Rethinking Eliminative Connectionism

Cognitive Psychology

Volumn 37, Issue 3, 1998, Pages 243-282

  Marcus, Gary F ^a  

^a NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HUMAN; LANGUAGE; REVIEW; VERBAL BEHAVIOR;

HUMANS; LANGUAGE; VERBAL BEHAVIOR;

EID: 0032247463     PISSN: 00100285     EISSN: None     Source Type: Journal    
DOI: 10.1006/cogp.1998.0694     Document Type: Article

References (95)

1. Abler, W. L. (1989). On the particulate principle of self-diversifying systems. Journal of Social and Biological Structures, 12, 1-13.
2. Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard Univ. Press.
3. Barnden, J. A. (1984). On short-term information processing in connectionist theories. Cognition and Brain Theory, 7, 285-328.
4. Barnden, J. A. (1992). Connectionism, generalization, and prepositional attitudes: A catalogue of challenging issues. In J. Dinsmore (Ed.), The symbolic and connectionist paradigms: Closing the gap (pp. 149-178). Hillsdale, NJ: Erlbaum.
5. Barto, A. G. (1992). Reinforcement learning and adaptive critic methods. In D. A. White & D. A. Sofge (Eds.), Handbook of intelligent control (pp. 469-491). New York: Van Nostrand-Reinhold.
6. Berent, I., Everett, D. L., & Shimron, J. (1998). Do phonological representations specify variables? Evidence from the Obligatory Contour Principle. Manuscript submitted for publication.
7. Busemeyer, J., McDaniel, M. A., & Byun, E. (1997). The abstraction of intervening concepts from experience with multiple input-multiple output causal environments. Cognitive Psychology, 32, 1-48.
8. Chalmers, D. J. (1990). Syntactic transformations on distributed representations. Connection Science, 2, 53-62.
9. Chomsky, N. (1957). Syntactic structures. The Hague: Mouton.
10. Clark, A., & Thornton, C. (1997). Trading spaces: Computation, representation, and the limits of uninformed learning. Behavioral and Brain Sciences, 20, 57-90.
11. Crick, F. (1988). What mad pursuit. New York: Basic Books.
12. Crick, F., & Asunama, C. (1986). Certain aspects of anatomy and physiology of the cerebral cortex. In J. L. McClelland, D. E. Rumelhart, & The PDP Research Group (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition: Vol. 2. Psychological and biological models. Cambridge, MA: MIT Press.
13. DeLosh, E. L., Busemeyer, J. R., & McDaniel, M. (1997). Extrapolation: The sine qua non of abstraction in function learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 968-986.
14. Denker, J., Schwartz, D., Wittner, B., Solla, S., Howard, R., Jackel, L., & Hopfield, J. (1987). Large automatic learning, rule extraction, and generalization. Complex Systems, 1, 877-892.
15. Dominey, P. F. (1997). An anatomically structured sensory-motor sequence learning system displays some general linguistic capacities. Brain and Language, 59, 50-75.
16. Dyer, M. G. (1995). Connectionist natural language processing: A status report. In R. Sun & L. A. Bookman (Eds.), Computational architectures integrating neural and symbolic processes. Dordrecht: Kluwer Academic.
17. Elman, J. L. (1988). Finding structure in time (CRL Tech. Rep. No. 8801). La Jolla, CA: UCSD.
18. Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14, 179-211.
19. Elman, J. L. (1991). Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning, 7, 195-224.
20. Elman, J. L. (1993). Learning and development in neural networks: The importance of starting small. Cognition, 48, 71-99.
21. Fahlman, S. E. (1979). NETL: A system for representing and using real-word knowledge. Cambridge, MA: MIT Press.
22. Fahlman, S. E., & Lebiere, C. (1990). The cascade-correlation learning architecture. In D. S. Touretzky (Ed.), Advances in neural information processing systems 2 (pp. 38-51). Los Angeles: Morgan Kaufmann.
23. Feldman, J. A., & Ballard, D. H. (1982). Connectionist models and their properties. Cognitive Science, 6, 205-254.
24. Fodor, J. A. (1975). The language of thought. New York: Crowell.
25. Fodor, J. A., & Pylyshyn, Z. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28, 3-71.
26. Garson, J. W. (1993). Must we solve the binding problem in neural hardware? Behavioral and Brain Sciences, 16, 459-460.
27. Geman, S., Bienstock, E., & Doursat, R. (1992). Neural networks and the bias/variance dilemma. Neural Computation, 4, 1-58.
28. Ghahramani, Z., Wolpert, D. M., & Jordan, M. I. (1996). Generalization to local remappings of the visuomotor coordinate transformation. Journal of Neuroscience, 16, 7085-7096.
29. Goodman, N. (1955). Fact, fiction, and forecast. Indianapolis: Bobbs-Merrill.
30. Hadley, R. F. (1994). Systematicity in connectionist language learning. Mind & Language, 9, 247-272.
31. Halford, G. S., Wilson, W. H., Gray, B., & Phillips, S. (1997). A neural net model for mapping hierarchically structured analogs. Paper presented at the Proceedings of the Fourth Conference of the Australasian Cognitive Science Society, Newcastle, Australia.
32. Hare, M., Elman, J., & Daugherty, K. (1995). Default generalisation in connectionist networks. Language and Cognitive Processes, 10, 601-630.
33. Hinton, G. E. (1986). Learning distributed representations of concepts. In Proceedings of the Eighth Annual Conference of the Cognitive Science Society (pp. 1-12). Hillsdale, NJ: Erlbaum.
34. Hinton, G. E. (Ed.). (1990). Connectionist symbol processing. Cambridge: MIT Press.
35. Hinton, G. E., McClelland, J. L., & Rumelhart, D. E. (1986). Distributed representations. In D. E. Rumelhart, J. L. McClelland, & The PDP Research Group (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition: Vol. 1. Foundations. Cambridge, MA: MIT Press.
36. Holyoak, K. (1991). Symbolic connectionism. In K. A. Ericsson & J. Smith (Eds.), Toward a general theory of expertise. Cambridge: Cambridge Univ. Press.
37. Holyoak, K. J., & Hummel, J. E. (in press). The proper treatment of symbols in a connectionist architecture. In E. Deitrich & A. Markman (Eds.), Cognitive dynamics: Conceptual change in humans and machines. Cambridge, MA: MIT Press.
38. Hong, F. S., & Marcus, G. F. (1996). Neural networks and finite state grammars. Unpublished manuscript, University of Massachusetts.
39. Hummel, J. E., & Biederman, I. (1992). Dynamic binding in a neural network for shape recognition. Psychological Review, 99, 480-517.
40. Hummel, J. E., & Holyoak, K. J. (1993). Distributing structure over time. Brain and Behavioral Sciences, 16, 464.
41. Hummel, J. E., & Holyoak, K. J. (1997). Distributed representations of structure: A theory of analogical access and mapping. Psychological Review, 104, 427-466.
42. Jackendoff, R. (1983). Semantics and cognition. Cambridge, MA: MIT Press.
43. Jacobs, R. A., Jordan, M. a. I., & Barto, A. G. (1991). Task decomposition through competition in a modular connectionist architecture: The what and where vision tasks. Cognitive Science, 15, 219-250.
44. Kirsh, D. (1987). Putting a price on cognition. The Southern Journal of Philosophy, 26 (Suppl.), 119-135. (Reprinted, 1991, in T. Horgan & J. Tienson (Eds.), Connectionism and the philosophy of mind. Dordrecht: Kluwer).
45. Kirsh, D. (1987). Putting a price on cognition. The Southern Journal of Philosophy, 26 (Suppl.), 119-135. (Reprinted, 1991, in T. Horgan & J. Tienson (Eds.), Connectionism and the philosophy of mind. Dordrecht: Kluwer).
46. Kohonen, T. (1984). Self-organization and associative memory. Berlin: Springer-Verlag.
47. Lachter, J., & Bever, T. G. (1988). The relation between linguistic structure and associative theories of language learning: A constructive critique of some connectionist learning models. Cognition, 28, 195-247.
48. Lange, T. E., & Dyer, M. G. (1996). Parallel reasoning in structured connectionist networks: Signatures versus temporal synchrony. Behavioral and Brain Sciences, 19, 328-331.
49. Marcus, G. F. (1996). Why do children say "breaked"? Current Directions in Psychological Science, 5, 81-85.
50. Marcus, G. F. (1999). The algebraic mind: Integrating connectionism and cognitive science. Cambridge, MA: MIT Press.
51. Marcus, G. F., Brinkmann, U., Clahsen, H., Wiese, R., & Pinker, S. (1995). German inflection: The exception that proves the rule. Cognitive Psychology, 29, 186-256.
52. Marcus, G. F., Vijayan, S., Bandi Rao, S., & Vishton, P. M. (in press). 7-month-old infants can learn rules. Science.
53. Marr, D. (1982). Vision. San Francisco: Freeman.
54. McClelland, J. L. (1989). Parallel distributed processing: Implications for cognition and development. In R. G. M. Morris (Ed.), Parallel distributed processing: Implications for psychology and neurobiology (pp. 9-45). Oxford: Oxford Univ. Press.
55. McClelland, J. L., & Rumelhart, D. E. (1986). A distributed model of human learning and memory. In J. L. McClelland, D. E. Rumelhart, & The PDP Research Group (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition: Vol. 2. Psychological and biological models (pp. 170-215). Cambridge, MA: MIT Press.
56. McClelland, J. L., Rumelhart, D. E., & Hinton, G. E. (1986). The appeal of parallel distributed processing. In J. L. McClelland, D. E. Rumelhart, & The PDP Research Group (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition: Vol. 1. Foundations (pp. 365-422). Cambridge, MA: MIT Press.
57. McCloskey, M. (1991). Networks and theories: The place of connectionism in cognitive science. Psychological Science, 2, 387-395.
58. McCloskey, M., & Cohen, N. J. (1989). Catastrophic interference in connectionist networks: The sequential learning problem. In G. H. Bower (Ed.), The Psychology of learning and motivation: Advances in research and theory (pp. 109-165). San Diego: Academic Press.
59. Miller, G., & Chomsky, N. A. (1963). Finitary models of language users. In R. D. Luce, R. R. Bush, & E. Galanter (Eds.), Handbook of mathematical psychology (Vol. II). New York: Wiley.
60. Minsky, M. L., & Papert, S. A. (1969). Perceptions. Cambridge, MA: MIT Press.
61. Mozer, M. C. (1991). The perception of multiple objects: A connectionist approach. Cambridge, MA: MIT Press.
62. Newell, A. (1980). Physical symbol systems. Cognitive Science, 4, 135-183.
63. Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Harvard Univ. Press.
64. Newell, A., & Simon, H. A. (1975). Computer science as empirical inquiry: Symbols and search. Communications of the Association for Computing Machinery, 19, 113-136.
65. Niklasson, L. F., & Gelder, T. V. (1994). On being systematically connectionist. Mind & Language, 9, 288-302.
66. Pavel, M., Gluck, M. A., & Henkle, V. (1988). Generalization by humans and multi-layer adaptive networks. Proceedings of the Cognitive Science Society (pp. 680-687). Hillsdale, NJ: Erlbaum.
67. Pazzani, M., & Dyer, M. (1987). A comparison of concept identification in human learning and network learning with the generalized delta rule. Proceedings of the Tenth International Joint Conference on Artificial Intelligence (pp. 147-150). Los Altos, CA: Kaufmann.
68. Pendlebury, D. A. (1996). Which psychology papers, places, and people have made a mark. APS Observer, 9, 14-18.
69. Phillips, S. (1994). Connectionism and systematicity. Paper presented at the Proceedings of the Fifth Australian Conference on Neural Networks, Brisbane, Australia.
70. Phillips, S., & Halford, G. S. (1997). Systematicity: Psychological evidence with connectionist implications. Paper presented at the Proceedings of the Nineteenth Annual Conference of the Cognitive Science Society, Stanford University.
71. Pinker, S. (1991). Rules of language. Science, 253, 530-555.
72. Pinker, S., & Prince, A. (1988). On language and connectionism: Analysis of a parallel distributed processing model of language acquisition. Cognition, 28, 73-193.
73. Plaut, D. C., McClelland, J. L., Seidenberg, M. S., & Patterson, K. E. (1996). Understanding normal and impaired word reading: Computational principles in quasi-regular domains. Psychological Review, 103, 56-115.
74. Pollack, J. B. (1990). Recursive distributed representations. Artificial Intelligence, 46, 77-105.
75. Prasada, S., & Pinker, S. (1993). Similarity-based and rule-based generalizations in inflectional morphology. Language and Cognitive Processes, 8, 1-56.
76. Pylyshyn, Z. (1984). Computation and cognition: Toward a foundation for cognitive science. Cambridge, MA: MIT Press.
77. Pylyshyn, Z. (1986). Cognitive science and the study of cognition and language. In E. C. Schwab & H. C. Nusbaum (Eds.), Pattern recognition by humans and machines (pp. 295-314). Orlando: Academic Press.
78. Rosenberg, C. R., & Sejnowski, T. J. (1987). Parallel networks that learn to pronounce English text. Complex Systems, 1, 145-168.
79. Rosenblatt, F. (1962). Principles of neurodynamics. New York: Spartan.
80. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986a). Learning internal representations by error propagation. In J. L. McClelland, D. E. Rumelhart, & The PDP Research Group (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition: Vol. I. Foundations. Cambridge, MA: MIT Press.
81. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986b). Learning representations by back-propagating errors. Nature, 323, 533-536.
82. Rumelhart, D. E., & McClelland, J. L. (1986). On learning the past tenses of English verbs. In J. L. McClelland, D. E. Rumelhart, & The PDP Research Group (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition: Vol. 2. Psychological and biological models. Cambridge, MA: MIT Press.
83. Seidenberg, M. S., & McClelland, J. L. (1989). A distributed, developmental model of word recognition and naming. Psychological Review, 96, 523-568.
84. Shastri, L., & Ajjanagadde, V. (1993). From simple associations to systematic reasoning: A connectionist representation of rules, variables, and dynamic bindings using temporal synchrony. Behavioral and Brain Sciences, 16, 417-494.
85. Shultz, T. R., Schmidt, W. C., Buckingham, D., & Mareschal, D. (1995). Modeling cognitive development with a generative connectionist algorithm. In T. J. Simon & G. S. Halford (Eds.), Developing cognitive competence: New approaches to process modeling (pp. 205-261): Hillsdale, NJ: Erlbaum.
86. Smith, E. E., Langston, C., & Nisbett, R. E. (1992). The case for rules in reasoning. Cognitive Science, 16, 1-40.
87. Smolensky, P. (1988). On the proper treatment of connectionism. Behavioral and Brain Sciences, 11, 1-74.
88. Smolensky, P. (1995). Reply: Constituent structure and explanation in an integrated connectionist/symbolic cognitive architecture. In C. Macdonald & G. Macdonald (Eds.), Connectionism: Debates on psychological explanation. Oxford: Blackwell Sci.
89. Sun, R. (1992). On variable binding in connectionist networks. Connection Science, 4, 93-124.
90. Tomasello, M., & Olguin, R. (1993). Twenty-three-month-old children have a grammatical category of noun. Cognitive Development, 8, 451-464.
91. Touretzky, D. S. (1991). Connectionism and compositional semantics. In J. A. Barnden & J. B. Pollack (Eds.), High-level connectionist models (pp. 17-31). Hillsdale, NJ: Erlbaum.
92. Touretzky, D. S., & Hinton, G. E. (1985). Symbols among the neurons. Proceedings IJCAI-85, Los Angeles.
93. Vera, A. H., & Simon, H. A. (1994). Reply to Touretzky and Pomerleau. Cognitive Science, 18, 355-360.
94. von der Malsburg, C. (1981). The correlation theory of brain function (Internal Report No. 81-2). Max-Planck-Insittut for Biophysical Chemistry, Department of Neurobiology.
95. Accepted August 3, 1998