Comprendiendo los paradigmas de Kuhn a la luz de la ciencia cognitiva
DOI:
https://doi.org/10.29393/CF38-7ABCP10007Palavras-chave:
racionalidad, ejemplar, cognición científica, analogíaResumo
En 1962 Thomas Kuhn publicó La estructura de las revoluciones científicas y, como consecuencia, el término ‘paradigma’ se convirtió en una expresión común entre los científicos. Sin embargo, no se ha entendido adecuadamente que Kuhn utilizó el término con una teoría de la cognición científica en mente que surge a partir del trabajo empírico en psicología. En cambio, en muchos círculos se consideró que Kuhn sostenía una visión irracionalista o escéptica de la ciencia. Me propongo articular la teoría de los paradigmas de Kuhn como teoría de ejemplares y mostrar cómo el trabajo en psicología y ciencia cognitiva después de la publicación de La estructura ofrece un respaldo a la teoría de Kuhn. Esta teoría merece un mejor reconocimiento y una investigación más profunda por parte de los científicos.
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Andersen, H., Barker, P., & Chen, X. (2006). The cognitive structure of scientific revolutions. Cambridge University Press.
Anderson, R. C., Reynolds, R. E., Schallert, D. L., & Goetz, E. T. (1977). Frameworks for comprehending discourse. American Educational Research Journal, 14(4), 367–381. https://doi.org/10.3102/00028312014004367
Bareiss, E. R., Porter, B. W., & Wier, C. C. (1988). Protos: An exemplar-based learning apprentice. *International Journal of Man-Machine Studies, 29*(5), 549–561. https://doi.org/10.1016/S0020-7373(88)80013-0
Bartlett, F. C. (1932). Remembering: A study in experimental and social psychology. Cambridge University Press.
Bird, A. (2005). Naturalizing Kuhn. Proceedings of the Aristotelian Society, 105(1), 109–127. https://doi.org/10.1111/j.0066-7372.2005.00106.x
Bird, A. (2007). Incommensurability naturalized. In L. Soler, H. Sankey, & P. Hoyningen-Huene (Eds.), Rethinking scientific change and theory comparison (pp. 21–39). Springer. https://doi.org/10.1007/978-1-4020-6279-7_2
Blanchette, I., & Dunbar, K. (2000). How analogies are generated: The roles of structural and superficial similarity. Memory & Cognition, 28(1), 130–135. https://doi.org/10.3758/BF03211580
Bruner, J. S., & Postman, L. (1949). On the perception of incongruity: A paradigm. Journal of Personality, 18(2), 206–223. https://doi.org/10.1111/j.1467-6494.1949.tb01241.x
Campbell, N. R. (1920). Physics, the elements. Cambridge University Press.
Carbonell, J. G. (1982). Learning by analogy: Formulating and generalizing plans from past experience (Technical Report No. 2442). Carnegie Mellon University, Computer Science Department.
Champagne, A. B., Klopfer, L. E., & Gunstone, R. F. (1982). Cognitive research and the design of science instruction. Educational Psychologist, 17(1), 31–53. https://doi.org/10.1080/00461528209529243
Clement, J. (1982). Students’ preconceptions in introductory mechanics. American Journal of Physics, 50(1), 66–71. https://doi.org/10.1119/1.12989
Didierjean, A., & Cauzinille-Marmeche, E. (1998). Reasoning by analogy: Is it schema-mediated or case-based? European Journal of Psychology of Education, 13(3), 385–398. https://doi.org/10.1007/BF03172950
Duhem, P. (1954). The aim and structure of physical theory (P. P. Wiener, Trans.). Princeton University Press. (Original work published 1914).
Dunbar, K. (1996). How scientists really reason: Scientific reasoning in real-world laboratories. In R. J. Sternberg & J. E. Davidson (Eds.), The nature of insight (pp. 365–395). MIT Press.
Dunbar, K. (1999). How scientists build models: In vivo science as a window on the scientific mind. In L. Magnani, N. J. Nersessian, & P. Thagard (Eds.), Model-based reasoning in scientific discovery (pp. 85–99). Kluwer Academic/Plenum Publishers. https://doi.org/10.1007/978-1-4615-4813-3_6
Falconer, I. (2004). Charles Augustin Coulomb and the fundamental law of electrostatics. Metrologia, 41(2), S107–S114. https://doi.org/10.1088/0026-1394/41/2/S05
Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7(2), 155–170. https://doi.org/10.1207/s15516709cog0702_3
Goldin-Meadow, S., Nusbaum, H., Garber, P., & Church, R. B. (1993). Transitions in learning: Evidence for simultaneously activated strategies. Journal of Experimental Psychology: Human Perception and Performance, 19(1), 92–107. https://doi.org/10.1037/0096-1523.19.1.92
Green, A. E., Fugelsang, J. A., Kraemer, D. J. M., & Dunbar, K. N. (2008). The micro-category account of analogy. Cognition, 106(2), 1004–1016. https://doi.org/10.1016/j.cognition.2007.04.003
Heering, P. (1992). On Coulomb’s inverse square law. American Journal of Physics, 60(11), 988–994. https://doi.org/10.1119/1.17003
Heering, P. (1994). The replication of the torsion balance experiment: The inverse square law and its refutation by early 19th-century German physicists. In C. Blondel & M. Dörries (Eds.), Restaging Coulomb: Usages, controverses et réplications autour de la balance de torsion (pp. 47–66). Leo S. Olschki Editore.
Hesse, M. B. (1963). Models and analogies in science. Sheed and Ward.
Kuhn, T. S. (1962). The structure of scientific revolutions. University of Chicago Press.
Mahon, B. (2004). The man who changed everything: The life of James Clerk Maxwell. Wiley.
Martínez, A. (2006). Replication of Coulomb’s torsion balance experiment. Archive for History of Exact Sciences, 60(6), 517–563. https://doi.org/10.1007/s00407-006-0113-9
Masterman, M. (1970). The nature of a paradigm. In I. Lakatos & A. Musgrave (Eds.), Criticism and the growth of knowledge (pp. 59–89). Cambridge University Press. https://doi.org/10.1017/CBO9781139171434.006
Medawar, P. B. (1978). March of paradigms [Review of the book The essential tension: Selected studies in scientific tradition and change, by T. S. Kuhn]. Nature, 273(5661), 575–575. https://doi.org/10.1038/273575a0
Minsky, M. (1975). A framework for representing knowledge. In P. H. Winston (Ed.), The psychology of computer vision (pp. 211–277). McGraw-Hill.
Nersessian, N. J. (2003). Kuhn, conceptual change, and cognitive science. In T. Nickles (Ed.), Thomas Kuhn (pp. 178–211). Cambridge University Press. https://doi.org/10.1017/CBO9780511999951.008
Nersessian, N. J. (1989). Conceptual change in science and in science education. Synthese, 80(1), 163–183. https://doi.org/10.1007/BF00869953
Nickles, T. (2003). Normal science: From logic to case-based and model-based reasoning. In T. Nickles (Ed.), Thomas Kuhn (pp. 142–177). Cambridge University Press. https://doi.org/10.1017/CBO9780511999951.007
Rumelhart, D. E. (1980). Schemata: The building blocks of cognition. In R. J. Spiro, B. C. Bruce, & W. F. Brewer (Eds.), Theoretical issues in reading comprehension (pp. 33–58). Lawrence Erlbaum Associates.
Schank, R. C., & Abelson, R. P. (1977). Scripts, plans, goals, and understanding: An inquiry into human knowledge structures. Lawrence Erlbaum Associates.
Watson, I., & Marir, F. (1994). Case-based reasoning: A review. The Knowledge Engineering Review, 9(4), 327–354. https://doi.org/10.1017/S0269888900007098
Weinberg, S. (1998, October 8). The revolution that didn’t happen. The New York Review of Books, 45(15), 48–52.
Winston, P. H. (1980). Learning and reasoning by analogy. Communications of the ACM, 23(12), 689–703. https://doi.org/10.1145/359038.359042
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