In this chapter, we explore the relationship between mind-wandering (broadly defined as task-unrelated thought) and creativity. We begin with an exploration of the evidence that mind-wandering may contribute to creative insights (Aha! experiences) and then explore its relationship to creativity more generally. Although assorted lines of evidence support a relationship between mind-wandering and creativity, this literature has proven to be somewhat mixed: an outcome that we speculate arises because only certain types of mind-wandering are helpful. We then consider the relationship between different types of mind-wandering and creativity, examining both differences between individuals in the frequency with which they engage in assorted types of mind-wandering and fluctuations within individuals across days. This review offers suggestive evidence that particular forms of mind-wandering may facilitate creativity and, in particular, that curious daydreaming (or “mind wondering”) may do so. However, we acknowledge the case remains equivocal as supportive research is limited. We close with a discussion of future directions that may help to more conclusively identify and potentially foster the kinds of mind-wandering that are most likely to promote creative insights and advances

Perspectives on how to define, operationalize, and measure insight have evolved due to developments in theory, methodology, and technology. Research on insight can be broken into several waves. In the first wave, Gestalt psychologists introduced the concept of insight as a discontinuous form of learning and problem solving that arises from changes in one’s global representation of a problem, in opposition to contemporary associationist views. In the second wave, psychologists examined insight in deliberate contrast with analytical problem-solving and found that insight involves nonreportable mental operations leading to a discrete, all-or-none availability of representational change. In the third wave, thanks to advances in behavioral methods and neuroimaging technology, cognitive neuroscientists began to examine how insight occurs in the brain with the goal of studying the neural states that co-occur with and precede insight to better understand its cognitive mechanisms. The advances made during these initial waves enabled the proliferation of research on insight over recent decades and inspired new discoveries. This chapter provides a brief retrospective on the first two waves of insight research and a more in-depth overview of the third wave of research on the cognitive neuroscience of insight, and ends by discussing current and future directions in insight research

Aha! experiences are often very well recalled. The idea of insight leaving an indelible mark on memory was put forward by the Gestalt psychologists, yet has remained under-studied. This chapter provides an overview the existing empirical evidence for an insight memory advantage and possible explanations for why insight enhances memory. Several studies have focused on the phenomenological aspect of insight by showing that solutions associated with Aha! experiences are remembered better than those without, even after delays of up to two weeks. Confidence (i.e., a metacognitive reaction) and pleasure (i.e., an affective reaction) were also shown to play a role. On the other hand, there could also be a potential association between solution processes involving restructuring (i.e., the cognitive aspect of insight) and better memory. At present a clearer role is seen for the phenomenological effects of insight experiences on memory, but more work is needed that explores both phenomenological and cognitive aspects of insight in the same studies.

In this chapter, we argue that differences in problem-solving experiences can be traced to differences in the activation of brain structures involved in the unconscious processing of information (what we refer to as “the backstage”). Scientists commonly distinguish between two major types of problem-solving experiences: via insight and via analysis. Three properties are often mentioned when describing how insight solutions differ from analytic solutions: (1) Solvers are unable to report much of the processing that leads to the solution which comes to mind in an off–on manner; (2) Solvers experience their solutions together with a feeling of pleasure and reward; (3) Solutions via insight feel correct and they actually are. This is captured by a distinctive response: the Aha! This chapter focuses on these three properties and argues that unconscious processes are important for problem-solving in general, but especially important for insight experiences because most of the processing that leads to the solution happens below awareness. It also argues that the positive affect associated with insight serves an adaptive function.

Where do insights come from? What causes those moments when, unexpectedly, a marvelous new idea flashes into consciousness, possibly accompanied by feelings of surprise and delight? Sudden insights are rare, yet everyone appears to be familiar with the experience that may be alternatively described as an “Aha!” or “Eureka” moment: a sudden realization, an epiphany, illumination, revelation, or satori. The ideas resulting from insight experiences range from mundane to historic. Insight is defined not so much by the importance or significance of the content produced, but rather by the cognition and the phenomenology of the event. At its core, the insightful solution process begins with the solver holding an incorrect representation, and ends (if successful) with a nonobvious solution. But there is much more to know: What is insight, and how does a solution emerge unexpectedly into awareness? Is there a set of steps, a pathway that leads to insight?

Problems can be difficult to solve when individuals become fixated by misleading information. A popular method for studying fixation in problem solving externally induces it by priming misleading solutions. However, fixation can also arise internally from incorrect solutions that are strongly activated by prior knowledge. The work summarized in this chapter considers both sources of fixation. It also considers the effects of warnings, and the countervailing influences that individual differences in working-memory capacity (WMC) may have. Higher WMC or attentional control may sometimes help individuals to retrieve solutions, maintain task-relevant goals, and use hints or warnings, but at other times may make individuals more prone to fixation. This chapter describes studies that explored both sources of fixation, how warning participants about possible sources of fixation might affect problem solving success, and whether benefits from warnings of what to avoid relate to individual differences in WMC. Both internal fixation from prior knowledge and external fixation from exposure to misleading primes led to poorer performance on a word-fragment completion task. Providing participants with a warning about the misleading solutions sometimes led to poorer performance (rather than better). Within the conditions where individuals received warnings, the likelihood of reaching correct solutions depended on WMC. Several results highlight potential differences between internal fixation from prior knowledge and external fixation from recent exposure. An important direction for future research is to continue to explore the differences that might be seen in insightful solution processes and experiences depending on the source of the initial fixation, and the extent to which fixation from prior knowledge and from recent exposure may need to be overcome differently.

Insight experiences are powerful: They feel true, they are remembered, and they can shift our decisions and our beliefs. Feelings of insight are also accurate most of the time. However, recent work shows that it is possible to systematically induce false insights and even misattribute our Aha! moments to make false facts seem true. Insights, therefore, seem to be adaptive on average but error prone. This chapter suggests that these results can be integrated by thinking of insights as a metacognitive heuristic for selecting ideas from the stream of consciousness (dubbed the “Eureka heuristic”), reviews key findings about the accuracy of insights and where and why insights go wrong, and discusses implications for our understanding of the development of delusions, false beliefs, and misinformation. Ultimately, understanding the role that feelings of insight play in human cognition may make us better decision-makers in an uncertain world.

So-called incubation effects refer to better resolution of an initially unsolved problem after putting the problem aside rather than working on it continuously. Although the effect is a familiar experience to most people, and the term appeared in 1926, incubation was not observed reliably in laboratory studies until the late twentieth century, when research began to focus on causes of and relief from fixation. We review research on incubation effects in creative problem solving, divergent thinking, and memory recovery. Although the term “incubation” erroneously implies the underlying mechanism is unconscious work, we refer to the beneficial effect of a break as an “incubation effect.” We review research showing that creative responses can be blocked by more dominant ones, a fixation effect, and forgetting dominant responses can enable incubation effects. Forgetting fixating responses can occur via temporal delays, retrieval inhibition, and context shifts, all of which can lead to incubation effects. Future research may discover what activity during the incubation interval is optimal for incubation effects, and should also examine the moments preceding an insight experience, a nascent period that may occur when one returns to an unsolved problem after fixation has been diminished.

Research has shown that taking a break, or an "incubation interval," can facilitate creative problem solving. One interpretation of this phenomenon is that it allows for task-switching and attentional flexibility, which can improve creative performance. Task-switching may allow individuals to break their mental set and identify solutions that were previously unavailable. It may also encourage the alternation between idea generation and evaluation, leading to attentional flexibility. This chapter discusses the evidence for the benefits of attentional flexibility and its relationship to mind-wandering, and presents a new study on the potential sources of benefit for task-switching on creativity.

Research on creative problem solving has shown that the generation of new ideas and solutions can be impeded by existing ideas and solutions. This phenomenon, known as mental fixation, has been observed in many problem-solving contexts, including the remote associates test (RAT). In the RAT, participants are presented with three cue words and are asked to come up with a fourth word related to each of the cue words. The task can be made more difficult by exposing participants to unhelpful associates that cause mental fixation before they attempt to generate the fourth word. The current chapter reviews research on the mechanisms by which people overcome the effects of mental fixation, focusing on research using the RAT, and on the potential roles of forgetting and inhibition. The results suggest that, at least under certain conditions, the ability to forget, inhibit retrieval, or stop a response can help people overcome mental fixation and thus lead to the experience of creative insight.

We propose that the processes underlying insight problem solving in humans depend on two distinctly different forms of curiosity: Curiosity1 (which is associated with a habit-based, goal-centered, reinforcement learning processing system), and Curiosity2 (which depends on the discursive, default mode, medial-temporal-lobe based processing system). The former kind of curiosity is goal directed and increases with approach to the rewarding answer. The latter is exploratory and goal averse: “twiddling.” The possibility of insight, we suggest, depends upon the individual initiating a deliberate system switch upon apprehension of an impasse. Problem solving involves engaging in a habitual mode of responding and motivation by Curiosity1. With insight problems, however, this normal mode fails to lead to a solution, and impasse results. Acknowledgment of the impasse may trigger a strategic switch to a different kind of curiosity and information processing system: the discursive, default mode Curiosity2 system, wherein the solution that was previously unavailable may be found. This view is consistent with traditional stages posited to be involved in insight problem solving. However, several paradigms used to study creativity or investigate 'Aha!' reactions do not fit easily with this view of insight. Using this perspective, we evaluate the evidence for insight in nonhuman animals.

Although insight experiences are recognized to be cognitively, physiologically, and neurologically distinct from other forms of reasoning and problem solving, it also appears that the experience of insight is not the same for all types of individuals or in all circumstances. Researchers in our lab and others have begun to examine the individual differences associated with the insight experience: what makes certain individuals more likely to report solving problems insightfully, and how might the subjective experience of insight differ among people according to their underlying psychological and neurobiological characteristics? Research demonstrates that the tendency to experience insight during problem-solving varies among individuals as a function of transient differences in one’s psychological state as well as stable, trait-like differences in neurocognitive dynamics. We argue that an individual differences approach can further disambiguate the components of insight on the behavioral and neural levels and help us understand when, and for whom, such experiences are most likely to occur, and how they may affect us depending on our individual motivations, goals, and underlying neurobiology.

This chapter posits a prepared mind as key to later insight experiences. Following Wallas's (1926) four-stage model, preparation through failures experienced during initial solution attempts anticipates opportunities. At the time of impasse, solvers can predict necessary solution qualities by thinking through failed attempts at a more abstract level. These predictive features (Johnson & Seifert, 1994) describe needed resources, missing information, and solution characteristics, and are “seeded” into memory with the unsolved problem. Later, during incubation, attended features in the current context spontaneously retrieve the unsolved problem from memory, called opportunistic assimilation. This conscious reminding of the unsolved problem is the experience of sudden insight (Aha!). The surprised solver must then puzzle through why the current contextual features brought the problem back to mind and, in the process, restructure the old and new representational pieces into a novel solution. In this account, the insight process depends on effortful thinking during both preparation and illumination, but the incubation stage involves the simple process of associative memory as the source of insight experiences.

Chapter 1 introduces what the fair process effect entails, and what it does not entail. Important caveats when studying the effect are noted. The chapter further explains that, given the current state of knowledge and the impressive body of research, the fair process effect now can be used effectively to start developing approaches and interventions to start countering important instances of societal discontent, in particular, distrust, polarization, and conspiracy thinking.

Newell & Simon’s 1972 book Human Problem Solving continues to influence theories of problem solving. Their theory provides a general framework for specifying how the structure of the problem, strategies, and different sources of knowledge influence progress from the initial state to the goal state. In contrast, discovering solutions for problems studied by Gestalt psychologists typically require a perceptual reorganization to identify the correct relations among the components of the problem. A rapid shift to a correct organization is referred to as ‘insight’. Solving mathematics and scientific problems requires utilizing information learned in the classroom. This information is organized into clusters of knowledge that are typically called ‘schema’. Design problems are usually ill-structured in which the initial, goal, and intermediate states are incompletely specified. There are no right or wrong answers, only better and worse ones. The size and complexity of the problems require decomposition into smaller problems or modules.

This chapter explores little-c, or everyday creativity, by providing an overview of the creative process. First, Graham Wallas’s initial model, which features the concepts of incubation and insight, is discussed. Then J. P. Guilford’s creative problem-solving model and subsequent additions and revisions are highlighted. I talk about the importance of problem finding before proceeding to the well-known concepts of divergent and convergent thinking. I proceed to discuss Paul Torrance and the Torrance Tests of Creative Thinking, along with other divergent thinking measures. I finish up with a quick mention of associational thinking and the Remote Associates Test.

Creativity is typically defined as producing something that is novel, useful, and surprising. Such endeavor plays a critical role in the arts and scientific discovery. However, not all creativity is groundbreaking or historically important. As a common cognitive activity, creativity is amenable to scientific investigation leading to a process-based understanding, so it should be possible to propose models and write computer programs simulating the creativity process. However, the path from cognitive models to computational models is still not trodden as often as would be beneficial. This chapter reviews common concepts underlying many computational creativity efforts, namely idea generation, search, and evaluation. Two example computational models are described in more detail, namely the explicit-implicit interaction theory and the CreaCogs architecture. The chapter concludes with a discussion of current shortcomings and future directions for computational creativity as well as discussing promising avenues and successes of current models.