Abstract
In this paper I demonstrate that connectionism is, or can be, largely in line with most recent trends in cognitive science. The core of my argument is a distinction of several types or uses of representation in cognitive science. I demonstrate how connectionism helps in abandoning one of these types—the notion that representation is a mirror of an objectively existent world—while maintaining the important other two types—causal correlates of physical states, and internal mental states standing in for past perceptions. With this distinction the question “Does representation need reality?” can be answered. I further depict a connectionist route to embodied and situated cognitive models, as they are put forward by recent cognitive theories. After a short discussion of connectionism’s role for dynamicist theories of cognition, I conclude that much of current connectionist research is highly relevant to modern cognitive science, even if the models are apparently remote from truly embodied or situated ones.
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References
Anthony, M. & Biggs, N. (1992) Computational Learning Theory. Cambridge: Cambridge University Press.
Bickhard, M. H. & Terveen, L. (1995) Foundational Issues in Artificial Intelligence and Cognitive Science. Elsevier Science Publishers.
Brooks, R. A. (1991) Intelligence without Representation. Artificial Intelligence, Special Volume: Foundations of Artificial Intelligence 47(1–3): 139–160.
Charniak, E. & McDermott, D. (1985) Introduction to Artificial Intelligence. Reading, MA: Addison-Wesley.
Cummins, R. & Schwarz, G. (1987) Radical Connectionism. Proc. of Spindel Conf. 1987: Connectionism and the Philosophy of Mind.
Dorffner, G. (1991) Konnektionismus. Stuttgart: Teubner.
Dorffner, G. (1996) Categorization in early language acquisition—Accounts from a connectionist model. Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, TR-96-16.
Dorffner, G. (1997) Radical Connectionism—A Neural Bottom-Up Approach to AI. In: Dorffner, G. (ed.) Neural Networks and a New AI. London: International Thomson Computer Press.
Dorffner, G., Hentze, M. & Thurner, G. (1996) A Connectionist Model of Categorization and Grounded Word Learning. In: Koster, C. & Wijnen, F. (eds.) Proceedings of the Groningen Assembly on Language Acquisition (GALA’ 95).
Elman, J. L. (1990) Finding Structure in Time. Cognitive Science 2(14): 179–212.
Elman, J. L., Bates, E. A., Johnson, M. H., Karmiloff-Smith A., Parisi, D. & Plunkett, K. (1996) Rethinking Innateness. Cambridge, MA: MIT Press.
Fodor, J. A., Pylyshyn, Z. W. (1988) Connectionism and Cognitive Architecture: A Critical Analysis, Cognition 28: 3–71.
Gelder, T. van (1995) Modeling, connectionist and otherwise. In: Niklasson, L. & Boden, M. (eds.) Current Trends in Connectionism. Hillsdale, NJ: Lawrence Erlbaum, pp. 217–235.
Glasersfeld, E. von (1988) The Construction of Knowledge. Seaside: Intersystems Publications.
Harnad, S. (1990) The Symbol Grounding Problem. Physica D 42: 335–346.
Kruschke, J. K. (1993) Human Category Learning: Implications for Backpropagation Models. Connection Science 5(1), 3–36.
Lakoff, G. (1987) Women, Fire and Dangerous Things; What Categories Reveal about the Mind. Chicago: University of Chicago Press.
Markman, A. B. & Dietrich, E. (1998) In defense of representation as mediation. PSYCOLOQUY 8.9.48.
Maturana, H. R. & Varela, F. J. (1987) The Tree of Knowledge. Boston: Shambhala.
McClelland, J. L., Rumelhart, D. E. (1986) Parallel Distributed Processing, Explorations in the Microstructure of Cognition, Vol II: Psychological and Biological Models. Cambridge, MA: MIT Press.
Murre, J. M. J., Phaf, R. H. & Wolters, G. (1992) CALM: Categorizing and Learning Module. Neural Networks 5(1): 55–82.
Nehmzow, U. & Smithers, T. (1991) Mapbuilding using Self-Organising Networks in “Really Useful Robots”. In: Meyer, J.-A. & Wilson, S. W. (eds.) From Animals to Animats. Cambridge, MA: MIT Press.
Pfeifer, R., Verschure, P. (1992) Beyond Rationalism: Self-Organizing, Sensory-Based Systems. Connection Science 4(3–4): 313–326.
Plunkett, K. & Marchman, V. (1991) U-shaped learning and frequency effects in a multi-layered perceptron: Implications for child language acquisition. Cognition 38: 43–102.
Port, R. F. & Gelder, T. J. van (eds.) (1995) Mind as Motion. Cambridge, MA: MIT Press.
Rumelhart, D. E. & McClelland, J. L. (1986) Parallel Distributed Processing, Explorations in the Micro structure of Cognition, Vol 1: Foundations. Cambridge, MA: MIT Press.
Seidenberg, M. S. & McClelland, J. L. (1989) A distributed, developmental model of word recognition and naming. Psychological Review 96(4): 523–568.
Skarda, A. & Freeman, W. J. (1987) How brains make chaos in order to make sense of the world. Behavioral and Brain Sciences 2(10) 161–196.
Smolensky P. (1988) On the Proper Treatment of Connectionism. Behavioral and Brain Sciences 11(88): 1–74.
Steels, L. (1994) Emergent Functionality in Robotic Agents through On-Line Evolution. In: Brooks, R. A. & Maes, P. (eds.) Artificial Life IV. Cambridge, MA: MIT Press, pp. 8–16.
Steels, L. (1996) The Spontaneous Self-organization of an Adaptive Language. In: Muggleton, S. (ed.) Machine Intelligence 15. Oxford: Oxford Univ. Press.
Varela, F. J. (1990) Kognitionswissenschaft—tKognitionstechnik. Frankfurt/Main: Suhrkamp.
Varela, F. J., Thompson, E. & Rosch, E. (1991) The Embodied Mind. Cambridge, MA: MIT Press.
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Dorffner, G. (1999). The Connectionist Route to Embodiment and Dynamicism. In: Riegler, A., Peschl, M., von Stein, A. (eds) Understanding Representation in the Cognitive Sciences. Springer, Boston, MA. https://doi.org/10.1007/978-0-585-29605-0_3
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DOI: https://doi.org/10.1007/978-0-585-29605-0_3
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-306-46286-3
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