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REFERENCES

  • Ahn, W.K., & Medin, D.L. (1992). A two-stage model of category construction. Cognitive Science, 16, 81121.
  • Anderson, J.R. (1990). The adaptive character of thought. Hillsdale , NJ : Erlbaum.
  • Asch, S. (1946). Forming impressions of personality. Journal of Abnormal and Social Psychology, 41, 258290.
  • Brewer, W.F., & Lambert, B.L. (September, 1991). The theory-ladedness of observation; Evidence from cognitive psychology. Paper presented at the University of Minnesota, Learning Center Reunion, St. Paul , MN .
  • Bruner, J.S., Goodnow, J.J., & Austion, G.A. (1956). A study of thinking. New York : Wiley.
  • Bugelski, B.R., & Alampay, D.A. (1961). The role of frequency in developing perceptual sets. Canadian Journal of Psychology, 15, 205211.
  • Busemeyer, J.R., & Myung, J. (1988). A new method for investigating prototype learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 311.
  • Carey, S. (1985). Conceptual change in childhood. Cambridge , MA : MIT Press.
  • Chapman, L.J., & Chapman, J.P. (1967). The genesis of popular but erroneous psychodiagnostic observations. Journal of Abnormal Psychology, 72, 193204.
  • Cheeseman, P., Kelly, J., Self, M., Stutz, J., Taylor, W., & Freeman, D. (1988). Autoclass: A Bayesian classification system. Proceedings of the Fifth International Workshop on Machine Learning (pp. 5464). San Mateo , CA : Morgan Kaufmann.
  • Collins, A., & Loftus, E.F. (1975). A spreading activation theory of semantic processing. Psychological Review, 82, 407428.
  • Dejong, G. (1988). An introduction to explanation-based learning. In H.E. Shrobe (Ed.), Exploring artificial intelligence. San Mateo , CA : Morgan Kaufmann.
  • Dejong, G., & Mooney, R.J. (1986). Explanation-based learning: An alternative view. Machine Learning, 1, 145176.
  • Dietterich, T.G., London, B., Clarkson, K., & Dromey, G. (1982). Learning and inductive inference. In P.R. Cohen & E.A. Feigenbaum (Eds.), The handbook of artificial intelligence. Los Altos , CA : Kaufman.
  • Ellman, T. (1989). Explanation-based learning: A survey of programs and perspectives. Computing Surveys, 21, 163221.
  • Estes, W.K., Campbell, J.A., Hatsopoulos, N., & Hurwitz, J. (1989). Base-rate effects in category learning: A comparison of parallel network and memory storage-retrieval models. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15, 556571.
  • Fisher, D.H. (1987). Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2, 139172.
  • Fisher, G. (1968). Ambiguity in form: Old and new. Perception and Psychophysics, 4, 189192.
  • Flann, N.S., & Dietterich, T.G. (1989). A study of explanation-based methods for inductive learning. Machine Learning, 4, 187226.
  • Gentner, D. (1989). The mechanisms of analogical learning. In S. Vosniadou & A. Ortony (Eds.), Similarity, analogy, and thought. New York : Cambridge University Press.
  • Gluck, M.A., & Bower, G.H. (1988), Evaluating an adaptive network model of human learning. Journal of Memory and Language, 27, 166195.
  • Goodenough, F.L., & Harris, D.B. (1950). Studies in the psychology of children's drawings II, 1928-1949. Psychological Bulletin, 47, 363433.
  • Hampton, J.A. (1979). Polymorphous concepts in semantic memory. Journal of Verbal Learning and Verbal Behavior, 18, 441461.
  • Hanson, S.J., & Bauer, M. (1989). Conceptual clustering, categorization, and polymorphy. Machine Learning, 3, 343372.
  • Harris, D.B. (1963). Children's drawings as measures of intellectual maturity. New York : Harcourt Brace & World.
  • Haygood, R.C., & Bourne, L.E., Jr. (1965). Attribute- and rule-learning aspects of conceptual behavior. Psychological Review, 72, 175195.
  • Heit, E. (1992). Categorization using chains of examples. Cognitive Psychology, 24, 341380.
  • Hintzman, D.L. (1986). Schema abstraction in a multiple-trace memory model. Psychological Review, 93, 411428.
  • Keil, F.C. (1989). Concepts, kinds, and conceptual development. Cambridge , MA : Bradford Books/MIT Press.
  • Koppitz, E.M. (1984). Psychological evaluation of human figure drawings by middle school pupils. Orlando , FL : Grune & Stratton.
  • Laird, P.E., Rosenbloom, P.S., & Newell, A. (1986). Chunking in SOAR: The anatomy of a general learning mechanism. Machine Learning, 1, 4780.
  • Langley, P. (1981). Data-driven discovery of physical laws. Cognitive Science, 5, 3154.
  • Lebowitz, M. (1986). Integrated learning: Controlling explanation. Cognitive Science, 10, 219240.
  • Lesgold, A.M., Rubinson, H., Feltovich, P., Glaser, R., Klopfer, D., & Wang, Y. (1988). Expertise in complex skill: Diagnosing x-ray pictures. In M.T.H. Chi, R. Glaser, & M. Farr (Eds.), The nature of expertise. Hillsdale , NJ : Erlbaum.
  • Lien, Y., & Cheng, P.W. (1989). A framework for psychological causal induction: Integrating the power and covariation views. Proceedings of the 11th Annual Conference of the Cognitive Science Society (pp. 729733). Hillsdale , NJ : Erlbaum.
  • Markman, A.B., & Gentner, D. (1993a). Splitting the differences: A structural alignment view of similarity. Journal of Memory and Language, 32, 517535.
  • Markman, A.B., & Gentner, D. (1993b). Structural alignment during similarity comparisons. Cognitive Psychology, 25, 431467.
  • McClelland, J.L., & Rumelhart, D.E. (1981). An interactive activation model of context effects in letter perception: Part 1. An account of the basic findings. Psychological Review, 88, 375407.
  • McCloskey, M., & Glucksberg, S. (1979). Decision processes in verifying category membership statements: Implications for models of semantic memory. Cognitive Psychology, 11, 137.
  • Medin, D.L., & Schaffer, M.M. (1978). A context theory of classification learning. Psychological Review, 85, 207238.
  • Medin, D.L., Goldstone, R.L., & Gentner, D. (1993). Respects for similarity. Psychological Review, 100, 254278.
  • Medin, D.L., & Shoben, E.J. (1988). Context and structure in conceptual combination. Cognitive Psychology, 20, 158190.
  • Medin, D.L., & Wattenmaker, W.D. (1987). Category cohesiveness, theories, and cognitive archeology. In U. Neisser (Ed.), Concepts and conceptual development: Ecological and intellectual factors in categorization. Cambridge , MA : Cambridge University Press.
  • Medin, D.L., Wattenmaker, W.D., & Michalski, R.S. (1987). Constraints and preferences in inductive learning: An experimental study of human and machine performance. Cognitive Science, 11, 299339.
  • Michalski, R.S. (1983a). A theory and methodology of inductive learning. In R.S. Michalski, J.G. Carbonell, & T.M. Mitchell (Eds.), Machine learning: An artificial intelligence approach (Vol. 1). Palo Alto , CA : Tioga.
  • Michalski, R.S. (1983b). A theory and methodology of inductive learning. Artificial Intelligence, 20, 111161.
  • Michalski, R.S., & Chilausky, R.L. (1980). Learning by being told and learning from examples: An experimentsl comparison of the two methods of knowledge acquisition in the context of developing an expert system for soybean disease diagnosis. Policy Analysis and Information Systems, 4, 125161.
  • Miller, G.A., & Johnson-Laird, P.N. (1976). Language and perception. Cambridge , MA : Harvard University Press.
  • Minton, S. (1988). Quantitative results concerning the utility of explanation-based learning. Proceedings of the Seventh National Conference on Artificial Intelligence. San Mateo , CA : Morgan Kaufmann.
  • Mitchell, T.M. (1982). Generalization as search. Artificial Intelligence, 18, 203226.
  • Mitchell, T.M., Keller, R.M., & Kedar-Cabelli, S.T. (1986). Explanation-based generalization: A unifying view. Machine Learning, 1, 4780.
  • Mooney, R.J., & Bennett, S.W. (1986). A domain independent explanation-based generalizer. Proceedings of the Fifth National Conference on Artificial Intelligence (pp. 551555). San Mateo , CA : Morgan Kaufmann.
  • Mooney, R.J., & Ourston, D. (1989). Induction over the unexplained: Integrated learning of concepts with both explainable and conventional aspects. Proceedings of the Sixth International Workshop on Machine Learning (pp. 57). San Mateo , CA : Morgan Kaufmann.
  • Mooney, R.J., & Ourston, D. (1992). A multistrategy approach to theory refinement. Proceedings of the First Annual Workshop on Multistrategy Learning (pp. 115130). Fairfax , VA : George Mason University, Center for Artificial Intelligence.
  • Mooney, R.J., Ourston, D., & Wu, S. (1989). Induction over the unexplained: A new approach to combining empirical and explanation-based learning (Tech. Rep. No. A189-110) Austin : University of Texas at Austin, Department of Computer Science.
  • Murphy, G.L. (1993). Theories and concept formation: In I.V. Mechelen, J. Hampton, R. Michalski, & P. Theuens (Eds.), Categories and concepts: Theoretical views and inductive data analysis. London : Academic.
  • Murphy, G.L., & Medin, D.L. (1985). The role of theories in conceptual coherence. Psychological Review, 92, 289316.
  • Murphy, G.L., & Wisniewski, E.J. (1989). Feature correlations in conceptual representations. In G. Tiberghien (Ed.), Advances in cognitive science: Vol. 2. Theory and applications. Chichester , England : Ellis Horwood.
  • Nakumura, G.V. (1985). Knowledge-based classification of ill-defined categories. Memory and Cognition, 13, 377384.
  • Nisbett, R.E., & Ross, L. (1980). Human inference: Strategies and shortcomings of social judgment. Englewood Cliffs , NJ : Prentice-Hall.
  • Nisbett, R.E., & Wilson, T.D. (1977). Telling more than we know: Verbal reports on mental processes. Psychological Review, 84, 231259.
  • Norman, G.R., Brooks, L.R., Coblentz, C.L., & Babcook, C.J. (1992). The correlation of feature identification and category judgements in diagnostic radiology. Memory and Cognition, 20, 344355.
  • Nosofsky, R.M. (1986a). Attention and learning processes in the identification and categorization of integral stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 87108.
  • Nosofsky, R.M. (1986b). Attention, similarity and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 3957.
  • Nosofsky, R.M. (1988). Similarity, frequency and category representations. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 5465.
  • Nosofsky, R.M. (1991). Exemplars, prototypes and similarity rules. In A. Healy, S. Kosslyn, & R. Shiffrin (Eds.), From learning theory to connectionist theory: Essays in honor of W.K. Estes (Vol. 1). Hillsdale , NJ : Erlbaum.
  • Nosofsky, R.M., Clark, S.E., & Shin, H.J. (1989). Rules and exemplars in categorization, identification, and recognition. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15, 282304.
  • Pagallo, G. (1989). Learning DNF by decision trees. Proceedings of the 11th International Joint Conference of Artificial Intelligence (pp. 639644). San Mateo , CA : Morgan Kaufmann.
  • Pazzani, M.J. (1985). Explanation and generalization-based memory. Proceedings of the Seventh Annual Conference of the Cognitive Science Society. Irvine , CA .
  • Pazzani, M.J. (1991). Influence of prior knowledge on concept acquisition: Experimental and computational results. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15, 416432.
  • Quinlan, J.R. (1983). Learning efficient classification procedures and their application to chess end games. In R.S. Michalski, J.G. Carbonell, & T.M. Mitchell (Eds.), Machine learning: An artificial intelligence approach (Vol. 1). Palo Alto , CA : Tioga.
  • Quinlan, J.R. (1986). Induction of decision trees. Machine Learning, 1, 81106.
  • Rips, L. (1989). Similarity, typicality, and categorization. In S. Vosniadou & A. Ortony, (Eds.), Similarity and analogical reasoning. Cambridge , MA : Cambridge University Press.
  • Schank, R.C., Collins, G.C., & Hunter, L.E. (1986). Transcending inductive category formation in learning. Behavioral and Brain Sciences, 9, 639686.
  • Shavlik, J.W., & Towell, G.G. (1989). Combining explanation-based and neural learning: An algorithm and empiricial results. (Tech. Rep. No. 859). Madison : University of Wisconsin-Madison.
  • Smith, C., Carey, S., & Wiser, M. (1985). On differentiation: A case study of the development of the concepts of size, weight and density. Cognition, 21, 177237.
  • Smith, E., & Medin, D.L. (1981). Categories and concepts. Cambridge , MA : Harvard University Press.
  • Sutton, R.S., & Matheus, C.J. (1989). Learning polynomial functions by feature construction. Proceedings of the Eighth International Workshop on Machine Learning (pp. 208212). San Mateo , CA : Morgan Kaufmann.
  • Tecuci, G.D. (1991). Learning as understanding the external world. Proceedings of the First International Workshop on Multistrategy Learning (pp. 4964). Harpers Ferry, WVA .
  • Towell, G.G., Shavlik, J.W., & Noordewier, M.O. (1990). Refinement of approximate domain theories by knowledge-based neural networks. Proceedings of the Eighth National Conference on Artificial Intelligence (pp. 861866). Menlo , CA : AAAI Press/The MIT Press.
  • Wattenmaker, W.D., Dewey, G.I., Murphy, T.D., & Medin, D.L. (1986). Linear separability and concept learning: Context, relational properties, and concept naturalness. Cognitive Psychology, 18, 158194.
  • Wisniewski, E.J., & Medin, D.L. (1991). Harpoons and long sticks: The interaction of theory and similarity in rule induction. In D.H. Fisher, M.J. Pazzani, & P. Langley (Eds.), Concept formation: knowledge and experience in unsupervised learning. San Mateo , CA : Morgan Kaufmann.
  • Wisniewski, E.J., & Medin, D.L. (1994). The fiction and nonfiction of features. In R.S. Michalski & G. Tecuci, (Eds.), Machine learning (Vol. 4). San Mateo , CA : Morgan Kaufmann.
  • Yoo, J., & Fisher, D.H. (1991). Concept formation over problem-solving experience. In D.H. Fisher, M.J. Pazzani, & P. Langley (Eds.), Concept formation: Knowledge and experience in unsupervised learning. San Mateo , CA : Morgan Kaufmann.