Psychonomic Bulletin & Review

, Volume 9, Issue 4, pp 829–835 | Cite as

Comparing supervised and unsupervised category learning

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Abstract

Two unsupervised learning modes (incidental and intentional unsupervised learning) and their relation to supervised classification learning are examined. The approach allows for direct comparisons of unsupervised learning data with the Shepard, Hovland, and Jenkins (1961) seminal studies in supervised classification learning. Unlike supervised classification learning, unsupervised learning (especially under incidental conditions) favors linear category structures over compact nonlinear category structures. Unsupervised learning is shown to be multifaceted in that performance varies with task conditions. In comparison with incidental unsupervised learning, intentional unsupervised learning is more rule like, but is no more accurate. The acquisition and application of knowledge is also more laborious under intentional unsupervised learning.

References

  1. Anderson, J. (1991). The adaptive nature of human categorization.Psychological Review,98, 409–429.CrossRefGoogle Scholar
  2. Barsalou, L. W. (1990). On the indistinguishability of exemplar memory and abstraction in category representation. In T. K. Srull & R. S. Wyer (Eds.),Content and process specificity in the effects of prior experiences: Advances in social cognition (pp. 61–88). Hillsdale, NJ: Erlbaum.Google Scholar
  3. Berry, D. C., &Dienes, Z. (1993).Implicit learning: Theoretical and empirical issues. Hillsdale, NJ: Erlbaum.Google Scholar
  4. Billman, D., &Knutson, J. (1996). Unsupervised concept learning and value systematicity: A complex whole aids learning the parts.Journal of Experimental Psychology: Learning, Memory, & Cognition,22, 458–475.CrossRefGoogle Scholar
  5. Cohen, N. J., &Eichenbaum, H. (1993).Memory, amnesia, and the hippocampal system. Cambridge, MA: MIT Press.Google Scholar
  6. Goldstone, R. L. (1996). Isolated and interrelated concepts.Memory & Cognition,24, 608–628.CrossRefGoogle Scholar
  7. Goldstone, R. L., &Kruschke, J. K. (1994). Are rules and instances subserved by separate systems?Behavioral & Brain Sciences,17, 405.Google Scholar
  8. Hayes, N., &Broadbent, D. E. (1988). Two modes of learning for interactive tasks.Cognition,28, 249–276.PubMedCrossRefGoogle Scholar
  9. Hock, H. S., Malcus, L., &Hasher, L. (1986). Frequency discrimination: Assesing global and elemental letter units in memory.Journal of Experimental Psychology: Learning, Memory, & Cognition,12, 232–240.CrossRefGoogle Scholar
  10. Johnston, W. A., Hawley, K. J., &Elliot, J. M. (1991). Contribution of perceptual fluency to recognition judgments.Journal of Experimental Psychology: Learning, Memory, & Cognition,17, 210–223.CrossRefGoogle Scholar
  11. Kellogg, R. T. (1982). When can we introspect accurately about mental processes?Memory & Cognition,10, 141–144.CrossRefGoogle Scholar
  12. Knowlton, B. J., &Squire, L. R. (1994). The information acquired during artificial grammar learning.Journal of Experimental Psychology: Learning, Memory, & Cognition,20, 79–91.CrossRefGoogle Scholar
  13. Kruschke, J. K. (1992). ALCOVE: An exemplar-based connectionist model of category learning.Psychological Review,99, 22–44.PubMedCrossRefGoogle Scholar
  14. Love, B. C. (2001). Three deadly sins of category learning modelers.Behavioral & Brain Sciences,24, 687–688.CrossRefGoogle Scholar
  15. Love, B. C. (in press). The multifaceted nature of unsupervised category learning.Psychonomic Bulletin & Review.Google Scholar
  16. Love, B. C., Markman, A. B., &Yamauchi, T. (2000). Modeling classification and inference learning. InProceedings of the Fifteenth National Conference on Artificial Intelligence (pp. 136–141). Cambridge, MA: MIT Press.Google Scholar
  17. Love, B. C., &Medin, D. L. (1998). SUSTAIN: A model of human category learning. InProceedings of the Fifteenth National Conference on Artificial Intelligence (pp. 671–676). Cambridge, MA: MIT Press.Google Scholar
  18. Markman, A. B., &Makin, V. S. (1998). Referential communication and category acquisition.Journal of Experimental Psychology: General,127, 331–254.CrossRefGoogle Scholar
  19. Medin, D. L., &Schwanenflugel, P. J. (1981). Linear separability in classification learning.Journal of Experimental Psychology: Human Learning & Memory,7, 355–368.CrossRefGoogle Scholar
  20. Medin, D. L., Wattenmaker, W. D., &Hampson, S. E. (1987). Family resemblance, conceptual cohesiveness, and category construction.Cognitive Psychology,19, 242–279.PubMedCrossRefGoogle Scholar
  21. Nosofsky, R. M. (1986). Attention, similarity, and the identification— categorization relationship.Journal of Experimental Psychology: General,115, 39–57.CrossRefGoogle Scholar
  22. Nosofsky, R. M. (1988). Exemplar-based accounts of relations between classification, recognition, and typicality.Journal of Experimental Psychology: Learning, Memory, & Cognition,14, 700–708.CrossRefGoogle Scholar
  23. Nosofsky, R. M., Gluck, M. A., Palmeri, T. J., McKinley, S. C., &Glauthier, P. (1994). Comparing models of rule-based classification learning: A replication and extension of Shepard, Hovland, and Jenkins (1961).Memory & Cognition,22, 352–369.CrossRefGoogle Scholar
  24. Nosofsky, R. M., &Palmeri, T. J. (1996). Learning to classify integraldimension stimuli.Psychonomic Bulletin & Review,3, 222–226.CrossRefGoogle Scholar
  25. Nosofsky, R. M., Palmeri, T. J., &McKinley, S. C. (1994). Rule-plus-exception model of classification learning.Psychological Review,101, 53–79.PubMedCrossRefGoogle Scholar
  26. Roediger, H. L., III,Weldon, M. S., &Challis, B. H. (1989). Explaining dissociations between implicit and explicit measures of retention: A processing account. In H. L. Roediger III & F. I. M. Craik (Eds.),Varieties of memory and consciousness: Essays in honour of Endel Tulving (pp. 3–41). Hillsdale, NJ: Erlbaum.Google Scholar
  27. Rosch, E., Mervis, C. B., Gray, W. D., Johnson, D. M., &Boyes-Braem, P. (1976). Basic objects in natural categories.Cognitive Psychology,8, 382–439.CrossRefGoogle Scholar
  28. Ross, B. H. (1996). Category representations and the effects of interacting with instances.Journal of Experimental Psychology: Learning, Memory, & Cognition,22, 1249–1265.CrossRefGoogle Scholar
  29. Schank, R. C., Collins, G. C., &Hunter, L. E. (1986). Transcending inductive category formation in learning.Behavioral & Brain Sciences,9, 639–686.CrossRefGoogle Scholar
  30. Seger, C. A. (1994). Implicit learning.Psychological Bulletin,115, 163–196.PubMedCrossRefGoogle Scholar
  31. Shepard, R. N., Hovland, C. L., & Jenkins, H. M. (1961). Learning and memorization of classifications.Psychological Monographs,75 (13, Whole No. 517).Google Scholar
  32. Sloman, S. A. (1996). The empirical case for two systems of reasoning.Psychological Bulletin,119, 3–22.CrossRefGoogle Scholar
  33. Squire, L. R. (1992). Memory and the hippocampus: A synthesis from findings with rats, monkeys, and humans.Psychological Review,99, 195–231.PubMedCrossRefGoogle Scholar
  34. Wattenmaker, W. D. (1991). Learning modes, feature correlations, and memory-based categorization.Journal of Experimental Psychology: Learning, Memory, & Cognition,17, 908–923.CrossRefGoogle Scholar
  35. Whittlesea, B. W. A., &Dorken, M. D. (1993). Incidentally, things in general are particularly determined: An episodic-processing account of implicit learning.Journal of Experimental Psychology: General,23, 227–248.CrossRefGoogle Scholar
  36. Wittgenstein, L. (1953).Philosophical investigations (G. E. M. Anscombe, Trans.). Oxford: Blackwell.Google Scholar
  37. Yamauchi, T., Love, B. C., &Markman, A. B. (2002). Learning nonlinearly separable categories by inference and classification.Journal of Experimental Psychology: Learning, Memory, & Cognition,28, 585–593.CrossRefGoogle Scholar
  38. Yamauchi, T., &Markman, A. B. (1998). Category learning by inference and classification.Journal of Memory & Language,39, 124–149.CrossRefGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2002

Authors and Affiliations

  1. 1.Department of PsychologyUniversity of TexasAustin

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