Stop Sugar-coating Creativity!

Stop Sugar-coating Creativity!

Stop Sugar-coating Creativity!

In response to a high demand for answers, the bunkum and balderdash of oversimplified creativity solutions are continuously shoved down a hungry market’s throat. The question remains: Is the current state of knowledge about creativity in a position to deliver meaningful, scientifically sound conclusions to what creativity is and how to foster it?

The Creativity Post just turned six years old. I want to thank our amazing authors and loyal audience for their active participation in this ongoing project exploring creativity from every possible angle. To mark this happy occasion, I would like to share with you a few remarks on how creativity studies are looking.

Before I start my soliloquy, I would like to express my most profound appreciation to all researchers working on understanding creativity. It takes a lot of guts to swing at this complex and vague subject with scientific tools. At the same time, I also want to emphasize that creativity, like every subject of science, needs constant reexamination—a permanent state of epistemological vigilance—which is even more critical when it comes to a discipline so young and yet so important to all of us.

Science is a noble pursuit of the truth in all things, and if you fully understand the process of scientific discovery, then you know that it rarely happens “on demand.” Science has its own dynamics, including a hypothesis and an empirical experiment, which make for a grueling and often slow process full of detours and retractions.

Right now, a rigorous science of creativity is still emerging. In fact, it’s a miracle we’re able to comprehend anything about creativity, knowing that our precise understanding of high mental processes is still in its infancy.

Here are some challenges we’re currently facing in the field of creativity research:

Supply and Demand

The biggest problem in the more “mainstream” side of popular creativity studies comes from a mismatch between supply and demand. While there is a high demand for definitive answers to questions like  “How does one foster creativity?” and “What makes some people more creative than others?” the supply, or a systematic and rigorous knowledge of the subject matter, is often limited, vague or incomplete.

So who is driving this demand? The eager audience comes from two sources: education and business. Smart and caring teachers know all too well that in order for kids to compete and succeed in the new, unknown world before us (a place that is increasingly more fast-paced and technology-driven than ever before), they must foster skills like mental flexibility, open-mindedness and the ability to come up with ingenious solutions to problems. We have no clue what jobs will be available for next generations, therefore nurturing creativity in students is a safe bet for the future. Creativity, to teachers, seems to be the last bastion of natural human excellence, especially when pitted against automation.

Simultaneously, the business world is interested in fostering creativity for their bottom line. It’s simply much cheaper (and more cost-effective) to retain the employees they already have and support those employees’ creativity skills than continuously rotate their workforce. However, creativity and open-mindedness diminish proportionally to the time employees work in a particular field. While they will gain experience and knowledge about their industry after enough time, the more employees settle and become comfortable with the status quo, the more they produce routine solutions and become less innovative.

When all businesses large and small feel pressured to innovate and adjust quickly to continually changing environments, the ability to come up with creative solutions has significantly high value.

The One-Size-Fits-All Solution

Historically speaking, when we rush science to produce certain simplified answers, bad things happen. Let me give you an example from recent history.

The rising obesity epidemic in the U.S. demanded a rapid response. In 1992, using preliminary studies agglomerated by scientists, USDA approved a “pyramid of nutrition,” featuring carbs at the bottom and fat at the tiny top, as it was assumed to not be necessary and to be avoided. The food pyramid was soon in every school—every cafeteria, every physical education classroom—and dieticians endorsed it in pamphlets which rarely questioned the pyramid’s validity. The result was opposite to what incentivized the food pyramid’s creation in the first place.

Manufacturers responded to these incomplete “science-based" findings with newly concocted and marketed foods. But to make low-fat products taste good, they had to add sugar and other simple carbohydrates, making the obesity problem even worse. Researchers who built the food pyramid actually had access to the appropriate and accurate data (i.e., simple carbohydrates are bad; complex carbohydrates are good), but those distinctions were omitted for the sake of “simplicity” over accuracy. The obesity epidemic in the U.S. continued to rise and still rages on to this day with a vengeance The food pyramid is a mistake with severe consequences.  

Similar to nutrition, a very complex subject in which rarely does a one-size-fits-all solution work well, the study of creativity is a series of multifaceted, dense inquiries. Because we are still unsure on how creativity works, we should be extremely cautious in our search for simple answers.

Unfortunately, however, we’re not always so careful. Creativity gurus and coaches are having a field day serving “low-fat/high-sugar” quick fixes which may promise a lot, but might potentially cause harm and severe disappointment instead. We live in an era of binging on questionable “creativity enhancers.”

Where Creativity Testing Falls Short

About three-quarters of what you’ve read about creativity in the fields of psychology and neuroscience is derived from some type of divergent thinking test. To be more precise, this includes the Torrance Test; Remote Association Test (RAT); and Alternative Uses Test (AUT). All of these are based on the assumption that someone’s ability to produce multiple solutions to an inquiry is a proxy for creativity.

I was always very skeptical about this sweeping assumption. Recently, some acclaimed researchers have also expressed similar doubts. John Baer explains the entirety of his argument in “How Divergent Thinking Tests Mislead Us,” but let me quote from his most recent book:

“(…) calling divergent-thinking tests creativity tests would be rather like calling tests of one’s ability to recall strings of random numbers intelligence tests.” (Domain Specificity of Creativity, page 27)

Currently, there is a prevalent consensus that creativity needs both “divergent” and “convergent” thinking, which doesn't make things any clearer. Arne Dietrich delivers a scorching criticism of this dichotomy by asking a highly logical question:

“(…) if both divergent and convergent thinking lead to both creative and non-creative thinking, what is there about either divergent or convergent thinking that is creative or non-creative?” (How Creativity Happens in the Brain, page 27)

Convergent and divergent thinking are vague categories, and it is challenging to establish what role they play in different types of creativity. Therefore, testing divergent thinking doesn’t tell us much about creativity or creative potential, for that matter. This presents a significant problem since tests based on divergent thinking are used continuously in both psychology and neuroscience.

What A “Creative Personality" You Have

Another way psychology conceptualizes creativity is by mapping certain personality traits considered more conducive towards creativity. You’ve probably heard that creative people score higher on openness to experience. This might be statistically true, but still. You cannot tell anything about a person’s creativity from just drawing on a single personality trait. Creative people come in all different shapes and sizes, and being “open to experience” is neither necessary nor sufficient in being considered creative.

If this technical jargon gives you a headache and you’re doubting whether there really is any significance in possible testing pitfalls, then I want to offer you a handy intuition pump:

Take a look at these two images. Who do you think would score higher on any readily available creativity test?


Picture number 1: A member of The Fabulous of Unicorns, a “hedonistic, polyamorous unicorn cult


Picture number 2: Nikola Tesla—a brilliant inventor, physicist and engineer

  I bet heavily that the person in Picture 1 would score much higher on any type of divergent thinking test and personality test, especially one that measures openness to experience. Let’s remember that divergent thinking tests are language tests. You would need a rich vocabulary and fortified worldview to pass it with flying colors. I assume the person in Picture 1 must have some type of security net to comfortably roll in glitter all day, so I’m betting she has middle-class parents. (Ergo, she went to decent schools, has a broad worldview and well-developed artistic abilities.) 

When it comes to Tesla, though, things get complicated. Consider this scenario: When asked about multiple uses of a brick, let’s say Tesla comes up with just three or four possible uses, effectively scoring him low on ideational fluency. And all of his possible uses would somehow be related to the concept of electromagnetism, since he was clearly obsessed with the subject, so again he would score very low, this time on flexibility. But what if one of his answers were genuinely, groundbreakingly brilliant? Who would be able to see and evaluate the depth of his insight and its true originality?  

Without question, we need a creativity test which works across disciplines—a creativity test which will not label people like Tesla non-creative, much like a divergent thinking or personality test might. The current tests are not sufficient at the task of diagnosing and identifying creativity in individuals.

Due to these testing complications, some researchers have proposed abandoning the search for general creativity altogether and instead advocate focusing on domain-specific creativity. They’ve noticed that a set of required skills in creativity varies dramatically from one discipline to another. A creative dancer and a creative entrepreneur might need a completely different set of skills in order to display creativity in their own realm.

As measured today, creativity is not a transferable skill. Being a creative writer doesn’t make you a creative dancer, nor does it make you a creative engineer. Note that in contrast, an IQ test (with all its imperfections) actually does measure certain aspects of intelligence and has predictive value (e.g., with higher IQ, you will do better in math and science and if your score is statistically low, you might be treated differently by the legal system.)

When it comes to creativity, outlining the proper criteria for evaluation is not a trivial matter. After all, in the very definition of creativity, context-dependent terms like “originality,” “newness,” and “value” are lurking at us with every step. If we were to stay focused solely on “personal creativity”—a subjective feeling of something being creative—it might be sufficient, but I’m not so sure researchers are aiming at such a humble goal.

It is much more likely that we all try to comprehend creativity in more universal terms. Certainly, people who are reaching out for answers to questions like "What is creativity?" are less concerned about personal feelings. What they want to know is how to foster creativity in real-world situations, outside of our heads. And it might be that even in science we are held hostage by certain stereotypes, which are leading us to look for meaningful regularities in the wrong places.

In my opinion, creativity is more related to critical thinking than it is to simply generating a wide array of options. Creativity isn’t about just any answer; it’s about finding the right, novel and valuable answer to a complex question. The constant dance between a high level of knowledge and the ability to stay critical—this is what sets apart brilliant creators in every discipline. There is no meaningful creativity without solid data and know-how. Imagination is important, but so is knowledge. Constant production of ideas might give you personal satisfaction, but in reality, your creativity will be judged by the quality (not quantity) of your work.

“Art Equals Creativity” Blunder

We have as many creative artists as we do creative engineers and creative scientists. But for some reason, we habitually glue together artistic behavior and creativity. I think this habit has horrible consequences for creativity studies. Some artists are dramatically non-creative. At the same time, in many disciplines, we don’t even have the competency to judge if any given output is creative or not. (In a sense, we are “tone-deaf” to novelty, surprise, and even functionality in areas we know very little about.)

Do some soul searching here. How competent do you feel evaluating creativity in rocket science? To properly assess a creative output, you more often than not need to know something (or several things) about that particular discipline.

To use Dan Dennett's framework, art is a design space much like many others, but I think a disconnect happens because of how familiar and seemingly competent we feel towards art. After all, making art is as prevalent in our species’ history as walking on two legs. Art is a vital component of every culture on the planet. Even if you haven't stepped foot in an art gallery in years, artifacts surround you.

Just look around. Unless you’re currently sitting in a survival-style underground bunker, you’re likely participating in an ongoing display of “aesthetic bonanza.” Your clothes are signaling if you are “hip” or not. You feel “moved” by certain tunes. You prefer certain styles of architecture and decor over others.  

Dennis Dutton explains the complexity of our aesthetic experience from a Darwinian perspective1. He proposes altering the very definition of art; instead of any singular description, he advocates for a “cluster criteria” of evaluation.

Some evolutionary psychologists consider art as a byproduct of other biological adaptations. We all remember the famous proclamation made by Steven Pinker that “music (...) is a cheesecake for the mind.” Some theoreticians suspect that art is a form of exaptation, or a form of pre-adaptation or spandrel. But all naturalistically inclined thinkers will agree that art is diabolically difficult to study and trying to reverse-engineer the very process of artistic appreciation and value is an epistemological nightmare. Look at paroxysmal trials of defining what “art” is in studies of Aesthetics and art theory over thousands of years. Art is hard to capture because we use a variety of tools to determine its value.

By observing the complex relationship between artifacts created by a computer and the reaction of an audience, you can further see that our perception of art is very far from objective. Some artwork created by computers would thrive in galleries, but for us to appreciate it, we need a person and a story, as well as a curator who presents them as a package. Somehow we struggle with a version of the uncanny valley problem here: We are psychologically inclined to contemplate art in connection with the person who (presumably) made it.

Art strengthens social interaction and communication; it signals fitness. This is the reason why we develop a visceral sense of competency in spotting creativity in art. We perceive art as available for everyone’s judgment—art “speaks to us.” (Or it doesn’t.) However, creativity appears in every discipline with the same frequency; we just insist on this false “art is creativity” equivalence due to art’s perceived accessibility. We are evolutionarily enabled to observe creativity in art, while creativity in different disciplines and subsequent different design spaces is more elusive to our insight.


Algorithmic Approaches to Creativity

Let’s not forget that running parallel to psychometric studies in psychology is a plethora of knowledge gathered in multiple algorithmic approaches to creativity. (Yes, creativity can be understood as an algorithmic process 2.) For those who are not familiar with this method, Tony Veale describes what an algorithmic approach to creativity entails (emphasis my own):

“But this is more or less what we mean when we talk of an algorithmic process: a well-defined sequence of tasks and sub-tasks that allows us to achieve a complex goal in a somewhat orderly fashion. There may well be a great many algorithms that lead to creative results in different contexts, and we should not caricature the computational perspective by assuming that there is a single grand algorithm that reduces the whole of human creativity to a few simple functions and procedures. Rather, we may have to explore many specific forms of creativity before we can arrive at any substantial high-level understanding of the phenomenon.” (Exploding the Creativity Myth, page IX)

As I’ve mentioned multiple times, creativity is a high-level mental process and the path to understanding it will be long and bumpy. But why prefer the computational route of thinking about creative process, rather than just thinking about creative people and their associated traits or characteristics? The answer is twofold.

First, trying to grasp creativity via a well-intentioned-but-not-so-precise set of studies resembles eating a bowl of soup with a fork. While your fork may scoop up a carrot, that one carrot doesn’t make an entire soup. Neither does a piece of potato or chicken. So although we can pick up some regularities in human behavior captured by studies, their findings may be misleading when potentially accounting for the whole of creativity. Extreme caution is needed when attempting to generalize and transfer a study’s findings on the whole population.

Second, computer science has made tremendous strides and can and should be used more effectively to study complex processes. Computers and their systems have the capacity to create models and test hypotheses. I recently had the honor to attend the Eighth International Conference on Computational Creativity this past June, and the advances being made with building creative systems and agents are beyond impressive. And they’re only getting better.

Computational creativity wizards aim to build creative systems 3 capable of things as varied as scientific invention, visual artistry, music composition and story generation. Some systems are designed to be co-creative and aid scientific discovery by intelligently searching a database of studies according to an interest inputted by a researcher. Other systems assist creative writing by helping transform text and evaluate spelling and grammar. Others are designed to play music with you; Musebots can hold their ground during improvisation sessions with professional musicians and even play in bands with each other. And there are other systems devoted to generating spontaneous dance moves.

This is not your parent's computer science. In today’s computational creativity research, even sophisticated problems like non-verbal communication and embodied cognition are being explored and probed 4.

An instant benefit of using co-creativity agents doesn't require any elaborate endorsement—it’s self-explanatory. Imagine that you could use a highly skilled research buddy to help you come up with new ideas in your discipline. Who would pass on that?

And the benefits don’t stop there. I’m not ready to give up on developing a concept of general creativity. I think the alternative—chopping creativity into separate domains—might create even more problems. Disciplines are not isolated entities; they overlap, too. In domains like entrepreneurship and innovation, it would be purely impossible to map all the necessary skills. But if we persevere and try to understand creativity from an algorithmic perspective, the search for generalized creativity might not be in vain.

Subsequently, as we begin to understand the algorithmic nature of creativity better, we will be in a better position to create more adequate, precise and robust testing tools. Then we could use those tools for research which would have more gravitas and potential for practical implementation. (After all, IQ testing didn’t jump from Zeus’ head either. Rather, IQ Test is based on in-depth understandings of processes related to human cognition, including quantitative reasoning, processing speed, memory retrieval, etc.)

Psychology is a critical discipline in cognitive science and it aids algorithmic approaches to creativity as well. Studies on intentionality; a sense of insight, perception, autonomy; and language processing and learning will help build systems which will in turn become even more adequate and autonomous. On the other hand, testing hypotheses by using computer models which mimic human mental processes might be a source of incredible insight into our own cognitive architecture.

This is why I strongly believe that “blended approaches” to creativity have the most potential to move the discipline of creativity studies forward.

Final Thoughts

The word “interdisciplinarity” is thrown around a lot, but in cases of solid, epistemologically sound research on complex phenomena, it is an absolute necessity. An integration of distinct perspectives and their methodological approaches, a tension between ontological and epistemological frameworks—all can produce knowledge at once rigorous, applicable and relevant. Understanding creativity from only one vantage point will never provide a necessary, satisfactory answer. We were not able to describe cognition in the realm of one discipline, and we will not be able to crack the enigma of creativity by using only one set of tools.

Yes, imagination, free associations, and even techniques like mindfulness might aid creativity in some form, but if we single them out and claim them as essential, then we run the risk of distorting and foregoing other important aspects critical for meaningful creativity. Getting “sugar high” on mind-wandering and random associations will not replace the boring-but-powerful “protein” of solid knowledge gained in a particular design space.

The more you know about a discipline, the more likely you’ll produce valuable, transformative solutions to problems. Meaningful creativity requires a high level of competence as well as the ability to independently view things with which one is already familiar. This makes creativity more aligned with critical thinking than with pure imagination. Creativity is not random—creativity is clever!

We have a ton of books and unsolicited advice coming from every angle these days, and sadly, science-based books on creativity have a dramatically short lifespan. (Not for lack of trying, though.) I believe it is always good to err on the side of caution at this stage, when we still lack definitive answers to questions like “How does creativity work?”

Be skeptical about simple answers in enthusiastic articles espousing the “Simple Keys To Creativity!” These sources have value if you find them personally inspiring, but remain vigilant. Many popular books create myths of knowledge which, while easy to digest, are quite hard to unlearn and question. We know from Sturgeon’s law that 90% of everything is crap, so while we are crying for creativity enhancement and encouraging mass production of nonsense, we might accidentally be moving in the wrong direction.

Just like the food pyramid fiasco, undercooked creativity research might become a series of mistakes with severe consequences.



1.  Dennis Dutton captured the difficulty of art in his “cluster criteria theory of art,” outlined in The Art Instinct (2009.) We evaluate art according to many criteria. We have a visceral reaction to beauty in direct pleasure; we admire skill and virtuosity in any high-skill performance; we have innate or developed preferences towards a certain style. Sometimes we respond to novelty and creativity in a given artwork. In some instances, we evaluate a work of art following the judgment of esteemed critics (criticism). Sometimes, we just enjoy a beautiful or intriguing representation of things and how an artist accomplished it. Sometimes, a random object could be placed in the space of a museum and we automatically are primed to treat it like art (special focus). We are still very sensitive to expressive individuality—the phenomenon of fan-clubs and groupies is not going away. Some of us search for emotional saturation while others prefer an intellectual challenge in the art they admire. Art traditions and institutions heavily guide our understanding of art. (For example, Marcel Duchamp is completely inaccessible for aesthetic appreciation without preexisting knowledge of art history.) Art is also an imaginative experience; although it might theoretically broaden the landscape of our imagination, more often than not, it simply sends us back in time and conjures up vivid memories.

2. Understanding creativity in terms of a process is not new. Margaret Boden underscored three types of creativity: combinational, exploratory and transformational. Douglas Hofstadter emphasized thinking as an analogy-making process. John McCarthy and Marvin Minsky, the fathers of AI, underlined the role of insight in creative process, while Gilles Fauconnier and Mark Turner introduced “conceptual blending” as a vehicle for creative output. The list here is long and full of very interesting ways to understand what creativity really is.

3. Many programs and agents utilize evolutionary algorithms, including reproduction, random mutation and selection, and fitness function, as well as deep-learning approaches and Hierarchical Bayesian Programs. Some agents are designed to learn like children by using a computational model for early cognitive development as a creative process. Some simply learn by continuous cooperation with a teacher.

4. Some AI researchers are pointing out that the Turing test is a poor tool for detecting traces of “deeper” phenomena in computers. (Turns out, we can be fooled by clever simulation.) What some propose instead is the Lovelace test, in which the computer creates original ideas which can surprise even its programmers. It seems like computational creativity might take us there faster.

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