Abstract
Traditional associationist models represent an organism’s knowledge state by a single strength of association on each associative link. Bayesian models instead represent knowledge by a distribution of graded degrees of belief over a range of candidate hypotheses. Many traditional associationist models assume that the learner is passive, adjusting strengths of association only in reaction to stimuli delivered by the environment. Bayesian models, on the other hand, can describe how the learner should actively probe the environment to learn optimally. The first part of this article reviews two Bayesian accounts of backward blocking, a phenomenon that is challenging for many traditional theories. The broad Bayesian framework, in which these models reside, is also selectively reviewed. The second part focuses on two formalizations of optimal active learning: maximizing either the expected information gain or the probability gain. New analyses of optimal active learning by a Kalman filter and by a noisy-logic gate show that these two Bayesian models make different predictions for some environments. The Kalman filter predictions are disconfirmed in at least one case.
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Akgün, A. E., Byrne, J. C., Lynn, G. S., & Keskin, H. (2007). Organizational unlearning as changes in beliefs and routines in organizations. Journal of Organizational Change Management, 20, 794–812.
Baron, J. (1985). Rationality and intelligence. Cambridge: Cambridge University Press.
Courville, A. C., Daw, N. D., Gordon, G. J., & Touretzky, D. S. (2004). Model uncertainty in classical conditioning. In S. Thrun, L. K. Saul, & B. Schölkopf (Eds.), Advances in neural information processing systems (Vol. 16, pp. 977–984). Cambridge, MA: MIT Press, Bradford Books.
Courville, A. C., Daw, N. D., & Touretzky, D. S. (2006). Bayesian theories of conditioning in a changing world. Trends in Cognitive Sciences, 10, 295–300.
Danks, D., Griffiths, T. L., & Tenenbaum, J. B. (2003). Dynamical causal learning. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in neural information processing systems (Vol. 15, pp. 83–90). Cambridge, MA: MIT Press, Bradford Books.
Dayan, P., Kakade, S., & Montague, P. R. (2000). Learning and selective attention. Nature Neuroscience, 3, 1218–1223.
Deneve, S. (2008). Bayesian spiking neurons II: Learning. Neural Computation, 20, 118–145.
Denzler, J., & Brown, C. M. (2002). Information theoretic sensor data selection for active object recognition and state estimation. IEEE Transactions on Pattern Analysis & Machine Intelligence, 24, 145–157.
Doyle, A. C. (1890). The sign of four. London: Spencer Blackett.
Goodman, N. D., Tenenbaum, J. B., Feldman, J., & Griffiths, T. L. (2008). A rational analysis of rule-based concept learning. Cognitive Science, 32, 108–154.
Griffiths, T. L., & Tenenbaum, J. B. (2007). Two proposals for causal grammars. In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation (pp. 323–346). Oxford: Oxford University Press.
Kakade, S., & Dayan, P. (2002). Acquisition and extinction in autoshaping. Psychological Review, 109, 533–544.
Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82, 34–45.
Kamin, L. J. (1968). “Attention-like” processes in classical conditioning. In M. R. Jones (Ed.), Miami symposium on the prediction of behavior: Aversive stimulation (pp. 9–33). Coral Gables, FL: University of Miami Press.
Kemp, C. (2008). The acquisition of inductive constraints. Unpublished doctoral dissertation, Massachusetts Institute of Technology.
Kemp, C., Perfors, A., & Tenenbaum, J. B. (2004). Learning domain structures. In K. Forbus, D. Gentner, & T. Regier (Eds.), Proceedings of the 26th Annual Conference of the Cognitive Science Society (pp. 672–677). Mahwah, NJ: Erlbaum.
Kruschke, J. K. (2001). Toward a unified model of attention in associative learning. Journal of Mathematical Psychology, 45, 812–863.
Kruschke, J. K. (2006a). Locally Bayesian learning. In R. Sun (Ed.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 453–458). Mahwah, NJ: Erlbaum.
Kruschke, J. K. (2006b). Locally Bayesian learning with applications to retrospective revaluation and highlighting. Psychological Review, 113, 677–699.
Laporte, C., & Arbel, T. (2006). Efficient discriminant viewpoint selection for active Bayesian recognition. International Journal of Computer Vision, 68, 267–287.
Lu, H., Yuille, A., Liljeholm, M., Cheng, P. W., & Holyoak, K. J. (2006). Modeling causal learning using Bayesian generic priors on generative and preventive powers. In R. Sun (Ed.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 519–524). Mahwah, NJ: Erlbaum.
Markman, A. B. (1989). LMS rules and the inverse base-rate effect: Comment on Gluck and Bower (1988). Journal of Experimental Psychology: General, 118, 417–421.
Meinhold, R. J., & Singpurwalla, N. D. (1983). Understanding the Kalman filter. American Statistician, 37, 123–127.
Nelson, J. D. (2005). Finding useful questions: On Bayesian diagnosticity, probability, impact, and information gain. Psychological Review, 112, 979–999.
Paletta, L., Prantl, M., & Pinz, A. (2000). Learning temporal context in active object recognition using Bayesian analysis. In Proceedings of the 15th International Conference on Pattern Recognition (Vol. 1, pp. 695–699). New York: IEEE Press.
Pearce, J. M. (1994). Similarity and discrimination: A selective review and a connectionist model. Psychological Review, 101, 587–607.
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.
Shanks, D. R. (1985). Forward and backward blocking in human contingency judgement. Quarterly Journal of Experimental Psychology, 37B, 1–21.
Spence, K. W. (1937). The differential response in animals to stimuli varying within a single dimension. Psychological Review, 44, 430–444.
Spence, K. W. (1950). Cognitive versus stimulus-response theories of learning. Psychological Review, 57, 159–172.
Spence, K. W. (1956). Behavior theory and conditioning. New Haven, CT: Yale University Press.
Spiker, C. C. (1977). Behaviorism, cognitive psychology and the active organism. In N. Datan & H. W. Reese (Eds.), Life-span developmental psychology: Dialectical perspectives on experimental research (pp. 93–103). New York: Academic Press.
Sutton, R. S. (1992). Gain adaptation beats least squares? In Proceedings of the Seventh Annual Yale Workshop on Adaptive and Learning Systems (pp. 161–166). New Haven, CT: Yale University.
Tassoni, C. J. (1995). The least mean squares network with information coding: A model of cue learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 21, 193–204.
Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences, 10, 309–318.
Tenenbaum, J. B., Griffiths, T. L., & Niyogi, S. (2007). Intuitive theories as grammars for causal inference. In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation (pp. 301–322). Oxford: Oxford University Press.
Tong, S., & Koller, D. (2001a). Active learning for parameter estimation in Bayesian networks. In T. K. Leen, T. G. Dietterich, & V. Tresp (Eds.), Advances in neural information processing systems (Vol. 13, pp. 647–653). Cambridge, MA: MIT Press, Bradford Books.
Tong, S., & Koller, D. (2001b). Active learning for structure in Bayesian networks. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI) (Vol. 2, pp. 863–869). Seattle, WA: IJCAI.
Vandorpe, S., & De Houwer, J. (2006). People want to see information that will help them make valid inferences in human causal learning. Memory & Cognition, 34, 1133–1139.
Van Hamme, L. J., & Wasserman, E. A. (1994). Cue competition in causality judgments: The role of nonpresentation of compound stimulus elements. Learning & Motivation, 25, 127–151.
Wagner, A. R. (2003). Context-sensitive elemental theory. Quarterly Journal of Experimental Psychology, 56B, 7–29.
Yin, H., Barnet, R. C., & Miller, R. R. (1994). Second-order conditioning and Pavlovian conditioned inhibition: Operational similarities and differences. Journal of Experimental Psychology: Animal Behavior Processes, 20, 419–428.
Yuille, A., & Lu, H. (in press). The noisy-logical distribution and its application to causal inference. In Advances in neural information processing systems (Vol. 20). Cambridge, MA: MIT Press, Bradford Books.
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For helpful comments on drafts of this article, I thank Rick Hullinger and Ed Wasserman. Additional thanks to Ed Wasserman, John Freeman, and the other organizers of the Spence Fest.
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Kruschke, J.K. Bayesian approaches to associative learning: From passive to active learning. Learning & Behavior 36, 210–226 (2008). https://doi.org/10.3758/LB.36.3.210
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DOI: https://doi.org/10.3758/LB.36.3.210