Synthese 193 (5):1457-1478 (2016)

Authors
Marcin Miłkowski
Polish Academy of Sciences
Abstract
The claim defended in the paper is that the mechanistic account of explanation can easily embrace idealization in big-scale brain simulations, and that only causally relevant detail should be present in explanatory models. The claim is illustrated with two methodologically different models: Blue Brain, used for particular simulations of the cortical column in hybrid models, and Eliasmith’s SPAUN model that is both biologically realistic and able to explain eight different tasks. By drawing on the mechanistic theory of computational explanation, I argue that large-scale simulations require that the explanandum phenomenon is identified; otherwise, the explanatory value of such explanations is difficult to establish, and testing the model empirically by comparing its behavior with the explanandum remains practically impossible. The completeness of the explanation, and hence of the explanatory value of the explanatory model, is to be assessed vis-à-vis the explanandum phenomenon, which is not to be conflated with raw observational data and may be idealized. I argue that idealizations, which include building models of a single phenomenon displayed by multi-functional mechanisms, lumping together multiple factors in a single causal variable, simplifying the causal structure of the mechanisms, and multi-model integration, are indispensable for complex systems such as brains; otherwise, the model may be as complex as the explanandum phenomenon, which would make it prone to so-called Bonini paradox. I conclude by enumerating dimensions of empirical validation of explanatory models according to new mechanism, which are given in a form of a “checklist” for a modeler
Keywords Human brain project  Blue brain  SPAUN  Mechanistic explanation  Idealization  Bonini’s paradox
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Reprint years 2016
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DOI 10.1007/s11229-015-0731-3
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References found in this work BETA

Origins of Objectivity.Tyler Burge - 2010 - Oxford University Press.
Explaining the Brain.Carl F. Craver - 2009 - Oxford University Press.
Thinking About Mechanisms.Peter Machamer, Lindley Darden & Carl F. Craver - 2000 - Philosophy of Science 67 (1):1-25.

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Citations of this work BETA

Integrating Cognitive (Neuro)Science Using Mechanisms.Marcin Miłkowski - 2016 - Avant: Trends in Interdisciplinary Studies (2):45-67.
Models and Mechanisms in Network Neuroscience.Carlos Zednik - 2018 - Philosophical Psychology 32 (1):23-51.

View all 14 citations / Add more citations

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