Navegar

Colección

Datos Estadísticas

Conferencia

Estamos trabajando para incorporar este artículo al repositorio

Abstract:

A neural network model of operant conditioning for appetitive and aversive stimuli is proposed. From neurobiological and behavioral bases it is assumed that animals are able to compute the prediction of the unconditioned stimulus. The prediction controls the learning of the correct response to obtain reward and to avoid punishment. The model has as inputs: all the conditioned stimuli and the unconditioned stimulus. The outputs are all the possible responses of the animal; each one is computed by one neuron. Based on Hebbian or anti-Hebbian learning, depending on the prediction, the synaptic weights of the response neurons are calculated. The synaptic weights of the neuron computing the prediction are calculated based on the Rescorla-Wagner model. The simulated and experimental data have been compared, showing that the model predicts relevant features of operant conditioning. This model is a theory of operant conditioning and provides principles to design autonomous systems.

Registro:

Documento: Conferencia
Título:Role of unconditioned stimulus prediction in the operant learning: A neural network model
Autor:Lew, S.E.; Wedemeyer, C.; Zanutto, B.S.
Ciudad:Washington, DC
Filiación:Fac. Ing. Univ. de Buenos Aires, 1428 Buenos Aires, Argentina
Palabras clave:Cognitive systems; Computer simulation; Learning systems; Neurophysiology; Probability; Random processes; Hebbian learning; Operant learning; Synaptic weights; Neural networks
Año:2001
Volumen:1
Página de inicio:331
Página de fin:336
Título revista:International Joint Conference on Neural Networks (IJCNN'01)
Título revista abreviado:Proc Int Jt Conf Neural Networks
CODEN:85OFA
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS20731_v1_n_p331_Lew

Referencias:

  • Rescorla, R.A., Wagner, A.R., A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement (1972) Classical Conditioning II: Current Research and Theory, , In: A.H. Black and W.F. Prokasy (Eds.); (New York: Appleton-Century-Crofts)
  • Staddon, J.E.R., (1983) Adaptive Behavior and Learning, , (Cambridge, Cambridge Univ. Press)
  • Schultz, W., Dayan, P., Montague, R., A neural substrate of prediction and reward (1997) Science, 275, pp. 1593-1598
  • Herrnstein, R.J., Method and theory in the study of avoidance (1969) Psychological Review, 76, pp. 49-69
  • Mowrer, O.H., On the dual nature of learning, an interpretation of conditioning and problem solving (1947) Harvard Educational Review, 17, pp. 102-148
  • Seligman, M.E.P., Johnston, J.C., A cognitive theory of avoidance learning (1973) Contemporary Approaches to Conditioning and Learning, , In: F. J. McGuigan and D. B. Lumsdenm (Eds.); (Washington D.C.: Winston-Wiley)
  • Overmier, J.B., Seligman, M.E.P., Effects of inescapable shock upon subsequent escape and avoidance responding (1967) Journal of Comparative and Physiological Psychology, 63, pp. 28-33
  • Amsel, A., (1992) Frustration Theory, , (Cambridge, Cambridge Univ. Press)
  • Dragoi, V., Staddon, J.E.R., The dynamics of operant conditioning (1999) Psychological Review, 106, pp. 20-61
  • Schmajuk, N., Zanutto, B.S., Escape, avoidance and imitation: A neural network approach (1997) Adaptive Behavior, 6, pp. 63-129
  • Zanutto, B.S., Lew, S., A neural network model of aversive behavior (2000) Proceedings of the IASTED International Conference on Neural Networks, pp. 118-123. , Ed: M.H. Hamza. IASTED/ACTA Press. (Anaheim, Calgary)
  • Pycock, C.J., Kerwin, R.W., Carter, C.J., (1980) Nature, 286, pp. 74-76
  • Fuster, J.M., The anatomy of prefrontal cortex (1997) The Prefrontal Cortex, p. 25. , edited by Lippincott-Raven; Philadelphia. New York
  • Watanabe-Saguaguchi, K., Kubota, K., Arikuni, T., Cytoarchitecture and intrafrontal conections of the frontal cortex of the brain of the Hamadryas baboom (Papio hamadryas) (1991) J. Comp. Neurol, 311, pp. 108-133
  • Bates, J.F., Goldman-Rakic, P.S., Prefrontal conections of medial motor areas in the rhesus monkey (1993) J. Comp. Neurol., 336, pp. 211-228
  • Kohonen, T., (1988) Self-Organization and Associative Memory, , Springer-Verlag
  • Crespi, L.P., Quantitative variation in incentive and performance in the whit rat (1942) Am. J. of Psych, 5, pp. 467-517
  • Hebb, D.O., (1949) The Organization of Behavior, a Neuropsychological Theory, , (New York: Wiley)
  • Herrnstein, R.J., Relative and absolute strength of response as a function of frequency of reinforcement (1961) JEAB, 4, pp. 267-272
  • Staddon, J.E.R., Zhang, Y., On the assignment-of-credit problem in operant learning (1991) Neural Network Models of Conditioning and Action, , In: M.L. Commons, S. Grossberg and J.E.R. Staddon (Eds.); (Hillsdale, NJ, Erlbaum)
  • Kacelnik, A., Kreps, J.R., Ens, B., Foraging in a changing environment: An experiment with starlings (1987) Quantitative Analyses of Behavior VI: Foraging, pp. 63-87. , In: M.L. Commons, A. Kacelnik and S.L. Shettleworth (Eds.); (Hillsdale, NJ: Erlbaum)
  • Mackintosh, N.J., (1974) The Psychology of Animal Learning, , (San Diego, CA: Academic Press)
  • Bolles, R.C., Avoidance and escape learning: Simultaneous acquisition of different responses (1969) Journal of Comparative Psychology and Physiological, 68, pp. 355-358
  • Kamin, L.J., The gradient of delay of secondary reward in avoidance learning (1957) Journal of Comparative and Physiological Psychology, 50, pp. 445-449
  • Rescorla, R.A., LoLordo, V.M., Inhibition of avoidance behavior (1965) J. of Comp. and Physiol. Psych., 59, pp. 406-412
  • Overmier, J.B., Bull, J.A., On the independence of stimulus control of avoidance (1969) Journal of Experimental Psychology, 79, pp. 464-467
  • Seligman, M.E.P., Rosellini, R.A., Kozak, M.J., Learned helplessness in the rat: Time course, immunization and reversibility (1975) Journal of Comparative and Physiological Psychology, 88, pp. 542-547
  • Gray, J.A., (1971) The Psychology of Fear and Stress, , (London: Weidenfeld and Nicholson)
  • Herrnstein, R.J., Hineline, D.H., Negative reinforcement as shock-frequency reduction (1966) Journal of the Experimental Analysis of Behavior, 9, pp. 421-430

Citas:

---------- APA ----------
Lew, S.E., Wedemeyer, C. & Zanutto, B.S. (2001) . Role of unconditioned stimulus prediction in the operant learning: A neural network model. International Joint Conference on Neural Networks (IJCNN'01), 1, 331-336.
Recuperado de https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS20731_v1_n_p331_Lew [ ]
---------- CHICAGO ----------
Lew, S.E., Wedemeyer, C., Zanutto, B.S. "Role of unconditioned stimulus prediction in the operant learning: A neural network model" . International Joint Conference on Neural Networks (IJCNN'01) 1 (2001) : 331-336.
Recuperado de https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS20731_v1_n_p331_Lew [ ]
---------- MLA ----------
Lew, S.E., Wedemeyer, C., Zanutto, B.S. "Role of unconditioned stimulus prediction in the operant learning: A neural network model" . International Joint Conference on Neural Networks (IJCNN'01), vol. 1, 2001, pp. 331-336.
Recuperado de https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS20731_v1_n_p331_Lew [ ]
---------- VANCOUVER ----------
Lew, S.E., Wedemeyer, C., Zanutto, B.S. Role of unconditioned stimulus prediction in the operant learning: A neural network model. Proc Int Jt Conf Neural Networks. 2001;1:331-336.
Available from: https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS20731_v1_n_p331_Lew [ ]