Notas para una evaluación actualizada del enfoque computacional de la mente
DOI:
https://doi.org/10.15448/1984-6746.2024.1.44571Palabras clave:
representaciones mentales, conexionismo, computacionalismo estructural, mecanicismoResumen
El artículo propone una evaluación actualizada del enfoque computacional de la mente, detallando cuestiones conceptuales y críticas. La evaluación
se guía por tres tesis – α) La mente humana es un sistema computacional; β) La
mente humana puede describirse como un sistema computacional; γ) Los sistemas
computacionales necesitan contenido representacional –, a partir de las cuales
muestro que el computacionalismo clásico se articula en términos de α∧γ y que
las vertientes contemporáneas se entienden mejor en términos de α∧~γ o β∧~γ.
Finalmente, después de analizar una serie de objeciones, argumentamos que el
computacionalismo del siglo XXI es un programa de investigación filosóficamente
relevante y que los críticos del enfoque computacional de la mente incurren en
un anacronismo cuando se limitan a criticar vertientes clásicas.
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ARISTÓTELES. Ética a Nicômaco. In: ARISTÓTELES. Coleção Os Pensadores. São Paulo: Abril, 1984. p. 47-236.
BAARS, B. J. A cognitive theory of consciousness. Cambridge, MA: Cambridge University Press, 1988.
BARRETT, L. Why brains are not computers, why behaviorism is not satanism, and why dolphins are not aquatic apes. The Behavior Analyst, v. 39, nº. 1, p. 09-23, 2016.
BECHTEL, W.; SHAGRIR, O. The non-redundant contributions of Marr's three levels of analysis for explaining information-processing mechanisms. Topics in Cognitive Science, v. 7, nº. 2, p. 312-322, 2015. <https://doi.org/10.1111/tops.12141>
BECHTEL, W. Mental mechanisms: philosophical perspectives on cognitive neuroscience. New York: Routledge, 2008.
BICKHARD, M. H.; TERVEEN, L. Foundational issues in artificial intelligence and cognitive science: impasse and solution. Amsterdam: North-Holland, 1995.
BLOCK, N.; FODOR, J. A. What psychological states are not. The Philosophical Review, v. 81, p. 159-181, 1972. <https://doi.org/10.2307/2183991>
BLOCK, N. Troubles with functionalism. In: BLOCK, N. Readings in the philosophy of psychology. Cambridge, MA: Harvard University Press, 1980. p. 268-305.
CHALMERS, D. A computational foundation for the study of cognition. Journal of Cognitive Science, v. 12, p. 323-357, 2011. <https://doi.org/10.17791/jcs.2011.12.4.325>
CHALMERS, D. The conscious mind: in search of a fundamental theory. Oxford: Oxford University Press, 1996.
CHEMERO, A. Information for perception and information processing. Minds and Machines, v. 13, p. 577-588, 2003. <https://doi.org/10.1023/A:1026209002908>
CHRISTIANSEN, M.; CHATER, N. Connectionist natural language processing: the state of the art. Cognitive Science, v. 23, nº. 4, p. 417-437, 1999. <https://doi.org/10.1207/s15516709cog2304_2>
CHURCHLAND, P. Brain-wise: studies in neurophilosophy. Cambridge, MA: MIT Press, 2002.
CRAVER, C. F. Explaining the brain: mechanisms and the mosaic unity of neuroscience. Oxford: Oxford University Press, 2007.
DAUGMAN, J. Brain metaphor and brain theory. In: SCHWARTZ, E. L. (Ed.) Computational neuroscience. Cambridge, MA: MIT Press, 1990. p. 9-18.
EDELMAN, G. M. Bright air, brilliant fire: on the matter of the mind. New York: Basic Books, 1992.
EPSTEIN, R. The empty brain: your brain does not process information, retrieve knowledge or store memories. Aeon, 2016. <https://aeon.co/essays/your-brain-does-not-process-information-and-it-is-not-a-computer>
FODOR, J. A.; McLAUGHLIN, B. P. Connectionism and the problem of systematicity: why Smolensky’s solution doesn’t work. Cognition, v. 35, nº. 2, p. 183-204, 1990. <https://doi.org/10.1016/0010-0277(90)90014-B>
FODOR, J. A.; PYLYSHYN, Z. Connectionism and cognitive architecture: a critical analysis. Cognition, v. 28, nº. 1-2, p. 03-71, 1988. <https://doi.org/10.1016/0010-0277(88)90031-5>
FODOR, J. A. Psychosemantics: the problem of meaning in the philosophy of mind. Cambridge, MA: MIT Press, 1987.
FODOR, J. A. Representations: philosophical essays on the foundations of cognitive science. Cambridge, MA: MIT Press, 1981a.
FODOR, J. A. The language of thought. New York: Thomas Y. Crowell, 1975.
GIBSON, J. J. The ecological approach to visual perception. Hove: Psychology Press, 1986.
HADLEY, R. F. Systematicity in connectionist language learning. Mind & Language, v. 9, nº. 3, p. 247-272, 1994 (1994a). <https://doi.org/10.1111/j.1468-0017.1994.tb00225.x>
HADLEY, R. F. Systematicity revisited: reply to Christiansen and Chater and Niklasson and van Gelder. Mind & Language, v. 9, nº. 4, p. 431-444, 1994. (1994b) <https://doi.org/10.1111/j.1468-0017.1994.tb00317.x>
HARBECKE, J.; SHAGRIR, O. The role of the environment in computational explanations. European Journal for Philosophy of Science, v. 9, art. 37, p. 01-19, 2019. <https://doi.org/10.1007/s13194-019-0263-7>
HARNAD, S. The symbol grounding problem. Physica D: Nonlinear Phenomena, v. 42, nº. 1-3, p. 335-346, 1990. <https://doi.org/10.1016/0167-2789(90)90087-6>
HASSABIS, D.; KUMARAN, D.; SUMMERFIELD, C.; BOTVINICK, M. Neuroscience-inspired artificial intelligence. Neuron, v. 95, nº. 2, p. 245-258, 2017. <https://doi.org/10.1016/j.neuron.2017.06.011>
HINTON, G. How neural networks learn from experience. Scientific American, v. 267, nº. 3, p. 145-151, 1992. <https://www.scientificamerican.com/article/how-neural-networks-learn-from-expe/>
HUTTO, D.; MYIN, E.; PEETERS, A.; ZAHNOUN, F. The cognitive basis of computation: putting computation in its place. In: SPREVAK, M.; COLOMBO, M. (Eds.) The Routledge handbook of the computational mind. London: Routledge, 2018. p. 272-282.
KELLOGG, R. T. Fundamentals of cognitive psychology. 2ª. ed. New York: Sage Publications, 2012.
KREMPEL, R. Notas sobre a teoria computacional da mente. In: BARBOSA, E. C.; CID, R. L. (Orgs.) Filósofas analíticas contemporâneas. Pelotas: Ed. UFPel, 2022. p. 56-79. <https://wp.ufpel.edu.br/nepfil/>
LAKOFF, G. Women, fire, and dangerous things: what categories reveal about the mind. Chicago: University of Chicago Press, 1987.
LECLERC, A. Uma introdução à filosofia da mente. Curitiba: Appris, 2018.
LEITE, D. A. The twenty-first century mechanistic theory of human cognition: a critical analysis. Cham: Springer, 2021.
LOULA, J.; BARONI, M.; LAKE, B. Rearranging the familiar: testing compositional generalization in recurrent networks. In: Proceedings of the 2018 EMNLP Workshop BlackboxNLP: analyzing and interpreting neural networks for NLP. Brussels: Association for Computational Linguistics, 2018. p. 108-114. <https://doi.org/10.18653/v1/W18-5413>
LUCAS, J. R. Minds, machines and Gödel. Philosophy, v. 36, nº. 137, p. 219-227, 1961. <https://www.jstor.org/stable/3749270>
LUPYAN, G. The difficulties of executing simple algorithms: why brains make mistakes computers don’t. Cognition, v. 129, nº. 3, p. 615-636, 2013. <https://doi.org/10.1016/j.cognition.2013.08.015>
MACKAY, D. M. Information, mechanism and meaning. Cambridge, MA: MIT Press, 1969.
MARR, D. C. Vision: a computational approach. San Francisco: Freeman & Co., 1982.
MEURER, C. F. Sobre a viabilidade computacional de uma arquitetura cognitiva não-modular. Cognitio-Estudos, v. 14, p. 55-73, 2017. <https://doi.org/10.23925/1809-8428.2017v14i1p55-73>
MEURER, C. F. Challenging evolutionary psychology with the eternalist view of the world. Cognitio-Estudos, v. 15, p. 247-262, 2018. <https://doi.org/10.23925/1809-8428.2018v15i2p247-262>
MEURER, C. F. É a modularidade massiva um programa de pesquisa empiricamente progressivo? Veritas, v. 64, e34192, 2019. <https://doi.org/10.15448/1984-6746.2019.3.34192>
MIŁKOWSKI, M. Beyond formal structure: a mechanistic perspective on computation and implementation. Journal of Cognitive Science, v. 12, p. 359-379, 2011. <http://dx.doi.org/10.17791/jcs.2011.12.4.361>
MIŁKOWSKI, M. Explaining the computational mind. Cambridge, MA: The MIT Press, 2013.
MIŁKOWSKI, M. From computer metaphor to computational modeling: the evolution of computationalism. Minds and Machines, v. 28, p. 515-541, 2018. (2018a) <https://doi.org/10.1007/s11023-018-9468-3>
MIŁKOWSKI, M. Objections to computationalism: a survey. Roczniki Filozoficzne, v. 66, nº. 3, p. 57-75, 2018. (2018b) <https://doi.org/10.18290/rf.2018.66.3-3>
MÜLLER, V. C. Pancomputationalism: theory or metaphor? In: HAGENGRUBER, R.; RISS, U. V. (Eds.) The relevance of philosophy for information science. London, New York: Routledge, 2014. p. 213-222.
NAGY, N.; AKL, S. Computations with uncertain time constraints: effects on parallelism and universality. In: CALUDE, C.; KARI, J.; PETRE, I.; ROZENBERG, G. (Eds.) Unconventional computation. Berlin, Heidelberg: Springer, 2011. p. 152-163. <http://dx.doi.org/10.1007/978-3-642-21341-0_19>
O'BRIEN, G.; OPIE, J. How do connectionist networks compute? Cognitive Processing, v. 7, p. 30-41, 2006. <https://doi.org/10.1007/s10339-005-0017-7>
PENROSE, R. The emperor’s new mind. Oxford: Oxford University Press, 1989.
PICCININI, G. Neurocognitive mechanisms: explaining biological cognition. Oxford: Oxford University Press, 2020.
PICCININI, G.; BAHAR, S. Neural computation and the computational theory of cognition. Cognitive Science, v. 37, nº. 3, p. 453-488, 2013. <https://doi.org/10.1111/cogs.12012>
PICCININI, G. Computationalism. In: MARGOLIS, E.; SAMUEL, R.; STICH, S. (Eds.) Oxford Handbook of Philosophy of Cognitive Science. Oxford: Oxford University Press, 2012. p. 222-249.
PICCININI, G. Computing mechanisms. Philosophy of Science, v. 74, nº. 4, p. 501-526, 2007. <https://doi.org/10.1086/522851>
PINKER, S. So how does the mind work? Mind & Language, v. 20, p. 01-24, 2005. <https://doi.org/10.1111/j.0268-1064.2005.00274.x>
POLGER, T. Computational functionalism. In: CALVO, P.; SYMONS, J. (Eds.) The Routledge companion to the philosophy of psychology. London: Routledge, 2008. p. 148-163.
PUTNAM, H. Representation and reality. Cambridge, MA: MIT Press, 1991.
PUTNAM, H. Psychophysical predicates. In: CAPITAN, W.; MERRILL, D. (Eds.) Art, mind, and religion. Pittsburgh: University of Pittsburgh Press, 1967. p. 37-48.
PUTNAM. H. Minds and machines. In: HOOK, S. (Ed.) Dimensions of mind. New York: University of New York Press, 1960. p. 148-180.
PYLYSHYN, Z. W. Computation and cognition: toward a foundation for cognitive science. Cambridge, MA: MIT Press, 1984.
RAMSEY, W. Do connectionist representations earn their explanatory keep. Mind & Language, v. 12, nº. 1, p. 34-66, 1997. <https://doi.org/10.1111/j.1468-0017.1997.tb00061.x>
RESCORLA, M. A teoria computacional da mente. In: CARVALHO, E. M. (Org.) Textos selecionados de filosofia da cognição. Pelotas: NEPFIL On-line, 2022. p. 42-111. <https://wp.ufpel.edu.br/nepfil/>
ROLLA, G. A mente enativa. Porto Alegre: Editora Fi, 2021. <https://doi.org/10.22350/9786559173341>
ROSENBLATT, F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review, v. 65, nº. 6, p. 386-408, 1958. <https://doi.org/10.1037/h0042519>
ROSENTHAL, D. Consciousness and mind. Oxford: Oxford University Press, 2005.
RUMELHART, D.; McCLELLAND, J. PDP models and general issues in cognitive science. In: RUMELHART, D.; McCLELLAND, J. (Eds.) Parallel distributed processing: explorations in the microstructure of cognition. Cambridge, MA: MIT Press, 1986. p. 110-146.
SCHNEIDER, S. The language of thought: a new philosophical direction. Cambridge, MA: MIT Press, 2011.
SCHWEIZER, P. Algorithms implemented in space and time. In: KIBBLE, R. (Ed.) Proceedings of the 50th anniversary convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour. Bath, UK: AISB, 2014. p. 128-136. <http://doc.gold.ac.uk/aisb50/>
SEARLE, J. R. Minds, brains, and programs. Behavioral and Brain Sciences, v. 3, nº. 03, p. 01-19, 1980. <https://doi.org/10.1017/S0140525X00005756>
SEARLE, J. The rediscovery of mind. Cambridge, MA: MIT Press, 1992.
SIEGELMANN, H. T.; SONTAG, E. D. Analog computation via neural networks. Theoretical Computer Science, v. 131, nº. 2, p. 331-360, 1994. <https://doi.org/10.1016/0304-3975(94)90178-3>
TALLIS, R.; ALEKSANDER, I. Computer models of the mind are invalid. Journal of Information Technology, v. 23, p. 55-62, 2008. <https://doi.org/10.1057/palgrave.jit.2000128>
THOMPSON, E. Mind in life: biology, phenomenology, and the sciences of mind. Cambridge, MA: Harvard University Press, 2007.
TONONI, G. An information integration theory of consciousness. BMC Neuroscience, v. 5, nº. 1, article 42, p. 01-22, 2004. <https://doi.org/10.1186/1471-2202-5-42>
TOWNSEND, A. Representation in neural networks. Dissertação (Mestrado em Filosofia). Universidade de Adelaide, Austrália, 2023. <https://digital.library.adelaide.edu.au/dspace/bitstream/2440/139686/1/Townsend2023_MPhil.pdf>
VARELA, F.; THOMPSON, E.; ROSCH, E. The embodied mind: cognitive science and human experience. Cambridge, MA: MIT Press, 2016. [Primeira edição publicada em 1991]
WHEELER, M. Reconstructing the cognitive world. Cambridge, MA: MIT Press, 2005.
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