The Art of Invention

Nancy Nersessıan is driven by a simple question that doesn’t have a simple answer: “Where does creativity come from?”

As a Regents professor of cognitive science at Georgia Tech, she conducts groundbreaking research into the art of innovation—how scientists and inventors actually think. Her work bridges the philosophy of creativity and the hard science of math and physics, and it has revealed the process of innovation to be far different from what long has been hailed as sacrosanct.

Nersessian has been fascinated by math and science since childhood when, as a 4-year-old, she would eavesdrop on her older sister’s sessions with a math tutor. Later, she was the lone female physics major in her class at Boston University and at the same time helped program the Apollo 11 computers for the moon landing. In 2011 she was the inaugural recipient of the Patrick Suppes Prize in Philosophy of Science from the American Philosophical Society. Oh, and she’s an accomplished opera singer, too.

So in her studies on the culture and source of creativity, she draws upon plenty of personal experience. And Georgia Tech is a fitting setting for her topic of inquiry: The Institute’s faculty, alumni and students always have generated inventions that have saved lives and changed the way people live, and the Tech campus is home to world-renowned researchers, startup incubators and invention competitions that foster the next generation of innovators.

The Alumni Magazine picked Nersessian’s brain about the limits of the scientific method, the intersection of physics and philosophy, the future of creativity and more.

How did you become interested in this area of study?
I loved math from the moment I encountered it. I really didn’t like science labs in school, especially biology labs. Anyone who was paired with me in science lab was unfortunate and often in danger since I was quite clumsy. I was always theoretically oriented. I won third place in the Boston Science Fair for my project, which was a mathematical analysis of hyperbolic and elliptical functions.

[In college] my physics professors were just interested in teaching the formulas. I was asking, “What does it all mean?” But they didn’t encourage me to pursue this question. In my junior year, I accidentally signed up for a class with Milic Capek, a professor of philosophy and brother of the Czech science fiction writer Karel Capek. His class was on the philosophy of space and time, and I was hooked. It was then that I started to understand what Einstein’s theory of relativity told us about nature. I wanted to find out, “Where did these theories come from? What’s the process?”

I wanted to look at ordinary scientists and engineers doing frontier research and how they think. How do they solve problems and compare that to the struggles of the great scientists and thinkers like [Michael] Faraday, [James Clerk] Maxwell and Einstein? How did they solve their problems?

How do you go about researching that?
The best advice I ever received came when I started grad school, from my mentor, Howard Stein, now an emeritus professor at the University of Chicago. He said: “Don’t just read what philosophers say about science, read the scientists themselves.” As a physics student, it had never occurred to me that it was possible to read the writings of the people who had created the theories in the textbooks.

What I began to find wasn’t just mathematical problems, but their letters and diaries, notebooks filled with sketches and drawings. They made lots of analogies. They ran lots of thought experiments. I was surprised when I first encountered the numerous sketches in Faraday’s diary, the analogical models in Maxwell’s writings and Einstein’s use of thought experiments. These didn’t fit the view of “the scientific method” I’d been indoctrinated with, and yet I was convinced that they were key to understanding how scientists think creatively. However, you can’t talk to dead scientists, so I also began studying scientists and engineers in their research labs.

How does their process differ from the perceived view of the scientific method?
What we’re taught often is that you make a hypothesis, deduce a result and then test it empirically. But that’s not what they did. They went through a different process. I call it model-based reasoning. It’s the engine of creativity. It’s what drove people to their solutions.

How does model-based reasoning aid creativity?
A model is an integrated representation that provides an interpretation of the phenomena under investigation. Models are selective (you can’t model everything) and are constructed to exemplify what are considered to be the important features of phenomena, and so a good model focuses the mind on the cognitively relevant features and enables manipulation of these.

The processes of building models integrate constraints from a variety of resources so that, over many iterations, genuinely novel behaviors or structures can emerge. And models can be represented in different formats, which enable different kinds of manipulations and support different kinds of inferential processes. Transforming models from one format to another can lead to novel insights (e.g., language affords logical inferences, diagrams enable perceptual inferences).

What’s an example of this?
In constructing the electromagnetic field equations, Maxwell built a series of conceptual models that incrementally merged what was known experimentally about electricity and magnetism with constraints from fluid mechanics and machine mechanics to create imaginary models that enabled him to tap into the representational power of the mathematics of continuum mechanics—something he and others at Cambridge had been working on for years before he took on the electromagnetism problem. The other thing he did was to make diagrams of the models that facilitated thinking about the complex interrelations of electricity and magnetism through perceptual inferences and mental simulations.

You hold appointments in the College of Computing, the College of Architecture and the Ivan Allen College of Liberal Arts. Why are you such a proponent of interdisciplinary research?
This merging of constraints from various sources is part of what makes interdisciplinary research a source of creativity. For instance, the biomedical engineering researchers my research group has studied often build physical simulation models that merge constraints from biology and engineering—they can’t experiment on the phenomena directly, so they build physical models that capture what they consider to be relevant aspects, manipulate these hybrid bio-engineered models, and again novel behaviors and structures can emerge. Something new is created in the course of representing these (usually in math).

The systems biologists we’ve studied build computational models to produce simulations that integrate data from a vast range of literature, creating a synthesis that exists nowhere else, and building and running the simulations through numerous iterations often leads to novel behaviors that provide insight into system-level phenomena about which little is currently understood.

Do people have a predisposition to being creative? How can an institution like Georgia Tech foster creativity?
That’s a driving question. There are a lot of smart, creative people who never produce anything. And there are many people who might not think that they are all that creative, but they do produce. There’s a persistence factor in this. They keep working a problem, looking at it from different angles. They struggle. Now, if we can understand that cognitive and neural activity, what happens there, that would be something. And I think we can get there. But without that we can still figure out what the characteristics are that promote creative thinking and foster them.

Do you have any ideas of what those characteristics might be?
A major one is cognitive flexibility—the ability to see something from different perspectives. One way to foster this is to provide opportunities for students to engage a problem from multiple points of view. Also, I think philosophy is great training for any scientist. It teaches you how to formulate problems. It teaches you how to think—how to understand things conceptually. We shouldn’t be restricted to just looking at formulas. Music also fosters creativity more broadly. Einstein played the violin.

What I tell my PhD students is that they need to have real intellectual problems driving their research and feel a passion for the research that will sustain them through the hard work, failures and difficulties that they will inevitably encounter along the way. This points to the significant role of emotion in creativity. It’s what cognitive scientists call “hot cognition.” The moments of insight come with elation, things going well can be exciting, impasses produce despair. To stick with it requires resilience in the face of impasse. Resilience is something that can be fostered in the learning environments designed to promote creativity and innovation.

Where do you think creativity comes from?
The short answer is: from a lot of hard work. I like Einstein’s paraphrase of the old adage: “Genius is 1 percent talent (inspiration) and 99 percent hard work (perspiration).” Some people focus on creativity as an act—the “Aha!” moments of insight. But this leaves out all the prior thinking that went into preparing the mind for that moment. Others focus on creativity as an attribute or characteristic—there are psychological tests to measure the creative predisposition of an individual.

I focus on creativity as a process and, specifically, as a problem-driven process. Thinking of it as a process enables us to see how it takes place within a cognitive-social-cultural nexus that can facilitate or impede it. Importantly, as educators, it also enables us to think of ways in which we can design learning environments that cultivate and facilitate ways of thinking and working that promote creativity and innovation.

How does artistic creativity relate to scientific creativity?
I see them as lying on a continuum. Creative thinking across the arts, humanities, sciences and engineering make use of various forms of model-based reasoning: analogies, visualizations, thought experiments. The problems and resources for solving them are contextual in the arts as for science and engineering. There is support for this from countless accounts by writers, artists and musicians that detail their struggles to solve problems in trying to create something novel. In the final telling, these struggles are often omitted or underplayed.

What’s an example of that?
There’s the myth that Jack Kerouac wrote On the Road in one continuous stream of “spontaneous writing.” However, that myth leaves out the fact that he struggled for years and across many drafts both with how to tell that story and how to perfect the art of “spontaneous writing.” Renaissance artists struggled with the problem of perspective, 20th century musicians with tonality. I think even performance artists go through problem-solving processes. As an opera singer, my struggles were not only with problems of technique and vocal production but also with how to portray the character I was singing—finding the experiential and imaginative resources that would tell the story of that character.

What is the future of creativity in science and engineering?
The area that’s exploding with creative research is the interface of computation, biology and engineering. Computational power and sophisticated algorithms are enabling us to begin to understand complex biological systems and to design synthetic organisms. New technologies are enabling us to merge biological and engineered materials (including in the human brain). Bio-computing is opening the possibility of reprograming or repairing biological processes. Biologically inspired design is creating novel products. These developments—if we consider the ethical implications—have the potential to transform human life in positive directions.

When I was a student, everyone pointed me in the direction of physics. As much as I love that subject, when science and engineering students ask me where the action is—where they have the possibility to be most creative—I send them in the bio-computing-engineering direction.

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