight – total number
produced) and Creativity (Foresight – CAT) and measures of
cortical thickness and subcortical volumes across the entire brain.
CAQ scores were log10 transformed before further analysis
as this measure was highly skewed. We did not control for
multiple comparisons, although with 66 cortical regions and
14 subcortical regions being analyzed, there are 80 contrasts
being made per regression. Given that five in 100 Type I
errors are considered to be generally acceptable in research
designs, we would expect roughly four regions of 80 to be
related to our measures by chance. We have adjusted our
significance levels to P<0.005 to account for such possible
chance relationships.
Results
Subjects were higher than average in terms of intellectual
ability (Mean =111.7; SD =12.1), as is characteristic of a
college cohort, and ranged in IQ from 80 to 153. Creativity
scores on the Foresight measure were reliable across the
three judges with scores being “good” in terms of internal
consistency (Cronbach alpha =0.76). The relationship between
“fluency” and “creativity” scores obtained from the Foresight
measure of divergent thinking and other behavioral measures of
intelligence, creativity, and personality are presented in Tab l e 1 .
The significant relationship observed between measures of both
“fluency” and “creativity” with other proxy measures of creativity,
including the CAQ, and Openness, demonstrates convergent
validity of this divergent thinking measure. Importantly,
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Jung et al. Quantity yields quality
TABLE 1 | Bivariate relationships between Foresight measures of fluency
and creativity.
Age Full scale
intelligence
quotient
Openness Creative
achievement
questionnaire
Fluency 0.09 0.01 0.15 0.17
Creativity 0.07 0.05 0.18∗0.26∗
∗p<0.05.
“fluency” was highly related to “creativity” in this sample
(r=0.73, p<0.001), supporting our hypothesis that ideational
quantity is associated with ideational creativity. The relationship
between “fluency” and “creativity” on the Foresight measure,
across all 246 subjects, is presented in Figure 2.
Next, we regressed all brain measures of cortical thickness
and subcortical volumes against measures of “fluency” and
“creativity,” controlling for age, sex, handedness, and FSIQ.
“Fluency” was negatively correlated with the volume of the right
thalamus (β=−0.24) as well as with the cortical thickness of
the right inferior parietal lobe (β=−0.20; Figure 3), and caudal
anterior cingulate (β=−0.13; Figure 4). In contrast, “fluency”
was positively correlated with the cortical thickness of the left
frontal pole (β=0.25; F=5.02, p<0.001, r2=0.15).
“Creativity” was negatively correlated with the volume of
the left entorhinal cortex (β=−0.20). In contrast, “creativity”
was positively correlated with volume of the left frontal pole
(β=0.17) and left parahippocampal gyrus (β=0.12; F=3.3,
p=0.002, r2=0.09; Figure 5).
FIGURE 3 | FreeSurfer rendering of the right hemisphere pial surface
with the inferior parietal region indicated in blue, showing decreased
cortical thickness in this region associated with increased fluency on
the Foresight task.
Discussion
We found that quantity was associated with quality on measures
of divergent thinking customarily associated with creative
FIGURE 2 | Scatterplot of “fluency” measures from the Foresight task (y-axis) versus the “creativity” measure (x-axis) obtained from 246 subjects.
Significant overlap in subject scores results in fewer than 246 individual points being observed on the graph.
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Jung et al. Quantity yields quality
FIGURE 4 | FreeSurfer rendering of the right hemisphere pial surface
with the caudal anterior cingulate indicated in blue, showing
decreased cortical thickness in this region associated with increased
fluency on the Foresight task.
FIGURE 5 | FreeSurfer rendering of the left hemisphere pial surface
with the left frontal pole, parahippocampal gyrus, and entorhinal gyrus
indicated (inferior frontal view), showing positive (red) and negative
(blue) associations between cortical thickness and increased
creativity on the Foresight task.
cognition. Subjects who produced more descriptions of abstract
visual designs produced more creative descriptions of the designs
as measured by judges who were blind to subject demographics.
These results provide compelling support for the equal-odds
rule which underlie BVSR theories of creative cognition, and
which have been demonstrated repeatedly in Big C cohorts
throughout history. Importantly, these results were obtained
in a college sample ranging in creative achievement (0–144),
and intellectual capacity (80–153), thus spanning the normal
ranges of both creative and intellectual abilities. We found that
fluency and creativity were highly related to one another when
measured using a test of divergent thinking and the consensual
assessment technique. Finally, we found that fronto-subcortical
brain networks were implicated in performance of both fluency
and creativity measures, with a common locus across both
measures being the frontal pole.
These results partially replicate our previous findings where
we found an inverse relationship between fluency measures
of Foresight and volume of the right thalamus in a smaller
sample of 107 subjects (Jung et al., 2014). This result is now
confirmed in a much larger sample (N=246) that includes
those original subjects, and extends these findings into cortical
thickness measures. Specifically, we found that a thicker left
frontal pole was associated with both higher fluency and higher
creativity across subjects. Of interest to this finding, a network
that includes the frontal pole and the medial temporal lobes has
been implicated in thinking about one’s own future (Okuda et al.,
2003). More specific studies undertaken with patients suffering
brain lesions have found that frontal pole damage is associated
with (1) preference for immediate versus future reward (Bechara
et al., 1996), (2) abnormal strategy application (Shallice and
Burgess, 1991), and (3) disrupted decision making (Damasio
et al., 1991). Researchers have further parcellated the frontal lobe
to indicate time valence when thinking about the near future
versus far future, and integration with parahippocampal regions
when “extracting future prospects” (Okuda et al., 2003). Thus, our
results, implicating both left frontopolar and parahippocampal
thickening appear to comport well with this particular network
implicated in “thinking about the future.” In a large meta-
analysis of all functional neuroimaging studies of “episodic
future thinking” (EFT) researchers noted the specificity of the
medial prefrontal cortex in EFT, indicating its likely role in
(1) adaptive decision making processes, (2) the creation of
abstract knowledge or schemas, and (3) the integration of novel
experiences into pre-existing knowledge networks (Stawarczyk
and D’Argembeau, 2015). Our results, demonstrating cortical
thickening in the medial frontal lobe, is interpreted to reflect
strengthening of neural networks underlying such cognitive
processes.
We have previously interpreted thalamic volume decrements
and right inferior parietal thinning within a “disinhibitory
framework of brain regions associated with increased behavioral
output,” and the current findings are consistent with our
previous findings implicating thalamic and inferior parietal
regions with increased creative capacity (Jung et al., 2013).
Other researchers have found lower thalamic dopamine D2
receptor densities to be inversely related to creative cognition
in healthy individuals (de Manzano et al., 2010). Importantly,
these researchers used a measure of divergent thinking, and
the results were related to fluency of uses (as opposed to
originality). These authors noted that the thalamus contains
the highest level of dopamine D2 receptors in the brain, and
that decreased D2 binding has been linked with decreased
“filtering and autoregulation of information flow,” as well as
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Jung et al. Quantity yields quality
decreased inhibition of prefrontal pyramidal neurons (Seamans
and Yang, 2004;Trantham-Davidson et al., 2004). They refer to
this decreased inhibition as producing a “creative bias” which
benefits tasks requiring continuous generation and increases
fluency and flexibility of associations. Our results, reflecting both
lower thalamic and anterior cingulate volume (Bush et al., 2000)
would be broadly consistent with this hypothesis, and provides
further neurobiological support to better explain the equal-
odds phenomenon underlying variation/selection mechanisms
associated with creativity.
There are several limitations to our approach. First, we utilized
a relatively young, healthy sample, and whether our results would
generalization to older populations and/or clinical samples is
unknown. Second, we are making inferences regarding brain
function in spite of using measures of brain structure. These
inferences may or may not be correct, although some studies
suggest correspondence between structure and function (Segall
et al., 2012). Our measure of divergent thinking is not one
commonly used in the creativity literature, although it was
found to have high reliability, as well as correspondence to
other measures commonly used as proxy measures (e.g., CAQ,
Openness) for creativity. Finally, we have not conducted full
Bonferroni correction for all possible multiple corrections (which
would increase Type II error), but have adopted an intermediate
approach of adjusting our significance level to p<0.005 to
account for the 80 contrasts being made per regression (leaving
possible Type I error). This balance between Type I and Type II
error was seen as appropriate for this exploratory study. Future
studies using broader samples comprised of both older and
younger subjects, using other well-validated measures of fluency-
originality, and exploiting multimodal neuroimaging measures
[e.g., structural Magnetic Resonance Imaging (MRI), functional
MRI, diffusion tensor imaging] would help significantly to
support these findings and to implicate particular brain networks
associated with BVSR.
This study supports the notion that BVSR is a central
component of creative cognition, working via an equal-odds
rule, wherein higher output of ideas is associated with higher
likelihood of creative ideas. This paradigm is amenable to both
psychological and neuroscientific manipulation to determine
the interaction of fluency–creativity relationships with other
cognitive components hypothesized to be relevant to creative
cognition (e.g., cognitive control, flexibility, etc.). Our results
demonstrate key nodes within the brain, including the right
thalamus, right caudal anterior cingulate, left medial temporal
lobe, left medial frontal cortex, and right temporo-parietal
junction that constrain both fluency and originality in a manner
that would suggest mutually inhibitory network interactions
constraining both variation and selection processes. Specific
nodes (e.g., medial fronto-temporal cortices) within this network
have been implicated in highly adaptive human cognitive
processes including EFT and extracting future prospects, while
other regions (e.g., thalamus, anterior cingulate) have been
implicated in modulating the fluency and flexibility of ongoing
cognition. Future research will be critical in determining the
specific roles that these structures play in the interactions between
broad cognitive networks that have been implicated in creative
cognition (e.g., default mode network).
In summary, early theories regarding creative cognition
have broadly implicated fronto-temporal and fronto-subcortical
networks that operate in “mutually inhibitory” balance that, when
disease (Miller et al., 1998)orlesion(Shamay-Tsoory et al.,
2011;Abraham et al., 2012) disrupt this balance, can result in
greater creative drive and/or novelty generation (Flaherty, 2005).
More current theories implicate an interaction between broad
networks of the brain including the default, executive control,
and salience, which interact in service of variation and selection
tasks organized around adaptive behavior (Jung et al., 2013;
Beaty, 2015;Liu et al., 2015). Research is converging around
two main aspects of creative cognition involving variation (i.e.,
divergence, fluency, elaboration) on the one hand and selection
(i.e., convergence, usefulness, constraint) broadly conforming to
notions of BVSR (Campbell, 1960). This “variation-selection”
model is evolutionarily sound, conforms to humans and other
living species, and produces adaptive behaviors within “design
space” (Dennett, 1996).
Acknowledgments
This work was funded by a grant from the Johnson O’Connor
Research Support Corporation by a grant entitled “The
Neuroscience of Aptitude” and the John Templeton Foundation
entitled “The Neuroscience of Scientific Creativity.” The paper
was written by the lead author in one day, between the hours of 9
and 5, as a demonstration of the equal-odds rule.
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
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Copyright © 2015 Jung, Wertz, Meadows, Ryman, Vakhtin and Flores. This is an
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