This timeline displays the year-by-year progress of the Human Genome Project in the context of genetics since 1865. Starting in 1990, by 1999, Chromosome 22 became the first human chromosome to be completely sequenced.
By 1982, researchers shared information via punch cards.
The amount of data grew exponentially by the end of the 1980s,
requiring new computational methods for quickly interpreting relevant
information.
Perhaps the best-known example of computational biology, the Human Genome Project, officially began in 1990. By 2003, the project had mapped around 85% of the human genome, satisfying its initial goals. Work continued, however, and by 2021 level "a complete genome" was
reached with only 0.3% remaining bases covered by potential issues. The missing Y chromosome was added in January 2022.
Since the late 1990s, computational biology has become an important part of biology, leading to numerous subfields. Today, the International Society for Computational Biology recognizes 21 different 'Communities of Special Interest', each representing a slice of the larger field. In addition to helping sequence the human genome, computational biology has helped create accurate models of the human brain, map the 3D structure of genomes, and model biological systems. Much of the original progress in computational biology emerged from the United States and Western Europe,
due to their large computational infrastructures. Recent decades have
seen growing contributions from less-wealthy nations, however. For
example, Colombia
has had an international computational biology effort since 1998,
focusing on genomics and disease in nationally-important crops like coffee and potatoes. Poland, similarly, has recently been a leader in biomolecular simulations and macromolecular sequence analysis.
Computational anatomy is the study of anatomical shape and form at the visible or gross anatomical scale of morphology.
It involves the development of computational mathematical and
data-analytical methods for modeling and simulating biological
structures. It focuses on the anatomical structures being imaged, rather
than the medical imaging devices. Due to the availability of dense 3D
measurements via technologies such as magnetic resonance imaging, computational anatomy has emerged as a subfield of medical imaging and bioengineering for extracting anatomical coordinate systems at the morpheme scale in 3D.
The original formulation of computational anatomy is as a
generative model of shape and form from exemplars acted upon via
transformations. The diffeomorphism group is used to study different coordinate systems via coordinate transformations as generated via the Lagrangian and Eulerian velocities of flow from one anatomical configuration in to another. It relates with shape statistics and morphometrics, with the distinction that diffeomorphisms are used to map coordinate systems, whose study is known as diffeomorphometry.
Mathematical biology is the use of mathematical models of
living organisms to examine the systems that govern structure,
development, and behavior in biological systems. This entails a more theoretical approach to problems, rather than its more empirically minded counterpart of experimental biology. Mathematical biology draws on discrete mathematics, topology (also useful for computational modeling), Bayesian statistics, linear algebra and Boolean algebra.
These mathematical approaches have enabled the creation of databases and other methods for storing, retrieving, and analyzing biological data, a field known as bioinformatics. Usually, this process involves genetics and analyzing genes.
Gathering and analyzing large datasets have made room for growing research fields such as data mining, and computational biomodeling, which refers to building computer models and visual simulations
of biological systems. This allows researchers to predict how such
systems will react to different environments, which is useful for
determining if a system can "maintain their state and functions against
external and internal perturbations". While current techniques focus on small biological systems, researchers
are working on approaches that will allow for larger networks to be
analyzed and modeled. A majority of researchers believe this will be
essential in developing modern medical approaches to creating new drugs
and gene therapy. A useful modeling approach is to use Petri nets via tools such as esyN.
Until recent decades theoretical ecology has largely dealt with analytic models that were detached from the statistical models used by empirical ecologists. More recently, computational methods have aided in developing theories via simulation of ecological systems, in addition to increasing application of methods from computational statistics in ecological analyses.
Systems biology consists of computing the interactions
between various biological systems ranging from the cellular level to
entire populations with the goal of discovering emergent properties.
This process usually involves networking cell signaling and metabolic pathways. Systems biology often uses computational techniques from biological modeling and graph theory to study these complex interactions at cellular levels.
Computational genomics is the study of the genomes of cells and organisms. The Human Genome Project
is one example of computational genomics. This project looks to
sequence the entire human genome into a set of data. Once fully
implemented, this could allow for doctors to analyze the genome of an
individual patient. This opens the possibility of personalized medicine, prescribing
treatments based on an individual's pre-existing genetic patterns.
Researchers are looking to sequence the genomes of animals, plants, bacteria, and all other types of life.
One of the main ways that genomes are compared is by sequence homology. Homology is the study of biological structures and nucleotide sequences in different organisms that come from a common ancestor. Research suggests that between 80 and 90% of genes in newly sequenced prokaryotic genomes can be identified this way.
Sequence alignment
is another process for comparing and detecting similarities between
biological sequences or genes. Sequence alignment is useful in a number
of bioinformatics applications, such as computing the longest common subsequence of two genes or comparing variants of certain diseases.
An untouched project in computational genomics is the
analysis of intergenic regions, which comprise roughly 97% of the human
genome. Researchers are working to understand the functions of non-coding
regions of the human genome through the development of computational and
statistical methods and via large consortia projects such as ENCODE and the Roadmap Epigenomics Project.
Understanding how individual genes contribute to the biology of an organism at the molecular, cellular, and organism levels is known as gene ontology. The Gene Ontology Consortium's mission is to develop an up-to-date, comprehensive, computational model of biological systems,
from the molecular level to larger pathways, cellular, and
organism-level systems. The Gene Ontology resource provides a
computational representation of current scientific knowledge about the
functions of genes (or, more properly, the protein and non-coding RNA molecules produced by genes) from many different organisms, from humans to bacteria.
3D genomics is a subsection in computational biology that focuses on the organization and interaction of genes within a eukaryotic cell. One method used to gather 3D genomic data is through Genome Architecture Mapping (GAM). GAM measures 3D distances of chromatin and DNA in the genome by combining cryosectioning,
the process of cutting a strip from the nucleus to examine the DNA,
with laser microdissection. A nuclear profile is simply this strip or
slice that is taken from the nucleus. Each nuclear profile contains
genomic windows, which are certain sequences of nucleotides - the base unit of DNA. GAM captures a genome network of complex, multi enhancer chromatin contacts throughout a cell.
Biomarker discovery
Computational biology also plays a role in identifying biomarkers for diseases such as cardiovascular conditions, with the integration of various 'Omic' data - such as genomics, proteomics, and metabolomics
- researchers can uncover potential biomarkers that aid in disease
diagnosis, prognosis, and treatment strategies. For instance,
metabolomic analyses have identified specific metabolites capable of
distinguishing between coronary artery disease and myocardial infarction.
Computational neuroscience is the study of brain function in terms of the information processing properties of the nervous system. A subset of neuroscience, it looks to model the brain to examine specific aspects of the neurological system. Models of the brain include:
Realistic Brain Models: These models look
to represent every aspect of the brain, including as much detail at the
cellular level as possible. Realistic models provide the most
information about the brain, but also have the largest margin for error.
More variables in a brain model create the possibility for more error
to occur. These models do not account for parts of the cellular
structure that scientists do not know about. Realistic brain models are
the most computationally heavy and the most expensive to implement.
Simplifying Brain Models: These models look to limit the scope of a model in order to assess a specific physical property
of the neurological system. This allows for the intensive computational
problems to be solved, and reduces the amount of potential error from a
realistic brain model.
It is the work of computational neuroscientists to improve the algorithms and data structures currently used to increase the speed of such calculations.
Computational neuropsychiatry is an emerging field that uses mathematical and computer-assisted modeling of brain mechanisms involved in mental disorders.
Several initiatives have demonstrated that computational modeling is an
important contribution to understand neuronal circuits that could
generate mental functions and dysfunctions.
Computational biology plays a crucial role in discovering signs of new, previously unknown living creatures and in cancer research. This field involves large-scale measurements of cellular processes, including RNA, DNA,
and proteins, which pose significant computational challenges. To
overcome these, biologists rely on computational tools to accurately
measure and analyze biological data. In cancer research, computational biology aids in the complex analysis of tumor
samples, helping researchers develop new ways to characterize tumors
and understand various cellular properties. The use of high-throughput
measurements, involving millions of data points from DNA, RNA, and other
biological structures, helps in diagnosing cancer at early stages and
in understanding the key factors that contribute to cancer development.
Areas of focus include analyzing molecules that are deterministic in
causing cancer and understanding how the human genome relates to tumor
causation.
Computational toxicology is a multidisciplinary area of
study, which is employed in the early stages of drug discovery and
development to predict the safety and potential toxicity of drug
candidates.
Computational pharmacology is "the study of the effects of genomic data to find links between specific genotypes and diseases and then screening drug data".
Drug discovery
A growing application of computational biology is drug discovery. For example, simulations of intracellular and intercellular signaling events, using data from proteomic or metabolomic experiments, may reduce dependence on experimentation in elucidating pharmacokinetics and pharmacodynamics of drug candidates in living organisms.
Increasingly, artificial intelligence plays a central role
in the drug discovery process. Using chemical structures of known
pharmaceutical agents as inputs, AI models can suggest structures of
lead compounds or predict novel modes of drug-protein binding. AI is
also used for virtual screening of candidate molecules, avoiding the need to synthesize large numbers of molecules for screening.
Techniques
Computational biologists use a wide range of software and algorithms to carry out their research.
Unsupervised learning
Unsupervised learning is a type of algorithm that finds patterns in unlabeled data. One example is k-means clustering, which aims to partition n data points into k clusters, in which each data point belongs to the cluster with the nearest mean. Another version is the k-medoids
algorithm, which, when selecting a cluster center or cluster centroid,
will pick one of its data points in the set, and not just an average of
the cluster.
A heat-map of the Jaccard distances of nuclear profiles
The algorithm follows these steps:
Randomly select k distinct data points. These are the initial clusters.
Measure the distance between each point and each of the 'k' clusters. (This is the distance of the points from each point k).
Assign each point to the nearest cluster.
Find the center of each cluster (medoid).
Repeat until the clusters no longer change.
Assess the quality of the clustering by adding up the variation within each cluster.
Repeat the processes with different values of k.
Pick the best value for 'k' by finding the "elbow" in the plot of which k value has the lowest variance.
One example of this in biology is used in the 3D mapping
of a genome. Information of a mouse's HIST1 region of chromosome 13 is
gathered from Gene Expression Omnibus. This information contains data on which nuclear profiles show up in certain genomic regions. With this information, the Jaccard distance can be used to find a normalized distance between all the loci.
Graph analytics
Graph analytics, or network analysis,
is the study of graphs that represent connections between different
objects. Graphs can represent all kinds of networks in biology such as protein-protein interaction
networks, regulatory networks, Metabolic and biochemical networks and
much more. There are many ways to analyze these networks. One of which
is looking at centrality
in graphs. Finding centrality in graphs assigns nodes rankings to their
popularity or centrality in the graph. This can be useful in finding
which nodes are most important. For example, given data on the activity
of genes over a time period, degree centrality can be used to see what
genes are most active throughout the network, or what genes interact
with others the most throughout the network. This contributes to the
understanding of the roles certain genes play in the network.
There are many ways to calculate centrality in graphs all
of which can give different kinds of information on centrality. Finding
centralities in biology can be applied in many different circumstances,
some of which are gene regulatory, protein interaction and metabolic
networks.
Supervised learning
Supervised learning
is a type of algorithm that learns from labeled data and learns how to
assign labels to future data that is unlabeled. In biology supervised
learning can be helpful when we have data that we know how to categorize
and we would like to categorize more data into those categories.
Diagram showing a simple random forest
A common supervised learning algorithm is the random forest, which uses numerous decision trees
to train a model to classify a dataset. Forming the basis of the random
forest, a decision tree is a structure which aims to classify, or
label, some set of data using certain known features of that data. A
practical biological example of this would be taking an individual's
genetic data and predicting whether or not that individual is
predisposed to develop a certain disease or cancer. At each internal
node the algorithm checks the dataset for exactly one feature, a
specific gene in the previous example, and then branches left or right
based on the result. Then at each leaf node, the decision tree assigns a
class label to the dataset. So in practice, the algorithm walks a
specific root-to-leaf path based on the input dataset through the
decision tree, which results in the classification of that dataset.
Commonly, decision trees have target variables that take on discrete
values, like yes/no, in which case it is referred to as a classification tree, but if the target variable is continuous then it is called a regression tree.
To construct a decision tree, it must first be trained using a training
set to identify which features are the best predictors of the target
variable.
Open source software provides a platform for computational biology where everyone can access and benefit from software developed in research. PLOS cites four main reasons for the use of open source software:
Reproducibility: This allows for researchers to use the exact methods used to calculate the relations between biological data.
Faster development: developers and researchers do not
have to reinvent existing code for minor tasks. Instead they can use
pre-existing programs to save time on the development and implementation
of larger projects.
Increased quality: Having input from multiple researchers
studying the same topic provides a layer of assurance that errors will
not be in the code.
Long-term availability: Open source programs are not tied
to any businesses or patents. This allows for them to be posted to
multiple web pages and ensure that they are available in the future.
Computational biology, bioinformatics and mathematical biology are all interdisciplinary approaches to the life sciences that draw from quantitative disciplines such as mathematics and information science. The NIH
describes computational/mathematical biology as the use of
computational/mathematical approaches to address theoretical and
experimental questions in biology and, by contrast, bioinformatics as
the application of information science to understand complex
life-sciences data.
Specifically, the NIH defines
Computational
biology: The development and application of data-analytical and
theoretical methods, mathematical modeling and computational simulation
techniques to the study of biological, behavioral, and social systems.
Bioinformatics:
Research, development, or application of computational tools and
approaches for expanding the use of biological, medical, behavioral or
health data, including those to acquire, store, organize, archive,
analyze, or visualize such data.
While each field is distinct, there may be significant overlap at their interface, so much so that to many, bioinformatics and computational biology are terms that are used interchangeably.
The terms computational biology and evolutionary computation
appear similar but are not identical. Evolutionary computation is a
field of computer science comprising algorithms inspired by evolution in
biology. Algorithms from within the field of evolutionary computation
can be applied to computational biology.
In philosophy of mind, the computational theory of mind (CTM), also known as computationalism, is a family of views that hold that the human mind is an information processing system and that cognition and consciousness together are a form of computation. It is closely related to functionalism, a broader theory that defines mental states by what they do rather than what they are made of.
History
Warren McCulloch and Walter Pitts (1943) were the first to suggest that neural activity is computational. They argued that neural computations explain cognition. A version of the theory was put forward by Peter Putnam and Robert W. Fuller in 1964. The theory was proposed in its modern form by Hilary Putnam in 1960 and 1961, aided by his then PhD student, philosopher and cognitive scientist Jerry Fodor, who continued the research as a post-doc in the 1960s, 1970s, and 1980s.It was later criticized by Putnam himself, John Searle, and others.
Classical computational theory of mind
The CTM holds that the human mind is a computational system
that is realized (i.e., physically implemented) by neural activity in
the brain. The theory can be elaborated in many ways and varies largely
based on how the term computation is understood.
In classical computational theory of mind (CCTM), computation is modeled in terms of Turing machines which manipulate symbols according to a rule, in combination with the internal state of the machine. A Turing machine is an abstract machine
with unlimited time and storage. CCTM does not pretend that the mind
looks like a Turing machine, but instead uses Turing machines as a
formalism. Alan Turing argued that any symbolic algorithm executed by a human brain can in theory be replicated on a Turing machine.
The critical aspect of such a computational model is that it allows to abstract away from particular physical details of the machine that is implementing the computation. For example, the appropriate computation could be implemented either by
silicon chips or biological neural networks, so long as there is a
series of outputs based on manipulations of inputs and internal states,
performed according to a rule.
Computational theories of mind are often said to require mental representation
because 'input' into a computation comes in the form of symbols or
representations of other objects. A computer cannot compute an actual
object but must interpret and represent the object in some form and then
compute the representation. Unlike CTM, the representational theory of mind shifts the focus to the symbols being manipulated. This approach better accounts for systematicity and productivity. In Fodor's view, the mind is a computational system that processes the language of thought.
Connectionist computationalism
Connectionist computationalism models the mind as a neural network.Steven Pinker and Alan Prince distinguish two types of connectionists: eliminative and implementationist. Eliminative connectionists generally reject classical
CTMs and the idea of a structured, symbolic mind, whereas
implementationists view neural networks and Turing machines as two
potentially complementary levels of analysis. It is indeed possible in
theory to implement a neural network in a Turing machine, or a Turing
machine in a neural network.
"Computer metaphor"
The CTM is not the same as the computer metaphor, comparing the mind to a modern-day digital computer. While the computer metaphor draws an analogy between the mind as
software and the brain as hardware, CTM is the claim that the mind is
literally a computational system. "Computational system" is not intended
to mean a modern-day electronic computer. The CTM is also different from the computational theory of cognition (CTC), which merely states that neural computations explain cognition, without regard to whether they explain phenomenal consciousness.
Pancomputationalism
The CTM raises a question that remains a subject of debate:
what does it take for a physical system (such as a mind, or an
artificial computer) to perform computations? A very straightforward
account is based on a simple mapping between abstract mathematical
computations and physical systems: a system performs computation C if
and only if there is a mapping between a sequence of states individuated
by C and a sequence of states individuated by a physical description of
the system.
Putnam (1988) and Searle (1992) argue that this simple mapping account (SMA) trivializes the empirical import of computational descriptions. As Putnam put it, "everything is a Probabilistic Automaton under some Description". Even rocks, walls, and buckets of water—contrary to appearances—are computing systems. Gualtiero Piccinini identifies different versions of pancomputationalism. Searle wrote:
the wall behind my back is right now implementing the WordStar
program, because there is some pattern of molecule movements that is
isomorphic with the formal structure of WordStar. But if the wall is
implementing WordStar, if it is a big enough wall it is implementing any
program, including any program implemented in the brain.
In
response to the trivialization criticism, and to restrict SMA,
philosophers of mind have offered different accounts of computational
systems. These typically include causal account, semantic account,
syntactic account, and mechanistic account. Instead of a semantic restriction, the syntactic account imposes a syntactic restriction. The mechanistic account was first introduced by Gualtiero Piccinini in 2007.
Criticism
A range of arguments have been proposed against physicalist conceptions used in computational theories of mind.
An early, though indirect, criticism of the computational theory of mind comes from philosopher John Searle. In his thought experiment known as the Chinese room, Searle attempts to refute the claims that artificially intelligent agents can be said to have intentionality and understanding and that these systems, because they can be said to be minds themselves, are sufficient for the study of the human mind. Searle asks us to imagine that there is a man in a room with no way of
communicating with anyone or anything outside of the room except for a
piece of paper with symbols written on it that is passed under the door.
With the paper, the man is to use a series of provided rule books to
return paper containing different symbols. Unknown to the man in the
room, these symbols are of a Chinese language, and this process
generates a conversation that a Chinese speaker outside of the room can
actually understand. Searle contends that the man in the room does not
understand the Chinese conversation. This was originally written as a
repudiation of the idea that computers work like minds.
Objections like Searle's might be called insufficiency
objections. They claim that computational theories of mind fail because
computation is insufficient to account for some capacity of the mind.
Arguments from qualia, such as Frank Jackson's knowledge argument,
can be understood as objections to computational theories of mind in
this way—though they take aim at physicalist conceptions of the mind in
general, and not computational theories specifically.
Objections have also been put forth that are directly tailored for computational theories of mind.
Jerry Fodor himself argues that the mind is still a very
long way from having been explained by the computational theory of mind.
The main reason for this shortcoming is that most cognition is abductive
and global, hence sensitive to all possibly relevant background beliefs
to (dis)confirm a belief. This creates, among other problems, the frame problem
for the computational theory, because the relevance of a belief is not
one of its local, syntactic properties but context-dependent.
Putnam himself (see in particular Representation and Reality and the first part of Renewing Philosophy)
became a prominent critic of computationalism for a variety of reasons,
including ones related to Searle's Chinese room arguments, questions of
world-word reference relations, and thoughts about the mind-body problem.
Regarding functionalism in particular, Putnam has claimed along lines
similar to, but more general than Searle's arguments, that the question
of whether the human mind can implement computational
states is not relevant to the question of the nature of mind, because
"every ordinary open system realizes every abstract finite automaton." Computationalists have responded by aiming to develop criteria describing what exactly counts as an implementation.
Roger Penrose
has proposed the idea that the human mind does not use a knowably sound
calculation procedure to understand and discover mathematical
intricacies. This would mean that a normal Turing complete computer would not be able to ascertain certain mathematical truths that human minds can. The application of Gödel's theorem by Penrose to demonstrate it, however, was widely criticized, and is considered erroneous.
Notable theorists
Daniel Dennett proposed the multiple drafts model, in which consciousness seems linear
but is actually blurry, distributed over space and time in the brain.
Consciousness is the computation, there is no extra step in which you
become conscious of the computation.
Jerry Fodor
argues that mental states, such as beliefs and desires, are relations
between individuals and mental representations. He maintains that these
representations can only be correctly explained in terms of a language of thought
(LOT) in the mind. Further, this language of thought itself is codified
in the brain, not just a useful explanatory tool. Fodor adheres to a
species of functionalism, maintaining that thinking and other mental
processes consist primarily of computations operating on the syntax of
the representations that make up the language of thought. In later work (Concepts and The Elm and the Expert),
Fodor has refined and even questioned some of his original
computationalist views, and adopted LOT2, a highly modified version of
LOT.
David Marr proposed that cognitive processes have three levels of description: the computational level, which describes what operations the system performs and why it performs them; the algorithmic level, which presents the algorithm used for computing it; and the implementational level, which describes the physical implementation of the algorithm postulated at the algorithmic level.
Ulric Neisser popularized the term cognitive psychology in his book with that title published in 1967. Neisser characterizes people as dynamic information-processing systems
whose mental operations might be described in computational terms.
Steven Pinker argued in his 1997 book How the Mind Works that "thinking and feeling consist of information-processing in the brain" and sought to explain the brain from an evolutionary psychology
perspective. He wrote that similarly to organs, human mental faculties
exist because they serve a role for survival or reproduction.
Hilary Putnam proposed functionalism
to describe consciousness, asserting that it is the computation that
equates to consciousness, regardless of whether the computation is
operating in a brain or in a computer.
Creativity may also describe the ability to find new solutions to problems or new methods to accomplish a goal. Therefore, creativity enables people to solve problems in new ways.
Most ancient cultures (including ancient Greece, ancient China, and ancient India) lacked the concept of creativity, seeing art as a form of discovery rather than a form of creation. In the Judeo-Christian-Islamic tradition,
creativity is seen as the sole province of God, and human creativity
was considered an expression of God's work; the modern conception of
creativity came about during the Renaissance, influenced by humanist ideas.
The English word "creativity" comes from the Latin term creare (meaning "to create"). Its derivational suffixes
also come from Latin, such as the etymological root “crescere”, which
means “to let things grow”. This aspect of creativity is emphasized more
in indigenous and Eastern concepts of creativity. The word "create" appeared in English as early as the 14th century—notably in Chaucer's The Parson's Tale to indicate divine creation. The modern meaning of creativity in reference to human creation did not emerge until after the Age of Enlightenment.
Definition
In a summary of scientific research into creativity,
psychology professor Michael Mumford wrote, "We seem to have reached a
general agreement that creativity involves the production of novel, useful products." Similarly, in psychologist Robert Sternberg's words, creativity produces "something original and worthwhile".
Authors have diverged dramatically in their precise
definitions beyond these general commonalities: social geographer Peter
Meusburger estimated that over a hundred different definitions can be
found in the literature. One definition given by Dr. E. Paul Torrance
in the context of assessing an individual's creative ability is "a
process of becoming sensitive to problems, deficiencies, gaps in
knowledge, missing elements, disharmonies; identifying the difficulty;
searching for solutions, making guesses, or formulating hypotheses about
the deficiencies: testing and retesting these hypotheses and possibly
modifying and retesting them; and finally communicating the results."
Philosophy professor Ignacio L. Götz, following the
etymology of the word, argued that creativity is not necessarily
"making". He confined it to the act of creating without thinking about
the end product. While many definitions of creativity seem almost synonymous with
originality, Götz also emphasized the difference between creativity and
originality. Götz asserted that one can be creative without necessarily
being original. When someone creates something, they are certainly
creative at that point, but they may not be original in the sense that
their creation is not something new.
Creativity in general is usually distinguished from innovation in particular, where the emphasis is on implementation. Academics and authors Teresa Amabile
and Michael Pratt defined creativity as the production of novel and
useful ideas and innovation as the implementation of creative ideas, while the OECD and Eurostat
stated that "innovation is more than a new idea or an invention; an
innovation requires implementation, either by being put into active use
or by being made available for use by other parties, firms, individuals,
or organizations."
There is also emotional creativity, which is described as a pattern of cognitive abilities and personality
traits related to originality and appropriateness in emotional
experience.
From an interdisciplinary point a view, creativity can
serve to increase neuronal connectivity, cognitive and emotional
coherence as well as social cohesion. In this respect, creativity can help to cope with despair, hate and violence.
Greek philosophers like Plato rejected the concept of creativity, preferring to see art as a form of discovery. When asked in The Republic, "Will we say, of a painter, that he makes something?", Plato answers, "Certainly not, he merely imitates."
Ancient
Most ancient cultures, including ancient Greece, ancient China, and ancient India, lacked the concept of creativity, seeing art as a form of discovery and
not creation. The ancient Greeks had no terms for "to create" or
"creator" except for the expression poiein (to make), which only applied to poiesis (poetry) and to the poietes (poet, or "maker", who made it). Plato did not believe in art as a form of creation. He asks in the Republic, "Will we say of a painter that he makes something?" He answers, "Certainly not, he merely imitates."
It is commonly argued that the notion of "creativity" originated in Western cultures through Christianity, as a matter of divine inspiration. According to scholars, the earliest Western conception of creativity was the Biblical story of the creation given in Genesis. However, this is not creativity in the modern sense, which did not arise until the Renaissance.
In the Judeo-Christian-Islamic tradition, creativity was the sole
province of God; humans were not considered to have the ability to
create something new except as an expression of God's work. A similar concept existed in Greek culture, where the Muses were seen as mediating inspiration from the gods. Romans and Greeks invoked the concept of an external creative "daemon" (Greek) or "genius"
(Latin), linked to the sacred or the divine. However, none of these
views are similar to the modern concept of creativity, and the rejection
of creativity in favor of discovery and the belief that individual
creation was a conduit of the divine would dominate the West until the
Renaissance and even later.
Renaissance
It was during the Renaissance that creativity was first
conceived not as a conduit from the divine, but as arising from the
abilities of "great men". This could be attributed to the leading intellectual movement of the time, aptly named humanism, which developed an intensely anthropocentric outlook on the world, valuing the intellect and achievement of the individual. From this philosophy arose the Renaissance man (or polymath), an individual who embodies the principles of humanism in their ceaseless courtship with knowledge and creation. One of the most well-known and immensely accomplished examples is Leonardo da Vinci.
From the 17th to the 19th centuries
However, the shift from divine inspiration to the abilities
of the individual was gradual and would not become immediately apparent
until the Age of Enlightenment. By the 18th century, creativity (notably in aesthetics) linked with the concept of imagination became more frequent. In the writing of Thomas Hobbes, imagination became a key element of human cognition. William Duff
was one of the first to identify imagination as a quality of genius,
typifying the separation being made between talent (productive, but not
breaking new ground) and genius.
As an independent topic of study, creativity received little attention until the 19th century. Psychologist Mark Runco and Robert Albert argue that creativity as the
subject of proper study began seriously to emerge in the late 19th
century with the increased interest in individual differences inspired
by the arrival of Darwinism. In particular, they refer to the work of Francis Galton, who, through his eugenicist outlook, took a keen interest in the heritability of intelligence, with creativity taken as an aspect of genius.
Modern
In the late 19th and early 20th centuries, leading mathematicians and scientists such as Hermann von Helmholtz (1896) and Henri Poincaré (1908) began to reflect on and publicly discuss their creative processes. The
insights of Poincaré and von Helmholtz inspired the accounts of the
creative process by pioneering theorists such as Graham Wallas and Max Wertheimer. In his work Art of Thought, published in 1926, Wallas presented one of the first models of the creative process. In
the Wallas model, creative insights and illuminations may be explained
by a process consisting of five stages:
preparation (preparatory work on a problem that focuses the individual's mind on the problem and explores the problem's dimensions),
incubation (in which the problem is internalized into the unconscious mind although nothing appears externally to be happening),
intimation (the creative person gets a "feeling" that a solution is on its way),
illumination or insight (in which the creative idea bursts forth from its preconscious processing into conscious awareness);
verification (in which the idea is consciously verified, elaborated, and then applied).
Wallas's model is also often treated as four stages, with "intimation" seen as a sub-stage.
Wallas considered creativity to be a legacy of the evolutionary process, which allowed humans to quickly adapt to rapidly changing environments. Simonton provides an updated perspective on this view in his book, Origins of Genius: Darwinian Perspectives on Creativity.
In 1927, mathematician and philosopher Alfred North Whitehead gave the Gifford Lectures at the University of Edinburgh, later published as Process and Reality. He is credited with having coined the term "creativity" to serve as the ultimate category of his metaphysical scheme.
Although psychometric studies of creativity had been
conducted by The London School of Psychology as early as 1927 with the
work of H.L. Hargreaves into the Faculty of Imagination. The formal psychometric measurement of creativity, from the standpoint of orthodox psychological literature, is usually considered to have begun with psychologist J.P. Guilford's address to the American Psychological Association in 1950. That address helped to popularize the study of creativity and to focus
attention on scientific approaches to conceptualizing creativity.
Statistical analyzes led to the recognition of creativity as an aspect
of human cognition separate from IQ-type
intelligence, under the study of which it had previously been subsumed.
Guilford's work suggested that above a threshold level of IQ, the
relationship between creativity and classically measured intelligence
broke down.
Across cultures
Creativity is viewed differently in different countries. For example, cross-cultural research centered in Hong Kong found that
Westerners view creativity more in terms of the individual attributes of
a person, such as their aesthetic taste, while Chinese people view
creativity more in terms of the social influence of creative people
(i.e. what they can contribute to society). Mpofu, et al., surveyed 28 African languages and found that 27 had no
word which directly translated to "creativity", with Arabic being the
exception. The linguistic relativity
hypothesis (i.e. that language can affect thought) suggests that the
lack of an equivalent word for "creativity" may affect the views of
creativity among speakers of such languages. However, more research
would be needed to establish this, and there is certainly no suggestion
that this linguistic difference makes people any less—or more—creative.
Nevertheless, it is true that there has been very little research on
creativity in Africa and Latin America. Creativity has been more thoroughly researched in the northern
hemisphere, but there are cultural differences between northern
countries. In Scandinavia, creativity is seen as an individual attitude
that helps people cope with life's challenges, while in Germany, creativity is seen more as a process that can be applied to help solve problems.
Classification
"Four C" model
Psychologists James Kaufman and Ronald Beghetto introduced a "four C" model of creativity. The four "C's" are:
mini-c ("transformative learning" involving "personally meaningful interpretations of experiences, actions, and insights").
little-c (everyday problem-solving and creative expression).
Pro-C (exhibited by people who are professionally or vocationally creative, though not necessarily eminent).
Big-C (creativity considered great in a given field).
This model was intended to help accommodate models and
theories of creativity that stressed competence as an essential
component and a historic transformation of a creative domain as the
highest mark of creativity. It also, the authors argued, made a useful
framework for analyzing creative processes in individuals.
The contrast signified by the terms "Big C" and "little C"
has been widely used. Kozbelt, Beghetto, and Runco used a
little-c/Big-C model to review major theories of creativity. Margaret Boden distinguished between h-creativity (historical) and p-creativity (personal).
Ken Robinson and Anna Craft focused on creativity in a general population, particularly with
respect to education. Craft makes a similar distinction between "high"
and "little c" creativity and cites Robinson as referring to "high" and "democratic" creativity. Mihaly Csikszentmihalyi defined creativity in terms of individuals judged to have made significant creative and perhaps domain-changing contributions. Simonton analyzed the career trajectories of eminent creative people in
order to map patterns and predictors of creative productivity.
"Four P's" aspects
Theories of creativity (and empirical investigations of
why some people are more creative than others) have focused on a variety
of aspects. The dominant factors are usually identified as "the four
P's," a framework first put forward by Mel Rhodes:
Process
A focus on process
is shown in cognitive approaches that try to describe thought
mechanisms and techniques for creative thinking. Theories invoking divergent rather than convergent thinking (such as that of Guilford), or those describing the staging of the creative process (such as that of Wallas) are primarily theories of the creative process.
Product
A focus on a creative product
usually attempts to assess creative output, whether for psychometrics
(see below) or to understand why some objects are considered creative.
It is from a consideration of product that the standard definition of
creativity as the production of something both novel and useful arises.
Person
A focus on the nature of the creative person considers more general intellectual habits, such as openness, levels of ideation, autonomy, expertise, exploratory behavior, and so on.
Press and place
A focus on place (or press)
considers the circumstances in which creativity flourishes, such as
degrees of autonomy, access to resources, and the nature of gatekeepers.
Creative lifestyles are characterized by nonconforming attitudes and
behaviors, as well as flexibility.
"Five A's" aspects
In 2013, based on a sociocultural critique of the Four-P's
model as individualistic, static, and decontextualized, psychology
professor and author Vlad Petre Glăveanu proposed a "Five A's" model
consisting of actor, action, artifact, audience, and affordance. In this model, the actor is the person with attributes but who is also located within social networks; action is the process of creativity not only in internal cognitive terms but also external, bridging the gap between ideation and implementation; artifacts
emphasize how creative products typically represent cumulative
innovations over time rather than abrupt discontinuities; and
"press/place" is divided into audience and affordance,
which consider the interdependence of the creative individual with the
social and material world, respectively. Although not supplanting the
Four P's model in creativity research, the Five A's model has exerted
influence over the direction of some creativity research, and has been credited with bringing coherence to studies across a number of creative domains.
Process theories
There has been significant research conducted in the fields of psychology and cognitive science
towards better understanding the processes by which creativity occurs.
The results of these studies have led to several possible explanations
of the sources and methods of creativity.
"Incubation" is a temporary break from creative problem solving that can result in insight. Empirical research has investigated whether, as the concept of "incubation" in Wallas's
model implies, a period of interruption or rest from a problem may aid
creative problem-solving. Early work proposed that creative solutions to
problems arise mysteriously from the unconscious mind while the
conscious mind is occupied with other tasks. This hypothesis is included in Csikszentmihalyi's
five-phase model of the creative process, which describes incubation as
a time when one's unconscious takes over. This was supposed to allow
for unique connections to be made without the conscious mind trying to
make logical order out of the problem.
Ward listed various hypotheses that have been advanced to explain why
incubation may aid creative problem-solving and notes how some empirical evidence
is consistent with a different hypothesis: incubation aids creative
problems in that it enables "forgetting" of misleading clues. The
absence of incubation may lead the problem solver to become fixated on inappropriate problem-solving strategies.
Divergent thinking
J. P. Guilford drew a distinction between convergent and divergent production, or convergent and divergent thinking.
Convergent thinking involves aiming for a single, correct, or best
solution to a problem (e.g. "How can we get a crewed rocket to land on
the moon safely and within budget?"). Divergent thinking, on the other
hand, involves the creative generation of multiple answers to an
open-ended prompt (e.g. "How can a chair be used?"). Divergent thinking is sometimes used as a synonym for creativity in
psychological literature or is considered the necessary precursor to
creativity. However, as Runco pointed out, there is a clear distinction between creative thinking and divergent thinking. Creative thinking focuses on the production, combination, and
assessment of ideas to formulate something new and unique, while
divergent thinking focuses on conceiving a variety of ideas that are not
necessarily new or unique. Other researchers have occasionally used the
terms flexible thinking or fluid intelligence, which are also roughly similar to (but not synonymous with) creativity. While convergent and divergent thinking differ greatly in terms of approach to problem solving, it is believed that both are employed to some degree in solving most real-world problems.
Geneplore model
In 1992, Finke, et al., proposed the "Geneplore model", in
which creativity takes place in two phases: a generative phase, where
an individual constructs mental representations called "preinventive"
structures, and an exploratory phase where those structures are used to
come up with creative ideas. Some evidence shows that when people use their imagination to develop
new ideas, those ideas are structured in predictable ways in accordance
with properties of existing categories and concepts. Weisberg argued, in contrast, that creativity involves ordinary cognitive processes yielding extraordinary results.
Explicit–implicit interaction theory
Helie and Sun proposed a framework for understanding creativity in problem solving,
namely the explicit–implicit interaction (EII) theory of creativity.
This theory attempts to provide a more unified explanation of relevant
phenomena (in part by reinterpreting/integrating various fragmentary
existing theories of incubation and insight).
The EII theory relies mainly on five basic principles:
co-existence of, and the difference between, explicit and implicit knowledge
simultaneous involvement of implicit and explicit processes in most tasks
redundant representation of explicit and implicit knowledge
integration of the results of explicit and implicit processing
iterative (and possibly bidirectional) processing
A computational implementation of the theory was developed based on the CLARION cognitive architecture
and used to simulate relevant human data. This work is an initial step
in the development of process-based theories of creativity, encompassing
incubation, insight, and various other related phenomena.
In The Act of Creation, Arthur Koestler
introduced the concept of "bisociation" – that creativity arises as a
result of the intersection of two quite different frames of reference. In the 1990s, various approaches in cognitive science that dealt with metaphor, analogy,
and structure mapping converged, and a new integrative approach to the
study of creativity in science, art, and humor emerged under the label conceptual blending.
Honing theory
Honing theory, developed principally by psychologist Liane Gabora,
posits that creativity arises due to the self-organizing, self-mending
nature of a worldview. The creative process is a way by which the
individual hones (and re-hones) an integrated worldview. Honing theory
places emphasis not only on the externally visible creative outcome but
also on the internal cognitive restructuring and repair of the worldview
brought about by the creative process. When one is faced with a creatively demanding task, there is an
interaction between one's conception of the task and one's worldview.
The conception of the task changes through interaction with the
worldview, and the worldview changes through interaction with the task.
This interaction is reiterated until the task is complete, at which
point the task is conceived of differently and the worldview is subtly
or drastically transformed, following the natural tendency of a
worldview to attempt to resolve dissonance and seek internal consistency
amongst its components, whether they be ideas, attitudes, or bits of
knowledge. Dissonance in a person's worldview is, in some cases,
generated by viewing their peers' creative outputs, and, so, people
pursue their own creative endeavors to restructure their worldviews and
reduce dissonance. This shift in worldview and cognitive restructuring through creative
acts has also been considered as a way to explain possible benefits of
creativity for mental health. The theory also addresses challenges not addressed by other theories of
creativity, such as the factors guiding restructuring and the evolution
of creative works.
A central feature of honing theory is the notion of a "potentiality state". Honing theory posits that creative thought proceeds not by searching
through and randomly "mutating" predefined possibilities but by drawing
upon associations that exist due to overlap in the distributed
neural-cell assemblies that participate in the encoding of experiences
in memory. Midway through the creative process, one may have made
associations between the current task and previous experiences but not
yet disambiguated which aspects of those previous experiences are
relevant to the current task. Thus, the creative idea may feel
"half-baked". At that point, it can be said to be in a potentiality
state, because how it will actualize depends on the different internally
or externally generated contexts it interacts with.
Honing theory is held to explain certain phenomena not
dealt with by other theories of creativity—for example, how different
works by the same creator exhibit a recognizable style or "voice" even
in different creative outlets. This is not predicted by theories of
creativity that emphasize chance processes or the accumulation of
expertise, but it is predicted by honing theory, according to which
personal style reflects the creator's uniquely structured worldview.
Another example is the environmental stimulus for creativity. Creativity
is commonly considered to be fostered by a supportive, nurturing, and
trustworthy environment conducive to self-actualization. In line with
this idea, Gabora posits that creativity is a product of culture and
that our social interactions evolve our culture in way that promotes
creativity.
In everyday thought, people often spontaneously imagine alternatives to reality when they think "if only...". Their counterfactual thinking is viewed as an example of everyday creative processes. It has been proposed that the creation of counterfactual alternatives
to reality depends on cognitive processes that are similar to rational
thought.
Imaginative thought in everyday life can be categorized based on whether it involves perceptual or motor-related mental imagery,
novel combinatorial processing, or altered psychological states. This
classification aids in understanding the neural foundations and
practical implications of imagination.
Creative thinking is a central aspect of everyday life,
encompassing both controlled and undirected processes. This includes
divergent thinking and stage models, highlighting the importance of
extra- and meta-cognitive contributions to imaginative thought.
Brain-network dynamics play a crucial role in creative
cognition. The default and executive control networks in the brain
cooperate during creative tasks, suggesting a complex interaction
between these networks in facilitating everyday imaginative thought.
Dialectical theory
The term "dialectical theory of creativity" dates back to psychoanalyst Daniel Dervin and was later developed into an interdisciplinary theory. This theory starts with the ancient concept that creativity takes place
in an interplay between order and chaos. Similar ideas can be found in
neuroscience and psychology. Neurobiologically, it can be shown that the
creative process takes place in a dynamic interplay between coherence
and incoherence that leads to new and usable neuronal networks.
Psychology shows how the dialectics of convergent and focused thinking
with divergent and associative thinking leads to new ideas and products.
Personality traits such as the "Big Five" seem to bedialectically intertwined in the creative process: emotional instability versus stability,
extraversion versus introversion, openness versus reserve, agreeableness
versus antagonism, and disinhibition versus constraint. The dialectical theory of creativity also applies to counseling and psychotherapy.
Neuroeconomic framework
Lin and Vartanian developed a neurobiological description of creative cognition. This interdisciplinary framework integrates theoretical principles and empirical results from neuroeconomics, reinforcement learning, cognitive neuroscience, and neurotransmission research on the locus coeruleus system. It describes how decision-making processes studied by neuroeconomists as well as activity in the locus coeruleus system underlie creative cognition and the large-scale brain network dynamics associated with creativity. It suggests that creativity is an optimization and utility maximization problem that requires individuals to determine the optimal way to exploit and explore ideas (e.g., the multi-armed bandit problem). This utility-maximization process is thought to be mediated by the locus coeruleus system, and this creativity framework describes how tonic
and phasic locus coeruleus activities work in conjunction to facilitate
the exploiting and exploring of creative ideas. This framework not only
explains previous empirical results but also makes novel and
falsifiable predictions at different levels of analysis (ranging from
neurobiological to cognitive and personality differences).
Behaviorism theory
B.F. Skinner attributed creativity to accidental behaviors that are reinforced by the environment. In behaviorism, creativity can be understood as novel or unusual
behaviors that are reinforced if they produce a desired outcome. Spontaneous behaviors by living creatures are thought to reflect past learned behaviors. In this way, a behaviorist may say that prior learning caused novel behaviors to be
reinforced many times over, and the individual has been shaped to
produce increasingly novel behaviors. A creative person, according to this definition, is someone who has
been reinforced more often for novel behaviors than others. Behaviorists
suggest that anyone can be creative, they just need to be reinforced to
learn to produce novel behaviors.
Investment theory
The "investment theory of creativity" suggests that many
individual and environmental factors must exist in precise ways for
extremely high, as opposed to average, levels of creativity to result.
In the "investment" sense, a person with their particular
characteristics in their particular environment may see an opportunity
to devote their time and energy to something that has been overlooked by
others. The creative person develops an undervalued or under-recognized
idea to the point where it is established as a new and creative idea.
Just as in the financial world, some investments are worth the buy-in,
while others are less productive and do not generate returns to the
extent that the investor expected. This "investment theory of
creativity" asserts that creativity might rely to some extent on the
right investment of effort being added to a field at the right time in
the right way.
Jürgen Schmidhuber's formal theory of creativity postulates that creativity, curiosity, and interestingness are by-products of a simple computational principle for measuring and optimizing learning progress.
Consider an agent able to manipulate its environment and thus its own sensory inputs. The agent can use a black box optimization method such as reinforcement learning to learn, through informed trial and error, sequences of actions that maximize the expected sum of its future reward
signals. There are extrinsic reward signals for achieving externally
given goals, such as finding food when hungry. But for Schmidhuber's objective function
to be maximized also includes an additional, intrinsic term to model
"wow-effects". This non-standard term motivates purely creative behavior
of the agent, even when there are no external goals.
A wow-effect is formally defined as follows: as the agent
is creating and predicting and encoding the continually growing history
of actions and sensory inputs, it keeps improving the predictor or
encoder, which can be implemented as an artificial neural network, or some other machine learning
device, that can exploit regularities in the data to improve its
performance over time. The improvements can be measured precisely, by
computing the difference in computational costs (storage size, number of
required synapses, errors, time) needed to encode new observations
before and after learning. This difference depends on the encoder's
present subjective knowledge, which changes over time, but the theory formally takes this
into account. The cost difference measures the strength of the present
wow-effect due to sudden improvements in data compression or computational speed. It becomes an intrinsic reward signal for the action selector. The objective function thus motivates the action optimizer to create action sequences that cause more wow-effects.
Irregular, random data (or noise) do not permit any
wow-effects or learning progress, and thus are "boring" by nature
(providing no reward). Already-known and predictable regularities also
are boring. Temporarily interesting are only the initially unknown,
novel, regular patterns in both actions and observations. This motivates
the agent to perform continual, open-ended, active, creative
exploration.
According to Schmidhuber, his objective function explains the activities of scientists, artists, and comedians. For example, physicists are motivated to create experiments leading to observations that obey previously unpublished physical laws, permitting better data compression.
Likewise, composers receive intrinsic reward for creating non-arbitrary
melodies with unexpected but regular harmonies that permit wow-effects
through data compression improvements. Similarly, a comedian gets an
intrinsic reward for "inventing a novel joke with an unexpected punch line,
related to the beginning of the story in an initially unexpected but
quickly learnable way that also allows for better compression of the
perceived data."
J. P. Guilford's group, which pioneered the modern psychometric
study of creativity, constructed several performance-based tests to
measure creativity in 1967, including asking participants to write
original titles for a story with a given plot, asking participants to
come up with unusual uses for everyday objects such as bricks, and
asking participants to generate a list of consequences of unexpected
events, such as the loss of gravity. Guilford was trying to create a
model for intellect as a whole, but in doing so, he also created a model
for creativity. Guilford assumed that creativity was not an abstract
concept, which was an important assumption needed for creativity
research. The idea that creativity was a category, rather than a single concept, enabled other researchers to look at creativity from a new perspective.
Additionally, Guilford hypothesized one of the first
models that specified the components of creativity. He explained that
creativity was a result of having three qualities: the ability to
recognize problems, "fluency", and "flexibility". "Fluency" encompassed
"ideational fluency", or the ability to rapidly produce a variety of
ideas fulfilling stated requirements; "associational fluency", or the
ability to generate a list of words associated with a given word; and
"expressional fluency", or the ability to organize words into larger
units such as phrases, sentences, and paragraphs. "Flexibility"
encompassed both "spontaneous flexibility", or the general ability to be
flexible, and "adaptive flexibility", or the ability to produces
responses that are novel and of high quality.
This represents the base model which several researchers
would alter to produce their own new theories of creativity years later. Building on Guilford's work, tests were developed, sometimes called "divergent thinking" (DT) tests, which have been both praised and criticized. One example is the Torrance Tests of Creative Thinking developed in 1966. These test set forth tasks requiring divergent thinking, as well as
other problem-solving skills, the tests being scored according to four
categories: "fluency", the total number of meaningful, and relevant,
ideas generated; "flexibility", the number of different categories of
responses; "originality", the statistical rarity of the responses; and
"elaboration", the amount of detail given.
Computer scoring
Considerable progress has been made in the automated
scoring of divergent-thinking tests, using a semantic approach. When
compared to human raters, natural language processing (NLP) techniques are reliable and valid for the scoring of originality. Computer programs were able to achieve a correlation to human graders of 0.60 and 0.72.
Semantic networks also devise originality scores that yield significant correlations with socio-personal measures. A team of researchers led by James C. Kaufman
and Mark A. Runco combined expertise in creativity research, natural
language processing, computational linguistics, and statistical data
analysis to devise a scalable system for computerized automated testing:
the SparcIt Creativity Index Testing system. This system enabled
automated scoring of DT tests that is reliable, objective, and scalable,
thus addressing most of the issues of DT tests that had been found and
reported. The resultant computer system was able to achieve a correlation to human graders of 0.73.
Social-personality approaches
Researchers have taken a social-personality approach by
using personality traits such as independence of judgement,
self-confidence, attraction to complexity, aesthetic orientation, and
risk-taking as measures of personal creativity. Within the framework of the Big Five personality traits, a consistent few of these traits have emerged as being correlated to creativity. Openness to experience is consistently related to a host of different assessments of creativity. Investigation of the other Big Five traits has demonstrated subtle
differences between different domains of creativity. Compared to
non-artists, artists tend to have higher levels of openness to
experience and lower levels of conscientiousness, while scientists are
more open to experience, conscientious, and higher in the confidence-dominance facets of extraversion compared to non-scientists.
Self-reporting questionnaires
Biographical methods use quantitative characteristics,
such as the number of publications, patents, or artistic performances
that can be credited to a person. While this method was originally
developed for highly creative personalities, today it is also available as self-report questionnaires supplemented with frequent, less outstanding creative behaviors such as writing a short story or creating recipes. The self-report questionnaire most frequently used in research is the Creative Achievement Questionnaire, a self-report test that measures creative achievement across ten
domains, which was described in 2005 and shown to be reliable when
compared to other measures of creativity and to independent evaluations
of creative output.
Factors
Intelligence
The potential relationship between creativity and intelligence
has been of interest since the last half of the twentieth century, when
many influential studies extensively studied both. This joint focus
highlighted both the theoretical and practical importance of the
relationship: researchers were interested in not only if the two qualities were related, but also how and why.
There are multiple theories accounting for their relationship, with there being three main theories. Threshold theory states that intelligence is a necessary, but not
sufficient, condition for creativity, and that there is a moderate
positive relationship between creativity and intelligence until IQ ~120. Certification theory states that creativity is not intrinsically
related to intelligence. Instead, individuals are required to meet the
requisite level of intelligence in order to gain a certain level of
education or work, which in turn offers the opportunity to be creative.
In this theory, displays of creativity are moderated by intelligence. Interference theory states, in contrast, that extremely high intelligence might interfere with creative ability.
Sternberg and O'Hara proposed a different framework of
five possible relationships between creativity and intelligence: that
creativity was a subset of intelligence; that intelligence was a subset
of creativity; that the two constructs overlapped; that they were both
part of the same construct (coincident sets); or that they were distinct
constructs (disjoint sets).
Creativity as a subset of intelligence
A number of researchers include creativity, either explicitly or implicitly, as a key component of intelligence, for example:
Sternberg's Theory of Successful Intelligence includes creativity as a main component and comprises three
sub-theories: contextual (analytic), contextual (practical), and
experiential (creative). Experiential sub-theory—the ability to use
pre-existing knowledge and skills to solve new and novel problems—is
directly related to creativity.
The Cattell–Horn–Carroll theory
(CHC) includes creativity as a subset of intelligence, associated with
the broad group factor of long-term storage and retrieval (Glr). Glr narrows abilities relating to creativity include ideational
fluency, associational fluency, and originality/creativity. Silvia et al. conducted a study to look at the relationship between divergent
thinking and verbal fluency tests and reported that both fluency and
originality in divergent thinking were significantly affected by the
broad-level Glr factor. Martindale extended the CHC-theory by proposing that people who are creative are
also selective in their processing speed. Martindale argues that in the
creative process, larger amounts of information are processed more
slowly in the early stages, and as a person begins to understand the
problem, the processing speed is increased.
The Dual Process Theory of Intelligence posits a two-factor or type model of intelligence. Type 1 is a
conscious process and concerns goal-directed thoughts. Type 2 is an
unconscious process, and concerns spontaneous cognition, which
encompasses daydreaming and implicit learning ability. Kaufman argues
that creativity occurs as a result of Type 1 and Type 2 processes
working together in combination. Each type in the creative process can
be used to varying degrees.
Intelligence as a subset of creativity
In this relationship model, intelligence is a key component in the development of creativity, for example:
Sternberg & Lubart's Investment Theory, using the metaphor of a stock market, demonstrates that creative
thinkers are like good investors—they buy low and sell high (in their
ideas). Like undervalued or low-valued stock, creative individuals
generate unique ideas that are initially rejected by other people. The
creative individual has to persevere and convince others of the idea's
value. After convincing others, and thus increasing the idea's value,
the creative individual "sells high" by leaving the idea with the other
people and moving on to generate another idea. According to this theory,
six distinct, but related elements contribute to successful creativity:
intelligence, knowledge, thinking styles, personality, motivation, and
environment. Intelligence is just one of the six factors that can,
either solely or in conjunction with the other five factors, generate
creative thoughts.
Amabile's Componential Model of Creativity posits three within-individual components needed for
creativity—domain-relevant skills, creativity-relevant processes, and
task motivation—and one component external to the individual—their
surrounding social environment. Creativity requires the confluence of
all components. High creativity will result when a person is
intrinsically motivated, possesses both a high level of domain-relevant
skills and has high skills in creative thinking, and is working in a
highly creative environment.
The Amusement Park Theoretical Model is a four-step theory in which domain-specific and generalist views are
integrated into a model of creativity. The researchers make use of the
metaphor of the amusement park to demonstrate that, within each of the
following creative levels, intelligence plays a key role:
To get into the amusement park, there are
initial requirements (e.g., time and transportation needed to go to the
park). Initial requirements (such as intelligence) are necessary, but
not sufficient for creativity. They are more like prerequisites for
creativity, and if a person does not possess the basic level of the
initial requirement (intelligence), then they will not be able to
generate creative thoughts and behaviour.
Secondly, there are the subcomponents—general thematic
areas—that increase in specificity. Like choosing which type of
amusement park to visit (e.g., a zoo or a water park), these areas
relate to the areas in which someone could be creative (e.g., poetry).
Thirdly, there are specific domains. After choosing the
type of park to visit (e.g., if one chooses a waterpark, that person has
to choose which specific park to go to). For example, within the poetry
domain there are many different forms (e.g., free verse, riddles,
sonnets, etc.).
Lastly, there are micro-domains. These are the specific
tasks that reside within each domain (e.g., individual rides at the
waterpark equate to individual lines in a poem in free-verse).
Creativity and intelligence as overlapping yet distinct constructs
These concepts posit creativity and intelligence as distinct, but intersecting constructs, for example:
In Renzulli's Three-Ring Conception of Giftedness, giftedness is an overlap of above-average intellectual ability,
creativity, and task commitment. Under this view, creativity and
intelligence are distinct constructs, but they overlap under the correct
conditions.
In the PASS theory of intelligence,
the planning component—the ability to solve problems, make decisions,
and take action—strongly overlaps with the concept of creativity.
Threshold Theory (TT) derives from a number of previous
research findings that suggested that a threshold exists in the
relationship between creativity and intelligence—both constructs are
moderately positively correlated up to an IQ of ~120. Above this
threshold, if there is a relationship at all, it is small and weak.TT posits that a moderate level of intelligence is necessary for creativity.
Creativity and intelligence as coincident sets
Under this view, researchers posit that there are no
differences in the mechanisms underlying creativity from those used in
normal problem solving, and in normal problem solving there is no need
for creativity. Thus, creativity and intelligence (problem solving) are
the same thing. Perkins referred to this as the "nothing-special" view.
Creativity and intelligence as disjoint sets
In this view, creativity and intelligence are completely
different, unrelated constructs. Along with the coincident set view,
this is quite a rare position taken within the literature.
Affective influence
Some theories suggest that creativity may be particularly susceptible to affective influence.
The term "affect" in this context refers to liking or disliking key
aspects of the subject in question. This work largely follows from
findings in psychology regarding the ways in which affective states are
involved in human judgment and decision-making.
According to Alice Isen,
positive affect has three primary effects on cognitive activity. First,
it makes additional cognitive material available for processing,
increasing the number of cognitive elements available for association.
Second, it leads to defocused attention and a more complex cognitive
context, increasing the breadth of those elements that are treated as
relevant to the problem. Third, it increases cognitive flexibility,
increasing the probability that diverse cognitive elements will in fact
become associated. Together, these processes enable creativity.
Barbara Fredrickson, in her broaden-and-build
model, suggests that positive emotions, such as joy and love, broaden a
person's available repertoire of cognitions and actions, thus enhancing
creativity.
According to these researchers, positive emotions increase
the number of cognitive elements available for association (attention
scope) and the number of elements that are relevant to the problem
(cognitive scope). Day-by-day psychological experiences—including
emotions, perceptions, and motivation—significantly impact creative
performance. Creativity is higher when emotions and perceptions are more
positive and when intrinsic motivation is stronger.
Some meta-analyses, such as Baas, et al., (2008) analyzing
66 studies of creativity and affect, support the link between
creativity and positive affect.
Links have been identified between creativity and mood disorders, particularly manic-depressive disorder (a.k.a. bipolar disorder) and depressive disorder (a.k.a. unipolar disorder). However, different artists have described mental illness as having both positive and negative effects on their work. In general, people who have worked in the arts industry throughout
history have faced many environmental factors that are associated with,
and can sometimes influence, mental illness—things such as poverty,
persecution, social alienation, psychological trauma, substance abuse,
and high stress.
Studies
A study by psychologist J. Philippe Rushton found creativity to correlate with intelligence and psychoticism. Another study found creativity to be greater in people with schizotypal personality disorder than in people with either schizophrenia or those without mental health disorders.While divergent thinking was associated with activation of both sides of the prefrontal cortex, schizotypal individuals were found to have much greater activation of their right prefrontal cortex. That study hypothesized that such individuals are better at accessing
both hemispheres, allowing them to make novel associations at a faster
rate. Consistent with this hypothesis, ambidexterity is also more common in people with schizotypal personality disorder and schizophrenia. Three studies by Mark Batey and Adrian Furnham demonstrated the relationships between schizotypal personality disorder, hypomanic personality, and several different measures of creativity.
A study of 300,000 persons with schizophrenia, bipolar
disorder, or unipolar depression, and their relatives, found
overrepresentation in creative professions of those with bipolar
disorder as well as for undiagnosed siblings of those with schizophrenia
or bipolar disorder. There
was no overall overrepresentation, but overrepresentation for artistic
occupations, among those diagnosed with schizophrenia. There was no association for those with unipolar depression or their relatives.
Another study, involving more than one million people,
conducted by Swedish researchers at the Karolinska Institute, reported a
number of correlations between creative occupations and mental
illnesses. Writers had a higher risk of anxiety and bipolar disorders,
schizophrenia, unipolar depression, and substance abuse, and were almost
twice as likely as the general population to kill themselves. Dancers
and photographers were also more likely to have bipolar disorder. Those in the creative professions were no more likely to have
psychiatric disorders than other people, although they were more likely
to have a close relative with a disorder, including anorexia and, to
some extent, autism, the Journal of Psychiatric Research reported.
Nancy Andreasen was one of the first researchers to carry
out a large-scale study of creativity and whether mental illnesses have
an impact on someone's ability to be creative. She expected to find a
link between creativity and schizophrenia, but her research sample (the
book-authors she pooled) had no history of schizophrenia. Her findings
instead showed that 80% of the creative group previously had some
episode of mental illness in their lifetime. When she performed follow-up studies over a 15-year period, she found
that 43% of the authors had bipolar disorder, compared to 1% of the
general public.
In 1989 another study, by Kay Redfield Jamison, reaffirmed
those statistics, with 38% of her sample of authors having a history of
mood disorders. Anthony Storr, a prominent psychiatrist, remarked:
The
creative process can be a way of protecting the individual against being
overwhelmed by depression, a means of regaining a sense of mastery in
those who have lost it, and, to a varying extent, a way of repairing the
self-damaged by bereavement or by the loss of confidence in human
relationships which accompanies depression from whatever cause.
Bipolar disorders
People diagnosed with bipolar disorder report themselves
as having a larger range of emotional understanding, heightened states
of perception, and an ability to connect better with those in the world
around them. Other reported traits include higher rates of productivity, higher
senses of self-awareness, and greater empathy. Those who have bipolar
disorder also understand their own sense of heightened creativity and
ability to get immense numbers of tasks done all at once. In one study,
of 219 participants (aged 19 to 63) diagnosed with bipolar disorder, 82%
of them reported having elevated feelings of creativity during their
hypomanic swings.
A study done by Shapiro and Weisberg also showed a
positive correlation between the manic upswings of the cycles of bipolar
disorder and the ability of an individual to be more creative. The data showed, however, that it was not the depressive swing that
brings forth dark creative spurts, but the act of climbing out of the
depressive episode that sparks creativity. The reason behind this spur
of creative genius could come from the type of self-image that the
person has during a time of hypomania. A hypomanic person may feel a
bolstered sense of self-confidence, creative confidence, and sense of
individualism.
Opinions
Vaitsa Giannouli believes that the creativity a person
diagnosed with bipolar disorder feels comes as a form of "stress
management". In the realm of music, one might be expressing one's stress or pains
through the pieces one writes in order to better understand those same
feelings. Famous authors and musicians, along with some actors, would
often attribute their wild enthusiasm to something like a hypomanic
state. The artistic side of society has been notorious for behaviors that are
seen as maladapted to societal norms. Symptoms of bipolar disorder
correlate with behaviors in high-profile creative personalities such as
alcohol addiction; drug abuse including stimulants, depressants,
hallucinogens and dissociatives, opioids, inhalants, and cannabis;
difficulties in holding regular occupations; interpersonal problems;
legal issues; and a high risk of suicide.
Robert Weisberg believes that the state of mania sets
"free the powers of a thinker". He implies that not only has the person
become more creative, but they have fundamentally changed the kind of
thoughts they produce. In a study of poets, who are especially highly afflicted with bipolar
disorders, over a period of three years those poets would have cycles of
creating really creative and powerful works of poetry. The timelines
over the three-year study looked at the poets' personal journals and
their clinical records, and found that the timelines between their most
powerful poems matched that of their upswings in bipolar disorder.
Personal traits
Creativity can be expressed in a variety of ways,
depending on the uniqueness of people and environments. Theorists have
suggested a number of different models of the creative person. However,
the creativity-profiling approach must take into account the tension
between predicting the creative profile of an individual, as
characterized by the psychometric approach, and the evidence that group creativity is founded on diversity and difference.
From a personality-traits perspective, there are a number of traits that are associated with creativity in people. Creative people tend to be more open to new experiences, are more
self-confident, are more ambitious, self-accepting, impulsive, driven,
dominant, and hostile, compared to people who are less creative.
Divergent production
One characteristic of creative people, as measured by some
psychologists, is what is called "divergent production"—the ability of a
person to generate a diverse assortment, yet an appropriate amount, of
responses to a given situation. One way to measure divergent production is by administering the Torrance Tests of Creative Thinking, which assess the diversity, quantity, and appropriateness of
participants' responses to a variety of open-ended questions. Some
researchers also emphasize how creative people are better at balancing
between divergent and convergent production, which depends on an
individual's innate preference or ability to explore and exploit ideas.
Dedication and expertise
Other researchers of creativity see that what
distinguishes creative people as a cognitive process of dedication to
problem-solving and developing expertise in the field of their creative
expression. Hardworking people study the work of people before them in
their milieu, become experts in their fields, and then have the ability
to add to and build upon previous information in innovative and creative
ways. In a study of projects by design students, students who had more
knowledge of their subject on average exhibited greater creativity in
carrying out their projects.
Motivation
A person's motivation may also be predictive of their
level of creativity. Motivation stems from two different sources:
intrinsic and extrinsic. Intrinsic motivation is an internal drive
within a person to participate as a result of personal interest,
desires, hopes, goals, etc. Extrinsic motivation is a drive from outside
and might take the form of payment, rewards, fame, approval from
others, etc. Although intrinsic and extrinsic motivation can both
increase creativity in certain cases, strictly extrinsic motivation
often impedes creativity in people.
Environment
In studying exceptionally creative people in history, some
common traits in lifestyle and environment are often found. Creative
people usually had supportive, but rigid and non-nurturing, parents.
Most had an interest in their field at an early age, and most had a
highly supportive and skilled mentor in their field of interest. Often
the field they chose was relatively uncharted, allowing for their
creativity to be expressed more. Most exceptionally creative people
devoted almost all of their time and energy into their craft, and after about a decade had a creative breakthrough of fame. Their lives were marked with
extreme dedication and a cycle of hard-work and breakthroughs as a
result of their determination.
Creativity is a fundamental component of the creative arts
and design practice. It allows artists and designers to generate
innovative ideas, solve complex problems, create products and
experiences that are meaningful and impactful, stay ahead of trends, and
anticipate future needs. Author Austin Kleon
asserts that all creative work builds on what came before. Embracing
influences and educating oneself in the work of others is conducive to
creativity.
Neuroscience
Distributed functional brain network associated with divergent thinking
The neuroscience
of creativity looks at the operation of the brain during creative
behavior. One article writes that "creative innovation might require
coactivation and communication between regions of the brain that
ordinarily are not strongly connected." People who excel at creative innovation tend to differ from others in
three ways: first, they have a high level of specialized knowledge;
second, they are capable of divergent thinking mediated by the frontal lobe; and, third, they are able to modulate neurotransmitters such as norepinephrine in their frontal lobe. Thus, the frontal lobe appears to be the part of the cortex that is most important for creativity.
A 2015 study of creativity found that it involves the
interaction of multiple neural networks, including those that support
associative thinking, along with other default mode network functions. In 2018, some experiments showed that when the brain suppresses obvious
or "known" solutions, the outcome is solutions that are more creative.
This suppression is mediated by alpha oscillations in the right temporal
lobe and activity in the right frontal pole.
REM sleep
Creativity involves the forming of associative elements
into new combinations that are useful or meet some requirement. Sleep
aids this process. REM rather than NREM sleep appears to be responsible. This may be due to changes in cholinergic and noradrenergicneuromodulation that occurs during REM sleep. During this period of sleep, high levels of acetylcholine in the hippocampus suppress feedback from the hippocampus to the neocortex,
and lower levels of acetylcholine and norepinephrine in the neocortex
encourage the spread of associational activity within neocortical areas
without control from the hippocampus. This is in contrast to waking consciousness, during which higher levels
of norepinephrine and acetylcholine inhibit recurrent connections in
the neocortex. REM sleep may aid creativity by allowing "neocortical
structures to reorganize associative hierarchies, in which information
from the hippocampus would be reinterpreted in relation to previous
semantic representations or nodes."
Vandervert model
Vandervert described how the brain's frontal lobes and the cognitive functions of the cerebellum
collaborate to facilitate creativity and innovation. Vandervert's
explanation rests on considerable evidence that all processes of working memory (responsible for processing all thought) are adaptively modeled for increased efficiency by the cerebellum. The cerebellum (consisting of 100 billion neurons, which is more than in the entirety of the rest of the brain) also adaptively models all bodily movement for efficiency. The
cerebellum's adaptive models of working memory processing are then fed
back to especially frontal lobe working memory control processes, where creative and innovative thoughts arise. (Apparently, creative insight or the "aha" experience is then triggered in the temporal lobe.)
According to Vandervert, the details of creative
adaptation begin in "forward" cerebellar models, which are
anticipatory/exploratory controls for movement and thought. These
cerebellar processing and control architectures have been termed
Hierarchical Modular Selection and Identification for Control (HMOSAIC). New, hierarchically-arranged levels of the cerebellar control
architecture (HMOSAIC) develop as mental mulling in working memory is
extended over time. These new levels of the control architecture are fed
forward to the frontal lobes. Since the cerebellum adaptively models
all movement and all levels of thought and emotion, Vandervert's approach helps explain creativity and innovation in
sports, art, music, the design of video games, technology, mathematics,
the child prodigy, and thought in general.
Vandervert argues that when a person is confronted with a
challenging new situation, visual-spatial working memory and
speech-related working memory are decomposed and re-composed
(fractionated) by the cerebellum and then blended in the cerebral cortex
in an attempt to deal with the new situation. With repeated attempts to
deal with challenging situations, the cerebro-cerebellar blending
process continues to optimize the efficiency of how working memory deals
with the situation or problem. He also argues that this is the same process (only involving
visual-spatial working memory and pre-language vocalization) that led to
the evolution of language in humans. Vandervert and Vandervert–Weathers have pointed out that this blending
process, because it continuously optimizes efficiencies, constantly
improves prototyping attempts toward the invention or innovation of new
ideas, music, art, or technology. Prototyping, they argue, not only produces new products, it trains the
cerebro-cerebellar pathways involved to become more efficient at
prototyping itself. Furthermore, Vandervert and Vandervert-Weathers
believe that this repetitive "mental prototyping", or mental rehearsal
involving the cerebellum and the cerebral cortex, explains the success
of the self-driven, individualized patterning of repetitions initiated
by the teaching methods of the Khan Academy.
The model proposed by Vandervert has, however, received incisive critique from several authors.
Flaherty model
In 2005, Alice Flaherty presented a three-factor model of
the creative drive. Drawing from evidence in brain imaging, drug
studies, and lesion analysis, she described the creative drive as
resulting from an interaction of the frontal lobes, the temporal lobes, and dopamine from the limbic system.
The frontal lobes may be responsible for idea generation, and the
temporal lobes for idea editing and evaluation. Abnormalities in the
frontal lobe (such as depression or anxiety) generally decrease
creativity, while abnormalities in the temporal lobe often increase
creativity. High activity in the temporal lobe typically inhibits
activity in the frontal lobe, and vice versa. High dopamine levels
increase general arousal and goal-directed behaviors and reduce latent inhibition, with all three effects increasing the drive to generate ideas.
Lin and Vartanian model
In 2018, Lin and Vartanian proposed a neuroeconomic framework that precisely describes norepinephrine's role in creativity and modulating large-scale brain networks associated with creativity. This framework describes how neural activity in different brain regions and networks, such as the default mode network, track utility or subjective values of ideas.
Economics
Economic approaches to creativity have focused on three
aspects – the impact of creativity on economic growth, methods of
modeling markets for creativity, and the maximization of economic
creativity (innovation).
In the early 20th century, Joseph Schumpeter introduced the economic theory of creative destruction to describe the way in which old ways of doing things are destroyed and replaced by the new. Some economists (such as Paul Romer)
view creativity as an important element in the recombination of
elements to produce new technologies and products and, consequently,
economic growth. Creativity leads to capital, and creative products are protected by intellectual property laws.
Mark A. Runco and Daniel Rubenson have tried to describe a "psychoeconomic" model of creativity. In such a model, creativity is the product of endowments and active
investments in creativity; the costs and benefits of bringing creative
activity to market determine the supply of creativity. Such an approach
has been criticized for its view of creativity consumption as always
having positive utility, and for the way it prematurely analyzes the value of future innovations.
In his 2002 book, The Rise of the Creative Class, economistRichard Florida
popularized the notion that regions with "3 T's of economic
development: Technology, Talent, and Tolerance" also have high
concentrations of creative professionals and tend to have a higher level of economic development.
Sociology
Creativity research for most of the twentieth century was
dominated by psychology and business studies, with little work done in
sociology. Since the turn of the millennium, there has been more
attention paid by sociological researchers, but sociology has yet to establish creativity as a specific research
field, with reviews of sociological research into creativity a rarity in
high-impact literature.
While psychology has tended to focus on the individual as
the locus of creativity, sociological research is directed more at the
structures and context within which creative activity takes place,
primarily based in sociology of culture, which finds its roots in the works of Marx, Durkheim, and Weber.
This has meant a focus on the cultural and creative industries as
sociological phenomena. Such research has covered a variety of areas,
including the economics and production of culture, the role of creative
industries in development, and the rise of the "creative class".
Education
For those who view the conventional system of schooling as stifling creativity, an emphasis is made (particularly in the preschool/kindergarten and early school years) to provide a creativity-friendly, rich, imagination-fostering environment for young children. Researchers have seen this as important because technology is advancing
at an unprecedented rate and creative problem-solving will be needed to
cope with these challenges as they arise. In addition to helping with problem solving, creativity also helps
students identify problems where others have failed to do so. The Waldorf School is an example of an education program that promotes creative thought.
Promoting intrinsic motivation and problem solving are two
areas where educators can foster creativity in students. Students are
more creative when they see a task as intrinsically motivating, valued
for its own sake. To promote creative thinking, educators need to identify what motivates
their students and to structure teaching around it. Providing students
with a choice of activities allows them to become more intrinsically
motivated and therefore creative in completing the tasks.
Teaching students to solve problems that do not have
well-defined answers is another way to foster their creativity. This is
accomplished by allowing students to explore problems and redefine them,
possibly drawing on knowledge that at first may seem unrelated to the
problem in order to solve it. In adults, mentoring individuals is another way to foster their creativity. However, the benefits of mentoring creativity apply only to creative contributions considered great in a given field, not to everyday creative expression.
Musical creativity is a gateway to the flow state, which
is conducive to spontaneity, improvisation, and creativity. Studies show
that it is beneficial to emphasize students' creative side and
integrate more creativity into their curriculums, with a notable
strategy being through music. One reason for this is that students are able to express themselves
through musical improvisation in a way that taps into higher order brain
regions, while connecting with their peers and allowing them to go
beyond typical pattern generation. In this sense, improvisation is a form of self-expression that can
generate connectivity between peers and surpass the age-old rudimentary
aspects of school.
Scotland
In the Scottish education system,
creativity is identified as a core skillset for learning, life, and
work, and is defined as "a process which generates ideas that have value
to the individual. It involves looking at familiar things with a fresh
eye, examining problems with an open mind, making connections, learning
from mistakes, and using imagination to explore new possibilities." The need to develop a shared language and understanding of creativity
and its role across every aspect of learning, teaching, and continuous
improvement was identified as a necessary aim; and a set of four skills is used to allow educators to discuss and
develop creativity across all subjects and sectors of education –
curiosity, open-mindedness, imagination, and problem solving. Distinctions are made between creative learning (when learners are
using their creativity skills), creative teaching (when educators are
using their own creativity skills), and creative change (when creativity
skills are applied to planning and improvement). Scotland's national
Creative Learning Plan supports the development of creativity skills in all learners and of
educators' expertise in developing creativity skills. A range of
resources has been created to support and assess this, including a
national review of creativity learning by Her Majesty's Inspectorate for
Education.
China
China recognizes that creativity is crucial for national security, social development, and generally benefitting the people. Measures have been proposed to enhance creative ability in the country.
European Union
The European Union
sees creativity as important for the development of basic skills, and
has declared 2009 the Year of Creativity and Innovation. Countries such
as France, Germany, Italy, and Spain have made the encouragement of creativity a part of their educational and economic policies.
Organizational creativity
Training meeting in an eco-design stainless steel company in Brazil. The leaders, among other things, wish to cheer and encourage the workers in order to achieve a higher level of creativity.
Various research studies set out to establish that
organizational effectiveness depends to a large extent on the creativity
of the workforce. For any given organization, measures of effectiveness
vary, depending upon the organization's mission, environmental context,
nature of work, the product or service it produces, and customer
demands. Thus, the first step in evaluating organizational effectiveness
is to understand the organization itself – how it functions, how it is
structured, and what it emphasizes.
Similarly, social psychologists, organizational
scientists, and management scientists (who research factors that
influence creativity and innovation in teams and organizations) have
developed integrative theoretical models that emphasize the elements of
team composition, team processes, and organizational culture. These
theoretical models also emphasize the mutually reinforcing relationships
between those elements in promoting innovation.
Research studies of the knowledge economy may be
classified into three levels: macro, meso, and micro. Macro studies are
at a societal or transnational level. Meso studies focus on
organizations. Micro investigations center on the working of workers.
There is also an interdisciplinary dimension when researching business, economics, education, human resource management, knowledge and organizational management, sociology, psychology, knowledge economy-related sectors – especially software, and advertising.
Organizational culture
Supportive and motivational environments that create psychological safety, encourage risk-taking, and tolerate mistakes increase team creativity. Organizations in which help-seeking, help-giving, and collaboration
are rewarded promote innovation by providing opportunities and contexts
in which team processes that lead to collective creativity can occur. Additionally, leadership styles
that downplay hierarchies or power differences within an organization,
and empower people to speak up about their ideas or opinions, also help
to create cultures that are conducive to creativity.
Team composition
The diversity of team members' backgrounds and knowledge
can increase team creativity by expanding the collection of unique
information that is available to the team and by introducing different
perspectives that can be integrated in novel ways. The Millennium Conferences on Creativity,
a two-year-long Canadian review of the subject, for example, advocated
for new linkages between the arts and science communities and targeted
funding for multidisciplinary research. However, under some conditions, diversity can also decrease team
creativity by making it more difficult for team members to communicate
about ideas and causing interpersonal conflicts between those with
different perspectives. Thus, the potential advantages of diversity must be supported by
appropriate team processes and organizational cultures in order to
enhance creativity.
Team processes
Team communication norms,
such as respecting others' expertise, paying attention to others'
ideas, expecting information sharing, tolerating disagreements, negotiating,
remaining open to others' ideas, learning from others, and building on
each other's ideas, increase team creativity by facilitating the social
processes involved with brainstorming and problem solving.
Through these processes, team members can access their collective pool
of knowledge, reach shared understandings, identify new ways of
understanding problems or tasks, and make new connections between ideas.
Engaging in these social processes also promotes positive team affect, which facilitates collective creativity.
There is a long-standing debate on how material
constraints (e.g., lack of money, materials, or equipment) affect
creativity. In psychological and managerial research, there are two
competing views. In one view, scholars propose a negative effect of
material constraints on innovation and claim that material constraints
starve creativity. Proponents argue that adequate material resources are needed to engage
in creative activities such as experimenting with new solutions and idea
exploration. In an opposing view, scholars assert that people tend to stick to
established routines or solutions as long as they are not forced to
deviate from them by constraints. For example, material constraints facilitated the development of jet engines in World War II.
To reconcile these competing views, contingency models were proposed. The rationale behind these models is that certain contingency factors (e.g., creativity climate or creativity-relevant skills) influence the relationship between constraints and creativity. These contingency factors reflect the need for higher levels of
motivation and skills when working on creative tasks under constraints. Depending on these contingency factors, there is either a positive or negative relationship between constraints and creativity.
An empirical synthesis, of which methods work best in enhancing creativity, was published by Haase et al. Summarising the results of 84 studies, the authors found that complex
training courses, meditation, and cultural exposure were most effective
in enhancing creativity, while the use of cognitive-manipulation drugs
was noneffective.
Need for closure
Experiments suggest the need for closure of task participants, whether as a reflection of personality or induced (through time pressure), negatively impacts creativity. Accordingly, it has been suggested that reading fiction, which can
reduce the cognitive need for closure, may encourage creativity.
"Malevolent creativity" is the "dark side" of creativity. This type of creativity is not typically accepted within society and is
defined by the intention to cause harm to others through original and
innovative means. While it is often associated with criminal behavior,
it can also be observed in ordinary day-to-day life as lying, cheating,
and betrayal.
Malevolent creativity should be distinguished from
negative creativity in that negative creativity may unintentionally
cause harm to others, whereas malevolent creativity is malevolently
motivated.
Crime
Malevolent creativity is a key contributor to crime and in
its most destructive form can even manifest as terrorism. As creativity
requires deviating from the conventional, there is permanent tension
between being creative and going too far—in some cases to the point of
breaking the law. Aggression is a key predictor of malevolent
creativity, and increased levels of aggression correlate with a higher
likelihood of committing crime.
Predictive factors
Although everyone shows some levels of malevolent
creativity under certain conditions, those that have a higher propensity
towards it have increased tendencies to deceive and manipulate others
for their own gain. While malevolent creativity appears to dramatically
increase when an individual is treated unfairly, personality,
particularly aggressiveness, is also a key predictor in anticipating
levels of malevolent thinking. Researchers Harris and Reiter-Palmon
investigated the role of aggression in levels of malevolent creativity,
in particular levels of implicit aggression and the tendency to employ
aggressive actions in response to problem solving. The personality
traits of physical aggression, conscientiousness, emotional intelligence, and implicit aggression all seem to be related with malevolent creativity. Harris and Reiter-Palmon's research showed that when subjects were
presented with a problem that designed to trigger malevolent creativity,
participants high in implicit aggression and low in premeditation
expressed the largest number of malevolently themed solutions. When
presented with the more benign problem designed to trigger prosocial
motives of helping others and cooperating, those high in implicit
aggression, even if they tended to be highly impulsive, were far less
destructive in their imagined solutions. The researchers concluded
premeditation, more than implicit aggression, controlled an individual's
expression of malevolent creativity.
The current measure for malevolent creativity is the 13-item Malevolent Creativity Behaviour Scale (MCBS).
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