In informal terms, abductive reasoning involves inferring the best or most plausible explanation from a given set of facts or data. It is a common occurrence in everyday life and crops up in such diverse places as medical diagnosis, scientific theory formation, accident investigation, language understanding, and jury deliberation. In recent years, it has become a popular and fruitful topic in artificial intelligence research. This volume breaks new ground in the scientific, philosophical, and technological study of abduction. It presents new (...) ideas about inferential and information-processing foundations for knowledge and certainty. The authors argue that knowledge arises from experience by processes of abductive inference, in contrast to the view that it arises non-inferentially, or that deduction and inductive generalization are enough to account for knowledge. Much AI research is hypothetical, so the importance of this book is that it reports key discoveries about abduction that have been made as a result of designing, building, testing, and analyzing actual working knowledge-based systems for medical diagnosis and other abductive tasks. The book tells the story of six generations of increasingly sophisticated generic abduction machines, RED-1, RED-2, PEIRCE, MDX2, TIPS, QUAWDS, and the discovery of reasoning strategies that make it computationally feasible to form well-justified composite explanatory hypotheses, despite the threat of combinatorial explosion. The final chapter argues that perception is logically abductive and presents a layered-abduction computational model of perceptual information processing. This book will be of great interest to researchers in AI, cognitive science, and philosophy of science. (shrink)

Debate has been growing in developmental psychology over how much the cognitive development of children is like theory change in science. Useful debate on this topic requires a clear understanding of what it would be for a child to have a theory. I argue that existing accounts of theories within philosophy of science and developmental psychology either are less precise than is ideal for the task or cannot capture everyday theorizing of the sort that children, if they theorize, must do. (...) I then propose an account of theories that ties theories and explanation very closely together, treating theories primarily as products of a drive to explain. I clarify some of the positions people have taken regarding the "theory theory" of development, and I concl.ude by proposing that psychologists interested in the 'theory theory' look for patterns of affect and arousal in development that would accompany the existence of a drive to explain. (shrink)

Bruner sets forth nothing less than a new agenda for the study of the mind. He examines the irrepressibly human acts of imagination that allow us to make experience meaningful; he calls this side of mental activity the “narrative mode,” and his book makes important advances in the effort to unravel its nature.

Argues that educators should help students piece together authentic narratives about themselves and about society, and not to focus so much on teaching students to process information.

We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or “Bayes nets”. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children (...) construct new causal maps and that their learning is consistent with the Bayes net formalism. (shrink)

"Every word, like a sacred object, has its place. No _précis_ is possible. This extraordinary book must be read."—Edmund Carpenter, _New York Times Book Review _ "No outline is possible; I can only say that reading this book is a most exciting intellectual exercise in which dialectic, wit, and imagination combine to stimulate and provoke at every page."—Edmund Leach, _Man _ "Lévi-Strauss's books are tough: very scholarly, very dense, very rapid in argument. But once you have mastered him, human history (...) can never be the same, nor indeed can one's view of contemporary society. And his latest book, _The Savage Mind_, is his most comprehensive and certainly his most profound. Everyone interested in the history of ideas _must_ read it; everyone interested in human institutions _should _read it."—J. H. Plumb, _Saturday Review_ "A constantly stimulating, informative and suggestive intellectual challenge."—Geoffrey Gorer, _The Observer_, London. (shrink)

One of our more fundamental beliefs is that causal chains are continuous in time: we believe that every influence from the past upon the future runs through the present. I argue that this tenet, given certain data, can force conceptual changes upon us. I attempt to formulate a heuristic for discovery, based as explicitly as possible upon this tenet, and illustrate it by means of several examples, one of which is Mendel's discovery of genes.

I am going to attempt to argue, given certain premises, there are reasons, not only empirical, but also logical, for expecting a certain division of labor in the processing of information by the human brain. This division of labor consists specifically of a functional bifurcation into what may be called, to a first approximation, "verbal" and "nonverbal" modes of information- processing. That this dichotomy is not quite satisfactory, however, will be one of the principal conclusions of this chapter, for I (...) shall attempt to show that metaphor, which in its most common guise is a literary, and hence a fortiori a "verbal" phenomenon, may in fact be more a function of the "nonverbal" than the "verbal" mode. (For alternative attempts to account for cognitive lateralization, see e.g. Bever, 1975; Wickelgren, 1975; Pendse, 1978.). (shrink)

Behavioral scientists studied behavior; cognitive scientists study what generates behavior. Cognitive science is hence theoretical behaviorism (or behaviorism is experimental cognitivism). Behavior is data for a cognitive theorist. What counts as a theory of behavior? In this paper, a methodological constraint on theory construction -- "neoconstructivism" -- will be proposed (by analogy with constructivism in mathematics): Cognitive theory must be computable; given an encoding of the input to a behaving system, a theory must be able to compute (an encoding of) (...) its outputs. It is a mistake to conclude, however, that this constraint requires cognitive theory to be computational, or that it follows from this that cognition is computation. (shrink)