This paper surveys historical and recent philosophical discussions of the relations between imagination and creativity. In the first two sections, it covers two insufficiently studied analyses of the creative imagination, that of Kant and Sartre, respectively. The next section discusses imagination and its role in scientific discovery, with particular emphasis on the writings of Michael Polanyi, and on thought experiments and experimental design. The final section offers a brief discussion of some very recent work done on conceptual relations between imagination (...) and creativity. (shrink)

How can we explain the intentional nature of an expert’s actions, performed without immediate and conscious control, relying instead on automatic cognitive processes? How can we account for the differences and similarities with a novice’s performance of the same actions? Can a naturalist explanation of intentional expert action be in line with a philosophical concept of intentional action? Answering these and related questions in a positive sense, this dissertation develops a three-step argument. Part I considers different methods of explanations in (...) cognitive neuroscience (Bennett & Hacker’s philosophical, conceptual analysis; Marr’s three levels of explanation; Neural Correlates of Consciousness research; mechanistic explanation), defending ‘mechanistic explanation’ as a method that provides the necessary tools for integrating interdisciplinary insights into human action. Furthermore, a dynamic, explanatory mechanism allows the assessment of the impact of learning and development on expert action in a valuable way that other methods don’t. Part II continues by scrutinizing several cognitive neuroscientific theories of learning and development (neuroconstructivism; dual-processing theories; simulation theory; extended mind/cognition hypothesis), arguing for the complex interactions between different types of processing and different action representations involved in expert action performances. Moreover, according to our discussion of a particular ‘simulation theory’ these interactions can be influenced in several ways with the use of language, allowing an agent to configure a specific action representation for performance at a later stage. The results of Parts I and II are then applied in Part III to a parallel discussion of philosophical analyses of intentional action (discussing i.a. Frankfurt, Bratman, Pacherie and Ricoeur) and cognitive neuroscientific insights in it. Both approaches are found to converge in emphasizing the importance for an expert to develop stable patterns of actions that comply maximally not only with his intentions, but also with his motor expertise and with situational conditions. Consequently, his actions – automatic, or not – rely on this ‘sculpted space of actions’. (shrink)

According to David Chalmers, the hard problem of consciousness consists of explaining how and why qualitative experience arises from physical states. Moreover, Chalmers argues that materialist and reductive explanations of mentality are incapable of addressing the hard problem. In this chapter, I suggest that Chalmers’ hard problem can be usefully distinguished into a ‘how question’ and ‘why question,’ and I argue that evolutionary biology has the resources to address the question of why qualitative experience arises from brain states. From this (...) perspective, I discuss the different kinds of evolutionary explanations (e.g., adaptationist, exaptationist, spandrel) that can explain the origins of the qualitative aspects of various conscious states. This argument is intended to clarify which parts of Chalmers’ hard problem are amenable to scientific analysis. (shrink)

The improbability of a spontaneously generated self-assembling molecule has suggested that life began with a set of simpler, collectively replicating elements, such as an enclosed autocatalytic set of polymers (or autocell). Since replication occurs without a self-assembly code, acquired characteristics are inherited. Moreover, there is no strict distinction between alive and dead; one can only infer that an autocell was alive if it replicates. These features of early life render natural selection inapplicable to the description of its change-of-state because they (...) defy its underlying assumptions. Moreover, natural selection describes only randomly generated novelty; it cannot describe the emergence of form at the interface between organism and environment. Self-organization is also inadequate because it is restricted to interactions amongst parts; it too cannot account for context-driven change. A modified version of selection theory or self-organization would not work because the description of change-of-state through interaction with an incompletely specified context has a completely different mathematical structure, i.e. entails a non-Kolmogorovian probability model. It is proposed that the evolution of early life is appropriately described as lineage transformation through context-driven actualization of potential, with self-organized change-of-state being a special case of no contextual influence, and competitive exclusion of less fit individuals through a selection-like process possibly (but not necessarily) playing a secondary role. It is argued that natural selection played an important role in evolution only after genetically mediated replication was established. (shrink)

Online collection of papers by Devitt, Dretske, Guarino, Hochberg, Jackson, Petitot, Searle, Tye, Varzi and other leading thinkers on philosophy and the foundations of cognitive Science. Topics dealt with include: Wittgenstein and Cognitive Science, Content and Object, Logic and Foundations, Language and Linguistics, and Ontology and Mereology.

This paper offers a critical survey and analysis of empirical studies on creativity, with emphasis on how imagination plays a role in the creative process. It takes as a foil the romantic view that, given features like novelty, incubation, and insight, we should be skeptical about the prospects for naturalistic explanation of creativity. It rebuts this skepticism by first distinguishing stages or operations in the creative process. It then works through various behavioral and neural studies, and corresponding philosophical theorizing, that (...) concern the preparation, generation, insight, and evaluation stages of creative processes. The result is not a complete explanation of creativity, but it identifies an explanatory path towards such an explanation, including the features of creativity that motivate that skepticism. (shrink)

This entry provides a substantive overview of research and debates concerning creativity in philosophy and related fields. Topics covered include definitions of creativity, whether creativity can be learned, whether it can be explained, attempts to explain creativity in cognitive science, and whether computer programs or AI systems can be creative.

The relative efficiency of an action is a central criterion in action control and can be used to predict others’ behavior. Yet, it is unclear when the ability to predict on and reason about the efficiency of others’ actions develops. In three main and two followup studies, 3- to 6-year-old children (n = 242) were confronted with vignettes in which protagonists could take a short (efficient) path or a long path. Children predicted which path the protagonist would take and why (...) the protagonist would take a specific path. The 3-year-olds did not take efficiency into account when making decisions even when there was an explicit goal, the task was simplified and made more salient, and children were questioned after exposure to the agent’s action. Four years is a transition age for rational action prediction, and the 5-year-olds reasoned on the efficiency of actions before relying on them to predict others’ behavior. Results are discussed within a representational redescription account. (shrink)

To investigate the cognitive processes underlying creative inspiration, we tested the extent to which viewing or copying prior examples impacted creative output in art. In Experiment 1, undergraduates made drawings under three conditions: copying an artist's drawing, then producing an original drawing; producing an original drawing without having seen another's work; and copying another artist's work, then reproducing that artist's style independently. We discovered that through copying unfamiliar abstract drawings, participants were able to produce creative drawings qualitatively different from the (...) model drawings. Process analyses suggested that participants' cognitive constraints became relaxed, and new perspectives were formed from copying another's artwork. Experiment 2 showed that exposure to styles of artwork considered unfamiliar facilitated creativity in drawing, while styles considered familiar did not do so. Experiment 3 showed that both copying and thoroughly viewing artwork executed using an unfamiliar style facilitated creativity in drawing, whereas merely thinking about alternative styles of artistic representation did not do so. These experiments revealed that deep encounters with unfamiliar artworks—whether through copying or prolonged observation—change people's cognitive representations of the act of drawing to produce novel artwork. (shrink)

The present experiment investigated the development of implicit and explicit knowledge during concept learning. According to Cleeremans and Jiménez , the content of a representation can be conscious only when the representation is of a sufficiently good quality; on this theory, increasing explicit and decreasing implicit knowledge might be expected with training. The view that implicit knowledge arises from compilation of explicit knowledge makes the opposite prediction. The present research tested these possibilities using subjective measures based on confidence ratings. One (...) group of participants was presented with blocks of category exemplars that activated prior knowledge, whereas another group was presented with blocks of categories that did not elicit any useful prior knowledge. The results showed that, irrespective of the knowledge group participants were allocated to, explicit knowledge increased over the course of learning, whereas implicit knowledge either stayed the same or decreased, consistent with Cleeremans and Jiménez’s prediction. (shrink)

What is creativity? One new idea may be creative, whereas another is merely new: What's the difference? And how is creativity possible? These questions about human creativity can be answered, at least in outline, using computational concepts. There are two broad types of creativity, improbabilist and impossibilist. Improbabilist creativity involves novel combinations of familiar ideas. A deeper type involves METCS: the mapping, exploration, and transformation of conceptual spaces. It is impossibilist, in that ideas may be generated which – with respect (...) to the particular conceptual space concerned – could not have been generated before. The more clearly conceptual spaces can be defined, the better we can identify creative ideas. Defining conceptual spaces is done by musicologists, literary critics, and historians of art and science. Humanist studies, rich in intuitive subtleties, can be complemented by the comparative rigour of a computational approach. Computational modelling can help to define a space, and to show how it may be mapped, explored, and transformed. Impossibilist creativity can be thought of in “classical” Al terms, whereas connectionism illuminates improbabilist creativity. Most Al models of creativity can only explore spaces, not transform them, because they have no self-reflexive maps enabling them to change their own rules. A few, however, can do so. A scientific understanding of creativity does not destroy our wonder at it, nor does it make creative ideas predictable. Demystification does not imply dehumanization. (shrink)

Depictive expressions of thought predate written language by thousands of years. They have evolved in communities through a kind of informal user testing that has refined them. Analyzing common visual communications reveals consistencies that illuminate how people think as well as guide design; the process can be brought into the laboratory and accelerated. Like language, visual communications abstract and schematize; unlike language, they use properties of the page (e.g., proximity and place: center, horizontal/up–down, vertical/left–right) and the marks on it (e.g., (...) dots, lines, arrows, boxes, blobs, likenesses, symbols) to convey meanings. The visual expressions of these meanings (e.g., individual, category, order, relation, correspondence, continuum, hierarchy) have analogs in language, gesture, and especially in the patterns that are created when people design the world around them, arranging things into piles and rows and hierarchies and arrays, spatial-abstraction-action interconnections termed spractions. The designed world is a diagram. (shrink)

Many animals can be trained to perform novel tasks. People, too, can be trained, but sometime in early childhood people transition from being trainable to something qualitatively more powerful—being programmable. We argue that such programmability constitutes a leap in the way that organisms learn, interact, and transmit knowledge, and that what facilitates or enables this programmability is the learning and use of language. We then examine how language programs the mind and argue that it does so through the manipulation of (...) embodied, sensorimotor representations. The role language plays in controlling mental representations offers important insights for understanding its origin and evolution. (shrink)

This piece is a commentary on a precis of Maggie Boden's book "The creative mind" published in Behavioral and Brain Sciences.

This chapter offers a brief characterization of creativity, followed by a review of some of the reasons people have been skeptical about the possibility of explaining creativity. It surveys some of the recent work on creativity that is naturalistic in the sense that it presumes creativity is natural, as opposed to magical, occult, or supernatural, and is therefore amenable to scientific inquiry. The chapter divides into two categories. The broader category is empirical philosophy, which draws on empirical research while addressing (...) questions that have traditionally been regarded as philosophical. The second category is experimental philosophy, a special branch of empirical philosophy in which experiments are designed by philosophers with the explicit purpose of addressing philosophical questions. This categorization is admittedly somewhat artificial, but it is a familiar one in the prevailing division of intellectual labor, and it will be a useful organizational tool for the chapter. (shrink)

This paper distinguishes two conceptions of representation at work in the philosophical literature. On the first, "contentive" conception (found, for example, in Searle and Fodor), something is a representation, roughly, if it has "propositional content". On the second, "indicative" conception (found, for example, in Dretske), representations must not only have content but also have the function of indicating something about the world. Desire is representational on the first view but not on the second. This paper argues that philosophers and psychologists (...) have sometimes conflated these two conceptions, and it examines the consequences of this conflation for the developmental literature on the child's understanding of mind. Specifically, recent research by Gopnik and Perner on the child's understanding of desire is motivated by an argument that equivocates between the two conceptions of representation. Finally, the paper suggests that an examination of when the child understands the possibility of misrepresentation in art would be helpful in charting the child's understanding of indicative representation. (shrink)

According to some recent empirical studies revealing that creativity is linked to sensorimotor components, the current research was aimed at evaluating whether sensory afferences could modulate originality in drawing of children and adolescents. Sixty-nine children from 1st, 3rd, 6th, and 8th grades were required to produce a man who exists and a man who doesn’t exist with fingers or stylus on a tablet and with a pen on paper. Drawings were assessed with an originality scale comparing original drawings to unoriginal (...) ones. Since, in comparison to drawings made on paper with a pen, drawing with fingers enhances proprioceptive information, this condition was expected, according to cognitive load theory, to favor originality in drawing by reducing cognitive resources devoted to motor control of the graphic gesture. On the contrary, since the use of a stylus involves a proprioceptive loss of information, which enhances intrinsic load by increasing cognitive resources devoted to motor control, it was expected that drawing with a stylus on the tablet would lead to the least original drawings. Results only partially confirmed these hypotheses. While the use of fingers on the tablet led to the highest original scores, using a stylus on the tablet did not impair originality in drawing of children and adolescents. On the opposite, the use of a stylus led 3rd–8th graders to perform better than with pen on paper. This modulation of the tool on originality does not confirm the hypotheses formulated in accordance with the cognitive load framework. However, it could be explained according to an embodied perspective of creativity considering the creative process as relying on a sensorimotor prediction process in which sensory afferences are central to generating and evaluate creative ideas. This research opens new avenues on creativity and proposes to consider the development of predictive motor control as a significant part of creativity development. (shrink)

Production and comprehension have long been viewed as inseparable components of language. The study of vision, by contrast, has centered almost exclusively on comprehension. Here we investigate drawing—the most basic form of visual production. How do we convey concepts in visual form, and how does refining this skill, in turn, affect recognition? We developed an online platform for collecting large amounts of drawing and recognition data, and applied a deep convolutional neural network model of visual cortex trained only on natural (...) images to explore the hypothesis that drawing recruits the same abstract feature representations that support natural visual object recognition. Consistent with this hypothesis, higher layers of this model captured the abstract features of both drawings and natural images most important for recognition, and people learning to produce more recognizable drawings of objects exhibited enhanced recognition of those objects. These findings could explain why drawing is so effective for communicating visual concepts, they suggest novel approaches for evaluating and refining conceptual knowledge, and they highlight the potential of deep networks for understanding human learning. (shrink)

Like the information patterns that evolve through. biological processes, mental representations or memes evolve through adaptive exploration and transformation of an information space through variation, selection, and transmission. However since memes do not contain instructions for their replication our brains do it for them, strategically, guided by a fitness landscape that reflects both internal drives and a worldview that forms through meme assimilation. This paper presents a tentative model for how an individual becomes a meme evolving agent via the emergence (...) of an autocatalytic network of sparse, distributed memories, and discusses implications for complex creative thought processes and why they are unique to humans. A hypothetical scenario for the evolutionary dynamics of a given meme in a society of interacting individuals is presented. (shrink)

One characteristic of the transition from the Middle Paleolithic to the Upper Paleolithic in Europe was the emergence of representational charcoal drawings and engravings by Aurignacian and Gravettian artists. European Neanderthals never engaged in representational drawing during the Middle and Early Upper Paleolithic, a property that might reflect less developed visuomotor coordination. This article postulates a causal relationship between an evolved ability of anatomically modern humans to throw spears accurately while hunting and their ability to draw representational images from working (...) memory. Unlike Neanderthals, archaic and anatomically modern humans in sub-Saharan Africa adopted longer-range hunting practices using hand-cast spears as a compensation for the emergence of increasingly wary game. For Neanderthals, paleoclimatic fluctuations likely precluded consistent hunting of cold-adapted game, a property making game more approachable for close-range hunting with thrusting spears. As evidence of less historical wariness of humans, many of the species hunted by Neanderthals were eventually domesticated. Due to strong sources of natural selection on archaic and anatomically modern humans for effective hunting, the parietal cortex that integrates visual imagery and motor coordination expanded progressively, yielding the globular shape of the human cranium that is not evident in Neanderthals. To characterize how the cognitive properties employed for throwing spears and drawing line work are similar, the Upper Paleolithic drawings of animals in Chauvet cave, France, are discussed in the speculative context of how these artists engaged simultaneously in overt attention to guide their hand movements and covert attention to their mental images during the drawing process. (shrink)

Production and comprehension have long been viewed as inseparable components of language. The study of vision, by contrast, has centered almost exclusively on comprehension. Here we investigate drawing—the most basic form of visual production. How do we convey concepts in visual form, and how does refining this skill, in turn, affect recognition? We developed an online platform for collecting large amounts of drawing and recognition data, and applied a deep convolutional neural network model of visual cortex trained only on natural (...) images to explore the hypothesis that drawing recruits the same abstract feature representations that support natural visual object recognition. Consistent with this hypothesis, higher layers of this model captured the abstract features of both drawings and natural images most important for recognition, and people learning to produce more recognizable drawings of objects exhibited enhanced recognition of those objects. These findings could explain why drawing is so effective for communicating visual concepts, they suggest novel approaches for evaluating and refining conceptual knowledge, and they highlight the potential of deep networks for understanding human learning. (shrink)