Vol. 40 Núm. 1 (2020)

El problema de ensamble

Mariela Destefano
Universidad de Buenos Aires, Argentina / CONICET

Publicado 2020-05-01

Palabras clave

  • Language Faculty,
  • Digital Operation,
  • Analog Operation,
  • Neural Computation
  • Facultad del lenguaje,
  • Operación digital,
  • Operación analógica,
  • Computación neural


Desde la bioloinguistica, ensamble sería una operación digital realizada en el cerebro que, en tanto tal, estaría asociada a principios específicos de la computación neural. En una primera aproximación, la computación digital consiste en el procesamiento de cadenas de dígitos de acuerdo a reglas generales. Sin embargo, los procesos neurales no se desarrollarían de acuerdo a los principios de la computación digital. Estas afirmaciones en conflicto, e.g., la caracterización digital de ensamble y la caracterización no digital del cerebro, llevan al siguiente escenario: o bien ensamble es una operación que no realiza el cerebro, o bien es realizada por el cerebro pero no digitalmente. El propósito de este artículo es evaluar los problemas de estas dos tesis.


  1. Al-Mutairi, F. R. (2014). The minimalist program: The nature and plausibility of Chomsky´s biolinguisitics. Cambridge Press.
  2. Arbib, M. (1983). Brains, machines, and mathematics. Springer.
  3. Beim Graben, P., Pinotsis, D., Saddy, D., & Potthast, R. (2008). Language processing with dynamic fields. Cognitive Neurodynamics, 2(2), 79-88. https://doi.org/10.1007/s11571-008-9042-4
  4. Bermúdez, J. L. (2014). Cognitive science: An introduction to the science of the mind. Cambridge University Press.
  5. Bilgrami, A., & Rovane, C. (2005). Mind, language, and the limits of inquiry. In J. McGilvray (Ed.), The Cambridge Companion to Chomsky (pp. 181-203). Cambridge University Press.
  6. Boeckx, C. (2009). The nature of merge: Consequences for language, mind and biology. In M. Piattelli-Palmarini, J. Uriagereka & P. Salaburu (Eds.), A dialogue with Noam Chomsky in the Basque Country (pp. 44-57). Oxford University Press.
  7. Boeckx, C. (2012). The I-languages mosaic. In C. Boeckx, M. C. Horno-Cheliz & J.L. Mendivil-Giro (Eds.), Language from a biological point of view: Current issues on biolinguistics (pp. 23-51). Cambridge Scholars Publishing.
  8. Boeckx, C. (2013). Biolinguistics: Facts, fiction, and forecast. Biolinguistics, 7, 316-328.
  9. Boeckx, C. (2013). Merge: Biolinguistic considerations. English Linguistics, 30, 463-484.
  10. Boeckx, C., & Benítez-Burraco, A. (2014). The shape of language-ready brain. Frontiers in Psychology, 5, 282. https://doi.org/10.3389/fpsyg.2014.00282
  11. Boeckx, C., & Grohmann, K. (2007). The biolinguistics manifesto. Biolinguistics, 1, 1-8.
  12. Boeckx, C., & Piattelli-Palmarini, M. (2005). Language as a natural object: Linguistics as a natural science. The Linguistic Review, 22, 467-471.
  13. Boolos, G. (1971). The iterative conception of set. The Journal of Philosophy, 68(8), 215-231. https://doi.org/10.2307/2025204
  14. Carandini, M., & Heeger, D. J. (2012). Normalization as a canonical neural computation. Nature Reviews Neuroscience, 13, 51-62.
  15. Chomsky, N. (1995). The minimalist program. The MIT Press.
  16. Chomsky, N. (2002). On nature and language. Cambridge University Press.
  17. Chomsky, N. (2007). Approaching UG from below. In U. Sauerland & H.-M. Gärtner (Eds.), Interfaces + recursion = language?: Chomsky’s minimalism and the view from semantics (pp. 1-29). Mouton de Gruyter.
  18. Chomsky, N. (2008). On phases. In R. Freidin, C. P. Otero, & M. L. Zubizarreta (Eds.), Foundational issues of philosophical studies: Essays in honor of Jean-Roger Vergnaud (pp. 133-166). The MIT Press. https://10.7551/mitpress/9780262062787.003.0007
  19. Chomsky, N. (2014). Minimal recursion: Exploring the prospects. In T. Roeper & M. Speas (Eds.), Recursion: Complexity in cognition. Springer. https://doi.org/10.1007/978-3-319-05086-7_1
  20. Copeland, B. J. (1996). What is Computation? Synthese, 3, 335-359.
  21. Craver, C. (2007). Explaining the brain: Mechanisms and the mosaic unity of neuroscience. Oxford University Press.
  22. De Mol, L. (2018). Turing Machines. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy (Winter 2019 Edition). https://plato.stanford.edu/archives/win2019/entries/turing-machine/
  23. Fodor, J. (1987). Psychosemantics: The problem of meaning in the philosophy of mind. The MIT Press.
  24. Fodor, J.. & Pylyshyn, Z. W. (1995). Connectionism and cognitive architecture: A critical analysis. In C. Macdonald & G. Macdonald (Eds.), Connectionism: Debates on psychological explanation, vol. 2; Blackwell.
  25. Fodor, J. (1994). The elm and the expert. The MIT Press.
  26. Fodor, J. (2000). The mind doesn´t work that way. The MIT Press.
  27. Fujita, K. (2017). On the parallel evolution of syntax and lexicon: A merge-only view. Journal of Neurolinguistics, 43, 178-192.
  28. Fukui, N. (2011). Merge and bare phrase sturcture. In C. Boeckx (Ed.), The Oxford handbook of linguistic minimalism. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199549368.013.0004
  29. Gerard, R. W. (1951). Some of the problems concerning digital notions in the central nervous system. In H. Foerster, M. Mead & H. L. Teuber (Eds.), Cybernetics: Circular causal and feedback mechanisms in biological and social systems. Transactions of the Seventh Conference, March 23-24, 1951 (pp. 11-574). Macy Foundation.
  30. Getz, R., & Moeckel, B. (1996). Understanding and eliminating EMI in microcontroller applications. National Semiconductor Corporation, Application Note 1050.
  31. González-Villar, A. J., Samartin-Veiga, N., Arias, M. & Carrillo-de-la-Peña, M. T. (2017). Increased neural noise and impaired brain synchronization in fibromyalgia patients during cognitive interference. Scientific Reports, 7(1), 1-8.
  32. Haugeland, J. (1981). Analog and Analog. Philosophical Topics, 12, 213-225.
  33. Haugeland, J. (1989). AI: The very idea. The MIT Press.
  34. Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, 117, 500-544.
  35. Hornstein, N., Nunes, J., & Grohmann K. (2005). Understanding minimalism: An introduction to minimalist syntax. Cambridge University Press
  36. Hulst, H. G. van der (2010). Re recursion. In H. van der Hulst (Ed.), Recursion and human language (pp. 15-53). Mouton de Gruyter.
  37. Jackendoff, R. (2011). What is the human language faculty? Two views. Language, 87, 586-624.
  38. Kass, R. E. (2018). Computational neuroscience: Mathematical and statistical perspectives. Annual Review of Statistics and Its Application, 5, 183-214.
  39. King, D. (1996). Is the human mind a Turing machine? Synthese, 108, 379-389.
  40. Kleene, S. C. (1952). Recursive predicates and quantifiers. In M. Davis (Ed.) (2004) The undecidable: Basic papers on undecidable propositions, unsolvable problems and computable functions (pp. 254-286). Dover Publications.
  41. Le Bon-Jego, M., & Yuste, R. (2007). Persistently active, pacemaker-like neurons in neocortex. Frontiers in Neuroscience, 1, 123–129.
  42. Lobina, D. (2011). A running back and forth: A review of recursion and human language. Biolinguistics, 5, 151-169.
  43. Lobina, D. (2014). Probing Recursion. Cognitive processing, 15(4), 435-450.
  44. Lobina, D. (2017). Recursion: A Computational investigation into the representation and procesing of language. Oxford University Press.
  45. Lorenzo, G. (2013). Biolingüística: La nueva síntesis. Open Libra.
  46. Machamer, P., Darden, L., & Craver, C. (2000). Thinking about Mechanisms. Philosophy of Science, 67, 1-25.
  47. Maley, C. J. (2011). Analog and digital, continuous and discrete. Philosophical Studies: An International Journal for Philosophy in the Analytic Tradition, 155, 117-131.
  48. Marantz, A. (1995). The minimalist program. In G. Webelhuth (Ed.) Government and Binding Theory and the Minimalist Program: Principles and Parameters in Syntactic Theory (pp. 351-382). Blackwell.
  49. Marr, D. (1982). Vision. Freeman Press.
  50. McCulloch, W. S., & Pitts, W. H. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115-133.
  51. Mochizuki, Y., & Shinomoto, S. (2014). Analog and digital codes in the brain. Physical Review E, 89, 02275-1-02275-8.
  52. Newell, A. (1980). Physical symbol systems. Cognitive Science, 4(2), 135-183.
  53. O’Reilly, R. (2006). Biological based computational models of high-level cognition. Science: New Series, 314, 91-94.
  54. Piccinini, G. (2012). Computationalism. In E. Margolis, R. Samuels & S. P. Stich (Eds.), The Oxford Handbook of Philosophy of Cognitite Science. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780195309799.013.0010
  55. Piccinini, G. (2006). Computational explanation in neuroscience. Synthese, 153, 343-353.
  56. Piccinini, G. (2007). Some neural networks compute, others don’t. Neural Networks, 21, 311-321.
  57. Piccinini, G. (2008). Computers. Pacific Philosophical Quarterly, 89, 32-73.
  58. Piccinini, G. (2010). The resilience of computationalism. Philosophy of Science, 77, 852-861.
  59. Piccinini, G., & Bahar, S. (2013). Neural computation and the computational theory of cognition. Cognitive Science, 37(3), 453-488.
  60. Piccinini, G., & Scarantino, A. (2010). Computation vs. information processing: Why their differences matters to cognitive science. Studies in History and Philosophy of Science, 41, 237-246.
  61. Poeppel, D., & Embick, D. (2005). Defining the relation between linguistics and neuroscience. In A. Cutler (Ed.), Twenty-first century psycholinguistics: Four cornerstones, 1 (pp. 103-118). Lawrence Erlbaum Associates.
  62. Pour-El, M. B. (1974). Abstract computability and its relation to the general purpose analog computer: Some connections between logic, differential equations and analog computers. Transactions of the American Mathematical Society, 199, 1-28.
  63. Raichle, M. E., & Mintun, M. A. (2006). Brain work and brain imaging. Annual Review of Neuroscience, 29, 449–476.
  64. Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain, Psychological Review, 65(6), 386-408.
  65. Rubel, L. A. (1985). The brain as an analog computer. Journal of theoretical neurobiology, 4(2), 73-81.
  66. Rubel, L. (1989). Digital simulation of analog computation and Church´s thesis. The Journal of Symbolic Logic, 54 (3), 1011-1017.
  67. Sarpeshkar, R. (1998). Analog versus digital: Extrapolating from electronics to neurobiology. Neural computation, 10, 1601-1638.
  68. Schlesewsky, M., & Bornkessel-Schlesewsky, B. (2013). Computational primitive in syntax and possible brain correlates. In C. Boeckx & K. K. Grohmann (Eds.), The Cambridge Handbook of Biolinguistics (pp. 257-282). Cambridge University Press.
  69. Schneider, S. (2011). The language of thought: A new philosophical direction. The MIT Press.
  70. Spivey, M. (2007). The continuity of mind. Oxford University Press.
  71. Turing, A. M. (1936). On computable numbers, with an application to the entscheidungsproblem. Proceedings of the London Mathematical Society, 42(1), 230–265.
  72. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 236: 433-460.
  73. Weiskopf, D. (2011). The functional unity of special science kinds. The British Journal for the Philosophy of Science, 62(2), 233-258.
  74. Wetzel, L. (2006). Types and tokens. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy (Fall 2018 ed.). https://plato.stanford.edu/archives/fall2018/entries/types-tokens/
  75. Yang, Ch. (2010). Three factors in language variation. Lingua, 120, 1160–1177.
  76. Yusa, N. (2016). Syntax in the brain. In K. Fujita & C. Boeckx (Eds.), Advances in biolinguistics: The human language faculty and its biological basis (pp. 217-229). Routledge.
  77. Zaccarella, E., & Friederici, D. G. (2015). Merge in the human brain: A sub-region based functional investigation in the left pars opercularis. Frontiers in psychology, 6, 1818.