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)

Images of mindmarks a new era in human cognitive neuroscience. Despite the difficult conceptual problems associated with using group-averaged data and paired subtractions, human PET images converge well with existing data from other areas of cognitive neuroscience while opening up new theoretical and experimental possibilities. However, greater attention to individual differences might prove necessary in the study of culturally driven adaptations such as literacy.

The authors ofImages of the Mindhave made a significant contribution to our understanding of the brain through imaging technology. The book is well written, timely, beautifully illustrated and conveys a sense of history. It will appeal to alay audience as well as a professional audience.

The authors ofImages of mindhave been highly successful in unravelling the neural basis of complex brain functions. Their emphasis on top-down processingin experimental neuroscience is especially important and, it is hoped, influential. Tracking brain activation accurately botli in space and in time would benefit from studiesofindividual subjects without relying on grand average data.

Comparing cognitive functions between humans and nonhuman primates is helpful for understanding human tool use. We comment on the latest insights from comparative research on executive control functions. Based on our own work, we discuss how even a mental function in which non-human primates outperform humans might have played a key role in the development of tool use.

This article has two goals. First, it synthesizes and critically assesses current scientific knowledge about the cognitive bases of human tool use. Second, it shows how the cognitive traits reviewed help to explain why technological accumulation evolved so markedly in humans, and so modestly in apes.

Posner & Raichle's book presents methods and data that increase support for mind-brain unity and provide a method for studying and verifying brain dysfunction objectively. Their incorporation into the assessment technology of neuropsychology should accordingly constitute a major advance. In addition, these techniques may help clarify longstanding controversies in cognitive psychology such as whether perception is multimodal or amodal.

This bold and brilliant book asks the ultimate question of the life sciences: How did the human mind acquire its incomparable power? In seeking the answer, Merlin Donald traces the evolution of human culture and cognition from primitive apes to the era of artificial intelligence, and presents an original theory of how the human mind evolved from its presymbolic form. In the emergence of modern human culture, Donald proposes, there were three radical transitions. During the first, our bipedal but still (...) apelike ancestors acquired "mimetic" skill—the ability to represent knowledge through voluntary motor acts—which made Homo erectus successful for over a million years. The second transition—to "mythic" culture —coincided with the development of spoken language. Speech allowed the large-brained Homo sapiens to evolve a complex preliterate culture that survives in many parts of the world today. In the third transition, when humans constructed elaborate symbolic systems ranging from cuneiforms, hieroglyphics, and ideograms to alphabetic languages and mathematics, human biological memory became an inadequate vehicle for storing and processing our collective knowledge. The modern mind is thus a hybrid structure built from vestiges of earlier biological stages as well as new external symbolic memory devices that have radically altered its organization. According to Donald, we are symbol-using creatures, more complex than any that went before us, and we may have not yet witnessed the final modular arrangement of the human mind. (shrink)

We focus on Karmiloff-Smith's Representational redescription model, arguing that it poses some problems concerning the architecture of a redescribing system. To discuss the topic, we consider the implicit/explicit dichotomy and the relations between natur al language and the language of thought. We argue that the model regards how knowledge is employed rather than how it is represented in the system.

Beyond modularityattempts a synthesis of Fodor's anticonstructivist nativism and Piaget's antinativist constructivism. Contra Fodor, I argue that: (1) the study of cognitive development is essential to cognitive science, (2) the module/central processing dichotomy is too rigid, and (3) the mind does not begin with prespecified modules; rather, development involves a gradual process of “modularization.” Contra Piaget, I argue that: (1) development rarely involves stagelike domain-general change and (2) domainspecific predispositions give development a small but significant kickstart by focusing the infant's (...) attention on proprietary inputs. Development does not stop at efficient learning. A fundamental aspect of human development (“representational redescription”) is the hypothesized process by which information that isina cognitive system becomes progressively explicit knowledgetothat system. Development thus involves two complementary processes of progressive modularization and progressive “explicitation.” Empirical findings on the child as linguist, physicist, mathematician, psychologist, and notator are discussed in support of the theoretical framework. Each chapter concentrates first on the initial state of the infant mind/brain and on subsequent domain-specific learning in infancy and early childhood. It then goes on to explore data on older children's problem solving and theory building, with particular focus on evolving cognitive flexibility. Emphasis is placed throughout on the status of representations underlying different capacities and on the multiple levels at which knowledge is stored and accessible. Finally, consideration is given to the need for more formal developmental models, and a comparison is made between representational redescription and connectionist simulations of development. In conclusion, I consider what is special about human cognition by speculating on the status of representations underlying the structure of behavior in other species. (shrink)

Research reported by Posner & Raichle may be usefully applied to the rehabilitation of persons with brain damage. Their findings are related to the “dual premotorsystems hypothesis” that reciprocally interactive medial and lateral brain systems are involved in attention and learning. Recent studies show that “brain healing” occurs through dynamic reorganization involving attentional networks postulated by Posner & Raichle.

On the basis of neuroiinaging studies, Posner & Raichle summarily report that the prefrontal cortex is involved in executive functioning and attention. In contrast to that superficial view, we briefly describe a testable model of the kinds of representations that are stored in prefrontal cortex, which, when activated, are expressed via plans, actions, thematic knowledge, and schemas.

Future developments in artificial intelligence (AI) will likely allow for a greater degree of human‐machine convergence, with machines becoming more humanlike and intelligent machinery becoming more integrated into human brain function. This will pose many ethical challenges, and the necessity for a moral framework for evaluating these challenges will grow. This paper argues that community concern constitutes a central factor in both the evolution of religion and the human brain, and as such it should be used as the organizing principle (...) for moral evaluations of AI technologies. (shrink)