Mechanism, explanatory pluralism and efficient coding explanation in neuroscience

Main Article Content

Sergio Daniel Barberis

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.

Downloads

Download data is not yet available.

Article Details

How to Cite
Barberis, S. D. (2017). Mechanism, explanatory pluralism and efficient coding explanation in neuroscience. Argentinean Journal of Behavioral Sciences, 9(1), 9–18. https://doi.org/10.32348/1852.4206.v9.n1.14650
Section
Original Articles
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.

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.