Abstract
Proponents of mechanistic explanations have recently proclaimed that all explanations in the neurosciences appeal to mechanisms. The purpose of the paper is to critically assess this statement and to develop an integrative account that connects a large range of both mechanistic and dynamical explanations. I develop and defend four theses about the relationship between dynamical and mechanistic explanations: that dynamical explanations are structurally grounded, that they are multiply realizable, possess realizing mechanisms and provide a powerful top-down heuristic. Four examples shall support my points: the harmonic oscillator, the Haken–Kelso–Bunz model of bimanual coordination, the Watt governor and the Gierer–Meinhardt model of biological pattern formation. I also develop the picture of “horizontal” and “vertical” directions of explanations to illustrate the different perspectives of the dynamical and mechanistic approach as well as their potential integration by means of intersection points.
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Notes
As a reviewer has pointed out, the assumption that generalizability and scope are explanatory virtues is already controversial and should particularly be argued for. While a general discussion of the nature of scientific explanation is beyond the scope of the present paper, I like to add this much: explanation raises understanding, hence, an explanatory pattern with a wider scope raises a bigger understanding than with a narrow scope. So far, I take sides with the basic idea of unificationism. I do, on the other hand, agree that many of the well-known standard problems of the classic DN- and unificationist approach stem from the fact that causation has not suitably been taken into account. The purpose of the present approach (and the present section in particular) is to present an integrated picture of a “horizontal” and a “vertical” direction of explanation and to eventually stress the idea that a full-fledged understanding of many phenomena is only brought forth once we understand their particular mechanistic realizations as well as their deep and generalizable structural core. In this respect, the present approach is in general harmony with a recent paper by Bangu (2017) who presents a revised unificationist account that aims to incorporate the causal account as a sub-component.
Of course, as Bechtel (2009) for instance discusses, the possibility of conservation of mechanisms through evolutionary descent allows for a certain, limited generalization of mechanistic explanations.
Note that what is ‘predicted’ here is the existence of a mechanism of a certain type (the GM type, i.e. with a structural core represented by the GM model equations). For instance, to take a most recent research result, the particular GM-type mechanism responsible for the color and pattern of skin scales in lizards (Manukyan et al. 2017). The prediction of mechanism types serves as a research heuristics and should of course not be conflated with the prediction of the temporal evolution of a certain system (e.g. by means of its underlying dynamical equations). To consider prediction in this latter sense as already explanatory is highly controversial (and of course dismissed by most mechanists). But it should be clear that this is a different sense of prediction than what I have in mind here.
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Many thanks to Carlos Zednik and two anonymous reviewers for valuable inputs and comments that helped to improve the paper.
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Lyre, H. Structures, dynamics and mechanisms in neuroscience: an integrative account. Synthese 195, 5141–5158 (2018). https://doi.org/10.1007/s11229-017-1616-4
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DOI: https://doi.org/10.1007/s11229-017-1616-4