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The Conceptual Structure of the Chemical Revolution

Published online by Cambridge University Press:  01 April 2022

Paul Thagard*
Affiliation:
Cognitive Science Laboratory, Princeton University

Abstract

This paper investigates the revolutionary conceptual changes that took place when the phlogiston theory of Stahl was replaced by the oxygen theory of Lavoisier. Using techniques drawn from artificial intelligence, it represents the crucial stages in Lavoisier's conceptual development from 1772 to 1789. It then sketches a computational theory of conceptual change to account for Lavoisier's discovery of the oxygen theory and for the replacement of the phlogiston theory.

Type
Research Article
Copyright
Copyright © 1989 by the Philosophy of Science Association

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Footnotes

For valuable comments I am grateful to Susan Brison, Lindley Darden, Philip Johnson-Laird, Trevor Levere, Michael Mahoney, and two anonymous referees. Conversations with Nancy Nersessian, Gregory Nowak, and Michael Ranney have also been helpful.

References

REFERENCES

Barr, A. and Feigenbaum, E. (1981), Handbook of Artificial Intelligence, vol. 1. Los Altos: Kaufmann.Google Scholar
Brachman, R., and Levesque, H. (eds.) (1985), Readings in Knowledge Representation. Los Altos, CA: Morgan Kaufmann.Google Scholar
Carey, S. (1985), Conceptual Change in Childhood. Cambridge, MA: Bradford Books/MIT Press.Google Scholar
Cohen, I. B. (1985), Revolution in Science. Cambridge, MA: Harvard University Press.Google Scholar
Conant, J. (1964), Harvard Case Histories in Experimental Science, vol. 1. Cambridge, MA: Harvard University Press.Google Scholar
Cruse, D. (1986), Lexical Semantics. Cambridge: Cambridge University Press.Google Scholar
Darden, L., and Rada, R. (1988), “Hypothesis Formation Using Part-Whole Interrelations”, in D. Hellman (ed.), Analogical Reasoning. Dordrecht: Reidel, pp. 341375.CrossRefGoogle Scholar
Guerlac, H. (1961), Lavoisier—The Crucial Year. Ithaca, NY: Cornell University Press.Google Scholar
Holland, J. H. (1986), “Escaping Brittleness: The Possibilities of General Purpose Machine Learning Algorithms Applied to Parallel Rule-based Systems”, in R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, (eds.), Machine Learning: An Artificial Intelligence Approach, vol. 2. Los Altos: Kaufmann, pp. 593623.Google Scholar
Holland, J., Holyoak, K., Nisbett, R., and Thagard, P. (1986), Induction: Processes of Inference, Learning, and Discovery. Cambridge, MA: Bradford Books/MIT Press.Google Scholar
Holmes, F. (1985), Lavoisier and the Chemistry of Life. Madison, WI: University of Wisconsin Press.Google Scholar
Ihde, A. (1980), “Lavoisier and Priestley”, in L. Kieft and B. Willeford (eds.), Joseph Priestley. Lewisburg, PA: Bucknell University Press, pp. 6291.Google Scholar
Kirwan, R. (1789/1968), An Essay on Phlogiston and the Constitution of Acids. New impression of second English Edition. London: Cass.Google Scholar
Knickerbocker, W. (1962), Classics of Modern Science. Boston: Beacon.Google Scholar
Kuhn, T. (1970), Structure of Scientific Revolutions (2nd edn.). Chicago: University of Chicago Press.Google Scholar
Kunda, Z. (1987), “Motivation and Inference: Self-serving Generation and Evaluation of Causal Theories”, Journal of Personality and Social Psychology 53: 636647.CrossRefGoogle Scholar
Langley, P., Simon, H., Bradshaw, G., and Zytkow, J. (1987), Scientific Discovery. Cambridge, MA: Bradford Books/MIT Press.CrossRefGoogle Scholar
Lavoisier, A. (1862), Oeuvres. 6 vols. Paris: Imprimerie Impériale.Google Scholar
Lavoisier, A. (1774/1970), Essays Physical and Chemical. Trans. by Henry, Thomas of Opuscules Physiques et Chimiques, 1774. Second English edition. London: Cass.Google Scholar
Lavoisier, A. (1789), Traité Elémentaire de chimie.Google Scholar
Leicester, H., and Krickstein, (1952), A Source Book in Chemistry. New York: McGraw-Hill.CrossRefGoogle Scholar
McCloskey, M. (1983), Intuitive Physics. Scientific American 24: 122130.CrossRefGoogle Scholar
Michalski, R., Carbonell, J., and Mitchell, T. (eds.) (1983), Machine Learning: An Artificial Intelligence Approach. Palo Alto: Tioga.CrossRefGoogle Scholar
Michalski, R., Carbonell, J., and Mitchell, T. (eds.) (1986), Machine Learning. volume II. (eds.) Los Altos: Morgan Kaufmann.Google Scholar
Miller, G., and Johnson-Laird, P. (1976), Language and Perception. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Neressian, N., (1988), “Conceptual Change in Science and in Science Education”, Synthese 80: 163183.CrossRefGoogle Scholar
Neressian, N., and Resnick, L., (1989), “Comparing Historical and Intuitive Explanations of Motion: Does Naive Physics Have a Structure?”, Proceedings of the Eleventh Annual Meeting of the Cognitive Science Society. Hillsdale, NJ: Erlbaum, pp. 412418.Google Scholar
Partington, J. (1961), A History of Chemistry. 4 vols. London: Macmillan.Google Scholar
Peirce, C. (1931–1958), Collected Papers. 8 vols. Edited by Hartshorne, C., Weiss, P., and Burks, A. Cambridge, MA: Harvard University Press.Google Scholar
Perrin, C. (1981), “The Triumph of the Antiphlogistinians”, in H. Woolf (ed.), The Analytic Spirit. Ithaca: Cornell University Press, pp. 4063.Google Scholar
Perrin, C. (1988), “The Chemical Revolution: Shifts in Guiding Assumptions”, in A. Donovan, L. Laudan, R. Laudan (eds.), Scrutinizing Science: Empirical Studies of Scientific Change. Dordrecht: Klewer, pp. 105124.CrossRefGoogle Scholar
Priestley, J. (1796/1929), Considerations on the Doctrine of Phlogiston, and the Decomposition of Water. Princeton: Princeton University Press.Google Scholar
Ranney, M., and Thagard, P. (1988), “Explanatory Coherence and Belief Revision in Naive Physics”. Proceedings of the Tenth Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Erlbaum, pp. 426432.Google Scholar
Rose, D., and Langley, P. (1986), “Chemical Discovery as Belief Revision”, Machine Learning 1, pp. 423452.CrossRefGoogle Scholar
Smith, C., Carey, S., and Wiser, M. (1985), “On Differentiation: A Case Study of the Development of the Concepts of Size, Weight, and Density”, Cognition 21: 177237.CrossRefGoogle ScholarPubMed
Stahl, G. (1723/1730), Philosophical Principles of Universal Chemistry. Trans. by Shaw, Peter of Fundamenta Chymiae, 1723. London: John Osborn and Peter Longman.Google Scholar
Thagard, P. (1984), “Frames, Knowledge, and Inference”, Synthese 61: 233259.CrossRefGoogle Scholar
Thagard, P. (1988), Computational Philosophy of Science. Cambridge, MA: The MIT Press/Bradford Books.CrossRefGoogle Scholar
Thagard, P. (1989), “Explanatory Coherence”. Behavioral and Brain Sciences 12: 435467.CrossRefGoogle Scholar
Thagard, P. (1990), “Concepts and Conceptual Change”, Synthese.CrossRefGoogle Scholar
Thagard, P., and Holyoak, K. (1985), “Discovering the Wave Theory of Sound: Induction in the Context of Problem Solving”. Proceedings of the Ninth International Joint Conference on Artificial Intelligence. Los Altos: Morgan Kaufmann. pp. 610612.Google Scholar
Thagard, P., and Nowak, G. (1988), “The Explanatory Coherence of Continental Drift”, in A. Fine and J. Leplin (eds.), PSA 1988, vol. 1. East Lansing, Mich.: Philosophy of Science Association, pp. 118126.Google Scholar
Thagard, P., and Nowak, G. (forthcoming), “The Conceptual Structure of the Geological Revolution”, in J. Shrager and P. Langley (eds.), Computational Models of Discovery and Theory Formation. Hillsdale, NJ: Erlbaum.Google Scholar
Vosniadou, S., and Brewer, W. (1989), “The Concept of the Earth's Shape”, unpublished manuscript, University of Illinois at Urbana-Champaign.Google Scholar
Winston, M., Chaffin, R., and Herrmann, D. (1987), “A Taxonomy of Part-Whole Relations”, Cognitive Science 11: 417444.CrossRefGoogle Scholar
Zytkow, J. and Simon, H. (1986), “A Theory of Historical Discovery: The Construction of Componential Models”, Machine Learning 1: 107137.CrossRefGoogle Scholar