ArticlePDF Available

Abstract and Figures

A novel game-like and creativity-conducive fMRI paradigm is developed to assess the neural correlates of spontaneous improvisation and figural creativity in healthy adults. Participants were engaged in the word-guessing game of Pictionary(TM), using an MR-safe drawing tablet and no explicit instructions to be "creative". Using the primary contrast of drawing a given word versus drawing a control word (zigzag), we observed increased engagement of cerebellum, thalamus, left parietal cortex, right superior frontal, left prefrontal and paracingulate/cingulate regions, such that activation in the cingulate and left prefrontal cortices negatively influenced task performance. Further, using parametric fMRI analysis, increasing subjective difficulty ratings for drawing the word engaged higher activations in the left pre-frontal cortices, whereas higher expert-rated creative content in the drawings was associated with increased engagement of bilateral cerebellum. Altogether, our data suggest that cerebral-cerebellar interaction underlying implicit processing of mental representations has a facilitative effect on spontaneous improvisation and figural creativity.
Content may be subject to copyright.
SCIENTIFIC RepoRts | 5:10894 | DOI: 10.1038/srep10894
Pictionary-based fMRI paradigm
to study the neural correlates of
spontaneous improvisation and
gural creativity
Manish Saggar1, Eve-Marie Quintin1, Eliza Kienitz1,2, Nicholas T. Bott1,2, Zhaochun Sun1,7,
Wei-Chen Hong3, Yin-hsuan Chien1,4, Ning Liu1, Robert F. Dougherty5, Adam Royalty6,
Grace Hawthorne6 & Allan L. Reiss1,8
A novel game-like and creativity-conducive fMRI paradigm is developed to assess the neural
correlates of spontaneous improvisation and gural creativity in healthy adults. Participants were
engaged in the word-guessing game of PictionaryTM, using an MR-safe drawing tablet and no explicit
instructions to be “creative”. Using the primary contrast of drawing a given word versus drawing
a control word (zigzag), we observed increased engagement of cerebellum, thalamus, left parietal
cortex, right superior frontal, left prefrontal and paracingulate/cingulate regions, such that activation
in the cingulate and left prefrontal cortices negatively inuenced task performance. Further, using
parametric fMRI analysis, increasing subjective diculty ratings for drawing the word engaged
higher activations in the left pre-frontal cortices, whereas higher expert-rated creative content in the
drawings was associated with increased engagement of bilateral cerebellum. Altogether, our data
suggest that cerebral-cerebellar interaction underlying implicit processing of mental representations
has a facilitative eect on spontaneous improvisation and gural creativity.
Creativity – the ability to create novel but appropriate outcomes, is considered as the driving force behind
all human progress. Given the wide import of creativity and its association with mental health across
the life span1,2, it is quintessential to examine the neural networks associated with creative thinking so
that novel interventions to foster creativity can be developed. Previously several neuroimaging studies
of creativity have been conducted. However, these studies have produced varied ndings3, with little
overlap4. Methodological issues might account for this variation, particularly, the inherent elusiveness of
the creativity construct itself, diversity in assessments, and the wide range of experimental procedures
currently employed5,6.
Recent neuroimaging studies have devised new avenues for exploring the neural basis of applied
creativity. For example, by comparing functional brain activation in artists with non-artists, research-
ers examined the neural correlates of enhanced artistic creativity7–9. Similarly, the neural correlates of
1Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford
University School of Medicine, 401 Quarry Road, Stanford, CA 94305. 2Pacic Graduate School of Psychology-
Stanford University Psy.D. Consortium, 1791 Arastradero Road, Palo Alto, CA 94304. 3Institute of Biomedical
Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan. 4Taipei City Hospital,
Zhong-Xing Branch, No. 145, Datong Rd, 10341, Taipei, Taiwan. 5Center for Cognitive and Neurobiological Imaging,
Stanford University, 450 Serra Mall, Building 420, Stanford, CA 94305. 6Hasso Plattner Institute of Design, Stanford
University, Building 550, 416 Escondido Mall, Stanford, CA 94305. 7Brain and Language Lab, School of English
for International Business, Guangdong University of Foreign Studies, Guangzhou, 510420.China. 8Department of
Radiology, Stanford University School of Medicine, 300 Pasteur Road, Stanford, CA 94305. Correspondence and
requests for materials should be addressed to M.S. (email:
Received: 29 October 2014
Accepted: 22 April 2015
Published: 28 May 2015
SCIENTIFIC RepoRts | 5:10894 | DOI: 10.1038/srep10894
musical improvisation have been examined to better understand the brain processes that give rise to
enhanced extemporaneity and creativity in musicians10–15. Additionally, the brain basis of specic com-
ponents of creativity, e.g., the “Aha! moment“16 and visual creativity17, have also been recently examined.
Despite this recent progress, experimental paradigms that are both conducive to creative thinking
and facilitate examination of applied creativity remain scarce. Such paradigms could play an essential
role in reducing variation in creativity neuroimaging results by minimizing confounding inuences of
cognitive processes that might not be related to creative thinking but are employed, in part, due to the
task design. For example, administering creativity assessments in a test-like setting as opposed to a
fun/game-like style can negatively inuence creativity3,18. However, most previous neuroimaging studies
of creativity have used traditional test-like assessments. Similarly, performance anxiety can negatively
impact creativity19, thereby potentially leading to methodological confounds when researchers explicitly
ask participants to be “creative. Lastly, few neuroimaging paradigms allow participants to express their
creative potential in a direct/unrestricted manner, as opposed to pressing buttons or “thinking” creatively.
To address some of these issues, we present a novel game-like and creativity-conducive fMRI par-
adigm to assess the neural correlates of spontaneous improvisation and gural creativity. Here, par-
ticipants played the word-guessing game of PictionaryTM, using an MR-safe drawing tablet, and drew
representations of a given word in 30s with a caveat that others would later guess the word by their
drawing alone (Fig.1). e drawings were later scored for creative content and subjective ease of guessing
by two experts. us, with no explicit instructions to be “creative, our game-like paradigm was designed
to putatively reveal the neural correlates of spontaneous improvisation and applied creativity in healthy
Behavioral Results on the fMRI task. e mean rating scores for representation and creativity
(on a scale of 1 to 5), across participants, were 3.56 (SD = 0.39) and 2.69 (SD = 0.25), while the mean
self-reported diculty rating (on a scale of 1 to 3) score was 1.83 (SD = 0.25). e representation and
creativity rating scores were positively correlated (r(30) = 0.71, p < 0.001), indicating that drawings that
were good representations of the given word were also creative. No other signicant correlation was
observed between diculty ratings and representation or creativity ratings (ps > 0.05).
Figure 1. (A) Task was setup as a block design with two conditions (word-drawing and zigzag-drawing).
(B) MR-safe tablet and pen. (C) Cartoon depicting how the participants used MR-safe tablet while lying
down in the fMRI scanner. (D) Representative drawings from the word-drawing condition, drawn while
participants were lying down in the fMRI scanner.
SCIENTIFIC RepoRts | 5:10894 | DOI: 10.1038/srep10894
Neural correlates of spontaneous improvisation and gural creativity. We hypothesized that
by contrasting word-drawing blocks with control zigzag-drawing blocks we could reveal the neural corre-
lates of spontaneous improvisation and creativity. Using this contrast, increased activation was observed
in six dierent clusters with peak cluster activations bilaterally in the areas of paracingulate gyrus, mid-
dle frontal gyrus, superior frontal gyrus, precentral gyrus, thalamus, cerebellum, le lateralized in the
occipital cortex, superior parietal lobule, precuneus, and right lateralized in the inferior frontal gyrus
(pars triangularis). e cluster with peak activation in the paracingulate gyrus, also extended to the
regions of anterior cingulate cortex (ACC), le dorsolateral prefrontal cortex (DLPFC), and le frontal
operculum/anterior insula complex (fO-AI). Figure2 shows the activation map for the primary contrast
(in red-yellow color scale) and Table 1A provides information pertaining to the number of voxels and
cluster-corrected p-values for each cluster.
For the reverse contrast, i.e., comparing zigzag-drawing with word-drawing condition, we observed
widespread activation in the medial-prefrontal cortices, posterior cingulate/precuneus cortex, inferior
parietal lobule, lingual gyrus, temporal pole, middle/superior temporal gyrus (posterior division), infe-
rior temporal gyrus (anterior division), postcentral gyrus, paracingulate gyrus, parahippocampal gyrus
(posterior division), central/parietal opercular cortex, planum polare, heschl’s gyrus, and planum tem-
porale (Fig.2 and Table1B). is result, consistent with known activation of resting-state networks20–22,
is not surprising given the fact that, compared to word-drawing, zigzag-drawing required minimal cog-
nitive eort from the participants.
To examine how dierential activation from the primary contrast (i.e., word- versus zigzag-drawing)
is associated with fMRI task performance, we examined the relations between beta-estimates from all
the six clusters and behavioral measures of task performance. We observed a signicant negative relation
between the percentage beta values extracted from the cluster with a peak activation in the paracingulate
gyrus (with activations extending into ACC/DLPFC) and representation rating scores (r(30) = 0.430
(95% CI: 0.6843 to 0.0825), p = 0.018; Fig.2B). is negative relation suggests that higher engage-
ment of the paracingulate (and ACC/DLPFC) regions could potentially lead to lower performance on
the fMRI task of word-drawing.
Parametric modulation of brain activity pattern using rating scores. For each presented word,
we obtained a self-reported subjective diculty rating score from the participants at a post-scan session.
As noted above, experts also rated each drawing on task performance (representation and creativity).
By parametrically modulating fMRI activation during the word-drawing condition with these three rat-
ing scores (in a multiple regression with the two task conditions also included), we identied brain
regions that are uniquely and increasingly recruited with corresponding increases in subjective di-
culty in word-drawing, and expert ratings of representation and creativity (Fig.3,Table 1C). Increased
recruitment of the le lateralized middle frontal gyrus, frontal pole, precentral gyrus, and DLPFC was
observed in association with increasing subjective diculty in word-drawing. Increased recruitment in
the bilateral cerebellum, lingual gyrus, brain stem, le occipital fusiform, right temporal fusiform, and
right inferior temporal gyrus was observed with increasing creativity ratings. No signicant results were
observed with increasing representation scores. In summary, the le prefrontal regions were increasingly
Figure 2. (A) Neural correlates of spontaneous improvisation and gural creativity. e red-yellow scale
depicts contrast of word-drawing versus zigzag-drawing, while the blue-green scale represents the reverse
contrast. (B) Correlations between beta-estimates from the word-drawing versus zigzag-drawing contrast
and expert representation ratings.
SCIENTIFIC RepoRts | 5:10894 | DOI: 10.1038/srep10894
(A) Primary contrast: Word-drawing versus Zigzag-drawing
MNI Coordinates
Cluster size
(number of
voxels) P-value Z-max X Y Z Hemisphere Brain Region
6 9761 1.35E-17 6.22 4 14 46 Bilateral Paracingulate Gyrus
6.02 30 4 46 Le Middle Frontal Gyrus
5.92 24 6 50 Le Superior Frontal Gyrus
5.63 42 2 24 Le Precentral Gyrus
5.26 38 4 46 Le Precentral Gyrus
5.17 42 2 48 Le Precentral Gyrus
5 5875 2.91E-12 6.15 0 54 28 Vermis Cerebellum
6.12 34 46 36 Le VI Cerebellum
6.04 10 58 26 Cerebellum
5.88 34 66 32 R. Crus Cerebellum
5.82 0 58 32 Vermis Cerebellum
5.8 2 62 30 Vermis Cerebellum
4 3349 5.96E-08 5.34 34 78 22 Le Lateral Occ. Cortex
5.33 34 70 18 Le Lateral Occ. Cortex
5.17 28 48 40 Le Superior Par. Lobule
4.88 26 74 28 Le Lateral Occ. Cortex
4.85 8 68 52 Le Precuneus
4.82 38 82 14 Le Lateral Occ. Cortex
3 1175 0.0015 4.53 10 8 6 Le alamus
4.27 10 4 2 Le alamus
4.23 0 10 4 Le alamus
4.22 0 22 6 Le alamus
4.19 2 1 4 Le alamus
3.64 12 14 2Right alamus
2 801 0.0156 4.91 28 4 50 Right Middle Frontal Gyrus
4.09 30 6 44 Right Precentral Gyrus
3.95 32 2 58 Right Middle Frontal Gyrus
3.74 20 4 50 Right Superior Frontal Gyrus
3.24 18 12 56 Right Superior Frontal Gyrus
3.06 42 2 62 Right Middle Frontal Gyrus
1 649 0.0442 5.29 44 6 22 Right Precentral Gyrus
2.46 46 26 8 Right Inf. Frontal Gyrus
2.4 40 22 14 Right Inferior Frontal Gyrus
(B) Reverse contrast: Zigzag-drawing versus Word-drawing
2 71900 0 7.65 46 8 8 Right Central Oper. Cortex
6.59 42 10 22 Right Temporal Pole
6.51 56 14 2 Le Planum Temporale
6.48 4 54 10 Bilateral Frontomedial Cortex
6.39 38 4 22 Right Temporal Pole
6.37 56 18 4 Le Planum Temporale
1 744 0.0229 5.08 48 66 42 Le Lateral Occ. Cortex
4.99 54 64 30 Le Lateral Occ. Cortex
4.55 52 64 38 Le Lateral Occ. Cortex
3.16 42 64 30 Le Lateral Occ. Cortex
SCIENTIFIC RepoRts | 5:10894 | DOI: 10.1038/srep10894
recruited as subjective diculty increased, while the bilateral cerebellum and inferior temporal gyrus was
increasingly recruited in association with more highly rated creative drawings.
We present a novel game-like and creativity-conducive fMRI paradigm to assess the neural correlates of
spontaneous improvisation and gural creativity in healthy adults. We strived to keep the environment
conducive to intuitive creative thinking by providing an MR-safe drawing tablet and no explicit instruc-
tions to be “creative” during the word-guessing game of PictionaryTM. e primary contrast of word- ver-
sus zigzag-drawing revealed increased engagement of the cerebellum, thalamus, le parietal cortex, right
superior frontal, le prefrontal and paracingulate/cingulate regions. Further, higher activation in the
cingulate and prefrontal regions was linked to lower expert representation rating scores. e parametric
fMRI approach revealed increasing subjective word-drawing diculty was associated with increasing
activation in the le pre-frontal cortices, whereas increasing expert creativity rating was associated with
higher activation of the bilateral cerebellum.
Since J. P. Guilford’s seminal lecture on the need to study creativity23, a wide range of behavioral and
neuroimaging studies have been undertaken to better understand creativity. Unfortunately, the extant
literature does not provide converging evidence in terms of specic brain regions/networks that are
associated with and/or engaged during creative thinking3,4. Apart from the methodological issues (e.g.,
varied experimental designs), such lack of convergence could also be due to several theoretical factors.
For example, it has been argued that treating creativity as a monolithic entity is one reason for such var-
iegated ndings24. Creative thinking, like any other thought process, undoubtedly requires a multitude of
explicit brain processes and networks to dene the problem/opportunity at hand, to ideate and evaluate
dierent solutions, and to prototype solutions in an iterative fashion. Further, each of these brain pro-
cesses can in turn be facilitated and adapted to a given problem/situation as a result of the implicit brain
processing (e.g., cerebellar facilitation of mental representation manipulations25). us, moving forward,
it is essential to assess creative thinking in terms of neural models of brain processes and their associated
interactions. Such neural models would also provide a framework for generating testable hypotheses and
for making valid inferences from neuroimaging studies of creativity, with the goal of moving the eld of
creativity neuroscience towards convergence.
As a starting point, we propose to adapt the neural model proposed by Ito (2008), which includes
both explicit and implicit processes potentially engaged during a problem-solving thought25. e explicit
processes include (a) the working-memory system (to retain information regarding the problem and
its constraints within a mentally “graspable” range26,27); (b) the two task-control attentional systems
(A) Primary contrast: Word-drawing versus Zigzag-drawing
MNI Coordinates
Cluster size
(number of
voxels) P-value Z-max X Y Z Hemisphere Brain Region
(C) Parametric fMRI Analysis
With subjective diculty rating
1 2152 1.08E-05 3.91 32 12 54 Le Middle frontal gyrus
3.88 32 10 58 Le Middle frontal gyrus
3.74 30 40 40 Le Frontal pole
3.65 52 22 24 Le Inferior frontal gyrus
3.56 32 2 34 Le Precentral gyrus
3.52 46 6 46 Le Middle frontal gyrus
With expert creativity rating
1 4822 5.96E-08 4.65 32 50 36 Right Cerebellum, Right VI
4.36 20 82 28 Right Cerebellum, Right Crus
4.36 8 32 44 Brain Stem
3.88 6 36 44 Brain Stem
3.85 20 70 30 Rights Cerebellum, Right Cru
3.82 36 48 34 le Cerebellum, Le VI
Table 1. Cluster statistics and locations for the (a-b) primary and reverse contrasts, and (c) parametric
SCIENTIFIC RepoRts | 5:10894 | DOI: 10.1038/srep10894
(adaptive system and stable goal-directed system)28; (c) a novelty system to evaluate whether each ten-
tative solution is novel or not29; and (d) a system to store mental models and representations, on which
all other systems perform actions. e implicit processes, on the other hand, include cerebral-cerebellar
interactions to create inverse and forward models that facilitate and increase eciency of repetitive
actions on mental representations. ese implicit processes are thought to enhance the likelihood of
more creative solutions25,26. Previous theoretical papers have suggested extending Ito’s and related models
for understanding creative thinking25,26. For example, when using divergent thinking tasks to assess cre-
ative capacity, the model predicts explicit systems (especially, adaptive attentional and novelty systems)
to be highly activated as the participants are explicitly trying to generate alternative, novel and unique
solutions to an open-ended problem. Similarly, for an ‘intuitive leap’ or Aha! moment to happen, the
model predicts use of implicit processing (via inverse/forward modeling), where the leap occurs when
the solution reaches conscious awareness.
Using Ito’s model as a framework, here we did not expect signicant engagement of the novelty sys-
tem because the participants were not explicitly asked to create novel solutions. Further, as the partic-
ipants were asked to spontaneously improvise drawings for a given verb/action, we expected increased
engagement of regions implicated in implicit processing for both ecient manipulation of mental rep-
resentations and enhanced creative content in the drawings. As expected, by contrasting word-drawing
with zigzag-drawing, we did not observe dierential recruitment in the well described novelty system
(consisting of hippocampal regions and ventral tegmental area30). However, increased engagement of
the paracingulate cortex, dorsal ACC, le DLPFC, and le fO-AI complex during the word-drawing
condition as compared to zigzag-drawing was observed. Activation in the le fronto-parietal regions
suggest involvement of the central executive and “visual sketchpad” of the working memory system31,32.
Further, engagement of the DLPFC, dorsal ACC, fO-AI complex, superior parietal lobule and thalamus
suggest activation of both fronto-parietal and cingulo-opercular components of task-control attentional
systems during the word-drawing condition. e fronto-parietal component has been proposed to initi-
ate and adjust control on a trial-by-trial basis, whereas the cingulo-opercular component provides stable
goal-maintenance’ over the entire task33.
In a recent study, and the only other to use an MR-safe drawing tablet, Ellamil and colleagues (2012)
used a book-cover design task to examine the neural correlates of creative thinking34. e authors sep-
arately administered and compared generative and evaluative phases of designing book-covers and
observed preferential recruitment of the DLPFC, ACC, and le fO-AI regions during the evaluative as
Figure 3. Parametric modulation of fMRI activation during word-drawing condition using self-reported
diculty ratings (in red-yellow color scale) and expert creativity ratings (in blue-green color scale) . No
signicant eect was found for the expert representation ratings.
SCIENTIFIC RepoRts | 5:10894 | DOI: 10.1038/srep10894
compared to generative phase. Other neuroimaging studies, where participants were asked to gener-
ate a unique/unusual response to a given stimuli, have also suggested increased recruitment of similar
task-control attentional networks during creative thinking7,17. In our fMRI task, we did not separate
generate and evaluate phases of the task to keep the creative thought process as close to a real-world
experience as possible. However, building upon previous studies, recruitment of the task-control regions
during word-drawing suggests that even with no explicit instructions to produce novel solutions, partic-
ipants were accentuating idea evaluation more than idea generation during our task.
To choose the most unique or unusual response, idea selection and evaluation is required and is evi-
dently facilitated by task-control networks. It is, however, unclear how such preferential recruitment of
task-control networks facilitates creativity and spontaneity during an improvisation. Interestingly, during
a musical improvisation task, Limb and Braun found that enhanced creativity in expert musicians was
associated with reduced recruitment of task-control networks13. Other, more recent studies, also done in
expert musicians, show deactivations in the DLPFC during musical improvisation as a sign of reduced
monitoring and volitional control10,12. In line with their ndings, we also observed a negative relation
between the beta-estimates from the cluster encompassing the le ACC/DLPFC regions and representa-
tion ratings on our fMRI task in a non-artist population, thereby providing suggestive evidence for a
negative role of higher engagement of task-control regions during spontaneous improvisation.
e role of implicit processing, especially via cerebral-cerebellar connectivity, during creative thinking
has been previously hypothesized25,26, based on the anatomical claim that the cerebellum can facilitate
ecient manipulation of movements and mental representations alike26,35–38. Recent work by Pinho et al.
bolsters this claim, by showing increased cerebral-cerebellar functional connectivity in expert musicians
during improvisation10. In our fMRI task, participants would have required both manipulations of move-
ments as well as mental representations to successfully draw the given word. us, one would expect
the cerebellum to be progressively engaged with increasing representation as well as creativity ratings of
each word. Interestingly, we found activation in the cerebellum to uniquely and linearly increase with
increasing creativity ratings only and not with representation ratings.
e extant literature, mainly from the work in primates, points towards motor control and motor
learning as a primary role for cerebral-cerebellar interactions39–41. However, recent research compar-
ing topographical organization and origins of cerebral peduncle bers in human and macaque brains
provides support for the role of cerebral-cerebellar interactions in higher order cognitive function in
humans. For example, Ramnani et al. (2006) showed that while macaque brains had a large propor-
tion of cerebral peduncle bers originating from the cortical motor system, human brains, on the other
hand, had the largest contribution of cerebral peduncle bers arising from the prefrontal cortex42. e
prefrontal cortex has been associated with processing of more abstract information as compared to the
cortical motor system43, suggesting that the human cerebellum is involved in neural functions beyond
that associated with control of movement.
A theoretical analogue of control theory models that were used to explain the role of the cerebel-
lum in motor control in cerebellum25,39 could also be employed to hypothesize how cerebral-cerebellar
interaction might facilitate enhanced improvisation and creativity skills. To achieve speed, accuracy,
and automaticity in motor command executions, researchers have proposed that the motor commands
directed towards the movement control systems are also copied as “internal models” in the cerebel-
lum. Accordingly, these internal models serve as cerebellar representations that can simulate natural
body movements44. rough repeated and parallel simulations, the cerebellum facilitates acquisition of
advanced motor skills and eventually provides automaticity. As proposed by others25,26,39,43, this theo-
retical model of motor control and learning can be extended to higher order cognitive functioning and
thought processing. Along the same lines, we extend the putative role of the cerebellum to improvisation
and creative thinking.
During our fMRI task, participants manipulate and amalgamate existing mental representations to
express the given word in a sketch using the MR-safe tablet. We hypothesize that internal models of the
cerebellum could facilitate such manipulations of mental representations, by simulating and parallelizing
the sketching of the given word in multiple ways. Such simulations, would in turn, allow participants to
more eciently draw the target word. It remains unclear, however, how such greater eciency is trans-
lated to creativity. Future research paradigms are required to systematically dissociate the cerebellum’s
role in dierent aspects of creative thinking (e.g., elaboration, exibility, uency, originality, etc.). In sum,
our results provide preliminary evidence of cerebellar activity associated with spontaneous improvisation
and gural creativity and extend previous results to non-musicians/artists.
A potential limitation of our fMRI task arises from the possibility that zigzag-drawing might not
fully account for the amount of language processing and/or overall cognitive load that is required during
word-drawing. us, while contrasting these two conditions some activation could also be attributed to
language processing and/or higher cognitive load. However, this potential limitation would not inuence
the results from the parametric analysis of the word-drawing condition. In the future, we plan to use
control conditions that can better account for overall cognitive load (e.g., moving a pen through a maze,
without touching boundaries) and language processing associated with the word-drawing condition.
We specically developed our novel fMRI task using an ecient block design, with a total of 10
blocks per condition. However, the lack of signicant correlation between subjective diculty ratings
and expert rating scores (both on creativity and representation scales) could be partially attributed to
SCIENTIFIC RepoRts | 5:10894 | DOI: 10.1038/srep10894
the small number of blocks per condition. Lastly, due to the nature of our experimental design and the
fact that we could not record a timestamp for every stroke made by the participants, we cannot discern
the neural resources employed purely during (pre-drawing) creative thinking versus implementation of
the drawings. In the future, we can achieve such discernment by instructing participants to “imagine” or
visually construct the representation before drawing/depicting one using the MR-safe tablet.
In sum, our results indicate a putative negative role of conscious monitoring and volitional control
and a potentially positive role of implicit processing via cerebral-cerebellar interaction during spontane-
ous improvisation and gural creative thinking.
Participants and study design. irty-six healthy adults (18M, 18F) were initially enrolled in the
study. Of these, one participant was excluded due to the use of prescription antidepressants; two par-
ticipants were excluded due to excessive motion in the scanner, while three participants had incom-
plete data. us, the nal data analyses were limited to 30 adult participants (16F, Mean Age = 28.77
years (S.D. = 5.54 years) and Mean IQ = 120 (S.D. = 10.53)). Participants were included in the study if
they could undergo a magnetic resonance imagining (MRI) scan of the head and were right-handed.
Participants were excluded if they self-reported a current or past history of psychiatric or neurological
conditions that had lead them to consult a medical professional, or had metallic devices or implants in
the head or body that are contraindicated for MRI. We recruited participants by sending out yers via
emails, message boards, list-servers, and word of mouth. Participants were recruited on or around the
Stanford campus and surrounding areas. All experiments were performed in accordance with the rel-
evant guidelines and regulations of Stanford University’s Institutional Review Board (Human Subjects
Division), which approved all the experimental protocol and procedures. Written informed consent was
obtained for every participant in the study.
The fMRI Task. e word-guessing fMRI task was based on the game of PictionaryTM, 45, and was
developed using Matlab ( and Psychtoolbox version 3 (http://psychtoolbox.
org) soware. We used a block-design with 30 seconds block duration for each of the two conditions
(word-drawing and zigzag-drawing). In the rst condition, word-drawing, participants were asked to
draw a given word (mainly actions or verbs) to the best of their ability using the MR-safe drawing tablet,
with the caveat that others would later try to guess the word by their drawing alone. To control for the
basic motor and visuospatial aspect during the word-drawing condition, participants were also asked to
make a drawing representing the control word (“zigzag”) in the second condition. Each block was sepa-
rated by a xation period with a random duration within the range of 10–15 seconds (see Fig.1). ere
were a total of 10 blocks per condition and the total duration of the task was approximately 14.5 minutes.
In each condition, participants were shown a word on the top-le corner of the screen. Participants were
asked to fully utilize the given 30 seconds in each block and continue to add elements to the illustration
in case they wanted to nish early.
e words in the word-drawing condition were chosen from the pool of “action words” from the game
of PictionaryTM. To balance the diculty level in drawing dierent words, across participants, the chosen
words were rated by a separate set of participants (N = 10) as “Dicult”, “Medium, and “Easy” to draw.
For example, drawing “cry” was rated as easy, while drawing “exhaust” was rated as dicult. Overall, we
chose 3 dicult, 4 medium, and 3 easy words. e order of words was randomly chosen and was kept
consistent across all participants. Additionally, participants in the fMRI study self-rated diculty level
(dicult, medium, or easy) for drawing each word during a post-scan questionnaire.
e MR-safe drawing tablet was designed and developed specically for this study using an
MR-compatible touch-sensitive surface. It uses a KEYTEC 4-wire resistive touch glass connected to a
Teensy 2.0 with custom rmware. is device connected via USB port. It streamed the absolute posi-
tion using a simple serial protocol. e tablet case was build out of clear acrylic using a laser cutter.
e rmware for this tablet is made open-source and is available at
Behavioral Assessments. General intelligence. e Wechsler Abbreviated Scale of Intelligence-II
(WASI-II) was used to measure general intelligence46. e WASI-II is designed to be administered indi-
vidually in approximately 30 min. e measure consists of four subtests: Vocabulary, Similarities, Block
Design, and Matrix Reasoning were used to obtain the Full Scale IQ (FSIQ). e WASI-II has a mean
standard score of 100 with a standard deviation of 15.
Task performance. Two expert raters from the Stanford Design School (authors A.R. and G.H.) were
chosen to blindly rate each drawing (from the word-drawing condition) on the scales of (a) representa-
tion and (b) creativity. Each drawing was de-identied and a web-based interface was developed for the
raters for easy access to all illustrations. e instructions for the “representation” scale were as follows:
“how easily do you think another person can guess the word represented by the drawing”. e ratings
were obtained on a ve-point scale (1-5), where 1 is “Not Representative, 2 is “Little Representative, 3
is “Moderately Representative”, 4 is “Representative”, and 5 is “Very Representative”.
SCIENTIFIC RepoRts | 5:10894 | DOI: 10.1038/srep10894
e “creativity” rating of each drawing was assessed based on the three subscales of – uency, elab-
oration, and originality. ese subscales were chosen based on established standardized tests of gural
creativity47. Scores from these three subscales were averaged to get the nal score of creativity. Each
subscale was dened as follows: (a) Fluency - total number of elements in the drawing; (b) Elaboration
- imagination and exposition of detail; and (c) Originality - the statistical infrequency and unusualness/
uniqueness of the response. e rating for each subscale was also done on a ve-point scale (1-5).
Importantly, if any drawing was not clear (e.g., due to unintentional lines drawn by a multi-touch
on the tablet), the raters marked those drawings as “confusing” and were not included in the analysis.
Overall, less than 4% out of 300 drawings were excluded. e two raters were trained on a small sample
of drawings (36 drawings) and their inter-rater reliability index for all the drawings (as measured by
Intra Class Correlation Coecient (ICC)) was 0.80 for representation and 0.884 for the creativity scale.
Lastly, as mentioned before, participants also self-rated the diculty level for each drawing (as di-
cult, medium, or easy) in terms of diculty in drawing during the post-scan questionnaire.
MRI image acquisition. Participants were imaged on a 3Tesla scanner (GE MR750, Milwaukee, WI) at
the Stanford University’s Center for Cognitive and Neurobiological Imaging (CNI) using a 32-channel
radiofrequency receive head coil (Nova Medical, Inc., Wilmington, MA). e participant’s head was
stabilized by packing foam between the temples and the inner surface of the receiver coil to mini-
mize motion during the scan, and a plethysmograph was placed on a nger on the le hand to moni-
tor peripheral pulse. To restrict additional movement of hands, cushions were placed under the tablet
and under participants’ arms. A total of 435 whole-brain volumes were collected on 42 axial-oblique
slices (2.9 mm thick) prescribed parallel to the intercommissural (AC-PC) line, using a T2*-weighted
gradient echo pulse sequence sensitive to blood oxygen level-dependence (BOLD) contrast with the
following acquisition parameters: Echo Time (TE) = 30 ms, repetition time (TR) = 2000 msec, ip
angle = 77°, FOV = 23.2 cm, acquisition matrix = 80 × 80, approximate voxel size = 2.9 × 2.9 × 2.9 mm.
To reduce blurring and signal loss arising from eld in-homogeneities, an automated high-order shim-
ming method based on gradient echo acquisitions was used before acquisition of functional MRI scans.
A high-resolution T1-weighted three-dimensional BRAVO pulse sequence acquisition was acquired for
co-registration with the following parameters: Echo Time (TE) = 2.8 ms, repetition time (TR) = 7.2 ms, ip
angle = 12°, FOV = 23 cm, slice thickness = 0.9 mm, 190 slices in the sagittal plane; matrix = 256 × 256;
acquired resolution = 0.9 × 0.9 × 0.9 mm. e images were reconstructed as a 256 × 256 × 190 matrix.
fMRI Data Analysis. Functional MRI data processing was carried out using FEAT (FMRI Expert
Analysis Tool) Version 6.00, part of FSL (FMRIB’s Soware Library, e fol-
lowing pre-statisticsal processing steps were applied: motion correction using MCFLIRT48, non-brain
removal using BET49, spatial smoothing using a Gaussian kernel of FWHM 5 mm, grand-mean inten-
sity normalization of the entire 4D dataset by a single multiplicative factor, highpass temporal ltering
(Gaussian-weighted least-squares straight line tting, with sigma= 50.0s), and probabilistic independent
component analysis49,50 as implemented in MELODIC (Multivariate Exploratory Linear Decomposition
into Independent Components) Version 3.10, part of FSL. Aer preprocessing, the functional data were
registered to each individual’s high-resolution T1-weighted image, followed by registration to the MNI152
standard-space by ane linear registration using FMRIB’s Linear Image Registration Tool (FLIRT)50. For
each participant, independent components were classied as “artifact” or non-artifact using an in-house
semi-automatic artifact removal tool (SMART; similar to a tool made for the EEG data51). SMART uses
the following rules to categorize each component as an artifact: (a) when the time series of a component
is highly correlated (r > 0.4) with motion prole only and not at all with the task design; or (b) when
a component has most of its power (> 70%) in the high frequency range. Once categorized, SMART
produces an HTML based web-tool for quality check, where the operator can easily override SMART’s
automatic classication. e quality check step was incorporated to make sure that the categorization
of a component as an artifact was accomplished conservatively; e.g., if the time course of a component
showed transient correlation with the task design, the components were retained as potentially con-
taining BOLD signal. Aer this quality check, SMART uses fsl_reglt utility (supplied with FSL) to
regress out the artifactual components to recreate 4-D datasets to be used in generalized linear model
analysis. Additionally, sharp motion peaks were detected using fsl_motion_outliers script (supplied with
FSL) and were regressed out in addition to the six motion parameters (from MCFLIRT). Registration to
high-resolution structural and standard space images was carried out using FLIRT. Time-series statisti-
cal analysis was carried out using FILM with local autocorrelation correction. Group-level analysis was
carried out using FEAT (FMRI Expert Analysis Tool). Z (Gaussianised T/F) statistic images were thresh-
olded using clusters determined by Z > 2.3 and a (corrected) cluster signicance threshold of P = 0.0548,52.
Featquery tool (supplied by FSL) was used to extract percent change in parameter estimates for function-
ally dened (clusters of activations) regions of interests. MRIcron was used to visualize neuroimaging
results on structural brain images. Talaraich Client was used to search for Brodmann areas for peak clus-
ter activations. For the fMRI parametric modulation analysis, at the participant (Pi) level, we subtracted
the average value (across the 10 drawings made by participant Pi) of expert creativity ratings from each
creativity rating received by Pi. Similarly, we subtracted the average value of subjective diculty ratings
from each of the 10 subjective diculty rating provided by Pi for his/her drawings. is procedure of
SCIENTIFIC RepoRts | 5:10894 | DOI: 10.1038/srep10894
demeaning (or normalizing) was done before running the individual level multiple regression to reduce
the eect of individual variance during the group-level analysis.
1. Torrance, E. P. A Longitudinal Examination of the Fourth Grade Slump in Creativity. Gied Child Quarterly 12, 195–199 (1968).
2. Srivastava, S. & etter, T. A. e lin between bipolar disorders and creativity: evidence from personality and temperament
studies. Curr Psychiatry ep 12, 522–530 (2010).
3. Dietrich, A. & anso, . A review of EEG, EP, and neuroimaging studies of creativity and insight. Psychol Bull 136, 822–848
4. Arden, ., Chavez, . S., Grazioplene, . & Jung, . E. Neuroimaging creativity: a psychometric view. Behav Brain es 214,
143–156 (2010).
5. Fin, A. et al. Stimulating creativity via the exposure to other people’s ideas. Hum. Brain Mapp. 33, 2603–2610 (2012).
6. Sawyer, . e Cognitive Neuroscience of Creativity: A Critical eview. Creativity esearch Journal 23, 137–154 (2011).
7. owatari, Y. et al. Neural networs involved in artistic creativity. Hum. Brain Mapp. 30, 1678–1690 (2009).
8. Bhattacharya, J. & Petsche, H. Drawing on mind’s canvas: dierences in cortical integration patterns between artists and non-
artists. Hum. Brain Mapp. 26, 1–14 (2005).
9. Gibson, C., Folley, B. S. & Par, S. Enhanced divergent thining and creativity in musicians: a behavioral and near-infrared
spectroscopy study. Brain Cogn 69, 162–169 (2009).
10. Pinho, A. L., de Manzano, Ö., Fransson, P., Erisson, H. & Ullén, F. Connecting to create: expertise in musical improvisation is
associated with increased functional connectivity between premotor and prefrontal areas. Journal of Neuroscience 34, 6156–6163
11. Villarreal, M. F. et al. Neural correlates of musical creativity: dierences between high and low creative subjects. PLoS ONE 8,
e75427 (2013).
12. Liu, S. et al. Neural correlates of lyrical improvisation: an FMI study of freestyle rap. Sci ep 2, 834 (2012).
13. Limb, C. J. & Braun, A. . Neural substrates of spontaneous musical performance: an FMI study of jazz improvisation. PLoS
ONE 3, e1679 (2008).
14. Bengtsson, S. L., Csíszentmihályi, M. & Ullén, F. Cortical regions involved in the generation of musical structures during
improvisation in pianists. J Cogn Neurosci 19, 830–842 (2007).
15. Berowitz, A. L. & Ansari, D. Expertise-related deactivation of the right temporoparietal junction during musical improvisation.
NeuroImage 49, 712–719 (2010).
16. Aziz-Zadeh, L., aplan, J. T. & Iacoboni, M. ‘Aha!’: e neural correlates of verbal insight solutions. Hum. Brain Mapp. 30,
908–916 (2009).
17. Aziz-Zadeh, L., Liew, S.-L. & Dandear, F. Exploring the neural correlates of visual creativity. Soc Cogn Aect Neurosci.
doi:10.1093/scan/nss021 (2012).
18. Torrance, E. P. in Guidelines for administration and scoring/comments on using the Torrance Tests of Creative ining.
(Scholastic Testing Service, 1987)
19. Smith, . L. ., Michael, W. B. & Hocevar, D. Performance on creativity measures with examinationtaing instructions intended
to induce high or low levels of test anxiety. Creativity esearch Journal 3, 265–280 (1990).
20. Damoiseaux, J. S. et al. Consistent resting-state networs across healthy subjects. Proc. Natl. Acad. Sci. U.S.A. 103, 13848–13853
21. Greicius, M. D., rasnow, B., eiss, A. L. & Menon, V. Functional connectivity in the resting brain: a networ analysis of the
default mode hypothesis. Proc. Natl. Acad. Sci. U.S.A. 100, 253–258 (2003).
22. aichle, M. E. et al. A default mode of brain function. Proc. Natl. Acad. Sci. U.S.A. 98, 676–682 (2001).
23. Guilford, J. P. Creativity. Am Psychol 5, 444–454 (1950).
24. Dietrich, A. Who’s afraid of a cognitive neuroscience of creativity? Methods 42, 22–27 (2007).
25. Ito, M. Control of mental activities by internal models in the cerebellum. Nat. ev. Neurosci. 9, 304–313 (2008).
26. Vandervert, L. ., Schimpf, P. H. & Liu, H. How Woring Memory and the Cerebellum Collaborate to Produce Creativity and
Innovation. Creativity esearch Journal 19, 1–18 (2007).
27. Baddeley, A. Woring memory. Science 255, 556–559 (1992).
28. Dosenbach, N. U. F. et al. Distinct brain networs for adaptive and stable tas control in humans. Proceedings of the National
Academy of Sciences 104, 11073–11078 (2007).
29. Schmaju, N. A., Gray, J. A. & Lam, Y. W. Latent inhibition: a neural networ approach. J Exp Psychol Anim Behav Process 22,
321–349 (1996).
30. Lisman, J. E. & Grace, A. A. e Hippocampal-VTA Loop: Controlling the Entry of Information into Long-Term Memory.
Neuron 46, 703–713 (2005).
31. Baddeley, A. Exploring the Central Executive. e Quarterly Journal of Experimental Psychology Section A 49, 5–28 (1996).
32. Baddeley, A. Woring memory: looing bac and looing forward. Nat. ev. Neurosci. 4, 829–839 (2003).
33. Dosenbach, N. U. F., Fair, D. A., Cohen, A. L., Schlaggar, B. L. & Petersen, S. E. A dual-networs architecture of top-down control.
Trends in Cognitive Sciences 12, 99–105 (2008).
34. Ellamil, M., Dobson, C., Beeman, M. & Christo, . Evaluative and generative modes of thought during the creative process.
NeuroImage 59, 1783–1794 (2012).
35. Ito, M. Movement and thought: identical control mechanisms by the cerebellum. Trends Neurosci 16, 448–450 (1993).
36. Ito, M. Cerebellar circuitry as a neuronal machine. Prog. Neurobiol. 78, 272–303 (2006).
37. Schmahmann, J. D. An emerging concept. e cerebellar contribution to higher function. Archives of neurology 48, 1178–1187
38. Bucner, . L. e cerebellum and cognitive function: 25 years of insight from anatomy and neuroimaging. Neuron 80, 807–815
39. amnani, N. e primate cortico-cerebellar system: anatomy and function. Nat. ev. Neurosci. 7, 511–522 (2006).
40. Gilbert, P. F. & ach, W. T. Purinje cell activity during motor learning. Brain esearch 128, 309–328 (1977).
41. Graon, S. T., Woods, . P. & Tysza, M. Functional imaging of procedural motor learning: elating cerebral blood ow with
individual subject performance. Hum. Brain Mapp. 1, 221–234 (1994).
42. amnani, N. et al. e evolution of prefrontal inputs to the cortico-pontine system: diusion imaging evidence from Macaque
moneys and humans. Cereb. Cortex 16, 811–818 (2006).
43. oziol, L. F. et al. Consensus paper: the cerebellum’s role in movement and cognition. in Cerebellum 13, 151–177 (2014).
44. Ito, M. Bases and implications of learning in the cerebellum–adaptive control and internal model mechanism. Prog. Brain es.
148, 95–109 (2005).
45. Angel, . & Everson, G. in PictionaryTM. Hasbro Inc.
SCIENTIFIC RepoRts | 5:10894 | DOI: 10.1038/srep10894
46. Wechsler, D. in WASI: Wechsler Abbreviated Scale of Intelligence. San Antonio, TX: e Psychological Corporation, (A Harcourt
Assessment Company, 1999).
47. Torrance, E. P. in Torrance tests of creative thining. Figural forms A and B. (Scholastic Testing Service, 1990).
48. Jeninson, M., Bannister, P. & Brady, M. Improved Optimization for the obust and Accurate Linear egistration and Motion
Correction of Brain Images. NeuroImage 17, 825–841 (2002).
49. Smith, S. Fast robust automated brain extraction. Hum. Brain Mapp. 17, 143–155 (2002).
50. Becmann, C. F. & Smith, S. M. Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE
Trans Med Imaging 23, 137–152 (2004).
51. Saggar, M. et al. Intensive training induces longitudinal changes in meditation state-related EEG oscillatory activity. Front Hum
Neurosci 6, 256 (2012).
52. Worsley, . in Functional MI: an introduction to methods (eds. Jezzard, P. et al.) Ch. 14 (Oxford University Press, 2001).
is work was supported by a Hasso Plattner Design inking Research Program grant to A.L.R. We thank
the sta at Stanford’s Hasso Plattner Institute of Design (aka: the, Center for Interdisciplinary
Brain Sciences Research, and Center for Cognitive and Neurobiological Imaging for their support.
Author Contributions
M.S. designed the task, collected and analyzed data, and wrote the manuscript. E.M.Q., E.K., N.T.B.,
Z.S., D.H., N.L., Y.H.C. and A.R. helped in study design and data collection. G.H. and A.R. blindly
rated the drawings for representation and creativity scores. R.F.D. and M.S. contributed to design and
development of MR-safe drawing tablet and R.F.D. also helped with development of neuroimaging
protocol. A.L.R. contributed to all aspects of the study, including design, interpretation of results and
writing of manuscript.
Additional Information
Competing nancial interests: e authors declare no competing nancial interests.
How to cite this article: Saggar, M. et al. Pictionary-based fMRI paradigm to study the neural
correlates of spontaneous improvisation and gural creativity. Sci. Rep. 5, 10894; doi: 10.1038/
srep10894 (2015).
is work is licensed under a Creative Commons Attribution 4.0 International License. e
images or other third party material in this article are included in the article’s Creative Com-
mons license, unless indicated otherwise in the credit line; if the material is not included under the
Creative Commons license, users will need to obtain permission from the license holder to reproduce
the material. To view a copy of this license, visit
... Although cerebellar activity is sometimes observed in neuroimaging studies on creativity (Chamberlain et al. 2014;Saggar et al. 2015;Sunavsky and Poppenk 2020; for reviews, see Beaty et al. 2016;Chen et al. 2020) and aesthetic experience (Ishizu and Zeki 2013Kirk et al. 2009a;Lacey et al. 2011;Vartanian and Goel 2004; for a meta-analysis see ), its role is rarely discussed. More often, the cerebellum is excluded from analysis. ...
... For art creativity, many investigations report cerebellar involvement (Chamberlain et al. 2014;Cogdell-Brooke et al. 2020;Fornazzari et al. 2020;Gao et al. 2017Gao et al. , 2020Makuuchi et al. 2003;Miall et al. 2009;Ogawa et al. 2018;Saggar et al. 2015;Schlegel et al. 2015;Sunavsky and Poppenk 2020). Most of these studies report an increased involvement of circumscribed locations within the cerebellum accompany specific tasks like the development of drawing skills. ...
... As a result, this increased activitiy is accompanied by perceptual enhancement through specific visuo-motor and visuo-spatial mechanisms. Beyond these primary visual avenues, the observed increased activities of prefrontal-parietal and associated motor cortices might point to involvement of cognitive and affective aspects of visuo-creative perception and thinking, such as elaboration, flexibility, fluency, and originality, guiding spontaneous improvisation and figural creativity along several domains of a large-scaled sketchpad including the central executive and (visual and visuo-spatial) working memory of the drawing person (Baddeley 2003;Saggar et al. 2015). Interestingly, Chamberlain et al. (2014) found more creative thinking in participants after a drawing training course, suggesting that neural plasticity in the cerebellum integrates visual perception and creative action. ...
Full-text available
This chapter addresses how the embodiment approach may represent a unifying perspective for examining the cerebellar role in emotional behavior and psychological traits. It is not intended to be exhaustive, but rather it can be a good starting point for advancing the cerebellar neural mechanism underlying embodiment. Our goal is to provide illustrative examples of embodied emotions and psychological traits in the emerging field of emotional and cognitive cerebellum. We illustrate how the cerebellum could be an important hub in the embodiment processes, associated with empathic abilities, impaired emotional identification and expression (as occurring for example in the presence of alexithymia), and specific psychological constructs (i.e., hypnotizability).
... Although related to taking a more objective assessment of designs/ideas (see the ndings of [186] above), these regions are also related to inhibition or 'self-censorship' and thus may impede creative generation or improvisation while making art. An fMRI study [198] in which participants drew solutions to Pictionary cues (Mattel Inc., El Segundo CA) also showed a negative correlation between activation in left-lateralized dlPFC and the drawing ratings. ...
... Focusing again on the technical ability to make realistic drawings, Chamberlain et al. [48] report a positive correlation between drawing ability and grey matter density in the right medial frontal gyrus as well as the left anterior cerebellum (see also Ref. [189]), both tied to ne motor control and to procedural memory (see also Ref. [198] for a study showing higher cerebellum activity when making drawings judged to be particularly creative and speci c patterns of activity in the right cerebellum and left motor cortex, perhaps tied speci cally hand-eye coordination). ...
Full-text available
This book, of which this chapter is a part, is about people who changed the ways in which they related to being visually creative or made art. Maybe they suddenly found themselves with a heightened interest in producing artworks, ramping up, greatly, their artistic production. Maybe they found themselves spontaneously able to see or think in novel ways, to make new associations, act with new confidence or courage; without inhibition. Maybe they found themselves producing in new media; in different styles or colors. Maybe they started up as artists for the first time ever. Or, maybe they felt their artistic interests and abilities slipping, changing, becoming something different—whether worse or better. Within the forthcoming chapters, these changes serve as the basis for a number of intriguing discussions of the equally changing lives, bodies, and especially brains of individuals living with neurological diseases, and with the overarching possibil- ity, if not explicit hypothesis, that these changes may be connected. Whether in the emerging body of case studies, discussions of caregivers, causative approaches, or even the reflections of artists about their lives and output, it is this bridge that holds the promise of this book’s very topic. Might—by changing our brain or our actions— we reveal something about what it means to have these disorders, about how we typically think and perceive; about how and why we make art? Similar interests, given the existence of this book, are evidently held by clinicians, neurologists, and working artists. However, this also begs a fundamental question: In order to discuss individuals becoming more, less, or differently involved in art, we must first have an idea of from what and to where these changes might proceed.
... Although related to taking a more objective assessment of designs/ideas (see the ndings of [186] above), these regions are also related to inhibition or 'self-censorship' and thus may impede creative generation or improvisation while making art. An fMRI study [198] in which participants drew solutions to Pictionary cues (Mattel Inc., El Segundo CA) also showed a negative correlation between activation in left-lateralized dlPFC and the drawing ratings. ...
... Focusing again on the technical ability to make realistic drawings, Chamberlain et al. [48] report a positive correlation between drawing ability and grey matter density in the right medial frontal gyrus as well as the left anterior cerebellum (see also Ref. [189]), both tied to ne motor control and to procedural memory (see also Ref. [198] for a study showing higher cerebellum activity when making drawings judged to be particularly creative and speci c patterns of activity in the right cerebellum and left motor cortex, perhaps tied speci cally hand-eye coordination). ...
Full-text available
For a book devoted to the overlap of visual art making and creativity with the putatively changing brain, it makes sense to talk about what we know of the typical artist and the artistic brain. What are the regions of interest—in fact, are there any—that can be connected to the specific production of art? Are there any particular areas or neurobiological aspects that show differences between more or less successful artists? What, thinking more broadly, techniques, perceptual abilities, cognitive processes, or cultural or life factors may contribute to shaping artistic production and development? What, so to speak, makes an artist? And, returning to the aim of this book itself, might we find some overlap when considering the changing circumstances—be they behavioral or biological—in neurodegenerative disorders? In this chapter, we review these topics, providing a walk-through of the present state of knowledge on art making, as it relates to the brain, but also considering theories and factors underpinning artistic production as well as current arguments on specific factors that may contribute to making art and relatively more successful artists. This is geared primarily at the researcher interested in artistic creativity and/or neurodegeneration but who may not be well-versed in current empirical art study. By putting this collection of findings together, however, it is also our hope that this chapter will be of use to a broader audience and serve as a guide and a set of sign-posts and ideas to navigate the discussions throughout this book.
... Neuroimaging research with participants playing a Pictionary-like game showed that more brain areas are involved and interacting when performing creative activities than during usual activities [Saggar et al., 2015], and thus facilitate spontaneous improvisation and visual creativity. Pictionary has also been explored for language acquisition purposes and it has been shown to increase students' creativity, interest and participation [Hamer and Lely, 2019;Fadirsair et al., 2021]. ...
Our ability to perceive, represent and understand the surrounding visual world is one of the most fascinating, but equally intricate, parts of our nervous system. Supported by a sufficiently complex brain, we start to learn and develop these cognitive abilities and skills such as communication from the moment we are born. These capabilities are essential in numerous tasks we carry out in our daily life. The development of such intelligence is promoted by exploration of the environment and social interaction. As such, advancing artificial intelligent agents capable of interaction through communication with each other and with humans has been a long-standing goal. This thesis seeks to uncover how inter-agent communication about the visual world, emerging in a completely self-supervised way, can be modelled and its interpretability improved. This research draws inspiration from how human communication developed and first compares the processes involved in transmitting meaningful information between humans and machines. In the context of referential signalling games played with realistic images, intelligent agents modelled as deep neural networks have previously been shown to develop successful token-based communication protocols to achieve a shared goal. This thesis analyses the factors which influence the emergence of meaningful protocols and shows that visual semantics can be learned in a self-supervised way. Nonetheless, qualitative and quantitative insights into emergent token-based communication are not easily explainable to humans. We thus propose drawing as a communication channel which is a much simpler and more directly interpretable modality than language. To enable end-to-end learnable models of visual communication, a differentiable relaxation of the process of drawing vector primitives into pixel rasters is proposed. Using this approach, the physical act of drawing with a pen on paper can be modelled. We then demonstrate that agents cooperating on a signalling game learn to communicate through sketching. An extensive analysis of the factors which influence the meaning and intent of agents’ drawings is presented. The final two chapters show how interpretable sketches emerge when inducing visual perceptual similarity constraints. Through human evaluation of the emergent visual communication, we explore how, with appropriate inductive biases, artificial agents learn to draw in a fashion that humans can interpret.
... To confirm the participants' compliance during the task, participants were asked to recall and/or portray their creative products outside the scanner (* Hahm et al., 2017;Cai et al., 2018;Huang et al., 2013) or to name the object they mentally produced (Aziz-Zadeh et al., 2013). Finally, other studies focused on spontaneous improvisation, and they required the creative drawing of objects/scenes/faces on fMRI-compatible drawing tablet systems (Ellamil et al., 2012;Fan et al., 2014;Saggar et al., 2017;Saggar et al., 2015), or to provide solutions to ill-structured visual problem-solving tasks with no predetermined final state or criterion for taking decisions (* Gilbert et al., 2010). ...
Creative production (related to art-making) and aesthetic appreciation (related to art-viewing) are inherently linked in visual arts, but their relationship has never been explored explicitly in cognitive neuroscience, nor the nature of such connection. The available literature suggests two cognitive processes as possible foundations of these two experiences: motor simulation or inhibitory control. In a metanalysis of fMRI studies we addressed this issue: we investigated whether there are shared neurofunctional underpinnings behind aesthetic and creative experiences in the visual domain; further, we examined whether any shared brain activation may reflect either motor simulation or inhibitory processes. A conjunction analysis revealed a common involvement of the pre-SMA in both classes of studies, a brain region, if anything, more concerned with top-down inhibitory motor and cognitive control rather than mirror motor simulation. In the art-viewing domain this finding was primarily driven by figurative rather than abstract art. The methodological limitations in the available literature are discussed together with possible new ways to expand the existing findings.
Full-text available
This chapter outlines the recent developments, such as Neuroscience on Design, Design Neurocognition, and NeuroDesign, in the intersection of neuroscience and design. This intersection of diverse disciplines, including psychology, neurophysiology, engineering, interaction design, and architecture, provides various opportunities and challenges to advance areas, such as design thinking, neuro-technology, embodied artificial intelligence (AI), and human-centered AI. We outline some of the opportunities and challenges with several examples, such as methodological and technological developments, necessary to develop this promising pan-disciplinary field. We emphasize the importance of educating researchers (i.e., NeuroDesign Researchers) and practitioners (Neuro-Designer/Engineers) to advance this intersection toward a new area that could be greater than the sum of its parts.
There is growing interest in the cerebellum's contributions to higher order functions of the human brain. When considering specific activities of the human cerebellum related to art, we differentiate two broad areas. Neural activity within different locations of the cerebellum is involved in art perception and in artistic creativity. The cerebellum plays an underappreciated role in neuroaesthetics, including the perception and evaluation of art objects, their appreciation and affective aesthetic experience. Certain areas of the cerebellum presumably are of particular relevance, incorporating cognitive and affective issues within large-scaled neural networks in perceiving and appraising artworks. For art creativity, many investigations report cerebellar implementations. Important areas in these domains are evolutionary younger parts of the cerebellar hemispheres, in particular the lobule VII with its Crus I and II, influencing crucial networks such as the Default Mode Network in optimizing creativity. These structures help guide pattern recognition and in art appreciation as they may play a role in predicting ongoing neural network activities through a crucial frontoparietal axis. In this chapter, we consider how our current neuroscientific understanding of cerebellar functions point to a likely role of the cerebellum in art appreciation and creativity.
Two experiments examined the dual influence of mind wandering (MW) on the incubation of both deliberate and spontaneous modes of creativity. Specifically, using a modified version of Sustained Attention Response Task as the incubation task, this study assessed whether taking a break from a creative task and engaging in either an MW‐allowed task or an MW‐prevented task can exert differential effects on different aspects of creativity. Results showed that after engaging in an incubation task that allowed MW rather than prevented MW, participants generated ideas more flexibly but less persistently in the subsequent divergent thinking tasks, and were more likely to solve creative insight problems through intuitive insight but not systematic analysis. The results suggest that MW during incubation may simultaneously facilitate the spontaneous mode of creativity while suppressing the deliberate mode of creativity. These findings also indicate that creativity must be parsed into different subtypes in order to identify more specific ways to enhance creativity.
Full-text available
Musicians have been used extensively to study neural correlates of long-term practice, but no studies have investigated the specific effects of training musical creativity. Here, we used human functional MRI to measure brain activity during improvisation in a sample of 39 professional pianists with varying backgrounds in classical and jazz piano playing. We found total hours of improvisation experience to be negatively associated with activity in frontoparietal executive cortical areas. In contrast, improvisation training was positively associated with functional connectivity of the bilateral dorsolateral prefrontal cortices, dorsal premotor cortices, and presupplementary areas. The effects were significant when controlling for hours of classical piano practice and age. These results indicate that even neural mechanisms involved in creative behaviors, which require a flexible online generation of novel and meaningful output, can be automated by training. Second, improvisational musical training can influence functional brain properties at a network level. We show that the greater functional connectivity seen in experienced improvisers may reflect a more efficient exchange of information within associative networks of importance for musical creativity.
The subject of creativity has been neglected by psychologists. The immediate problem has two aspects. (1) How can we discover creative promise in our children and our youth, (2) How can we promote the development of creative personalities. Creative talent cannot be accounted for adequately in terms of I.Q. A new way of thinking about creativity and creative productivity is seen in the factorial conceptions of personality. By application of factor analysis a fruitful exploratory approach can be made. Carefully constructed hypotheses concerning primary abilities will lead to the use of novel types of tests. New factors will be discovered that will provide us with means to select individuals with creative personalities. The properties of primary abilities should be studied to improve educational methods and further their utilization. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
• Recent clinical and research reports suggest that the cerebellum may contribute to the modulation of higher order behavior. This article presents a critical review of both earlier and more current clinical observations that raise this possibility, as well as a review of the salient laboratory data that appear to support this contention. It also summarizes the relevant anatomic work concerning the contributions to the cortico-pontocerebellar pathways from the higher order cerebral association areas, which have been implicated as partial anatomic substrates for this putative cerebellar role in higher function. Finally, it provides a framework for the understanding of this correlation, concludes with suggestions for future areas of investigation, and recommends that patients with cerebellar lesions be studied from a neurobehavioral point of view.
The term working memory refers to a brain system that provides temporary storage and manipulation of the information necessary for such complex cognitive tasks as language comprehension, learning, and reasoning. This definition has evolved from the concept of a unitary short-term memory system. Working memory has been found to require the simultaneous storage and processing of information. It can be divided into the following three subcomponents: (i) the central executive, which is assumed to be an attentional-controlling system, is important in skills such as chess playing and is particularly susceptible to the effects of Alzheimer's disease; and two slave systems, namely (ii) the visuospatial sketch pad, which manipulates visual images and (iii) the phonological loop, which stores and rehearses speech-based information and is necessary for the acquisition of both native and second-language vocabulary.
Twenty-five years ago the first human functional neuroimaging studies of cognition discovered a surprising response in the cerebellum that could not be attributed to motor demands. This controversial observation challenged the well-entrenched view that the cerebellum solely contributes to the planning and execution of movement. Recurring neuroimaging findings combined with key insights from anatomy and case studies of neurological patients motivated a reconsideration of the traditional model of cerebellar organization and function. The majority of the human cerebellum maps to cerebral association networks in an orderly manner that includes a mirroring of the prominent cerebral asymmetries for language and attention. These findings inspire exploration of the cerebellum's contributions to a diverse array of functional domains and neuropsychiatric disorders.