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

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.

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.

This chapter examines suitable statistics questions for investigation by children of different ages, using a cycle of problem, plan, data, analysis and conclusion (PPDAC) (Wild & Pfannkuch 1999). The importance of variation in data and different types of variables and the difference between a population and a sample are investigated. Readers will explore different ways of displaying data to ‘tell a story’. The importance of drawing inferences from data and the uncertainty associated with these inferences are discussed. Readers will engage in activities that use technology to support the development of statistical understanding.

In recent times, self-interest has been seen as the main driving force of behaviour and function in organisms. This is particularly evident in the concept of the selfish gene. However, as elaborated in this book, living systems strongly depend on cooperative behaviour, which is found everywhere in nature. All the way from millions of minute bacteria cooperating in the way they feed and grow, to massive whales talking with each other across oceans, organisms communicate with each other, and that communication is used as the glue of cooperation, even between distinct species. The idea of nature as ‘red in tooth and claw’ is at best a distorted perspective of the entirety of nature. However, in the grand scheme of things, both cooperation and competition are part of the story, and – whether wittingly or unwittingly – organisms form part of and interact with their ecosystems.

The view of living systems as machines is based on the idea of a fixed sequence of cause and effect: from genotype to phenotype, from genes to proteins and to life functions. This idea became the Central Dogma: the genotype maps to the phenotype in a one-way causative fashion, making us prisoners of our genes.

Living systems are characterised by intelligence. Treating organisms as gene-driven automata, blindly reacting to events, does not take account of their social or ecological being. Living systems anticipate the actions and reactions of other living systems. As in a chess game, anticipation can consider many options. Nevertheless, the chess analogy only gets us part of the way to understanding this characteristic of life. It is more like a chess game in which the players can create the rules, much as happens in a game of poker, in which anticipation is the key to success, including assessment of the other’s power of anticipation. Life is rule-creating, rather than rigidly rule-following. This does not mean there is no logic to what happens or how organisms behave; there is, and often it involves a clear strategy. But this is not regulated by genes. Much behaviour may be programmed, and much is learned; the logic, however, is situational (that is, dependent on circumstances) and subject to change. The ability to adapt to circumstances is an example of evolved functionality. Therefore, dogmatic models of life, seeking to reduce behaviour to little more than a set of algorithms, misunderstand the intelligence of organisms.

Where is the living mind that thinks? Culture is the matrix of the mind. Organisms owe their social and mental abilities to the ‘nesting’ of causation between all levels of their functioning. Higher levels mould what the lower levels can do. This is how living systems can use their flexibility, from cultural and linguistic variability to the water-based jiggling around of their molecules, to enable the evolution of rational and ethical social organisation. It is within this purposiveness that genuine freedom and responsibility are to be found.

Artificial intelligence (AI) is a tool created by living organisms, us humans. Like the hydraulic robots of the seventeenth century which inspired Descartes’ mechanical view of organisms, AI has become the latest in a list of mechanical metaphors for life. Yet it is just as limited, just as much a mistaken view of organisms. It views life as just processing further and further information faster and faster. Computers exist to process rapidly. That is their function, given to them by the humans who created them. Organisms use processing to help them create objectives, purpose.

Standard evolutionary theory represents genes as the target of evolution. But organisms may functionally develop without alterations in their DNA, and they may also buffer changes in the DNA to retain function. It is organisms that are the agents in the process of evolution. Outside a living system, DNA is inactive, dead. Furthermore, many significant transitions in evolution have not depended on new DNA mutations. They arose from the fusion or hybridisation of organisms with existing but different DNA. All the molecular processes in a living system are constrained by its purpose. Viewed this way, genes are the most constrained elements in organisms. Evolution of different species has occurred through extraordinarily creative and varied processes that include cooperation and fusion of existing species and the exchange of DNA and organelles. It is much more like nature using preformed tried and tested functionality than through slow gradual mutation. Evolution can occur in leaps and bounds.

If the dichotomy between replicator and vehicle is wrong, then what is it that replicates? The purpose of reproduction is not replication, at least not exactly. Reproduction brings about change. It shakes up the templates and provides new avenues to explore in adapting to a changing environment. It creates and propagates variation. But it also provides a way for the lessons learned in one generation to be passed on to the next. Reproduction is sensitive to the environment of the parent generation and enables change through the germ line.

We are writing this book as agents with a purpose. Agency and purposeful action is a defining property of all living systems. Yet modern science has presented a reductionist, gene-centred view of life, where life is reduced to biochemistry, particularly DNA and proteins. It has even carved out its own areas of study – genomics and proteomics – as if these components can be understood in isolation from the organisms themselves. But they cannot. The gene-centric view of life creates a fundamental problem. If all action can be reduced to genes, or is controlled by them, then purposeful agency cannot exist. Indeed, it has been referred to as an illusion. At best, modern science gives this problem to philosophers, assuming that the answer does not lie in biology itself. This is a mistake. Casting the issue aside ignores the most creative aspect of living things: problem-solving and the agency of organisms.

In the preceding chapters, we showed why the idea that living organisms are really driven by their genes is a profound misunderstanding of how living systems work. On the contrary, they are open systems at all levels of organisation. How things work at the molecular level is constrained and regulated at the cellular level. The interaction of cells is regulated at the tissue level, and tissues at the organ level, and organs at the system level. The system is regulated by the behaviour of the organisms, and organisms by social and ecological interactions. The psychosocial level is unique. If there is a privileged level of causation, then it lies at the psychosocial level and not at the level of genes. This is the level at which wilful agency is initiated and organisms can be genuinely selfish or altruistic. In truth you cannot be selfish if you do not have the choice to be altruistic, which is why selfishness cannot be applied at a genetic level, neither metaphorically nor literally.