Dynamics of a classical conditioning model

Autonomous Robots

Volumn 7, Issue 1, 1999, Pages 41-56

  Balkenius, Christian ^a   Morén, Jan ^a  

^a LUND UNIVERSITY COGNITIVE SCIENCE   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

CONVERGENCE OF NUMERICAL METHODS; MATHEMATICAL MODELS; SENSORS;

REINFORCEMENT LEARNING;

ROBOT LEARNING;

EID: 0032682232     PISSN: 09295593     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1008965713435     Document Type: Article

References (47)

1. Balkenius, C. 1995. Natural intelligence in artificial creatures, Lund University Cognitive Studies 37.
2. Balkenius, C. 1996. Generalization in instrumental learning. In From Animals to Animats 4: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, P. Maes, M. Mataric, J.-A. Meyer, J. Pollack, and S.W. Wilson (Eds.), MIT Press/Bradford Books: Cambridge, MA.
3. Balkenius, C. 1998. A neural network model of classical conditioning I: The dynamics of learning, Lund University Cognitive Studies 68.
4. Balkenius, C. and Morén, J. 1998. Computational models of classical conditioning: A comparative study. In From Animals to Animals 5: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior, R. Pfeifer, B. Blumberg, J.-A. Meyer, and S.W. Wilson (Eds.), MIT Press/Bradford Books: Cambridge, MA.
5. Balleine, B. 1992. Instrumental performance following a shift in primary motivation depends on incentive learning. Journal of Experimental Psychology: Animal Behavior Processes, 18:236-250.
6. Balleine, B., Garner, C., Gonzalez, F., and Dickinson, A. 1995. Motivational control of heterogeneous instrumental chains. Journal of Experimental Psychology: Animal Behavior Processes, 21:203-217.
7. Barto, A.G., Sutton, R.S., and Watkins, C.J.C.H. 1990. Learning and sequential decision making. In Learning and Computational Neuroscience: Foundations of Adaptive Networks, M. Gabriel and J. Moore (Eds.), MIT Press: Cambridge, MA, pp. 539-602.
8. Bouton, M.E. 1991. Context and retrieval in extinction and in other examples of interference in simple associative learning. In Current Topics in Animal Learning: Brain, Emotion and Cognition, L. Dachowski and C.F. Flaherty (Eds.), Erlbaum: Hillsdale, NJ.
9. Desmond, J.E. 1990. Temporally adaptive responses in neural models: The stimulus trace. In Learning and Computational Neuroscience: Foundations of Adaptive Networks, M. Gabriel and J. Moore (Eds.), MIT Press: Cambridge, MA, pp. 421-456.
10. Donahoe, J.W. and Palmer, D.C. 1994. Learning and Complex Behavior, Allyn & Bacon: Boston.
11. Gaffan, D. 1992. Amygdala and the memory of reward. In The Amygdala: Neurobiological Aspects of Emotion, Memory, and Mental Dysfunction, J.P. Aggleton (Ed.), Wiley-Liss: New York, pp. 471-484.
12. Gallistel, C.R. 1990. The Organization of Learning, MIT Press: Cambridge, MA.
13. Gray, J.A. 1975. Elements of a Two-Process Theory of Learning, Academic Press: London.
14. Grossberg, S. 1974. Classical and Instrumental Learning by Neural Networks. In Progress in Theoretical Biology, Academic Press: New York, Vol. 3.
15. Grossberg, S. 1987. The Adaptive Brain, North-Holland: Amsterdam.
16. Hassoun, M.H. 1995. Fundamentals of Artificial Neural Networks, MIT Press: Cambridge, MA.
17. Holland, P.C. 1992. Occasion setting in Pavlovian conditioning. In The Psychology of Learning and Motivation, D. Medin (Ed.), Academic Press: San Diego, CA, Vol. 28, pp. 69-125.
18. Hull, C.L. 1932. The goal-gradient hypothesis and maze learning. Psychological Review, 39(1):25-43.
19. Kaelbling, L.P., Littman, M.L., and Moore, A.W 1996. Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4:237-285.
20. Kamin, L.J. 1968. Attention-like processes in classical conditioning. In Miami Symposium on the Prediction of Behavior: Aversive Stimulation, M.R. Jones (Ed.), University of Miami Press: Miami, pp. 9-31.
21. Kehoe, E.J. 1982. Conditioning with serial compound stimuli: Theoretical and empirical issues. Experimental Animal Behavior, 1:30-65.
22. Kehoe, E.J. 1990. Classical conditioning: Fundamental issues for adaptive network models. In Learning and Computational Neuroscience: Foundations of Adaptive Networks , M. Gabriel and J. Moore (Eds.), MIT Press: Cambridge, MA, pp. 390-420.
23. Klopf, A.H. 1988. A neuronal model of classical conditioning. Psychobiology, 16(2):85-125.
24. Klopf, A.H., Morgan, J.S., and Weaver, S.E. 1993. A hierarchical network of control systems that learn: Modeling nervous system function during classical and instrumental conditioning. Adaptive Behavior, 1(3):263-319.
25. Machado, A. 1997. Learning the temporal dynamics of behavior. Psychological Review, 104:241-265.
26. Mackintosh, N.J. 1975. A theory of attention: Variations in the associability of stimuli with reinforcement. Psychological Review, 82:276-298.
27. Mackintosh, N.J. 1983. Conditioning and Associative Learning, Oxford University Press: Oxford.
28. Minsky, M. and Papert, S. 1988. Perceptrons - Expanded Edition, MIT Press: Cambridge, MA.
29. Moore, J.W. and Choi, J.-C. 1998. Conditioned stimuli are occasion setters. In Occasion Setting: Associative Learning and Cognition in Animals, N.A. Schmajuk and P.C. Holland (Eds.), American Psychological Association: Washington, D.C.
30. Mowrer, O.H. 1973. Learning Theory and Behavior, Wiley: New York.
31. Pavlov, I.P. 1927. Conditioned Reflexes, Oxford University Press: Oxford.
32. Rescorla, R.A. and Wagner, A.R. 1972. A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and non-reinforcement. In Classical Conditioning II: Current Research and Theory, A.H. Black and W.F. Prokasy (Eds.), Appleton-Century-Crofts: New York, pp. 64-99.
33. Rescorla, R.A. 1985. Inhibition and facilitation. In Information Processing in Animals: Conditioned Inhibition, R.R. Miller and N.E. Spear (Eds.), Erlbaum: Hillsdale, NJ, pp. 299-326.
34. Rosenblatt, F. 1959. Principles of Neurodynamics, Spartan Books: New York.
35. Schmajuk, N.A. 1990. Role of the hippocampus in temporal and spatial navigation: An adaptive neural network. Behavioral Brain Research, 39:205-229.
36. Schmajuk, N.A. and DiCarlo, J.J. 1992. Stimulus configuration, classical conditioning, and hippocampal function. Psychological Review, 99:268-305.
37. Schmajuk, N.A. and Thieme, A.D. 1992. Purposive behavior and cognitive mapping: A neural network model. Biological Cybernetics, 67:165-174.
38. Schmajuk, N.A., 1997. Animal Learning and Cognition: A Neural Network Approach, Cambridge University Press.
39. Schmajuk, N.A. and Holland, P.C. (Eds.). 1998. Occasion Setting: Associative Learning and Cognition in Animals, American Psychological Association: Washington, DC.
40. Schneiderman, N. 1966. Interstimulus interval function of the nictitating membrane response of the rabbit under delay versus trace conditioning. Journal of Comparative and Physiological Psychology, 62:397-402.
41. Schneiderman, N., Fuentes, I., and Gormezano, I. 1962. Acquisition and extinction of the classically conditioned eyelid response in the albino rabbit. Science, 136:650-652.
42. Schneiderman, N. and Gormezano, I. 1964. Conditioning of the nictitating membrane of the rabbit as a function as the CS-US interval. Journal of Comparative and Physiological Psychology, 57:188-195.
43. Smith, M.C., Coleman, S.R., and Gormezano, I. 1969. Classical conditioning of the rabbits nictitating membrane response at backward, simultaneous, and forward CS-US interval. Journal of Comparative Physiological Psychology, 69:226-231.
44. Staddon, J.E.R and Higa, J.J. 1996. Multiple time scales in simple habituation. Psychological Review, 103:720-733.
45. Sutton, R.S. and Barto, A.G. 1990. Time-derivative models of Pavlovian reinforcement. In Learning and Computational Neuroscience: Foundations of Adaptive Networks, M. Gabriel and J. Moore (Eds.), MIT Press: Cambridge, MA, pp. 497-538.
46. Sutton, R.S. and Barto, A.G. 1998. Reinforcement Learning: An Introduction, MIT Press: Cambridge, MA.
47. Watkins, C.J.C.H. 1992. Q-learning. Machine Learning, 8:279-292.