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Intuition and Insight: Two Processes That Build on Each Other or Fundamentally Differ?

Thea zander.

1 Department of Psychology, University of Basel, Basel, Switzerland

Michael Öllinger

2 Parmenides Foundation, Munich, Germany

3 Department Psychology, Ludwig-Maximilians-Universität München, Munich, Germany

Kirsten G. Volz

4 Werner Reichardt Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany

Intuition and insight are intriguing phenomena of non-analytical mental functioning: whereas intuition denotes ideas that have been reached by sensing the solution without any explicit representation of it, insight has been understood as the sudden and unexpected apprehension of the solution by recombining the single elements of a problem. By face validity, the two processes appear similar; according to a lay perspective, it is assumed that intuition precedes insight. Yet, predominant scientific conceptualizations of intuition and insight consider the two processes to differ with regard to their (dis-)continuous unfolding. That is, intuition has been understood as an experience-based and gradual process, whereas insight is regarded as a genuinely discontinuous phenomenon. Unfortunately, both processes have been investigated differently and without much reference to each other. In this contribution, we therefore set out to fill this lacuna by examining the conceptualizations of the assumed underlying cognitive processes of both phenomena, and by also referring to the research traditions and paradigms of the respective field. Based on early work put forward by Bowers et al. (1990 , 1995 ), we referred to semantic coherence tasks consisting of convergent word triads (i.e., the solution has the same meaning to all three clue words) and/or divergent word triads (i.e., the solution means something different with respect to each clue word) as an excellent kind of paradigm that may be used in the future to disentangle intuition and insight experimentally. By scrutinizing the underlying mechanisms of intuition and insight, with this theoretical contribution, we hope to launch lacking but needed experimental studies and to initiate scientific cooperation between the research fields of intuition and insight that are currently still separated from each other.

Introduction

There are situations, in which decision makers arrive at an idea or a decision not by analytically inferring the solution but by either sensing the correct solution without being able to give reasons for it, or by realizing the solution all of a sudden without being able to report on the solution process. Roughly, the former phenomenon has been called intuition, the latter insight. Both have fascinated the public as well as the scientific audience.

Here are two historical cases that illustrate the two phenomena ( Gladwell, 2005 ; Mclean, as cited in Klein and Jarosz, 2011 ): The first is known as the Getty kouros and happened to the J. Paul Getty Museum in Los Angeles at the end of the 20th century. The museum was offered to add an over-life-sized statue in form of a kouros – allegedly from Ancient Greece, and thus several millions worth – to its art collection. Before the contract could be concluded, several experts set out to assure the authenticity of the statue and its origin thereby using a substantial number of high-tech methods for their analyses. After a year of thorough inspection, the experts reached the conclusion that the statue was authentic. At the same time, the former curator of the Metropolitan Museum of Art in New York, by chance, cast a glance at the artwork and spontaneously raised doubts regarding its authenticity. Thereupon, other men of renown who were asked for their spontaneous assessment of the kouros, also reported that they felt that something was wrong with it – without being able to tell the reason for this impression (cf. Gladwell, 2005 ). Interestingly, up to now, it could not be entirely cleared whether the statue stems from Ancient Greece or whether it is a modern forgery. Yet, the curator – instantaneously “feeling” that something was wrong and acting upon this impression although not being able to name a specific reason – is a paramount example of what it means to have an intuition being strong enough to act accordingly.

For an example of a sudden insight into the solution of a complex problem, consider Wagner Dodge, a smokejumper who survived the Mann Gulch Fire in August 1949 (Mclean, as cited in Klein and Jarosz, 2011 ). On a very hot day, a fire broke out in Mann Gulch, a canyon near Helena in Montana. Sixteen smokejumpers were flown close to the fire in order to extinguish it. After they had parachuted out of the aircraft, they realized that the fire was much worse than expected: They faced an uncontrollable blaze. The biggest problem was that they were in the danger of being entrapped by the fire. They could not escape and thus their lives were immediately threatened. For a moment they were desperately helpless and bustled around without a plan. They faced an impasse : well-known routines would not bring them forward and they might be caught in a mental set , that is, the tendency to try to solve a problem based on previous successful solution attempts to similar kinds of problems that are inefficient or cannot be transferred to the problem at hand (see Luchins and Luchins, 1959 , as well as Öllinger et al., 2008 ). After a while, all at once, Wagner Dodge had the sudden idea to ignite an “escape fire” ahead of the group (i.e., he had a sudden aha-experience ). Although he had never heard of such a possibility, he abruptly realized that when he could quickly stub an area of vegetation, the blaze would have no basis to continue when arriving at the cinder. He put his idea into action, ignited an additional fire and stepped into the middle of the newly burnt area. This way, he could save his life; the other smokejumpers who did not trust him lost their lives in the fire. Today, escape fires belong to the standard practice of fire services in the wild (Mclean, as cited in Klein and Jarosz, 2011 ).

Based on these examples, both phenomena – intuition and insight – may be conceived of as non-analytical thought processes that result in certain behavior that is not based on an exclusively deliberate and stepwise search for a solution. Non-analytical thought means a thought process in which no deliberate deduction takes place: individuals are not engaged in the consecutive testing of the obvious and/or typical routes to solution that define deliberate analysis. Instead, intuitions are characterized by the decision maker feeling out the solution without an available, tangible explanation for it; insights are characterized by the fact that the solution suddenly and unexpectedly pops into the mind of the decision maker or problem solver being instantaneously self-evident. Despite these apparent similarities of the two phenomena, intuition and insight have been conceptualized rather differently in the scientific literature up to now with regard to the underlying cognitive mechanisms as well as to the experimental designs routinely being used to gain empirical evidence. The aim of our contribution is therefore to scrutinize the similarities and differences of the cognitive mechanisms underlying intuition and insight by drawing on and extending early ideas by Bowers et al. (1990 , 1995 ). The gripping question is whether intuition and insight are two qualitatively distinct phenomena, appearing similar only by face validity, or whether they are indeed similar/related and may only unfold on different levels of processing. To address this question, we draw on the latest contributions in the field and include recent research findings that have not been available in Bowers et al. (1990 , 1995 ) time.

First, we will give an overview of predominant definitions of intuition and insight from a cognitive-psychological perspective. Second, we will elaborate on the underlying cognitive processes of both phenomena, thereby aiming to pin down similarities and differences. Both, similarities and differences will be addressed against the background of the research history of intuition and insight as well as in light of predominant, experimental paradigms that have been used to investigate the two phenomena. The paper ends by outlining open questions and highlighting future directions in scientific research that may progress our understanding of the underlying cognitive processes of intuition and insight (as well as on their relatedness).

Defining Intuition and Insight

Theoretical characterization of intuition.

Although most people “intuitively” know what an intuition is, the scientific community is split over its definition as well as its conceptualization. Despite disagreement about any definition, common ground is that intuition is an experienced-based process resulting in a spontaneous tendency toward a hunch or a hypothesis ( Bowers et al., 1990 ; Volz and Zander, 2014 ). Taking all major definitions into consideration, it is possible to distil certain characteristics that prominent definitions of intuition have in common ( Glöckner and Witteman, 2010 ; Volz and Zander, 2014 ).

Firstly, there is the aspect of non-conscious processing , which means that intuition occurs with very little awareness about the underlying cognitive processes so that people are mostly not able to report on these. Yet, intuitive processes can partly or completely be made conscious at some point in the entire judgmental process (e.g., Gigerenzer, 2008 ). In this regard, intuitive processing is not directly conscious or non-conscious, but can be viewed as reflecting cognitive processing on the fringe of human consciousness ( Mangan, 1993 , 2001 , 2015 ; Norman, 2002 , 2016 ; Price, 2002 ; Norman et al., 2006 , 2010 ). Secondly, there is the aspect of automaticity or uncontrollability . Intuitive processing appears in the form of spontaneous and instantaneous ideas or hunches that cannot be intentionally controlled in the way that they cannot be neither intentionally evoked nor ignored (e.g., Topolinski and Strack, 2008 ). The unintentional nature of intuition implies that intuition comes along without attentional effort and thus intuitive processing has been described as fast and effortless (e.g., Hogarth, 2001 ). Thirdly, there is the aspect of experientiality . Intuitive processing is based on tacit knowledge that has been acquired without attention during a person’s life and is thus fueled by it (e.g., Bowers et al., 1990 ). In combination these aspects result in the subjective experience of “knowing without knowing why” as Claxton (1998 , p. 217) put it. Lastly, there is the aspect of the initiation of action . The non-conscious, experience-based, and unintentional process finally results in a strong tendency toward a hunch, which serves as a go-signal that is strong enough to initiate action. As a result, people act in accordance with their intuitive impression or feeling (e.g., Gigerenzer, 2008 ). For a more detailed overview of the different aspects, consult Glöckner and Witteman (2010) or Volz and Zander (2014) .

In line with these aspects, Gigerenzer (2008) has focused, inter alia, on the experiential basis of intuition and states that intuition may hardly be possible without pre-existing knowledge and experiences. To revert to the example of the Getty kouros, the interplay of the given (visible) information was dissonant for someone who had seen lots of antique statues before; a beginner to the field may have arrived at a completely different judgment. By intuitively apprehending the situation, the curator relied on specific long-term-memory content that had been primarily acquired by studying, analyzing, and reflecting about a great number of statues resulting in associative and unattended learning. Volz and Zander (2014) refer to this kind of memory content as tacitly (in)formed cue-criterion relationships . On this view, different environmental cues can have different predictive power with respect to the criterion at hand; the situational validity of the cues will moderate whether the cue is used outright. In the above example, the curator judged the grade of authenticity of the kouros (criterion) from the subjective impression that the statue’s outer appearance had on him (cue). By doing this, the curator could not only rely on the given information (i.e., the visible kouros), but had to non-consciously activate further relevant knowledge from memory, that is to activate associatively learned cue-criterion relationships. Thus, the mental representation constructed during intuitive processing goes beyond the existing, perceivable information. Consequently, the curator’s feeling of unease when having a look at the statue resulted from an incomplete cue-criterion relationship that was taken as diagnostic for the assessment of the statue’s authenticity.

In addition to the aspect of experientiality and the unconscious read-out of implicitly learned cue-criterion relationships, Gigerenzer (2008) describes intuition as felt knowledge that aids decision making not only in cases, in which the decision maker already has a huge amount of prior experiences with a particular situation, but also when time and cognitive capacity is limited. According to the author, shadowy situations – either caused by a blurry sensory input that is only hardly detectable, or by the temporary non-availability of necessary information about the individual decisional components, which does not allow for foreseeing all consequences of a decision – foster intuitive processing. Intuition then manifests itself in the use of certain heuristics that may form highly successful, cognitive shortcuts ( Gigerenzer, 2008 ; Gigerenzer and Gaissmaier, 2011 ).

Insight and Aha-Experience

In contrast to the above elaborations on intuition, the term insight has been used to refer to the sudden and unexpected understanding of a previously incomprehensible problem or concept. In this sense, Jung-Beeman et al. (2004 , p. 506) explicate the nature of insight as “the recognition of new connections across existing knowledge.” Sometimes the solution to a difficult problem may suddenly pop out in the mind and the decision maker or problem solver may immediately recognize the complex nexuses, as formerly illustrated in the episode of the smokejumper Wagner Dodge. Problems seem to be processed and solved by re-grouping or re-combining (i.e., re-structuring) existing information in a new way so that self-imposed constraints can elegantly be relaxed ( Duncker, 1935 ; Wertheimer, 1959 ; Ohlsson, 1992 ). Wagner Dodge had prior knowledge: For instance, he knew how fires most commonly can be extinguished and that fires need vegetation or some other foundation to burn on. Furthermore, he knew about terrestrial conditions, and most important, he knew that smoke and fire could kill him. The solution to the problem occurred when he non-consciously combined all pieces of knowledge with each other in a new way so as to circumvent the fire death.

Such insightful solutions are associated with a privileged storage in long-term memory. Likewise as single trial learning. Recent studies observed a memory advantage for items that were solved by insight compared with non-insight solutions ( Danek et al., 2013 ) as well as compared with items that were not self-generated ( Kizilirmak et al., 2015 ). So, it is very likely, that Wagner Dodge never forgot how to ignite escape fires in the wild.

Yet, it has to be emphasized that an exact definition of the term insight has proven to be difficult, not least because the term insight has been used in many different ways in problem-solving research. Another hindrance is that it is very difficult to empirically operationalize the psychological construct of insight ( Knoblich and Öllinger, 2006 ), which is a similar problem as in research on intuition. Hitherto, researchers disagree whether there are certain necessary and/or sufficient conditions to determine whether an insight has occurred. For example, due to the absence of objective physiological markers indicating the occurrence of an insight, mainly reports in form of the subjective aha-experience have been used ex post to determine whether an insight has occurred during the solution process of a certain problem (e.g., Gick and Lockhardt, 1995 ; Bowden et al., 2005 ; Danek et al., 2013 ). Danek et al. (2013 , p. 2) state that the aha-experience is “the clearest defining characteristic of insight problem solving.” Topolinski and Reber (2010) define the aha-experience as the sudden and unexpected understanding of the solution, which comes with ease and is accompanied by positive affect as well as confidence in the truth of the solution. Given scientific endeavors to (objectively) pin down whether an insight had occurred, it can be summarized that insight and aha-experience have been equated. However, to date, there is disagreement whether (a) every insight is accompanied by an aha-experience, and (b) aha-experiences can only accompany insights and do never occur for presented solutions (i.e., solutions that are not generated by the individual herself; cf. Klein and Jarosz, 2011 ; Kizilirmak et al., 2015 ).

In order to help clarifying the conceptual muddle on insight, Knoblich and Öllinger (2006) proposed a classification of insight on three dimensions: first, on a phenomenological dimension, insight is opposed to a systematic and stepwise solution approach. Instead, it can be described as the sudden, unintended, and unexpected appearance of a solution idea, which is accompanied by a strong emotional component – the subjective and involuntary aha-experience. Second, on a task dimension, the literature on insight distinguishes between predefined insight problems and non-insight problems, with insight problems requiring sudden solution ideas and non-insight problems requiring a rather incremental solution approach. In case such an insight problem is solved, it is inferred that it is very likely that an insight has taken place. For example, the nine-dot problem ( Maier, 1930 ), the eight-coin problem ( Ormerod et al., 2002 ), and the candle problem ( Duncker, 1935 ) belong to such classical insight problems. However, a disadvantage of this distinction is that there are no unique criteria for an insight problem, and most of these problem could be solved with or without having an insight ( Öllinger et al., 2014 ); the most proposed criteria refer back to the subjective experience of aha, which has led to a circular definition of insight and insight problems. To circumvent this disadvantage, Bowden et al. (2005) have suggested using a class of problems that can be solved either with insight or without insight. Last, on a process dimension, recent research is concerned with the underlying cognitive mechanisms of insight and how these are different from non-insight problem solving. The predominant assumption here is that the non-conscious cognitive process of a mental set shift enables a changed representation of the problem’s elements ( Ohlsson, 1992 , 2011 ), which in turn leads to a sudden insight into the solution. For instance, in the nine-dot problem, the sudden realization that moves beyond the virtual nine-dot square are possible may lead to the relaxation of the perceptually driven boundary constraints and thus to a representational change of the problem space, which in the following enable insightful solutions (for a detailed explanation of the three dimensions consult Knoblich and Öllinger, 2006 ) 1 .

Different Research Traditions of Intuition and Insight

After having defined both cognitive phenomena, intuition and insight, it becomes obvious that both share a similarity in terms of persisting conceptual difficulties. Moreover, with regard to the subjective phenomenology they reveal a distinct picture: While intuition means to non-consciously understand environmental patterns and to act according with this first impression without being able to justify it ( Bowers et al., 1990 ), insight problem solving deals with situations in which a solution pops into a person’s mind out of the blue ( Durso et al., 1994 ). Yet, both processes can be viewed as non-analytical solution or thought processes, where no incremental search takes place. In the following, we will critically elaborate on the cognitive processes assumed to underlie intuition and insight. Starting point will be a few words on the research history of both, which allow to understand why both fields of research have developed independently over time.

The Single- vs. Dual-System View on Intuition

Intuition research has been deeply integrated in research on judgment and decision making that investigates how humans decide between alternatives and judge situations ( Plessner et al., 2008 ). Yet this took some time, in which intuition had been neglected due to its elusiveness ( Betsch, 2008 ). Now researchers agree that “intuition need not to be “magical” – it can be defined and explained scientifically” ( Sadler-Smith, 2008 , p. 1). It has to be emphasized, though, that, historically, the concept of intuition has fallen between (at least) two stools: The fast-and-frugal-heuristic approach – which sees the concept in a positive light as it serves as the basis for heuristics and thus is a valid strategy successfully be used when time and cognitive capacity is limited in a fuzzy real world ( Gigerenzer et al., 1999 ) –, and the heuristics-and-biases approach – which conceives of heuristics based on intuition as a source of erroneous and biased thinking that demonstrates human cognitive fallibility ( Kahneman and Tversky, 1974 ). Both approaches have localized the concept of intuition completely differently within human thought processes and assign qualitatively different functions to it. Today, due to their continuing, fundamentally contradictory assumptions concerning human cognition, the fast-and-frugal-heuristic approach and the heuristics-and-biases approach pit themselves against each other. Conceptually, the key difference may be that Kahneman and Tversky (1974) and Kahneman (2011) advocate a dual-system view on human thinking (intuition vs. deliberation), whereas Kruglanski and Gigerenzer (2011) and Mega et al. (2015) favor a single system view of unified processes in thinking and reasoning. Additionally, it has to be emphasized that, since interest in intuition has mainly originated from the area of judgment and decision making, implications for intuition with respect to problem solving processes (and insight) are rather hard to derive from this kind of research. This may have complicated experimentally clarifying the relationship between intuition and insight.

Intuition As Experienced-Based Perception of Coherence and As an Antecedent of Insight

To anticipate elaboration taking place later in this contribution, we mention a third approach in intuition research, which has developed independently from any dual- or single perspective and has its roots in the creativity and problem-solving literature ( Mednick, 1962 ; Bowers et al., 1995 ; Dorfman et al., 1996 ). Intuition is here conceived as the experience-based perception or recognition of environmental meaning/coherence in terms of a sensitization toward the detection of hidden patterns whose structure cannot be immediately verbalized. For example, in the different versions of the semantic coherence task originally developed by Bowers et al. (1990) , participants are asked to judge the semantic coherence of word triads and to name a forth word that may be the semantic link between the words, if it exists. Research found out that in these tasks participants are able to correctly categorize word triads as semantic coherent or incoherent – intriguingly even when they are not able to name the forth word, which is a paramount example of intuitive processing (e.g., Bowers et al., 1990 ; Bolte and Goschke, 2005 ). They rather feel the semantic link between the three words, but are not (yet) able to report on the reasons in terms of a solution concept that describes the semantic associations between the triad’s constituents. The concept of fringe consciousness ( Mangan, 1993 , 2001 , 2015 ) may be helpful to further understand intuition as the preliminary perception of environmental coherence. Price and Norman (2008) , referring to the concept of fringe consciousness, have explained that the stream of consciousness does not only include a nucleus of consciously available information , but also a non-conscious fringe that contains cognitive signals of temporarily unavailable, non-conscious information processing that is constantly going on in the background (as it accompanies cognition). These signals are continuously going on as cognitive byproducts of cognitive processes . Yet, they are only consciously experienced when attention is drawn to them ( Reber et al., 2004 ). Regarding the semantic coherence task, the product of this non-conscious processing on the fringe (i.e., the subjectively experienced intuition) is consciously perceivable, but its antecedents, direct content, and underlying processing mechanisms are outside of awareness (see also Topolinski and Strack, 2009a ).

On this view, intuitive responses have been understood as “intuitive antecedents of insight” ( Bowers et al., 1995 , p. 27). As far as we know, this has been the first (and only) conception that up to now has addressed a potential link between intuition and insight. Their early work allows deriving assumptions concerning the interaction of intuition and insight in more detail. Moreover, this conceptualization produced valuable empirical paradigms (e.g., semantic and visual coherence judgment tasks) that are particularly suited to investigate insight and its intuitive precursors. Therefore, we will elaborate on this conception later in this contribution when aiming to clarify the conceptual relationship between intuition and insight 2 .

The Special-Process vs. Nothing-Special View on Insight

In contrast, research on insightful thinking has its roots in Gestalt psychology, which investigated the integration and ordering mechanisms of human perception and problem solving (e.g., Köhler, 1921 ; Duncker, 1945 ; Metzger, 1953 ). Similar to intuition research, the research on insight problem solving is also located between two different views: The special-process view – which posits that insight problem solving involves a unique cognitive process that is qualitatively different from the processes non-insight problem solving utilizes – and the business-as-usual or nothing-special view – which assumes that mainly the same cognitive processes are involved in insight and non-insight problem solving ( Seifert et al., 1995 ). Despite these two views, scientists have been highly fascinated by the topic since its early description by the Gestalt psychologists. This great interest culminated in the seminal book “The nature of insight,” which mainly deals with the Gestalt psychologist’s view on insight problem solving ( Sternberg and Davidson, 1995 ).

Interim Summary I

In sum, both concepts, due to their elusiveness, had to fight for recognition as an established field of research. Nevertheless, regrettably, research on intuition and research on insight has developed mostly independently from each other. However, this is in sharp contrast to a lay perspective on the two phenomena, which would rather endorse the perspective that intuition and insight are inherently intertwined with intuition being an antecedent of insight (in terms of a slight previous impression on the fringe of consciousness). Yet, the two branches of research evolved from different research traditions using different scientific paradigms and, unfortunately, have referred to one another only marginally (i.e., for instance by Bowers et al., 1990 ). Therefore, we think it is now time to scrutinize the relationship between the two phenomena in greater depth. Based on Bowers et al. (1990 , 1995 ) work, we will do this by elaborating on the cognitive similarities and differences of the two phenomena and by offering preliminary process ideas on their relationship.

Differences in the Cognitive Processes Assumed to Underlie Intuition and Insight

The continuity model of intuition: intuition as a gradual process.

In the majority of conceptualizations, intuitive processing has been described within a continuity model locating intuition on one end of the continuum and insight on the other. A prominent example is the two-stage model put forward by Bowers et al. (1990) . The authors determine intuition as the preliminary perception of coherence in the environment triggered by tacit knowledge that has been acquired unintentionally during a person’s life (i.e., the cue-criterion relationships that we addressed earlier in this contribution, see also Volz and Zander, 2014 ). While tacit, or implicit, knowledge is seen as the foundation on which intuitions are based (e.g., Lieberman, 2000 ), in our view, intuition must not be regarded solely as a phenomenon of or even be equated with implicit memory processing. As Volz and Zander (2014) clarify, there are several important differences between intuition and implicit memory concerning both the format in which information is stored in memory and the kind of signal that accompanies the respective cognitive process. The fact that implicit knowledge is seen only as one component of processing is similar to the field of implicit cognition in general. Here, implicit knowledge is assumed to be supplemented and/or completed by antecedent hunches of correct solution, the subjectively experienced nearness to the solution ( Reber et al., 2007 ).

Based on Polanyi’s (1966) concept of tacit knowledge, Bowers (1984 , p. 256) defined intuition as “sensitivity and responsiveness to information that is not consciously represented, but which nevertheless guides inquiry toward productive and sometimes profound insights.” According to the author, the cognitive processing from an intuitive hunch toward an explicit insight is gradual and proceeds in two stages. In the first stage, the guiding or intuitive stage , environmental cues trigger the activation of tacit knowledge associatively connected in semantic memory, which results in an implicit perception of coherence that (yet) cannot be explained verbally. This process is characterized by the automatic spread of activation proposed by Collins and Loftus (1975) . In the second stage of intuition, the integrative or insight stage , information becomes consciously available, which is enabled via a gradual accumulation of the previously activated concepts. The previous, implicit activation becomes now explicitly represented, which may thus be also interpreted as a form of insight processing. Hence, in Bowers et al. (1990 , 1995 ) conception, intuition precedes insight in the way that explicit representations are anticipated by the sensitization of environmental pattern or structure. Yet, besides the idea of a gradual, successive accumulation of activated concepts in associative memory, unfortunately, it has remained unclear which cognitive and/or physiological conditions foster the transition from sensed intuition to justified insight.

Bowers et al. (1990) approach is not only theoretically important it also carries paradigmatic weight. In order to empirically test their model’s assumptions, the authors developed several novel paradigms (verbal as well as perceptual ones), which today, after slight revisions, belong to the standard paradigms of intuition research (e.g., Bolte and Goschke, 2005 ; Volz and von Cramon, 2006 ; Topolinski and Strack, 2009b ; Hicks et al., 2010 ; Remmers et al., 2014 ; Zander et al., 2015 ). One of them is the semantic coherence task mentioned above, consisting of word triads that can be either semantically coherent (e.g., SALT, DEEP, and FOAM) or incoherent (DREAM; BALL; BOOK). Semantic coherence is determined via a fourth word each word of the word triad’s constituents associatively hints at (e.g., SEA for the coherent triad). Participants are instructed to perform a semantic coherence judgment , that is, to indicate via button press whether a given triad is coherent or incoherent. Researchers found that people showed an above-chance discrimination between coherent and incoherent triads even when they are not able to name the forth word (e.g., Bowers et al., 1990 ; Bolte and Goschke, 2005 ). In other words, people were intuitively sensitized to the detection of coherence prior to its explicit recognition (i.e., before having an explicit insight into the underlying semantic structure). Using a similar task, which consists of up to 15 semantically target-related clue words (i.e., the Accumulated Clues Task), it could be observed that participants continuously approached the explicit representation of environmental patterns/meaning ( Bowers et al., 1990 ; Reber et al., 2007 ), which could be recently also demonstrated on a neuronal level when using the semantic coherence task ( Zander et al., 2015 ). These results are perfectly in line with Bowers et al. (1990) definition of intuition and the corresponding gradual two-stage model. As another important aspect concerning the link between intuition and insight, Bowers et al. (1990) suggested the concept of semantic convergence to differentiate between triads that are rather easily solved by non-consciously reading out the common association (i.e., convergent triads) and triads that require a reorganization of semantic associations (i.e., divergent triads; see also the section Bridging the gap between the underlying processes of insight and intuition , second part).

To put it in a nutshell, according to the continuity model, – as Bowers et al. (1990) defined and tested it by means of verbal and visual coherence tasks – intuition and insight (in terms of an explicit representation that can be verbalized) are inherently intertwined: intuition and insight build upon each other and the one can hardly occur without the other. That is, intuitive processing is the non-conscious precursor of insight and thus, intuition and insight build on each other evolving on different processing stages. Accordingly, intuition and insight are not considered qualitatively distinct or mutually exclusive. Instead a crosstalk between the two is possible and even required to some extent. Importantly, Bowers et al. (1995) noted, that a thought process that appears to be sudden on a phenomenological level (like an aha-experience) nevertheless could have continuous underlying processes that have led to the particular subjective experience. Thus, they do not exclude the existence of subjective aha-experiences accompanying the successful solution generation in their verbal tasks.

Along these lines, when investigating insights from a naturalistic perspective (i.e., in a field setting and not in controlled laboratory settings), Klein and Jarosz (2011) found out that a substantial number of insights occurred gradually and in an (non-conscious) evidence-accumulating fashion. Following the naturalistic-decision-making approach ( Zsambok and Klein, 1997 ), the authors aimed at investigating the natural occurrence of insights by analyzing a collection of reported insight incidents (comprising a radical shift in understanding) having occurred in the different domains of everyday life of different occupation (e.g., invention, firefighting, management, and the like). The authors found out that (a) impasses did not occur in each insight case, (b) not every incident of an insight was accompanied by an aha-experience, and (c) an intuitive feeling of how near the solution might be occurred in many cases before the actual solution was reached. These results indicate that insights in a naturalistic setting may differ from insights synthetically induced by the class of pre-defined insight problems (e.g., eight-coin-problem, Ormerod et al., 2002 ) according to the degree with which the solution is derived gradually. Thus, in the naturalistic setting, a continuous solution approach (as advocated in intuition research) may be adoptable.

The Discontinuity Model of Insight: Insight As the Result of a Mental Restructuring Process

Contrary to the idea of a gradual solution approach, there is the discontinuity model of problem solving: insight is strongly linked to cognitive processes that restructure mental problem representations in order to allow the generation of a solution to a complex problem. A prominent example of a discontinuity model is the representational change theory put forward by Ohlsson (1992 , 2011 ) that combines the Gestalt psychological approach (characterized by a person being unable to report conscious solution strategies, cf. Duncker, 1945 ) and the information-processing view on problem solving (characterized by a conscious search through alternatives in a problem space, which is a controllable and reportable process, cf. Newell and Simon, 1972 ). According to the representational change theory, and in sharp contrast to the two-stage model developed by Bowers et al. (1990) , prior knowledge and experiences are postulated to hamper (instead of promote) the generation of solutions since they easily turn into constraints ( Knoblich et al., 1999 ). Based on this, Ohlsson (1992) introduced the idea that an impasse, that is a “blind lane” where one is caught in wrong solution attempts finding no expedient or problem solving attempts ceases, is the precondition for a representational change that results in an insight. According to the author, a restructuring process is required, during which self-imposed constraints of the problem representation change and the problem solver obtains a “fresh look” at the problem. Problem solvers may then be able to rearrange either the individual components or the general assumptions how to solve the problem. A putative mechanism assumed to drive such restructuring processes is the relaxation of self-imposed constraints . The representational change theory became very influential; there are several studies that have tested and could corroborate its assumptions (e.g., Knoblich et al., 2001 ; Kershaw and Ohlsson, 2004 ; Öllinger et al., 2006 , 2013 ).

In an eye movement study, for example, participants were asked to transform an incorrect arithmetic statement, which is made up of Roman numbers made of matchsticks, into a correct one moving only one single matchstick. Interestingly, it could be observed that before the correct solution of difficult problems was generated, suddenly, solvers attended such problem elements of the equation (e.g., the operators) longer that they had hardly noticed before. This was taken as evidence that successful solvers overcame self-imposed constraints ( Knoblich et al., 2001 ). Research on the underlying cognition of the representational change theory could also help in understanding the subjective aha-experience as a subjective marker of insight: a recent study conducted by Danek et al. (2016) provides first evidence that the self-reported rates of aha-experiences depend on the degree of constraint relaxation that is necessary to solve the given problem. The authors found that the more constraints had to be relaxed, the less aha-experiences were reported, which was interpreted such that the execution of several necessary solution steps (that are needed to gain a representational change) minimizes or even eliminates the experience of suddenness as a key attribute of subjective aha-experiences.

Interim Summary II

To summarize, according to a discontinuity model, the cognitive processes of intuition and insight seem to be qualitatively distinct. No crosstalk between them is possible. Moreover, the first (intuitive) look on a problem resulting in a mental impasse biases the subsequent solution. To be more precise, the intuitive apprehension of a problem necessarily leads to an impasse and restructuring processes are needed so as to overcome the bias and to solve the problem. This can be demonstrated, for example, via the utilization of magic tricks in order to probe insight problem solving. To explicate, Danek et al. (2013) recently introduced a novel paradigm consisting of magic tricks to investigate the cognitive underpinnings of insight problem solving. When viewing these magic tricks, the intuitive viewing pattern, which the magician intentionally utilizes, will very likely prohibit the understanding of the trick, that is, to first impede the solution to the problem. The solution is only within reach when the intuitive apprehension of the magic-trick situation, that is the first and rapidly formed impression, can be overcome. Classical insight problems as for example the famous candle problem ( Duncker, 1935 ) utilize the same rationale.

Bridging the Gap between the Underlying Processes of Insight and Intuition

Dual-system models of thinking and reasoning.

This discontinuity approach resembles the experimental procedure in typical judgment and decision-making studies conducted within the heuristics-and-biases framework ( Kahneman, 2011 ). This framework draws on a class of psychological models that are very well known in social and cognitive psychology and are called dual-system or dual-process models (e.g., Evans and Frankish, 2009 ; Kahneman, 2011 ). These models assume two different modes of thinking, which Stanovich and West (2000) called System 1 (described as e.g., non-conscious, fast, associative, holistic, automatic, and emotional) and System 2 (described as e.g., conscious, slow, analytic, serial, controlled, and affect-free). In other words, according to dual-system models, judgments may be formed via two qualitatively distinct processes or systems – an intuitive one (System 1) or a deliberate one (System 2). The intuitive strategy, thereby, is thought to require some sort of a feeling that “tells” a person which option is the optimal one. Thus, affective feelings are here seen as a crucial component that is inherent to the entire decision process. In contrast, when thoroughly deliberating on the pros and cons of multiple options, the solution to the decision process is considered to come to mind by way of logic and exhaustively sensible considerations of probable consequences. Thus, System 2 processing is here thought to not need or even to not involve any affective contribution.

Despite the large number of contributions that support the dual-systems view both theoretically and empirically, such theories have nevertheless recently come under strong fire ( Keren and Schul, 2009 ; Kruglanski and Gigerenzer, 2011 ). The main point of criticism put forward by Keren and Schul (2009 , p. 534) is that “the different dual-system theories lack conceptual clarity, that they are based upon methodological methods that are questionable, and that they rely on insufficient (and often inadequate) empirical evidence.” Kruglanski and Gigerenzer (2011) provide a unified approach and explain that both, intuition and deliberation, rely on the same functional principles (i.e., they are based on if – then rules), which is dependent on environmental conditions. As a reply to such criticism, Evans and Stanovich (2013) recently riposted that it is overstated since such criticism refers to dual-system models as a class of purely the same theoretical assumptions. They clarify that there are indeed different assumptions and terminologies subsumed under the dual-system framework, which needs to be considered. Nevertheless, there is also neuronal evidence against the assumptions of the dual-system approach ( Mega et al., 2015 ). The authors did a functional-magnetic-resonance-imaging study and asked participants to judge either intuitively or deliberately the authenticity of emotional facial expressions. Interestingly, the authors found that intuition and deliberation recruit the same neuronal networks – a finding well in line with Kruglanski and Gigerenzer’s (2011) proposal. It can be summarized that the dual-system framework is being much debated at the moment (see also volume 8 of Perspectives on Psychological Science , 2013) and therefore, it is very likely that there will be a revised conception in the foreseeable future.

Dual-System Models and the Discontinuity Model of Insight: Intuition As the First and Biased Problem Representation

After having shortly named the key assumptions of the dual-system framework as well as potential critical points, we will continue by elaborating on why we think the experimental approach of the insight problem solving literature (e.g., Danek et al., 2013 ) is similar to the one pursued by the heuristics-and-biases framework ( Kahneman, 2011 ). A typical task used by researchers of the heuristics-and-biases approach is the bat and the ball problem . Participants are told that a bat and ball together cost $ 1.10 in total and that the bat costs $ 1 more than the ball. Then they are asked to state how much the ball costs. A vast number of experiments showed that the first “intuitive answer,” following Kahneman’s terminology, is 10 cent, but after a while of conscious deliberation (i.e., analytical thought) participants find out that the correct answer is 5 cent ( Kahneman, 2011 ). Here is employed the same principle as in the magic-trick paradigm: the first and rapidly formed judgment, which is intentionally induced by the task material, is incorrect and hampers the generation of the correct solution (here 5 cent). In terms of the representational change theory an over-constraint problem representation is activated, where a simple goal representation is set up: total sum minus bat results immediately in the cost of the ball. Overcoming these assumptions seems difficult and requires a more sophisticated goal representation that combines two sets of information: (1) bat - ball = 1 AND (2) bat + ball = 1.10 => 1 in (2) ball + ball + 1 = 1.10 => ball = 0.05).

Together, experiments from both scientific fields show that by exploiting peoples’ intuitive apprehension of a problem, the solution is precluded from the beginning. To overcome the impasse or bias, it is suggested that the problem solver may engage in restructuring the problem space or in analytic strategies so as to eventually being able to solve the problem and to arrive at the objectively correct answer. Thus, there might be a reasonable mapping of the discontinuity model to the common dual-system model: first, the intuitive system starts (whether by default first or in parallel to System 2), and will lead to an over-constrained or biased problem representation that subsequently may lead to an impasse or conflict. Essential for reaching a solution is, (i) that the problem solver or decision maker realizes that the fast initial apprehension of the problem precludes its solution and (ii) engages in a representational change to overcome the initial problem representation ( Öllinger et al., 2014 ). Since, by definition, System 2 processing is slower than System 1 processing it can smooth out the first and hasty attempts made by System 1. In the diction of dual system theorists, the analytic mind is called up when encountering an impasse or conflict and will attempt to deliberately solve the problem by applying certain rational strategies. Importantly, Systems 1 and 2, or intuition and insight, are here considered to be qualitatively different – “hare and tortoise.”

Equally important, System 1 is considered subordinate to System 2 and its hasty responses needs to be tamed (cf. Kahneman, 2011 , p. 185). Kahneman (2011 , p. 44) states: “One of the main functions of System 2 is to monitor and control thought and actions “suggested” by System 1, allowing some to be expressed directly in behavior and suppressing or modifying others.” Given such an understanding of intuition and insight, the discontinuity model may suffer from the very same conceptual problem as a dual-system account of reasoning: that is, how and by which factors is a conflict or impasse detected? “Who” eventually launches restructuring processes that are needed to overcome the error? How does restructuring of the first problem representation take place? This may be viewed as a variation of the “homunculus problem.”

Hence, within the discontinuity conception of insight, intuition is not regarded as helpful or diagnostic for the generation of a pending insight. In line with this idea, Metcalfe and Wiebe (1987) investigated feeling of warmth accompanying insight and incremental problem solving using classical insight problems and algebraic problems. They used feeling-of-warmth ratings as the assessment of how close participants intuitively felt to the solution, which was taken to indicate the subjective nearness to the solution . Interestingly, they found out that these subjective feelings of warmth differed for insight and non-insight solutions insofar that they could predict performance only on incremental algebra problems. For insight problems such intuitive feelings were lacking. Given this result, one may conclude that intuition differs from insight concerning the (introspective) access to non-conscious processing: whereas decision makers intuit the solution to a problem, people solving the problem by insight show to lack such hunches. Thus, additionally to the continuity/discontinuity distinction, insightful solutions as in contrast to intuitive ones seem to be discrete phenomena in terms of availability to awareness. However, it could be also possible that the conscious assessment of how close/far the solution is, just easier for non-insight tasks. Since non-insight tasks are well-defined insofar that there are clear starts, solution paths, and goals, which enables exact planning of the necessary steps and its order (as for example in algebraic problems). Conversely, classical insight problems may be technically well-defined (in that there is also a clear start and goal, see e.g., the famous nine-dot problem), but since the problem’s different components are unhelpfully represented in the problem solvers mental set, it is difficult or rather impossible to estimate how far/close the solution is.

Interim Summary III

As an interim summary, it may be concluded that intuition research advocates a continuity model, in which intuition and insight build upon each other in a gradual and cumulative fashion: people are non-consciously sensitized toward pattern or meaning in the environment and act accordingly (e.g., Bowers et al., 1990 ). In contrast, insight research focuses on a discontinuity model, in which the initial representation of the problem (i.e., early intuition) biases later solution attempts and has to be overcome in order to reach a solution. Here, no intuitive precursors of insight in terms of a subjectively felt nearness toward the solution are assumed. This latter model resembles famous, yet recently heavily criticized, dual-system models in judgment and decision-making research insofar as in both approaches the participants first intuitive apprehension of a problem biases its later solution.

Semantic Coherence Tasks Used in Intuition and Insight Research: Word Triads and Remote Associates

Interestingly, in the semantic domain, intuition research following Bowers et al. (1990) approach and contemporary insight research do have used similar stimuli yet with different task rationales, which could be used as an excellent starting point for necessary, and up to now lacking, common investigations. As described earlier in this contribution, in the tradition of Bowers et al. (1990 , 1995 ), typical coherence judgment tasks include semantically coherent and incoherent word triads – a task that dates back to the work of Mednick (1962) . Here, response patterns of both triad types (i.e., coherent vs. incoherent) are compared to each other. In recent research on insight problem solving, Bowden et al. (2005) presented a novel framework and a new class of problems in order to probe insight problem solving. The authors equate subjectively reported aha-experiences with insight. The authors have used word triads based on Mednick’s (1962) task to investigate the neuronal underpinnings of insight. They presented a large number of problems that can be solved either by insight or by non-insight (i.e., Aha! vs. Non-Aha!) and do not require a lot of time to be solved ( Kounios and Beeman, 2014 ). As a result they found that Aha! solutions revealed distinguish neural patterns than Non-Aha!-solutions. Unlike intuition research, they (1) only applied word triads that are principally solvable (i.e., no incoherent triads), and (2) word triads that consist of compound remote associate.

Bowers et al. (1990) , distinguished two types of triads and termed them convergent and divergent triads , respectively. For convergent triads the common associate means the same with respect to each clue word, whereas for divergent triads the common associate is more remote and changes its meaning with respect to each clue word. An example for a coherent convergent triad is SALT DEEP FOAM– SEA; and an example for a divergent triad is AGE MILE SAND– STONE. Unlike convergent triads, divergent triads are built in a way one need to detect the multiple meanings of the solution word to associate it with the meanings of the three clue words. As divergent triads may require a restructuring of the different meanings of the clues with respect to the solution, these kinds of triads could be nicely seen as an insight condition.

According to Bowden and Jung-Beeman (2007) , divergent triads are not as complex as classical insight problems, but they can nevertheless be used as a kind of insight problems. Like typical insight tasks (1) they misdirect retrieval processes (i.e., the first word of a divergent triad biases later thought toward a specific, yet wrong direction), (2) the strategy that has led to the correct solution cannot be reported by the problem solver, and (3) aha-experiences can occur.

For such divergent triads, Cranford and Moss (2012) , using a verbal protocol method, found out that there are two different types of insight problems, for which only one type shows the typical traditional characteristics of an insight. It has to be emphasized that, unlike Bowden et al. (2005) , the authors consider all three components, subjective aha-experience, impasse, and restructuring, as necessary for an insight to occur. They could show that some problems, consisting of divergent triads, could be solved via immediate insight , whereas others were solved by non-immediate or delayed insight . Interestingly, only the latter type of insights showed the supposed phases of insight. Fedor et al. (2015) detailed on this question and found that the classical insight sequence (i.e., constrained search, impasse, insight, extended search, and solution) is a rather rare event. They found that participants showed much more often fairly different insight sequences (i.e., a flexible order of the different problem-solving stages), which has to be further specified in the future. We consider this line of research ( Cranford and Moss, 2012 ; Kounios and Beeman, 2014 ; Fedor et al., 2015 ) as promising and important for future endeavors, which may initiate the common investigations of intuition and insight.

Conclusion, Open Research Questions, and Future Directions

To conclude, we set out to disentangle the underlying mechanisms of intuition and insight so as to clarify their relationship. At first sight, intuition and insight seem to be very differently conceptualized: while the intuition literature favors a continuity model, insight has been described within in a discontinuity model. In a continuity model, early (semantic) readout processes are taken as diagnostic for the non-conscious detection of environmental patterns and/or meaning (in terms of an antecedent of later explicit mental representation or insight). Intuition is described as aiding decision making and problem solving when time and cognitive capacity is limited and necessary information is temporarily unavailable. Contrary to this, in a discontinuity model early intuitive responses misdirect the generation of a correct solution or are experimentally utilized to bias solution attempts. In this case, intuitions lead people astray. Instead of employing intuition, mental restructuring processes (i.e., qualitative changes in the non-conscious search processes) are needed to overcome biased intuitive impressions or apprehensions so as to eventually solve the problem. In that respect, a discontinuity model resembles dual-process accounts in judgment and decision making.

Except early work by Bowers et al. (1990 , 1995 ) and Dorfman et al. (1996) , there have not been much empirical investigations so far aiming at exploring similarities and differences in the underlying neurocognitive mechanisms of intuition and insight. A major drawback here may be that there are no tasks that easily enable a direct empirical comparison between the two concepts. Nevertheless, we consider it very important to test intuitive and insight solution processes by means of exactly the same task and within the same participants. Such a task needs to be created. With this theoretical contribution, we therefore aim to initiate common investigations of both fields of research to detect neurocognitive similarities and differences between intuitive processing and insight problem solving. A good starting point for common empirical investigations may be the use of different types of triads [as for example divergent and convergent triads, as formerly suggested by Bowers et al. (1990) ] in order to induce gradual and discontinuous solution attempts. We also consider it important to investigate not only the cognitive processes that may underlie intuition and insight, but also the neuronal processes involved. Future studies may shed light on the specific (and maybe distinct) neuronal correlates, which will then also allow drawing conclusions about the theoretical conceptualization of the two phenomena. Interesting research questions would be (as non-exhaustive list): (1) Are the neuronal correlates different for the two types of triads (convergent versus divergent triads)? (2) Do aha-experiences also occur for convergent triads? (3) Do feelings-of-warmth ratings occur for both types of triads or only for convergent triads? (4) Do verbal protocols differ for the two types of triads? (5) How can the assumed recursive coherence building process be neuronally mapped? The further investigation of the underlying cognitive and neuronal processes of restructuring may also deeply progress our understanding of the topic. Here, Öllinger et al. (2006 , 2013 ) reached influential results that may be carried forward in future research. Equally important, following Kounios and Beeman (2014) in using current neuroimaging techniques may promote the detection of objective physiological markers of insight (in form of a specific neuronal or electrophysiological activation pattern accompanying the experience of impasses and aha’s as well as correlating mental restructuring processes). Kounios and Beeman (2014) as well as Sandkühler and Bhattacharya (2008) already gained promising results in this respect, thus their research may be a good starting point for the future. To sum up, intuition and insight are intriguing (non-analytical) mental phenomena that need to be further investigated in the future.

Author Contributions

TZ developed the theoretical conception; wrote the article. MÖ developed the theoretical conception; revised the manuscript. KV developed the theoretical conception; revised the manuscript.

Conflict of Interest Statement

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

Acknowledgments

This work was funded by the Werner Reichardt Centre for Integrative Neuroscience (CIN) at the University of Tübingen (an Excellence Cluster within the framework of the Excellence Initiative (EXG 307) funded by the Deutsche Forschungsgemeinschaft (DFG).

1 There is the idea that a period, in which a person after encountering an impasse is not being consciously engaged in finding the solution anymore and puts the problem aside (i.e., the incubation period ) fosters sudden insights of the solution (e.g., Gilhooly et al., 2012 ). Ritter and Dijksterhuis (2014) explain that unconscious thought processes continue to find the problem’s solution by re-organizing memory content eventually resulting in gist-based representations. This occurs in the absence of a person’s conscious attempts. It has to be emphasized, however, that empirical studies revealed different results as to whether incubation periods are beneficial for problem solving. The specific conditions under which positive incubation effects take place have to be further investigated ( Sio and Ormerod, 2009 ).

2 For the sake of completeness, it has to be emphasized that metacognitive processes may play a role as well in intuitive processing. To strengthen the scope of our argumentation, we decided not to detail on this notion. Please see Mealor and Dienes (2013) ; Storm and Hickman (2015) , or Thompson et al. (2011) . A particular emphasize may be laid on the concept of experience-based metacognitive feelings (e.g., Koriat and Levy-Sadot, 1999 ).

  • Betsch T. (2008). “The nature of intuition and its neglect in research on judgment and decision making,” in Intuition in Judgment and Decision Making , eds Plessner H., Betsch C., Betsch T. (New York, NY: Lawrence Erlbaum Associates; ), 3–22. [ Google Scholar ]
  • Bolte A., Goschke T. (2005). On the speed of intuition: intuitive judgments of semantic coherence under different response deadlines. Mem. Cognit. 33 1248–1255. 10.3758/BF03193226 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Bowden E. M., Jung-Beeman M. (2007). Methods for investigating the neural components of insight. Methods 42 87–99. 10.1016/j.ymeth.2006.11.007 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Bowden E. M., Jung-Beeman M., Fleck J., Kounios J. (2005). New approaches to demystifying insight. Trends Cogn. Sci. 9 322–328. 10.1016/j.tics.2005.05.012 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Bowers K. S. (1984). “On being unconsciously influenced and informed,” in The Unconscious Reconsidered , eds Bowers K. S., Meichenbaum D. (New York, NY: John Wiley & Sons; ), 227–272. [ Google Scholar ]
  • Bowers K. S., Farvolden P., Mermigis L. (1995). “Intuitive antecedents of insight,” in The Creative Cognition Approach , eds Smith S. M., Ward T. B., Finke R. A. (Cambridge, MA: The MIT Press; ), 27–51. [ Google Scholar ]
  • Bowers K. S., Regehr G., Balthazard C., Parker K. (1990). Intuition in the context of discovery. Cognit. Psychol. 22 72–110. 10.1016/0010-0285(90)90004-N [ CrossRef ] [ Google Scholar ]
  • Claxton G. (1998). Investigating human intuition: knowing without knowing why. Psychologist 11 217–220. 10.3758/s13415-014-0286-7 [ CrossRef ] [ Google Scholar ]
  • Collins A. M., Loftus E. F. (1975). A spreading-activation theory of semantic processing. Psychol. Rev. 82 407–428. 10.1037/0033-295X.82.6.407 [ CrossRef ] [ Google Scholar ]
  • Cranford E. A., Moss J. (2012). Is insight always the same? A verbal protocol analysis of insight in compound remote associate problems. J. Probl. Solving 4 128–153. [ Google Scholar ]
  • Danek A. H., Fraps T., von Müller A., Grothe B., Öllinger M. (2013). Aha! experiences leave a mark: facilitated recall of insight solutions. Psychol. Res. 77 659–669. 10.1007/s00426-012-0454-8 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Danek A. H., Wiley J., Öllinger M. (2016). Solving classical insight problems without Aha! experience: 9 dot, 8 coin, and matchstick arithmetic problems. J. Probl. Solving 9 47–57. 10.7771/1932-6246.1183 [ CrossRef ] [ Google Scholar ]
  • Dorfman J., Shames V. A., Kihlstrom J. F. (1996). “Intuition, incubation and insight: implicit cognition in problem solving,” in Implicit Cognition , ed. Underwood G. (Oxford: Oxford University Press; ), 257–296. [ Google Scholar ]
  • Duncker K. (1935). Zur Psychologie des Produktiven Denkens [On the psychology of productive Thinking]. Berlin: Springer. [ Google Scholar ]
  • Duncker K. (1945). On problem solving. Psychol. Monogr. 58 : 270 10.1037/h0093599 [ CrossRef ] [ Google Scholar ]
  • Durso F. T. F., Rea C. C. B., Dayton T. (1994). Graph-theoretic confirmation of restructuring during insight. Psychol. Sci. 5 94–97. 10.1111/j.1467-9280.1994.tb00637.x [ CrossRef ] [ Google Scholar ]
  • Evans J., Frankish K. (eds) (2009). In Two Minds: Dual Processes and Beyond . Oxford: Oxford University Press. [ Google Scholar ]
  • Evans J., Stanovich K. S. (2013). Dual-process theories of higher cognition: advancing the debate. Perspect. Psychol. Sci. 8 223–241. 10.1177/1745691612460685 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Fedor A., Szatmary E., Öllinger M. (2015). Problem solving stages in the five square problem. Front. Psychol. 6 : 1050 10.3389/fpsyg.2015.01050 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Gick M. L., Lockhardt R. S. (1995). “Cognitive and affective components of insight,” in The Nature of Insight , eds Sternberg R. J., Davidson J. E. (Cambridge, MA: The MIT Press; ), 197–228. [ Google Scholar ]
  • Gigerenzer G. (2008). Gut Feelings: The Intelligence of the Unconscious . New York, NY: Viking. [ Google Scholar ]
  • Gigerenzer G., Gaissmaier W. (2011). Heuristic decision making. Annu. Rev. Psychol. 62 451–482. 10.1146/annurev-psych-120709-145346 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Gigerenzer G., Todd P. M., The Abc Research Group (eds) (1999). Simple Heuristics that Make us Smart. New York, NY: Oxford University Press. [ Google Scholar ]
  • Gilhooly K. J., Georgiou G. J., Garrison J., Reston J. D., Sirota M. (2012). Don’t wait to incubate: immediate versus delayed incubation in divergent thinking. Mem. Cognit. 40 966–975. 10.3758/s13421-012-0199-z [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Gladwell M. (2005). Blink. The Power of Thinking Without Thinking. London: Penguin Books. [ Google Scholar ]
  • Glöckner A., Witteman C. (2010). “Foundations for tracing intuition: models, findings, categorizations,” in Foundations for Tracing Intuition: Challenges and Methods , eds Glöckner A., Witteman C. (East Sussex: Psychology Press; ), 1–23. [ Google Scholar ]
  • Hicks J. A., Burton C. M., Cicero D. C., Trent J., King L. A. (2010). Positive affect, intuition, and feelings of meaning. J. Pers. Soc. Psychol. 89 967–979. 10.1037/a0019377 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Hogarth R. M. (2001). Educating Intuition. Chicago, IL: University of Chicago Press. [ Google Scholar ]
  • Jung-Beeman M., Bowden E. M., Haberman J., Frymiare J. L., Arambel-Liu S., Greenblatt R., et al. (2004). Neural activity when people solve verbal problems with insight. PLoS Biol. 2 : E97 10.1371/journal.pbio.0020097 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Kahneman D. (2011). Thinking, Fast and Slow . London: Penguin Books. [ Google Scholar ]
  • Kahneman D., Tversky A. (1974). Judgment under uncertainty: heuristics and biases. Science 185 1124–1131. 10.1126/science.185.4157.1124 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Keren G., Schul Y. (2009). Two is not always better than one: a critical evaluation of two-system theories. Perspect. Psychol. Sci. 4 533–550. 10.1111/j.1745-6924.2009.01164.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Kershaw T. C., Ohlsson S. (2004). Multiple causes of difficulty in insight: the case of thenine-dot problem. J. Exp. Psychol. Learn. Mem. Cogn. 30 3–13. 10.1037/0278-7393.30.1.3 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Kizilirmak J., Galvao Gomes da Silva J., Imamoglu F., Richardsohn-Klavehn R. (2015). Generation and the subjective feeling of “aha!” are independently related to learning from insight. Psychol. Res. 10.1007/s00426-015-0697-2 [Epub ahead of print]. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Klein G., Jarosz A. (2011). A naturalistic study of insight. J. Cogn. Eng. Decis. Mak. 5 335–351. 10.1177/1555343411427013 [ CrossRef ] [ Google Scholar ]
  • Knoblich G., Ohlsson S., Haider H., Rhenius D. (1999). Constraint relaxation and chunk decomposition in insight problem solving. J. Exp. Psychol. Learn. Mem. Cogn. 25 1534–1555. 10.1037/0278-7393.25.6.1534 [ CrossRef ] [ Google Scholar ]
  • Knoblich G., Ohlsson S., Raney G. E. (2001). An eye movement study of insight problem solving. Mem. Cognit. 29 1000–1009. 10.3758/BF03195762 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Knoblich G., Öllinger M. (2006). “Einsicht und umstrukturierung beim problemlösen [insight and restructering in problem solving],” in Denken und Problemlösen. Enzyklopädie der Psychologie [Thinking and Problem Solving. Encyclopedia of Psychology] , ed. Funke J. (Göttingen: Hogrefe; ), 3–86. [ Google Scholar ]
  • Köhler W. (1921). Intelligenzprüfungen am Menschenaffen [Investigating intelligence in great apes] . Berlin: Springer. [ Google Scholar ]
  • Koriat A., Levy-Sadot R. (1999). “Processes underlying metacognitive judgments: information-based and experience-based monitoring of one’s own knowledge,” in Dual-Process Theories in Social Psychology , eds Chaiken S., Trope Y. (New York, NY: Guilford Press; ), 483–502. [ Google Scholar ]
  • Kounios J., Beeman M. (2014). The cognitive neuroscience of insight. Annu. Rev. Psychol. 65 71–93. 10.1146/annurev-psych-010213-115154 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Kruglanski A. W., Gigerenzer G. (2011). Intuitive and deliberate judgments are based on common principles. Psychol. Rev. 118 97–109. 10.1037/a0020762 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Lieberman M. D. (2000). Intuition: a social cognitive neuroscience approach. Psychol. Bull. 126 109–137. 10.1037/0033-2909.126.1.109 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Luchins A. S., Luchins E. H. (1959). Rigidity of Behavior: A Variational Approach to the Effect of Einstellung . Eugene, OR: University of Oregon Books. [ Google Scholar ]
  • Maier N. R. F. (1930). Reasoning in humans. I. On direction. J. Comp. Psychol. 10 115–143. 10.1037/h0073232 [ CrossRef ] [ Google Scholar ]
  • Mangan B. (1993). Taking phenomenology seriously: the “fringe” and its implications for cognitive research. Consci. Cogn. 2 89–108. 10.1006/ccog.1993.1008 [ CrossRef ] [ Google Scholar ]
  • Mangan B. (2001). Sensation’s ghost. The non-sensory “fringe” of consciousness. Psyche 7 . [ Google Scholar ]
  • Mangan B. (2015). The uncanny valley as fringe experience. Interact. Stud. 16 193–199. 10.1075/is.16.2.05man [ CrossRef ] [ Google Scholar ]
  • Mealor A. D., Dienes Z. (2013). The speed of metacognition: taking time to get to know one’s structural knowledge. Conscious. Cogn. 22 123–136. 10.1016/j.concog.2012.11.009 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Mednick S. A. (1962). The associative basis of the creative process. Psychol. Rev. 69 220–232. 10.1037/h0048850 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Mega L. F., Gigerenzer G., Volz K. G. (2015). Do intuitive and deliberate judgments rely on two distinct neural systems? A case study in face processing. Front. Hum. Neurosci. 9 : 456 10.3389/fnhum.2015.00465 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Metcalfe J., Wiebe D. (1987). Intuition in insight and noninsight problem solving. Mem. Cognit. 15 238–246. 10.3758/BF03197722 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Metzger W. (1953). Gesetze des Sehens [Rules of vision]. Frankfurt: Kramer. [ Google Scholar ]
  • Newell A., Simon H. A. (1972). Human Problem Solving. Englewood Cliffs, NJ: Prentice Hall. [ Google Scholar ]
  • Norman E. (2002). Subcategories of “fringe consciousness” and their related nonconscious contexts. Psyche 8 1–15. [ Google Scholar ]
  • Norman E. (2016). Metacognition and minfulness: the role of fringe consciousness. Mindfulness 10.1007/s12671-016-0494-z [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Norman E., Price M. C., Duff S. C. (2006). Fringe consciousness in sequence learning: the influence of individual differences. Consci. Cogn. 15 723–760. 10.1016/j.concog.2005.06.003 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Norman E., Price M. C., Duff S. C. (2010). “Fringe consciousness: a useful framework for clarifying the nature of experience-based feelings,” in Trends and Prospects in Metacognition Research , eds Efklides A., Misailidi P. (New York, NY: Springer; ), 63–89. [ Google Scholar ]
  • Ohlsson S. (1992). “Information-processing explanations of insight and related phenomena,” in Advances in the Psychology of Thinking , eds Keane M., Gilhooly K. (London: Harvester-Wheatsheaf; ), 1–44. [ Google Scholar ]
  • Ohlsson S. (2011). Deep Learning. How the Mind Overrides Experience. New York, NY: Cambridge University Press. [ Google Scholar ]
  • Öllinger M., Jones G., Faber A. H., Knoblich G. (2013). Cognitive mechanisms of insight: the role of heuristics and the representational change in solving the eight-coin problem. J. Exp. Psychol. Learn. Mem. Cogn. 39 931–939. 10.1037/a0029194 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Öllinger M., Jones G., Knoblich G. (2006). Heuristics and representational change in two-move matchstick arithmetic task. Adv. Cogn. Psychol. 2 239–253. 10.2478/v10053-008-0059-3 [ CrossRef ] [ Google Scholar ]
  • Öllinger M., Jones G., Knoblich G. (2008). Investigating the effect of mental set on insight problem solving. Exp. Psychol. 55 270–282. [ PubMed ] [ Google Scholar ]
  • Öllinger M., Jones G., Knoblich G. (2014). The dynamics of search, impasse, and representational change provide a coherent explanation of difficulty in the nine-dot problem. Psychol. Res. 78 266–275. 10.1007/s00426-013-0494-8 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Ormerod T. C., MacGregor J. N., Chronicle E. P. (2002). Dynamics and constraints in insight problem solving. J. Exp. Psychol. Learn. Mem. Cogn. 28 791–799. 10.1037/0278-7393.28.4.791 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Plessner H., Betsch C., Betsch T. (eds) (2008). Intuition in Judgment and Decision Making. New York, NY: Lawrence Erlbaum Associates. [ Google Scholar ]
  • Polanyi M. (1966). Implizites Wissen [The tacit dimension]. Frankfurt: Suhrkamp. [ Google Scholar ]
  • Price M. C. (2002). Measuring the fringe of experience. Psyche 8 1–24. [ Google Scholar ]
  • Price M. C., Norman E. (2008). Intuitive feelings on the fringe of consciousness: are they conscious and does it matter? Judgm. Decis. Mak. 3 28–41. [ Google Scholar ]
  • Reber R., Ruch-Monachon M.-A., Perrig W. J. (2007). Decomposing intuitive components in a conceptual problem solving task. Conscious. Cogn. 16 294–309. 10.1016/j.concog.2006.05.004 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Reber R., Schwarz N., Winkielmann P. (2004). Processing fluency and aesthetic pleasure: is beauty in the perceiver’s processing experience? Pers. Soc. Psychol. Rev. 8 364–382. 10.1207/s15327957pspr0804_3 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Remmers C., Topolinski T., Dietrich D. E., Michalak J. (2014). Impaired intuition in pateints with major depressive disorder. Br. J. Clin. Psychol. 54 200–213. 10.1111/bjc.12069 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Ritter S., Dijksterhuis A. (2014). Creativity – the unconscious foundations of the incubation period. Front. Hum. Neurosci. 8 : 215 10.3389/fnhum.2014.00215 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Sadler-Smith E. (2008). Inside Intuition. Abingdon: Routledge. [ Google Scholar ]
  • Sandkühler S., Bhattacharya J. (2008). Deconstructing insight: EEG correlates of insightful problem solving. PLoS ONE 3 : 1459 10.1371/journal.pone.0001459 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Seifert C. M., Meyer D. E., Davidson N., Patalano A. L., Yaniv I. (1995). “Demystification of cognitive insight: opportunistic assimilation and the prepared-mind perspective,” in The Nature of Insight , eds Sternberg R. J., Davidson J. E. (Cambridge, MA: The MIT Press; ), 65–124. [ Google Scholar ]
  • Sio U. N., Ormerod T. C. (2009). Does incubation enhance problem solving? A meta- analytic review. Psychol. Bull. 135 94–120. 10.1037/a0014212 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Stanovich K. E., West R. F. (2000). Individual differences in reasoning: implications for the rationality debate. Behav. Brain Sci. 23 645–665. 10.1017/S0140525X00003435 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Sternberg R. J., Davidson J. E. (eds) (1995). The Nature of Insight. Cambridge, MA: The MIT Press. [ Google Scholar ]
  • Storm B. C., Hickman M. L. (2015). Mental fixation and metacognitive predictions of insight in creative problem solving. Q. J. Exp. Psychol. 68 802–813. 10.1080/17470218.2014.966730 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Thompson V. A., Prowse Turner J. A., Pennycook G. (2011). Intuition, reason, and metacognition. Cognit. Psychol. 63 107–140. 10.1016/j.cogpsych.2011.06.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Topolinski S., Reber R. (2010). Gaining insight into the “aha” experience. Curr. Dir. Psychol. Sci. 19 402–405. 10.3389/fpsyg.2014.01408 [ CrossRef ] [ Google Scholar ]
  • Topolinski S., Strack F. (2008). Where there’s a will – there’s no intuition. The unintentional basis of semantic coherence judgments. J. Mem. Lang. 58 1032–1048. 10.1016/j.jml.2008.01.002 [ CrossRef ] [ Google Scholar ]
  • Topolinski S., Strack F. (2009a). Scanning the “fringe” of consciousness: what is felt and what is not felt in intuitions about semantic coherence. Consci. Cogn. 18 608–618. 10.1016/j.concog.2008.06.002 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Topolinski S., Strack F. (2009b). The analysis of intuition: processing fluency and affect in judgments of semantic coherence. Cogn. Emot. 23 1465–1503. 10.1080/02699930802420745 [ CrossRef ] [ Google Scholar ]
  • Volz K. G., von Cramon D. Y. (2006). What neuroscience can tell about intuitive processes in the context of perceptual discovery. J. Cogn. Neurosci. 18 1–11. 10.1162/jocn.2006.18.12.2077 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Volz K. G., Zander T. (2014). Primed for intuition? Neurosci. Decis. Mak. 1 26–34. 10.2478/ndm-2014-0001 [ CrossRef ] [ Google Scholar ]
  • Wertheimer M. (1959). Productive Thinking. New York, NY: Harper. [ Google Scholar ]
  • Zander T., Horr N. K., Bolte A., Volz K. G. (2015). Intuition as a gradual process: investigating intuition-based and priming-based decisions with fMRI. Brain Behav. 6 : e00420 10.1002/brb3.420 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Zsambok C. E., Klein G. (eds) (1997). Naturalistic Decision Making. New York, NY: Routlege. [ Google Scholar ]

The interpretative heuristic in insight problem solving

  • Published: 04 April 2014
  • Volume 13 , pages 97–108, ( 2014 )

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  • Laura Macchi 1 &
  • Maria Bagassi 1  

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The study of insight problem solving could well become one of the most important topics in the contemporary debate on thought. Dealing with insight problems today requires of necessity reconsidering the concept of bounded rationality. Simon’s work has inspired us to reflect on the specific quality of the type of boundaries which, by limiting the search, allow and guarantee the act of creativity; finding the solution to insight problems is emblematic of this creativity and provides a paradigmatic case. According to Simon, the solution to insight problems requires a search for an alternative space. He considered the “Notice Invariants Heuristic” to be a powerful tool for focusing this search which must always be guided by salience. Therefore, in the case of insight problems the heuristic is not a weak method of solving problems; indeed, it is the only way, an innovative and creative approach to reach the solution. In our view, the solution to these problems is not attained by abstraction, but only by a pertinent interpretation of the context ( interpretative heuristic ) in the light of the goal, allowing the problem solver to abandon the default representation. We therefore propose that this interpretative heuristic is inherent to all insight problem solving processes and, in more general terms, is an adaptive characteristic of the human cognitive system; this of course implies that the dual process theory will have to be challenged and discussed.

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It is not clear in this case whether it is necessary to maintain the area of the sum of the two specific shapes dealt with in the text, or to shift to the area of the shape, however organized, as the sum of the two right-angled triangles, or the area of one rectangle only, as well as the sum of the square and the parallelogram.

We consider as default contextualization the preferred organization of stimulus and the generalized interpretation of the text of the problem (Macchi and Bagassi 2012 ).

The inference to the stereotype, given its importance, must be processed with care. Take for example “The pencil is in the cup”, suggesting “The standard-type pencil is projecting out of, but is supported by the cup”. Compare this with “The coffee is in the cup” where the inference is liquid rather than beans , completely in the cup rather than projecting from it , or with “The key is in the lock” where the key projects horizontally , not vertically . The particular relation intended by ‘in’ has to be inferred by reference to the context and the objects to which it relates (Levinson 1995 , p. 234).

Spatial orientation is a decisive factor in the perception of forms (Mach 1914 ): two identical shapes seen from different orientations take on a different phenomenic identity.

Bagassi M, Macchi L (2006) Pragmatic approach to decision making under uncertainty: the case of the disjunction effect. Think Reason 12(3):329–350

Article   Google Scholar  

Carruthers P (2011) The opacity of mind: an integrative theory of self-knowledge. Oxford University Press, Oxford

Book   Google Scholar  

Evans JSBT (2009) How many dual-process theories do we need? In: Evans JSBT, Frankish K (eds) In two minds. Oxford University Press, Oxford, pp 33–55

Google Scholar  

Evans JSBT (2012) Spot the difference: distinguishing between two kinds of processing. Mind Soc 11:121–131

Evans JSBT, Over DE (1996) Rationality and reasoning. Psychology Press, Hove

Evans JSBT, Stanovich KE (2013) Dual-process theories of higher cognition: advancing the debate. Perspect Psychol Sci 8:223–241

Frederick S (2005) Cognitive reflection and decision making. J Econ Perspect 19(4):25–42

Gilhooly KJ, Macchi L, Ball L (2014) Special issue on Insight and creative problem solving. Think Reason 20(4) (in press)

Greeno JG (1974/04) Hobbits and orcs: acquisition of a sequential concept. Cognit Psychol 6(2):270–292

Hammond KR (1996) Human judgment and social policy. Oxford University Press, New York

Hayes JR, Simon HA (1974) Understanding written problem instructions. In: Gregg L (ed) Knowledge and cognition. Lawrennce Erlbaum Associates, Potomac

Hélie S, Sun R (2010) Incubation, insight, and creative problem solving: a unified theory and a connectionist model. Psychol Rev 117:994–1024

Kaplan CA, Simon HA (1990) In search of insight. Cognit Psychol 22:374–419

Levinson SC (1995) Interactional biases in human thinking. In: Goody EN (ed) Social intelligence and interaction. Cambridge University Press, Cambridge, pp 221–261

Chapter   Google Scholar  

Levinson SC (2000) Presumptive meanings: the theory of generalized conversational implicature. MIT press, Cambridge

Macchi L, Bagassi M (2006) Biased communication and misleading intuition of probability. Meeting on Intuition and Affect in Risk Perception and Decision Making, Bergen

Macchi L, Bagassi M (2007) The underinformative formulation of conditional probability. Behav Brain Sci 30(3):274–275

Macchi L, Bagassi M (2012) Intuitive and analytical processes in insight problem solving: a psycho-rhetorical approach to the study of reasoning. Mind Soc 11:53–67

Mach E (1914) The analysis of sensations. Open Court, Chicago

Mosconi G, D’Urso V (1974) Il farsi e il disfarsi del problema. Giunti-Barbera, Firenze

Newell A, Simon HA (1972) Human problem solving Englewood Cliffs. Prentice-Hall, NJ

Ohlsson S (1984) Restructuring revisited I: summary and critique of the Gestalt theory of problem solving. Scand J Psychol 25:65–78

Simon HA (1990) Invariants of human behavior. Annu Rev 4:1–19

Simon HA, Hayes JR (1976) The understanding process: problem isomorphs. Cognit Psychol 8:165–190

Simon HA, Newell A (1971) Human problem solving: the state of theory. Am Psychol 21(2):145–159

Sloman SA (1996) The empirical case for two systems of reasoning. Psychol Bull 119:3–22

Stanovich KE, Toplak ME (2012) Defining features versus incidental correlates of type 1 and type 2 processing. Mind Soc 11:3–13

Stanovich KE, West RE (2000) Individual differences in reasoning: implications for the rationality debate? Behav Brain Sci 23:645–726

Sternberg RJ, Davidson JE (eds) (1986) Conceptions of giftedness. Cambridge University Press, New York

Wertheimer M (1925) Uber Schlussprozesse im produktiven Denken. In Drei Abhandlungen zur Gestalttheorie. Verlag der Philosophischen Akademie, Erlangen

Wertheimer M (1985) A gestalt perspective on computer simulations of cognitive processes. Comput Hum Behav 1:19–33

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Macchi, L., Bagassi, M. The interpretative heuristic in insight problem solving. Mind Soc 13 , 97–108 (2014). https://doi.org/10.1007/s11299-014-0139-7

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What makes an insight problem? The roles of heuristics, goal conception, and solution recoding in knowledge-lean problems

Affiliation.

  • 1 Departmentof Psychology, University of Hawaii, Manoa, HI, USA.
  • PMID: 14736293
  • DOI: 10.1037/0278-7393.30.1.14

Four experiments investigated transformation problems with insight characteristics. In Experiment 1, performance on a version of the 6-coin problem that had a concrete and visualizable solution followed a hill-climbing heuristic. Experiment 2 demonstrated that the difficulty of a version of the problem that potentially required insight for solution stems from the same hill-climbing heuristic, which creates an implicit conceptual block. Experiment 3 confirmed that the difficulty of the potential insight solution is conceptual, not procedural. Experiment 4 demonstrated the same principles of move selection on the 6-coin problem and the 10-coin (triangle) problem. It is argued that hill-climbing heuristics provide a common framework for understanding transformation and insight problem solving. Postsolution receding may account for part of the phenomenology of insight.

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8.2 Problem-Solving: Heuristics and Algorithms

Learning objectives.

  • Describe the differences between heuristics and algorithms in information processing.

When faced with a problem to solve, should you go with intuition or with more measured, logical reasoning? Obviously, we use both of these approaches. Some of the decisions we make are rapid, emotional, and automatic. Daniel Kahneman (2011) calls this “fast” thinking. By definition, fast thinking saves time. For example, you may quickly decide to buy something because it is on sale; your fast brain has perceived a bargain, and you go for it quickly. On the other hand, “slow” thinking requires more effort; applying this in the same scenario might cause us not to buy the item because we have reasoned that we don’t really need it, that it is still too expensive, and so on. Using slow and fast thinking does not guarantee good decision-making if they are employed at the wrong time. Sometimes it is not clear which is called for, because many decisions have a level of uncertainty built into them. In this section, we will explore some of the applications of these tendencies to think fast or slow.

We will look further into our thought processes, more specifically, into some of the problem-solving strategies that we use. Heuristics are information-processing strategies that are useful in many cases but may lead to errors when misapplied. A heuristic is a principle with broad application, essentially an educated guess about something. We use heuristics all the time, for example, when deciding what groceries to buy from the supermarket, when looking for a library book, when choosing the best route to drive through town to avoid traffic congestion, and so on. Heuristics can be thought of as aids to decision making; they allow us to reach a solution without a lot of cognitive effort or time.

The benefit of heuristics in helping us reach decisions fairly easily is also the potential downfall: the solution provided by the use of heuristics is not necessarily the best one. Let’s consider some of the most frequently applied, and misapplied, heuristics in the table below.

In many cases, we base our judgments on information that seems to represent, or match, what we expect will happen, while ignoring other potentially more relevant statistical information. When we do so, we are using the representativeness heuristic . Consider, for instance, the data presented in the table below. Let’s say that you went to a hospital, and you checked the records of the babies that were born on that given day. Which pattern of births do you think you are most likely to find?

Most people think that list B is more likely, probably because list B looks more random, and matches — or is “representative of” — our ideas about randomness, but statisticians know that any pattern of four girls and four boys is mathematically equally likely. Whether a boy or girl is born first has no bearing on what sex will be born second; these are independent events, each with a 50:50 chance of being a boy or a girl. The problem is that we have a schema of what randomness should be like, which does not always match what is mathematically the case. Similarly, people who see a flipped coin come up “heads” five times in a row will frequently predict, and perhaps even wager money, that “tails” will be next. This behaviour is known as the gambler’s fallacy . Mathematically, the gambler’s fallacy is an error: the likelihood of any single coin flip being “tails” is always 50%, regardless of how many times it has come up “heads” in the past.

The representativeness heuristic may explain why we judge people on the basis of appearance. Suppose you meet your new next-door neighbour, who drives a loud motorcycle, has many tattoos, wears leather, and has long hair. Later, you try to guess their occupation. What comes to mind most readily? Are they a teacher? Insurance salesman? IT specialist? Librarian? Drug dealer? The representativeness heuristic will lead you to compare your neighbour to the prototypes you have for these occupations and choose the one that they seem to represent the best. Thus, your judgment is affected by how much your neibour seems to resemble each of these groups. Sometimes these judgments are accurate, but they often fail because they do not account for base rates , which is the actual frequency with which these groups exist. In this case, the group with the lowest base rate is probably drug dealer.

Our judgments can also be influenced by how easy it is to retrieve a memory. The tendency to make judgments of the frequency or likelihood that an event occurs on the basis of the ease with which it can be retrieved from memory is known as the availability heuristic (MacLeod & Campbell, 1992; Tversky & Kahneman, 1973). Imagine, for instance, that I asked you to indicate whether there are more words in the English language that begin with the letter “R” or that have the letter “R” as the third letter. You would probably answer this question by trying to think of words that have each of the characteristics, thinking of all the words you know that begin with “R” and all that have “R” in the third position. Because it is much easier to retrieve words by their first letter than by their third, we may incorrectly guess that there are more words that begin with “R,” even though there are in fact more words that have “R” as the third letter.

The availability heuristic may explain why we tend to overestimate the likelihood of crimes or disasters; those that are reported widely in the news are more readily imaginable, and therefore, we tend to overestimate how often they occur. Things that we find easy to imagine, or to remember from watching the news, are estimated to occur frequently. Anything that gets a lot of news coverage is easy to imagine. Availability bias does not just affect our thinking. It can change behaviour. For example, homicides are usually widely reported in the news, leading people to make inaccurate assumptions about the frequency of murder. In Canada, the murder rate has dropped steadily since the 1970s (Statistics Canada, 2018), but this information tends not to be reported, leading people to overestimate the probability of being affected by violent crime. In another example, doctors who recently treated patients suffering from a particular condition were more likely to diagnose the condition in subsequent patients because they overestimated the prevalence of the condition (Poses & Anthony, 1991).

The anchoring and adjustment heuristic is another example of how fast thinking can lead to a decision that might not be optimal. Anchoring and adjustment is easily seen when we are faced with buying something that does not have a fixed price. For example, if you are interested in a used car, and the asking price is $10,000, what price do you think you might offer? Using $10,000 as an anchor, you are likely to adjust your offer from there, and perhaps offer $9000 or $9500. Never mind that $10,000 may not be a reasonable anchoring price. Anchoring and adjustment does not just happen when we’re buying something. It can also be used in any situation that calls for judgment under uncertainty, such as sentencing decisions in criminal cases (Bennett, 2014), and it applies to groups as well as individuals (Rutledge, 1993).

In contrast to heuristics, which can be thought of as problem-solving strategies based on educated guesses, algorithms are problem-solving strategies that use rules. Algorithms are generally a logical set of steps that, if applied correctly, should be accurate. For example, you could make a cake using heuristics — relying on your previous baking experience and guessing at the number and amount of ingredients, baking time, and so on — or using an algorithm. The latter would require a recipe which would provide step-by-step instructions; the recipe is the algorithm. Unless you are an extremely accomplished baker, the algorithm should provide you with a better cake than using heuristics would. While heuristics offer a solution that might be correct, a correctly applied algorithm is guaranteed to provide a correct solution. Of course, not all problems can be solved by algorithms.

As with heuristics, the use of algorithmic processing interacts with behaviour and emotion. Understanding what strategy might provide the best solution requires knowledge and experience. As we will see in the next section, we are prone to a number of cognitive biases that persist despite knowledge and experience.

Key Takeaways

  • We use a variety of shortcuts in our information processing, such as the representativeness, availability, and anchoring and adjustment heuristics. These help us to make fast judgments but may lead to errors.
  • Algorithms are problem-solving strategies that are based on rules rather than guesses. Algorithms, if applied correctly, are far less likely to result in errors or incorrect solutions than heuristics. Algorithms are based on logic.

Bennett, M. W. (2014). Confronting cognitive ‘anchoring effect’ and ‘blind spot’ biases in federal sentencing: A modest solution for reforming and fundamental flaw. Journal of Criminal Law and Criminology , 104 (3), 489-534.

Kahneman, D. (2011). Thinking, fast and slow. New York, NY: Farrar, Straus and Giroux.

MacLeod, C., & Campbell, L. (1992). Memory accessibility and probability judgments: An experimental evaluation of the availability heuristic.  Journal of Personality and Social Psychology, 63 (6), 890–902.

Poses, R. M., & Anthony, M. (1991). Availability, wishful thinking, and physicians’ diagnostic judgments for patients with suspected bacteremia.  Medical Decision Making,  11 , 159-68.

Rutledge, R. W. (1993). The effects of group decisions and group-shifts on use of the anchoring and adjustment heuristic. Social Behavior and Personality, 21 (3), 215-226.

Statistics Canada. (2018). Ho micide in Canada, 2017 . Retrieved from https://www150.statcan.gc.ca/n1/en/daily-quotidien/181121/dq181121a-eng.pdf

Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability.  Cognitive Psychology, 5 , 207–232.

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ORIGINAL RESEARCH article

Unconscious processing of prototype heuristics in scientific innovation problem-solving.

Yushi Ling

  • 1 Faculty of Psychology, Southwest University, Chongqing, China
  • 2 The High School Attached to Hunan Normal University-Meixihu High School, Changsha, China
  • 3 Institute of Marxism, Chongqing Medical and Pharmaceutical College, Chongqing, China

Previously published studies on the effect of how different levels of unconsciousness (UC) and different loads of executive functions (EFs) affect insight problem solving are inconsistent. In a set of three experiments, we used scientific innovation problems (SIP) as insight metrics and distractor tasks to induce UC. Experiment 1 confirmed that, compared with conscious processing, unconscious processing is more conducive to obtaining prototype heuristics for correctly solving scientific innovation problems creatively. Furthermore, Experiment 2 found that different levels of unconscious processing, which were induced by different distractor tasks, made a different impact on high or low difficulty creative problem solving. Experiment 3 indicated that unconscious processing could improve prototype activation and the ability to use key heuristics information in prototype heuristics processing by improving working memory, inhibitory control, and shifting ability of EFs. Overall, the present results provide additional evidence for the role of consciousness levels in insight problem solving.

1. Introduction

1.1. the prototype heuristics theory in the scientific invention problems.

Creativity is the ability to produce both novel and appropriate work ( Sternberg and Lubart, 1991 , 1996 ), and insight is an important topic in the research of creativity in psychology ( Dietrich and Kanso, 2010 ; Tong et al., 2013 ). Sometimes, a flash of inspiration or intuition may trigger a critical moment of thought that leads to an “aha” moment and solves a problem, known as insight. For example, the well-known golden crown problem. That is, Archimedes, the famous Ancient Greek philosopher, was asked to estimate whether the golden crown was made from pure gold. He was very confused at the beginning, but a solution suddenly hit him during his bath time because he found that when he got into the bathtub, once the water has been drained from the bathtub, objects of the same weight and density should drain the same volume of water. He realized that this discovery could solve the golden crown problem. This anecdote is a good example of insight ( Hao et al., 2013 ; Zhu et al., 2019 ).

To further illustrate, we can treat the phenomenon that the object drains water out of a bathtub as a prototype, realize the connection between that prototype and the golden crown problem, and then apply that prototype to that problem; this is a way to solve the insight problem, that is, the prototype heuristic ( Zhang et al., 2004 ). During the prototype heuristic process, the activation of a semantic representation of a prototype that benefits insight problem-solving is known as prototype activation, and the application of the heuristic information implied by the prototype (such as principles, rules, and methods) leads to successfully solving the insight problem of creativity ( Zhang et al., 2012 ).

Many recorded insights, such as the golden crown problem, were derived from scientists and inventors ( Ovington et al., 2018 ), which may suggest that a large part of real-world epiphanies come from scientific inventions. Most published studies that concentrate on the cognitive mechanism of insight more frequently adopt the compound remote associate problem (RAT; Beaty et al., 2014 ) and the puzzle task ( Wu, 2007 ), and they all have a common problem, that is, artificial materials and lack of ecological validity ( Tong et al., 2013 , 2015 ; Yang et al., 2016 ). To solve this problem, Zhu (2011) used scientific innovation problems (SIPs) as experimental materials and compiled the Scientific Innovation Problems Database. Unlike divergent thinking, convergent thinking, or analogical transfer problems, SIPs comprise knowledge-rich problems ( Yang et al., 2022 ). In this experimental material, each SIP includes contextual information of the scientific innovation problem, a prototype associated with it, and a reference answer. For example, groundwater needs to be pumped to irrigate crops in arid areas, but it uses too much electricity and is expensive to drill wells. This makes it difficult to scale up in the vast arid areas of the west (contextual information). The question is how to use groundwater without electricity or drilling wells. The relevant prototype information is that trees use capillary action in their roots to transport underground water from their roots to leaves hundreds of feet above the ground. Researchers always combine the SIP with the “learning-testing” paradigm to explore the effect of the prototype heuristic. Participants need to learn the prototype information first; the specific operation is to present one or more prototypes to the participants for learning without limitation of time, and after the participants report the completion of learning, researchers present them with target problems to explore whether the participants could activate the previously learned prototype to help solve the current creative problem ( Yan et al., 2011 ). This is known as the prototype heuristic paradigm, which considers knowledge-rich contexts and enables more ecologically valid investigations of creative problem-solving in the laboratory ( Yang et al., 2022 ). Moreover, the Scientific Innovation Problems Database has also been widely used in research ( Zhu et al., 2017 , 2019 ).

1.2. The influence of levels of consciousness on prototype heuristic and insight

Dijksterhuis and Meurs (2006) suggest that, compared with conscious thinking, unconscious thinking is more “liberal” and leads to “less obvious, less accessible, and more creative” ideas. Compared with non-insight problem-solving, insight problem-solving relies more on implicit, bottom-up, unconscious processes ( Lebed and Korovkin, 2017 ; Stuyck et al., 2022 ). Simultaneously, the processing of the prototype heuristic includes unconscious thinking as well. For example, Cao et al. (2006) found that prototype activation has no difference between participants who implicitly or explicitly learned the prototype. Furthermore, they suggested that prototype activation could occur unconsciously and does not need conscious induction and summary. Hereafter, the process of matching various information of the prototype with the problem to find the solution to the problem is completed through conscious processing. A recent study by Xing et al. (2018) also showed that heuristics from prototypes probably involve an implicit, unconscious process.

Zhao (2018) identified that conscious and unconscious processing are both involved in the creative problem-solving process, meanwhile, Zhao also verified that the level of UC has deep and shallow processing by the sandwich masking and distractor task paradigms. The sandwich masking paradigm reduces or disappears the visibility of the target stimulus through the continuous and rapid presentation of the two stimuli, thus achieving unconscious-level processing. The distractor task is a task that causes the unconscious level processing of the target task through a task that occupies more cognitive resources. Sio and Ormerod (2009) used meta-analysis to assess the incubation effect of RAT tasks; their results also show that, compared with those difficult distractor tasks that consume more cognitive resources, the remote association of target words was observed faster in the easy distractor tasks with less cognitive resources. Overall, conscious and unconscious processing is involved in creative problem-solving, and different levels of UC will have different effects on creative problem-solving. Moreover, it could be argued that different levels of UC will also have different effects on prototype heuristics.

1.3. Executive functions

As mentioned earlier, conscious thinking, which involves bottom-up processing, and insight problem-solving appear to be closely linked. However, EFs, which involve top-down processing, do not rely on instinct or intuition ( Diamond, 2013 ). Furthermore, an important factor of insight problem-solving, such as progress monitoring theory ( Knoblich et al., 1999 ) developed by modern cognitive psychology, is that effective insight problem-solving involves substantial loads on working memory (an EF process). Chrysikou (2019) further points out that the importance of cognitive control mechanisms for creative thinking is a consensus in the field of creative neuroscience.

EFs refer to a series of higher cognitive abilities of individual consciousness and effective control of thinking and behavior, which include inhibition, shifting, and updating ( Miyake et al., 2000 ). Inhibition is the repression of automatic reactions in the cognitive process or content, which mainly prevents irrelevant information from entering and being stored in working memory. Shifting means individuals respond to new situations with appropriate reactions and maintain cognitive and behavioral flexibility. That is, when faced with multiple tasks competing for a cognitive resource, the control process of attentional switching in these tasks takes precedence. Updating is the process by which an individual continuously incorporates new information and discards irrelevant information to the current task to change the contents of working memory based on the information presented. These three sub-functions are related to each other, but they play different roles in complex cognitive processes and play an important role in insight problem-solving.

For example, four fluid reasoning tests, 13 working memory tasks, and an intensive range of insight tasks were used by Chuderski and Jastrzebski (2018) to verify the relationships among the three; they found a strong positive correlation between EFs and insight problem-solving of 0.795, which verified a strong link between the two of them. Moreover, they also found that the working memory capacity factor explained 51.8% of insight variance, as well as 87.0% of reasoning variance. Xing et al. (2018) also found positive correlations between EFs and insight problem-solving, and updating (an EF) significantly predicted insight performance. Cassotti et al. (2016) suggested that inhibitory control is a central process in creative problem-solving and idea generation from childhood through adulthood because developing a solution to a creative problem requires suppressing inappropriate thoughts. In addition, EEG research by Benedek et al. (2012) showed that inhibitory control resources were positively correlated with creative task scores ( Benedek et al., 2012 ; Beaty et al., 2014 ).

To summarize, an antagonistic relationship between UC and EFs may exist; however, unconsciousness, EFs, and its three components play a role in promoting the performance of insight problem-solving. So, what are the true relationships among UC, EFs, and insight problem-solving? Previous research has established some models to explore the relationships among these three constructs. For example, the associative theory contends that UC promotes insight problem-solving ( Mednick, 1962 ); however, this theory does not consider the role of EFs. Recent research by Stuyck et al. (2022) claimed that they proved the associative theory as they found that cognitive control did not influence the performance of insight problem-solving. In their research, the performance of insight problem-solving was measured by RAT grades with an accuracy of 91%–94%. Of note, however, previous research has already provided evidence that when RAT difficulty was very high (all but one of 39 participants were able to solve no more than one problem out of nine), the promoting effect of unconscious processing on RAT performance could be observed, but when RAT difficulty was medium (correct answer rates were between 41% and 59% with 39 participants), the promoting effect disappeared ( Zhong et al., 2008 ). The results of Zhong et al. (2008) may reveal that the research by Stuyck et al. (2022) is insufficient to conclude that cognitive control did not influence the performance of insight problem-solving. In addition to the association theory, some researchers claimed that only EFs could promote insight problem-solving, such as Chuderski and Jastrzebski (2018) who attributed their results to working memory playing a central role in insight problem-solving and “nothing special with special add-ons.” Although this view considers the role of EFs, it ignores the UC that already exists. Early in 2007, Schmeichel (2007) found that working memory tasks could deplete EFs. Therefore, the working memory task can also play the role of a distractor task, making the insight problem-solving processing into the unconscious thinking state. Thus, it is inappropriate to consider only the role of working memory. The view of associative processes and executive control both playing a role in insight problem-solving has also been proposed. To illustrate, Beaty et al. (2016) argued that the default network influences the generation of candidate ideas, but the control network can constrain and guide the process through top-down monitoring and executive control to meet the goals of a particular task. However, they also make it clear that the framework does not include cases of creative insight. Therefore, how EFs and UC play a role in insight problem-solving remains to be explored.

According to Beaty et al. (2016) , in creative problem-solving, the default network influences the generation of candidate ideas before the constraint and guidance of executive control. Therefore, we believe that only after the UC induces the generation of ideas, will EFs play a role. Otherwise, the process of logical reasoning or functional fixation can only occur. On the other hand, if the EFs are not functioning, individuals may not be able to report correct opinions even with unconscious thinking. Therefore, we hypothesize that EFs mediate the relationship between UC and insight problem-solving.

1.4. Current research

To sum up, in solving creative problems, sometimes unconscious processing may be better than conscious processing results. In addition, the effect of unconscious processing induced by a low cognitive load distractor task (low-distractor task) on creative problem-solving is greater than that of a high cognitive load distractor task (high-distractor task). Research has also shown that improvements in certain cognitive abilities in EF can also boost creative performance. However, although sufficient research exists on the relationships among UC, EFs, and insight problem-solving, no clear explanation has been determined. Therefore, we report three experiments to explore the relationships between UC and EFs as well as how they play a role in insight problem-solving.

To this end, the purpose of Experiment 1 was to verify whether unconscious processing is better than conscious processing in SIP solving and whether item difficulty will influence performance. Most researchers investigating UC have utilized the distractor task ( Ding et al., 2019 ), mask priming paradigm ( Huber-Huber and Ansorge, 2018 ; Silva et al., 2018 ), and dual-task paradigm ( Lebed and Korovkin, 2017 ). In Experiment 1, a distractor task was used wherein participants were required to perform several comparison tasks in the process of problem-solving. Such concurrent cognitive control tasks would prevent cognitive control from playing a role in the main task ( Huber-Huber and Ansorge, 2018 ); thus inducing the participants to engage in unconscious thinking. Based on the empirical results mentioned earlier, we predicted that participants who performed the distractor task performed better on insight problem-solving than participants who performed conscious thinking, especially when the problem was difficult.

Experiment 2 added different levels of consciousness. We speculate that the more difficult the distractor task, the more cognitive resources will be occupied, and the higher the level of UC. According to previous studies, too much occupation of cognitive resources will lead to poorer performance in SIP solving than less occupation.

A measure of EFs was introduced in Experiment 3 to further explain the results of Experiment 2. EFs are a higher-level cognitive ability used in careful research and goal realization ( Cristofori et al., 2019 ). As an ability, researchers assume that it will not change in the short term. Therefore, many researchers have focused their attention on individual differences in EFs ( Steward et al., 2018 ; Stolte et al., 2020 ; Tsai et al., 2021 ). However, Schmeichel (2007) believed that, similar to the ego depletion hypothesis ( Baumeister et al., 1998 ), EFs have a depletable capacity. Their study found that, compared with the control group, participants who completed the distractor task showed decreased performance in the next EFs measuring. Moreover, participants who previously completed the inhibition task had a negative influence on the subsequent working memory updating task and vice versa (the previous working memory updating task had a negative impact on inhibition task performance). This suggests that the completion of tasks involving EFs impacts subsequent measures of EFs. Specifically, the previous task consumes a portion of the limited resources of EFs, thus reducing the amount of EFs available later. To test this idea, EFs were measured immediately after the distractor task in Experiment 3 to explore whether the distractor task affected EFs and whether the affected EFs would lead to different SIP-solving results. In addition, although the EFs measurement task itself also occupied cognitive resources, the three groups of subjects completed the same EFs measurement after the level of consciousness manipulation; therefore, theoretically, the occupied cognitive resources are equal and can be balanced.

2. Experiment 1

Given the evidence that UC has been effective in creativity, especially in difficulty problem-solving, we verified whether, when SIP is used to test creativity, different levels of consciousness have different effects on it. We hypothesize that when the SIP is difficult, UC can promote prototype heuristics in solving problems more than consciousness.

2.1. Method

2.1.1. participants.

Seventy-eight participants (aged between 18 and 27 years, mean age = 21.48 years, SD = 1.68) from Southwest University were recruited. Participants were randomly assigned to the conscious condition ( n = 38) and the unconscious condition ( n = 40). After removing one subject who failed to complete all the experimental tasks, the final number of effective subjects was 77. All participants provided informed consent before participating and received some remuneration after the experiment. Experimental protocols for all three experiments were approved by the University’s local ethics committee.

2.1.2. Materials

2.1.2.1. scientific innovation problem.

We chose 84 SIPs from the Scientific Innovation Problems Database ( Zhu, 2011 ) based on difficulty. Three students with psychology as a major were asked to rate the difficulty of the questions on a 7-point scale (1 = lowest difficulty and 7 = highest difficulty). The order of each question was presented randomly. The scorer reliability was 0.89. According to the scoring results, 20 questions were selected for the high-difficulty condition (MD = 4.70, SD = 0.52) and 20 questions for the low-difficulty condition (MD = 2.10, SD = 0.48).

2.1.2.2. Distractor task

Several comparison tasks were adopted to induce UC. A random set of numbers appeared on both the left and right sides of the computer screen. The numbers were random integers between 10 and 99 and appeared for only 1 s. The participants were asked to quickly and accurately determine which side of the screen had a larger number and to respond accordingly. If the number on the left side of the screen was larger, the participant pressed “Q,” and if the number on the right side of the screen was larger, the participant pressed “P.” Participants were asked to perform a 3-min distractor task. This is because previous studies have found that when the distractor task lasts 3 min, it has the best effect on creativity compared to 1 or 5 min ( Gilhooly, 2016 ).

2.1.3. Procedure

The experiment was programmed using E-Prime 2.0 and consisted of four phases: problems presentation, prototypes learning, consciousness level manipulation, and answer.

In the problems presentation phase, the participants were presented with eight blocks. Each block had five trials that would randomly present a SIP on the computer screen (high-and low-difficulty problems were presented randomly too), resulting in 40 trials presenting 40 problems. The participants had 30 s to memorize it carefully. To eliminate participants answering questions based on personal experience, instead of learning from the prototype we provided, we also required the participants to judge whether they already knew the answer to the question due to personal life experience or education. Participants were asked to press the “F” key if they already knew the answer before participating in this study and the “J” key if not. The answer was eliminated in the data analysis phase if participants pressed the “F” key.

The prototypes learning phase used the same design as the problems presentation phase, but each trial was randomly presented with the prototype information corresponding to the problem in the first phase (but the problem itself was not presented), such as “When the nurse gives the injection, she can use a small needle to inject the medicine like squeezing toothpaste,” and participants did not have to press any key, then after 60 s, the screen will automatically display the next prototype until all prototypes corresponding to the problem are presented.

In the consciousness level manipulation phase, participants in the unconscious group were informed to complete a 3-min number comparison task; participants in the conscious group were instructed to recall the problems and prototypes they had seen before and to try to think of solutions to the problems.

In the answer phase, the participants began to answer the SIP with paper and pen.

2.1.4. Data analysis

For the SIP, we rated the answers on a scale from 0 to 2, based on the criteria shown in previous studies ( Yang et al., 2016 ). If the participant had recalled the correct prototype and correctly solved the problem, the score was 2; if the participant had only recalled the correct prototype but failed to solve the problem, the score was 1; and if the participant had failed to answer the question correctly, the score was 0. To assess how well the participants solved the problem, we computed two indices, one was the prototype activation rate, which refers to the number of questions with a non-zero individual score divided by the number of all questions after eliminating the questions with known answers. The other was the accuracy rate, which refers to the number of questions with an individual score of 2 divided by the number of all questions, after excluding questions to which the participant knew the answer. Hereafter, SPSS 22.0 was used for statistical analysis. Repeated measures ANOVA was performed for the prototype activation rate and problem-solving accuracy rate.

2.2. Results

Three trained psychology majors were asked to rate participants’ SIP-solving scores according to the method we presented in the Data Analysis section earlier. The scorer reliability was 0.918. Descriptive statistics for the prototype activation rate and accuracy rate are provided in Table 1 .

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Table 1 . Descriptive statistics of prototype activation rate and accuracy rate in conscious and unconscious processing conditions (M ± SD).

2.2.1. The prototype activation rate

A 2 × 2 repeated-measures ANOVA was performed to assess the effects of the within-subjects factor difficulty (high difficulty vs. low difficulty) and the between-subjects factor group (conscious vs. unconscious) on the prototype activation rate; age and gender of participants were used as covariables. A significant main effect of the group [ F (1,76) = 32.732, p < 0.001, η p 2 = 0.304] revealed that the prototype activation rate of the conscious condition (M = 0.827, SD = 0.085) was significantly lower than the unconscious condition (M = 0.920, SD = 0.049). The main effect on difficulty was also significant [ F (1,76) = 46.172, p < 0.01, η p 2 = 0.381]; the high-difficulty prototype activation rate (M = 0.840, SD = 0.116) was significantly lower than the low-difficulty prototype activation rate (M = 0.907, SD = 0.077). Moreover, the interaction between group and difficulty was significant, F (1,76) = 40.509, p < 0.001, η p 2 = 0.351.

As we found a significant interaction between group and difficulty, we followed up with a simple effect analysis. The outcomes showed that the prototype activation rate was not significantly different ( p = 0.095) between the unconscious (M = 0.922, SD = 0.055) and conscious groups (M = 0.893, SD = 0.092) under the low difficulty condition. However, in the high-difficulty condition, the prototype activation rate of the unconscious group (M = 0.918, SD = 0.055) was significantly higher than the conscious group (M = 0.765, SD = 0.110; p < 0.01). In the conscious condition, the prototype activation rate of the high-difficulty task (M = 0.765, SD = 0.110) was significantly lower than the low-difficulty task (M = 0.893, SD = 0.092). However, there was no difference between the high-difficulty prototype activation (M = 0.918, SD = 0.055) and low-difficulty prototype activation rates (M = 0.922, SD = 0.055) in the unconscious group. The results are shown in Figure 1 .

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Figure 1 . The prototype activation rate of Experiment 1.

2.2.2. The accuracy rate

A 2 × 2 repeated measures ANOVA was performed with the accuracy rate as the dependent variable, difficulty (high vs. low difficulty) as the within-subject variable, group (conscious vs. unconscious) as the between-subjects variable, and age and gender as the covariables. The results showed that the main effect was highly significant, F (1,76) = 71.199, p < 0.001, η p 2 = 0.487. The accuracy rate of the conscious condition (M = 0.455, SD = 0.138) was significantly lower than the unconscious condition (M = 0.693, SD = 0.106). The main effect on difficulty was significant, F (1,76) = 193.755, p  < 0.001, η p 2  = 0.721; the high difficulty accuracy rate (M = 0.488, SD = 0.216) was significantly lower than the low difficulty accuracy rate (M = 0.657, SD = 0.156). The interaction between group and difficulty was significant, F (1,76)  = 94.566, p  < 0.001, η p 2  = 0.558.

Further simple effect analysis showed that the accuracy rate of the unconscious group (M = 0.718, SD = 0.110) was significantly higher than the conscious group (M = 0.598, SD = 0.172; p  < 0.01) under the low-difficulty condition, and the accuracy rate of the unconscious group (M = 0.667, SD = 0.125) was significantly higher than the conscious group (M = 0.313, SD = 0.121; p  < 0.01) under the high-difficulty condition. In the unconscious processing group, the accuracy rate of the high-difficulty task (M = 0.668, SD = 0.125) was significantly lower than the low-difficulty task (M = 0.598, SD = 0.172; p  = 0.004). Moreover, there was no difference between the high-difficulty (M = 0.667, SD = 0.125) and low-difficulty accuracy rates (M = 0.598, SD = 0.172). The results are shown in Figure 2 .

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Figure 2 . The accuracy rate of Experiment 1.

2.3. Discussion

Taken together, these results showed that if we solve SIP consciously, two indices of prototype heuristics (the prototype activation and accuracy rates) can be quite different because of the different task difficulties. Otherwise, the effect of prototype heuristics is good regardless of the difficulty level of the task if unconscious processing is used. When the task difficulty was low, the prototype activation rate of the unconscious group was no different from that of the conscious group, but the accuracy rate was significantly higher than that of the conscious group. When the task difficulty was high, the prototype activation rate and accuracy rate of the unconscious group were significantly higher than that of the conscious group. This result is consistent with our hypothesis, that is, UC promotes SIP solving. According to previous research, the prototype heuristic is an automatic process ( Zhu et al., 2019 ), specifically, there is a semantic similarity between the “need function” in problem representation and the “characteristic function” in prototype representation. When participants map the “characteristic function” to the “need function,” the problem will be solved, and such structural mapping is an automatic process ( Zhang et al., 2012 ), also known as representation-connection ( Zhu et al., 2019 ). In SIP solving, individuals need to find the prototype character that plays a key role in the current problem among the numerous prototype characters, which requires a wide range of information processing. Unconscious processing, with its powerful searching and associative abilities, can help individuals find corresponding archetypes and solve problems.

Both simple and difficult scientific inventions benefited from unconscious processing, which is somewhat different from previous studies. Zhong et al. (2008) , using RAT as a creative task, showed that when the difficulty of the task was simple, there was no significant difference in the impact of conscious and unconscious thought on creative problem-solving, but when the difficulty of the task was medium, unconscious thought had a more prominent role in promoting creative problem-solving. In our study, UC was significantly more conducive to creative problem-solving than consciousness, regardless of whether the task was easy. This occurred presumably because different creative tasks were used. From the perspective of semantic processing, RAT requires the semantic processing of words, while SIP requires a semantic connection between sentences. It may even be that the low-difficulty SIP is more difficult than the RAT task, so it is more suitable for unconscious processing. Due to different creative tasks, the definition of difficulty may also be different. To illustrate, the difficulty of the materials in this experiment was subjectively assessed by three students majoring in psychology, while in the Scientific Innovation Problem Database, each question has a corresponding heuristic index. A heuristic index refers to the accuracy rate of solving problems obtained by the participant after learning the prototype minus the accuracy rate of solving problems without learning the prototype. Therefore, in Experiment 2, the heuristic index was used as the difficulty standard of SIP to further explore whether the facilitation of unconscious processing in creative problem-solving was related to difficulty.

Overall, the results provide strong support that distractor tasks can promote problem-solving after leading to the individual unconscious thought. It is worth further discussing that if the distractor task occupies too many cognitive resources, will the promotion effect of UC on creative problem-solving be weakened? To address this, we conducted Experiment 2.

3. Experiment 2

The results of Experiment 1 suggested that UC has a facilitatory effect on prototype heuristics. Will this phenomenon be affected by different cognitive loads induced by different difficulties of distractor tasks? We hypothesized that UC’s positive effect on prototype heuristics would be reduced when the distractor task becomes more difficult and consumes more cognitive resources.

3.1. Method

3.1.1. participants.

Ninety participants (aged between 18 and 23 years, mean age = 19.67 years, SD = 1.29, 36 male participants) from Southwest University were recruited by advertising. Participants were randomly assigned to the conscious condition ( n  = 30), low-distractor task condition ( n  = 30), and high-distractor task condition ( n  = 30). All participants provided informed consent before participating and received some remuneration after the experiment.

3.1.2. Materials

3.1.2.1. scientific innovation problem.

Twenty-four SIPs were selected from the Scientific Innovation Problems Database ( Zhu, 2011 ). Twelve of them were low difficulty (M = 0.81, SD = 0.05) and the others were high difficulty (M = 0.57, SD = 0.02). Importantly, we measured difficulty by heuristics rate.

3.1.2.2. Distractor task

Experiment 2 adopted the same distractor task as Experiment 1; the only difference was that different numeric types were used to induce high and low cognitive loads. As comparisons between fractions are more complicated compared with integer comparisons, fraction comparisons require a higher cognitive load to process. Therefore, we induced low cognitive loads by requiring participants to compare random two-digit numbers and induced high cognitive loads by requiring participants to make comparisons between random fractions, in which the numerator and denominator were both random integers between 10 and 99. A preliminary experiment was used to examine the cognitive load distinction between integer and fraction comparisons. As a result, the accuracy of fraction comparison tasks (M = 0.73, SD = 0.89) was significantly lower than the accuracy of integer comparison tasks (M = 0.92, SD = 0.45), reaction time (M = 928.79 ms, SD = 205.03) was significantly higher than that of integer comparison tasks (M = 613.77 ms, SD = 135.93), and the difficulty score (M = 5.90, SD = 0.89), which was subjectively assessed by participants, was significantly lower than the difficulty score of integer comparison tasks (M = 1.33, SD = 0.69). It could be argued that the integer and fraction comparison tasks have a reliable effect on distinguishing between low and high cognitive loads.

3.1.3. Procedure

The basic procedure in Experiment 2 was identical to Experiment 1, with two alterations. One was that in the problems presentation phase, the participants were presented with four blocks instead of eight, and each block had six trials that would randomly present a SIP on the computer screen, resulting in 24 trials presenting 24 problems. The other alteration was that in the consciousness level manipulation phase, participants in the low-distractor task condition were informed to complete a 3-min integer comparison task, participants in the h-distractor task condition were informed to complete a 3-min fraction comparison task, and the conscious condition participants were informed to recall the problems and prototypes they had seen before and to try to think of solutions to the problems.

3.2. Results

SPSS 22.0 was used for statistical analysis. Repeated measures ANOVA was performed for the prototype activation and problem-solving accuracy rates. Three trained psychology majors were asked to rate participants’ SIP-solving scores according to the method we presented in the Data Analysis section in Experiment 1. The scorer reliability was 0.848. Descriptive statistics for the prototype activation and the accuracy rates are provided in Table 2 .

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Table 2 . Descriptive statistics of prototype activation rate and accuracy rate in different conditions (M ± SD).

3.2.1. The prototype activation rate

A 2 × 3 repeated-measures ANOVA was performed to assess the effects of the within-subjects factor difficulty (high difficulty vs. low difficulty) and the between-subjects factor group (conscious vs. low-distractor task vs. h-distractor task) on prototype activation rate; age and gender of participants were used as covariables. A significant main effect of the group [F (2,85)  = 26.552, p  < 0.001, η p 2  = 0.358] revealed that the prototype activation rate of the low-distractor task condition (M = 0.933, SD = 0.050) was significantly higher than the high-distractor task condition (M = 0.876, SD = 0.061), and the prototype activation rate of the high-distractor task condition was significantly higher than that of the conscious condition (M = 0.778, SD = 0.125). The main effect on difficulty was not significant [ F (2,85)  = 0.015, p  = 0.903, η p 2  < 0.001]. The interaction between groups and difficulty was significant, F (2,85)  = 3.625, p  = 0.031, η p 2  = 0.079.

Simple effect analysis showed that the outcomes indicated that the prototype activation rate in the conscious condition (M = 0.805, SD = 0.017) was significantly lower than the low-distractor task condition (M = 0.928, SD = 0.016) and high-distractor task condition (M = 0.905, SD = 0.016; p  < 0.001). However, the prototype activation rate was not significantly different between the high-and low-distractor task conditions ( p  = 0.319). However, when the problems were highly difficult, the prototype activation rate in the conscious condition (M = 0.750, SD = 0.021) was significantly lower than in the low-distractor task condition (M = 0.939, SD = 0.020) and high-distractor task condition (M = 0.848, SD = 0.020; p  < 0.001), and the prototype activation rate in the high-distractor task condition was significantly lower than the low-distractor task condition ( p  < 0.001). Simultaneously, in the conscious condition, the prototype activation rate in high-difficulty problems (M = 0.750, SD = 0.021) was significantly lower than that in low-difficulty problems (M = 0.805, SD = 0.017; p  = 0.011). In the low-distractor task condition, the prototype activation rate in high-and low-difficulty problems was not significantly different ( p  = 0.582). In the high-distractor task condition, the prototype activation rate in high-difficulty problems (M = 0.848, SD = 0.020) was significantly lower than that in low-difficulty problems (M = 0.905, SD = 0.016; p  = 0.006). A visual display is provided in Figure 3 .

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Figure 3 . The prototype activation rate of Experiment 2.

3.2.2. The accuracy rate

A 2 × 3 repeated-measures ANOVA was performed to assess the effects of the within-subjects factor difficulty (high difficulty vs. low difficulty) and the between-subjects factor group (conscious vs. low-distractor task vs. high-distractor task) on the accuracy rate; age and gender of participants were used as covariables. A significant main effect of the group [ F (2,85)  = 15.109, p  < 0.001, η p 2  = 0.258] revealed that the accuracy rate of the low-distractor task (M = 0.644, SD = 0.080) and high-distractor task conditions (M = 0.613, SD = 0.098) were significantly higher than that of the conscious condition (M = 0.492, SD = 0.149), but the accuracy rate of the high-distractor task and low-distractor task conditions were not differentiated. The main effect on difficulty was significant [ F (2,85)  = 5.217, p  = 0.025, η p 2 = 0.057]. Moreover, the interaction between groups and difficulty was significant, F (2,85)  = 6.092, p  = 0.003, η p 2  = 0.123.

A simple effect analysis was conducted and the outcomes indicated that when SIPs were high-difficulty problems, the accuracy rate in the conscious condition (M = 0.510, SD = 0.138) was significantly lower than the low-distractor task (M = 0.630, SD = 0.808) and high-distractor task conditions (M = 0.651, SD = 0.102; p  < 0.001), but the accuracy rate was not significantly different between the high-and low-distractor task conditions ( p  = 0.846). When SIP were low-difficulty problems, the accuracy rate in the conscious condition (M = 0.474, SD = 0.175) was significantly lower than the low-distractor task (M = 0.658, SD = 0.118; p  < 0.001) and high-distractor task conditions (M = 0.575, SD = 0.127; p  = 0.021), but the accuracy rate was not significantly different between the high-and low-distractor task conditions ( p  = 0.069). The accuracy rates of high-and low-difficulty problem-solving were not significantly different under the conscious and low-distractor task conditions ( p  = 0.10; p  = 0.195), and a significant difference was found under the high-distractor task condition ( p  = 0.001). More specifically, the accuracy rate of high-difficulty problem-solving was significantly greater than that of low-difficulty problem-solving. A visual display is presented in Figure 4 .

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Figure 4 . The accuracy rate of Experiment 2.

3.3. Discussion

In low-difficulty SIP solving, the prototype activation rate of the two unconscious groups was better than that of the conscious group, which partially supports the results of Experiment 1. Simultaneously, the prototype activation rate of the high-distractor task was significantly lower than that of the low-distractor task condition, which demonstrates that with an increase in cognitive load induced by the distractor task, the effect of unconscious thinking promoting creative problem-solving declined. This occurred presumably because although the participants were still unconsciously processing the SIPs when they were doing irrelevant tasks, the level of cognitive load of distractor tasks might affect the degree of involvement in the target and irrelevant tasks ( Damian and Sherman, 2013 ). In prototype heuristics, to realize the connection between prototype information and SIPs, the individual needs to search out the corresponding information from all the currently learned prototypes to activate the prototype successfully. Previous research suggests that unconscious thinking made individuals conduct a wide range of searches, including information that may seem irrelevant to the problem ( Ding et al., 2019 ). If the degree of involvement of the individual in the distractor task is too high, even though the activation of the prototype can be realized in unconscious processing, the individual may not be aware of it at the conscious level, thus reducing the individual’s activation rate in the prototype.

The accuracy rates were not significantly different between the three levels of consciousness. However, in the high-distractor task condition, the accuracy rate of high-difficulty problem-solving was significantly higher than the accuracy rate of low-difficulty problem-solving. These findings verified Zhong’s assumption, showing that conscious or unconscious thinking makes no difference in the promoting effect of problem-solving when the difficulty of problems is low; only high-difficulty problems could distinguish the promoting effect between conscious and unconscious thinking. According to Zhao (2018) , unconscious processing could be divided into deep and shallow processing; deep processing could improve the accessibility of answers. Therefore, do the different levels of cognitive load induced by distractor tasks lead to changes in the depth of unconscious processing and further lead to differences in problem resolution rates? Furthermore, how does the depth of unconscious processing affect individual cognitive activities in problem-solving? To address this, we conducted Experiment 3.

4. Experiment 3

The results of Experiments 1 and 2 suggested that, compared with consciousness, UC had a facilitatory effect on prototype heuristics under high-difficulty problem-solving conditions, and the size of the facilitation effect is related to the cognitive load induced by the distractor task. This occurred presumably because UC induced by a distractor task changes individual EFs and influences creative problem-solving. Thus, we only chose high-difficulty SIPs to further investigate the internal mechanism of the promotion effect of unconscious processing on prototype heuristics. The experiments reported here try to verify whether unconscious thinking promotes individual EF, which is conducive to creative problem-solving.

We hypothesized the following: a. compared with conscious processing, unconscious processing occupied fewer EFs, and compared with low-distractor task, high-distractor task depleted more EFs; b. residual EFs in the unconscious state can promote SIP solving, and EFs play a mediating role between UC and SIP solving.

4.1. Method

4.1.1. participants.

Eighty-six participants (aged between 18 and 23 years, mean age = 19.67 years, SD = 1.43, male = 31) from Southwest University were recruited by advertising. Participants were randomly assigned to the conscious condition ( n  = 28), low-distractor task condition ( n  = 29), and high cognitive loads condition ( n  = 29). All participants provided informed consent before participating and received some remuneration after the experiment.

4.1.2. Materials

4.1.2.1. scientific innovation problem.

Twenty-four high-difficulty (M = 0.63, SD = 0.05) SIPs were selected from the Scientific Innovation Problems Database ( Zhu, 2011 ); and the difficulty was measured by heuristics rate.

4.1.2.2. Distractor task

The distractor task in Experiment 3 was identical to Experiment 2.

4.1.2.3. Executive functions measurement

The current study used a two-back task to examine the ability of individuals to update. During the task, random integer numbers between 0 and 9 were presented one at a time, and participants were asked to compare each number with the second number before it. If the two numbers are the same, a key response is required. If the two numbers are different, participants do not react. Each number was presented for only 1 s, requiring participants to react as quickly as possible. In data processing and analysis, we excluded the reaction time of the wrong reaction of the participants and then calculated all the reaction times of the correct trial.

The shifting number task examined the ability of individuals to shift. In each trial, a single letter and a single number were presented on the screen concurrently, the word color could be red or green, and stimuli would change color randomly. Participants responded to stimuli by pressing keys. If the word color was green, participants needed to respond to the parity of numbers; they were asked to press “F” in response to any odd number (1,3,5,7,9) and “J” in response to any even number (2,4,6,8). If the word color was red, participants needed to decide whether the letter was a vowel or consonant; they were asked to press “F” in response to any vowels (A, E, I, O, U) and “J” in response to any consonants (G, K, M, R, etc.). After learning the rules in the practice phase, the participants entered the formal experiment. At the end of Experiment 3, data processing and analysis were performed on the total conversion response time of the shifting number task.

The Stroop task is used to examine individuals’ inhibition ability ( Zhang et al., 2020 ). The task involved presenting a single-color word at the center of the screen; in the current experiment, one Chinese character was presented at a time, which was red, green, yellow, or blue and meant “red,” “green,” “yellow,” or “blue.” Sometimes the character and its color were the same, for example, the character “red” was red, and sometimes the character and its color were not the same, for example, the character “red” was green; every trial contained consistencies and inconsistencies. Participants were inquired to ignore the word and give a key-press in response to the color. The colors corresponded to the keys one by one (“D” for red, “F” for green, “J” for yellow, and “K” for blue). One block in the emotional Stroop task comprised 30 stimuli (i.e., trials). During each trial, each Chinese character remained until the participant responded or 2,000 ms passed, and after a 1,500 ms fixation was presented, the next stimulus appeared. Ten practice trials and a 3-min formal test were designed. The program gave feedback on correct or incorrect responses after the participant pressed the button during the practice trials, and there was no feedback during the formal tests irrespective of whether the response was correct or incorrect. At the end of Experiment 3, data processing and analysis were conducted by subtracting the response time of the inconsistent Stroop test from the response time of the consistent Stroop test.

4.1.3. Procedure

The procedure was the same as in Experiment 2, but there was an extra phase before the answer phase. The new phase was the EFs measurement phase, which measured three dimensions in random order: working memory was measured by a two-back task, shifting by a shifting number task, and inhibition by the Stroop task. Participants had at most 3 min to complete each task. Note that the problems presentation and the prototypes learning phases presented different problems and prototypes from Experiment 2; in the current experiment, 24 high-difficulty problems were selected as materials.

4.2. Results

SPSS 22.0 was used for statistical analysis; relative mediation analyses were performed using the mediation package. To investigate the relations among the levels of consciousness, three sub-functions of EFs, two rates of prototype heuristics, and descriptive statistics are summarized in Table 3 . Among them, the three sub-functions (inhibition, shifting, and updating) scores were calculated by the reaction time of the correct response to the task, in milliseconds. The correlation between each variable was analyzed and is presented in Table 4 .

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Table 3 . Descriptive statistics of variables ( n = 86; M ± SD; the total EF score has been deleted).

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Table 4 . Correlation analysis between variables (the total EF score has been deleted).

As can be seen from Table 4 , there were significant positive correlations between all conditions. Hereafter, mediation analyses were performed. As independent variables are categorical variables, and intermediate variables and dependent variables were continuous variables in the current experiment, bootstrap relative mediation analysis was performed using the mediation package ( Hayes and Preacher, 2014 ; Jie and Wen, 2017 ). The independent variable levels of consciousness were coded, with the conscious condition as the reference variable, the high-distractor task condition as dummy variable 1, and the low-distractor task condition as dummy variable 2. Bootstrap set random sampling to 5,000 times, with the prototype activation rate and the accuracy rate as dependent variables under a 95% confidence interval. The global mediation analysis and relative mediation analysis were conducted with the three sub-functions of EFs as three parallel mediation variables. The results were as follows.

The total effect of the global mediation analysis with the prototype activation rate as the dependent variable was significant [ F (4,81)  = 43.5, p  < 0.001], indicating that the two relative total effects are not 0. The total direct effect of the global mediation analysis with the prototype activation rate as the dependent variable was also significant [ F (7,78)  = 114.95, p  < 0.001] and indicated that the two relative direct effects are not 0. The total effect of the global mediation analysis with the accuracy rate as the dependent variable was significant [ F (4,81)  = 11.96, p  < 0.001], indicating that the two relative total effects are not 0. The total direct effect of the global mediation analysis with the accuracy rate as the dependent variable was also significant [ F (7,78)  = 13.31, p  < 0.001], indicating that the two relative direct effects are not 0. Therefore, further relative mediation analysis had to be conducted.

4.2.1. The prototype activation rate as the dependent variable

As shown in Figure 5 , the relative mediation analysis, with the prototype activation rate as the dependent variable and levels of consciousness as the reference variable, showed that the working memory ability (updating) as the intermediate variable and the 95% bootstrap confidence interval between the high-distractor task and the conscious conditions was [0.15, 0.28], excluding 0, indicating significant relative mediation effect (a 11  = 0.14, b 1  = 0.38, a 11 b 1  = 0.053). The 95% bootstrap confidence interval of the relative mediation analysis between the low-distractor task and conscious conditions was [0.19, 0.62], excluding 0, indicating a significant relative mediation effect (a 12  = 1.03, b 1  = 0.38, a 12 b 1  = 0.39). These results suggest that high-and low-distractor tasks promote the updating ability of individuals and thus promote the ability of individuals to activate prototypes. However, the indirect mediating effect of the updating function was higher in the low-distractor task than in the high-distractor task condition.

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Figure 5 . Mediation analysis with prototype activation rate as the dependent variable. ** indicates that the correlation is significant at the level of 0.01.

With the prototype activation rate as the dependent variable, levels of consciousness as the reference variable, and shifting ability as the intermediate variable, the 95% bootstrap confidence interval between the high-distractor task and conscious conditions was [0.36, 0.79], excluding 0, indicating a significant relative mediation effect (a 21  = 1.14, b 2  = 0.51, a 21 b 2  = 0.58); and the 95% bootstrap confidence interval between the low-distractor task and conscious conditions was [0.66, 1.17], excluding 0, indicating a significant relative mediation effect (a 22  = 1.83, b 2  = 0.51, a 22 b 2  = 0.93). These results suggest that high-and low-distractor tasks promote the shifting ability of individuals and thus promote the ability of individuals to activate prototypes. However, the indirect mediating effect of the shifting function was higher in the low-distractor task than in the high-distractor task condition.

With the prototype activation rate as the dependent variable, levels of consciousness as the reference variable, and the inhibition ability as the intermediate variable, the 95% bootstrap confidence interval between the high cognitive load condition and the conscious condition was [0.26, 0.97], excluding 0, indicating a significant relative mediation effect (a 31  = 1.04, b 3  = 0.56, a 31 b 3  = 0.58); and the 95% bootstrap confidence interval between the low-distractor task and conscious conditions was [0.51, 1.39], excluding 0, indicating a significant relative mediation effect (a 32  = 1.65, b 3  = 0.56, a 32 b 3  = 0.92). These results suggest that high-and low-distractor tasks promote the inhibition ability of individuals, and thus promote the ability of individuals to activate prototypes. However, the indirect mediating effect of the inhibition function was higher in the low-distractor task than in the high-distractor task condition.

4.2.2. The accuracy rate as the dependent variable

As shown in Figure 6 , relative mediation analysis with the accuracy rate as the dependent variable, levels of consciousness as the reference variable, and working memory ability (updating) as the intermediate variable, showed that the 95% bootstrap confidence interval between the high-distractor task and conscious conditions was [−0.16, 0.33], including 0, indicating no significant relative mediation effect; and the 95% bootstrap confidence interval between the low-distractor task and conscious conditions was [0.18, 0.71], excluding 0, indicating a significant relative mediation effect (a 12  = 1.04, b 1  = 0.40, a 12 b 1  = 0.42). These results suggest that, compared with the conscious condition, the high-distractor task condition does not promote the working memory ability of individuals, but low-distractor tasks promote individuals’ working memory ability and thus promote individuals’ ability to solve SIPs. In addition, the indirect mediating effect of the updating function was higher in the low-distractor task than in the high-distractor task condition.

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Figure 6 . Mediation analysis with an accuracy rate as the dependent variable. ** indicates that the correlation is significant at the level of 0.01.

The relative mediation analysis with the accuracy rate as the dependent variable, levels of consciousness as the reference variable, and the shifting ability as the intermediate variable showed that the 95% bootstrap confidence interval between the high-distractor task and conscious conditions was [0.09, 0.82], excluding 0, indicating a significant relative mediation effect (a 21  = 1.14, b 2  = 0.37, a 21 b 2  = 0.42); and the 95% bootstrap confidence interval between the low-distractor task and conscious conditions was [0.15, 1.30], excluding 0, indicating a significant relative mediation effect (a 22  = 1.83, b 2  = 0.37, a 22 b 2  = 0.68). These results suggest that, compared with the conscious condition, the high-and low-distractor task conditions promote the shifting ability of individuals, and thus promote individuals’ ability to solve SIPs. However, the indirect mediating effect of the shifting function was higher in the low-distractor task than in the high-distractor task condition.

The relative mediation analysis with the accuracy rate as the dependent variable, levels of consciousness as the reference variable, and the inhibition ability as the intermediate variable showed that the 95% bootstrap confidence interval between the high-distractor task and conscious conditions was [0.17, 0.76], excluding 0, indicating a significant relative mediation effect (a 31  = 1.04, b 3  = 0.42, a 31 b 3  = 0.44); and the 95% bootstrap confidence interval between the low-distractor task and conscious conditions was [0.39, 1.10], excluding 0, indicating a significant relative mediation effect (a 32  = 1.65, b 3  = 0.42, a 32 b 3  = 0.69). These results suggest that, compared with the conscious condition, the high-and low-distractor task conditions promote the inhibition ability of individuals, and thus promote individuals’ ability to solve SIPs. However, the indirect mediating effect of the inhibition function was higher in the low-distractor task than in the high-distractor task condition.

4.3. Discussion

The positive results of Experiment 3 supported our hypothesis and showed that, compared with the conscious condition, participants in the unconscious condition (low-distractor task and high-distractor task) had higher EFs. Moreover, participants who performed the low-distractor task also had higher EFs than participants who performed the high-distractor task, supporting the viewpoint that EFs can be depleted ( Schmeichel, 2007 ). This means that previously conscious SIP solving occupies the largest amount of EFs resources, followed by the high-distractor task, and the low-distractor task occupies the least cognitive resources. Thus, conscious SIP solving occupies more cognitive resources than the distractor task. On the one hand, research has found that the correlation between insight and reasoning ability is as high as 0.920, but when the correlation between the two abilities is assumed to be 1, the model has a significant loss of fit, indicating that insight problem-solving and reasoning abilities highly overlap, although differently ( Chuderski and Jastrzebski, 2018 ). Reasoning is an important ability that constitutes EFs ( Chrysikou, 2019 ), so consciously solving insight problems will involve more EFs. On the other hand, Schmeichel (2007) also mentioned that distractor tasks (such as simple mathematical calculations) are achieved through automatic or regular cognitive processes that do not require a lot of EFs. Therefore, it is not surprising that SIP requires more EFs than distractor tasks.

Based on the above reasoning, after manipulating the level of consciousness, the rest of the EFs in the conscious condition was less than the high-distractor task and low-distractor task conditions, verifying that the EFs used in SIP in the conscious condition were less than the high-distractor task and low-distractor task conditions. The remaining EFs were positively correlated with the SIP-solving performance, which suggests that EFs contribute to unconscious SIP-solving. Specifically, when the prototype activation rate was used as the dependent variable, the three dimensions of EFs in the two distractor task groups had partial mediating effects compared with the control group, but the mediating effects of the three mediating variables in the high-distractor task condition were all smaller than those in the low-distractor task condition, which verified hypothesis b.

Of note, however, Korovkin et al. (2018) used the dual task to investigate the effect of different working memory systems’ load on insight problem-solving. They found that insight reorganization relies on fairly low levels of processing occurring in the working memory storage system, and the closer a person is to an insight solution, the more important the role of working memory in insight problem-solving becomes. This suggests that working memory is involved in insight problem-solving but at a very low level. Specifically, the difficulty of recalling memory content rather than the organization form affects an individual’s ability to make creative associations ( Beaty et al., 2014 ). In the current experiment, the link between the prototype and the problem was already established at the unconscious level. To this end, bringing the connection to the conscious level requires very little updating ability, and the closer individuals get to the insight solution, the more important the role of updating working memory becomes. This reasoning also explains why updating has a significant mediating effect on the high and low cognitive load of prototype activation and a significant mediating effect on the low cognitive load of problem-solving, but not on the high cognitive load of problem-solving. This is because the prototype activation by working memory updating only needs to extract the key prototype, and the requirement of working memory updating is very small, but the problem solving of working memory updating needs to extract and problem solve the related characteristic of the prototype of the function and get the solution, which is more demanding on working memory updating. Therefore, working memory updating cannot be supported enough under a high cognitive load.

5. General discussion

The current study performed three experiments to investigate the difference in the effect of prototype heuristics in SIP solving with different levels of consciousness and explore its internal mechanism. The results found that after learning prototypes, distractor tasks induced unconscious processing, and when solving scientific innovation problems creatively by unconscious thinking, especially when the difficulty of the problem increased, the facilitation effect of unconscious processing became more prominent. The effect of unconscious processing was also related to the cognitive load of distractor tasks. This is generally consistent with previous studies on the relationship between unconscious processing and creative problems. Based on previous research, this research also studied the relationships among UC, EFs, and SIP and found that three dimensions of EFs (working memory, shifting, and inhibition) mediated the relationship between the level of consciousness and SIP solving. However, what is the specific process and mechanism of this action?

First, it is worth thinking about whether the executive function is a trait or an ability because different perspectives can lead to the opposite result. When we think of the executive function as a trait, the researchers will treat the measured executive function scores as a general level of executive control, and participants who have high executive function scores will have more resources to complete any task. However, if we think of the executive function as an ability, then the resource depletion hypothesis ( Schmeichel, 2007 ) tells us that prior tasks deplete our executive control, and the executive function scores measured in later tasks was the amount of executive control ability that the participant has left available for this measuring task, the lower these scores, the higher the level of executive control the subject used in the previous task. In our experiment, the results of Experiment 3 can only be explained by taking the executive function as a kind of ability, that is, the differences in the executive function of different groups are caused by the differences in the operations that induce different levels of consciousness previously, rather than the differences in the pre-existing traits of different groups. Moreover, the order of such differences has been reasonably explained in the discussion of Experiment 3. Therefore, this study also provides additional support for the conclusion that executive function is an ability.

In addition, according to the prototype heuristic theory, the insight of SIP includes at least two stages: prototype activation and obtaining heuristics from a prototype. Prototype activation is automatic and obtaining heuristics requires executive control ( Cao et al., 2006 ), which implies a cooperative mode of UC and EFs in SIP solving. Similarly, Beaty et al. (2016) summarized brain imaging research on creative thinking and found that many studies have pointed out the important role of default network and episodic memory in creative cognition; they suggested that the default network influences the generation of candidate ideas, while executive control guides and monitors them. The results of Experiment 3 supported this view, that is, compared to the high-distractor task condition, although the low-distractor task condition had a larger set of available EFs resources that led to better performance on SIP, participants who only performed conscious SIP solving had the worst performance, despite all their EFs resources used to solve the SIP. This means that although EFs are important for SIP solving, the result of problem-solving will be very poor if there is no unconscious processing, and EFs and UC are both indispensable in difficult insight problem-solving.

While many studies documented the positive role of EFs in creativity, others provide evidence to the contrary. For example, Chuderski and Jastrzebski (2018) concluded that to date, researchers’ studies on working memory and insight problem-solving have reported highly inconsistent results, ranging from moderately positive to zero and even negative effects. Zhu et al. (2019) discussed brain structure and resting brain function in SIP solving; they reported that decreased response inhibition, as well as the automatic association of semantics, will support representation-connection in the insight process. This suggests that the decrease in inhibition ability promotes semantic linkage during insight problem-solving, and thus facilitates problem-solving. We suggest that a paradoxical result of the different roles of EFs in insight problem-solving is that the UC and EFs resources required are different at different stages of insight problem-solving. That is, with the unconscious processing of the problem situation, key information retrieval, and the formation of semantic links, excessive EFs will hinder the process. Contrastingly, if the solution to the problem has already been found in the unconscious state, too little executive control will make individuals unable to extract the results to the level of consciousness and report them, thus affecting the performance of the subjects.

Past research has outlined this process. For example, the role of UC is to generate ideas by searching for materials in episodic memory ( Beaty et al., 2016 ), or generate “structural mapping” and “representation-connection” between prototypes and problems ( Zhu et al., 2019 ), while before, during, and after unconscious action, different levels of EFs play different roles. For example, many researchers believe that working memory is important in the early stage of insight problem-solving, such as problem understanding and goal orientation ( Chrysikou, 2019 ), and in the later stage, Korovkin et al. (2018) suggested that working memory is more important the closer it is to problem-solving. Simple creativity tasks were not affected by working memory loads ( Stuyck et al., 2022 ); it can be inferred that working memory also plays a role in extracting thoughts or links from the unconscious to the conscious level. In addition, inhibition plays an important role in suppressing conventional thoughts that are not novel when the individual is in cognitive fixation ( Camarda et al., 2018 ), but also blocks UC-dominated representation-connection ( Zhu et al., 2019 ). This suggests a complex relationship between the negative role of inhibitory control in unconscious processes and the important role it plays in the top-down overcoming of functional fixity. Lu et al. (2017) found that task switching can enhance creativity by reducing cognitive fixation, suggesting the role of switching in fixation, similar to inhibition. Ding et al. (2019) found that subjects’ performance in the Creative Scientific Problem Finding Test, regardless of the field, had no significant difference after conscious and unconscious thinking. However, in the Creative Scientific Problem Finding Test of a specific field, conscious thinking is superior to unconscious thinking, which may reveal that the role of conscious thinking is to screen and control creative thoughts in a specific situation so that creativity can better meet the direction required by the question.

Finally, to further understand the role of UC and EFs in SIP solving, we propose a conjecture about this process based on the viewpoints of previous studies (see Figure 7 ). As can be seen from Figure 7 , we divided the process of UC involving difficult insight problem-solving into three phases: prepared, problem-solving, and answer. Among them, the problem-solving phase was further divided into the first half dominated by UC and the second half dominated by EFs. In the preparation phase, working memory capacity and updating are used to learn and memorize insight problems (and prototype in our experiments), and EFs also help individuals form goal orientation. In the first half of the problem-solving phase, UC plays an important role that assesses its powerful search capabilities to retrieve questions and relevant prototypes and experiences, make new connections, and try to come up with answers. At this time, if EFs (such as inhibition) are too strong, it will cause certain damage to this part. In the second half of the problem-solving phase, working memory tries to extract related information to consciousness and to pick up the semantic links that had formed at the unconscious level; if the participants think they found the right solution, the method is reported (the answer phase), and if it is wrong, one can inhibit the wrong solution, suppress interference or irrelevant information, and use the ability to switch and overcome the fixation, think from a new perspective, and re-enter the cycle until a satisfactory answer is obtained and reported (the answer phase), or a satisfactory answer is not obtained and the problem is not solved (the answer phase).

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Figure 7 . An insight prototype heuristic model of executive functions and unconsciousness. This model is only applicable to the problem-solving process of difficult insight problems with unconscious effects.

Moreover, Yang et al. (2022) argued that it is unclear whether the state of creativity can have an impact on knowledge-rich creative problem-solving and whether interventions, that support analogical transfer in the heuristic prototype paradigm, can be used to improve knowledge-rich creative problem-solving. Current experimental manipulation and findings of this study provide definitive answers that, through certain distractor tasks, it is possible to improve knowledge-rich creative problem-solving, such as SIPs.

To summarize, we reported three experiments to explore the relationships among UC, EFs, and insight problem-solving, found that low cognitive load UC promotes prototype heuristics in SIPs, and proved more evidence for research in this area. To further understand the role of UC and EFs in SIP solving, we propose a conjecture about this process based on the viewpoints of previous studies.

5.1. Limitations

The current study first explored the relationships among UC, EFs, and insight problem-solving and proposed a new conjecture. However, direct evidence of the internal mechanism is somewhat insufficient, and future research can further verify the fuzzy zone. Second, this study uses SIPs as the insight problem, which can only show that the EFs and UC collaboration mode are such in solving the SIPs. The conclusion should be cautiously generalized, and future research can use other insight paradigms for more exploration.

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 human participants were reviewed and approved by the Human Research Ethics Committee, Faculty of Psychology, Southwest University. The patients/participants provided their written informed consent to participate in this study.

Author contributions

YL provided ideas for the argumentation and wrote the submitted manuscript. LT contributed to the conception and design of the study and wrote the first draft of the manuscript. LZ reviewed and edited the manuscript. GC contributed to study design and to manuscript drafting and reviewing. All authors contributed to the article and approved the submitted version.

This study was supported by Southwest University open access funding and Subject on Social Science of Chongqing Medical and Pharmaceutical College (ygz2022203).

Acknowledgments

We thank the supports of Faculty of Psychology, Southwestern University and Subject on Social Science of Chongqing Medical and Pharmaceutical College (ygz2022203).

Conflict of interest

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

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.

Baumeister, R. F., Bratslavsky, E., Muraven, M., and Tice, D. M. (1998). Ego depletion: is the active self a limited resource? J. Pers. Soc. Psychol. 74, 1252–1265. doi: 10.1037/0022-3514.74.5.1252

PubMed Abstract | CrossRef Full Text | Google Scholar

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

Beaty, R. E., Silvia, P. J., Nusbaum, E. C., Jauk, E., and Benedek, M. (2014). The roles of associative and executive processes in creative cognition. Mem. Cognit. 42, 1186–1197. doi: 10.3758/s13421-014-0428-8

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

Camarda, A., Salvia, E., Vidal, J., Weil, B., Poirel, N., Houde, O., et al. (2018). Neural basis of functional fixedness during creative idea generation: an EEG study. Neuropsychologia 118, 4–12. doi: 10.1016/j.neuropsychologia.2018.03.009

Cao, G., Yang, D., and Zhang, Q. (2006). Activation of prototypal matters in insight problem solving: an automatic or controllable processing? Chin. Psychol. Sci. 29, 1123–1127. doi: 10.16719/j.cnki.1671-6981.2006.05.023

CrossRef Full Text | Google Scholar

Cassotti, M., Agogue, M., Camarda, A., Houde, O., and Borst, G. (2016). Inhibitory control as a Core process of creative problem solving and idea generation from childhood to adulthood. Persp. Creat. Dev. 151, 61–72. doi: 10.1002/cad.20153

Chrysikou, E. G. (2019). Creativity in and out of (cognitive) control. Curr. Opin. Behav. Sci. 27, 94–99. doi: 10.1016/j.cobeha.2018.09.014

Chuderski, A., and Jastrzebski, J. (2018). Much ado about aha!: insight problem solving is strongly related to working memory capacity and reasoning ability. J. Exp. Psychol. 147, 257–281. doi: 10.1037/xge0000378

Cristofori, I., Cohen-Zimerman, S., and Grafman, J. (2019). Executive functions. Handb. Clin. Neurol. 163, 197–219. doi: 10.1016/B978-0-12-804281-6.00011-2

Damian, R. I., and Sherman, J. W. (2013). A process-dissociation examination of the cognitive processes underlying unconscious thought. J. Exp. Soc. Psychol. 49, 228–237. doi: 10.1016/j.jesp.2012.10.018

Diamond, A. (2013). Executive functions. Annu. Rev. Psychol. 64, 135–168. doi: 10.1146/annurev-psych-113011-143750

Dietrich, A., and Kanso, R. (2010). A review of EEG, ERP, and neuroimaging studies of creativity and insight. Psychol. Bull. 136, 822–848. doi: 10.1037/a0019749

Dijksterhuis, A., and Meurs, T. (2006). Where creativity resides: the generative power of unconscious thought. Conscious. Cogn. 15, 135–146. doi: 10.1016/j.concog.2005.04.007

Ding, R., Han, Q., Li, R. F., Li, T. N., Cui, Y., and Wu, P. Q. (2019). Unconscious versus conscious thought in creative science problem finding: unconscious thought showed no advantage! Conscious. Cogn. 71, 109–113. doi: 10.1016/j.concog.2019.03.010

Gilhooly, K. J. (2016). Incubation and intuition in creative problem solving. Front. Psychol. 7, 1–9. doi: 10.3389/fpsyg.2016.01076

Hao, X., Cui, S., Li, W. F., Yang, W. J., Qiu, J., and Zhang, Q. L. (2013). Enhancing insight in scientific problem solving by highlighting the functional features of prototypes: an fMRI study. Brain Res. 1534, 46–54. doi: 10.1016/j.brainres.2013.08.041

Hayes, A. F., and Preacher, K. J. (2014). Statistical mediation analysis with a multicategorical independent variable. Br. J. Math. Stat. Psychol. 67, 451–470. doi: 10.1111/bmsp.12028

Huber-Huber, C., and Ansorge, U. (2018). Unconscious conflict adaptation without feature-repetitions and response time carry-over. J. Exp. Psychol. Hum. Percept. Perform. 44, 169–175. doi: 10.1037/xhp0000450

Jie, F., and Wen, Z.-L. (2017). Mediation analysis of categorical variables[J]. Psychol. Sci. 40, 471–477. doi: 10.16719/j.cnki.1671-6981.20170233

Knoblich, G., Ohlsson, S., Haider, H., and Rhenius, D. (1999). Constraint relaxation and chunk decomposition in insight problem solving. J. Exp. Psychol. Learn. Mem. Cogn. 25, 1534–1555. doi: 10.1037/0278-7393.25.6.1534

Korovkin, S., Vladimirov, I., Chistopolskaya, A., and Savinova, A. (2018). How working memory provides representational change during insight problem solving. Front. Psychol. 9, 1–16. doi: 10.3389/fpsyg.2018.01864

Lebed, A. A., and Korovkin, S. Y. (2017). The unconscious nature of insight: a dual-task paradigm investigation. Psychol. Russia State Art 10, 107–119. doi: 10.11621/pir.2017.0307

Lu, J. G., Akinola, M., and Mason, M. F. (2017). "switching on" creativity: task switching can increase creativity by reducing cognitive fixation. Organ. Behav. Hum. Decis. Process. 139, 63–75. doi: 10.1016/j.obhdp.2017.01.005

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

Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., and Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex "frontal lobe" tasks: a latent variable analysis. Cogn. Psychol. 41, 49–100. doi: 10.1006/cogp.1999.0734

Ovington, L. A., Saliba, A. J., Moran, C. C., Goldring, J., and MacDonald, J. B. (2018). Do people really have insights in the shower? The when, where and who of the aha! Moment. J. Creat. Behav. 52, 21–34. doi: 10.1002/jocb.126

Schmeichel, B. J. (2007). Attention control, memory updating, and emotion regulation temporarily reduce the capacity for executive control. J. Exp. Psychol. Gen. 136, 241–255. doi: 10.1037/0096-3445.136.2.241

Silva, F., Dias, J., Silva, S., Bem-Haja, P., Silva, C. F., and Soares, S. C. (2018). Unconscious influence over executive control: absence of conflict detection and adaptation. Conscious. Cogn. 63, 110–122. doi: 10.1016/j.concog.2018.06.021

Sio, U. N., and Ormerod, T. C. (2009). Does incubation enhance problem solving? A Meta-Analytic Review. Psychol. Bull. 135, 94–120. doi: 10.1037/a0014212

Sternberg, R. J., and Lubart, T. I. (1991). An investment theory of creativity and its development. Hum. Dev. 34, 1–31. doi: 10.1159/000277029

Sternberg, R. J., and Lubart, T. I. (1996). Investing in creativity. Am. Psychol. 51, 677–688. doi: 10.1037/0003-066X.51.7.677

Steward, T., Mestre-Bach, G., Vintro-Alcaraz, C., Lozano-Madrid, M., Aguera, Z., Fernandez-Formoso, J. A., et al. (2018). Food addiction and impaired executive functions in women with obesity. Eur. Eat. Disord. Rev. 26, 574–584. doi: 10.1002/erv.2636

Stolte, M., Garcia, T., Van Luit, J. E. H., Oranje, B., and Kroesbergen, E. H. (2020). The contribution of executive functions in predicting mathematical creativity in typical elementary school classes: a twofold role for updating. Journal of. Intelligence 8, 1–20. doi: 10.3390/jintelligence8020026

Stuyck, H., Cleeremans, A., and Van den Bussche, E. (2022). Aha! Under pressure: the aha! Experience is not constrained by cognitive load. Cognition 219:104946. doi: 10.1016/j.cognition.2021.104946

Tong, D. D., Zhu, H. X., Li, W. F., Yang, W. J., Qiu, J., and Zhang, Q. L. (2013). Brain activity in using heuristic prototype to solve insightful problems. Behav. Brain Res. 253, 139–144. doi: 10.1016/j.bbr.2013.07.017

Tong, D. D., Li, W. F., Tang, C. Y., Yang, W. J., Tian, Y., Zhang, L., et al. (2015). An illustrated heuristic prototype facilitates scientific inventive problem solving: A functional magnetic resonance imaging study. Consciousness and Cognition 34, 43–51. doi: 10.1016/j.concog.2015.02.009

Tsai, T. H., Chen, Y. L., and Gau, S. S. F. (2021). Relationships between autistic traits, insufficient sleep, and real-world executive functions in children: a mediation analysis of a national epidemiological survey. Psychol. Med. 51, 579–586. doi: 10.1017/S0033291719003271

Wu, Z. (2007). A study on the activation of Protoyepal matters in logograph insight problem solving , Chongqing, China: Southwest University.

Google Scholar

Xing, Q., Rong, C. L., Lu, Z. Y., Yao, Y. F., Zhang, Z. L., and Zhao, X. (2018). The effect of the embodied guidance in the insight problem solving: an eye movement study. Front. Psychol. 9, 1–14. doi: 10.3389/fpsyg.2018.02257

Yan, T. I. A. N., Jun-Long, L. U. O., Wen-Fu, L. I., Jiang, Q. I. U., and Qing-Lin, Z. H. A. N. G. (2011). Influence of prototype representation on elicitation effect in creative problem solving. Acta Psychol. Sin. 43, 619–628. doi: 10.3724/24/SP.J.1041.2011.00619

Yang, W. J., Dietrich, A., Liu, P. D., Ming, D., Jin, Y. L., Nusbaum, H. C., et al. (2016). Prototypes are key heuristic information in insight problem solving. Creat. Res. J. 28, 67–77. doi: 10.1080/10400419.2016.1125274

Yang, W., Green, A. E., Chen, Q., Kenett, Y. N., Sun, J., Wei, D., et al. (2022). Creative problem solving in knowledge-rich contexts. Trends Cogn. Sci. 26, 849–859. doi: 10.1016/j.tics.2022.06.012

Zhang, Q., Qiu, J., and Cao, G. (2004). A review and hypothesis about the cognitivemechanism of insight. Psychol. Sci. 27, 1435–1437. doi: 10.16719/j.cnki.1671-6981.2004.06.041

Zhang, Q., Tian, Y., and Qiu, J. (2012). Automatic Activation of Prototype Representation in Insight: The Sources of Inspiration. Journal of Southwest University (Natural Science Edition) 34, 1–10. doi: 10.13718/j.cnki.xdzk.2012.09.019

Zhang, H., Zhang, X. L., Liu, X. P., Yang, H. B., and Shi, J. N. (2020). Inhibitory process of collaborative inhibition: assessment using an emotional Stroop task. Psychol. Rep. 123, 300–324. doi: 10.1177/0033294118805007

Zhao, X. (2018). Dissociation of levels of unconscious processing in creative problem solving , Chongqing, China: Southwest University.

Zhong, C. B., Dijksterhuis, A., and Galinsky, A. D. (2008). The merits of unconscious thought in creativity. Psychol. Sci. 19, 912–918. doi: 10.1111/j.1467-9280.2008.02176.x

Zhu, D. (2011). The effect and mechanism of the common words and the highlighted principle to the prototype elicitation to the scientific innovation , Chongqing, China: Southwest University.

Zhu, W. F., Chen, Q. L., Xia, L. X., Beaty, R. E., Yang, W. J., Tian, F., et al. (2017). Common and distinct brain networks underlying verbal and visual creativity. Hum. Brain Mapp. 38, 2094–2111. doi: 10.1002/hbm.23507

Zhu, W., Yang, W. J., Qiu, J., Tian, F., Chen, Q. L., Cao, G. K., et al. (2019). Individual differences in brain structure and resting brain function underlie representation-connection in scientific problem solving. Creat. Res. J. 31, 132–148. doi: 10.1080/10400419.2019.1602461

Keywords: levels of consciousness, creative problem-solving, prototype heuristics, distractor tasks, scientific invention, insight, executive function

Citation: Ling Y, Tan L, Zhang L and Cao G (2023) Unconscious processing of prototype heuristics in scientific innovation problem-solving. Front. Psychol . 14:1056045. doi: 10.3389/fpsyg.2023.1056045

Received: 09 November 2022; Accepted: 26 January 2023; Published: 23 February 2023.

Reviewed by:

Copyright © 2023 Ling, Tan, Zhang and Cao. 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: Liyi Zhang, ✉ [email protected] ; Guikang Cao, ✉ [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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What Are Heuristics?

These mental shortcuts can help people make decisions more efficiently

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

insight problem solving heuristic

Steven Gans, MD is board-certified in psychiatry and is an active supervisor, teacher, and mentor at Massachusetts General Hospital.

insight problem solving heuristic

Verywell / Cindy Chung 

  • History and Origins
  • Heuristics vs. Algorithms
  • Heuristics and Bias

How to Make Better Decisions

Heuristics are mental shortcuts that allow people to solve problems and make judgments quickly and efficiently. These rule-of-thumb strategies shorten decision-making time and allow people to function without constantly stopping to think about their next course of action.

However, there are both benefits and drawbacks of heuristics. While heuristics are helpful in many situations, they can also lead to  cognitive biases . Becoming aware of this might help you make better and more accurate decisions.

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The History and Origins of Heuristics

Nobel-prize winning economist and cognitive psychologist Herbert Simon originally introduced the concept of heuristics in psychology in the 1950s. He suggested that while people strive to make rational choices, human judgment is subject to cognitive limitations. Purely rational decisions would involve weighing all the potential costs and possible benefits of every alternative.

But people are limited by the amount of time they have to make a choice as well as the amount of information they have at their disposal. Other factors such as overall intelligence and accuracy of perceptions also influence the decision-making process.

During the 1970s, psychologists Amos Tversky and Daniel Kahneman presented their research on cognitive biases. They proposed that these biases influence how people think and the judgments people make.

As a result of these limitations, we are forced to rely on mental shortcuts to help us make sense of the world. Simon's research demonstrated that humans were limited in their ability to make rational decisions, but it was Tversky and Kahneman's work that introduced the study of heuristics and the specific ways of thinking that people rely on to simplify the decision-making process.

How Heuristics Are Used

Heuristics play important roles in both  problem-solving  and  decision-making , as we often turn to these mental shortcuts when we need a quick solution.

Here are a few different theories from psychologists about why we rely on heuristics.

  • Attribute substitution : People substitute simpler but related questions in place of more complex and difficult questions.
  • Effort reduction : People use heuristics as a type of cognitive laziness to reduce the mental effort required to make choices and decisions.
  • Fast and frugal : People use heuristics because they can be fast and correct in certain contexts. Some theories argue that heuristics are actually more accurate than they are biased.

In order to cope with the tremendous amount of information we encounter and to speed up the decision-making process, our brains rely on these mental strategies to simplify things so we don't have to spend endless amounts of time analyzing every detail.

You probably make hundreds or even thousands of decisions every day. What should you have for breakfast? What should you wear today? Should you drive or take the bus? Fortunately, heuristics allow you to make such decisions with relative ease and without a great deal of agonizing.

There are many heuristics examples in everyday life. When trying to decide if you should drive or ride the bus to work, for instance, you might remember that there is road construction along the bus route. You realize that this might slow the bus and cause you to be late for work. So you leave earlier and drive to work on an alternate route.

Heuristics allow you to think through the possible outcomes quickly and arrive at a solution.

Are Heuristics Good or Bad?

Heuristics aren't inherently good or bad, but there are pros and cons to using them to make decisions. While they can help us figure out a solution to a problem faster, they can also lead to inaccurate judgments about other people or situations.

Types of Heuristics

There are many different kinds of heuristics. While each type plays a role in decision-making, they occur during different contexts. Understanding the types can help you better understand which one you are using and when.

Availability

The availability heuristic  involves making decisions based upon how easy it is to bring something to mind. When you are trying to make a decision, you might quickly remember a number of relevant examples. Since these are more readily available in your memory, you will likely judge these outcomes as being more common or frequently occurring.

For example, if you are thinking of flying and suddenly think of a number of recent airline accidents, you might feel like air travel is too dangerous and decide to travel by car instead. Because those examples of air disasters came to mind so easily, the availability heuristic leads you to think that plane crashes are more common than they really are.

Familiarity

The familiarity heuristic refers to how people tend to have more favorable opinions of things, people, or places they've experienced before as opposed to new ones. In fact, given two options, people may choose something they're more familiar with even if the new option provides more benefits.

Representativeness

The representativeness heuristic  involves making a decision by comparing the present situation to the most representative mental prototype. When you are trying to decide if someone is trustworthy, you might compare aspects of the individual to other mental examples you hold.

A soft-spoken older woman might remind you of your grandmother, so you might immediately assume that she is kind, gentle, and trustworthy. However, this is an example of a heuristic bias, as you can't know someone trustworthy based on their age alone.

The affect heuristic involves making choices that are influenced by the emotions that an individual is experiencing at that moment. For example, research has shown that people are more likely to see decisions as having benefits and lower risks when they are in a positive mood. Negative emotions, on the other hand, lead people to focus on the potential downsides of a decision rather than the possible benefits.

The anchoring bias involves the tendency to be overly influenced by the first bit of information we hear or learn. This can make it more difficult to consider other factors and lead to poor choices. For example, anchoring bias can influence how much you are willing to pay for something, causing you to jump at the first offer without shopping around for a better deal.

Scarcity is a principle in heuristics in which we view things that are scarce or less available to us as inherently more valuable. The scarcity heuristic is one often used by marketers to influence people to buy certain products. This is why you'll often see signs that advertise "limited time only" or that tell you to "get yours while supplies last."

Trial and Error

Trial and error is another type of heuristic in which people use a number of different strategies to solve something until they find what works. Examples of this type of heuristic are evident in everyday life. People use trial and error when they're playing video games, finding the fastest driving route to work, and learning to ride a bike (or learning any new skill).

Difference Between Heuristics and Algorithms

Though the terms are often confused, heuristics and algorithms are two distinct terms in psychology.

Algorithms are step-by-step instructions that lead to predictable, reliable outcomes; whereas heuristics are mental shortcuts that are basically best guesses. Algorithms always lead to accurate outcomes, whereas, heuristics do not.

Examples of algorithms include instructions for how to put together a piece of furniture or a recipe for cooking a certain dish. Health professionals also create algorithms or processes to follow in order to determine what type of treatment to use on a patient.

How Heuristics Can Lead to Bias

While heuristics can help us solve problems and speed up our decision-making process, they can introduce errors. As in the examples above, heuristics can lead to inaccurate judgments about how commonly things occur and about how representative certain things may be.

Just because something has worked in the past does not mean that it will work again, and relying on a heuristic can make it difficult to see alternative solutions or come up with new ideas.

Heuristics can also contribute to stereotypes and  prejudice . Because people use mental shortcuts to classify and categorize people, they often overlook more relevant information and create stereotyped categorizations that are not in tune with reality.

While heuristics can be a useful tool, there are ways you can improve your decision-making and avoid cognitive bias at the same time.

We are more likely to make an error in judgment if we are trying to make a decision quickly or are under pressure to do so. Whenever possible, take a few deep breaths . Do something to distract yourself from the decision at hand. When you return to it, you may find you have a fresh perspective, or notice something you didn't before.

Identify the Goal

We tend to focus automatically on what works for us and make decisions that serve our best interest. But take a moment to know what you're trying to achieve. Are there other people who will be affected by this decision? What's best for them? Is there a common goal that can be achieved that will serve all parties?

Process Your Emotions

Fast decision-making is often influenced by emotions from past experiences that bubble to the surface. Is your decision based on facts or emotions? While emotions can be helpful, they may affect decisions in a negative way if they prevent us from seeing the full picture.

Recognize All-or-Nothing Thinking

When making a decision, it's a common tendency to believe you have to pick a single, well-defined path, and there's no going back. In reality, this often isn't the case.

Sometimes there are compromises involving two choices, or a third or fourth option that we didn't even think of at first. Try to recognize the nuances and possibilities of all choices involved, instead of using all-or-nothing thinking .

Rachlin H. Rational thought and rational behavior: A review of bounded rationality: The adaptive toolbox . J Exp Anal Behav . 2003;79(3):409–412. doi:10.1901/jeab.2003.79-409

Shah AK, Oppenheimer DM. Heuristics made easy: An effort-reduction framework . Psychol Bull. 2008;134(2):207-22. doi:10.1037/0033-2909.134.2.207

Marewski JN, Gigerenzer G. Heuristic decision making in medicine .  Dialogues Clin Neurosci . 2012;14(1):77–89. PMID: 22577307

Schwikert SR, Curran T. Familiarity and recollection in heuristic decision making .  J Exp Psychol Gen . 2014;143(6):2341-2365. doi:10.1037/xge0000024

Finucane M, Alhakami A, Slovic P, Johnson S. The affect heuristic in judgments of risks and benefits . J Behav Decis Mak . 2000; 13(1):1-17. doi:10.1002/(SICI)1099-0771(200001/03)13:1<1::AID-BDM333>3.0.CO;2-S

Cheung TT, Kroese FM, Fennis BM, De Ridder DT. Put a limit on it: The protective effects of scarcity heuristics when self-control is low . Health Psychol Open . 2015;2(2):2055102915615046. doi:10.1177/2055102915615046

Mohr H, Zwosta K, Markovic D, Bitzer S, Wolfensteller U, Ruge H. Deterministic response strategies in a trial-and-error learning task . Inman C, ed. PLoS Comput Biol. 2018;14(11):e1006621. doi:10.1371/journal.pcbi.1006621

Lang JM, Ford JD, Fitzgerald MM.  An algorithm for determining use of trauma-focused cognitive-behavioral therapy .  Psychotherapy   (Chic) . 2010;47(4):554-69. doi:10.1037/a0021184

Bigler RS, Clark C. The inherence heuristic: A key theoretical addition to understanding social stereotyping and prejudice. Behav Brain Sci . 2014;37(5):483-4. doi:10.1017/S0140525X1300366X

del Campo C, Pauser S, Steiner E, et al.  Decision making styles and the use of heuristics in decision making .  J Bus Econ.  2016;86:389–412. doi:10.1007/s11573-016-0811-y

Marewski JN, Gigerenzer G. Heuristic decision making in medicine .  Dialogues Clin Neurosci . 2012;14(1):77-89. doi:10.31887/DCNS.2012.14.1/jmarewski

Zheng Y, Yang Z, Jin C, Qi Y, Liu X. The influence of emotion on fairness-related decision making: A critical review of theories and evidence .  Front Psychol . 2017;8:1592. doi:10.3389/fpsyg.2017.01592

Bazerman MH. Judgment and decision making. In: Biswas-Diener R, Diener E, eds.,  Noba Textbook Series: Psychology.  DEF Publishers.

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

4 Main problem-solving strategies

problem solving

In Psychology, you get to read about a ton of therapies. It’s mind-boggling how different theorists have looked at human nature differently and have come up with different, often somewhat contradictory, theoretical approaches.

Yet, you can’t deny the kernel of truth that’s there in all of them. All therapies, despite being different, have one thing in common- they all aim to solve people’s problems. They all aim to equip people with problem-solving strategies to help them deal with their life problems.

Problem-solving is really at the core of everything we do. Throughout our lives, we’re constantly trying to solve one problem or another. When we can’t, all sorts of psychological problems take hold. Getting good at solving problems is a fundamental life skill.

Problem-solving stages

What problem-solving does is take you from an initial state (A) where a problem exists to a final or goal state (B), where the problem no longer exists.

To move from A to B, you need to perform some actions called operators. Engaging in the right operators moves you from A to B. So, the stages of problem-solving are:

  • Initial state

The problem itself can either be well-defined or ill-defined. A well-defined problem is one where you can clearly see where you are (A), where you want to go (B), and what you need to do to get there (engaging the right operators).

For example, feeling hungry and wanting to eat can be seen as a problem, albeit a simple one for many. Your initial state is hunger (A) and your final state is satisfaction or no hunger (B). Going to the kitchen and finding something to eat is using the right operator.

In contrast, ill-defined or complex problems are those where one or more of the three problem solving stages aren’t clear. For example, if your goal is to bring about world peace, what is it exactly that you want to do?

It’s been rightly said that a problem well-defined is a problem half-solved. Whenever you face an ill-defined problem, the first thing you need to do is get clear about all the three stages.

Often, people will have a decent idea of where they are (A) and where they want to be (B). What they usually get stuck on is finding the right operators.

Initial theory in problem-solving

When people first attempt to solve a problem, i.e. when they first engage their operators, they often have an initial theory of solving the problem. As I mentioned in my article on overcoming challenges for complex problems, this initial theory is often wrong.

But, at the time, it’s usually the result of the best information the individual can gather about the problem. When this initial theory fails, the problem-solver gets more data, and he refines the theory. Eventually, he finds an actual theory i.e. a theory that works. This finally allows him to engage the right operators to move from A to B.

Problem-solving strategies

These are operators that a problem solver tries to move from A to B. There are several problem-solving strategies but the main ones are:

  • Trial and error

1. Algorithms

When you follow a step-by-step procedure to solve a problem or reach a goal, you’re using an algorithm. If you follow the steps exactly, you’re guaranteed to find the solution. The drawback of this strategy is that it can get cumbersome and time-consuming for large problems.

Say I hand you a 200-page book and ask you to read out to me what’s written on page 100. If you start from page 1 and keep turning the pages, you’ll eventually reach page 100. There’s no question about it. But the process is time-consuming. So instead you use what’s called a heuristic.

2. Heuristics

Heuristics are rules of thumb that people use to simplify problems. They’re often based on memories from past experiences. They cut down the number of steps needed to solve a problem, but they don’t always guarantee a solution. Heuristics save us time and effort if they work.

You know that page 100 lies in the middle of the book. Instead of starting from page one, you try to open the book in the middle. Of course, you may not hit page 100, but you can get really close with just a couple of tries.

If you open page 90, for instance, you can then algorithmically move from 90 to 100. Thus, you can use a combination of heuristics and algorithms to solve the problem. In real life, we often solve problems like this.

When police are looking for suspects in an investigation, they try to narrow down the problem similarly. Knowing the suspect is 6 feet tall isn’t enough, as there could be thousands of people out there with that height.

Knowing the suspect is 6 feet tall, male, wears glasses, and has blond hair narrows down the problem significantly.

3. Trial and error

When you have an initial theory to solve a problem, you try it out. If you fail, you refine or change your theory and try again. This is the trial-and-error process of solving problems. Behavioral and cognitive trial and error often go hand in hand, but for many problems, we start with behavioural trial and error until we’re forced to think.

Say you’re in a maze, trying to find your way out. You try one route without giving it much thought and you find it leads to nowhere. Then you try another route and fail again. This is behavioural trial and error because you aren’t putting any thought into your trials. You’re just throwing things at the wall to see what sticks.

This isn’t an ideal strategy but can be useful in situations where it’s impossible to get any information about the problem without doing some trials.

Then, when you have enough information about the problem, you shuffle that information in your mind to find a solution. This is cognitive trial and error or analytical thinking. Behavioral trial and error can take a lot of time, so using cognitive trial and error as much as possible is advisable. You got to sharpen your axe before you cut the tree.

When solving complex problems, people get frustrated after having tried several operators that didn’t work. They abandon their problem and go on with their routine activities. Suddenly, they get a flash of insight that makes them confident they can now solve the problem.

I’ve done an entire article on the underlying mechanics of insight . Long story short, when you take a step back from your problem, it helps you see things in a new light. You make use of associations that were previously unavailable to you.

You get more puzzle pieces to work with and this increases the odds of you finding a path from A to B, i.e. finding operators that work.

Pilot problem-solving

No matter what problem-solving strategy you employ, it’s all about finding out what works. Your actual theory tells you what operators will take you from A to B. Complex problems don’t reveal their actual theories easily solely because they are complex.

Therefore, the first step to solving a complex problem is getting as clear as you can about what you’re trying to accomplish- collecting as much information as you can about the problem.

This gives you enough raw materials to formulate an initial theory. We want our initial theory to be as close to an actual theory as possible. This saves time and resources.

Solving a complex problem can mean investing a lot of resources. Therefore, it is recommended you verify your initial theory if you can. I call this pilot problem-solving.

Before businesses invest in making a product, they sometimes distribute free versions to a small sample of potential customers to ensure their target audience will be receptive to the product.

Before making a series of TV episodes, TV show producers often release pilot episodes to figure out whether the show can take off.

Before conducting a large study, researchers do a pilot study to survey a small sample of the population to determine if the study is worth carrying out.

The same ‘testing the waters’ approach needs to be applied to solving any complex problem you might be facing. Is your problem worth investing a lot of resources in? In management, we’re constantly taught about Return On Investment (ROI). The ROI should justify the investment.

If the answer is yes, go ahead and formulate your initial theory based on extensive research. Find a way to verify your initial theory. You need this reassurance that you’re going in the right direction, especially for complex problems that take a long time to solve.

memories of murder movie scene

Getting your causal thinking right

Problem solving boils down to getting your causal thinking right. Finding solutions is all about finding out what works, i.e. finding operators that take you from A to B. To succeed, you need to be confident in your initial theory (If I do X and Y, they’ll lead me to B). You need to be sure that doing X and Y will lead you to B- doing X and Y will cause B.

All obstacles to problem-solving or goal-accomplishing are rooted in faulty causal thinking leading to not engaging the right operators. When your causal thinking is on point, you’ll have no problem engaging the right operators.

As you can imagine, for complex problems, getting our causal thinking right isn’t easy. That’s why we need to formulate an initial theory and refine it over time.

I like to think of problem-solving as the ability to project the present into the past or into the future. When you’re solving problems, you’re basically looking at your present situation and asking yourself two questions:

“What caused this?” (Projecting present into the past)

“What will this cause?” (Projecting present into the future)

The first question is more relevant to problem-solving and the second to goal-accomplishing.

If you find yourself in a mess , you need to answer the “What caused this?” question correctly. For the operators you’re currently engaging to reach your goal, ask yourself, “What will this cause?” If you think they cannot cause B, it’s time to refine your initial theory.

hanan parvez

Hi, I’m Hanan Parvez (MBA, MA Psychology). My work has been featured in Forbes , Business Insider , Reader’s Digest , and Entrepreneur . When I’m not thinking about human behavior, I… No wait! I’m always thinking about human behavior!

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COMMENTS

  1. The interpretative heuristic in insight problem solving.

    The study of insight problem solving could well become one of the most important topics in the contemporary debate on thought. Dealing with insight problems today requires of necessity reconsidering the concept of bounded rationality. Simon's work has inspired us to reflect on the specific quality of the type of boundaries which, by limiting the search, allow and guarantee the act of ...

  2. The interpretative heuristic in insight problem solving

    We therefore propose that this interpretative heuristic is inherent to all insight problem solving processes and, in more general terms, is an adaptive characteristic of the human cognitive system; this of course implies that the dual process theory will have to be challenged and discussed. Similar content being viewed by others

  3. PDF The Eureka Heuristic: Relying on insight to appraise the quality of ideas

    insight experiences, without participants knowing that they were being primed. Hattori, Sloman, and Orita (2013) also found that subliminal primes improved insight problem solving across three experiments, in some cases leading to a fivefold improvement. Since the problem solving process—and therefore the reasoning that underlies the

  4. Intuition and Insight: Two Processes That Build on Each Other or

    Intuition then manifests itself in the use of certain heuristics that may form highly successful, cognitive shortcuts (Gigerenzer, 2008; Gigerenzer and Gaissmaier, 2011). ... In recent research on insight problem solving, Bowden et al. (2005) presented a novel framework and a new class of problems in order to probe insight problem solving. The ...

  5. PDF What Makes an Insight Problem? The Roles of Heuristics ...

    What are lacking from current theories of insight problem solving are general problem-solving heuristics that might apply across a wider range of insight problems.

  6. What Makes an Insight Problem? The Roles of Heuristics, Goal Conception

    What Makes an Insight Problem? The Roles of Heuristics, Goal Conception, and Solution Recoding in Knowledge-Lean Problems. Four experiments investigated transformation problems with insight characteristics.

  7. PDF Prototypes are Key Heuristic Information in Insight Problem Solving

    Prototypes are Key Heuristic Information in Insight Problem Solving Wenjing Yang Faculty of Psychology, Southwest University, Chongqing, China and Faculty of Education, ... dition and the heuristic condition showed that solving the problems in the prototype condition was significantly better, t(83) = 23.058, p < 0.001. Solution time was also ...

  8. [PDF] What makes an insight problem? The roles of heuristics, goal

    It is argued that hill-climbing heuristics provide a common framework for understanding transformation and insight problem solving and may account for part of the phenomenology of insight. Four experiments investigated transformation problems with insight characteristics. In Experiment 1, performance on a version of the 6-coin problem that had a concrete and visualizable solution followed a ...

  9. Prototypes are Key Heuristic Information in Insight Problem Solving

    The results suggest that problem activation was the key process in the real-life problem solving enhanced by heuristic prototype, and the semantic similarity between the feature function of the prototype and the required function ofThe problem is the mechanism of the problem. 2.

  10. The interpretative heuristic in insight problem solving

    We therefore propose that this interpretative heuristic is inherent to all insight problem solving processes and, in more general terms, is an adaptive characteristic of the human cognitive...

  11. The interpretative heuristic in insight problem

    The interpretative heuristic in insight problem solving. 123. 98 L. Macchi, M. Bagassi. One of the most intriguing insight problems, which at rst sight appears impossible to solve, is the Study Window problem (Mosconi and DUrso 1974): The study window measures one meter in height and one meter wide. The owner decides to enlarge it and calls in ...

  12. The interpretative heuristic in insight problem solving

    It is proposed that this interpretative heuristic is inherent to all insight problem solving processes and, in more general terms, is an adaptive characteristic of the human cognitive system; this of course implies that the dual process theory will have to be challenged and discussed. The study of insight problem solving could well become one of the most important topics in the contemporary ...

  13. PDF The interpretative heuristic in insight problem solving

    In problem solving, the characteristics of the processes attributed to System 2 identify a form of reasoning by which the solution is reached by a deliberate step by step search, typical of non-insight problem solving, and do not take into consideration other types of reasoning, such as the solution of insight problems.

  14. PDF Cognitive Mechanisms of Insight: The Role of Heuristics and

    Keywords: insight, heuristics, representational change, problem solving Some problems initially appear trivial yet prove to be very difficult and time-consuming. They often have no obvious solu-tion, and solution strategies used in the past cannot successfully be appliedtothem.Sometimes,asudden,unintended,andunexpected

  15. What makes an insight problem? The roles of heuristics, goal ...

    10.1037/0278-7393.30.1.14 Abstract Four experiments investigated transformation problems with insight characteristics. In Experiment 1, performance on a version of the 6-coin problem that had a concrete and visualizable solution followed a hill-climbing heuristic.

  16. How can we gain insight in scientific innovation? Prototype heuristic

    Controversy remains as to the cognitive mechanism of insight problem solving, in spite of a shared point of the information-processing heuristics. Among these theories, there are two overarching models: the Representation Change (RC) Theory (Kaplan & Simon, 1990) and the Progress Monitoring (PM) Theory (Knoblich, Ohlsson, Haider, & Rhenius, 1999).

  17. Insight and the selection of ideas

    Insight - prediction - active inference - ideas - heuristics - Aha - problem solving We do not just think about ideas, we feel them too. Whenever we listen to a presentation by a scientist (or a politician), we quickly sense whether we agree with the ideas being shared. The same is true for our own ideas.

  18. Prototypes Are Key Heuristic Information in Insight Problem Solving

    To sum up, the experiments suggested that activating a memory of the heuristic prototype was important in solving scientific innovation insight problems. Activating the feature function of right heuristic prototype and linking it by way of semantic similarity to the required function of the problem was the key mechanism people used to solve ...

  19. 8.2 Problem-Solving: Heuristics and Algorithms

    A heuristic is a principle with broad application, essentially an educated guess about something. We use heuristics all the time, for example, when deciding what groceries to buy from the supermarket, when looking for a library book, when choosing the best route to drive through town to avoid traffic congestion, and so on.

  20. Brain activity in using heuristic prototype to solve insightful

    When confronted with a real-world problem, heuristic knowledge and experience can guide the solution of a specific technical problem as the key step toward innovation. In particular, a heuristic prototype must be used correctly to cue the technical problem that exists in a particular situation. ... Insight in problem solving has been studied ...

  21. Frontiers

    During the prototype heuristic process, the activation of a semantic representation of a prototype that benefits insight problem-solving is known as prototype activation, and the application of the heuristic information implied by the prototype (such as principles, rules, and methods) leads to successfully solving the insight problem of ...

  22. Heuristics: Definition, Examples, and How They Work

    Heuristics are mental shortcuts that allow people to solve problems and make judgments quickly and efficiently. These rule-of-thumb strategies shorten decision-making time and allow people to function without constantly stopping to think about their next course of action. However, there are both benefits and drawbacks of heuristics.

  23. 4 Main problem-solving strategies

    Goal state The problem itself can either be well-defined or ill-defined. A well-defined problem is one where you can clearly see where you are (A), where you want to go (B), and what you need to do to get there (engaging the right operators). For example, feeling hungry and wanting to eat can be seen as a problem, albeit a simple one for many.