David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jonathan Jenkins Ichikawa
Jack Alan Reynolds
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Minds and Machines 15 (1):57-71 (2005)
Traditional approaches to modeling cognitive systems are computational, based on utilizing the standard tools and concepts of the theory of computation. More recently, a number of philosophers have argued that cognition is too subtle or complex for these tools to handle. These philosophers propose an alternative based on dynamical systems theory. Proponents of this view characterize dynamical systems as (i) utilizing continuous rather than discrete mathematics, and, as a result, (ii) being computationally more powerful than traditional computational automata. Indeed, the logical possibility of such super-powerful systems has been demonstrated in the form of analog artificial neural networks. In this paper I consider three arguments against the nomological possibility of these automata. While the first two arguments fail, the third succeeds. In particular, the presence of noise reduces the computational power of analog networks to that of traditional computational automata, and noise is a pervasive feature of information processing in biological systems. Consequently, as an empirical thesis, the proposed dynamical alternative is under-motivated: What is required is an account of how continuously valued systems could be realized in physical systems despite the ubiquity of noise
|Keywords||Cognition Computation Dynamic Metaphysics System|
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References found in this work BETA
Jerry A. Fodor (1975). The Language of Thought. Harvard University Press.
Jerry A. Fodor (1998). Concepts: Where Cognitive Science Went Wrong. Oxford University Press.
Tim van Gelder (1995). What Might Cognition Be If Not Computation? Journal of Philosophy 92 (7):345-81.
Terence E. Horgan & John L. Tienson (1996). Connectionism and the Philosophy of Psychology. MIT Press.
Citations of this work BETA
Whit Schonbein (2014). Varieties of Analog and Digital Representation. Minds and Machines 24 (4):415-438.
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