The effect of true and false unreportable hints on anagram problem solving, restructuring, and the Aha!-experience

The lack of Aha! experience can be dependent on the problem difficulty

Previous research on how problem-difficulty affects solution-types of insight-problems has yielded contradictory findings. Thus, we aimed to examine the impact of problem-difficulty on solution-types in both inter- and intra-problem-difficulty contexts. For this, we employed the original 8-coin, and 9-dot problems and four hinted-versions of those that were manipulated by using hints-to-remove-sources-of-difficulty to alter their difficulty level. Those manipulations were executed based on the assumptions of constraint-relaxation and chunk-decomposition as posited by representational change theory. The study involved a total of 165 participants who were tested in five groups (33 per se), with each group receiving an original or hinted problem. Following their correct solutions, problem-solvers classified their solution-types (insight or non-insight solutions) by whether they had an Aha!-experience during the solution. Across all groups, 56.1% of correctly solved insight problems were solved with Aha!-experience, based on participants' self-reports, implying that correct solutions should not be equated with insight. Subsequently, the solution-type rates were compared for both original problems (inter-problem-difficulty) and hinted versions of those at each difficulty level (intra-problem-difficulty). Inter-problem-difficulty comparisons demonstrated that the easier 8-coin problem was more likely to be solved with insight than the harder 9-dot problem. In contrast, intra-problem-difficulty comparisons revealed that harder problems were more likely to be solved with insight. These findings suggest that problem-difficulty should be considered in future studies of insight. Finally, separate analyses on the predictive values of the cognitive-affective-dimensions on solution-types revealed that, after adjusting for problem-difficulty, problem-solvers with higher suddenness scores in both problems exhibited a significantly higher probability of generating insight solutions.

The involvement of decomposition and composition processes in restructuring during problem solving

Decomposition of chunks has been widely accepted as a critical proxy of restructuring, but the role of composition in forming new representations has been largely neglected. This study aims to investigate the roles of both decomposition and composition processes in chunk restructuring, as well as their relationships with "aha" experiences during problem-solving. Participants were asked to move a part of a character to another character to create two new characters. Across three experiments, the characters to be decomposed or composed were varied in terms of tight or loose chunks. The results showed that decomposition or composition of tight chunks led to lower success rates, longer response times, and significantly stronger "Aha!" emotional experiences (mainly in terms of surprise and suddenness). This study provides evidence for the contribution of both decomposition and composition processes to restructuring in creative insight.

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.

Does Thinking in Opposites in Order to Think Differently Improve Creativity?

In this paper, we focus on the link between thinking in opposites and creativity. Thinking in opposites requires an intuitive, productive strategy, which may enhance creativity. Given the importance of creativity for the well-being of individuals and society, finding new ways to enhance it represents a valuable goal in both professional and personal contexts. We discuss the body of evidence that exists concerning the importance of the first representation of the structure of a problem to be solved, which determines the baseline representation and sets limits on the area within which a problem solver will explore. We then review a variety of interventions described in the literature on creativity and insight problem solving that were designed to overcome fixedness and encourage people to move away from stereotypical solutions. Special attention is paid to the research carried out in the context of problem solving, which provides evidence that prompting people to "think in opposites" is beneficial. We suggest that an extended investigation of the effects of this strategy in various types of tasks related to creativity is an interesting line of research to follow. We discuss the rationale supporting this claim and identify specific questions, both theoretical and methodological, for future research to address.

Where Does Eureka Come From? The Effect of Unreportable Hints on the Phenomenology of Insight

Insight interests researchers given its special cognitive mechanisms and phenomenology (an Aha! experience or Eureka moment). There is a considerable amount of research on the effect of hints on performance in insight problem solving. However, only a few studies address the effect of hints on the subjective experiences of solvers, and the picture their results provide is unclear. We analyze the effect of unreportable true and false hints on different dimensions of the Aha! experience (subjective suddenness, Aha! experience as an effect, and certainty). Using the processing fluency framework, we predict that true hints lead to more insights and stronger Aha! experience and certainty, while false hints lead to the opposite results due to the controlled inhibition of the inappropriate representation. The results showed that false hints decreased the chance of finding a correct solution. The true-hint condition did not lead to more correct solutions but made solutions feel sudden more often than the control condition. The ratings of the Aha! experience and certainty were higher for solutions obtained after true hints than after false hints. We obtained partial support for the effect of unreportable hints on "Eureka!" moments.

Surprise! Why Insightful Solution Is Pleasurable

Insight problems-as a type of ill-defined problems-are often solved without an articulate plan, and finding their solution is accompanied by the Aha! experience (positive feeling from suddenly finding a solution). However, the solution of such problems can also be guided, for example, by expectations in terms of criteria for achieving the goal. We hypothesize that adjusting the expectation accuracy based on the reward prediction error (discrepancy between the reward and its prediction) affects the strength of affective components of the Aha! experience (pleasure and surprise), allowing to learn how to solve similar problems. We manipulated expectation accuracy by varying the similarity in problem solution principle and structure in a short learning set. Each set was followed by a critical problem where both the structure and solution principle were changed (except for control set). Subjective feelings, solution time, and expectation were measured after each problem. The results revealed that problems with similarities become more expected at the end of the set and their solution time is decreased. However, the critical problem featured a rapid increase in pleasure and surprise and decrease in expectedness only in the condition where both the solution principle and structure were expected, suggesting that problem structure is a key feature determining expectedness in insight problem solving. The Aha! experience is not an epiphenomenon; it plays a role in learning of problem solving through adjusting expectations.

Eliciting false insights with semantic priming

The insight experience (or ‘Aha moment’) generally evokes strong feelings of certainty and confidence. An ‘Aha’ experience for a false idea could underlie many false beliefs and delusions. However, for as long as insight experiences have been studied, false insights have remained difficult to elicit experimentally. That difficulty, in turn, highlights the fact that we know little about what causes people to experience a false insight. Across two experiments (total N = 300), we developed and tested a new paradigm to elicit false insights. In Experiment 1 we used a combination of semantic priming and visual similarity to elicit feelings of insight for incorrect solutions to anagrams. These false insights were relatively common but were experienced as weaker than correct ones. In Experiment 2 we replicated the findings of Experiment 1 and found that semantic priming and visual similarity interacted to produce false insights. These studies highlight the importance of misleading semantic processing and the feasibility of the solution in the generation of false insights.