Abstract
The central claim of computationalism is generally taken to be that the brain is a computer, and that any computer implementing the appropriate program would ipso facto have a mind. In this paper I argue for the following propositions: (1) The central claim of computationalism is not about computers, a concept too imprecise for a scientific claim of this sort, but is about physical calculi (instantiated discrete formal systems). (2) In matters of formality, interpretability, and so forth, analog computation and digital computation are not essentially different, and so arguments such as Searle's hold or not as well for one as for the other. (3) Whether or not a biological system (such as the brain) is computational is a scientific matter of fact. (4) A substantive scientific question for cognitive science is whether cognition is better modeled by discrete representations or by continuous representations. (5) Cognitive science and AI need a theoretical construct that is the continuous analog of a calculus. The discussion of these propositions will illuminate several terminology traps, in which it's all too easy to become ensnared.
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References
Blum, L. (1989), Lectures on a Theory of Computation and Complexity over the Reals (or an arbitrary ring) (Report No. TR-89-065). Berkeley, CA: International Computer Science Institute.
Blum, L., Shub, M., and Smale, S. (1988), ‘On a Theory of Computation and Complexity over the Real Numbers: NP Completeness, Recursive Functions and Universal Machines’,The Bulletin of the American Mathematical Society 21, 1–46.
Burghardt, G. M. (1970), ‘Defining “Communication”’, in J. W. Johnston Jr., D. G. Moulton and A. Turk (eds.),Communication by Chemical Signals, New York: Century-Crofts, pp. 5–18.
Franklin, S. and Garzon, M. (1990), ‘Neural Computability’, in O. M. Omidvar (ed.),Progress in Neural Networks, Norwood, NJ: Ablex, Vol. 1, pp. 127–145.
Garzon, M. and Franklin, S. (1989), ‘Neural Computability II (extended abstract)’, inProceedings, International Joint Conference on Neural Networks, New York, NY: Institute of Electrical and Electronic Engineers, Vol. 1, pp. 631–637.
Goodman, N. (1968).Languages of Art: An Approach to a Theory of Symbols, Indianapolis, IN & New York, NY: Bobbs-Merrill.
Harnad, S. (1989), ‘Minds, Machines and Searle’,Journal of Theoretical and Experimental Artificial Intelligence 1, 5–25.
Harnad, S. (1990), ‘The Symbol Grounding Problem’,Physica D 42, 335–346.
Harnad, S. (1991), ‘Other Bodies, Other Minds: A Machine Incarnation of an Old Philosophical Problem’,Minds and Machines 1, 43–54.
Harnad, S. (in press-a), ‘Grounding Symbols in the Analog World”,Think 2, 48–51.
Harnad, S. (in press-b), ‘Artificial Life: Synthetic vs. Virtual, inArtificial Life III.
Haugeland, J. (1981), ‘Analog and Analog’,Philosophical Topics 12, 213–225.
Hayes, P., Harnad, S., Perlis, D., and Block, N. (1992), ‘Virtual Symposium on the Virtual Mind’,Minds and Machines 2, 217–238.
Lewis, D. (1971), ‘Analog and Digital”,Noûs 5, 321–327.
MacLennan, B. J. (1987), Technology-independent Design of Neurocomputers: The Universal Field Computer, in M. Caudill and C. Butler (eds.),Proceedings, IEEE First International Conference on Neural Networks, New York NY: Institute of Electrical and Electronic Engineers, Vol. 3, pp. 39–49
MacLennan, B. J. (1988), ‘Logic for the New AI’, in J. H. Fetzer (ed.),Aspects of Artificial Intelligence, Dordrecht: Kluwer, pp. 163–192.
MacLennan, B. J. (1989), The Calculus of Functional Differences and Integrals (Technical Report CS-89-80). Knoxville, TN: Computer Science Department, University of Tennessee.
MacLennan, B. J. (1990a), Evolution of Communication in a Population of Simple Machines (Technical Report CS-90-99). Knoxville, TN: Computer Science Department, University of Tennessee; submitted for publication.
MacLennan, B. J. (1990b),Functional Programming: Practice and Theory, Reading, MA: Addison-Wesley.
MacLennan, B. J. (1990c), ‘The Discomforts of Dualism’,Behavioral and Brain Sciences 13, 673–674.
MacLennan, B. J. (1990d), Field Computation: A Theoretical Framework for Massively Parallel Analog Computation; Parts I–IV (Technical Report CS-90-100) Knoxville, TN: University of Tennessee, Computer Science Department.
MacLennan, B. J. (1992), ‘Synthetic Ethology: An Approach to the Study of Communication’, in C. G. Langton, C. Taylor, J. D. Farmer and S. Rasmussen (eds.),Artificial Life II, Redwood City: Addison-Wesley, pp. 631–658.
MacLennan, B. J. (1993a), ‘Characteristics of Connectionist Knowledge Representation’,Information Sciences 70, 119–143.
MacLennan, B. J. (1993b), ‘Information Processing in the Dendritic Net’, in Karl Pribram (ed.),Rethinking neural networks: Quantum fields and biological data, Hillsdale: Lawrence Erlbaum, pp. 161–197.
MacLennan, B. J. (1993c), ‘Field Computation in the Brain’, in Karl Pribram (ed.),Rethinking neural networks: Quantum fields and biological data, Hillsdale: Lawrence Erlbaum, pp. 199–232.
MacLennan, B. J. (1993d), ‘Grounding Analog Computers’,Think 2 (June 1993), 48–51.
MacLennan, B. J. (1994), ‘Continuous Symbol Systems: The Logic of Connectionism’, in Daniel S. Levine and Manuel Aparicio IV (eds.),Neural networks for knowledge representation and inference, Hillsdale, NJ: Lawrence Erlbaum, pp. 83–120.
MacLennan, B. J. (in press), ‘Image and Symbol: Continuous Computation and the Emergence of the Discrete’, in V. Honavar and L. Uhr (eds.),Artificial intelligence and neural networks: Steps toward principled integration, Volume I: Basic paradigms; learning representational issues; and integrated architectures, New York, NY: Academic Press.
MacLennan, B. J. and Burghardt, G. M. (1993), ‘Synthetic Ethology and the Evolution of Cooperative Communication’,Adaptive Behavior 2, 161–187.
Pour-El, M. B. and Richards, I. (1979), ‘A Computable Ordinary Differential Equation Which Possesses no Computable Solution’,Annals of Mathematical Logic 17, 61–90.
Pour-El, M. B. and Richards, I. (1981), ‘The Wave Equation with Computable Initial Data Such That its Unique Solution is Not Computable’,Advances in Mathematics 39, 215–239.
Pour-El, M. B. and Richards, I. (1982), ‘Noncomputability in Models of Physical Phenomena’,International Journal of Theoretical Physics 21, 553–555.
Rogers, A. E. and Connolly, T. W. (1960),Analog Computation in Engineering Design, New York, NY: McGraw-Hill.
Searle, J. R. (1990), ‘Is the Brain a Digital Computer?Proceedings of the American Philosophical Association.
Stannett, M. (1990), ‘X-machines and the Halting Problem: Building a Super-Turing Machine’,Formal Aspects of Computing 2, 331–341.
Truitt, T. D. and Rogers, A. E. (1960),Basics of Analog Computers, New York NY: John F. Rider.
von Neumann, J. (1956), ‘The General and Logical Theory of Automata’, in J. R. Newmann (ed.),The world of mathematics, New York, NY: Simon and Schuster, Vol. 4, pp. 2066–2098.
von Neumann, J. (1958),The Computer and the Brain, New Haven and London: Yale University Press.
Wolpert, D. and MacLennan, B. J. (submitted), ‘A Computationally Universal Field Computer Which is Purely Linear’.
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MacLennan, B.J. “Words lie in our way”. Mind Mach 4, 421–437 (1994). https://doi.org/10.1007/BF00974168
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DOI: https://doi.org/10.1007/BF00974168