Mechanism, explanatory pluralism and efficient coding explanation in neuroscience

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Published: Apr 3, 2017
DOI: https://doi.org/10.32348/1852.4206.v9.n1.14650
Keywords:
mechanism, unity of science, explanatory pluralism, causal explanation, efficient coding explanation

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Sergio Daniel Barberis
Universidad de Buenos Aires. Facultad de Filosofía y Letras. Instituto de Filosofía "Alejandro Korn"

Abstract

There is a growing debate within the philosophical community about the unity or disunity of neuroscience. The new Mechanist philosophers claim that neuroscience exhibits a mosaic unity –one in which different explanatory models may contribute to the explanation of some explanandum phenomenon ? by setting causal constraints on the space of possible mechanisms for ?. Non-mechanist philosophers frequently adopt some form or another of explanatory pluralism. In this paper I argue, first, that Mechanism is compatible with a popular version of explanatory pluralism, which I call causally restricted pluralism. Then, I present a liberalized version of explanatory pluralism –one according to which there are models in neuroscience that are explanatory of some phenomenon ? but that do not set any causal constraint on the space of possible mechanisms for ?. Finally, I argue that there is at least one pattern of explanation in neuroscience –namely, efficient coding explanation– that is better accounted for by liberal pluralism.

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How to Cite
Mechanism, explanatory pluralism and efficient coding explanation in neuroscience. (2017). Argentinean Journal of Behavioral Sciences, 9(1), 9-18. https://doi.org/10.32348/1852.4206.v9.n1.14650
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Vol. 9 No. 1 (2017): Revista Argentina de Ciencias del Comportamiento
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Original Articles

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Author Biography

Sergio Daniel Barberis, Universidad de Buenos Aires. Facultad de Filosofía y Letras. Instituto de Filosofía "Alejandro Korn"

Doctor en Filosofía por la Universidad de Buenos Aires. Becario Post-doctoral CONICET 2013-2105. Auxiliar Docente en Filosofía de las Ciencias, FFyL-UBA. Auxiliar Docente en Metafìsica, FFyL-UBA. Profesor Adjunto de Historia de la Ciencia, Universidad Nacional de Moreno. Becario Fulbright 2016-2017.

How to Cite

Mechanism, explanatory pluralism and efficient coding explanation in neuroscience. (2017). Argentinean Journal of Behavioral Sciences, 9(1), 9-18. https://doi.org/10.32348/1852.4206.v9.n1.14650

References

Abney, D., Dale, R., Yoshimi, J., Kello, Ch., Tylén, K., Fusaroli, R. (2014) “Joint perceptual decision-making: a case study in explanatory pluralism”, Frontiers in Psychology, 5, 330, p. 1-12.

Barros, B. (2008) “Natural selection as a mechanism”, Philosophy of Science, 75(3), pp. 306-322

Batterman, R. (2002) The devil in the details, Oxford: Oxford University Press.

Bechtel, W. (2008) Mental Mechanisms: Philosophical perspective on cognitive neuroscience, New York: Routledge.

Bickle, J. (2006) “Reducing mind to molecular pathways: Explicating the reductionism implicit in current cellular and molecular neuroscience”, Synthese, 151, pp. 411-434.

Boone, W. and Piccinini, G. (2015) “The cognitive neuroscience revolution”, Synthese, 193(5), pp 1509-1534.

Piccinini, G. y Boone, T. (2016) “Mechanistic Abstraction”, Philosophy of Science, online first.

Abney, D., Dale, R., Yoshimi, J., Kello, Ch., Tylén, K., & Fusaroli, R. (2014). Joint perceptual decision-making: a case study in explanatory pluralism. Frontiers in Psychology, 5(330), 1-12.

Batterman, R. (2002). The devil in the details. Oxford: Oxford University Press.

Bechtel, W. (2008). Mental Mechanisms: Philosophical perspective on cognitive neuroscience. New York: Routledge.

Bechtel, W., & Abrahamsen, A. (2005). Explanation: A Mechanistic Alternative. Studies in History and Philosophy of the Biological and Biomedical Sciences, 36, 421-441.

Boone, W., & Piccinini, G. (2016a). The cognitive neuroscience revolution. Synthese, 193(5), 1509-1534.

Boone, W., & Piccinini, G. (2016b). Mechanistic abstraction. Philosophy of Science, 83(5), 686-697.

Caddick, S., Carandini, M., Hausser, M., Martin, K., Priebe, N., Reynolds, … Yokoyama, C. (2009). Physiology: Mechanisms. In D. Heegger, E. Simoncelli, J. Reynolds, & M. Carandini (Eds.), Canonical neural computation: a summary and a roadmap (pp. 8-12). Recovered from: http://www.theswartzfoundation.org/docs/Canonical-Neural-Computation-April-2009.pdf

Carandini, M., & Heeger, D. (2012). Normalization as a canonical neural computation. Nature Neuroscience, 13, 51-62.

Chirimuuta, M. (2014). Minimal Models and Canonical Neural Computations: The Distinctness of Computational Explanation in Neuroscience. Synthese, 191, 127-153.

Chirimuuta, M. (forthcoming). Explanation in computational neuroscience: causal and non-causal.

Churchland, P. M. (1989). A neurocomputational perspective: The nature of mind and the structure of science. Cambridge: MIT Press.

Clatworthy, P., Chirimuuta, M., Lauritzen, J., & Tolhurst, D. (2003). Coding of the contrasts in natural images by populations of neurons in primary visual cortex (V1). Vision Research, 43, 1983-2001.

Craver, C. (2006). When mechanistic models explain. Synthese, 28(2), 141-163.

Craver, C. (2007). Explaining the Brain: Mechanisms and the mosaic unity of neuroscience. Oxford: Clarendon.

Craver, C. (2009). Mechanisms and natural kinds. Philosophical Psychology, 22, 575-594.

Craver, C. (2015). The ontic account of scientific explanation. In M. Kaiser, O. Scholz, D. Plenge, & A. Hüttemann (2015) Explanation in the Special Sciences: The case of biology and history (pp. 27-52). Dordretch: Springer.

Dale, R., Dietrich, E., & Chemero, A. (2009). Explanatory Pluralism in Cognitive Science. Cognitive Science, 33, 739-742.

Darden, L., & Maull, N. (1977). Interfield theories. Philosophy of Science, 44, 43-64.

Dayan, P., & Abbott, L. (2005). Theoretical Neuroscience: Computational and mathematical modeling of neural systems. Cambridge: MIT Press.

Feyerabend, P. (1975). Against method: Outline of an anarchistic theory of knowledge. London: New Left Books.

Glennan, S. (2002). Rethinking mechanistic explanation. Philosophy of Science, 69(3), S342-S353.

Glennan, S. (2010). Mechanisms, Causes, and the Layered Model of the World. Philosophy and Phenomenological Research, 81, 362–381.

Harris, C., & Wolpert, D. (2006). The main sequence of saccades optimizes speed-accuracy trade-off. Biological Cybernetics, 95(1), 25-29.

Heeger, D. (1992). Normalization of cell responses in the cat striate cortex. Visual Neuroscience, 9, 181-197.

Krickel, B. (forthcoming). A regularist approach to mechanistic type-level explanation.

Lange, M. (2013). What makes a scientific explanation distinctively mathematical?. British Journal for the Philosophy of Science, 64, 485-511.

Levins, R. (1966). The strategy of model building in population biology. American Scientist, 54(4), 421-431.

Levy, A. (2013). What was Hodgkin and Huxley’s Achievement. British Journal for the Philosophy of Science, 65, 469-492.

Machamer, P., Darden, L., & Craver, C. (2000). Thinking about mechanisms. Philosophy of Science, 57, 1-25.

Marr, D. (1982). Vision. San Francisco, CA: Freeman Press.

Mayr, E. (1961). Cause and effect in biology. Science, 134, 1501-1506.

Nagel, E. (1961). The structure of science. Problems in the Logic of Scientific Explanation. New York: Harcourt, Brace and World, Inc.

Oppenheim, O., & Putnam, H. (1958). Unity of Science as a Working Hypothesis. In H. Feigl, M. Scriven, & G. Maxwell (Eds.). Concepts, Theories and the Mind-Body Problem, Minnesota Studies in the Philosophy of Science II (pp. 3-36). Minneapolis: University of Minnesota Press.

Piccinini, G. (2007). Computing mechanisms. Philosophy of Science, 74(4), 501-526.

Piccinini, G., & Craver, C. (2011). Integrating psychology and neuroscience: Functional analyses as mechanism sketches. Synthese, 183(3), 283-311.

Pincock, C. (2012). Mathematical and Scientific Representation. Oxford: Oxford University Press.

Ramón y Cajal, S. (1909). Histology of the Nervous System of Man and Vertebrates. Oxford: Oxford University Press.

Rosen, R. (1967). Optimality Principles in Biology. US: Springer.

Salmon, W. (1984). Scientific explanation and the causal structure of the world. Princeton: Princeton University Press.

Sundaram, R. (1996). A First Course in Optimization Theory. Cambridge: Cambridge University Press.

Thagard, P. (2003). Pathways to biomedical discovery. Philosophy of science, 70(2), 235-254.

Weisberg, M. (2006). Forty years of ‘The Strategy’. Biology and Philosophy, 21(5), 623-645.

Weisberg, M. (2007). Three kinds of idealization. Journal of Philosophy, 104(2), 639-659.

Weiskopf, D. (2011). Models and mechanisms in psychological explanation. Synthese 183, 313-338.

Woodward, J. (2003). Making things happen: a theory of causal explanation. Oxford: Oxford University Press.

Wright, C. (2012). Mechanistic explanation without the ontic conception. European Journal of Philosophy of Science, 2(3), 375-394.

Wright, C., & Bechtel, W. (2007). Mechanisms and psychological explanation. In P. Thagard (Ed.), Philosophy of Psychology and Cognitive Science (pp. 31-79). New York: Elsevier.