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Reasoning From Imagery and Analogy in Scientific Concept Formation

Published online by Cambridge University Press:  31 January 2023

Nancy J. Nersessian
Nancy J. Nersessian*
Affiliation:
Princeton University
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Extract

How do vague notions about how one might understand certain physical phenomena get transformed into scientific concepts such as “field”, “quark”, and “gene”? Philosophers of as disparate views as Reichenbach and Feyerabend have held that the process through which scientific concepts emerge is not a reasoned process. In a manner completely mysterious and unanalyzable, scientific concepts emerge fully grown, like Athena from the head of Zeus. However, when one examines actual cases of concept formation in science, a different picture can be painted: Scientific concepts do not pop out of heads, but are constructed in response to specific problems by utilizing methods appropriate to the solution of the problem. Thus, as with other aspects of the scientific enterprise, concept formation is a problem-solving process that involves the application of systematic procedures in the attempt to solve specific problems. It is a reasoned process.

Type
Part II. Epistemology and the Dynamics of Science
Information
PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association , Volume 1988 , Issue 1: Volume One: Contributed Papers 1988 , 1988 , pp. 41 - 47
Copyright
Copyright © Philosophy of Science Association 1988

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Footnotes

1

The preparation of this paper was supported in part by the Office of Naval Research Grant N00014-85-K-0337 to the Learning Research and Development Center at the University of Pittsburgh. The opinions expressed do not necessarily reflect those of the ONR, and no official endorsement should be inferred. I wish to thank the “analogies reading group” at LRDC for many stimulating discussions and Paul Thagard for his comments on the draft of this paper.

References

Berkson, W. (1974). Fields of Force: The Development of a World View from Faraday to Einstein. New York: John Wiley and Sons.Google Scholar
Bromberg, J. (1968). “Maxwell’s Displacement Current and his Theory of Light.” Archive for the History of the Exact Sciences 4: 218–234.CrossRefGoogle Scholar
Carbonell, J. (1986). “Derivational Analogy: A Theory of Reconstructive Problem Solving and Expertise Acquisition.” In Machine Learning: An Artificial Intelligence Approach. Edited by Michalski, R., Carbonell, J. and Mitchell, T. Los Altos: Morgan Kaufman pp. 371–392.Google Scholar
Chalmers, A.F. (1986). “The Heuristic Role of Maxwell’s Mechanical Model of Electromagnetic Phenomena.” Studies in the History and Philosophy of Science 17: 415–427.CrossRefGoogle Scholar
Chi, M.T.H., Feltovich, P.J., and Glaser, R. (1981). “Categorization and Representation of Physics Problems by Experts and Novices.” Cognitive Science 5: 121–152.CrossRefGoogle Scholar
Duhem, P. (1902). Les theories electriques de J. Clerk Maxwell. Paris: A. Hermann & Cie.Google Scholar
Faraday, M. (1839-55). Experimental Researches in Electricity. Reprinted, New York: Dover, 1965.Google Scholar
Gentner, D. (in press). “The Mechanisms of Analogical Learning.” In Similarity and Analogical Reasoning. Edited by Vosniadou, S. and Ortony, A.Google Scholar
Gentner, D. and Gentner, D.R. (1983). “Flowing Waters and Teeming Crowds: Mental Models of Electricity.” In Mental Models. Edited by Gentner, D. and Stevens, A. Hillsdale, NJ: Lawrence Earlbaum Associates. Pages 99–130.Google Scholar
Hesse, M. (1973). “Logic of Discovery in Maxwell’s Electromagnetic Theory.” In Foundations of Scientific Method: The 19th Century. Edited by Giere, R.N. and Westfall, R.S. Bloomington: Indiana University Press.Google Scholar
Holyoak, K. and Thagard, P. (1987). “Analogical Mapping by Constraint satisfaction: A Computational Theory.” Unpublished Manuscript.Google Scholar
Maxwell, J.C. (1890). The Scientific Papers of J.C. Maxwell. Edited by Niven, W.D. Cambridge: Cambridge University Press.Google Scholar
Miller, A.I. (1984). Imagery in Scientific Thought. Boston: Birkhauser.Google Scholar
Nersessian, N.J. (1984). Faraday to Einstein: Constructing Meaning in Scientific Theories. Dordrecht: Martinus Nijhoff.CrossRefGoogle Scholar
Nersessian, N.J. (1986). The Process of Science: Contemporary Philosophical Approaches to Understanding Scientific Practice. Dordrecht: Martinus Nijhoff.Google Scholar
Nickles, T. (1986). “‘Twixt Method and Madness.” In Nersessian (1986). Pages 41-68.Google Scholar
Simon, H.A., Langley, P.W., and Bradshaw, G.L. (1981). “Scientific Discovery as Problem Solving.” Synthese 47: 1–27.CrossRefGoogle Scholar
Simon, H.A. and Larkin, J.H. (1987). “Why a Diagram is (Sometimes) Worth Ten Thousand Words.” Cognitive Science 11: 65 100.Google Scholar
Wise, N. (1979). “The Mutual Embrace of Electricity and Magnetism.” Science 203: 1313–1318.CrossRefGoogle ScholarPubMed