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Home > Books > Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human - Machine Collaborations

The Aha! Moment: The Science Behind Creative Insights

Submitted: 01 June 2018 Reviewed: 05 February 2019 Published: 27 November 2019

DOI: 10.5772/intechopen.84973

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Insight, often referred to as an “aha moment,” has been defined as a sudden, conscious change in a person’s representation of a stimulus, situation, event, or problem. Recent advances in neuroimaging technology and neurophysiological techniques have allowed researchers an opportunity to hone in on the neural circuitry that governs insight, a phenomenon that has been theorized about by cognitive psychologists for over a century. Studies show that insight is not a sudden flash that comes from nowhere, but in fact is the result of the unconscious mind piecing together loosely connected bits of information stemming from prior knowledge and experiences and forming novel associations among them. This conceptualization of insight naturally gives rise to comparisons between insight and creativity. Creativity, however, involves many cognitive processes, occurring in many regions of the brain and thus cannot be laterally localized as insight can. Thus, creativity is not considered synonymous with insight; however, insight can certainly result in creative solutions during creative problem solving.

• Aha! moment
• analytical problem solving
• creative problem solving
• functional fixedness

Author Information

Wesley carpenter *.

• The University of Akron, Akron, Ohio, USA

*Address all correspondence to: [email protected]

1. Introduction

Undoubtedly, we have all had them, that moment of extraordinary clarity in which the solution to a difficult problem suddenly seems to just “pop in there.” Or perhaps it is a punchline to a joke that you all of a sudden get, or the perfect metaphor that suddenly comes into awareness. Where do these whiffs of inspiration come from? Do they just magically pop in there, as if given to us by some muse? Or is there perhaps a more scientific explanation? Insight, or an “Aha!” moment as it is commonly referred to, is not mysterious at all. In fact, recent advances in neuroimaging technology have made it seem less mysterious than ever. Insight has been defined as any sudden comprehension, realization, or problem solution that involves a reorganization of the elements of a person’s mental representation of a stimulus, situation, or event to yield a nonobvious or nondominant interpretation. Insights may appear suddenly, but are preceded by incremental unconscious processing. Research by cognitive psychologists and cognitive neuroscientists has shown that moments of insight are merely the result of the brain making connections between weakly and strongly activated bits of information, and then bringing them to consciousness.

2. Insight versus analytical problem-solving

Some of the earliest research on insight sought to conclude whether there really was a difference between solving a problem via insight versus solving a problem via a heuristic driven type of problem solving methodology. Firstly, there are definitional differences between the two. Insight, commonly referred to as an “aha moment,” has been defined as a sudden, conscious change in a person’s representation of a stimulus, situation, event, or problem [ 1 ]. It should be noted that insights, while they do suddenly merge into one’s stream of consciousness, are proceeded by unconscious processing to arrive at the insight. This is in contrast to analytical problem solving which involves the use of a systematic process or simply logical reasoning to arrive at a solution to a problem. It is deliberate and conscious, and often involves the use of some type of strategy which allow the individual to progress incrementally toward a solution. Because this type of methodology involves storing and manipulating information in the prefrontal cortex utilizing the individuals working memory capacity, individuals can typically fully explain the steps taken to arrive at the solution [ 2 ], whereas with insight, individuals cannot readily reconstruct the procedure followed to reach the solution. Albert Einstein summarized the unconscious nature of insight when he said, “At times I feel certain I am right while not knowing the reason” [ 3 ].

Differences between the two problem solving methods vary beyond differences in definition and accuracy of solutions, neuroimaging studies suggest that patterns of brain activity during and prior to solving by insight versus analysis are fundamentally different as well [ 4 , 5 , 6 ]. This suggests different cognitive strategies are being employed depending upon whether the solution arrives via insight or analytical means. Studies have shown that the brain actually predicts in advance whether the problem will be solved analytically or by insight [ 6 , 7 ]. For example, Salvi et al. [ 8 ] showed that people blink and move their eyes differently prior to solving by insight versus solving analytically.

Other findings using the compound remote associates (CRA) test have provided additional support for the notion that insight processing is qualitatively different from analysis type problem solving. Compound remote associate problems are similar to items on the remote associates test developed by Mednick in 1962. Subjects must produce a solution word (e.g., sweet) that can form compounds with each of three problem words (e.g., tooth, potato, and heart). This type of test, while not considered a classic insight test, often give rise to Aha! moments. They are frequently used when studying creativity, problem solving, and insight.

Bowden and Jung-Beeman [ 9 ] presented compound remote associates test problems to participants followed by a single word that they were instructed to verbalize as quickly as possible. This known as cognitive priming. For unsolved problems, following verbalization participants indicated whether the word was the solution to the problem they had just been given. If it was, subjects had to indicate whether this realization had come to them suddenly, which would indicate insight, or incrementally, which would indicate an analytical solution strategy was employed.

Another type of cognitive priming was used to induce abstract thinking in subjects as opposed to concrete thinking by asking subjects to thinking about distant ideas (past or future), remote locations or other’s perspectives versus asking subjects to think about ideas related to the here and now. According to construal level theory, increasing the psychological distance, that is, thinking about things that are increasingly far away in space or time or about people that are different from oneself tends to engage abstract thinking [ 10 ], which in turn is hypothesized to produce more creative and insightful ideas. Subjects who were primed to think in the abstract by considering ideas at far psychological distances performed better on insight related tasks whereas those primed to think concretely by considering ideas at short psychological distances did considerably better on problems requiring analysis [ 11 ].

2.1 Differences in cognitive strategies

A study by Salvi et al. [ 8 ] suggest additional evidence that there are differences between insight and analysis problem solving wherein it was revealed that solutions provided by insight were correct more often that solutions garnered by analysis. A possible explanation of this is that insights are typically all or nothing, i.e., there is no intermediate opportunity to alter one’s information or solution strategy, ideas, thought processes, etc., when there is a looming deadline whereas analytical problem solving, due to its conscious nature, allows for individuals to make errors of commission, becoming fixated on irrelevant information (i.e., functional fixedness), etc., as a looming deadline approaches [ 7 ].

A pattern of errors made by subjects using either of the two methods suggests differences in cognitive strategies for problem solving via insight and analysis. They found that participants who solve predominantly by insight tend to make errors of omission (i.e., time outs) rather than errors of commission, whereas participants who tend to solve analytically make errors of commission rather than errors of omission (i.e., incorrect responses).

3. The neuroscience of insight

Recent technological advances have allowed neuroscientists to begin getting closer to understanding the complex neural underpinnings of the Aha! moment, i.e., insight. Neuroimaging studies on the insight phenomenon typically involve the use of either electroencephalography (EEG) or functional magnetic resonance imaging (fMRI), or commonly a combination of both to investigate the temporal dynamics and neural correlates of insight. Electroencephalography affords the researcher high temporal resolution which provides highly precise time measurements which are necessary to capture the rapidly changing electrical activity in the brain when subjected to stimulation. A disadvantage of EEG, however, is poor spatial resolution. Thus, functional magnetic resonance imaging is commonly used to provide high spatial resolution for precise localization of brain activity. Together these techniques are able to isolate the neural correlates of insight in both space and time.

As discussed above, the development of short compound remote associates problems readily solvable by insight by Bowden and Jung-Beeman has proved useful in neuroscientific studies as well. Early studies of insight typically posed a small number of complex problems to participants. Most participants take many minutes to solve such problems, when they are able to solve them at all. However, neuroimaging and electrophysiological methods require many trials to accurately record brain activity. Compound remote associates problems are well suited to neuroimaging and electrophysiological studies.

These types of problems afford the researcher two primary advantages. First, they can be solved via insight or through analysis. Furthermore, each problem presented, whether solved with insight or analysis, does not differ in complexity or solving duration [ 2 , 12 ]. Essentially, this test controls for all confounding variables for the actual cognitive strategy used, therefore whether insight or analysis was used can be more easily identified without error. Secondly, a response utilizing either method can be given relatively quickly, thereby allowing a large number of trials per condition in a short time period [ 7 ].

As described above, each compound-remote-associates problem consists of three words (e.g., potato, tooth, heart). Participants are instructed to think of a single word that can form a compound or familiar two-word phrase with each of the three problem words (e.g., sweet can join with potato, tooth, and heart to form sweet potato, sweet tooth, and sweetheart). The instant subjects think of the word that can combine with all three, they press a button as quickly as possible. Subjects are instructed to not take any time to analyze the solution, simply press the button as soon as they become aware of the solution. They are then prompted to verbalize the solution and then to press a button to indicate whether that solution had popped into awareness suddenly (insight) or whether the solution had resulted from a more methodical hypothesis-testing approach.

When participants indicated that the solution had popped into awareness suddenly, thus indicating insight, the EEG showed a burst of high-frequency gamma waves over the right temporal lobe (just above the right ear in the right hemisphere) as shown in Figure 1 , and the fMRI showed a corresponding change in blood flow in the medial aspect of the right anterior superior temporal gyrus (aSTG) [ 4 ]. No gamma wave activity was reported in the left hemisphere. This activity in the right hemisphere (RH) is interpreted as the sudden availability of the solution coming into consciousness, i.e., the Aha! moment.

The image on the left shows a topographic distribution of gamma-band activity during the insight solutions and the image on the right shows area of activation corresponding to insight effect during functional magnetic resonance imaging (fMRI). Adapted from Kounios and Beeman [ 13 ].

The spatial and temporal correspondence of the EEG and fMRI signals suggests they were triggered by the same underlying neural event [ 13 ]. Activity was also reported in the bilateral hippocampus, para-hippocampal gyri and anterior and posterior cingulate cortex, but further studies suggest activity in these areas were relatively weak compared to the strong signals produced in the right anterior superior temporal gyrus. Moreover, the signal produced in the right temporal region of the brain occurred nearly the same time as when subjects realized the solution to each of the problems; the same region that is implicated in other tasks requiring semantic integration [ 14 ]. Furthermore, high frequency gamma-wave signals have been proposed to be a mechanism for assimilating and ultimately making connections among information as it emerges into consciousness [ 15 ].

Figure 2 highlights differences in EEG power just before, during and after the solution to the problem was given by the individual. The figure clearly shows a distinct difference in EEG power when the participant reported a solution via insight whereas virtually no change in EEG power when a solution was arrived at via an analysis type of problem solving method. Thus, clear differences in neural activity just before a solution comes to consciousness validates distinct differences between solution by insight and solution by analysis. It should be noted that one of the advantages of problem solving via insight is that sometimes it brings nonobvious solutions to problems to conscious awareness. The anterior cingulate cortex (ACC) is thought to prepare the brain for the integration of weakly activated ideas and solutions [ 5 ]. When a problem is presented, one’s attention is typically dominated by obvious solutions to a given problem, however, if there exists inconsistent or competing information, the ACC is can become activated, and thus allow more distant, weakly activated ideas to come to consciousness.

Time course of insight- and analysis-related gamma-band EEG power. Adapted from Kounios and Beeman [ 38 ].

In addition to the increase in gamma wave activity, Figure 3 shows a sudden increase in power in the alpha-band frequency occurred about 1.5 s before insight solutions, suggesting a decrease in neural activity within the right visual cortex. These effects are not attributable to emotional responses, because the neural activity preceded the solutions. Alpha waves reflect cortical deactivation or inhibition of certain brain areas [ 5 ], thus the increase in alpha waves just before solution is analogous to looking away, closing one’s eyes, or looking up at the ceiling, all of which are common tactics employed by individuals to minimize visual distractions when solving problems. The burst of alpha waves and then gamma waves suggest before insight solutions suggest the brain is changing the focus of its efforts to limit visual distractions thereby facilitating the integration of remote semantic elements and allowing a pathway for it to emerge into conscious awareness. This is in contrast to solutions produced via analysis which shows increased neural activity (i.e., decreased alpha-band activity) in the visual cortex. A decrease in alpha waves indicates a response to demands on one’s attention, thus the decrease in alpha waves suggests subjects were focusing on the external environment while solving problems rather than making attempts to minimize distractions.

Graph showing large increase of power in the alpha-band frequency just prior to increase in gamma band activity, known as the alpha insight effect. Adapted from Kounios and Beeman [ 38 ].

The primary take-away appears to be that a subject’s neural activity during resting state, i.e., task-free state, prior to each compound remote associates problem suggest that distinct patterns of neural activity precede problems that people eventually solve by insight versus those solved by analysis. These changes in the brains resting state prior to solving insight problems suggest it is possible to predict a priori whether a subject is likely to use insight to solve a problem rather than analysis.

4. The psychology of insight

The neuroscientific view of insight allows to understand the neurological processes that underpin the moment of insight, but what exactly is insight from a cognitive psychology point of view? Indeed, Aha! moments are one of the most intriguing and unexplained processes of the human mind [ 16 ]. From a cognitive psychology perspective, attempting to place the insight phenomena into a proper theoretical framework to provide scientifically valid explanations of why the insight phenomena occurs has been difficult.

Famous American psychologist William James [ 17 ] put forth the first psychological theory of insight known as the associationist theory of insight which proposed that new ideas are combinations of existing ideas, that sudden insights are merely the result of having a lot of information in being able to make connections between fax. These connections are made during a suitable incubation period, an unguided, unconscious process whereby individuals simply take time off from the problem. A competing view of insight was put forth by the German Gestaltist Karl Duncker who was attempting to explain the psychology of insight and thus put forth proper definition of insight [ 18 ]. The Gestalt view of insight described it as “a process based on reconstructing the core of a problem, rethinking its basic assumptions and originating a new and creative solution, a process usually occurring in an unexpected and unpredictable manner” [ 19 , 20 ].

The Gestalt view of insight differed in that they believed insight problems are solved suddenly and therefore no chain of connections could explain the discovery. This view suggests that insights occur while performing an analysis of the problem in which you are drawn to a potential solution, but then realize it cannot work. This is referred to as an impasse in which your mind becomes fixated on a particular solution and you therefore become incapable of exploring the problem from other angles. The solution arrives not by making incremental associations but by overcoming the fixation thus allowing a restructuring of a problem that allows you to eventually arrive at a solution. Restructuring is conceptualizing the problem differently, essentially seeing the problem in a whole new way, hence the solution is sudden and surprising. Individuals are not consciously aware of how they overcame the problem.

Other theories have been proposed to provide theoretical framework to explain insight. For example, The Progress Monitoring Theory by MacGregor et al. [ 21 ], is based on the hill-climbing idea that problem solving proceeds with the problem solver seeking to minimize the gap between the current state of the problem and the goal state. Individuals begin attempting to solve a problem by putting forth what they believe is an informed solution, which is then subsequently altered by making incremental improvements to the solution thereby getting closer and closer to the correct solution. When such incremental improvements do not result in the correct solution, the individual reaches an impasse, often likely due to the individual becoming fixated on an incorrect strategy or incomplete information. Now the individual must search for a new approach to solve the problem. This theory implies that individuals constantly monitor their own progress in order to promptly switch to a different problem-solving strategy in case the current one is not successful. This theory suggests that the Aha! moment may be achieved with an incremental approach, with constant monitoring of one’s own cognitive processes as a pivotal feature, making the Aha! moment more like a conscious epiphenomenon of a general problem-solving process rather than a burst of uncommon cognitive processes [ 22 , 23 ].

In contrast to the Progress Monitoring Theory, Knoblich and colleagues introduced the Representational Change Theory [ 24 ] which offered an alternative explanation of how an impasse is overcome, that is, through a reorganization of a problem’s representation. Representation can be thought of as the distribution of activation across pieces of knowledge in memory [ 25 ]. This theory suggests that the problem is first represented using information or knowledge that is not relevant for the solution, hence an impasse is reached. Once this impasse is reached, the representation is altered such that relevant information becomes active and a viable solution merges into consciousness. Knoblich et al. [ 24 , 26 ] suggest that the main issue of problem-solving is an individual’s tendency to set unnecessary constraints through a very restricted representation of the problem, which is a function of limited, incomplete or ambiguous prior knowledge. Once the impasse is reached, by relaxing the unnecessary constraints that have been placed on the problem by deactivating the recalled knowledge linked to the problem or decomposing elements of the task by dividing it into perceptual chunks, a new representation of the problem can be reached [ 23 , 25 ].

Progress Monitoring Theory and Representational Change Theory differs primarily in how one deals with an eventual impasse that impedes a solution. Bowden and Beeman have proposed another theoretical framework to explain insight by attempting to link a cognitive psychological model to actual neurological processes within particular regions of the brain. The theory proposes that insights occur when the initial representation of the problem initiates a strong semantic activation of information that allows for the generation of obvious solutions to a problem and a weak (unconscious) semantic activation of remote, alternative information important for the generation of non-obvious solutions to a problem. The weak semantic activation which is responsible for allowing remote associations to be made is thought to be produced in the right hemisphere whereas the strong semantic activation is thought to be produced in the left hemisphere [ 22 ]. Initially, solvers may be unable to take advantage of weak solution activation because it is weak, and therefore might be blocked by stronger, more focused, but misdirected semantic activations [ 22 ]. A new restructured representation of the problem emerges when integration of weakly activated information and subsequent associations made therein are reinforced, strengthened, and ultimately emerge into consciousness.

It is important to recognize that both hemispheres of the brain involve complimentary processes that work synergistically to produce a solution. Information is shared between the two hemispheres, it is the presence of this laterality that allows the solution to merge into consciousness. However, it is thought that the right hemisphere s predominantly responsible for the generation of non-obvious solutions to a given problem, i.e., creative problem solving. Psychological studies of insight suggest that the good gestalt theory is largely false. The consensus among scholars is that insight is primarily a function of previous experience and acquired knowledge [ 27 ]. Rather than a sudden restructuring, the mind seems to gradually get closer to the correct solution. And that’s pretty consistent with the association theory and the Bowden and Beeman theory that creativity occurs when existing ideas combine together. The existing ideas on the new metal structure our new, they’re familiar ideas and Conventions that are already in the domain and then have been internalized by the creator.

5. The relationship between insight and creativity

One of the most enduring theories of creativity is the Wallas [ 28 ] model of creativity. It begins with a preparation stage where the individual properly identifies and defines the problem, and then proceeds to gather information necessary to solve the problem. Next comes incubation which involves taking some time away from a problem to allow the unconscious mind to process the information to produce a solution. This is the state where information is assimilated, and remote associations are thought to be formed [ 29 ].

The third stage in the Wallas model is illumination, or more commonly referred to as insight because it results in the familiar Aha! experience. During this stage, a solution suddenly emerges into consciousness, light a lightbulb being turned on. This sudden illumination is still controversial however. Weisberg [ 30 ] wrote, “there seems very little reason to believe that solutions to novel problems come about in leaps of insight. At every step of the way, the process involves small movements away from what is known” (p. 50). Perhaps we only perceive it as sudden because the processing that led up to the insight is below conscious awareness [ 31 ]. Prominent creativity researcher Sawyer [ 27 ] suggests insights only seem sudden because we didn’t notice the many incremental steps, or mini-insights, that immediately preceded it. He suggests rather than the familiar light bulb turning on metaphor, perhaps the tip of an iceberg or final brick in the wall is more appropriate.

The final stage was verification. At that point, the individual tests the idea or applies the solution. Although the four stages of the creative process included in the Wallas model are generally accepted to be accurate, it is generally accepted that the creative process is much more recursive than the linear Wallas model is depicted as being. It is worth noting that while other models have dissected the four stages of the Wallas model into further stages, the fundamental four of the Wallas model still remain.

With respect to the second stage of the Wallas stage model of creativity, namely incubation, one of the oldest observations in the psychology of creativity is that a creative idea is often preceded by a period of unconscious incubation [ 17 , 32 ]. There is much research studying the incubation effect and its relationship with creative insight [ 16 , 33 , 34 , 35 ]. It is generally agreed upon that there exists an incubation effect, although the exact nature of the associated unconscious processes remains uncertain. Hypotheses include mental relaxation, selective forgetting, random subconscious recombination, and spreading activation.

The relationship between insight and creativity is still a controversial one. Whether insight is a component of creativity (or a component of the creative process), simply a form of problem solving that may or may not produce a creative solution to a given problem [ 36 ], or something else entirely is as yet unanswered. Experimental and theoretical work support conflicting views regarding this question [ 37 ]. Sternberg and Davidson [ 16 ] conceptualized creativity as the ability to change existing thinking patterns, producing something that is useful, novel and generative. One cannot help but notice similarities between this conception of creativity and the generally accepted definition of insight, namely “a reorganization of the elements of a person’s mental representation of a stimulus, situation or event to yield a nonobvious or nondominant interpretation” [ 38 ]. Thus, it is likely that both conceptions are correct. We know from experience that insight is not always involved in creative problem solving and therefore must not be a necessary component of it. Creative solutions can also arise through a conscious, deliberate analysis of the problem [ 39 ].

Creativity and insight have similar neurological correlates as well. Deliberate creativity that results from analysis is primarily controlled by the prefrontal cortex. However, creativity that comes as a sudden flash of insight involves three brain regions, namely the temporal, occipital, and parietal (TOP). Moreover, a prominent view of creativity is that it is based on the processing of remote or loose connections between ideas [ 40 ]. Research suggests the brain’s right hemisphere is primarily responsible for the processing of remote associations and the brains left hemisphere is responsible for the processing of close or obvious associations [ 4 ]. Research suggests it is this rightward asymmetry that allows for weak activation of a broad semantic field, thus allowing for nondominant, remote associations between disparate ideas to take place. Hence the Bowden and Beeman theory seems to provide a neurological basis for Mednick’s theory of creativity.

6. Conclusion

Insight is any sudden comprehension, realization, or problem solution that involves a reorganization of the elements of a person’s mental representation of a stimulus, situation, or event to yield a nonobvious or nondominant interpretation. Insight is sudden, but it is preceded by incremental unconscious processing, sometimes referred to as mini-insights [ 27 ]. This unconscious processing appears to involve the integration of information contained within a weakly activated broad semantic field thus allowing remote associations of knowledge to stream into consciousness culminating in what we often refer to as an insight. It comes to consciousness suddenly, thus giving rise to the familiar Aha! moment. Such activation of remote associates naturally gives rise to comparisons to creativity, and the potential relationship between insight and creativity.

Insights are considered simply another way individuals produce creative solutions to problems. Neuroimaging studies suggest insights emanate predominantly from the right anterior superior temporal gyrus region of the brain, thus our understanding of the neural correlates involved in insight has increased considerably. It is generally accepted however, that creativity cannot be localized to a single region of the brain. Creativity appears to be highly lateralized in that several regions of the brain are active simultaneously. This makes sense, creativity involves many cognitive abilities, each of which involve many regions of the brain. Thus, creativity is not a moment of insight; however, insight can produce creativity if creativity happens to be the desired output [ 27 ]. In addition, it is worth noting that while the weak activation of a broad semantic field involved in insight is thought to be localized to the right hemisphere, thus perhaps giving rise to the popular myth that creative individuals are right-brained, there is no evidence to support such distinct brain lateralization, both hemispheres are active and contribute equally to creative problem solving.

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• Published: 07 April 2022

Going beyond the AHA! moment: insight discovery for transdisciplinary research and learning

• BinBin J. Pearce   ORCID: orcid.org/0000-0003-0982-3743 1 ,
• Lisa Deutsch   ORCID: orcid.org/0000-0002-8961-357X 2 , 3 ,
• Patricia Fry 4 , 5 ,
• Francesco Femi Marafatto 6 &
• Jenny Lieu 1  

Humanities and Social Sciences Communications volume  9 , Article number:  123 ( 2022 ) Cite this article

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• Environmental studies

In this paper, we develop and apply the concept of ‘insight discovery’ as a key competence for transdisciplinary research and learning. To address complex societal and environmental problems facing the world today, a particular expertise that can identify new connections between diverse knowledge fields is needed in order to integrate diverse perspectives from a wide range of stakeholders and develop novel solutions. The capacity for “insight discovery” means becoming aware of personal mental representations of the world and being able to shape and integrate perspectives different from one’s own. Based on experiences and empirical observations within the scope of an educational programme for Masters students, PhD candidates and post-doctoral researchers, we suggest that insights are the outcome of a learning process influenced by the collective and environment in which they are conceived, rather than instant moments of individual brilliance. The process which we describe, named the insight discovery process (IDP), is made up of five aspects. Within a group setting, a person begins with an “original mental model”, experiences an “insight trigger”, processes new information within the “liminal space”, “formulates an insight” and eventually forms an “adapted mental model”. There is a potential for incorporating such process as a fundamental competence for transdisciplinary curricula in undergraduate and graduate programmes by cultivating specific practices and safe learning environments, focused on the enquiry, exchange and integration of diverse perspectives.

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Introduction.

What are the necessary competences to tackle the challenges related to sustainable development, climate change, social unrest and other societal dilemmas? Authors have referred to such problems as “wicked” (Rittel and Webber, 1973 ), “complex” or “ill-defined” (Dörner, 1996 ). A common characteristic of this type of problem is that its definition depends on the perspective of the individual confronting the problem. The solution or the means by which to arrive at a solution to the problem is therefore also open-ended (Rittel and Webber, 1973 ; Dörner and Funke, 2017 ; Alford and Head, 2017 ). This paper argues that what enables effective engagement with this complexity is “insight discovery”, defined as the ability and willingness to identify and overturn one’s own assumptions by assimilating new experiences and knowledge. Insights are important for addressing the complex problems in transdisciplinary research and learning, because they cannot be effectively addressed by “reproductive solutions” (Weisberg, 2014 , p. 6) or technical answers that can be easily transferable between different contexts. In the case of climate change, for example, effective solutions are likely not to be derived from established ways of thinking and will also depend on the cooperation of diverse groups of people to be implemented. They require “new ways of knowledge production” (Lang et al., 2012 , p. 25), learning from a wide range of disciplines and the inclusion of knowledge from outside of academia. While the need for such a transdisciplinary approach to enquiry has been acknowledged (Gibbons et al., 1994 ), how this approach can be implemented in practice is still being developed. For example, how can different perspectives be brought together in a way that consistently results in a greater understanding that goes beyond divisions? How might we, as transdisciplinary researchers and practitioners, restructure our own assumptions such that these new understandings will be incorporated into future climate change actions? We propose that one way to address these questions is to foster a willingness and capacity for discovering and acting on insights. The focus of this “insight discovery process” (IDP) is not only on acquiring a greater quantity of information, but on improving how we can better interpret information that is already available to us.

In the following sections, we first give an overview of concepts and knowledge gaps related to the concept of “insight” in the literature. We then present the components of our IDP framework, together with evidence of how this process is experienced by Masters students, PhD candidates and post-doctoral researchers as a part of a transdisciplinary winter school that took place in Switzerland. Finally, we discuss possible implications of the IDP for transdisciplinary research, learning and societal transformation.

What do we already know about insights?

A dictionary definition of “insight” is the “capacity to gain an accurate and deep understanding of someone or something” (OUP, 2021 ). Common terms associated with insights include—the “AHA! Moment” (Kaplan and Simon, 1990 ; Kounios and Beeman, 2009 ), the “Eureka Moment” (Klein, 2013 ), the “lightbulb moment” (Danek et al., 2014 ) or a “flash of illumination” (Metcalfe and Wiebe, 1987 , p. 239). The concept of “insight” as a research subject was introduced in the 1920s in Austria and Germany by Gestalt psychologists interested in understanding the process of problem solving (Köhler, 1925 ; Davidson, 2003 ; Maier, 1940 ; Wertheimer, 2020 ). This work revealed that people solved problems by restructuring available information that, suddenly, leads to the emergence of a new understanding. These can be moments in which an impasse is overcome, for example, by solving a puzzle. Initial ideas about the role of insights in problem solving were developed in the context of well-defined problems, characterized by a fixed framing of the problem and existing solutions.

When taken into the context of complex problem solving in groups, there is an opportunity to further develop the insight concept in relation to the process of joint problem framing in transdisciplinary research. In an attempt to engage with the complexity of real world problems, scholars in the field have identified problem framing as an important element of transdisciplinary research (Hirsch Hadorn et al., 2006 ; Pearce and Ejderyan, 2019 ; Pohl and Hadorn, 2007 ; Rossini, 2009 ). Problem framing is the process of eliciting, searching and selecting relevant perspectives that restructure one’s perception of a situation, to determine the appropriate goals and criteria for the creation of effective solutions (Pearce and Ejderyan, 2019 ). Joint problem framing takes place in a group setting, when diverse points of views are integrated to create a shared understanding of a problem and its possible solutions. The integration is made possible when individuals are open to changing their individual mental representation of the problem—their mental model—by identifying, exchanging and incorporating insights from inside and outside the group such that a shared group mental model can be developed (details of this process are described in Pearce and Ejderyan, 2019 ). This paper continues the development of the insight concept from this perspective.

In a transdisciplinary learning setting, the use of insights as the basis of joint problem framing has formed the foundation of the transdisciplinary “integrated systems and design thinking” methodology (Pohl et al., 2020 )—intended to help Bachelors students develop the capacity for environmental problem solving. Working in groups, students have to discover “insights” from literature reviews, discussions with stakeholders and field visits to identify the problem and solutions they want to work on during a year-long course. As opposed to a fact, or a single piece of data, an insight has explanatory power, addressing the “why” or “how” of a situation, rather than only the “what”. Based on observation, insights often also indicate a contraposition in the current understanding that defies intuition and can be explained concisely. Following existing concepts, insights are also explained as being information that restructures previously held assumptions, resulting in an “AHA!” experience for the individual.

The literature provides support for this definition of insight based on these key characteristics:

Subjectivity: Although a group of people might receive the same piece of information, it is not automatically guaranteed that all of them will arrive at an insight (Klein, 2013 , p. 30, 116; Danek et al., 2014 ). Insights can also yield a “realization about oneself” that is unique to each individual (Kounios and Beeman, 2009 , p. 210).

Suddenness : In contrast to incremental problem solving, where the solver has an estimation on how to solve the problem and reach the solution in an incremental, analytical manner (Wieth and Burns, 2000 ), having an insightful experience can be compared to a light bulb that suddenly switches on.

Certainty: People who have an insight are confident “that the solution is correct without having to check it” (Danek et al., 2014 , p. 4).

Emotions: People who have insights report positive feelings such as “a jolt of excitement” (Klein, 2013 , p. 9), but also experience a release of tension in sight of having overcome the experienced impasse.

In the next section, we identify gaps in the definitions and understandings of insights in the literature and propose how we will address them within the context of promoting transdisciplinary practices in higher education and research.

What is missing from existing definitions?

The existing literature and practice provides a starting point for advancing our understanding of insight discovery as a competence. However, there are three knowledge gaps in operationalizing the concept for transdisciplinary teaching and learning. The first aspect of the insight concept yet to be fully developed is that insight discovery is a part of an emergent, collective process , rather than merely a single moment in time. Conventional wisdom would have us believe that insights are ideas that “pop[s] into mind, as if from nowhere” (Schooler and Melcher, 1995 , p. 97). In our empirical work, however, we observe that insight formation is the result of a dynamic process occurring over a period of time. This process includes assimilating observations and reflections of an individual within a collective and environmental context in which the individual is situated. Insights are therefore created through an individual’s engagement with their environment and context. It follows that the discovery of insights can be enabled or hindered by factors of an individual’s environment and group interactions. External conditions, such as a deviation from the routine, learning that takes place in a new setting, having to adapt to new surroundings, exposure to a diversity of people and contexts that requires confrontation with different ways of thinking, and/or having a need to solve a problem may facilitate the process of insight discovery.

The second aspect of the insight concept to be fully developed is the inclusion of affective capacities within the insight discovery process. The existing literature emphasizes mainly the cognitive nature of insights. For example, puzzle-solving exercises, where factual information serves as the trigger for a change of perspective that enables a solution to be found, are the basis of many of the Gestalt experiments. In our work, we noticed that insight discovery also requires a high tolerance for ambiguity, openness for plurality, curiosity, reflexivity and perceptiveness, resilience as well as engagement with crises. These affective qualities have to do with a mindset that enables the discovery of insights. They are related to an ability to reflect upon one’s position in relation to others and the ability to observe and assess one’s own thinking and reaction in relation to newly available knowledge. This observation is supported by findings in educational psychology. Bloom et al. ( 1956 ), for example, describe the affective abilities as those that pertain to feelings, emotions and attitudes. The five subdomains of this area of learning include “receiving”, “responding”, “valuing”, “organizing”, and “characterizing”. For example, the ability to perceive self and others accurately and the ability to identify, prioritize and act according to one’s values belong to this affective domain. Overlooking the role that affective capabilities can play in the insight discovery process would limit our understanding of the full potential of insights for transformative and transdisciplinary learning.

The third aspect of the insight concept to be fully developed is to understand how insight discovery occurs in educational settings, rather than in closed experimental settings, as is the case for most studies (Ohlsson, 1984 ). These studies focus on factors that could be easily manipulated and quantified (i.e., Kaplan and Simon, 1990 ; Kuonios and Beeman, 2009 ) and therefore tend to emphasise the importance of cognitive capacities linked to insights. This study, on the other hand, takes place in a natural setting in which people interpret insights within a transdisciplinary and multicultural learning setting. We are therefore able to explore the group and affective components of insight discovery in tandem—aspects of insights often overlooked in the literature.

The concept of “insight discovery” was first developed within the context of a Bachelors level course at Swiss Federal Institute of Technology (ETH Zurich), known as “Tackling Environmental Problem Solving” (for more details of this course, see Pohl et al., 2020 ). Over 4 years of conducting the course, we observed that students’ ability to understand complex systems, pinpoint key leverage points for transformation and to find viable solutions for problems was rooted in their capacity for the stage of problem solving that we identified as “insight discovery”. As a result, the Transdisciplinarity Lab (TdLab) Winter School, a programme that was run for 10 years, was also the responsibility of the co-lecturer and co-curriculum developer of the original “Tackling Environmental Problem Solving” course. The co-lecturer saw the potential for deepening the concept of “insight discovery” by examining whether the concept is also relevant for researchers with a specific interest in transdisciplinary research. This paper is based on the experiences during the last year of this programme.

The goal of the 8-day TdLab Winter School was to help participants learn and apply concepts and tools from transdisciplinary research while working on a real-life issue of immediate interest to the local community. The topic was chosen by the mayor and community secretary (‘Gemeindeschreiber’) of the town together with the coordinators of the winter school. Seventeen Masters students, PhD candidates and postdoctoral researchers worked together with local community members from the small village of Wislikofen in Switzerland on the topic of “community amalgamation”. The participants came from 13 countries, 10 universities and spoke more than 10 languages. The students stayed, worked and ate together in a former monastery (Propstei Wislikofen) which also serves as a center for community life in rural Wislikofen, about 1.5 h travel by train and bus from the city of Zurich. The Propstei served as the key meeting point for all workshops and events of the TdLab Winter School, such that all stakeholders knew where to find the group at all times.

The task of the participants was to design a “community interaction event” for the residents of Wislikofen and neighbouring villages that would help the local stakeholders in the process of community amalgamation in some way. This task was intentionally kept open to give participants the opportunity to apply the concepts and tools learnt during the course of the winter school. The community interaction event took place at the end of the TdLab Winter School. In the first four days, participants were asked to collect their ongoing learnings about the town and the theme of community amalgamation in Wislikofen through conversations with local stakeholders and visits of the surroundings. They made “rich pictures” of new information, which were image-rich system maps that connected pieces of information they were finding out about the topic, adapted from original use as a part of soft systems methodology (for more details see Checkland, 2000 , pp. 22–23). They also had the opportunity to discuss their learnings with local stakeholders to check if their initial understanding was indeed correct. In the last 4 days, building on these learnings, groups were asked to identify the key insights that they thought represented the most important learnings they got from the first 4 days. They identified the stakeholders most affected by the agreed upon insight and the formed “problem statements” about the insight. Each problem statement named the original insight, who the stakeholder was and the particular need that the stakeholder had. This problem statement formed the motivation for the design of the “community interaction event”. The lecturers used an ideation process from design thinking to help participants arrive at a final design. In parallel, the participants: (1) were introduced to concepts and tools of transdisciplinary research, (2) practiced moving out of disciplinary silos by learning about one another’s research projects, and (3) were introduced to joint problem framing, soft systems thinking and design thinking that serve as the foundation of integrated systems and design thinking (Pohl et al., 2020 ).

Data collection

During the programme, participants were asked to document and reflect on their learning and moments of insights with the help of an online insight journal. The following guiding question was provided to the participants in order to steer their reflection:

“What was your AHA! Moment of today? If there was none, what was something new that you learned?”

Participants were asked to answer this question each day for the whole duration of the winter school as a journal entry on an online platform provided by the coordinator. Although strongly encouraged, there were no consequences if the participant chose not to fill out the journal. Each participant completed at least 6 out of the possible 8 entries. No participant voiced any concerns about keeping such a journal. Participants expressed interest in reading others’ entries out of curiosity to learn what others’ experiences were during the programme, thus entries were available to all participants, anonymized. The coordinator of the winter school informed the participants that the answers could potentially be used for research on insight discovery. The participants, whose qualitative quotes were used to illustrate the insight discovery process, were asked for permission prior to publishing.

This self-reporting approach allowed us to explore the nature of insights beyond a laboratory setting, as was also suggested by previous studies (Danek et al., 2014 , p. 8). In that study, the authors stated that “there is a wealth of information to be gained through subjective self-reports”. They recommend the use of such direct, qualitative self-reports as a tool to “learn more about the phenomenological aspects of insight problem solving” (Danek et al., 2014 ).

Data analysis

Insight diaries were sequentially analysed by all co-authors by identifying quotes relevant to the concept of insights and deriving categories by “open coding” (Strauss and Corbin, 1998 ; Flick, 2018 ). By differentiating these categories and relating them to each other, we could obtain an initial model for an insight discovery process. Finally, we went back to the most relevant citations and identified quotes for illustrating the core phases, as well as quotes that captured the entire process in which multiple categories of the latter could be identified.

Our choice to use personal journal entries, thus self-reports, for studying insight discovery processes presents learning opportunities and limitations. Since insights are discovery processes that are inherently subjective, internal and personal learning processes that cannot always be captured by an external observer—at least not in its entirety. Thus, such processes need to be reported by the individuals themselves. In addition, as insights are subjective, it is key to include the participant’s interpretation of this process instead of exclusively relying on interpretation by third parties. A potential limitation of this approach are perceptual differences (Thomas et al., 2000 ), that is when participants have different definitions of ‘insight’ in mind when writing their journal entries. Nevertheless, this is in line with our perspective that insights can have different facets and mean different things to different people, which enriches our final definition. Additionally, the potential bias of social desirability (Edwards, 1957 ) may have influenced the journal writing process. The organizers tried to limit this bias by emphasizing that the journal entries were voluntary and anonymous, and ensured that they could be easily entered online.

We engaged with the perils of self-reporting by relying and balancing the multiple perspectives of analysis that each member of the writing team brings. The collective nature of this analysis is what Naomi Oreskes ( 2019 , p. 104) describes as “the social processes of collective interrogation” which offers a means for conclusions to be arrived that is non-idiosyncratic, especially when those carrying out this interrogation form a diverse collective of many different professional and cultural backgrounds. For example, the concept of insight was described and analysed independently by each author. This was based on their diverse experiences (i.e., as previous winter school participants, as professional intermediaries, as coordinator and coaches of the programme). Although different in our training, disciplinary backgrounds (i.e., sociology, ecological economics, biology, microbiology, environmental chemistry) and experiences working at the boundary between science and practice. We looked for a common understanding of the insight discovery process. With multiple iterations between the data and the abstraction of the data, we reached an integrated framework that all co-authors agreed upon, which will be presented in the following section.

Components of the insight discovery process

Based on the analysis of the insight diaries we were able to differentiate the insight discovery process into two states and three different phases. These are (Fig. 1 ):

State 1: Original mental model

Phase 1: insight trigger.

Phase 2: Liminal space (including reflection, re-framing and signal processing)

Phase 3: Insight formulation

State 2: Adapted mental model

The process begins with the original mental model (State 1), which is disrupted by an insight trigger (Phase 1), moves into a liminal space characterised by reframing, reflecting and signal processing (Phase 2), leads to insight formulation (Phase 3) which contributes to an adapted mental model of the problem or situation (State 2).

We give a detailed description of each in the following paragraphs.

The original mental model represents the original state of knowledge before the introduction of any new experiences or information. It is an individual’s mental representation of any situation before any new encounters. An individual’s mental model is determined by their set of personal experiences, attitudes and prior knowledge, which defines the way in which an individual perceives, understands and frames a problem or a specific situation (Johnson-Laird, 1983 ; Morgan et al., 2002 ; Newell and Simon, 1972 ).

The first phase of the IDP model is the trigger in which an ‘AHA!’ moment occurs. A trigger is caused by the acquisition and incorporation of new pieces of information, both individually as well as collectively (i.e. such as in a group), which challenges the current mental model. An analogy to a trigger is the concept of activation energy in thermodynamics. In order to initiate a transformation bringing a system to a more stable state, energy is required to “jump-start” the system. The trigger is information that does not fit into an existing mental model of an individual—leading to cognitive dissonance (Festinger, 1957 ; Aronson, 1969 ). This is the mental discomfort that arises from holding conflicting values, beliefs or attitudes. This tension can be relieved by rejecting, creating rationale for or avoiding new information. However, we propose that in the case where insights are discovered, individuals move towards a deeper reflection and exploration of this tension rather than avoid it. Klein ( 2013 , p. 104) developed a typology for systematizing different (insight) triggers, the Triple Path Model: First, identifying contradictions can be an insight trigger. Klein noted that paradigm shifts qualify as insights because “the result is a shift from a mediocre frame to one that provides a better understanding of the same phenomenon” (p. 75). Secondly, Klein emphasizes the role of making connections. Connections happen when one receives new information and “sees how it combines with other information to form a new idea” (p. 41) or refers to a new combination of ‘old’ information and ideas. The third path is creative desperation and refers to brilliant ideas and solutions people come up with when they feel trapped in a troublesome situation. In order to do so, it becomes necessary to disarm flawed assumptions, which are trapping people in the first place (Klein, 2013 ).

Phase 2: Liminal space

The insight process is also characterized by the presence of a liminal space. In making the decision to leave the comfort zone, one acknowledges the limits of the original mental model. The individual is moving into “unchartered territory” at this point. The process requires time, is challenging and can be associated with contrasting emotions, including those of uncertainty or ambiguity. The need of individuals to feel safe and supported during their time in the liminal space is important (Freeth and Caniglia, 2020 ; Förster et al., 2019 ).

“It is exactly this discomfort that opens room for new insights— leaving your comfort zone as a crucial prerequisite for learning processes” (Winter School participant A, 2020).

The liminal space requires a willingness to learn which underpins three sub-phases, which are reflection, re-framing and signal processing, all of which are conceptually distinct, yet intertwined. Willingness to learn has been defined as an individual’s psychological state, which shows a desire to learn new things and an impulse or readiness to acquire new knowledge (Hotifah et al., 2020 ). In psychological studies, it has also been associated with the high value that students attribute to tasks (Gorges et al., 2013 ). Additionally, it is a student’s “…engagement with, and appreciation of, the values and ideologies that go along with the discursive structures of educational activities.” (Cekaite, 2012 , p. 643). In this framework, we understand the “willingness to learn” as the setting within which the other elements of the liminal space take place. Hotifah et al. ( 2020 ) identified that the factors affecting willingness to learn, repeatedly found across studies, include “internal” factors of individual attitudes and personality, as well as “external” factors of family and school environment.

We explore in more detail the three processes that are a part of the liminal space:

Reflection : This is the process of questioning, carefully examining and evaluating one’s own assumptions. It requires the individual to make their implicit assumptions explicit in the first place. While reflection is a prerequisite for re-framing, it does not automatically lead up to it. The reflecting individuals can either arrive to the conclusion that their assumptions hold in sight of new information and experiences (and will be even more confident about them) or conclude that they need to be adjusted. In the latter case, reframing can take place (Schön, 1992 ).

“Keep asking ‘why’ is key for obtaining interesting observations […]. Formulating problem statements is an exercise of making the implicit, explicit.” (Winter school participant B, 2020)

Problem reframing and iteration: This is the process of iteratively re-adjusting one’s assumptions or perspective on a problem situation or topic. Reframing occurs when individuals conclude that their original mental model (i.e. assumptions and beliefs) has become inadequate for understanding a problem situation or topic. Individuals try to assimilate new information into existing knowledge structures. This process can be uncomfortable (letting go), joyful (relieve) or a mix of both. Reframing is a nonlinear process requiring time, repetition or several loops until a new frame emerges (Pearce and Ejderyan, 2019 ).

“We are following a non-linear path, overall converging on an outcome but with individual, iterative phases/steps which diverge (opening up) and converge (zooming in).” (Winter school participant C, 2020)

Signal processing : This is the means by which individuals try to make sense of the external ‘signals’ the individual is exposed to while they are in the liminal space and making the shift between the original and the adapted mental model. These signals can be, for instance, new information, indicators from other individuals that the individual is on the “right track”. These signals encourage the individual to continue the search for clarity within the liminal space.

“It is essential to test your ideas iteratively with different people to discover your blind spots or implicit assumptions that participants might or might not share” (Winter school participant D, 2020)

Phase 3: Forming insights

The third phase of our model is the formation of an insight—this is a moment of clarity and new-found understanding, which leads to a shift in an individual’s mental model. This phase is often characterized by a strong positive feeling of accomplishment and concludes with the formulation of knowledge that is understood and shareable by the individual. The formation of an insight is comparable to a threshold concept. Insights, like threshold concepts, are concepts that, once understood, transform the perception of a given phenomenon or subject (Meyer and Land, 2005 ). Once an individual has formed an insight, they cannot go back to seeing the problem space from their old mental model and will make use of a new adapted mental model to think about a problem or situation.

“…I got to see the land and understand the place from a geographical point-of-view and how that could impact on the mindset of the community. I also think that gives an insight into the current concerns that have come up for the community of Wislikofen […] This experience was somewhat like an outer body experience.” (Winter school participant E, 2020)

Once an insight has formed, individuals enter a new state of knowledge. With a formed insight, the individual is able to apply and adapt their new knowledge. In this phase participants have an adapted mental model of the problem space, that incorporates the insight(s) gained. Going forward, this adapted mental model will replace or enrich the previous original mental model to assess future problem situations that are relevant to the topics covered by the mental model.

Non-linearity of the IDP

The iterative analysis of the winter school participants’ insight diaries revealed that insight discovery entails going through three dynamic phases, followed by the emergence of a new mental model. Our analysis reveals that these sub-processes of the liminal space, i.e., reflecting, reframing and signal processing, do not necessarily occur in a linear manner. Some diary entries described these sub-processes in a well-defined order, but others did not. Of the latter, we made three core observations: (1) multiple sub-processes can occur concurrently and might repeat themselves iteratively; (2) not all participants described all of the identified sub-processes of the liminal space explicitly in their diaries and/or different participants assigned more importance to one sub-process than to another; (3) the diaries suggest that not all participants necessarily arrived at an insight that led to a new, adapted mental model. Therefore, entering the liminal space alone does not guarantee the formulation of a new insight.

The following journal entries by two Winter School participants exemplifies these three observations (also visualized in the figures in Example A and B):

“Having done the reading before coming to the Winter School I felt like I had a good grasp of what [the programme] and transdisciplinarity meant [original mental model]. However, after today’s ‘fish bowl’ session [trigger], I felt I left feeling more confused and having more questions than answers [liminal space]. So I felt like I did not contribute as much as I would have liked to. For me, my whole career and the way I approach life I feel I have the ‘Td spirit’ but I want to consolidate this feeling into something more tangible in the days to come [willingness to learn]. I am happy that this is a safe environment to share ideas and questions for when I am ready [external context—collective norms, collective practices].

(Winter school participant F, 2020)

“Slowly throughout the days, I began to think of myself as a researcher [adapted mental model] and not a passive student [original mental model]

The focus started to widen to include me as an active and important actor to be observed and cared for throughout the process [liminal space—reframing].

As such, I also have different levels of involvement in the process, and also different levels of interaction with other actors [liminal space—reflection]:

sometimes the process requires a high intensity of engagement and sometimes more quiet times for me as a researcher to process the information and connect the dots [liminal space—signal processing].

I understood that the graph of varying degrees of involvement is not only a cold and rational representation of tools and stages [original mental model] but rather a flow of energy among people throughout a process [formed insight].”

(Winter school participant G, 2020)

Both examples display multiple stages and phases of the insight discovery process within a single journal entry. Noteworthy of these examples is that the recognition of the initial state of knowledge occurs only after reaching the formed insight. This demonstrates a non-linear process of learning. By going through the IDP and reaching a new state of knowledge, individuals are able to acknowledge how the original mental model could be adapted and enriched through interaction with others’ perspectives and new information. This process requires reflection and assimilation before it can proceed further (Figs. 2 and 3 ).

Applying the insight discovery process for Example A.

Applying the insight discovery process for Example B.

Enabling conditions for insights discovery process (IDP)

The IDP is dependent on both individual, internal factors as well the collective, external context. The enabling conditions for the IDP, then, stem from both these internal and external factors. The internal enabling condition is the willingness to learn. This condition creates the openness for reflection, reframing and signal processing. Without such a willingness, the act of exploring an uncertain and sometimes uncomfortable liminal space seems unlikely. When students are willing to learn new things, we assume that they are also more open to accepting and assimilating new insights. The question remains—what factors might affect this willingness to learn? In this paper, we posit that this willingness is only in part a function of individual desire and circumstances, but rather also results from a set of external factors which make it more likely for an individual to cultivate this willingness. We define these as the external enabling conditions of the IDP.

These external enabling conditions can be broadly categorized in how an individual relates to: (1) the physical environment in which the learning is taking place; (2) the collective identity, collective norms and goals shared by those taking part in learning and teaching activities; (3) the specific types of activities and practices that are taking place and available tools to carry out the learning. The combination of the enabling conditions then creates a safe space that is important in the insight discovery process.

Various authors have shown that the physical setting of the learning environment has a significant effect on the ability of learners to foster critical thinking, social skills and creativity (Jindal-Snape et al., 2013 ; Lippman, 2010 ; Weinstein, 1979 ). The responsive approach in the design of learning environments, for example, recognizes the contribution of the physical setting and could contribute to “a culture of inquisitiveness” (Lippman, 2010 ; Altman, 1992 ). The physical setting of the TdLab Winter School, for example, was at a monastery-turned seminar hotel located in a small, rural village that serves as the centre of community life and activities. The participants shared rooms in pairs during their stay. Wislikofen is situated amongst farms and rolling hills, providing a possibility for students to spend time outdoors and to explore the landscape. In the evenings, students socialized in the former monastery cellar where local beer and specialities were available.

The setting served as a retreat from the normal routine of participants’ usual academic life at a university. They were led on a tour of the village by local residents and sometimes even spent time at the local bar. The residents were invited for cake and coffee at the hotel as a part of the programme. This intimate setting enabled participants to form bonds with each other and with local residents.

Collective norms and goals are also a part of the enabling environment. These norms and goals are collective through the sense of purpose and mission shared by the individuals in the group. The idea that the individual is affected, and affects, the system in which one is a part of belongs to practice theory (Bourdieu, 1972 ; Giddens, 1984 ). Social phenomena are not only the result of individual intention alone, but rather also mediated by the collective structures and norms by which individuals are conditioned. This is in line with critical realism, which emphasizes the dialectical and dynamic interplay of agency and structure (Archer, 1995 ). It is this complex entanglement of the individual and the collective (and collective artefacts) that contributes to the social outcomes that we are able to observe. The social identity model of pro-environmental action (SIMPEA) (Fritsche et al., 2018 ) posits that individual actions are driven by identity, but that identity is determined collectively, through the individual linking to collective norms, goals and emotions. The IDP acknowledges the permeable boundary between the individual and the group such that the group context mediates insights reached by the individual. The TdLab Winter School sought to create a collective identity as a group of researchers and scholars more interested in listening than telling. The collective identity was that of facilitating a process in which we share with them transdisciplinary tools for stakeholder engagement that the participants are also learning about during the Winter School. The collective norms and goals included valuing listening over speaking, encouraging reflection of oneself and the situation, encouraging the questioning of one’s own assumptions and firmly held beliefs and to the welcoming of uncertainty by staying flexible and adaptable to changing situations.

To encourage the willingness to learn, specific activities and practices were implemented, and were a part of the enabling conditions. The diversity of the participant group itself made it possible that all were confronted with foreign languages, cultures different views and unfamiliar topics. These participants, coming from all over the world, suddenly landed in a small Swiss village where they were exposed to its social, political and economic developments and related apprehensions and aspirations by its residents. With the help of Swiss participants and simultaneous translation, we encouraged participants, also those who did not speak German, to communicate and connect with those whom they would normally not be comfortable speaking. Additionally, the daily practice of journaling helped to create the norm of reflection as a part of growth and becoming more comfortable with being in the liminal space. Kligyte et al. ( 2019 ) proposed that these so-called third spaces can emerge when the enabling practices and conditions are present. “Such processes can be encouraged, tended to, and guided, but are usually spoilt if attempts are made to control them” (Hasan, 2014 in Kligyte et al., 2019 , p. 15).

Other tools introduced to the participants, such as the use of rich pictures, systems thinking, joint problem framing and design thinking encouraged them to see stakeholder engagement as a means not only to draw out information from people in the community, but as a means of creating dialogue and understanding. All tools were focused on helping participants to get to the heart of complex problems, based on using a variety of mediums, rather than tools such as modelling or scenario analysis, that operates more on scientific data and knowledge rather than a bottom-up understanding of human interests. We made use of the methods and tools from the td-net toolbox ( https://naturalsciences.ch/co-producing-knowledge-explained/methods/td-net_toolbox ), an online resource for co-producing knowledge.

In creating the enabling conditions of the IDP from the physical setting, collective norms and goals and putting into place specific practices and activities, we are able to encourage learning processes by creating a safe space that allowed the participants to come out of their comfort zone (Fry and Thieme, 2021 ). That this, the importance of the safe space is not only supported by our own experience, but also in the literature: “To develop attitudes and values enabling them to address real-world sustainability issues, students need a “safe space” where they can experience the emotional learning edge that triggers transformative learning moments through disruptive learning” (Trechsel et al., 2021 , p. 2).

The safe space is important when experiencing a disruption of what one is used to or comfortable with. There are strong emotions first of shock, uncertainty as well as denial (Förster et al., 2019 ). A safe space allows the students to share what they are really thinking and to be vulnerable, without the burden of judgement. This safe environment—an atmosphere of openness, cooperation, collaboration, creativity and mutual appreciation are important external enabling conditions which, combined with the willingness to learn, helps manoeuvre through the liminal space and helps to enter the phase were the individuals can formulate their insight. This is also in line with Pohl et al. ( 2021 ) and Boix Mansilla et al. ( 2016 ), who emphasize that integrating diverse perspectives include not only a cognitive dimension, but also its interplay with emotional (e.g. mutual respect) and a social dimensions (e.g. climate of conviviality).

Why do we care about insights? What are the implications of the insight discovery process (IDP) for inter- and transdisciplinary research and learning? Our paper proposes three main implications for the concept of the IDP:

The IDP can be a key process for confronting social dilemmas.

The IDP is an important aspect of knowledge integration expertise for inter- and transdisciplinary collaboration.

The IDP can aid transformative learning.

Each of these implications are discussed in more detail below.

Insight discovery process for confronting societal dilemmas

The IDP has an important role in addressing sustainability and climate change challenges, particularly in mobilizing change through social innovation (Hoppe and de Vries, 2018 ) at the individual and community level. As mentioned at the opening of the paper, these problems can be referred to as “wicked” problems (Rittel and Webber, 1973 ). The problem is dependent on the perspective of the one experiencing the problem, meaning that there is little agreement on what the problem is. This means that the solution or the means by which to arrive at a solution is also open-ended (Rittel and Webber, 1973 ; Dörner and Funke, 2017 ; Alford and Head, 2017 ). The IDP can be of particular importance for these problems because it presents a process showing how different points of view can be integrated and accepted. With the IDP, we are able to see how we, as transdisciplinary researchers and practitioners, can create external conditions that foster the willingness to learn between stakeholders in a real-world problem. Integrating diverse perspectives through insights can enable, for example, transformation processes in the energy transition. For such a social challenge, communities have the complex task of being able to bridge the global challenges of climate change with diverse local needs and perspectives. For instance, some people might see renewable energy projects as a viable investment, but some may resist change as an immediate financial burden requiring difficult trade-offs in household spending. Energy technology and service providers may have specific interests and expert knowledge in either keeping or changing the current energy system for decarbonization. Diverse insights can sometimes pull initiatives apart but the IDP process can facilitate the creation of a new collective mental model by helping to find overlaps across different stakeholders. Currently, the research project Energy Citizens for Inclusive Decarbonization (ENCLUDE) ( http://www.encludeproject.eu ), an H2020 research project, has an application of the IDP to explore the potential for scaling up new energy citizenship initiatives while considering the internal and external context.

The insight discovery process and inter- and transdisciplinary knowledge integration

There are two means by which the capacity for IDP assists with knowledge integration for inter- and transdisciplinary collaboration. First, the mindset needed for the discovery of insights is similar to that needed for carrying out inter- and transdisciplinary knowledge integration. According to Augsburg ( 2014 , p. 240), transdisciplinary researchers need to have “curiosity about, and willingness to learn from other[s]”, to be able to “think in a complex, interlinked manner” and use “creative enquiry”, the capacity to suspend “one’s own point of view”, and have the ability to “acknowledge the pain inherent in abandoning one’s intellectual comfort zone”. These requirements of having a curious and open mind, thinking critically and being persistent are what we also observed as needed for going through each phase of the IDP. Insight discovery processes are key to inter- and trans-disciplinary research (ITDR) as they help people leave a fixed frame of reference (i.e., their mental model, influenced by their disciplinary silos), and allow them to engage in new ways of thinking and create new knowledge (Godemann, 2008 ; Defila and Di Giulio, 2015 ; Pohl et al., 2021 ).

Second, the formulation of insights requires going beyond routine knowledge acquisition from within a single field, making it inherently an inter- and/or transdisciplinary process. In the process of sense making that is a part of arriving at insights, there is a requirement to bring together elements of one’s understanding, which have diverse origins. This also requires stepping outside of academic thought itself. The integrative nature of insights is what contributes to the emergence of a bigger picture and allows a team or group to bridge diverse perspectives and knowledge fields. In these inter- and transdisciplinary processes there is an aim at creating something larger than just the sum of its parts, where an enquiry of separate parts leads to an emergent understanding of the whole. This is especially relevant for university students transitioning into the ‘future’ workforce, where some of the predicted skills for 2025 include: analytical thinking and innovation; complex problem solving; critical thinking and analysis; creativity, initiative and analysis; reasoning, problem solving and ideation (WEF, 2020 ).

Insight discovery process and the transformative learning theory

The IDP is a means for transformative learning. Mezirow ( 1996 , 1997 ) developed a theory of transformative learning with the purpose of designing adult education. He describes the adult learning as a process where the “frame of reference” is changed, synonymous with the “mental model” in the IDP. Mezirow ( 1997 ) explains that for adults, these frames define their world, made up of concepts, values, beliefs and conditioned responses. He further argues that these frames can be transformed through critical reflection when actions dictated by this frame of reference become problematic or fail in some way. It is exactly this process that the insight discovery process (IDP) tries to uncover. Mezirow also refers to “reflective insights” (Mezirow, 1996 , p. 163) and implies that these insights are the products of critical reflection. The concept of the liminal space within the IDP can be linked to the need of a “critical reflection” for transformation to occur in the transformative learning theory.

Both transformative learning and the IDP approach learning from what Jürgen Habermas ( 1981 ) refers to as the communicative mode of learning and engagement for problem solving. Communicative learning is collective, involving at least two people, and includes learning about the meaning of an interpretation or the justification of a belief. Communicative learning is aimed at not only learning the “what” of a situation, but also the purposes, values, beliefs and feelings which belong to a set of facts and not revealed by the facts alone. Transformation is enabled when we are able to change our frame of reference, or mental model, through critical reflection of our habits of mind and points of view. The concepts of the mental model, liminal space and problem (re-)framing all find parallels within the transformative learning theory (see also Fry and Thieme, 2019 ). Therefore, the IDP could be a means to understand how transformation occurs in learning.

The IDP has been built into an award-winning transdisciplinary curriculum (“integrated systems and design thinking”) for transformative learning for Bachelors students in the Department of Environmental Systems Science at ETH Zurich. To date, it has been introduced to more than 1000 students in Switzerland.

Traditionally, institutions of higher education have been organized around providing students with the competences to succeed in individual disciplines rather than to have the capacity to solve problems in the real world. However, there is growing recognition that higher education should impart both skills needed for conducting high quality research, as well as for solving wicked sustainability problems in our societies. In this article, we argued that insight discovery is a key competence for: (i) conducting inter- and transdisciplinary research; (ii) eliciting transformative learning; and (iii) addressing wicked problems and societal dilemmas. We proposed a non-linear, dynamic and interactive model to advance the understanding of insight processes with regard to these three key areas. We went beyond a classic laboratory setting and based our analysis of insight journal entries, created by winter school students in a group learning setting. This model for an insight discovery process consists of three different phases-—the trigger, liminal space and insight formation—culminating in a peak transitional experience leading to a new state of “knowledge”. When undergoing these phases, it requires not only cognitive abilities (linking different concepts or ideas), but also affective abilities (dealing with uncertainty and ambiguity) as it requires individuals to leave their comfort zone. We showed that, despite being a very subjective experience, insight discovery processes do not take place in a vacuum and need to be understood in relation to their physical and/or group settings. Hence, insight discovery can be enabled or hindered through external factors. For instance, creating a safe environment to share new ideas, providing sufficient space to explore different points of views and encouraging people to go beyond their current mental model with the help of different tools and methods are conducive for an insight discovery process. These external factors can then facilitate leaving fixation and arriving at a new, adapted mental model.

We believe that by providing a clear IDP model and showing the integrative and transformative potential of insights, this article can support other transdisciplinary researchers and instructors in enabling insight discoveries in their own projects, programmes or university courses.

Data availability

The datasets generated and analysed during the current study are not publicly available due to the personal nature of the statements provided, but may be partially available from the corresponding author on reasonable request, with the consent of affected data subjects.

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Acknowledgements

The authors of the paper would like to thank the support of the co-directors of the Transdisciplinarity Lab, Pius Krütli, Michael Stauffacher and Christian Pohl who contributed much of their time and abilities in realizing the TdLab Winter School. Without the residents of Wislikofen, with their openness and generosity for all the students and coaches, none of this work would have been possible. In particular, Andi Meier, the community secretary who made all the stakeholder engagement activities possible, and the mayor of Wislikofen, Heinrich Rohner. We would also like to acknowledge Carolina Adler and Claudia Zingerli, who were the past coordinators of the Winter School and were instrumental for building a trusting relationship between ETH Zurich and the community.

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Pearce, B.J., Deutsch, L., Fry, P. et al. Going beyond the AHA! moment: insight discovery for transdisciplinary research and learning. Humanit Soc Sci Commun 9 , 123 (2022). https://doi.org/10.1057/s41599-022-01129-0

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Insight Learning Theory: Definition, Stages, and Examples

Categories Learning

Insight learning theory is all about those “lightbulb moments” we experience when we suddenly understand something. Instead of slowly figuring things out through trial and error, insight theory says we can suddenly see the solution to a problem in our minds. 

This theory is super important because it helps us understand how our brains work when we learn and solve problems. It can help teachers find better ways to teach and improve our problem-solving skills and creativity. It’s not just useful in school—insight theory also greatly impacts science, technology, and business.

Table of Contents

What Is Insight Learning?

Insight learning is like having a lightbulb moment in your brain. It’s when you suddenly understand something without needing to go through a step-by-step process. Instead of slowly figuring things out by trial and error, insight learning happens in a flash. One moment, you’re stuck, and the next, you have the solution. 

This type of learning is all about those “aha” experiences that feel like magic. The key principles of insight learning involve recognizing patterns, making connections, and restructuring our thoughts. It’s as if our brains suddenly rearrange the pieces of a puzzle, revealing the big picture. So, next time you have a brilliant idea pop into your head out of nowhere, you might just be experiencing insight learning in action!

Three Components of Insight Learning Theory

Insight learning, a concept rooted in psychology, comprises three distinct properties that characterize its unique nature:

1. Sudden Realization

Unlike gradual problem-solving methods, insight learning involves sudden and profound understanding. Individuals may be stuck on a problem for a while, but then, seemingly out of nowhere, the solution becomes clear. This sudden “aha” moment marks the culmination of mental processes that have been working behind the scenes to reorganize information and generate a new perspective .

2. Restructuring of Problem-Solving Strategies

Insight learning often involves a restructuring of mental representations or problem-solving strategies . Instead of simply trying different approaches until stumbling upon the correct one, individuals experience a shift in how they perceive and approach the problem. This restructuring allows for a more efficient and direct path to the solution once insight occurs.

3. Aha Moments

A hallmark of insight learning is the experience of “aha” moments. These moments are characterized by a sudden sense of clarity and understanding, often accompanied by a feeling of satisfaction or excitement. It’s as if a mental lightbulb turns on, illuminating the solution to a previously perplexing problem. 

These moments of insight can be deeply rewarding and serve as powerful motivators for further learning and problem-solving endeavors.

Four Stages of Insight Learning Theory

Insight learning unfolds in a series of distinct stages, each contributing to the journey from problem recognition to the sudden realization of a solution. These stages are as follows:

1. Problem Recognition

The first stage of insight learning involves recognizing and defining the problem at hand. This may entail identifying obstacles, discrepancies, or gaps in understanding that need to be addressed. Problem recognition sets the stage for the subsequent stages of insight learning by framing the problem and guiding the individual’s cognitive processes toward finding a solution.

2. Incubation

After recognizing the problem, individuals often enter a period of incubation where the mind continues to work on the problem unconsciously. During this stage, the brain engages in background processing, making connections, and reorganizing information without the individual’s conscious awareness. 

While it may seem like a period of inactivity on the surface, incubation is a crucial phase where ideas gestate, and creative solutions take shape beneath the surface of conscious thought.

3. Illumination

The illumination stage marks the sudden emergence of insight or understanding. It is characterized by a moment of clarity and realization, where the solution to the problem becomes apparent in a flash of insight. 

This “aha” moment often feels spontaneous and surprising, as if the solution has been waiting just below the surface of conscious awareness to be revealed. Illumination is the culmination of the cognitive processes initiated during problem recognition and incubation, resulting in a breakthrough in understanding.

4. Verification

Following the illumination stage, individuals verify the validity and feasibility of their insights by testing the proposed solution. This may involve applying the solution in practice, checking it against existing knowledge or expertise, or seeking feedback from others. 

Verification serves to confirm the efficacy of the newfound understanding and ensure its practical applicability in solving the problem at hand. It also provides an opportunity to refine and iterate on the solution based on real-world feedback and experience.

Famous Examples of Insight Learning

Examples of insight learning can be observed in various contexts, ranging from everyday problem-solving to scientific discoveries and creative breakthroughs. Some well-known examples of how insight learning theory works include the following:

Archimedes’ Principle

According to legend, the ancient Greek mathematician Archimedes experienced a moment of insight while taking a bath. He noticed that the water level rose as he immersed his body, leading him to realize that the volume of water displaced was equal to the volume of the submerged object. This insight led to the formulation of Archimedes’ principle, a fundamental concept in fluid mechanics.

Köhler’s Chimpanzee Experiments

In Wolfgang Köhler’s experiments with chimpanzees on Tenerife in the 1920s, the primates demonstrated insight learning in solving novel problems. One famous example involved a chimpanzee named Sultan, who used sticks to reach bananas placed outside his cage. After unsuccessful attempts at using a single stick, Sultan suddenly combined two sticks to create a longer tool, demonstrating insight into the problem and the ability to use tools creatively.

Eureka Moments in Science

Many scientific discoveries are the result of insight learning. For instance, the famed naturalist Charles Darwin had many eureka moments where he gained sudden insights that led to the formation of his influential theories.

Everyday Examples of Insight Learning Theory

You can probably think of some good examples of the role that insight learning theory plays in your everyday life. A few common real-life examples include:

• Finding a lost item : You might spend a lot of time searching for a lost item, like your keys or phone, but suddenly remember exactly where you left them when you’re doing something completely unrelated. This sudden recollection is an example of insight learning.
• Untangling knots : When trying to untangle a particularly tricky knot, you might struggle with it for a while without making progress. Then, suddenly, you realize a new approach or see a pattern that helps you quickly unravel the knot.
• Cooking improvisation : If you’re cooking and run out of a particular ingredient, you might suddenly come up with a creative substitution or alteration to the recipe that works surprisingly well. This moment of improvisation demonstrates insight learning in action.
• Solving riddles or brain teasers : You might initially be stumped when trying to solve a riddle or a brain teaser. However, after some time pondering the problem, you suddenly grasp the solution in a moment of insight.
• Learning a new skill : Learning to ride a bike or play a musical instrument often involves moments of insight. You might struggle with a certain technique or concept but then suddenly “get it” and experience a significant improvement in your performance.
• Navigating a maze : While navigating through a maze, you might encounter dead ends and wrong turns. However, after some exploration, you suddenly realize the correct path to take and reach the exit efficiently.
• Remembering information : When studying for a test, you might find yourself unable to recall a particular piece of information. Then, when you least expect it, the answer suddenly comes to you in a moment of insight.

These everyday examples illustrate how insight learning is a common and natural part of problem-solving and learning in our daily lives.

Exploring the Uses of Insight Learning

Insight learning isn’t an interesting explanation for how we suddenly come up with a solution to a problem—it also has many practical applications. Here are just a few ways that people can use insight learning in real life:

Problem-Solving

Insight learning helps us solve all sorts of problems, from finding lost items to untangling knots. When we’re stuck, our brains might suddenly come up with a genius idea or a new approach that saves the day. It’s like having a mental superhero swoop in to rescue us when we least expect it!

Ever had a brilliant idea pop into your head out of nowhere? That’s insight learning at work! Whether you’re writing a story, composing music, or designing something new, insight can spark creativity and help you come up with fresh, innovative ideas.

Learning New Skills

Learning isn’t always about memorizing facts or following step-by-step instructions. Sometimes, it’s about having those “aha” moments that make everything click into place. Insight learning can help us grasp tricky concepts, master difficult skills, and become better learners overall.

Insight learning isn’t just for individuals—it’s also crucial for innovation and progress in society. Scientists, inventors, and entrepreneurs rely on insight to make groundbreaking discoveries and develop new technologies that improve our lives. Who knows? The next big invention could start with someone having a brilliant idea in the shower!

Overcoming Challenges

Life is full of challenges, but insight learning can help us tackle them with confidence. Whether it’s navigating a maze, solving a puzzle, or facing a tough decision, insight can provide the clarity and creativity we need to overcome obstacles and achieve our goals.

The next time you’re feeling stuck or uninspired, remember: the solution might be just one “aha” moment away!

Alternatives to Insight Learning Theory

While insight learning theory emphasizes sudden understanding and restructuring of problem-solving strategies, several alternative theories offer different perspectives on how learning and problem-solving occur. Here are some of the key alternative theories:

Behaviorism

Behaviorism is a theory that focuses on observable, overt behaviors and the external factors that influence them. According to behaviorists like B.F. Skinner, learning is a result of conditioning, where behaviors are reinforced or punished based on their consequences. 

In contrast to insight learning theory, behaviorism suggests that learning occurs gradually through repeated associations between stimuli and responses rather than sudden insights or realizations.

Cognitive Learning Theory

Cognitive learning theory, influenced by psychologists such as Jean Piaget and Lev Vygotsky , emphasizes the role of mental processes in learning. This theory suggests that individuals actively construct knowledge and understanding through processes like perception, memory, and problem-solving. 

Cognitive learning theory acknowledges the importance of insight and problem-solving strategies but places greater emphasis on cognitive structures and processes underlying learning.

Gestalt Psychology

Gestalt psychology, which influenced insight learning theory, proposes that learning and problem-solving involve the organization of perceptions into meaningful wholes or “gestalts.” 

Gestalt psychologists like Max Wertheimer emphasized the role of insight and restructuring in problem-solving, but their theories also consider other factors, such as perceptual organization, pattern recognition, and the influence of context.

Information Processing Theory

Information processing theory views the mind as a computer-like system that processes information through various stages, including input, processing, storage, and output. This theory emphasizes the role of attention, memory, and problem-solving strategies in learning and problem-solving. 

While insight learning theory focuses on sudden insights and restructuring, information processing theory considers how individuals encode, manipulate, and retrieve information to solve problems.

Kizilirmak, J. M., Fischer, L., Krause, J., Soch, J., Richter, A., & Schott, B. H. (2021). Learning by insight-like sudden comprehension as a potential strategy to improve memory encoding in older adults .  Frontiers in Aging Neuroscience ,  13 , 661346. https://doi.org/10.3389/fnagi.2021.661346

Lind, J., Enquist, M. (2012). Insight learning and shaping . In: Seel, N.M. (eds) Encyclopedia of the Sciences of Learning . Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1428-6_851

Osuna-Mascaró, A. J., & Auersperg, A. M. I. (2021). Current understanding of the “insight” phenomenon across disciplines . Frontiers in Psychology , 12, 791398. https://doi.org/10.3389/fpsyg.2021.791398

Salmon-Mordekovich, N., & Leikin, M. (2023). Insight problem solving is not that special, but business is not quite ‘as usual’: typical versus exceptional problem-solving strategies .  Psychological Research ,  87 (6), 1995–2009. https://doi.org/10.1007/s00426-022-01786-5

Problem Solving in Science Learning

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Introduction: Problem Solving in the Science Classroom

Problem solving plays a central role in school science, serving both as a learning goal and as an instructional tool. As a learning goal, problem-solving expertise is considered as a means of promoting both proficiency in solving practice problems and competency in tackling novel problems, a hallmark of successful scientists and engineers. As an instructional tool, problem solving attempts to situate the learning of scientific ideas and practices in an applicative context, thus providing an opportunity to transform science learning into an active, relevant, and motivating experience. Problem solving is also frequently a central strategy in the assessment of students’ performance on various measures (e.g., mastery of procedural skills, conceptual understanding, as well as scientific and learning practices).

A problem is often defined as an unfamiliar task that requires one to make judicious decisions when searching through a problem...

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Yerushalmi, E., Eylon, BS. (2015). Problem Solving in Science Learning. In: Gunstone, R. (eds) Encyclopedia of Science Education. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2150-0_129

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ORIGINAL RESEARCH article

Q-learning model of insight problem solving and the effects of learning traits on creativity.

• Graduate School of Business Administration, Kobe University, Kobe, Japan

Despite the fact that insight is a crucial component of creative thought, the means by which it is cultivated remain unknown. The effects of learning traits on insight, specifically, has not been the subject of investigation in pertinent research. This study quantitatively examines the effects of individual differences in learning traits estimated using a Q-learning model within the reinforcement learning framework and evaluates their effects on insight problem solving in two tasks, the 8-coin and 9-dot problems, which fall under the umbrella term “spatial insight problems.” Although the learning characteristics of the two problems were different, the results showed that there was a transfer of learning between them. In particular, performance on the insight tasks improved with increasing experience. Moreover, loss-taking, as opposed to loss aversion, had a significant effect on performance in both tasks, depending on the amount of experience one had. It is hypothesized that loss acceptance facilitates analogical transfer between the two tasks and improves performance. In addition, this is one of the few studies that attempted to analyze insight problems using a computational approach. This approach allows the identification of the underlying learning parameters for insight problem solving.

Introduction

Creativity occasionally depends on insight, the ability of an individual to alter their existing thought patterns, break the status quo, and create something new without being aware of the process by which the solution was reached. While analytical problems are solved through a step-by-step, incremental process, insight problems require an “a-ha” moment that leads to a solution. The information gained in this way transcends current informational boundaries and contributes to solving the problem.

The underlying mechanisms of creative thinking in which insight plays a critical role, have been the subject of intensive research efforts that have led to a number of studies using a variety of insight tasks as summarized in Table 1 . Several conceptual models have been developed as a result, such as the representational change theory ( Ohlsson, 1992 ; Knöblich et al., 1999 ), the breakthrough thinking model ( Perkins, 2000 ), and “Geneplore” model ( Finke et al., 1999 ). These cognitive models of insight generation appear less reliant on analytical processes. According to these models, attempts to solve problems failed, impasses were reached, restructuring occurred, and the “a-ha” moment led to a solution ( Weisberg, 2015 ). However, some studies have suggested that creativity is identical to analytical problem solving, and that insight and impasse have no influence on it ( Weisberg, 2006 , 2013 ; Ball and Stevens, 2009 ; Chein and Weisberg, 2014 ). According to this view, insight tasks differ due to their high domain specificity. Therefore, an integrated model has been proposed that includes solutions by transfer, heuristic methods, restructuring, and insight, primarily based on analytic thinking processes ( Weisberg, 2015 ). Although problem solving through insight is the final step, most problems can be solved before reaching an impasse and gaining insight. However, because this integrated model is a categorical stage model, it is difficult to quantitatively assess the relative contribution of analytical thinking and other learning traits to problem solving (for a systematic review of insight problem solving, see van Steenburgh et al., 2012 ).

Table 1 . Insight tasks in problem-solving.

This study investigated the contribution of learning traits such as exploitation/exploration trade-offs, risk attitude, and loss aversion to problem solving in insight tasks. Although insight problems can be solved analytically without insight, which is extremely rare under laboratory conditions ( Fleck and Weisberg, 2013 ), solving insight problems requires removing assumptions that are implicitly imposed by the problem solver, making it challenging to solve the problem analytically. For example, in the 9-dot problem, despite the absence of imposed assumptions, participants usually assume that the lines should be drawn within the square box. To solve the problem, the line must be drawn outside the square box, and participants might arrive at this conclusion through insight, analysis or sheer luck. While related studies primarily examined the occurrence of insight in such problems, this study focused on identifying the factors, especially learning characteristics, that facilitated the removal of implicit assumptions, rather than the reasons (such as insight, analysis, and sheer luck) that led to these assumptions being directly relaxed.

To accomplish this, a reinforcement learning (RL) framework was used in this study to provide a simple and rigorous account of problem solving and learning activities. The RL framework is supported by considerable empirical evidence, including neural signals in various cortical and subcortical structures that behave as predicted ( Schultz et al., 1997 ; Glimcher and Rustichini, 2004 ; Hikosaka et al., 2006 ; Rangel et al., 2008 ). While this framework has been applied to studies of decision-making and learning in various social contexts ( Delgado et al., 2005 ; Montague et al., 2006 ; Behrens et al., 2008 ; Hampton et al., 2008 ; Coricelli and Nagel, 2009 ; Bhatt et al., 2010 ; Yoshida et al., 2010 ), only a few studies have applied this to creative thinking ( Harada, 2020a , b , 2021 , 2023 ).

In this study the effect of learning characteristics measured by the RL framework in removing implicit assumptions and developing appropriate solutions was empirically investigated. Using our RL framework, which incorporates the prospect utility function, the exploitation/exploration ratio and the risk-taking and loss-taking attitudes can be estimated. The novelty of this approach in this study is simply that it allows us to examine the effects of learning traits such as exploitation/exploration ratio and loss-taking attitudes on insight problem solving, which would be impossible to assess without the computational model used in this study. Attitudes towards risk-taking have been extensively studied in the relevant literature by evaluating them using questionnaires. However, this method is subject to the subjective assessments of the participants, which could distort the measurement of risk attitudes. In contrast, risk attitude was determined on this study by estimating the underlying utility function based on objective behavioral data. Relevant literature emphasizes the role of risk-taking in fostering creativity as creative people are more likely to be motivated by challenging and risky situations ( Albert, 1990 ; Perkins, 1990 ), suggesting a strong connection between risk-taking and creativity. Several empirical studies that examined this relationship reported that creativity and risk-taking are positively correlated ( Eisenman, 1987 ; El-Murad and West, 2003 ; Dewett, 2007 ; Simmons and Ren, 2009 ; Tyagi et al., 2017 Harada, 2020a ). However, Shen et al. (2018) found that, while low risk-taking was associated with convergent thinking, it was not significantly correlated with divergent thinking. Nevertheless, risk-taking and loss-taking in insight problems facilitate navigation through risky and unpromising sequences, which could help to relax or eliminate existing constraints that hinder problem solving and creative thinking. While related studies have investigated the effects of risk-taking on creativity, to our knowledge, the effects of loss-taking attitudes have not been examined because they could not be assessed without explicit consideration of a prospect utility function. This study tested the hypotheses that risk-taking and loss-taking are positively associated with performance in insight problem solving.

In addition, this study assigned two insight tasks (8-coin and 9-dot problems) and examined the possibility of knowledge transfer in insight problem solving, i.e., the contribution of experience in one task to problem solving in another task. The computational approach used in this study enabled the systematic assessment of the relative importance of the learning characteristics, especially risk- and loss-taking, for knowledge transfer in insight problem solving, which is not possible in the categorical sequential models that use multiple-comparison procedures for insight problem solving.

Participants

The insight tasks (8-coin and 9-dot problems) were assigned to 364 healthy undergraduates at Kobe University. Seven students were excluded because they did not participate in all tests, while the data of 32 students were dropped from the sample because they had previously experienced at least one of the two tasks. The remaining sample of 325 students was analyzed (111 women, age range = 18–26 years, SD = 0.47). The Ethics Committee of the Graduate School of Business Administration, Kobe University approved all experimental protocols in this study. The study conducted in compliance with the relevant guidelines and regulations. An informed consent form was signed by all participants and their parents (for those under 20 years of age).

Experiments

In Test 1, participants completed a two-armed bandit (TAB) problem. In Test 2, they completed two insight tasks, 8-coin and 9-dot problems, in a randomly chosen order. For Tests 1 and 2, a TAB and two insight tasks were uploaded to our experimental server during class time. Each participant received Tests 1 and 2 at random. Programs for Tests 1 and 2 were developed, and the participants accessed the programs on the server from their PCs.

In the 8-coin problem, the goal is to move only two coins from their respective starting positions such that each coin touches three other coins. Figure 1 shows the initial problem configuration and the final solution. To solve the problem requires switching from moving coins in two-dimensions to three-dimensions. Ormerod et al. (2002) reported low success rates without any hints.

Figure 1 . In the 8-coin problem, the figure on the left shows the participants. They were asked to move only two coins, so that each coin touched exactly three others. The figure on the right shows this solution.

In the 9-dot problem, the participant must connect all nine dots with four straight lines without lifting the pencil or retracing any lines. Figure 2 shows the initial problem configuration and the final solution. The insight required for this problem was to draw lines outside the 9-dot square box. The key to solving this problem lies in “thinking outside the box.” According to Weisberg and Alba (1981) , all participants in their study reached an impasse, and none of them solved the problem. Even providing hints did not improve the situation. When they provided relatively detailed information on how to reach the solution, the success rate increased by the practice of solving simpler connect-the-dot problems. From this, they concluded that problem-specific experience was crucial to solving the problem.

Figure 2 . In the 9-dot problem, the figure on the left shows the participants. They were asked to connect all nine dots to four straight lines without lifting their pencils or retracing any lines. The figure on the right shows this solution.

Both the 8-coin and 9-dot problems had a 30-min time limit. If the participants were unable to solve the task within the time limit, the solution was displayed for 10 s and the program automatically proceeded to the next task (if it was the first problem) or Test 2 was terminated (if it was the second problem). If participants submitted a wrong answer, the message “incorrect” appeared immediately on the screen, followed by the solution.

The solutions to both problems require constraint relaxation. The 9-dot problem requires drawing lines outside the 9-dot square, whereas the 8-coin problem requires switching from two-dimensional to three-dimensional movement. Additionally, these are spatial puzzles (see Table 1 ), which are often categorized as spatial insight problems ( Dow and Mayer, 2004 ). Therefore, learning transfer across two tasks was expected.

In our study, the success rates for the 8-coin and 9-dot problems were 31 and 70%, respectively ( Table 2 ) which are significantly higher than those reported in similar studies. This difference could be attributed to the time limits for each problem. For example, the time limit for the 8-coin problem in Ormerod et al. (2002) was 6 min, whereas the time limits in this study were 30 min for both 8-coin and 9-dot problems. However, the successful participants completed the 8-coin problem in 1 min and 19 s and 9-point problem in 1 min and 44 s on average. Thus, the time limit in this study did not directly affect the success rates. A possible reason could be the occurrence of learning transfer. Although participants failed in the first test, they were able to succeed in the next test because they quickly learned that relaxing implicit assumptions is the key to success in the problems.

Table 2 . Descriptive statistics.

Q-learning model

To account for decision-making in the TAB, a simple Q-learning reinforcement learning algorithm was used in this study ( Watkins and Dayan, 1992 ). In Test 1, participants selected either the right or left box on the screen ( Figure 3 ). Upon selection, participants were immediately awarded either 10 or − 10 points. The goal of this test was to maximize the sum of the rewards over a series of 100 choices. The probability of gaining 10 points was higher for one box (70%) and lower for the other (30%). These probabilities were switched twice over 100 choices to eliminate the possibility of learning convergence, where the participants learn the box with the higher probability of gaining 10 points and choose that box in the future. For the first 30 choices, the right and left boxes had a 70 and 30% probability of gaining 10 points, respectively. From the 31st to the 70th choice, the probabilities switched such that the probability of earning 10 points for the right and left boxes fell to 30 and 70%, respectively. Subsequently, for the last 30 choices, the probabilities of the right and left boxes returned to initial levels of 70 and 30%, respectively. These shifts in probabilities were built in to prevent participants from continuing to select the same deck with a higher expected reward in the early stages of the 100 trials.

Figure 3 . Example of a trial in the two-armed bandits (TAB) in which the participant chose the right box first, then the left, and finally the right box, with rewards of 10, 10, and −10 points, respectively.

Q-learning assumes that a decision-maker calculates the action value for choice i at trial t ( i = right or left box), which is denoted by Q i t as

where R i t is the reward associated with choice i at trial t , either 10 or − 10 points, and δ t is the reward prediction error. α ± indicates the learning rate, which measures the sensitivity to gains and losses when updating the action value. ϕ is added to Equation 1 , because participants may tend to make the same choice over time. This autocorrelation of choices could bias the magnitude of the learning rate α ± ( Katahira, 2018 ). ϕ was added to correct this bias.

Following Harada (2023) , the prospect utility function ( Tversky and Kahneman, 1986 ) was incorporated in U R i t because it facilitates the measurement of risk and loss attitudes without additional paper and pencil tests. μ and ν in Equation 3 measure the degree of risk aversion and risk-taking, respectively. In this specification, risk-taking (aversion) is associated with lower (higher) μ and higher (lower) ν . λ evaluates losses relative to gains, which is usually referred to as loss aversion. A higher λ implies that agents want to avoid losses. Note that λ measures sensitivity to negative rewards, whereas risk attitudes evaluate sensitivity to changes in rewards.

When box j is not chosen by the decision-maker, its action value remains the same, such that

Faced with the action values of the two boxes, it is assumed that the decision maker chooses one of the two according to the SoftMax decision rule.

where a t represents the choice made at trial t and P a t = i refers to the probability of choosing box i at trial t . Parameter β is the inverse temperature indicating the relative strength of exploitation versus exploration (exploitation/exploration ratio), which was originally proposed in the RL framework ( Sutton and Barto, 2018 ). Exploitation refers to the optimization of current tasks under existing information and memory conditions, whereas exploration implies wider, and sometimes random, searches and trials. Consequently, exploitation and exploration usually generate different solutions, resulting in a trade-off between the two. A higher inverse temperature indicates that the decision-maker chooses the box with the higher Q values. In contrast, a lower inverse temperature suggests that the choice is more likely to be made randomly, independent of the Q values.

In this study, it was hypothesized that this Q-learning model could also specify creative thinking processes in insight tasks. In the 9-dot problem, Weisberg and Alba (1981) highlighted the importance of providing relatively detailed information about the problem to improve the success rate. In particular, problem-specific knowledge is required to solve a problem. In the 8-coin problem, Ormerod et al. (2002) emphasized the importance of current constraints and preferred strategic moves when changing the search direction. These findings suggest that existing beliefs and knowledge regarding strategic activities and directions play a role in finding solutions, which can be formalized as the action values of each option. The action values are derived from an individual’s prior beliefs and experiences, as specified in Equations 1 – 4 .

Moreover, unrealized options can be represented by options that have the maximum possible action values after the “a-ha” moment and zero action values prior to it. The above Q-learning model may seem to represent only incremental learning while the “a-ha” moment entails sudden learning wherein a zero-valued option swiftly increases to its maximum value. However, this sudden shift in the option values could be triggered by a lower value of the inverse temperature β (exploration) in Equation 5 . A random choice of a low valued option might result in an extremely higher reward R i t and δ t in Equation 2 , leading to an immediate shift in its Q value in Equation 1 . Thus, the Q-learning model described above could be applied not only to the TAB, but also to the 8-coin and 9-dot problems. The research strategy in this study was to estimate the parameter values of the Q-learning model from the TAB in Test 1 and evaluate their effects on the performance of the two insight tasks in Test 2.

Estimation method

The parameters specified in Equations 1 – 5 were estimated by optimizing the maximum a posteriori objective function.

where p D s | θ s is the likelihood of data D s for subject s under the condition of the parameters θ s = β S μ S ν S α ± S λ S ϕ S . p θ s is the prior probability of θ s . Note that α ± should be bound between 0 and 1 and β , μ , ν , and λ , take non-negative values. Following a standard procedure in Bayesian statistics, the priors for α ± were specified as beta distributions with shape parameters of 2 and 2, and the priors for β , μ , ν , and λ were gamma distributions, f, with a shape parameter of 2 and a scale parameter of 3. ϕ was assumed to follow a standard normal distribution with a mean of 0 and variance of 1.

This section examines the effects of the learning characteristics in the Q-learning model. The descriptive statistics (mean, SD, and correlation) for all the variables used in the empirical analyses are listed in Table 2 .

For this purpose, the parameters of inverse temperature (β), the risk-aversion index for gains (μ), the risk-taking index for losses (ν), learning rates ( α ± ), loss aversion ( λ ), and autocorrelation ( ϕ ) were estimated from the data obtained in the TAB by the MAP estimation described above using R and the Rsolnp and tidyverse libraries. Regression analyses were then performed on the determinants of success in the 8-coin and 9-dot problems. Performance in TAB (TAB performance) was also added as a regressor. As the measures indicating success in these two tasks were dummy variables (1 and 0 for success and failure, respectively), the probit regression method was used to maintain statistical consistency. The results are listed in Table 3 .

Table 3 . Probit regression results (SE in parentheses).

Columns (1) and (2) of Table 3 show the results for the 8-coin problem. Column (2) contains a dummy variable indicating success in the 9-dot problem. In both columns, the learning rate for the negative reward prediction errors ( α − ) exerted a negative effect. This suggests that successful individuals tend to respond to negative results positively in updating the Q value. Moreover, column (2) clearly indicates that successful individuals in the 9-dot problem were more likely to be successful in the 8-coin problem. A possibility of learning transfer exists between these two insight tasks, as they belong to the same category of insight problems, so-called the spatial insight problems ( Dow and Mayer, 2004 ). The ability to solve the 9-dot problem was carried over to the 8-coin problem, suggesting that problem-solving ability is not limited to problem-specific knowledge.

Columns (3) and (4) show the results for the 9-dot problem. Column (4) contains a dummy variable indicating success in the 8-coin problem. In both columns, loss aversion λ exerted a negative effect, implying that successful individuals in the 8-coin problem tend to react positively to negative rewards. Furthermore, 8-coin problem success had a positive effect on 9-dot-problem success. Thus, problem solving ability for the 8-coin problem also contributed to the 9-dot problem.

These results imply that the determinants of success in the two insight tasks differ completely in terms of the learning characteristics of the Q-learning model. Nevertheless, the negative effects of α − and λ suggest that insight problem solving must respond positively in updating the Q value. Moreover, the results indicated that problem solving abilities in both tasks were closely related.

However, these results do not account for the order effect of the two insight tasks. If something is learned from an insight task, the lessons could provide useful guidance in the next insight task. The sample was split into two subsamples to comprehend this order effect. In these subsamples, participants performed one of the two tasks for the second time such that they had already experienced another insight task. The results are listed in Table 4 .

Table 4 . Probit regression results for the second-time tasks (SE in parentheses).

First, based on the results in Table 4 , success in the previous insight task positively affected success in the following insight task. Second, a positive effect of α − is observed in the 9-dot problem, but it is no longer significant in the 8-coin problem. Interestingly, in contrast to the previous results, all columns in Table 4 show significant negative effects of loss aversion λ . In both the 8-coin and 9-dot problems, the success of the insight tasks in the second time critically depended on their insensitivity to avoid reward losses. In particular, successful individuals were more willing to accept losses than to avoid them. Lower loss aversion appears to be critical for transferring what has been learned to other tasks.

To check the robustness of this result, subsamples in which participants undertook insight tasks for the first time were also examined. In this analysis, no common effects of learning characteristics were observed between the two tasks. The significant parameters were α − for the 8-coin problem and a constant term for the 9-dot problem. The success rates for the first and second-time tasks were 0.25 and 0.36 for the 8-coin problem ( χ 2 = 4.84, p  = 0.03) and 0.64 and 0.77 for the 9-dot problem ( χ 2 = 5.74, p  = 0.02), respectively, indicating that prior learning was transferred to the next task. Hence, for this learning transfer to occur, loss-taking, rather than loss aversion, played a critical role in both insight tasks.

In this study, a novel methodology for studying insight problem solving was proposed and the effects of learning parameters specified in the Q-learning model in insight problem solving performance were investigated. To the best of our knowledge, this is one of the first attempts to use a computational approach to study insight tasks, such as the 8-coin and 9-dot problems. Although there are several studies that have empirically investigated insight problem solving and cognitive strategies, most of them have not explicitly modeled the underlying mechanism of problem solving in insight tasks. In addition to the categorical conceptual models of insight thinking processes ( Ohlsson, 1992 ; Finke et al., 1999 ; Knöblich et al., 1999 ; Perkins, 2000 ; Weisberg, 2015 ), a frequently used method in related studies was retrospective reporting such as feeling-of-warmth rating, in which participants were asked to assess “how warm/close do you feel you are to the solution?” or respond to a verbal protocol, in which they were asked what they are thinking while working on the solution ( Chu and Macgregor, 2011 ). Evidently, these methods are subjective and unreliable. In addition, retrospective reporting during the tasks themselves has been reported to affect performance ( Berardi-Coletta et al., 1995 ), which could bias the results, and make it more difficult to assess the effect of the underlying mechanism. Undoubtedly, pertinent research has also investigated the preconditions for insight such as mind-wandering thoughts ( Zedelius and Schooler, 2015 ; Gable et al., 2019 ) and looking away behavior ( Salvi and Bowden, 2016 ) after being unsuccessful at solving a problem. However, these preconditions have not been integrated into a coherent model of insight problem solving.

In contrast, a computational approach to insight problem solving was described in this study. This algorithm allows for a more accurate understanding of the processes that occur while people solve insight problems, as it can identify the parameters that influence learning. In particular, detailed individual differences in learning traits could be examined in insight problem solving using this approach which could further our understanding on insight problem solving processes and help enhance creative thinking. Of course, it must be noted that our computational approach was not directly applied to insight problem solving. Instead, the learning parameters were estimated in the TAB tasks. Nevertheless, we believe that the Q-learning framework could also be applied to insight problem solving by interpreting insight as a sudden shift of a low- or zero-valued option triggered by exploration.

It should also be noted that the proposed Q-learning model replicates actual brain activity as it is based on the underlying neural mechanism. This RL framework is supported by a growing number of studies on neural mechanisms ( Schultz et al., 1997 ; Glimcher and Rustichini, 2004 ; Hikosaka et al., 2006 ; Rangel et al., 2008 ). For example, research supports the existence of a connection between behavior and dopamine neurons in the midbrain of humans and monkeys that encode reward-prediction errors ( Schultz et al., 1997 ; Bayer and Glimcher, 2005 ; Cohen et al., 2012 ). The Q-learning model proposed in this study belongs to this class of models that can be used to model brain activity. Thus, the Q-learning model suffers less from the arbitrariness and ad hoc nature typically observed in the related conceptual models.

Regarding the hypotheses that risk-taking and loss-taking improve performance in insight problems, no significant effects of risk-taking were observed. This result supports the findings of Shen et al. (2018), according to which risk-taking was not significantly correlated with divergent thinking. In contrast, loss-taking was positively related to performance in the 9-dot problem but not in the 8-coin problem. These results suggest that loss-taking, rather than risk-taking, was partially responsible for insight problem solving performance.

However, when the learning transferability between the two problems is taken into account, loss-taking assumes a substantial role in both tests. The performance in the second insight problem solving improved with loss-taking attitudes. Therefore, the hypothesis must be modified to the effect that loss-taking is positively related to performance in insight problems under learning transfer.

The learning transfer has also been confirmed in related studies. Ansburg and Dominowski (2000) found that insight problem solving can be construed as a general strategic thinking skill for which training is useful. Chrysikou (2006) also reported that additional general training that does not directly target insight problems can improve insight problem solving. However, several studies questioned the generalizability of problem-solving ability. They claimed that training for one insight problem is not transferable to other insight problems ( Dow and Mayer, 2004 ; Cunningham and Mac Gregor, 2008 ). One possible reason for these differences could be that different types of insight problems require different cognitive abilities ( Chu and Macgregor, 2011 ). In this study, the learning transfer could have occurred between the 8-coin and 9-dot problems because of their similarity. In the debate on the transferability or learning in insight problems, this research made a unique contribution by identifying the factor that facilitates learning transfer, namely, the attitude toward loss-taking. In addition to the differences in the nature of insight problems, a lack of this attitude may prevent learning transfer. Hence, individual differences in learning characteristics play a role in establishing learning transfer across insight problems.

The literature on analogical transfer in insight problems argues that providing a problem analogy, such as similes, metaphors, and case-based reasoning, improves solution rates ( Reeves and Weisberg, 1994 ). A positive attitude towards failure (loss-taking in the context of Q-learning) could facilitate this analogical transfer. If lessons from failure are appropriately generalized in analogies or case-based reasoning, it could serve as a guide. Accepting and learning from failure leads to the creation of useful analogies that reflects previous experiences of failure to overcome the next insight problem.

According to prospect theory, people are willing to take risks to avoid losses ( Tversky and Kahneman, 1992 ). One of the implications of this study is that the creativity of those who do not attempt to avoid losses can be enhanced. Although loss-taking only partially responsible for performance in insight problems, it facilitated problem solving in both 8-coin and 9-dot problems under learning transfer. It is our conviction that this attitude can often be trained such that loss-averting individuals strive for more loss-seeking. Even if this is difficult, appropriate incentives can be created to encourage loss-seeking by rewarding (constructive) failure. For example, a global mobility company, Honda, introduced a challenging goal system in which employees were evaluated on the basis of processes rather than performance (results). The criteria for process evaluation included the number of instances in which employees experienced constructive failure ( Harada, 2010 ). Alternatively, reducing actual losses due to failure by introducing simulations, virtual experiments or rapid prototyping could also improve creativity ( Ries, 2011 ).

However, the results of this study have several limitations. First, the Q-learning model was only applied to TAB tasks and the effects of its learning parameters over different insight problems were assessed. Therefore, while we argued that the Q-learning model could model the insight problem solving activity, the computational approach in this study was limited in the sense that it was not applied for analyzing insight problem solving directly. When the cognitive activities in the TAB and insight problem share the same mechanism, the results showed the direct effect of learning traits in insight problem solving. However, even if non-insight and insight problem solving follow the Q-learning mechanism, it is possible that the parameter values differ in the different problems (even across different insight problems). It is evident that this possibility should be further investigated in future studies by applying the computational approach directly to insight problems. To achieve this, more sophisticated computer programs must be developed to track detailed thought processes during insight problem solving.

Second, only two insight problems were investigated in this study. However, it would be more interesting to examine the learning transfer not only across similar types of insight problems, but also for different types of insight problems. A more systematic study on a variety of insight problems will reveal the domain-free determinants of learning transfer in insight problems.

Third, this study examined the determinant of performance in insight problem solving, In related studies, the occurrence of insight has typically been investigated using retrospective reports after insight solving ( Chu and Macgregor, 2011 ). However, as described above, this method is subjective and unreliable. As a result, this study did not examine whether insight actually occurred for each participant. Therefore, the results of this study might also reflect solutions without insight. Hence, the results should be interpreted as a determinant of performance of so-called “insight problems” in which no distinction was made as to whether insight actually occurs or not. In future studies, we should more objectively determine whether insight occurs or not to examine the determinant of problem solving with insight, which would probably require a neuroscientific approach.

Finally, we point out that our results critically depend on the cultural and social background of the participants. Results may differ when similar experiments are conducted in different contexts, although any psychological study is subject to this type of limitation. Even if different results are obtained, we believe that the computational approach to insight problem solving and the simple Q-learning framework in this study remain valid and useful.

This study examined the effects of learning traits on insight problem solving, using a computational approach to uncover the correlational factors linked with insight problem solving. The result revealed that positively reacting to loss and errors is a crucial characteristic for successful insight problem solving in both 8-coin and 9-dot problems, facilitating analogical transfer between the two tasks and improving performance. This assessment was made possible by implementing a simple Q-learning model and estimating learning parameters. To the best of our knowledge, this study is one of the few attempts to apply the RL framework to insight problem solving and learning transfer.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by The Ethics Committee of the Graduate School of Business Administration, Kobe University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

TH: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by JSPS KAKENHI under Grant (Number 19H00597).

Conflict of interest

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.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: insight problem solving, creativity, individual differences, Q-learning, learning transfer

Citation: Harada T (2024) Q-learning model of insight problem solving and the effects of learning traits on creativity. Front. Psychol . 14:1287624. doi: 10.3389/fpsyg.2023.1287624

Received: 04 September 2023; Accepted: 18 December 2023; Published: 08 January 2024.

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Copyright © 2024 Harada. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Tsutomu Harada, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Teaching Creativity and Inventive Problem Solving in Science

• Robert L. DeHaan

Division of Educational Studies, Emory University, Atlanta, GA 30322

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Engaging learners in the excitement of science, helping them discover the value of evidence-based reasoning and higher-order cognitive skills, and teaching them to become creative problem solvers have long been goals of science education reformers. But the means to achieve these goals, especially methods to promote creative thinking in scientific problem solving, have not become widely known or used. In this essay, I review the evidence that creativity is not a single hard-to-measure property. The creative process can be explained by reference to increasingly well-understood cognitive skills such as cognitive flexibility and inhibitory control that are widely distributed in the population. I explore the relationship between creativity and the higher-order cognitive skills, review assessment methods, and describe several instructional strategies for enhancing creative problem solving in the college classroom. Evidence suggests that instruction to support the development of creativity requires inquiry-based teaching that includes explicit strategies to promote cognitive flexibility. Students need to be repeatedly reminded and shown how to be creative, to integrate material across subject areas, to question their own assumptions, and to imagine other viewpoints and possibilities. Further research is required to determine whether college students' learning will be enhanced by these measures.

INTRODUCTION

Dr. Dunne paces in front of his section of first-year college students, today not as their Bio 110 teacher but in the role of facilitator in their monthly “invention session.” For this meeting, the topic is stem cell therapy in heart disease. Members of each team of four students have primed themselves on the topic by reading selected articles from accessible sources such as Science, Nature, and Scientific American, and searching the World Wide Web, triangulating for up-to-date, accurate, background information. Each team knows that their first goal is to define a set of problems or limitations to overcome within the topic and to begin to think of possible solutions. Dr. Dunne starts the conversation by reminding the group of the few ground rules: one speaker at a time, listen carefully and have respect for others' ideas, question your own and others' assumptions, focus on alternative paths or solutions, maintain an atmosphere of collaboration and mutual support. He then sparks the discussion by asking one of the teams to describe a problem in need of solution.

Science in the United States is widely credited as a major source of discovery and economic development. According to the 2005 TAP Report produced by a prominent group of corporate leaders, “To maintain our country's competitiveness in the twenty-first century, we must cultivate the skilled scientists and engineers needed to create tomorrow's innovations.” ( www.tap2015.org/about/TAP_report2.pdf ). A panel of scientists, engineers, educators, and policy makers convened by the National Research Council (NRC) concurred with this view, reporting that the vitality of the nation “is derived in large part from the productivity of well-trained people and the steady stream of scientific and technical innovations they produce” ( NRC, 2007 ).

For many decades, science education reformers have promoted the idea that learners should be engaged in the excitement of science; they should be helped to discover the value of evidence-based reasoning and higher-order cognitive skills, and be taught to become innovative problem solvers (for reviews, see DeHaan, 2005 ; Hake, 2005 ; Nelson, 2008 ; Perkins and Wieman, 2008 ). But the means to achieve these goals, especially methods to promote creative thinking in scientific problem solving, are not widely known or used. An invention session such as that led by the fictional Dr. Dunne, described above, may seem fanciful as a means of teaching students to think about science as something more than a body of facts and terms to memorize. In recent years, however, models for promoting creative problem solving were developed for classroom use, as detailed by Treffinger and Isaksen (2005) , and such techniques are often used in the real world of high technology. To promote imaginative thinking, the advertising executive Alex F. Osborn invented brainstorming ( Osborn, 1948 , 1979 ), a technique that has since been successful in stimulating inventiveness among engineers and scientists. Could such strategies be transferred to a class for college students? Could they serve as a supplement to a high-quality, scientific teaching curriculum that helps students learn the facts and conceptual frameworks of science and make progress along the novice–expert continuum? Could brainstorming or other instructional strategies that are specifically designed to promote creativity teach students to be more adaptive in their growing expertise, more innovative in their problem-solving abilities? To begin to answer those questions, we first need to understand what is meant by “creativity.”

What Is Creativity? Big-C versus Mini-C Creativity

How to define creativity is an age-old question. Justice Potter Stewart's famous dictum regarding obscenity “I know it when I see it” has also long been an accepted test of creativity. But this is not an adequate criterion for developing an instructional approach. A scientist colleague of mine recently noted that “Many of us [in the scientific community] rarely give the creative process a second thought, imagining one either ‘has it’ or doesn't.” We often think of inventiveness or creativity in scientific fields as the kind of gift associated with a Michelangelo or Einstein. This is what Kaufman and Beghetto (2008) call big-C creativity, borrowing the term that earlier workers applied to the talents of experts in various fields who were identified as particularly creative by their expert colleagues ( MacKinnon, 1978 ). In this sense, creativity is seen as the ability of individuals to generate new ideas that contribute substantially to an intellectual domain. Howard Gardner defined such a creative person as one who “regularly solves problems, fashions products, or defines new questions in a domain in a way that is initially considered novel but that ultimately comes to be accepted in a particular cultural setting” ( Gardner, 1993 , p. 35).

But there is another level of inventiveness termed by various authors as “little-c” ( Craft, 2000 ) or “mini-c” ( Kaufman and Beghetto, 2008 ) creativity that is widespread among all populations. This would be consistent with the workplace definition of creativity offered by Amabile and her coworkers: “coming up with fresh ideas for changing products, services and processes so as to better achieve the organization's goals” ( Amabile et al. , 2005 ). Mini-c creativity is based on what Craft calls “possibility thinking” ( Craft, 2000 , pp. 3–4), as experienced when a worker suddenly has the insight to visualize a new, improved way to accomplish a task; it is represented by the “aha” moment when a student first sees two previously disparate concepts or facts in a new relationship, an example of what Arthur Koestler identified as bisociation: “perceiving a situation or event in two habitually incompatible associative contexts” ( Koestler, 1964 , p. 95).

In this essay, I maintain that mini-c creativity is not a mysterious, innate endowment of rare individuals. Instead, I argue that creative thinking is a multicomponent process, mediated through social interactions, that can be explained by reference to increasingly well-understood mental abilities such as cognitive flexibility and cognitive control that are widely distributed in the population. Moreover, I explore some of the recent research evidence (though with no effort at a comprehensive literature review) showing that these mental abilities are teachable; like other higher-order cognitive skills (HOCS), they can be enhanced by explicit instruction.

Creativity Is a Multicomponent Process

Efforts to define creativity in psychological terms go back to J. P. Guilford ( Guilford, 1950 ) and E. P. Torrance ( Torrance, 1974 ), both of whom recognized that underlying the construct were other cognitive variables such as ideational fluency, originality of ideas, and sensitivity to missing elements. Many authors since then have extended the argument that a creative act is not a singular event but a process, an interplay among several interactive cognitive and affective elements. In this view, the creative act has two phases, a generative and an exploratory or evaluative phase ( Finke et al. , 1996 ). During the generative process, the creative mind pictures a set of novel mental models as potential solutions to a problem. In the exploratory phase, we evaluate the multiple options and select the best one. Early scholars of creativity, such as J. P. Guilford, characterized the two phases as divergent thinking and convergent thinking ( Guilford, 1950 ). Guilford defined divergent thinking as the ability to produce a broad range of associations to a given stimulus or to arrive at many solutions to a problem (for overviews of the field from different perspectives, see Amabile, 1996 ; Banaji et al. , 2006 ; Sawyer, 2006 ). In neurocognitive terms, divergent thinking is referred to as associative richness ( Gabora, 2002 ; Simonton, 2004 ), which is often measured experimentally by comparing the number of words that an individual generates from memory in response to stimulus words on a word association test. In contrast, convergent thinking refers to the capacity to quickly focus on the one best solution to a problem.

The idea that there are two stages to the creative process is consistent with results from cognition research indicating that there are two distinct modes of thought, associative and analytical ( Neisser, 1963 ; Sloman, 1996 ). In the associative mode, thinking is defocused, suggestive, and intuitive, revealing remote or subtle connections between items that may be correlated, or may not, and are usually not causally related ( Burton, 2008 ). In the analytical mode, thought is focused and evaluative, more conducive to analyzing relationships of cause and effect (for a review of other cognitive aspects of creativity, see Runco, 2004 ). Science educators associate the analytical mode with the upper levels (analysis, synthesis, and evaluation) of Bloom's taxonomy (e.g., Crowe et al. , 2008 ), or with “critical thinking,” the process that underlies the “purposeful, self-regulatory judgment that drives problem-solving and decision-making” ( Quitadamo et al. , 2008 , p. 328). These modes of thinking are under cognitive control through the executive functions of the brain. The core executive functions, which are thought to underlie all planning, problem solving, and reasoning, are defined ( Blair and Razza, 2007 ) as working memory control (mentally holding and retrieving information), cognitive flexibility (considering multiple ideas and seeing different perspectives), and inhibitory control (resisting several thoughts or actions to focus on one). Readers wishing to delve further into the neuroscience of the creative process can refer to the cerebrocerebellar theory of creativity ( Vandervert et al. , 2007 ) in which these mental activities are described neurophysiologically as arising through interactions among different parts of the brain.

The main point from all of these works is that creativity is not some single hard-to-measure property or act. There is ample evidence that the creative process requires both divergent and convergent thinking and that it can be explained by reference to increasingly well-understood underlying mental abilities ( Haring-Smith, 2006 ; Kim, 2006 ; Sawyer, 2006 ; Kaufman and Sternberg, 2007 ) and cognitive processes ( Simonton, 2004 ; Diamond et al. , 2007 ; Vandervert et al. , 2007 ).

Creativity Is Widely Distributed and Occurs in a Social Context

Although it is understandable to speak of an aha moment as a creative act by the person who experiences it, authorities in the field have long recognized (e.g., Simonton, 1975 ) that creative thinking is not so much an individual trait but rather a social phenomenon involving interactions among people within their specific group or cultural settings. “Creativity isn't just a property of individuals, it is also a property of social groups” ( Sawyer, 2006 , p. 305). Indeed, Osborn introduced his brainstorming method because he was convinced that group creativity is always superior to individual creativity. He drew evidence for this conclusion from activities that demand collaborative output, for example, the improvisations of a jazz ensemble. Although each musician is individually creative during a performance, the novelty and inventiveness of each performer's playing is clearly influenced, and often enhanced, by “social and interactional processes” among the musicians ( Sawyer, 2006 , p. 120). Recently, Brophy (2006) offered evidence that for problem solving, the situation may be more nuanced. He confirmed that groups of interacting individuals were better at solving complex, multipart problems than single individuals. However, when dealing with certain kinds of single-issue problems, individual problem solvers produced a greater number of solutions than interacting groups, and those solutions were judged to be more original and useful.

Consistent with the findings of Brophy (2006) , many scholars acknowledge that creative discoveries in the real world such as solving the problems of cutting-edge science—which are usually complex and multipart—are influenced or even stimulated by social interaction among experts. The common image of the lone scientist in the laboratory experiencing a flash of creative inspiration is probably a myth from earlier days. As a case in point, the science historian Mara Beller analyzed the social processes that underlay some of the major discoveries of early twentieth-century quantum physics. Close examination of successive drafts of publications by members of the Copenhagen group revealed a remarkable degree of influence and collaboration among 10 or more colleagues, although many of these papers were published under the name of a single author ( Beller, 1999 ). Sociologists Bruno Latour and Steve Woolgar's study ( Latour and Woolgar, 1986 ) of a neuroendocrinology laboratory at the Salk Institute for Biological Studies make the related point that social interactions among the participating scientists determined to a remarkable degree what discoveries were made and how they were interpreted. In the laboratory, researchers studied the chemical structure of substances released by the brain. By analysis of the Salk scientists' verbalizations of concepts, theories, formulas, and results of their investigations, Latour and Woolgar showed that the structures and interpretations that were agreed upon, that is, the discoveries announced by the laboratory, were mediated by social interactions and power relationships among members of the laboratory group. By studying the discovery process in other fields of the natural sciences, sociologists and anthropologists have provided more cases that further illustrate how social and cultural dimensions affect scientific insights (for a thoughtful review, see Knorr Cetina, 1995 ).

In sum, when an individual experiences an aha moment that feels like a singular creative act, it may rather have resulted from a multicomponent process, under the influence of group interactions and social context. The process that led up to what may be sensed as a sudden insight will probably have included at least three diverse, but testable elements: 1) divergent thinking, including ideational fluency or cognitive flexibility, which is the cognitive executive function that underlies the ability to visualize and accept many ideas related to a problem; 2) convergent thinking or the application of inhibitory control to focus and mentally evaluate ideas; and 3) analogical thinking, the ability to understand a novel idea in terms of one that is already familiar.

LITERATURE REVIEW

What do we know about how to teach creativity.

The possibility of teaching for creative problem solving gained credence in the 1960s with the studies of Jerome Bruner, who argued that children should be encouraged to “treat a task as a problem for which one invents an answer, rather than finding one out there in a book or on the blackboard” ( Bruner, 1965 , pp. 1013–1014). Since that time, educators and psychologists have devised programs of instruction designed to promote creativity and inventiveness in virtually every student population: pre–K, elementary, high school, and college, as well as in disadvantaged students, athletes, and students in a variety of specific disciplines (for review, see Scott et al. , 2004 ). Smith (1998) identified 172 instructional approaches that have been applied at one time or another to develop divergent thinking skills.

Some of the most convincing evidence that elements of creativity can be enhanced by instruction comes from work with young children. Bodrova and Leong (2001) developed the Tools of the Mind (Tools) curriculum to improve all of the three core mental executive functions involved in creative problem solving: cognitive flexibility, working memory, and inhibitory control. In a year-long randomized study of 5-yr-olds from low-income families in 21 preschool classrooms, half of the teachers applied the districts' balanced literacy curriculum (literacy), whereas the experimenters trained the other half to teach the same academic content by using the Tools curriculum ( Diamond et al. , 2007 ). At the end of the year, when the children were tested with a battery of neurocognitive tests including a test for cognitive flexibility ( Durston et al. , 2003 ; Davidson et al. , 2006 ), those exposed to the Tools curriculum outperformed the literacy children by as much as 25% ( Diamond et al. , 2007 ). Although the Tools curriculum and literacy program were similar in academic content and in many other ways, they differed primarily in that Tools teachers spent 80% of their time explicitly reminding the children to think of alternative ways to solve a problem and building their executive function skills.

Teaching older students to be innovative also demands instruction that explicitly promotes creativity but is rigorously content-rich as well. A large body of research on the differences between novice and expert cognition indicates that creative thinking requires at least a minimal level of expertise and fluency within a knowledge domain ( Bransford et al. , 2000 ; Crawford and Brophy, 2006 ). What distinguishes experts from novices, in addition to their deeper knowledge of the subject, is their recognition of patterns in information, their ability to see relationships among disparate facts and concepts, and their capacity for organizing content into conceptual frameworks or schemata ( Bransford et al. , 2000 ; Sawyer, 2005 ).

Such expertise is often lacking in the traditional classroom. For students attempting to grapple with new subject matter, many kinds of problems that are presented in high school or college courses or that arise in the real world can be solved merely by applying newly learned algorithms or procedural knowledge. With practice, problem solving of this kind can become routine and is often considered to represent mastery of a subject, producing what Sternberg refers to as “pseudoexperts” ( Sternberg, 2003 ). But beyond such routine use of content knowledge the instructor's goal must be to produce students who have gained the HOCS needed to apply, analyze, synthesize, and evaluate knowledge ( Crowe et al. , 2008 ). The aim is to produce students who know enough about a field to grasp meaningful patterns of information, who can readily retrieve relevant knowledge from memory, and who can apply such knowledge effectively to novel problems. This condition is referred to as adaptive expertise ( Hatano and Ouro, 2003 ; Schwartz et al. , 2005 ). Instead of applying already mastered procedures, adaptive experts are able to draw on their knowledge to invent or adapt strategies for solving unique or novel problems within a knowledge domain. They are also able, ideally, to transfer conceptual frameworks and schemata from one domain to another (e.g., Schwartz et al. , 2005 ). Such flexible, innovative application of knowledge is what results in inventive or creative solutions to problems ( Crawford and Brophy, 2006 ; Crawford, 2007 ).

Promoting Creative Problem Solving in the College Classroom

In most college courses, instructors teach science primarily through lectures and textbooks that are dominated by facts and algorithmic processing rather than by concepts, principles, and evidence-based ways of thinking. This is despite ample evidence that many students gain little new knowledge from traditional lectures ( Hrepic et al. , 2007 ). Moreover, it is well documented that these methods engender passive learning rather than active engagement, boredom instead of intellectual excitement, and linear thinking rather than cognitive flexibility (e.g., Halpern and Hakel, 2003 ; Nelson, 2008 ; Perkins and Wieman, 2008 ). Cognitive flexibility, as noted, is one of the three core mental executive functions involved in creative problem solving ( Ausubel, 1963 , 2000 ). The capacity to apply ideas creatively in new contexts, referred to as the ability to “transfer” knowledge (see Mestre, 2005 ), requires that learners have opportunities to actively develop their own representations of information to convert it to a usable form. Especially when a knowledge domain is complex and fraught with ill-structured information, as in a typical introductory college biology course, instruction that emphasizes active-learning strategies is demonstrably more effective than traditional linear teaching in reducing failure rates and in promoting learning and transfer (e.g., Freeman et al. , 2007 ). Furthermore, there is already some evidence that inclusion of creativity training as part of a college curriculum can have positive effects. Hunsaker (2005) has reviewed a number of such studies. He cites work by McGregor (2001) , for example, showing that various creativity training programs including brainstorming and creative problem solving increase student scores on tests of creative-thinking abilities.

Model creativity—students develop creativity when instructors model creative thinking and inventiveness.

Repeatedly encourage idea generation—students need to be reminded to generate their own ideas and solutions in an environment free of criticism.

Cross-fertilize ideas—where possible, avoid teaching in subject-area boxes: a math box, a social studies box, etc; students' creative ideas and insights often result from learning to integrate material across subject areas.

Build self-efficacy—all students have the capacity to create and to experience the joy of having new ideas, but they must be helped to believe in their own capacity to be creative.

Constantly question assumptions—make questioning a part of the daily classroom exchange; it is more important for students to learn what questions to ask and how to ask them than to learn the answers.

Imagine other viewpoints—students broaden their perspectives by learning to reflect upon ideas and concepts from different points of view.

How Is Creativity Related to Critical Thinking and the Higher-Order Cognitive Skills?

It is not uncommon to associate creativity and ingenuity with scientific reasoning ( Sawyer, 2005 ; 2006 ). When instructors apply scientific teaching strategies ( Handelsman et al. , 2004 ; DeHaan, 2005 ; Wood, 2009 ) by using instructional methods based on learning research, according to Ebert-May and Hodder ( 2008 ), “we see students actively engaged in the thinking, creativity, rigor, and experimentation we associate with the practice of science—in much the same way we see students learn in the field and in laboratories” (p. 2). Perkins and Wieman (2008) note that “To be successful innovators in science and engineering, students must develop a deep conceptual understanding of the underlying science ideas, an ability to apply these ideas and concepts broadly in different contexts, and a vision to see their relevance and usefulness in real-world applications … An innovator is able to perceive and realize potential connections and opportunities better than others” (pp. 181–182). The results of Scott et al. (2004) suggest that nontraditional courses in science that are based on constructivist principles and that use strategies of scientific teaching to promote the HOCS and enhance content mastery and dexterity in scientific thinking ( Handelsman et al. , 2007 ; Nelson, 2008 ) also should be effective in promoting creativity and cognitive flexibility if students are explicitly guided to learn these skills.

Creativity is an essential element of problem solving ( Mumford et al. , 1991 ; Runco, 2004 ) and of critical thinking ( Abrami et al. , 2008 ). As such, it is common to think of applications of creativity such as inventiveness and ingenuity among the HOCS as defined in Bloom's taxonomy ( Crowe et al. , 2008 ). Thus, it should come as no surprise that creativity, like other elements of the HOCS, can be taught most effectively through inquiry-based instruction, informed by constructivist theory ( Ausubel, 1963 , 2000 ; Duch et al. , 2001 ; Nelson, 2008 ). In a survey of 103 instructors who taught college courses that included creativity instruction, Bull et al. (1995) asked respondents to rate the importance of various course characteristics for enhancing student creativity. Items ranking high on the list were: providing a social climate in which students feels safe, an open classroom environment that promotes tolerance for ambiguity and independence, the use of humor, metaphorical thinking, and problem defining. Many of the responses emphasized the same strategies as those advanced to promote creative problem solving (e.g., Mumford et al. , 1991 ; McFadzean, 2002 ; Treffinger and Isaksen, 2005 ) and critical thinking ( Abrami et al. , 2008 ).

In a careful meta-analysis, Scott et al. (2004) examined 70 instructional interventions designed to enhance and measure creative performance. The results were striking. Courses that stressed techniques such as critical thinking, convergent thinking, and constraint identification produced the largest positive effect sizes. More open techniques that provided less guidance in strategic approaches had less impact on the instructional outcomes. A striking finding was the effectiveness of being explicit; approaches that clearly informed students about the nature of creativity and offered clear strategies for creative thinking were most effective. Approaches such as social modeling, cooperative learning, and case-based (project-based) techniques that required the application of newly acquired knowledge were found to be positively correlated to high effect sizes. The most clear-cut result to emerge from the Scott et al. (2004) study was simply to confirm that creativity instruction can be highly successful in enhancing divergent thinking, problem solving, and imaginative performance. Most importantly, of the various cognitive processes examined, those linked to the generation of new ideas such as problem finding, conceptual combination, and idea generation showed the greatest improvement. The success of creativity instruction, the authors concluded, can be attributed to “developing and providing guidance concerning the application of requisite cognitive capacities … [and] a set of heuristics or strategies for working with already available knowledge” (p. 382).

Many of the scientific teaching practices that have been shown by research to foster content mastery and HOCS, and that are coming more widely into use, also would be consistent with promoting creativity. Wood (2009) has recently reviewed examples of such practices and how to apply them. These include relatively small modifications of the traditional lecture to engender more active learning, such as the use of concept tests and peer instruction ( Mazur, 1996 ), Just-in-Time-Teaching techniques ( Novak et al. , 1999 ), and student response systems known as “clickers” ( Knight and Wood, 2005 ; Crossgrove and Curran, 2008 ), all designed to allow the instructor to frequently and effortlessly elicit and respond to student thinking. Other strategies can transform the lecture hall into a workshop or studio classroom ( Gaffney et al. , 2008 ) where the teaching curriculum may emphasize problem-based (also known as project-based or case-based) learning strategies ( Duch et al. , 2001 ; Ebert-May and Hodder, 2008 ) or “community-based inquiry” in which students engage in research that enhances their critical-thinking skills ( Quitadamo et al. , 2008 ).

Another important approach that could readily subserve explicit creativity instruction is the use of computer-based interactive simulations, or “sims” ( Perkins and Wieman, 2008 ) to facilitate inquiry learning and effective, easy self-assessment. An example in the biological sciences would be Neurons in Action ( http://neuronsinaction.com/home/main ). In such educational environments, students gain conceptual understanding of scientific ideas through interactive engagement with materials (real or virtual), with each other, and with instructors. Following the tenets of scientific teaching, students are encouraged to pose and answer their own questions, to make sense of the materials, and to construct their own understanding. The question I pose here is whether an additional focus—guiding students to meet these challenges in a context that explicitly promotes creativity—would enhance learning and advance students' progress toward adaptive expertise?

Assessment of Creativity

To teach creativity, there must be measurable indicators to judge how much students have gained from instruction. Educational programs intended to teach creativity became popular after the Torrance Tests of Creative Thinking (TTCT) was introduced in the 1960s ( Torrance, 1974 ). But it soon became apparent that there were major problems in devising tests for creativity, both because of the difficulty of defining the construct and because of the number and complexity of elements that underlie it. Tests of intelligence and other personality characteristics on creative individuals revealed a host of related traits such as verbal fluency, metaphorical thinking, flexible decision making, tolerance of ambiguity, willingness to take risks, autonomy, divergent thinking, self-confidence, problem finding, ideational fluency, and belief in oneself as being “creative” ( Barron and Harrington, 1981 ; Tardif and Sternberg, 1988 ; Runco and Nemiro, 1994 ; Snyder et al. , 2004 ). Many of these traits have been the focus of extensive research of recent decades, but, as noted above, creativity is not defined by any one trait; there is now reason to believe that it is the interplay among the cognitive and affective processes that underlie inventiveness and the ability to find novel solutions to a problem.

Although the early creativity researchers recognized that assessing divergent thinking as a measure of creativity required tests for other underlying capacities ( Guilford, 1950 ; Torrance, 1974 ), these workers and their colleagues nonetheless believed that a high score for divergent thinking alone would correlate with real creative output. Unfortunately, no such correlation was shown ( Barron and Harrington, 1981 ). Results produced by many of the instruments initially designed to measure various aspects of creative thinking proved to be highly dependent on the test itself. A review of several hundred early studies showed that an individual's creativity score could be affected by simple test variables, for example, how the verbal pretest instructions were worded ( Barron and Harrington, 1981 , pp. 442–443). Most scholars now agree that divergent thinking, as originally defined, was not an adequate measure of creativity. The process of creative thinking requires a complex combination of elements that include cognitive flexibility, memory control, inhibitory control, and analogical thinking, enabling the mind to free-range and analogize, as well as to focus and test.

More recently, numerous psychometric measures have been developed and empirically tested (see Plucker and Renzulli, 1999 ) that allow more reliable and valid assessment of specific aspects of creativity. For example, the creativity quotient devised by Snyder et al. (2004) tests the ability of individuals to link different ideas and different categories of ideas into a novel synthesis. The Wallach–Kogan creativity test ( Wallach and Kogan, 1965 ) explores the uniqueness of ideas associated with a stimulus. For a more complete list and discussion, see the Creativity Tests website ( www.indiana.edu/∼bobweb/Handout/cretv_6.html ).

The most widely used measure of creativity is the TTCT, which has been modified four times since its original version in 1966 to take into account subsequent research. The TTCT-Verbal and the TTCT-Figural are two versions ( Torrance, 1998 ; see http://ststesting.com/2005giftttct.html ). The TTCT-Verbal consists of five tasks; the “stimulus” for each task is a picture to which the test-taker responds briefly in writing. A sample task that can be viewed from the TTCT Demonstrator website asks, “Suppose that people could transport themselves from place to place with just a wink of the eye or a twitch of the nose. What might be some things that would happen as a result? You have 3 min.” ( www.indiana.edu/∼bobweb/Handout/d3.ttct.htm ).

In the TTCT-Figural, participants are asked to construct a picture from a stimulus in the form of a partial line drawing given on the test sheet (see example below; Figure 1 ). Specific instructions are to “Add lines to the incomplete figures below to make pictures out of them. Try to tell complete stories with your pictures. Give your pictures titles. You have 3 min.” In the introductory materials, test-takers are urged to “… think of a picture or object that no one else will think of. Try to make it tell as complete and as interesting a story as you can …” ( Torrance et al. , 2008 , p. 2).

Figure 1. Sample figural test item from the TTCT Demonstrator website ( www.indiana.edu/∼bobweb/Handout/d3.ttct.htm ).

How would an instructor in a biology course judge the creativity of students' responses to such an item? To assist in this task, the TTCT has scoring and norming guides ( Torrance, 1998 ; Torrance et al. , 2008 ) with numerous samples and responses representing different levels of creativity. The guides show sample evaluations based upon specific indicators such as fluency, originality, elaboration (or complexity), unusual visualization, extending or breaking boundaries, humor, and imagery. These examples are easy to use and provide a high degree of validity and generalizability to the tests. The TTCT has been more intensively researched and analyzed than any other creativity instrument, and the norming samples have longitudinal validations and high predictive validity over a wide age range. In addition to global creativity scores, the TTCT is designed to provide outcome measures in various domains and thematic areas to allow for more insightful analysis ( Kaufman and Baer, 2006 ). Kim (2006) has examined the characteristics of the TTCT, including norms, reliability, and validity, and concludes that the test is an accurate measure of creativity. When properly used, it has been shown to be fair in terms of gender, race, community status, and language background. According to Kim (2006) and other authorities in the field ( McIntyre et al. , 2003 ; Scott et al. , 2004 ), Torrance's research and the development of the TTCT have provided groundwork for the idea that creative levels can be measured and then increased through instruction and practice.

SCIENTIFIC TEACHING TO PROMOTE CREATIVITY

How could creativity instruction be integrated into scientific teaching.

Guidelines for designing specific course units that emphasize HOCS by using strategies of scientific teaching are now available from the current literature. As an example, Karen Cloud-Hansen and colleagues ( Cloud-Hansen et al. , 2008 ) describe a course titled, “Ciprofloxacin Resistance in Neisseria gonorrhoeae .” They developed this undergraduate seminar to introduce college freshmen to important concepts in biology within a real-world context and to increase their content knowledge and critical-thinking skills. The centerpiece of the unit is a case study in which teams of students are challenged to take the role of a director of a local public health clinic. One of the county commissioners overseeing the clinic is an epidemiologist who wants to know “how you plan to address the emergence of ciprofloxacin resistance in Neisseria gonorrhoeae ” (p. 304). State budget cuts limit availability of expensive antibiotics and some laboratory tests to patients. Student teams are challenged to 1) develop a plan to address the medical, economic, and political questions such a clinic director would face in dealing with ciprofloxacin-resistant N. gonorrhoeae ; 2) provide scientific data to support their conclusions; and 3) describe their clinic plan in a one- to two-page referenced written report.

Throughout the 3-wk unit, in accordance with the principles of problem-based instruction ( Duch et al. , 2001 ), course instructors encourage students to seek, interpret, and synthesize their own information to the extent possible. Students have access to a variety of instructional formats, and active-learning experiences are incorporated throughout the unit. These activities are interspersed among minilectures and give the students opportunities to apply new information to their existing base of knowledge. The active-learning activities emphasize the key concepts of the minilectures and directly confront common misconceptions about antibiotic resistance, gene expression, and evolution. Weekly classes include question/answer/discussion sessions to address student misconceptions and 20-min minilectures on such topics as antibiotic resistance, evolution, and the central dogma of molecular biology. Students gather information about antibiotic resistance in N. gonorrhoeae , epidemiology of gonorrhea, and treatment options for the disease, and each team is expected to formulate a plan to address ciprofloxacin resistance in N. gonorrhoeae .

In this project, the authors assessed student gains in terms of content knowledge regarding topics covered such as the role of evolution in antibiotic resistance, mechanisms of gene expression, and the role of oncogenes in human disease. They also measured HOCS as gains in problem solving, according to a rubric that assessed self-reported abilities to communicate ideas logically, solve difficult problems about microbiology, propose hypotheses, analyze data, and draw conclusions. Comparing the pre- and posttests, students reported significant learning of scientific content. Among the thinking skill categories, students demonstrated measurable gains in their ability to solve problems about microbiology but the unit seemed to have little impact on their more general perceived problem-solving skills ( Cloud-Hansen et al. , 2008 ).

What would such a class look like with the addition of explicit creativity-promoting approaches? Would the gains in problem-solving abilities have been greater if during the minilectures and other activities, students had been introduced explicitly to elements of creative thinking from the Sternberg and Williams (1998) list described above? Would the students have reported greater gains if their instructors had encouraged idea generation with weekly brainstorming sessions; if they had reminded students to cross-fertilize ideas by integrating material across subject areas; built self-efficacy by helping students believe in their own capacity to be creative; helped students question their own assumptions; and encouraged students to imagine other viewpoints and possibilities? Of most relevance, could the authors have been more explicit in assessing the originality of the student plans? In an experiment that required college students to develop plans of a different, but comparable, type, Osborn and Mumford (2006) created an originality rubric ( Figure 2 ) that could apply equally to assist instructors in judging student plans in any course. With such modifications, would student gains in problem-solving abilities or other HOCS have been greater? Would their plans have been measurably more imaginative?

Figure 2. Originality rubric (adapted from Osburn and Mumford, 2006 , p. 183).

Answers to these questions can only be obtained when a course like that described by Cloud-Hansen et al. (2008) is taught with explicit instruction in creativity of the type I described above. But, such answers could be based upon more than subjective impressions of the course instructors. For example, students could be pretested with items from the TTCT-Verbal or TTCT-Figural like those shown. If, during minilectures and at every contact with instructors, students were repeatedly reminded and shown how to be as creative as possible, to integrate material across subject areas, to question their own assumptions and imagine other viewpoints and possibilities, would their scores on TTCT posttest items improve? Would the plans they formulated to address ciprofloxacin resistance become more imaginative?

Recall that in their meta-analysis, Scott et al. (2004) found that explicitly informing students about the nature of creativity and offering strategies for creative thinking were the most effective components of instruction. From their careful examination of 70 experimental studies, they concluded that approaches such as social modeling, cooperative learning, and case-based (project-based) techniques that required the application of newly acquired knowledge were positively correlated with high effect sizes. The study was clear in confirming that explicit creativity instruction can be successful in enhancing divergent thinking and problem solving. Would the same strategies work for courses in ecology and environmental biology, as detailed by Ebert-May and Hodder (2008) , or for a unit elaborated by Knight and Wood (2005) that applies classroom response clickers?

Finally, I return to my opening question with the fictional Dr. Dunne. Could a weekly brainstorming “invention session” included in a course like those described here serve as the site where students are introduced to concepts and strategies of creative problem solving? As frequently applied in schools of engineering ( Paulus and Nijstad, 2003 ), brainstorming provides an opportunity for the instructor to pose a problem and to ask the students to suggest as many solutions as possible in a brief period, thus enhancing ideational fluency. Here, students can be encouraged explicitly to build on the ideas of others and to think flexibly. Would brainstorming enhance students' divergent thinking or creative abilities as measured by TTCT items or an originality rubric? Many studies have demonstrated that group interactions such as brainstorming, under the right conditions, can indeed enhance creativity ( Paulus and Nijstad, 2003 ; Scott et al. , 2004 ), but there is little information from an undergraduate science classroom setting. Intellectual Ventures, a firm founded by Nathan Myhrvold, the creator of Microsoft's Research Division, has gathered groups of engineers and scientists around a table for day-long sessions to brainstorm about a prearranged topic. Here, the method seems to work. Since it was founded in 2000, Intellectual Ventures has filed hundreds of patent applications in more than 30 technology areas, applying the “invention session” strategy ( Gladwell, 2008 ). Currently, the company ranks among the top 50 worldwide in number of patent applications filed annually. Whether such a technique could be applied successfully in a college science course will only be revealed by future research.

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Submitted: 31 December 2008 Revised: 14 May 2009 Accepted: 28 May 2009

© 2009 by The American Society for Cell Biology

April 11, 2017

The Science of Problem-Solving

It turns out practices that might seem a little odd—like talking to yourself—can be pretty effective

By Ulrich Boser

justgrimes Flickr   (CC BY-SA 2.0)   

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

For Gurpreet Dhaliwal, just about every decision is a potential opportunity for effective problem solving. What route should he take into the office? Should Dhaliwal write his research paper today or next week? "We all do problem solving all day long," Dhaliwal told me.

An emergency medicine physician, Dhaliwal is one of the leaders in a field known as clinical reasoning , a type of applied problem solving. In recent years, Dhaliwal has mapped out a better way to solve thorny issues, and he believes that his problem solving approach can be applied to just about any field from knitting to chemistry.

For most of us, problem solving is one of those everyday activities that we do without much thought. But it turns out that many common approaches like brainstorming don’t have much research behind them. In contrast, practices that might seem a little odd—like talking to yourself —can be pretty effective.

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I came across the new research on problem solving as part of my reporting on a book on the science of learning, and it was mathematician George Polya who first established the field, detailing a four-step approach to cracking enduring riddles.

Credit: Ulrich Boser

For Polya, the first phase of problem solving is “understanding.” In this phase, people should look to find the core idea behind a problem. “You have to understand the problem,” Polya argued. “What is the unknown? What are the data?”

The second phase is “devising a plan,” in which people map out how they’d address the problem. “Find the connection between the data and the unknown,” Polya counseled. 

The third phase of problem solving is “carrying out the plan.” This is a matter of doing—and vetting: “Can you prove that it is correct?”

The final phase for Polya is “looking back.” Or learning from the solution: People should "consolidate their knowledge.”

While Dhaliwal broadly follows this four-step method, he stresses that procedures are not enough. While a focused method is helpful, thorny issues don’t always fit nicely into categories.

This idea is clear in medicine. After all, symptoms rarely match up perfectly with an illness. Dizziness can be the signal of something serious—or a symptom of a lack of sleep. “What is tricky is to figure out what’s signal and what’s noise,” Dhaliwal told me.

In this regard, Dhaliwal argues that what’s at the heart of effective problem solving is making a robust connection between the problem and the solution. "Problem solving is part craft and part science, " Dhaliwal says, a type of "matching exercise. "

To get a sense of Dhaliwal’s approach, I once watched him solve a perplexing case. It was at a medical conference, and Dhaliwal stood at a dais as a fellow doctor explained the case: Basically, a man came into ER one day—let’s call him Andreas—and he spat up blood, could not breath very well, and had a slight fever.

At the start of the process, Dhaliwal recommends developing a one-sentence description of the problem. "It’s like a good Google search,” he said. “You want a concise summary,” and in this case, it was: Sixty-eight-year-old man with hemoptysis, or coughing up blood.

Dhaliwal also makes a few early generalizations, and he thought that Andreas might have a lung infection or an autoimmune problem. There wasn’t enough data to offer any sort of reliable conclusion, though, and really Dhaliwal was just gathering information.

Then came an x-ray, an HIV test, and as each bit of evidence rolled in, Dhaliwal detailed various scenarios, assembling the data in different ways. “To diagnosis, sometimes we are trying to lump, and sometimes trying to split,” he said.

Dhaliwal’s eyes flashed, for instance, when it became apparent that Andreas had worked in a fertilizer factory. It meant that Andreas was exposed to noxious chemicals, and for a while, it seemed like a toxic substance was at the root of Andreas’s illness.

Dhaliwal had a few strong pieces of evidence that supported the theory including some odd-looking red blood cells. But Dhaliwal wasn't comfortable with the level of proof. “I'm like an attorney presenting in a court of law,” Dhaliwal told me. “I want evidence.”

As the case progressed, Dhaliwal came across a new detail, and there was a growth in the heart. This shifted the diagnosis, knocking out the toxic chemical angle because it doesn't spark tumors.

Eventually, Dhaliwal uncovered a robust pattern, diagnosing Andreas with a cardiac angiosarcoma, or heart cancer. The pattern best explained the problem. “Diagnosing often comes down the ability to pull things together,” he said.

Dhaliwal doesn’t always get the right answer. But at the same time, it was clear that a more focused approach to problem solving can make a clear difference. If we’re more aware of how we approach an issue, we are better able to resolve the issue.

This idea explains why people who talk to themselves are more effective at problem solving. Self-queries—like is there enough evidence? —help us think through an issue.

As for Dhaliwal, he had yet another problem to solve after his diagnosis of Andreas: Should he take an Uber to the airport? Or should he grab a cab? After a little thought, Dhaliwal decided on an Uber. It was likely to be cheaper and equally comfortable. In other words, it was the solution that best matched the problem.

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Intervention based on science of reading and math boosts comprehension and word problem-solving skills

by University of Kansas

New research from the University of Kansas has found that an intervention based on the science of reading and math effectively helped English learners boost their comprehension, visualize and synthesize information, and make connections that significantly improved their math performance.

The intervention , performed for 30 minutes twice a week for 10 weeks with 66 third-grade English language learners who displayed math learning difficulties, improved students' performance when compared to students who received general instruction. This indicates that emphasizing cognitive concepts involved in the science of reading and math are key to helping students improve, according to researchers.

"Word problem-solving is influenced by both the science of reading and the science of math. Key components include number sense, decoding, language comprehension and working memory. Utilizing direct and explicit teaching methods enhances understanding and enables students to effectively connect these skills to solve math problems . This integrated approach ensures that students are equipped with necessary tools to navigate both the linguistic and numerical demands of word problems," said Michael Orosco, professor of educational psychology at KU and lead author of the study.

The intervention incorporates comprehension strategy instruction in both reading and math, focusing and decoding, phonological awareness, vocabulary development, inferential thinking, contextualized learning and numeracy.

"It is proving to be one of the most effective evidence-based practices available for this growing population," Orosco said.

The study, co-written with Deborah Reed of the University of Tennessee, was published in the journal Learning Disabilities Research and Practice .

For the research, trained tutors implemented the intervention, developed by Orosco and colleagues based on cognitive and culturally responsive research conducted over a span of 20 years. One example of an intervention session tested in the study included a script in which a tutor examined a word problem explaining that a person made a quesadilla for his friend Mario and gave him one-fourth of it, then asked students to determine how much remained.

The tutor first asked students if they remembered a class session in which they made quesadillas and what shape they were, and demonstrated concepts by drawing a circle on the board, dividing it into four equal pieces, having students repeat terms like numerator and denominator. The tutor explains that when a question asks how much is left, subtraction is required. The students also collaborated with peers to practice using important vocabulary in sentences. The approach both helps students learn and understand mathematical concepts while being culturally responsive.

"Word problems are complex because they require translating words into mathematical equations, and this involves integrating the science of reading and math through language concepts and differentiated instruction," Orosco said. "We have not extensively tested these approaches with this group of children. However, we are establishing an evidence-based framework that aids them in developing background knowledge and connecting it to their cultural contexts."

Orosco, director of KU's Center for Culturally Responsive Educational Neuroscience, emphasized the critical role of language in word problems, highlighting the importance of using culturally familiar terms. For instance, substituting "pastry" for "quesadilla" could significantly affect comprehension for students from diverse backgrounds. Failure to grasp the initial scenario could impede subsequent problem-solving efforts.

The study proved effective in improving students' problem-solving abilities, despite covariates including an individual's basic calculation skills, fluid intelligence and reading comprehension scores. That finding is key, as while ideally all students would begin on equal footing and there would be few variations in a classroom, in reality, covariates exist and are commonplace.

The study had trained tutors deliver the intervention, and its effectiveness should be further tested with working teachers, the authors wrote. Orosco said professional development to help teachers gain the skills is necessary, and it is vital for teacher preparation programs to train future teachers with such skills as well. And helping students at the elementary level is necessary to help ensure success in future higher-level math classes such as algebra.

The research builds on Orosco and colleagues' work in understanding and improving math instruction for English learners. Future work will continue to examine the role of cognitive functions such as working memory and brain science, as well as potential integration of artificial intelligence in teaching math.

"Comprehension strategy instruction helps students make connections, ask questions, visualize, synthesize and monitor their thinking about word problems," Orosco and Reed wrote. "Finally, applying comprehension strategy instruction supports ELs in integrating their reading, language and math cognition…. Focusing on relevant language in word problems and providing collaborative support significantly improved students' solution accuracy."

Provided by University of Kansas

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