A great deal of philosophy of mind in the modern era has been driven by an intense aversion to Cartesian dualism. In the 1950s, materialists claimed to have succeeded once and for all in exorcising the Cartesian ghost by identifying the mind with the brain. In subsequent decades, cognitive science put scientific meat on this metaphysical skeleton by explicating mental processes as digital computation implemented in the brain's hardware.

Reexamines the Western philosophical tradition, looking at the basic concepts of the mind, time, causation, morality, and the self.

In this paper I consider whether traditional behaviors of animals, like traditions of humans, are transmitted by imitation learning. Review of the literature on problem solving by captive primates, and detailed consideration of two widely cited instances of purported learning by imitation and of culture in free-living primates (sweet-potato washing by Japanese macaques and termite fishing by chimpanzees), suggests that nonhuman primates do not learn to solve problems by imitation. It may, therefore, be misleading to treat animal traditions and human (...) culture as homologous (rather than analogous) and to refer to animal traditions as cultural. (shrink)

"More than any thing else technology creates our world. It creates our wealth, our economy, our very way of being," says W. Brian Arthur. Yet, until now the major questions of technology have gone unanswered. Where do new technologies come from -- how exactly does invention work? What constitutes innovation, and how is it achieved? Why are certain regions -- Cambridge, England, in the 1920s and Silicon Valley today -- hotbeds of innovation, while others languish? Does technology, like biological life, (...) evolve? How do new industries, and the economy itself, emerge from technologies? In this groundbreaking work, pioneering technology thinker and economist W. Brian Arthur sets forth a boldly original way of thinking about technology that gives answers to these questions. The Nature of Technology is an elegant and powerful theory of technology's origins and evolution. It achieves for the progress of technology what Thomas Kuhn's The Structure of Scientific Revolutions did for scientific progress. Arthur explains how transformative new technologies arise and how innovation really works. Conventional thinking ascribes the invention of technologies to "thinking outside the box," or vaguely to genius or creativity, but Arthur shows that such explanations are inadequate. Rather, technologies are put together from pieces -- themselves technologies -- that already exist. Technologies therefore share common ancestries and combine, morph, and combine again to create further technologies. Technology evolves much as a coral reef builds itself from activities of small organisms -- it creates itself from itself; all technologies are descended from earlier technologies. Drawing on a wealth of examples, from historical inventions to the high-tech wonders of today, and writing in wonder fully engaging and clear prose, Arthur takes us on a mind-opening journey that will change the way we think about technology and how it structures our lives. (shrink)

Innovation is a key component of most definitions of culture and intelligence. Additionally, innovations may affect a species' ecology and evolution. Nonetheless, conceptual and empirical work on innovation has only recently begun. In particular, largely because the existing operational definition (first occurrence in a population) requires long-term studies of populations, there has been no systematic study of innovation in wild animals. To facilitate such study, we have produced a new definition of innovation: Innovation is the process that generates in an (...) individual a novel learned behavior that is not simply a consequence of social learning or environmental induction. Using this definition, we propose a new operational approach for distinguishing innovations in the field. The operational criteria employ information from the following sources: (1) the behavior's geographic and local prevalence and individual frequency; (2) properties of the behavior, such as the social role of the behavior, the context in which the behavior is exhibited, and its similarity to other behaviors; (3) changes in the occurrence of the behavior over time; and (4) knowledge of spontaneous or experimentally induced behavior in captivity. These criteria do not require long-term studies at a single site, but information from multiple populations of a species will generally be needed. These criteria are systematized into a dichotomous key that can be used to assess whether a behavior observed in the field is likely to be an innovation. (shrink)

Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the (...) brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components – not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored. (shrink)

Hutchins examines a set of phenomena that have fallen between the established disciplines of psychology and anthropology, bringing to light a new set of relationships between culture and cognition.

Anthropology combines two quite different enterprises: the ethnographic study of particular people in particular places and the theorizing about the human species. As such, anthropology is part of cognitive science in that it contributes to the unitary theoretical aim of understanding and explaining the behavior of the animal species Homo sapiens. This article draws on our own research experience to illustrate that cooperation between anthropology and the other sub-disciplines of cognitive science is possible and fruitful, but it must proceed from (...) the recognition of anthropology’s unique epistemology and methodology. (shrink)

In treating cognition as problem solving, Andy Clark suggests, we may often abstract too far from the very body and world in which our brains evolved to guide...