Miłkowski, Marcin (2018) From Computer Metaphor to Computational Modeling: The Evolution of Computationalism. Minds and Machines, 28 (3). pp. 515-541. ISSN 0924-6495

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Abstract

In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working memory to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evidenced by the way in which models are empirically validated. Moreover, the methodological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deservedly successful in cognitive (neuro)science. Its successes are related to deep conceptual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year.

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Item Type:	Published Article or Volume
Creators:	Creators Email ORCID Miłkowski, Marcin mmilkows@ifispan.waw.pl 0000-0001-7646-5742
Keywords:	computationalism, research tradition
Subjects:	Specific Sciences > Cognitive Science General Issues > Explanation Specific Sciences > Neuroscience
Depositing User:	Dr. Marcin Miłkowski
Date Deposited:	06 Jan 2019 02:02
Last Modified:	06 Jan 2019 02:02
Item ID:	15557
Journal or Publication Title:	Minds and Machines
Official URL:	http://doi.org/10.1007/s11023-018-9468-3
DOI or Unique Handle:	10.1007/s11023-018-9468-3
Subjects:	Specific Sciences > Cognitive Science General Issues > Explanation Specific Sciences > Neuroscience
Date:	2018
Page Range:	pp. 515-541
Volume:	28
Number:	3
ISSN:	0924-6495
URI:	https://philsci-archive.pitt.edu/id/eprint/15557

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