The Connectionist Construction of Psychological Spaces

Connection Science

Volumn 9, Issue 4, 1997, Pages 323-352

  Lee, Michael D ^a  

^a DEFENCE SCIENCE AND TECHNOLOGY ORGANISATION   (Australia)

Author keywords

Connectionism; Mental representation; Multi dimensional scaling; Psychological space

Indexed keywords

EID: 0031326670     PISSN: 09540091     EISSN: None     Source Type: Journal    
DOI: 10.1080/095400997116586     Document Type: Article

References (71)

1. Arabie, P. (1991) Was Euclid an unnecessarily sophisticated psychologist? Psychometrika, 56, 567-587.
2. Ash, T. & Cottrell, G. (1995) Topology-modifying neural network algorithms. In M.A. Arbib (Ed.), Handbook of Brain Theory and Neural Networks. Cambridge, MA: MIT Press, pp. 990-993.
3. Bourne, L.E. (1974) An inference model of conceptual rule learning. In R.L. Solso (Ed.), Theories in Cognitive Psychology: The Layola Symposium. Potomac, MD: Erlbaum, pp. 231-256.
4. Buntine, W.R. & Weigend, A.S. (1991) Bayesian back-propagation. Complex Systems, 3, 603-643.
5. Chalmers, D.J., French, R.M. & Hofstadter, D.R. (1991) High-level Perception, Representation, and Analogy: A Critique of Artificial Intelligence Methods. CRCC Technical Report 49, Indiana University, Bloomington, IN.
6. Choi, S., McDaniel, M. A. & Busemeyer, J.R. (1993) Incorporating prior biases in network models of conceptual rule learning. Memory & Cognition, 21, 413-423.
7. Ekman, G. (1954) Dimensions of color vision. The Journal of Psychology, 38, 467-474.
8. Ennis, D.M. (1988a) Confusable and discriminable stimuli: Comment on Nosofsky (1986) and Shepard (1986). Journal of Experimental Psychology: General, 117, 408-411.
9. Ennis, D.M. (1988b) Toward a universal law of generalization. Science, 242, 944.
10. Ennis, D.M. (1992) Modeling similarity and identification when there are momentary fluctuations in psychological magnitudes. In F.G. Ashby (Ed.), Multidimensional Models of Perception and Cognition. Hillsdale, NJ: Erlbaum, pp. 279-298.
11. Garner, W. R. (1974) The Processing of Information and Structure. Potomac, MD: Erlbaum.
12. Gati, I. & Tversky, A. (1982) Representations of qualitative and quantitative dimensions. Journal of Experimental Psychology: Human Perception and Performance, 8, 325-340.
13. Getty, D.J., Swets, J.A. Swets, J.B. & Green, D.M. (1979) On the prediction of confusion matrices from similarity judgments. Perception & Psychophysics, 26, 1-19.
14. Hanson, S.J. & Burr, D.J. (1990) What connectionist models learn: Learning and representation in connectionist networks. Behavioral and Brain Sciences, 13, 471-518.
15. Hartman, E.J., Keeler, J.D. & Kowalski, J.M. (1990) Layered neural networks with Gaussian units as universal approximations. Neural Computation, 2, 210-215.
16. Hertz, J., Krogh, A. & Palmer, R.G. (1991) Introduction to the Theory of Neural Computing. Redwood City, CA: Addison-Wesley.
17. Hinton, G.E. (1989) Connectionist learning procedures. Artificial Intelligence, 40, 185-234.
18. Hoffman, T. & Buhmann, J. (1995) Multidimensional scaling and data clustering. In D.S. Touretzky, M.C. Mozer & M.E. Hasselmo (Eds), Advances in Neural Information Processing Systems 8. Cambridge, MA: MIT Press, pp. 104-111.
19. Hofstadter, D.R. (1988) Foreword. In P. Kanerva, Sparse Distributed Memory. Cambridge, MA: MIT Press, pp. xi-xviii.
20. Hubert, L., Arabie, P. & Hesson-Mcinnis, M. (1992) Multidimensional scaling in the city-block metric: A combinatorial approach. Journal of Classification, 9, 211-236.
21. Kohonen, T. (1982) Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43, 59-69.
22. Kohonen, T. (1990) The self-organizing map. Proceedings of the IEEE, 78, 1464-1480.
23. Kruschke, J.K. (1990) A Connectionist Model of Category Learning. Unpublished doctoral dissertation, University of California at Berkeley.
24. Kruschke, J.K. (1992) ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99, 22-44.
25. Kruschke, J.K. (1993a) Three principles for models of category learning. The Psychology of Learning and Motivation, 29, 57-90.
26. Kruschke, J.K. (1993b) Human category learning: Implications for backpropagation models. Connection Science, 5, 3-36.
27. Kruskal, J.B. (1964) Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29, 1-27.
28. Lewandowsky, S. (1995) Base-rate neglect in ALCOVE: A critical reevaluation. Psychological Review, 102, 185-191.
29. Lindman, H. & Caelli, T. (1978) Constant curvature Riemannian scaling. Journal of Mathematical Psychology, 17, 89-109.
30. Lowe, D. (1995) Radial basis function networks. In M.A. Arbib (Ed.), Handbook of Brain Theory and Neural Networks. Cambridge, MA: MIT Press, pp. 779-782.
31. MacKay, D.J.C. (1992) A practical Bayesian framework for backpropagation networks. Neural Computation, 4, 448-472.
32. McClelland, J.L., O'Reilly, R.C. & McNaughton, B.L. (1995) Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102, 419-457.
33. McMichael, D.W. (1995) Automatic complexity determination of Gaussian mixture models with the EMS algorithm. In P. Bartlett, A. Burkitt & R.C. Williamson (Eds), Proceedings of the Seventh Australian Conference on Neural Networks, Australian National University, Canberra, ACT, pp. 103-108.
34. Medin, D.L. & Edelson, S.M. (1988) Problem structure and the use of base-rate information from experience. Journal of Experimental Psychology: General, 117, 68-85.
35. Medin, D.L. & Schwanenflugel, P.J. (1981) Linear separability in classification learning. Journal of Experimental Psychology: Human Learning and Memory, 7, 355-368.
36. Medin, D.L., Altom, M.W., Edelson, S.M. & Freko, D. (1982) Correlated symptoms and simulated medical classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 8, 37-50.
37. Moody, J. & Darken, C.J. (1989) Fast learning in networks of locally-tuned processed units. Neural Computation, 1, 281-294.
38. Mozer, M.C. & Soukup, T. (1991) Connectionist music composition based on melodic and stylistic constraints. In R.P. Lippmann, J.E. Moody & D.S. Touretzky (Eds), Advances in Neural Information Processing Systems 3. San Mateo, CA: Morgan Kauffman, pp. 789-796.
39. Nosofsky, R.M. (1984) Choice, similarity, and the context theory of classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10, 104-114.
40. Nosofsky, R.M. (1986) Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39-57.
41. Nosofsky, R.M. (1988a) Similarity, frequency, and category representations. Journal of Experimental Psychology: Learning, Memory and Cognition, 14, 54-65.
42. Nosofsky, R.M. (1988b) Exemplar-based accounts of relations between classification, recognition, and typicality. Journal of Experimental Psychology: Learning, Memory and Cognition, 14, 700-708.
43. Nosofsky, R.M. (1988c) On exemplar-based representations: Reply to Ennis (1988). Journal of Experimental Psychology: General, 117, 412-414.
44. Nosofsky, R.M. (1992) Similarity scaling and cognitive process models. Annual Review of Psychology, 43, 25-53.
45. Nosofsky, R.M. & Kruschke, J.K. (1992) Investigations of an exemplar-based connectionist model of category learning. The Psychology of Learning and Motivation, 28, 207-250.
46. Nosofsky, R.M., Kruschke, J.K. & McKinley, S.C. (1992) Combining exemplar-based category representations and connectionist learning rules. Journal of Experimental Psychology: Learning, Memory and Cognition, 18, 211-233.
47. Nowlan, S.J. & Hinton, G.E. (1992) Simplifying neural networks by soft weight-sharing. Neural Computation, 4, 473-493.
48. Piaget, J. (1970) Piaget's theory. In P.H. Mussen (Ed.), Carmichael's Manual of Child Psychology. New York: Wiley, Vol. 1, pp. 703-732.
49. Poggio, T. & Girosi, F. (1990) Networks for approximation and learning. Proceedings of the IEEE, 78, 1481-1497.
50. Ratcliff, R. (1990) Connectionist models of recognition memory: Constraints imposed by learning and forgetting functions. Psychological Review, 97, 285-308.
51. Ritter, H. (1990) Self-organizing maps for internal representations. Psychological Research, 52, 128-136.
52. Rumelhart, D.E. & McClelland, J.L. (1986) On learning the past tenses of English verbs. In D.E. Rumelhart & J.L. McClelland (Eds), Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Cambridge, MA: MIT Press, Vol. 1, pp. 216-271.
53. Rumelhart, D.E. & Todd, P.M. (1993) Learning and connectionist representations. In D.E. Meyer & S. Kornblum (Eds), Attention and Performance 14: Synergies in Experimental Psychology, Artificial Intelligence and Cognitive Neuroscience. Cambridge, MA: MIT Press, pp. 2-37.
54. Shanks, D.R. & Gluck, M.A. (1994) Tests of an adaptive network model for the identification and categorization of continuous-dimension stimuli. Connection Science, 6, 59-89.
55. Shepard, R.N. (1957) Stimulus and response generalization: A stochastic model relating generalization to distance in psychological space. Psychometrika, 22, 325-345.
56. Shepard, R.N. (1958) Stimulus and response generalization: Deduction of the generalization gradient from a trace model. Psychological Review, 65, 242-256.
57. Shepard, R.N. (1962a) The analysis of proximities: Multidimensional scaling with an unknown distance function. I. Psychometrika, 27, 125-140.
58. Shepard, R.N. (1962b) The analysis of proximities: Multidimensional scaling with an unknown distance function. II. Psychometrika, 27, 219-246.
59. Shepard, R.N. (1974) Representation of structure in similarity data: Problems and prospects. Psychometrika, 39, 373-422.
60. Shepard, R.N. (1987a) Toward a universal law of generalization for psychological science. Science, 237, 1317-1323.
61. Shepard, R.N. (1987b) Evolution of a mesh between principles of the mind and regularities of the world. In J. Dupré (Ed.), The Latest on the Best: Essays on Evolution and Optimality. Cambridge, MA: MIT Press, pp. 251-275.
62. Shepard, R.N. (1988) Time and distance in generalization and discrimination: Reply to Ennis (1988). Journal of Experimental Psychology: General, 117, 415-416.
63. Shepard, R.N. (1991) Integrality versus separability of stimulus dimensions: From an early convergence of evidence to a proposed theoretical basis. In J.R. Pomerantz & G.L. Lockhead (Eds), The Perception of Structure: Essays in Honor of Wendell R. Garner. Washington, DC: American Psychological Association, pp. 53-71.
64. Shepard, R.N. (1994) Perceptual-cognitive universals as reflections of the world. Psychonomic Bulletin & Review, 1, 2-28.
65. Shepard, R.N. & Kannappan, S. (1991) Connectionist implementation of a theory of generalization. In R.P. Lippmann, J.E. Moody & D.S. Touretzky (Eds), Advances in Neural Information Processing Systems 3. San Mateo, CA: Morgan Kauffman, pp. 665-671.
66. Shepard, R.N., Hovland, C.L. & Jenkins, H. M. (1961) Learning and memorization of classifications. Psychological Monographs, 75.
67. Smolensky, P. (1988) On the proper treatment of connectionism. Behavioral and Brain Sciences, 11, 1-23.
68. Todd, P.M. & Rumelhart, D.E. (1995) Feature Abstraction from Similarity Ratings: A Connectionist Approach. Unpublished manuscript, Stanford University.
69. Tversky, A. & Gati, I. (1982) Similarity, separability and the triangle inequality. Psychological Review, 89, 123-154.
70. Webb, A.R. (1995) Multidimensional scaling by iterative majorization using radial basis functions. Pattern Recognition, 28, 753-759.
71. Weigend, A.S., Rumelhart, D.E. & Huberman, B.A. (1991) Generalization by weight-elimination with application to forecasting. In R.P. Lippmann, J.E. Moody & D.S. Touretzky (Eds), Advances in Neural Information Processing Systems 3. San Mateo, CA: Morgan Kauffman, pp. 875-882.