creative problem solving in psychology

Creative Problem Solving: Overview and educational implications

• Published: September 1995
• Volume 7 , pages 301–312, ( 1995 )

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• Donald J. Treffinger 1  

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Creative Porblem Solving (CPS) is framework which individuals or groups can use to: formulate problems, opportunities, or challenges; generate and analyze many, varied, and novel options; and plan for effective implementation of new solutions or courses of action. Today's CPS framework bulds on more than four decades of theory, research, and application in a variety of contexts. CPS involves the integration of both creative and critical thinking skills. Using CPS effective also requires drawing upon several metacognitive and task appraisal skills. Current research and applications focus on flexible, dynamic, descriptive uses of CPS, moving away from traditional linear, prescriptive stepor stage-sequential models. CPS offfers a powerful set of tools for productive thinking; these can be learned an used successfully by children, adolescents, and adults.

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Treffinger, D.J. Creative Problem Solving: Overview and educational implications. Educ Psychol Rev 7 , 301–312 (1995). https://doi.org/10.1007/BF02213375

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

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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

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

• 01 Feb 2022

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

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

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

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What Is Creative Problem-Solving?

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

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

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

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

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

1. Balance Divergent and Convergent Thinking

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

2. Reframe Problems as Questions

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

3. Defer Judgment of Ideas

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

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

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

Creative Problem-Solving and Design Thinking

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

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

The four stages are:

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

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

Creative Problem-Solving Tools

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

Creating a Problem Story

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

1. Identify a UDP

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

2. Move Forward in Time

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

3. Move Backward in Time

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

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

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

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

4. Break the Chains

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

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

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

Brainstorming

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

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

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

Alternate Worlds

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

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

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

Continue Developing Your Skills

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

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

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Understanding the Psychology of Creativity

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

Michael H / DigitalVision / Getty Images

What Is Creativity?

When does creativity happen, types of creativity, what does it take to be creative, creativity and the big five, how to increase creativity, frequently asked questions.

What is creativity? Creativity involves the ability to develop new ideas or utilize objects or information in novel ways. It can involve large-scale ideas that have the potential to change the world, such as inventing tools that impact how people live, or smaller acts of creation such as figuring out a new way to accomplish a task in your daily life.

This article explores what creativity is and when it is most likely to happen. It also covers some of the steps that you can take to improve your own creativity.

Studying creativity can be a tricky process. Not only is creativity a complex topic in and of itself, but there is also no clear consensus on how exactly to define creativity. Many of the most common definitions suggest that creativity is the tendency to solve problems or create new things in novel ways.

Two of the primary components of creativity include:

• Originality: The idea should be something new that is not simply an extension of something else that already exists.
• Functionality: The idea needs to actually work or possess some degree of usefulness.

In his book Creativity: Flow and the Psychology of Discovery and Invention , psychologist Mihaly Csikszentmihalyi suggested that creativity can often be seen in a few different situations.  

• People who seem stimulating, interesting, and have a variety of unusual thoughts.
• People who perceive the world with a fresh perspective, have insightful ideas and make important personal discoveries. These individuals make creative discoveries that are generally known only to them.
• People who make great creative achievements that become known to the entire world. Inventors and artists such as Thomas Edison and Pablo Picasso would fall into this category.

Experts also tend to distinguish between different types of creativity. The “four c” model of creativity suggests that there are four different types:

• “Mini-c” creativity involves personally meaningful ideas and insights that are known only to the self.
• “ Little-c” creativity involves mostly everyday thinking and problem-solving. This type of creativity helps people solve everyday problems they face and adapt to changing environments.
• “Pro-C” creativity takes place among professionals who are skilled and creative in their respective fields. These individuals are creative in their vocation or profession but do not achieve eminence for their works.
• “Big-C” creativity involves creating works and ideas that are considered great in a particular field. This type of creativity leads to eminence and acclaim and often leads to world-changing creations such as medical innovations, technological advances, and artistic achievements.

Csikszentmihalyi suggests that creative people tend to possess are ​a variety of traits that contribute to their innovative thinking. Some of these key traits include:

• Energy: Creative people tend to possess a great deal of both physical and mental energy. However, they also tend to spend a great deal of time quietly thinking and reflecting.
• Intelligence: Psychologists have long believed that intelligence plays a critical role in creativity. In Terman’s famous longitudinal study of gifted children, researchers found that while high IQ was necessary for great creativity, not all people with high IQs are creative. Csikszentmihalyi believes that creative people must be smart, but they must be capable of looking at things in fresh, even naïve, ways.
• Discipline: Creative people do not just sit around waiting for inspiration to strike. They ​are playful, yet they are also disciplined in the pursuit of their work and passions.

Certain personality traits are also connected to creativity. According to the big five theory of personality , human personality is made up of five broad dimensions:

• Conscientiousness
• Extroversion
• Agreeableness
• Neuroticism

Each dimension represents a continuum, so for each trait, people can be either high, low, or somewhere between the two. 

Openness to experience is a big five trait that is correlated with creativity. People who are high on this trait are more open to new experiences and ideas. They tend to seek novelty and enjoy trying new things, meeting new people, and considering different perspectives. 

However, other personality traits and characteristics can also play a role in creativity. For example, intrinsic motivation , curiosity, and persistence can all determine how much people tend to pursue new ideas and look for novel solutions.

While some people seem to come by creativity naturally, there are things that you can do to increase your own creativity .

Some strategies that can be helpful for improving creativity include: 

• Being open to new ideas : Openness to experience is the personality trait that is most closely correlated with creativity. Focus on being willing to try new things and explore new ideas.
• Be persistent : Creativity is not just about sitting around waiting for inspiration to strike. Creative people spend time working to produce new things. Their efforts don't always work out, but continued practice builds skills that contribute to creativity.
• Make time for creativity : In addition to being persistent, you also need to devote time specifically toward creative efforts. This might mean setting aside a little time each day or each week specifically to brainstorm, practice, learn, or create.

Csikszentmihalyi has noted that creativity requires both a fresh perspective combined with discipline. As Thomas Edison famously suggested, genius is 1% inspiration and 99% perspiration.

A Word From Verywell

Creativity is a complex subject and researchers are still working to understand exactly what factors contribute to the ability to think creatively. While some people seem to come by creativity naturally, there are also things you can do to build and strengthen this ability.

The late Maya Angelou also suggested that thinking creativity helps foster even greater creativity, "The important thing is to use it. You can’t use up creativity. The more you use it, the more you have," she suggested.

Creativity does not reside in one single area of the brain; many areas are actually involved. The frontal cortex of the brain is responsible for many of the functions that play a part in creativity.

However, other parts of the brain impact creativity as well, including the hippocampus (which is important to memory) and the basal ganglia (which is essential in the memory of how to perform tasks). The white matter of the brain, which keeps the various parts of the brain connected, is also essential for creative thinking.

Research suggests that people can train their brains to be more creative. Engaging in cognitively stimulating tasks, going on a walk, finding sources of inspiration, and meditating are a few strategies that may help boost creative thinking abilities. 

The "big five" are the broad categories of traits that make up personality. The five dimensions are openness, conscientiousness, extroversion, agreeableness, and neuroticism. Each trait involves a range between two extremes, and people can be either at each end or somewhere in the middle.

American Psychological Association. The science of creativity .

Csikszentmihalyi M. Creativity: Flow and the Psychology of Discovery and Invention .   New York: HarperCollins; 2013.

Kaufman J, Beghetto R. Beyond big and little: The four C model of creativity .  Review of General Psychology . 2009;13(1):1-12. doi:10.1037/a0013688

Kaufman SB, Quilty LC, Grazioplene RG, et al. Openness to experience and intellect differentially predict creative achievement in the arts and sciences .  J Pers . 2016;84(2):248-258. doi:10.1111/jopy.12156

Elliot J.  Conversations With Maya Angelou . Jackson, Miss.: University Press of Mississippi; 1998.

Cavdarbasha D, Kurczek J. Connecting the dots: your brain and creativity . Front Young Minds . 2017;5:19. doi:10.3389/frym.2017.00019

Sun J, Chen Q, Zhang Q, Li Y, Li H, Wei D, Yang W, Qiu J.  Training your brain to be more creative: brain functional and structural changes induced by divergent thinking training .  Hum Brain Mapp . 2016;37(10):3375-87. doi:10.1002/hbm.23246

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

Creative Problem-Solving Test

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HYPOTHESIS AND THEORY article

Incubation and intuition in creative problem solving.

A commentary has been posted on this article:

Commentary: Incubation and Intuition in Creative Problem Solving

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• 1 Psychology Department, University of Hertfordshire, Hatfield, UK
• 2 Department of Clinical Sciences, Brunel University London, London, UK

Creative problem solving, in which novel solutions are required, has often been seen as involving a special role for unconscious processes (Unconscious Work) which can lead to sudden intuitive solutions (insights) when a problem is set aside during incubation periods. This notion of Unconscious Work during incubation periods is supported by a review of experimental studies and particularly by studies using the Immediate Incubation paradigm. Other explanations for incubation effects, in terms of Intermittent Work or Beneficial Forgetting are considered. Some recent studies of divergent thinking, using the Alternative Uses task, carried out in my laboratory regarding Immediate vs. Delayed Incubation and the effects of resource competition from interpolated activities are discussed. These studies supported a role for Unconscious Work as against Intermittent Conscious work or Beneficial Forgetting in incubation.

What form might unconscious work take? On theoretical grounds, the notion that Unconscious Work involves the same processing steps as Conscious Work but minus conscious awareness is discounted, despite some recent arguments that the unconscious can duplicate any conscious function. A candidate account in terms of spreading activation, coupled with below-threshold but active goal representations, is put forward. This account could explain the emergence of subjectively sudden intuitive solutions (Aha-insight solutions) as a result of unconscious processes (Unconscious Work) during incubation periods.

“Intuition: the power of the mind by which it immediately perceives the truth of things without reasoning or analysis; a truth so perceived, immediate, instinctive knowledge or belief.

Latin, in , into, upon, and tueri , tuitus , to look.” The Chambers Dictionary, 9 th Edition , 2003, p. 778. Edinburgh: Chambers Harrap.

Creative problem solving involves the production of approaches and solutions that are novel to the solver even if not historically novel ( Boden, 2004 ). Explaining the generation of personally novel solutions is an unresolved issue for the psychology of thinking and problem solving. Sometimes, problems seem to be solved by an immediate intuition or insight (e.g., Salvi et al., 2016 ) but, with difficult problems, a period of conscious analysis is usually needed, even if it does not directly lead to solution and the problem is set aside before solution. Why might setting a problem aside facilitate solution? One popular explanation is that setting creative problems aside for a period can allow unconscious processes to generate solution ideas, which are then experienced, either as spontaneous breakthroughs into consciousness while attention is focussed on other matters, or as very rapid solutions on returning to previously intractable problems. These solutions occurring apparently rapidly and without awareness of intermediate steps, will be experienced as akin to the dictionary idea of an intuition as a truth (a solution in this case) perceived without reasoning or analysis.

The value of setting a problem aside for facilitating solutions has been a concern of theorists in the area for at least the past 100 years. Wallas (1926 , p. 80) drew on Poincaré’s (1910 ) earlier analysis of mathematical creation and labeled the stage in which a problem is not consciously processed as “Incubation.” (It is noteworthy that Poincaré himself did not use the term “Incubation” in his 1910 paper, although he reported four examples of incubation periods from his own experience of creative work in mathematics). In Wallas’s analysis, Incubation is proposed as a useful stage after conscious Preparation but preceding Illumination (or Inspiration) and Verification. Clues to processes underlying creative thinking should be found from analyses of when and why Incubation can be useful. Subjective reports by acknowledged creative thinkers over many areas of work have supported the existence of incubation phenomena (e.g., Poincaré, 1910 ; Ghiselin, 1952 ; Csikszentmihalyi, 1996 ). However, since such personal reports have often been given many years after the events described, the reliability of such reports is highly questionable. For example, frequently cited accounts by Coleridge (composition of poem Kubla Khan in a dream), Mozart (complete compositions coming to mind without error) and Kekulé (discovery of benzene ring in a dream) have proven to be false ( Weisberg, 2006 , pp. 73–78). Poincaré (1910) himself based his own analysis of creative thinking on self reports of problem solving episodes he had experienced nearly 30 years previously. This is actually rather curious, as Poincaré was an active researcher in mathematics at the time of making his analysis of creative thinking and could presumably have drawn on more recent episodes which would be less susceptible to recall problems. However, after Poincaré (1910) and Wallas (1926) , who had relied on their own introspections and on subjective reports by others (e.g., Wallas drew on daydream reports by Varendonck, 1921 ), a substantial body of experimental work research has been carried outusing both (a) insight problems, in which t the solver has to develop a re-structuring of the task to reach a unique solution and (b) divergent problems, that have no single unique solution but in which many novel potential solutions are to be generated. A typical divergent task, often used in research studies, is the Alternative Uses Task. In this task, participants are to produce as many uses as they can which are different from the normal use in response to one or more everyday items, such as a house building brick, a coat hanger, a pencil, a paperclip, and so on ( Guilford, 1967 ; Guilford et al., 1978 ; Gilhooly et al., 2007 ).

Early work on incubation used a laboratory paradigm, known as the Delayed Incubation Paradigm , in which participants work on the target problem for an experimenter set preparation time before being given an interpolated activity different from the target task for a setincubation period before returning to the target problem for a set post-incubation work time. Performance in the incubation condition is compared with that of the control condition in which participants work without a break on the target task for a time equal to the sum of preparation and post-incubation conscious working times in the incubation condition. A recent alternative, the Immediate Incubation paradigm , has an interpolated task immediately after the instructions on the main problem before any conscious work has been undertaken on that problem, followed by uninterrupted work on the maint problem for a set time ( Dijksterhuis and Meurs, 2006 ).

Delayed and Immediate Incubation Effects

There is now considerable evidence from laboratory studies for the benefits of Delayed Incubation, i.e., that setting a problem aside after a period of work is beneficial (see Dodds et al., 2012 , for a qualitative review). A quantitative meta-analysis by Sio and Ormerod (2009) , of 117 studies identified a positive effect of Delayed Incubation, where the overall average effect size was in the low-medium band (mean d = 0.29) over a range of insight and divergent tasks. Sio and Ormerod’s review also revealed that the benefits of an incubation period are greater when participants are occupied by an undemanding interpolated task than when they engage in a demanding interpolated task or no task at all. Overall, from narrative reviews and meta-analysis, it can be concluded that the basic existence of Delayed Incubation effects is clearly established, especially for divergent problem solving.

Concerning the effectiveness of Immediate Incubation opportunities, Dijksterhuis and Nordgren (2006) found that better performances when Immediate Incubation occurred after decision problems or divergent tasks were initially presented. Indeed, Nordgren et al. (2011) reported that Delayed Incubation resulted in better decisions than Immediate Incubation and both types of incubation were beneficial relative to No Incubation.

A meta –analysis ( Strick et al., 2011 ) of 92 decision studies found a significant beneficial aggregate effect size of g = 0.224 for Immediate Incubation. Their results also pointed to a number of moderating factors, for example, beneficial effects were greater, with more options, with shorter presentation times, with shorter incubation times and with induction of a configural mindset vs. a feature based mindset.

In creative divergent tasks Dijksterhuis and Meurs (2006) , reported that responses were more creative on average, when the divergent task instructions were followed immediately by a short distracting task before producing uses for a brick, compared to a control condition. We may note that the instructions in this study did not ask for unusual uses, which is the norm in divergent thinking tasks, and so it is not clear whether participants had the goal of being creative. Participants may have been reporting infrequent uses, that they happened to know, rather than generating uses novel to them at the time of test. Raters tend to score infrequent responses as creative, although such uses may have been pre-known and therefore could reflect memory retrieval rather than generation of subjectively novel responses ( Quellmalz, 1985 ). However, Gilhooly et al. (2012) using more standard instructions with a stress on unusual uses found a stronger beneficial effect of Immediate Incubation than of Delayed Incubation with both incubation effects being superior to control effects, scored for fluency and novelty of responses. Thus, the benefit of immediate incubation was also found when the task involved novelty ( Gilhooly et al., 2012 ) as well as fluency ( Dijksterhuis and Meurs, 2006 ).

Zhong et al. (2008) , applied the Immediate Incubation paradigm to the Remote Associates Task (RAT), in which solvers have to generate an associate common to three words (e.g., cottage, blue, mouse ? Answer : cheese ), and found that, Immediate Incubation activated solution words more on unsolved trials. compared to solution word activation on unsolved trials where that had been no Immediate Incubation.

Overall, it may be concluded from both meta-analyses ( Sio and Ormerod, 2009 ; Strick et al., 2011 ) and from recent studies ( Gilhooly et al., 2012 , 2013 , 2015 ) that incubation periods, whether delayed or immediate, do have beneficial effects. The main theories regarding mechanisms underlying incubation effects will now be outlined.

Theories of Incubation Effects

Intermittent conscious work.

This approach proposes that participants carry out intermittent conscious work during the incubation period despite instructions to be fully engaged on the interpolated task used to fill the incubation period ( Seifert et al., 1995 , p. 82; Weisberg, 2006 , pp. 443–445). Any conscious work during the supposed incubation period would help reduce the time required when the target problem was re-addressed – but conscious work on the target task would be expected to impair performance on the interpolated task. This theory has the merit of parsimony and essentially explains incubation away as not involving any special processes, such as intuitive unconscious thinking.

Beneficial Forgetting

This view (e.g., Woodworth, 1938 ; Simon, 1966 ; Smith and Blankenship, 1991 ; Smith, 1995 ; Segal, 2004 ; see also, Dijksterhuis and Meurs, 2006 ) argues that “mental sets,” weaken during the incubation period. Such “beneficial forgetting” facilitates fresh starts or “set shifting” when the problem is taken up again after the incubation period. As well as decay and interference, misleading approaches may conceivably be weakened through inhibition as proposed in the theory of retrieval-induced forgetting ( Anderson et al., 1994 ; Storm and Angello, 2010 ). Segal (2004) proposed a variant (known as “Fresh Look”) in which simply switching attention away from the main task allowed a new start, with no forgetting or unconscious work proposed. The Fresh Look view does not predict effects of Immediate Incubation because with in that condition, there is insufficient opportunity for sets or fixations to develop that need to be forgotten to enable later progress.

Unconscious Work

On this account incubation effects involve active, but unconscious, or intuitive processing. The term “unconscious work” seems to first appear in the problem solving literature in Poincaré’s (1910 ) paper (p. 328). Related phrases such as “non-conscious idea generation” ( Snyder et al., 2004 ) and “unconscious thought” ( Dijksterhuis and Nordgren, 2006 ; Ritter and Dijksterhuis, 2014 ) are also used in the literature, but I will use the phrase “unconscious work” throughout the present paper.

Theoretically, what form might unconscious work take? For example, could unconscious work be exactly like conscious work, but with just one difference, namely that it is carried out without any conscious awareness? Or is unconscious work better thought of as some form of automatic spreading activation along associative links, as against a conscious rule or strategy governed activity? Wallas (1926) proposed the idea of spreading “associative chains” as being active during incubation, which can be seen as anticipating modern ideas of spreading activation. Poincaré (1910) argued for quite specific mechanisms of automatic idea generation and selection tailored to his domain of interest which was mathematical creation. Both Poincaré and Wallas argued that the suddenness of Illumination or Inspiration coupled with the feeling of confidence in the sudden insight arose from prolonged unconscious work. Wallas’s analysis is often labeled as a Four Stage theory, incorporating Preparation, Incubation, Illumination, and Verification, but he also proposed a sub-stage of Illumination which he dubbed “Intimation” ( Wallas, 1926 , p. 97). This sub-stage is often overlooked in discussions of Wallas’s analysis, although Wallas considered it was important, practically and theoretically (see also, Sadler-Smith, 2015 , for an extended discussion of Intimation in Wallas’s model). Intimation is the moment at the very start of the Illumination period when the solver becomes aware that a flash of success is imminent. Theoretically, Wallas saw Intimation as reflecting increasing activation of a successful association train which was about to become conscious. Thus, Intimation was consistent with the view that Incubation involved unconscious work. Practically, Wallas felt it was important that the solver recognize the Intimation feeling and desist from distracting activities to allow the solution to continue rising into consciousness. Overall, unconscious work has long been favored as a possible explanation of incubation effects. The question of what specific processes might be involved in unconscious work will be considered further in the Theoretical Discussion section.

The possible mechanisms indicated above are not mutually exclusive (or exhaustive). Delayed Incubation could involve all three suggested mechanisms, with some intermittent conscious work taking place when attention strays from the distracting task during the incubation period and with some beneficial forgetting and unconscious work also occurring when the solver is consciously processing to the distracting incubation task. However, a beneficial effect of Immediate Incubation would not be consistent with the Beneficial Forgetting hypothesis in that there is not time in the Immediate paradigm for sets or misleading directions to be established, but the Immediate paradigm would permit some intermittent conscious work and/or some unconscious work.

Theories of Incubation: Empirical Evidence

Intermittent work.

As a check for intermittent conscious work during an incubation period, performance on the interpolated task, during incubation, should be compared with performance by a control group using the interpolated task as a stand-alone activity. Impaired interpolated task performance during incubation would be consistent with the hypothesis of some conscious work on the target task during incubation. The argument here being that intermittent conscious work represents a diversion of resources away from the interpolated task and that should impair performance on the interpolated task. Although this may seem a basic methodological check for intermittent conscious work, it does not appear to have been carried out ( Sio and Ormerod, 2009 ; Dodds et al., 2012 ) until quite recently. In particular, Gilhooly et al. (2012 , 2015 ) incorporated suitable checks for intermittent conscious work on a target divergent thinking task during the incubation period. In an experiment involving delayed and immediate incubation and two different interpolated activities ( Gilhooly et al., 2012 ), there was no evidence of impairment to the interpolated incubation period tasks (which were mental rotations and anagram solving) as a result of the tasks being carried out during incubation periods, as against being carried out as stand-alone tasks in control conditions. These studies also found positive incubation effects, despite a lack of evidence for intermittent conscious work. If anything, the trends in the data were the opposite of those that would be predicted by the intermittent work hypothesis. Mental rotation and anagrams were somewhat (but not significantly) facilitated by being carried out as distractor tasks during incubation. None of the one tail predictions of the intermittent conscious work hypothesis were upheld. An additional analysis examined the correlations between performance scores on the interpolated tasks and post-incubation scores on the target, divergent thinking task. The Intermittent Work Hypothesis would predict negative correlations in that the more attention given to the interpolated task, the better the interpolated task scores would be, and the worse would be the target task scores. Over eight Pearson correlations examined, two were negative and six positive; the average Pearson correlation between target task and interpolated task performance measures was 0.11. Only one correlation was significant ( r = 0.36, p < 0.05, two tail) and this was in the direction opposite to that predicted by the Intermittent Work Hypothesis. This analysis of correlations between interpolated task and target task performance measures thus did not support the Intermittent Work hypothesis. A later study ( Gilhooly et al., 2015 ) using a target divergent thinking task and mental rotations as the interpolated task in a delayed incubation paradigm, also found no impairment in the interpolated task relative to controls. Indeed, mental rotations were significantly better performed as an interpolated task as against as a stand-alone task, contrary to the Intermittent Work Hypothesis.

In a related study, Baird et al. (2012) , using thought monitoring techniques, found that frequency of target task related intermittent thoughts during incubation was not related to quality of performance after the incubation period. So, it seems that even if intermittent thoughts about the target task occurred they were ineffective and did not explain the beneficial effects of incubation. In conclusion, from Baird et al. (2012) and Gilhooly et al. (2012 , 2015 ), it seems safe to rule out the Intermittent Work explanation of incubation effects.

On this view, solvers often develop initial approaches that are misleading and become fixated on these approaches. A break allows such tendencies to become weaker and so a fresh start is possible when the problem is resumed after an incubation break.

Smith (1995) investigated this possibility using word problems presented either with helpful or with misleading cues. After failures to solve, participants were given breaks of varying lengths and then on returning to the task tried to recall the cues and to solve. In the case of misleading cues, participants were more likely to solve when they had forgotten the cues and likelihood of forgetting increased with length of the break. The results thus supported the idea that beneficial forgetting of misleading information could be a factor underlying incubation effects.

Segal (2004) examined a variant of the Beneficial Forgetting approach which may be labeled the Fresh Look hypothesis. On this variant, simply switching attention from the target task is enough and length of break is not important. His study involved a spatial insight problem, in which a square has a parallelogram superimposed on it and the task is to find the sum of the areas of the two shapes. The problem is made easier when the solver realizes that the shapes can be restructured as two equal sided right angle triangles which, if slid, form a rectangle whose area is easily calculated. Participants engaged in this target task until they felt they were experiencing an impasse.

After impasse , participants were given 4 or 12 min on either a demanding verbal task (crossword) or undemanding task (browsing through newspapers) and then returned to the main task for up to 6 more min.

Results indicated significant benefits for incubation break v. no break, but no effects for length of break or for the demandingness of the activity during the break. Segal argued that these results were consistent with a the Fresh Look view, that simply removing attention from the target task was sufficient and that it was not important what was done in the incubation period or how long it was. This study thus supports a role for attentional shifting as a mechanism for Delayed Incubation. Together, Smith (1995) and Segal (2004) are consistent with a role for Beneficial Forgetting in the Delayed Incubation paradigm.

In contrast to Smith (1995) , Segal (2004) , and Dijksterhuis and Meurs (2006) argued that in the Immediate Incubation paradigm, the Beneficial Forgetting approach may be ruled out as there is no period of initial work in which misleading fixations and sets could be developed. Thus, if Immediate Incubation is shown to be effective, the unconscious work hypothesis must remain in contention for Immediate Incubation effects at least and would also be a candidate explanation as one possible mechanism for Delayed Incubation. Dijksterhuis and Meurs (2006) took the beneficial effects of the Immediate Incubation paradigm on a divergent task in their Experiment 3 as support for the role of unconscious work in incubation. However, as already mentioned, the task in this study did not clearly meet the usual criteria for a creative task and the scoring did not distinguish infrequent from genuinely novel responses. Hence, this study did not unequivocally address creative thinking as against free recall of possibly rare but previously experienced events from episodic and semantic memory.

Gilhooly et al. (2012) using explicit instructions to generate novel responses did find that both delayed and immediate incubation were effective in the Alternative Uses task and that immediate incubation produced more facilitation than delayed incubation. These results were consistent with a role for unconscious work in divergent thinking, particularly for Immediate Incubation, to which the Beneficial Forgetting approach is not applicable.

Snyder et al. (2004) investigated the role of unconscious work in the Delayed Incubation paradigm using a surprise return to the target task. In this case, beneficial effects of incubation emerged, consistent with the view that an automatic continuation of work but unconsciously may have occurred after the task was set aside. We may note that Snyder et al.’s (2004) task required simply production of uses for a piece of paper as against novel uses. Thus, this study did not necessarily require creative thinking as against recall of previously known uses.

The interpolated tasks used by Segal (2004) and by Dijksterhuis and Meurs (2006) were different in modality from the main tasks. Segal’s main task was spatial while the interpolated tasks were verbal and Dijksterhuis and Meurs’s study showed the opposite pattern in that their target task was verbal but the interpolated task was spatial. The similarity–dissimilarity relationship between target and interpolated tasks could be important theoretically as the main competing hypotheses suggest different effects of similarity between target and interpolated tasks. If unconscious work is the main process then interpolated tasks similar to the target task should interfere with any unconscious work using the same mental resources and so lead to weaker (or even reversed) incubation effects when compared with effects of dissimilar interpolated tasks. The unconscious work hypothesis suggests that when it comes to incubation it would be helpful to “do something different” from the target task. On the other hand, a forgetting account would suggest that interpolated tasks similar to the target task would cause greater interference, which would lead to more forgetting of misleading approaches and thus enhanced incubation benefits.

Helie et al. (2008) explored the effects of different interpolated tasks on the reminiscence paradigm in free recall. This is relevant to our present concerns because the reminiscence paradigm is analogous to incubation, in that an initial free recall is followed by interpolated tasks for a set period and then the same free recall is attempted a second time. The reminiscence score is the number of items recalled on re-test that were not recalled on the initial free recall. Helie et al. (2008) found that the more executively demanding the interpolated tasks were, the lower were the reminiscence scores for picture recall These results fitted well with Helie and Sun’s (2010) Explicit–Implicit Interaction model which envisages unconscious implicit processes running in parallel with conscious explicit processes. Helie et al.’s (2008) result is consistent with the Unconscious Work hypothesis for incubation in that more demanding interpolated tasks will leave less resources available for unconscious work. However, Helie et al.’s (2008) focus was free recall from episodic memory rather than creative thinking, which requires novel combinations and so, although suggestive, and consistent with Unconscious Work, this result does not directly address creative thinking which is the focus of the present paper.

Ellwood et al. (2009) found a beneficial effect on number of responses post-incubation of a dissimilar interpolated task in a Delayed Incubation experiment. However, this study used a fluency of uses task rather than a novel uses task. Also, as Ellwood et al. (2009) pointed out, although their findings are consistent with an explanation in terms of unconscious work, an explanation in terms of selective relief of fatigue could also be invoked to account for the effects of similarity between incubation and target tasks. On this view, for example, a spatial Delayed Incubation task very different from a main verbal task could facilitate more recovery from fatigue specific to verbal processing than might an interpolated verbal task. Gilhooly et al. (2013) included tests of the effects of the similarity between incubation and target tasks in an Immediate Incubation paradigm, so that where fatigue as an explanation could be examined. The Gilhooly et al. (2013) study factorially varied incubation activities (verbal – anagram solving vs. spatial – mental rotations), used either a clearly creative verbal divergent task (alternate uses) or a clearly spatial divergent task (mental synthesis) and both divergent tasks were scored for novelty as well as fluency. Significant incubation effects were found, but of most interest were the interactions, in that spatial incubation benefitted verbal divergent thinking more than did verbal incubation activity and verbal incubation activity benefitted spatial divergent thinking more than did spatial incubation activity. These results supported a role for unconscious work during incubation periods in creative thinking tasks and did not support the hypotheses that incubation effects are due to Beneficial Forgetting or attention shifting. The Beneficial Forgetting account predicted the opposite pattern of facilitation (i.e., that similar incubation and target tasks would be more beneficial than different modality incubation and target tasks).

Theoretical Discussion

From recent research discussed above relating to the three main explanations for incubation effects, viz., Unconscious Work, Intermittent Work, and Beneficial Forgetting, it seems that given the effectiveness of Immediate Incubation, in which sets are unlikely to have been developed, the Beneficial Forgetting hypothesis can be ruled out for immediate incubation at least. In addition, Gilhooly et al. (2012 , 2015 ) found no support for the idea of Intermittent Work, from studies in which suitable control conditions were included. Unconscious Work thus remains as the best candidate explanation for the effects of Immediate Incubation periods and it handles the effects of similarity between incubation and target task Gilhooly et al. (2013) . Gilhooly et al. (2013) found that Delayed Incubation was beneficial, but less so than Immediate Incubation in a divergent thinking task (Alternative Uses). It could be that in Delayed Incubation, sets do build up during the initial period of conscious work, and are then reduced by Beneficial Forgetting, after which useful unconscious work could come into play. In contrast, with Immediate Incubation, there are no sets to be overcome and beneficial unconscious work can start sooner than in the Delayed paradigm leading to better performance than with Delayed incubation. Overall, however, the Unconscious Work hypothesis is in contention for both Delayed and Immediate Incubation.

However, the question still arises of what processes might be involved in unconscious work? Could unconscious work processes be identical toe conscious work processes with the sole difference that they are executed without conscious awareness? This issue will now be addressed.

Unconscious Work?

Conscious work is generally rule or strategy governed. Could unconscious work also be rule governed? Poincaré (1910 , p. 329) considered the possibility of a “subliminal self” that worked in the same way as the conscious self, but without consciousness, and might even be a superior “self” since it could find solutions that evaded the conscious mind. Kounios and Beeman (2015) illustrate this notion of a subliminal self by supposing that a man has the job of solving long anagrams during office hours. Suppose the person concerned works systematically all day, on the day shift, from 9 am to 5 pm, trying to solve say, “iaiaeiaeiiamsnrtnmhslbtssdtn,” but when he leaves at 5 pm it is still not solved. Another worker takes over and continues the systematic search on the night shift, from where the first worker left off. At 7 pm the night shift worker phones through to the day shift worker with the answer (cf., insight) saying “It’s “antidisestablishmentarianism!””. In this example, the second shift worker represents the unconscious and works just the same way, using systematic search, as the day shift worker; but, the day shift worker is not aware of the night shift worker’s activities until the answer is phoned through.

To explore further the idea that unconscious work might be a subliminal version of conscious work let us consider conscious processing in the Alternate Uses task. This was addressed in a think aloud study of the Brick Uses task by Gilhooly et al. (2007) in which it was found that participants used strategies, such as scanning the target object’s properties (“Bricks are heavy”) and using the retrieved properties to cue and infer uses from semantic memory (“Heavy objects can hold down things like sheets, rugs, tarpaulin and so on, so a heavy brick could do those things too”). Could unconscious work essentially duplicate this form of conscious work but with no awareness. As we have argued previously ( Gilhooly et al., 2012 , p. 976).

“The standard view in cognitive science is (a) that mental contents vary in activation levels, (b) that above some high activation level mental contents become available to consciousness, (c) that we are conscious of only a limited number of highly activated mental elements at any one time (that is, the contents of working memory) and (d) that strategy or rule based processing, as found in Gilhooly et al.’s (2012) think aloud study, requires such highly activated (conscious) material as inputs and generates highly activated (conscious) outputs.”

On the standard view then, conscious work requires the highly activated contents of working memory and highly activated material is necessarily in consciousness. Overall, it seems impossible that unconscious processes could really be exactly like conscious processes in every respect except that of being conscious. For example, using the rules of arithmetic and temporary working memory storage processes to multiply two 3 digit numbers (e.g., 364 × 279 = ?) is surely impossible without highly activated representations in working memory of the numbers, goals, and intermediate results. The short term representations involved in mental arithmetic would seem to be necessarily conscious. It seems impossible to carry out unconscious multiplication of two or three digit numbers. (With practice of course, one can learn and store three digit multiplication results in long term memory which can be directly retrieved by a type of unconscious process. However, this t is not mental multiplication). Poincaré (1910 , p. 334) made a very similar point when he wrote “It never happens that the unconscious work gives us the result of a somewhat long calculation all made , where we only have to apply fixed rules.” In conclusion, the idea that unconscious work or thought processes could be just the same as conscious work processes with the sole difference that they lack awareness of any mental content, seems unlikely.

However, a challenge to this conclusion has been recently put forward by Hassin (2013) who argues in favor of what he labels a “Yes, It Can” (YIC) principle. According to YIC, unconscious processes can perform the same fundamental, high level functions that conscious processes perform. While it would be generally accepted that the elementary (fundamental?) component processes in carrying out 364 × 279 = ?, are unconscious (e.g., the first step of 364 × 279 is likely to be 9 × 4 = 32, which involves a direct retrieval process that occurs without conscious concomitants in adults practiced in basic multiplication at least) and many such steps and processes are needed, yet precise results need to be held in working memory and precise goals need to be formulated in an organized way (executive processes) all of which seems impossible without mental contents activated to conscious levels. Hassin cites some experiments ( Sklar et al., 2012 ) which appear to show priming in subliminally presented additions and subtractions involving two and even three digits. However, these are far from the long calculations with intermediate results that Poincare discussed as difficult for the subliminal self. Exact calculations cannot realistically be made purely by priming which would activates associatively related numbers and not just the correct ones which are needed at every step of a long calculation if it is to be successful. Similar points apply to all types of problem solving which require multiple steps to be carried out and multiple intermediate results to be held along the way between presentation and solution.

Assuming unconscious work cannot actually be just the same in terms of processing steps as conscious work, of what then, might unconscious work consist?

Poincaré (1910 , p. 333) drew on Epicurus’s (341–270 BC) ancient-world theory of atoms as having hooks so that these elementary building blocks of nature could combine with each other. He imagined ideas like hooked atoms hanging on a wall before relevant ideas/atoms are set in motion during Preparation and continue in motion during Incubation. As with molecules of a gas in a container, the atoms/ideas collide at random and sometimes the hooks snag and a new combination is formed. The atoms initially set in motion can strike atoms at rest and may combine with them. This would represent initial ideas being combined with new ideas so that the products of random combination would always have some relation to the starting conditions of the problem.

Campbell (1960) drew on a range of pre-cursors of his view who had stressed the role of extensive trial-and-error in creative work ( Bain, 1874 ; James, 1880 ) and he was strongly influenced by Poincaré (1910) . Campbell argued that creative problem solving involves a quasi-random generation of associations between mental elements (“Blind Variation”) to produce novel combinations of ideas, some of which may be useful and so be subject to Selective Retention. This approach draws an analogy with biological evolution in which random changes in genetic material lead to changes in organisms, some of which are useful and hence retained by natural selection. Similarly, it is argued that ideas are modified in creative problem solving in ways which are blind to the final solution and only by chance lead ultimately to modifications that solve the problem and are retained for future use. Campbell (1960) quoted extensively from Poincaré’s (1910 ) account of creative thinking in mathematics, as involving extensive quasi-random search, although Campbell did not stress any special role for unconscious processing. His concern was very much with the role of blind trial-and-error, whether carried out at a conscious or an unconscious level. It could be argued that Campbell saw productive conscious creative thinking as like the unconscious work proposed by Poincaré (1910) .

Simonton (1995 , 2003 ) developed Campbell’s ideas and used the notion of “mental elements” which are similar to Poincaré’s (1910 ) “hooked atoms.” However, unlike Campbell, Simonton stresses the role of unconscious processes which lead to new combinations, some of which are retained and selected to enter consciousness on the basis of their “stability.”

In terms of current approaches to cognitive processing, how might novel combinations come about? Parallel spreading activation processes in a semantic network could lead to remote and unusual associations ( Jung-Beeman et al., 2004 ). One specific proposal is that of Helie and Sun’s (2010) Explicit–Implicit Interaction model. In this model, incubation is regarded as involving unconscious, implicit, stochastic associative processes that demand little attentional capacity in contrast with conscious explicit rule governed attentionally demanding processes that run in parallel. In this model, activation spreading through implicit networks during incubation periods leads to novel associations which could facilitate later work when conscious processing resumes and the explicit level processes and knowledge interact with the implicit level processes and knowledge. The model does not seem to deal with incubation leading to a breakthrough of solutions into consciousness without an explicit return to the task. According to Dijksterhuis and Nordgren’s (2006) Unconscious Thought Theory (UTT), unconscious thought, or work, is parallel, bottom-up, inexact, and divergent; whereas conscious thought is, serial, exact, and convergent. Thus, the characteristics of unconscious thought, as envisaged by UTT are consistent with incubation effects.

Overall, there is general agreement among many theorists that unconscious thinking, or unconscious work, in the form of implicit associative processes involving spreading activation [similar to Wallas’s (1926) concept of “associative trains”], is a possible explanation of incubation effects.

How might the suddenness of inspiration be explained? Both Poincaré and Wallas saw this feature of creative thinking as indicative of prolonged unconscious work that found a solution and delivered it to consciousness. However, here Poincaré identified a problem for the unconscious work account. How did the good idea become selected for promotion to consciousness? Poincaré was focussed on mathematical creation and he proposed that in this domain selection was based on the mathematician’s special intuitive sensibility to beauty in mathematics and further that the subliminal self possessed this intuitive sensibility. Poincaré’s theory, as stated in the 1910 paper, is narrow in solely addressing mathematical creation; generalization to other fields, such as poetry, music, physics, and so on, would require specific intuitive sensibilities to be proposed for those fields. An alternative possibility that has general applicability, is that when a problem is set aside, a goal representation remains active for extended time periods, although below the threshold for consciousness. The active goal representation would tend to boost activation flow into associated solution-relevant paths and when a solution combination of associations or a single relevant association became active, the solution and the goal representations would mutually activate each other in a positive feedback loop leading both to become conscious as their activations pass threshold levels. It is suggested that this rising activation (or “rising train of association” as Wallas put it) is experienced as Intimation. The present account has the benefit of automaticity and is parsimonious in not requiring special sensibilities to be invoked. The sub-threshold but active goal representation automatically does the work of selecting promising solution –relevant associations.

Concluding Comments and Limitations

Overall, it can be concluded that the field, although still acknowledging the pioneering work of Poincaré and Wallas, has made considerable progress. The existence of incubation as a beneficial stage in creative thinking has been established through a large number of empirical studies ( Sio and Ormerod, 2009 ), so that the field does not depend on potentially unreliable introspective accounts. New paradigms, such as Immediate Incubation have been established and have helped justify a role for implicit Unconscious Work. Theoretical ideas have been sharpened and refined and the joint effects of spreading activation and subconscious goal activation provide a candidate 9 explanation for insight or intuitive solutions following incubation. The approach put forward here, in terms of spreading activation and goal representations, is most applicable to relatively small scale but knowledge rich problems such as divergent thinking tasks. Further work is needed to develop the present approach for knowledge lean problems, such as laboratory insight problems on the one hand and for larger scale real life problems on the other hand.

Author Contributions

The author confirms being the sole contributor of this work and approved it for publication.

This paper is based on research funded by grants from UK Economic and Social Research Council (RES-000-22-2191) and Leverhulme Trust (F008281G) to KG.

Conflict of Interest Statement

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

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Keywords : creativity, intuition, problem-solving, incubation effect, insight problem solving

Citation: Gilhooly KJ (2016) Incubation and Intuition in Creative Problem Solving. Front. Psychol. 7:1076. doi: 10.3389/fpsyg.2016.01076

Received: 28 April 2016; Accepted: 01 July 2016; Published: 22 July 2016.

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*Correspondence: Kenneth J. Gilhooly, [email protected]

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