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The question that frames this chapter is how humans have managed to learn such amazing things as the age of the universe. After briefly reviewing logical, methodological, historical, and social approaches to this question, the chapter focuses on contributions of the cognitive study of science. This leads to a comparison of the cognitive study of science and the psychology of science, which study how fundamental cognitive mechanisms operate in the context of generating scientific knowledge. There is, however, a second way humans use their psychological powers in the pursuit of knowledge, namely, by designing material and symbolic artifacts that greatly increase their cognitive powers. The resulting physical-computational-human systems have been incorporated into the cognitive sciences as “distributed cognitive systems.” The chapter proposes adoption of an agent-centered approach, in which ever more ubiquitous distributed cognitive systems can be fully cognitive without being fully computational.

In order to streamline design processes, it is crucial to understand where new ideas come from. This chapter starts by outlining the ways that designers can generate novel solutions to problems. The many chapters in this volume explore a variety of different paths to innovation. To put these chapters into perspective, this chapter starts with a broad discussion of the nature of innovation. Then, it highlights the contributions of each of the chapters both for their basic research on the design process as well as for their suggestions about how to create new tools to support innovation.

There has always been a close association between psychology and the philosophy of science. Although some individuals over the past 200 years have suggested that psychology had little to offer philosophy (Kant and Popper), many more have expressed a contrary opinion. The chapter shows that psychology influenced philosophy of science mainly through four areas: perception in the form of psychophysics (on Mach, Peirce, and James) and in the form of Gestalt psychology (on Carnap, Hanson, and Kuhn), animal behavior in the form of behaviorism (on Russell, Bergmann, and the logical positivists), and cognitive psychology (on Popper, Giere, and Thagard). On the basis of the present findings, the conclusion is reached that better understanding of science is considerably dependent on knowing how the mind works, which is an idea as old as the British empiricists and the Würzburg school and as young as contemporary cognitive science.

This chapter considers the implications of the picture of conceptual development offered in this book for a theory of concepts. It introduces new material, placing work on the origin of concepts in the context of selected controversies from cognitive science and philosophy concerning the very nature of concepts.

Building upon work which considers the implications dual-process theory for the great rationality debate in cognitive science, this chapter advances that discussion, first by discussing additions and complications to dual-process theory and then by working through the implications of these ideas for our view of human rationality. It argues that System 2 needs to be understood in terms of two levels of processing: the algorithmic and the reflective.

This chapter describes cognitive science. In its widest sense, the term cognitive science is used to indicate that the study of mind is in itself a worthy scientific pursuit. At this time, cognitive science is not yet established as a mature science. It does not have a clearly agreed upon sense of direction and a large number of researchers constituting a community. Rather, it is really more of a loose affiliation of disciplines than a discipline of its own. Interestingly, an important pole is occupied by artificial intelligence—thus, the computer model of the mind is a dominant aspect of the entire field. The other affiliated disciplines are generally taken to consist of linguistics, neuroscience, psychology, sometimes anthropology, and the philosophy of mind. Each discipline would give a somewhat different answer to the question of what is mind or cognition, an answer that would reflect its own specific concerns.

This chapter aims to reflect on themes and points of difference raised in the book; consider relationships between this work and broader developments in the cognitive sciences; and to give some personal views concerning possible future directions for the field. It draws out themes which discuss: the meaning of rational analysis; whether probability is what the brain is computing; the relation between models of ‘low-high’ and ‘high-level’ cognition; and the relation of probability to traditional concepts in cognitive science namely, logic and structured mental representations.

This chapter analyses developments in cognitive science during the past fifty years from a personal perspective. It describes psychology issues and topics that were of personal interest to the author of this chapter. These include a paper titled ‘A logical calculus of the ideas immanent in nervous activity’ and Donald Broadbent's Perception and Communication, which not only used flowcharts to express models of cognition but also made a clear and explicit statement of cognitive science as abstracting computational procedures from the physical systems that implement them.

This chapter aims to discuss the function of consciousness within a broad framework of cognitive science and phenomenology, and to relate these two approaches to each other. This chapter's treatment of the role of consciousness, while sharing the computational approach, is quite different. It proposes a tentative taxononomy, partly of the typical phenomenology and content of consciousness. The chapter distinguishes four aspects of consciousness, two involuntary and two voluntary. The latter are built on the former and serve the functions of developing plans and goals based on a model of the self. This chapter emphasizes the social construction of consciousness, providing persuasive illustrations. The bringing together of the domain of emotions with insights from the artificial intelligence metaphor is an exciting enterprise.

This chapter presents an overview of the concepts of representation in cognitive psychology and developmental psychology. It then discusses theories based on levels of representation and their development in childhood. The chapter begins with the function of representation in human cognition as conceptualized by cognitive psychologist and psycholinguist George Miller, who declared that language serves the representational function for humans that is otherwise served for non-language creatures. But it leaves open the question of how—if at all—representation is managed by nonhumans or by non-language using humans, such as pre-linguistic infants and very young children. These questions reflect contentious issues in cognitive science based on different computational models of symbolic processing and neural network processing. Specific alleged domains such as space, number, object knowledge, and theory of mind are integrated in the knowledge structures of the early years and serve as background to the pragmatics of everyday life, organized in terms of domains of practice.

This chapter reviews how human reasoning and decision making evolves from the cognitive sciences, challenging basic assumptions of objectivism-rationalism along with ethical models based on reason. It emphasizes the significance of effortless attention in human reasoning and suggests that virtue ethics is preferable to authoritative thinking. By examining an early text from China, entitled “Analects of Confucius,” the chapter demonstrates how effortless attention and action can be developed and incorporated into moral behavior. This text is an important source of evidence regarding the earliest examples of virtue ethic in world literature. These findings also reveal the presence of different human thinking systems that work on implicit and explicit levels, with knowledge operating at the implicit level.

This chapter looks at Marvin Minsky's and Seymour Papert's recent proposal to study the mind as a society, which takes the patchwork architecture of cognition as a central element. Minsky and Papert present a view in which minds consist of many “agents” whose abilities are quite circumscribed: each agent taken individually operates only in a microworld of small-scale or “toy” problems. This model of the mind as a society of numerous agents is intended to encompass a multiplicity of approaches to the study of cognition, ranging from distributed, self-organizing networks to the classical, cognitivist conception of localized, serial symbolic processing. The society of mind purports to be, then, something of a middle way in present cognitive science. This middle way challenges a homogenous model of the mind, whether in the form of distributed networks at one extreme or symbolic processers at the other extreme.

Cognitive Science has come a long way – even the old Greeks have put forward intricate thoughts about what our minds can know and what seems inaccessible. This chapter addresses the historical development of cognitive science. The fundamental homunculus and qualia problems are introduced, and tentative answers from embodied cognitive science are sketched-out. A look at biology emphasizes that our bodies and brains are evolutionarily shaped, making us ready to develop our minds. Psychologically, minds develop and adapt given environmental feedback, striving to optimize behavior. Only by means of constructive, generative processes, however, can a behavior-oriented “understanding” of the environment develop. These “understanding”-oriented structures also make our mind language ready. Finally, in order to comprehend how the human mind actually comes into being, based on the considered principles, it is necessary to pursue functional, computational, and where possible, algorithmic and hardware perspectives in the sense of David Marr’s three levels of understanding.