Functional Analyses, Mechanistic Explanations, and Explanatory Tradeoffs

Journal of Cognitive Science 14:229-251 (2013)

Authors
Sergio Daniel Barberis
Universidad de Buenos Aires (UBA)
Abstract
Recently, Piccinini and Craver have stated three theses concerning the relations between functional analysis and mechanistic explanation in cognitive sciences: No Distinctness: functional analysis and mechanistic explanation are explanations of the same kind; Integration: functional analysis is a kind of mechanistic explanation; and Subordination: functional analyses are unsatisfactory sketches of mechanisms. In this paper, I argue, first, that functional analysis and mechanistic explanations are sub-kinds of explanation by scientific (idealized) models. From that point of view, we must take into account the tradeoff between the representational/explanatory goals of generality and precision that govern the practice of model-building. In some modeling scenarios, it is rational to maximize explanatory generality at the expense of mechanistic precision. This tradeoff allows me to put forward a problem for the mechanist position. If mechanistic modeling endorses generality as a valuable goal, then Subordination should be rejected. If mechanists reject generality as a goal, then Integration is false. I suggest that mechanists should accept that functional analysis can offer acceptable explanations of cognitive phenomena.
Keywords functional analysis  mechanistic explanation  model explanation  generality  precision
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References found in this work BETA

Laws and Symmetry.Bas C. van Fraassen - 1989 - Oxford University Press.
Explaining the Brain.Carl F. Craver - 2009 - Oxford University Press.
Thinking About Mechanisms.Peter K. Machamer, Lindley Darden & Carl F. Craver - 2000 - Philosophy of Science 67 (1):1-25.

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Citations of this work BETA

Explanation in Computational Neuroscience: Causal and Non-Causal.M. Chirimuuta - 2018 - British Journal for the Philosophy of Science 69 (3):849-880.

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