Advertisement

Synthese

, Volume 101, Issue 3, pp 401–431 | Cite as

Doing without representing?

  • Andy Clark
  • Josefa Toribio
Article

Abstract

Connectionism and classicism, it generally appears, have at least this much in common: both place some notion of internal representation at the heart of a scientific study of mind. In recent years, however, a much more radical view has gained increasing popularity. This view calls into question the commitment to internal representation itself. More strikingly still, this new wave of anti-representationalism is rooted not in ‘armchair’ theorizing but in practical attempts to model and understand intelligent, adaptive behavior. In this paper we first present, and then critically assess, a variety of recent anti-representationalist treatments. We suggest that so far, at least, the sceptical rhetoric outpaces both evidence and argument. Some probable causes of this premature scepticism are isolated. Nonetheless, the anti-representationalist challenge is shown to be both important and progressive insofar as it forces us to see beyond the bare representational/non-representational dichotomy and to recognize instead a rich continuum of degrees and types of representationality.

Keywords

Internal Representation Scientific Study Adaptive Behavior Radical View Rich Continuum 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abraham, R. H. and Shaw C. D.: 1992,Dynamics. The Geometry of Behavior, 2nd ed., Addison-Wesley, Redwood City, California.Google Scholar
  2. Beer, R.: 1990,Intelligence and Adaptive Behavior, Academic Press, San Diego, California.Google Scholar
  3. Beer, R.: to appear, ‘A Dynamical Systems Perspective on Environment Agent Interactions’,Artificial Intelligence.Google Scholar
  4. Beer, R. and Gallagher, J. C.: 1992, ‘Evolving Dynamical Neural Networks for Adaptive Behavior’,Adaptive Behavior 1, 91–122.Google Scholar
  5. Beer, R., Chiel, H. J., Quinn, R. D., Espenschied, K. S. and Larsson, P.: 1992, ‘A Distributed Neural Network Architecture for Hexapod Robot Locomotion’,Neural Comp. 4, 356–565.Google Scholar
  6. Brooks, R.: 1991, ‘Intelligence Without Representation’,Artificial Intelligence 47, 139–159.Google Scholar
  7. Churchland, P. M.: 1989,A Neurocomputational Perspective, MIT Press, Cambridge, Massachusetts.Google Scholar
  8. Churchland, P. S. and Sejnowski, T. J.: 1992,The Computational Brain, MIT Press, Cambridge, Massachusetts.Google Scholar
  9. Clark, A.: 1989,Microcognition, MIT Press, Cambridge, Massachusetts.Google Scholar
  10. Clark, A.: 1992, ‘The Presence of a Symbol’,Connection Science 4 193–206.Google Scholar
  11. Clark, A.: 1993,Associative Engines, MIT Press, Cambridge, Massachusetts.Google Scholar
  12. Cleeremans, A.: 1993,Mechanisms of Implicit Learning: Connectionist Models of Sequence Processing, MIT Press/Bradford Books, Cambridge, Massachusetts.Google Scholar
  13. Corbetta, M., Miezin, F. M., Dobmeyer, S., Gordon, L. S. and Peterson, S. E.: 1991, ‘Selective and Divided Attention During Visual Discriminations of Shape, Color and Speed: Functional Anatomy by Positron Emission Tomography’,The Journal of Neuroscience 11, 2383–402.Google Scholar
  14. Dennett, D.: 1981,Brainstorms, MIT Press, Cambridge, Massachusetts.Google Scholar
  15. Dreyfus, H. L.: 1991,Being-in-the-World. A Commentary on Heidegger's Being and Time. Division I, MIT Press, Cambridge, Massachusetts.Google Scholar
  16. Elman, J. L.: 1991, ‘Distributed Representations, Simple Recurrent Networks and Grammatical Structure’,Machine Learning 7, 195–225.Google Scholar
  17. Fodor, J.: 1975,The Language of Thought, Crowell, New York.Google Scholar
  18. Fodor, J.: 1987,Psychosemantics. The Problem of Meaning in the Philosophy of Mind, MIT Press, Cambridge, Massachusetts..Google Scholar
  19. Fodor, J. and Pylyshyn, Z.: 1988, ‘Connectionism and Cognitive Architecture: A Critical Analysis’,Cognition,28, 3–71.Google Scholar
  20. Giunti, M.: 1992,Computers, Dynamical Systems, Phenomena and the Mind, Ph.D thesis, Indiana University.Google Scholar
  21. Harcourt, A.: 1988, ‘All's Fair in Play and Politics’,New Scientist 108, 35–42.Google Scholar
  22. Haugeland, J.: 1991, ‘Representational Genera’, In W. Ramsey, S. Stich and D. Rumelhart (eds.),Philosophy and Connectionist Theory, Erlbaum, New Jersey, pp. 61–90.Google Scholar
  23. Heidegger, M.: 1962,Being and Time, Harper and Row, New York.Google Scholar
  24. Hinton, G., Plut, D. and Shallice, T.: 1993, ‘Simulating Brain Damage’,Scientific American 269, 76–82.Google Scholar
  25. Livingstone, M. S. and Hubel, D. H.: 1987, ‘Psychophysical Evidence for Separate Channels for the Perception of Form, Color, Movement, and Depth’,The Journal of Neuroscience 7, 3416–68.Google Scholar
  26. McClelland, J., Rumelhart, D. and the PDP Research Group: 1986,Parallel Distributed Processing: Explorations in the Micro-Structure of Cognition, Vols. I and II, MIT Press, Cambridge, Massachusetts.Google Scholar
  27. Newell, A. and Simon, H.: 1972,Human Problem Solving, Prentice-Hall, New Jersey.Google Scholar
  28. Pinker, S. and Prince, A.: 1988, ‘On Language and Connectionism. Analysis of a Parallel Distributed Processing’,Cognition 28, 73–193Google Scholar
  29. Port, R.: 1990, ‘Representation and Recognition of Temporal Patterns’,Connection Science 2, 151–76.Google Scholar
  30. Rumelhart, D. and McClelland, J.: 1986, ‘On Learning the Past Tenses of English Verbs’, in J. McClelland et al. (eds.),Parallel Distributed Processing: Exploration in the Microstructure of Cognition, Vol. 2, MIT Press, Cambridge, Massachusetts., pp. 216–71.Google Scholar
  31. Seidenberg, M. and McClelland, J.: 1989, ‘A Distributed, Developmental Model of Word Recognition and Naming’,Psychological Review 96, 523–68.Google Scholar
  32. Smolensky, P.: 1988, ‘On the Proper Treatment of Connectionism’,Behavioral and Brain Sciences 11, 1–74.Google Scholar
  33. van Essen, D., Anderson, C. and Olshausen, B.: in press, ‘Dynamic Routing Strategies in Sensory, Motor and Cognitive Processing’, in C. Koch and J. Davis (eds.),Large Scale Neuronal Theories of the Brain, MIT Press, Cambridge, Massachusetts.Google Scholar
  34. van Gelder, T. J.: 1990, ‘Compositionality: A Connectionist Variation on a Classical Theme’,Cognitive Science 14, 335–84.Google Scholar
  35. van Gelder, T. J.: 1991, ‘What is the “D” in “PDP”?. A Survey of the Concept of Distribution’, in R. W. Ramsey et al. (eds.),Philosophy and Connectionist Theory, Erlbaum, New Jersey.Google Scholar
  36. van Gelder, T. J.: in press — a, ‘Is Cognition Categorization?’, in G. V. Nakamura, R. M. Taraban and D. L. Medin (eds.),Categorization by Humans and Machines, Academic Press, San Diego, California.Google Scholar
  37. van Gelder, T. J.: in press — b, ‘What Might Cognition Be if Not Computation?’, in R. Port and T. J. van Gelder (eds.),Mind as Motion: Dynamics, Behavior and Cognition, MIT Press, Cambridge, Massachusetts.Google Scholar
  38. van Gelder, T. J. and Port, R.: 1993, ‘Beyond Symbolic: Prolegomena to aKama-Sutra of Compositionality’, in V. Honavar and L. Uhr (eds), Symbol Processing and Connectionist Models in Artificial Intelligence and Cognition: Steps Toward Integration, Academic Press, San Diego, California.Google Scholar

Copyright information

© Kluwer Academic Publishers 1994

Authors and Affiliations

  • Andy Clark
    • 1
  • Josefa Toribio
    • 1
  1. 1.Philosophy DepartmentPhilosophy/Neuroscience/Psychology Program Washington University in St. LouisSt. LouisUSA

Personalised recommendations