Abstract
A series of simulations is reported in which extant formal categorization models are applied to human rule-learning data (Salatas & Bourne, 1974). These data show that there are clear differences in the ease with which humans learn rules, with the conjunctive the easiest and the biconditional the hardest. The original ALCOVE model (an exemplar-based model), a configuralcue model, and two-layer backpropagation models did not fit the rule-learning data. ALCOVE successfully fit the data, however, when prior biases observed in human rule learning were implemented into weights of the network. Thus, current empirical learning models may not fare well in situations in which learners enter the concept-formation situation with preconceived biases regarding the kinds of concepts that are possible, but such biases might nevertheless be captured within these models. By incorporating preexperimental biases, ALCOVE may hold promise as a comprehensive category-learning model.
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Bourne, L. E., Jr. (1967). Learning and utilization of conceptual rules. In B. Kleinmuntz (Ed.),Concepts and the structure of memory (pp. 1–32). New York: Wiley.
Bourne, L. E., Jr. (1974). An inference model of conceptual rule learning. In R. L. Solso (Ed.),Theories in cognitive psychology: The Loyola symposium (pp. 231–256). Potomac, MD: Erlbaum.
Bourne, L. E., Jr.,Dominowski, R. L., &Lokius, E. F. (1979).Cognitive processes. Englewood Cliffs, NJ: Prentice-Hall.
Bourne, L. E., Jr., &Guy, D. E. (1968). Learning conceptual rules: I. Some interrule transfer effects.Journal of Experimental Psychology,76, 423–429.
Brooks, L. (1978). Nonanalytic concept formation and memory for instances. In E. Rosen & B. B. Lloyd (Eds.),Cognition and categorization (pp. 169–211). Hillsdale, NJ: Erlbaum.
Brunkr, J. S., Goodndw, J. J., &Austin, G. A. (1956).A study of thinking. New York: Wiley.
Busemeyer, J. R., &Myung, I. J. (1992). An adaptive approachto human decision making: Learning theory, decision theory, and human performance.Journal of Experimental Psychology: General,121, 177–194.
Estes, W. K., Campbell, J. A., Hatsopoulus, N., &Hurwitz, J. B. (1989). Base-rate effects in category learning: A comparison of parallel networkand memory storage-retrieval models.Journal of Experimental Psychology: teaming, Memory, & Cognition,15, 556–571,
Gluck, M. A., &Bower, G. H. (1988a). From conditioning to category learning: An adaptive network model.Journal of Experimental Psychology: General,117, 227–247.
Gluck, M. A., &Bower, G. H. (1988b). Evaluating an adaptive network model of human learning.Journal of Memory & Language,27, 166–195.
Gluck, M. A., Bower, G. H., &Hee, M. R. (1989). A configuraicue network model of animal and human associative learning.Proceedings of the Eleventh Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Erlbaum.
Haygood, R. C., &Bourne, L. E., Jr. (1965). Attribute-and rulelearning aspects of conceptual behavior.Psychological Review,72, 175–195.
Hintzman, D. L. (1986) “Schema abstraction” in a multiple-trace memory model.Psychological Review,93, 411–428.
Homa, D. (1984). Ort the nature of categories. In G. B. Bower (Ed.),The psychology of learning and motivation (Vol. 18, pp. 49–94). New York: Academic Press.
Homa, D., Sterling, S., &Trepel, L. (1981). Limitations of exemplar-based generalization and the abstraction of categorical information.Journal of Experimental Psychology: Human learning & Memory,7, 418–439.
Horton, D. L., &Turnage, T. W. (1976).Human learning. Englewood, NJ: Prentice-Hall.
Hull, C. L. (1920). Quantitative aspects of the evolution of concepts.Psychological Monographs,28(3, Whole No.123).
Hunt, E. B., &Hovland, C. I. (1960). Order of consideration of different types of concepts.Journal of Experimental Psychology,59, 220–225.
Kellogg, T. T., &Bourne, L. E., Jr. (1989). Nonanalytic-automatic abstraction of concepts. In J. B. Sidowski (Ed.),Conditioning, cognition, and methodology: Contemporary issues in experimental psychology (pp. 89–111). Lanham, MD: University Press of America.
Kozminsky, E., Kintsch, W., &Bourne, L. E., Jr. (1981). Decision making with texts: Information analysis and schema acquisition.Journal of Experimental Psychology: General,110, 363–380.
Kruschke, J. K. (1992). ALCOVE: An exemplar-based conneclionist model of category learning.Psychological Review,99, 22–44.
McCloskey, M. (1991). Networks and theories: The place of connectionism in cognitive science.Psychological Science,2, 387–395.
Medin, D. L., &Schaffer, M. M. (1978). Context theory of classification learning.Psychological Review,85, 207–238.
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, 299–339.
Minsky, M. L., &Papert, S. (1969).Perceptrons: An introduction to computational geometry. Cambridge, MA: MIT Press.
Neisskr, U., &Weene, P. (1962). Hierarchies in concept attainment.Journal of Experimental Psychology,64, 640–645.
Neuman, P. G. (1973).Directional and neutral category labels in bidirectional concept identification problems. Unpublished master’s thesis, University of Colorado, Boulder.
Nosofsky, R. M. (1984). Choice, similarity, and the context theory of classification.Journal of Experimental Psychology: Learning, Memory, & Cognition,10, 104–114.
Nosoi-Sky, R. M. (1986). Attention, similarity, and the identification-categorization relationship.Journal of Experimental Psychology: General,115, 39–57.
Nosofsky, R. M. (1987). Attention and learning processes in the identification and categorization of integral stimuli.Journal of Experimental Psychology: Learning, Memory,& Cognition,13, 87–108.
Nosofsky, R. M. (1988). Exemplar-based accounts of relations between classification, recognition, and typicality.Journal of Experimental Psychology: Learning, Memory, & Cognition,14, 700–708.
Nosofsky, R. M. (1991). Typicality in logically defined categories: Exemplar-similarity versus rule instantiation.Memory & Cognition,19, 131–150.
Pavel, M., Gluck, M. A., &Henkle, V. (1988). Constraints on adaptive networks for modeling human generalization. InProceedings of the November 1988 Neural Information Processing Systems Conference. Hillsdale, NJ: Erlbaum.
Pazzani, M. J. (1991). Influence of prior knowledge on concept acquisition: Experimental and computational results.Journal of Experimental Psychology: Learning, Memory, & Cognition,17, 416–432.
Posner, M. I., &Keele, S. W. (1968). On the genesis of abstract ideas.Journal of Experimental Psychology,77, 353–363.
Rosch, E. (1975). Cognitive representation of semantic categories.Journal of Experimental Psychology: General,104, 192–233.
Rumelhart, D. E., Hinton, G. E., &Williams, R. J. (1986). Learning internal representations by error propagation. In D. E. Rumelhart, J. L. McClelland, & the PDP Research Group (Eds.),Parallel distributed processing: Explorations in the microstructure of cognition: Vol. I. Foundations (pp. 318–362). Cambridge, MA: MIT Press.
Rumelhart, D. E., &McClelland, J. L. (1986). PDP models and general issues in cognitive science. In D. E. Rumelhart, J. L. McClelland, & the PDP Research Group (Eds.),Parallel distributed processing: Explorations in the microstructure of cognition: Vol. 1. Foundations (pp. 110–146). Cambridge, MA: MIT Press.
Salatas, H., &Bourne, L. E., Jr. (1974). Learning conceptual rules: III. Processes contributing to rule difficulty.Memory & Cognition,2, 549–553.
Shanks, D. R. (1991). Categorization by a conneclionist network.Journal of Experimental Psychology: Learning, Memory, & Cognition,17, 433–443.
Shepard, R., Hovland. C, & Jenkins, H. M. (1961). Learning and memorization of classifications.Psychological Monographs,75(13, Whole No. 517).
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This work was supported by National Institute of Mental Health Grant MH 47126 to J.R.B, and M. A.M.
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Choi, S., McDaniel, M.A. & Busemeyer, J.R. Incorporating prior biases innetwork models of conceptual rule learning. Memory & Cognition 21, 413–423 (1993). https://doi.org/10.3758/BF03197172
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DOI: https://doi.org/10.3758/BF03197172