Insight Problem Solving Research Articles

The irreconcilability of insight

We are said to experience insight when we suddenly and unexpectedly become aware of the solution to a problem that we previously took ourselves to be unable to solve. In the field of comparative cognition, there is rising interest in the question of whether non-human animals are capable of insightful problem-solving. Putative cases of animals demonstrating insight have generally attracted two types of criticism: first, that insight is being conflated with other cognitive capacities (e.g., causal cognition, or mental trial and error); and, second, that the relevant performances merely reflect associative learning—and on the received understanding of insight within comparative cognition, insight necessarily involves non-associative processes. I argue that even if we grant that some cases of animal insight do withstand these two criticisms, these cases of purported animal insight cannot shed light on the nature of insightful problem-solving in humans. For the phenomenon studied by cognitive psychologists under the heading of insight is fundamentally different from that studied in comparative cognition. In light of this impasse, I argue that the reinterpretation of the extant research on animal insight in terms of other high-level cognitive capacities (means-end reasoning in particular) can improve the prospect of a successful comparative research program.

FRAMED: a framework facilitating insight problem solving.

Insight has been studied as an element of problem solving in the field of cognitive psychology and may play an important role in clinical reasoning. We propose a new strategy based on theories that promote insight that may help generate further diagnostic hypotheses by reviewing the interpretation of a case and an individual's list of differential diagnoses from multiple perspectives: formation (F), re-encoding (R), analogy (A), modification (M), elaboration (E), and deliberation (D) (FRAMED). The FRAMED strategy may help clinicians overcome misinterpretations and cognitive bias by systematically reflecting on previous clinical reasoning processes from multiple perspectives.

Impasse-Driven problem solving: The multidimensional nature of feeling stuck

This study reports findings across four preregistered experiments (total N = 856) that establish the multidimensional nature of impasse and resolve two paradoxes implicit in the problem-solving literature: how a state of impasse can be at once necessary to solve a problem with insight yet also have appear to have a catastrophic effect on solution rates, and why individuals such as problem-solving and gaming enthusiasts seem to seek out this apparently aversive state. We introduce a new way of measuring impasse based on qualitative reports and subsequently confirmed through quantitative analysis that exploits two aspects of impasse: its dynamic and unstable nature (it can be resolved or unresolved) and its multidimensionality in terms of feelings of cognitive speediness, motivation, and affect. The feeling of being stuck varies between resolved and unresolved impasse in terms of feelings of speediness and positive affect, but not motivation, which remains constant. We demonstrate that the feeling of insight can be reliably elicited by experiencing and resolving impasse but also in the absence of impasse, which suggests that there is more than one path to an insight experience. This adds depths to current proposals of the cognitive mechanisms underlying both insight problem-solving and impasse. Our findings are robust across a range of problem types. The novel conception of impasse in this paper as dynamic and multidimensional has implications for theories of insight problem solving, and also wider implications for understanding how impasse can be resolved across different domains such as education and design.

Funny? Think About It! Selective effect of cognitive mechanisms of humour on insight problems

ABSTRACT The present study aims to elucidate whether insight problem solving could be facilitated by the cognitive component of humour. The authors take interest in whether the logical mechanisms of humour can affect how fast insight problems are solved. To that end, the authors conducted two experiments where participants solved insight problems after watching visual humorous stimuli such as videos and slideshows. The first experiment demonstrated the overall impact of facilitation by humour on insight problem solving; however, it did not show any difference in how particular logical mechanisms of humour affect the solution time of insight problems. The second experiment showed that the cognitive component of humour could selectively affect insight problems whose difficulty stems from different sources. These results suggest that the cognitive component of humour, when operationalised as logical mechanisms and schema switching, contributes to solving insight problems.

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

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

Restructuring processes and Aha! experiences in insight problem solving

Restructuring processes and Aha! experiences in insight problem solving

Gestalt's Perspective on Insight: A Recap Based on Recent Behavioral and Neuroscientific Evidence.

The Gestalt psychologists' theory of insight problem-solving was based on a direct parallelism between perceptual experience and higher-order forms of cognition (e.g., problem-solving). Similarly, albeit not exclusively, to the sudden recognition of bistable figures, these psychologists contended that problem-solving involves a restructuring of one's initial representation of the problem's elements, leading to a sudden leap of understanding phenomenologically indexed by the "Aha!" feeling. Over the last century, different scholars have discussed the validity of the Gestalt psychologists' perspective, foremost using the behavioral measures available at the time. However, in the last two decades, scientists have gained a deeper understanding of insight problem-solving due to the advancements in cognitive neuroscience. This review aims to provide a retrospective reading of Gestalt theory based on the knowledge accrued by adopting novel paradigms of research and investigating their neurophysiological correlates. Among several key points that the Gestalt psychologists underscored, we focus specifically on the role of the visual system in marking a discrete switch of knowledge into awareness, as well as the perceptual experience and holistic standpoints. While the main goal of this paper is to read the previous theory in light of new evidence, we also hope to initiate an academic discussion and encourage further research about the points we raise.

The Receptive Brain: Up-Regulated Right Temporal Alpha Oscillation Boosting Aha!

ABSTRACT Chance favors the prepared mind, said Louis Pasteur. Sometimes, significant breakthroughs occur when we creatively integrate new information, leading to a creative insight or an Aha! moment, while at other times when we fail to use a clue, we remain stuck in our habitual thinking patterns. In this study, we hypothesized that the brain’s transient oscillatory states would characterize its receptivity or preparedness for such insights. We conducted a real-time brain-state-dependent cognitive stimulation experiment during insightful problem-solving. We showed that participants were more successful in utilizing clues and experienced more Aha responses when these clues were presented at the spontaneously up-regulated state of right temporal alpha oscillation, as opposed to the down-regulated state. Furthermore, we observed an inverse correlation between the coupling of alpha oscillation phase and gamma oscillation power and the frequency of insight. These results shed light on the neural mechanism underpinning the brain’s receptivity to integrate upcoming semantic information, emphasizing the pivotal role of dynamical brain oscillations in the Aha! experience.

The difference between induced insight by hints and induced insight by answers: an event-related potential study.

We can solve insight problems by ourselves, by hints or by answers. This study compared the temporal features of different types of insight (spontaneous insight, induced insight by hints and induced insight by answers). Fifteen college students participated in the Chinese Remote Association Task. If they did not come up with an answer, the cue word was presented. Finally, they needed to judge whether the answer was correct or not. Participants' brain electroencephalography-event-related potentials (ERPs) were recorded. Induced insight by hints elicited a more negative ERP deflection than spontaneous insight within 260-400 ms (N2). Induced insight by hints and induced insight by answers elicited a more negative late negative component (LNC) than spontaneous insight. Induced insight by hints elicited a more positive ERP deflection than induced insight by answers and spontaneous insight in the right frontal area. Spontaneous insight and induced insight by answers elicited a more positive ERP deflection than induced insight by hints in the right part of the central region. When solving insight problems, the N2 may be related to representation restructuring. The first LNC may be related to the breaking of mental set. The positive component of the right frontal area before pressing the button may be related to the formation of novel associations, and the positive component of the right part of the central region may be related to the intensity of the 'Aha!' experience.

The influence of insight on risky decision making and nucleus accumbens activation

During insightful problem solving, the solution appears unexpectedly and is accompanied by the feeling of an AHA!. Research suggests that this affective component of insight can have consequences beyond the solution itself by motivating future behavior, such as risky (high reward and high uncertainty) decision making. Here, we investigate the behavioral and neural support for the motivational role of AHA in decision making involving monetary choices. The positive affect of the AHA! experience has been linked to internal reward. Reward in turn has been linked to dopaminergic signal transmission in the Nucleus Accumbens (NAcc) and risky decision making. Therefore, we hypothesized that insight activates reward-related brain areas, modulating risky decision making. We tested this hypothesis in two studies. First, in a pre-registered online study (Study 1), we demonstrated the behavioral effect of insight-related increase in risky decision making using a visual Mooney identification paradigm. Participants were more likely to choose the riskier monetary payout when they had previously solved the Mooney image with high compared to low accompanied AHA!. Second, in an fMRI study (Study 2), we measured the effects of insight on NAcc activity using a similar Mooney identification paradigm to the one of Study 1. Greater NAcc activity was found when participants solved the Mooney image with high vs low AHA!. Taken together, our results link insight to enhanced NAcc activity and a preference for high but uncertain rewards, suggesting that insight enhances reward-related brain areas possibly via dopaminergic signal transmission, promoting risky decision making.

Роль рабочей памяти на разных этапах решения инсайтных задач: критический обзор

This paper offers a critical review of studies published over the past few years that explore the role of working memory at different stages of insight problem solving. The overall contribution of working memory (WM) to insight problem solving remains a matter of controversy, since there is supporting evidence for both the positive and the negative roles of executive control and WM in insight problem solving. A promising way to approach this contradiction is to trace WM loading dynamics in the course of a solution. Data analysis revealed that insight problem solving is generally WM-demanding, although to a lesser extent than analytic problem solving. The WM load peaks at the beginning and at the end of insight problem solving. The initial WM load may be linked to the interpretation of a problem description, creating the initial representation, and analytic reasoning within the initial problem space. Subsequently, in the case of insight problems, the WM load decreases noticeably, which can be linked to a search for a new representation or to an impasse. At the last stage of problem solving, a short peak in the WM load precedes the solution detection, which can be linked to the restructuring of representation and the beginning of a new solution process in a new problem space.

Human-like problem-solving abilities in large language models using ChatGPT.

The field of Artificial Intelligence (AI) has seen a major shift in recent years due to the development of new Machine Learning (ML) models such as Generative Pre-trained Transformer (GPT). GPT has achieved previously unheard-of levels of accuracy in most computerized language processing tasks and their chat-based variations. The aim of this study was to investigate the problem-solving abilities of ChatGPT using two sets of verbal insight problems, with a known performance level established by a sample of human participants. A total of 30 problems labeled as "practice problems" and "transfer problems" were administered to ChatGPT. ChatGPT's answers received a score of "0" for each incorrectly answered problem and a score of "1" for each correct response. The highest possible score for both the practice and transfer problems was 15 out of 15. The solution rate for each problem (based on a sample of 20 subjects) was used to assess and compare the performance of ChatGPT with that of human subjects. The study highlighted that ChatGPT can be trained in out-of-the-box thinking and demonstrated potential in solving verbal insight problems. The global performance of ChatGPT equalled the most probable outcome for the human sample in both practice problems and transfer problems as well as upon their combination. Additionally, ChatGPT answer combinations were among the 5% of most probable outcomes for the human sample both when considering practice problems and pooled problem sets. These findings demonstrate that ChatGPT performance on both set of problems was in line with the mean rate of success of human subjects, indicating that it performed reasonably well. The use of transformer architecture and self-attention in ChatGPT may have helped to prioritize inputs while predicting, contributing to its potential in verbal insight problem-solving. ChatGPT has shown potential in solving insight problems, thus highlighting the importance of incorporating AI into psychological research. However, it is acknowledged that there are still open challenges. Indeed, further research is required to fully understand AI's capabilities and limitations in verbal problem-solving.

Moderating Role of Creative Mindset in the Effect of Metacognitive Experience on Insight Problem Solving.

Metacognitive experience, measured by processing fluency, contributes to divergent thinking performance; however, whether it exhibits varying effects on insight problem-solving remains unknown. Additionally, as individuals' interpretation of metacognitive experience is influenced by their creative mindset, whether creative mindset plays a role in the relationship between metacognitive experience and insight problem-solving is another issue. In Experiment 1, a Chinese logogriph task was used to investigate insight problem-solving performance. The font style of logogriphs (easy versus difficult) was used to alter the ease of processing. The results showed that individuals had lower performance accuracy for logogriphs presented in difficult font styles, suggesting the negative effect of metacognitive disfluency experience on logogriph solving. In Experiment 2, different creative mindsets (entity versus incremental) were activated in individuals via prime manipulation. Individuals with an incremental creative mindset had a significantly higher performance accuracy and longer reaction time for logogriphs presented in difficult font styles than individuals with an entity creative mindset, suggesting that an incremental creative mindset might counteract the negative effect of metacognitive disfluency experience on logogriphs solving. These findings suggest that metacognitive disfluency experience has a negative effect on insight problem-solving and that a creative mindset moderated this effect.

Tracing Cognitive Processes in Insight Problem Solving: Using GAMs and Change Point Analysis to Uncover Restructuring

Insight problems are likely to trigger an initial, incorrect mental representation, which needs to be restructured in order to find the solution. Despite the widespread theoretical assumption that this restructuring process happens suddenly, leading to the typical "Aha!" experience, the evidence is inconclusive. Among the reasons for this lack of clarity is that many measures of insight rely solely on the solvers' subjective experience of the solution process. In our previous paper, we used matchstick arithmetic problems to demonstrate that it is possible to objectively trace problem-solving processes by combining eye movements with new analytical and statistical approaches. Specifically, we divided the problem-solving process into ten (relative) temporal phases to better capture possible small changes in problem representation. Here, we go a step further to demonstrate that classical statistical procedures, such as ANOVA, cannot capture sudden representational change processes, which are typical for insight problems. Only nonlinear statistical models, such as generalized additive (mixed) models (GAMs) and change points analysis, correctly identified the abrupt representational change. Additionally, we demonstrate that explicit hints reorient participants' focus in a qualitatively different manner, changing the dynamics of restructuring in insight problem solving. While insight problems may indeed require a sudden restructuring of the initial mental representation, more sophisticated analytical and statistical approaches are necessary to uncover their true nature.

Dual-Process Accounts of the Creative Problem Solving and Human Connectome

Abstract The association of different networks in the human brain with creativity has received increasing attention as an active research topic. Creativity is a multidimensional concept reflecting different cognitive processes and associations mediated by the brain network. In this study, we first identified connections between the topological properties of the white matter network and divergent thinking and insight problem solving. Then, we performed a graph-theoretical analysis to calculate the efficiency of the brain network and to predict divergent thinking and insight-based problem solving. We found that effective information transmission across brain regions was significantly correlated with creative problem solving. Divergent thinking requires few connections between brain regions to produce novel ideas, whereas insight problem solving requires simultaneous processing by more brain regions to restructure the presentation of the problem. The results show that the integrated efficiency of the brain network varies between open- and closed-ended creative problem solving.

Convergent thinking and insight problem solving relate to semantic memory network structure

The associative theory of creativity has long held that creative thinking involves connecting remote concepts in semantic memory. Network science tools have recently been applied to map the organization of concepts in semantic memory, and to study the link between semantic memory and creativity. Yet such work has largely overlooked the domain of convergent thinking, despite the theoretical importance of semantic memory networks for facilitating associative processes relevant for convergent problem solving (e.g., spreading activation). Convergent thinking problems, such as the Compound Remote Associates (CRA) test, can be solved with insight (the sudden “aha” experience) or analysis (deliberately and incrementally working towards the solution). In a sample of 477 participants, we adopted network science methods to compare semantic memory structure across two grouping variables: 1) convergent thinking ability (i.e., CRA accuracy), and 2) the self-reported tendency to solve problems with insight or analysis. Semantic memory networks were constructed from a semantic fluency task, and problem solving style (insight or analysis) was determined from judgments provided during solving of CRAs. We found that, compared to the low-convergent thinking group, the high-convergent thinking group exhibited a more flexible and interconnected semantic network—with short paths and many connections between concepts. Moreover, participants who primarily solved problems with insight (compared to analysis) showed shorter average path distances between concepts, even after controlling for accuracy. Our results extend the literature on semantic memory and creativity, and suggest that the organization of semantic memory plays a key role in convergent thinking, including insight problem solving.

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

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.

The optimal balance of controlled and spontaneous processing in insight problem solving: fMRI evidence from Chinese idiom guessing.

Cognitive control is a key factor in insight generation. However, the neurocognitive mechanisms underlying the generation of insight for different cognitive control remain poorly understood. This study developed a parametric fMRI design, wherein hints for solving Chinese idiom riddles were gradually provided in a stepwise manner (from the first hint, H1, to the final hint, H4). By classifying the step-specific items solved in different hint-uncovering steps/conditions, we could identify insightful responses for different levels of spontaneous or controlled processing. At the behavioral level, the number of insightful problem solving trials reached the maximum at a intermediate level of the cognitively controlled processing and the spontaneously idea generating in H3, while the bilateral insular cortex and thalamus showed the robust engagement, implying the function of these regions in making the optimal balance between external hint processing and internal generated ideas. In addition, we identified brain areas, including the dorsolateral prefrontal cortex (dlPFC), angular gyrus (AG), dorsal anterior cingulate cortex (dACC), and precuneus (PreC), whose activities were parametrically increased with the levels of controlled (from H1 to H4) insightful processing which were increasingly produced by the sequentially revealed hints. Further representational similarity analysis (RSA) found that spontaneous processing in insight featured greater within-condition representational variabilities in widely distributed regions in the executive, salience, and default networks. Altogether, the present study provided new evidence for the relationship between the process of cognitive control and that of spontaneous idea generation in insight problem solving and demystified the function of the insula and thalamus as an interactive interface for the optimal balance of these two processes.

Exploring the effects of risk-taking, exploitation, and exploration on divergent thinking under group dynamics.

This study examined the effects of risk-taking and exploitation/exploration trade-off on divergent thinking in individuals, dyads, and triads. We adopted a simple Q-learning model to estimate risk attitudes, exploitation, and exploration parameters. The results showed that risk-taking, exploitation, and exploration did not affect divergent thinking in dyads. Instead, loss aversion was negatively related to divergent thinking. In contrast, risk attitudes and the inverse temperature as a ratio between exploitation and exploration were significant but with contrasting effects in individuals and triads. For individuals, risk-taking, exploitation and loss aversion played a critical role in divergent thinking. For triads, risk aversion and exploration were significantly related to divergent thinking. However, the results also indicated that balancing risk with exploitation/exploration and loss aversion is critical in enhancing divergent thinking in individuals and triads when learning coherence emerges. These results could be interpreted consistently with related literature such as the odd-vs. even-numbered group dynamics, knowledge diversity in group creativity, and representational change theory in insight problem-solving.

Insight problem solving is not that special, but business is not quite 'as usual': typical versus exceptional problem-solving strategies.

The intriguing phenomenon of insight (also known as the "Aha!" moment) has provoked a long-standing conflict over its cognitive mechanism. The special-process theory posits insight as a unique, unconscious mechanism. Conversely, the business-as-usual theory conceptualizes insight processing as ordinary and similar to non-insight, i.e., analytic, incremental, and attention demanding. To resolve this conflict, participants completed cognitive tests and solved four types of problems: verbal insight, spatial insight, verbal non-insight, and spatial non-insight. These problems were solved under three conditions: silence (control), inner speech suppression (articulatory suppression), and non-verbal attentional demands (spatial tapping). Interestingly, insight problem solving differed from verbal non-insight, but resembled spatial non-insight problem solving. Solving insight and spatial non-insight problems substantially benefitted from spatial and near verbal analogical thinking and convergent thinking, and little from divergent thinking. Both were unaffected by secondary tasks. Analogical thinking was associated more strongly with the generation of new solution procedures than with the retrieval of known procedures from memory, as in verbal non-insight problem solving. Analogical and convergent thinking seem to be key skills for the creation of new solutions, whether or not they are insight based. The results indicate a typical, analytic solution method consistent with the business-as-usual theory. Yet, there is also evidence for an exceptional solving method that includes rare attributes of the insightful process delineated by the special-process theory. Thus, we endorse an unequally integrated assertion that each theory reflects a different mode of thinking, the common versus the uncommon, by which insightful solutions can be produced.