​Imagination will remain a mystery—we will not be able to explain imagination—until we can break it into parts we already understand. Explaining Imagination is a guidebook for doing just that, where the parts are other ordinary mental states like beliefs, desires, judgments, and decisions. In different combinations and contexts, these states constitute cases of imagining. This reductive approach to imagination is at direct odds with the current orthodoxy, according to which imagination is a sui generis mental state or process—one with (...) its own inscrutable principles of operation. Explaining Imagination upends that view, showing how, on closer inspection, the imaginings at work in hypothetical reasoning, pretense, the enjoyment of fiction, and creativity are reducible to other familiar mental states—judgments, beliefs, desires, and decisions among them. Crisscrossing contemporary philosophy of mind, cognitive science, and aesthetics, Explaining Imagination argues that a clearer understanding of imagination is already well within reach. (shrink)

The flow construct has been influential within positive psychology, sport psychology, the science of consciousness, the philosophy of agency, and popular culture. In spite of its longstanding influence, it remains unclear [a] how the constituents of the flow state ‘hang together’—how they relate to each other causally and functionally—[b] in what sense flow is an ‘optimal experience,’ and [c] how best to describe the unique phenomenology of the flow state. As a result, difficulties persist for a clear understanding of the (...) flow state’s structure and function. After explicating the standard view of the flow construct, I articulate several basic questions regarding its nature and functional roles, and I argue that these questions are best answered by integrating flow within broader streams of research on the dynamics of thought, on cognitive control resource allocation, and on creative thought. (shrink)

Currie’s (2010) argument that “i-desires” must be posited to explain our responses to fiction is critically discussed. It is argued that beliefs and desires featuring ‘in the fiction’ operators—and not sui generis imaginings (or "i-beliefs" or "i-desires")—are the crucial states involved in generating fiction-directed affect. A defense of the “Operator Claim” is mounted, according to which ‘in the fiction’ operators would be also be required within fiction-directed sui generis imaginings (or "i-beliefs" and "i-desires"), were there such. Once we appreciate that (...) even fiction-directed sui generis imaginings would need to incorporate ‘in the fiction’ operators, the main appeal of the idea that sui generis imaginings (or "i-beliefs" or "i-desires") are at work in fiction-appreciation dissipates. [This is Chapter 10 of Explaining Imagination (OUP, 2020)]. (shrink)

Comparatively easy questions we might ask about creativity are distinguished from the hard question of explaining transformative creativity. Many have focused on the easy questions, offering no reason to think that the imagining relied upon in creative cognition cannot be reduced to more basic folk psychological states. The relevance of associative thought processes to songwriting is then explored as a means for understanding the nature of transformative creativity. Productive artificial neural networks—known as generative antagonistic networks (GANs)—are a recent example of (...) how a system’s ability to generate novel products can both be finely tuned by prior experience and grounded in strategies that cannot be articulated by the system itself. Further, the kinds of processes exploited by GANs need not be seen as incorporating something akin to sui generis imaginative states. The chapter concludes with reflection on the added relevance of personal character to explanations of creativity. [This is Chapter 12 of the book Explaining Imagination.]. (shrink)

Do artists have control over their ideas for new artworks? This is often treated as a question about spontaneity, or the experience of control: does the event of having an idea for a new artwork occur unexpectedly and without foresight? I suggest another way of interpreting the question—one that has mostly been neglected by philosophers, and that is not settled by claims about spontaneity. According to that interpretation, the question is about agency: are the events of having ideas for new (...) artworks exercises of mental agency? I argue that the answer is no. I apply the results of this argument to questions about what is and is not intentional in the creative process. I conclude by examining another type of control artists might exercise over their creative mental events, which I call ‘facilitation’. (shrink)

Idea generation, the process of creating and developing candidate solutions that when implemented can solve ill-defined and complex problems, plays a pivotal role in creativity and innovation. The algorithms that underlie classical evolutionary, cognitive, and process models of idea generation, however, appear too inefficient to effectively help solve the ill-defined and complex problems for which one would engage in idea generation. To address this, these classical models have recently been redesigned as forward models, drawing heavily on the “predictive mind” literature. (...) These pose that more efficiency can be achieved by making predictions based on heuristics, previous experiences, and domain knowledge about what material to use to generate ideas with, and evaluate these subsequently generated ideas based on whether they indeed match the initial prediction. When a discrepancy occurs between prediction and evaluation, new predictions are made, and thus shaping what actions, and how these actions, are undertaken. Although promising, forward models of idea generation remain theoretical and thus no empirical evidence exists about whether such predictions and evaluations indeed form part of the idea generation process. To take a first empirical look at this, a mixed-methods study was conducted by analyzing people’s self-reports for the reasons of the actions that they take during an idea generation task. The results showed that predictions and evaluations are pervasive in the idea generation process. Specifically, switching between concept selection and conceptual combination and idea generation, as well as repeating idea generation based on earlier selected conceptual combination, and possibly (but to a lesser extent) concept selection and the repetition thereof, are likely to be driven by predictions and evaluations. Moreover, the frequencies of the predictions and evaluations that drive these actions influenced the amount of ideas generated, amount of concepts used, and within-concept fluency (the ratio of the amount of ideas generated per concept used). Therefore, the contribution of this paper is the first empirical evidence that indicates that the idea generation process is driven by both predictions and evaluations. (shrink)