Technological development has changed the nature of industrial production so that it is no longer a question of humans working with a machine, but rather that a joint human machine system is performing the task. This development, which started in the 1940s, has become even more pronounced with the proliferation of computers and the invasion of digital technology in all wakes of working life. It may appear that the importance of human work has been reduced compared to what can be achieved by intelligent software systems, but in reality, the opposite is true: the more complex a system, the more vital the human operator's task. The conditions have changed, however, whereas people used to be in control of their own tasks, today they have become supervisors of tasks which are shared between humans and machines. A considerable effort has been devoted to the domain of administrative and clerical work and has led to the establishment of an internationally based human-computer interaction (HCI) community at research and application levels. The HCI community, however, has paid more attention to static environments where the human operator is in complete control of the situation, rather than to dynamic environments where changes may occur independent of human intervention and actions. This book's basic philosophy is the conviction that human operators remain the unchallenged experts even in the worst cases where their working conditions have been impoverished by senseless automation. They maintain this advantage due to their ability to learn and build up a high level of expertise -- a foundation of operational knowledge -- during their work. This expertise must be taken into account in the development of efficient human-machine systems, in the specification of training requirements, and in the identification of needs for specific computer support to human actions. Supporting this philosophy, this volume *deals with the main features of cognition in dynamic environments, combining issues coming from empirical approaches of human cognition and cognitive simulation, *addresses the question of the development of competence and expertise, and *proposes ways to take up the main challenge in this domain -- the design of an actual cooperation between human experts and computers of the next century.

An understanding of cause-effect relationships is fundamental to the study of cognition. In this book, outstanding specialists from comparative psychology, social psychology, developmental psychology, anthropology, and philosophy present the newest developments in the study of causal cognition and discuss their different perspectives. They reflect on the role and forms of causal knowledge, both in animal and human cognition, on the development of human causal cognition from infancy, and on the relationship between individual and cultural aspects of causal understanding. The result is a state-of-the-art, informative, insightful, and interdisciplinary debate aimed at the non-specialist.

Causality is one of the core concepts in any attempt to make sense of the world, and the explanations people come up with shape their judgments, emotions, intentions and actions. This renders causal cognition a core topic for the social as well as the cognitive sciences. In the past, however, research has been split into diverging paradigms, each pertaining to a distinct (sub)discipline and focusing on a specific domain, thus creating a rather fragmented picture of causal cognition. Furthermore, most of this previous research paid only incidental attention to culture as a possibly constitutive factor, leaving important questions unanswered: Is causality always perceived in the same way? Are causal explanations affected by the concepts to which people refer and/or the language they use? Is causal cognition domain-specific, and if so, how does it differ from agency construal? Is causal reasoning always based on the same cognitive mechanisms, or does the cultural background of people shape how they process respective information - and perhaps even their willingness to search for causal explanations in the first place? By soliciting contributions that address questions like these, this research topic aimed at assessing the extent to which causal cognition may vary across species, cultures, or individuals at various stages of their development, and at integrating different perspectives across a broad range of disciplines. Originating from the work of a research group funded by the Center for Interdisciplinary Research (ZiF) at Bielefeld University, Germany, the scope of this research topic was broadened by inviting additional contributions from researchers with expertise in different fields of causal cognition, agency construal, and/or cultural impacts on cognition. In order to fully exploit the potential of cognitive science, we explicitly encouraged submissions from scholars from all its classic sub-disciplines (i.e., anthropology, artificial intelligence, linguistics, neuroscience, philosophy, psychology) as well as scholars from comparative psychology, cognitive archeology, economics, and any other discipline interested in causal cognition. We welcomed empirical findings as well as theoretical contributions, with an emphasis on those factors that do – or may – constrain, trigger, or shape the way in which humans and other primates think about causal relationships and inform us about both the diversity and the universality of causal cognition.

Studies of tool use have been used to examine an exceptionally wide range of aspects of cognition, such as planning, problem-solving and insight, naive physics, social relationship between action and perception.

Thinking Computers and Virtual Persons: Essays on the Intentionality of Machines explains how computations are meaningful and how computers can be cognitive agents like humans. This book focuses on the concept that cognition is computation. Organized into four parts encompassing 13 chapters, this book begins with an overview of the analogy between intentionality and phlogiston, the 17th-century principle of burning. This text then examines the objection to computationalism that it cannot prevent arbitrary attributions of content to the various data structures and representations involved in a computational process. Other chapters consider that the notion of original intentionality is incoherent. This book argues as well that the only way to build an intelligent machine is to build a neural network. The final chapter claims that an entire theoretical framework in cognitive psychology is incompatible with the view that human brains are computers of some sort. This book is a valuable resource for cognitive scientists.

Understanding causal structure is a central task of human cognition. Causal learning underpins the development of our concepts and categories, our intuitive theories, and our capacities for planning, imagination and inference. During the last few years, there has been an interdisciplinary revolution in our understanding of learning and reasoning: Researchers in philosophy, psychology, and computation have discovered new mechanisms for learning the causal structure of the world. This new work provides a rigorous, formal basis for theory theories of concepts and cognitive development, and moreover, the causal learning mechanisms it has uncovered go dramatically beyond the traditional mechanisms of both nativist theories, such as modularity theories, and empiricist ones, such as association or connectionism.