Abstract
There have been recent disagreements in the philosophy of neuroscience regarding which sorts of scientific models provide mechanistic explanations, and which do not (e.g. computational models, dynamical models, topological models). These disagreements often hinge on two commonly adopted, but conflicting, ways of understanding mechanistic explanations: what I call the “representation-as” account, and the “representation-of” account. In this paper, I argue that neither account does justice to neuroscientific practice. In their place, I offer a new alternative that can defuse some of these disagreements. I argue that individual models do not provide mechanistic explanations by themselves (regardless of what type of model they are). Instead, individual models are always used to complement a huge body of background information and pre-existing models about the target system. With this in mind, I argue that mechanistic explanations are distributed across sets of different, and sometimes contradictory, scientific models. Each of these models contributes limited, but essential, information to the same mechanistic explanation, but none can be considered a mechanistic explanation in isolation of the others.
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Notes
Carandini and Heeger (2012) define CNCs as “standard computational modules that apply the same fundamental operations in a variety of contexts” (p. 51).
This is arguably still consistent with Kaplan’s claim that the 3M requirement is intended as a tool to help “guide the direction of theoretical inquiry” (2011, p. 347).
Eliasmith, for instances, explicitly argues that most kinds of statistical, dynamical, and computational models fail to provide mechanistic explanations on these grounds (2010).
Another historical example is the study of the mechanism for protein synthesis. The mechanism for protein synthesis was not explained by any single representation, but by the piecemeal collection of various different models generated within different fields (Machamer et al. 2000, pp. 18–21).
I would like to offer special thanks to a blind referee for highlighting this worry.
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Acknowledgments
I would like to offer my thanks to the Centre for Theoretical Neuroscience at the University of Waterloo, and the PNP Work in Progress Seminar at Washington University in St Louis, for feedback and advice on earlier drafts of this paper. Special thanks go to Carl Craver, Gualtiero Piccinini, Doreen Fraser, Anya Plutynski, Kate Shrumm, Mark Povich, Kurt Holukoff, and Robyn Holukoff, for helpful suggestions and guidance in shaping this paper. This research was supported by the Social Sciences and Humanities Research Council of Canada Postdoctoral Award.
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This paper was funded by the Social Sciences and Humanities Research Council of Canada (award number: 756-2013-0215).
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Hochstein, E. One mechanism, many models: a distributed theory of mechanistic explanation. Synthese 193, 1387–1407 (2016). https://doi.org/10.1007/s11229-015-0844-8
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DOI: https://doi.org/10.1007/s11229-015-0844-8