Creat ive Educati on
2012. Vol.3, Supplement, 90-94
Published Online December 2012 in SciRes (http://www.SciRP.org/journal/ce) DOI:10.4236/ce.2012.38b020
90
Cogniton-based Enlightenment of Creative Thinking:
Examplars in Computer Sci e nce
Zhi-Quan Cheng, Shiyao Jin
School of Computer, N ational University of Defense T echnolog y, Hunan Province, China
Received 20 1 2
Abstract: It is rep uted that “Genius i s 1% ins piration a nd 99% per spira tion”, but it can al so be noted t hat
“sometimes, 1% inspirat ion is more import ant than 99% perspiration.” As this 1% is s o important, ca n it
be understood, and even learned? If so, how can cognition be used to enlighten a scientist's inspiration
(cr eative thinki ng)? Both ques tions are cons idered on the bas is of cognitive the ory in the pap er. We illu-
strat e our ideas with examples fr om comp uter science.
Key words: Creat ive Thinking; Enlightenment; Cognit ion; Computer Graphics, Comp uter Simulati on
Introduction
“Genius is one percent inspiration and ninety-nine percent
perspiration, but sometimes, one-percent inspiration is more
important than ninety-nine percent perspiration” is a quote
usually attributed to Edison, when discussing his remarkable
achievements. Generally, the later part of this saying is neg-
lected when quoted, as the goal is to encourage hard work,
rather than to point out the key role of distinguished scientists,
like Edison, as a creati ve elite.
Scientific research, searching for new knowledge, appeals
especially to individual creative people. Edward De Bono (De
Bono, 2008), the father of creative thinking, suggested that
creativeness is a particular way of thinking, and postu lated that
there are some basic principles and mental techniques that are
commonly used while being creative. 150 years ago , Claude
Bernard, the great French physiologist said (Bernard, 1865):
“The genius of inventiveness maybe diminished or even smo-
thered by a poor method, while a good method may increase
and develop it… In biological science, the role of method is
even more important…”. These statements argue that the
one-percent perspiration can be understood, and even learnt, in
some way.
In our paper, using cognitive theory (Bermúdez, 2010), we
explore how to understand creativity, and enlighten researchers
in creative thinking (Sternberg, 2006). Our arguments are
mainly addressed by using advances in computer science as
exemplars, particularly in the areas of computer graphics and
simulation. We explore creati ve hab its of min d, and t ry to catch
the insights how to generally improve one's creative thinking
abilities, and how to apply them to new situations. Our work is
carrying out at the difficult state of traditional methods pausing
for about a decade (Mumford, 2003), and try to deal with it
with new progress of co mputer science.
Cognition and Creative Thinking for Scientists
De Bono (De B ono, 2008 ) stated: “Creative thinking is not a
talent, it is a skill that can be learnt. It empowers people, adding
strength to their natural abilities, which improves teamwork,
productivity and where appropriate, profits”. For a senior scien-
tist, mental pro cesses ar e the ess en ce and the engine of creati ve
endeavo r . When a mind containing a wealth of information
contemplates a problem, relevant information readily comes to
into focus during thinking. A critical issue in problem solving is
deciding whether the available information is sufficient or not.
Since the information available in the mind must be recognized,
we address the relationship between cognition and creative
thinking, particularly for scientists.
Cognition and Creativity Revisited
The cognition (Kozbelt, Beghetto, &Runco, 2010) that gives
rise to creative thinking is not a single process or operation
(Smith & Ward, 1995), but rather consists of many different
cognitive structures and processes that collaborate in a variety
of ways to construct different types of creative output. There
are two contrasting approaches to creativity in cognitive psy-
chology. P. J. Guilford (Guilford, 1950) beli eved that creativit y
can be measured in terms of divergent production, or the num-
ber of varied responses made to specific tests. Rather than one
good answer or single solution, divergent production results in
many possible ideas. However, sheer number of possible ideas
does not guarantee that they are useful, high quality and novel.
The second approach is Sternberg and Lubart’s investment
theory of creativity (Sternberg & Lubart, 1996). This theory
states that the appropriate attributes for creativity are know-
ledge, an encouraging environment, an appropriate personality,
intelligence, motivation and an appropriate thinking style.
Studies of creativity and cognition results (in terms of gener-
al intelligence) have found modest correlation between them
(Silvia, 2008). Some researchers believe that creativity is the
outcome of the same cognitive processes as intelligence, and
only judge creativity in terms of its consequences. Recent ad-
vances in neural science further show that general intelligence
reflects the combined performance of brains systems (Gläscher
et al., 2010), but the brain is still a functional black box, in
terms of how cognitive processes produce something novel.
In recen t years, two app roaches have d ominated the research
literature on cognition-based creativity: process-oriented mod-

Z. -Q. CHENG, S. Y. JIN
91
els of creati vit y, and systems-oriented models. Process-oriented
models concentrate on cognitive aspects of creativity; while
systems-oriented models take a broader approach to creativity
involving non-cognitive factors as well as cognitive ones. We
suggest a proces s-oriented model, which we suggest simulates
how th e cognitive process relates to creativity.
Framework of Menta l Cog nitio n
We firstly recall how cognition works, before it acts as a
stimulus for cr eativity.
When one thinks of Einstein, it is natural to assume that his
brain differed from that of the average person . In 1999, an an a-
tomical study was made of Einstein's brain. Interestingly, his
brain was smaller than average. However, the study (Witelson
et al., 1999) also found that Einstein's parietal lobes were 15%
wider than average. Science now points out that these lobes are
usually connected to spatial and visual cognition, as well as to
mathe matics. Of course, the brain is a complex and
still-mysterious organ, but it may be that we can glean some
additional insight from this study: the relation of cognition to
creativity has a physiological basi s.
In psychology, a cognitive process refers to how people
perceive, remember, think, speak, and solve problems. The
cognitive approach was brought to prominence by Donald
Broadbent (Broadbent, 1958), who put forward an information
processing model of cognition. This is a way of thinking and
reason ing about mental processes, envisioning them as akin to
software running on a computer that is the brain. Theories refer
to forms of input, representation, computation or processing,
and outputs. Because of the use of computational metaphors
and terminology, cognitive psychology was able to benefit
greatly from the flourishing of research in computer science.
Based o n such an information processing model of cognition,
we illustrate the cognition framework (Figure 1) using recent
concep tual terms. The terms describe input sensations and per-
ception, output behavior, intrinsic and learning cognition func-
tion units, and main memory. Memories (Atkinson & Shiffrin,
1968) may be stored in long-term memory (LTM), short-term
memory (STM), autobiographical memory (ABM), and sensory
memory (SM).
● SM. Sensory memory corresponds approximately to the
initial 200–500 milliseconds after an item is perceived. The
ability to look at an item, and remember what it looked like
within just a second of observation , is an example of sensory
memory.
● STM. Short-term memory allows recall for a period of sev-
eral seconds to a minute without rehearsal. It provides the
ability to hold a small amount of information in mind in an
active, re adily avail able state for a sho rt period of time. The
duration of short-term memory (when rehearsal or active
maintenance is prevented) is believed to be of the order of
seconds. A commonly cited capacity is 7 ± 2 stored items.
● LTM. Long-term memory is memory in which associations
among it ems are stored, according to the dual-st or e memo r y
model (Atkinson and Shiffrin, 1968). Memories can reside
in the short-term “buffer” for a limited time while they are
simultaneously strengthening their associations in long-term
memory.
● ABM. Autobiographical memory is a memory system con-
sisting of episodes recollected from an individual's life,
based on a combination of episodic (personal experiences
and specific objects, people and events experienced at par-
ticul ar ti me and place) an d seman tic ( general kno wledge an d
facts about the world) memory (Williams, Conway, & Co-
hen, 20 08).
We su gg est a mod el to mental cognition using an analogy to
the Von Neumann architecture (Neumann, 1945) fro m computer
science. This model is not meant to be a serious suggestion of
how the brain works, but rather, a simplified description which
is adequate for the purposes of discussing cognition and crea-
tivity.
The correspondences between mind and computer could be
can be considered to be: input devices to input sensory and
percept ual organs, processor to intrinsic and learning cognition,
main memory to STM, disk to LTM, output devices to output
motor and behavior organs, input channels to SM and output
channels to ABM. See Figure 1.
Using this model, the mental cognitive process can be de-
scribed as follows:
1) The recognition and under stand ing of events, objects, and
stimuli through the use of senses (sight, hearing, touch, etc.).
Several di fferent types o f perception exist, and the data merged
to give the input.
2) The mind performs intrinsic cognition as primary
processing of the input data, then more deeply operates on the
data using learned cognition.
3) Operations are performed by retrieving stored information
in response to cues, enable the information to be used in mul-
tiple processes or activities.
4) Learned information is stored in the STM or LTM ac-
cording to judgment, and if necessary, appropriate behaviors
are output.
Clearl y, our framework of mental cognition is a stored-
memory model. The memor y is th e unit in which information is
encoded, and stored, and from which it is retrieved. To sum up,
information results from cognition of reality.
Correlation between Cognition and Creativity
The correlation between cognition and creativity is an im-
portant problem in philosophy and psychology. We must con-
sider the relationship, its origins and its forms, as well as the
principles and laws of cognitive activity, and its development.
As a selective reflection of the world, cognition and filtering of
information underpins human reasoning and drives human
achievement.
Figure 1.
Framework of mental cognition .

Z. -Q. CHENG, S. Y. JIN
92
There is much truth in the saying that in science the mind of
the scientist can build only as high as the foundations con-
structed by existing information will support. One of the re-
search worker’s duties is to follow the scientific literature, but
learning needs to be done with a critical, reflective attitude if
originality and freshness of outlook are not to be lost. Merely to
accumulat e information as a sort of capital investmen t is insuf-
ficient.
It is usual to carefully gather information dealing with the
particular problem on which one is going to work. However,
surprising as it may seem at first, some scientists consider that
this is unwise. They contend that investigating what others have
said on the subject conditions the mind to see the problem in
the same way and make it difficult to find a new and fruitful
approach. There are even some grounds for discouraging an
excessive amount of reading in the general field of science in
which one is going to work. Many successful investigators
were not trained in the branch of science in which they made
their most brilliant discoveries. But these researchers still had
relevant knowledge and were well trained. The same dilemma
faces all creat i ve wo rker s.
We may anal yze this observation further. When a mind con-
taining a wealth of information contemplates a problem, rele-
vant information provides useful cues to the solution. It is ad-
visable to make a thorough study of all the relevant literature
early in the investigation, for much effort may be wasted if
even only one significant article is missed. Ho wever, if that
information is insufficient, then the mass of this information
makes it more difficult for the mind to conjure up original ideas.
Further, some of that information maybe actually inappropriate,
in which case it presents a more serious bar r i er to new and
produ ctive ideas. During the course of an investigation, as well
as watching for new papers on the problem, it is very useful to
read more generall y over a wide field keeping a const ant watch
for some n ew pr incipl e or technique that may be useable. Often,
taking or adapting existing ideas from a different area is a key
problem solving step.
The best way of meeting the dilemma of “knowing too
much” is to critically obtain information, striving to maintain
independence of mind and avoid becoming conventionalized.
Franci s Bacon said : “Read not to contradict and confute, nor to
believe and take for granted…but to weigh and consider”. The
scientist with the right outlook for research develops a habit of
correlating what is read with his knowledge, looking for signif-
icant analogies an d generalizat i ons.
Simulation of Cognition and Creative Thinking
In his pion eer in g work Art of Thought, Wallas (Wallas, 1926)
presented one of the first models of the creative process. In the
Wallas stage model, creative insights and illuminations may be
explained by a process comprising 5 stages:
1) Preparation. The scientist focus his mind on the problem
and explores the problem’s dimensions;
2) Incubation. The problem is internalized into the uncons-
cious mind and nothing appears externally to be happening;
3) Intimation. The creative perso n gets a feeling th at a solu-
tion is on its way;
4) Illumination or insight. The creative idea bursts forth
from its preconscious processing into conscious awareness;
5) Verification. The idea is consciously veri fied, elaborated,
and then applied.
Wallas’ model i s often treated as fou r stages, with intimation
seen as a su b-stage. Wallas con sidered creativity to b e a legacy
of the evolutionary process, which allowed humans to quickly
adapt to rapidly changing environments. The implied theory
behind Wallas’ model–that creative thinking is a subconscious
proces s that cann ot be direct ed, and that cr eative and anal ytical
thinking are complementary–is reflected to varying degrees in
other models of creativity. In contrast to the prominent role that
some models give to subconscious processes, Perkins (Perkins,
1981) argues that subconscious mental processes are b ehind all
thinking and, therefore, play no extraordinary role in creative
thinking. (Ram et al., 1995) proposed the five components for
creativity: inferential mechanisms, knowledge sources, tasks,
situation, and strategic control.
While there are many models for the process of creative
thinking, it is not difficult to see consistent themes that span
them all. 1) The creative process involves purposeful analysis,
imaginative idea generation, and critical evaluation–the overall
creative process is a balance of imagination and analysis. 2)
Older models tend to imply that creative ideas result fro m sub-
conscious processes, largely outside the control of the thinker.
Modern models tend to imply purposeful generation of new
ideas, under the direct control of the thinker. 3) The overall
creative process requires a drive to action and the implementa-
tion of ideas. We must do more than simply imagine new things,
we must work t o make them concrete realities.
These insi ghts from a revie w of the many model s of creative
thinking have encouraged us to produce a synthetic simulation
model (Humphreys, 2004) of creative thinking that combines
the concepts behind the various models proposed over the last
years.(Figure 2)
Our model has three main components as follows:
● Recognition. Recognition uses memories storing information
in SM, STM, and LTM, sensing and learning functional or-
gans, and cognition processors.
● Creativity. Creativity units (including creative thinking me-
chani sms) and skills (creativity mapping) work to gether to
produce novel and useful produces (Mumford, 2003). The
dominant factors are usually identified as "the four
Ps"–process, product, person and place (Kozb el t , 2010). A
focus on process is shown in cognitive approaches that try to
describe thought mechanisms and techniques for creative
integration
simulation
system simulation
mechanism
R
E
A
L
I
T
Y
sensing &
learning
cognition
system
creativity system
divergence
convergence
Intrinsic motivation
verification
integration
creative results
simulation
divergent convergent
direction focus
similarity
measuremen
system simulation
mechanism
stored information
STM
LTM
SM
creativity
mapping
Figure 2.
Simu lation model of c ognition and creativity.

Z. -Q. CHENG, S. Y. JIN
93
thinking. Theories invoking divergent rather than conver-
gent thinking (such as Guiford), or those describing the
staging o f the creative p rocess (such as Wallas) are pri mar-
ily theories of creative process. J. P. Guilford (Guiford,
1967) performed important work in the field of creativity,
drawing a distinction between convergent and divergent
production or thinking. Convergent thinking involves aim-
ing at a single, correct solution to a problem, whereas di-
vergent thinking involves creatively generating multiple
answers t o a prob lem. Divergent thinking is sometimes used
as a synonym for creativity in the psychology literature. In-
trinsic, task-focused motivation is also essential to creativi-
ty.
● Verification. After verifying, elaborating, and applying the
creative idea using similarity, a creative (original and
worthwhile) result is produced.
Note that the main characters of this model are the simulation
factors, which are seamlessly integrated into the mechanical
analysis of cognition and creativity. By using the computer
simulation units, we provide a foundation to simulate the ab-
stract mental model of cognition and creativity. The simulation
could be performed by finding analytical solutions to cogni-
tion-based creative thinking problems, which enables the re-
cording, verification, and even prediction of the behavior of the
cognition-based creativity from a set of parameters and initial
conditions. Furthermore, by concurrently performing simu la-
tion and real co gnition and creati vity tasks, our new framework
can effectively deal with the interplay between experiment,
simulation, and theory for the cognition and creativity correla-
tion investigation.
Our work continues in the tradition of others (e.g. (Gra-
ham-Rowe, 2005)) in asserting that creativity is a balance of
imagination and analysis by using information. The simulation
model also purposefully avoids taking a stand on the contro-
versy of whether creativity is a conscious or subconscious cog-
nitive result. While we perso nally believe th at intrinsic motiva-
tion is a conscious, non-magical mental action, the activity of
“produ cing creative resu lts” in the model accepts cr eative ideas
regardless of their source. Finally, note that this model clearly
supports the notion that creativity is a step beyond the simple
recognition of reality. The simulation model has value only
when it is implemented in the real world.
Creative Thinking Enlightenment
As it is still i mpossible to physically record the mental cog-
nition and creativity process, we use our former model to simu-
late the functionalities of learning and thinking. In the section,
we first review thinking, and discuss why visual analogical
thinking is an appropriate choice for enlightening creati ve
thinking. We then consider an example from computer graphics
of automatic 3D model creation.
Thinking Mechanism Review
Reasoning, visual thinking, intuition and inspiration are
standing thinking mechanisms. In the following, we discuss
which can be learned and are applicable for a scientist per-
forming creative resear ch.
The origin of creativity is somewhat beyond the reach of
logical r easonin g (Aldo, 2003). The role o f logical reasoning in
research is not in making discoveries (either factual or theoret-
ical), but verifying, interpreting and developing them and
building a general th eoretical scheme. Mo st scientific facts and
theories are only true under certain conditions and our know-
ledge is so in complete that at best we can on ly reason based on
probabilities and possibilities. Besides logical reasoning, ana-
logical reasoning is a mutually exclusive alternative for the
thinking. Analogs are achieved by a comparison that deter-
mines the d egree of similarity, or an inference b ased on resem-
blance or correspondence. As we know, while results from an
analo gy may or may not be true, analogical thinking can pro-
duce new ideas.
Visual thinking, or right brained thinking, is the common
phenomenon of thinking through visual processing using the
part of the brain that is emotional and creative to organize in-
formation in an intuitive and simultaneous way. During his
lifetime, Einstein often claimed that he thought in images and
sensations rather than in words.
Intuition and inspiration indicate a sudden enlightenment or
comprehension of a situation, a clarifying idea which dramati-
cally springs into the consciousness, often, though not neces-
sarily, when one scientist is not consciously thinking of special
subject. The most characteristic circumstances of an intuition
are a period of intense work on the subject accompanied by a
desire for its solution, followed by the appearance of the crea-
tive idea with dramatic suddenness and often a sense of cer-
tainty. Intuition is still a mystical issue, and we are a long way
from reall y understanding and simulate it.
Theobald Smith’s said that: “Discovery should come as an
adventure rather than as the result of a logical process of
thought. Sharp, prolonged thinking is necessary that we may
keep on the chosen road, but it does not necessar i ly lead to
disco very”. As we know, all scientific advances rest on a base
of previous knowledge. Often, the application or transfer of a
new principle or technique from another field provides the cen-
tral id ea upon which an investigation hinges. S uch transfer is a
typical analogical thinking scheme. In attempting to apply an
existin g technique to a new problem, some new knowledge
arises.
In the process of creativity, it is not the knowledge (informa-
tion) stored which is so important as the scientist making use of
knowledge. During scientific creative thinking, analogical and
visu a l thinking are both learnable and app licable tacti cs .
New 3D Model Creation
3D modeling is the process of developing a mathematical re-
presentation of any three-dimensional object, called a “3D
model”. It is one of the most fundamental tasks in computer
graphics. We demonstrate how analogical and visual thinking
tactics may be employed within a computer program to auto-
matically creati ve ly g en erat e novel 3D models.
Creating a 3D model of modest complexity is typically a
daunting task, so a sensible strategy is to generate a novel shape
as a variation of one or more existing models. In a typical paper
(Xu et al., 2010), new shapes ar e synthesized replicating a cer-
tain style extracted from a set of input shapes. The particular
style studied is the anisotropic scaling of the shape parts. The
key enabling concept is style-content separation which facili-
tates the computation of part correspondence across a whole set
of input shapes exhibiting large style variations. Style-content
separation then allows style transfer as a basis for synthesis of
new objects. Figure 3 show the style-content separation
process and automatic 3D model creation. Our idea is a typical
example of the use of analogical thinking, this time performed

Z. -Q. CHENG, S. Y. JIN
94
by
Figure 3.
Automat ic 3D model crea tion. Up: the proc ess of conten t-style separa-
tion, bottom: new model creation by style transfer.
the computer, to create different styles of model. Using the
transfer rule, some newly-created models do not meet or re-
quirements and expectations. This shows that when con cepts
are transferred to another area, they are often instrumental in
uncovering still further knowledge. The example gives some
hints on how best to go about various activities that constitute
research, but explicit rules can not be laid out since research is
an investigatory art.
The possibility of developments in the transfer method is
perhaps the main reason why the scient ist needs t o keep himself
informed of at least the principal developments taking place in
more than his own narrow field of work.
Conclusions
A scienti st works like a p ion eer as he expl ores th e front ier of
knowledge, an d req uires man y of the same attrib u tes: en terp rise
and initiative, readiness to face difficulties and overcome them
with his own resourcefulness and ingenuity, perseverance, a
spirit of adventure, a certain dissatisfaction with well-known
territory and prevailing ideas, and an eagerness to try his own
judgment. What is produced can come in many forms and is not
specific ally singled out in a sub ject or area.
In this paper, we have tried to suggest how cognition works
for creative thinking, which is more important than the 99%
perspiration. We have tried to solve the problem by using ex-
emplars from computer science. Firstly, we have made use of
computer simulation to investigate the correlation between
cogn iti on and creati ve thinking. Then, the 3D model creation in
computer graphics is used as an illustration to explain why the
analogical and visual thinking are enlightening for creative
thinking.
It is probably inevitable that any paper which attempts to
deal with such a wide and complex subject will have many
limitations. We hope the shortcomings of our work may pro-
voke others whose achievements and experience are greater
than ours to write about this subject and so build up a greater
body of organized knowledge than is available in the literature
at present.
Acknowledgements
The work is funded by the NSFC of China (No. 61103084
and 61272334) and NUDT Education Reforming grants.
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