Three Principles for Models of Category Learning

Psychology of Learning and Motivation - Advances in Research and Theory

Volumn 29, Issue C, 1993, Pages 57-90

  Kruschke, John K ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77957087048     PISSN: 00797421     EISSN: None     Source Type: Book Series    
DOI: 10.1016/S0079-7421(08)60136-X     Document Type: Chapter

References (63)

1. Anderson, J.A., Silverstein, J.W., Ritz, S.A., Jones, R.S., Distinctive features, categorical perception, and probability learning: Some applications of a neural model. Psychological Review 84 (1977), 413–451.
2. Anderson, J.R., The Adaptive character of thought., 1990, Erlbaum, Hillsdale, NJ.
3. Anderson, J.R., The adaptive nature of human categorization. Psychological Review 98 (1991), 409–429.
4. Ashby, F.G., Gott, R.E., Decision rules in the perception and categorization of multidimensional stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition 14 (1988), 33–53.
5. Ashby, F. G., & Maddox, W. T. (in press). Relations between prototype, exemplar and decision bound models of categorization. Journal of Mathematical Psychology.
6. Ballard, D.H., Interpolation coding: A representation for numbers in neural models. Biological Cybernetics 57 (1987), 389–402.
7. Broomhead, D.S., Lowe, D., Multivariable functional interpolation and adaptive networks. Complex Systems 2 (1988), 321–355.
8. Cohen, J.D., Dunbar, K., McClelland, J.L., On the control of automatic processes: A parallel distributed processing account of the Stroop effect. Psychological Review 97 (1990), 332–361.
9. Estes, W.K., Array models for category learning. Cognitive Psychology 18 (1986), 500–549.
10. Estes, W.K., Campbell, J.A., Hatsopoulos, N., Hurwitz, J.B., 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 (1989), 556–576.
11. Garner, W.R., The processing of information and structure., 1974, Erlbaum, Hillsdale, NJ.
12. Gluck, M.A., Stimulus generalization and representation in adaptive network models of category learning. Psychological Science 2 (1991), 50–55.
13. Gluck, M.A., Bower, G.H., Evaluating an adaptive network model of human learning. Journal of Memory and Language 27 (1988), 166–195.
14. Gluck, M.A., Bower, G.H., From conditioning to category learning: An adaptive network model. Journal of Experimental Psychology: General 117 (1988), 227–247.
15. Gottwald, R.L., Garner, W.R., Effects of focusing strategy on speeded classification with grouping, filtering and condensation tasks. Perception & Psychophysics 11 (1972), 179–182.
16. Gottwald, R.L., Garner, W.R., Filtering and condensation tasks with integral and separable dimensions. Perception & Psychophysics 18 (1975), 26–28.
17. Hancock, P.J.B., Data representation in neural nets: An empirical study. Touretzky, D., Hinton, G., Sejnowski, T., (eds.) Proceedings of the 1988 Connectionist Models Summer School, 1989, Morgan Kaufmann, San Mateo, CA, 11–20.
18. Hartman, E., Keeler, J.D., Predicting the future: Advantages of semilocal units. Neural Computation 3 (1991), 566–578.
19. Hurwitz, J. B. (1990). A hidden-pattern unit network model of category learning. Doctoral dissertation. Harvard University, Cambridge, MA.
20. Kamin, L.J., Predictability, surprise, attention, and conditioning. Campbell, B.A., Church, R.M., (eds.) Punishment, 1969, Appleton-Century-Crofts, New York, 279–296.
21. Kanerva, P., Sparse Distributed Memory., 1988, MIT Press, Cambridge, MA.
22. Keeler, J.D., Comparison between Kanerva's SDM and Hopfield-type neural networks. Cognitive Science 12 (1988), 299–329.
23. Knapp, A.G., Anderson, J.A., Theory of categorization based on distributed memory storage. Journal of Experimental Psychology: Learning, Memory and Language 10 (1984), 616–637.
24. Kruschke, J. K. (1990). A connectionist model of category learning. Doctoral dissertation, University of California, Berkeley.
25. Kruschke, J.K., Dimensional attention learning in models of human categorization. Proceedings of the Thirteenth Annual Conference of the Cognitive Science Society, 1991, Erlbaum, Hillsdale, NJ, 281–286.
26. Kruschke, J.K., ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review 99 (1992), 22–44.
27. Kruschke, J.K., Human category learning: Implications for backpropagation models. Connection Science 5 (1993), 3–36.
28. Levine, M., A cognitive theory of learning: Research on hypothesis testing., 1975, Erlbaum, Hillsdale, NJ.
29. Luce, R.D., Detection and recognition. Luce, R.D., Bush, R.R., Galanter, E., (eds.) Handbook of mathematical psychology, 1963, Wiley, New York, 103–189.
30. McClelland, J.L., Stochastic interactive processes and the effect of context on perception. Cognitive Psychology 23 (1991), 1–44.
31. McClelland, J.L., Toward a theory of information processing in graded, random, interactive networks, 1991, Carnegie Mellon University, Pittsburgh, PA Technical Report PDP.CNS.91.1).
32. McClelland, J.L., Jenkins, E., Nature, nurture and connections: Implications of connectionist models for cognitive development. Van Lehn, K., (eds.) Architectures for intelligence., 1991, Erlbaum, Hillsdale, NJ.
33. McClelland, J.L., Rumelhart, D.E., An interactive activation model of context effects in letter perception: Part 1. An account of basic findings. Psychological Review 88 (1981), 375–407.
34. McCloskey, M., Cohen, N.J., Catastrophic interference in connectionist networks: The sequential learning problem. Bower, G., (eds.) The Psychology of Learning and Motivation, 24, 1989, Academic Press, New York, 109–165.
35. Medin, D.L., Schaffer, M.M., Context theory of classification learning. Psychological Review 85 (1978), 207–238.
36. Mel, B.W., The sigma-pi column: A model of associative learning in cerebral neocortex (CNS Memo 6)., 1990, California Institute of Technology, Pasadena, CA.
37. Minsky, M.L., Papert, S.A., Perceptrons., 1969, MIT Press, Cambridge, MA 1988 expanded edition.
38. Moody, J., Darken, C.J., Fast learning in networks of locally-tuned processing units. Neural Computation 1 (1989), 281–294.
39. Niranjan, M., Fallside, F., Neural networks and radial basis functions in classifying static speech patterns (Technical Report CUES/F-INFENG/TR.22)., 1988, Cambridge University, Engineering Department, Cambridge, England.
40. Nosofsky, R.M., Attention, similarity and the identification-categorization relationship. Journal of Experimental Psychology: General 115 (1986), 39–57.
41. Nosofsky, R.M., Relation between the rational model and the context model of categorization. Psychological Science 2 (1991), 416–421.
42. Nosofsky, R.M., Typicality in logically defined categories: exemplar-similarity versus rule instantiation. Memory & Cognition 19 (1991), 131–150.
43. Nosofsky, R.M., Clark, S.E., Shin, H.J., Rules and exemplars in categorization, identification and recognition. Journal of Experimental Psychology: Learning, Memory, and Cognition 15 (1989), 282–304.
44. Nosofsky, R.M., Kruschke, J.K., Investigations of an exemplar-based connectionist model of category learning. Medin, D.L., (eds.) The Psychology of Learning and Motivation, 28, 1992, Academic Press, New York, 207–250.
45. Nosofsky, R.M., Kruschke, J.K., McKinley, S., Combining exemplar-based category representations and connectionist learning rules. Journal of Experimental Psychology: Learning, Memory and Cognition 18 (1992), 211–233.
46. Pavel, M., Gluck, M.A., Henkle, V., Constraints on adaptive networks for modeling human generalization. Touretzky, D.S., (eds.) Advances in Neural Information Processing Systems, 1, 1989, Morgan Kaufmann, San Mateo, CA, 2–10.
47. Pazzani, M.J., Influence of prior knowledge on concept acquisition: Experimental and computational results. Journal of Experimental Psychology: Learning, Memory and Cognition 17 (1991), 416–432.
48. Poggio, T., Girosi, F., Regularization algorithms for learning that are equivalent to multilayer networks. Science 247 (1990), 978–982.
49. Posner, M.I., Information reduction in the analysis of sequential tasks. Psychological Review 71 (1964), 491–504.
50. Powell, M.J.D., Radial basis functions for multivariable interpolation: A review (Technical Report DAMTP 1985/NA12)., 1985, Cambridge University, Department of Applied Mathematics and Theoretical Physics, Cambridge, England.
51. Ratcliff, R., Connectionist models of recognition memory: Constraints imposed by learning and forgetting functions. Psychological Review 97 (1990), 285–308.
52. Rescorla, R.A., Wagner, A.R., A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and non-reinforcement. Black, A.H., Prokasy, W.F., (eds.) Classical Conditioning: II. Current Research and Theory, 1972, Appleton-Century-Crofts, New York, 64–99.
53. Robinson, A.J., Niranjan, M., Fallside, F., Generalising the nodes of the error propagation network (Technical Report CUES/F-INFENG/TR.25)., 1988, Cambridge University, Engineering Department, Cambridge, England.
54. Rosenblatt, F., The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65 (1958), 386–408.
55. Rumelhart, D.E., Hinton, G.E., Williams, R.J., Learning internal representations by error propagation. McClelland, J.L., Rumelhart, D.E., (eds.) Parallel distributed processing: Vol. 1. Foundations, 1986, MIT Press, Cambridge, MA, 318–362.
56. Seidenberg, M.S., McClelland, J.L., A distributed, developmental model of word recognition and naming. Psychological Review 96 (1989), 523–568.
57. Sejnowski, T.J., Rosenberg, C.R., Learning and representation in connectionist networks. Gazzaniga, M.S., (eds.) Perspectives in memory research, 1988, MIT Press, Cambridge, MA, 135–178.
58. Shanks, D.R., Connectionism and the learning of probabilistic concepts. Quarterly Journal of Experimental Psychology 42A (1990), 209–237.
59. Shepard, R.N., Attention and the metric structure of the stimulus space. Journal of Mathematical Psychology 1 (1964), 54–87.
60. Shepard, R.N., Toward a universal law of generalization for psychological science. Science 237 (1987), 1317–1323.
61. Smith, E.E., Medin, D., Categories and concepts., 1981, Harvard U. Press, Cambridge, MA.
62. Taraban, R., McDonald, J.L., MacWhinney, B., Category learning in a connectionist model: Learning to decline the German definite article. Corrigan, R., Eckman, F., Noonan, M., (eds.) Linguistic categorization, 1989, Benjamins, Philadelphia, 163–193.
63. Walters, D., Properties of connectionist variable representations. Program of the Ninth Annual Conference of the Cognitive Science Society, 1987, Erlbaum, Hillsdale, NJ, 265–273.