A Three-Layer Configural Cue Model of Category Learning Rates

  • Paul Bartos
  • Martin Le Voi
Conference paper
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)


The relative difficulty of six different category structures revealed in Shepard, Hovland, and lenkins’ 1961 classic category learning paradigm [18] is predicted using simple channel capacity calculations. This approach is subsequently used to inform the design of a three-layer connectionist network based on Gluck and Bower’s configural cue model of category learning [3]. The extra layer of nodes in this model consists of intermediate or bottleneck nodes which lie between each spatial group of nodes, representing particular cues and cue configurations, and each category label node. The weights in these nodes learn to approximate the correlation between the output of the’ space’ and the target output. The model, using two free parameters, shows a superior fit to the human data than the configural cue model and its variants evaluated by Nosofsky, Gluck, Palmeri, McKinley, and Glauthier [16] in their replication of the Shepard et al. experiment [18]. The reason for this and the applicability of the model to other category learning paradigms is discussed.


Channel Capacity Category Structure Connectionist Model Category Label Label Activation 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127, 107–140.CrossRefGoogle Scholar
  2. 2.
    Estes, W. K. (1994). Classification and Cognition. New York: Oxford University Press.CrossRefGoogle Scholar
  3. 3.
    Gluck, M. A., & Bower, G. H. (1988). Evaluating an adaptive network model of human learning. Journal of Memory and Language, 27, 166–195.CrossRefGoogle Scholar
  4. 4.
    Gluck, M. A., Glauthier, P. T., & Sutton, R. S. (1992). Adaptation of cue-specific learning rates in network models of human category learning. Proceedings of the Fourteenth Annual Meeting of the Cognitive Science Society. (pp 540–545) Hillsdale, NJ: Erlbaum.Google Scholar
  5. 5.
    Jacobs, R. A. (1997). Nature, nurture, and the development of functional specializations: A computational approach. Psychonomic Bulletin and Review, 4, 299–309.CrossRefGoogle Scholar
  6. 6.
    Jacobs, R. A., Jordan, M. I., Nowlan, S. J., & Hinton, G. E. (1991). Adaptive mixtures of local experts. Neural Computation, 3, 79–87.CrossRefGoogle Scholar
  7. 7.
    Kruschke, J. K. (1992). ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99, 22–44.CrossRefGoogle Scholar
  8. 8.
    Kruschke, J. K. (1993). Human category learning: implications for backpropagation models. Connection Science, 5, 3–36.CrossRefGoogle Scholar
  9. 9.
    Kruschke, J. K. (1996). Dimensional relevance shifts in category learning. Connection Science, 8, 201–223.CrossRefGoogle Scholar
  10. 10.
    Kruschke, J. K., & Blair, N. J. (In Press). Blocking and backward blocking involve learned inattention. Psychonomic Bulletin&Review.Google Scholar
  11. 11.
    Luce, R. D. (1963). Detection and recognition. In R. D. Luce, R. R. Bush, & E. Galanter (Eds.), Handbook of mathematical psychology. (pp. 103–189). New York: Wiley.Google Scholar
  12. 12.
    Mackintosh, N. J. (1975). A Theory of attention: Variations in the associability of stimuli with reinforcement. Psychological Review, 82, 276–298.CrossRefGoogle Scholar
  13. 13.
    Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207–238.CrossRefGoogle Scholar
  14. 14.
    Nosofsky, R. M. (1984). Choice, similarity, and the context theory of classification. Journal of Experimental Psychology: Learning, Memory and Cognition, 10, 104–114.CrossRefGoogle Scholar
  15. 15.
    Nosofsky, R. M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39–57.CrossRefGoogle Scholar
  16. 16.
    Nosofsky, R. M., Gluck, M. A., Palmeri, T. J., McKinley, S. C., & Glauthier, P. (1994). Comparing models of rule-based classification leaming: A replication and extension of Shepard, Hovland, and Jenkins (1961). Memory and Cognition, 22, 352–369.CrossRefGoogle Scholar
  17. 17.
    Nosofsky, R. M., & Palmeri, T. J. (1996). Learning to classify integral-dimension stimuli. Psychonomic Bulletin and Review, 3, 222–226.CrossRefGoogle Scholar
  18. 18.
    Shepard, R. N., Hovland, C. J., & Jenkins, H. M. (1961). Learning and memorization of classifications. Psychological Monographs, 75, 13.Google Scholar

Copyright information

© Springer-Verlag London 2001

Authors and Affiliations

  • Paul Bartos
  • Martin Le Voi

There are no affiliations available

Personalised recommendations