psychology today creative problem solving test

Creative Problem-Solving Test

Do you typically approach a problem from many perspectives or opt for the same old solution that worked in the past? In his work on human motivation, Robert E. Franken states that in order to be creative, you need to be able to view things from different perspectives.

Creativity is linked to fundamental qualities of thinking, such as flexibility and tolerance of ambiguity. This Creative Problem-solving Test was developed to evaluate whether your attitude towards problem-solving and the manner in which you approach a problem are conducive to creative thinking.

This test is made up of two types of questions: scenarios and self-assessment. For each scenario, answer according to how you would most likely behave in a similar situation. For the self-assessment questions, indicate the degree to which the given statements apply to you. In order to receive the most accurate results, please answer each question as honestly as possible.

After finishing this test you will receive a FREE snapshot report with a summary evaluation and graph. You will then have the option to purchase the full results for $6.95

This test is intended for informational and entertainment purposes only. It is not a substitute for professional diagnosis or for the treatment of any health condition. If you would like to seek the advice of a licensed mental health professional you can search Psychology Today's directory here .

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5.20: Assignment- Creative Thinking Skills

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• Lumen Learning
• Evaluate your attitude toward problem-solving in the context of cultivating creative thinking.
• Access Psychology Today ’s Creative Problem-Solving Test at the Psychology Today  Web site.
• Read the introductory text, which explains how creativity is linked to fundamental qualities of thinking, such as flexibility and tolerance of ambiguity.
• Advance to the questions by clicking on the “Take The Test” button. The test has 20 questions and will take roughly 10 minutes.
• After finishing the test, you will receive a Snapshot Report with an introduction, a graph, and a personalized interpretation for one of your test scores.
• College Success. Authored by : Linda Bruce. Provided by : Lumen Learning. License : CC BY: Attribution

"Life is a continuous exercise in creative problem solving." - Michael J. Gelb

Do you typically approach a problem from many perspectives or opt for the same old solution that worked in the past? In his work on human motivation, Robert E. Franken states that in order to be creative, you need to be able to view things from different perspectives.

Creativity is linked to fundamental qualities of thinking, such as flexibility and tolerance of ambiguity. This Creative Problem-solving Test was developed to evaluate whether your attitude towards problem-solving and the manner in which you approach a problem are conducive to creative thinking.

This test is made up of two types of questions: scenarios and self-assessment. For each scenario, answer according to how you would most likely behave in a similar situation. For the self-assessment questions, indicate the degree to which the given statements apply to you. In order to receive the most accurate results, please answer each question as honestly as possible.

After finishing the test, you will receive a Snapshot Report with an introduction, a graph and a personalized interpretation for one of your test scores. You will then have the option to purchase the full results.

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• Austin Community College
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Education Standards

Ncccs combined course library.

Learning Domain: Humanities

Standard: Critical Thinking

Chapter 7: Critical and Creative Thinking

LEARNING OBJECTIVES

By the end of this section, you will be able to:

• Define critical thinking
• Describe the role that logic plays in critical thinking
• Describe how critical thinking skills can be used to evaluate information
• Apply the CRAAP test to evaluate sources of information
• Identify strategies for developing yourself as a critical thinker
• Identify applications in education and one's career where creative thinking is relevant and beneficial
• Explore key elements and stages in the creative process
• Apply specific skills for stimulating creative perspectives and innovative options
• Integrate critical and creative thinking in the process of problem-solving

Critical and Creative Thinking

Critical Thinking

As a college student, you are tasked with engaging and expanding your thinking skills. One of the most important of these skills is critical thinking because it relates to nearly all tasks, situations, topics, careers, environments, challenges, and opportunities. It is a “domain-general” thinking skill, not one that is specific to a particular subject area.

What Is Critical Thinking?

Critical thinking  is clear, reasonable, reflective thinking focused on deciding what to believe or do (Robert Ennis.) It means asking probing questions like “How do we know?” or “Is this true in every case or just in this instance?” It involves being skeptical and challenging assumptions rather than simply memorizing facts or blindly accepting what you hear or read.

Imagine, for example, that you’re reading a history textbook. You wonder who wrote it and why, because you detect certain biases in the writing. You find that the author has a limited scope of research focused only on a particular group within a population. In this case, your critical thinking reveals that there are “other sides to the story.”

Who are critical thinkers, and what characteristics do they have in common? Critical thinkers are usually curious and reflective people. They like to explore and probe new areas and seek knowledge, clarification, and new solutions. They ask pertinent questions, evaluate statements and arguments, and they distinguish between facts and opinion. They are also willing to examine their own beliefs, possessing a manner of humility that allows them to admit lack of knowledge or understanding when needed. They are open to changing their mind. Perhaps most of all, they actively enjoy learning, and seeking new knowledge is a lifelong pursuit. This may well be you!

No matter where you are on the road to being a critical thinker, you can always more fully develop and finely tune your skills. Doing so will help you develop more balanced arguments, express yourself clearly, read critically, and glean important information efficiently. Critical thinking skills will help you in any profession or any circumstance of life, from science to art to business to teaching. With critical thinking, you become a clearer thinker and problem solver.

The following video, from Lawrence Bland, presents the major concepts and benefits of critical thinking.

Critical Thinking and Logic

Critical thinking is fundamentally a process of questioning information and data and then reflecting on and assessing what you discover to arrive at a reasonable conclusion. You may question the information you read in a textbook, or you may question what a politician or a professor or a classmate says.

You can also question a commonly held belief or a new idea. It is equally important (and even more challenging) to question your own thinking and beliefs! With critical thinking, anything and everything is subject to question and examination for the purpose of logically constructing reasoned perspectives.

What Is Logic?

The word  logic  comes from the Ancient Greek  logike , referring to the science or art of reasoning. Using logic, a person evaluates arguments and reasoning and strives to distinguish between good and bad reasoning, or between truth and falsehood. Using logic, you can evaluate the ideas and claims of others, make good decisions, and form sound beliefs about the world. [1]

Questions of Logic in Critical Thinking

Let’s use a simple example of applying logic to a critical-thinking situation. In this hypothetical scenario, a man has a Ph.D. in political science, and he works as a professor at a local college. His wife works at the college, too. They have three young children in the local school system, and their family is well known in the community. The man is now running for political office. Are his credentials and experience sufficient for entering public office? Will he be effective in the political office? Some voters might believe that his personal life and current job, on the surface, suggest he will do well in the position, and they will vote for him. In truth, the characteristics described don’t guarantee that the man will do a good job. The information is somewhat irrelevant. What else might you want to know? How about whether the man had already held a political office and done a good job? In this case, we want to think critically about how much information is adequate in order to make a decision based on  logic  instead of  assumptions.

The following questions, presented in Figure 1, below, are ones you may apply to formulate a logical, reasoned perspective in the above scenario or any other situation:

• What’s happening?  Gather the basic information and begin to think of questions.
• Why is it important?  Ask yourself why it’s significant and whether or not you agree.
• What don’t I see?  Is there anything important missing?
• How do I know?  Ask yourself where the information came from and how it was constructed.
• Who is saying it?  What’s the position of the speaker and what is influencing them?
• What else?   What if?  What other ideas exist and are there other possibilities?

Problem-Solving with Critical Thinking

For most people, a typical day is filled with critical thinking and problem-solving challenges. In fact, critical thinking and problem-solving go hand-in-hand. They both refer to using knowledge, facts, and data to solve problems effectively. But with problem-solving, you are specifically identifying, selecting, and defending your solution. Below are some examples of using critical thinking to problem-solve:

• Your roommate was upset and said some unkind words to you, which put a crimp in the relationship. You try to see through the angry behaviors to determine how you might best support the roommate and help bring the relationship back to a comfortable spot.
• Your campus club has been languishing due to a lack of participation and funds. The new club president, though, is a marketing major and has identified some strategies to interest students in joining and supporting the club. Implementation is forthcoming.
• Your final art class project challenges you to conceptualize form in new ways. On the last day of class when students present their projects, you describe the techniques you used to fulfill the assignment. You explain why and how you selected that approach.
• Your math teacher sees that the class is not quite grasping a concept. She uses clever questioning to dispel anxiety and guide you to a new understanding of the concept.

You have a job interview for a position that you feel you are only partially qualified for, although you really want the job and you are excited about the prospects. You analyze how you will explain your skills and experiences in a way to show that you are a good match for the prospective employer.

• You are doing well in college, and most of your college and living expenses are covered. But there are some gaps between what you want and what you feel you can afford. You analyze your income, savings, and budget to better calculate what you will need to stay in college and maintain your desired level of spending.

Evaluating Information with Critical Thinking

Evaluating information can be one of the most complex tasks you will be faced with in college. But if you utilize the following four strategies, you will be well on your way to success:

• Read for understanding
• Examine arguments
• Clarify thinking
• Cultivate “habits of mind”

Read for Understanding

When you read, take notes or mark the text to track your thinking about what you are reading. As you make connections and ask questions in response to what you read,  you monitor your comprehension and enhance your long-term understanding of the material. You will want to mark important arguments and key facts. Indicate where you agree and disagree or have further questions. You don’t necessarily need to read every word, but make sure you understand the concepts or the intentions behind what is written. See the chapter on  Active Reading Strategies  for additional tips.

Examine Arguments

When you examine arguments or claims that an author, speaker, or other source is making, your goal is to identify and examine the hard facts. You can use the spectrum of authority strategy for this purpose. The spectrum of authority strategy assists you in identifying the “hot” end of an argument—feelings, beliefs, cultural influences, and societal influences—and the “cold” end of an argument—scientific influences. The most compelling arguments balance elements from both ends of the spectrum. The following video explains this strategy in further detail:

Clarify Thinking

When you use critical thinking to evaluate information, you need to clarify your thinking to yourself and likely to others. Doing this well is mainly a process of asking and answering probing questions, such as the logic questions discussed earlier. Design your questions to fit your needs, but be sure to cover adequate ground. What is the purpose? What question are we trying to answer? What point of view is being expressed? What assumptions are we or others making? What are the facts and data we know, and how do we know them? What are the concepts we’re working with? What are the conclusions, and do they make sense? What are the implications?

Cultivate “Habits of Mind”

“Habits of mind” are the personal commitments, values, and standards you have about the principle of good thinking. Consider your intellectual commitments, values, and standards. Do you approach problems with an open mind, a respect for truth, and an inquiring attitude? Some good habits to have when thinking critically are being receptive to having your opinions changed, having respect for others, being independent and not accepting something is true until you’ve had the time to examine the available evidence, being fair-minded, having respect for a reason, having an inquiring mind, not making assumptions, and always, especially, questioning your own conclusions—in other words, developing an intellectual work ethic. Try to work these qualities into your daily life.

In 2010, a textbook being used in fourth-grade classrooms in Virginia became big news for all the wrong reasons. The book,  Our Virginia  by Joy Masoff, had caught the attention of a parent who was helping her child do her homework, according to  an article in  The Washington Post . Carol Sheriff was a historian for the College of William and Mary and as she worked with her daughter, she began to notice some glaring historical errors, not the least of which was a passage that described how thousands of African Americans fought for the South during the Civil War.

Further investigation into the book revealed that, although the author had written textbooks on a variety of subjects, she was not a trained historian. The research she had done to write  Our Virginia,  and in particular the information she included about Black Confederate soldiers, was done through the Internet and included sources created by groups like the Sons of Confederate Veterans, an organization which promotes views of history that de-emphasize the role of slavery in the Civil War.

How did a book with errors like these come to be used as part of the curriculum and who was at fault? Was it Masoff for using untrustworthy sources for her research? Was it the editors who allowed the book to be published with these errors intact? Was it the school board for approving the book without more closely reviewing its accuracy?

There are a number of issues at play in the case of  Our Virginia , but there’s no question that evaluating sources is an important part of the research process and doesn’t just apply to Internet sources. Using inaccurate, irrelevant, or poorly researched sources can affect the quality of your own work. Being able to understand and apply the concepts that follow is crucial to becoming a more savvy user and creator of information.

When you begin evaluating sources, what should you consider? The  CRAAP test  is a series of common evaluative elements you can use to evaluate the  C urrency,  R elevance,  A uthority,  A ccuracy, and  P urpose of your sources. The CRAAP test was developed by librarians at California State University at Chico and it gives you a good, overall set of elements to look for when evaluating a resource. Let’s consider what each of these evaluative elements means. 

One of the most important and interesting steps to take as you begin researching a subject is selecting the resources that will help you build your thesis and support your assertions. Certain topics require you to pay special attention to how current your resource is—because they are time sensitive, because they have evolved so much over the years, or because new research comes out on the topic so frequently. When evaluating the currency of an article, consider the following:

• When was the item written, and how frequently does the publication come out?
• Is there evidence of newly added or updated information in the item?
• If the information is dated, is it still suitable for your topic?
• How frequently does information change about your topic?

Understanding what resources are most applicable to your subject and why they are applicable can help you focus and refine your thesis. Many topics are broad and searching for information on them produces a wide range of resources. Narrowing your topic and focusing on resources specific to your needs can help reduce the piles of information and help you focus in on what is truly important to read and reference. When determining relevance consider the following:

• Does the item contain information relevant to your argument or thesis?
• Read the article’s introduction, thesis, and conclusion.
• Scan main headings and identify article keywords.
• For book resources, start with the index or table of contents—how wide a scope does the item have? Will you use part or all of this resource?
• Does the information presented support or refute your ideas?
• If the information refutes your ideas, how will this change your argument?
• Does the material provide you with current information?
• What is the material’s intended audience?

Understanding more about your information’s source helps you determine when, how, and where to use that information. Is your author an expert on the subject? Do they have some personal stake in the argument they are making? What is the author or information producer’s background? When determining the authority of your source, consider the following:

• What are the author’s credentials?
• What is the author’s level of education, experience, and/or occupation?
• What qualifies the author to write about this topic?
• What affiliations does the author have? Could these affiliations affect their position?
• What organization or body published the information? Is it authoritative? Does it have an explicit position or bias?

Determining where information comes from, if the evidence supports the information, and if the information has been reviewed or refereed can help you decide how and whether to use a source. When determining the accuracy of a source, consider the following:

• Is the source well-documented? Does it include footnotes, citations, or a bibliography?
• Is information in the source presented as fact, opinion, or propaganda? Are biases clear?
• Can you verify information from the references cited in the source?
• Is the information written clearly and free of typographical and grammatical mistakes? Does the source look to be edited before publication? A clean, well-presented paper does not always indicate accuracy, but usually at least means more eyes have been on the information.

Knowing why the information was created is a key to evaluation. Understanding the reason or purpose of the information, if the information has clear intentions, or if the information is fact, opinion, or propaganda will help you decide how and why to use information:

• Is the author’s purpose to inform, sell, persuade, or entertain?
• Does the source have an obvious bias or prejudice?
• Is the article presented from multiple points of view?
• Does the author omit important facts or data that might disprove their argument?
• Is the author’s language informal, joking, emotional, or impassioned?
• Is the information clearly supported by evidence?

When you feel overwhelmed by the information you are finding, the CRAAP test can help you determine which information is the most useful to your research topic. How you respond to what you find out using the CRAAP test will depend on your topic. Maybe you want to use two overtly biased resources to inform an overview of typical arguments in a particular field. Perhaps your topic is historical and currency means the past hundred years rather than the past one or two years. Use the CRAAP test, be knowledgeable about your topic, and you will be on your way to evaluating information efficiently and well!

Next, visit the  ACC Library’s Website  for a tutorial and quiz on using the CRAAP test to evaluate sources.

Developing Yourself As a Critical Thinker

Critical thinking is a fundamental skill for college students, but it should also be a lifelong pursuit. Below are additional strategies to develop yourself as a critical thinker in college and in everyday life:

• Reflect and practice : Always reflect on what you’ve learned. Is it true all the time? How did you arrive at your conclusions?
• Use wasted time : It’s certainly important to make time for relaxing, but if you find you are indulging in too much of a good thing, think about using your time more constructively. Determine when you do your best thinking and try to learn something new during that part of the day.
• Redefine the way you see things : It can be very uninteresting to always think the same way. Challenge yourself to see familiar things in new ways. Put yourself in someone else’s shoes and consider things from a different angle or perspective.  If you’re trying to solve a problem, list all your concerns: what you need in order to solve it, who can help, what some possible barriers might be, etc. It’s often possible to reframe a problem as an opportunity. Try to find a solution where there seems to be none.
• Analyze the influences on your thinking and in your life : Why do you think or feel the way you do? Analyze your influences. Think about who in your life influences you. Do you feel or react a certain way because of social convention, or because you believe it is what is expected of you? Try to break out of any molds that may be constricting you.
• Express yourself : Critical thinking also involves being able to express yourself clearly. Most important in expressing yourself clearly is stating one point at a time. You might be inclined to argue every thought, but you might have greater impact if you focus just on your main arguments. This will help others to follow your thinking clearly. For more abstract ideas, assume that your audience may not understand. Provide examples, analogies, or metaphors where you can.
• Enhance your wellness : It’s easier to think critically when you take care of your mental and physical health. Try taking activity breaks throughout the day to reach 30 to 60 minutes of physical activity each day. Scheduling physical activity into your day can help lower stress and increase mental alertness. Also,  do your most difficult work when you have the most energy . Think about the time of day you are most effective and have the most energy. Plan to do your most difficult work during these times. And be sure to  reach out for help i f you feel you need assistance with your mental or physical health (see  Maintaining Your Mental and Physical Health  for more information).

Complete ACTIVITY 1:  REFLECT ON CRITICAL THINKING at the end of the chapter to deepen your understanding of critical thinking in action. 

Creative thinking.

Creative thinking  is an invaluable skill for college students because it helps you look at problems and situations from a fresh perspective. Creative thinking is a way to develop novel or unorthodox solutions that do not depend wholly on past or current solutions. It’s a way of employing strategies to clear your mind so that your thoughts and ideas can transcend what appears to be the limitations of a problem. Creative thinking is a way of moving beyond barriers and it can be understood as a  skill —as opposed to an inborn talent or natural “gift”—that can be taught as well as learned.

However, the ability to think and act in creative ways is a natural ability that we all exhibited as children. The curiosity, wonder, imagination, playfulness, and persistence in obtaining new skills are what transformed us into the powerful learners that we became well before we entered school. As a creative thinker now, you are curious, optimistic, and imaginative. You see problems as interesting opportunities, and you challenge assumptions and suspend judgment. You don’t give up easily. You work hard. Is this you? Even if you don’t yet see yourself as a competent creative thinker or problem-solver yet, you can learn solid skills and techniques to help you become one.

Creative Thinking in Education

College is a great ground for enhancing creative thinking skills. The following are some examples of college activities that can stimulate creative thinking. Are any familiar to you? What are some aspects of your own college experience that require you to think creatively?

• Design sample exam questions to test your knowledge as you study for a final.
• Devise a social media strategy for a club on campus.
• Propose an education plan for a major you are designing for yourself.
• Prepare a speech that you will give in a debate in your course.
• Arrange audience seats in your classroom to maximize attention during your presentation.
• Participate in a brainstorming session with your classmates on how you will collaborate on a group project.
• Draft a script for a video production that will be shown to several college administrators.
• Compose a set of requests and recommendations for a campus office to improve its services for students.
• Develop a marketing pitch for a mock business you are developing.
• Develop a plan to reduce energy consumption in your home, apartment, or dorm.

How to Stimulate Creative Thinking

The following video,  How to Stimulate the Creative Process , identifies six strategies to stimulate your creative thinking.

• Sleep on it . Over the years, researchers have found that the REM sleep cycle boosts our creativity and problem-solving abilities, providing us with innovative ideas or answers to vexing dilemmas when we awaken. Keep a pen and paper by the bed so you can write down your nocturnal insights if they wake you up.
• Go for a run or hit the gym . Studies indicate that exercise stimulates creative thinking, and the brainpower boost lasts for a few hours.
• Allow your mind to wander  a few times every day. Far from being a waste of time, daydreaming has been found to be an essential part of generating new ideas. If you’re stuck on a problem or creatively blocked, think about something else for a while.
• Keep learning . Studying something far removed from your area of expertise is especially effective in helping you think in new ways.
• Put yourself in nerve-racking situations  once in a while to fire up your brain. Fear and frustration can trigger innovative thinking.
• Keep a notebook  with you, or create a file for ideas on your smartphone or laptop, so you always have a place to record fleeting thoughts. They’re sometimes the best ideas of all.

The following video, Where Good Ideas Come From by Steven Johnson, reinforces the idea that time allows creativity to flourish.

Watch this supplemental video by PBS Digital Studies: How To Be Creative | Off Book | PBS Digital Studio for a more in-depth look on how to become a “powerful creative person.”

Below is an article by Professor Tobin Quereau, called In Search of Creativity . Perhaps the article can help you think about some simple principles that can enhance your own creative thinking.

In Search of Creativity Tobin Quereau As I was searching through my files the other day for materials on creativity, I ran across some crumpled, yellowed notes which had no clear identification as to their source. Though I cannot remember exactly where they came from, I pass them along to you as an example of the absurd lengths to which some authors will go to get people’s attention. The notes contained five principles or practices with accompanying commentary which supposedly enhance creativity. I reprint them here as I found them and leave you to make your own judgment on the matter.... 1. Do It Poorly! One has to start somewhere and hardly anyone I know starts perfectly at anything. As a result, hardly anyone seems to start very much at all. Often times the quest for excellence quashes any attempt at writing, thinking, doing, saying, etc., since we all start rather poorly in the beginning. Therefore, I advocate more mediocrity as a means to success. Whatever you want, need, or have to do, start doing it! (Apologies to Nike, but this was written long before they stole the concept....) Do it poorly at first with pleasure, take a look or listen to what you’ve done, and then do it again. If you can turn out four good, honest, poor quality examples, the fifth time you should have enough information and experience to turn out something others will admire. And if you do the first four tries in private, only you need to know how you got there. 2. Waste Time! Don’t spend it all doing things. Give yourself time and permission to daydream, mull over, muse about your task or goal without leaping into unending action. “But what,” you say, “if I find myself musing more about the grocery shopping than the gross receipts?” Fine, just see what relationships you can come up with between groceries and gross receipts. (How about increasing the volume and lowering mark-ups? Or providing comfortable seating in the local superstore so that people can relax while shopping and thus have more energy with which to spend their money??) Whatever you do, just pay attention to what comes and get it down in writing somewhere somehow before it goes again. No need to waste ideas.... 3. Be Messy! (Not hard for some of us.) Don’t go for clarity before confusion has had time to teach you something new. In fact, I advocate starting with a large sheet of blank paper–anything up to 2 feet by 4 feet in size–and then filling it up as quickly and randomly as possible with everything that is, might be, or ought to be related to the task at hand. Then start drawing arrows, underlining, scratching through, highlighting, etc., to make a real mess that no one but you can decipher. (If you can’t figure it out either, that’s O.K., too–it doesn’t have to make sense in the beginning.) Then go back to Principle #1 and start doing something. 4. Make Mistakes! Search out your stumbling blocks. Celebrate your errors. Rejoice in your “wrongs” for in them lie riches. Consider your faux pas as feedback not failure and you’ll learn (and possibly even earn!) a lot more. Be like a research scientist and get something publishable out of whatever the data indicates. As one creative consultant, Sidney X. Shore, suggests, always ask, “What’s Good About It?” Some of our most precious inventions have resulted from clumsy hands and creative insight. 5. Forget Everything You Have Learned! (Except, perhaps, these principles!) Give yourself a chance to be a neophyte, return to innocence, start with “beginner’s mind”. In the Zen tradition of Japan, there is a saying in support of this approach because in the beginner’s mind all things are possible, in the expert’s mind only one or two. What would a five-year-old do with your task, goal, project, or problem? Take a risk and be naive again. Many major advances in math and science have come from young, wet-behind-the-ears upstarts who don’t know enough to get stuck like everyone else. Even Picasso worked hard at forgetting how to draw.... But I must stop! There was more to this unusual manuscript, but it would be a poor idea to prolong this further. As a responsible author, I don’t want to waste any more of your time on such ramblings. You know as well as I that such ideas would quickly make a mess of things. I am sure that the original author, whoever that was, has by now repudiated these mistaken notions which could be quite dangerous in the hands of untrained beginners. I even recall a reference to these principles being advocated for groups and teams as well as for individual practice—if you can imagine such a thing! It is a pity that the author or authors did not have more to offer, however, “In Search of Creativity” could have made a catchy title for a book....

Problem Solving with Creative Thinking

Creative problem-solving is a type of problem-solving that involves searching for new and novel solutions to problems. It’s a way to think “outside of the box.” Unlike critical thinking, which scrutinizes assumptions and uses reasoning, creative thinking is about generating alternative ideas— practices and solutions that are unique and effective. It’s about facing sometimes muddy and unclear problems and seeing how things can be done differently.

Complete ACTIVITY 2:  ASSESS YOUR CREATIVE-PROBLEM SOLVING SKILLS  at the end of the chapter to see what skills you currently have and which new ones you can develop further. 

As you continue to develop your creative thinking skills, be alert to perceptions about creative thinking that could slow down progress. Remember that creative thinking and problem-solving are ways to transcend the limitations of a problem and see past barriers.

Critical and creative thinking complement each other when it comes to problem-solving. The process of alternatively focusing and expanding your thinking can generate more creative, innovative, and effective outcomes. The following words, by Dr. Andrew Robert Baker, are excerpted from his “Thinking Critically and Creatively ” essay. Dr. Baker illuminates some of the many ways that college students will be exposed to critical and creative thinking and how it can enrich their learning experiences.

THINKING CRITICALLY AND CREATIVELY Critical thinking skills are perhaps the most fundamental skills involved in making judgments and solving problems. You use them every day, and you can continue improving them. The ability to think critically about a matter—to analyze a question, situation, or problem down to its most basic parts—is what helps us evaluate the accuracy and truthfulness of statements, claims, and information we read and hear. It is the sharp knife that, when honed, separates fact from fiction, honesty from lies, and the accurate from the misleading. We all use this skill to one degree or another almost every day. For example, we use critical thinking every day as we consider the latest consumer products and why one particular product is the best among its peers. Is it a quality product because a celebrity endorses it? Because a lot of other people may have used it? Because it is made by one company versus another? Or perhaps because it is made in one country or another? These are questions representative of critical thinking. The academic setting demands more of us in terms of critical thinking than everyday life. It demands that we evaluate information and analyze myriad issues. It is the environment where our critical thinking skills can be the difference between success and failure. In this environment we must consider information in an analytical, critical manner. We must ask questions—What is the source of this information? Is this source an expert one and what makes it so? Are there multiple perspectives to consider on an issue? Do multiple sources agree or disagree on an issue? Does quality research substantiate information or opinion? Do I have any personal biases that may affect my consideration of this information? It is only through purposeful, frequent, intentional questioning such as this that we can sharpen our critical thinking skills and improve as students, learners and researchers. While critical thinking analyzes information and roots out the true nature and facets of problems, it is creative thinking that drives progress forward when it comes to solving these problems. Exceptional creative thinkers are people that invent new solutions to existing problems that do not rely on past or current solutions. They are the ones who invent solution C when everyone else is still arguing between A and B. Creative thinking skills involve using strategies to clear the mind so that our thoughts and ideas can transcend the current limitations of a problem and allow us to see beyond barriers that prevent new solutions from being found. Brainstorming is the simplest example of intentional creative thinking that most people have tried at least once. With the quick generation of many ideas at once, we can block-out our brain’s natural tendency to limit our solution-generating abilities so we can access and combine many possible solutions/thoughts and invent new ones. It is sort of like sprinting through a race’s finish line only to find there is new track on the other side and we can keep going, if we choose. As with critical thinking, higher education both demands creative thinking from us and is the perfect place to practice and develop the skill. Everything from word problems in a math class, to opinion or persuasive speeches and papers, call upon our creative thinking skills to generate new solutions and perspectives in response to our professor’s demands. Creative thinking skills ask questions such as—What if? Why not? What else is out there? Can I combine perspectives/solutions? What is something no one else has brought-up? What is being forgotten/ignored? What about ______? It is the opening of doors and options that follows problem-identification. Consider an assignment that required you to compare two different authors on the topic of education and select and defend one as better. Now add to this scenario that your professor clearly prefers one author over the other. While critical thinking can get you as far as identifying the similarities and differences between these authors and evaluating their merits, it is creative thinking that you must use if you wish to challenge your professor’s opinion and invent new perspectives on the authors that have not previously been considered. So, what can we do to develop our critical and creative thinking skills? Although many students may dislike it, group work is an excellent way to develop our thinking skills. Many times I have heard from students their disdain for working in groups based on scheduling, varied levels of commitment to the group or project, and personality conflicts too, of course. True—it’s not always easy, but that is why it is so effective. When we work collaboratively on a project or problem we bring many brains to bear on a subject. These different brains will naturally develop varied ways of solving or explaining problems and examining information. To the observant individual we see that this places us in a constant state of back and forth critical/creative thinking modes. For example, in group work we are simultaneously analyzing information and generating solutions on our own, while challenging other’s analyses/ideas and responding to challenges to our own analyses/ideas. This is part of why students tend to avoid group work—it challenges us as thinkers and forces us to analyze others while defending ourselves, which is not something we are used to or comfortable with as most of our educational experiences involve solo work. Your professors know this—that’s why we assign it—to help you grow as students, learners, and thinkers! —Dr. Andrew Robert Baker,  Foundations of Academic Success: Words of Wisdom

Problem-Solving Action Checklist

Problem-solving can be an efficient and rewarding process, especially if you are organized and mindful of critical steps and strategies. Remember to assume the attributes of a good critical thinker: if you are curious, reflective, knowledge-seeking, open to change, probing, organized, and ethical, your challenge or problem will be less of a hurdle, and you’ll be in a good position to find intelligent solutions. The steps outlined in this checklist will help you adhere to these qualities in your approach to any problem:

KEY TAKEAWAYS

• Critical thinking is logical and reflective thinking focused on deciding what to believe or do.
• Critical thinking involves questioning and evaluating information.
• Evaluating information is a complex, but essential, process. You can use the CRAAP test to help determine if sources and information are reliable.
• Creative thinking is both a natural aspect of childhood and a re-learnable skill as an adult.
• Creative thinking is as essential a skill as critical thinking and integrating them can contribute to  innovative and rewarding experiences in life.
• Critical and creative thinking both contribute to our ability to solve problems in a variety of contexts.
• You can take specific actions to develop and strengthen your critical and creative thinking skills.

ACTIVITY 1: REFLECT ON CRITICAL THINKING

• Apply critical thinking strategies to your life

Directions:

• Think about someone you consider to be a critical thinker (friend, professor, historical figure, etc). What qualities does he/she have?
• Review some of the critical thinking strategies discussed on this page. Pick one strategy that makes sense to you. How can you apply this critical thinking technique to your academic work?
• Habits of mind are attitudes and beliefs that influence how you approach the world (i.e., inquiring attitude, open mind, respect for truth, etc). What is one habit of mind you would like to actively develop over the next year? How will you develop a daily practice to cultivate this habit?
• Write your responses in journal form, and submit according to your instructor’s guidelines.

ACTIVITY 2: ASSESS YOUR CREATIVE PROBLEM-SOLVING SKILLS

• Access  Psychology Today ’s  Creative Problem-Solving Test  at the  Psychology Today  Web site.
• Read the introductory text, which explains how creativity is linked to fundamental qualities of thinking, such as flexibility and tolerance of ambiguity.
• Then advance to the questions by clicking on the “Take The Test” button. The test has 20 questions and will take roughly 10 minutes.
• After finishing the test, you will receive a Snapshot Report with an introduction, a graph, and a personalized interpretation for one of your test scores.

Complete any further steps by following your instructor’s directions.

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• Where Good Ideas Come From.  Authored by : Steven Johnson.  Located at :  https://www.youtube.com/watch?v=NugRZGDbPFU .  License :  Public Domain: No Known Copyright .  License Terms : Standard YouTube License
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ACTIVITY: ASSESS YOUR CREATIVE PROBLEM-SOLVING SKILLS

Directions:.

• Access  Psychology Today ’s  Creative Problem-Solving Test  at the  Psychology Today  Web site.
• Read the introductory text, which explains how creativity is linked to fundamental qualities of thinking, such as flexibility and tolerance of ambiguity.
• Then advance to the questions by clicking on the “Take The Test” button. The test has 20 questions and will take roughly 10 minutes.
• After finishing the test, you will receive a Snapshot Report with an introduction, a graph, and a personalized interpretation for one of your test scores.

Complete any further steps by following your instructor’s directions.

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What Is Creative Problem-Solving & Why Is It Important?

• 01 Feb 2022

One of the biggest hindrances to innovation is complacency—it can be more comfortable to do what you know than venture into the unknown. Business leaders can overcome this barrier by mobilizing creative team members and providing space to innovate.

There are several tools you can use to encourage creativity in the workplace. Creative problem-solving is one of them, which facilitates the development of innovative solutions to difficult problems.

Here’s an overview of creative problem-solving and why it’s important in business.

Access your free e-book today.

What Is Creative Problem-Solving?

Research is necessary when solving a problem. But there are situations where a problem’s specific cause is difficult to pinpoint. This can occur when there’s not enough time to narrow down the problem’s source or there are differing opinions about its root cause.

In such cases, you can use creative problem-solving , which allows you to explore potential solutions regardless of whether a problem has been defined.

Creative problem-solving is less structured than other innovation processes and encourages exploring open-ended solutions. It also focuses on developing new perspectives and fostering creativity in the workplace . Its benefits include:

• Finding creative solutions to complex problems : User research can insufficiently illustrate a situation’s complexity. While other innovation processes rely on this information, creative problem-solving can yield solutions without it.
• Adapting to change : Business is constantly changing, and business leaders need to adapt. Creative problem-solving helps overcome unforeseen challenges and find solutions to unconventional problems.
• Fueling innovation and growth : In addition to solutions, creative problem-solving can spark innovative ideas that drive company growth. These ideas can lead to new product lines, services, or a modified operations structure that improves efficiency.

Creative problem-solving is traditionally based on the following key principles :

1. Balance Divergent and Convergent Thinking

Creative problem-solving uses two primary tools to find solutions: divergence and convergence. Divergence generates ideas in response to a problem, while convergence narrows them down to a shortlist. It balances these two practices and turns ideas into concrete solutions.

2. Reframe Problems as Questions

By framing problems as questions, you shift from focusing on obstacles to solutions. This provides the freedom to brainstorm potential ideas.

3. Defer Judgment of Ideas

When brainstorming, it can be natural to reject or accept ideas right away. Yet, immediate judgments interfere with the idea generation process. Even ideas that seem implausible can turn into outstanding innovations upon further exploration and development.

4. Focus on "Yes, And" Instead of "No, But"

Using negative words like "no" discourages creative thinking. Instead, use positive language to build and maintain an environment that fosters the development of creative and innovative ideas.

Creative Problem-Solving and Design Thinking

Whereas creative problem-solving facilitates developing innovative ideas through a less structured workflow, design thinking takes a far more organized approach.

Design thinking is a human-centered, solutions-based process that fosters the ideation and development of solutions. In the online course Design Thinking and Innovation , Harvard Business School Dean Srikant Datar leverages a four-phase framework to explain design thinking.

The four stages are:

• Clarify: The clarification stage allows you to empathize with the user and identify problems. Observations and insights are informed by thorough research. Findings are then reframed as problem statements or questions.
• Ideate: Ideation is the process of coming up with innovative ideas. The divergence of ideas involved with creative problem-solving is a major focus.
• Develop: In the development stage, ideas evolve into experiments and tests. Ideas converge and are explored through prototyping and open critique.
• Implement: Implementation involves continuing to test and experiment to refine the solution and encourage its adoption.

Creative problem-solving primarily operates in the ideate phase of design thinking but can be applied to others. This is because design thinking is an iterative process that moves between the stages as ideas are generated and pursued. This is normal and encouraged, as innovation requires exploring multiple ideas.

Creative Problem-Solving Tools

While there are many useful tools in the creative problem-solving process, here are three you should know:

Creating a Problem Story

One way to innovate is by creating a story about a problem to understand how it affects users and what solutions best fit their needs. Here are the steps you need to take to use this tool properly.

1. Identify a UDP

Create a problem story to identify the undesired phenomena (UDP). For example, consider a company that produces printers that overheat. In this case, the UDP is "our printers overheat."

2. Move Forward in Time

To move forward in time, ask: “Why is this a problem?” For example, minor damage could be one result of the machines overheating. In more extreme cases, printers may catch fire. Don't be afraid to create multiple problem stories if you think of more than one UDP.

3. Move Backward in Time

To move backward in time, ask: “What caused this UDP?” If you can't identify the root problem, think about what typically causes the UDP to occur. For the overheating printers, overuse could be a cause.

Following the three-step framework above helps illustrate a clear problem story:

• The printer is overused.
• The printer overheats.
• The printer breaks down.

You can extend the problem story in either direction if you think of additional cause-and-effect relationships.

4. Break the Chains

By this point, you’ll have multiple UDP storylines. Take two that are similar and focus on breaking the chains connecting them. This can be accomplished through inversion or neutralization.

• Inversion: Inversion changes the relationship between two UDPs so the cause is the same but the effect is the opposite. For example, if the UDP is "the more X happens, the more likely Y is to happen," inversion changes the equation to "the more X happens, the less likely Y is to happen." Using the printer example, inversion would consider: "What if the more a printer is used, the less likely it’s going to overheat?" Innovation requires an open mind. Just because a solution initially seems unlikely doesn't mean it can't be pursued further or spark additional ideas.
• Neutralization: Neutralization completely eliminates the cause-and-effect relationship between X and Y. This changes the above equation to "the more or less X happens has no effect on Y." In the case of the printers, neutralization would rephrase the relationship to "the more or less a printer is used has no effect on whether it overheats."

Even if creating a problem story doesn't provide a solution, it can offer useful context to users’ problems and additional ideas to be explored. Given that divergence is one of the fundamental practices of creative problem-solving, it’s a good idea to incorporate it into each tool you use.

Brainstorming

Brainstorming is a tool that can be highly effective when guided by the iterative qualities of the design thinking process. It involves openly discussing and debating ideas and topics in a group setting. This facilitates idea generation and exploration as different team members consider the same concept from multiple perspectives.

Hosting brainstorming sessions can result in problems, such as groupthink or social loafing. To combat this, leverage a three-step brainstorming method involving divergence and convergence :

• Have each group member come up with as many ideas as possible and write them down to ensure the brainstorming session is productive.
• Continue the divergence of ideas by collectively sharing and exploring each idea as a group. The goal is to create a setting where new ideas are inspired by open discussion.
• Begin the convergence of ideas by narrowing them down to a few explorable options. There’s no "right number of ideas." Don't be afraid to consider exploring all of them, as long as you have the resources to do so.

Alternate Worlds

The alternate worlds tool is an empathetic approach to creative problem-solving. It encourages you to consider how someone in another world would approach your situation.

For example, if you’re concerned that the printers you produce overheat and catch fire, consider how a different industry would approach the problem. How would an automotive expert solve it? How would a firefighter?

Be creative as you consider and research alternate worlds. The purpose is not to nail down a solution right away but to continue the ideation process through diverging and exploring ideas.

Continue Developing Your Skills

Whether you’re an entrepreneur, marketer, or business leader, learning the ropes of design thinking can be an effective way to build your skills and foster creativity and innovation in any setting.

If you're ready to develop your design thinking and creative problem-solving skills, explore Design Thinking and Innovation , one of our online entrepreneurship and innovation courses. If you aren't sure which course is the right fit, download our free course flowchart to determine which best aligns with your goals.

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Creative problem solving and facial expressions: A stage based comparison

Mritunjay kumar.

1 Department of Design, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

Satyaki Roy

2 Department of Humanities and Social Sciences, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

Braj Bhushan

Ahmed sameer.

3 Department of Humanities and Social Sciences, Indian Institute of Technology (ISM) Dhanbad, Dhanbad, Jharkhand, India

Associated Data

All data files are available from the protocols.io database ( dx.doi.org/10.17504/protocols.io.4r3l2oq4jv1y/v1 ).

A wealth of research indicates that emotions play an instrumental role in creative problem-solving. However, most of these studies have relied primarily on diary studies and self-report scales when measuring emotions during the creative processes. There has been a need to capture in-the-moment emotional experiences of individuals during the creative process using an automated emotion recognition tool. The experiment in this study examined the process-related difference between the creative problem solving (CPS) and simple problem solving (SPS) processes using protocol analysis and Markov’s chains. Further, this experiment introduced a novel method for measuring in-the-moment emotional experiences of individuals during the CPS and SPS processes using facial expressions and machine learning algorithms. The experiment described in this study employed 64 participants to solve different tasks while wearing camera-mounted headgear. Using retrospective analysis, the participants verbally reported their thoughts using video-stimulated recall. Our results indicate differences in the cognitive efforts spent at different stages during the CPS and SPS processes. We also found that most of the creative stages were associated with ambivalent emotions whereas the stage of block was associated with negative emotions.

Introduction

The cognitive mechanisms underlying the creative process have been the focus of creativity research for decades [ 1 ]. Numerous sources of information, knowledge, skills, and emotions are utilized in different ways during the creative process [ 2 ]. Therefore, understanding how individuals take different approaches and learn to be creative becomes crucial to support their creative development.

Recent years have seen a rise in the number of studies in understanding the creative subprocesses and stages [ 3 – 5 ]. While most of the studies have focused on understanding the granularity of the creative process, it is essential to identify the creative act in its totality [ 6 ]. It is crucial that we evaluate the similarities and differences between the creative and non-creative acts to develop a holistic understanding [ 2 ]. Nevertheless, this area has received scant attention, and no direct study has empirically investigated the nature of the either processes [ 7 – 9 ].

It is no surprise that a significant part of the creative process is emotional [ 10 ], from the initial decision to try something new to the skills for maintaining enthusiasm and perseverance for creative endeavors [ 8 ]. Unfortunately, assessing emotions during the creative process remains a challenge due to methodological complexities. Methods such as experience sampling methods and diary studies have successfully captured the dynamics, but they do not account for the in-the-moment emotional experiences during the creative process. There is a strong need for a novel approach to capturing emotions in real-time during the creative process.

This article is exploratory in nature and examines the process-related differences between the CPS and SPS processes, as well as captures the in-the-moment emotional experience of the individuals during the CPS process. The specific questions addressed in this article include:

• RQ1: What stages of the CPS and SPS require the greatest amount of cognitive effort from the individuals?
• RQ2: What types of emotions are experienced during the various stages of the creative process?

Background of the study

The dynamics of the creative process.

The connotation of the word ‘dynamic’ supports the concept of the creative process not being linear [ 2 ] p. 295 explained the creative process as “a succession of thoughts and actions that leads to novel and adapted productions.” Research into the creative process has progressed rapidly since [ 11 ] four-stage model of creativity and several models have emerged over the past few years (for a detailed overview, see [ 2 ]) despite the absence of consensus on the definition of the stages. Cropley and Cropley [ 12 ] have mentioned seven stages—preparation, activation, generation, illumination, verification, communication, and validation. In Wallas’ four-stage model, Sadler-Smith [ 13 ] proposes a five-stage model, with an intimation phase added between incubation and illumination. Sawyer’s emergent model [ 14 ] examined six subprocesses (intuition, idea emergence, iteration, experimentation, and exploration). More recent work by [ 3 ] examined the concurrent sequence between the subprocess of generation, selection, exploration, evaluation, refinement, comparison, synthesis, and application. Other researchers have explored the stages of problem recognition, idea generation, idea evaluation, and solution validation in creative problem solving [ 15 ]. Furthermore, for an understanding of the creative act in its entirety, it is necessary to identify similarities and differences between the processes that result in creative outcomes and those that result in noncreative outcomes [ 2 , 6 ]. Lubart presented four hypotheses as possible explanations for the differences. Under the first approach, creativity and non-creativity can be viewed as separate constructs that lead to creative and non-creative outcomes. The second hypothesis doesn’t differentiate these two processes and considers a creative and noncreative process continuum. Third, depending on the quality of knowledge used, the same process can lead to highly creative, moderately creative, or non-creative outcomes. Lastly, these processes may entail the same stages and may also involve the same amount of time spent at each stage, with the only variation being the quality of execution at each stage. Mumford and colleagues also outlined four hypotheses regarding the differences between creative and regular problem-solving processes. First, creative problem solving (CPS) involves ill-defined problems, as opposed to regular problem-solving. Second, CPS allows higher degrees of divergent and convergent thinking wherein routine tasks permit applying previously known procedures and information to solve the problem. Third, the difference lies in the multiple cycles of divergent and convergent thinking involved in CPS instead of regular problem-solving. Finally, the CPS process involves reorganizing and restructuring the information, while the routine, non-creative process just recalls the information based on the existing knowledge. These differences, though, require further investigation as there is no empirical evidence that can be used as a comparison between CPS and the simple problem solving (SPS) processes.

Emotions and the creative process

Similarly, understanding the emotional processes that contribute to creativity becomes essential as they influence higher-level cognitive functions during the creative process, such as perception of the stimuli, judgment, decision making, and reasoning [ 16 , 17 ]. Phenomenology research into creativity and emotions shows a wide range of emotions occurring during the creative process across many domains. Several creative individuals, e.g., artists, musicians, scientists, designers, describe the feelings of mixed emotions such as joy, happiness, and pain during the long processes of working and reworking on the creative problem to realize the idea of the product or outcome [ 18 , 19 ]. Some long-standing views have examined the role of positive emotions in enhancing cognitive flexibility and creativity [ 8 ]. Others have explored the relationship between negative affects compared to neutral mood states in promoting creativity by helping individuals be more focused, critical, and determined in producing a creative outcome [ 20 , 21 ]. The third paradigm produced reliable evidence for ambivalent emotions association with creativity [ 22 ], where highly activated positive (excited) and negative (angry) states were linked to high creative engagement and deactivated positive (relaxed) and negative (discouraged) states were linked to lower creative engagement [ 23 ].

Notably, a few studies have also investigated the dynamics of emotions for different creative stages. Peilloux and Botella [ 24 ] for example, found that the creative stages of immersion, thinking, research, inspiration, and insight were associated with positive emotions and the stages of judgment, experimentation, and planning were associated with negative emotions. Most recently, Kumar et al. [ 25 ] examined the dynamics of emotions using the eight design subprocesses, where positive affects dominated the conceptual phase of the design process whereas negative affects dominated the embodiment phase of the design process.

Existing measures of the creative process

Protocol analysis . Protocol analysis has emerged as an important tool for behavior analysis during the dynamics of the creative process [ 3 , 5 , 26 , 27 ]. Concurrent verbal protocols or the think-aloud method involves participants verbalizing their thought processes while performing the task in real-time using their short-term memory [ 28 ]. In the retrospective protocol method, participants retrieve the information of the already completed task that has been stored in their memory. Both these methods have their own advantages and limitations. Gero and Tang [ 29 ] have emphasized that both these methodologies achieve similar results with respect to examining the process-oriented aspects of the creative design process.

Markov’s chain analysis . Recent years have seen a rise in utilizing the Markov’s chain analysis to study the creative subprocess sequence. A Markov Chain is a stochastic model for predicting, estimating, or guessing the result of an event based on the preceding state and its action [ 30 – 32 ]. Pringle and Sowden [ 5 ] compared shifts of the associative mode of thinking with the analytical mode of thinking during the processing of emotional input. Moreover, Kan and Gero [ 33 ] examined design protocols from the standpoint of the sequential order of the Function Behavior-Structure (FBS) processes using the first-order Markov chain for various design processes. Nevertheless, the Markov analysis can be performed on any coding scheme. Ergodic Markov chains have the property of achieving a single stationary state distribution as time progresses. Generally, it is represented as a row vector π whose entries are probabilities that add to 1, and given the transition matrix P, it satisfies

where π is referred to as the equilibrium distribution of a chain. Computing a stationary state distribution makes it easy to statistically compare different distribution probabilities.

Existing measures of emotions during the creative process

Most of the published research in measuring creativity and emotions together has relied primarily on diary studies and experience sampling methods [ 34 , 35 ]. Even though these methods offer greater ecological validity, they are costly, time consuming, and do not account for in-the-moment emotional experiences of the creators. A further step in this direction should include the use of various technological advances that enable the investigation of the physiological and behavioral measures of the creative process and emotions [ 36 ]. A recent study employed eight body postures to decode an individual’s emotions during the creative design process using Kinect and a machine learning classifier [ 37 ]. Several articles have also asserted that facial expressions can be used to study emotions during the creative process [ 17 , 38 ]. Maybe the concept of measuring facial Action Units (AUs) can be utilized to map the corresponding emotions that arise at different stages of the creative process.

Present study

The research gap.

Several articles have asserted that facial expressions can be leveraged to study emotions of the individuals during the creative process. To the best of our knowledge, no study has examined the dynamics of emotions during the CPS process using facial expression data.

Consequently, the two primary goals of this study are to examine the process related difference between the CPS and SPS processes and to capture in-the-moment emotional experiences of individuals using facial expressions during the CPS process.

Objective of this study

More specifically, the objectives of this study are -

• To compare the CPS and SPS processes using Protocol analysis and Markov chains.
• To classify different stages of CPS process based on automatic facial AU data combinations.

Participants

A total of 69 participants volunteered for the study, of which two opted out of the study, citing discomfort in wearing the headgear during the experiment, while the data of three participants were excluded due to the wrong orientation of the facial camera during the experiment. Thus, the study was carried out on a convenience sample of 64 adults randomly assigned to the CPS and SPS task groups. The CPS and SPS groups comprised 33 (17 men and 16 women) and 31 (18 men and 13 women) participants, respectively. The mean age of CPS group was 25.24 years (Men 25.59 years, SD 1.06; Women 24.88, SD 0.96), while the mean age of SPS group was 25.45 years (Men 25.78, SD 0.81; Women 25.00, SD 0.71). All the participants were postgraduate students ranging from several disciplines: mechanical engineering, electrical engineering, fine arts, design, civil engineering, psychology, literature, philosophy, and economics. The presence of glasses, beards, and mustaches were the exclusion criteria. The participants were recruited through an advertisement and were monetarily compensated. They were briefed about the study, and their participation was confirmed after they signed the informed consent form. The study protocol (IITK/IEC/2018-19/II/7) was approved by the Institute Ethics Committee of the Indian Institute of Technology Kanpur.

Two different sets of tasks, CPS and SPS, each comprising of three different activities, were used in this study. The CPS task consisted of three different tasks ( Fig 1 ), a drawing task [ 39 ], a writing task [ 40 ], and a structure making task [ 41 ]. The diversity of these creativity tasks allowed more room for creative engagement. They were chosen owing to the fact that working on a variety of creative tasks and switching between them enhances divergent and convergent thinking by reducing cognitive fixation [ 42 ]. The SPS task consisted of a drawing task, a writing task, and a structure-making task that were tailored to demand the same amount of effort as solving the creative tasks ( Fig 2 ). The difficulty levels of the two sets of tasks were assessed beforehand by eight judges who rated the creative components (1 = not at all creative, 5 = very creative) and difficulty levels (1 = very easy, 5 = very difficult) of the tasks on a 5-point Likert scale. The two tasks did not differ in terms of their difficulty levels (t = −0.323, df = 14, p = 0.751). In contrast, the CPS tasks were rated significantly higher on creativity as compared to the SPS tasks (t = 15.82, df = 14, p < .001).

Capturing the facial expressions

Participants’ facial expressions were captured using a custom-made head-worn camera. The aim was to ensure that the frontal faceview was always in view of the camera, regardless of the participant’s head movements during the task. An AKASO V50 pro sports camera was mounted on the headgear facing the participant at an approximate distance of 12.25 inches from their nose tip. The face was also illuminated by a strip of LED lights embedded in the headgear, as shown in Fig 3 . The participants’ facial expressions were recorded while solving the tasks for 30 minutes at the rate of 25 frames per second in 720p.

The left figure presents the 3/4th view of the face, while the right figure presents the frontal profile. In both instances, the face has been appropriately illuminated using LEDs.

A manipulation check was performed to ascertain that the headgear did not interfere with the task performance by administering the same to 18 randomly selected matched participants who were not part of the main study. Nine participants were required to wear the headgear, while the remaining nine did not and were asked to complete the alternative usage test [ 43 ] where they were asked to provide ‘five different usages of a brick’ within two minutes. The z-scores indicated no difference between those wearing the headgear and those who did not on fluency (z = −0.97, p = 0.39), flexibility (z = −0.225, p = 0.87) and originality scores (z = −0.94, p = 0.39).

The experiment was conducted in the one-way glass room of the psychology lab at IIT Kanpur. This room was equipped with three dome cameras to record all the activities of the participants. Besides observing the participant through the one-way glass, the experimenter could observe the session from the observation room by controlling the camera movements using a control panel. The three cameras captured the whole process providing a close-up view, a 3/4 profile, and a backside profile (see Fig 4 ). The one-way glass room had only a working table, and a chair in addition to a table meant for the raw materials. After establishing the rapport, the participants were provided with the informed consent form upon reaching the venue. Upon signing it, they were instructed to wear the headgear and continue the interaction with the experimenter. This was done in order to make them accustomed to the headgear. As soon as the participant approved of working for 30 minutes on the tasks while wearing the headgear, they were instructed to proceed to the one-way glass room. The instructions were read aloud to them, and thereafter the CPS or SPS set was provided to them. The participants could start with any task and switch between tasks or work simultaneously on different problems. At this point, the participants were instructed not to read the three tasks until they heard the 1st buzzer. Doing so ensured that we did not lose out on any possible observation of the spontaneous occurrence of stages just after reading the task/tasks. The participants began working on the tasks following the first buzzer, and their sessions were recorded. While the dome cameras recorded the entire session, the head-mounted camera made separate recordings of the facial expressions. The participants were instructed not to think aloud to avoid any manipulation of the facial AUs. Participants were also periodically reminded of the time passed every 10 minutes through the buzzer sound. The total time for completion of all three tasks was 30 minutes. The task activity was followed by a short break of five minutes. Following the short break, the participants were asked to describe their thinking process during the activity based on some questions and instructions. Using the video stimulated recall, participants watched the whole video recording of their activity, where they were asked to reflect retrospectively on the process, speak aloud, and report their experiences. They were free to pause the video and speak about the process. This session was audiotaped. The total time to administer the whole experimental procedure, including the retrospective recall, was approximately 110 minutes per participant.

(A) A close-up view of the output is captured by the CAM4, (B) a medium close-up 3/4th view is captured by the CAM2, (C) and a zoom-out view of the overall activity is captured by the CAM1, in the psychology lab.

Data analysis

Segmenting the protocols.

The video of facial expressions was synchronized with the timeline of the three dome camera recordings from the beginning to the end of the activity and the audio recordings with the video timelines. Adobe Premiere Pro CC was used to trim and synchronize videos and audios using the frame-matching features. Audio MP3 for each participant was mastered and boosted for better sound quality. This was followed by transcription of the verbal utterances on a word file. The transcribed verbal utterances were further divided into small units/segments/moves based on one main verb per parse [ 26 ] along with the timestamps in the datasheet for each participant. The start and end of a verbal utterance were timestamped based on the time interval of the recorded video that the participants were referring to. After parsing the verbal utterances, video recording observations that were not reported as verbs or actions during the retrospective verbal utterance process were separately segmented and were added to the same excel sheets with timestamps. They can be categorized as task-related actions (e.g., reading the task description, sketching, making the prototype, arranging the raw materials, etc.) and other actions (e.g., standing up, relaxing the body, etc.). The segmentation of actions based on video observation became more robust by capturing details that were obscured by verbal utterances alone.

Coding the segments

Following the review of the segmented protocols, seven categories, namely preparation, ideation, illumination, evaluation, verification, production, block, or other were developed as coding scheme with the attributes of the different modes of thinking based on the literature review. Each segment was assigned to one category only. Table 1 summarizes the coding scheme based on which a total of 18,355 segments were coded for all the 64 participants.

When dealing with a large number of categories, coding requires a high level of reliability. Accordingly, after the lead author completed this exercise, an independent coder who was blind to the experiment but was proficient in coding schemes and creativity research coded 1476 segments randomly chosen from the CPS and SPS groups. Cohen’s kappa was computed for these 1476 segments to determine intercoder reliability. We observed strong agreement in 82% of the segments (k = .816, p < .001) after correction for chance. In the event of disagreements between the coders, the coding categories were discussed between the two coders in order to categorize the protocols into different stages based upon different modes of thinking. Upon resolution of the discrepancy between the coders, the codes were adjusted to be consistent across all protocols for each participant.

Markov’s analysis to study the transition between different stages

Based on the coding for the segments in the excel sheet, we calculated the probability of transition from one state to the next for each of the eight categories, providing us with an 8 by 8 matrix for each participant. Using these probability matrices, a stationary state distribution matrix with one row and eight columns was created using R studio for each participant to represent the differences in cognitive effort exerted at different stages of the task solving process. The analysis of Markov’s stationary state distribution in this study was based on the work by Kan and Gero [ 33 ].

Transition state diagrams

The transition diagrams provide a visual representation of the transition of events with their probabilities. Based on the analysis of Jeong’s work [ 54 ], the transition state from one stage to another, for each participant, the sum of these frequencies was taken, and a relative frequency was computed for each transition. This provided us with a single transition matrix containing 64 probabilities of the transitions for the CPS and SPS groups, respectively.

In these transition diagrams, the creative stages are represented by the nodes that are linked to the other nodes using directional arrows. These directional arrows represent the relative frequency from one stage to another and the width of the arrow represents the strength of the transitional probability. Numbers in the transitional state diagrams represent the probability of one stage being followed by another stage.

Facial expression analysis

Automatic facial action coding system . Facial Action Coding System [ 55 ] is a rigorous and psychometrically robust system that utilizes facial muscle movements to represent them as action units (AUs) to describe facial activity. Emotion-specific AUs or the combination of AUs can be found in EMFACS [Ekman & Friesen, 1983 [Unpublished]]. Several researchers across different domains have used automatic facial expression recognition software to characterize a specific set of emotions [ 56 ].

This study leveraged the OpenFace 2.0 [ 57 ] program to extract the facial action units from the synchronized videos of all the sessions. Based on comparing the performance of automatic facial expression recognition tools, OpenFace has been shown to be superior to the high-paid commercial softwares, e.g., Nodlus FaceReader, and Affectiva in detecting automatic facials. AUs with better accuracies [ 58 ]. Another advantage of OpenFace is that it is available in the open-source domain ( https://github.com/TadasBaltrusaitis/OpenFace ). This program is trained on different facial datasets and uses a linear kernel support vector machine to recognize individual AUs. The system recognizes seventeen specific AUs (1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 20, 23, 25, 26, and 45). This study used only 12 AUs and their combinations that correspond to seven emotions. Details regarding the combination of AUs related to specific emotions used in this study can be found in [Ekman & Friesen, 1983 [Unpublished], 59 , 60 ].

For each participant, OpenFace generated a total of 45000 frames based on the video duration of 30 minutes. These 45000 frames (rows) contained continuous AU data from 0 to 5 (intensity of AUs) for 12 facial AUs, along with the timestamp and confidence level. In most cases, AUs is regressed with high levels of confidence, usually 98%. To increase internal validity, frames with confidence levels less than 95% were removed from the analysis. Fig 5 demonstrates the OpenFace graphical user interface while simultaneously collecting the variables in a spreadsheet.

Based on video and image outputs, this program detects and extracts participant facial features such as facial landmarks, head pose, eye gaze, and facial Aus.

Support vector machine to classify the creative stages based on facial AU combinations

The dataset generated by OpenFace required further processing before classification, as the facial AU datasets were highly imbalanced (typically a result of an unequal distribution of classes within a dataset) with some AUs with 0 values indicating no movement of specific muscles, while other AUs displayed continuous values (above 0), indicating movement intensity of the muscles at different time intervals.

As part of the feature set in this study, seven different combinations of AUs are used to represent seven different emotions to predict the target class, comprising of six stages. During the data analysis, the datasets of ‘illumination’ and ‘others’ stage were discarded as ‘illumination’ occurs within a fraction of a second [ 61 ] and is, therefore, inaccessible by retrospective protocol analysis and ‘others’ did not contain tasks-related information. Further, by eliminating these stages, the model’s performance improved by reducing the data imbalance issue. The final dataset for the CPS and SPS groups used for the SVM classification is presented in Table 2 .

Before the model generation in SVM, we used a random undersampling approach to balance our facial AUs dataset using python libraries. We leveraged the imblearn.undersampling_sampling package in python programming language to undersample the data and generate a new dataset. The RandomUnderSampler function results in random selections of examples from the majority class. The parameter ‘replacement’ is set to false to prevent the repetition of the same example that requires deletion from the training dataset. This is followed using the fit_resample function to under sample the dataset and thereafter, train_test_split function has been leveraged to split into the training and testing dataset. 80% of the data were split into training sets and 20% as test sets.

We used a polynomial kernel function with degree 4 using Python to classify the stages, as other kernels did not provide high accuracy in predicting the stages based on different combination of AUs. Beyond this degree, the model became saturated. Moreover, our SVM model utilized the C parameter to avoid misclassification of the training data. Decision function shape is used as an ovo (OnevsOne) classifier to serve as a binary classifier for all pairs of classes, as well as the training utilizes the fit function to obtain a fitting of the model to the input training instances while testing utilizes the predict function to make predictions about the testing instances.

Results and discussion

Comparing the distribution of stages during the sps and cps processes.

Since the computed stationary state distributions of the stages were not normally distributed, the Mann Whitney U test was conducted. The results of the statistical analyses are summarized in Tables ​ Tables3 3 and ​ and4 4 .

Statistical analysis revealed a significant difference in the distribution of stages during the CPS and SPS processes. Participants in the CPS group predominantly spent their cognitive efforts towards the stages of Ideation (z = −6.572, p < 0.05), Illumination (z = −2.696, p < 0.05), and Refinement (z = −3.437, p < 0.05).

Task A’s ideation phase involved participants generating pre-inventive structures from incomplete elements provided to them, which encouraged them to diverge and consider alternate solutions. For example, Participant 01 or P01 (CPS) commented, “This curvy shape looked like a girl and a vase” whereas, P02 (CPS) associated the shape with surrealism and reported, “ It looked like a Dali painting . ” The majority of participants for Task B expressed ideas based on the combination of the previously stored schema for having common festivals as interaction points between two countries that could facilitate cross-cultural exchanges. For example, P18 (CPS) reported, “we can have movies like Bajrangi Bhaijaan which will bring more unity among each other” while P12 (CPS) reported, “then I thought about the food as a medium to connect these two countries . ” During Task C, most participants were interested in creating a strong and stable foundation for the structure. For example, P28 (CPS) reported, “to fir I got the idea about making a strong base that will hold the tower , ” and P07 (CPS) reported, "I will have to make many legs where this long heightened thing can stand . ”

In the illumination stage, some of the participants credited the emergence of insight to random associations during idea generation that somehow emerged spontaneously as an appropriate solution. For example, P02 (CPS) during Task A reported, “you know when I saw this , I was thinking of many things , and immediately this idea suddenly came . ” Some participants attributed the spontaneous occurrence of insights to chance; for example, P30 (CPS) during Task A reported, “I mean what a coincidence that I bought a chair today and when I saw this it clicked here . ” During Task C, P06 (CPS) reported, “and then here the idea came to me just like that . ” It is worth noting that time constraints played a crucial role in some participants’ sudden appearance of insights. For example, P07 (CPS) reported during Task C, “and I am like very capable if the time is limited , like here where I came up with the leg spread concept . ”

A substantial number of enhancements were made during the refinement stage to produce a better output. Many participants returned to the previous tasks and refined their work as they worked on the current task. Since they were occupied with other tasks, they were more likely to incubate, evaluate their previous responses, and then make changes.

Participants in the SPS group spent majority of their cognitive efforts during the Preparation (z = −6.878, p < 0.05) and the Production stage (z = −5.914, p < 0.05). The preparation stage required a considerable amount of concentration and attention. Task A demanded considerable focus on the deliberation of mentally converting the shapes. For example, P06 (SPS) reported, “these shapes were simple but required a lot of attention as you need to think the reverse of it” while P12 (SPS) reported, “I became habituated with the drawings and the conversion thing . ” During the preparation stage of Task B, participants worked on gathering the information from the graphs, translating the data, and arriving at relevant conclusions. For example, P15 (SPS) reported “these graphs were simple but I was reading them again and again to find the connection” . Similarly, during Task C, P13 (SPS) reported, “so I read these instructions one by one and it required focus . ”

During the production stage, participants spent their cognitive efforts in creating or transforming their ideas or thoughts into a tangible solution. In task A, for example, participants focused most of their efforts on implementing their ideas through sketching on the grid answer sheet. We observed an average increase in participants’ drawing speed after the 6th to 7th element. As participants became familiar with the grid and the reference sheet, their drawing speed increased. During the task B production stage, participants spent their efforts in writing their conclusions after making sense of the data. During Task C, participants tried to develop the structure prototype based on the instructions provided. It is interesting to note that some participants spent considerable time unraveling the adhesive tape, while others cut multiple strands of tape and stuck them to the table for convenience and time savings.

Stage transition diagrams between different stages

As illustrated in Figs ​ Figs6 6 and ​ and7, 7 , the participants of CPS group expressed more thought sequence from the production to the ideation stage (57%) than the SPS group (1%). As participants developed the idea they produced them, and then immediately returned to the mode of ideation. By generating the outputs, the participants were able to generate a greater number of ideas, for instance, P03 in the CPS group stated— “I was doodling without much thought and started to visualize it when doodling” . This was followed by transition from preparation to preparation, which was 57% for the CPS group and 40% for the SPS group. Consequently, participants in the CPS group transitioned rapidly from the preparation-to-preparation stage to gather relevant information before moving on to the ideation stage. This is an important step as it is said that preparation requires 99% of perspiration which leads to 1% of inspiration [ 62 ]. For the CPS group, the transition from illumination to ideation was 65%, whereas there were no such instances in the SPS group. The reason for this is that the tasks in the SPS group did not require illumination activities, whereas in the CPS group, when the idea suddenly emerged, participants expected it to be developed further. For example, P06 in the CPS group reported— “the idea that came instantly was the biryani competition” (illumination) and then immediately reported— “This was funny idea and I thought to develop it further” (ideation).

Blo = Block; Ide = Ideation; Eva = Evaluation; Ref = Refinement; Prod = Production; Oth = Others; Illum = Illumination.

More events of transition from production to preparation (82% vs. 12%), preparation to the production (50% vs. 19%), others to preparation (49% vs. 33%), illumination to preparation (100% vs. 5%), refinement to preparation (50% vs. 22%) happened for the SPS group than the CPS group, respectively. As part of the SPS group, it was necessary to examine sample sheets (preparation) to produce appropriate outputs. The results indicate that the preparation stage was most likely to occur for the SPS and was at the center of most of the transitions. It is likely that this result is due to the nature of the tasks where the participants spent most of their time collecting information and setting up the problem.

SVM classification of stages based on facial AU combinations

For selecting the best separation between classes, the highest possible accuracy scores for each stage were considered. Among all the stages, only the target class block was classified with the highest accuracy scores (above 95%). The remaining stages were classified with moderate accuracy scores. The results are presented in Table 5 .

Preparation stage and associated emotions

The stage preparation observed moderate accuracy scores for the ambivalent emotions, happiness (67.65% and 64.73%), and fear (68.95% and 66.52%) during the CPS and SPS process respectively. Positive emotions at the beginning of the task led some of the participants to approach the problem with high levels of motivation. For example, P01 (CPS) during task A reported— “this question was very interesting and I was just staring at them to understand the question” and P15 (CPS) reported,— “I was totally excited so immediately I started counting the shapes . ” During task B, P21 (CPS) reported— “I like literature and writing and I can use my knowledge so this was really exciting , ” and P03 (CPS) reported— “this question was interesting as well as tough . ” During task C, P16 (CPS) reported— “this task was full of creativity and fun , ” and P17 (CPS) reported— “I thought why not first solve the 3rd task as it looks very interesting” . For some of the participants, preparation was accompanied by a sense of apprehension after appraisal of the task about failing to complete the task. For example, during task A, P27 (CPS) reported— “I was worried because I was losing on a lot of time in searching for the pen to write” ; P10 (CPS) reported— “I was a bit skeptical and stressed when I read this question . ” Nevertheless, some participants also experienced negative emotions when attempting to complete the tasks on time. For example, during task A, P06 (CPS) reported— “but these shapes were complex and I got scared to finish it on time . ” During task B, P13 (CPS) reported— “basically this was a political question and I realized that this task was going to take a lot of time . ” During task C, P21 (CPS) reported— “I was also thinking about how much time I have left for this task . Participants in the SPS reported negative feelings in response to the focus and attention demanded by the tasks. For example, P15 (SPS) during task A reported— “this one demanded focus and attention and I had to be careful in converting the shapes” ; P31 (SPS) during task B reported— “so I was also calculating the values in my head after looking at these graphs , that’s why it was taking time for me to see and then write the answers” ; P17 (SPS) during the task C reported— “this kind of task is like want you to be very attentive because you will have to measure everything and make them . ” Alternatively, the other perspective could be perhaps due to the nature of the tasks in the SPS group that were not exploratory and did not demand high divergent thinking abilities. For example, P06 (SPS) reported during task A— “there was nothing much to think about this task” ; P31 (SPS) reported— “actually this was a complicated problem due to the shapes and you can’t come up with ideas here . ”

Ideation stage and associated emotions

The ideation stage also involved ambivalent emotions. These observations are in accordance with previous findings [ 8 ]. Moderate accuracy scores were observed for happiness (73.81% and 70.19%) for the CPS and SPS groups, respectively. For fear, accuracy scores were significantly higher (76.05%) for the SPS group than for the CPS group (56.90%). Perhaps the gap between ideation and implementation of those ideas during the SPS process seemed farfetched due to the nature of the tasks that resulted in such a difference. Some of the participants dedicated their cognitive efforts on making richer associations with positive emotions, thereby facilitating flexibility. For example, P27 (CPS) during task A reported— “so at first glance I thought that it was something like Picasso’s work and I was like very happy that I have direction now” ; P02 (CPS) during task A reported— “it looked like a Dali painting somehow from which I got inspired . ” During task B, P15 (CPS) reported— “I felt really happy that I came up with this idea of one nation one flag . ” While during task C, P06 (CPS) reported— “I was thinking of making something similar to the Eiffel tower . ” Perhaps, for some of the participants, not being able to transform pre-inventive structures into a concrete mental representation or idea caused fear during ideation. For example, during task B, P33 (CPS) reported— “I was scared if I will be able to complete this vague idea of UN ka intervention with Pakistan . ” In the SPS group, negative affects encouraged participants to develop self-efficacy necessary to reach the right solution, resulting in positive affect. The participant initially struggled with converting a few shapes in Task A, but later came up with an idea for transforming the shapes. For example, P02 (SPS) stated— “I was not sure here but then I came across a rough idea of converting these arcs into semi triangle that made me feel lighter” .

Block stage and associated emotions

Block is the only stage that observed very high accuracy scores for negative emotions—anger (96.04% and 97.26%), fear (94.67% and 97.26%), sadness (95.01% and 95.95%), and disgust (96.90% and 95.57%) respectively for the CPS and SPS groups. This makes sense as there are several factors that can be attributed to an impasse, for example, creative fixation, fear of failure, or lack of faith in oneself [ 63 ]. For example, during the task A, P1 (CPS) reported— “this was a sad moment because I couldn’t think of anything at this moment” ; P2 (CPS) reported— “I was not able to come up with an idea right away which pissed me off” ; P11 (CPS) reported— “It was very irritating that why I am not able to move forward” . During the task C, P19 (CPS) reported— “this paper was frustrating me because there was nothing I can do with this” . It is interesting to note, despite this, that some of the participants learned to accept these negative emotions, helping them to persist and complete their tasks, while others switched to an alternative activity.

Several participants in the SPS group were stuck on task A’s shapes, specifically the third element with two semi-circular shapes and the eleventh element with a star shape. For example, participant P01 (SPS) during task A reported— “at this point I was confused and irritated about converting this circle or not” ; P02 (SPS) reported— “then at this figure the confusion happened and I was fearing that I could make this one wrong” ; P30 (SPS) reported— “this was not triangle not circle both which was frustrating to decode” . In addition, some participants had difficulty rolling an 81-cm paper in Task C and encountered an impasse. For example, P04 (SPS) reported— “First I was not satisfied and I did not understand what I have to do to make it 81 cms” ; P31 (SPS) reported— “even after trying so hard it was not 81 cms which was irritating”; P22 (SPS) reported— “I was like there was no option to make the length bigger which was very sad” . Participants in both the groups either abandoned the tasks immediately and switched to the other task or worked on the task for some time.

Evaluation stage and associated emotions

The evaluation stage observed moderate accuracy scores for ambivalent emotions, happiness (72.25% and 74.09%), pride (64.63% and 68.08%), fear (60.74% and 67.83%) and stress (59.72% and 68.58%) respectively for the CPS and SPS groups. This state, which requires focused attention to determine whether the idea is weak, is an emotionally taxing process. For example, P19 (CPS) during task C reported— “but then this was not standing which gave me a lot of stress . ” P05 (CPS), during task A reported— “but something was not very right here and it was very disappointing . ” Similarly, in the SPS group, P01 (SPS) during task A reported— “in between I was checking whether I am doing it correctly as I was little worried” ; P11 (SPS) reported— “I was having a lot of problem and stress in drawing this circle on this line” . For task C, most of the participants utilized their cognitive efforts on measuring the length of the rolled sheet. For example, P13 (SPS) reported— “mine was coming a bit long which was mind boggling” ; P27 (SPS) reported— “when I realized this one to be 10 cms short I was like shit … ”

It is interesting to note that some participants appraised their results positively which perhaps resulted in experiencing happiness. For example, P03 (CPS), during task A, reported— “I just looked at this thing and it came out to be nice . ” P22 (CPS) during task C, reported— “here , when it stood na , I was very happy” ; P06 (CPS) reported— “here I was happy that this structure could stand” . Our findings are consistent with previous research that showed designers experience high levels of arousal and positive emotions during the verification stage [ 64 ].

Refinement stage and associated emotions

For the state of refinement, the CPS and SPS groups scored higher accuracy scores for ambivalent emotions—happiness (85.81% and 83.49%), pride (81.58% and 82.13%), and stress (77.19% and 81.19%) respectively. During this stage, additional layers of work, refinements, and finishing touches were added. Some participants were successful in resolving the issues and thus experienced happiness. For example, P01 (CPS) during task A reported— “I felt extremely happy when I made more flames to provide a completeness in this photograph . ” Nonetheless, there were some participants who struggled to resolve the issues, which resulted in additional time and stress. For example, P14 (CPS) during task C reported— “even after spreading these legs , my idea did not work that tensed me . ” P19 (SPS), during task A, reported— “I shaded the rest of the shapes to be free from tension”; P18 (SPS) reported— “I was having problem in redrawing this circle again and again . ”

Production stage and associated emotions

This stage observed moderate accuracy scores for ambivalent emotions—happiness (74.35% and 74.09%), fear (60.55% and 65.71%), pride (70.29% and 71.31%), and stress (60.71% and 71.83%) for both the CPS and SPS groups respectively. At this stage, all the tasks required the participants to produce tangible outcomes to implement an idea they had already conceptualized. Perhaps, those who were successful in transforming their ideas into successful outcomes felt happiness and pride. For example, during task A, P17 (CPS) reported— “once I had this face idea , I was successful in making it here . ” During task C, P02 (CPS) reported— “I was enjoying making firm base out of this newspaper sheet . ” During task A, participant P18 (SPS) reported— “I kept drawing like this because I was enjoying it . ” During task C, P22 (SPS) reported— “I folded the sheet into exactly three parts and woaahhhh !! ” . Perhaps, some of the participants may have realized that they could not produce a quality outcome and, as a result, might have experienced fear and stress. The realization of this ongoing process of production differs from the process of evaluation where the produced outcome or an idea is verified. It becomes difficult to capture the evaluation of the ongoing production process during the retrospective think-aloud protocol.

General discussion and conclusion

For a more comprehensive understanding of creativity, it is necessary to capture creativity act in its totality and the underlying emotional mechanisms. This work presents a novel method of capturing in-the-moment emotional experiences of the individuals during the creative process using the facial expressions data and a headgear setup. Previous studies have leveraged self-report scales and diary studies in examining the emotion influences on the creative work. This study is first to capture the emotional states associated with each stage during the CPS process using real-time facial action unit combinations and support vector machine classification. Moreover, this work also compared the CPS and SPS processes using the protocol analysis and Markov chains.

In order to answer the first research question (RQ1—what stages of the CPS and SPS require the greatest amount of effort from the individuals?), protocol analysis and Markov’s analysis results revealed that the participants in the CPS group spent most of their cognitive effort during the stages of ‘ideation,’ ‘illumination,’ and ‘refinement,’ whereas the participants in the SPS group spent most of their cognitive effort during preparation and production. Perhaps, this difference is attributable the tasks assigned, as the tasks in the CPS group offered more opportunities for divergent thinking. In the ‘ideation’ process, participants spent most of their time deriving pre-inventive structures, although some participants were more successful than others in generating pre-inventive structures quickly and solving the tasks on time. Different components, such as task motivation, and skillsets impacted the quality of output produced for a specific task as not all tasks were attended with equal efforts. The same is true for the other stages. For example, most participants showed more interest in solving tasks A and C than task B for the CPS group. Although all these tasks encouraged divergent thinking, drawing and structure-making tasks were considered more playful and enjoyable than the written tasks. Task A and Task B were given priority over task C for the SPS group. This requirement arose from the requirement for greater focus and attention in reading the instructions and then developing the prototype. Furthermore, all the tasks in the SPS group involved high attentional demands, which meant some participants were able to focus more and produce better results. As a result of this study, one of the approaches proposed by [ 2 ] to differentiate between the CPS and SPS is supported: these two processes entail the same stages, but the variation is in the level of execution at each stage. These results provide the first empirical evidence in understanding the difference between the CPS and SPS process at a micro-level and lend support to studies suggesting that the creative process must allow higher degrees of divergent and convergent thinking [ 6 ]. There were also differences in transition state diagrams between the CPS and SPS processes. The CPS group experienced more transitions from ‘preparation’ → ‘preparation,’ ‘illumination’ → ‘ideation’ and ‘production’ → ‘ideation.’ The SPS group, on the other hand, experienced more transitions from ‘production’ → ‘preparation,’ ‘refinement’ → ‘preparation,’ ‘illumination’ → ‘preparation,’ ‘others’ → ‘preparation’ and from ‘preparation’ → ‘production.’

To address the second research question (RQ2—What kinds of emotions occur at different stages of the creative process?), the exploratory part of this study investigated the in-the-moment experience of emotions during the CPS processes by fulfilling the research gap posited in the literature regarding the use of automatic facial expression analysis in researching the role of emotions in creativity. Six stages were classified using SVM based on seven facial expressions (or facial AU combinations). Although ambivalent emotions were majorly present during most of the creative stages, considering the results, specific emotions were found to be present for different stages where the negative and positive emotions have a predominance presence. Happiness and pride were found to be predominant during the stages of evaluation, refinement, and production whereas negative emotions were predominant during the stage of block. These results are unique and corroborate with the previous findings. During the evaluation and refinement process, one may see a silver lining that induces happiness whereas, during a creative block, the idea becomes fixated, and the emotions become predominantly negative.

The findings of this study are consistent with theories relating to the role of ambivalent emotions in creative work [ 18 , 22 , 25 ]. The appraisal of the tasks and the appraisal of one’s performance during the completion of the tasks caused most participants to experience both positive and negative emotions at different time intervals. Furthermore, the time pressure to finish the three tasks on time allowed some participants to stay motivated and experience ambivalent emotions to be more creative, but for others, this time pressure acted as a guillotine to their creative performance. On the contrary, the stage of ‘block’ was highly dominated by negative emotions, and high accuracy scores were observed for anger, fear, sadness, and disgust. This makes sense as the previous research has indicated that a diverse set of negative thoughts is encountered during the resolution of this state, e.g., loss of focus, fear of failure, worry, depression, etc [ 53 ]. Interestingly, we did not observe much difference in the emotional profiles for each stage between the CPS and SPS processes, even though CPS and SPS have different distributions of stages.

Limitations of the study

Unlike any other study, this study has its limitations. We did not postulate a dichotomy between the CPS and SPS processes nor examine the different categories of stages between the two processes. Segmenting and coding different categories of stages for the CPS and SPS processes might pose a construct validity issue since the literature fails to distinguish stage-based differences for the CPS and SPS processes. In addition, conducting a statistical test becomes difficult based on the difference in categories of stages between the CPS and SPS stages. Another limitation of the study is its lack of ecological validity. The creative process in the real physical world is influenced and mediated by many factors, and thus, the laboratory-based study may yield a different result from those in the natural situation [ 65 ]. As of now, the prospect of recording and analyzing moment-to-moment facial expressions in a real-life situation seems distant at this moment. Additionally, this research was constrained by the retrospective protocol analysis, which lowered the accuracy of the in-the-moment CPS and SPS process analyses. Nevertheless, concurrent verbal utterances could have introduced bias in the data due to redundant facial muscles movements. Furthermore, the small sample size didn’t permit us to account for the differences between the creative performances of the high and low scorers.

Implications of this research

Despite these limitations, this research can have significant practical implementations in creativity research. Novel methods and tools could be developed to train individuals to recognize and regulate their emotions during the creative process to create a deeper understanding of what emotions to expect at each stage and implement effective strategies to manage those emotions. The process of ideation could, for instance, be enhanced by inducing positive emotions and allowing flexibility. Similarly, creative individuals can also be trained to regulate their negative emotions during the creative block to stay motivated and find ways to solve the problem. Few studies have developed personalized gaming experiences based on different emotions. E.g., the game alters its difficulty level by sensing negative emotions in gamers through recognizing facial expressions in real-time to help them stay motivated throughout the gameplay [ 66 , 67 ]. Consequently, the results of this study may serve as a baseline for developing creative support systems for various professionals. For instance, an individual could receive personalized visual cues or affective cues in real-time based on the changes in their facial expression captured by their personal device to enhance their creative performance. This claim may be considered immature at this point due to the limitations of current technological advances, as well as laws and policies about the monitoring of sensitive facial information. Furthermore, designing such affective solutions must demonstrate excellent reliability and validity to be generalized.

Future work

There is a need for further research regarding creativity and facial expressions. Replication of our study with a larger sample size among various ethnic groups may demonstrate the similarities and differences with our findings. Future work could include longitudinal studies that capture facial expressions at different stages of the creative process to clarify the relationship between creativity and emotions. Moreover, the benefits of such studies could also improve ecological validity by determining the causal connection. A further step could be to triangulate this study by utilizing other technological interventions, e.g., EEG and biofeedback sensors. In addition, different automatic facial expression recognition algorithms could be used to cross-validate the accuracy scores obtained in this study.

Supporting information

Acknowledgments.

The authors would like to thank Mr. Gregory Boldt (University of Calgary, Canada) who offered his feedback on the methodology and analysis on a video conference call. We thank Mrs. Shweta Mittal (Excel expert) and Mrs. Swati Mittal (Design Researcher, Microsoft) for providing tips on Excel for extracting the data efficiently.

Funding Statement

The author(s) received no specific funding for this work.

Data Availability

HYPOTHESIS AND THEORY article

Intelligence and creativity in problem solving: the importance of test features in cognition research.

• Center for Cognitive Science, Cognitive and Developmental Psychology Unit, University of Kaiserslautern, Kaiserslautern, Germany

This paper discusses the importance of three features of psychometric tests for cognition research: construct definition, problem space, and knowledge domain. Definition of constructs, e.g., intelligence or creativity, forms the theoretical basis for test construction. Problem space, being well or ill-defined, is determined by the cognitive abilities considered to belong to the constructs, e.g., convergent thinking to intelligence, divergent thinking to creativity. Knowledge domain and the possibilities it offers cognition are reflected in test results. We argue that (a) comparing results of tests with different problem spaces is more informative when cognition operates in both tests on an identical knowledge domain, and (b) intertwining of abilities related to both constructs can only be expected in tests developed to instigate such a process. Test features should guarantee that abilities can contribute to self-generated and goal-directed processes bringing forth solutions that are both new and applicable. We propose and discuss a test example that was developed to address these issues.

Much of cognition research is based on psychometric tests, such as tests assessing the constructs of intelligence or creativity. We argue that three test features must be explicitly considered, however, in order to reliably infer from individual’s test performance the underlying cognitive processes. These three test features are: definition of the construct, problem space , and knowledge domain .

The definition of the construct a test is to measure is most important in test construction and application, because cognitive processes reflect the possibilities a task offers. For instance, a test constructed to assess intelligence will operationalize the definition of this construct, being, in short, finding the correct answer. Also, the definition of a construct becomes important when selecting tests for the confirmation of a specific hypothesis. One can only find confirmation for a hypothesis if the chosen task instigates the necessary cognitive operations. For instance, in trying to confirm the assumed intertwining of certain cognitive abilities (e.g., convergent thinking and divergent thinking), tasks should be applied that have shown to yield the necessary cognitive process.

The second test feature, problem space , determines the degrees of freedom cognition has to its disposal in solving a problem. For instance, cognition will go through a wider search path when problem constraints are less well defined and, consequently, data will differ accordingly.

The third test feature, knowledge domain , is important when comparing results from two different tests. When tests differ in problem space, it is not advisable they should differ in knowledge domain. For instance, when studying the differences in cognitive abilities between tests constructed to asses convergent thinking (mostly defined problem space) and divergent thinking (mostly ill-defined problem space), in general test practice, both tests also differ in knowledge domain. Hence, data will reflect cognition operating not only in different problem spaces, but also operating on different knowledge domains, which makes the interpretation of results ambiguous.

The proposed approach for test development and test application holds the promise of, firstly, studying cognitive abilities in different problem spaces while operating on an identical knowledge domain. Although cognitions’ operations have been studied extensively and superbly in both contexts separately, they have rarely been studied in test situations where one or the other test feature is controlled for. The proposed approach also presents a unique method for studying thinking processes in which cognitive abilities intertwine. On the basis of defined abilities, tasks can be developed that have a higher probability of yielding the hypothesized results.

The construct of intelligence is defined as the ability to produce the single best (or correct) answer to a clearly defined question, such as a proof to a theorem ( Simon, 1973 ). It may also be seen as a domain-general ability ( g -factor; Spearman, 1904 ; Cattell, 1967 ) that has much in common with meta cognitive functions, such as metacognitive knowledge, metacognitive monitoring, and metacognitive control ( Saraç et al., 2014 ).

The construct of creativity, in contrast, is defined as the ability to innovate and move beyond what is already known ( Wertheimer, 1945/1968 ; Ghiselin, 1952/1985 ; Vernon, 1970 ). In other words, it emphasizes the aspect of innovation. This involves the ability to consider things from an uncommon perspective, transcend the old order ( Ghiselin, 1952/1985 ; Chi, 1997 ; Ward, 2007 ), and explore loosely associated ideas ( Guilford, 1950 ; Mednick, 1962 ; Koestler, 1964 ; Gentner, 1983 ; Boden, 1990 ; Christensen, 2007 ). Creativity could also be defined as the ability to generate a solution to problems with ill-defined problem spaces ( Wertheimer, 1945/1968 ; Getzels and Csikszentmihalyi, 1976 ). In this sense it involves the ability to identify problematic aspects of a given situation ( Ghiselin, 1952/1985 ) and, in a wider sense, the ability to define completely new problems ( Getzels, 1975 , 1987 ).

Guilford (1956) introduced the constructs of convergent thinking and divergent thinking abilities. Both thinking abilities are important because they allow us insights in human problem solving. On the basis of their definitions convergent and divergent thinking help us to structurally study human cognitive operations in different situations and over different developmental stages. Convergent thinking is defined as the ability to apply conventional and logical search, recognition, and decision-making strategies to stored information in order to produce an already known answer ( Cropley, 2006 ). Divergent thinking, by contrast, is defined as the ability to produce new approaches and original ideas by forming unexpected combinations from available information and by applying such abilities as semantic flexibility, and fluency of association, ideation, and transformation ( Guilford, 1959 , as cited in Cropley, 2006 , p. 1). Divergent thinking brings forth answers that may never have existed before and are often novel, unusual, or surprising ( Cropley, 2006 ).

Guilford (1967) introduced convergent and divergent thinking as part of a set of five operations that apply in his Structure of Intellect model (SOI model) on six products and four kinds of content, to produce 120 different factors of cognitive abilities. With the SOI model Guilford wanted to give the construct of intelligence a comprehensive model. He wanted the model to include all aspects of intelligence, many of which had been seriously neglected in traditional intelligence testing because of a persistent adherence to the belief in Spearman’s g ( Guilford, 1967 , p. vii). Hence, Guilford envisaged cognition to embrace, among other abilities, both convergent and divergent thinking abilities. After these new constructs were introduced and defined, tests for convergent and divergent thinking emerged. Despite the fact that Guilford reported significant loadings of tests for divergent production on tests constructed to measure convergent production ( Guilford, 1967 , p. 155), over the years, both modes of thinking were considered as separate identities where convergent thinking tests associated with intelligence and divergent thinking tests with creativity ( Cropley, 2006 ; Shye and Yuhas, 2004 ). Even intelligence tests that assess aspects of intelligence that supposedly reflect creative abilities do not actually measure creativity ( Kaufman, 2015 ).

The idea that both convergent and divergent thinking are important for solving problems, and that intelligence helps in the creative process, is not really new. In literature we find models of the creative process that define certain stages to convergent and divergent thinking; the stages of purposeful preparation at the start and those of critical verification at the end of the process, respectively ( Wallas, 1926 ; Webb Young , 1939/2003 ). In this view, divergent thinking enables the generation of new ideas whereas the exploratory activities of convergent thinking enable the conversion of ideas into something new and appropriate ( Cropley and Cropley, 2008 ).

We argue that studying the abilities of divergent and convergent thinking in isolation does not suffice to give us complete insight of all possible aspects of human problem solving, its constituent abilities and the structure of its processes. Processes that in a sequence of thoughts and actions lead to novel and adaptive productions ( Lubart, 2001 ) are more demanding of cognition for understanding the situation at hand and planning a path to a possible solution, than abilities involved in less complex situations ( Jaušovec, 1999 ). Processes that yield self-generated and goal-directed thought are the most complex cognitive processes that can be studied ( Beaty et al., 2016 ). Creative cognition literature is moving toward the view that especially in those processes that yield original and appropriate solutions within a specific context, convergent and divergent abilities intertwine ( Cropley, 2006 ; Ward, 2007 ; Gabora, 2010 ).

The approach of intertwining cognitive abilities is also developed within cognitive neuroscience by focusing on the intertwining of brain networks ( Beaty et al., 2016 ). In this approach divergent thinking relates to the default brain network. This network operates in defocused or associative mode of thought yielding spontaneous and self-generated cognition ( Beaty et al., 2015 ). Convergent thinking relates to the executive control network operating in focused or analytic modes of thought, yielding updating, shifting, and inhibition ( Benedek et al., 2014 ). Defocused attention theory ( Mendelssohn, 1976 ) states that less creative individuals operate with a more focused attention than do creative individuals. This theory argues that e.g., attending to two things at the same time, might result in one analogy, while attending to four things might yield six analogies ( Martindale, 1999 ).

In the process of shifting back and forth along the spectrum between associative and analytic modes of thinking, the fruits of associative thought become ingredients for analytic thought processes, and vice versa ( Gabora, 2010 ). In this process, mental imagery is involved as one sensory aspect of the human ability to gather and process information ( Jung and Haier, 2013 ). Mental imagery is fed by scenes in the environment that provide crucial visual clues for creative problem solving and actuates the need for sketching ( Verstijnen et al., 2001 ).

Creative problem solving processes often involve an interactive relationship between imagining, sketching, and evaluating the result of the sketch ( van Leeuwen et al., 1999 ). This interactive process evolves within a type of imagery called “visual reasoning” where forms and shapes are manipulated in order to specify the configurations and properties of the design entities ( Goldschmidt, 2013 ). The originality of inventions is predicted by the application of visualization, whereas their practicality is predicted by the vividness of imagery ( Palmiero et al., 2015 ). Imaginative thought processes emerge from our conceptual knowledge of the world that is represented in our semantic memory system. In constrained divergent thinking, the neural correlates of this semantic memory system partially overlap with those of the creative cognition system ( Abraham and Bubic, 2015 ).

Studies of convergent and divergent thinking abilities have yielded innumerable valuable insights on the cognitive and neurological aspects involved, e.g., reaction times, strategies, brain areas involved, mental representations, and short and long time memory components. Studies on the relationship between both constructs suggest that it is unlikely that individuals employ similar cognitive strategies when solving more convergent than more divergent thinking tasks ( Jaušovec, 2000 ). However, to arrive at a quality formulation the creative process cannot do without the application of both, convergent and divergent thinking abilities (e.g., Kaufmann, 2003 ; Runco, 2003 ; Sternberg, 2005 ; Dietrich, 2007 ; Cropley and Cropley, 2008 ; Silvia et al., 2013 ; Jung, 2014 ).

When it is our aim to study the networks addressed by the intertwining of convergent and divergent thinking processes that are considered to operate when new, original, and yet appropriate solutions are generated, then traditional thinking tests like intelligence tests and creativity tests are not appropriate; they yield processes related to the definition of one or the other type of construct.

Creative Reasoning Task

According to the new insights gained in cognition research, we need tasks that are developed with the aim to instigate precisely the kind of thinking processes we are looking for. Tasks should also provide a method of scoring independently the contribution of convergent and divergent thinking. As one possible solution for such tasks we present the Creative Reasoning Task (CRT; Jaarsveld, 2007 ; Jaarsveld et al., 2010 , 2012 , 2013 ).

The CRT presents participants with an empty 3 × 3 matrix and asks them to fill it out, as original and complex as possible, by creating components and the relationships that connect them. The created matrix can, in principle, be solved by another person. The creation of components is entirely free, as is the generation of the relationships that connects them into a completed pattern. Created matrices are scored with two sub scores; Relations , which scores the logical complexity of a matrix and is, therefore, considered a measure for convergent thinking, and Components and Specifications , which scores the originality, fluency, and flexibility and, therefore, is considered an indication for divergent thinking (for a more detailed description of the score method, see Appendix 1 in Supplementary Material).

Psychometric studies with the CRT showed, firstly, that convergent and divergent thinking abilities apply within this task and can be assessed independently. The CRT sub score Relations correlated with the Standard Progressive Matrices test (SPM) and the CRT sub score Components and Specifications correlated with a standard creativity test (TCT–DP, Test of Creative Thinking–Drawing Production; Urban and Jellen, 1995 ; Jaarsveld et al., 2010 , 2012 , 2013 ). Studies further showed that, although a correlation was observed for the intelligence and creativity test scores, no correlation was observed between the CRT sub scores relating to intelligent and creative performances ( Jaarsveld et al., 2012 , 2013 ; for further details about the CRT’s objectivity, validity, and reliability, see Appendix 2 in Supplementary Material).

Reasoning in creative thinking can be defined as the involvement of executive/convergent abilities in the inhibition of ideas and the updating of information ( Benedek et al., 2014 ). Jung (2014) describes a dichotomy for cognitive abilities with at one end the dedicated system that relies on explicit and conscious knowledge and at the other end the improvisational system that relies more upon implicit or unconscious knowledge systems. The link between explicit and implicit systems can actually be traced back to Kris’ psychoanalytic approach to creativity dating from the 1950s. The implicit system refers to Kris’ primary process of adaptive regression, where unmodulated thoughts intrude into consciousness; the explicit system refers to the secondary process, where the reworking and transformation of primary process material takes place through reality-oriented and ego-controlled thinking ( Sternberg and Lubart, 1999 ). The interaction between explicit and implicit systems can be seen to form the basis of creative reasoning, i.e., the cognitive ability to solve problems in an effective and adaptive way. This interaction evolved as a cognitive mechanism when human survival depended on finding effective solutions to both common and novel problem situations ( Gabora and Kaufman, 2010 ). Creative reasoning solves that minority of problems that are unforeseen and yet of high adaptability ( Jung, 2014 ).

Hence, common tests are insufficient when it comes to solving problems that are unforeseen and yet of high adaptability, because they present problems that are either unforeseen and measure certain abilities contained in the construct of creativity or they address adaptability and measure certain abilities contained in the construct of intelligence. The CRT presents participants with a problem that they could not have foreseen; the form is blank and offers no stimuli. All tests, even creativity tests, present participants with some kind of stimuli. The CRT addresses adaptability; to invent from scratch a coherent structure that can be solved by another person, like creating a crossword puzzle. Problems, that are unforeseen and of high adaptability, are solved by the application of abilities from both constructs.

Neuroscience of Creative Cognition

Studies in neuroscience showed that cognition operating in ill-defined problem space not only applies divergent thinking but also benefits from additional convergent operations ( Gabora, 2010 ; Jung, 2014 ). Understanding creative cognition may be advanced when we study the flow of information among brain areas ( Jung et al., 2010 ).

In a cognitive neuroscience study with the CRT we focused on the cognitive process evolving within this task. Participants performed the CRT while EEG alpha activity was registered. EEG alpha synchronization in frontal areas is understood as an indication of top-down control ( Cooper et al., 2003 ). When observed in frontal areas, for divergent and convergent thinking tasks, it may not reflect a brain state that is specific for creative cognition but could be attributed to the high processing demands typically involved in creative thinking ( Benedek et al., 2011 ). Top-down control, relates to volitionally focusing attention to task demands ( Buschman and Miller, 2007 ). That this control plays a role in tasks with an ill-defined problem space showed when electroencephalography (EEG) alpha synchronization was stronger for individuals engaged in creative ideation tasks compared to an intelligence related tasks ( Fink et al., 2007 , 2009 ; Fink and Benedek, 2014 ). This activation was also found for the CRT; task related alpha synchronization showed that convergent thinking was integrated in the divergent thinking processes. Analyzes of the stages in the CRT process showed that this alpha synchronization was especially visible at the start of the creative process at prefrontal and frontal sites when information processing was most demanding, i.e., due to multiplicity of ideas, and it was visible at the end of the process, due to narrowing down of alternatives ( Jaarsveld et al., 2015 ).

A functional magnetic resonance imaging (fMRI) study ( Beaty et al., 2015 ) with a creativity task in which cognition had to meet specific constraints, showed the networks involved. The default mode network which drives toward abstraction and metaphorical thinking and the executive control network driving toward certainty ( Jung, 2014 ). Control involves not only maintenance of patterns of activity that represent goals and the means to achieve those ( Miller and Cohen, 2001 ), but also their voluntary suppression when no longer needed, as well as the flexible shift between different goals and mental sets ( Abraham and Windmann, 2007 ). Attention can be focused volitionally by top-down signals derived from task demands and automatically by bottom-up signals from salient stimuli ( Buschman and Miller, 2007 ). Intertwining between top-down and bottom-up attention processes in creative cognition ensures a broadening of attention in free associative thinking ( Abraham and Windmann, 2007 ).

These studies support and enhance the findings of creative cognition research in showing that the generation of original and applicable ideas involves an intertwining between different abilities, networks, and attention processes.

Problem Space

A problem space is an abstract representation, in the mind of the problem solver, of the encountered problem and of the asked for solution ( Simon and Newell, 1971 ; Simon, 1973 ; Hayes and Flowers, 1986 ; Kulkarni and Simon, 1988 ; Runco, 2007 ). The space that comes with a certain problem can, according to the constraints that are formulated for the solution, be labeled well-defined or ill-defined ( Simon and Newell, 1971 ). Consequently, the original problems are labeled closed and open problems, respectively ( Jaušovec, 2000 ).

A problem space contains all possible states that are accessible to the problem solver from the initial state , through iterative application of transformation rules , to the goal state ( Newell and Simon, 1972 ; Anderson, 1983 ). The initial state presents the problem solver with a task description that defines which requirements a solution has to answer. The goal state represents the solution. The proposed solution is a product of the application of transformation rules (algorithms and heuristics) on a series of successive intermediate solutions. The proposed solution is also a product of the iterative evaluations of preceding solutions and decisions based upon these evaluations ( Boden, 1990 ; Gabora, 2002 ; Jaarsveld and van Leeuwen, 2005 ; Goldschmidt, 2014 ). Whether all possible states need to be passed through depends on the problem space being well or ill-defined and this, in turn, depends on the character of the task descriptions.

When task descriptions clearly state which requirements a solution has to answer then the inferences made will show little idiosyncratic aspects and will adhere to the task constraints. As a result, fewer options for alternative paths are open to the problem solver and search for a solution evolves in a well-defined space. Vice versa, when task or problem descriptions are fuzzy and under specified, the problem solver’s inferences are more idiosyncratic; the resulting process will evolve within an ill-defined space and will contain more generative-evaluative cycles in which new goals are set, and the cycle is repeated ( Dennett, 1978 , as cited in Gabora, 2002 , p. 126).

Tasks that evolve in defined problem space are, e.g., traditional intelligence tests (e.g., Wechsler Adult Intelligence Scale, WAIS; and SPM, Raven, 1938/1998 ). The above tests consist of different types of questions, each testing a different component of intelligence. They are used in test practice to assess reasoning abilities in diverse domains, such as, abstract, logical, spatial, verbal, numerical, and mathematical domains. These tests have clearly stated task descriptions and each item has one and only one correct solution that has to be generated from memory or chosen from a set of alternatives, like in multiple choice formats. Tests can be constructed to assess crystallized or fluid intelligence. Crystallized intelligence represents abilities acquired through learning, practice, and exposure to education, while fluid intelligence represents a more basic capacity that is valuable to reasoning and problem solving in contexts not necessarily related to school education ( Carroll, 1982 ).

Tasks that evolve in ill-defined problem space are, e.g., standard creativity tests. These types of test ask for a multitude of ideas to be generated in association with a given item or situation (e.g., “think of as many titles for this story”). Therefore, they are also labeled as divergent thinking test. Although they assess originality, fluency, flexibility of responses, and elaboration, they are not constructed, however, to score appropriateness or applicability. Divergent thinking tests assess one limited aspect of what makes an individual creative. Creativity depends also on variables like affect and intuition; therefore, divergent thinking can only be considered an indication of an individual’s creative potential ( Runco, 2008 ). More precisely, divergent thinking explains just under half of the variance in adult creative potential, which is more than three times that of the contribution of intelligence ( Plucker, 1999 , p. 103). Creative achievement , by contrast, is commonly assessed by means of self-reports such as biographical questionnaires in which participants indicate their achievement across various domains (e.g., literature, music, or theater).

Studies with the CRT showed that problem space differently affects processing of and comprehension of relationships between components. Problem space did not affect the ability to process complex information. This ability showed equal performance in well and ill-defined problem spaces ( Jaarsveld et al., 2012 , 2013 ). However, problem space did affect the comprehension of relationships, which showed in the different frequencies of relationships solved and created ( Jaarsveld et al., 2010 , 2012 ). Problem space also affected the neurological activity as displayed when individuals solve open or closed problems ( Jaušovec, 2000 ).

Problem space further affected trends over grade levels of primary school children for relationships solved in well-defined and applied in ill-defined problem space. Only one of the 12 relationships defined in the CRT, namely Combination, showed an increase with grade for both types of problem spaces ( Jaarsveld et al., 2013 ). In the same study, cognitive development in the CRT showed in the shifts of preference for a certain relationship. These shifts seem to correspond to Piaget’s developmental stages ( Piaget et al., 1977 ; Siegler, 1998 ) which are in evidence in the CRT, but not in the SPM ( Jaarsveld et al., 2013 ).

Design Problems

A sub category of problems with an ill-defined problem space are represented by design problems. In contrast to divergent thinking tasks that ask for the generation of a multitude of ideas, in design tasks interim ideas are nurtured and incrementally developed until they are appropriate for the task. Ideas are rarely discarded and replaced with new ideas ( Goel and Pirolli, 1992 ). The CRT could be considered a design problem because it yields (a) one possible solution and (b) an iterative thinking process that involves the realization of a vague initial idea. In the CRT a created matrix, which is a closed problem, is created within an ill-defined problem space. Design problems can be found, e.g., in engineering, industrial design, advertising, software design, and architecture ( Sakar and Chakrabarti, 2013 ), however, they can also be found in the arts, e.g., poetry, sculpting, and dance geography.

These complex problems are partly determined by unalterable needs, requirements and intentions but the major part of the design problem is undetermined ( Dorst, 2004 ). This author points out that besides containing an original and a functional value, these types of problems contain an aesthetic value. He further states that the interpretation of the design problem and the creation and selection of possible suitable solutions can only be decided during the design process on the basis of proposals made by the designer.

In design problems the generation stage may be considered a divergent thinking process. However, not in the sense that it moves in multiple directions or generates multiple possibilities as in a divergent thinking tests, but in the sense that it unrolls by considering an initially vague idea from different perspectives until it comes into focus and requires further processing to become viable. These processes can be characterized by a set of invariant features ( Goel and Pirolli, 1992 ), e.g., structuring. iteration , and coherence .

Structuring of the initial situation is required in design processes before solving can commence. The problem contains little structured and clear information about its initial state and about the requirements of its solution. Therefore, design problems allow or even require re-interpretation of transformation rules; for instance, rearranging the location of furniture in a room according to a set of desirable outcomes. Here one uncovers implicit requirements that introduce a set of new transformations and/or eliminate existing ones ( Barsalou, 1992 ; Goel and Pirolli, 1992 ) or, when conflicting requirements arise, one creates alternatives and/or introduces new trade-offs between the conflicting constraints ( Yamamoto et al., 2000 ; Dorst, 2011 ).

A second aspect of design processes is their iterative character. After structuring and planning a vague idea emerges, which is the result of the merging of memory items. A vague idea is a cognitive structure that, halfway the creative process is still ill defined and, therefore, can be said to exist in a state of potentiality ( Gabora and Saab, 2011 ). Design processes unroll in an iterative way by the inspection and adjustment of the generated ideas ( Goldschmidt, 2014 ). New meanings are created and realized while the creative mind imposes its own order and meaning on the sensory data and through creative production furthers its own understanding of the world ( Arnheim, 1962/1974 , as cited in Grube and Davis, 1988 , pp. 263–264).

A third aspect of design processes is coherence. Coherence theories characterize coherence in, for instance, philosophical problems and psychological processes, in terms of maximal satisfaction of multiple constraints and compute coherence by using, a.o., connectionist algorithms ( Thagard and Verbeurgt, 1998 ). Another measure of coherence is characterized as continuity in design processes. This measure was developed for a design task ( Jaarsveld and van Leeuwen, 2005 ) and calculated by the occurrence of a given pair of objects in a sketch, expressed as a percentage of all the sketches of a series. In a series of sketches participants designed a logo for a new soft drink. Design series strong in coherence also received a high score for their final design, as assessed by professionals in various domains. Indicating that participants with a high score for the creative quality of their final sketch seemed better in assessing their design activity in relation to the continuity in the process and, thereby, seemed better in navigating the ill-defined space of a design problem ( Jaarsveld and van Leeuwen, 2005 ). In design problems the quality of cognitive production depends, in part, on the abilities to reflect on one’s own creative behavior ( Boden, 1996 ) and to monitor how far along in the process one is in solving it ( Gabora, 2002 ). Hence, design problems are especially suited to study more complex problem solving processes.

Knowledge Domain

Knowledge domain represents disciplines or fields of study organized by general principles, e.g., domains of various arts and sciences. It contains accumulated knowledge that can be divided in diverse content domains, and the relevant algorithms and heuristics. We also speak of knowledge domains when referring to, e.g., visuo-spatial and verbal domains. This latter differentiation may refer to the method by which performance in a certain knowledge domain is assessed, e.g., a visuo-spatial physics task that assesses the content domain of the workings of mass and weights of objects.

In comparing tests results, we should keep in mind that apart from reflecting cognitive processes evolving in different problem spaces, the results also arise from cognition operating on different knowledge domains. We argue that, the still contradictory and inconclusive discussion about the relationship between intelligence and creativity ( Silvia, 2008 ), should involve the issue of knowledge domain.

Intelligence tests contain items that pertain to, e.g., verbal, abstract, mechanical and spatial reasoning abilities, while their content mostly operates on knowledge domains that are related to contents contained in school curricula. Items of creativity tests, by contrast, pertain to more idiosyncratic knowledge domains, their contents relating to associations between stored personal experiences ( Karmiloff-Smith, 1992 ). The influence of knowledge domain on the relationships between different test scores was already mentioned by Guilford (1956 , p. 169). This author expected a higher correlation between scores from a typical intelligence test and a divergent thinking test than between scores from two divergent thinking tests because the former pair operated on identical information and the latter pair on different information.

Studies with the CRT showed that when knowledge domain is controlled for, the development of intelligence operating in ill-defined problem space does not compare to that of traditional intelligence but develops more similarly to the development of creativity ( Welter et al., in press ).

Relationship Intelligence and Creativity

The Threshold theory ( Guilford, 1967 ) predicts a relationship between intelligence and creativity up to approximately an intelligence quotient (IQ) level of 120 but not beyond ( Lubart, 2003 ; Runco, 2007 ). Threshold theory was corroborated when creative potential was found to be related to intelligence up to certain IQ levels; however, the theory was refuted, when focusing on achievement in creative domains; it showed that creative achievement benefited from higher intelligence even at fairly high levels of intellectual ability ( Jauk et al., 2013 ).

Distinguishing between subtypes of general intelligence known as fluent and crystallized intelligence ( Cattell, 1967 ), Sligh et al. (2005) observed an inverse threshold effect with fluid IQ: a correlation with creativity test scores in the high IQ group but not in the average IQ group. Also creative achievement showed to be affected by fluid intelligence ( Beaty et al., 2014 ). Intelligence, defined as fluid IQ, verbal fluency, and strategic abilities, showed a higher correlation with creativity scores ( Silvia, 2008 ) than when defined as crystallized intelligence. Creativity tests, which involved convergent thinking (e.g., Remote Association Test; Mednick, 1962 ) showed higher correlations with intelligence than ones that involved only divergent thinking (e.g., the Alternate Uses Test; Guilford et al., 1978 ).

That the Remote Association test also involves convergent thinking follows from the instructions; one is asked, when presented with a stimulus word (e.g., table) to produce the first word one thinks of (e.g., chair). The word pair table–chair is a common association, more remote is the pair table–plate, and quite remote is table–shark. According to Mednick’s theory (a) all cognitive work is done essentially by combining or associating ideas and (b) individuals with more commonplace associations have an advantage in well-defined problem spaces, because the class of relevant associations is already implicit in the statement of the problem ( Eysenck, 2003 ).

To circumvent the problem of tests differing in knowledge domain, one can develop out of one task a more divergent and a more convergent thinking task by asking, on the one hand, for the generation of original responses, and by asking, on the other hand, for more common responses ( Jauk et al., 2012 ). By changing the instruction of a task, from convergent to divergent, one changes the constraints the solution has to answer and, thereby, one changes for cognition its freedom of operation ( Razumnikova et al., 2009 ; Limb, 2010 ; Jauk et al., 2012 ). However, asking for more common responses is still a divergent thinking task because it instigates a generative and ideational process.

Indeed, studying the relationship between intelligence and creativity with knowledge domain controlled for yielded different results as defined in the Threshold theory. A study in which knowledge domain was controlled for showed, firstly, that intelligence is no predictor for the development of creativity ( Welter et al., 2016 ). Secondly, that the relationship between scores of intelligence and creativity tests as defined under the Threshold theory was only observed in a small subset of primary school children, namely, female children in Grade 4 ( Welter et al., 2016 ). We state that relating results of operations yielded by cognitive abilities performing in defined and in ill-defined problem spaces can only be informative when it is ensured that cognitive processes also operate on an identical knowledge domain.

Intertwining of Cognitive Abilities

Eysenck (2003) observed that there is little justification for considering the constructs of divergent and convergent thinking in categorical terms in which one construct excludes the other. In processes that yield original and appropriate solutions convergent and divergent thinking both operate on the same large knowledge base and the underlying cognitive processes are not entirely dissimilar ( Eysenck, 2003 , p. 110–111).

Divergent thinking is especially effective when it is coupled with convergent thinking ( Runco, 2003 ; Gabora and Ranjan, 2013 ). A design problem study ( Jaarsveld and van Leeuwen, 2005 ) showed that divergent production was active throughout the design, as new meanings are continuously added to the evolving structure ( Akin, 1986 ), and that convergent production was increasingly important toward the end of the process, as earlier productions are wrapped up and integrated in the final design. These findings are in line with the assumptions of Wertheimer (1945/1968) who stated that thinking within ill-defined problem space is characterized by two points of focus; one is to work on the parts, the other to make the central idea clearer.

Parallel to the discussion about the intertwining of convergent and divergent thinking abilities in processes that evolve in ill-defined problem space we find the discussion about how intelligence may facilitate creative thought. This showed when top-down cognitive control advanced divergent processing in the generation of original ideas and a certain measure of cognitive inhibition advanced the fluency of idea generation ( Nusbaum and Silvia, 2011 ). Fluid intelligence and broad retrieval considered as intelligence factors in a structural equation study contributed both to the production of creative ideas in a metaphor generation task ( Beaty and Silvia, 2013 ). The notion that creative thought involves top-down, executive processes showed in a latent variable analysis where inhibition primarily promoted the fluency of ideas, and intelligence promoted their originality ( Benedek et al., 2012 ).

Definitions of the Constructs Intelligence and Creativity

The various definitions of the constructs of intelligence and creativity show a problematic overlap. This overlap stems from the enormous endeavor to unanimously agree on valid descriptions for each construct. Spearman (1927) , after having attended many symposia that aimed at defining intelligence, stated that “in truth, ‘intelligence’ has become a mere vocal sound, a word with so many meanings that finally it has none” (p. 14).

Intelligence is expressed in terms of adaptive, goal-directed behavior; and the subset of such behavior that is labeled “intelligent” seems to be determined in large part by cultural or societal norms ( Sternberg and Salter, 1982 ). The development of the IQ measure is discussed by Carroll (1982) : “Binet (around 1905) realized that intelligent behavior or mental ability can be ranged along a scale. Not much later, Stern (around 1912) noticed that, as chronological age increased, variation in mental age changes proportionally. He developed the IQ ratio, whose standard deviation would be approximately constant over chronological age if mental age was divided by chronological age. With the development of multiple-factor-analyses (Thurstone, around 1935) it could be shown that intelligence is not a simple unitary trait because at least seven somewhat independent factors of mental ability were identified.”

Creativity is defined as a combined manifestation of novelty and usefulness ( Jung et al., 2010 ). Although it is identified with divergent thinking, and performance on divergent thinking tasks predicts, e.g., quantity of creative achievements ( Torrance, 1988 , as cited in Beaty et al., 2014 ) and quality of creative performance ( Beaty et al., 2013 ), it cannot be identified uniquely with divergent thinking.

Divergent thinking often leads to highly original ideas that are honed to appropriate ideas by evaluative processes of critical thinking, and valuative and appreciative considerations ( Runco, 2008 ). Divergent thinking tests should be more considered as estimates of creative problem solving potential rather than of actual creativity ( Runco, 1991 ). Divergent thinking is not specific enough to help us understand what, exactly, are the mental processes—or the cognitive abilities—that yield creative thoughts ( Dietrich, 2007 ).

Although current definitions of intelligence and creativity try to determine for each separate construct a unique set of cognitive abilities, analyses show that definitions vary in the degree to which each includes abilities that are generally considered to belong to the other construct ( Runco, 2003 ; Jaarsveld et al., 2012 ). Abilities considered belonging to the construct of intelligence such as hypothesis testing, inhibition of alternative responses, and creating mental images of new actions or plans are also considered to be involved in creative thinking ( Fuster, 1997 , as cited in Colom et al., 2009 , p. 215). The ability, for instance, to evaluate , which is considered to belong to the construct of intelligence and assesses the match between a proposed solution and task constraints, has long been considered to play a role in creative processes that goes beyond the mere generation of a series of ideas as in creativity tasks ( Wallas, 1926 , as cited in Gabora, 2002 , p. 1; Boden, 1990 ).

The Geneplore model ( Finke et al., 1992 ) explicitly models this idea; after stages in which objects are merely generated, follow phases in which an object’s utility is explored and estimated. The generation phase brings forth pre inventive objects, imaginary objects that are generated without any constraints in mind. In exploration, these objects are evaluated for their possible functionalities. In anticipating the functional characteristics of generated ideas, convergent thinking is needed to apprehend the situation, make evaluations ( Kozbelt, 2008 ), and consider the consequences of a chosen solution ( Goel and Pirolli, 1992 ). Convergent reasoning in creativity tasks invokes criteria of functionality and appropriateness ( Halpern, 2003 ; Kaufmann, 2003 ), goal directedness and adaptive behavior ( Sternberg, 1982 ), as well as the abilities of planning and attention. Convergent thinking stages may even require divergent thinking sub processes to identify restrictions on proposed new ideas and suggest requisite revision strategies ( Mumford et al., 2007 ). Hence, evaluation, which is considered to belong to the construct of intelligence, is also functional in creative processes.

In contrast, the ability of flexibility , which is considered to belong to the construct of creativity and denotes an openness of mind that ensures the generation of ideas from different domains, showed, as a factor component for latent divergent thinking, a relationship with intelligence ( Silvia, 2008 ). Flexibility was also found to play an important role in intelligent behavior where it enables us to do novel things smartly in new situations ( Colunga and Smith, 2008 ). These authors studied children’s generalizations of novel nouns and concluded that if we are to understand human intelligence, we must understand the processes that make inventiveness. They propose to include the construct of flexibility within that of intelligence. Therefore, definitions of the constructs we are to measure affect test construction and the resulting data. However, an overlap between definitions, as discussed, yields a test diversity that makes it impossible to interpret the different findings across studies with any confidence ( Arden et al., 2010 ). Also Kim (2005) concluded that because of differences in tests and administration methods, the observed correlation between intelligence and creativity was negligible. As the various definitions of the constructs of intelligence and creativity show problematic overlap, we propose to circumvent the discussion about which cognitive abilities are assessed by which construct, and to consider both constructs as being involved in one design process. This approach allows us to study the contribution to this process of the various defined abilities, without one construct excluding the other.

Reasoning Abilities

The CRT is a psychometrical tool constructed on the basis of an alternative construct of human cognitive functioning that considers creative reasoning as a thinking process understood as the cooperation between cognitive abilities related to intelligent and creative thinking.

In generating relationships for a matrix, reasoning and more specifically the ability of rule invention is applied. The ability of rule invention could be considered as an extension of the sequence of abilities of rule learning, rule inference, and rule application, implying that creativity is an extension of intelligence ( Shye and Goldzweig, 1999 ). According to this model, we could expect different results between a task assessing abilities of rule learning and rule inference, and a task assessing abilities of rule application. In two studies rule learning and rule inference was assessed with the RPM and rule application was assessed with the CRT. Results showed that from Grades 1 to 4, the frequencies of relationships applied did not correlate with those solved ( Jaarsveld et al., 2010 , 2012 ). Results showed that performance in the CRT allows an insight of cognitive abilities operating on relationships among components that differs from the insight based on performance within the same knowledge domain in a matrix solving task. Hence, reasoning abilities lead to different performances when applied in solving closed as to open problems.

We assume that reasoning abilities are more clearly reflected when one formulates a matrix from scratch; in the process of thinking and drawing one has, so to speak, to solve one’s own matrix. In doing so one explains to oneself the relationship(s) realized so far and what one would like to attain. Drawing is thinking aloud a problem and aids the designer’s thinking processes in providing some “talk-back” ( Cross and Clayburn Cross, 1996 ). Explanatory activity enhances learning through increased depth of processing ( Siegler, 2005 ). Analyzing explanations of examples given with physics problems showed that they clarify and specify the conditions and consequences of actions, and that they explicate tacit knowledge; thereby enhancing and completing an individual’s understanding of principles relevant to the task ( Chi and VanLehn, 1991 ). Constraint of the CRT is that the matrix, in principle, can be solved by another person. Therefore, in a kind of inner explanatory discussion, the designer makes observations of progress, and uses evaluations and decisions to answer this constraint. Because of this, open problems where certain constraints have to be met, constitute a powerful mechanism for promoting understanding and conceptual advancement ( Chi and VanLehn, 1991 ; Mestre, 2002 ; Siegler, 2005 ).

Convergent and divergent thinking processes have been studied with a variety of intelligence and creativity tests, respectively. Relationships between performances on these tests have been demonstrated and a large number of research questions have been addressed. However, the fact that intelligence and creativity tests vary in the definition of their construct, in their problem space, and in their knowledge domain, poses methodological problems regarding the validity of comparisons of test results. When we want to focus on one cognitive process, e.g., intelligent thinking, and on its different performances in well or ill-defined problem situations, we need pairs of tasks that are constructed along identical definitions of the construct to be assessed, that differ, however, in the description of their constraints but are identical regarding their knowledge domain.

One such possible pair, the Progressive Matrices Test and the CRT was suggested here. The CRT was developed on the basis of creative reasoning , a construct that assumes the intertwining of intelligent and creativity related abilities when looking for original and applicable solutions. Matched with the Matrices test, results indicated that, besides similarities, intelligent thinking also yielded considerable differences for both problem spaces. Hence, with knowledge domain controlled, and only differences in problem space remaining, comparison of data yielded new results on intelligence’s operations. Data gathered from intelligence and creativity tests, whether they are performance scores or physiological measurements on the basis of, e.g., EEG, and fMRI methods, are reflections of cognitive processes performing on a certain test that was constructed on the basis of a certain definition of the construct it was meant to measure. Data are also reflections of the processes evolving within a certain problem space and of cognitive abilities operating on a certain knowledge domain.

Data can unhide brain networks that are involved in the performance of certain tasks, e.g., traditional intelligence and creativity tests, but data will always be related to the characteristics of the task. The characteristics of the task, such as problem space and knowledge domain originated at the construction of the task, and the construction, on its turn, is affected by the definition of the construct the task is meant to measure.

Here we present the CRT as one possible solution for the described problems in cognition research. However, for research on relationships among test scores other pairs of tests are imaginable, e.g., pairs of tasks operating on the same domain where one task has a defined problem space and the other one an ill-defined space. It is conceivable that pairs of test could operate, besides on the domain of mathematics, on content of e.g., visuo-spatial, verbal, and musical domains. Pairs of test have been constructed by changing the instruction of a task; instructions instigated a more convergent or a more a divergent mode of response ( Razumnikova et al., 2009 ; Limb, 2010 ; Jauk et al., 2012 ; Beaty et al., 2013 ).

The CRT involves the creation of components and their relationships for a 3 × 3 matrix. Hence, matrices created in the CRT are original in the sense that they all bear individual markers and they are applicable in the sense, that they can, in principle, be solved by another person. We showed that the CRT instigates a real design process; creators’ cognitive abilities are wrapped up in a process that should produce a closed problem within an ill-defined problem space.

For research on the relationship among convergent and divergent thinking, we need pairs of test that differ in the problem spaces related to each test but are identical in the knowledge domain on which cognition operates. The test pair of RPM and CRT provides such a pair. For research on the intertwining of convergent and divergent thinking, we need tasks that measure more than tests assessing each construct alone. We need tasks that are developed on the definition of intertwining cognitive abilities; the CRT is one such test.

Hence, we hope to have sufficiently discussed and demonstrated the importance of the three test features, construct definition, problem space, and knowledge domain, for research questions in creative cognition research.

Author Contributions

All authors listed, have made substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpsyg.2017.00134/full#supplementary-material

Abraham, A., and Bubic, A. (2015). Semantic memory as the root of imagination. Front. Psychol. 6:325. doi: 10.3389/fpsyg.2015.00325

PubMed Abstract | CrossRef Full Text | Google Scholar

Abraham, A., and Windmann, S. (2007). Creative cognition: the diverse operations and the prospect of applying a cognitive neuroscience perspective. Methods 42, 38–48. doi: 10.1016/j.ymeth.2006.12.007

Akin, O. (1986). Psychology of Architectural Design London: Pion.

Google Scholar

Anderson, J. R. (1983). The Architecture of Cognition Cambridge, MA: Harvard University Press.

Arden, R., Chavez, R. S., Grazioplene, R., and Jung, R. E. (2010). Neuroimaging creativity: a psychometric view. Behav. Brain Res. 214, 143–156. doi: 10.1016/j.bbr.2010.05.015

Arnheim, R. (1962/1974). Picasso’s Guernica Berkeley: University of California Press.

Barsalou, L. W. (1992). Cognitive Psychology: An Overview for Cognitive Scientists Hillsdale, NJ: LEA.

Beaty, R. E., Benedek, M., Silvia, P. J., and Schacter, D. L. (2016). Creative cognition and brain network dynamics. Trends Cogn. Sci. 20, 87–95. doi: 10.1016/j.tics.2015.10.004

Beaty, R. E., Kaufman, S. B., Benedek, M., Jung, R. E., Kenett, Y. N., Jauk, E., et al. (2015). Personality and complex brain networks: the role of openness to experience in default network efficiency. Hum. Brain Mapp. 37, 773–777. doi: 10.1002/hbm.23065

Beaty, R. E., Nusbaum, E. C., and Silvia, P. J. (2014). Does insight problem solving predict real-world creativity? Psychol. Aesthet. Creat. Arts 8, 287–292. doi: 10.1037/a0035727

CrossRef Full Text | Google Scholar

Beaty, R. E., and Silvia, R. E. (2013). Metaphorically speaking: cognitive abilities and the production of figurative language. Mem. Cognit. 41, 255–267. doi: 10.3758/s13421-012-0258-5

Beaty, R. E., Smeekens, B. A., Silvia, P. J., Hodges, D. A., and Kane, M. J. (2013). A first look at the role of domain-general cognitive and creative abilities in jazz improvisation. Psychomusicology 23, 262–268. doi: 10.1037/a0034968

Benedek, M., Bergner, S., Konen, T., Fink, A., and Neubauer, A. C. (2011). EEG alpha synchronization is related to top-down processing in convergent and divergent thinking. Neuropsychologia 49, 3505–3511. doi: 10.1016/j.neuropsychologia.2011.09.004

Benedek, M., Franz, F., Heene, M., and Neubauer, A. C. (2012). Differential effects of cognitive inhibition and intelligence on creativity. Pers. Individ. Dif. 53, 480–485. doi: 10.1016/j.paid.2012.04.014

Benedek, M., Jauk, E., Sommer, M., Arendasy, M., and Neubauer, A. C. (2014). Intelligence, creativity, and cognitive control: the common and differential involvement of executive functions in intelligence and creativity. Intelligence 46, 73–83. doi: 10.1016/j.intell.2014.05.007

Boden, M. A. (1990). The Creative Mind: Myths and Mechanisms London: Abacus.

Boden, M. A. (1996). Artificial Intelligence New York, NY: Academic.

Buschman, T. J., and Miller, E. K. (2007). Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science 315, 1860–1862. doi: 10.1126/science.1138071

Carroll, J. B. (1982). “The measurement of Intelligence,” in Handbook of Human Intelligence , ed. R. J. Sternberg (New York, NY: Cambridge University Press), 29–120.

Cattell, R. B. (1967). The theory of fluid and crystallized general intelligence checked at the 5-6 year-old level. Br. J. Educ. Psychol. 37, 209–224. doi: 10.1111/j.2044-8279.1967.tb01930.x

Chi, M. T. H. (1997). “Creativity: Shifting across ontological categories flexibly,” in Creative Thought: An Investigation of Conceptual Structures and Processes , eds T. Ward, S. Smith, and J. Vaid (Washington, DC: American Psychological Association), 209–234.

Chi, M. T. H., and VanLehn, K. A. (1991). The content of physics self-explanations. J. Learn. Sci. 1, 69–105. doi: 10.1207/s15327809jls0101_4

Christensen, B. T. (2007). The relationship of analogical distance to analogical function and preinventive structure: the case of engineering design. Mem. Cogn. 35, 29–38. doi: 10.3758/BF03195939

Colom, R., Haier, R. J., Head, K., Álvarez-Linera, J., Quiroga, M. A., Shih, P. C., et al. (2009). Gray matter correlates of fluid, crystallized, and spatial intelligence: testing the P-FIT model. Intelligence 37, 124–135. doi: 10.1016/j.intell.2008.07.007

Colunga, E., and Smith, L. B. (2008). Flexibility and variability: essential to human cognition and the study of human cognition. New Ideas Psychol. 26, 158–192. doi: 10.1016/j.newideapsych.2007.07.012

Cooper, N. R., Croft, R. J., Dominey, S. J. J., Burgess, A. P., and Gruzelier, J. H. (2003). Paradox lost? Exploring the role of alpha oscillations during externally vs. internally directed attention and the implications for idling and inhibition hypotheses. Int. J. Psychophysiol. 47, 65–74. doi: 10.1016/S0167-8760(02)00107-1

Cropley, A. (2006). In praise of convergent thinking. Creat. Res. J. 18, 391–404. doi: 10.1207/s15326934crj1803_13

Cropley, A., and Cropley, D. (2008). Resolving the paradoxes of creativity: an extended phase model. Camb. J. Educ. 38, 355–373. doi: 10.1080/03057640802286871

Cross, N., and Clayburn Cross, A. (1996). Winning by design: the methods of Gordon Murray, racing car designer. Des. Stud. 17, 91–107. doi: 10.1016/0142-694X(95)00027-O

Dennett, D. (1978). Brainstorms: Philosophical Essays on Mind and Psychology Montgomery, VT: Bradford Books.

Dietrich, A. (2007). Who’s afraid of a cognitive neuroscience of creativity? Methods 42, 22–27. doi: 10.1016/j.ymeth.2006.12.009

Dorst, K. (2004). The problem of design problems: Problem solving and design expertise. J. Design Res. 4. doi: 10.1504/JDR.2004.009841

Dorst, K. (2011). The core of ‘design thinking’ and its application. Des. Stud. 32, 521–532. doi: 10.1016/j.destud.2011.07.006

Eysenck, H. J. (2003). “Creativity, personality and the convergent-divergent continuum,” in Critical Creative Processes , ed. M. A. Runco (Cresskill, NJ: Hampton Press), 95–114.

Fink, A., and Benedek, M. (2014). EEG alpha power and creative ideation. Neurosci. Biobehav. Rev. 44, 111–123. doi: 10.1016/j.neubiorev.2012.12.002

Fink, A., Benedek, M., Grabner, R. H., Staudt, B., and Neubauer, A. C. (2007). Creativity meets neuroscience: experimental tasks for the neuroscientific study of creative thinking. Methods 42, 68–76. doi: 10.1016/j.ymeth.2006.12.001

Fink, A., Grabner, R. H., Benedek, M., Reishofer, G., Hauswirth, V., Fally, M., et al. (2009). The creative brain: investigation of brain activity during creative problem solving by means of EEG and FMRI. Hum. Brain Mapp. 30, 734–748. doi: 10.1002/hbm.20538

Finke, R. A., Ward, T. B., and Smith, S. M. (1992). Creative Cognition: Theory, Research, and Applications Cambridge, MA: MIT Press.

Fuster, J. M. (1997). Network memory. Trends Neurosci. 20, 451–459. doi: 10.1016/S0166-2236(97)01128-4

Gabora, L. (2002). “Cognitive mechanisms underlying the creative process,” in Proceedings of the Fourth International Conference on Creativity and Cognition , eds T. Hewett and T. Kavanagh (Loughborough: Loughborough University), 126–133.

Gabora, L. (2010). Revenge of the ‘neurds’: Characterizing creative thought in terms of the structure and dynamics of human memory. Creat. Res. J. 22, 1–13. doi: 10.1080/10400410903579494

Gabora, L., and Kaufman, S. B. (2010). “Evolutionary approaches to creativity,” in The Cambridge Handbook of Creativity , eds J. S. Kaufman and R. J. Sternberg (Cambridge: Cambridge University Press), 279–300.

Gabora, L., and Ranjan, A. (2013). “How insight emerges in a distributed, content-addressable memory,” in The Neuroscience of Creativity , eds A. Bristol, O. Vartanian, and J. Kaufman (Cambridge: MIT Press), 19–43.

Gabora, L., and Saab, A. (2011). “Creative inference and states of potentiality in analogy problem solving,” in Proceedings of the Annual Meeting of the Cognitive Science Society , Boston, MA, 3506–3511.

Gentner, D. (1983). Structure mapping: a theoretical framework for analogy. Cogn. Sci. 7, 155–170. doi: 10.1207/s15516709cog0702_3

Getzels, J. W. (1975). Problem finding and the inventiveness of solutions. J. Creat. Behav. 9, 12–18. doi: 10.1002/j.2162-6057.1975.tb00552.x

Getzels, J. W. (1987). “Creativity, intelligence, and problem finding: retrospect and prospect,” in Frontiers of Creativity Research: Beyond the Basics , ed. S. G. Isaksen (Buffalo, NY: Bearly Limited), 88–102.

Getzels, J. W., and Csikszentmihalyi, M. (1976). The Creative Vision: A Longitudinal Study of Problem Finding in Art New York, NY: Wiley.

Ghiselin, B. (ed.). (1952/1985). The Creative Process Los Angeles: University of California.

Goel, V., and Pirolli, P. (1992). The structure of design problem spaces. Cogn. Sci. 16, 395–429. doi: 10.1207/s15516709cog1603_3

Goldschmidt, G. (2013). “A micro view of design reasoning: two-way shifts between embodiment and rationale,” in Creativity and Rationale: Enhancing Human Experience by Design, Human-Computer Interaction Series , ed. J. M. Carroll (London: Springer Verlag). doi: 10.1007/978-1-4471-2_3

Goldschmidt, G. (2014). Linkography: Unfolding the Design Process Cambridge, MA: MIT Press.

Grube, H. E., and Davis, S. N. (1988). “Inching our way up mount Olympus: The evolving-systems approach to creative thinking,” in The Nature of Creativity , ed. R. J. Sternberg (New York, NY: Cambridge University Press), 243–270.

Guilford, J. P. (1950). Creativity. Am. Psychol. 5, 444–454. doi: 10.1037/h0063487

Guilford, J. P. (1956). The structure of intellect model. Psychol. Bull. 53, 267–293. doi: 10.1037/h0040755

Guilford, J. P. (1959). “Traits of creativity,” in Creativity and its Cultivation , ed. H. H. Anderson (New York: Harper), 142–161.

Guilford, J. P. (1967). The Nature of Human Intelligence New York, NY: McGraw-Hill, Inc.

Guilford, J. P., Christensen, P. R., Merrifield, P. R., and Wilson, R. C. (1978). Alternate Uses: Manual of Instructions and Interpretation Orange, CA: Sheridan Psychological Services.

Halpern, D. F. (2003). “Thinking critically about creative thinking,” in Critical Creative Processes , ed. M. A. Runco (Cresskill, NJ: Hampton Press), 189–208.

Hayes, J. R., and Flowers, L. S. (1986). Writing research and the writer. Am. Psychol. 41, 1106–1113. doi: 10.1037/0003-066X.41.10.1106

Jaarsveld, S. (2007). Creative Cognition: New Perspectives on Creative Thinking Kaiserslautern: University of Kaiserslautern Press.

Jaarsveld, S., Fink, A., Rinner, M., Schwab, D., Benedek, M., and Lachmann, T. (2015). Intelligence in creative processes; an EEG study. Intelligence 49, 171–178. doi: 10.1016/j.ijpsycho.2012.02.012

Jaarsveld, S., Lachmann, T., Hamel, R., and van Leeuwen, C. (2010). Solving and creating Raven Progressive Matrices: reasoning in well and ill defined problem spaces. Creat. Res. J. 22, 304–319. doi: 10.1080/10400419.2010.503541

Jaarsveld, S., Lachmann, T., and van Leeuwen, C. (2012). Creative reasoning across developmental levels: convergence and divergence in problem creation. Intelligence 40, 172–188. doi: 10.1016/j.intell.2012.01.002

Jaarsveld, S., Lachmann, T., and van Leeuwen, C. (2013). “The impact of problem space on reasoning: Solving versus creating matrices,” in Proceedings of the 35th Annual Conference of the Cognitive Science Society , eds M. Knauff, M. Pauen, N. Sebanz, and I. Wachsmuth (Austin, TX: Cognitive Science Society), 2632–2638.

Jaarsveld, S., and van Leeuwen, C. (2005). Sketches from a design process: creative cognition inferred from intermediate products. Cogn. Sci. 29, 79–101. doi: 10.1207/s15516709cog2901_4

Jauk, E., Benedek, M., Dunst, B., and Neubauer, A. C. (2013). The relationship between intelligence and creativity: new support for the threshold hypothesis by means of empirical breakpoint detection. Intelligence 41, 212–221. doi: 10.1016/j.intell.2013.03.003

Jauk, E., Benedek, M., and Neubauer, A. C. (2012). Tackling creativity at its roots: evidence for different patterns of EEG alpha activity related to convergent and divergent modes of task processing. Int. J. Psychophysiol. 84, 219–225. doi: 10.1016/j.ijpsycho.2012.02.012

Jaušovec, N. (1999). “Brain biology and brain functioning,” in Encyclopedia of Creativity , eds M. A. Runco and S. R. Pritzker (San Diego, CA: Academic Press), 203–212.

Jaušovec, N. (2000). Differences in cognitive processes between gifted, intelligent, creative, and average individuals while solving complex problems: an EEG Study. Intelligence 28, 213–237. doi: 10.1016/S0160-2896(00)00037-4

Jung, R. E. (2014). Evolution, creativity, intelligence, and madness: “here be dragons”. Front. Psychol 5:784. doi: 10.3389/fpsyg.2014.00784

Jung, R. E., and Haier, R. J. (2013). “Creativity and intelligence,” in Neuroscience of Creativity , eds O. Vartanian, A. S. Bristol, and J. C. Kaufman (Cambridge, MA: MIT Press), 233–254.

Jung, R. E., Segall, J. M., Bockholt, H. J., Flores, R. A., Smith, S. M., Chavez, R. S., et al. (2010). Neuroanatomy of creativity. Hum. Brain Mapp. 31, 398–409. doi: 10.1002/hbm.20874

Karmiloff-Smith, A. (1992). Beyond Modularity: A Developmental Perspective on Cognitive Science Cambridge, MA: MIT Press.

Kaufman, J. C. (2015). Why creativity isn’t in IQ tests, why it matters, and why it won’t change anytime soon probably. Intelligence 3, 59–72. doi: 10.3390/jintelligence303005

Kaufmann, G. (2003). What to measure? A new look at the concept of creativity. Scand. J. Educ. Res. 47, 235–251. doi: 10.1080/00313830308604

Kim, K. H. (2005). Can only intelligent people be creative? J. Second. Gift. Educ. 16, 57–66.

Koestler, A. (1964). The Act of Creation London: Penguin.

Kozbelt, A. (2008). Hierarchical linear modeling of creative artists’ problem solving behaviors. J. Creat. Behav. 42, 181–200. doi: 10.1002/j.2162-6057.2008.tb01294.x

Kulkarni, D., and Simon, H. A. (1988). The processes of scientific discovery: the strategy of experimentation. Cogn. Sci. 12, 139–175. doi: 10.1016/j.coph.2009.08.004

Limb, C. J. (2010). Your Brain on Improve Available at: http://www.ted.com/talks/charles_limb_your_brain_on_improv

Lubart, T. I. (2001). Models of the creative process: past, present and future. Creat. Res. J. 13, 295–308. doi: 10.1207/S15326934CRJ1334_07

Lubart, T. I. (2003). Psychologie de la Créativité. Cursus. Psychologie Paris: Armand Colin.

Martindale, C. (1999). “Biological basis of creativity,” in Handbook of Creativity , ed. R. J. Sternberg (New York, NY: Cambridge University Press), 137–152.

Mednick, S. A. (1962). The associative basis of the creative process. Psychol. Rev. 69, 220–232. doi: 10.1037/h0048850

Mendelssohn, G. A. (1976). Associational and attentional processes in creative performance. J. Pers. 44, 341–369. doi: 10.1111/j.1467-6494.1976.tb00127.x

Mestre, J. P. (2002). Probing adults’ conceptual understanding and transfer of learning via problem posing. Appl. Dev. Psychol. 23, 9–50. doi: 10.1016/S0193-3973(01)00101-0

Miller, E. K., and Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24, 167–202. doi: 10.1146/annurev.neuro.24.1.167

Mumford, M. D., Hunter, S. T., Eubanks, D. L., Bedell, K. E., and Murphy, S. T. (2007). Developing leaders for creative efforts: a domain-based approach to leadership development. Hum. Res. Manag. Rev. 17, 402–417. doi: 10.1016/j.hrmr.2007.08.002

Newell, A., and Simon, H. A. (1972). “The theory of human problem solving,” in Human Problem Solving , eds A. Newell and H. Simon (Englewood Cliffs, NJ: Prentice Hall), 787–868.

Nusbaum, E. C., and Silvia, P. J. (2011). Are intelligence and creativity really so different? Intelligence 39, 36–40. doi: 10.1016/j.intell.2010.11.002

Palmiero, M., Nori, R., Aloisi, V., Ferrara, M., and Piccardi, L. (2015). Domain-specificity of creativity: a study on the relationship between visual creativity and visual mental imagery. Front. Psychol. 6:1870. doi: 10.3389/fpsyg.2015.01870

Piaget, J., Montangero, J., and Billeter, J. (1977). “La formation des correlats,” in Recherches sur L’abstraction Reflechissante I , ed. J. Piaget (Paris: Presse Universitaires de France), 115–129.

Plucker, J. (1999). Is the proof in the pudding? Reanalyses of torrance’s (1958 to present) longitudinal study data. Creat. Res. J. 12, 103–114. doi: 10.1207/s15326934crj1202_3

Raven, J. C. (1938/1998). Standard Progressive Matrices, Sets A, B, C, D & E Oxford: Oxford Psychologists Press.

Razumnikova, O. M., Volf, N. V., and Tarasova, I. V. (2009). Strategy and results: sex differences in electrographic correlates of verbal and figural creativity. Hum. Physiol. 35, 285–294. doi: 10.1134/S0362119709030049

Runco, M. A. (1991). The evaluative, valuative, and divergent thinking of children. J. Creat. Behav. 25, 311–319. doi: 10.1177/1073858414568317

Runco, M. A. (2003). “Idea evaluation, divergent thinking, and creativity,” in Critical Creative Processes , ed. M. A. Runco (Cresskill, NJ: Hampton Press), 69–94.

Runco, M. A. (2007). Creativity, Theories and Themes: Research, Development, and Practice New York, NY: Elsevier.

Runco, M. A. (2008). Commentary: divergent thinking is not synonymous with creativity. Psychol. Aesthet. Creat. Arts 2, 93–96. doi: 10.1037/1931-3896.2.2.93

Sakar, P., and Chakrabarti, A. (2013). Support for protocol analyses in design research. Des. Issues 29, 70–81. doi: 10.1162/DESI_a_00231

Saraç, S., Önder, A., and Karakelle, S. (2014). The relations among general intelligence, metacognition and text learning performance. Educ. Sci. 39, 40–53.

Shye, S., and Goldzweig, G. (1999). Creativity as an extension of intelligence: Faceted definition and structural hypotheses. Megamot 40, 31–53.

Shye, S., and Yuhas, I. (2004). Creativity in problem solving. Tech. Rep. doi: 10.13140/2.1.1940.0643

Siegler, R. S. (1998). Children’s Thinking , 3rd Edn. Upper Saddle River, NJ: Prentice Hall, 28–50.

Siegler, R. S. (2005). Children’s learning. Am. Psychol. 60, 769–778. doi: 10.1037/0003-066X.60.8.769

Silvia, P. J. (2008). Creativity and intelligence revisited: a reanalysis of Wallach and Kogan (1965). Creat. Res. J. 20, 34–39. doi: 10.1080/10400410701841807

CrossRef Full Text

Silvia, P. J., Beaty, R. E., and Nussbaum, E. C. (2013). Verbal fluency and creativity: general and specific contributions of broad retrieval ability (Gr) factors to divergent thinking. Intelligence 41, 328–340. doi: 10.1016/j.intell.2013.05.004

Simon, H. A. (1973). The structure of ill structured problems. Artif. Intell. 4, 1012–1021. doi: 10.1016/0004-3702(73)90011-8

Simon, H. A., and Newell, A. (1971). Human problem solving: state of theory in 1970. Am. Psychol. 26, 145–159. doi: 10.1037/h0030806

Sligh, A. C., Conners, F. A., and Roskos-Ewoldsen, B. (2005). Relation of creativity to fluid and crystallized intelligence. J. Creat. Behav. 39, 123–136. doi: 10.1002/j.2162-6057.2005.tb01254.x

Spearman, C. (1904). ‘General intelligence,’ objectively determined and measured. Am. J. Psychol. 15, 201–293. doi: 10.2307/1412107

Spearman, C. (1927). The Abilities of Man London: Macmillan.

Sternberg, R. J. (1982). “Conceptions of intelligence,” in Handbook of Human Intelligence , ed. R. J. Sternberg (New York, NY: Cambridge University Press), 3–28.

Sternberg, R. J. (2005). “The WICS model of giftedness,” in Conceptions of Giftedness , 2nd Edn, eds R. J. Sternberg and J. E. Davidson (New York, NY: Cambridge University Press), 237–243.

Sternberg, R. J., and Lubart, T. I. (1999). “The concept of creativity: Prospects and paradigms,” in Handbook of Creativity , ed. R. J. Sternberg (New York, NY: Cambridge University Press), 3–15.

Sternberg, R. J., and Salter, W. (1982). “The nature of intelligence and its measurements,” in Handbook of Human Intelligence , ed. R. J. Sternberg (New York, NY: Cambridge University Press), 3–24.

Thagard, P., and Verbeurgt, K. (1998). Coherence as constraint satisfaction. Cogn. Sci. 22, l–24. doi: 10.1207/s15516709cog2201_1

Torrance, E. P. (1988). “The nature of creativity as manifest in its testing,” in The Nature of Creativity: Contemporary Psychological Perspectives , ed. R. J. Sternberg (New York, NY: Cambridge University Press), 43–75.

Urban, K. K., and Jellen, H. G. (1995). Test of Creative Thinking – Drawing Production Frankfurt: Swets Test Services.

van Leeuwen, C., Verstijnen, I. M., and Hekkert, P. (1999). “Common unconscious dynamics underlie uncommon conscious effect: a case study in the iterative nature of perception and creation,” in Modeling Consciousness Across the Disciplines , ed. J. S. Jordan (Lanham, MD: University Press of America), 179–218.

Vernon, P. E. (ed.). (1970). Creativity London: Penguin.

Verstijnen, I. M., Heylighen, A., Wagemans, J., and Neuckermans, H. (2001). “Sketching, analogies, and creativity,” in Visual and Spatial Reasoning in Design, II. Key Centre of Design Computing and Cognition , eds J. S. Gero, B. Tversky, and T. Purcell (Sydney, NSW: University of Sydney).

Wallas, G. (1926). The Art of Thought New York, NY: Harcourt, Brace & World.

Ward, T. B. (2007). Creative cognition as a window on creativity. Methods 42, 28–37. doi: 10.1016/j.ymeth.2006.12.002

Webb Young, J. (1939/2003). A Technique for Producing Ideas New York, NY: McGraw-Hill.

Welter, M. M., Jaarsveld, S., and Lachmann, T. (in press). Problem space matters: development of creativity and intelligence in primary school children. Creat. Res. J.

Welter, M. M., Jaarsveld, S., van Leeuwen, C., and Lachmann, T. (2016). Intelligence and creativity; over the threshold together? Creat. Res. J. 28, 212–218. doi: 10.1080/10400419.2016.1162564

Wertheimer, M. (1945/1968). Productive Thinking (Enlarged Edition) London: Tavistock.

Yamamoto, Y., Nakakoji, K., and Takada, S. (2000). Hand on representations in two dimensional spaces for early stages of design. Knowl. Based Syst. 13, 357–384. doi: 10.1016/S0950-7051(00)00078-2

Keywords : creative cognition, cognitive neuroscience, creative reasoning, problem space, knowledge domain

Citation: Jaarsveld S and Lachmann T (2017) Intelligence and Creativity in Problem Solving: The Importance of Test Features in Cognition Research. Front. Psychol. 8:134. doi: 10.3389/fpsyg.2017.00134

Received: 05 July 2016; Accepted: 19 January 2017; Published: 06 February 2017.

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Copyright © 2017 Jaarsveld and Lachmann. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Saskia Jaarsveld, [email protected] Thomas Lachmann, [email protected]

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Problem-Solving Strategies and Obstacles

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Sean is a fact-checker and researcher with experience in sociology, field research, and data analytics.

JGI / Jamie Grill / Getty Images

• Application
• Improvement

From deciding what to eat for dinner to considering whether it's the right time to buy a house, problem-solving is a large part of our daily lives. Learn some of the problem-solving strategies that exist and how to use them in real life, along with ways to overcome obstacles that are making it harder to resolve the issues you face.

What Is Problem-Solving?

In cognitive psychology , the term 'problem-solving' refers to the mental process that people go through to discover, analyze, and solve problems.

A problem exists when there is a goal that we want to achieve but the process by which we will achieve it is not obvious to us. Put another way, there is something that we want to occur in our life, yet we are not immediately certain how to make it happen.

Maybe you want a better relationship with your spouse or another family member but you're not sure how to improve it. Or you want to start a business but are unsure what steps to take. Problem-solving helps you figure out how to achieve these desires.

The problem-solving process involves:

• Discovery of the problem
• Deciding to tackle the issue
• Seeking to understand the problem more fully
• Researching available options or solutions
• Taking action to resolve the issue

Before problem-solving can occur, it is important to first understand the exact nature of the problem itself. If your understanding of the issue is faulty, your attempts to resolve it will also be incorrect or flawed.

Problem-Solving Mental Processes

Several mental processes are at work during problem-solving. Among them are:

• Perceptually recognizing the problem
• Representing the problem in memory
• Considering relevant information that applies to the problem
• Identifying different aspects of the problem
• Labeling and describing the problem

Problem-Solving Strategies

There are many ways to go about solving a problem. Some of these strategies might be used on their own, or you may decide to employ multiple approaches when working to figure out and fix a problem.

An algorithm is a step-by-step procedure that, by following certain "rules" produces a solution. Algorithms are commonly used in mathematics to solve division or multiplication problems. But they can be used in other fields as well.

In psychology, algorithms can be used to help identify individuals with a greater risk of mental health issues. For instance, research suggests that certain algorithms might help us recognize children with an elevated risk of suicide or self-harm.

One benefit of algorithms is that they guarantee an accurate answer. However, they aren't always the best approach to problem-solving, in part because detecting patterns can be incredibly time-consuming.

There are also concerns when machine learning is involved—also known as artificial intelligence (AI)—such as whether they can accurately predict human behaviors.

Heuristics are shortcut strategies that people can use to solve a problem at hand. These "rule of thumb" approaches allow you to simplify complex problems, reducing the total number of possible solutions to a more manageable set.

If you find yourself sitting in a traffic jam, for example, you may quickly consider other routes, taking one to get moving once again. When shopping for a new car, you might think back to a prior experience when negotiating got you a lower price, then employ the same tactics.

While heuristics may be helpful when facing smaller issues, major decisions shouldn't necessarily be made using a shortcut approach. Heuristics also don't guarantee an effective solution, such as when trying to drive around a traffic jam only to find yourself on an equally crowded route.

Trial and Error

A trial-and-error approach to problem-solving involves trying a number of potential solutions to a particular issue, then ruling out those that do not work. If you're not sure whether to buy a shirt in blue or green, for instance, you may try on each before deciding which one to purchase.

This can be a good strategy to use if you have a limited number of solutions available. But if there are many different choices available, narrowing down the possible options using another problem-solving technique can be helpful before attempting trial and error.

In some cases, the solution to a problem can appear as a sudden insight. You are facing an issue in a relationship or your career when, out of nowhere, the solution appears in your mind and you know exactly what to do.

Insight can occur when the problem in front of you is similar to an issue that you've dealt with in the past. Although, you may not recognize what is occurring since the underlying mental processes that lead to insight often happen outside of conscious awareness .

Research indicates that insight is most likely to occur during times when you are alone—such as when going on a walk by yourself, when you're in the shower, or when lying in bed after waking up.

How to Apply Problem-Solving Strategies in Real Life

If you're facing a problem, you can implement one or more of these strategies to find a potential solution. Here's how to use them in real life:

• Create a flow chart . If you have time, you can take advantage of the algorithm approach to problem-solving by sitting down and making a flow chart of each potential solution, its consequences, and what happens next.
• Recall your past experiences . When a problem needs to be solved fairly quickly, heuristics may be a better approach. Think back to when you faced a similar issue, then use your knowledge and experience to choose the best option possible.
• Start trying potential solutions . If your options are limited, start trying them one by one to see which solution is best for achieving your desired goal. If a particular solution doesn't work, move on to the next.
• Take some time alone . Since insight is often achieved when you're alone, carve out time to be by yourself for a while. The answer to your problem may come to you, seemingly out of the blue, if you spend some time away from others.

Obstacles to Problem-Solving

Problem-solving is not a flawless process as there are a number of obstacles that can interfere with our ability to solve a problem quickly and efficiently. These obstacles include:

• Assumptions: When dealing with a problem, people can make assumptions about the constraints and obstacles that prevent certain solutions. Thus, they may not even try some potential options.
• Functional fixedness : This term refers to the tendency to view problems only in their customary manner. Functional fixedness prevents people from fully seeing all of the different options that might be available to find a solution.
• Irrelevant or misleading information: When trying to solve a problem, it's important to distinguish between information that is relevant to the issue and irrelevant data that can lead to faulty solutions. The more complex the problem, the easier it is to focus on misleading or irrelevant information.
• Mental set: A mental set is a tendency to only use solutions that have worked in the past rather than looking for alternative ideas. A mental set can work as a heuristic, making it a useful problem-solving tool. However, mental sets can also lead to inflexibility, making it more difficult to find effective solutions.

How to Improve Your Problem-Solving Skills

In the end, if your goal is to become a better problem-solver, it's helpful to remember that this is a process. Thus, if you want to improve your problem-solving skills, following these steps can help lead you to your solution:

• Recognize that a problem exists . If you are facing a problem, there are generally signs. For instance, if you have a mental illness , you may experience excessive fear or sadness, mood changes, and changes in sleeping or eating habits. Recognizing these signs can help you realize that an issue exists.
• Decide to solve the problem . Make a conscious decision to solve the issue at hand. Commit to yourself that you will go through the steps necessary to find a solution.
• Seek to fully understand the issue . Analyze the problem you face, looking at it from all sides. If your problem is relationship-related, for instance, ask yourself how the other person may be interpreting the issue. You might also consider how your actions might be contributing to the situation.
• Research potential options . Using the problem-solving strategies mentioned, research potential solutions. Make a list of options, then consider each one individually. What are some pros and cons of taking the available routes? What would you need to do to make them happen?
• Take action . Select the best solution possible and take action. Action is one of the steps required for change . So, go through the motions needed to resolve the issue.
• Try another option, if needed . If the solution you chose didn't work, don't give up. Either go through the problem-solving process again or simply try another option.

You can find a way to solve your problems as long as you keep working toward this goal—even if the best solution is simply to let go because no other good solution exists.

Sarathy V. Real world problem-solving .  Front Hum Neurosci . 2018;12:261. doi:10.3389/fnhum.2018.00261

Dunbar K. Problem solving . A Companion to Cognitive Science . 2017. doi:10.1002/9781405164535.ch20

Stewart SL, Celebre A, Hirdes JP, Poss JW. Risk of suicide and self-harm in kids: The development of an algorithm to identify high-risk individuals within the children's mental health system . Child Psychiat Human Develop . 2020;51:913-924. doi:10.1007/s10578-020-00968-9

Rosenbusch H, Soldner F, Evans AM, Zeelenberg M. Supervised machine learning methods in psychology: A practical introduction with annotated R code . Soc Personal Psychol Compass . 2021;15(2):e12579. doi:10.1111/spc3.12579

Mishra S. Decision-making under risk: Integrating perspectives from biology, economics, and psychology . Personal Soc Psychol Rev . 2014;18(3):280-307. doi:10.1177/1088868314530517

Csikszentmihalyi M, Sawyer K. Creative insight: The social dimension of a solitary moment . In: The Systems Model of Creativity . 2015:73-98. doi:10.1007/978-94-017-9085-7_7

Chrysikou EG, Motyka K, Nigro C, Yang SI, Thompson-Schill SL. Functional fixedness in creative thinking tasks depends on stimulus modality .  Psychol Aesthet Creat Arts . 2016;10(4):425‐435. doi:10.1037/aca0000050

Huang F, Tang S, Hu Z. Unconditional perseveration of the short-term mental set in chunk decomposition .  Front Psychol . 2018;9:2568. doi:10.3389/fpsyg.2018.02568

National Alliance on Mental Illness. Warning signs and symptoms .

Mayer RE. Thinking, problem solving, cognition, 2nd ed .

Schooler JW, Ohlsson S, Brooks K. Thoughts beyond words: When language overshadows insight. J Experiment Psychol: General . 1993;122:166-183. doi:10.1037/0096-3445.2.166

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."