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.


Cognitive Science Cognitive Model Cognitive Agent Connectionist Model Grammatical Category 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Anthony, M. & Biggs, N. (1992) Computational Learning Theory. Cambridge: Cambridge University Press.Google Scholar
  2. Bickhard, M. H. & Terveen, L. (1995) Foundational Issues in Artificial Intelligence and Cognitive Science. Elsevier Science Publishers.Google Scholar
  3. Brooks, R. A. (1991) Intelligence without Representation. Artificial Intelligence, Special Volume: Foundations of Artificial Intelligence 47(1–3): 139–160.Google Scholar
  4. Charniak, E. & McDermott, D. (1985) Introduction to Artificial Intelligence. Reading, MA: Addison-Wesley.Google Scholar
  5. Cummins, R. & Schwarz, G. (1987) Radical Connectionism. Proc. of Spindel Conf. 1987: Connectionism and the Philosophy of Mind.Google Scholar
  6. Dorffner, G. (1991) Konnektionismus. Stuttgart: Teubner.Google Scholar
  7. Dorffner, G. (1996) Categorization in early language acquisition—Accounts from a connectionist model. Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, TR-96-16.Google Scholar
  8. 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.Google Scholar
  9. 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).Google Scholar
  10. Elman, J. L. (1990) Finding Structure in Time. Cognitive Science 2(14): 179–212.CrossRefGoogle Scholar
  11. Elman, J. L., Bates, E. A., Johnson, M. H., Karmiloff-Smith A., Parisi, D. & Plunkett, K. (1996) Rethinking Innateness. Cambridge, MA: MIT Press.Google Scholar
  12. Fodor, J. A., Pylyshyn, Z. W. (1988) Connectionism and Cognitive Architecture: A Critical Analysis, Cognition 28: 3–71.PubMedCrossRefGoogle Scholar
  13. 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.Google Scholar
  14. Glasersfeld, E. von (1988) The Construction of Knowledge. Seaside: Intersystems Publications.Google Scholar
  15. Harnad, S. (1990) The Symbol Grounding Problem. Physica D 42: 335–346.CrossRefGoogle Scholar
  16. Kruschke, J. K. (1993) Human Category Learning: Implications for Backpropagation Models. Connection Science 5(1), 3–36.CrossRefGoogle Scholar
  17. Lakoff, G. (1987) Women, Fire and Dangerous Things; What Categories Reveal about the Mind. Chicago: University of Chicago Press.Google Scholar
  18. Markman, A. B. & Dietrich, E. (1998) In defense of representation as mediation. PSYCOLOQUY 8.9.48.Google Scholar
  19. Maturana, H. R. & Varela, F. J. (1987) The Tree of Knowledge. Boston: Shambhala.Google Scholar
  20. 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.Google Scholar
  21. Murre, J. M. J., Phaf, R. H. & Wolters, G. (1992) CALM: Categorizing and Learning Module. Neural Networks 5(1): 55–82.CrossRefGoogle Scholar
  22. 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.Google Scholar
  23. Pfeifer, R., Verschure, P. (1992) Beyond Rationalism: Self-Organizing, Sensory-Based Systems. Connection Science 4(3–4): 313–326.CrossRefGoogle Scholar
  24. 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.PubMedCrossRefGoogle Scholar
  25. Port, R. F. & Gelder, T. J. van (eds.) (1995) Mind as Motion. Cambridge, MA: MIT Press.Google Scholar
  26. Rumelhart, D. E. & McClelland, J. L. (1986) Parallel Distributed Processing, Explorations in the Micro structure of Cognition, Vol 1: Foundations. Cambridge, MA: MIT Press.Google Scholar
  27. Seidenberg, M. S. & McClelland, J. L. (1989) A distributed, developmental model of word recognition and naming. Psychological Review 96(4): 523–568.PubMedCrossRefGoogle Scholar
  28. 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.Google Scholar
  29. Smolensky P. (1988) On the Proper Treatment of Connectionism. Behavioral and Brain Sciences 11(88): 1–74.CrossRefGoogle Scholar
  30. 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.Google Scholar
  31. Steels, L. (1996) The Spontaneous Self-organization of an Adaptive Language. In: Muggleton, S. (ed.) Machine Intelligence 15. Oxford: Oxford Univ. Press.Google Scholar
  32. Varela, F. J. (1990) Kognitionswissenschaft—tKognitionstechnik. Frankfurt/Main: Suhrkamp.Google Scholar
  33. Varela, F. J., Thompson, E. & Rosch, E. (1991) The Embodied Mind. Cambridge, MA: MIT Press.Google Scholar

Copyright information

© Kluwer Academic/Plenum Publishers 1999

Authors and Affiliations

  • Georg Dorffner
    • 1
  1. 1.Austrian Research Institute for Artificial Intelligence, and Dept. of Medical Cybernetics and Artificial IntelligenceUniversity of ViennaAustria

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