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A Rudimentary Version of Cognitive Structural Realism

  • Majid Davoody Beni
Chapter
Part of the Studies in Brain and Mind book series (SIBM, volume 14)

Abstract

The chapter launches a new attempt at addressing the problem of representation. In this chapter, I shall argue that to deal with the problem, we may specify the underlying structure of scientific theories in terms of cognitive structures. To introduce the desirable cognitive structures, I shall rely on the preceding work of Churchland and construe it as a new version of structural realism. My construal of Churchland’s work paves the way for a synthesis between CMSA and SR. The chapter outlines a rudimentary version of Cognitive SR and its solution to the problem of representation. A more advanced version that includes further details regarding the underpinning neurological mechanisms and their biological viability will be presented in the next chapters of this book.

References

  1. Beni, M. D. (2017a, October). The downward path to epistemic informational structural realism. Acta Analytica, 33, 181–197.  https://doi.org/10.1007/s12136-017-0333-4. SpringerCrossRefGoogle Scholar
  2. Beni, M. D. (2017b). Reconstructing the upward path to structural realism. European Journal for Philosophy of Science, 7(3), 393–409.  https://doi.org/10.1007/s13194-016-0167-8. Springer.CrossRefGoogle Scholar
  3. Beni, M. D. (2018a). Syntactical informational structural realism. Minds and Machines, 1–21. Springer Netherlands. Accessed April 5.  https://doi.org/10.1007/s11023-018-9463-8.CrossRefGoogle Scholar
  4. Beni, M. D. (2018b). Reconstructing Probabilistic Realism: Re-enacting syntactical structures. Journal for General Philosophy of Science, 1–21 Springer Netherlands. Accessed September 27.  https://doi.org/10.1007/s10838-018-9426-z.
  5. Churchland, P. M. (1979). Scientific realism and the plasticity of mind. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  6. Churchland, P. M. (1989). On the nature of theories: A neurocomputational perspective. In C. W. Savage (Ed.), Minnesota studies in the philosophy of science (Scientific Theories, Vol 14, pp. 59–101). Minneapolis: University of Minnesota Press.Google Scholar
  7. Churchland, P. M. (1991). A deeper unity: Some feyerabendian themes in neurocomputational form. In G. Munévar (Ed.), Beyond reason essays on the philosophy of Paul Feyerabend (pp. 1–23). Dordrecht: Springer.  https://doi.org/10.1007/978-94-011-3188-9_1.CrossRefGoogle Scholar
  8. Churchland, P. M. (1998). Conceptual similarity across sensory and neural diversity: The Fodor/Lepore challenge answered. The Journal of Philosophy, 95(1), 5.  https://doi.org/10.2307/2564566.CrossRefGoogle Scholar
  9. Churchland, P. M. (2012). Plato’s camera: How the physical brain captures a landscape of abstract universals. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
  10. da Costa, N. C. A., & French, S. (2003). Science and partial truth. Oxford: Oxford University Press.  https://doi.org/10.1093/019515651X.001.0001.CrossRefGoogle Scholar
  11. Esfeld, M., & Lam, V. (2008). Moderate structural realism about space-time. Synthese, 160(1), 27–46.  https://doi.org/10.1007/s11229-006-9076-2. Springer.CrossRefGoogle Scholar
  12. Fitelson, B., & Sober, E. (1998). Plantinga’s probability arguments against evolutionary naturalism. Pacific Philosophical Quarterly, 79(2), 115–129.  https://doi.org/10.1111/1468-0114.00053. Blackwell Publishers Ltd.CrossRefGoogle Scholar
  13. Fodor, J., & Lepore, E. (1999). All at sea in semantic space: Churchland on meaning similarity. The Journal of Philosophy, 96(8), 381.  https://doi.org/10.2307/2564628.CrossRefGoogle Scholar
  14. French, S. (2011). Metaphysical underdetermination: Why worry? Synthese, 180(2), 205–221.  https://doi.org/10.1007/s11229-009-9598-5. Springer.CrossRefGoogle Scholar
  15. French, S., & Ladyman, J. (2003). Remodelling structural realism: Quantum physics and the metaphysics of structure. Synthese, 136(1), 31–56.  https://doi.org/10.1023/A:1024156116636.CrossRefGoogle Scholar
  16. Giere, R. N. (1992). Cognitive models of science. Minnesota Studies in the Philosophy of Science, XV, 239–250.  https://doi.org/10.1177/030631293023004005.CrossRefGoogle Scholar
  17. Laakso, A., & Cottrell, G. (2000). Content and cluster analysis: Assessing representational similarity in neural systems. Philosophical Psychology, 13(1), 47–76.  https://doi.org/10.1080/09515080050002726. Taylor & Francis Group.CrossRefGoogle Scholar
  18. Ladyman, J., Ross, D., Collier, J., & Spurrett, D. (2007). Every thing must go. Oxford: Oxford University Press.  https://doi.org/10.1093/acprof:oso/9780199276196.001.0001.CrossRefGoogle Scholar
  19. Miłkowski, M. (2013). Explaining the computational mind. Cambridge, MA: MIT Press.Google Scholar
  20. Muller, F. A. (2011). Withering away, weakly. Synthese, 180(2), 223–233.  https://doi.org/10.1007/s11229-009-9609-6.CrossRefGoogle Scholar
  21. Nakano, R., & Saito, K. (1998). Computational characteristics of law discovery using neural networks. Lecture Notes in Computer Science, 1532, 342–351.  https://doi.org/10.1007/3-540-49292-5_30. Berlin: Springer.CrossRefGoogle Scholar
  22. Piccinini, G., & Bahar, S. (2013). Neural computation and the computational theory of cognition. Cognitive Science, 37(3), 453–488.  https://doi.org/10.1111/cogs.12012. Blackwell Publishing Ltd.CrossRefGoogle Scholar
  23. Piccinini, G., & Scarantino, A. (2011). Information processing, computation, and cognition. Journal of Biological Physics, 37(1), 1–38.  https://doi.org/10.1007/s10867-010-9195-3. Springer.CrossRefGoogle Scholar
  24. Psillos, S. (2007). The fine structure of inference to the best explanation. Philosophy and Phenomenological Research, 74(2), 441–448.  https://doi.org/10.1111/j.1933-1592.2007.00030.x. Blackwell Publishing Ltd.CrossRefGoogle Scholar
  25. Saito, K., & Nakano, R. (1997). Law discovery using neural networks. In Proceedings of the fifteenth international joint conference on artificial intelligence (Vol. 2, pp. 1078–1083). https://dl.acm.org/citation.cfm?id=1622312. Morgan Kaufmann Publishers Inc.Google Scholar
  26. Suppe, F. (1998). Understanding scientific theories: An assessment of developments, 1969–1998. Philosophy of Science Biennial Meetings of the Philosophy of Science Association. Part II: Symposia Papers, 67, 102–115 http://www.jstor.org/stable/188661.CrossRefGoogle Scholar
  27. Turing, A. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460.  https://doi.org/10.2307/2251299. Oxford University Press.
  28. van Fraassen, B. C. (1980). The scientific image. Oxford: Oxford University Press.  https://doi.org/10.1093/0198244274.001.0001.CrossRefGoogle Scholar
  29. Worrall, J. (1989). Structural realism: The best of both worlds? Dialectica, 43(1–2), 99–124.  https://doi.org/10.1111/j.1746-8361.1989.tb00933.x.CrossRefGoogle Scholar
  30. Worrall, J. (2011). Underdetermination, realism and empirical equivalence. Synthese, 180(2), 157–172.  https://doi.org/10.1007/s11229-009-9599-4.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Majid Davoody Beni
    • 1
  1. 1.Department of Management, Science, and TechnologyAmirkabir University of TechnologyTehranIran

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