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Brain and Mind

, Volume 4, Issue 2, pp 129–149 | Cite as

Interpreting the Internal Structure of a Connectionist Model of the Balance Scale Task

  • Michael R. W. Dawson
  • Corinne Zimmerman
Article

Abstract

One new tradition that has emerged from early research on autonomous robots is embodied cognitive science. This paper describes the relationship between embodied cognitive science and a related tradition, synthetic psychology. It is argued that while both are synthetic, embodied cognitive science is antirepresentational while synthetic psychology still appeals to representations. It is further argued that modern connectionism offers a medium for conducting synthetic psychology, provided that researchers analyze the internal representations that their networks develop. The paper then provides a detailed example of the synthetic approach by showing how the construction (and subsequent analysis) of a connectionist network can be used to contribute to a theory of how humans solve Piaget's classic balance scale task.

balance scale task cognitive informatics connectionism embodied cognitive science synthetic psychology 

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References

  1. Anderson, J. R. and Bower, G. H., 1973: Human Associative Memory, Erlbaum, Hillsdale, NJ.Google Scholar
  2. Ashby, W. R. (1960). Design for a Brain, 2nd edn., Wiley, New York.Google Scholar
  3. Bever, T. G., Fodor, J. A. and Garrett, M., 1968: A formal limitation of associationism, in T. R. Dixon and D. L. Horton (eds.), Verbal Behavior and General Behavior Theory, Prentice-Hall, Englewood Cliffs, NJ, pp. 582-585.Google Scholar
  4. Braitenberg, V., 1984: Vehicles: Explorations in Synthetic Psychology, MIT Press, Cambridge, MA.Google Scholar
  5. Brooks, R. A., 1999: Cambrian Intelligence: The Early History of the New AI, MIT Press, Cambridge, MA.Google Scholar
  6. Chletsos, P. N., de Lisi, R., Turner, G. and McGillicuddy-de Lisi, A.V., 1989: Cognitive assessment of proportional reasoning strategies, J.Res.Dev.Edu. 22, 18-27.Google Scholar
  7. Chomsky, N., 1959: A review of B.F. Skinner's Verbal Behavior, Language 35, 26-58.Google Scholar
  8. Chomsky, N., 1965: Aspects of the Theory of Syntax, MIT Press, Cambridge, MA.Google Scholar
  9. Chomsky, N., 1995: The Minimalist Program, MIT Press, Cambridge, MA.Google Scholar
  10. Chomsky, N. and Halle, M., 1991: The Sound Pattern of English, MIT Press, Cambridge, MA.Google Scholar
  11. Dawson, M. R. W., 1998: Understanding Cognitive Science, Blackwell, Oxford.Google Scholar
  12. Dawson, M. R.W., 2003: Minds and Machines: Connectionism and Psychological Modeling, Blackwell, Oxford.Google Scholar
  13. Dawson, M. R.W., Boechler, P.M. and Valsangkar-Smyth, M, 2000: Representing space in a PDP network: Coarse allocentric coding can mediate metric and nonmetric spatial judgments, Spat.Cogn.Comput. 2, 181-218.Google Scholar
  14. Dawson, M. R. W., Medler, D. A. and Berkeley, I. S. N., 1997: PDP networks can provide models that are not mere implementations of classical theories, Philos.Psychol. 10, 25-40.Google Scholar
  15. Dawson, M. R. W. and Piercey, C. D., 2001: On the subsymbolic nature of a PDP architecture that uses a nonmonotonic activation function, Minds Machines 11, 197-218.Google Scholar
  16. Dawson, M. R. W. and Schopflocher, D. P., 1992: Modifying the generalized delta rule to train networks of nonmonotonic processors for pattern classification, Connect.Sci. 4, 19-31.Google Scholar
  17. diSessa, A. A., 1993: Toward an epistemology of physics, Cogn.Inst. 10, 105-225.Google Scholar
  18. Fahlman, S. E. and Lebiere, C., 1990: The Cascade-Correlation Learning Algorithm (CMU-CS-90-100), School of Computer Science, Carnegie Mellon University, Pittsburgh.Google Scholar
  19. Ferretti, R. P. and Butterfield, E. C., 1986: Are children's rule-assessment classifications invariant across instances of problem types? Child Dev. 57, 1419-1428.Google Scholar
  20. Ferretti, R. P., Butterfield, E. C., Cahn, A. and Kerkman, D., 1985: The classification of children's knowledge: Development on the balance scale and inclined-plane tasks. J.Exp.Child Psychol. 39, 131-160.Google Scholar
  21. Flavell, J. H., 1985: Cognitive Development, Prentice-Hall, Englewood Cliffs, NJ.Google Scholar
  22. Fodor, J. A., 1975: The Language of Thought, Harvard University Press, Cambridge, MA.Google Scholar
  23. Grey Walter, W., 1950: An imitation of life, Sci.Am. 182(5), 42-45.Google Scholar
  24. Grey Walter, W., 1951: A machine that learns, Sci.Am. 184(8), 60-63.Google Scholar
  25. Grey Walter, W., 1963: The Living Brain, W.W. Norton & Co., New York.Google Scholar
  26. Hinton, G. E., McClelland, J. and Rumelhart, D., 1986: Distributed representations, in D. Rumelhart and J. McClelland (eds.), Parallel Distributed Processing, Vol. 1, MIT Press, Cambridge, MA. pp. 77-109.Google Scholar
  27. Inhelder, B. and Piaget, J., 1958: The Growth of Logical Thinking From Childhood to Adolescence, Basic Books, New York.Google Scholar
  28. Jackendoff, R., 1992: Languages of the Mind, MIT Press, Cambridge, MA.Google Scholar
  29. Jansen, B. R. J. and van der Mass, H. L. J., 1997: Statistical test of the rule assessment methodology by latent class analysis, Dev.Rev., 17, 321-357.Google Scholar
  30. Klahr, D. and Siegler, R. S., 1978: The representation of children's knowledge, in H. W. Reese and L. P. Lipsitt (eds.), Advances in Child Development and Behavior, Vol. 12, Academic Press, New York, pp. 61-116.Google Scholar
  31. Kliman, M., 1987: Children's learning about the balance scale. Inst.Sci. 15, 307-340.Google Scholar
  32. Laviree, S., Normandeau, S., Roulin, J. L. and Longeot, F., 1987: Siegler's balance scale: A critical analysis of the rule-assessment approach, L'Anee Psychol. 87, 509-534.Google Scholar
  33. Leighton, J. P. and Dawson, M. R. W., 2001: A parallel distributed processing model of Wason's selection task, Cogn.Syst.Res. 2, 207-231.Google Scholar
  34. Mareschal, D. and Shultz, T. R., 1996: Generative connectionist networks and constructivist cognitive development, Cogn.Dev. 11, 571-603.Google Scholar
  35. Marr, D., 1982: Vision, W.H. Freeman, San Francisco.Google Scholar
  36. McClelland, J., 1989: Parallel distributed processing: Implications for cognition and development, in R. G. M. Morris (ed.), Parallel Distributed Processing: Implications for Psychology and Neurobiology, Oxford University Press, Oxford, pp. 8-45.Google Scholar
  37. McCloskey, M., 1991: Networks and theories: The place of connectionism in cognitive science, Psychol.Sci. 2, 387-395.Google Scholar
  38. Minsky, M. and Papert, S., 1988: Perceptrons, 3rd edn., MIT Press, Cambridge, MA.Google Scholar
  39. Moravec, H., 1999: Robot, Oxford University Press, New York.Google Scholar
  40. Newell, A., 1990: Unified Theories of Cognition, Harvard University Press, Cambridge, MA.Google Scholar
  41. Normandeau, S., Laviree, S., Roulin, J. L. and Longeot, F., 1989: The balance-scale dilemma: Either the subject or the experimenter muddles through, J.Genet.Psychol. 150, 237-249.Google Scholar
  42. Paivio, A., 1969: Mental imagery in associative learning and memory, Psychol.Rev. 76, 241-263.Google Scholar
  43. Paivio, A., 1971: Imagery and Verbal Processes, Holt, Rinehart and Winston, New York.Google Scholar
  44. Pfeifer, R. and Scheier, C., 1999: Understanding Intelligence, MIT Press, Cambridge, MA.Google Scholar
  45. Piaget, P. and Inhelder, B., 1969: The Psychology of the Child, Routledge and Kegan Paul, London.Google Scholar
  46. Pylyshyn, Z. W., 1984: Computation and Cognition, MIT Press, Cambridge, MA.Google Scholar
  47. Quinlan, J. R., 1993: C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, CA.Google Scholar
  48. Rumelhart, D. E., Hinton, G. E. and Williams, R. J., 1986: Learning representations by back-propagating errors. Nature 323, 533-536.Google Scholar
  49. Sage, S. and Langley, P., 1983: Modeling cognitive development on the balance scale task, in Proc Eighth International Joint Conference on Artificial Intelligence, Vol. 1, 94-96.Google Scholar
  50. Schmidt, W. C. and Ling, C. X., 1996: A decision-tree model of balance scale development, Machine Learn. 24, 203-230.Google Scholar
  51. Schmidt, W. C. and Shultz, T. R., 1992: An investigation of balance scale success in Paper, in Presented at the Fourteenth Annual Conference of the Cognitive Science Society.Google Scholar
  52. Seidenberg, M., 1993: Connectionist models and cognitive theory. Psychol.Sci. 4, 228-235.Google Scholar
  53. Shultz, T. R., Mareschal, D. and Schmidt, W. C., 1994: Modeling cognitive development on balance scale phenomena, Machine Learn. 16, 57-86.Google Scholar
  54. Shultz, T. R. and Schmidt, W. C., 1992: A cascade-correlation model of balance scale phenomena, in Paper, Presented at the Thirteenth Annual Conference of the Cognitive Science Society.Google Scholar
  55. Shultz, T. R., Schmidt, W. C., Buckingham, D. and Mareschal, D., 1995: Modeling cognitive development with a generative connectionist algorithm, in T. J. Simon and G. S. Halford (eds.), Developing Cognitive Competence: New Approaches to Process Modeling, Erlbaum Hillsdale, NJ, pp. 205-261.Google Scholar
  56. Siegler, R. S., 1976: Three aspects of cognitive development, Cogn.Psychol. 8, 481-520.Google Scholar
  57. Siegler, R. S., 1978: The origins of scientific reasoning, in R. S. Siegler (ed.), Children's Thinking: What Develops? Erlbaum, Hillsdale, NJ.Google Scholar
  58. Siegler, R. S., 1981: Developmental sequences within and between concepts. Monogr.Soc.Res.Child Dev. 46 (Whole No. 189).Google Scholar
  59. Skinner, B. F., 1957: Verbal Behavior, Appleton-Century-Crofts, New York.Google Scholar
  60. Smolensky P., 1988: On the proper treatment of connectionism, Behav.Brain Sci. 11, 1-74.Google Scholar
  61. van Maanen, L., Been, P. and Sijtsma, K., 1989: The linear logistic test model and heterogeneity of cognitive strategies, in E. E. Roskam (ed.), Mathematical Psychology in Progress, Springer-Verlag, Berlin, (pp. 267-287).Google Scholar
  62. Watson, J. B., 1913: Psychology as the behaviorist views it. Psychol.Rev. 20, 158-177.Google Scholar
  63. Wilkening, F. and Anderson, N. H., 1982: Comparison of two rule-assessment methodologies for studying cognitive development and knowledge structures, Psychol.Bull. 92, 215-237.Google Scholar
  64. Zimmerman, C. L., 1999: A network interpretation approach to the balance scale task, Unpublished PhD, Thesis, University of Alberta, Edmonton, Canada.Google Scholar

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Michael R. W. Dawson
    • 1
  • Corinne Zimmerman
    • 2
  1. 1.Biological Computation ProjectUniversity of AlbertaEdmonton, AlbertaCanada
  2. 2.Department of PsychologyIllinois State UniversityNormalU.S.A.

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