relationship between problem solving and creativity

• Business Essentials
• Leadership & Management
• Credential of Leadership, Impact, and Management in Business (CLIMB)
• Entrepreneurship & Innovation
• Digital Transformation
• Finance & Accounting
• Business in Society
• For Organizations
• Support Portal
• Media Coverage
• Founding Donors
• Leadership Team

• Harvard Business School →
• HBS Online →
• Business Insights →

Business Insights

Harvard Business School Online's Business Insights Blog provides the career insights you need to achieve your goals and gain confidence in your business skills.

• Career Development
• Communication
• Decision-Making
• Earning Your MBA
• Negotiation
• News & Events
• Productivity
• Staff Spotlight
• Student Profiles
• Work-Life Balance
• AI Essentials for Business
• Alternative Investments
• Business Analytics
• Business Strategy
• Business and Climate Change
• Creating Brand Value
• Design Thinking and Innovation
• Digital Marketing Strategy
• Disruptive Strategy
• Economics for Managers
• Entrepreneurship Essentials
• Financial Accounting
• Global Business
• Launching Tech Ventures
• Leadership Principles
• Leadership, Ethics, and Corporate Accountability
• Leading Change and Organizational Renewal
• Leading with Finance
• Management Essentials
• Negotiation Mastery
• Organizational Leadership
• Power and Influence for Positive Impact
• Strategy Execution
• Sustainable Business Strategy
• Sustainable Investing
• Winning with Digital Platforms

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.

About the Author

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Front Psychol

Intelligence and Creativity in Problem Solving: The Importance of Test Features in Cognition Research

Associated data.

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.

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., Bubic A. (2015). Semantic memory as the root of imagination. Front. Psychol. 6 : 325 10.3389/fpsyg.2015.00325 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Abraham A., Windmann S. (2007). Creative cognition: the diverse operations and the prospect of applying a cognitive neuroscience perspective. Methods 42 38–48. 10.1016/j.ymeth.2006.12.007 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Akin O. (1986). Psychology of Architectural Design London: Pion. [ Google Scholar ]
• Anderson J. R. (1983). The Architecture of Cognition Cambridge, MA: Harvard University Press. [ Google Scholar ]
• Arden R., Chavez R. S., Grazioplene R., Jung R. E. (2010). Neuroimaging creativity: a psychometric view. Behav. Brain Res. 214 143–156. 10.1016/j.bbr.2010.05.015 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Arnheim R. (1962/1974). Picasso’s Guernica Berkeley: University of California Press. [ Google Scholar ]
• Barsalou L. W. (1992). Cognitive Psychology: An Overview for Cognitive Scientists Hillsdale, NJ: LEA. [ Google Scholar ]
• Beaty R. E., Benedek M., Silvia P. J., Schacter D. L. (2016). Creative cognition and brain network dynamics. Trends Cogn. Sci. 20 87–95. 10.1016/j.tics.2015.10.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• 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. 10.1002/hbm.23065 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Beaty R. E., Nusbaum E. C., Silvia P. J. (2014). Does insight problem solving predict real-world creativity? Psychol. Aesthet. Creat. Arts 8 287–292. 10.1037/a0035727 [ CrossRef ] [ Google Scholar ]
• Beaty R. E., Silvia R. E. (2013). Metaphorically speaking: cognitive abilities and the production of figurative language. Mem. Cognit. 41 255–267. 10.3758/s13421-012-0258-5 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Beaty R. E., Smeekens B. A., Silvia P. J., Hodges D. A., Kane M. J. (2013). A first look at the role of domain-general cognitive and creative abilities in jazz improvisation. Psychomusicology 23 262–268. 10.1037/a0034968 [ CrossRef ] [ Google Scholar ]
• Benedek M., Bergner S., Konen T., Fink A., Neubauer A. C. (2011). EEG alpha synchronization is related to top-down processing in convergent and divergent thinking. Neuropsychologia 49 3505–3511. 10.1016/j.neuropsychologia.2011.09.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Benedek M., Franz F., Heene M., Neubauer A. C. (2012). Differential effects of cognitive inhibition and intelligence on creativity. Pers. Individ. Dif. 53 480–485. 10.1016/j.paid.2012.04.014 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Benedek M., Jauk E., Sommer M., Arendasy M., 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. 10.1016/j.intell.2014.05.007 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Boden M. A. (1990). The Creative Mind: Myths and Mechanisms London: Abacus. [ Google Scholar ]
• Boden M. A. (1996). Artificial Intelligence New York, NY: Academic. [ Google Scholar ]
• Buschman T. J., Miller E. K. (2007). Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science 315 1860–1862. 10.1126/science.1138071 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Carroll J. B. (1982). “The measurement of Intelligence,” in Handbook of Human Intelligence , ed. Sternberg R. J. (New York, NY: Cambridge University Press; ), 29–120. [ Google Scholar ]
• 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. 10.1111/j.2044-8279.1967.tb01930.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chi M. T. H. (1997). “ Creativity: Shifting across ontological categories flexibly ,” in Creative Thought: An Investigation of Conceptual Structures and Processes , eds Ward T., Smith S., Vaid J. (Washington, DC: American Psychological Association; ), 209–234. [ Google Scholar ]
• Chi M. T. H., VanLehn K. A. (1991). The content of physics self-explanations. J. Learn. Sci. 1 69–105. 10.1207/s15327809jls0101_4 [ CrossRef ] [ Google Scholar ]
• 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. 10.3758/BF03195939 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• 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. 10.1016/j.intell.2008.07.007 [ CrossRef ] [ Google Scholar ]
• Colunga E., Smith L. B. (2008). Flexibility and variability: essential to human cognition and the study of human cognition. New Ideas Psychol. 26 158–192. 10.1016/j.newideapsych.2007.07.012 [ CrossRef ] [ Google Scholar ]
• Cooper N. R., Croft R. J., Dominey S. J. J., Burgess A. P., 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. 10.1016/S0167-8760(02)00107-1 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cropley A. (2006). In praise of convergent thinking. Creat. Res. J. 18 391–404. 10.1207/s15326934crj1803_13 [ CrossRef ] [ Google Scholar ]
• Cropley A., Cropley D. (2008). Resolving the paradoxes of creativity: an extended phase model. Camb. J. Educ. 38 355–373. 10.1080/03057640802286871 [ CrossRef ] [ Google Scholar ]
• Cross N., Clayburn Cross A. (1996). Winning by design: the methods of Gordon Murray, racing car designer. Des. Stud. 17 91–107. 10.1016/0142-694X(95)00027-O [ CrossRef ] [ Google Scholar ]
• Dennett D. (1978). Brainstorms: Philosophical Essays on Mind and Psychology Montgomery, VT: Bradford Books. [ Google Scholar ]
• Dietrich A. (2007). Who’s afraid of a cognitive neuroscience of creativity? Methods 42 22–27. 10.1016/j.ymeth.2006.12.009 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dorst K. (2004). The problem of design problems: Problem solving and design expertise. J. Design Res. 4 10.1504/JDR.2004.009841 [ CrossRef ] [ Google Scholar ]
• Dorst K. (2011). The core of ‘design thinking’ and its application. Des. Stud. 32 521–532. 10.1016/j.destud.2011.07.006 [ CrossRef ] [ Google Scholar ]
• Eysenck H. J. (2003). “Creativity, personality and the convergent-divergent continuum,” in Critical Creative Processes , ed. Runco M. A. (Cresskill, NJ: Hampton Press; ), 95–114. [ Google Scholar ]
• Fink A., Benedek M. (2014). EEG alpha power and creative ideation. Neurosci. Biobehav. Rev. 44 111–123. 10.1016/j.neubiorev.2012.12.002 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fink A., Benedek M., Grabner R. H., Staudt B., Neubauer A. C. (2007). Creativity meets neuroscience: experimental tasks for the neuroscientific study of creative thinking. Methods 42 68–76. 10.1016/j.ymeth.2006.12.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• 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. 10.1002/hbm.20538 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Finke R. A., Ward T. B., Smith S. M. (1992). Creative Cognition: Theory, Research, and Applications Cambridge, MA: MIT Press. [ Google Scholar ]
• Fuster J. M. (1997). Network memory. Trends Neurosci. 20 451–459. 10.1016/S0166-2236(97)01128-4 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gabora L. (2002). “Cognitive mechanisms underlying the creative process,” in Proceedings of the Fourth International Conference on Creativity and Cognition , eds Hewett T., Kavanagh T. (Loughborough: Loughborough University; ), 126–133. [ Google Scholar ]
• 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. 10.1080/10400410903579494 [ CrossRef ] [ Google Scholar ]
• Gabora L., Kaufman S. B. (2010). “Evolutionary approaches to creativity,” in The Cambridge Handbook of Creativity , eds Kaufman J. S., Sternberg R. J. (Cambridge: Cambridge University Press; ), 279–300. [ Google Scholar ]
• Gabora L., Ranjan A. (2013). “How insight emerges in a distributed, content-addressable memory,” in The Neuroscience of Creativity , eds Bristol A., Vartanian O., Kaufman J. (Cambridge: MIT Press; ), 19–43. [ Google Scholar ]
• Gabora L., 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. [ Google Scholar ]
• Gentner D. (1983). Structure mapping: a theoretical framework for analogy. Cogn. Sci. 7 155–170. 10.1207/s15516709cog0702_3 [ CrossRef ] [ Google Scholar ]
• Getzels J. W. (1975). Problem finding and the inventiveness of solutions. J. Creat. Behav. 9 12–18. 10.1002/j.2162-6057.1975.tb00552.x [ CrossRef ] [ Google Scholar ]
• Getzels J. W. (1987). “Creativity, intelligence, and problem finding: retrospect and prospect,” in Frontiers of Creativity Research: Beyond the Basics , ed. Isaksen S. G. (Buffalo, NY: Bearly Limited; ), 88–102. [ Google Scholar ]
• Getzels J. W., Csikszentmihalyi M. (1976). The Creative Vision: A Longitudinal Study of Problem Finding in Art New York, NY: Wiley. [ Google Scholar ]
• Ghiselin B. (ed.) (1952/1985). The Creative Process Los Angeles: University of California. [ Google Scholar ]
• Goel V., Pirolli P. (1992). The structure of design problem spaces. Cogn. Sci. 16 395–429. 10.1207/s15516709cog1603_3 [ CrossRef ] [ Google Scholar ]
• 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. Carroll J. M. (London: Springer Verlag; ). 10.1007/978-1-4471-2_3 [ CrossRef ] [ Google Scholar ]
• Goldschmidt G. (2014). Linkography: Unfolding the Design Process Cambridge, MA: MIT Press. [ Google Scholar ]
• Grube H. E., Davis S. N. (1988). “Inching our way up mount Olympus: The evolving-systems approach to creative thinking,” in The Nature of Creativity , ed. Sternberg R. J. (New York, NY: Cambridge University Press; ), 243–270. [ Google Scholar ]
• Guilford J. P. (1950). Creativity. Am. Psychol. 5 444–454. 10.1037/h0063487 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Guilford J. P. (1956). The structure of intellect model. Psychol. Bull. 53 267–293. 10.1037/h0040755 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Guilford J. P. (1959). “Traits of creativity,” in Creativity and its Cultivation , ed. Anderson H. H. (New York: Harper; ), 142–161. [ Google Scholar ]
• Guilford J. P. (1967). The Nature of Human Intelligence New York, NY: McGraw-Hill, Inc. [ Google Scholar ]
• Guilford J. P., Christensen P. R., Merrifield P. R., Wilson R. C. (1978). Alternate Uses: Manual of Instructions and Interpretation Orange, CA: Sheridan Psychological Services. [ Google Scholar ]
• Halpern D. F. (2003). “Thinking critically about creative thinking,” in Critical Creative Processes , ed. Runco M. A. (Cresskill, NJ: Hampton Press; ), 189–208. [ Google Scholar ]
• Hayes J. R., Flowers L. S. (1986). Writing research and the writer. Am. Psychol. 41 1106–1113. 10.1037/0003-066X.41.10.1106 [ CrossRef ] [ Google Scholar ]
• Jaarsveld S. (2007). Creative Cognition: New Perspectives on Creative Thinking Kaiserslautern: University of Kaiserslautern Press. [ Google Scholar ]
• Jaarsveld S., Fink A., Rinner M., Schwab D., Benedek M., Lachmann T. (2015). Intelligence in creative processes; an EEG study. Intelligence 49 171–178. 10.1016/j.ijpsycho.2012.02.012 [ CrossRef ] [ Google Scholar ]
• Jaarsveld S., Lachmann T., Hamel R., van Leeuwen C. (2010). Solving and creating Raven Progressive Matrices: reasoning in well and ill defined problem spaces. Creat. Res. J. 22 304–319. 10.1080/10400419.2010.503541 [ CrossRef ] [ Google Scholar ]
• Jaarsveld S., Lachmann T., van Leeuwen C. (2012). Creative reasoning across developmental levels: convergence and divergence in problem creation. Intelligence 40 172–188. 10.1016/j.intell.2012.01.002 [ CrossRef ] [ Google Scholar ]
• Jaarsveld S., Lachmann T., 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 Knauff M., Pauen M., Sebanz N., Wachsmuth I. (Austin, TX: Cognitive Science Society; ), 2632–2638. [ Google Scholar ]
• Jaarsveld S., van Leeuwen C. (2005). Sketches from a design process: creative cognition inferred from intermediate products. Cogn. Sci. 29 79–101. 10.1207/s15516709cog2901_4 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jauk E., Benedek M., Dunst B., 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. 10.1016/j.intell.2013.03.003 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jauk E., Benedek M., 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. 10.1016/j.ijpsycho.2012.02.012 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jaušovec N. (1999). “Brain biology and brain functioning,” in Encyclopedia of Creativity , eds Runco M. A., Pritzker S. R. (San Diego, CA: Academic Press; ), 203–212. [ Google Scholar ]
• 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. 10.1016/S0160-2896(00)00037-4 [ CrossRef ] [ Google Scholar ]
• Jung R. E. (2014). Evolution, creativity, intelligence, and madness: “here be dragons”. Front. Psychol 5 : 784 10.3389/fpsyg.2014.00784 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jung R. E., Haier R. J. (2013). “Creativity and intelligence,” in Neuroscience of Creativity , eds Vartanian O., Bristol A. S., Kaufman J. C. (Cambridge, MA: MIT Press; ), 233–254. [ Google Scholar ]
• 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. 10.1002/hbm.20874 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Karmiloff-Smith A. (1992). Beyond Modularity: A Developmental Perspective on Cognitive Science Cambridge, MA: MIT Press. [ Google Scholar ]
• 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. 10.3390/jintelligence303005 [ CrossRef ] [ Google Scholar ]
• Kaufmann G. (2003). What to measure? A new look at the concept of creativity. Scand. J. Educ. Res. 47 235–251. 10.1080/00313830308604 [ CrossRef ] [ Google Scholar ]
• Kim K. H. (2005). Can only intelligent people be creative? J. Second. Gift. Educ. 16 57–66. [ Google Scholar ]
• Koestler A. (1964). The Act of Creation London: Penguin. [ Google Scholar ]
• Kozbelt A. (2008). Hierarchical linear modeling of creative artists’ problem solving behaviors. J. Creat. Behav. 42 181–200. 10.1002/j.2162-6057.2008.tb01294.x [ CrossRef ] [ Google Scholar ]
• Kulkarni D., Simon H. A. (1988). The processes of scientific discovery: the strategy of experimentation. Cogn. Sci. 12 139–175. 10.1016/j.coph.2009.08.004 [ CrossRef ] [ Google Scholar ]
• Limb C. J. (2010). Your Brain on Improve Available at: http://www.ted.com/talks/charles_limb_your_brain_on_improv [ Google Scholar ]
• Lubart T. I. (2001). Models of the creative process: past, present and future. Creat. Res. J. 13 295–308. 10.1207/S15326934CRJ1334_07 [ CrossRef ] [ Google Scholar ]
• Lubart T. I. (2003). Psychologie de la Créativité. Cursus. Psychologie Paris: Armand Colin. [ Google Scholar ]
• Martindale C. (1999). “Biological basis of creativity,” in Handbook of Creativity , ed. Sternberg R. J. (New York, NY: Cambridge University Press; ), 137–152. [ Google Scholar ]
• Mednick S. A. (1962). The associative basis of the creative process. Psychol. Rev. 69 220–232. 10.1037/h0048850 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mendelssohn G. A. (1976). Associational and attentional processes in creative performance. J. Pers. 44 341–369. 10.1111/j.1467-6494.1976.tb00127.x [ CrossRef ] [ Google Scholar ]
• Mestre J. P. (2002). Probing adults’ conceptual understanding and transfer of learning via problem posing. Appl. Dev. Psychol. 23 9–50. 10.1016/S0193-3973(01)00101-0 [ CrossRef ] [ Google Scholar ]
• Miller E. K., Cohen J. D. (2001). An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24 167–202. 10.1146/annurev.neuro.24.1.167 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mumford M. D., Hunter S. T., Eubanks D. L., Bedell K. E., Murphy S. T. (2007). Developing leaders for creative efforts: a domain-based approach to leadership development. Hum. Res. Manag. Rev. 17 402–417. 10.1016/j.hrmr.2007.08.002 [ CrossRef ] [ Google Scholar ]
• Newell A., Simon H. A. (1972). “The theory of human problem solving,” in Human Problem Solving , eds Newell A., Simon H. (Englewood Cliffs, NJ: Prentice Hall; ), 787–868. [ Google Scholar ]
• Nusbaum E. C., Silvia P. J. (2011). Are intelligence and creativity really so different? Intelligence 39 36–40. 10.1016/j.intell.2010.11.002 [ CrossRef ] [ Google Scholar ]
• Palmiero M., Nori R., Aloisi V., Ferrara M., Piccardi L. (2015). Domain-specificity of creativity: a study on the relationship between visual creativity and visual mental imagery. Front. Psychol. 6 : 1870 10.3389/fpsyg.2015.01870 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Piaget J., Montangero J., Billeter J. (1977). “La formation des correlats,” in Recherches sur L’abstraction Reflechissante I , ed. Piaget J. (Paris: Presse Universitaires de France; ), 115–129. [ Google Scholar ]
• 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. 10.1207/s15326934crj1202_3 [ CrossRef ] [ Google Scholar ]
• Raven J. C. (1938/1998). Standard Progressive Matrices, Sets A, B, C, D & E Oxford: Oxford Psychologists Press. [ Google Scholar ]
• Razumnikova O. M., Volf N. V., Tarasova I. V. (2009). Strategy and results: sex differences in electrographic correlates of verbal and figural creativity. Hum. Physiol. 35 285–294. 10.1134/S0362119709030049 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Runco M. A. (1991). The evaluative, valuative, and divergent thinking of children. J. Creat. Behav. 25 311–319. 10.1177/1073858414568317 [ CrossRef ] [ Google Scholar ]
• Runco M. A. (2003). “Idea evaluation, divergent thinking, and creativity,” in Critical Creative Processes , ed. Runco M. A. (Cresskill, NJ: Hampton Press; ), 69–94. [ Google Scholar ]
• Runco M. A. (2007). Creativity, Theories and Themes: Research, Development, and Practice New York, NY: Elsevier. [ Google Scholar ]
• Runco M. A. (2008). Commentary: divergent thinking is not synonymous with creativity. Psychol. Aesthet. Creat. Arts 2 93–96. 10.1037/1931-3896.2.2.93 [ CrossRef ] [ Google Scholar ]
• Sakar P., Chakrabarti A. (2013). Support for protocol analyses in design research. Des. Issues 29 70–81. 10.1162/DESI_a_00231 [ CrossRef ] [ Google Scholar ]
• Saraç S., Önder A., Karakelle S. (2014). The relations among general intelligence, metacognition and text learning performance. Educ. Sci. 39 40–53. [ Google Scholar ]
• Shye S., Goldzweig G. (1999). Creativity as an extension of intelligence: Faceted definition and structural hypotheses. Megamot 40 31–53. [ Google Scholar ]
• Shye S., Yuhas I. (2004). Creativity in problem solving. Tech. Rep. 10.13140/2.1.1940.0643 [ CrossRef ] [ Google Scholar ]
• Siegler R. S. (1998). Children’s Thinking , 3rd Edn Upper Saddle River, NJ: Prentice Hall, 28–50. [ Google Scholar ]
• Siegler R. S. (2005). Children’s learning. Am. Psychol. 60 769–778. 10.1037/0003-066X.60.8.769 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Silvia P. J. (2008). Creativity and intelligence revisited: a reanalysis of Wallach and Kogan (1965). Creat. Res. J. 20 34–39. 10.1080/10400410701841807 [ CrossRef ] [ Google Scholar ]
• Silvia P. J., Beaty R. E., 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. 10.1016/j.intell.2013.05.004 [ CrossRef ] [ Google Scholar ]
• Simon H. A. (1973). The structure of ill structured problems. Artif. Intell. 4 1012–1021. 10.1016/0004-3702(73)90011-8 [ CrossRef ] [ Google Scholar ]
• Simon H. A., Newell A. (1971). Human problem solving: state of theory in 1970. Am. Psychol. 26 145–159. 10.1037/h0030806 [ CrossRef ] [ Google Scholar ]
• Sligh A. C., Conners F. A., Roskos-Ewoldsen B. (2005). Relation of creativity to fluid and crystallized intelligence. J. Creat. Behav. 39 123–136. 10.1002/j.2162-6057.2005.tb01254.x [ CrossRef ] [ Google Scholar ]
• Spearman C. (1904). ‘General intelligence,’ objectively determined and measured. Am. J. Psychol. 15 201–293. 10.2307/1412107 [ CrossRef ] [ Google Scholar ]
• Spearman C. (1927). The Abilities of Man London: Macmillan. [ Google Scholar ]
• Sternberg R. J. (1982). “Conceptions of intelligence,” in Handbook of Human Intelligence , ed. Sternberg R. J. (New York, NY: Cambridge University Press; ), 3–28. [ Google Scholar ]
• Sternberg R. J. (2005). “The WICS model of giftedness,” in Conceptions of Giftedness , 2nd Edn, eds Sternberg R. J., Davidson J. E. (New York, NY: Cambridge University Press; ), 237–243. [ Google Scholar ]
• Sternberg R. J., Lubart T. I. (1999). “The concept of creativity: Prospects and paradigms,” in Handbook of Creativity , ed. Sternberg R. J. (New York, NY: Cambridge University Press; ), 3–15. [ Google Scholar ]
• Sternberg R. J., Salter W. (1982). “The nature of intelligence and its measurements,” in Handbook of Human Intelligence , ed. Sternberg R. J. (New York, NY: Cambridge University Press; ), 3–24. [ Google Scholar ]
• Thagard P., Verbeurgt K. (1998). Coherence as constraint satisfaction. Cogn. Sci. 22 l–24. 10.1207/s15516709cog2201_1 [ CrossRef ] [ Google Scholar ]
• Torrance E. P. (1988). “The nature of creativity as manifest in its testing,” in The Nature of Creativity: Contemporary Psychological Perspectives , ed. Sternberg R. J. (New York, NY: Cambridge University Press; ), 43–75. [ Google Scholar ]
• Urban K. K., Jellen H. G. (1995). Test of Creative Thinking – Drawing Production Frankfurt: Swets Test Services. [ Google Scholar ]
• van Leeuwen C., Verstijnen I. M., 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. Jordan J. S. (Lanham, MD: University Press of America; ), 179–218. [ Google Scholar ]
• Vernon P. E. (ed.) (1970). Creativity London: Penguin. [ Google Scholar ]
• Verstijnen I. M., Heylighen A., Wagemans J., Neuckermans H. (2001). “Sketching, analogies, and creativity,” in Visual and Spatial Reasoning in Design, II. Key Centre of Design Computing and Cognition , eds Gero J. S., Tversky B., Purcell T. (Sydney, NSW: University of Sydney; ). [ Google Scholar ]
• Wallas G. (1926). The Art of Thought New York, NY: Harcourt, Brace & World. [ Google Scholar ]
• Ward T. B. (2007). Creative cognition as a window on creativity. Methods 42 28–37. 10.1016/j.ymeth.2006.12.002 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Webb Young J. (1939/2003). A Technique for Producing Ideas New York, NY: McGraw-Hill. [ Google Scholar ]
• Welter M. M., Jaarsveld S., Lachmann T. Problem space matters: development of creativity and intelligence in primary school children. Creat. Res. J. (in press) [ Google Scholar ]
• Welter M. M., Jaarsveld S., van Leeuwen C., Lachmann T. (2016). Intelligence and creativity; over the threshold together? Creat. Res. J. 28 212–218. 10.1080/10400419.2016.1162564 [ CrossRef ] [ Google Scholar ]
• Wertheimer M. (1945/1968). Productive Thinking (Enlarged Edition) London: Tavistock. [ Google Scholar ]
• Yamamoto Y., Nakakoji K., Takada S. (2000). Hand on representations in two dimensional spaces for early stages of design. Knowl. Based Syst. 13 357–384. 10.1016/S0950-7051(00)00078-2 [ CrossRef ] [ Google Scholar ]

The science behind creativity

Psychologists and neuroscientists are exploring where creativity comes from and how to increase your own

Vol. 53 No. 3 Print version: page 40

• Neuropsychology
• Creativity and Innovation

Paul Seli, PhD, is falling asleep. As he nods off, a sleep-tracking glove called Dormio, developed by scientists at the Massachusetts Institute of Technology, detects his nascent sleep state and jars him awake. Pulled back from the brink, he jots down the artistic ideas that came to him during those semilucid moments.

Seli is an assistant professor of psychology and neuroscience at the Duke Institute for Brain Sciences and also an artist. He uses Dormio to tap into the world of hypnagogia, the transitional state that exists at the boundary between wakefulness and sleep. In a mini-experiment, he created a series of paintings inspired by ideas plucked from his hypnagogic state and another series from ideas that came to him during waking hours. Then he asked friends to rate how creative the paintings were, without telling them which were which. They judged the hypnagogic paintings as significantly more creative. “In dream states, we seem to be able to link things together that we normally wouldn’t connect,” Seli said. “It’s like there’s an artist in my brain that I get to know through hypnagogia.”

The experiment is one of many novel—and, yes, creative—ways that psychologists are studying the science of creativity. At an individual level, creativity can lead to personal fulfillment and positive academic and professional outcomes, and even be therapeutic. People take pleasure in creative thoughts, research suggests—even if they don’t think of themselves as especially creative. Beyond those individual benefits, creativity is an endeavor with implications for society, said Jonathan Schooler, PhD, a professor of psychological and brain sciences at the University of California, Santa Barbara. “Creativity is at the core of innovation. We rely on innovation for advancing humanity, as well as for pleasure and entertainment,” he said. “Creativity underlies so much of what humans value.”

In 1950, J. P. Guilford, PhD, then president of APA, laid out his vision for the psychological study of creativity ( American Psychologist , Vol. 5, No. 9, 1950). For half a century, researchers added to the scientific understanding of creativity incrementally, said John Kounios, PhD, an experimental psychologist who studies creativity and insight at Drexel University in Philadelphia. Much of that research focused on the personality traits linked to creativity and the cognitive aspects of the creative process.

But in the 21st century, the field has blossomed thanks to new advances in neuroimaging. “It’s become a tsunami of people studying creativity,” Kounios said. Psychologists and neuroscientists are uncovering new details about what it means to be creative and how to nurture that skill. “Creativity is of incredible real-world value,” Kounios said. “The ultimate goal is to figure out how to enhance it in a systematic way.”

Streaming Audio

Creativity in the brain.

What, exactly, is creativity? The standard definition used by researchers characterizes creative ideas as those that are original and effective, as described by psychologist Mark A. Runco, PhD, director of creativity research and programming at Southern Oregon University ( Creativity Research Journal , Vol. 24, No. 1, 2012). But effectiveness, also called utility, is a slippery concept. Is a poem useful? What makes a sculpture effective? “Most researchers use some form of this definition, but most of us are also dissatisfied with it,” Kounios said.

Runco is working on an updated definition and has considered at least a dozen suggestions from colleagues for new components to consider. One frequently suggested feature is authenticity. “Creativity involves an honest expression,” he said.

Meanwhile, scientists are also struggling with the best way to measure the concept. As a marker of creativity, researchers often measure divergent thinking—the ability to generate a lot of possible solutions to a problem or question. The standard test of divergent thinking came from Guilford himself. Known as the alternate-uses test, the task asks participants to come up with novel uses for a common object such as a brick. But measures of divergent thinking haven’t been found to correlate well with real-world creativity. Does coming up with new uses for a brick imply a person will be good at abstract art or composing music or devising new methods for studying the brain? “It strikes me as using way too broad a brush,” Seli said. “I don’t think we measure creativity in the standard way that people think about creativity. As researchers, we need to be very clear about what we mean.”

One way to do that may be to move away from defining creativity based on a person’s creative output and focus instead on what’s going on in the brain, said Adam Green, PhD, a cognitive neuroscientist at Georgetown University and founder of the Society for the Neuroscience of Creativity . “The standard definition, that creativity is novel and useful, is a description of a product,” he noted. “By looking inward, we can see the process in action and start to identify the characteristics of creative thought. Neuroimaging is helping to shift the focus from creative product to creative process.”

That process seems to involve the coupling of disparate brain regions. Specifically, creativity often involves coordination between the cognitive control network, which is involved in executive functions such as planning and problem-solving, and the default mode network, which is most active during mind-wandering or daydreaming (Beaty, R. E., et al., Cerebral Cortex , Vol. 31, No. 10, 2021). The cooperation of those networks may be a unique feature of creativity, Green said. “These two systems are usually antagonistic. They rarely work together, but creativity seems to be one instance where they do.”

Green has also found evidence that an area called the frontopolar cortex, in the brain’s frontal lobes, is associated with creative thinking. And stimulating the area seems to boost creative abilities. He and his colleagues used transcranial direct current stimulation (tDCS) to stimulate the frontopolar cortex of participants as they tried to come up with novel analogies. Stimulating the area led participants to make analogies that were more semantically distant from one another—in other words, more creative ( Cerebral Cortex , Vol. 27, No. 4, 2017).

Green’s work suggests that targeting specific areas in the brain, either with neuromodulation or cognitive interventions, could enhance creativity. Yet no one is suggesting that a single brain region, or even a single neural network, is responsible for creative thought. “Creativity is not one system but many different mechanisms that, under ideal circumstances, work together in a seamless way,” Kounios said.

In search of the eureka moment

Creativity looks different from person to person. And even within one brain, there are different routes to a creative spark, Kounios explained. One involves what cognitive scientists call “System 1” (also called “Type 1”) processes: quick, unconscious thoughts—aha moments—that burst into consciousness. A second route involves “System 2” processes: thinking that is slow, deliberate, and conscious. “Creativity can use one or the other or a combination of the two,” he said. “You might use Type 1 thinking to generate ideas and Type 2 to critique and refine them.”

Which pathway a person uses might depend, in part, on their expertise. Kounios and his colleagues used electroencephalography (EEG) to examine what was happening in jazz musicians’ brains as they improvised on the piano. Then skilled jazz instructors rated those improvisations for creativity, and the researchers compared each musician’s most creative compositions. They found that for highly experienced musicians, the mechanisms used to generate creative ideas were largely automatic and unconscious, and they came from the left posterior part of the brain. Less-experienced pianists drew on more analytical, deliberative brain processes in the right frontal region to devise creative melodies, as Kounios and colleagues described in a special issue of NeuroImage on the neuroscience of creativity (Vol. 213, 2020). “It seems there are at least two pathways to get from where you are to a creative idea,” he said.

Coming up with an idea is only one part of the creative process. A painter needs to translate their vision to canvas. An inventor has to tinker with their concept to make a prototype that actually works. Still, the aha moment is an undeniably important component of the creative process. And science is beginning to illuminate those “lightbulb moments.”

Kounios examined the relationship between creative insight and the brain’s reward system by asking participants to solve anagrams in the lab. In people who were highly sensitive to rewards, a creative insight led to a burst of brain activity in the orbitofrontal cortex, the area of the brain that responds to basic pleasures like delicious food or addictive drugs ( NeuroImage , Vol. 214, 2020). That neural reward may explain, from an evolutionary standpoint, why humans seem driven to create, he said. “We seem wired to take pleasure in creative thoughts. There are neural rewards for thinking in a creative fashion, and that may be adaptive for our species.”

The rush you get from an aha moment might also signal that you’re onto something good, Schooler said. He and his colleagues studied these flashes of insight among creative writers and physicists. They surveyed the participants daily for two weeks, asking them to note their creative ideas and when they occurred. Participants reported that about a fifth of the most important ideas of the day happened when they were mind-wandering and not working on a task at hand ( Psychological Science , Vol. 30, No. 3, 2019). “These solutions were more likely to be associated with an aha moment and often overcoming an impasse of some sort,” Schooler said.

Six months later, the participants revisited those ideas and rated them for creative importance. This time, they rated their previous ideas as creative, but less important than they’d initially thought. That suggests that the spark of a eureka moment may not be a reliable clue that an idea has legs. “It seems like the aha experience may be a visceral marker of an important idea. But the aha experience can also inflate the meaningfulness of an idea that doesn’t have merit,” Schooler said. “We have to be careful of false ahas.”

Boosting your creativity

Much of the research in this realm has focused on creativity as a trait. Indeed, some people are naturally more creative than others. Creative individuals are more likely than others to possess the personality trait of openness. “Across different age groups, the best predictor of creativity is openness to new experiences,” said Anna Abraham, PhD, the E. Paul Torrance Professor and director of the Torrance Center for Creativity and Talent Development at the University of Georgia. “Creative people have the kind of curiosity that draws them toward learning new things and experiencing the world in new ways,” she said.

We can’t all be Thomas Edison or Maya Angelou. But creativity is also a state, and anyone can push themselves to be more creative. “Creativity is human capacity, and there’s always room for growth,” Runco said. A tolerant environment is often a necessary ingredient, he added. “Tolerant societies allow individuals to express themselves and explore new things. And as a parent or a teacher, you can model that creativity is valued and be open-minded when your child gives an answer you didn’t expect.”

One way to let your own creativity flow may be by tapping into your untethered mind. Seli is attempting to do so through his studies on hypnagogia. After pilot testing the idea on himself, he’s now working on a study that uses the sleep-tracking glove to explore creativity in a group of Duke undergrads. “In dream states, there seems to be connectivity between disparate ideas. You tend to link things together you normally wouldn’t, and this should lead to novel outcomes,” he said. “Neurally speaking, the idea is to increase connectivity between different areas of the brain.”

You don’t have to be asleep to forge those creative connections. Mind-wandering can also let the ideas flow. “Letting yourself daydream with a purpose, on a regular basis, might allow brain networks that don’t usually cooperate to literally form stronger connections,” Green said.

However, not all types of daydreams will get you there. Schooler found that people who engage in more personally meaningful daydreams (such as fantasizing about a future vacation or career change) report greater artistic achievement and more daily inspiration. People who are prone to fantastical daydreaming (such as inventing alternate realities or imaginary worlds) produced higher-quality creative writing in the lab and reported more daily creative behavior. But daydreams devoted to planning or problem-solving were not associated with creative behaviors ( Psychology of Aesthetics, Creativity, and the Arts , Vol. 15, No. 4, 2021).

It’s not just what you think about when you daydream, but where you are when you do it. Some research suggests spending time in nature can enhance creativity. That may be because of the natural world’s ability to restore attention, or perhaps it’s due to the tendency to let your mind wander when you’re in the great outdoors (Williams, K. J. H., et al., Journal of Environmental Psychology , Vol. 59, 2018). “A lot of creative figures go on walks in big, expansive environments. In a large space, your perceptual attention expands and your scope of thought also expands,” Kounios said. “That’s why working in a cubicle is bad for creativity. But working near a window can help.”

Wherever you choose to do it, fostering creativity requires time and effort. “People want the booster shot for creativity. But creativity isn’t something that comes magically. It’s a skill, and as with any new skill, the more you practice, the better you get,” Abraham said. In a not-yet-published study, she found three factors predicted peak originality in teenagers: openness to experience, intelligence, and, importantly, time spent engaged in creative hobbies. That is, taking the time to work on creative pursuits makes a difference. And the same is true for adults, she said. “Carve out time for yourself, figure out the conditions that are conducive to your creativity, and recognize that you need to keep pushing yourself. You won’t get to where you want to go if you don’t try.”

Those efforts can benefit your own sense of creative fulfillment and perhaps lead to rewards on an even grander scale. “I think everyday creativity is the most important kind,” Runco said. “If we can support the creativity of each and every individual, we’ll change the world.”

How to become more creative

1. Put in the work: People often think of creativity as a bolt of inspiration, like a lightbulb clicking on. But being creative in a particular domain—whether in the arts, in your work, or in your day-to-day life—is a skill. Carve out time to learn and practice.

2. Let your mind wander: Experts recommend “daydreaming with purpose.” Make opportunities to let your daydreams flow, while gently nudging them toward the creative challenge at hand. Some research suggests meditation may help people develop the habit of purposeful daydreaming.

3. Practice remote associations: Brainstorm ideas, jotting down whatever thoughts or notions come to you, no matter how wild. You can always edit later.

4. Go outside: Spending time in nature and wide-open spaces can expand your attention, enhance beneficial mind-wandering, and boost creativity.

5. Revisit your creative ideas: Aha moments can give you a high—but that rush might make you overestimate the merit of a creative idea. Don’t be afraid to revisit ideas to critique and tweak them later.

Further reading

Creativity: An introduction Kaufman, J. C., and Sternberg, R. J. (Eds.), Cambridge University Press, 2021

The eureka factor: Aha moments, creative insight, and the brain Kounios, J., & Beeman, M., Random House, 2015

Creativity anxiety: Evidence for anxiety that is specific to creative thinking, from STEM to the arts Daker, R. J., et al., Journal of Experimental Psychology: General , 2020

Predictors of creativity in young people: Using frequentist and Bayesian approaches in estimating the importance of individual and contextual factors Asquith, S. L., et al., Psychology of Aesthetics, Creativity, and the Arts , 2020

Recommended Reading

Contact apa, you may also like.

Last updated 10th July 2024: Online ordering is currently unavailable due to technical issues. We apologise for any delays responding to customers while we resolve this. For further updates please visit our website https://www.cambridge.org/news-and-insights/technical-incident

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings .

Login Alert

• > The Cambridge Handbook of Creativity
• > Motivation and Creativity

Book contents

• The Cambridge Handbook of Creativity
• Copyright page
• Contributors
• Acknowledgments
• An Introduction to the Second Edition
• Part I An Introduction to Creativity
• Part II Underpinnings of Creativity
• Part III Differential Bases for Creativity
• Individual Differences in Creativity
• 16 The Relation of Creativity to Intelligence and Wisdom
• 17 The Function of Personality in Creativity
• 18 Motivation and Creativity
• 19 Creative Self-Beliefs
• Environmental Differences in Creativity
• Part IV Creativity in the World

18 - Motivation and Creativity

from Individual Differences in Creativity

Published online by Cambridge University Press:  12 April 2019

Access options

Save book to kindle.

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle .

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service .

• Motivation and Creativity
• By Beth A. Hennessey
• Edited by James C. Kaufman , University of Connecticut , Robert J. Sternberg , Cornell University, New York
• Book: The Cambridge Handbook of Creativity
• Online publication: 12 April 2019
• Chapter DOI: https://doi.org/10.1017/9781316979839.020

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox .

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive .

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

The Relationship between Creativity and Interpersonal Problem-Solving Skills in Adults

1996, The Journal of Creative Behavior

Related Papers

Indian Journal of Psychiatry

Vishal Indla

The Journal of Creative Behavior

Jan te Nijenhuis

Motivation and Emotion

Núria Pérez-Escoda

Intelligence

Kenneth Wilson

The Journal of Humanistic Counseling, Education and Development

Christopher McNally

Proceedings of the 9th ACM Conference on Creativity & Cognition

The New Educational Review

Procedia - Social and Behavioral Sciences

Cristina Chirila

Lecture Notes in Computer Science

Ahmed Abdelfattah

Alf Beauman (PI) 🇪🇺 , Carlton Pomeroy

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

European Proceedings of Social & Behavioural Sciences

Ivanna Shubina

Psychology in the Schools

David Feldman

Clinical Psychology Review

Sheri Johnson

The Leadership Quarterly

Child Neuropsychology

Anna Abraham , Onur Güntürkün

Arthur Cropley

Theoretical & Applied Science

Victoria Moroz

HANIFAH HOSEN

Creativity Under Duress in Education?

Thinking Skills and Creativity

Solange Wechsler

Topics in Case-Based Reasoning

Janet Kolodner

Norman Huang

Audrey Friedman

Journal of Education and Practice

Ahmad Isnaini

Tatiana Paley

International Journal of Educational Sciences

Sulaiman O L U S E G U N Atiku

International Journal of Human-Computer Interaction

Ernest Edmonds

Creative Education

Juliana Berg

Journal of Psychology & Behavior Research

FELIX-KINGSLEY OBIALO

Suzanna Ramos

International Journal of Hospitality Management

Levent Altinay

Entrepreneurship Education and Training

Sílvio Manuel Da Brito

Gabriela Krumm

Marzuki Mustafa

Ileana Monteiro

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

Memory and creativity: A meta-analytic examination of the relationship between memory systems and creative cognition

• Theoretical/Review
• Published: 25 May 2023
• Volume 30 , pages 2116–2154, ( 2023 )

Cite this article

• Courtney R. Gerver 1 ,
• Jason W. Griffin 1 ,
• Nancy A. Dennis 1 &
• Roger E. Beaty 1 , 1  

2869 Accesses

13 Citations

30 Altmetric

Explore all metrics

Increasing evidence suggests that specific memory systems (e.g., semantic vs. episodic) may support specific creative thought processes. However, there are a number of inconsistencies in the literature regarding the strength, direction, and influence of different memory (semantic, episodic, working, and short-term) and creativity (divergent and convergent thinking) types, as well as the influence of external factors (age, stimuli modality) on this purported relationship. In this meta-analysis, we examined 525 correlations from 79 published studies and unpublished datasets, representing data from 12,846 individual participants. We found a small but significant ( r  = .19) correlation between memory and creative cognition. Among semantic, episodic, working, and short-term memory, all correlations were significant, but semantic memory – particularly verbal fluency, the ability to strategically retrieve information from long-term memory – was found to drive this relationship. Further, working memory capacity was found to be more strongly related to convergent than divergent creative thinking. We also found that within visual creativity, the relationship with visual memory was greater than that of verbal memory, but within verbal creativity, the relationship with verbal memory was greater than that of visual memory. Finally, the memory-creativity correlation was larger for children compared to young adults despite no impact of age on the overall effect size. These results yield three key conclusions: (1) semantic memory supports both verbal and nonverbal creative thinking, (2) working memory supports convergent creative thinking, and (3) the cognitive control of memory is central to performance on creative thinking tasks.

Similar content being viewed by others

The role of memory in creative ideation

Is there a role of creativity in the relationship between working memory consolidation and long-term memory?

The scope and control of attention: sources of variance in working memory capacity.

Avoid common mistakes on your manuscript.

Introduction

General overview.

The act of remembering is an attempt to retrieve concepts or events that have been learned or experienced at some point in the past. In contrast, generating creative ideas and discoveries involves combining learned concepts into new information and perspectives that were not previously apparent (Stein, 1989 ). How does remembering the past impact creative thinking? While at first glance memory and creativity may appear distinct, creative thought is often conceptualized as a high-level cognitive ability that is supported (or scaffolded) by “lower-level” cognitive processes, including memory, attention, and cognitive control (cf., Abraham, 2014 ; Benedek & Fink, 2019 ; Finke et al., 1992 ). Understanding the nature of creative thought ultimately requires mapping relevant underlying constructs, including identifying how and when memory may support or constrain creative thought. At the same time, examining the memory-creativity link provides critical insight into consequences and higher-level functions of different memory systems (e.g., the episodic system supports both remembering the past and imagining the future; Schacter & Addis, 2007b ).

Yet studying the memory-creativity relationship is a complex endeavor: there are many types of memory (e.g., semantic, episodic, working, short-term) and creativity (e.g., divergent, convergent thinking) that can be elicited and influenced depending on the demands of a given task. Additionally, while classic creativity theories assume a central role of memory in generating creative thoughts (Mednick, 1962 ), memory is also error-prone, and it can act as a source of interference – particularly when people are reminded of old and unoriginal ideas. Further, the relationship between memory and creativity can also be influenced by individual factors such as age (e.g., Arenberg, 1973 ; Hess, 2005 ; Palmiero et al., 2017 ) or task-specific factors such as stimulus modality (verbal vs. visuospatial response format; Chrysikou et al., 2016 ; Farah et al., 1989 ; Freides, 1974 ; Penney, 1989 ). Therefore, understanding the relationship between memory and creativity must involve defining the type of memory and creativity under investigation, as well as task parameters and individual differences that can influence the strength and direction of the relationship.

Here, we assess the links between memory and creativity by examining how each memory type uniquely relates to creative performance across a wide range of task contexts. Specifically, our review focuses on the following memory systems: semantic memory (a measurement of verbal ability, concepts, and their relations; Collins & Loftus, 1975 ), episodic memory (memory for unique experiences; Tulving & Patterson, 1968 ), working memory (temporary storage and manipulation of information; Baddeley & Hitch, 1974 ), and short-term memory (holding limited information in a temporarily accessible, non-manipulated state; Atkinson & Shiffrin, 1968 ). Regarding creativity, our review focuses on divergent thinking (solving open-ended problems with multiple solutions; Guilford, 1950 ) and convergent thinking (solving problems with only one correct solution; Runco et al., 2010 ). Together, we leverage meta-analytic tools to identify which memory systems reliably support specific modes of creative thought, thus providing clarity on over 50 years of cognitive research on creativity.

Overview of “domain-general” creativity tasks and metrics

Although the study of creativity is a broad and diverse field of research, the study of “domain-general” creativity – the ability to come up with ideas and solve problems that do not require domain-specific knowledge or expertise – has converged on a handful of measures to assess divergent and convergent creative thinking. One of the most common measures of divergent thinking is the Alternate Uses Task (AUT; Torrance, 1972 ), which requires people to think of unusual uses for everyday objects. AUT scores can reflect the number of ideas generated (fluency; Runco et al., 2011 ), the variety of ideas across categories or themes (flexibility; Guilford, 1968 ; Runco & Okuda, 1991 ), and the novelty (statistical infrequency or quality) of an idea (Wallach & Kogan, 1965 ), among others. Divergent thinking tasks have shown evidence for predictive validity, including moderate to large correlations with real-world creative achievement (Beaty et al., 2018 ; Jauk et al., 2014 ). Convergent thinking is often assessed with the Remote Associates Test (RAT; Mednick, 1962 ), which presents a triplet of apparently unrelated words (e.g., cream, skate, water) and requires people to find a fourth word that conceptually unites them (e.g., ice). Scoring the RAT and other convergent thinking tasks typically involves simply counting the number of problems correctly solved. Convergent thinking tasks can be solved by either analysis or insight. Analysis is the deliberate search of a problem space to find solutions (Ericsson & Simon, 1998 ; Kounios et al., 1987 ; Newell & Simon, 1972 ), whereas with insight, a solution emerges spontaneously into awareness (i.e., the “aha” experience; Metcalfe & Wiebe, 1987 ; Smith & Kounios, 1996 ). However, divergent and convergent creative thinking are both susceptible to fixation, or a mental block to problem solving (Smith & Blankenship, 1991 .) Despite increasing attempts to map the cognitive mechanisms of divergent and convergent creative thinking, the roles of specific memory systems in specific modes of creative cognition remain inconclusive. Below, we synthesize previous research efforts toward this goal.

Explicit long-term memory and creative cognition

Semantic memory and divergent thinking.

Semantic memory, or the organization of facts and concepts into networks, is embedded in classic theories of creative cognition. According to the associative theory (Mednick, 1962 ), creativity involves combining weakly related, remote concepts in semantic memory into novel and useful ideas, a process that is thought to occur through spreading activation (Collins & Loftus, 1975 ). On this view, as the relative “semantic distance” between two concepts increases, so does the likelihood that a conceptual combination will be perceived as creative. The associative theory also suggests that highly creative individuals have a more efficient, “flat” associative hierarchy (numerous and weakly related associations to a given concept) compared to less creative people, who have more “steep” associative hierarchies (few, strong associations to a given concept; Mednick, 1962 ).

The associative theory has received support from empirical investigations linking individual semantic memory structure to creative task performance, particularly divergent thinking. For example, the semantic networks of highly creative individuals, defined by divergent thinking performance, have higher connectivity (the extent to which two neighbors of a node in a network will be neighbors), shorter path length (average number of steps (edges) between any pair of nodes in a network), and fewer subcommunities (subcategories, or smaller networks, within the overall network) than less creative individuals (Kenett et al., 2014 , 2018 ). That is, denser, highly connected, and less modular networks facilitate more efficient activation spread beyond closely connected (unoriginal) semantic concepts to more remote ones (Kenett et al., 2014 , 2018 ), which in turn leads to the formation of creative ideas (Mednick, 1962 ; Schilling, 2005 ). Highly creative individuals have also exhibited a more complex lexical network structure, and they tend to activate a wider range of associations, potentially increasing the number of novel ideas from which to choose (Gruszka & Necka, 2002 ; Kenett et al., 2018 ).

Further support for associative processes in creative thought comes from studies of the serial order effect (Parnes, 1961 ; Ward, 1969 ), a phenomenon in which idea production often follows a temporal tendency where ideas become less frequent and more original over time during divergent thinking tasks (Beaty & Silvia, 2012 ). Initially high idea fluency is attributed to activating the dense semantic neighborhood directly surrounding the stimuli prompt (e.g., a brick, during the AUT) and producing responses similar to the prompt (Gilhooly et al., 2007 ). Then, as spreading activation unfolds with time, originality increases later in the task, when distant concepts within the semantic networks can be reached (Collins & Loftus, 1975 ; Mednick, 1962 ).

Alongside such passive activation spread, contemporary theories of semantic memory also emphasize the importance of top-down, strategic, and controlled processes to guide memory retrieval (Rosen & Engle, 1997 ; Unsworth et al., 2009 ). Indeed, there is now considerable evidence linking creative cognition to aspects of controlled semantic retrieval, with several studies reporting positive effects of verbal fluency (a measure of sematic retrieval ability) on divergent thinking ability (Benedek et al., 2012 ; Forthmann et al., 2019 ; Gilhooly et al., 2007 ; Silvia et al., 2013 ). For example, category fluency tasks require people to produce as many unique words as possible within a semantic category (e.g., animals), and phonological fluency tasks require people to produce as many unique words starting with a given letter (e.g., F, A, and S); the tasks are scored by summing the unique/correct words (Shao et al., 2014 ). Given the close resemblance in task requirements for verbal fluency and divergent thinking tasks – both involve open-ended retrieval from memory – classic models of intelligence have viewed divergent thinking as a lower-level factor of broad retrieval ability (McGrew, 2009 ). A critical distinction, however, is that divergent thinking tasks often consider the quality of response, whereas canonical verbal fluency tasks only consider the number of responses. Together, top-down retrieval strategies are thought to facilitate divergent creative thinking, in addition to the aforementioned passive process of spreading activation.

While access to semantic memory can facilitate divergent thinking, prior knowledge can also constrain creative thought. Indeed, the semantic system is organized to facilitate efficient and appropriate linguistic functions, many of which do not call for creativity. Within the spreading activation framework (Collins & Loftus, 1975 ), one must overcome strongly activated semantic interference to generate a creative response, as reflected in the beginning stages of the serial order effect (Beaty & Silvia, 2012 ; Christensen et al., 1957 ). One type of interference associated with semantic retrieval is functional fixedness, whereby stereotypical object information impedes generating novel ideas during creative problem solving (Duncker, 1945 ), including on open-ended tasks (Glucksberg & Weisberg, 1966 ) such as the AUT (Chrysikou et al., 2016 ). Further, increased knowledge can lead to the fan effect (Anderson, 1974 ), whereby increasing knowledge about concepts leads to increased interference from related information (Beaty et al., 2019 ). Although the fan effect is known as an episodic phenomenon, a semantic analog – increasing associative elements linked to a given cue – has been shown to impact the quality and quantity of responses on the AUT: low-association AUT cues yield higher originality but less fluency than high-association AUT, potentially due to less interference from closely related concepts in semantic memory (Beaty et al., 2019 ). Thus, semantic memory has shown both costs and benefits to divergent creative thinking.

Semantic memory and convergent thinking

The semantic system has also been implicated in convergent thinking. The RAT was constructed in such a way that only one solution is possible and that the first solution is commonly incorrect, thus requiring one to overcome the incorrect solution and identify the correct, “remote” association (Akbari Chermahini & Hommel, 2012 ). Spreading activation (Collins & Loftus, 1975 ) accounts of the RAT (Smith et al., 2013 ) suggest the cue words activate close associates in semantic space, and the activation spreads until people ultimately converge on a solution. If the solution to a RAT problem readily comes to mind, then the cues are considered to be “closer” together in the underlying network. According to this research (Smith et al., 2013 ), the RAT can be solved using two semantic search strategies. First, participants will select a set of possible answers constrained by just one word from the triplet at a time. Second, they’ll adopt a local search strategy and make new guesses based in part on their previous guesses (Smith et al., 2013 ). This semantic search approach also applies to other cognitive tasks such as generating hypotheses (Thomas et al., 2008 ) and analogies (Forbus et al., 1995 ). If this search process is biased in any way, such as forcing participants to respond quickly, then high-frequency words are produced even if they are not correct (Gupta et al., 2012 ). Successful convergent creative thinking is therefore thought to require bypassing high-frequency responses that passively activate in semantic space via spreading activation.

In contrast to passive activation, another line of research suggests individuals take a more controlled, top-down memory search approach when problem-solving known as information foraging. Foraging theory was first applied to non-human animals searching for food (Stephens & Krebs, 1986 ), and it has since been adopted to explain information foraging in cognitive systems (Hills et al., 2012 ; Pirolli, 2007 ). Foraging has specifically been used to describe semantic memory search behaviors when solving creative problems, such as the RAT. Specifically, the three RAT cues may activate adjacent semantic neighborhoods and eventually intersect. Information within this intersection is activated more strongly than the individual neighborhoods, but not to the point where individual cue-specific items would get excluded. An optimal memory forage would involve focusing one’s search on the intersection of the cue’s semantic neighborhood to maximize the difference in activation between targets and distractors (Davelaar, 2015 ). In contrast, when completing a verbal fluency task (e.g., listing animals), search behavior typically involves staying within a “patch,” or neighborhood cluster, until it is exhausted (Hills et al., 2012 ). Searching the intersection of a RAT triplet is particularly advantageous when the target is weak and cue patches contain strong interference (Davelaar, 2015 ). Thus, compared to passive activation spread and controlled retrieval, memory foraging may allow one to intentionally bypass distractors, allowing more efficient retrieval of the target solution.

On the other hand, more spontaneous, insight-based problem-solving is thought to be the result of using shortcuts (or creating links) between semantic concepts when searching semantic memory (Schilling, 2005 ). One study (Samsonovich & Kuznetsova, 2018 ) attempted to map memory search processes when solving classic insight problems (DeYoung et al., 2008 ) and found people take a less linear approach through semantic concepts, particularly at the very end of the task – just prior to the insight experience – compared to moving linearly towards a single solution (Samsonovich & Kuznetsova, 2018 ). These findings suggest the anticipated end of an insight problem is enough to alter a semantic search path. Notably, performance on classic insight problems has shown questionable validity evidence – including near-zero correlations reported between insight problem solving and creative achievement (Beaty et al., 2014a , b ) – so the generalization of such findings to real-world creativity is currently unclear.

Similar to the relationship between semantic memory and divergent thinking, semantic memory can also lead to mental fixation and impede problem solving (Duncker, 1945 ; Maier, 1931). For example, simply exposing participants to inappropriate or misleading semantic associates can impair performance on the RAT, leading to fixation (Smith & Blankenship, 1991 ). People also tend to naturally fixate on salient but incorrect solutions (e.g., high-frequency words), a phenomenon that can be redirected with clues and other types of priming (Vul & Pashler, 2007 ). Therefore, bypassing inappropriate ideas to formulate new and creative ones appears to be relevant to problem solving (Storm et al., 2011 ). Convergent thinking is also susceptible to negative transfer, which is when prior learning causes poorer subsequent performance. For example, when the test words of RAT problems (e.g., cottage, Swiss, cake) are paired with conceptually related words (e.g., hut, chocolate, icing) that are unrelated to solutions (e.g., cheese), performance on the RAT decreases due to fixating on what had recently been learned (Beda & Smith, 2018 ). Bypassing or forgetting fixation-inducing semantic associates thus seems to be important for solving creative problems in this task (Storm et al., 2011 ). Yet the literature is still mixed on the broader role of semantic memory in convergent creative thinking, particularly regarding whether individual differences in semantic memory abilities (e.g., verbal fluency) reliably predict performance on convergent thinking tasks, such as the RAT.

Episodic memory and divergent thinking

Although a majority of research on memory and creativity has focused on the semantic system, recently, researchers have begun to explore the potential role of episodic memory in the creative process. Episodic memory retrieval is considered to be a constructive process , wherein past events are reconstructed by piecing together individual-stored memories of people, contexts, and actions. The constructive episodic simulation hypothesis (Schacter & Addis, 2007a , b ) suggests episodic memory provides a source of details for the retrieval of past events. The hypothesis also contends that the constructive nature of the episodic memory system allows for the recombination of such details into a simulation of a novel event, like when one imagines future experiences that have not yet occurred (Schacter & Addis, 2007a ). There is considerable evidence demonstrating an overlap between memory retrieval and imagination, including neuroimaging studies showing a substantial overlap in the brain regions engaged during tasks involving episodic retrieval and future simulation (Addis et al., 2009 ; Schacter et al., 2012 ; Szpunar & Schacter, 2018 ). Regarding creativity, more recent evidence has demonstrated individuals sometimes draw on episodic memories when performing divergent thinking tasks (e.g., Addis et al., 2016 ; Benedek et al., 2014a , b ; Duff et al., 2013 ; Ellamil et al., 2012 ), suggesting that the constructive nature of the episodic system may extend to creative tasks that similarly require flexibly combining information.

Divergent thinking may also benefit from direct recall of solution-relevant past experiences (Sheldon et al., 2011 ; Vandermorris et al., 2013 ). For example, participants who completed a think-aloud version of the AUT occasionally drew on their personal past experiences when generating object uses, though this type of retrieval primarily occurred at the beginning of the task (Gilhooly et al., 2007 ). Drawing on previous experiences can also be beneficial for real-world creative problems, such as when experienced engineers educate novice engineers by sharing hints and previously used examples (Smith et al., 1993 ). Neuroimaging work has found that brain regions typically associated with episodic memory, including the hippocampus, show increased activity when performing divergent thinking tasks such as the AUT (Benedek et al., 2014a , b ) and when generating ideas on a drawing task (Ellamil et al., 2012 ). Researchers have also causally tested this relationship using inhibitory transcranial magnetic stimulation (TMS; Thakral et al., 2020 ). Specifically, inhibitory TMS to the hippocampus (via the angular gyrus) – core regions of the episodic system – led participants to produce fewer ideas on the AUT and fewer episodic details when imagining future events.

To assess the extent to which episodic memory contributes to divergent creative thinking, researchers have used an experimental procedure known as episodic-specificity induction (ESI). ESI trains participants in recollecting specific details of recent experiences (e.g., recalling the details of events from a video), which activates constructive retrieval mechanisms and thus can be used to test the involvement of the episodic system on a subsequent behavioral task. Across several studies, ESI has been found to boost the number of categories of appropriate uses, as well as episodic details (but not semantic details) in both young and older adults (Madore & Schacter, 2014 ; Madore et al., 2014 , 2015 ), despite the observation that age-related differences in remembering the past extend to imagining the future (Schacter et al., 2013 ). At the individual level, performance on the AUT was shown to positively correlate with the amount of episodic detail when younger and older adults imagine future personal scenarios (Addis et al., 2016 ). Further, an fMRI study of divergent thinking found that the ESI engages the hippocampus (Madore et al., 2019 ). Notably, however, the behavioral effects of ESI appear to be limited to increasing the number (i.e., fluency) of ideas on divergent tasks, and not their creative quality, indicating that episodic memory may make people more generative but not necessarily more original (Madore et al., 2016 ). Together, ESI studies lend further support to the constructive episodic hypothesis and the involvement of episodic memory in divergent thinking (van Genugten et al., 2021 ).

While the above research supports the role of episodic memory in divergent creative thinking, access to past experiences can also negatively impact creative output as well. For example, past experiences can bias people toward schemas that are not conducive to creativity. In the aforementioned study of engineers (Smith et al., 1993 ), biasing retrieval through conformity was found to render expert engineers unable to think beyond hints and examples to generate novel designs (Linsey et al., 2010 ). In another study (Smith et al., 1993 ), participants were asked to create new toys and new animals to inhabit a foreign planet. Participants who were shown pictorial examples prior to creation tended to conform to these examples, despite explicitly being asked to avoid using components of the examples. Such effects hold even if the examples contain design flaws, and participants will replicate such flaws even when explicitly instructed not to (Chrysikou & Weisberg, 2005 ). Thus, prior experience can be both a cost and benefit to divergent thinking.

Episodic memory and convergent thinking

Episodic memory may also influence convergent thinking skills such as problem-solving (Roediger et al., 2007 ). For example, insights are typically incorporated into long-term memory, facilitating more efficient problem-solving in the future (Holland & Gallagher, 2006 ; Ludmer et al., 2011 ). One line of research suggests that solving RAT problems with insight necessitates a fundamental, unconscious change to the initial problem representation (Ohlsson, 1992 , 2011 ). A separate line of research examines how episodic false memories, or erroneously remembering an experience that did not actually happen, interact with higher cognitive abilities such as problem-solving. In the Deese/Roediger-McDermott (DRM) paradigm, for example, participants are given word lists (e.g., bed, rest, awake) the members of which are all associates of an unpresented critical lure (e.g., sleep). Despite having never been presented during the study phase, participants often falsely remember the critical lure as being presented in the list (Deese, 1959 ; Roediger & McDermott, 1995 ). Of relevance to creativity, researchers primed RAT performance with a preceding DRM list whose critical lure was also the solution to one of the RAT problems (Howe et al., 2010 ). They found that when the critical lure was falsely recalled in the DRM, RAT problems were solved more often and faster than when problems were not primed. Critically, there were no differences between primed and unprimed RAT problem solution rates and reaction times when the critical lure was not falsely recalled. These results demonstrate that episodic false memory can influence performance on creative problem-solving tasks.

On the other hand, previous experiences can negatively influence how one solves a single-solution problem. For example, in the classic “water jug problem” (the Einstellung effect; Luchins, 1942 ), participants were required to take jugs filled with water and find a sequence of pouring that would produce a prespecified amount of water in each jug. After the researchers performed a demonstration, participants would continuously attempt to use the solution they saw demonstrated, even when it was not practical. Such experience-induced inflexibility can become an even greater problem when someone is an expert in a topic: coming up with new ideas may be challenging simply because one knows how things should be done (De Bono, 1968 ). Further, even if one generates a solution via insight, memory for the solution decays (Ormerod et al., 2002 ). Thus, moving beyond previous experiences stored in memory appears to be important for creatively solving single-solution problems.

Explicit short-term memory and creative cognition

Working memory and divergent thinking.

A longstanding question in the creativity literature concerns the role of attention control via working memory in creative thought. Does creative thinking require focused attention, or rather a relaxation of attention control? The controlled-attention theory of working memory (Engle, 2002 ) suggests working memory capacity contributes to higher order cognition, such as language comprehension (King & Just, 1991 ) and reasoning (Kyllonen, 1996 ). On this view, attentional control is critical to facilitating more efficient maintenance of task-relevant information in working memory (Drabant et al., 2006 ), efficient switching between tasks (Baddeley et al., 2001 ), and sustaining general attention (Unsworth et al., 2009 ) – abilities strongly related to fluid intelligence, or the ability to solve novel problems (Unsworth et al., 2014 ). Although working memory plays a critical role in such cognitive abilities, the contribution of working memory to divergent thinking is less clear.

The controlled attention theory has been adopted by some creativity researchers. According to this theory, attention control facilitates divergent thinking by directing search processes away from strong, common associates (Beaty et al., 2014a , b ; Benedek et al., 2014a , b ; Jauk et al., 2013 ). In other words, controlled attentional processes may intervene in an otherwise spontaneous process of spreading activation within semantic memory networks by suppressing unoriginal mnemonic information (Frith et al., 2021 ). Additional findings suggest working memory capacity supports divergent thinking through cognitive persistence, or sustained task-relevant processing that is robust to proactive interference (De Dreu et al., 2012 ). In addition, because creativity appears to involve pulling concepts from long term-memory into working memory, which are then manipulated to find a solution, working memory may allow for the discrimination of task-relevant and -irrelevant information (De Dreu et al., 2012 ; Unsworth et al., 2009 ). Together, working memory has been hypothesized to benefit divergent thinking through attentional control mechanisms that manage and direct complex search processes.

A number of individual differences studies have investigated links between working memory, executive functions, and divergent thinking (e.g., Beck et al., 2016 ; Lee & Therriault, 2013 ; Menashe et al., 2020 ; Vartanian et al., 2013 ). In one study on divergent thinking, researchers (Benedek et al., 2014a , b ) examined three executive functions utilized within working memory: shifting, updating, and inhibition. They found shifting (switching between different tasks and mental sets) did not relate to divergent thinking, but updating (monitoring and revising working memory content) and inhibition (suppressing dominant but irrelevant response tendencies) showed significant and positive associations with divergent thinking originality. The researchers concluded working memory (as assessed by updating tasks) uniquely contributes to divergent thinking (Benedek et al., 2014a , b ). On the other hand, several studies found no specific relationship between working memory capacity and divergent thinking tasks (Furley & Memmert, 2015 ; Smeekens & Kane, 2016 ). Still others maintain top-down control actually harms divergent thinking because it restricts mind wandering, which can sometimes facilitate creativity, particularly during incubation periods (Gable et al., 2019 ; Leszczynski et al., 2017 ). These conflicting findings motivate a meta-analytic investigation into the relationship between working memory capacity and divergent thinking.

Working memory and convergent thinking

Because working memory involves the storing and processing of information online (Baddeley, 1986 ), and given its strong association with novel problem solving (i.e., fluid intelligence; Shelton et al., 2010 ), one might assume working memory is relevant for creative problem-solving (Ericsson & Simon, 1998 ). Notably, although early reports examining working memory’s benefit to convergent thinking were mixed (see Wiley & Jarosz, 2012 , for a review), recent studies point to a stronger relationship between the two constructs (Chein & Weisberg, 2014 ; Chuderski & Jastrzębski, 2018 ; Lee et al., 2014 ). The executive-attention framework suggests maintaining information in working memory is critical to success across higher-order cognitive domains by sustaining attentional focus in the face of distraction (Kane & Engle, 2002 ). In the context of convergent problem-solving, working memory is hypothesized to help focus attention, narrow search through a problem space, and inhibit distractions. This ability may be particularly useful, since such problems typically yield initial failed attempts at finding a solution, requiring subsequent iterative attempts to more remote solutions. For example, when reaching an impasse in solving, one might make incremental modifications by back-tracking and systematically searching semantic space. This search process, enabled in part by working memory, ultimately results in solutions that are generally weakly related to their initial representations and hence more creative (Kaplan & Simon, 1990 ).

The present study

Decades of research has sought to characterize the relationship between memory and creativity. And yet, the field remains marked by inconsistent findings, with no clear view on which memory systems reliably support creative cognition. For the field to progress, a systematic analysis of the literature is necessary. Despite longstanding interest in the topic of memory and creativity, to our knowledge, only two book chapters have provided qualitative overviews (Nęcka, 1999 ; Stein, 1989 ). Critically, no attempt has been made to quantitatively summarize the memory-creativity relationship. Here, we conducted a systematic meta-analysis to synthesize and quantify the overall association between memory and creative cognition. Across 50 years of research, we aimed to clarify the strength and direction of the memory-creativity relationship. We also examined whether any relationship is affected by memory system (episodic, semantic, short-term, working) and creativity type (convergent, divergent), and examine whether other study-specific factors affect the strength of the relationship, such as stimulus modality of response (visual (e.g., drawing, selecting shapes), verbal (e.g., writing words, selecting multiple choice)) and participant age.

In our view, the psychology of creativity has matured to a point where a quantitative review of memory and creativity is warranted and necessary. There is now a critical mass of data available to reliably assess the memory-creativity relationship, and clarifying this association is critical to resolving persistent controversies in the field. Therefore, in this meta-analysis, we sought to answer the following questions:

What is the general relationship between memory and creative cognition (across all types of memory and creativity)?

If a general relationship between memory and creative cognition exists, is the summary effect size between constructs influenced by the type of memory (e.g., semantic) or creativity (e.g., divergent thinking)?

If a general relationship between memory and creative cognition exists, is the summary effect size influenced by study-specific factors, like age or stimulus modality (verbal vs. visual)?

For transparency, we first define and operationalize all variables used in subsequent analyses. Semantic memory refers to a person’s capacity to remember facts, meanings, and general knowledge about the world, including comprehension of characteristic item properties and the semantic labels used to describe them (Barsalou, 2003 ; Menon et al., 2002 ; Patterson et al., 2007 ; Quillan, 1966 ; Smith, 1978 ; Squire & Zola, 1998 ; Tulving, 1972 ). Specifically, semantic memory concerns representing and retrieving, or mentally operating on, stored information about the world that is abstracted from episodic experiences and is describable (e.g., not present to the senses; Barsalou, 2003 ; Menon et al., 2002 ; Patterson et al., 2007 ; Smith, 1978 ; Tulving, 1972 ). Common semantic memory tasks (Saumier & Chertkow, 2002 ) emphasize retrieving as many items as possible related to a specific cue (e.g., fluency tasks) and are typically scored by the total number of valid, unique responses provided.

Episodic memory refers to the ability to remember personally experienced events (Baddeley, 2001 ; Craik, 2002 ; Ezzyat & Davachi, 2011 ; Hassabis & Maguire, 2007 ; Squire & Zola, 1998 ; Tulving, 1972 , 1983 , 1993 , 2002 ). Specifically, episodic memory receives and stores spatial and temporal information (and spatial–temporal relationships) among events experienced between event boundaries for later retrieval (Hassabis & Maguire, 2007 ; Squire & Zola, 1998 ; Tulving, 1972 , 1983 , 1993 , 2002 ). Episodic memories can be recalled , which is when the memory of stimulus items is evaluated without the presence of the to-be-remembered information available (e.g., “Tell me all the words you remember”), or  recognized , when memory evaluation of stimulus items occurs in the presence of the to-be-remembered items (e.g., “Did you see this word previously?”; Tulving & Thomson, 1973 ). Common episodic memory tasks involve encoding stimuli (e.g., word lists, picture presentations) and a later recognition or recall phase. While these tasks can be scored on several types of metrics, we focus on veridical memory “hits,” which represents how much information during encoding was accurately remembered during retrieval.

Working memory refers to the ability to maintain and manipulate a limited amount of information held in a highly accessible mental state (Cowan, 2008 ). Although not completely distinct from short-term memory, it is thought to uniquely function as an interface between perception, long-term memory, and action (Aben et al., 2012 ; Andrade, 2001 ; Baddeley, 2003 ; Baddeley & Hitch, 1974 ; Conway et al., 2007 ; Miyake & Shah, 1999 ). Recalling information from working memory requires engaging in an activity interleaved between the presentation of to-be-remembered information and recall (Unsworth & Engle, 2007 ). Working memory tasks involve the simultaneous demands of short, uninterrupted sequences of information for immediate recall (e.g., assessed via backwards digit span, complex span, n-back tasks) and are scored on the correct recognition or recall of one set of information.

Short-term memory refers to the ability to temporarily hold and recall a limited amount of information in a highly accessible mental state, including sensory events, movements, and information from long-term memory (Atkinson & Shiffrin, 1971 ; Cowan, 1988 , 2008 ; Kail & Hall, 2001 ). Short-term memory tasks often involve the presentation of short, uninterrupted sequences of information for immediate recall or recognition (e.g., serial recall tasks such as forwards digit span) and are commonly scored on the number or length of correctly recalled or recognized consecutively presented sequences of information.

Divergent creative thinking refers to the ability to solve open-ended problems with multiple solutions (Guilford, 1950 ). This ability is often tested with ill-defined problems, where multiple solutions are often requested (Mumford & Gustafson, 1988 ) and are traditionally scored on the number of ideas generated (fluency; Runco et al., 2011 ), the variety of ideas across categories or themes (flexibility; Guilford, 1968 ; Runco & Okuda, 1991 ), and the originality (statistical infrequency or quality) of an idea (Wallach & Kogan, 1965 ), though labels may differ by researcher. Common divergent thinking tasks (e.g., Alternate/Unusual Uses Task, Torrance Test of Creative Thinking) are scored by fluency, originality, flexibility, cleverness, or uniqueness, variably operationalized by different researchers of the original studies presented here.

Convergent creative thinking is the ability to solve problems with only one correct solution (Runco et al., 2010 ). Convergent thinking tasks can be solved by either analysis or insight. Analysis is the deliberate search of a problem space to find solutions (Ericsson & Simon, 1998 ; Kounios et al., 1987 ; Newell & Simon, 1972 ), whereas with insight, a solution emerges spontaneously into awareness (i.e., the “aha” experience; Metcalfe & Wiebe, 1987 ; Smith & Kounios, 1996 ). Common convergent creative thinking tasks (e.g., Remote Associates Test, classic insight problems) are measured by summing the number of problems correctly solved.

Power analysis

To determine the feasibility of this meta-analysis, we conducted an a priori power analysis using the R package metapower version 0.2.0 (Griffin, 2020 ). Based on the current state of the literature, we expected that 40 studies would meet inclusion criteria with an average study size of 100 and moderate-large heterogeneity among effect sizes. Overall, we expected that the correlation between measures of creative cognition and memory would be small (i.e., r  = 0.25). Under these expectations, power to detect a statistically significant summary effect size was 100%. For two-group moderator analysis (divergent vs. convergent thinking), power to detect group differences was 90.2%. Since power for subgroups is generally low for meta-analyses, we had no stopping rules and intended to include as many studies as possible to generate a representative dataset of the relevant literature (see Cuijpers et al., 2021 ; Griffin, 2021 ).

Literature search

With the aim of adhering to transparent and rigorous psychological practices (Johnson, 2021 ), we identified, screened, and determined eligibility of empirical studies in accordance with all Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA; Page et al., 2021 ) guidelines, including the content checklist (see Online Supplemental Material File 1 ) and study search flow diagram. On 21 April 2020 we conducted searches using the online databases PsycINFO, PubMed, Web of Science, and Scopus (Bramer, 2017). Each search contained the following terms and Boolean operators: ((memory OR recollection OR familiarity OR recognition OR recall OR retrieval OR “verbal fluency”) AND (creativity OR creative OR “divergent thinking” OR “convergent thinking” OR “idea generation” OR originality)). We collected peer-reviewed published/in-press research articles, preprints (e.g., unreviewed work posted to PsyArxiv, bioRxiv, etc.), and dissertations/theses. Additionally, we solicited relevant unpublished datasets using topic-relevant listservs and Twitter to reduce selective reporting bias. From these hits, we first removed duplicates, then applied our inclusion criteria in a sequential three-step screening procedure: title only screening, title and abstract screening, and full-text review. Finally, we conducted a manual reference screen by extracting the references of all articles meeting inclusion criteria from the database search and applied the same three step screening procedure. The entire study identification, screening, and eligibility process is shown in Fig.  1 . The review and protocol for this study were unregistered and were considered exempt by the The Pennsylvania State University Institutional Review Board.

PRISMA flow diagram illustrating study identification, screening, and selection processes. Blue boxes = records interrogated for inclusion; Red boxes = excluded records; Green boxes = included in meta-analysis

Study selection

Inclusion and exclusion criteria.

For inclusion, articles must have (1) administered at least one direct, observable measure of memory and one direct, observable measure of creativity; (2) administered memory and creativity tasks that reflect semantic memory, episodic memory, working memory, short-term memory, divergent creative thinking, and/or convergent creative thinking; (3) reported the correlation coefficient between memory and creativity performance (or information to calculate the correlation coefficient); (4) been published in English, and (5) included a neurotypical sample.

Articles were excluded if (1) memory or creativity tasks were measured via self-report or interviews from which a memory could not be verified in the lab (e.g., autobiographical memories for events purported to have been experienced outside of the lab); (2) performance could not be verified by a researcher (e.g., tasks reflecting autobiographical memory, dream recall, or prospective memory); and (3) the creativity task was domain-specific (e.g., musical).

Database search

The searches from PsycINFO, PubMed, Web of Science, and Scopus were concatenated, and all duplicates were removed. All titles and abstracts were then screened for inclusion. This step was completed such that records were excluded only if there were clear examples of exclusion, such as the full-text record was not available, or it was non-empirical. After this, full-text articles were screened for the aforementioned primary inclusion and exclusion components. We used a double-screening approach where the first author and a research assistant completed all screening steps independently of each other. Inclusion disagreements were resolved by the second author.

Data extraction

For each included study, we extracted values for the sample size and correlation coefficients between measures of creativity and veridical memory. Specifically, we extracted data computed from raw scores (e.g., summative totals) on semantic memory and convergent thinking tasks; recall or recognition for episodic, short-term, or working memory tasks; and fluency, originality, flexibility, cleverness, or uniqueness for divergent thinking tasks – all operationalized by the researchers of the original study.

When access to the raw data were provided (five unpublished datasets), we calculated the correlation coefficient manually. Since meta-analysis of correlation coefficients are performed on Fisher’s r -to- z transformation scores, we used the metafor package (Viechtbauer, 2010 ) to convert all study-specific correlation coefficients to Fisher’s z scores with the respective variances. Many studies included more than one effect size that met inclusion criteria. To maximize and include as much data as possible, we accounted for this multilevel structure by coding each study and unique effect size.

Moderator variables

To evaluate the influence of age-related variation among effect sizes, we extracted participant information on age (in years) for each study. Specifically, we extracted the average sample age relevant to each effect size. Additionally, to assess the influence of methodological variation among effect sizes, we extracted information related to the type of memory (semantic memory, episodic memory, short-term memory, working memory) and creativity (divergent thinking, convergent thinking), as well as the paradigm modality used to measure memory and creativity (i.e., verbal, visual).

Statistical analysis

We used the metafor package version 2.40 for the statistical software R to analyze all data (R Core Team, 2020; Viechtbauer, 2010 ). To estimate a summary effect size for the correlation between memory and creative cognition, we fit a three-level model to partition the sampling variance of the observed effect sizes, between-study variability, and within-study variability. Unlike traditional meta-analysis, this modeling approach allows for the inclusion of multiple effect sizes per study, while accounting for the interdependency among effect sizes within studies (Assink & Wibbelink, 2016 ; Cheung, 2014 , 2019 ). To determine if there was significant between-study heterogeneity among effect sizes, we evaluated Cochran’s Q and calculated the percentage of variation across studies that was not due to sampling variability (i.e., I 2 ), with values of 75%, 50%, and 25% reflecting large, moderate, and small degrees of heterogeneity (Higgins & Thompson, 2002 ). In the presence of moderate-large heterogeneity, we evaluated potential sources of heterogeneity through moderator analyses and sensitivity to small-study effects (e.g., publication bias).

Moderator analyses

We extended the three-level model to evaluate the influence of sample- and methodological-related study characteristics, including sample age, type of memory, type of creativity, and paradigm modality. Since some studies did not report information for all moderator variables, we evaluated the influence of each moderator separately to maximize statistical power (Assink & Wibbelink, 2016 ; Cheung, 2014 ; Viechtbauer, 2010 ). Lastly, we only included a moderator category if there was a substantive cluster ( k  > 5).

Sensitivity to small-study effects

To evaluate the sensitivity of meta-analyses to small-study effects, visual inspection of funnel plot asymmetry and the Egger’s regression test are standard methods for evaluating the potential presence of publication bias (Egger et al., 1997 ). However, visual inspection is subjective and the Egger’s regression test is not appropriate for multilevel data (Nakagawa & Santos, 2012 ). Therefore, to evaluate sensitivity of our meta-analytic estimates to small-study effects, we objectively evaluated funnel plot asymmetry by regressing our summary effect size estimate onto the study-specific standard errors. This method is conceptually identical to the Egger’s regression test, but preserves the multilevel form (Nakagawa & Santos, 2012 ; see also Griffin et al., 2021 ). In the presence of statistically significant funnel plot asymmetry, we planned to conduct sensitivity analysis by excluding effect sizes that contributed to funnel plot asymmetry. Finally, to evaluate the potential risk of bias due to unpublished results, we evaluated the summary effect size with and without published data.

The full study identification, screening, and selection process are displayed in Fig.  1 . In total, we included 525 effect sizes from 79 unique empirical articles and unpublished datasets (see Table 1 ). Representing data from 12,846 individual participants. On average, each study included 6.65 effect sizes ( SD  = 9.45). Overall, the average study sample age was 22.09 ( SD  = 11.05). Table 1 reports the included studies’ sample sizes, study characteristics, participant demographics, memory type, creativity type, and paradigm modality.

Summary effect size

Overall, the effect size between creativity and memory was statistically significant and small in magnitude ( r  = 0.19, se  = 0.02, 95% CI [0.15, 0.22], p  < 0.0001; see Fig.  2 ). Consistent with our choice of a random-effects model, we also observed considerable heterogeneity among effect sizes ( Q  = 1897.64, I 2  = 79.41%, p  < 0.0001). Specifically, our three-level model revealed that 20.59% of the total variance was attributed to sampling variability, 19.96% was attributed to variation among effect sizes of the same study, and 59.44% was attributed to between-study variability. We evaluated potential sources of this heterogeneity with moderator analysis.

Meta-analytic forest plot containing all effect sizes nested within studies (organized by publication year). Effect sizes vary by memory type (color) and creativity type (shape). The dashed grey lines reflect the 95% confidence interval for the overall summary effect for all studies

Memory type

While all memory types (semantic, episodic, short-term, and working memory) were related to creative cognition, the magnitude varied by type. Specifically, the summary effect size was strongly moderated by memory type ( Q  = 33.56, p  < 0.0001). The largest correlation was found between creative cognition and semantic memory ( r  = 0.25), followed by working memory ( r  = 0.17), episodic memory ( r  = 0.16), and short-term memory ( r  = 0.15). Statistically, the correlation between memory and creative cognition was significantly larger for semantic memory compared to all other memory types, including working memory ( b  = -0.08, se  = 0.01, 95% CI[-0.11, -0.05], p  < 0.0001), episodic memory ( b  = -0.09, se  = 0.03, 95% CI[-0.15, -0.03], p  = 0.002), and short-term memory ( b  = -0.10, se  = 0.03, 95% CI[-0.16, -0.04], p  = 0.003; see Fig.  3 and Table 2 ). The correlation between semantic memory and creative cognition was not different across memory paradigm modality (verbal vs. visual; b  = 0.34, se  = 0.20, 95% CI[-0.04, 0.72], p  = 0.09). Additionally, we found that the summary effect size was moderated by episodic retrieval demands (i.e., recognition, recall, cued recall; Q  = 8.91, p  = 0.01). Specifically, the summary effect size was larger for cued recall compared to recognition ( b  = 0.10, se  = 0.03, 95% CI[-0.03, -0.17], p  = 0.003).

The top panel displays the summary effect sizes for each Memory Type (Short-term Memory, Working Memory, Episodic Memory, Semantic Memory) as function of Creativity Type (Divergent Thinking, Convergent Thinking). The bottom panel displays the summary effect sizes for Verbal and Visual memory as a function of Creativity Modality (Visual, Verbal). Points reflect point estimates and error bars reflect 95% confidence intervals. k  = number of effect sizes contributing to summary effect size estimates. Gray vertical bands in each panel represent a summary effect size confidence interval at 95%. * =  p  < .05; ***  =  p  < .001

Creativity type

Similarly, the summary effect size was moderated by creativity type ( Q  = 16.74, p  < 0.0001). Specifically, the correlation between creative cognition and memory was different for convergent ( r  = 0.23) compared to divergent ( r  = 0.17) creativity tasks ( b  = -0.06, se  = 0.02, 95% CI[-0.09, -0.03], p  < 0.0001). Since divergent thinking tasks evaluate numerous dimensions of creativity (e.g., fluency, originality, flexibility, cleverness, uniqueness), we also tested for moderation among these dimensions. We did not find evidence of significant variation across effect size metrics ( Q  = 3.96, p  = 0.78).

Memory type as a function of creativity type

We also evaluated whether the summary effect size between creative cognition and memory was conditional on whether the creativity task was measuring either convergent or divergent thinking. We found that working memory was more strongly correlated with convergent thinking ( r  = 0.23) than divergent thinking ( r  = 0.15; p  < 0.001), though we did not find a significant Creativity Type × Memory Type interaction ( Q  = 5.52, p  = 0.14).

Overall, the summary effect size was not moderated by average sample age ( Q  = 0.67 , p  = 0.41). Specifically, the average sample age was not significantly related to the strength of correlation between creative cognition and memory performance ( b  = 0.001, se  = 0.001, 95% CI [-0.002, 0.005], p  = 0.41).

Paradigm modality

The summary effect size was not moderated by paradigm modality of the memory task ( Q  = 3.15, p  = 0.08). Overall, the summary effect size was similar for visual compared to verbal memory tasks ( b  = -0.03, se  = 0.02, 95% CI [-0.06, -0.003], p  = 0.08). Similarly, the summary effect size was not different for visual and verbal creativity tasks ( b  = -0.002, se  = 0.02, 95% CI [-0.04, 0.03], p  = 0.86). These findings were qualified by a two-way interaction ( Q  = 13.15 , p  = 0.0003). Specifically, within visual creativity, the relationship with visual memory was greater than that of verbal memory, but within verbal creativity, the relationship with verbal memory was greater than that of visual memory. See Fig.  3 for a visualization of these results.

We evaluated the influence of small-study effects on the overall summary estimate using a modified Egger’s regression that preserves the multilevel structure of the data to quantify funnel plot asymmetry (Nakagawa & Santos, 2012 ). Overall, we found no evidence of significant plot asymmetry ( b  = -0.11, se  = 0.37, 95% CI [-0.84, 0.61], p  = 0.76; see Fig.  4 ), suggesting that the results presented here were not impacted by small-study effects, including publication bias. In addition, there were 56 effect sizes that were from unpublished studies or datasets. We found that without these effect sizes, the overall summary effect was virtually identical ( r  = 0.19, se  = 0.02, 95% CI [0.16, 0.22], p  < 0.0001).

Funnel plot displaying the summary effect size estimates as a function of precision (i.e., standard error). The funnel plot is centered on the overall summary effect size for all effect sizes ( k  = 525) indicated by the vertical black line ( r  = .19). Effect sizes vary by memory type (color) and creativity type (shape)

The present meta-analysis sought to quantitatively summarize the relationship between memory and creative cognition. To our knowledge, this is the first quantitative attempt to summarize over 50 years of research in the literature. Overall, we found a small positive correlation between memory and creative cognition. Importantly, this association varied as a function of the type of memory and creative thinking under investigation. Semantic memory ( r  = 0.25) shared the largest overall relationship with general creative cognition compared to all other memory types ( ps  < 0.001), convergent thinking ( r  = 0.23) shared a stronger relationship with general memory than divergent thinking ( r  = 0.17, p  < 0.001), and working memory was more strongly correlated with convergent than divergent thinking ( p  < 0.001). Moreover, within visual creativity, the relationship with visual memory was greater than that of verbal memory, but within verbal creativity, the relationship with verbal memory was greater than that of visual memory. Our findings thus provide insight into the role of memory in creative thinking, addressing a longstanding question in the psychology of creativity regarding the relative importance of specific memory systems for creative cognition.

The general relationship between memory and creative cognition

Despite decades of research describing the relationship between memory and creativity, a consensus on the strength and direction of this relationship has never been reached. We synthesized 525 effect sizes from 79 unique empirical studies to quantitatively summarize the overall association (correlation) between memory and creative cognition. We found that better memory – averaged across semantic, episodic, working, and short-term memory – is related to higher creativity (collapsed across divergent and convergent thinking tasks). This suggests memory systems reliably support creative cognition. In addition, the magnitude of the effect size ( r  = 0.19) suggests memory and creativity have modest similarities, with substantial variance in creative ability left unexplained by memory ability alone. Additionally, follow-up analyses showed that the modesty of the correlation can be attributed to the variance of memory and creativity type.

Impact of memory and creativity type on the relationship between memory and creative cognition

Semantic memory showed the largest correlation with creative cognition compared to all other memory types (episodic, working, and short-term). This finding provides some support for the classic associative theory (Mednick, 1962 ), which first suggested creativity involves connecting weakly related, remote concepts. However, given that semantic memory was primarily assessed with tasks involving goal-directed retrieval (e.g., verbal fluency), our findings are perhaps more consistent with a growing literature emphasizing the roles of strategic semantic retrieval ability in creative cognition (Avitia & Kaufman, 2014 ; Forthmann et al., 2019 ; Silvia et al., 2013 ). Importantly, the meta-analytic effect of semantic memory on creative performance was not moderated by creativity modality (verbal vs. visuospatial) and it was consistent across creativity type (divergent and convergent), indicating that semantic memory’s role in creative cognition is broad and not limited to verbal tasks only. Thus, the ability to retrieve items from long-term memory reliably predicts creative performance across a diverse range of tasks. This result suggests that semantic memory is a cognitive system fundamentally supporting people’s ability to think creatively.

Analyses also revealed that working memory was more strongly related to convergent than divergent thinking. While the effect size was modest, this finding is in line with previous work raising questions about whether convergent creative thinking tasks (such as the RAT) are measures of creativity or intelligence (Lee & Therriault, 2013 ). Recently, several latent variable studies have reported large correlations between RAT performance and working memory capacity, as well as other cognitive abilities such as fluid and crystallized intelligence (Chein & Weisberg, 2014 ; Lee & Therriault, 2013 ). Importantly, however, our meta-analysis could not disentangle the roles of insight versus analytical problem solving, which have been shown to differentially relate to working memory (i.e., stronger working memory associations for analytical over insight; Fleck, 2008 ). Nevertheless, to the extent that the RAT and other convergent thinking tasks index creative thought, our meta-analytic finding emphasizes the importance of cognitive control processes that allow people to maintain and manipulate multiple items from memory in an active state to solve complex creative problems (Benedek et al., 2014a , b ). Future work may wish to examine other memory variables commonly examined alongside the RAT, such as false memories (e.g., Howe et al., 2010 ).

Notably, compared to convergent thinking, the contribution of working memory to divergent thinking was smaller, despite this being the most well-powered comparison in the analysis, with 179 effect sizes reported in the literature. On the one hand, a relatively weaker relationship between working memory and divergent thinking may raise questions about the executive nature of divergent creativity; on the other hand, the finding may call for increased specificity in the field. Perhaps some executive functions relate to divergent thinking more strongly than others (cf., Benedek et al., 2014a , b ). In other words, although working memory is a broad construct that tends to correlate with other higher cognitive abilities that relate to divergent thinking (e.g., fluid intelligence), the specific ability to update items in working memory – as indexed by complex span tasks – appears to be less relevant to divergent creativity compared to convergent thinking.

We also found that episodic memory was associated with divergent thinking to a weaker degree than that of semantic memory. Although a growing number of studies have found a contribution of the episodic system to divergent thinking, via an experimental manipulation that boosts episodic memory known as the episodic specificity induction (Madore et al., 2014 ), our meta-analytic results do not support a strong relationship between episodic memory ability and divergent thinking. One possibility is that episodic memory can support divergent thinking in a state-dependent manner. That is, experimentally activating the episodic system may temporarily boost some aspects of divergent thinking, but trait-level episodic memory ability may not impact people’s ability to think divergently. Further meta-analytic work is needed, however, before such conclusions can be made.

Impacts of paradigm modality and age on the relationship between memory and creative cognition

Our results showed that the summary effect size was similar for visual compared to verbal memory tasks and for visual and verbal creativity tasks. However, a difference was found between memory modalities for verbal compared to visual creativity tasks with respect to direction. Specifically, within visual creativity, the relationship with visual memory was greater than that of verbal memory – but within verbal creativity, the relationship with verbal memory was greater than that of visual memory. This indicates that whether the task involves verbal or visual stimuli impacts relationships between memory and creative thinking. Although our analysis did not detect a visual/verbal difference for creativity tasks, our finding for verbal memory highlights a consideration for future research on memory and creativity. Specifically, prior work demonstrates functional fixedness may be differentially induced depending on stimulus modality, such as whether people are presented with pictures or words in a divergent thinking task (Chrysikou et al., 2016 ). The Matched Filter Hypothesis contends task demands influence the level of cognitive control required to complete the task (Chrysikou, 2019 ). In this context, stimulus modality can bias retrieval strategy during divergent thinking, either towards top-down (visual, e.g., pictures) or bottom-up (verbal, e.g., words), which has implications for both the type of memory engaged and the level of cognitive control required. We encourage future researchers to carefully consider stimulus modality and other task parameters when designing cognitive experiments on creativity to avoid unintentional confounds in their data.

Finally, the summary effect size between memory and creativity was not moderated by age. Most memory types (Schneider & Pressley, 2013 ) and performance on convergent thinking abilities (Kleibeuker et al., 2013 ) continue to develop well into young adulthood. However, divergent thinking does not follow a linear developmental pattern. For example, fluency and flexibility are already well developed by adolescence, and in one study, adolescents excelled on a visuospatial divergent thinking task compared to other older individuals (age range: 10–30 years; Kleibeuker et al., 2013 ). There are also reports of slumps and jumps in creative abilities as a function of age/school grade (Claxton et al., 2005 ; Saggar et al., 2019 ; Said-Metwaly et al., 2021 ; Torrance, 1962 ). An alternate speculative interpretation of our finding could be that children rely more on memory because their executive functions are less developed than younger adults aged 18–30 (Schneider & Pressley, 2013 ). After young adulthood, one model suggests a general decline in creative potential as a function of old age (e.g., over age 60 years) due to changing underlying cognitive processes (Simonton, 1984 ). Indeed, with the exception of semantic memory (Bäckman & Nilsson, 1996 ), there is a general decline in cognitive abilities, particularly in the memory domain, around the time one transitions from middle age to older age (Josefsson et al., 2012 ; Olaya et al., 2017 ). Empirical work on creativity and aging has primarily focused on divergent thinking, producing mixed results. Some findings suggest creativity is maintained into older adulthood (Addis et al., 2016 ; Foos & Boone, 2008 ; Palmiero et al., 2014 ), perhaps related to preserved crystallized intelligence (Palmiero et al., 2014 ). Others findings suggest aging is marked with a reduction of fluency and originality (Alpaugh & Birren, 1977 ), with this deficit first present in middle adulthood (Reese et al., 2001 ). Coupling this prior research with the findings from our meta-analysis, individuals may differentially rely on memory for creative thinking across the lifespan, and this inconsistency may be too subtle to detect with binned age categories, as was done in the current study.

Creative cognition and the cognitive control of memory

Taken together, the current findings emphasize the central importance of cognitive control to creative cognition. Specifically, we found semantic memory – assessed primarily by verbal fluency tasks, which require controlled semantic retrieval – consistently predicted performance on both divergent and convergent creative thinking tasks. After semantic memory, we found working memory to be the second strongest predictor of convergent thinking, pointing to the role of controlled attention (Kane & Engle, 2002 ). Here, we explore the implications of these meta-analytic results in the context of the ongoing debate on the role of cognitive control in creative thought.

Longstanding theories in the creativity literature emphasized the role of unconscious processes in creative cognition (see Abraham, 2018 ; Campbell, 1960 ; Martindale, 2007 ; Mednick, 1962 ; Mendelsohn, 1976 ; Wallas, 1926 ), particularly with respect to insight problem solving. On this view, cognitive control plays a minimal role – and in some cases, even a detrimental role – in solving creative problems. In a similar vein, the Blind Variation and Selective Retention (BVSR) theory of creativity (Campbell, 1960 ; Simonton, 2011 ) posits that creative idea generation is largely spontaneous and unpredictable (i.e., blind). Likewise, several theories propose that cognitive disinhibition (or defocused attention) supports creative performance by “releasing” attentional control (Martindale, 2007 ; Mendelsohn, 1976 ), allowing diffuse semantic activation and extraneous sensory information to be entertained when thinking creatively (Zabelina et al., 2016 ).

On the other hand, more recently, researchers have begun to theorize about how cognitive control may support creative cognition, particularly in light of evidence linking the two cognitive abilities (Silvia, 2015 ). For example, studies linking divergent thinking to facets of intelligence, such as verbal fluency (or broad retrieval ability, Gr), have informed the view that divergent thinking in part relies on controlled retrieval from long-term memory (Avitia & Kaufman, 2014 ; Forthmann et al., 2019 ; Silvia et al., 2013 ). Common verbal fluency tasks require participants to retrieve specific exemplars from memory, such as category fluency tasks (e.g., foods, animals, etc.) and phonemic fluency (e.g., words that start with the letters F, A, or S). Verbal fluency is considered a canonical task of cognitive control: performance reliable engages prefrontal brain regions, particularly the inferior frontal gyrus (Costafreda et al., 2006 ; Hirshorn & Thompson-Schill, 2006 ; Phelps et al., 1997 ; Schlösser et al., 1998 ). Verbal fluency is thought to require selective and goal-directed memory retrieval mechanisms, such as generating and maintaining search cues (Unsworth et al., 2011 ).

In the context of divergent thinking, and given evidence linking verbal fluency to divergent thinking, researchers have theorized that similar selective retrieval mechanisms contribute to divergent thinking performance. Although the goals of verbal fluency and divergent thinking tasks differ in terms of what is to be retrieved from memory, i.e., typical versus atypical exemplars, how information is retrieved from long-term memory may be at least partly similar with respect to controlled retrieval mechanisms (e.g., maintaining a retrieval cue in mind while strategically searching memory for candidate responses). Despite commonalties, however, selection demands may be even higher for divergent thinking, particularly when many salient and unoriginal items become activated during search. Of course, elaborative processing, beyond simply retrieving information from memory, is required to formulate creative ideas, which may require more or less controlled aspects of cognition. The current meta-analysis could not provide such mechanistic insight into specific cognitive subprocesses of divergent thinking (e.g., generating vs. evaluating ideas), but we see this as a fruitful direction for future research, with an eye toward dissociating contributions of controlled versus spontaneous semantic retrieval, or the relative roles of semantic search processes vs. the semantic network structure (Kenett & Hills, 2022 ).

Regarding convergent thinking, cognitive control may likewise support performance on tasks such as the RAT, particularly when participants solve problems analytically (compared to insightfully). Given large correlations between performance on classic convergent thinking tasks like the RAT and cognitive ability – including large latent correlations between convergent creative thinking tasks and WMC (Chuderski & Jastrzębski, 2018 ) – researchers have recently raised the question of whether convergent creativity tasks actually measure creativity or rather working memory/intelligence (Chein & Weisberg, 2014 ; Lee et al., 2014 ). The present meta-analysis indeed supports the role of working memory in solving convergent thinking tasks. However, it is important to mention that our analysis could not dissociate insightful versus analytical problem solving, which may contribute to the WMC-convergent thinking relation (Kounios & Beeman, 2014 ; Salvi et al., 2016 ). Nevertheless, our findings clearly implicate cognitive control (via WMC) to overall performance on classic tests of convergent creative thinking, suggesting that the ability to actively maintain and manipulate information in working memory is a reliable path toward successful creative problem solving.

The current meta-analysis is partly consistent with the recently proposed minimal theory of creative ability (MTCA; Stevenson et al., 2021 ). According to MTCA, individual creative performance can be largely explained by two factors: intelligence (domain-general cognitive ability) and expertise (domain-specific knowledge). Our meta-analysis provides support for the first factor of MTCA, with respect to general cognitive ability (e.g., verbal fluency, working memory), and it is aligned with another recent meta-analysis by Gerwig et al. ( 2021 ), who reported a meta-analytic correlation between general intelligence and divergent thinking. Importantly, the current work points more directly at the cognitive control of memory; that is, although general cognitive control abilities (e.g., fluid intelligence) have previously been shown to support creative cognition, our findings provide specificity on the role of cognitive control by demonstrating meta-analytic relations between creative performance and cognitive abilities that require the control of memory (via working memory and verbal fluency).

Limitations

Some limitations of this meta-analytic review merit attention. First, while the current review attempted to cover a wide breadth of research, the number of studies for each memory and creativity type included for analysis was unequal, which could impact Type I error rates. Second, Pearson correlation coefficients cannot capture potential non-linear effects that may exist between variables. Third, regarding convergent thinking, we could not examine analytical vs. insight problem solving separately, and prior work highlights key cognitive differences between these two modes of solving convergent thinking problems (Kounios & Beeman, 2009 , 2014 ), with specific implications for semantic and working memory. Fourth, the current review focused exclusively on explicit behaviors that were observed in a laboratory setting (i.e., psychometric creativity tests), and not domain-specific creative performance. Future reviews could also focus on implicit, primed, or subjective facets of creativity and memory. Also, with respect to memory, there was not a large enough quantity of papers to pull in memory metrics other than hit rate. This influences the interpretation of the present study, as the results may only generalize to accurate episodic memory (as opposed to other memory metrics that can account for false alarm and miss rates, such as discriminability). Finally, the meta-analysis did not account for the potential contribution of general cognitive ability (i.e., general intelligence), which may partly explain associations between memory abilities (e.g., working memory) and creative cognition.

By aggregating over 50 years of research on memory and creativity, we provide the first quantitative and conclusive meta-analytic evidence that memory supports creative cognition. Collapsing across types of memory and creativity, we found a small but significant ( r  = 0.19) general relationship between these two constructs. A closer examination of memory type revealed this association to be driven largely by semantic memory, assessed primarily by performance on verbal fluency tasks. Further, despite previously mixed evidence, we showed working memory capacity is more strongly related to convergent than to divergent creative thinking. Regarding paradigm modality, we found that within visual creativity, the relationship with visual memory was greater than that of verbal memory, but within verbal creativity, the relationship with verbal memory was greater than that of visual memory. Finally, there was no impact of age on the general effect size. These findings provide clarity regarding the nature of the relationship between memory and creative cognition – pointing to the cognitive control of memory as central to creative task performance – and they help to address longstanding controversies around how, and to what extent, specific cognitive systems support specific modes of creative thought.

Data and code availability

All data and code are publicly available via the Open Science Framework and can be accessed at https://osf.io/kudvy/?view_only=cd19b0a438a4486b8b5a0dab39339e1d .

* = contains data included in analysis.

Aben, B., Stapert, S., & Blokland, A. (2012). About the Distinction between Working Memory and Short-Term Memory. Frontiers in Psychology, 3 , 301. https://doi.org/10.3389/fpsyg.2012.00301

Article   PubMed   PubMed Central   Google Scholar  

Abraham, A. (2014). Creative thinking as orchestrated by semantic processing vs. Cognitive control brain networks. Frontiers in Human Neuroscience , 8 . https://doi.org/10.3389/fnhum.2014.00095

Abraham, A. (2018). The Neuroscience of Creativity. Cambridge University Press . https://doi.org/10.1017/9781316816981

Article   Google Scholar  

*Addis, D. R., Pan, L., Musicaro, R., & Schacter, D. L. (2016). Divergent thinking and constructing episodic simulations. Memory , 24 (1), 89–97. https://doi.org/10.1080/09658211.2014.985591

Addis, D. R., Pan, L., Vu, M.-A., Laiser, N., & Schacter, D. L. (2009). Constructive episodic simulation of the future and the past: Distinct subsystems of a core brain network mediate imagining and remembering. Neuropsychologia, 47 (11), 2222–2238. https://doi.org/10.1016/j.neuropsychologia.2008.10.026

Article   PubMed   Google Scholar  

Akbari Chermahini, S., & Hommel, B. (2012). Creative mood swings: Divergent and convergent thinking affect mood in opposite ways. Psychological Research Psychologische Forschung, 76 (5), 634–640. https://doi.org/10.1007/s00426-011-0358-z

Alpaugh, P. K., & Birren, J. E. (1977). Variables Affecting Creative Contributions across the Adult Life Span. Human Development, 20 (4), 240–248. https://doi.org/10.1159/000271559

Anderson, J. R. (1974). Retrieval of propositional information from long-term memory. Cognitive Psychology, 6 (4), 451–474. https://doi.org/10.1016/0010-0285(74)90021-8

Andrade, J. (Ed.). (2001). An introduction to working memory. Working memory in perspective. Psychology Press . https://doi.org/10.4324/9780203194157

*Arán Filippetti, V., & Krumm, G. (2020). A hierarchical model of cognitive flexibility in children: Extending the relationship between flexibility, creativity and academic achievement. Child Neuropsychology , 26 (6), 770-800.

Arenberg, D. (1973). Cognition and aging: Verbal learning, memory, problem solving, and aging. In Eisdorfer, C., & Lawton, M. P. (eds.),  The psychology of adult development and aging (pp. 74–97). American Psychological Association. https://doi.org/10.1037/10044-005

Assink, M., & Wibbelink, C. (2016). Fitting three-level meta-analytic models in R: A step-by-step tutorial. The Quantitative Methods for Psychology , 12 (3), 154–174. https://doi.org/10.20982/tqmp.12.3.p154

Atkinson, R. C., & Shiffrin, R. M. (1971). The Control of Short-Term Memory. Scientific American, 225 (2), 82–91.

Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In Psychology of learning and motivation (Vol. 2, pp. 89–195). Cambridge: Academic Press.

*Avila, B. N. (2018). Creating divergence: Examining the development of creativity through executive function, language and mindset. (Doctoral dissertation).

*Avitia, M. J., & Kaufman, J. C. (2014). Beyond g and c: The relationship of rated creativity to long-term storage and retrieval (Glr). Psychology of Aesthetics, Creativity, and the Arts , 8 (3), 293–302. http://dx.doi.org.ezaccess.libraries.psu.edu/ https://doi.org/10.1037/a0036772

Bäckman, L., & Nilsson, L.-G. (1996). Semantic Memory Functioning Across the Adult Life Span. European Psychologist, 1 (1), 27–33. https://doi.org/10.1027/1016-9040.1.1.27

Baddeley, A. (2001). The concept of episodic memory. Philosophical Transactions of the Royal Society of London. Series B , 356 (1413), 1345–1350. https://doi.org/10.1098/rstb.2001.0957

Baddeley, A. (2003). Working memory: Looking back and looking forward. Nature Reviews Neuroscience, 4 (10), 829–839. https://doi.org/10.1038/nrn1201

Baddeley, A., Chincotta, D., & Adlam, A. (2001). Working memory and the control of action: Evidence from task switching. Journal of Experimental Psychology: General, 130 (4), 641–657. https://doi.org/10.1037/0096-3445.130.4.641

Baddeley, A. D. (1986). Working Memory (Oxford Psychology Series No. 11). Journal of Neurology, Neurosurgery & Psychiatry , 50 . https://doi.org/10.1136/jnnp.50.5.654-a

Baddeley, A. D., & Hitch, G. (1974). Working Memory. In G. H. Bower (Ed.), Psychology of Learning and Motivation (Vol. 8, pp. 47–89). Academic Press. https://doi.org/10.1016/S0079-7421(08)60452-1

*Bahar, A. (2013). The influence of cognitive abilities on mathematical problem solving performance. (Doctoral dissertation).

Bahar, M., & Hansell, M. H. (2000). The relationship between some psychological factors and their effect on the performance of grid questions and word association tests. Educational Psychology, 20 (3), 349–364. https://doi.org/10.1080/713663739

Barsalou, L. (2003). Situated simulation in the human conceptual system. Language and Cognitive Processes, 18 (5–6), 513–562. https://doi.org/10.1080/01690960344000026

Beaty, R. E., Kenett, Y. N., Christensen, A. P., Rosenberg, M. D., Benedek, M., Chen, Q., Fink, A., Qiu, J., Kwapil, T. R., Kane, M. J., & Silvia, P. J. (2018). Robust prediction of individual creative ability from brain functional connectivity. Proceedings of the National Academy of Sciences, 115 (5), 1087–1092. https://doi.org/10.1073/pnas.1713532115

Beaty, R. E., Nusbaum, E. C., & Silvia, P. J. (2014a). Does insight problem solving predict real-world creativity? Psychology of Aesthetics, Creativity, and the Arts, 8 (3), 287–292. https://doi.org/10.1037/a0035727

Beaty, R. E., & Silvia, P. (2012). Why Do Ideas Get More Creative Across Time? An Executive Interpretation of the Serial Order Effect in Divergent Thinking Tasks. Psychology of Aesthetics Creativity and the Arts, 6 , 309–319. https://doi.org/10.1037/a0029171

*Beaty, R. E., Silvia, P. J., Nusbaum, E. C., Jauk, E., & Benedek, M. (2014b). The roles of associative and executive processes in creative cognition. Memory & Cognition , 42 (7), 1186–1197. https://doi.org/10.3758/s13421-014-0428-8

Beaty, R. E., Kenett, Y., Hass, R., & Schacter, D. L. (2019). A Fan Effect for Creative Thought: Semantic Richness Facilitates Idea Quantity but Constrains Idea Quality . PsyArXiv. https://doi.org/10.31234/osf.io/pfz2g

*Beaty, R. E., Smeekens, B. A., Silvia, P. J., Hodges, D. A., & Kane, M. J. (2013). A first look at the role of domain-general cognitive and creative abilities in jazz improvisation. Psychomusicology: Music, Mind, and Brain ,  23 (4), 262. https://doi.org/10.1037/a0034968

Beck, S. R., Williams, C., Cutting, N., Apperly, I. A., & Chappell, J. (2016). Individual differences in children’s innovative problem-solving are not predicted by divergent thinking or executive functions. Philosophical Transactions of the Royal Society B: Biological Sciences, 371 (1690), 20150190. https://doi.org/10.1098/rstb.2015.0190

Beda, Z., & Smith, S. M. (2018). Chasing red herrings: Memory of distractors causes fixation in creative problem solving. Memory & Cognition, 46 (5), 671–684. https://doi.org/10.3758/s13421-018-0799-3

Benedek, M., & Fink, A. (2019). Toward a neurocognitive framework of creative cognition: The role of memory, attention, and cognitive control. Current Opinion in Behavioral Sciences, 27 , 116–122. https://doi.org/10.1016/j.cobeha.2018.11.002

Benedek, M., Jauk, E., Fink, A., Koschutnig, K., Reishofer, G., Ebner, F., & Neubauer, A. C. (2014a). To create or to recall? Neural mechanisms underlying the generation of creative new ideas. NeuroImage, 88 , 125–133. https://doi.org/10.1016/j.neuroimage.2013.11.021

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

*Benedek, M., Kenett, Y. N., Umdasch, K., Anaki, D., Faust, M., & Neubauer, A. C. (2017). How semantic memory structure and intelligence contribute to creative thought: A network science approach.  Thinking & Reasoning ,  23 (2), 158-183. https://doi.org/10.1080/13546783.2016.1278034

Benedek, M., Könen, T., & Neubauer, A. C. (2012). Associative abilities underlying creativity. Psychology of Aesthetics, Creativity, and the Arts , 6 (3), 273–281. http://dx.doi.org.ezaccess.libraries.psu.edu/ https://doi.org/10.1037/a0027059

*Benedek, M., Jurisch, J., Koschutnig, K., Fink, A., & Beaty, R. E. (2020). Elements of creative thought: Investigating the cognitive and neural correlates of association and bi-association processes.  NeuroImage ,  210 , 116586. https://doi.org/10.1016/j.neuroimage.2020.116586

*Bentley, J. C. (1966). Creativity and academic achievement. The Journal of Educational Research , 59 (6), 269-272.

*Bromage, B. K., & Mayer, R. E. (1981). Relationship between what is remembered and creative problem-solving performance in science learning. Journal of Educational Psychology , 73 (4), 451.

*Bucik, V., & Neubauer, A. C. (1996). Bimodality in the Berlin model of intelligence structure (BIS): A replication study.  Personality and Individual Differences ,  21 (6), 987-1005. https://doi.org/10.1016/S0191-8869(96)00129-8

Campbell, D. T. (1960). Blind variation and selective retentions in creative thought as in other knowledge processes. Psychological Review, 67 (6), 380–400. https://doi.org/10.1037/h0040373

*Chein, J. M., & Weisberg, R. W. (2014). Working memory and insight in verbal problems: Analysis of compound remote associates. Memory & Cognition , 42 (1), 67–83. https://doi.org/10.3758/s13421-013-0343-4

Chein, J. M., Weisberg, R. W., Streeter, N. L., & Kwok, S. (2010). Working memory and insight in the nine-dot problem. Memory & Cognition, 38 , 883–892.

*Chen, Q., Beaty, R. E., Wei, D., Yang, J., Sun, J., Liu, W., ... & Qiu, J. (2018). Longitudinal alterations of frontoparietal and frontotemporal networks predict future creative cognitive ability.  Cerebral Cortex ,  28 (1), 103–115. https://doi.org/10.1093/cercor/bhw353

Cheung, M.W.-L. (2014). Modeling dependent effect sizes with three-level meta-analyses: A structural equation modeling approach. Psychological Methods, 19 (2), 211–229. https://doi.org/10.1037/a0032968

Cheung, M.W.-L. (2019). A Guide to Conducting a Meta-Analysis with Non-Independent Effect Sizes. Neuropsychology Review, 29 (4), 387–396. https://doi.org/10.1007/s11065-019-09415-6

*Chiappe, D. L., & Chiappe, P. (2007). The role of working memory in metaphor production and comprehension.  Journal of Memory and Language ,  56 (2), 172-188. https://doi.org/10.1016/j.jml.2006.11.006

Christensen, P. R., Guilford, J. P., & Wilson, R. C. (1957). Relations of creative responses to working time and instructions. Journal of Experimental Psychology, 53 (2), 82–88. https://doi.org/10.1037/h0045461

Chrysikou, E. G. (2019). Creativity in and out of (cognitive) control. Current Opinion in Behavioral Sciences, 27 , 94–99. https://doi.org/10.1016/j.cobeha.2018.09.014

Chrysikou, E. G., Motyka, K., Nigro, C., Yang, S.-I., & Thompson-Schill, S. L. (2016). Functional Fixedness in Creative Thinking Tasks Depends on Stimulus Modality. Psychology of Aesthetics, Creativity, and the Arts, 10 (4), 425–435. https://doi.org/10.1037/aca0000050

Chrysikou, E., & Weisberg, R. (2005). Following the Wrong Footsteps: Fixation Effects of Pictorial Examples in a Design Problem-Solving Task. Journal of Experimental Psychology . Learning, Memory, and Cognition, 31 , 1134–1148. https://doi.org/10.1037/0278-7393.31.5.1134

*Chuderski, A., & Jastrzębski, J. (2018). Much ado about aha!: Insight problem solving is strongly related to working memory capacity and reasoning ability—ProQuest . https://search-proquest-com.ezaccess.libraries.psu.edu/docview/1955061175?accountid=13158&pq-origsite=summon

*Clabby, Jr, J. F. (1980). The wit: A personality analysis. Journal of Personality Assessment , 44 (3), 307-310.

Claxton, A. F., Pannells, T. C., & Rhoads, P. A. (2005). Developmental Trends in the Creativity of School-Age Children. Creativity Research Journal, 17 (4), 327–335. https://doi.org/10.1207/s15326934crj1704_4

Collins, A. M., & Loftus, E. F. (1975). A spreading-activation theory of semantic processing. Psychological Review, 82 (6), 407–428. https://doi.org/10.1037/0033-295X.82.6.407

Conway, A., Jarrold, C., Kane, M., Miyake, A., & Towse, J. (Eds.). (2007). Variation in working memory . Oxford: Oxford University Press.

*Coskun, H. (2011). Close associations and memory in brainwriting groups.  The Journal of Creative Behavior ,  45 (1), 59-75. https://doi.org/10.1002/j.2162-6057.2011.tb01085.x

*Cushen, P. J., & Wiley, J. (2018). Both attentional control and the ability to make remote associations aid spontaneous analogical transfer.  Memory & Cognition ,  46 (8), 1398-1412. https://doi.org/10.3758/s13421-018-0845-1

Costafreda, S. G., Fu, C. H. Y., Lee, L., Everitt, B., Brammer, M. J., & David, A. S. (2006). A systematic review and quantitative appraisal of fMRI studies of verbal fluency: Role of the left inferior frontal gyrus. Human Brain Mapping, 27 (10), 799–810. https://doi.org/10.1002/hbm.20221

Cowan, N. (1988). Evolving conceptions of memory storage, selective attention, and their mutual constraints within the human information-processing system. Psychological Bulletin, 104 (2), 163–191. https://doi.org/10.1037/0033-2909.104.2.163

Cowan, N. (2008). Chapter 20 What are the differences between long-term, short-term, and working memory? In W. S. Sossin, J.-C. Lacaille, V. F. Castellucci, & S. Belleville (Eds.), Progress in Brain Research (Vol. 169, pp. 323–338). Elsevier. https://doi.org/10.1016/S0079-6123(07)00020-9

Craik, F. I. M. (2002). Levels of processing: Past, present... and future? Memory , 10 (5–6), 305–318. https://doi.org/10.1080/09658210244000135

Cuijpers, P., Griffin, J. W., & Furukawa, T. A. (2021). The lack of statistical power of subgroup analyses in meta-analyses: A cautionary note. Epidemiology and Psychiatric Sciences , 30 . https://doi.org/10.1017/S2045796021000664

Davelaar, E. J. (2015). Semantic Search in the Remote Associates Test. Topics in Cognitive Science, 7 (3), 494–512. https://doi.org/10.1111/tops.12146

De Bono, E. (1968). New Think: The Use of Lateral Thinking in the Generation of New Ideas . Avon Books.

*De Dreu, C. K. W., Nijstad, B. A., Baas, M., Wolsink, I., & Roskes, M. (2012). Working Memory Benefits Creative Insight, Musical Improvisation, and Original Ideation Through Maintained Task-Focused Attention. Personality and Social Psychology Bulletin , 38 (5), 656–669. https://doi.org/10.1177/0146167211435795

Deese, J. (1959). Influence of inter-item associative strength upon immediate free recall. Psychological Reports, 5 (3), 305–312.

*DeYoung, C. G., Flanders, J. L., & Peterson, J. B. (2008). Cognitive Abilities Involved in Insight Problem Solving: An Individual Differences Model. Creativity Research Journal , 20 (3), 278–290. https://doi.org/10.1080/10400410802278719

Drabant, E. M., Hariri, A. R., Meyer-Lindenberg, A., Munoz, K. E., Mattay, V. S., Kolachana, B. S., Egan, M. F., & Weinberger, D. R. (2006). Catechol O-methyltransferase Val158Met Genotype and Neural Mechanisms Related to Affective Arousal and Regulation. Archives of General Psychiatry , 63 (12). https://doi.org/10.1001/archpsyc.63.12.1396

Duff, M. C., Kurczek, J., Rubin, R., Cohen, N. J., & Tranel, D. (2013). Hippocampal amnesia disrupts creative thinking. Hippocampus, 23 (12), 1143–1149. https://doi.org/10.1002/hipo.22208

*Dumas, D., Schmidt, L. C., & Alexander, P. A. (2016). Predicting creative problem solving in engineering design.  Thinking Skills and Creativity ,  21 , 50-66.

Duncker, K. (1945). On problem-solving (L. S. Lees, Trans.). Psychological Monographs, 58 (5), i–113. https://doi.org/10.1037/h0093599

Dygert, S. K., & Jarosz, A. F. (2020). Individual differences in creative cognition.  Journal of Experimental Psychology: General ,  149 (7), 1249. https://doi.org/10.1037/xge0000713

Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315 (7109), 629–634. https://doi.org/10.1136/bmj.315.7109.629

Ellamil, M., Dobson, C., Beeman, M., & Christoff, K. (2012). Evaluative and generative modes of thought during the creative process. NeuroImage, 59 (2), 1783–1794. https://doi.org/10.1016/j.neuroimage.2011.08.008

Engle, R. W. (2002). Working Memory Capacity as Executive Attention. Current Directions in Psychological Science, 11 (1), 19–23. https://doi.org/10.1111/1467-8721.00160

Ericsson, K. A., & Simon, H. A. (1998). How to Study Thinking in Everyday Life: Contrasting Think-Aloud Protocols With Descriptions and Explanations of Thinking. Mind, Culture, and Activity, 5 (3), 178–186. https://doi.org/10.1207/s15327884mca0503_3

Ezzyat, Y., & Davachi, L. (2011). What Constitutes an Episode in Episodic Memory? Psychological Science, 22 (2), 243–252. https://doi.org/10.1177/0956797610393742

Farah, M. J., Hammond, K. M., Mehta, Z., & Ratcliff, G. (1989). Category-specificity and modality-specificity in semantic memory. Neuropsychologia, 27 (2), 193–200. https://doi.org/10.1016/0028-3932(89)90171-1

*Feingold, A., & Mazzella, R. (1991). Psychometric intelligence and verbal humor ability.  Personality and Individual Differences ,  12 (5), 427-435. https://doi.org/10.1016/0191-8869(91)90060-O

*Figueroa, I. J. (2017). Do working memory capacity and cognitive flexibility predict creative thinking and production? (Doctoral dissertation).

Finke, R. A., Ward, T. B., & Smith, S. M. (1992). Creative cognition: Theory, research, and applications (pp. vi, 239). The MIT Press.

Fleck, J. I. (2008). Working memory demands in insight versus analytic problem solving. European Journal of Cognitive Psychology, 20 (1), 139–176. https://doi.org/10.1080/09541440601016954

Foos, P. W., & Boone, D. (2008). Adult Age Differences in Divergent Thinking: It’s Just a Matter of Time. Educational Gerontology, 34 (7), 587–594. https://doi.org/10.1080/03601270801949393

Forbus, K. D., Gentner, D., & Law, K. (1995). MAC/FAC: A Model of Similarity-Based Retrieval. Cognitive Science, 19 (2), 141–205. https://doi.org/10.1207/s15516709cog1902_1

Forthmann, B., Jendryczko, D., Scharfen, J., Kleinkorres, R., Benedek, M., & Holling, H. (2019). Creative ideation, broad retrieval ability, and processing speed: A confirmatory study of nested cognitive abilities. Intelligence, 75 , 59–72. https://doi.org/10.1016/j.intell.2019.04.006

Freides, D. (1974). Human information processing and sensory modality: Cross-modal functions, information complexity, memory, and deficit. Psychological Bulletin, 81 (5), 284–310. https://doi.org/10.1037/h0036331

*Freund, P. A., Holling, H., & Preckel, F. (2007). A multivariate, multilevel analysis of the relationship between cognitive abilities and scholastic achievement.  Journal of Individual Differences ,  28 (4), 188-197. https://doi.org/10.1027/1614-0001.28.4.188

Frith, E., Kane, M. J., Welhaf, M. S., Christensen, A. P., Silvia, P. J., & Beaty, R. E. (2021). Keeping Creativity under Control: Contributions of Attention Control and Fluid Intelligence to Divergent Thinking. Creativity Research Journal , 1–20. https://doi.org/10.1080/10400419.2020.1855906

*Fugate, C. M., Zentall, S. S., & Gentry, M. (2013). Creativity and working memory in gifted students with and without characteristics of attention deficit hyperactive disorder: Lifting the mask.  Gifted Child Quarterly ,  57 (4), 234-246. https://doi.org/10.1177/0016986213500069

*Furley, P., & Memmert, D. (2015). Creativity and working memory capacity in sports: Working memory capacity is not a limiting factor in creative decision making amongst skilled performers. Frontiers in Psychology , 6 . https://doi.org/10.3389/fpsyg.2015.00115

Gable, S. L., Hopper, E. A., & Schooler, J. W. (2019). When the Muses Strike: Creative Ideas of Physicists and Writers Routinely Occur During Mind Wandering. Psychological Science, 30 (3), 396–404. https://doi.org/10.1177/0956797618820626

Gerwig, A., Miroshnik, K., Forthmann, B., Benedek, M., Karwowski, M., & Holling, H. (2021). The Relationship between Intelligence and Divergent Thinking—A Meta-Analytic Update. Journal of Intelligence, 9 (2), 23. https://doi.org/10.3390/jintelligence9020023

Gilhooly, K. J., Fioratou, E., Anthony, S. H., & Wynn, V. (2007). Divergent thinking: Strategies and executive involvement in generating novel uses for familiar objects. British Journal of Psychology, 98 (4), 611–625. https://doi.org/10.1111/j.2044-8295.2007.tb00467.x

Glucksberg, S., & Weisberg, R. W. (1966). Verbal behavior and problem solving: Some effects of labeling in a functional fixedness problem. Journal of Experimental Psychology, 71 (5), 659–664. https://doi.org/10.1037/h0023118

*Gras, R. M. L., Bordoy, M., Ballesta, G. J., & Berna, J. C. (2010). Creativity, intelectual abilities and response styles: Implications for academic performance in the secondary school.[Creatividad, aptitudes intelectuales y estilos de respuesta: implicaciones para el rendimiento académico en secundaria].  Anales de Psicología/Annals of Psychology ,  26 (2), 212–219. https://doi.org/10.6018/analesps

Griffin, J. W. (2020). MetapoweR: An R package for computing meta-analytic statistical power (R package version 0.2.0) . https://CRAN.R-project.org/package=metapower

Griffin, J. W. (2021). Calculating statistical power for meta-analysis using metapower. The Quantitative Methods for Psychology , 17 (1), 24–39. https://doi.org/10.20982/tqmp.17.1.p024

Griffin, J. W., Bauer, R., & Scherf, K. S. (2021). A quantitative meta-analysis of face recognition deficits in autism: 40 years of research. Psychological Bulletin , No Pagination Specified-No Pagination Specified. https://doi.org/10.1037/bul0000310

Gruszka, A., & Necka, E. (2002). Priming and Acceptance of Close and Remote Associations by Creative and Less Creative People. Creativity Research Journal, 14 (2), 193–205. https://doi.org/10.1207/S15326934CRJ1402_6

*Gubbels, J., Segers, E., & Verhoeven, L. (2017). Predicting the development of analytical and creative abilities in upper elementary grades.  Creativity Research Journal ,  29 (4), 433-441. https://doi.org/10.1080/10400419.2017.1376548

*Gubbels, J., Segers, E., & Verhoeven, L. (2018). How children’s intellectual profiles relate to their cognitive, socio-emotional, and academic functioning.  High Ability Studies ,  29 (2), 149-168. https://doi.org/10.1080/13598139.2018.1507902

Guilford, J. P. (1950). Fundamental statistics in psychology and education (2nd ed.). McGraw-Hill.

Google Scholar  

Guilford, J. P. (1968). Intelligence Has Three Facets. Science, 160 (3828), 615–620.

Gupta, N., Jang, Y., Mednick, S. C., & Huber, D. E. (2012). The Road Not Taken: Creative Solutions Require Avoidance of High-Frequency Responses. Psychological Science, 23 (3), 288–294. https://doi.org/10.1177/0956797611429710

*Hakstian, A. R., & Cattell, R. B. (1974). The checking of primary ability structure on a broader basis of performances. British Journal of Educational Psychology , 44 (2), 140-154.

*Hao, N., Yuan, H., Cheng, R., Wang, Q., & Runco, M. A. (2015).Interaction effect of response medium and working memory capacity on creative idea generation.  Frontiers in Psychology ,  6 , 1582. https://doi.org/10.3389/fpsyg.2015.01582

*Hass, R. W. (2015). Feasibility of online divergent thinking assessment.  Computers in Human Behavior ,  46 , 85-93. https://doi.org/10.1016/j.chb.2014.12.056

Hassabis, D., & Maguire, E. A. (2007). Deconstructing episodic memory with construction. Trends in Cognitive Sciences, 11 (7), 299–306. https://doi.org/10.1016/j.tics.2007.05.001

Hess, T. M. (2005). Memory and Aging in Context. Psychological Bulletin, 131 (3), 383–406. https://doi.org/10.1037/0033-2909.131.3.383

*Hetzroni, O., Agada, H., & Leikin, M. (2019). Creativity in autism: An examination of general and mathematical creative thinking among children with autism spectrum disorder and children with typical development.  Journal of Autism and Developmental Disorders ,  49 (9), 3833-3844. https://doi.org/10.1007/s10803-019-04094-x

Higgins, J. P. T., & Thompson, S. G. (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21 (11), 1539–1558. https://doi.org/10.1002/sim.1186

Hills, T. T., Jones, M. N., & Todd, P. M. (2012). Optimal foraging in semantic memory. Psychological Review, 119 (2), 431–440. https://doi.org/10.1037/a0027373

Hirshorn, E. A., & Thompson-Schill, S. L. (2006). Role of the left inferior frontal gyrus in covert word retrieval: Neural correlates of switching during verbal fluency. Neuropsychologia, 44 (12), 2547–2557. https://doi.org/10.1016/j.neuropsychologia.2006.03.035

Holland, P. C., & Gallagher, M. (2006). Different Roles for Amygdala Central Nucleus and Substantia Innominata in the Surprise-Induced Enhancement of Learning. Journal of Neuroscience, 26 (14), 3791–3797. https://doi.org/10.1523/JNEUROSCI.0390-06.2006

Howe, M. L., Garner, S. R., Dewhurst, S. A., & Ball, L. J. (2010). Can false memories prime problem solutions? Cognition, 117 (2), 176–181. https://doi.org/10.1016/j.cognition.2010.08.009

*Hsieh, C. Y., & Chen, T. (2019). Effect of Pokémon GO on the Cognitive Performance and Emotional Intelligence of Primary School Students.  Journal of Educational Computing Research ,  57 (7), 1849-1874. https://doi.org/10.1177/0735633119854006

Jauk, E., Benedek, M., Dunst, B., & Neubauer, A. C. (2013). The relationship between intelligence and creativity: New support for the threshold hypothesis by means of empirical breakpoint detection. Intelligence, 41 (4), 212–221. https://doi.org/10.1016/j.intell.2013.03.003

Jauk, E., Benedek, M., & Neubauer, A. C. (2014). The Road to Creative Achievement: A Latent Variable Model of Ability and Personality Predictors. European Journal of Personality, 28 (1), 95–105. https://doi.org/10.1002/per.1941

Johnson, B. T. (2021). Toward a more transparent, rigorous, and generative psychology. Psychological Bulletin, 147 (1), 1–15. https://doi.org/10.1037/bul0000317

Josefsson, M., de Luna, X., Pudas, S., Nilsson, L.-G., & Nyberg, L. (2012). Genetic and Lifestyle Predictors of 15-Year Longitudinal Change in Episodic Memory. Journal of the American Geriatrics Society, 60 (12), 2308–2312. https://doi.org/10.1111/jgs.12000

Kail, R., & Hall, L. K. (2001). Distinguishing short-term memory from working memory. Memory & Cognition, 29 (1), 1–9. https://doi.org/10.3758/BF03195735

*Kaltsounis, B. (1975). Psycholinguistic abilities and their influence on creative expression. Perceptual and Motor Skills , 40 (3), 937-938.

Kane, M. J., & Engle, R. W. (2002). The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: An individual-differences perspective. Psychonomic Bulletin & Review, 9 (4), 637–671. https://doi.org/10.3758/BF03196323

Kaplan, C. A., & Simon, H. A. (1990). In search of insight. Cognitive Psychology, 22 (3), 374–419. https://doi.org/10.1016/0010-0285(90)90008-R

Kenett, Y. N., Anaki, D., & Faust, M. (2014). Investigating the structure of semantic networks in low and high creative persons. Frontiers in Human Neuroscience , 8 . https://doi.org/10.3389/fnhum.2014.00407

Kenett, Y. N., & Hills, T. T. (2022). Networks of the Mind: How Can Network Science Elucidate Our Understanding of Cognition? Topics in Cognitive Science, 14 (1), 45–53. https://doi.org/10.1111/tops.12598

Kenett, Y. N., Levy, O., Kenett, D. Y., Stanley, H. E., Faust, M., & Havlin, S. (2018). Flexibility of thought in high creative individuals represented by percolation analysis. Proceedings of the National Academy of Sciences, 115 (5), 867–872. https://doi.org/10.1073/pnas.1717362115

King, J., & Just, M. A. (1991). Individual Differences in Syntactic Processing: The Role of Working Memory. Journal of Memory and Language, 30 (5), 580–602.

Kleibeuker, S. W., Dreu, C. K. W. D., & Crone, E. A. (2013). The development of creative cognition across adolescence: Distinct trajectories for insight and divergent thinking. Developmental Science, 16 (1), 2–12. https://doi.org/10.1111/j.1467-7687.2012.01176.x

Kounios, J., & Beeman, M. (2009). The Aha! Moment: The Cognitive Neuroscience of Insight. Current Directions in Psychological Science, 18 (4), 210–216. https://doi.org/10.1111/j.1467-8721.2009.01638.x

Kounios, J., & Beeman, M. (2014). The Cognitive Neuroscience of Insight. Annual Review of Psychology, 65 (1), 71–93. https://doi.org/10.1146/annurev-psych-010213-115154

Kounios, J., Osman, A. M., & Meyer, D. E. (1987). Structure and process in semantic memory: New evidence based on speed-accuracy decomposition. Journal of Experimental Psychology. General, 116 (1), 3–25. https://doi.org/10.1037//0096-3445.116.1.3

*Krumm, G., Filippetti, V. A., & Gutierrez, M. (2018). The contribution of executive functions to creativity in children: What is the role of crystallized and fluid intelligence?.  Thinking Skills and Creativity ,  29 , 185-195. https://doi.org/10.1016/j.tsc.2018.07.006

Kyllonen, P. C. (1996). Is working memory capacity Spearman’s g? In Human abilities: Their nature and measurement (pp. 49–75). Mahwah: Lawrence Erlbaum Associates, Inc.

*Lang, R. J., & Ryba, K. A. (1976). The identification of some creative thinking parameters common to the artistic and musical personality. British Journal of Educational Psychology , 46 (3), 267-279.

*Leder, J., Häusser, J. A., Krumm, S., Germar, M., Schlemmer, A., Kaiser, S., ... & Mojzisch, A. (2018). The cognitive underpinnings of option generation in everyday life decision‐making: A latent variable analysis.  Cognitive Science ,  42 (8), 2562–2591. https://doi.org/10.1111/cogs.12678

*Lee, C. S., Huggins, A. C., & Therriault, D. J. (2014). A measure of creativity or intelligence? Examining internal and external structure validity evidence of the Remote Associates Test. Psychology of Aesthetics, Creativity, and the Arts , 8 (4), 446–460. http://dx.doi.org.ezaccess.libraries.psu.edu/ https://doi.org/10.1037/a0036773

*Lee, C. S., & Therriault, D. J. (2013). The cognitive underpinnings of creative thought: A latent variable analysis exploring the roles of intelligence and working memory in three creative thinking processes. Intelligence , 41 (5), 306–320. https://doi.org/10.1016/j.intell.2013.04.008

*Leon, S. A., Altmann, L. J., Abrams, L., Gonzalez Rothi, L. J., & Heilman, K. M. (2014). Divergent task performance in older adults: Declarative memory or creative potential?.  Creativity Research Journal ,  26 (1), 21-29. https://doi.org/10.1080/10400419.2014.873657

Leszczynski, M., Chaieb, L., Reber, T. P., Derner, M., Axmacher, N., & Fell, J. (2017). Mind wandering simultaneously prolongs reactions and promotes creative incubation. Scientific Reports, 7 (1), 10197. https://doi.org/10.1038/s41598-017-10616-3

Linsey, J. S., Tseng, I., Fu, K., Cagan, J., Wood, K. L., & Schunn, C. (2010). A Study of Design Fixation, Its Mitigation and Perception in Engineering Design Faculty. Journal of Mechanical Design , 132 (4), 041003. https://doi.org/10.1115/1.4001110

Luchins, A. S. (1942). Mechanization in problem solving: The effect of Einstellung. Psychological Monographs, 54 (6), i–95. https://doi.org/10.1037/h0093502

Ludmer, R., Dudai, Y., & Rubin, N. (2011). Uncovering Camouflage: Amygdala Activation Predicts Long-Term Memory of Induced Perceptual Insight. Neuron, 69 (5), 1002–1014. https://doi.org/10.1016/j.neuron.2011.02.013

*Lunke, K., & Meier, B. (2016).Disentangling the impact of artistic creativity on creative thinking, working memory, attention, and intelligence: Evidence for domain-specific relationships with a new self-report questionnaire.  Frontiers in Psychology ,  7 , 1089. https://doi.org/10.3389/fpsyg.2016.01089

*Lv, K. (2015). The involvement of working memory and inhibition functions in the different phases of insight problem solving.  Memory & Cognition ,  43 (5), 709-722. https://doi.org/10.3758/s13421-014-0498-7

Madore, K. P., Addis, D. R., & Schacter, D. L. (2015). Creativity and Memory: Effects of an Episodic-Specificity Induction on Divergent Thinking. Psychological Science, 26 (9), 1461–1468. https://doi.org/10.1177/0956797615591863

Madore, K. P., Gaesser, B., & Schacter, D. L. (2014). Constructive episodic simulation: Dissociable effects of a specificity induction on remembering, imagining, and describing in young and older adults. Journal of Experimental Psychology. Learning, Memory, and Cognition , 40 (3), 609–622. https://doi.org/10.1037/a0034885

Madore, K. P., Jing, H. G., & Schacter, D. L. (2016). Divergent creative thinking in young and older adults: Extending the effects of an episodic specificity induction. Memory & Cognition, 44 (6), 974–988. https://doi.org/10.3758/s13421-016-0605-z

Madore, K. P., & Schacter, D. L. (2014). An episodic specificity induction enhances means-end problem solving in young and older adults. Psychology and Aging, 29 (4), 913–924. https://doi.org/10.1037/a0038209

Madore, K. P., Thakral, P. P., Beaty, R. E., Addis, D. R., & Schacter, D. L. (2019). Neural Mechanisms of Episodic Retrieval Support Divergent Creative Thinking. Cerebral Cortex, 29 (1), 150–166. https://doi.org/10.1093/cercor/bhx312

Martindale, C. (2007). Creativity, primordial cognition, and personality. Personality and Individual Differences, 43 (7), 1777–1785. https://doi.org/10.1016/j.paid.2007.05.014

McGrew, K. S. (2009). CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research. Intelligence, 37 (1), 1–10. https://doi.org/10.1016/j.intell.2008.08.004

Mednick, S. (1962). The associative basis of the creative process. Psychological Review, 69 (3), 220–232. https://doi.org/10.1037/h0048850

*Menashe, S., Leshem, R., Heruti, V., Kasirer, A., Yair, T., & Mashal, N. (2020). Elucidating the role of selective attention, divergent thinking, language abilities, and executive functions in metaphor generation. Neuropsychologia , 142 , 107458. https://doi.org/10.1016/j.neuropsychologia.2020.107458

Mendelsohn, G. A. (1976). Associative and attentional processes in creative performance1. Journal of Personality, 44 (2), 341–369. https://doi.org/10.1111/j.1467-6494.1976.tb00127.x

Menon, V., Boyett-Anderson, J. M., Schatzberg, A. F., & Reiss, A. L. (2002). Relating semantic and episodic memory systems. Cognitive Brain Research, 13 (2), 261–265. https://doi.org/10.1016/S0926-6410(01)00120-3

Metcalfe, J., & Wiebe, D. (1987). Intuition in insight and noninsight problem solving. Memory & Cognition, 15 (3), 238–246. https://doi.org/10.3758/BF03197722

Miyake, A., & Shah, P. (1999). Models of Working Memory: Mechanisms of Active Maintenance and Executive Control . https://doi.org/10.2307/1423383

Mumford, M. D., & Gustafson, S. B. (1988). Creativity syndrome: Integration, application, and innovation. Psychological Bulletin, 103 (1), 27–43. https://doi.org/10.1037/0033-2909.103.1.27

Murray, G., & Johnson, S. L. (2010). The clinical significance of creativity in bipolar disorder. Clinical Psychology Review, 30 (6), 721–732.

Nakagawa, S., & Santos, E. S. A. (2012). Methodological issues and advances in biological meta-analysis. Evolutionary Ecology, 26 (5), 1253–1274. https://doi.org/10.1007/s10682-012-9555-5

Nęcka, E. (1999). Memory and creativity. In Runco M. A., & Pritzker S. R. (eds.), Encyclopedia of creativity (Vol. 2, pp. 193–199).

*Nęcka, E., Żak, P., & Gruszka, A. (2016). Insightful imagery is related to working memory updating.  Frontiers in Psychology ,  7 , 137. https://doi.org/10.3389/fpsyg.2016.00137

*Nemoto, T., Mizuno, M., & Kashima, H. (2005). Qualitative evaluation of divergent thinking in patients with schizophrenia.  Behavioural Neurology ,  16 (4), 217-224. https://doi.org/10.1155/2005/386932

*Neuman, G. A., Bolin, A. U., & Briggs, T. E. (2000). Identifying general factors of intelligence: A confirmatory factor analysis of the Ball Aptitude Battery.  Educational and Psychological Measurement ,  60 (5), 697-712. https://doi.org/10.1177/00131640021970853

Newell, A., & Simon, H. A. (1972). Human problem solving (pp. xiv, 920). Prentice-Hall.

Ohlsson, S. (1992). Information-processing explanations of insight and related phenomena. Advances in the Psychology of Thinking . /paper/Information-processing-explanations-of-insight-and-Ohlsson/0966fde1dec92c2e719bb4baa97b8ea3294a5622.

Ohlsson, S. (2011). Deep learning: How the mind overrides experience (pp. xiii, 523). Cambridge University Press. https://doi.org/10.1017/CBO9780511780295

Olaya, B., Bobak, M., Haro, J. M., & Demakakos, P. (2017). Trajectories of Verbal Episodic Memory in Middle-Aged and Older Adults: Evidence from the English Longitudinal Study of Ageing. Journal of the American Geriatrics Society, 65 (6), 1274–1281. https://doi.org/10.1111/jgs.14789

Ormerod, T., Macgregor, J., & Chronicle, E. (2002). Dynamics and Constraints in Insight Problem Solving. Journal of Experimental Psychology . Learning, Memory, and Cognition, 28 , 791–799. https://doi.org/10.1037/0278-7393.28.4.791

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ , 372 , n71. https://doi.org/10.1136/bmj.n71

Palmiero, M., Giacomo, D. D., & Passafiume, D. (2014). Divergent Thinking and Age-Related Changes. Creativity Research Journal, 26 (4), 456–460. https://doi.org/10.1080/10400419.2014.961786

Palmiero, M., Nori, R., & Piccardi, L. (2017). Verbal and visual divergent thinking in aging. Experimental Brain Research, 235 (4), 1021–1029. https://doi.org/10.1007/s00221-016-4857-4

Parnes, S. J. (1961). Effects of extended effort in creative problem solving. Journal of Educational Psychology, 52 (3), 117–122. https://doi.org/10.1037/h0044650

Patterson, K., Nestor, P. J., & Rogers, T. T. (2007). Where do you know what you know? The representation of semantic knowledge in the human brain. Nature Reviews Neuroscience, 8 (12), 976–987. https://doi.org/10.1038/nrn2277

*Pelowski, M., Markey, P. S., Goller, J., Förster, E. L., & Leder, H. (2019). But, how can we make “art?” Artistic production versus realistic copying and perceptual advantages of artists.  Psychology of Aesthetics, Creativity, and the Arts ,  13 (4), 462. https://doi.org/10.1037/aca0000178

Penney, C. G. (1989). Modality effects and the structure of short-term verbal memory. Memory & Cognition, 17 (4), 398–422. https://doi.org/10.3758/BF03202613

Phelps, E. A., Hyder, F., Blamire, A. M., & Shulman, R. G. (1997). FMRI of the prefrontal cortex during overt verbal fluency. NeuroReport, 8 (2), 561–565.

*Piers, E. V., & Kirchner, E. P. (1971). Productivity and uniqueness in continued word association as a function of subject creativity and stimulus properties. Journal of Personality , 39 (2), 264-276.

Pirolli, P. (2007). Information Foraging Theory: Adaptive Interaction with Information . Oxford University Press.

Book   Google Scholar  

*Pollert, L. H., Feldhusen, J. F., Van Mondfrans, A. P., & Treffinger, D. J. (1969). Role of memory in divergent thinking. Psychological reports , 25 (1), 151-156.

*Polner, B., Simor, P., & Kéri, S. (2018). Insomnia and intellect mask the positive link between schizotypal traits and creativity.  PeerJ ,  6 , e5615. https://doi.org/10.7717/peerj.5615

Quillan, M. R. (1966). SEMANTIC MEMORY . BOLT BERANEK AND NEWMAN INC CAMBRIDGE MA. https://apps.dtic.mil/sti/citations/AD0641671

*Redifer, J. L., Bae, C. L., & DeBusk-Lane, M. (2019). Implicit theories, working memory, and cognitive load: Impacts on creative thinking.  Sage Open ,  9 (1). 2158244019835919

Reese, H. W., Lee, L.-J., Cohen, S. H., & Puckett, J. M. (2001). Effects of intellectual variables, age, and gender on divergent thinking in adulthood. International Journal of Behavioral Development, 25 (6), 491–500. https://doi.org/10.1080/01650250042000483

*Ricks, T. R., Turley-Ames, K. J., & Wiley, J. (2007). Effects of working memory capacity on mental set due to domain knowledge.  Memory & Cognition ,  35 (6), 1456-1462. https://doi.org/10.3758/BF03193615

*Ritter, S. M., Abbing, J., & Van Schie, H. T. (2018). Eye-closure enhances creative performance on divergent and convergent creativity tasks.  Frontiers in Psychology ,  9 , 1315. https://doi.org/10.3389/fpsyg.2018.01315

Roediger, H. L., Dudai, Y., & Fitzpatrick, S. M. (Eds.). (2007). Science of memory: Concepts . Oxford University Press.

Roediger, H. L., & McDermott, K. B. (1995). Creating false memories: Remembering words not presented in lists. Journal of Experimental Ppsychology: Learning, Memory, and Cognition, 21 (4), 803.

Rosen, V. M., & Engle, R. W. (1997). The role of working memory capacity in retrieval. Journal of Experimental Psychology. General, 126 (3), 211–227. https://doi.org/10.1037//0096-3445.126.3.211

*Roskos‐ewoldsen, B., Black, S. R., & McCown, S. M. (2008). Age‐related changes in creative thinking.  The Journal of Creative Behavior ,  42 (1), 33-59. https://doi.org/10.1002/j.2162-6057.2008.tb01079.x

Runco, M. A., Millar, G., Acar, S., & Cramond, B. (2010). Torrance Tests of Creative Thinking as Predictors of Personal and Public Achievement: A Fifty-Year Follow-Up. Creativity Research Journal, 22 (4), 361–368. https://doi.org/10.1080/10400419.2010.523393

Runco, M. A., Noble, E. P., Reiter-Palmon, R., Acar, S., Ritchie, T., & Yurkovich, J. M. (2011). The Genetic Basis of Creativity and Ideational Fluency. Creativity Research Journal, 23 (4), 376–380. https://doi.org/10.1080/10400419.2011.621859

Runco, M. A., & Okuda, S. M. (1991). The instructional enhancement of the flexibility and originality scores of divergent thinking tests. Applied Cognitive Psychology, 5 (5), 435–441. https://doi.org/10.1002/acp.2350050505

Saggar, M., Xie, H., Beaty, R. E., Stankov, A. D., Schreier, M., & Reiss, A. L. (2019). Creativity slumps and bumps: Examining the neurobehavioral basis of creativity development during middle childhood. NeuroImage, 196 , 94–101. https://doi.org/10.1016/j.neuroimage.2019.03.080

Said-Metwaly, S., Fernández-Castilla, B., Kyndt, E., Van den Noortgate, W., & Barbot, B. (2021). Does the Fourth-Grade Slump in Creativity Actually Exist? A Meta-analysis of the Development of Divergent Thinking in School-Age Children and Adolescents. Educational Psychology Review, 33 (1), 275–298. https://doi.org/10.1007/s10648-020-09547-9

Salvi, C., Bricolo, E., Kounios, J., Bowden, E., & Beeman, M. (2016). Insight solutions are correct more often than analytic solutions. Thinking & Reasoning, 22 (4), 443–460. https://doi.org/10.1080/13546783.2016.1141798

Samsonovich, A. V., & Kuznetsova, K. (2018). Semantic-map-based analysis of insight problem solving. Biologically Inspired Cognitive Architectures, 25 , 37–42. https://doi.org/10.1016/j.bica.2018.07.017

*Sanders, K. E., Osburn, S., Paller, K. A., & Beeman, M. (2019). Targeted memory reactivation during sleep improves next-day problem solving.  Psychological Science ,  30 (11), 1616-1624. https://doi.org/10.1177/0956797619873344 .

Saumier, D., & Chertkow, H. (2002). Semantic memory. Current Neurology and Neuroscience Reports, 2 (6), 516–522. https://doi.org/10.1007/s11910-002-0039-9

Schacter, D. L., & Addis, D. R. (2007a). The cognitive neuroscience of constructive memory: Remembering the past and imagining the future. Philosophical Transactions of the Royal Society B: Biological Sciences, 362 (1481), 773–786. https://doi.org/10.1098/rstb.2007.2087

Schacter, D. L., & Addis, D. R. (2007b). The ghosts of past and future. Nature, 445 (7123), 27–27. https://doi.org/10.1038/445027a

Schacter, D. L., Addis, D. R., Hassabis, D., Martin, V. C., Spreng, R. N., & Szpunar, K. K. (2012). The Future of Memory: Remembering, Imagining, and the Brain. Neuron, 76 (4), 677–694. https://doi.org/10.1016/j.neuron.2012.11.001

Schacter, D. L., Gaesser, B., & Addis, D. R. (2013). Remembering the Past and Imagining the Future in the Elderly. Gerontology, 59 (2), 143–151. https://doi.org/10.1159/000342198

Schilling, M. A. (2005). A “Small-World” Network Model of Cognitive Insight. Creativity Research Journal, 17 (2–3), 131–154. https://doi.org/10.1080/10400419.2005.9651475

Schlösser, R., Hutchinson, M., Joseffer, S., Rusinek, H., Saarimaki, A., Stevenson, J., Dewey, S. L., & Brodie, J. D. (1998). Functional magnetic resonance imaging of human brain activity in a verbal fluency task. Journal of Neurology, Neurosurgery & Psychiatry, 64 (4), 492–498. https://doi.org/10.1136/jnnp.64.4.492

Schneider, W., & Pressley, M. (2013). Memory Development Between Two and Twenty . Psychology Press.

*Shakeel, M. K., & Goghari, V. M. (2017). Measuring fluid intelligence in healthy older adults.  Journal of Aging Research ,  2017 . https://doi.org/10.1155/2017/8514582

Shao, Z., Janse, E., Visser, K., & Meyer, A. S. (2014). What do verbal fluency tasks measure? Predictors of verbal fluency performance in older adults. Frontiers in Psychology , 5 . https://doi.org/10.3389/fpsyg.2014.00772

Sheldon, S., McAndrews, M. P., & Moscovitch, M. (2011). Episodic memory processes mediated by the medial temporal lobes contribute to open-ended problem solving. Neuropsychologia, 49 (9), 2439–2447. https://doi.org/10.1016/j.neuropsychologia.2011.04.021

Shelton, J. T., Elliott, E. M., Matthews, R. A., Hill, B. D., & Gouvier, Wm. D. (2010). The relationships of working memory, secondary memory, and general fluid intelligence: Working memory is special. Journal of Experimental Psychology: Learning, Memory, and Cognition , 36 (3), 813–820. https://doi.org/10.1037/a0019046

*Schweizer, T. S., Schmalenberger, K. M., Eisenlohr-Moul, T. A., Mojzisch, A., Kaiser, S., & Funke, J. (2016). Cognitive and affective aspects of creative option generation in everyday life situations.  Frontiers in Psychology ,  7 , 1132. https://doi.org/10.3389/fpsyg.2016.01132

*Shaw, S. T. (unpublished). Memory and creativity meta-analysis data collection . [Unpublished raw data]. University of California, Los Angeles.

*Shaw, G. A., & DeMers, S. T. (1986). The relationship of imagery to originality, flexibility and fluency in creative thinking. Journal of Mental Imagery, 10 (1), 65–74.

Silvia, P. J. (2015). Intelligence and Creativity Are Pretty Similar After All. Educational Psychology Review, 27 (4), 599–606. https://doi.org/10.1007/s10648-015-9299-1

Silvia, P. J., Beaty, R. E., & Nusbaum, E. C. (2013). Verbal fluency and creativity: General and specific contributions of broad retrieval ability (Gr) factors to divergent thinking. Intelligence, 41 (5), 328–340. https://doi.org/10.1016/j.intell.2013.05.004

Simonton, D. K. (1984). Creative productivity and age: A mathematical model based on a two-step cognitive process. Developmental Review, 4 (1), 77–111. https://doi.org/10.1016/0273-2297(84)90020-0

Simonton, D. K. (2011). Creativity and Discovery as Blind Variation: Campbell’s (1960) BVSR Model after the Half-Century Mark. Review of General Psychology, 15 (2), 158–174. https://doi.org/10.1037/a0022912

*Smeekens, B. A., & Kane, M. J. (2016). Working memory capacity, mind wandering, and creative cognition: An individual-differences investigation into the benefits of controlled versus spontaneous thought. Psychology of Aesthetics, Creativity, and the Arts , 10 (4), 389–415. http://dx.doi.org.ezaccess.libraries.psu.edu/ https://doi.org/10.1037/aca0000046

Smith, E. E. (1978). Handbook of Learning and Cognitive Processes (Volume 6): Linguistic Functions in Cognitive Theory (W. K. Estes, Ed.). Psychology Press.

Smith, K. A., Huber, D. E., & Vul, E. (2013). Multiply-constrained semantic search in the Remote Associates Test. Cognition, 128 (1), 64–75. https://doi.org/10.1016/j.cognition.2013.03.001

Smith, R. W., & Kounios, J. (1996). Sudden insight: All-or-none processing revealed by speed–accuracy decomposition. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22 (6), 1443–1462. https://doi.org/10.1037/0278-7393.22.6.1443

Smith, S. M., & Blankenship, S. E. (1991). Incubation and the Persistence of Fixation in Problem Solving. The American Journal of Psychology, 104 (1), 61–87. https://doi.org/10.2307/1422851

Smith, S. M., Ward, T. B., & Schumacher, J. S. (1993). Constraining effects of examples in a creative generation task. Memory & Cognition, 21 (6), 837–845. https://doi.org/10.3758/BF03202751

Squire, L. R., & Zola, S. M. (1998). Episodic memory, semantic memory, and amnesia. Hippocampus, 8 (3), 205–211. https://doi.org/10.1002/(SICI)1098-1063(1998)8:3%3c205::AID-HIPO3%3e3.0.CO;2-I

Stein, B. S. (1989). Memory and Creativity. In J. A. Glover, R. R. Ronning, & C. R. Reynolds (Eds.), Handbook of Creativity (pp. 163–176). Springer US. https://doi.org/10.1007/978-1-4757-5356-1_9

Stephens, D. W., & Krebs, J. R. (1986). Foraging Theory . Princeton University Press.

Stevenson, C., Baas, M., & van der Maas, H. (2021). A Minimal Theory of Creative Ability. Journal of Intelligence, 9 (1), 9. https://doi.org/10.3390/jintelligence9010009

Storm, B. C., Angello, G., & Bjork, E. L. (2011). Thinking can cause forgetting: Memory dynamics in creative problem solving. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37 (5), 1287–1293. https://doi.org/10.1037/a0023921

Szpunar, K. K., & Schacter, D. L. (2018). Memory and future imagining. In Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience (pp. 1–24). American Cancer Society. https://doi.org/10.1002/9781119170174.epcn105

Thakral, P. P., Madore, K. P., Kalinowski, S. E., & Schacter, D. L. (2020). Modulation of hippocampal brain networks produces changes in episodic simulation and divergent thinking. Proceedings of the National Academy of Sciences of the United States of America, 117 (23), 12729–12740. https://doi.org/10.1073/pnas.2003535117

Thomas, R. P., Dougherty, M. R., Sprenger, A. M., Harbison, J. (2008). Diagnostic hypothesis generation and human judgment. Psychological Review, 115 (1), 185.

Torrance, E. P. (1962). Guiding creative talent (pp. xi, 278). Prentice-Hall, Inc. https://doi.org/10.1037/13134-000

Torrance, E. P. (1972). Predictive Validity of the Torrance Tests of Creative Thinking*. The Journal of Creative Behavior, 6 (4), 236–262. https://doi.org/10.1002/j.2162-6057.1972.tb00936.x

Tulving, E. (1972). Episodic and semantic memory. In E. Tulving & W. Donaldson, Organization of memory . Cambridge: Academic Press.

Tulving, E. (1983). Elements of Episodic Memory . Oxford University Press.

Tulving, E. (1993). What Is Episodic Memory? Current Directions in Psychological Science, 2 (3), 67–70. https://doi.org/10.1111/1467-8721.ep10770899

Tulving, E. (2002). Episodic Memory: From Mind to Brain. Annual Review of Psychology, 53 (1), 1–25. https://doi.org/10.1146/annurev.psych.53.100901.135114

Tulving, E., & Patterson, R. D. (1968). Functional units and retrieval processes in free recall. Journal of Experimental Psychology, 77 (2), 239–248. https://doi.org/10.1037/h0025788

Tulving, E., & Thomson, D. M. (1973). Encoding specificity and retrieval processes in episodic memory. Psychological Review, 80 (5), 352–373. https://doi.org/10.1037/h0020071

Unsworth, N., & Engle, R. W. (2007). On the division of short-term and working memory: An examination of simple and complex span and their relation to higher order abilities. Psychological Bulletin, 133 (6), 1038–1066. https://doi.org/10.1037/0033-2909.133.6.1038

Unsworth, N., Redick, T. S., Heitz, R. P., Broadway, J. M., & Engle, R. W. (2009). Complex working memory span tasks and higher-order cognition: A latent-variable analysis of the relationship between processing and storage. Memory, 17 (6), 635–654.

Unsworth, N., Fukuda, K., Awh, E., & Vogel, E. K. (2014). Working memory and fluid intelligence: Capacity, attention control, and secondary memory retrieval. Cognitive Psychology, 71 , 1–26. https://doi.org/10.1016/j.cogpsych.2014.01.003

Unsworth, N., Spillers, G. J., & Brewer, G. A. (2011). Variation in verbal fluency: A latent variable analysis of clustering, switching, and overall performance. Quarterly Journal of Experimental Psychology, 64 (3), 447–466. https://doi.org/10.1080/17470218.2010.505292

van Genugten, R. D. I., Beaty, R. E., Madore, K. P., & Schacter, D. L. (2021). Does Episodic Retrieval Contribute to Creative Writing? An Exploratory Study. Creativity Research Journal , 0 (0), 1–14. https://doi.org/10.1080/10400419.2021.1976451

Vandermorris, S., Sheldon, S., Winocur, G., & Moscovitch, M. (2013). Differential Contributions of Executive and Episodic Memory Functions to Problem Solving in Younger and Older Adults. Journal of the International Neuropsychological Society, 19 (10), 1087–1096. https://doi.org/10.1017/S1355617713000982

Vartanian, O., Jobidon, M.-E., Bouak, F., Nakashima, A., Smith, I., Lam, Q., & Cheung, B. (2013). Working memory training is associated with lower prefrontal cortex activation in a divergent thinking task. Neuroscience, 236 , 186–194. https://doi.org/10.1016/j.neuroscience.2012.12.060

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software , 36 (3), 1–48. https://doi.org/10.18637/jss.v036.i03

Vul, E., & Pashler, H. (2007). Incubation benefits only after people have been misdirected. Memory & Cognition, 35 (4), 701–710. https://doi.org/10.3758/BF03193308

*Wagner, F. L., Rammsayer, T. H., Schweizer, K., & Troche, S. J. (2014). Relations between the attentional blink and aspects of psychometric intelligence: A fixed-links modeling approach.  Personality and Individual Differences ,  58 , 122–127. https://doi.org/10.1016/j.paid.2013.10.023

Wallach, M. A., & Kogan, N. (1965). A new look at the creativity‐intelligence distinction. Journal of Personality, 33 (3), 348–369.

Wallas, G. (1926). The Art of Thought . Harcourt.

*Wang, L., Long, H., Plucker, J. A., Wang, Q., Xu, X., & Pang, W. (2018). High schizotypal individuals are more creative? The mediation roles of overinclusive thinking and cognitive inhibition.  Frontiers in Psychology ,  9 , 1766. https://doi.org/10.3389/fpsyg.2018.01766

*Ward, T. B., & Wickes, K. N. (2009). Stable and dynamic properties of category structure guide imaginative thought.  Creativity Research Journal ,  21 (1), 15-23. https://doi.org/10.1080/10400410802633376

Ward, W. C. (1969). Rate and Uniqueness in Children’s Creative Responding. Child Development, 40 (3), 869–878. https://doi.org/10.2307/1127195

*Weinstein, S., & Graves, R. E. (2002). Are creativity and schizotypy products of a right hemisphere bias?.  Brain and Cognition ,  49 (1), 138-151. https://doi.org/10.1006/brcg.2001.1493

Wiley, J., & Jarosz, A. F. (2012). Chapter Six—How Working Memory Capacity Affects Problem Solving. In B. H. Ross (Ed.), Psychology of Learning and Motivation (Vol. 56, pp. 185–227). Academic Press. https://doi.org/10.1016/B978-0-12-394393-4.00006-6

Wronska, M. K., Kolańczyk, A., & Nijstad, B. A. (2018). Engaging in creativity broadens attentional scope. Frontiers in Psychology, 9 , 1772.

Zabelina, D., Saporta, A., & Beeman, M. (2016). Flexible or leaky attention in creative people? Distinct patterns of attention for different types of creative thinking. Memory & Cognition, 44 (3), 488–498. https://doi.org/10.3758/s13421-015-0569-4

Download references

Acknowledgements

N.A.D. is supported by a grant from the National Science Foundation [2000047 BSC]. R.E.B. is also supported by a grant from the National Science Foundation [DRL-1920653]. We would also like to thank Rebecca Henry for help with text screening and data entry.

Author information

Authors and affiliations.

Department of Psychology, Pennsylvania State University, 140 Moore Building, University Park, PA, 16802, USA

Courtney R. Gerver, Jason W. Griffin, Nancy A. Dennis, Roger E. Beaty & Roger E. Beaty

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Roger E. Beaty .

Ethics declarations

Conflict of interest.

There are no conflicts of interest to be declared.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Public significance statement

We synthesize over 50 years of research on creativity and memory to clarify their relationship. Our findings indicate creativity is positively related to memory, with semantic memory supporting both verbal and visuospatial creativity. People’s ability to think creatively is therefore reliably related to their ability to selectively retrieve information from long-term memory. Our findings have implications for education and interventions aimed at fostering creative thinking.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 32 KB)

Rights and permissions.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Gerver, C.R., Griffin, J.W., Dennis, N.A. et al. Memory and creativity: A meta-analytic examination of the relationship between memory systems and creative cognition. Psychon Bull Rev 30 , 2116–2154 (2023). https://doi.org/10.3758/s13423-023-02303-4

Download citation

Accepted : 30 April 2023

Published : 25 May 2023

Issue Date : December 2023

DOI : https://doi.org/10.3758/s13423-023-02303-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Semantic memory
• Working memory
• Creative cognition
• Divergent thinking
• Convergent thinking
• Find a journal
• Publish with us
• Track your research