David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Minds and Machines 22 (4):353-380 (2012)
Which notion of computation (if any) is essential for explaining cognition? Five answers to this question are discussed in the paper. (1) The classicist answer: symbolic (digital) computation is required for explaining cognition; (2) The broad digital computationalist answer: digital computation broadly construed is required for explaining cognition; (3) The connectionist answer: sub-symbolic computation is required for explaining cognition; (4) The computational neuroscientist answer: neural computation (that, strictly, is neither digital nor analogue) is required for explaining cognition; (5) The extreme dynamicist answer: computation is not required for explaining cognition. The first four answers are only accurate to a first approximation. But the “devil” is in the details. The last answer cashes in on the parenthetical “if any” in the question above. The classicist argues that cognition is symbolic computation. But digital computationalism need not be equated with classicism. Indeed, computationalism can, in principle, range from digital (and analogue) computationalism through (the weaker thesis of) generic computationalism to (the even weaker thesis of) digital (or analogue) pancomputationalism. Connectionism, which has traditionally been criticised by classicists for being non-computational, can be plausibly construed as being either analogue or digital computationalism (depending on the type of connectionist networks used). Computational neuroscience invokes the notion of neural computation that may (possibly) be interpreted as a sui generis type of computation. The extreme dynamicist argues that the time has come for a post-computational cognitive science. This paper is an attempt to shed some light on this debate by examining various conceptions and misconceptions of (particularly digital) computation.
|Keywords||Computation Connectionism Dynamicism Computationalism Classicism Computational neuroscience Cognitive science Mechanistic explanation Representation|
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References found in this work BETA
Gilbert Ryle (1949/2002). The Concept of Mind. Hutchinson and Co.
Francisco Varela, Evan Thompson & Eleanor Rosch (1991). The Embodied Mind: Cognitive Science and Human Experience. MIT Press.
Evan Thompson (2007). Mind in Life: Biology, Phenomenology, and the Sciences of Mind. Harvard University Press.
Citations of this work BETA
Hyundeuk Cheon (2014). Distributed Cognition in Scientific Contexts. Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 45 (1):23-33.
Alexandre Castro (2013). The Thermodynamic Cost of Fast Thought. Minds and Machines 23 (4):473-487.
Raoul Gervais (2015). Mechanistic and Non-Mechanistic Varieties of Dynamical Models in Cognitive Science: Explanatory Power, Understanding, and the ‘Mere Description’ Worry. Synthese 192 (1):43-66.
Prakash Mondal (2014). Does Computation Reveal Machine Cognition? Biosemiotics 7 (1):97-110.
Alexandre de Castro (2013). The Thermodynamic Cost of Fast Thought. Minds and Machines 23 (4):473-487.
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