CAN CONNECTIONIST THEORIES ILLUMINATE COGNITION?
Comment on Green on Connectionist-Explanation

Abstract

In this commentary I attempt to show in what sense we can speak of connectionist theory as illuminating cognition. It is usually argued that distributed connectionist networks do not explain brain function because they do not use the appropriate explanatory vocabulary of propositional attitudes, and because their basic terms, being theoretical, do not refer to anything. There is a level of analysis, however, at which the propositional attitude vocabulary can be reconstructed and used to explain the performance of networks; and the basic terms of networks are not theoretical but observable entities that purport to refer to terms used to describe the brain.

Keywords

References

• Achinstein, P. (1968). Concepts of Science. Baltimore, MD: The Johns Hopkins University Press.
• Braithwaite, R. B. (1953). Scientific explanation. Cambridge: Cambridge University. Press.
• Cartwright, N. (1983). How the Laws of Physics Lie. Oxford: Oxford Un. Press.
• Churchland, P. (1989). A Neurocomputational Perspective: The Nature of Mind and the Structure of Science. Cambridge, MA: The MIT Press.
• Clark, A. (1991). Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing. Cambridge, MA: The MIT Press.
• Clark, A. (1993). Associative Engines: Connectionism, Concepts, and Representational Change. Cambridge, MA: The MIT Press.
• Clark, A. (1995). Connectionist minds. In: Debates on Psychological Explanation, eds. MacDonald, C., and MacDonald, G. , Oxford: Blackwell, 339-356.
• Clark, A. (1997). From text to process: Connectionism's contribution to the future of cognitive science. In: The Future of Cognitive Revolution, eds. Johnson, D. M., and Erneling, C. E., Oxford: Oxford University Press, 169-186.
• Crick, F. H. C., and Asanuma, C. (1986). Certain aspects of the anatomy and physiology of the cerebral cortex. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol. 2: Psychological and Biological Models, eds., McClelland, J. L., Rumelhart, D. E., and the PDP Research Group. Cambridge, MA: The MIT Press, 333-371.
• Durbin, R., and Rumelhart, D. E. (1989). Product Units: A computationally powerful and biologically plausible extension to backpropagation networks. Neural Computation, 1: 133-142.
• Elman, J. L., Bates E. A., Johnson, M. H., Karmiloff-Smith, A., Parisi, D., and Plunkett K. (1996). Rethinking Innateness: A Connectionist Perspective on Development. Cambridge, MA: MIT Press.
• Feldman, J., and Ballard, D. (1982). Connectionist models and their properties. Cognitive Science, 6: 205-254.
• Fine, A. (1984). And not anti-realism either. Nous, 18: 51-66.
• Fodor, J. A., and Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28: 3-71.
• Fodor, J. A., and McLaughin B. P. (1995). Connectionism and the problem of systematicity: Why Smolensky's solution doesn't work. In: Debates on Psychological Explanation, eds. MacDonald, C., and MacDonald, G., Oxford: Blackwell, 199-222.
• Green, C. D. (1998). Are connectionist models theories of cognition? PSYCOLOQUY 9(4) ftp://ftp.princeton.edu/pub/harnad/Psycoloquy/1998.volume.9/ psyc.98.9.04.connectionist-explanation.1.green
• Hacking, I. (1983). Representing and Intervening. Cambridge: Cambridge University Press.
• Hanson, S. J. (1990). Meiosis networks. In: Advances in neural- information processing systems II, ed., Touretzky, D. S., San Mateo: Morgan Kaufman, 533-542.
• Mel, B. W. (1990). The sigma-pi column: A model for associative learning in cerebral neocortex. Pasadena, CA: California Institute of Technology, Computational and Neural Systems Program.
• Milner, P. M. (1957). The cell assembly: Mark II. Psychological Review, 64: 242-252.
• Murre, J.M.J. (1992) Precis of: Learning and categorization in modular neural networks. PSYCOLOQUY 3(68) ftp://ftp.princeton.edu/pub/harnad/Psycoloquy/1992.volume.3/ psycoloquy.92.3.68.categorization.1.murre
• Nersessian, N. J. (1984). Faraday to Einstein: Constructing Meaning in Scientific Theories. Dordrecht: Kluwer Academic Publishers.
• Plunkett, K., and Marchman, V. (1991). U-shaped learning and frequency effects in a multilayered perceptron: Implications for child language acquisition. Cognition, 38: 43-102.
• Poggio, T., and Girosi, F. (1990). Regularizing algorithms for learning that are equivalent to multilayer networks. Science, 247: 978-982.
• Pollack, J. B. (1990). Recursive distributed representations. In: Connectionist Symbol Processing, ed., Hinton, G, Cambridge, MA: The MIT Un. Press, 77-105.
• Ramsey, W., Stich, S. P., and Garon, J. (1991). Connectionism, eliminativism and the future of folk psychology. In: Philosophy and Connectionist Theory, eds., Ramsey, W., Stich, S. P., and Rumelhart, D. E., Hillsdale, NJ: Erlbaum , 199-228.
• Rumelhart, D. E., Hinton, G. E. and McClelland, J. L. (1986). A general framework for parallel distributed processing. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol. 1: Foundations, eds., J. L., Rumelhart, D. E., McClelland, and the PDP Research Group. Cambridge, MA: The MIT Press, 45-76.
• Sejnowski, T., and Rosenberg, C. (1986). NETtalk: A parallel network that learns to read aloud. Johns Hopkins University, Technical Report JHU/EEC-86/01.
• Shapere, D. (1982). The concept of observation in science and philosophy. Philosophy of Science, 49: 231-267.
• Shultz, T. R, and Schmidt, W. C. (1991). A Cascade-Correlation model of balance scale phenomena. Proceedings of the Thirteenth Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Lawrence Erlbaum, 635-40.
• Shultz, T. R, Schmidt, W. C., Buckingham, D., and Mareschal, D. (1995). Modeling cognitive development with a generative connectionist algorithm. In: Developing Cognitive Competence: New Approaches to Process Modeling, eds., Simon T. J., and Halford, G. S., Hillsdale, NJ: Lawrence Erlbaum, 205-262.
• Smolensky. P (1995). On the projectible predicates of connectionist psychology: A case for belief. In: Connectionism: Debates on Psychological Explanation, eds. MacDonald, C., and MacDonald, G. , Oxford: Blackwell, 357-394.
• Spector, M. (1965). Models and theories. The British Journal for the Philosophy of Science, reprinted in: Readings in the Philosophy of Science, eds. Brody, B. A., and Grandy, R. E., Englewood Cliffs, NJ: Prentice Hall., 1989, 44-57.
• Van Gelder, T. (1990). Compositionality: A connectionist variation of a classical theme. Cognition, 14: 355-384.