In this path-breaking work, Paul Thagard draws on history and philosophy of science, cognitive psychology, and the field of artificial intelligence to develop a ...

This target article presents a new computational theory of explanatory coherence that applies to the acceptance and rejection of scientific hypotheses as well as to reasoning in everyday life, The theory consists of seven principles that establish relations of local coherence between a hypothesis and other propositions. A hypothesis coheres with propositions that it explains, or that explain it, or that participate with it in explaining other propositions, or that offer analogous explanations. Propositions are incoherent with each other if they (...) are contradictory, Propositions that describe the results of observation have a degree of acceptability on their own. An explanatory hypothesis is acccpted if it coheres better overall than its competitors. The power of the seven principles is shown by their implementation in a connectionist program called ECHO, which treats hypothesis evaluation as a constraint satisfaction problem. Inputs about the explanatory relations are used to create a network of units representing propositions, while coherende and incoherence relations are encoded by excitatory and inbihitory links. ECHO provides an algorithm for smoothly integrating theory evaluation based on considerations of explanatory breadth, simplicity, and analogy. It has been applied to such important scientific cases as Lovoisier's argument for oxygen against the phlogiston theory and Darwin's argument for evolution against creationism, and also to cases of legal reasoning. The theory of explanatory coherence has implications for artificial intelligence, psychology, and philosophy. (shrink)

Keith Holyoak and Paul Thagard provide a unified, comprehensive account of the diverse operations and applications of analogy, including problem solving, ...

The philosophical theory of scientific explanation proposed here involves a radically new treatment of causality that accords with the pervasively statistical character of contemporary science. Wesley C. Salmon describes three fundamental conceptions of scientific explanation--the epistemic, modal, and ontic. He argues that the prevailing view is untenable and that the modal conception is scientifically out-dated. Significantly revising aspects of his earlier work, he defends a causal/mechanical theory that is a version of the ontic conception. Professor Salmon's theory furnishes a robust (...) argument for scientific realism akin to the argument that convinced twentieth-century physical scientists of the existence of atoms and molecules. To do justice to such notions as irreducibly statistical laws and statistical explanation, he offers a novel account of physical randomness. The transition from the "reviewed view" of scientific explanation to the causal/mechanical model requires fundamental rethinking of basic explanatory concepts. (shrink)

Kenneth F. Schaffner compares the practice of biological and medical research and shows how traditional topics in philosophy of science—such as the nature of theories and of explanation—can illuminate the life sciences. While Schaffner pays some attention to the conceptual questions of evolutionary biology, his chief focus is on the examples that immunology, human genetics, neuroscience, and internal medicine provide for examinations of the way scientists develop, examine, test, and apply theories. Although traditional philosophy of science has regarded scientific discovery—the (...) questions of creativity in science—as a subject for psychological rather than philosophical study, Schaffner argues that recent work in cognitive science and artificial intelligence enables researchers to rationally analyze the nature of discovery. As a philosopher of science who holds an M.D., he has examined biomedical work from the inside and uses detailed examples from the entire range of the life sciences to support the semantic approach to scientific theories, addressing whether there are "laws" in the life sciences as there are in the physical sciences. Schaffner's novel use of philosophical tools to deal with scientific research in all of its complexity provides a distinctive angle on basic questions of scientific evaluation and explanation. (shrink)

Since the 1950s, Donald T. Campbell has been one of the most influential contributors to the methodology of the social sciences. A distinguished psychologist, he has published scores of widely cited journal articles, and two awards, in social psychology and in public policy, have been named in his honor. This book is the first to collect his most significant papers, and it demonstrates the breadth and originality of his work.

The thirty-five chapters in this book describe various judgmental heuristics and the biases they produce, not only in laboratory experiments but in important...

Change in View offers an entirely original approach to the philosophical study of reasoning by identifying principles of reasoning with principles for revising one's beliefs and intentions and not with principles of logic. This crucial observation leads to a number of important and interesting consequences that impinge on psychology and artificial intelligence as well as on various branches of philosophy, from epistemology to ethics and action theory. Gilbert Harman is Professor of Philosophy at Princeton University. A Bradford Book.

My description of the cognitive processes involved in the discovery, development, and acceptance of the bacterial theory of ulcers might have left the impression that science is all in the mind (Thagard, forthcoming-b). But only part of the story of the bacterial theory of ulcers is psychological. This paper discusses the important role of physical interaction with the world by means of instruments and experiments, and the equally important role of social interactions among the medical researchers who developed the theory. (...) The main questions I want to answer are the following: 1. What instruments contributed to the development and acceptance of the new theory? 2. What kinds of experiments contributed to the development and acceptance of the new theory? 3. How did theorizing and experimentation interact in the development of new experiments and hypotheses? 4. How did social processes such as collaboration, communication, and consensus contribute to the development and widespread acceptance of the bacterial theory of ulcers? I conclude with a sketch of science as a complex system of interacting psychological, physical, and social processes. (shrink)

By contrasting Hippocratic and nineteenth century theories of disease, this paper describes important conceptual changes that have taken place in the history of medicine. Disease concepts are presented as causal networks that represent the relations among the symptoms, causes, and treatment of a disease. The transition to the germ theory of disease produced dramatic conceptual changes as the result of a radically new view of disease causation. An analogy between disease and fermentation was important for two of the main developers (...) of the germ theory of disease, Pasteur and Lister. Attention to the development of germ concepts shows the need for a referential account of conceptual change to complement a representational account. (shrink)

By applying research in artificial intelligence to problems in the philosophy of science, Paul Thagard develops an exciting new approach to the study of scientific reasoning. This approach uses computational ideas to shed light on how scientific theories are discovered, evaluated, and used in explanations. Thagard describes a detailed computational model of problem solving and discovery that provides a conceptually rich yet rigorous alternative to accounts of scientific knowledge based on formal logic, and he uses it to illuminate such topics (...) as the nature of concepts, hypothesis formation, analogy, and theory justification. (shrink)

This paper provides a computational characterization of coherence that applies to a wide range of philosophical problems and psychological phenomena. Maximizing coherence is a matter of maximizing satisfaction of a set of positive and negative constraints. After comparing five algorithms for maximizing coherence, we show how our characterization of coherence overcomes traditional philosophical objections about circularity and truth.

Peter Achinstein (1990, 1991) analyses the scientific debate that took place in the eighteenth and nineteenth centuries concerning the nature of light. He offers a probabilistic account of the methods employed by both particle theorists and wave theorists, and rejects any analysis of this debate in terms of coherence. He characterizes coherence through reference to William Whewell's writings concerning how "consilience of inductions" establishes an acceptable theory (Whewell, 1847) . Achinstein rejects this analysis because of its vagueness and lack of (...) reference to empirical data, concluding that coherence is insufficient to account for the belief change that took place during the wave-particle debate. (shrink)

The main message of this volume is that the creative process of discovery is not a purely rational enterprise in the traditional sense which equates rationality with logical reasoning, yet it is a manifestation of a universal phenomenon ...

The purpose of the two studies reported here was to develop an integrated model of the scientific reasoning process. Subjects were placed in a simulated scientific discovery context by first teaching them how to use an electronic device and then asking them to discover how a hitherto unencountered function worked. To do this task, subjects had to formulate hypotheses based on their prior knowledge, conduct experiments, and evaluate the results of their experiments. In the first study, using 20 adult subjects, (...) we identified two main strategies that subjects used to generate new hypotheses. One strategy was to search memory and the other was to generalize from the results of previous experiments. We described the former group as searching an hypothesis space, and the latter as searching an experiment space. In a second study, with 10 adults, we investigated how subjects search the hypothesis space by instructing them to state all the hypotheses that they could think of prior to conducting any experiments. Following this phase, subjects were then allowed to conduct experiments. Subjects who could not think of the correct rule in the hypothesis generation phase discovered the correct rule only by generalizing from the results of experiments in the experimental phase.Both studies provide support for the view that scientific reasoning can be characterized as search in two problem spaces. By extending Simon and Lea's (1974) Generalized Rule Inducer, we present a general model of Scientific Discovery as Dual Search (SDDS) that shows how search in two problem spaces (an hypothesis space and an experiment space) shapes hypothesis generation, experimental design, and the evaluation of hypotheses. The model also shows how these processes interact with each other. Finally, we interpret earlier findings about the psychology of scientific reasoning in terms of the SDDS model. (shrink)

The purpose of the two studies reported here was to develop an integrated model of the scientific reasoning process. Subjects were placed in a simulated scientific discovery context by first teaching them how to use an electronic device and then asking them to discover how a hitherto unencountered function worked. To do this task, subjects had to formulate hypotheses based on their prior knowledge, conduct experiments, and evaluate the results of their experiments. In the first study, using 20 adult subjects, (...) we identified two main strategies that subjects used to generate new hypotheses. One strategy was to search memory and the other was to generalize from the results of previous experiments. We described the former group as searching an hypothesis space, and the latter as searching an experiment space. In a second study, with 10 adults, we investigated how subjects search the hypothesis space by instructing them to state all the hypotheses that they could think of prior to conducting any experiments. Following this phase, subjects were then allowed to conduct experiments. Subjects who could not think of the correct rule in the hypothesis generation phase discovered the correct rule only by generalizing from the results of experiments in the experimental phase.Both studies provide support for the view that scientific reasoning can be characterized as search in two problem spaces. By extending Simon and Lea's (1974) Generalized Rule Inducer, we present a general model of Scientific Discovery as Dual Search (SDDS) that shows how search in two problem spaces (an hypothesis space and an experiment space) shapes hypothesis generation, experimental design, and the evaluation of hypotheses. The model also shows how these processes interact with each other. Finally, we interpret earlier findings about the psychology of scientific reasoning in terms of the SDDS model. (shrink)