Statistical models of conditioning

Advances in Neural Information Processing Systems

Volumn , Issue , 1998, Pages 117-123

  Dayan, Peter ^a   Long, Theresa ^b  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b NONE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

INDIVIDUAL PREDICTION; MIXTURE OF EXPERTS; STANDARD MODEL;

FORECASTING;

EID: 84899031338     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. Barto, AG, Sutton, RS & Anderson, CW (1983). IEEE Transactions on Systems, Man, and Cybernetics, 13, pp 834-846.
2. Bordley, RF (1982). Journal of the Operational Research Society, 33, 171-174.
3. Broadbent, DE (1958). Perception and Communication. London: Pergamon.
4. Buhusi, CV & Schmajuk, NA. Hippocampus, 6, 621-642.
5. Dempster, AP, Laird, NM & Rubin, DB (1977). Proceedings of the Royal Statistical Society, B-39, 1-38.
6. Dickinson, A (1980). Contemporary Animal Learning Theory. Cambridge, England: Cambridge University Press.
7. Gabriel, M & Moore, J, editors (1989). Learning and Computational Neuroscience. Cam-bridge, MA: MIT Press.
8. Gallagher, M & Holland, PC (1994). PNAS, 91, 11771-6.
9. Gluck, MA & Thompson, RF (1987). Psychological Reviews, 94, 176-191.
10. Grossberg, S (1982). Psychological Review, 89, 529-572.
11. Holland, PC (1988). Journal of Experimental Psychology: Animal Behavior Processes, 14, 261-279.
12. Jacobs, RA, Jordan, MI & Barto, AG (1991). Cognitive Science, 15, 219-250.
13. Jacobs, RA, Jordan, MI, Nowlan, SJ & Hinton, GE (1991). Neural Computation, 3, 79-87.
14. Konorski, J (1967). Integrative Activity of the Brain. Chicago, II: Chicago University Press.
15. Mackintosh, NJ (1975). Psychological Review, 82, 276-298.
16. Mackintosh, NJ (1983). Conditioning and Associative Learning. Oxford, UK: Oxford University Press.
17. Marr, D (1982). Vision. New York, NY: Freeman.
18. Miller, RR & Matzel, LD (1989). In SB Klein & RR Mowrer, editors, Contemporary Learning Theories: Pavlovian Conditioning and the Status of Traditional Theory. Hillsdale, NJ: Lawrence Erlbaum.
19. Montague, PR, Dayan, P & Sejnowski, TK (1996). Journal of Neuroscience, 16, 1936-1947.
20. Nowlan, SJ (1991). Soft Competitive Adaptation: Neural Network Learning Algorithms Based on Fitting Statistical Mixtures. PhD Thesis, Department of Computer Science, Carnegie- Mellon University.
21. Pearce, JM & Hall, G (1980). Psychological Review, 87, 532-552.
22. Rescorla, RA & Wagner, AR (1972). In AH Black & WF Prokasy, editors, Classical Con-ditioning II: Current Research and Theory, pp 64-69. New York, NY: Appleton-Century- Crofts.
23. Schultz, W, Dayan, P & Montague, PR (1997). Science, 275, 1593-1599.
24. Solomon, RL & Corbit, JD (1974). Psychological Review, 81, 119-145.
25. Sutton, RS & Barto, AG (1981). Psychological Review, 88 2, pp 135-170.
26. Sutton, RS & Barto, AG (1989). In Gabriel & Moore (1989).