Hostname: page-component-76fb5796d-dfsvx Total loading time: 0 Render date: 2024-04-25T12:14:12.831Z Has data issue: false hasContentIssue false
  • English
  • Français

Toward automatic constructive learning

Published online by Cambridge University Press:  26 June 2008

Thomas R. Shultz
Thomas R. Shultz
Affiliation:
Department of Psychology, McGill University, Montreal, Quebec H3A 1B1, Canada. thomas.shultz@mcgill.cawww.psych.mcgill.ca/perpg/fac/shultz/personal/default.htm
Get access Rights & Permissions [Opens in a new window]

Abstract

Neuroconstructivist modeling can be usefully extended with algorithms that build their own topology and recruit existing knowledge, effectively constructing a hierarchy of network modules. Possible benefits include allowing abilities to emerge naturally, in a way that affords objective study, deeper insights, and more rapid progress, and provides more serious consideration of the implications of constructivism.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 31 , Issue 3 , June 2008 , pp. 344 - 345
Copyright
Copyright © Cambridge University Press 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Baillargeon, R. (1987) Object permanence in 3 1/2- and 4 1/2-month-old infants. Developmental Psychology 23:655–64.CrossRefGoogle Scholar
Baluja, S. & Fahlman, S. E. (1994) Reducing network depth in the cascade-correlation learning architecture. (Technical Report CMU-CS-94-209). School of Computer Science, Carnegie Mellon University.CrossRefGoogle Scholar
Egri, L. & Shultz, T. R. (2006) A compositional neural-network solution to prime-number testing. In: Proceedings of the Twenty-eighth Annual Conference of the Cognitive Science Society, ed. Sun, R. & Miyake, N., pp. 1263–68. Erlbaum.Google Scholar
Fahlman, S. E. & Lebiere, C. (1990) The cascade-correlation learning architecture. In: Advances in neural information processing systems 2, ed. Touretzky, D. S., pp. 524–32. Morgan Kaufmann.Google Scholar
Heit, E. (1994) Models of the effects of prior knowledge on category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition 20:1264–82.Google ScholarPubMed
Mareschal, D., Johnson, M. H., Sirois, S., Spratling, M., Thomas, M. & Westermann, G. (2007a) Neuroconstructivism, vol. I: How the brain constructs cognition. Oxford University Press.CrossRefGoogle Scholar
Mareschal, D., Plunkett, K. & Harris, P. (1999) A computational and neuropsychological account of object-oriented behaviours in infancy. Developmental Science 2:306–17.CrossRefGoogle Scholar
Pazzani, M. J. (1991) Influence of prior knowledge on concept acquisition: Experimental and computational results. Journal of Experimental Psychology: Learning, Memory, and Cognition 17:416–32.Google Scholar
Quinlan, P. T., van, derMaas, H. L. J., Jansen, B. R. J., Booij, O. & Rendell, M. (2007) Re-thinking stages of cognitive development: An appraisal of connectionist models of the balance scale task. Cognition 103:413–59.CrossRefGoogle ScholarPubMed
Shultz, T. R. (2003) Computational developmental psychology. MIT Press.Google Scholar
Shultz, T. R., Mysore, S. P. & Quartz, S. R. (2007a) Why let networks grow? In: Neuroconstructivism, Vol. 2: Perspectives and prospects, ed. Mareschal, D., Sirois, S., Westermann, G. & Johnson, M. H., pp. 65–98. Oxford University Press.CrossRefGoogle Scholar
Shultz, T. R., Rivest, F., Egri, L., Thivierge, J.-P. & Dandurand, F. (2007b) Could knowledge-based neural learning be useful in developmental robotics? The case of KBCC. International Journal of Humanoid Robotics 4:245–79.CrossRefGoogle Scholar
Shultz, T. R. & Rivest, F. (2001) Knowledge-based cascade-correlation: Using knowledge to speed learning. Connection Science 13:1–30.CrossRefGoogle Scholar
Siegler, R. S. & Klahr, D. (1982) When do children learn? The relationship between existing knowledge and the acquisition of new knowledge. In: Advances in instructional psychology, ed. Glazer, R., pp. 121–211. Erlbaum.Google Scholar
Wisniewski, E. J. (1995) Prior knowledge and functionally relevant features in concept learning. Journal of Experimental Psychology: Learning, Memory, and Cognition 21:449–68.Google ScholarPubMed