How do novel scientific concepts arise? In Creating Scientific Concepts, Nancy Nersessian seeks to answer this central but virtually unasked question in the problem of conceptual change. She argues that the popular image of novel concepts and profound insight bursting forth in a blinding flash of inspiration is mistaken. Instead, novel concepts are shown to arise out of the interplay of three factors: an attempt to solve specific problems; the use of conceptual, analytical, and material resources provided by the cognitive-social-cultural (...) context of the problem; and dynamic processes of reasoning that extend ordinary cognition. Focusing on the third factor, Nersessian draws on cognitive science research and historical accounts of scientific practices to show how scientific and ordinary cognition lie on a continuum, and how problem-solving practices in one illuminate practices in the other. (shrink)

The book Model-Based Reasoning in Scientific Discovery, aims to explain how specific modeling practices employed by scientists are productive methods of ...

: The increasing attention on experiment in the last two decades has led to important insights into its material, cultural and social dimensions. However, the role of experiment as a tool for generating knowledge has been comparatively poorly studied. What questions are asked in experimental research? How are they treated and eventually resolved? And how do questions, epistemic situations, and experimental activity cohere and shape each other? In my paper, I treat these problems on the basis of detailed studies of (...) research practice. After presenting several cases from the history of electricity—Dufay, Ampère, and Faraday—I discuss a specific type of experiment—the "exploratory experiment"—and analyze how it works in concept formation. I argue that a fuller understanding of experiment can only be achieved by intertwining historical and philosophical perspectives in such a way that the very separation of the two become questioable. (shrink)

Scientific discovery is often regarded as romantic and creative - and hence unanalyzable - whereas the everyday process of verifying discoveries is sober and more suited to analysis. Yet this fascinating exploration of how scientific work proceeds argues that however sudden the moment of discovery may seem, the discovery process can be described and modeled. Using the methods and concepts of contemporary information-processing psychology (or cognitive science) the authors develop a series of artificial-intelligence programs that can simulate the human thought (...) processes used to discover scientific laws. The programs - BACON, DALTON, GLAUBER, and STAHL - are all largely data-driven, that is, when presented with series of chemical or physical measurements they search for uniformities and linking elements, generating and checking hypotheses and creating new concepts as they go along. "Scientific Discovery examines the nature of scientific research and reviews the arguments for and against a normative theory of discovery; describes the evolution of the BACON programs, which discover quantitative empirical laws and invent new concepts; presents programs that discover laws in qualitative and quantitative data; and ties the results together, suggesting how a combined and extended program might find research problems, invent new instruments, and invent appropriate problem representations. Numerous prominent historical examples of discoveries from physics and chemistry are used as tests for the programs and anchor the discussion concretely in the history of science. Pat Langley is an Associate Professor in the Department of Information and Computer Science at the University of California, Irvine.Herbert Simon is a Professor in the Departments of Psychology, Computer Science, and Philosophy at Carnegie-Mellon University. Gary L. Bradshaw is an Assistant Professor in the Department of Psychology and Institute of Cognitive Science at the University of Colorado, Boulder. Jan M. Zytkow is an Associate Professor in the Computer Science Department at Wichita State University. (shrink)

The present paper attempts to define an experimental ethnography as an approach to the understanding of scientific thinking. Such an ethnography relies upon the replication of contemporary and historical scientific practices as a means of capturing the cultural and cognitive meanings of the practices in question. The approach is contrasted to the typical kind of laboratory experiment in psychology, and it is argued that replications of scientific practices can reveal dimensions of the microstructure of science and of its context that (...) otherwise may remain invisible. An extended example is presented, based upon replications of the experimental procedures used by Michael Faraday in 1856 during his examination of the optical properties of gold. (shrink)

: In 1856, Michael Faraday (1791–1867) conducted nearly a year's worth of research on the optical properties of gold, in the course of which he discovered the first metallic colloids. Following our own discovery of hundreds of the specimens prepared by Faraday for this research, the present paper describes the cognitive role of these "epistemic artifacts" in the dynamics of Faraday's research practices. Analysis of the specimens, Faraday's Diary records, and replications of selected procedures (partly to replace missing kinds of (...) specimens and partly to understand the "tacit knowledge" implicated in Faraday's research) are outlined, and a reconstruction of the events surrounding the initial discovery of metallic colloids is presented. (shrink)

: Faraday is often described as an experimentalist, but his work is a dialectical interplay of concrete objects, visual images, abstract, theoretically-informed visual models and metaphysical precepts. From phenomena described in terms of patterns formed by lines of force he created a general explanation of space-filling systems of force which obey both empirical laws and principles of conservation and economy. I argue that Faraday's articulation of situated experience via visual models into a theory capable of verbal expression owed much to (...) his strategy of moving—via conjectural visual models—between the phenomenology of particulars (often displayed as patterns) and the general features of dynamical phenomena which he depicted as structures. (shrink)

... the topic of 'meaning' is the one topic discussed in philosophy in which there is literally nothing but 'theory' - literally nothing that can be labelled or even ridiculed as the 'common sense view'. Putnam, 'The Meaning of Meaning' This book explores some truths behind the truism that experimentation is a hallmark of scientific activity. Scientists' descriptions of nature result from two sorts of encounter: they interact with each other and with nature. Philosophy of science has, by and large, (...) failed to give an account of either sort of interaction. Philosophers typically imagine that scientists observe, theorize and experiment in order to produce general knowledge of natural laws, knowledge which can be applied to generate new theories and technologies. This view bifurcates the scientist's world into an empirical world of pre-articulate experience and know how and another world of talk, thought and argument. Most received philosophies of science focus so exclusively on the literary world of representations that they cannot begin to address the philosophical problems arising from the interaction of these worlds: empirical access as a source of knowledge, meaning and reference, and of course, realism. This has placed the epistemological burden entirely on the predictive role of experiment because, it is argued, testing predictions is all that could show that scientists' theorizing is constrained by nature. Here a purely literary approach contributes to its own demise. The epistemological significance of experiment turns out to be a theoretical matter: cruciality depends on argument, not experiment. (shrink)

This book describes empirically ways to analyze and then to effectually utilize cognitive processes to advance discovery and invention in the sciences. It also explains how to teach these principles to students.

PARTI The Philosophical Situation: A Critical Appraisal We must begin with the mistake and find out the truth in it. That is, we must uncover the source of ...

Interest in thought experiments (TEs) derives from the paradox: “How can findings that carry conviction result from a new experiment conducted entirely within the head?” Historical studies have established the importance of TEs in science but have proposed disparate hypotheses concerning the source of knowledge in TEs, ranging from empiricist to rationalist accounts. This article analyzes TEs in think‐aloud protocols of scientifically trained experts to examine more fine‐grained information about their use. Some TEs appear powerful enough to discredit an existing (...) theory—a disconfirmatory purpose. In addition, confirmatory and generative purposes were identified for other TEs. One can also use details in transcript data, including imagery reports and gestures, to provide evidence for a central role played by imagistic simulations in many TEs, and to suggest that these simulations can generate new knowledge using several sources, including the “extended application” of perceptual motor schemas, implicit prior knowledge, and spatial reasoning operations, in contrast to formal arguments. These sources suggest what it means for TEs to be grounded in embodied processes that can begin to explain the paradox above. This leads to a rationalistic view of TEs as using productive internal reasoning, but the view also acknowledges the historical role that experience with the world can play in forming certain schemas used in TEs. Understanding such processes could help provide a foundation for developing a larger model of scientific investigation processes grounded on imagistic simulation (Clement, 2008). (shrink)