Computing Machinery and Understanding

Cognitive Science

Volumn 34, Issue 6, 2010, Pages 966-971

  Ramscar, Michael ^a  

^a STANFORD UNIVERSITY   (United States)

Author keywords

Computational metaphors; Natural language; Prediction; Symbolic models

Indexed keywords

EID: 78651503550     PISSN: 03640213     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1551-6709.2010.01120.x     Document Type: Article

References (23)

1. Chomsky, N. (1965). Cartesian linguistics. New York: Harper and Row.
2. Chomsky, N. (2000). New horizons in the study of language and mind. Cambridge, England: Cambridge University Press.
3. Doumas, L. A. A., Holyoak, K. J., & Hummel, J. E. (2006). The Problem with using associations to carry binding information. Behavioral and Brain Sciences, 29, 38-39.
4. Doumas, L. A. A., Hummel, J. E., & Sandhofer, C. M. (2008). A theory of the discovery and predication of relational concepts. Psychological Review, 115, 1-43.
5. Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A., & Vapnik, V. (1997). Support vector regression machines. Advances in Neural Information Processing Systems 9, NIPS 1996 (pp. 155-161). Cambridge, MA: The MIT Press.
6. Fodor, J. A. (1981). Representations: Philosophical essays on the foundations of cognitive science. Cambridge, MA: The MIT Press.
7. Fodor, J. (1998). Concepts: Where cognitive science went wrong. New York: Oxford University Press.
8. Gallistel, C. R. (2003). Conditioning from an information processing perspective. Behavioral Process, 62, 89-101.
9. Goodman, N. D., Tenenbaum, J. B., Feldman, J., & Griffiths, T. L. (2008). A rational analysis of rule-based concept learning. Cognitive Science, 32(1), 108-154.
10. Hawking, S. (1988). A brief history of time. New York: Bantam Books.
11. Hirschberg, J. (Ed.) (1989) Proceedings of the 27th annual meeting of the association for computational linguistics, Vancouver, British Columbia, June 1989. Association for Computational Linguistics.
12. Hummel, J. E. (2010) Symbolic vs. associative learning. Cognitive Science, 34(7), 1.
13. Hummel, J. E., & Holyoak, K. J. (1997). Distributed representations of structure: A theory of analogical access and mapping. Psychological Review, 104, 427-466.
14. Hummel, J. E., & Holyoak, K. J. (2003). A symbolic-connectionist theory of relational inference and generalization. Psychological Review, 110, 220-264.
15. Jaeger, T. F. (2010). Redundancy and reduction: Speakers manage syntactic information density. Cognitive Psychology, 61, 23-62.
16. Lee, G. G., & Im Walde, S. S. (2009) Proceedings of the 47th annual meeting of the association for computational linguistics, Singapore, August 2009. Association for Computational Linguistics.
17. Levy, R. (2008). Expectation-based syntactic comprehension. Cognition, 106(3), 1126-1177.
18. Marr, D. C. (1982). Vision: A computational investigation into the human representation and processing of visual information. New York: Freeman.
19. Ramscar, M., Yarlett, D., Dye, M., Denny, K., & Thorpe, K. (2010) Feature-label-order effects and their implications for symbolic learning. Cognitive Science, 34(7).
20. Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In A. H. Black & W. F. Prokasy (Eds.), Classical Conditioning II: Current research and theory (pp. 64-99). New York: Appleton-Century-Crofts.
21. Rogers, T. T., & McClelland, J. L. (2004). Semantic cognition: A parallel distributed processing approach. Cambridge, MA: MIT Press.
22. Scholz, B. C., & Pullum, G. K. (2006). Irrational nativist exuberance. In R. Stainton (Ed.), Contemporary debates in cognitive science (pp. 59-80). Oxford, England: Basil Blackwel.
23. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59, 433-460.