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May 12, 2014

The Philosophy of Creativity

There is little that shapes the human experience as profoundly and pervasively as creativity. Creativity drives progress in every human endeavor, from the arts to the sciences, business, and technology.

By Scott Barry Kaufman

This article was published in Scientific American’s former blog network and reflects the views of the author, not necessarily those of Scientific American

There is little that shapes the human experience as profoundly and pervasively as creativity. Creativity drives progress in every human endeavor, from the arts to the sciences, business, and technology. We celebrate and honor people for their creativity, identifying eminent individuals, as well as entire cultures and societies, in terms of their creative achievements. Creativity is the vehicle of self-expression and part of what makes us who we are. One might therefore expect creativity to be a major topic in philosophy, especially since it raises such a wealth of interesting philosophical questions, as we will soon see. Curiously, it isn’t.

To be sure, some of the greatest philosophers in history have been taken with the wonder of creativity. To name just few examples: Plato has Socrates say, in certain dialogues, that when poets produce truly great poetry, they do it not through knowl- edge or mastery, but rather by being divinely “inspired”—literally, breathed into— by the Muses, in a state of possession that exhibits a kind of madness. Aristotle, in contrast, characterized the work of the poet as a rational, goal-directed activity of making (poeisis), in which the poet employs various means (such as sympathetic characters and plots involving twists of fate) to achieve an end (of eliciting various emotions in the audience). Kant conceived of artistic genius as an innate capacity to produce works of “exemplary originality” through the free play of the imagination, a process which does not consist in following rules, can neither be learned nor taught, and is mysterious even to geniuses themselves. Schopenhauer stressed that the greatest artists are distinguished not only by the technical skill they employ in the production of art, but also by the capacity to “lose themselves” in the experience of what is beautiful and sublime. Nietzsche saw the greatest feats of creativity, exemplified in the tragic poetry of ancient Greece, as being born out of a rare cooperation between the “Dionysian” spirit of ecstatic intoxication, which imbues the work with vitality and passion, and the “Apollonian” spirit of sober restraint, which tempers chaos with order and form. This is just the barest glimpse of what each of these philosophers had to say about creativity, and many other figures could be added to their number.

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Nevertheless, while some of the topics explored by earlier thinkers have come to occupy a central place in philosophy today—such as freedom, justice, conscious- ness, and knowledge—creativity is not among them. Philosophy has seen some very important work on creativity in the last few decades, but not nearly at the rate that we see for subjects of comparable range and importance. Indeed, “the philosophy of creativity” is still a neologism in most quarters—just as, for example, “the philosophy of action” and “the philosophy of music” were not too long ago.

In contrast, psychology has seen a definite surge of interest in creativity. In 1950, J. P. Guilford gave a presidential address at the American Psychological Association calling for research on the topic. And the field soon took off with waves of research investigating the traits and dispositions of creative personalities; the cognitive and neurological mechanisms at play in creative thought; the motivational determinants of creative achievement; the interplay between individual and collective creativity; the range of institutional, educational, and environmental factors that enhance or inhibit creativity; and more. Today, the blossoming of this field can be seen in the flurry of popular writing reporting on its results; an official division of the American Psychological Association on the psychology of aesthetics, creativity, and the arts (Division 10); numerous academic conferences; multiple peer-reviewed journals; several textbooks; and a growing number of undergraduate and graduate courses all devoted to the psychology of creativity. According to one historical overview, creativity has been studied by nearly all of the most eminent psychologists of the 20th century, and “the field can only be described as explosive.”

The swell of interest in the science of creativity is an inspiring example for the philosophy of creativity, but more importantly, it offers a resource that philosophers should be mindful of as they pursue this effort. Unfortunately, philosophers writing on creativity have sometimes tended to ignore the scientific literature. In some cases, they have gone so far as to claim—after citing just a few studies—that creativity is by its very nature unpredictable and therefore beyond the scope of science. Although the question of whether creativity is explicable is a philosophical question, it is not one that is impervious to empirical work. After all, anyone who declares from the armchair that something cannot be explained is liable to be refuted in the event that researchers do find ways to uncover explanations. The question of whether creativity can be explained empirically is itself, at least partly, an empirical question.

In fact, a number of issues arise at the nexus between philosophy and psychology and are handled best with contributions from both. This interdisciplinary approach is embraced by a new school of creativity researchers who are part of much broader trend toward dialogue and collaboration between scientifically-minded philosophers and philosophically-minded scientists. And the essays in this volume illustrate numerous ways in which the exchange can be fruitful, as philosophers draw on scientific research and scientific work is informed by philosophical perspectives. Below, we present a bird’s-eye view of these chapters and the themes and issues they explore.

The Concept of Creativity

Perhaps the most fundamental question for any study of creativity, philosophical or otherwise, is What is creativity? The term “creative” is used to describe three kinds of things: a person, a process or activity, or a product, whether it is an idea in someone’s mind or an observable performance or artifact. There is an emerging consensus that a product must meet two conditions in order to be creative. It must be new, of course, but since novelty can be worthless (as in a meaningless string of letters), it must also be of value. (Researchers sometimes express this second condition by saying a product must be “useful,” “appropriate,” or “effective.”) This definition is anticipated, in a way, by Immanuel Kant, who viewed artistic genius as an ability to produce works that are not only original—“since there can be original nonsense”— but also “exemplary.”

In chapter 1, Bence Nanay argues that creativity is primarily an attribute not of products, but of mental processes. Some have suggested that what makes a mental process creative is the use of a certain kind of functional or computational mecha- nism, such as the recombination of old ideas or the transformation of one’s concep- tual space. Against this view, Nanay offers what he calls an experiential account of creativity. He contends that what is distinctive about the creative mental process is not any functional/computational mechanism, but the way in which it is experienced. In particular, the process yields an idea that the creator experiences as one she hadn’t taken to be possible before.

Aesthetics and Philosophy of Art

One might suppose that if creativity has been understudied in philosophy at large, this couldn’t be so when philosophers are focused on art in particular. Art was long thought to have a monopoly on human creativity; it is still the paradigm of a creative domain, as “creative” is sometimes used more or less as a synonym for “artistic” and, at least in modern times, artists are disparaged when seen as derivative and praised for originality. But while the philosophy of art has been concerned with such issues as the definition, interpretation, and ontology of art, it has tended not to reflect on the artist as a creator, or the artist’s labors as a creative process, or the work of art as an expression of creativity. Thus Gaut and Livingston observe that “[a]lthough the creation of art is a topic that should be a central one for aesthetics, it has been comparatively neglected in recent philosophical writing about art.”

Gregory Currie brings the issue of creativity to the fore in chapter 2, where he examines the popular idea that eminently creative works of literature provide insight into the workings of the human mind. Many advocates of this view write as if its truth were self-evident. Currie suggests that it is not, that indeed there is little evidence in its favor, and he considers how the claim might be tested. Recent experi- mental studies by Oatley and colleagues look promising in this regard, but Currie suggests that their results so far provide very weak evidence at best. In the absence of better evidence, Currie puts a new spin on the debate by emphasizing the creativ- ity that goes into producing such great works of fiction. Are there aspects of literary creativity that should reliably lead to insights about the mind? He considers two such aspects—the institutions of literary production and the psychology of literary creativity—and suggests that in both cases, there are some grounds for thinking that literary creativity is not reliably connected with the production of insight.

Noël Carroll brings another dimension of creativity into view in chapter 3. Although he agrees that we should attend to the creative activities of the artist, he suggests that we should also acknowledge the contribution of the audience. For in order for the artist to accomplish the effects to which she aspires, Carroll argues, the audience must creatively cooperate with what the artist has initiated. He explores how audiences co-create artworks through the play of imagination. Rather than treating the imagination as if it were a single monolithic phenomenon, however, he identifies and analyzes several different imaginative activities that are engaged in response to a variety of artworks, such as reasoning counterfactually, filling-in unspecified content, constructing story-worlds around fictional objects, mentally simulating characters’ experiences and points of view, and freely devising and play- ing with different meanings, interpretations, and unifying themes. By means of these activities, Carroll suggests, it is ultimately the audience’s contribution that makes a work of art “work.”

In chapter 4, Christopher Peacocke raises interesting questions for aesthetics that bear upon the study of creativity. While philosophers have long debated the question of what makes something a work of art, Peacocke asks: What makes a work an example of a particular artistic style? He suggests that answering this question is a precondition for research on creativity in musical composition. Just as researchers who study perception understand that we cannot account for how the content of a perception is computed without specifying what the content is, Peacocke suggests that we cannot explain how a composer creates in his particular style unless we identify what is distinctive about that musical style. Using the example of the Romantic style of music, Peacocke’s approach draws on the perception of expressive action in combination with an account of what is involved in hearing emotion and other mental states in music. The account can link the phenomenology of musi- cal perception with the ideas and ideals of the Romantic movement. He notes that by changing various parameters in the account, we can explain what is variously distinctive about impressionist music, expressionist music, and some neoclassical composing in the style of Stravinsky.

Ethics and Value Theory

One thing that makes creativity such a gripping topic is that we cannot fully under- stand ourselves without taking it into account. Creativity seems to be linked to our very identity; it is part of what makes us who we are both as human beings and individuals. With regard to the latter, each of us can ask, “What makes me who I am (as an individual)?” and we might wonder whether the answer has something to do with creativity.

According to an ancient and still influential view, the self (one’s life) is some kind of dramatic or artistic performance. Exploring this idea in chapter 5, Owen Flanagan notes that there are metaphysical and logical questions about whether and how self-creation and self-constitution are possible. But he points out that there are also normative questions associated with the idea that life is a performance and the self is something that both emerges in and is constituted by that performance. Are there norms or standards that apply to self-constituting performances, and if so, what are they? Flanagan examines three contemporary psychopoetic conceptions of person—“day-by-day persons,” “ironic persons,” and “strong poetic persons”—in order to explore potential normative constraints on “performing oneself.” Flanagan’s provocative paper has implications for a number of diverse views in philosophy and psychology, from Jerome Bruner’s narrative theory of “self-making stories” to David Velleman’s paradox of self-constitution.

In chapter 6, Matthew Kieran asks what it is to be a creative person, and whether it involves a kind of virtue or excellence of character. He notes that there is a minimal sense according to which being creative means nothing more than having the ability to produce novel and worthwhile artifacts. Yet, he argues, there is a richer sense of the term that presupposes agential insight, mastery, and sensitivity to reasons in bringing about what is aimed at. A stroke victim who reliably produces beautiful patterns as a byproduct of his actions is not creative in the richer sense in which an artist who aims to produce them and could have done so differently is. Is creativity in this richer sense ever more than just a skill? In the light of suggestive empirical work, Kieran argues that motivation is central to exemplary creativity. Exemplary creativity, he argues, involves intrinsic motivation and is a virtue or excellence of character. We not only praise and admire individuals whose creative activity is born from a passion for what they do but, other things being equal, we expect them to be more reliably creative across different situations than those who are extrinsically motivated. This is consistent with the recognition that intrinsic motivation is not required to be creative and people’s creative potentials differ. Creativity in people will flourish when intrinsic motivation is foregrounded, with the relevant values and socioeconomic structures lining up appropriately. It tends to wither when they do not (unless a person’s creativity, like Van Gogh’s, is exceptionally virtuous).

Philosophy of Mind and Cognitive Science

In chapter 7, Simon Blackburn briefly remarks on the history of the idea—voiced by Plato, echoed by philosophers and artists in the Romantic tradition, and still present in the popular imagination—that creativity involves something mystical or supernatural. Against this notion, Blackburn draws on findings of modern psy- chology to offer a tamer view. He argues that even the most extraordinary creative achievements are the result of ordinary cognitive processes.

In chapter 8, Dustin Stokes ventures to clarify exactly what the relation is between creativity and imagination. In his view, imagination is important for even the most minimally creative thought processes. This would be a pointless tautology if “imagination” just means (the capacity for) creativity. The key, then, is to identify what imagination is such that it is not the same thing as creativity but still essential for it nonetheless. As Stokes notes, few philosophers have thought through the distinction between imagination and creativity, and few psychologists have directly tested the difference between the two constructs. While grounding his paper in contemporary philosophy, Stokes also draws on cognitive and developmental psy- chology to identify the architectural features common to genius-level creativity, as well as more everyday forms of creativity. He starts by making a distinction between “truth-boundedness”—cognitive states that function to accurately represent the world—and “non truth-bound” states that do not function to accurately represent the world, but instead facilitate the manipulation of the information they represent. He argues that richly creative achievements in the arts and sciences, as well as more everyday breakthroughs, draw on cognitive manipulation processes. Stokes concludes that imagination serves the cognitive manipulation role and is typified by four features: It is non truth-bound, under immediate voluntary control, engages with affective and motivational systems, and drives inference and decision- making. Stokes’s essay has implications for a number of philosophical problems relating to imagination and fiction, as well as psychological issues relating to the role of conscious, deliberate thought in creativity.

On the latter question, there is a tendency that appears in various forms through- out intellectual and artistic history to regard conscious thought as irrelevant or even inimical to creativity. In the classical story where creative inspiration comes to an artist from an external muse, the artist’s consciousness is not the source, but rather the recipient, of creative work. The same is true when an insight is said to emerge from the unconscious mind, showing up in consciousness as a kind of pleasant surprise (Eureka!). There is also the popular perception that conscious thought impedes creativity; thus the familiar accounts of artists using drugs, alcohol, or other trance-inducing practices as a means of surrendering conscious control and giving free rein to the creative unconscious.

In chapter 9, however, psychologists Roy Baumeister, Brandon Schmeichel, and C. Nathan DeWall suggest that consciousness deserves more creative credit. They present evidence to support the notion that creativity requires an interactive collaboration of conscious and unconscious processes. In their view, creative impulses originate in the unconscious but require conscious processing to edit and integrate them into a creative product. They review psychological experiments showing that creativity declines sharply when consciousness is preoccupied (for example, improvising jazz guitar while counting backward by six, or drawing with colored pencils while listening closely to music). They conclude that the research contradicts the popular view in both psychology and philosophy that consciousness is irrelevant or an impediment to the creative process. Instead, they believe that the research fits well with recently emerging understandings of the special capabilities of conscious thought.

Earlier, when we discussed the potential connection between creativity and self-understanding, we were concerned with what makes each of us who we are as individuals. But we can also ask, more generally, what makes us who we are as a species, and there is a long tradition of Western thought that seeks to understand what makes us human in terms of what makes us distinctively human, and set apart from other animals in particular. Whatever we think of the existing proposals that highlight our allegedly unique possession of reason, language, and metacognition, creativity seems as good a candidate as any. The tricky question, of course, is how did creativity evolve in humans?

In chapter 10, Elizabeth Picciuto and Peter Carruthers provide an integrated evolutionary and developmental account of the emergence of distinctively human creative capacities. Their main thesis is that childhood pretend play (e.g., imagining battling spaceship invaders) is a uniquely human adaptation that functions in part to enhance adult forms of creativity.

In support of their view, they draw on a wide literature spanning evolutionary, cognitive, and developmental psychology. They begin by reviewing evolutionary accounts of what makes humans unique, including our language, enhanced working memory, culture, and convergent and divergent thinking. They consider pretend play as a distinctively human ability, noting its universality, and showing that nearly all children, cross-culturally, engage in it. They review existing views of the func- tional roles of pretend play, including the facilitation of social schemata and theory of mind. Unconvinced by these accounts, they argue instead that pretend play facilitates creative thought—a process that involves both defocused attention and cogni- tive control. They review a number of common capacities of both pretend play and creativity, including generativity, supposing, bypassing the obvious, and selection of valuable but less obvious ideas. They conclude that childhood pretense paves the way for creativity in adulthood. This chapter is a fine example of how philosophers can contribute to our understanding of issues that are also pursued by scientists, in this case concerning the emergence of the capacities we have as human beings to pretend and create.

In our technologically driven age, it is not uncommon to think of what makes us human in contrast not only to other animals but also to machines, computers, and robots. Artificial intelligence is becoming ever more sophisticated, and some programs already display certain marks of creativity, appearing in major art galleries and garnering patents. These are machines whose products are both valuable and new. In addition to these two standard conditions, Margaret Boden maintains in chapter 11 that a creative product is one that is surprising as a result of the combina- tion, exploration, or transformation involved in producing it. She gives examples of artificial intelligence systems that fit all of these criteria, and raises this intriguing question: Could a computer-based system ever “really” be creative? This leads to interesting philosophical issues about what constitutes “real” creativity. With some qualification, she argues that real creativity involves autonomy, intentionality, valu- ation, emotion, and consciousness. But as she points out, the problem is that each one of these elements is controversial in itself, even if we don’t consider it in rela- tion to creativity and/or artificial intelligence. Boden concludes that we will not be able to understand whether creativity and artificial intelligence are contradictions in terms until we have clear and credible accounts of all these matters. Her chapter thus highlights the important role that philosophy can play in both psychology and artificial intelligence by further clarifying the constructs involved.

Philosophy of Science

Today, it’s understood that creativity can be at work in virtually every human pursuit. In the past, however, thinking about creativity tended to be much less inclu- sive. Once again, Kant is a telling example. Having defined genius as the capacity to produce ideas that are both original and exemplary (i.e., “creative” in our terms), he asserted that genius could only be manifested in the fine arts.20 Scientists were not geniuses because they follow the set procedures of the scientific method rather than giving free rein to their imaginations. Even Isaac Newton, whom Kant called the “great man of science,” was not deemed to be a creative genius. Nor, for that matter, was Kant himself!

Despite the much broader scope that we now accord to creativity, there is still a remnant of the Kantian intuition in popular stereotypes of the creative person that are more strongly associated with the artist than with anyone else. In chapter 12, psychologist Dean Keith Simonton argues, in effect, that there is something right about this Kantian tendency, as he explores the question: How does creativity differ between domains? In so doing, he integrates two philosophical traditions. The first tradition, stemming back to Auguste Comte, is concerned with whether the sciences can be arrayed into a hierarchy. The second tradition, which includes Alexander Bain and William James, concerns whether creativity and discovery involve a pro- cess of blind-variation and selective-retention (BVSR). The key part for this issue is blind-variation. Roughly, a process is “blind” to the extent that the probability of it’s generating a certain idea is not a function of that idea’s utility or value. A completely random procedure would be an example, though not the only example, of a blind process. Drawing on psychological research, Simonton shows that a valid hierarchy can be formed based on objective criteria regarding creative ideas, products, and persons. In place of Kant’s stark dichotomy between the sciences and the fine arts, Simonton’s hierarchy comprises a wide range of disciplines in the sciences, the humanities, and the arts. Where a discipline falls in the hierarchy depends on the extent to which practitioners need to engage in BVSR processes in order to make contributions that are creative (new and useful). Domains at the top of the hierarchy (i.e., sciences) rely more on sighted variations, whereas domains at the bottom (i.e., arts) depend more on blind variations. Simonton also shows that a discipline’s position in the hierarchy depends on the characteristics and developmental experi- ences of the creator. Simonton’s chapter is an intriguing synthesis of issues in both psychology and philosophy regarding the classification of creativity across domains.

Philosophy of Education (and Education of Philosophy)

Our final two chapters deal with the teaching and learning of creativity. It is not unusual to find people who assume that creativity is an innate capacity that cannot be taught or learned. Edward Young and Immanuel Kant were part of a long tradi- tion of thinkers who held such a view, and in arguing for it, they did us the service of exposing the kinds of assumptions that make it seem compelling. In chapter 13, Berys Gaut identifies two key arguments: The first is that learning requires imitation, which is incompatible with creativity; the second is that learning consists in following rules, which is incompatible with creativity. After criticizing these arguments, Gaut develops a positive case for the teachability of creativity, based on the teachability of the kinds of abilities and motivations that are involved in creativity. There is a sense in which Gaut’s question can be settled empirically: We can show that creativity can be taught simply by pointing to cases where it has been taught. Gaut himself discusses such examples as they occur in mathematics and fiction writing, noting in particular how heuristics or rules of thumb are used in these domains. But while such cases may suffice to show that creativity can be taught, Gaut further enriches our understanding by explaining how this is possible despite the common misconceptions that may seem to rule it out. Having given a philosophical account of how creativity can be taught, he ends by applying his analysis to the teaching of creativity within philosophy itself.

With this last theme, Gaut has a kindred spirit in Alan Hájek, the author of our final chapter. In fact, between the two of them, we have an instance of “multiples” in creativity research, cases where people working independently arrive at the same discoveries at about the same time.21 Although Gaut and Hájek were unaware of each other’s essays before submitting them for this volume, they converged on an interesting proposal—that by using various heuristics, philosophers can enhance their abilities to make valuable contributions to their field, including ideas that are distinctively creative.

As Hájek notes, it is said that anyone of average talent can become a strong chess player by learning and internalizing certain chess heuristics—“castle early,” “avoid isolated pawns,” and so on. Analogously, Hájek suggests, philosophy has a wealth of heuristics—philosophical heuristics—although they have not been nearly so well documented and studied. Sometimes these take the form of useful heuristics for generating counterexamples, such as “check extreme cases.” Sometimes they sug- gest ways of generating new arguments out of old ones, as in “arguments involving possibility can often be recast as arguments involving time, or space.” Sometimes they provide templates for positive arguments (e.g., ways of showing that something is possible). Hájek offers this chapter partly as an introduction to a larger project of identifying and evaluating philosophical heuristics, illustrating them with numer- ous examples from the philosophical literature. This work is a creative contribution to the philosophy of education. And it offers insights for the philosophy of creativity too, as it shows in fine detail how, contrary to a common assumption, creativity can be compatible with and even enhanced by the following of rules.

We are thankful for the input, encouragement, and support of Taylor Carmen, Tamara Day, Michael Della Rocca, Milena Fisher, Eugene Ford, Nancy France, Don Garrett, Tamar Szabó Gendler, Lydia Goehr, Joy Hanson, Markus Labude, Rebecca McMillan, John Morrison, Emily Downing Muller, Fred Neuhouser, Carol Rovane, and our wonderful colleagues and students at Barnard College, Columbia University, and New York University. Special thanks to Liz Boylan, former provost of Barnard College, for generously sponsoring the conference we held on the philosophy of creativity in preparation for this volume. We thank film director Tao Ruspoli for making a video of the event, artists Jill Sigman and Paul D. Miller (a.k.a. “D.J. Spooky”) for their participation as special guests, and Geovanna Carrasco, Melissa Flores, and Emily Neil for their excellent work as research assistants. We thank Peter Ohlin, Lucy Randall, Stacey Victor, and their colleagues at Oxford University Press for helping us see this book to print. Last but not least, we are very grateful to our contributors for illustrating the value of interdisciplinary exchange, the intellectual richness of the philosophy of creativity, and the exciting possibilities for how this field can grow. We hope this volume helps to stimulate new insights, questions, and collaborations—new ways to illuminate (and perhaps even to exemplify) this magnificent facet of human life.

This was an excerpt from The Philosophy of Creativity, edited by Elliot Samuel Paul and Scott Barry Kaufman , now available on Amazon .

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The Philosophy of Creativity: New Essays

Assistant Professor of Philosophy

Adjunct Assistant Professor of Psychology

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Creativity pervades human life. It is the mark of individuality, the vehicle of self-expression, and the engine of progress in every human endeavor. It also raises a wealth of philosophical questions, but curiously, it hasn’t been a major topic in contemporary philosophy. The Philosophy of Creativity ventures to change that. Illustrating the value of interdisciplinary exchange, this is a series of new essays from some of today’s leading thinkers integrating philosophical insights with empirical research. Join them as they explore such issues as the role of consciousness in the creative process, the role of the audience in the creation of art, the emergence of creativity through childhood pretending, whether great works of literature give us insight into human nature, whether a computer program can really be creative, the definition of creativity, whether creativity is a virtue, the difference between creativity in science and art, and whether creativity can be taught—both in general and within philosophy itself.

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THE big ideas: why does art matter?

Five Theses on Creativity

It permeates life, and, like love, it can break your heart.

By Eric Kaplan

Mr. Kaplan is a television producer and writer.

This essay is part of The Big Ideas , a special section of The Times’s philosophy series, The Stone , in which more than a dozen artists, writers and thinkers answer the question, “Why does art matter?” The entire series can be found here .

The word “art” can seem pretentious: When people hear it, they worry someone will force them to read a novel, or go to a museum, or see a movie without any explosions in it.

To me, art simply refers to those aspects of our lives that can be suffused and transformed by creativity. And having creativity in our lives is important. Without it we’re just going through the motions, stuck in the past. With it we feel alive, even joyous.

But if I say that art is simply life imbued with creativity, isn’t that just a case of obscurum per obscurius — of explaining the murky with the even murkier? After all, what exactly is creativity?

To help unravel this puzzle, here are five theses on creativity:

Thesis No. 1: Creativity makes something new. A different way of talking can suddenly make our world seem new. Here’s an example: In the Middle Ages, a road was something people walked on, the ocean a terrifying expanse of blue. But when the anonymous author of the Old English epic poem “Beowulf” called the ocean a “whale-road,” he made his readers experience the ocean afresh. The ocean may be an obstacle for us land-bound humans, but for whales it’s a road.

Thesis No. 2: Creativity hides itself. Creativity is shy. It’s easy to miss that creativity is about making something new, because, as soon as we succeed, the new thing we’ve created appears obvious, as if it had always been there. “Whale” and “road” were just there hanging around when someone said “whale-road.” And then people said, “Of course! The ocean may be a barrier for us, but it’s not for whales. They swim in it.” All that one person did was say what there was to be said — except it wasn’t there to be said, until he or she said it.

Creativity can seem like a tool for solving problems: We need a new word for the ocean! But creativity doesn’t just solve problems; it also makes or discovers new problems to solve. Hundreds of years ago, nobody knew the old words for ocean weren’t cutting it, until someone said “whale-road.” And everyone was like, “Wow! It is a whale-road!” Creativity always hides itself — it makes itself disappear.

That’s a helpful point to keep in mind when thinking about science, because creativity is fundamental there, too. We tend to think of science as a series of nonoptional statements about how the world works — as a collection of things we must believe. But if that’s true, how can scientists be creative? They can’t really say anything new; they just have to passively express things as they are.

But, of course, that isn’t how science works at all. We actually have to create it. When Newton came up with his second law of motion (force equals mass times acceleration) he was being just as creative as the person who came up with “whale-road.” And as with “whale-road,” Newton’s creativity was concealed by the success of his creative act: His formulation pointed toward something that already existed, but also didn’t. The more successful we are, the more it will seem like the things we created didn’t need to be created. Creativity hides.

Thesis No. 3: Creativity permeates life. Creativity fills our lives like ocean water fills the grains of a sand castle — saturating the spaces between this moment and the next, this action and the next, this word and the next. As a consequence, you can be creative when you’re doing pretty much anything: You can be creative in the way you walk to work, respond to grief, make a friend, move your body when you wake up in the morning, or hum a tune on a sunny day.

We are constantly remaking our lives through acts of creativity. In fact, creativity makes life possible — just like water makes a sand castle possible. Without water a sand castle falls apart, and a life that is completely routinized and uncreative is no life at all.

Thesis No. 4: Creativity can break your heart. It’s inherently risky. You might say, “Creativity seems so joyous and fun — why isn’t everybody creative all the time? Why do people steal and plagiarize instead? Why do they follow rules when they’re trying to be creative? Why do they always make the hero a handsome man, or always make song lyrics rhyme? Why do they copy what’s worked before?”

Because creativity can fail. If you knew ahead of time that the thing you were making would work, you wouldn’t be engaged in creativity. And when it doesn’t work, it breaks your heart. You look like a fool; what’s worse, you feel like a fool. It’s very embarrassing. But you can’t get the joy of creativity without risking pain and failure — which is also true of love.

Thesis No. 5: Creativity is a kind of love. That’s why it can break your heart, and why, at the same time, it can make the world come alive. When you’re creative, you make things fresh and new; when you love someone or something, you do the same.

That’s also why creativity is shy, why it hides. We don’t want the way we love to be captured by someone else’s loveless formulation. We don’t want someone to say, “Oh, he loves everybody with blond hair” or “He loves everybody who reminds him of his mother.” We don’t like it when people think they can manipulate us by figuring out whom or what we love — it’s an insult to those we love, to us, to love itself. So we’re a bit guarded when we talk about love; we don’t want people using the way we love to take advantage of us.

Corruptio optimi pessima — the corruption of the best is the worst: Love is the best part of our lives and can permeate our entire being, but it’s the most terrible thing when it’s misused or misunderstood. It’s the same with creativity.

Eric Kaplan is an Emmy Award-winning television producer and writer who has worked on “The Big Bang Theory” and “Futurama,” among other shows. He is the author of “Does Santa Exist: A Philosophical Investigation.”

Now in print : “ Modern Ethics in 77 Arguments ,” and “ The Stone Reader: Modern Philosophy in 133 Arguments ,” with essays from the series, edited by Peter Catapano and Simon Critchley, published by Liveright Books.

The Times is committed to publishing a diversity of letters to the editor. We’d like to hear what you think about this or any of our articles. Here are some tips . And here’s our email: [email protected] .

Follow The New York Times Opinion section on Facebook , Twitter (@NYTopinion) and Instagram .

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Few things shape the human experience as profoundly or as pervasively as creativity does. And creativity raises a wealth of philosophical issues. Since art is such a salient domain of creativity, you might assume, at first, that the philosophy of creativity is the philosophy of art or aesthetics, or a branch thereof. But creativity invites questions of its own that go beyond the purview of those other fields.

Note that the adjective “creative” can be applied to three kinds of things: a person (“Beyoncé is creative”), a process or activity (“Tell us about your creative process”), or a product , where the latter is taken broadly to include an idea in someone’s mind or an observable performance or artifact (“That’s a creative design”).

Now suppose you are looking at a creative product, like a painting or sculpture. The philosophy of art may ask, “What makes this a work of art?” and aesthetics may ask, “What makes this beautiful?”. By contrast, the philosophy of creativity asks, “What makes this creative? Is it just that it’s new, or must it meet further conditions?” We may ask the same question not just of artworks but of any creative product, whether it be a new scientific theory, a technological invention, a philosophical breakthrough, or a novel solution to a mathematical or logical puzzle. Beyond creative products, we can ask about the creative process : Must it proceed without following rules? Is it conscious, unconscious, or both? Must it be an expression of the creator’s agency, and, if so, must that agency be exercised intentionally? Exactly how does the process manage to produce new things? Can it be explained scientifically? Furthermore, we can ask about creative persons, or more generally, creators. What does it mean for a person to be creative? Is it a virtue to be creative? What capacities and characteristics does a being need to have in order to be creative? Could a computer be creative? These are the kinds of questions animating the literature we’ll survey below.

Some of these questions have an empirical dimension, most obviously those which pertain to how the creative process is actually carried out. Thus, much of the research we’ll canvass falls under the inter-disciplinary umbrella of cognitive science, with contributions not only from philosophers but also from researchers in neighboring fields like psychology, neuroscience, and computer science.

1. The Philosophy of Creativity: Past and Present

2.1 challenges to the value condition, 2.2.1 surprise, 2.2.2 originality, 2.2.3 spontaneity, 2.2.4 agency, 2.3 is creativity a virtue, 3. can creativity be learned, 4. can creativity be explained, 5.1 preparation, 5.2.1 blind variation, 5.2.2 the default-mode network, 5.2.3 imagination, 5.2.4 incubation, 5.3 insight, 5.4 evaluation, 5.5 externalization, 5.6 worries and future directions, 6. creativity and artificial intelligence, 7. conclusion, other internet resources, related entries.

Given the significance creativity has in our lives and the deep philosophical questions it raises, one might expect creativity to be a major topic in philosophy. Curiously, it isn’t.

To be sure, some of the most prominent figures in the history of Western philosophy have been fascinated with creativity—or what we now call “creativity”. According to some scholars, the abstract noun for creativity did not appear until the nineteenth century—but the phenomenon certainly existed and many philosophers took an interest in it (McMahon 2013; Nahm 1956; Murray 1989; Tatarkiewicz 1980: chapter 8).

To name just a few examples: Plato (4 th century BCE) had Socrates say, in certain dialogues, that when poets produce truly great poetry, they do it not through knowledge or mastery, but rather by being divinely “inspired” by the Muses, in a state of possession that exhibits a kind of madness ( Ion and Phaedrus ). Aristotle (3 rd century BCE), in contrast, characterized the work of the poet as a rational, goal-directed activity of making ( poeisis ), in which the poet employs various means (such as sympathetic characters and plots involving twists of fate) to achieve an end (of eliciting various emotions in the audience). Margaret Cavendish (1623–1673) and Émilie du Châtelet (1706–1749) championed the creative use of the imagination to pursue freedom, overcome prejudice, and cultivate natural abilities even despite social and political oppression . Immanuel Kant (1724–1804) conceived of artistic genius as an innate capacity to produce original works through the free play of the imagination, a process which does not consist in following rules, can neither be learned nor taught, and is mysterious even to geniuses themselves. Schopenhauer (1788–1860) stressed that the greatest artists are distinguished not only by the technical skill they employ in the production of art, but also by the capacity to “lose themselves” in the experience of what is beautiful and sublime (Schopenhauer 1859: Vol. I: 184–194 and Vol. II: 376–402). Friedrich Nietzsche (1844–1900) argued that the greatest feats of creativity, which he took to be exemplified by the tragic poetry of ancient Greece, was being born out of a rare cooperation between the “Dionysian” spirit of ecstatic intoxication, which imbues the work with vitality and passion, and the “Apollonian” spirit of sober restraint, which tempers chaos with order and form (Nietzsche 1872 [1967]). William James (1842–1910) theorized about creative genius exerts the causal power to change the course of history (Simonton 2018). This is just a glimpse of what each of these philosophers had to say about creativity, and many other figures could be added to their number.

Nevertheless, while some of the topics explored by earlier thinkers have come to occupy a central place in philosophy today—such as freedom, justice, consciousness, and knowledge—creativity is not among them. Indeed, “philosophy of creativity” is still a neologism in most quarters, just as, for example, “philosophy of action” and “philosophy of gender” were not too long ago. However, philosophical work on creativity has been picking up steam over the last two decades (as shown, for example, in a few important collections of essays: B. Gaut & Livingston 2003; Krausz, Dutton, & Bardsley 2009; Paul & Kaufman 2014; B. Gaut & Kieran 2018). We’ll now dive into those contributions, along with earlier work, beginning with what is perhaps the most basic question one can ask in this field.

2. What is Creativity?

As we noted at the outset, the term “creative” can be applied to three kinds of things: a person , a process , or a product (where a product could be an idea, performance, or physical artifact).

Most definitions focus on the product. According to one common approach, persons or processes are creative to the extent that they produce creative products, and a product is creative if it meets two conditions: in addition to being new it must also be valuable . Many theorists argue that novelty is not sufficient, because something can be new but worthless (e.g., a meaningless string of letters), in which case it doesn’t merit the compliment of being called “creative”. Immanuel Kant is often cited as anticipating this definition of creativity in his discussion of (artistic) genius. According to a common interpretation, Kant defines (artistic) genius as the ability to produce works that are not only “original”—since “there can be original nonsense”—but also “exemplary” (Kant 1790: §§43–50 [2000: 182–197]). (Hills & Bird [2018] challenge this reading of Kant.) This definition is so widely accepted among psychologists that it has come to be known as “the standard definition” of creativity in psychology. In practice, “creativity is often not defined” (J.C. Kaufman 2009: 19) in psychological experiments—more on this in §5 below. When psychologists do explicitly adopt a definition, however, they usually say that creative products are not only new, but also valuable in some way, though they variously express the product’s value in terms of its being “useful”, “effective”, “worthwhile”, “fit”, or “appropriate to the task at hand” (Bruner 1962: 18; A. J. Cropley 1967: 67; Jackson & Messick 1965: 313; Kneller 1965: 7; Cattell & Butcher 1968; Heinelt 1974; J.C. Kaufman 2009: 19–20; S.B. Kaufman & Gregoire 2016; Stein 1953; Sternberg & Lubart 1999: 3—for an overview, see Runco & Jaeger 2012). A few psychologists have suggested that the standard definition doesn’t fully capture the concept of creativity (Amabile 1996; Simonton 2012b). As for philosophers, at least one of them defends the standard definition with qualifications (Klausen 2010), but many of them challenge it, as we’ll soon see.

While it is uncontroversial that novelty is required for creativity, philosophers have refined that point. Certain examples may seem, at first, to suggest that novelty isn’t really necessary for creativity. Newton’s discovery of calculus was creative even if, unbeknownst to him at the time, Leibniz got there first—one of many examples of what are called “multiples” in the history of science (Simonton 2004). A beginning student’s idea that freedom is compatible with causal determinism might be creative even if, as she will soon learn, philosophers have been defending such “compatibilist” theories for millennia. However, examples like these do not force us to abandon the novelty requirement, but only to qualify it. Newton’s calculus and the student’s compatibilism were not new in all of history, but they were new to their respective creators, and that is enough for them to count as creative. In the terminology of philosopher Margaret Boden, these ideas are “psychologically creative” (P-creative) even though they are not “historically creative” (H-creative). Notice that P-creativity is more fundamental. Anything that is new in all of history (H-creative) must also be new to its creator (P-creative). Thus, creativity always exhibits psychological novelty, though it doesn’t always exhibit historical novelty.

Again, no one denies that a creative product must be new, at least to its creator. But as we’ll now see, some philosophers depart from the standard definition of creativity by rejecting the value condition ( §2.1 ), or by proposing some further condition(s) ( §2.2 ), or by doing both.

Some theorists have argued that although creative things are valuable, we shouldn’t build value into the definition of creativity, because doing so is not informative or explanatory:

Knowing that something is valuable or to be valued does not by itself reveal why or how that thing is. By analogy, being told that a carburetor is useful provides no explanatory insight into the nature of a carburetor: how it works and what it does. (Stokes 2008: 119; Stokes 2011: 675–76)

Those who maintain that value is required for creativity might reply that it doesn’t need to be informative or explanatory. Being a man is required for being a bachelor even though it’s not informative or explanatory to say that bachelors are men. Stokes notes that “creative” is a term of praise, and uses this point to argue that what is creative must be produced intentionally (since we don’t rightly praise what is unintentional or accidental)—an idea we’ll return to below. But the same point also seems to imply that what is creative must also have value (since we don’t rightly praise what doesn’t have value). And while the concept “carburetor” is value-neutral, as shown by the fact that a carburetor can be worthless or useless (if it’s broken), “creative”, one might argue, is a value-laden concept, like “progress”. Progress necessarily involves novelty or change, but we don’t praise change as progress unless it’s good change. Likewise, defenders of the value condition urge, creativity necessarily involves novelty, but we don’t praise novelty as creative unless it’s good novelty.

Other critics use counterexamples to argue that value isn’t necessary for creativity, the most prominent cases being ones of immoral creativity. (For a collection of essays by psychologists on the phenomenon of immoral or so-called “dark” creativity’, see D. Cropley et al. 2010). Putative cases of immoral creativity include creative accounting to cheat investors or creative testimony to mislead jurors, and the stock example in the literature is creative torture or murder. One can imagine novel and well-designed murders, as Thomas De Quincey once did in a satirical essay:

[S]omething more goes to the composition of a fine murder than two blockheads to kill and be killed—a knife—a purse—and a dark lane. Design, gentlemen, grouping, light and shade, poetry, sentiment, are now deemed indispensable to attempts of this nature. Mr. Williams has exalted the ideal of murder to all of us […] Like Æschylus or Milton in poetry, like Michael Angelo in painting, he has carried his art to a point of colossal sublimity. (De Quincey 1827; see also discussion in Battin et al. 1989)

Innovative ways of inflicting needless agony and craftily designed murders are not good (they have no value), and yet they can be creative. If this is right, then it seems to follow that creativity doesn’t require value.

One way of trying to save the value condition is by flatly denying that torture methods can be creative, and by denying more generally that creative things can be bad (Novitz 1999). But such denial seems ad hoc and implausible—“evil creativity” is not a contradiction in terms—and some have argued that this denial faces other problems besides (Livingston 2018).

Other theorists revise or qualify the value condition in order to accommodate examples of immoral creativity. Paisley Livingston (2018) proposes that a creative product only needs to be instrumentally valuable or “effective” as means to its intended end, regardless of whether that end is morally good, bad, or indifferent. Berys Gaut (2018) distinguishes between something’s being good (or good, period) versus being good of its kind . In his view, a new way of wielding blades and pulleys may be creative if it’s a good of its kind—good as a method of torture—even though it isn’t good. In order for something to count as creative, Gaut says, it doesn’t need to be good; it just needs to be good of its kind.

Alison Hills and Alexander Bird (2018) are unconvinced by such qualifications. They contemplate an elaborate torture device that ends up killing its victims immediately, “without enough suffering on the way”. The device may still be creative, they hold, even though “as a method of torture, it’s no good” (2018: 98). Indeed, they argue, a creative item needn’t be good in any way at all, not even for its creator. The ineffective torture device just described doesn’t satisfy its creator’s preferences, it doesn’t give him pleasure, it isn’t an achievement, it doesn’t contribute at all to his well-being—and yet, they contend, it may be creative, provided that it’s new and was produced in the right way. Exactly what “the right way” amounts to is the topic we turn to next.

2.2 Other proposed conditions

With or without the value condition, some theorists argue that a product must satisfy one or more further conditions, beyond being new, in order to count as creative. The four most prominent proposals are that the product must be (i) surprising, (ii) original (i.e., not copied), (iii) spontaneous, and/or (iv) agential. Each of these is a condition on the process of creativity. To be clear, we are still concerned with what it means for a product to be creative, but the proposals we’ll now consider say that in order for a product to count as creative, it must be brought about in the right way.

Margaret Boden holds that a creative product must be “ new, surprising, and valuable ” (2004: 1; cf. Boden 2010; 2014). It is perhaps most natural to assume that being surprising—like being new and valuable—is a feature of a product. But while Boden does think of creative products as surprising, her interest is more fundamentally in the underlying generative process, in how a creator manages to make something surprising. In her view, there are “three types of creativity”—combinatorial, exploratory, and transformative—“which elicit different forms of surprise, [and] are defined by the different kinds of psychological processes that generate the new structures” (2010: 1, italics added).

Combinatorial creativity occurs when old ideas are combined in new ways. Obvious examples include fictional hybrid creatures or chimeras: add wings to a horse (Pegasus), add the tail of a fish to a woman’s head and upper-body (a mermaid), add a lion’s body to a woman’s head and torso (Sphinx), and so on. Other combinations are found in analogies, such as when Niels Bohr compared an atom to the solar system. The term “combination” can refer either to the product of things combined or to the process of combining them, but Boden’s focus is on the process here, on the fact that one way to generate new ideas is to begin with old ideas and combine them in new ways.

To explain her other two kinds of creativity, Boden invokes the notion of a “conceptual space”, which is roughly a system comprising a set of basic elements (e.g., basic ideas or representations) as well as rules or “constraints” for manipulating or re-combining those elements. A conceptual space is not a painting, song, or poem, for example; it’s a way of creating a painting, song, poem, or theory. The rules or constraints are “the organizing principles that unify and give structure to a given domain of thinking”. And so a conceptual space is

the generative system that underlies that domain and defines a certain range of possibilities: chess moves, or molecular structures, or jazz melodies. (1994: 79)

We could think of a conceptual space as not just a set of thoughts but also a style of thinking defined by rules for generating new thoughts.

“Within a given conceptual space”, Boden observes, “many thoughts are possible, only some of which may have been actually thought” (2004: 4). Some conceptual spaces contain more possibilities than others. Consider different games. Tic-tac-toe is such a simple game that all of its possible moves have already been made many times over. The same is not true in chess, by contrast, which allows for a mind-boggling number of possible moves. The range of possible ideas is also practically inexhaustible in literature, music, the visual and performing arts, as well as the various domains of theoretical inquiry. And within those pursuits, there are various “structured styles of thought”—genres, paradigms, methodological orientations—which Boden thinks of as conceptual spaces.

Boden argues that the elements as well as the operating rules of a conceptual space can be, and in some cases have been, captured in computer programs. She has used this point not only to argue that computers can be creative (a topic we’ll return to below in §5 ), but also to suggest that we should employ the computational model of the mind in order to explain how humans create.

With her notion of conceptual spaces in hand, Boden says that exploratory creativity occurs within a given conceptual space. The new idea that emerges is one that was already possible within that space, because it was permitted by its rules. “When Dickens described Scrooge as ‘a squeezing, wrenching, grasping, scraping, clutching, covetous old sinner,’” Boden writes, “he was exploring the space of English grammar” in which “the rules of grammar allow us to use any number of adjectives before a noun” (Boden 1994: 79). Dickens’s description may strike us somewhat surprising, unexpected, or improbable, but it doesn’t have an air of impossibility about it.

By contrast, Boden argues, another form of creativity does. In this kind of case, the creative result is so surprising that it prompts observers to marvel, “But how could that possibly happen?” (2004: 6). Boden calls this transformational creativity because it cannot happen within a pre-existing conceptual space; the creator has to transform the conceptual space itself, by altering its constitutive rules or constraints. Schoenberg crafted atonal music, Boden says, “by dropping the home-key constraint”, the rule that a piece of music must begin and end in the same key. Lobachevsky and other mathematicians developed non-Euclidean geometry by dropping Euclid’s fifth axiom. Kekulé discovered the ring-structure of the benzene molecule by negating the constraint that a molecule must follow an open curve (Boden 1994: 81–3). In such cases, Boden is fond of saying that the result was “downright impossible” within the previous conceptual space (Boden 2014: 228).

Boden’s definition of creativity has perhaps been most influential among researchers who share her intertest in computer creativity (e.g., Halina 2021; Miller 2019: ch. 3; du Sautoy 2019). In a variation of Boden’s account, one philosopher proposes that what makes a mental process creative is not that it actually involves “the recombination of old ideas or the transformation of one’s conceptual space”, but rather that the creator experiences the process as having one of those features (Nanay 2014).

Maria Kronfeldner (2009; 2018) argues that the process of making something creative must exhibit originality . As she uses the term “original”, it does not simply mean “new”; instead, it has to do with the kind of causal process the creator must employ. She motivates her view by asking why it’s the case that, as we noted earlier, psychological novelty is required for creativity while historical novelty is not. Why is it, for example, that Newton’s invention of calculus was creative even if Leibniz invented it first? The answer, of course, is that it’s because Newton didn’t copy his calculus from Leibniz. Insofar as Newton came up with calculus independently, on his own, then he exhibited originality in his discovery, even though someone else got there first. This originality, Kronfeldner argues, is essential to creativity.

Kronfeldner (2009; 2018) also argues that spontaneity is required for creativity. An idea occurs spontaneously to the extent that it is produced without foresight or intentional control. If you were to foresee the output of the creative process at the beginning of that process, then you wouldn’t need any further process to come up with it. So if an idea is creative, you cannot have fully seen it coming. To that extent, insight comes as a surprise, hence the common phenomenological observation that creative breakthroughs feel like they come unbidden or out of the blue: “Eureka!”, “Aha!”, a lightbulb turns on.

Gaut (2018: 133–137) agrees that creativity requires spontaneity, and he points out, as Kronfeldner does, that it comes in degrees. He explains that you do something spontaneously to the extent that do it without planning it in advance. If you are going to act creatively, he argues, you cannot set out to follow an “exact plan”—a mechanical procedure, routine, or algorithmic rule—which would give you advance knowledge of exactly what the outcome will be and exactly the means you'll take to achieve it. At the outset of a creative act, you have to be to some extent ignorant of the end, or the means, or both. That ignorance opens up room for spontaneity and creativity.

Some philosophers argue that an item does not count as creative unless it has been produced by an agent. Consider a unique snowflake with an intricate shape, a distinctive sunset with stunning layers of red-orange hues, a novel patterning of dunes across a wind-blown desert. All of these things are aesthetically valuable and new. None of them are creative, however, insofar as they all occurred naturally and were not made by an agent. Gaut uses examples like these to argue that creative things must be created by agents (B. Gaut 2018: 129–30; cf. B. Gaut 2010, and B. Gaut 2014b) and several other philosophers agree (Carruthers 2006, 2011; Kieran 2014a, 2014b; Stokes 2008, 2011, 2014; Paul & Stokes 2018).

Of course, many theists would maintain that everything in nature is the handiwork of an agent—namely, God—and so arguably it would make sense for them to regard a natural phenomenon as creative if it is valuable and new. For theists, the unparalleled beauty of nature is a reason to praise the Creator. But this only supports the conceptual point that creativity, by definition, requires agency. We may coherently regard valuable new things as creative if we attribute them to a creative agent, as the theist does with the natural world; otherwise, we can’t. So again, it seems, creativity requires agency.

This leaves open the question of exactly how a creator’s agency must be exercised in order for the result to count as creative. Some philosophers argue that the agent’s act of creation must be intentional . Suppose you are snowboarding on a powder day and, unbeknownst to you, the tracks from your board result in a pleasing new pattern as viewed from high above. The new pattern has aesthetic value, but it isn’t creative. And that is because you didn’t intend to make it. Underlying this intuition, as well as our intuitions about the natural phenomena above, is the fact that “creative” is a term of praise, and we do not extend praise (or blame) for things that are not done by an agent, or for things that an agent doesn’t do in some sense intentionally.

While a number of philosophers endorse some version of the agency requirement for creativity, many theorists make no mention of it, whether to endorse it or reject it, including all of the psychologists cited above. Further, at least two philosophers are willing to attribute creativity to natural phenomena like trees and evolutionary processes: Arnheim (2001) and, in recent work, Boden (2018). These latter theorists don’t discuss agency as such, but insofar as the natural phenomena they call creative are not the result of agency, their view would imply that agency isn’t required for creativity.

The four proposals we’ve just considered all say that a product must arise from a certain kind of process—a process that exhibits surprise, originality, spontaneity, or agency—in order to count as creative. While there is wide agreement among philosophers that creativity requires some special kind of process, not just a special product, there is no consensus on what is required of the process. Of the four process conditions described here, the agency condition seems to be the one that is explicitly endorsed by the greatest number of philosophers thus far, though even they are still just a handful. And as we’ve seen, the other proposed conditions have serious arguments in their favor as well.

Some philosophers argue that if any process requirement is correct, this has an intriguing corollary for judgements about creativity: Even when we are explicitly judging only that a product is creative, we are implicitly assuming something about the process by which it was made. Suppose, for illustration, that the agency requirement is correct—that being generated through an agential process is built into the very concept of a creative product. Suppose further that you are applying that concept competently. It follows that if you come across a captivating arrangement of stones on the beach and you judge it to be creative, you are at least implicitly assuming that it was created through an agential process. If someone later persuades you that the stones happened to be moved into place by the wind and waves, not by any agent but just by chance, then you may still regard the result as aesthetically interesting but you would have to rescind your judgement that it is creative. So if the agency condition is correct, whenever you point to some item and say, “This is creative”, what you are saying, in part is, “This resulted from a creative process”. Furthermore, on this view, analogous implications follow if any other process condition is correct (Paul & Stokes 2018).

Having considered what is required for something to count as a creative product , and whether it must be produced by a certain kind of process , we now turn to analysis of the creative person .

Some theorists suggest that creativity, as an attribute of persons, is an ability to perform creative acts or produce creative things (Boden 2004). Others argue, however, that creativity isn’t merely an ability. An ability is something you can possess without ever putting it to use. You might have the ability to learn Swahili, for example, without ever making the effort to learn that language, despite having ample opportunities to do so. Creativity is different in this regard. If someone has the ability to be creative but never uses that ability when given numerous chances to do so, we would not call that person creative. Creative people are not merely able to act creatively. They are, moreover, disposed to exercise that ability, such that they do act creatively, at least some of the time, when the occasion arises. On this view creativity is a disposition , also referred to as a trait (Grant 2012; cf. B. Gaut 2014b, 2018).

Philosophers have long distinguished virtues as a special subclass of dispositions or traits. In Western philosophy, the tradition of theorizing about virtues goes back to the ancient Greeks, and over the last half-century it has enjoyed a renaissance in ethics (see entry on virtue ethics ) and, more recently, in epistemology (see entry on virtue epistemology ) and aesthetics (Lopes 2008; Roberts 2018; Hills 2018). Traditional examples of virtues include wisdom, justice, temperance, and courage. Should creativity be added to the list?

The answer depends, of course, on what it means for a trait to be a virtue. At the very least, a virtue is a trait that is good or valuable. So whether creativity counts as a virtue in this minimal sense depends on whether creativity is necessarily valuable, a point which is contested, as we saw in the previous section. In fact, those who contend that creativity isn’t necessarily valuable often do so in order to prove that it isn’t a virtue.

But let’s suppose for the sake of argument that creativity is indeed a valuable trait. Is it also a virtue in some more robust sense? Virtue theorists commonly take their cue from Aristotle’s classic discussion in the Nichomachean Ethics . Citing justice and temperance as paradigm virtues, Aristotle asserts that a trait must meet at least three conditions to count as a virtue:

For actions in accord with the virtues to be done temperately or justly it does not suffice that they themselves have the right qualities. Rather, the agent must also be in the right state when he does them. First, he must know [that he is doing virtuous actions]; second he must decide on them, and decide on them for themselves; and thrid, he must also do them from a firm and unchanging state. ( EN II.4, 1105a28–1105a33)

So, for example, if you return something you’ve borrowed, that act exhibits the virtue of justice if and only if (1) you know that you’re returning what you borrowed, (2) you choose to do so because it is the just thing to do, and for no other reason, and (3) you are disposed to do the just thing across the range of circumstances when the opportunity arises. In addition to justice and temperance, Aristotle enumerates other ethical virtues like prudence, generosity, and courage, as well as the intellectual virtue of theoretical wisdom. In his view, each of these traits requires one to meet the three conditions above. While he does not consider whether creativity is a virtue, we may ask whether creativity also has these three criteria. Does one have to meet these three requirements in order to count as creative?

We’ll begin with the third requirement to set it to one side. Does a person’s act count as creative only “if he does it from a fixed and permanent disposition of character”? Examples suggest otherwise. Consider the poet Arthur Rimbaud, who abandoned poetry at the age of 21 to pursue a life of adventure. The fact that he never produced another poem after that does not count against the fact that he was a creative poet in his youth (B. Gaut 2014b). Unlike the Aristotelian virtues, then, creativity does not have to be a permanent disposition.

Even so, it would still be significant if creativity turned out to be like an Aristotelian virtue in meeting the first two requirements. And arguably, creativity does meet the first requirement. A person doesn’t count as doing something creative unless “he knows what he is doing”. This was already implied by the agency condition for creativity discussed earlier.

Where things get interesting is with Aristotle’s second criterion for virtue. In order for your action to count as virtuous, he says, you have to do it “for its own sake”—i.e., you have to do it because you value virtue as an end itself, and not as a means to some external reward like praise, money, status, fame, or winning a competition. Consider the virtue of generosity, for instance. If you give money to someone in need merely because it will make you look good in the eyes of your friends, then you aren’t really being generous. Your act may outwardly look like generosity, but it’s not the real thing. To exhibit real generosity, you have to pursue generosity as an end in itself; you have to help others just for the sake of helping others. Now contrast being generous with being polite. If you compliment your colleague on the good work she’s done, then even if you’re doing this in order to manipulate her, you are being polite to her. You can have an ulterior motive for being polite. So politeness is not a virtue the way generosity is.

Is creativity a virtue in this respect? That is, does being creative require acting creatively for its own sake? Matthew Kieran’s (2014a, 2014b, 2018) answer is a qualified yes. While he grants that you can be motivated by external rewards to exhibit “minimal creativity” in producing valuable new things, he maintains that “exemplary creativity” requires you to be motivated by the value of creativity itself. Thus, in his view, exemplary creativity is a virtue.

To support this claim, Kieran points to a research program in psychology which purports to show that creativity is driven by “intrinsic motivation” rather than “extrinsic motivation”. A classic experiment in this program is “the magic markers study”, in which kids end up producing less creative drawings when they are offered a prize (Lepper et al. 1973). Many other studies have reported similar results, which lead Teresa Amabile to conclude, at first without qualification, that creativity is enhances by intrinsic motivation and hampered by extrinsic motivation (Amabile 1983: 107).

Further research introduced complications. In some studies, subjects were given “immunization techniques” whereby they were first primed or trained to focus on intrinsically motivating factors like the pleasure or aesthetical value of engaging in artistic activities, and it was found that when they engaged in those activities afterward, external rewards actually enhanced their creativity.

As researchers interpreted these findings, offering reward can support one’s intrinsic motivation, provided that the reward works either to boost one’s sense of agency or to provide useful feedback about what’s working and what isn’t. Intrinsic motivation is still what fuels creativity, on this interpretation; rewards help only indirectly, when they reinforce intrinsic motivation. This lead Amabile to revise her hypothesis as the Intrinsic Motivation Principle (IMP):

Intrinsic motivation is conducive to creativity; controlling extrinsic motivation is detrimental to creativity, but informational or enabling extrinsic motivation can be conducive, particularly if initial levels of intrinsic motivation are high. (1996: 107)

Kieran takes this as evidence for his claim that creativity, or at least what he calls exemplary creativity, requires intrinsic motivation and is therefore a virtue in that respect.

Objecting to this proposal, Gaut cites evidence that extrinsic motivation is not always detrimental to creativity. In one study, students in an introductory psychology class came up with more creative short story titles if they were offered a financial reward (Eisenberger & Rhodes 2001). In the studies where immunization techniques were used, proponents of IMP argue that rewards enhance creativity only indirectly, by buttressing intrinsic motivation. But in this case no such techniques were used, and so it seems the prospect of a reward enhanced creativity directly.

Further, Gaut argues that this point coheres with the role that rewards seem to play in so many real-world cases of creative achievement. In their quest to discover the structure of the DNA molecule, Watson and Crick were driven “to imitate Linus Pauling and beat him at his own game” (Watson 1968 [1999: 46]). Picasso and Matisse were both spurred on by their rivalry with each other (Flam 2003: 37). Paul McCready says he was driven to invent his award-winning human-powered glider in 1977 because he needed the prize-money to pay off his debts:

I felt that I didn’t have the time to mess with such things, but I had this strong economic motivation to take an interest in man-powered flight, so I charged around trying to figure out a way to solve it. (quoted in Sternberg & Lubart 1995: 242)

One historian argues that in World War II the Poles beat the French in cracking the Germans’ Enigma Code because they were more terrified of German invasion (Singh 1999: ch. 4). Gaut quips: “Fear of death is a more powerful motivator than the intrinsic satisfactions of code breaking” (Gaut 2014b: 196).

Finally, Gaut points out that even if IMP is true, it is only a causal, probabilistic claim: intrinsic motivation is “conducive” to creativity; extrinsic motivation is “detrimental”. But for a trait to be a virtue, intrinsic motivation must be conceptually necessary for the exercise of that trait. If we learn that someone gave to charity just to enhance his reputation, we conclude that he wasn’t really being generous. By contrast, if we discover that someone created gorgeous artwork just for the fame and glory, we may then lose some of our admiration for her creativity, but we do not deny that she was being creative.

Kieran could remind us that, in his view, intrinsic motivation is not required for all creativity, but only for the special form of it that he calls exemplary creativity. Anticipating this reply, Gaut says that to distinguish between two forms of creativity is just to concede his point. There are not two forms of generosity, one that requires intrinsic motivation and another that does not. If your act of giving isn’t motivated by the right kind of reason, then it doesn’t count as an act of generosity at all. Thus, Gaut argues, to grant the possibility of non-exemplary creativity is to grant that, unlike generosity, creativity isn’t a virtue in the traditional Aristotelian sense.

Another way to examine relations between creativity and virtue is through the lens of virtue epistemology. Linda Zagzebksi defines a virtue

as a deep and enduring acquired excellence of a person, involving a characteristic motivation to produce a certain desired end and reliable success in bringing about that end. (1997: 137, italics added)

While there is a lot packed into this definition, what we’ll pinpoint here is the idea that virtue involves reliable success in achieving a desired end, and that the agent who is epistemically virtuous, in particular, is one who is reliably successful in achieving knowledge. Knowledge requires truth, of course, so an epistemic virtue is a trait that is “truth-conducive”. Epistemologists typically regard a process as truth-conducive to the extent that the beliefs it produces are more often true than false. But Zagzebksi proposes that a process or trait may be truth-conducive in a different sense, insofar as it is necessary for advancing knowledge in some area, even if it produces a very small proportion of true beliefs. Creativity, she claims, is truth-conducive in this sense, and thus it qualifies as an epistemic virtue (1997: 182). Also note the emphasis on agency. In contrast to contemporary western epistemology, virtue epistemology identifies the agent (rather than, say her beliefs) as the essential locus of epistemic valence; it is the agent who is epistemically good (or not). This emphasis comports well with the proposal, discussed above, that the creator’s agency is necessary for genuine creative achievement. A virtue-theoretic approach thus illuminates what may (as we will discuss again later) be essential to creativity, namely, a process that non-trivially involves a responsible agent.

We’ve seen that even after we fix a specific referent for the term “creative”—whether it be a person, process, or product—there are lively disagreements about what it means. These debates often seem to presuppose that the term always expresses the same concept, for which we can seek necessary and sufficient conditions. But we’ve also seen that some theorists distinguish between different concepts of creativity, corresponding to different senses of the term “creative”. In future work we may see theorists develop such pluralistic approaches in more detail. The trick, though, will be to give principled reasons for multiplying different concepts of creativity so that the analyses do not simply reduce to saying that anything goes.

There is a long tradition of thinkers who answer no to the question above. Two of the most influential are from the eighteenth century—Edward Young and Immanuel Kant—who were concerned specifically with genius , the capacity for achieving the very highest levels of creativity. In Conjectures on Original Composition (1759), Young says,

An Original may be said to be of a vegetable nature; it rises spontaneously from the vital root of genius; it grows , it is not made …. (1759 [1966: 7])

His idea is that originality emerges naturally from something implanted in us by nature, and it can only be hindered by learning. Young seems to think of learning as proceeding either through imitation or through the following of rules, and both, he thinks, are detrimental to originality. Regarding imitation he writes,

Born Originals , how comes it to pass that we die Copies ? That meddling ape Imitation … destroys all mental individuality…. (1759 [1966: 20])

And insofar as learning is “a great lover of rules”, he warns that it “sets rigid bounds to that liberty, to which genius often owes its supreme glory” (1759 [1966: 13]).

Kant makes similar claims in his Critique of Judgment (1790). Like Young, he takes genius to be a natural capacity, though a very rare one:

such a skill cannot be communicated, but is apportioned to each immediately from the hand of nature and dies with him. (1790: §47 5:309 [2000: 188])

It certainly cannot be learned through imitation:

genius is entirely opposed to the spirit of imitation . Now since learning is nothing but imitation, even the greatest aptitude for learning, facility for learning (capacity) as such, still does not count as genius. (1790: §47 5:308 [2000: 187])

Nor can it be learned through rules, Kant holds, for genius is

the talent (natural gift) that gives the rule to art … the inborn predisposition of the mind ( ingenium ) through which nature gives the rule to art. (1790: §46 5:307 [2000: 186])

For Kant, a genius does not follow rules; a genius invents the rules, indirectly, by creating exemplary works from which other artists might extract rules and undertake “a methodical instruction in accordance with rules” (1790: §49 5:318 [2000: 196]).

Young and Kant are concerned with genius, specifically, but if we extend their reasoning to creativity in general, as Berys Gaut (2014a) has noted, we can discern two lines of argument:

The imitation argument All learning is a form of imitation. Imitating someone or something is incompatible with being creative. So, one cannot learn to be creative. The rules argument All learning consists in the following of rules. Following rules is incompatible with being creative. So, one cannot learn to be creative. (2014a: 266)

Gaut points out, first of all, that both arguments are invalid. In both cases, what the premises would entail is that learning cannot be creative, that, in other words, you cannot learn creatively (a claim about how you can learn). But even if that were true, it wouldn’t follow that you cannot learn to be creative (a claim about what you can learn). If you absorb the advice of a creative writing manual then this act of learning may not itself be creative. But if the manual is effective—and we’ll see in a moment how it can be—then what you will learn is how to become more creative.

Gaut also challenges the premises of these arguments. To start with the first premise of the imitation argument, it simply isn’t true that all learning proceeds through imitation, as we learn many things through direct experience, trial and error, and many other means.

The second premise is also suspect. Something superficially close to it is true: mere copying is incompatible with being creative. But to the extent that we learn from others by imitating them, this is not merely a matter of copying them. When a child learns to speak the language of those around her, she doesn’t simply parrot the exact same sentences she hears; she absorbs the vocabulary and underlying grammar in a way that enables her to form new sentences of her own devising.

Now for the rules argument. Contrary to the first premise, it cannot be the case that all learning consists in following rules, Gaut argues, because for any given rule there will be hard cases where it is unclear whether or how the rule applies to them, and so an individual still has to use her own judgment in applying the rule.

The second premise is false too. Recall the distinction from §3 above between two kinds of rules. An algorithm serves as an exact plan, specifying both the outcome and the path for getting to it in exact detail. In contrast, a heuristic is a looser “rule of thumb” that leaves room for an agent to exercise her own judgment, choice, and creativity in determining whether, when, and how to follow the rule. While algorithms, in this sense, may preclude creativity, heuristics do not, which is why, as we’ll see below, the teaching of creativity so often takes the form of heuristics.

There is a sense in which the question at hand can be answered empirically: We can show that creativity can be taught simply by pointing to cases where it has been taught. Gaut himself discusses such examples as they occur in mathematics and fiction writing, which we’ll turn to below. But while such cases may suffice to show that creativity can be taught, Gaut further enriches our understanding by explaining how this is possible . He does so partly by articulating and then debunking the imitation and rules arguments to the contrary. But in addition, he offers the following positive argument to show that creativity can be taught and learned. He calls it “the constitutive argument” because it begins with his view of what constitutes or defines creativity itself.

The constitutive argument

• Creativity is a disposition—involving both the ability and the motivation —to produce things that are new and valuable, and to do so in ways that express one’s agency through “the exercise of choice, evaluation, understanding, and judgment” (Gaut 2014a: 273).
• At least some people can learn to enhance their creative motivation .
• At least some people can learn to enhance their creative abilities .
• So, at least some people can learn to become more creative.

Premise 1 recapitulates the point we’ve already seen Gaut and others defend (in §2.3 above), that creativity is not merely an ability but a disposition or trait, whereby the creative person is disposed or motivated to exercise that ability when given the opportunity.

In support of premise 2, Gaut argues that you can strengthen both your intrinsic motivation to be creative (when you take pleasure in your creative activities), as well as your extrinsic motivation to be creative (when you are rewarded with praise, grades, pay, etc. for your creative efforts).

Defending premise 3, Gaut points out that you can develop your ability to produce valuable new things by practising and strengthening the relevant skills. And this development can be substantially aided by learning certain heuristics.

Heuristics are indeed a staple of education in creative pursuits from mathematics (draw the figure; consider special cases; consider extreme cases; generalize the problem; look for a related problem, etc.—see Pólya 1945; Schoenfeld 1982, 1987a, 1987b) to creative writing (write what you know; be specific and detailed in describing sensory experiences; practice seeing similarities between dissimilar things; show, don’t tell, etc.—see Bell & Magrs 2001; Anderson 2006; Maybury 1967; S. Kaufman & J. Kaufman 2009). Gaut also identifies several heuristics that might be used to foster creativity in philosophy, even among children (cf. M. Gaut 2010; B. Gaut & M. Gaut 2011).

With this last theme, Gaut has a kindred spirit in Alan Hájek (2014, 2016, 2017, 2018), who has independently proposed that by using various heuristics, philosophers can enhance their abilities to make valuable contributions to their field, including ideas that are distinctively creative. It has been said that anyone of average talent can become a strong chess player by learning and internalizing certain chess heuristics: “castle early”, “avoid isolated pawns”, etc. Analogously, Hájek suggests, philosophy has a wealth of heuristics— philosophical heuristics —although they have not been as well documented and studied. Sometimes these take the form of useful heuristics for generating counterexamples, such as “check extreme cases”. Sometimes they suggest ways of generating new arguments out of old ones, as in “arguments involving possibility can often be recast as arguments involving time, or space”. Sometimes they provide templates for positive arguments (e.g., ways of showing that something is possible). Hájek offers a catalogue of such philosophical heuristics to show that, contrary to a common assumption, creativity, even in philosophy, can be compatible with, and enhanced by, following rules.

Upon observing the work of creative people, it is natural to wonder: How do they do that? How do people create? The issue we turn to now is whether we could, at least in principle, answer this question scientifically, using the methods of modern empirical psychology and other cognitive and behavioral sciences. Those who take a negative stance on this matter are not merely saying that, in practice, it would be exceedingly difficult for science to explain creativity. They are saying that it’s altogether impossible that science could ever explain creativity.

Hospers (1985) defends this kind of pessimism based on the variety and complexity of creativity, given that creativity occurs not only in art, but in science, theorizing of any sort, engineering, business, medicine, sport, gaming, and so on. At least two worries may follow. First, given the complexity of any one of these individual domains, one might worry that there are simply too many variables to allow for a clear explanation. Art provides a paradigmatic example. Consider an artwork that you judge to be masterful (a sculpture, a painting, a film). Now imagine attempting to describe or identify all the reasons for which you think it is masterful. Take as much time as you like but, the skeptic will urge, any long description you construct will invariably strike you as woefully incomplete by comparison to the artwork, and the experience thereof. So, if the creative achievements of artists, in all of their complexity, cannot even be adequately described, we have little reason to think that such achievements can be explained.

How can theorists respond to these skeptical worries? Both the complexity and generalizability worries might be partially disarmed by noting analogies between creativity and other phenomena. For instance, consider the range of bodily movement involved in some of the very domains of activities listed above: art, science, engineering, medicine, sport. The kinds of bodily action specific to these domains are complex and vary dramatically: the relevant physical movements of the surgeon are much different from the tennis player. However, it is not plausible that this complexity and variety precludes explanation of bodily action in those domains. It simply implies that some features of the explanation will be context-sensitive, that is, specific to that domain of activity. And further to the analogy: the fact that the long description of, say, the tennis serve is incomplete does not preclude it from being apt and explanatory. If this line of reasoning is sound for bodily action, why not also for creative action?

At this point, one might argue that while complexity and generalizability worries would only show that creativity is difficult to explain in practice, the very nature of creativity implies, more strongly, that it could never be explained, not even in principle. Resources to support this kind of pessimism may be adduced from various past philosophers. We need to tread carefully, however, since most of the figures we are about to consider were writing long before the rise of the relevant sciences, so they could not have made any explicit claim either way as to whether creativity could be explained by those sciences. Nevertheless, some of them did make claims which entail, or seem to entail, that creativity simply isn’t the kind of thing that could be explained through scientific inquiry as we understand it today.

The classic expression of such a view comes from Plato. In his dialogues, Plato features his teacher Socrates as a spokesperson for his own views, and in the Ion he has Socrates argue that poets do not produce poetry through knowledge or skill. When you exercise a skill ( technē ), you apply techniques, rules, or methods to perform a given activity, like charioteering, fishing, or commanding an army. In principle, one could explain these activities by identifying the techniques they involve, and a student or apprentice could learn these activities by applying and practicing those techniques. But poetry is not like that, in Socrates’ view. A poet can only imitate the application of rules or techniques, mimicking the surface appearance of skill. Voicing an idea that was familiar in Ancient Greek culture, Socrates suggests that poetry emerges instead through divine inspiration, whereby a human being is inspired —literally “filled with a spirit”, with a god or goddess, with a muse:

You know, none of the epic [or lyric] poets, if they’re good, are masters of their subject; they are inspired, possessed, and that is how they utter all those beautiful poems. … [They] are not in their right minds when they make those beautiful lyrics, but as soon as they sail into harmony and rhythm they are possessed by Bacchic frenzy. […] For a poet is an airy thing, winged and holy, and he is not able to make poetry until he becomes inspired and goes out of his mind and his intellect is no longer in him. As long as a human being has his intellect in his possession he will always lack the power to make poetry or sing prophecy. […] You see, it’s not mastery [ technē ] that enables them to speak those verses, but a divine power. That’s why the god takes their intellect away from them when he uses them as his servants, as he does prophets and godly diviners, so that we who hear should know that they are not the ones who speak those verses that are of such high value, for their intellect is not in them: the god himself is the one who speaks, and he gives voice through them to us. In this more than anything, then, I think, the god is showing us, so that we should be in no doubt about it, that these beautiful poems are not human, not even from human beings, but are divine and from gods; that poets are nothing but representatives of the gods, possessed by whoever possesses them. ( Ion 534a-d)

Socrates repeats this view in the Phaedrus : “Some of the greatest blessings come by way of madness, indeed madness that is heaven-sent” (244a). He adds that while a poet may have some kind of skill, anyone who aspires to make poetry purely by skill, without the madness or the muse, will fail (245a).

It’s important to note that “madness”, for Plato, is a supernatural affair. From the vantage of contemporary behavioral science, we think of madness—or rather, mental illness—as a pathology arising from some combination of genetic and environmental factors, and those factors can be studied scientifically. So even if creativity is linked to mental illness—a highly controversial proposition—it could still be entirely within the scope of science. However, Plato’s talk of “madness” does not refer to any naturally occurring pathology, but rather to the result of divine intervention: the poet is taken over or “possessed” by the muse and that is precisely why he is “out of his mind”. Plato’s poet suffers divine madness.

According to this story, then, the person we call a poet isn’t really a creator of poetry, but is merely the vessel through which a divine being delivers poetry. If it is literally true that the source of poetry is supernatural, then poetic creativity could never be explained by science, which is limited to the investigation of natural causes. (For more on Plato, see Asmis 1992.)

This kind of supernaturalism has enjoyed a long afterlife in Western thought. In ancient Rome, the Latin term “ genius ” referred to a guiding spirit that was thought to accompany each person throughout their lives. The genius of an artist would occasionally deliver art through that person in the manner of Platonic inspiration.

Conceptions of the artist take a new turn when the idea of genius is transformed in the eighteenth century. As we saw above, Immanuel Kant defines genius as a natural capacity that a certain kind of artist possesses innately and which partly constitutes that artist’s identity. So rather than saying that a gifted artist “has a genius”, Kant says that such a person “is a genius”. What distinguishes the genius is fundamentally an imaginative capacity—an ability to engage in a “free play” of imagination to produce artworks of “exemplary originality”. These works are exemplary not only in the sense that they have artistic or aesthetic value, unlike “original nonsense”; they are also exemplary in the more radical sense of providing an exemplar—a new paradigm and precedent—for lesser artists to follow. A work of genius sets a new standard of artistic value, and, looking to that exemplar, lesser artists may then extract techniques or rules for their own craft. The genius therefore “gives the rule to art”. In creating such works, the genius does not follow any rules or methods. Instead the genius creates art through a “free play of imagination”—where the terms “free” and “play” characterize the nature of an activity unconstrained by any pre-established methods or rules:

[G]enius … is a talent for producing that for which no determinate rule can be given, not a predisposition of skill for that which can be learned in accordance with some rule …. (1790: §46 5:307–8; 2000 trans., 186)

Kant thought that genius, so conceived, is limited to the fine arts, poetry being chief among them. Meanwhile, in Kant’s view, there is no room for genius in science, for example, where good theories and hypotheses must emerge from the careful application of scientific method, and so he said that even Isaac Newton, “that great man of science”, was not a genius. We’ll soon consider why this view might seem to entail that creativity is inexplicable, but first it will be helpful to bring another figure, Arthur Schopenhauer, who was deeply influenced both by Kant and by Plato.

Like Kant, Schopenhauer thought of genius as a natural capacity that is limited to the fine arts. He also echoes Plato’s sentiments about madness, famously stating that “genius and madness have a side where they touch and even pass over into each other” ( The World as Will and Representation , 1859, WWV I: 190), and that “Genius lives only one storey above madness” ( Parerga and Paralipomena , SW 2:53, PP 2:49). In a state of madness, Schopenhauer’s genius is like Plato’s poet in experiencing a momentary loss of self, but what displaces the self is not any divine being but rather a pure Idea which seizes the author’s being and becomes the object of both his fascination and his artistic expression:

We lose ourselves entirely in this object, to use a pregnant expression; in other words, we forget our individuality, our will, and continue to exist only as pure subject, as clear mirror of the object, so that it is as though the object alone existed without anyone to perceive it, and thus we are no longer able to separate the perceiver from the perception, but the two have become one, since the entire consciousness is filled and occupied by a single image of perception. ( World WWV I: 178–179, §34).

With their focus on genius construed as a natural capacity, figures like Kant and Schopenhauer abandon the supernaturalism of the Platonic muse. Nevertheless, they retain the idea that creativity—specifically genius-level creativity in the fine arts—is not a matter of exercising a skill or applying given rules, methods, or techniques.

As we noted earlier, these figures did not and could not have explicitly denied that creativity could be explained by the sciences of the twentieth and twenty-first centuries, but they are commonly taken to represent such a denial (Kronfeldner 2018). Why?

Perhaps figures like Kant and Schopenhauer seem to make creativity, or at least creative genius, inexplicable insofar they suppose it to be innate and as they have no story to tell about how one came to acquire an innate capacity except to say that it was either an accident of chance (which is no explanation at all) or a gift from God (which again is not a scientific explanation). But while these figures seemed to think of artistic genius as being endowed entirely by nature with no contribution from nurture, modern genetic theory rejects that dichotomy. Instead of positing all-or-nothing natural abilities, behavioral scientists today think in terms of genetically inherited predispositions. In order for a genetic predisposition to develop into a trait with an observable phenotype, it needs to be triggered and shaped through a complex interaction between an organism’s genes and certain kinds of stimuli or environmental conditions. There are still open questions about exactly how, and how much, genes and environment feed into the development of any given trait, but it’s misguided to pose the binary nature-versus-nurture question as if the two were mutually exclusive (see Tabery 2014). Many researchers agree that some people have a stronger natural predisposition toward creativity than others, and that genius-level creativity partly stems from such a predisposition. Even so, the predisposition itself can be understood scientifically in terms of genetic heritability. (For a sampling of the relevant studies, see the essays collected in S.B. Kaufman 2013.)

Perhaps creativity seems inexplicable according to these accounts because it doesn’t follow rules or methods. In order to explain how to do something—how to build a boat or lead an army etc.—perhaps I need to be able to identify the rules or methods you should follow in order to practice and apply those skills. How-to explanations are instructions. But scientific explanations needn’t be instructions. A lot of good science explains how something happens—e.g., how heat melts ice or how a bat navigates its environment by echolocation—without explaining how to do it yourself.

Perhaps creativity seems inexplicable according to these accounts because creators themselves do not know how they create. But a scientific explanation needn’t be available through introspection. Most people cannot explain how their own digestive, circulatory, or perceptual systems work, but scientists who study those systems can.

Another line of thought is perhaps implicit in Kant but comes to the fore in Schopenhauer, who says that “the nature of genius consists precisely in the preeminent ability” to

consider things independently of the principle of sufficient reason , in contrast to the way of considering which proceeds in exact accordance with this principle, and is the way of science and experience. ( World WWV: I: 192, §36)

The principle of sufficient reason says that for every fact there is a cause which completely explains that fact. So the defining ability of genius is to see things in a way that transcends the causal order and defies all explanation.

A version of this view is defended more recently by Carl Hausman (1975 [1984], 1979, 1985) who frames it in terms of novelty that creativity involves. Hausman asserts that if a product is creative, it must be metaphysically novel (or in his terms, “genuinely novel”) in the sense that it cannot be predicted from, or explained by, prior events—not even in principle. Creativity is therefore incompatible with causal determination and causal explanation: “A causal view of explanation sets a framework for ways of denying that there is anything new under the sun” (Hausman 1984: ix). If something can be explained by prior causes, it is not metaphysically novel, and is therefore, in Hausman’s view, not truly creative.

Against Hausman’s skeptical charge, Maria Kronfeldner (2009) argues that creativity is compatible with causal determination. First, causal determinism does not preclude novelty or change. Determinism says the emergence of new kinds of things can at least in principle be predicted in advance. Importantly, though, when this prediction becomes true, then something new is added to the world. Of course, not all novelty instantiates creativity. The question is whether the kind of novelty involved in creativity must be metaphysical novelty, which is by definition incompatible with causal determination. This is doubtful. Notice that, by definition, metaphysical novelty defies natural laws. The production of something metaphysically novel would therefore require supernatural powers. Traditional Western religions conceive of God as performing the miracle of creation ex nihilo . But are we positing a miracle every time we describe a human artifact or achievement as creative? Surely not. As noted above, human creativity is manifest in things that are novel relative to the agent producing them or new to human history, but both of those kinds of novelty (psychological and historical) are perfectly compatible with causal determination. As Kronfeldner explains, creativity does not preclude causes in general; it only precludes certain kinds of causes. A creative product, she argues, must be original —which means that it cannot be produced through a process of copying something prior. And it must be spontaneous (not produced through a routine or mechanical procedure)—which means that it is to some extent independent of the agent’s intentional control and previously acquired knowledge. (For more on originality and spontaneity, recall §2.2 above). Intuitively, the causes of something creative cannot simply be a matter of copying or following a routine. But it may have causes nonetheless, and cognitive science can investigate those causes, at least in principle. Indeed, as we’ll see next, it is doing so in practice.

5. The Cognitive Science of Creativity

Although creativity has been relatively understudied by contemporary philosophers, as we noted in §1 , it has been receiving a great deal of attention from psychologists over the past few decades. In 1950, J. P. Guilford gave a presidential address at the American Psychological Association calling for research on the topic, and the field soon took off with waves of research investigating the traits and dispositions of creative personalities; the cognitive and neurological mechanisms at play in creative thought; the motivational determinants of creative achievement; the range of institutional, educational, and environmental factors that enhance or inhibit creativity; and more. Today, the blossoming of this field can be seen in the flurry of popular writing on its results; an official division of the American Psychological Association for the psychology of aesthetics, creativity, and the arts (Division 10); numerous academic conferences; dedicated peer-reviewed journals ( Psychology of Aesthetics , Creativity and the Arts ; Creativity Research Journal ; Journal of Creative Behavior ; International Journal of Creativity and Problem Solving ); special issues of journals ( Current Opinion in Behavioral Sciences , Takeuchi & Jung 2019); literature surveys (Hennessey & Amabile 2010; Runco & Albert 2010; Runco 2017; Glaveanu 2014; Williams et al. 2016); textbooks (J.C. Kaufman 2009; Sawyer 2012; R. W. Weisberg 1986, 2006); and a comprehensive encyclopedia (Runco & Pritzker 2020). According to one overview, creativity has been studied by nearly all of the most eminent psychologists of the twentieth century, and “the field can only be described as explosive” (Albert & Runco 1999: 17). There is also a groundswell of new work on creativity in the fields of computer science, artificial intelligence (AI), and robotics.

The present section surveys empirical work in psychology along with some related work in neuroscience, while the next section ( §6 ) covers research in computing, AI, and robotics. Throughout, we’ll see that philosophers are actively in dialogue with these fields under the broad, interdisciplinary umbrella of cognitive science.

The vast body of empirical research of creativity can be seen as addressing a variety of issues, but the central question that concerns us here is the one we identified above as the challenge for explaining creativity: How are people creative? This question is analogous to a number of other questions in cognitive science: How do people perceive through sense modalities such as vision? How do they form concepts? How do they acquire a language? How do they make inferences? Just as psychologists investigate the psychological and neurological processes, systems, and mechanisms at work in these other mental operations, as well as the internal and external factors that either enhance or hinder these operations, they are doing the same for creativity. There is no pretension to achieving a complete explanation which would include each and every causal factor, and provide the basis for perfectly predicting creative outcomes in advance. But to the extent that we identify some of the relevant causal factors involved in creativity we thereby make progress in explaining creativity, just as we do with other features of the mind.

As we noted in §2 , the standard definition of creativity in psychology says that a product (idea or artefact) is creative to the extent that it is both new and valuable (“effective”, “useful” or “appropriate”), and, in turn, people and processes are creative to the extent that they produce new and valuable things. As we also noted, many psychologists do not actually employ this, or any, definition of creativity in conducting their research. In one sampling of studies of creativity published in peer-reviewed psychology journals, only 38% of them included an explicit definition of creativity (Plucker, Beghetto, & Dow 2004), as they rely in one way or another on the assumption that we know it when we see it. For example, many studies use the Consensual Assessment Technique (CAT), whereby experimental subjects produce things that are then rated for how creative they are by a panel of experts in the relevant field; so paintings are rated by professional painters, stories by published authors, etc. Many other research methodologies are used, as we’ll see below.

Empirical research on creativity departs in several ways from the traditional approaches that seemed to place creativity outside the scope of science. For starters, in stark contrast to Plato’s supernaturalism, empirical psychologists take creativity to be a completely natural phenomenon. Creative people may of course be “inspired” in the sense of feeling energized or filled with ideas, but rather than being literally “breathed into” by some god or muse, their thoughts and behaviors are presumed to have causes that are perfectly natural. While it is difficult in practice to identify these causes, they are not in principle beyond the reach of science.

Further, the range of phenomena that contemporary researchers countenance within the ambit of creativity is far broader and more diverse than the traditional focus on poetry and the fine arts, as creativity can be manifest in any kind of art or craft, as well as in the sciences, technology, entrepreneurship, cooking, humor, or indeed in any domain where people come up with ideas or things that are novel and valuable in some way or another. Departing from Kant, genius, the highest echelon of creativity, may be acknowledged in virtually any of these domains, not just in the fine arts. And while a few researchers (e.g., Simonton 1984, 1994, 1997, 2009; Root-Bernstein & Root-Bernstein 1999) venture to examine genius (so-called “Big-C” creativity), most of them focus instead on relatively ordinary creative feats (“little-c” creativity) including the kinds of story-making, drawing, and problem-solving that can be elicited on command from regular people in experimental settings. Some researchers propose that in order to understand how the mind generates new ideas, we should begin with even more rudimentary phenomena. For example, philosopher Jesse Prinz and psychologist Lawrence Barsalou focus on how we form new concepts to categorize the things we perceive, a process which they claim is creative, albeit in a “mundane” rather than “exceptional” way (Prinz & Barsalou 2002; Barsalou & Prinz 1997; cf. Child 2018).

Of course, many feats of human creativity, and the ones that are most interesting, go far beyond the basic formation of concepts. A major step toward explaining those feats is to recognize that what we call “the creative process”, as if it were a single, homogenous phenomenon, is in fact an assembly of multiple stages or operations. The simplest recognition of this fact is the Geneplore model which distinguishes just two stages: generating ideas and exploring ideas (Finke 1996; Smith, Ward, & Finke 1995). This distinction may be seen as echoing one made by philosophers of science in the early twentieth century, between the context of discovery and the context of justification (Popper 1934). Other theorists posit up to eight stages of creativity (for a summary of proposals, see Sawyer 2012: 89). But the most influential stage-theory traces back to Henri Poincaré’s lecture, “Mathematical Creation” (1908 [1913: 383–394]), in which he identifies four phases in his own innovative work as a mathematician:

• conscious hard work or preparation ,
• unconscious incubation ,
• illumination , and
• verification .

In his book, The Art of Thought (1926), the psychologist Graham Wallas endorses Poincaré’s four stages with corroborating evidence from the personal reports of other eminent scientists like Hermann von Helmholtz. Wallas’s scheme, as a development of Poincaré’s, is still the one that is most widely cited, and we employ a version of it here with some slightly different terminology and with two more substantive alterations: instead of “incubation”, we identify the second operation more generally as the “generation” of ideas, which may include unconscious incubation but may also occur in conscious, deliberate thought; and we add “externalization” for a total of five operations:

• Preparation —You invest a great deal of effort learning and practicing in order to acquire the knowledge, skills, and expertise required for work in a given domain.
• Generation —You produce new ideas, whether through conscious reflection or unconscious incubation.
• Insight —You consciously experience the emergence of a new idea, which would strike you with a feeling of surprise: “Aha!”, “Eureka!”
• Evaluation – You assess the idea to determine whether it should be discarded, retained, revised, or amended.
• Externalization —You express your idea in a concrete, observable form.

Artists provide compelling examples (though not the only ones) of each of these five operations. Such examples can be especially illustrative since they come straight from the artists’ mouths, as they reflect upon, and share, their creative process. The twentieth century painter Jacob Lawrence was known for painting in the style of visual narratives. Lawrence developed a system, much like a filmmaker’s storyboard, for the preparation of these paintings. He would lay as many as 60 wood panels on the studio floor, each with individual scenes and sometimes with captions. From these storyboards, Lawrence would generate and evaluate ideas and insights for a visual narrative, culminating in the paintings such as those in his Migration Series (see Whitney Museum, 2002, in Other Internet Resources ). Toni Morrison, the Nobel prize winning novelist, remarks on the labors and sustained effort required at the preparation, generation, evaluation, and externalization stages of a creative writing process. Commenting on her novel Jazz , she says,

I thought of myself as like the jazz musician—someone who practices and practices and practices in order to be able to invent and to make his art look effortless and graceful. I was always conscious of the constructed aspect of the writing process, and that art appears natural and elegant only as a result of constant practice and awareness of its formal structures.

She further notes that insight does not always come in a flash,

[I]t’s a sustained thing I have to play with. I always start out with an idea, even a boring idea, that becomes a question I don’t have any answers to. (T. Morrison 1993)

Writer Ishmael Reed claims that insight can come unexpectedly and in various contexts:

One can find inspiration from many sources. The idea of Japanese by Spring originated in a news item that claimed the endowment to a major university was traced to Japanese mob, the Yakuza. Flight to Canada began as a poem. The Terrible series began when I heard someone at party mention that there was a black figure, Black Peter, in the Dutch Christmas, and by coincidence I was invited to the Netherlands shortly afterwards, where I witnessed the arrival of Saint Nicholas and Peter on a barge that floated into Amsterdam with crowds looking on. I took photos of the ceremony …. (Howell 2020: 91)

And with signature profundity, James Baldwin suggested that all elements of the creative artistic process, from preparation to externalization, require a basic enabling condition: being (and willing to be) alone (Baldwin 1962).

As Wallas recognized (1926: 81), and as the above examples suggest, the “stages” of the creative process are not necessarily discrete steps that follow one another in a tidy sequence. Creative work is messy: over time you have numerous ideas, keeping some and abandoning others in multiple rounds of trial-and-error; you incubate new ideas for one problem while you’re busy externalizing your ideas for another; and your moments of insight, evaluation, and externalization trigger further generative processes that send you cycling through these operations many times over. It’s still important to distinguish these operations, however, because, as researchers are confirming, they are enabled and influenced by different causal factors.

Among the additional stages that researchers have posited, one of the most widely discussed is known as problem-finding. Psychologists often conceptualize creative thought in terms of problem-solving: the ideas generated within the creative process are seen as candidate solutions to a given problem—where “problems” are broadly construed to include any creative aim, like that of producing a particular kind of artwork or proving a particular theorem, etc. (Flavell & Draguns 1957: 201; Newell, Shaw, & Simon 1962). But following some early work by Mihalyi Csikszentmihalyi (1965), many researchers came to appreciate that a lot of creative work is done not just in solving problems but in finding the right problem to begin with (Abdulla et al. 2020; Csikszentmihalyi & Getzels 1970; Getzels 1965; Getzels & Csikszentmihalyi 1975). While we agree that problem-finding often plays a key role in creativity, we have not assigned it to a separate stage, for the following reasons. Consider that you might settle on a problem to work on in either of two ways. On one hand, you might choose a problem to work on from a pre-existing menu of options. In that case, your choice would fall under the evaluation phase; it’s just that the idea you select is a problem that calls for the pursuit of further ideas. If, on the other hand, you develop a new problem, you would thereby be engaging in the generation of a new idea—the new problem—which may emerge in a moment of insight . Einstein and his colleague celebrated the novelty in such problem-finding:

The formulation of a problem is often more essential than its solution, which may be merely a matter of mathematical or experimental skill. To raise new questions, new possibilities, to regard old problems from a new angle, requires creative imagination and marks real advance in science. (Einstein & Infeld 1938: 92)

Either way—whether you “find” a problem by picking a pre-existing one or by coming up with a new one yourself—problem-finding, though important, does not need to be seen as an additional operation beyond the five listed above; it’s just a special case of generation, insight, or evaluation.

The next five sub-sections will respectively examine the five operations of creative work. Notice that three of them—preparation, evaluation, and externalization—are uncontroversially ordinary activities that involve no apparent mystery; it’s a challenge to explain them but no one is tempted to regard them as inexplicable or as violating the laws of nature. As we saw in §4 , traditional skepticism about the possibility of explaining creativity is really focused on the two remaining phenomena: the generation of new ideas ( §5.2 ) and the experience of insight whereby an idea seems to come out of the blue, as if from a god ( §5.3 ).

It’s myth that outsiders are more creative. To put yourself in a position to create anything of value, you have to spend a great deal of time and effort acquiring the relevant knowledge, skills, and expertise. In what has come to be called “the ten-year rule”, Howard Gardner (1993) found that, on average, people spend about 10 years learning and being immersed in a domain before they make any significant creative contribution to it.

Though a certain amount of rote learning is required, gaining mastery in a field is not simply a matter of passively absorbing information. Much of it involves what Anders Ericsson calls deliberate practice, where you focus on tasks which are a little beyond your current abilities, but which you eventually conquer through feedback and repetition. Across a variety of domains—including physics, medicine, programming, dance, and music—Ericsson found that, on average, world-class performance becomes possible for people only after 10,000 hours of deliberate practice in their chosen activity. This finding also converges on the ten-year rule, because if you engage in deliberate practice four hours a day, five days a week, that would add up to 10,000 hours in ten years (Ericsson, Krampe, & Tesch-Römer 1993; Ericsson et al. 2006).

However, there seems to be a point at which too much formal training can dampen creativity. Simonton (1984: 70–73) has reported that the relationship between creativity and education level is an inverted-U, as too much schooling can reinforce familiar, pre-established styles of thought. Even so, the point remains that, before you run into diminishing returns, years of preparatory learning and practice are required for exceptional creativity.

5.2 Generation

In this section we discuss four kinds of mental capacities or processes that researchers have posited for generating new ideas.

Psychologist Donald T. Campbell (1960, 1965) proposed that creative thought proceeds through “blind variation and selective retention (BVSR)”. The “variations” he refers to are the various ideas that might occur to a creator, and the process of generating them is “blind” to the extent that it is not guided or directed by prior knowledge of how valuable or useful they will be: “Real gains must have been the products of explorations going beyond the limits of foresight or prescience , and in this sense blind” (Campbell 1960: 92, emphasis added). Once ideas have been generated, however, there is a subsequent stage where the creator selectively retains some of those ideas while discarding others, and Campbell says this stage is “sighted” rather than blind since it is guided by the creator’s judgments as to which ideas are valuable. While there is little debate that selective retention is sighted in this sense, there has been more controversy over whether the initial production of ideas is, by contrast, blind.

In his prolific body of work, Dean Keith Simonton has extended and refined Campbell’s proposal. His work nicely illustrates the interdisciplinary nature of creativity research as he, like Campbell, is a psychologist who engages with philosophers, some of whom are broadly sympathetic to the BVSR theory (Briskman, 2009; Nickles, 2003), while others are skeptical (Kronfeldner 2010, 2011, 2018). In earlier writings Simonton suggested, in a way Campbell did not, that BVSR is to be understood on the model of Darwinian evolution (Simonton 1999a, 1999b). But Simonton (forthcoming: 2–3) has come to rescind the Darwinian framing of BVSR, conceding that it is misleading. Reprising Campbell’s core idea, he says that a process of generating an idea is blind to the extent that it is not guided by “the creator’s prior knowledge of the variation’s utility” (Simonton forthcoming: 5; cf. Simonton 2011, 2012a, 2012b, 2018). He stresses that blindness is not all-or-nothing; it comes in degrees. An example of a highly sighted process is that of using the quadratic formula to find the roots of a quadratic equation: you know in advance that if you apply the formula correctly, it will yield the correct answer. Examples of relatively blind processes include remote association and mind wandering.

Despite the foregoing criticism of BVSR, recent neuroscientific studies suggest a network of brain activity that may serve the blind variation role. Brain activity doesn’t cease when one is not focusing on a task, when one is at rest, daydreaming, and so on. Following this insight, researchers have used neuroimaging methods to identify what is now called the default mode network (DMN). The precise anatomy of this network is still a matter of investigation, but it is supposed to be less active when one is focused on an external task (say a problem in the real world or in the lab) and more active when one is not so focused (Raichle et al. 2001; Buckner & DiNicola 2019). Notice then, that while this network is not creativity-specific—it is supposed to be active during memory recall, imagining future events, daydreaming, and so on—it does seem especially well-suited for creativity, and particularly for the random idea generation hypothesized by the BVSR (Jung et al. 2013). Creativity researchers in these fields often refer to this more “free” production of ideas as “divergent thinking”, and some argue on the basis of neuroimaging studies that creative thought requires cooperation between this mode of thought as well as that under “executive control”. As one team puts the point,

In general, we contend that the default network influences the generation of candidate ideas, but that the control network can constrain and direct this process to meet task-specific goals via top-down monitoring and executive control.. (Beaty, Benedek, et al. 2016; see also Mayseless, Eran, & Shamay-Tsoory 2015; Beaty, Seli, & Schacter 2019; Chrysikou 2019)

Notice how well this comports with both the Geneplore and the BVSR frameworks, perhaps identifying a way to keep some of the insights of both without commitment to a special creativity mechanism after all.

At least since Kant, theorists have identified an important link between creativity and imagination; indeed, the two are sometimes unfortunately conflated. Construed broadly, imagination can take various forms: sensory imagery, propositional imagination, supposition, free association. Berys Gaut (2003, 2009, 2010) and Stokes (2014, 2016) have both recently argued that, although imagination and creativity are distinct, imagination is especially well-suited to creative thought because of its characteristic flexibility. They both agree that imagination is decoupled from action (Gaut 2003) and “non-truthbound” (Stokes 2014) in the sense that, unlike belief, imagination is not limited by the proper function of accurately representing (some part of) the world. This freedom or playfulness of imagination is crucial to generating new ideas, since it allows one to safely “try out” hypotheses, conceptual combinations, strategies for solutions, and so on, without epistemic or behavioral commitment.

A series of studies illustrates both the need for non-truthbound capacities in creative thought, as well as the difficulty of employing them. When people—children and adults alike—are asked to imagine and draw non-existent houses, people, or animals, they depict things that are strikingly similar to their familiar counterparts in the real world: imagined people, for example, were generally drawn with some version of a head, limbs, eyes, and so forth. (Karmiloff-Smith 1990, 1992: 155–61; Cacciari et. al 1997; Ward 1994, 1995). This suggests that we are highly constrained in our creativity by the concepts we already have. Concepts of existing things are truth-bound: your concept of an animal, for example, has the proper function of accurately representing the range of things that are in fact animals. When you try to envision a new, fictional kind of animal, you begin with a mental image that exemplifies your existing concept of animal, which is why you are constrained by that concept. You then have to manipulate your initial image, varying its features in ways that abandon the aim of accuracy, using a capacity that isn’t truthbound. Generalizing this point yields the cognitive manipulation thesis , according to which creative thought requires cognitive manipulation, which involves thinking in ways that are not bound to the truth (Stokes 2014: 167). Plausibly, imagination is the mental capacity which is best suited to serve in this cognitive manipulation role. In the studies just cited, subjects must use their imagination to manipulate their existing concepts so as to form new ideas.

Recent empirical research on visual imagery seems to corroborate this claim. Various studies have identified positive correlations between creative problem solving and visual image generation, image transformation, and vividness of imagery (Finke 1990, 1996; Zemore 1995; R. Morrison & Wallace 2001; Pérez-Fabello and Campos 2007). A more recent study highlights the importance of image transformation ability—the ability to mentally manipulate a given image—and the ability to achieve high degrees of visual creativity. Further, the results of this study suggest that although vividness negatively correlates with the practicality of images created, vividness positively correlates with novel idea generation (Palmiero et al. 2015). The novelty involved is minimal, but again it appears that imagination, here in the form of imagery, well serves the role of cognitive manipulation.

Stokes observes further that we can voluntarily control imaginative states (in contrast with other non-truthbound states, like desires and wishes). And because imagination connects in important ways with inferential systems, as well as affective systems, the thoughts it produces can often be integrated with knowledge and skills to formulate an innovative strategy or solution to a problem. Finally, this role for imagination in creativity is not exclusive to the rich creativity of artists and scientists, but indeed seems to characterize the minimally creative behavior that we all enjoy. This claim is partly motivated by the empirical research just discussed. Here, as in the more radical cases, instances of novel achievement or learning by subjects requires more than rote memorization; it requires cognitive manipulation of the information in the relevant conceptual space (e.g., combining concepts about houses and persons). This kind of cognitive activity is best done by using the imagination.

Peter Carruthers has argued that imagination is important to creativity on evolutionary grounds (2002, 2006; see also Picciuto & Carruthers 2014). Like the above analyses, he focuses on the playfulness of imagination. Pretend play typically develops early in childhood in humans. And imagination in adults provides the right mechanisms for generating and exploring ideas (just as required by the Geneplore model). Carruthers argues that imagination evolves under adaptive advantage as a kind of practice for adult creativity—and may have been accordingly selected for, aligning with the putative creativity explosion of 40,000 years ago (Mithen 1996, 1998; Harris 2000). This, he argues, is the most parsimonious explanation of both the emergence and the ubiquity of creativity in the human species. See B. Gaut (2009) for a critique of Carruthers’ analysis.

While we may generate ideas consciously in imagination, we may also do so during a period of unconscious incubation, when we are focused on something else. This point is illustrated by any number of famous stories, though some are probably embellished after years of retelling. Isaac Newton witnessed an apple fall from a tree (on some accounts, falling upon Newton’s head) and thereby found the insight for his laws of gravity. August Kekulé is reported to have discovered the structure of the benzene molecule while daydreaming of a serpent circling upon and seizing its own tail. Henri Poincaré alleged that, while boarding a bus, he enjoyed a needed flash of insight that led to his discovery of non-Euclidian geometry. Richard Feynman, the Nobel prize winning physicist, claimed to find inspiration while sipping soda and doodling at adult clubs. And Einstein reported:

I was sitting in a chair in the patent office at Bern when all of a sudden a thought occurred to me. “If a person falls freely he will not feel his own weight”. I was startled. This simple thought made a deep impression on me. It impelled me toward a theory of gravitation. (Einstein, “Kyoto Lecture”, translated and quoted in Pais 1982: 179)

In each case, someone is suddenly struck with a flash of insight about one thing while engaged with something else entirely. The empirically-minded theorist rejects the notion that such ideas arise ex nihilo or through divine possession. So how are they explained in terms of natural mental phenomena?

Arthur Koestler, partly inspired by the work of Henri Poincaré (1908 [1913]), hypothesized that during creative thought processing, ideas are combined in novel ways, and this combination is performed largely unconsciously , by what Poincaré called the subliminal self (Koestler 1964: 164–5). For Poincaré there are only two ways we might think of the unconscious. One, we might think of the unconscious in Freudian terms, as a self capable of careful and fine discernment and, importantly, distinctions and combinations that the conscious self fails to make. Alternatively (and this is the option favored by both Poincaré and Koestler), we can think of the unconscious as a sub-personal automaton that mechanically runs through various combinations of ideas. Importantly, this unconscious process (or, if one likes, automaton) generates random conceptual associations and ideas. And these can then be further considered, examined, explored, and revised.

In the context of creativity in particular, there is precedent, or at least overlap, in Colin Martindale’s cortical arousal theory. This theory centers around the nature of focuses of attention (Martindale 1977, 1981, 1995, 1999; Martindale & Armstrong 1974; Martindale & Hines 1975). Martindale proposes a multi-stage model of problem solving, which if the right mechanism is possessed, leads to creative thought. In the initial stages, information is gathered, various approaches are taken to the problem, and there is a high level of cortical arousal with a narrow focus of attention. As information increases and the problem remains unsolved, two kinds of responses may occur. The first kind of response is to keep attempting the same solutions to the problem such that the arousal and attention focus stay high and narrow, respectively. Alternatively, some persons experience a decrease in cortical arousal coupled with a wider range of attention focus. Information then enters what Martindale calls primary processing: a kind of subconscious cognition not under the complete control of the agent. It is this kind of processing, and the arousal mechanisms that enable it, that distinguish creative insight or achievement from non-creative ones. The first kind of response typically results in frustration and failure (fixation), while the second often results in creative insight.

Some early studies on these phenomena centered around a familiar observation. Consider the tip-of the-tongue phenomenon, when you know that you know some bit of information (an actor’s name or the title of a song) but, try as you may, you just can’t recall it. It often helps to give up for a moment and allow the memory to surface without effort. Researchers found that the same approach—forgetting about a problem—works well to overcome fixation on ineffective ideas so as to allow the actual solution to pop up. Smith and Blankenship primed two groups of subjects with inappropriate or misleading solutions to problems. They left one group to continue struggling with the same problem, while they distracted the second group with a distinct but cognitively demanding task. The second group thereby overcame fixation and outperformed the first group when returning attention to the original target problem (Smith & Blankenship 1989, 1991; see also Smith, Ward, & Finke 1995).

These behavioral methods can be combined with contemporary understanding of neural plasticity and the effects of cognitive effort and attention. Neuroscientists have long recognized that the human brain is plastic —stable in genetic material but constantly undergoing functional change and development in neural networking in response to external stimuli, with the work of Donald Hebb in the middle of the twentieth century being one important early precedent. As Hebb put it, neural cells that “fire together, wire together”. Cell assemblies thus form as a result of the synchrony and proximity of the firing of individual cells.

[A]ny two cells or systems of cells that are repeatedly active at the same time will tend to become “associated”, so that activity in one facilitates activity in the other. (Hebb 1949 [2002: 70])

And continued attention to a problem, what some have called cerebral effort , causes changes in the networking of the brain’s cortex (Donald 2001: 175–8). Importantly, these changes can continue to take place, to “reverberate” even after one has removed attention from that problem. This motivates a simple (and somewhat unsurprising) hypothesis: attending to and performing cognitive tasks affects neural networking (Posner et al. 1997; Posner & Raichle 1994; see also Kami et al. 1995), and those changes can involve strengthening of synaptic connectivity (which correlate with conceptual connections and associations). These changes, again, can occur both when one is attending to a task and after one has diverted attention elsewhere. And, finally, the latter goes some way to explain a moment of insight after incubation (the so-called incubation effect): when one returns attention to the target problem, new or newly strengthened neural connectivity (as a result of previous cognitive effort) can give rise to a new idea. And because that neural process is not in any sense done by you, the emergence of the new idea can feel like a burst of insight (see Stokes 2007; Thagard & Stewart 2011; Ritter & Dijksterhuis 2014; and Heilman 2016).

There are also various recent studies on closely related topics: on mindwandering and spontaneous thought (Christoff et al. 2016; Irving & Thompson 2018; Murray et al. forthcoming), on so-called “divergent thinking” (Mekern et al. 2019), and more on the neural basis of insight (Jung-Beeman et al. 2004; Bowden et al. 2005; Limb & Braun 2008; Dietrich & Kanso 2010; Kounios & Beeman 2014).

It should be intuitive that creativity often involves solving problems and doing so in interesting or surprising ways. In exceptional cases, the individual identifies a problem solution that perhaps no one (including the creator) anticipated. But there are countless examples of more mundane instances of problem solving, where the solution may be surprising (or especially interesting) to only a few individuals, perhaps even only to the problem solver. One broad, standard experimental method used by researchers thus focuses on insight in problem solving. Some problems (thankfully!) can be solved by straightforward appeal to memory, or by applying some technique or method of calculation in a mechanical way. Solving the problem may still take time and effort, but the solution will come so long as one executes the appropriate strategy or applies the relevant knowledge from memory. An insight problem, by contrast, typically requires something new on the part of the individual, and one must often “change views” of the structure of the very problem. Predictably, there are a variety of definitions or characterizations of “insight” in the literature. Here are two recent, representative examples. Bowden et al. suggest that insight occurs

when a solver breaks free of unwarranted assumptions, or forms novel, task-related connections between existing concepts or skills. (Bowden et al. 2005: 322)

More recently, Kounios and Beeman write,

we define insight as any sudden comprehension, realization, or problem solution that involves a reorganization of the elements of a person’s mental representation of a stimulus, situation, or event to yield a nonobvious or nondominant interpretation. (2014: 74)

There are at least two, separable components of insight thus understood. First, an insight problem requires non-mechanical or non-algorithmic solution, and this in turn requires some kind of conceptual reorganization. A hackneyed phrase may come to mind here: one has to “think outside the box”.

The second element of insight as understood here is subjective or phenomenological. An insightful problem solution is often described as occurring suddenly and with little or no apparent effort. It is an aha moment, even if less dramatic than the traditionally romanticized Eureka moment. One way researchers have tested for this subjective feature is to ask subjects to report nearness or “warmth” relative to solving a problem. They find that for insight problems, by contrast to non-insight problems, subjects report that as they near solution they experience abrupt changes in the sense of warmth for solving the problem (Metcalfe & Wiebe 1987; see also Dominowski 1995; Laukkonen & Tangen 2018). More recently, researchers have begun to employ neuroimaging techniques to study insight and insightful problem solving (Luo & Niki 2003; Mai et al. 2004).

First, researchers have developed methods for using subjective report, where subjects rate whether they felt that they used insight in solving a designated problem (Bowden et al. 2005). And second, and coupled with those report methods, researchers have developed simple problems that can be solved with insight. One such example is the “Compound remote associates problem” (CRA). Here is an example of a CRA problem:

Each of the three words in (a) and (b) below can form a compound word or two-word phrase with the solution word. The solution word can come before or after any of the problem words. french, car, shoe boot, summer, ground [ 1 ] (Bowden et al. 2005: 324)

Because of their simplicity, these problems can be solved unambiguously and quickly, and with this speed comes better potential for neuroimaging study. In instances where subjects report insight solutions to these kinds of problems,

EEG shows a burst of high-frequency (gamma-band) EEG activity over the right temporal lobe, and fMRI shows a corresponding change in blood flow in the medial aspect of the right anterior superior temporal gyrus (Jung-Beeman et al. 2004). (Kounios & Beeman 2014: 78)

The question for neuroscientists is whether this convergence of evidence is sufficient to establish neural correlates of insight.

A moment of “insight” can be misleading, as what initially strikes you as a promising idea may ultimately turn out to be a dead end. You may have countless ideas in the course of undertaking a complex creative project, while only a few of them will make the final cut. A crucial part of your creative work therefore consists in evaluating your ideas. For any idea that occurs to you, you might have to ask: Will this work? Is it new? How does it fit in with other parts of your project? Do you have the resources and abilities to bring it to fruition? Is it worth the time and effort?

Much of the research on this phase of the creative process is concerned to identify and categorize the range of factors that people take into consider as they evaluate their ideas (Blair & Mumford 2007; Dailey & Mumford, 2006). Unsurprisingly, those factors vary from one domain to another. New culinary dishes are judged by factors like aroma, taste, texture, color, presentation (Horng & Lin 2009), whereas improved musical performances are judged according to their complexity, originality, and technical virtuosity (Eisenberg & Thompson 2003), and so on. Your understanding of the relevant factors is part of your internalized model of the domain (Bink & Marsh, 2000; Csikszentmihalyi & Sawyer 1995). And since you acquired and refined that model through years of preparation, your capacity for evaluation is largely a consequence of your efforts from that initial stage.

Somewhat more surprisingly, there is some evidence that people who are good at evaluating ideas are also good at generating them (Runco 1991; Runco & Dow 2004; Runco & Chand 1994; Runco & Vega 1990).

Other studies support what Sawyer calls Sawyer (2012: 131) calls the productivity theory, which says that the best way to get good ideas is to have lots of ideas and just throw away the bad ones. In historiometric studies, Simonton found that creators who yielded the greatest number of works over their lifetimes were mostly likely to produce works that were significant and stood the test of time. Even more striking, he discovered that, from year to year, the periods when creators were most productive were also the ones in which they were most likely to do exceptional work (Simonton 1988a, 1988b). Linus Pauling, who won the Nobel Prize in Chemistry in 1954 as well as the Nobel Peace Prize in 1962, summed up the productivity theory in a famous remark:

If you want to have good ideas you must have many ideas. Most of them will be wrong, and what you have to learn is which ones to throw away. (quoted by Crick 1995 [time 34:57])

The final operation of the creative process—externalizing ideas—may involve any number of disparate activities, which Keith Sawyer sums up as follows:

Creativity research has tended to focus on the early stages of the eight-stage creative process—particularly on the idea-generating stage. But a lot has to happen to make any idea a reality. Successful creators are skilled at executing their ideas, predicting how others might react to them and being prepared to respond, identifying the necessary resources to make them successful, forming plans for implementing the ideas, and improvising to adjust their plans as new information arrives. These activities are important in all creativity, but are likely to be even more important in practical domains such as technological invention and entrepreneurship (Mumford, 2003; Policastro & Gardner, 1999). (Sawyer 2012: 133–4)

It may be tempting to assume that the real creative work is finished once a new idea emerges in the moment of insight, and that externalization is just the uncreative, mechanical chore of making the idea public. But a closer look at the phenomenon reveals that externalization is often integral to creativity itself.

Vera John-Steiner (1985) interviewed, and examined the notebooks of, over 70 exceptional creators (ranging from author Anaïs Nin to composer Aaron Copland), and consulted the notebook of another 50 eminent historical creators such as Leo Tolstoy and Marie Curie. A recurring theme throughout was that at the beginning of each creative endeavor and continually throughout its development, creators manipulate and build upon their impressions, inklings, and tentative hunches using sketches, outlines, and other external representations.

Perkins (1981) corroborated this finding by analyzing the 61 sketches Picasso made en route to painting his famous work, Guernica , as well as Beethoven’s musical drafts and Darwin’s notebooks. In each case, the artist progressed by engaging with external representations.

Other studies found that people discovered and solved more problem when they used sketches during a task (Verstijnen 1997), and that people come up with better ideas for improving inventions when they work with visual diagrams (Mayer 1989).

One reason externalization is so vital to substantial creative work is because of our limited capacity to consciously hold and manipulate information in our minds. It helps to offload ideas and store them in the form of physical symbols and expressions in order to free up space for the mind to examine those ideas at arm’s length while entertaining new ones. Thus research shows that internal strategies like mental visualization can help with relatively simple tasks, but for more complex projects externalization is key (Finke et al. 1992: 60).

We close our survey of the cognitive science of creativity with a brief discussion of some general worries about current work, and some prescriptions for future research.

Some have worried about the validity of the psychometric measures employed in neuroimaging studies. One such concern regards the confidence that we should have that the tests employed are really tracking creative behavior. This is of course a general problem, partly symptomatic of the challenges that come with defining creativity (like other phenomena) and with the special challenges that attach to features such as insight and incubation. But there are particular challenges that come with using neuroimaging technologies such as fMRI scanning to attempt to study naturally occurring phenomena. Use of this technology is almost invariably ecologically invalid—one cannot run an fMRI in the artist’s studio. And because of the cost and sensitivity of these imaging systems, the correlative behavioral tests are often significantly abbreviated. This may impose constraints on space for occurrence of the target phenomena—novel thinking and insight—during the imaging session. As one researcher worries,

Too often single tests are used—or even single items! This is contrary of psychometric theory in general (where longer tests allow errors to cancel themselves out and are thus more reliable) and true of the research on creativity assessment in particular, where differences among items and even tests are common (Richards, 1976; Runco, Mohamad, & Paek, 2016 [sic should be Runco, Abdulla et al. 2016). Results from any one test will not generalize to other tests. Results from a single item of course have even less generalizability. (Runco 2017: 309–310; see also Abraham 2013)

Another empirical researcher criticizes what he sees as “the wild goose chase” in the neuroscience of creativity. Arne Dietrich (2019) recapitulates the above worries about validity of psychometric measures and their abbreviated and piecemeal application. He further worries about the now dominant emphasis on divergent thinking, and the default mode network (as well as the now mostly abandoned emphasis on notions such as madness, the right brain, and REM sleep). Dietrich’s concern in each case is that the research emphasis is unhelpfully myopic, and that while the imaging methods are sound and state of the art, the characterization of creativity is not. He decries the temptation to identify what may be a feature of creativity with the whole of the phenomenon. Divergent thinking, he suggests, is likely a cluster of various mental phenomena rather than a singular one, and

there is no effort underway to dissect divergent thinking and link it to the kinds of cognitive processes we use to operationalize all other psychological phenomena, such as working memory, cognitive control, semantic memory, perceptual processes, or executive attention. (2019: 37)

Notice, then, that the “wild goose” for Dietrich is to hastily conclude and then center studies around a singular, special creativity mechanism.

Dietrich also offers various prescriptions for remedy. To combat myopia, he suggests (as some have in other disciplines, e.g., Boden 2004) a plurality of types of creativity (and/or features of creativity). He cautions,

Since different types of creativity contain opposing brain mechanisms—focused versus defocused attention, for instance—any all-encompassing claim about creativity in the brain will almost certainly qualify as phrenology. (2019: 39)

He pairs this with a prescription for a more interdisciplinary approach to the topic. Others in the field have made the same prescription, advocating a “systems” approach sensitive both to the multi-faceted nature of creativity and the value of theorizing at multiple levels of explanation (Hennessy & Amabile 2010).

These directives for future research seem hard to resist. At the very least, it would seem advantageous to ensure that the full range of empirical method across the behavioral and brain sciences is communicated across the relevant sub-disciplines. This would ideally lead to better collaboration amongst such researchers. What’s interesting is that a cousin to this prescription is not well heeded by the same researchers advancing it here. However little crossover there is between, say, behavioral psychologists and neuroscientists in studies of creativity, there is comparatively even less crossover (almost none) between the psychological sciences and computational approaches to creativity. The next section thus begins by highlighting this “gap”, and identifying some of the potentially fruitful areas for interdisciplinary work on that front. It then continues with a discussion, generally, of research on creativity in the fields of computing science, artificial intelligence, and robotics.

Just as we find in psychology and neuroscience, there is a rich research literature on creativity in artificial intelligence and computer science, with devoted journals, special issues, and conferences ( The Journal of Artificial Creativity , The Journal of Creative Music Systems , Digital Creativity , Minds and Machines special issue on Computational Creativity [Gervás et al. 2010], The International Conference on Computational Creativity ). The question we focus on here is whether a computer could be creative . As background, it is worth considering how theorists approached the analogous question as to whether a computer could think .

Although theorists of various kinds have asked whether machines can think since at least the early modern period, the most important conceptual innovations on the topic came from Alan Turing, centering around his 1950 paper “Computing machinery and intelligence”. Here Turing provided a number of groundbreaking insights. Perhaps most familiar is Turing’s “imitation game”, now commonly known as “the Turing Test”. In brief, the test involved an unknowing interrogator who could ask an open-ended series of questions of both a human and a computer. If the interrogator could not distinguish computer from human, Turing postulated that this would suffice to illustrate genuine intelligence. There is no shortage of controversy regarding the aptness of the test for intelligence, and arguably no computer has yet passed it. (For more thorough discussion of Turing and the Turing test see entries on Alan Turing , Turing machines , and the Turing test ).

Successful performance in Turing’s game would require remarkable behavioral flexibility. And it is highly operational: specify a threshold for imitation, and then simply allow the interrogator to ask questions, then assess performance. If the behavior is sufficiently flexible to fool the interrogator, Turing claimed, the behavior was intelligent and, therefore, the computer intelligent.

With this background in mind, what are some of the cases in AI research lauded as success cases, and how do they align with some of Turing’s criteria?

Many of the familiar success cases are highly specialized. Deep Blue defeated chess master Garry Kasparov (Kasparov & Greengard 2017); some language processing systems managed to navigate social contexts such as ordering from a menu at a restaurant (Schank & Abelson 1977); AlphaGo more recently defeated the world champion Go player. This specialization is both a virtue and a limitation. On the one hand, achievement in such a specialized domain implies an exceptional amount of detailed memory and skill. On the other hand, this knowledge and skill does not generalize. Neither Deep Blue nor Alpha Go could successfully order from a menu, along with countless other basic human tasks. Put in terms of Turing’s imitation game, these systems would fail miserably to fool a human, or even remotely imitate one (except for their performance in a very narrow domain). What about systems such as IBM’s Watson , which famously won (against humans) on the television game show Jeopardy! This performance is more general, since topics on the show vary widely, and seemed to require both language comprehension and some minimal reasoning skills (see entry on artificial intelligence for extended discussion). Even so, Watson’s capabilities are still quite limited: it cannot make fluid conversation “in real time” and is largely insensitive to temporal and other factors that come with context.

There are many, many more examples of computational systems that display sophisticated behavior, from the highly specialized to the more general. On the language processing front, very recent AI systems such as OpenAI’s ChatGPT and Google’s LaMDA significantly outperform the systems described above. To be clear, these are remarkable achievements that display substantial complexity and, it appears in some cases, significant flexibility—features Turing highlighted in characteristically human behaviors. But this also underscores a distinction, often invoked by critics of artificial intelligence research. There is a difference between a computer’s displaying or merely imitating an intelligent behavior, and a computer’s instantiating intelligence through such behavior. And the critic will say, even if a computer behaves as if it is intelligent, this is just modeling or simulating intelligence. The greater ambition, though, is “genuine artificial intelligence”, a system that actually thinks. John Searle refers to this as the distinction between “weak AI” and “strong AI”, respectively.

• Weak AI : Could a computer behave as if it thinks?
• Strong AI: Could a computer genuinely think?

The general worry here is that however sophisticated a system’s behavior may appear “from the outside”, for all we know it may just be a “hollow shell” (Haugeland 1981 [1997]; Clark 2001). The worry has then been fleshed out in various ways by specifying what is missing from the shell, as it were. Here are three standard such candidates. And, again, in each case however sophisticated the computer’s behavior may appear it still may be lacking in any or all of the following. First, the computer may lack consciousness . Second, the computer may lack any understanding of the symbols over which it computes (Searle 1980). Finally, the computer may operate without caring about its own behavior or, as John Haugeland colorfully puts it, without “giving a damn”. In each case, any kind of response from the ambitious AI researcher encounters the substantial challenges that come with theorizing mental phenomena such as consciousness, understanding, linguistic competence, and emotion. (Turing 1950, for instance, recognized but largely eschewed these kinds of topics).

It’s one thing to ask whether computers could think, and another to ask whether they could be creative. And just as the prospect of artificial intelligence or thinking divides into two questions—of weak AI and strong AI—we may distinguish two analogous questions about artificial creativity, which we’ll refer to as the questions of “weak AC” and “strong AC”, respectively. To begin with the former:

• Weak AC : Could a computer behave as if it’s creative?

Something behaves as if it’s creative if it produces things which are psychologically new (new to that thing) and valuable . Arguably, a number of computers have already done that.

In the 1970s, Harold Cohen began using computational technologies to produce new drawings and paintings. The work of his computer painter, Aaron, has exhibited at galleries such as the Tate and the Victoria and Albert Museum in London. David Cope’s “EMI” (Experiments in Musical Intelligence) has composed musical works in the style of various known composers and styles, even a full-length opera. Some of these works have been recorded and produced by bona fide record labels. Just search “Emily Howell” on Spotify or Apple Music and give it a listen (Cope 1996, 2006). Simon Colton’s The Painting Fool is an ongoing project, involving a software that abstracts phrases, images, and other items from newspaper articles and creates collage-style pieces. It has also produced portraits, based on images of film characters, of the same individual in different emotional states (see Painting Fool in Other Internet Resources ; see Colton 2012 for theoretical discussion). Even more recently, there have been explosive developments in generative art systems like DALL•E, Midjourney, Stable Diffusion, VQGAN+CLIP. (For discussion see Paul & Stokes 2021). In all of these cases, the relevant outputs of the computer program are new relative to its past productions—so they are psychologically (or behaviorally) novel, which again is all the novelty that creativity requires. And although historical novelty isn’t required for creativity, it’s worth noting that these products appear to be to be new in all of history as well.

What about value? As noted above in §2.1 , some theorists reject the value condition, but even if value is required for creativity, that too is a condition these computer artworks seem to meet. Assessments of value can be controversial, but that is no less true for the outputs of human creativity. The fact that these works are critically acclaimed, showcased in prestigious galleries, and commissioned by selective record labels testifies to their artistic merit, and viewers find them pleasing, interesting, and appealing, even before being apprised of their unusual origin. So it is reasonable to conclude computer programs like the ones just described exhibit at least weak AC insofar as they produce works of valuable novelty, and one could cite many more examples in the same vein.

Some theorists have noted that, whether or not the original Turing test is a good test for intelligence or thinking, we might adopt an analogous test for creativity: If a computer can fool human observers into thinking that it is a human creator, then it is in fact creative (Pease & Colton 2011; see also Chen 2020 for useful discussion of artificial creativity, including many additional examples of particular cases, and so-called Dartmouth-based Turing tests). If we employ this test, we might find ourselves with an unexpected conclusion: computers can be creative; in fact, some of them already are. But one might reasonably worry that the test is inadequate and the conclusion is too quick (Berrar & Schuster 2014; Bringsjord et al. 2001). From the fact that a computer operates as if it’s creative, one might argue, it doesn’t follow that it really is. Which brings us to our next question:

• Strong AC : Could a computer genuinely be creative?

This obviously returns us to the question of what conditions something must meet in order to count as being genuinely creative. And here we need go beyond the outwardly observable product-features of novelty and value to consider the underlying processes of genuine creativity. As we saw in §2.2 , theorists have variously proposed that in order for a process to count as creative, it must be surprising, original, spontaneous, and/or agential. There is no consensus to appeal to here, but if any one of these conditions is indeed required for genuine creativity, then a computer could be genuinely creative only to the extent that it executes processes which satisfy that condition.

The classic statement of skepticism regarding the possibility of computer creativity is due to Lady Ada Lovelace who had this to say while remarking on “the Analytical Engine” designed by her friend Charles Babbage:

It is desirable to guard against the possibility of exaggerated ideas that might arise as to the powers of the Analytical Engine. The Analytical Engine has no pretensions whatever to originate anything. (Lovelace 1843, italics added)

Though Lovelace does not frame her comments in terms of “creativity” as such, she explicitly denied that a computer could satisfy at least one condition that is plausibly required for creativity, namely originality . A computer cannot be the originator, the author, or the creator of anything new, she contends; it can only do what it is programmed to do. We cannot get anything out of a computer that has not already been programmed into it. Further, Lovelace may also be interpreted as expressing or implying doubt about whether a computer could satisfy the three other proposed requirements for genuine creativity. Insofar as a computer’s outputs cannot be original, one might also suspect that they cannot be surprising . The image of a machine strictly following rules invokes precisely the kind of mechanical procedure that is the antithesis of spontaneity . And it may seem that such a machine could not be a genuine agent either. The problem isn’t just that a computer can’t produce anything original; it’s that it deserves no credit for whatever it does produce. Any praise or blame for the outputs of a computer rightly go to the engineers and programmers who made the machine, not to the machine itself. While these points may be intuitive, at least some of them are being challenged by modern technologies, which have come a long way since Babbage’s invention.

Consider AlphaGo again. This is a “deep learning” system, which involves two neural networks: a Policy network and a Value network. Very briefly: The system is trained using a vast number of legitimate moves made in actual games of Go played by professional human players (28.4 million moves from 160,000 games, to be precise; see Silver et al. 2016 and Halina 2021). The network is further trained, again using learning algorithms, by playing many games (some 100 million) against previous versions of itself (in the sense of a differently weighted neural network). The weights of nodes in the network are then adjusted by a learning algorithm that favors moves made in winning games. The value network is trained over a subset of these many games, with node weighting adjustments resulting in reliable probability assignments to moves vis-à-vis their potential to contribute to a win. Finally, the system employs a Monte Carlo search tree (MCT). Generally, this kind of algorithm is designed to simulate a decision process to optimize success given chosen parameters. In this case, the search algorithm selects a given path of moves, then adds some valid moves to this path, and then if this process does not terminate (end in win/loss), the system performs a “rollout”. A rollout essentially plays the game out for both players (using samples of possible moves) to its conclusion. The information that results from the MCT and processing by the value network are then fed back (back propagated) into the system. This entire process (once the system is trained) is rapid and determines how AlphaGo “decides” to move in any given game.

Here are some things to note. AlphaGo’s style of play is surprising . As commentators have noted, it is starkly unconventional relative to standards of human play (Halina cites Baker and Hui 2017 [ Other Internet Resources ]). Indeed, Lee Sodol, the world champion Go player defeated by AlphaGo in 2016, remarked that AlphaGo’s play revealed that much of human play is, contrary to prior common opinion, not creative after all—intimating that at least some of the play of AlphaGo is . Note further that this system is flexible. While there are learning algorithms and rules that adjust network weights, the system is not mechanical or predictable in the same fashion as earlier, classical systems (including Deep Blue , for example). In a recent paper, Marta Halina has made this argument (Halina 2021). She explicitly invokes Boden’s characterization, which requires novelty, value, and surprise of creativity. Again, the novelty and value should be plausibly attributed in this case. Regarding surprise, Halina suggests that it is AlphaGo’s employment of MCT that enables a kind of “insight”, flexibility, and unpredictable results. She writes,

It is the exploration parameter that allows AlphaGo to go beyond its training, encouraging it to simulate moves outside of those recommended by the policy network. As the search tree is constructed, the system starts choosing moves with the highest “action value” to simulate, where the action value indicates how good a move is based on the outcome of rollouts and value-network evaluations. (Halina 2021: 324)

Halina grants that given its domain-specificity, as we have already noted, this system’s particular abilities do not generalize in a way that may be required to properly attribute genuine intelligence. But she suggests that the complex use of the MCT search may amount to “mental scenario building” or, we might say, a kind of imagination. And insofar as this search algorithm technology can be applied to other systems in other domains, and imagination is a general component of intelligence, perhaps here lies space for generalizability. AlphaGo also affords at least some reply to the traditional Lovelace worry.

Artificial systems do not act only according to preprogrammed rules hand-coded by engineers. Moreover, current deep-learning methods are capable of producing systems that are superhuman in their abilities to discover novel and valuable solutions to problems within specific domains. (Halina 2021: 327)

If this is right, then AlphaGo exhibits originality . Finally, the flexibility with which this system operates may also satisfy Kronfeldner’s spontaneity requirement.

Some of these same features are found in a related approach in AI, namely research in evolutionary robotics. These systems also involve various forms of machine learning but in this case the learning is distributed, as it were, across a population of individuals rather than one individual. This approach can be understood, albeit imperfectly, as analogous to natural evolution. One begins, typically in computer simulation, with a population of agents. These agents are typically identified with individual neural networks, the connections and weightings of which are random to start. Relative to some task—for instance, avoiding obstacles, collecting objects, performing photo or phonotaxis—a genetic algorithm assigns a fitness value to each individual agent after a certain period of time or number of trials. Fitter agents are typically favored and used to generate the next population of agents. Also included in this generation are random mutation and genetic crossover (digital breeding!). Although it can take hundreds of generations, this is a discovery approach to engineering or constructing a system that successfully performs a task; it is “gradient descent learning” (Clark 1996). In this bottom-up approach, no single individual, nor even an entire population, are in any strict sense programmed. Rather, successful agents have “learned” as a result of generations of randomness, crossover, and small fitness improvements (and lots and lots of failures). Early success cases evolved robots that can follow trails (Koza 1992), locomote in insect-fashion (Beer & Gallagher 1992), guide themselves visually (Cliff, Husbands, & Harvey 1993), and collect garbage (Nolfi & Floreana 2000). See Bird and Stokes (2006, 2007) and Stokes and Bird (2008) for analysis and study of creativity in the context of evolutionary robotics.

These systems most certainly produce novelty. Later, fit individuals achieve novelty at their aimed task relative to whole generations and populations of previous agents. And this novelty is often surprising to the engineers and programmers that build them, indeed sometimes even unpredictably independent of any relevant task for individuals in the population. There are many examples in the literature. Indeed Lehman and others (2020) catalog a large range of cases where digital evolution surprises its creators, categorizing them in four representative groups: “mis-specified fitness functions”, “unintended debugging”, “exceeded experimenter expectations”, and “convergence with biology”. Here is one now relatively famous example of the first type of case. In early research in artificial life (A-Life), Karl Sims (1994) designed virtual creatures that were supposed to learn to walk (as well as swim and jump) in a simulated environment. The fitness function assessed individual agents on their average ground velocity across 10 seconds. Some of the fittest individuals to evolve were surprising: they grew tall and rigid and when they would fall over they would achieve high ground velocity, thus maximizing fitness given the (mis)specified parameters in unpredicted ways.

This is but one example of how systems like these can evolve in unpredictable or surprising ways. This unpredictability has occurred not just in simulated robotics, but in embodied robotics as well. In using a genetic algorithm to attempt to evolve oscillating sensors, researchers unintentionally evolved a radio antenna (Bird & Layzell 2002). This unexpected result arose from a combination of the particular algorithm used (which was intended) and various physical features of the space such as proximity to a PC monitor (which the researchers had presumably deemed irrelevant but which the evolved system, in a sense, did not). And one might be further inclined to describe some of these achievements as creative (and not just in the trivial sense that they are original instances of robotic success), since they also produce value, at least insofar as they are useful at performing a task, whether it is locomoting or locating a source of light or sensing radio waves.

Some theorists in this domain might argue that these systems achieve spontaneity as well. Given the substantial inclusion of randomness in the system’s development—both at the outset when the individual’s neural networks are randomized and more importantly with random mutation across populations—it is intuitive to describe the system’s as not following a mechanical procedure. Indeed, the way in which systems exploit fitness functions and data patterns further underscores this point. (Again, see the rich catalog of cases offered by Lehman et al. 2020).

On the face of it, then, recent technologies in AI, evolutionary robotics, and artificial life, seem to fulfill many of the conditions proposed for genuine creativity. These systems produce things that are novel and valuable, and do so through computational processes that are plausibly surprising, original, and spontaneous. The one requirement we have yet to address, however, is agency . Recall the suggestion, implicit in Lovelace’s remarks, that whatever a computer produces is to the credit of the programmer, not the computer. Notice that as sophisticated as current technologies in artificial creativity may be, presumably they are still not subject to praise or blame for what they do. If any beings are responsible for the work of these programs, it still seems to be the programmers and engineers who make them, not the programs themselves. The programs themselves do not seem to “give a damn”. So, if the creative process requires agency, arguably we have not yet created, programmed, or evolved a computational system that is really creative, however much they might appear to be. In the pursuit of strong AC, agency might be the final frontier (Paul & Stokes 2021).

It should be clear from the above discussions that there are rich and lively research programs, across a range of scientific disciplines, studying human creativity. These approaches substantiate the view that, contrary to the romantic tradition, creativity can be explained. Psychological functions and neural correlates have been identified, and remarkable advances are being made with computational and robotics technologies. What may be less clear is that, despite these advances, the distinct research programs in question are largely disjoint or siloed.

In a recent paper, Geraint Wiggins and Joydeep Bhattacharya (2014) highlight this “gap” between scientific studies of creativity. Their particular emphasis is on the gaps between research in neuroscience and research in computer science, and they advocate a bridge in the form of a neurocomputational approach. This kind of bridging may be called for even beyond what these authors prescribe, since there are gaps not just between these disciplines, but also between these and behavioral psychology, AI and A-Life research, and philosophical analysis. Creativity is a deeply complex and deeply important phenomenon. Fully understanding it will require us to integrate a variety of theoretical perspectives, and, as this survey reveals, philosophy has a vital role to play in that endeavor.

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Open Access

Peer-reviewed

Research Article

Modelling Creativity: Identifying Key Components through a Corpus-Based Approach

Contributed equally to this work with: Anna Jordanous, Bill Keller

* E-mail: [email protected] (AJ); [email protected] (BK)

Affiliation School of Computing, University of Kent, Chatham Maritime, Kent, United Kingdom

Affiliation Department of Informatics, University of Sussex, Falmer, Brighton, United Kingdom

• Anna Jordanous, 
• Bill Keller

• Published: October 5, 2016
• https://doi.org/10.1371/journal.pone.0162959
• Reader Comments

Creativity is a complex, multi-faceted concept encompassing a variety of related aspects, abilities, properties and behaviours. If we wish to study creativity scientifically, then a tractable and well-articulated model of creativity is required. Such a model would be of great value to researchers investigating the nature of creativity and in particular, those concerned with the evaluation of creative practice. This paper describes a unique approach to developing a suitable model of how creative behaviour emerges that is based on the words people use to describe the concept. Using techniques from the field of statistical natural language processing, we identify a collection of fourteen key components of creativity through an analysis of a corpus of academic papers on the topic. Words are identified which appear significantly often in connection with discussions of the concept. Using a measure of lexical similarity to help cluster these words, a number of distinct themes emerge, which collectively contribute to a comprehensive and multi-perspective model of creativity. The components provide an ontology of creativity: a set of building blocks which can be used to model creative practice in a variety of domains. The components have been employed in two case studies to evaluate the creativity of computational systems and have proven useful in articulating achievements of this work and directions for further research.

Citation: Jordanous A, Keller B (2016) Modelling Creativity: Identifying Key Components through a Corpus-Based Approach. PLoS ONE 11(10): e0162959. https://doi.org/10.1371/journal.pone.0162959

Editor: Peter Csermely, Semmelweis University, HUNGARY

Received: March 8, 2016; Accepted: August 31, 2016; Published: October 5, 2016

Copyright: © 2016 Jordanous, Keller. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: 90 academic publications dated 1950-2009 are analysed as part of this work. All of these articles were accessed via Scopus searches, through academic publishers. A full list of these publications is given in Jordanous’s thesis and the creativity corpus publications are listed in this article, in Fig 1 . All data produced during analysis from the texts of these publications are available via Open Science Framework ( https://osf.io/nqr76/ ). In particular, this includes the lexical data for both corpora, with frequencies, the similarity data scores that we produced during analysis, and the 694 ‘creativity words’. Data from the British National Corpus (BNC) was used during analysis. The BNC data is available from http://www.natcorp.ox.ac.uk/ The results data generated during analysis (the 694 key words for creativity and the 14 key components of creativity) are openly available online in the form of an ontology (also submitted as a Supporting Information file), published under the URL http://purl.org/creativity/ontology . As also stated in the paper, these data are made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://www.opendatacommons.org/licenses/pddl/1.0/ . These data are also available in the PhD thesis of Anna Jordanous (2012), which is openly available via the University of Sussex library ( http://sro.sussex.ac.uk/44741/ ) or via the University of Kent’s Academic Repository ( https://kar.kent.ac.uk/42388/1/Jordanous%252C_Anna_Katerina.pdf ). The creativity Semantic Web ontology links to data from the Wordnet lexical database ( http://wordnet-rdf.princeton.edu/ ), via the openly available data published at http://wordnet.rkbexplorer.com/ .

Funding: The author(s) received no specific funding for this work. Anna Jordanous undertook part of this work during her PhD, which was part-funded by a stipend provided by the School of Informatics, University of Sussex.

Competing interests: The authors have declared that no competing interests exist.

Introduction

What is creativity, and how can we better understand and learn about creativity using computational modelling? Computational creativity is a relatively youthful research area that has been growing with significant pace in recent years. Computational creativity is:

‘The philosophy, science and engineering of computational systems which, by taking on particular responsibilities, exhibit behaviours that unbiased observers would deem to be creative.’ [ 1 ] (p. 21).

Computational creativity research follows both theoretical and practical directions and crosses several disciplinary boundaries between the arts, sciences, and engineering. Research within the field is influenced by artificial intelligence, computer science, psychology and specific creative domains that have received attention from computational creativity researchers to date, such as art, music, reasoning and narrative/story telling (for examples, see [ 2 – 5 ]).

The evaluation of creative systems developed by researchers in the field of computational creativity has proven non-trivial. Creativity evaluation, a recurring topic for discussion, has been described as a ‘Grand Challenge’ for computational creativity research [ 6 ]. Difficulties are inherently linked to a question that both motivates and complicates the computational modelling of creativity: what do we mean when we talk about ‘creativity’ and what does it constitute?

Creativity is a complex, multi-faceted concept encompassing a variety of related aspects, abilities, properties and behaviours. There have been many attempts to capture this concept in words; indeed the work described in this paper is based on thirty such attempts (see the Methods section and the papers listed in S1 Appendix ). In the academic literature on creativity, many common themes have emerged. However, multiple viewpoints exist, prioritising different aspects of the concept according to what are traditionally considered to be the primary factors for a particular discipline. The need for a more over-arching, inclusive, multi-dimensional account of creativity has been widely recognised [ 7 – 10 ]. Such a meta-level account would assist our understanding of creativity, highlighting areas of common ground and avoiding the pitfalls of disciplinary bias [ 11 , 12 ].

There are many challenges to modelling a concept like creativity in a computational setting. Conceptually, creativity seems inherently fuzzy or vague, with a meaning that shifts depending on the domain of application. Tackling these challenges affords two key advantages, both of which motivate the current paper. First, we can take advantage of computing and artificial intelligence to perform or enhance creative activities using computational power and research expertise. Secondly, the act of modelling creativity requires us to more carefully identify what informs our intuitive notions about creativity and this can guide us towards a more rigorous and comprehensive understanding of the concept.

The aim of the work reported in this paper is to examine the nature of creativity and to identify within it a set of components, representing key dimensions, that are recognised across a combination of different viewpoints. We present a novel, empirical approach to the problem of modelling how creative behaviour is manifested, that focuses on what is revealed about our understanding of creativity and its attributes by the words we use to discuss and debate the nature of the concept. Analysis of this language provides a sound basis for constructing a sufficiently detailed and comprehensive model of creativity [ 13 , 14 ]. The current work is intended as a significant, methodological contribution towards addressing the Grand Challenge of evaluation in computational creativity research. It should provide researchers with a firm foundation for evaluating exactly how creative so-called c reative systems actually are.

On our approach, statistical language processing techniques are used to identify words significantly associated with creativity in a corpus of academic papers on the subject. A corpus spanning some 60 years of research into the nature of creativity was collected together. The papers were gathered from a wide variety of disciplines including psychology, educational testing and computational creativity, amongst others. The language data drawn from this collection was then analysed and contrasted with data from a corpus of matched papers on subjects unrelated to creativity. From this analysis, a set of 694 creativity words was identified, where each creativity word appeared significantly more often than expected in the corpus of creativity papers. A measure of lexical similarity provided a basis for clustering the creativity words into groups of words with similar or shared aspects of meaning. Through inspection of these clusters, a total of fourteen key components of creativity was identified, where each represents a key theme or attribute of creativity. The set of components yields information about the nature of creativity, based on what is collectively emphasised in discussions about the concept.

In the rest of this section we begin by noting a variety of attempts to define creativity. The representation of subjective, ambiguous, loosely structured concepts is considered. In the remaining sections, details are provided of the methodology used to identify components of creativity from an analysis of language data. The results of this analysis are then presented in terms of a model that encompasses fourteen key components. The derived set of components is evaluated in terms of how well it satisfies the need for a shared, inclusive and comprehensive account of creativity and provides a vocabulary of creativity that is accessible to both people and machines. Finally, conclusions are drawn and some directions for further work are outlined.

Background: The nature of creativity

As Torrance observes:

‘[c]reativity defies precise definition … even if we had a precise conception of creativity, I am certain we would have difficulty putting it into words’ [ 15 ] (p.43).

Many other authors have expressed similar difficulties [ 7 , 10 , 16 ]. In their review of research into human creativity, Hennessey and Amabile ask a significant follow-on question:

‘Even if this mysterious phenomenon can be isolated, quantified, and dissected, why bother? Wouldn’t it make more sense to revel in the mystery and wonder of it all?’ [ 11 ] (p.570).

Two answers to this question are offered by Hennessey and Amabile, both of which are identified as desirable: to gain a deeper understanding of creativity and to learn how to boost people’s creativity.

Creativity can and should be studied and measured scientifically, but the lack of a commonly-agreed understanding causes problems for measurement [ 10 ]. Plucker et al. make recommendations about best practice based on their own survey of the creativity literature:

‘we argue that creativity researchers must

• explicitly define what they mean by creativity,
• avoid using scores of creativity measures as the sole definition of creativity (e.g., creativity is what creativity tests measure and creativity tests measure creativity, therefore we will use a score on a creativity test as our outcome variable),
• discuss how the definition they are using is similar to or different from other definitions, and
• address the question of creativity for whom and in what context.’ [ 9 ] (p.92).

In short, we need to specify and justify the standards that we use to judge creativity. A more objective and well-articulated account of how creativity is manifested enables researchers to make a worthwhile contribution [ 8 – 10 ]. Particularly, in research we would like to focus on what processes and concepts relevant to creativity are ‘sufficiently important to warrant study’ [ 17 ] (p.15), based on an accumulation of the body of work on creativity to date [ 17 ].

Definitions of creativity.

To find out the meaning of a word, a natural first step might be to consult a dictionary. Dictionary definitions of creativity provide a brief introduction to the meaning of the word. However, for the purposes of research, the utility of such definitions is severely restricted by their format and brevity, and they generally provide only cursory, shallow insights into the nature of creativity. More problematic still, dictionary entries are often self-referential or circular, defining creativity in terms of “being creative” or “creative ability”. To illustrate these limitations, there follow several typical dictionary definitions of creativity and the related words ‘creative’ and ‘create’. For readability, some definitions are edited slightly to standardise formats and remove etymological/grammatical annotations:

Oxford English Dictionary 2nd ed. (1989) pp. 1134-5:

• creativity: creative power or faculty; ability to create
• creative: Having the quality of creating, able to create; of or relating to creation; originative. b. Inventive, imaginative; of, relating to, displaying, using, or involving imagination or original ideas as well as routine skill or intellect, esp. in literature or art. c. Esp. of a financial or other strategy: ingenious, esp. in a misleading way. 2. Providing the cause or occasion of, productive of.
• create: 1.a. Said of the divine agent: To bring into being, cause to exist; esp. to produce where nothing was before, ‘to form out of nothing’. b. with complemental extension. 2. To make, form, constitute, or bring into legal existence (an institution, condition, action, mental product, or form, not existing before). Sometimes of material works. 3. To constitute (a personage of rank or dignity); to invest with rank, title, etc. 4. To cause, occasion, produce, give rise to (a condition or set of circumstances).

The Penguin English Dictionary 2nd ed. (1969) p. 174:

• creative: having power to create; related to process of creation; constructive, original, producing an essentially new product; produced by original intellectual or artistic effort
• create: make out of nothing, bestow existence on; cause, bring about; produce or make something new or original; confer new rank etc on; (theat) be the first to act (a certain part); make a fuss

Webster’s 3rd New International Dictionary (1961) p. 532:

• creativity: the quality of being creative; ability to create
• creative: 1. having the power or quality of creating; given to creation 2: PRODUCTIVE—used with 3: having the quality of something created rather than imitated or assembled; expressive of the maker; IMAGINATIVE
• create: 1: to bring into existence; make out of nothing and for the first time 2: to cause to be or to produce by fiat or by mental, moral, or legal action 3: to cause or occasion—used of natural or physical causes and esp. of social and evolutionary or emergent forces 4a: to produce (as a work of art or of dramatic interpretation) along new or unconventional lines) b: to design (as a costume or dress)

Given the problems inherent in dictionary definitions of creativity, it is not surprising that a number of creativity researchers have set out to provide their own definitions of the concept. Some examples are:

‘creativity is that process which results in a novel work that is accepted as tenable or useful or satisfying by a group at some point in time’ [ 18 ] (p.28)

‘Creativity is the ability to produce work that is both novel (i.e., original, unexpected) and appropriate (i.e., useful, adaptive concerning task constraints)’ [ 16 ](p.3)

‘Creativity is the ability to come up with ideas or artefacts that are new , surprising and valuable ’ [ 19 ](p.1)

‘Creativity is the interaction among aptitude, process, and environment by which an individual or group produces a perceptible product that is both novel and useful as defined within a social context ’ [ 9 ](p.90)

‘Creativity: the generation of products or ideas that are both novel and appropriate’ [ 11 ](p.570)

‘The word creativity is a noun naming the phenomenon in which a person communicates a new concept (which is the product). Mental activity (or mental process) is implicit in this definition, and of course no one could conceive of a person living or operating in a vacuum, so the term press is also implicit’ [ 7 ](p.305)

These more research-oriented definitions avoid the problems of self-reference and circularity noted for the dictionary entries given previously. However, whilst the definitions may provide somewhat deeper insight into the nature of creativity, the brevity of the definitions means that they still only succeed in providing shallow, summary accounts of the concept.

A multitude of different perspectives.

The problem of identifying and quantifying creativity exists across many disciplines. How creative is this person? Does this person have the creative abilities to boost my business? Is this pupil’s story creative? Is this computational system an example of computational creativity? As a consequence, when attempts are made to define creativity, it is often from the perspective of a particular domain or research discipline. For example, psychometric tests for creativity such as [ 20 , 21 ] focus on problem solving and divergent thinking as key attributes of a creative person. In contrast, computational creativity research (for examples see [ 22 – 25 ]) has historially placed emphasis on the novelty and value of creative products. Whilst there is some consensus across academic fields, for example novelty and value are typically recognised as necessary (but arguably not sufficient) components of creativity [ 26 ], the differing emphases contribute to variations in the interpretation of creativity. These variations affect consistency across creativity research in different disciplines and potentially hinder interdisciplinary collaborations and cross-application of findings.

Several competing interpretations of creativity exist in the literature. Sometimes these differences of opinion do not need to be directly resolved but can be included alongside each other. Examples include whether creativity is centred around mental processes [ 19 , 27 , 28 ] or embodied and situated in an interactive environment [ 29 , 30 ]. Another example is whether creativity is domain-independent [ 31 ], or dependent on domain-specific context [ 32 ], or (as both Plucker and Baer have concluded) a combination of both [ 12 , 33 ].

Other conflicts arise where a previously narrow view of creativity has been widened in perspective. To resolve the conflict, an inclusive, all-encompassing view of creativity should adopt the wider perspective and incorporate the narrower perspective. For example rather than focussing narrowly on creative genius , through the study of people with exceptional creative achievements (see [ 34 , 35 ]) emphasis has shifted to encompass the broader study of everyday creativity, with genius as a special case: the notion that everyone can be creative to some degree [ 36 , 37 ].

Similarly, researchers distinguish between little-c and Big-C creativity, or psychological/P-creativity and historical/H-creativity [ 19 ], adjusting their focus accordingly to make their research more manageable. This is particularly the case in computational creativity, where endowing the computer with elements of general, human knowledge and experience is a major challenge. Little-c creative or p-creative work is perceived as creative by the creator personally but may replicate existing work (unknown to the creator) so is not necessarily creative in a wider social context. This encompasses the concept of Big-C creativity or h-creativity, where the work makes a creative contribution both to the creator and to society. To be Big-C creative/h-creative is to be little-c creative/p-creative in a way which has not been done before by anyone.

• Person/Producer: The individual that is creative
• Process: What the creative individual does to be creative
• Product: What is produced as a result of the creative process
• Press: The environment in which creative activity takes place

This framework presents creativity in a broader context, making our understanding of the concept more generally applicable and less specific to a domain or academic discipline. In contrast, models of the creative process [ 34 , 35 , 41 ], tests of people’s creativity [ 21 , 42 , 43 ] or tests based on creative artefact generation [ 25 , 44 ] are useful only within a limited sphere. Jordanous [ 40 ] has contextualised the Four Ps in a computational context, referring to the creative Producer (person or computational agent) carrying out Processes within the environmental context of a Press, to create computational Products.

The challenges of modelling creativity

Creativity can be seen as an essentially contested concept [ 45 ]: it is subjective, abstract and can be interpreted in a variety of acceptable ways, such that a fixed ‘proper general use’ is elusive [ 45 ] (p.167). Gallie [ 45 ] defines an essentially contested concept through several features: being internally complex in nature, but amenable to being broken down into identifiable constituent elements of varying relative importance, and dependent on a number of factors such as context and individual preference. Although there may be consensus on the meaning of such concepts in very general terms, they may defy precise interpretation. There is not a single agreed instantiation, but instead many reasonable possibilities, influenced by changing circumstances and contexts. It is more productive to acknowledge that these different interpretations exist and refer to ‘the respective contributions of its various parts or features’ [ 45 ] (p.172), rather than to argue for a single interpretation. Thus, different types of creativity manifest themselves in different ways while sharing certain characteristics (not necessarily the same across all creative instances). This is what Wittgenstein refers to as ‘family resemblances’ [ 14 ]:

[On discussing the example of what a ‘game’ is] ‘we see a complicated network of similarities overlapping and criss-crossing: sometimes overall similarities, sometimes similarities of detail. … I can think of no better expression to characterize these similarities than “family resemblances”; for the various resemblances between members of a family: build, features, colour of eyes, gait, temperament, etc. etc. overlap and criss-cross in the same way. And I shall say: “games” form a family.’ [ 14 ] (Part 1, Paragraphs 66-67).

Similarly, with creativity, different manifestations of creativity are not all necessarily required to share the same common, core elements in order to be identified as part of the creativity ‘family’. Rather, relationships between different manifestations reveal various shared characteristics that emerge in a similar way to Wittgenstein’s ‘family resemblances’ in language. We need to identify what those family resemblances are in the case of creativity. To understand creativity, we can investigate what resemblances exist across different instantiations of the concept.

Wittgenstein [ 14 ] has argued that ‘a clear view of the aim and functioning of the words’ helps us ‘dispers[e] the fog’ that obscures a clear vision of the ‘working of language’ [ 14 ] (Part 1, Paragraph 5). To understand the use of a word, one must have background information and context. Wittgenstein gives the example of a chess piece, which is introduced to someone as a ‘king’ (Paragraph 31): to understand this usage the person must already know the rules of chess, or must at least know what it means to have a piece in a game. To Wittgenstein, the semantics of words and statements are determined by how we use them, grounded in rules set by our habitual use of a word and our shared consensual practices, rather than being fixed by static, pre-assigned meanings.

Linguistics research advocates that the meaning of a word is dependent on the context it is used in [ 46 ]. In particular, Lakoff has argued that the study of language helps reveal how people think [ 13 , 47 ]. Words used frequently in discussions of the nature of a concept provide the context for the commonly understood meaning of that concept, as has been shown in various corpus linguistics contributions [ 48 – 51 ].

The key principle emerging across these present discussions is that the meaning of words like creativity can be modelled by identifying different aspects that collectively contribute to the meaning of the concept of creativity.

The need for a clearer, multi-perspective understanding of creativity is evident, but remains to be addressed. There is a large quantity of material contributory to a satisfactory model of creativity and a number of key contributions have been discussed during this section. What must be done now is to marshal this assortment of material and to unify different perspectives where possible, in order to avoid the disciplinary ‘blinkers’ or compartmentalisation that is often seen in creativity research [ 11 ]. In approaching the semantic representation of subjective and multi-faceted concepts, some useful guidance is offered through philosophical reflections on the meaning of such concepts.

Our approach makes use of an empirical study and analysis of the language used to talk about creativity in order to gather and collate knowledge about the concept. In addition, following from the observations above, a confluence approach to creativity is adopted [ 16 , 26 , 52 ]. This works on the principle that creativity results from several components converging and goes on to examine what these components are. Taking this approach in conjunction with the application of tools from computational linguistics and statistical analysis allows a wider disciplinary spectrum of perspectives on creativity to be captured than has previously been attempted. This is achieved by breaking down the whole into smaller and more tractable constituent parts identified through a broad cross-disciplinary examination of creativity research.

Tools from natural language processing and statistical analysis are used to identify words that appear to be highly associated with dimensions of creativity, as represented in a sample of academic papers on the topic. A key innovation is the use of a statistical measure of lexical similarity, which allows the words to be clustered into coherent and semantically-related groups. Clustering reveals a number of common themes or factors of creativity, allowing the identification of a set of fourteen components that serve as building blocks for creativity.

Corpus data

A sample of academic papers discussing the nature of creativity was assembled as a creativity corpus in 2010. This creativity corpus consisted of 30 papers examining creativity from various academic stand-points ranging from psychological studies to computational models.

Creativity corpus : a collection of thirty academic papers which explicitly discuss the nature of creativity.

The 30 papers selected for the creativity corpus are listed in S1 Appendix . The strategy used to select papers for this corpus is illustrated in a flow diagram, in Fig 1 .

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https://doi.org/10.1371/journal.pone.0162959.g001

The search strategy for identifying papers for the corpus involved a literature search for the term ‘creativity’ on the academic database Scopus to identify suitable papers. This literature search was supplemented with additional influential papers which may not have appeared in a Scopus search. For example, a Computer Science conference paper on cognitive models of creativity has been included, as in Computer Science, a number of conferences carry as much or more publication weight as some journals in the field. The eligibility of each identified article was verified for inclusion in the corpus via careful manual inspection.

• Papers must have, as their primary focus, discussion of the nature of creativity.
• Papers should be considered particularly influential. Influence was generally measured objectively, in terms of the number of times a paper had been cited by other academic authors. However, for papers published in recent years and which had consequently had little time to accrue citations, selection was based instead on a subjective judgement of influence grounded in a knowledge of the field.
• Papers selected should, as far as reasonable, represent a cross-section of years over the range 1950-2009. [The corpus was compiled in 2010.] 1950 was chosen as a starting point in recognition of the effect of J. P. Guilford’s presidential address to the American Psychological Association [ 20 ], which examined contemporary creativity research (or more specifically, the lack of thereof). His talk was highly influential in encouraging more creativity research activity [ 10 ].
• Papers selected should, as far as reasonable, represent a cross-section of disciplines relevant to discussions of creativity. Fig 2 illustrates the disciplinary distribution of the corpus as it changes over the time period covered by the selected papers. This distribution is based on the Scopus database, which classifies journals under their main subject area(s) covered. We should acknowledge here though that while many disciplines include creative practice, often the focus is on application rather than in depth discussion of what creativity entails. Hence, while we sought to cover creativity from a broad range of perspectives, we also felt it was important not to compromise the focus of our corpus as a representation of key discussions about the nature of creativity.

Disciplines are as specified for the paper’s journal, by the academic database Scopus . Note that Scopus may classify a journal under more than one discipline.

https://doi.org/10.1371/journal.pone.0162959.g002

• Authors were only represented more than once in the corpus if the relevant papers were written from different perspectives. For example, Mark Runco’s work is represented twice in the corpus, but covering two different topics relating to the nature of creativity (psychoeconomic approach to creativity; cognition and creativity). If the search process highlighted two or more papers with a shared author on the same or highly similar perspectives on creativity, then the more highly cited paper was chosen.
• Papers had to be written in English, as the language processing tools we were working with were for English language texts.
• Papers had to be available in a format that enabled us easily to extract plain text (this excluded books or book chapters).

The creativity corpus is relatively small and necessarily selective in terms of the papers that are included. As such it constitutes just a small fraction of the many academic works on creativity that have been published in the last 60 or so years. Indeed, the 30 papers in the creativity corpus cannot be regarded as comprehensively representative of the wide range of academic positions on creativity that have been discussed in the literature over the decades. However, the goal of this work is not to present a fine-grained analysis of language use drawn from this complete literature, nor to provide a comprehensive lexicon or dictionary of creativity. Rather, the goal is to identify the broader ontological themes or factors that recur in our understanding of the concept of creativity. For this purpose, what is required is a sufficiently representative sample of the academic discourse on creativity. This sample can be used to identify the way in which word use reflects key themes or factors that persist across different perspectives.

Our objective is to identify what is distinctive in the language used to discuss creativity, in contrast to the language used to discuss other topics. As a basis for comparison, therefore, a further sample of 60 academic papers on topics unrelated to creativity—the non-creativity corpus —was assembled alongside the creativity corpus, in 2010.

Non-creativity corpus : a collection of sixty academic papers on topics unrelated to creativity, from the same range of academic disciplines and publication years as the creativity corpus papers.

The non-creativity corpus papers were selected by a literature search retrieving, for each paper in the creativity corpus, the two most-cited papers in the same academic discipline (as categorised by Scopus ) and published in the same year, that did not contain any words with the prefix creat (i.e. creativity , creative , creation , and so on). In other words, the criteria for inclusion in this second corpus were whether a paper was one of the two papers that was most highly cited at the time of the search (2010), in the same academic discipline, and published in the same year, as a paper in the creativity corpus, and that satisfied the exclusion criteria of not containing any words with the above mentioned prefixes. The 60 papers selected for the creativity corpus are listed in S2 Appendix . The search strategy used to select papers for this non-creativity corpus is illustrated in a second flow diagram, in Fig 3 .

https://doi.org/10.1371/journal.pone.0162959.g003

• Ease of locating relevant and appropriate papers: e.g. availability of tools to perform targeted literature searches, electronic publication of papers for download, tagging of paper content by keywords, citations in papers to other related papers.
• Ability to access timestamped textual materials over a range of decades.
• Publication of academic papers in an appropriate format for computational analysis: most papers that are available electronically are in formats such as PDF or HTML, which can be converted to text fairly easily.
• Availability of citation data as a measure of how influential a paper is on others: whilst not a perfect reflection of a paper’s influence, citation data is often used for measuring the impact of a journal [ 53 ] or an individual researcher’s output [ 54 ].
• Availability of provenance data, such as who wrote the paper and for what audience (from the disciplinary classification of the journal).

Some pre-processing was undertaken for each paper in both the creativity corpus and non-creativity corpus prior to analysis. A plain text file was generated for each paper, containing the full text of that paper. All journal headers and copyright notices were removed from each paper, as were the author names and affiliations, list of references and acknowledgements. All files were also checked for any non-ASCII characters and anomalies that may have arisen during the creation of the text file.

Natural language processing

The corpus data was first pre-processed using the RASP natural language processing toolkit [ 55 ] in order to perform lemmatisation and part-of-speech tagging. Lemmatisation permits inflectional variants of a given word to be identified with a common ‘dictonary headword’ form or ‘lemma’. For example, performs , performed and performing all occur in the creativity corpus as distinct morphological variants of the verb, perform . Intuitively, we would like to count each of these inflectional variants as an instance of the same word, rather than as separate and distinct lexical tokens. Lemmatisation software enables us to do this by mapping such variants to a cannonical lemma form. As a further refinement, each lemma was also mapped to lower case to ensure that capitalised word forms (e.g. Novel ) were not counted separately from their non-capitalised forms ( novel ). While this has the potential for occasional confusion between proper names and common nouns (e.g. Apple v. apple ), it is not considered that the resulting level of ‘noise’ in the data is likely to adversely affect the results of the analysis.

Each word was assigned a part-of-speech tag identifying its grammatical category (i.e. whether the word was a noun, verb, preposition, etc.). Such tagging is useful because it allows us to distinguish between different grammatical uses of a common orthographic form. For example, the use of novel as a noun in a good novel can be properly differentiated from its use as an adjective in a novel idea . The data was further simplified and filtered so that only words of the four ‘major’ categories (i.e. noun, verb, adjective and adverb) were represented. Note that the major categories bear the semantic content of the papers making up the creativity corpus. They may be distinguished from minor categories or ‘function words’, such as pronouns ( something , itself ) prepositions (e.g. upon , by ) conjunctions ( but , or ) and quantifiers (e.g. many , more ). Because such words have little independent semantic content, they are of limited interest for the present study and may be removed from the data.

Following processing with RASP, a list of words found in the creativity corpus, together with their frequency counts was generated. The non-creativity corpus was pre-processed in the same way and a corresponding list of words and frequencies also generated.

Identifying words associated with creativity

The word frequency data derived from the two corpora was used to establish which words occur significantly more often in the creativity corpus than in the non-creativity corpus. This in turn can be regarded as providing evidence of which words are salient to the concept of creativity. Salient words were identified using the log-likelihood ratio (also referred to as the G 2 or G-squared statistic), which is a measure of how well observed data fit a model or expected distribution [ 48 – 50 , 56 ]. It provides an alternative to Pearson’s chi-squared ( χ 2 ) test and has been advocated as the more appropriate measure of the two for corpus analysis as it does not rely on the (unjustifiable) assumption of normality in word distribution [ 48 , 50 , 56 ]. This is a particular issue when analysing smaller corpora, such as those used in the present work. The log likelihood ratio statistic is more accurate in its treatment of infrequent words in the data, which often hold useful information. By contrast, the χ 2 statistic tends to under-emphasise such outliers at the expense of very frequently occurring data points.

As computed above, the log-likelihood ratio measures the extent to which the distribution of a given word deviates from what might be expected if its distribution is not corpus dependent. The higher the log likelihood ratio score for a given word, the greater the deviation from what is expected. It should be noted however, that the statistic tells us only that the observed distribution of a word in the two corpora is unexpected (and to what extent). It does not tell us whether the word is more or less frequent than expected in the creativity corpus. To identify words significantly associated with creativity therefore, it was necessary to select just those words with observed counts higher than that expected in the creativity corpus. It should perhaps be further noted that the resulting words may be either positively or negatively connoted with respect to creativity. In practice this is not a problem, as the significance of a given word lies in its semantic connection to creativity, not in its sentiment or affect. Affect is taken into account as part of the later manual examination of the data used to identify components of creativity.

The results of the calculations were filtered to remove any words with a log-likelihood score less than 10.83, representing a chi-squared significance value for p = 0.001 (one degree of freedom). In this way, the filtering process reduced the set of candidate words to just those that appear to occur significantly more often than expected in the creativity corpus. To avoid extremely infrequent words disproportionately affecting the data, any word occurring fewer than five times was also removed from the data. Finally, the words were inspected to remove any ‘spurious’ items such as proper nouns or misclassified or odd character sequences. This resulted in a total of 694 creativity words : a collection of 389 nouns, 205 adjectives, 72 verbs and 28 adverbs that occurred significantly more often than expected in the creativity corpus. Table 1 gives the top 20 results of these calculations.

A significant LLR score at p = 0.001 is 10.83. N.B. POS = Part Of Speech: N = noun, J = adjective, V = verb, R = adverb.

https://doi.org/10.1371/journal.pone.0162959.t001

Identifying components of creativity

It is important to note that our objective is to identify key themes in the lexical data, not to induce a comprehensive terminology of creativity. Despite the relatively small size of the corpora used, the resulting set of 694 creativity words is sufficiently rich for this purpose, but is still somewhat large to work with in its raw form. In previous, related work [ 57 ] an attempt was made to identify key components by manually clustering creativity words by inspection of the raw data. In practice, this proved laborious and made it impossible systematically to consider all of the identified words. It also raised issues of subjectivity and experimenter bias. These problems are addressed here, at least in part, by automatically clustering the words according to a statistical measure of distributional similarity [ 58 ]. The more manageable collection of clusters may then be examined to identify key components or dimensions of creativity.

The intuition underlying distributional measures of similarity derives from the distributional hypothesis due to Harris [ 59 ]. This hypothesis states that similarity of distribution correlates with similarity of meaning: two words that tend to appear in similar linguistic contexts will tend to have similar meanings. The notion of linguistic context here is not fixed and might plausibly be modelled in a variety of different ways. For example, two words might be considered to inhabit the same context if they appear in the same document or the same sentence or if they stand in the same grammatical relationship to some other word (e.g. both occur as subject of a particular verb or modifier of a given noun). In practice it has been shown that modelling distribution in terms of grammatical relations leads to a tighter correlation between distributional similarity and closeness of meaning [ 60 ].

In the present work, grammatical relations are used to represent linguistic context and distributional similarity is measured as a function of the number of relations that two words share. To illustrate, evidence that the words concept and idea are similar in meaning might be provided by occurrences such as the following:

(1) the concept/idea involves (subject of verb ‘involve’) (2) applied the concept/idea (object of verb ‘apply’) (3) the basic concept/idea (modified by adjective ‘basic’)

Grammatical relations were obtained from an analysis of the written portion of the British National Corpus [ 61 ], which had previously been processed using the RASP toolkit [ 55 ] in order to extract them. Using this data, each word in the creativity corpus was associated with a list of grammatical relations in which it occurred, together with corresponding counts of occurrence. In practice, not all of the grammatical relation information output by RASP was used to calculate distributional similarity. Just the subject, object and modifier relation types are used as these tend to give the best results [ 62 ]. A potential difficulty with obtaining word similarity data based on the BNC (i.e. using data from sources of everyday usage of English, rather than from more specialist sources) would arise if the majority of the creativity words were used with distinctive or technical senses within the creativity corpus. From inspection and from knowledge of creativity literature, however, this situation was found to be unlikely. While some narrowly specialised usage may be present to some small degree in the set of creativity words, most words retain general senses as reflected in the wider BNC data set. An advantage of using the BNC is that its size increases the chances of a comprehensive coverage of the general senses of each word of interest.

Distributional similarity of two words is measured in terms of the similarity of their associated lists of grammatical relations. A variety of different methods for calculating distributional similarity have been investigated in the literature, including standard techniques such as the cosine measure (for example [ 63 ]). The present work adopts an information-theoretic measure due to Lin [ 58 ], which has been widely used in language processing applications as a means of automatically discovering semantic relationships between words. In comparison to other similarity measures it has been shown to perform particularly well as a means of identifying near-synonyms [ 64 , 65 ].

Similarity scores were calculated between all pairs of creativity words of the same grammatical category. That is, scores were obtained separately for pairs of nouns, verbs, adjectives and adverbs. For a given set of words, word pair similarity data calculated in this way can be conveniently visualised as an edge-weighted graph, where nodes correspond to words and edges are weighted by similarity scores (for any score > 0), as in Fig 4 .

Nodes correspond to words and edges are weighted by similarity scores (for any score > 0).

https://doi.org/10.1371/journal.pone.0162959.g004

• Clustering:. The graph clustering software Chinese Whispers [ 66 ] was used to automatically identify word clusters (groups of closely interconnected words) in the dataset. This algorithm uses an iterative process to group together graph nodes that are located close to each other. By grouping words with similar meanings, the number of data items was effectively reduced and themes in the data could be recognised more readily from each distinct cluster. A sample of some of the resulting clusters can be seen in Fig 5 .
• Inspection:. To focus on the words most closely related to creativity, the top twenty creativity words (i.e. the twenty words with the highest log likelihood scores) were selected. Each word was then visualised as the root node of its own individual subgraph using the graph drawing software GraphViz ( http://www.graphviz.org/ , last accessed August 2016). In order to reduce the amount of data to be examined, similarity scores were discarded if they fell below a threshold value (adjusted manually for each graph to highlight the most strongly connected words). This made the size and complexity of the graphs smaller and therefore easier to inspect and analyse visually. Fig 6 illustrates, in diagram form, the process of using manual inspection to identify components.

https://doi.org/10.1371/journal.pone.0162959.g005

https://doi.org/10.1371/journal.pone.0162959.g006

As part of the manual inspection process, candidate components were further considered in terms of the Four Ps of creativity [ 7 , 10 , 38 , 67 ] described earlier in this paper. This additional analysis provided a means of identifying alternative perspectives and revealing subtle (but still important) aspects of creativity. For example, novelty is commonly associated with the results of creative behaviour (Product): how novel is the artefact or idea that has been produced? However, we could similarly recognise as creative an approach to a task (Process) that does things in a novel and different way. Also, if a product is new in a particular environment (Press), then it may well be regarded as creative to those in that environment. Viewing novelty from the perspectives of Product, Process and Press uncovers these subtle and interlinked distinctions.

Results and Discussion

Components of creativity.

From the analysis steps described in the previous section it was possible to extract a set of fourteen key components of creativity. These components are summarised in Fig 7 and are presented in more detail below. The components contribute collectively to the overall concept and may be regarded as providing an ontology of creativity . It is important to note, however that the fourteen components do not constitute a set of necessary and sufficient conditions for creativity, in all its possible manifestations. There are two reasons for this. Firstly, some of the components we have identified appear to be logically inconsistent with others in the set. Consider for example the apparent need for autonomous, independent behaviour identified in Independence and Freedom and contrast this with the requirement for social interaction implied by Social Interaction and Communication . Secondly, of course, creativity also manifests itself in rather different ways across different domains [ 12 ] and components will vary in importance, according to the requirements of a particular domain. As an illustration of this second point, creative behaviour in mathematical reasoning has more focus on finding a correct solution to a problem than is the case for creative behaviour in, say, musical improvisation [ 2 , 68 ].

https://doi.org/10.1371/journal.pone.0162959.g007

The following set of fourteen components is therefore presented as a collection of dimensions—attributes, abilities and behaviours, etc.—which contribute to our understanding of creativity. The components should be treated as building blocks for creativity that may be arranged in different ways and with different emphases to suit different modelling purposes. The analysis of creativity in terms of the dimensions should be informative for a human audience and provide a basis for machine-understanding of the concept. Each component is presented here with a brief explanation or gloss. These explanations will later be used for part of the semantic content in the creativity ontology.

Active Involvement and Persistence.

Being actively involved; reacting to and having a deliberate effect on the creative process. The tenacity to persist with the creative process throughout, even during problematic points .

Dealing with Uncertainty.

Coping with incomplete, missing, inconsistent, contradictory, ambiguous and/or uncertain information. Element of risk and chance—no guarantee that information problems will be resolved. Not relying on every step of the process to be specified in detail; perhaps even avoiding routine or pre-existing methods and solutions.

Domain Competence.

Domain-specific intelligence, knowledge, talent, skills, experience and expertise. Knowing a domain well enough to be equipped to recognise gaps, needs or problems that need solving and to generate, validate, develop and promote new ideas in that domain.

General Intellectual Ability.

General intelligence and IQ. Good mental capacity.

Generation of Results.

Working towards some end target, goal, or result. Producing something (tangible or intangible) that previously did not exist.

Independence and Freedom.

Working independently with autonomy over actions and decisions. Freedom to work without being bound to pre-existing solutions, processes or biases; perhaps challenging cultural or domain norms.

Intention and Emotional Involvement.

Personal and emotional investment, immersion, self-expression and involvement in the creative process. The intention and desire to be creative: creativity is its own reward, a positive process giving fulfilment and enjoyment.

Originality.

Novelty and originality; a new product, or doing something in a new way; seeing new links and relations between previously unassociated concepts. Results that are unpredictable, unexpected, surprising, unusual, out of the ordinary.

Progression and Development.

Movement, advancement, evolution and development during a process. Whilst progress may or may not be linear, and an actual end goal may be only loosely specified (if at all), the entire process should represent some progress in a particular domain or task.

Social Interaction and Communication.

Communicating and promoting work to others in a persuasive and positive manner. Mutual influence, feedback, sharing and collaboration between society and individual.

Spontaneity/Subconscious Processing.

No need to be in control of the whole process; thoughts and activities may inform the process subconsciously without being inaccessible for conscious analysis, or may receive less attention than others. Being able to react quickly and spontaneously when appropriate, without needing to spend too much time thinking about the options.

Thinking and Evaluation.

Consciously evaluating several options to recognise potential value in each and identify the best option, using reasoning and good judgement. Proactively selecting a decided choice from possible options, without allowing the process to stagnate under indecision.

Making a useful contribution that is valued by others and recognised as an achievement and influential advancement; perceived as special, ‘not just something anybody would have done’. The end product is relevant and appropriate to the domain being worked in.

Variety, Divergence and Experimentation.

Generating a variety of different ideas to compare and choose from, with the flexibility to be open to several perspectives and to experiment and try different options out without bias. Multi-tasking during the creative process.

Implementing a machine-readable ontology of creativity

The fourteen components provide a fuller and clearer account of the constituent parts of the concept of creativity. An important aim of the current work is to make the components available as a resource for other researchers in computational creativity and to provide a basis for the automated evaluation of creative systems. As a step in this direction, the components have been expressed in an open, machine-readable form within the Semantic Web. In this way, the characterization of the components benefits from and is enriched by concepts that are already represented within the Semantic Web.

In particular, the components are linked to the data in WordNet [ 69 ], a large lexical database of English that has recently been made available as a Semantic Web ontology ( http://wordnet.rkbexplorer.com/ , last accessed August 2016). In WordNet, words are grouped by sense and interlinked by lexical and conceptual relations. Note that, although the WordNet definition of the word such as ‘creativity’ is brief (‘the ability to create’), its utility lies in how it is linked to various concepts, such as its sense, hyponyms, type, ‘gloss’ (brief definition) and other related concepts. Each creativity component relates to a cluster of keywords from the original set of 694 creativity words. Following Linked Data principles, each can therefore be linked across the Semantic Web to an appropriate set of concepts from WordNet. In this way, associated semantic information is provided for each component.

The resulting encoding can be visualised as a graph, as shown in Fig 8 . The data has also been published under an Open Data Commons Public Domain Dedication and Licence (PDDL) [ 70 ] at:

https://doi.org/10.1371/journal.pone.0162959.g008

http://purl.org/creativity/ontology

The concept labelled Creativity has the unique URI:

http://purl.org/creativity/ontology#Creativity

Any Linked Data that needs to refer to the concept can use this identifier.

From a practical stand-point, the current work is part of an overarching project engaged with the question of the evaluation of creativity, particularly computational creativity [ 71 ]. It is clear that a rigorous and comparative evaluation process needs clear standards to use as guidelines or benchmarks [ 10 , 15 ].

• What does it mean to be creative in a general context, independent of any domain specifics?
• What aspects of creativity are particularly important in the domain your system works in (and what aspects of creativity are less important in that domain)?
• Using Step 1, clearly state what standards you use to evaluate the creativity of your system.
• Test your creative system against the standards stated in Step 2 and report the results.

In both case studies, the components of creativity were chosen as the way of characterising creativity for step 1a of SPECS, and were weighted according to their importance and relevance for creativity in the creative domains under study for each case study (step 1b of SPECS). Each component was treated as one standard to be used to evaluate the creativity of the creative systems in the case studies (step 2 of SPECS). Each case study system was then tested against each component using feedback provided by judges (step 3 of SPECS), resulting in a detailed set of evaluative feedback on the creativity of each system in the case studies.

Case Study 1 [ 71 , 72 ] evaluated the creativity of three different computational musical improvisation systems [ 71 ]. Case Study 2 used the components of creativity in an evaluation scenario where information and time was limited for evaluation, to simulate the forming of first impressions and snapshot judgements of the creativeness of a given computational creativity system [ 72 , 73 ].

The resulting component-based evaluation yielded detailed information about creative strengths and weaknesses of the systems under investigation, highlighting those components where a system performs strongly. Crucially, the evaluation feedback also highlighted areas where a given system performed poorly. For example, in the musical improvisation study, Case Study 1, we found that, in general, creativity could be improved most by improving performance in Social Interaction and Communication , Intention and Emotional Involvement and Domain Competence (the three components found to be most important for creativity in musical improvisation). Similarly, it is useful to be able to quickly obtain formative feedback on strengths and weaknesses in time-limited scenarios such as that replicated in Case Study 2 during the development of creative systems (when ongoing evaluation of progress ideally needs to be both timely and time-efficient). Insight can then be obtained on where future development effort is best spent.

The results described above were compared with those obtained from applying other evaluation models and with surveys of people’s opinions, where people were asked how creative they thought each system was. There was general agreement between evaluation approaches on the most and least creative systems. The approaches differed in the formative feedback they provided, particularly for identifying strengths of the system at being creative, and weaknesses of the system to be improved. The model of creativity offered in this paper gave the most detailed feedback, but required most information to be collected.

• establish what it means for something to be deemed creative; and
• identify appropriate evaluation standards that replicate typical human opinion on how creative something is or in comparing two or more creative systems.

Conclusions and directions for future work

This paper has described the methods used to identify a set of components of creativity using corpus-based, statistical language processing techniques. The motivation for the work is the need for a shared, comprehensive and multi-perspective model of creativity. Such a model should be of great value to researchers investigating the nature of creativity and in particular those concerned with the evaluation of creative practice. More broadly, the inter-disciplinary approach described here exemplifies a general approach to the investigation and representation of semantically fuzzy and essentially-contested concepts. For this reason, we expect that it will interest researchers investigating computational methods for analysing and representing other such concepts.

Rather than attempting to provide a unitary account of creativity, our approach extracts common, underlying themes that transcend discipline or domain bias. Our point of departure is the observation that the vocabulary used in discussions of the nature of creativity may be analysed in order to throw light on our understanding of the concept and its key attributes. Using techniques from corpus linguistics and natural language processing (as described in the Methods section), key components of creativity have been identified. The results of this novel, empirical analysis (presented in the Results section) inform the development of an ontology of creativity comprising a set of fourteen distinct components (see Fig 7 ). It is noted that each component makes a separate contribution to the overall meaning of the concept. At the same time, because creativity manifests itself in different ways across different domains [ 12 ], the individual components vary in importance and influence according to the requirements of a given domain. The components can be therefore be usefully thought of as ‘building blocks’ for the concept in its different manifestations. Taken together, the components make creativity more tractable to study and to evaluate.

The fourteen components provide a multi-perspective model of creativity that has been successfully applied in a comparative analysis and evaluation of computational creativity systems [ 71 – 73 ] (see the Discussions section). The outcome of the evaluation process provides relatively fine-grained information about the creative strengths of a given system. This information in turn evidences ways in which a system could be considered creative. In addition, evaluation based on the components is able to highlight areas of weakness. These can be used to inform future work aimed at further developing a system’s creative potential.

The components have been published in an open, machine-readable format, making them freely available to the research community. This has a number of implications. First, the set of components may be readily elaborated, extended or amended by other researchers investigating the concept of creativity. Second, the machine-readable format facilitates the development of creativity-aware applications, based on the components. Such applications might be developed to support manual evaluation of creative practice or as a significant step towards the development of methods for automated evaluation.

The problem of developing automated evaluation has elsewhere been described as ‘the Achilles’ heel of AI research on creativity’ [ 74 ]. An intriguing possibility that we are currently exploring is to further exploit language processing techniques to perform evaluation based on textual reviews, descriptions of system performance, or social media interactions [ 75 ]. Such an approach would be analogous to the way sentiment analysis techniques are now in common use to evaluate attitude and opinion based on reviews of products or services [ 76 ]. This is a fascinating direction for future work, with great potential for real progress towards tackling computational creativity’s ‘Achilles’ heel’.

Supporting Information

S1 appendix. creativity corpus..

These 30 papers were used as the creativity corpus for this work.

https://doi.org/10.1371/journal.pone.0162959.s001

S2 Appendix. Non-Creativity Corpus.

These 60 papers were used as the non-creativity corpus for this work.

https://doi.org/10.1371/journal.pone.0162959.s002

Acknowledgments

We would like to acknowledge Nick Collins and Chris Thornton for their helpful comments during this work.

Author Contributions

• Conceptualization: AJ.
• Data curation: AJ BK.
• Formal analysis: AJ BK.
• Investigation: AJ BK.
• Methodology: AJ BK.
• Project administration: AJ BK.
• Resources: AJ BK.
• Software: AJ BK.
• Supervision: AJ BK.
• Validation: AJ BK.
• Visualization: AJ BK.
• Writing – original draft: AJ BK.
• Writing – review & editing: AJ BK.
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BA Major Thesis Overview

The thesis—typically in the form of a collection of short stories, poems, essays, or a novel excerpt—is a significant, polished, original creative work; the culmination of your study at the University of Chicago; and an opportunity to deepen your understanding of writing craft. Over the course of four quarters, in consultation with a faculty advisor and a writing and research advisor (WARA), students produce work informed by aesthetic, literary, and critical influences, as well as engage in coursework, sustained readings, and research.

Students work on their BA theses/projects throughout their fourth year. In Spring Quarter of the third year, students will be assigned a WARA who will mentor student reading and research throughout the thesis process. Students, in conversation with their WARAs, will complete a preliminary project proposal during the Spring Quarter of their third year. The preliminary proposal will then be submitted to the Student Affairs Administrator.

During the following Summer Quarter, students will craft a reading journal centered on a field list of readings. Chosen texts will be based upon work, conversations, etc., that students have begun with their WARAs. In Autumn Quarter of their fourth year, students and WARAs will work together to adapt the reading journal into an annotated bibliography, a focus reading list, and a reading and research summary (a summary of student writing plan and goals for the BA thesis/project).

In Winter Quarter, students will continue meeting with their WARA and must also enroll in the appropriate Thesis/Major Projects Workshop in their primary genre ( CRWR 29200  Thesis/Major Projects: Fiction,  CRWR 29300  Thesis/Major Projects: Poetry,  CRWR 29400  Thesis/Major Projects: Nonfiction, or CRWR 29500 Thesis/Major Projects: Fiction/Nonfiction). The Thesis/Major Projects Workshop is mandatory and only offered during Winter Quarter.

The instructor for the Thesis/Major Projects Workshop will also serve as the faculty advisor for the BA thesis. Students should be aware that because of very high demand, students will not necessarily get their first choice of faculty advisor. 

Students will work closely with their faculty advisor and peers in their Thesis/Major Projects Workshop and will receive course credit, as well as a final grade for the course. In consultation with their faculty advisor and WARA, students will revise and submit a near-final draft of the BA thesis by the end of the second week of Spring Quarter. Students will submit the final version of their BA thesis to their WARA, faculty advisor, Student Affairs Administrator, and the Director of Undergraduate Studies by the beginning of the fifth week of Spring Quarter. 

All creative writing majors are encouraged to take the thesis workshop and write a BA thesis. Students following the original Major in Creative Writing are required to complete both the thesis workshop and the BA thesis to graduate with the major. For students following the 2023-24 updated requirements the thesis and thesis workshop are encouraged but optional, although the thesis workshop and thesis are required for consideration for the designation of honors. To opt out of the thesis process please email the Director of Undergraduate studies.

Creative Writing BA Thesis Timeline 2023-24

THE YEAR AT A GLANCE

**If you plan to graduate early, please contact the Student Affairs Administrator or DUS as soon as possible**

Spring (Rising Majors): Setting Up Summer Reading

• Tuesday, Week 5: WARA group meeting to discuss the preliminary BA proposal and general strategies for drawing up a summer reading list. This required information session will take place the same day as the group discussion with UChicago Library's  bibliographer for Literatures of Europe & the Americas  
• Friday, Week 7: Preliminary BA proposals are due to the Student Affairs Administrator
• Friday, Week 8: WARA groups will be finalized and confirmed. Your WARA will reach out to set up an individual meeting to discuss summer reading and research plans.
• Weeks 8 and 9: Individual meetings with WARAs to finalize summer reading and research plans (specifically field and focus reading lists)

Autumn: Reading, Research, Planning

• Week 0: Individual WARA and student check-ins regarding summer reading and research
• Week 1 or 2: WARA group meetings
• Week 4: Required Info Session for BA thesis writers
• Deadline to apply to the Thesis/Major Projects Workshop
• Submit annotated bibliography (composed of your field and focus reading lists ) to WARA
• Friday, Week 9: Submit completed BA reading & research summary form to WARA

Winter: Writing & Editorial Process (continue reading and research)

• Weeks 1-10: Work on projects in Thesis/Major Projects Workshops and continue supported reading; research with WARA groups
• Weeks 1-10: Submit Research Background Electives Petition
• Week 9/10: Submit Winter Thesis/Major Projects Workshop final to both your thesis advisor and WARA

Spring: Revising

• Friday, Week 2: Submit a second full (semi-final) draft of thesis to WARA and faculty advisor
• Monday, Week 5: Submit final draft of thesis to faculty advisors, WARA, and the Student Affairs Administrator
• Family & friends welcome
• Week 9: Students notified about Honors decisions

Program Honors and Eligibility

College Catalog on Program Honors: The faculty in the program will award program honors based on their assessment of BA theses and the assessment of WARAs. Students must complete all assignments set by WARAs to be considered for honors. To be eligible, students must have a major GPA of at least 3.6 and an overall GPA of at least 3.25. Honors will be awarded only to exceptional projects from a given cohort. 

Program Honors Criteria

1. GPA:  writer must have at least 3.6 major GPA & 3.25 cumulative GPA

2. Conception:  the BA project has emotional and intellectual resonance, and fulfills many of its artistic goals 

3.   Execution:  the BA project demonstrates strong technical knowledge, from its formal decisions to its execution of the fundamental mechanics of the genre:

            -BA project is developed through active writerly commitment

4. Revision:  writer is diligent throughout the revision process

5. Program Citizenship/Engagement:  the writer put in strong effort throughout the entirety of the BA writing and research process and worked well with both their faculty advisor and WARA:

-Submitted all BA assignments by their deadlines to WARAs

-Checked in with WARAs per pre-determined schedule

-Conscientious colleague in and out of the Thesis Workshop

6. Risk and ambition:  the BA project shows an impressive level of risk and ambition, whether through formal innovation or content

Assignment Checklist

• Preliminary BA Project Proposal  
• Summer Reading Journal (based on field reading list)  
• Annotated Bibliography  
• Focus Reading List  
• Reading and Research Summary

Forms and Guidelines

Princeton Correspondents on Undergraduate Research

Writing a Creative Thesis: An Interview with Edric Huang ’18

A couple of weeks ago, I interviewed Kristin Hauge about her independent work in the Music Department to highlight creative independent work in the arts. This week, I got in touch with Edric Huang, a senior in the Anthropology Department with certificates in Urban Studies and Creative Writing. Unlike most students on campus, he will be writing two theses this year. One is the classic research-based thesis that seniors in the sciences and humanities are familiar with, but the second will be a collection of poems for his Creative Writing Certificate. If you are unfamiliar with the kind of work that goes into creative theses, here’s what Edric had to share about his personal experience:

What topics do you write about in your work?

I’ve been thinking a lot about superstitions and hauntings lately, especially the ones that come up in conversations with my mom. Sometimes, she’ll tell me about Chinese folk superstitions that she or our relatives believe in, and it amazes me how these superstitions create worlds around us all — how they protect us, create conflict, stimulate us to action. I also write poems based on my anthropological fieldwork this past summer when I visited a migrant reception center in Paris and studied the survival strategies of Sudanese refugees who had made it there. Patterns of migration inevitably weave together a lot of my poems, both from a personal lens and through my observations.

How do you go about doing research for your poetry?

In some ways, attentively living is research for my poems. By paying attention to minute details, or lingering a little longer and taking a photo of a particularly arresting image, I naturally make connections to other life experiences or other topics. I recently walked along an industrial street in Brooklyn, and the scene, which reminded me of the 1992 LA Riots, created a starting point. An image creates the language from which I can begin to write.

But research for poetry can take many forms. I look to answer several questions: How do other poets write about certain topics, and how does the form of poetry create a chance to speak about something and speak to someone? How do these other writers use the page? I’ve read a lot of Asian-American writers lately, trying to figure out how I can write my “Asian-Americanness” into my poems. When I draw inspiration from specific poets, songs, etc., I will mention at the beginning of the poem that I am writing “after” something. For example, I wrote a poem after listening to Jhene Aiko’s new album, and included some lyrics from her song into mine.

I also do research on the topics I want to write about, especially if I don’t already know much. I believe that as someone who wants to use poems to grapple with myself and the larger processes that affect or surround me, proper representation of certain themes, histories, etc. cannot be done without a genuine investment in this research process.

Are there any work habits you find to be helpful?

I tend to set limits on how much time I allow myself to spend on this research because I often get so engrossed with some topics that I forget that I still have a poem (and a second thesis…) to write. I also have been trying to free-write by just spewing out lines onto a Word document — with my computer screen dimmed out completely. I edit and judge a little too much, and I’ve come to recognize that there’s a time and place for critique, but it can’t come too early or I won’t get past the first five lines of a poem. When I do edit  my work, I try to keep my writing minimal; I want each word to have gravity and to say as much as possible about the poem. Writing is such an individual process, though, and I’m still trying to find the best practices for me.

What are your plans for your creative thesis?

The standard poetry thesis consists of 30-40 pages of poems, and I hope to use this not only as a way to grow personally and sort through a lot of my complicated emotions around these topics,  but also as a starting point. I’m not sure what I’m doing post-grad yet, but no matter what, I hope to continue writing, editing through this manuscript, and revisiting a lot of these topics that I’ve been thinking about for the past three years.

Whether you’re a poet or not, Edric’s experience shows how there are several ways of going about research. When it comes to inspiration, you could draw from your personal experiences, from previous works that have intrigued you, or even from pausing to take a closer look at your surroundings. The writing process too is not set in stone and can involve a little experimentation to figure out what works best for you. If you’re looking for more tips on research and writing, you can visit the McGraw Center or the Writing Center . Moreover, if you’re interested in finding out more about creative writing, you can speak with a Peer Arts Advisor or apply for a class through the Lewis Center !

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• Thesis with creative works

With approval from your advisory committee your thesis may include a creative work or non-traditional research output (NTRO) component alongside a dissertation to fulfil the requirements of the degree. This is more common in some degrees and disciplines than others.

Both the dissertation and creative work must be passed, and a final version including a durable record of all components of your thesis must be submitted to the University’s digital repository, in order for you to be awarded the degree.

All theses must be presented as a unified whole and address a significant research question.

The creative work may take a variety of forms including:

• a performance,
• an exhibition,
• writing (poetry, fiction, script or other written literary forms),
• musical composition,
• e-portfolio or website,
• multimedia, or
• other new media technologies and modes of presentation.

If the creative work is not in writing it must be comprehensively documented. The work itself, or the documentation must be submitted with the dissertation through the Thesis Examination System (TES). If your thesis file consists of multiple files, upload the main file as part of the thesis submission process and upload the additional files via a cloud storage platform. Then add the shared link to TES as part of your submission.

The dissertation and documentation of the work  (where needed) must adhere to the Preparation of Graduate Research Theses Rules .  You must include a description of the form and presentation of the creative work in the Abstract and in your Preface, note the relative weighting of the creative work and dissertation.

The combined volume of work of the creative works and dissertation for a doctoral thesis would be equivalent to approximately 80,000 -100,000 words.  For a masters degree, the combined volume of work would be equivalent to approximately 40,000-50,000 words.

Any thesis that exceeds the maximum limit requires permission to proceed to examination, which must be sought via the  Graduate Research Examinations Office prior to submission.

Relationship between the Dissertation and Creative Work

The dissertation and the creative work should be considered as complementary, mutually reinforcing parts of a single project.  You may argue, however, that the relationship between the two parts contributes to the originality and creativity of the whole.

The dissertation is required to do more than simply describe the creative work and how it was undertaken.

The dissertation must:

• present the research questions address, and
• contextualise the research as new knowledge within the field of its production.

The dissertation may:

• include information on the materials and methodology used,
• elucidate the creative work, and
• place the creative work in an artistic, intellectual, or cultural context.

The weighting given to the components of the thesis describes the proportion of the research which is demonstrated through the creative component/s and the proportion which is demonstrated in the written dissertation. The relative weighting will inform the examiners’ assessment of the work so must be clearly explained in your Preface.  When registering your intention to submit via the Thesis Examination System (TES), include the weighting in your 80-word summary.

The weighting of the dissertation and creative work, and the expected word length of the dissertation should be agreed at Confirmation. Check the Handbook description for your course to see if the weighting is specified for the course. If not, the minimum weighting for the dissertation that can be agreed at Confirmation is 25%.

Examination

Where the creative work includes a performance or exhibition of visual art works, the examiners may be required to travel to the site of the performance or exhibition. Your Chair of Examiners will make the necessary arrangements for your examiners to attend the viewing of the performance/exhibition. In this situation, if the dissertation is not submitted at or around the same time, you must provide an extended abstract of 1000-3000 words to your Chair of Examiners two weeks prior to the viewing. You must then submit your dissertation by logging into the Thesis Examination System (TES) no more than six calendar months after the performance/exhibition. The role of Chair of Examiners is normally undertaken by the head of department/school or nominee. To find out your Chair of Examiners, contact your supervisor or the Examinations Office .

If one or more components of your thesis is a live website or content hosted online, there should be no alterations made to the website or online content while the examination is in progress.

As graduate researchers submitting creative works in the form of a performance, an exhibition, an e-portfolio, or a website have an obligation to avoid identifying their examiners, the following  Creative Works: Examiner Confidentiality Declaration form should be completed and submitted along with your thesis. Once you have submitted your thesis via TES, return the signed confidentiality declaration to the Examinations Office .

Additional criteria are specified for examiners who are examining creative works.

Final archival version of your thesis

To meet the University's digital repository (Minerva Access) requirements, once examiner comments and amendments have been incorporated, you will need to deposit a durable record of all components of your thesis. Methods of capturing and providing this durable representation of your creative work component vary widely depending on the nature and presentation of your creative component.  It is important for you, in discussion with your supervisor, to decide and capture your desired best quality representation.

If your thesis included a website, you must provide a durable copy of the website as it was during the examination with any amendments requested by the examiners.  You may also provide a link to the live website and have readers directed to that while it remains available,  in addition to the archived copy.

You can find further information about requirements for deposit, as well as options and implications of choosing some options at My thesis in the library and  Depositing multiple files for your final thesis record . You can request technical assistance for submitting the thesis to  Minerva Access .

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The Creativity Decline: Evidence from US Patents

Working Paper 2024-008A by Aakash Kalyani

Economists have long struggled to understand why aggregate productivity growth has dropped in recent decades while the number of new patents filed has steadily increased. I offer an explanation for this puzzling divergence: the creativity embodied in US patents has dropped dramatically over time. To separate creative from derivative patents, I develop a novel, text-based measure of patent creativity: the share of technical terminology that did not appear in previous patents. I show that only creative and not derivative patents are associated with significant improvements in firm level productivity. Using the measure, I show that inventors on average file creative patents upon entry, and file derivative patents with more experience. I embed this life-cycle of creativity in a growth model with endogenous creation and imitation of technologies. In this model, falling population growth explains 27% of the observed decline in patent creativity, 30% of the slowdown in productivity growth, and 64% of the increase in patenting.

Read Full Text

https://doi.org/10.20955/wp.2024.008

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Information for Visitors

i come into existence, only to fall apart: A Senior Thesis Exhibition by Vanessa Ayala '24 (CCS Art)

ミ★ 𝘪 𝘤𝘰𝘮𝘦 𝘪𝘯𝘵𝘰 𝘦𝘹𝘪𝘴𝘵𝘦𝘯𝘤𝘦, 𝘰𝘯𝘭𝘺 𝘵𝘰 𝘧𝘢𝘭𝘭 𝘢𝘱𝘢𝘳𝘵 ★彡

Vanessa Ayala 's '24 (CCS Art) senior thesis exhibition, “i come into existence, only to fall apart” is on display from April 8th to 11th , 2024 in the CCS Gallery at UCSB. Ayala specializes in world-building that traverses the liminal thresholds of their identity as a Queer Latinx raised in a traditional Catholic household. The exhibition explores the artist’s role as a nepantlera, forming new realities of abundance and an identity free from trauma and the marks of institutional violence. Let it all burn down; there is hope in starting anew. 

Join us in the CCS gallery  today, Tuesday, April 9th at 5:30pm  for an  artist-led tour. Light refreshments

for more information, visit  vanessaayala.info