nips nips2010 nips2010-155 nips2010-155-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Dan Navarro
Abstract: This paper outlines a hierarchical Bayesian model for human category learning that learns both the organization of objects into categories, and the context in which this knowledge should be applied. The model is fit to multiple data sets, and provides a parsimonious method for describing how humans learn context specific conceptual representations.
[1] W. G. Chase and H. A. Simon. Perception in chess. Cognitive Psychology, 4:55–81, 1973.
[2] S. Lewandowsky and K. Kirsner. Knowledge partitioning: Context-dependent use of expertise. Memory and Cognition, 28:295–305, 2000.
[3] L.-X. Yang and S. Lewandowsky. Context-gated knowledge partitioning in categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29:663–679, 2003.
[4] L.-X. Yang and S. Lewandowsky. Knowledge partitioning in categorization: Constraints on exemplar models. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30:1045–1064, 2004.
[5] M. L. Kalish, S. Lewandowsky, and J. K. Kruschke. Population of linear experts: Knowledge partitioning in function learning. Psychological Review, 111:1072–1099, 2004.
[6] S. Lewandowsky, L. Roberts, and L.-X. Yang. Knowledge partitioning in category learning: Boundary conditions. Memory and Cognition, 38:1676–1688, 2006.
[7] J. R. Anderson. The adaptive nature of human categorization. Psychological Review, 98: 409–429, 1991. ´
[8] D. Aldous. Exchangeability and related topics. In Ecole d’´ t´ de probabilit´ s de Saint-Flour, ee e XIII-1983, pages 1–198. Springer, Berlin, 1985.
[9] R. N. Shepard. Integrality versus separability of stimulus dimensions: From an early convergence of evidence to a proposed theoretical basis. In J. R. Pomerantz and G. L. Lockhead, editors, The Perception of Structure: Essays in Honor of Wendell R. Garner, pages 53–71. American Psychological Association, Washington, DC, 1991.
[10] A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin. Bayesian Data Analysis. Chapman and Hall, Boca Raton, 2nd edition, 2004.
[11] C. E. Antoniak. Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. Annals of Statistics, 2:1152–1174, 1974.
[12] L. Hubert and P. Arabie. Comparing partitions. Journal of Classification, 2:193–218, 1985.
[13] L. Valiant. A theory of the learnable. Communications of the ACM, 27:1134–1142, 1984.
[14] M. A. Erickson and J. K. Kruschke. Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127:107–140, 1998.
[15] C. Kemp, A. Perfors, and J. B. Tenenbaum. Learning overhypotheses with hierarchical Bayesian models. Developmental Science, 10:307–332, 2007.
[16] A. Perfors and J. B. Tenenbaum. Learning to learn categories. In N. Taatgen, H. van Rijn, L. Schomaker, and J. Nerbonne, editors, Proceedings of the 31st Annual Conference of the Cognitive Science Society, pages 136–141, Austin, TX, 2009. Cognitive Science Society.
[17] D. J. Navarro. From natural kinds to complex categories. In R. Sun and N. Miyake, editors, Proceedings of the 28th Annual Conference of the Cognitive Science Society, pages 621–626, Mahwah, NJ, 2006. Lawrence Erlbaum.
[18] T. L. Griffiths, K. R. Canini, A. N. Sanborn, and D. J. Navarro. Unifying rational models of categorization via the hierarchical Dirichlet process. In D. S. McNamara and J. G. Trafton, editors, Proceedings of the 29th Annual Conference of the Cognitive Science Society, pages 323–328, Austin, TX, 2007. Cognitive Science Society.
[19] K. Heller, A. N. Sanborn, and N. Chater. Hierarchical learning of dimensional biases in human categorization. In J. Lafferty and C. Williams, editors, Advances in Neural Information Processing Systems 22, Cambridge, MA, 2009. MIT Press.
[20] A. N. Sanborn, T. L. Griffiths, and D. J. Navarro. Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review, in press.
[21] J. K. Kruschke. ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99:22–44, 1992.
[22] R. Neal. Bayesian learning for neural networks. Springer-Verlag, New York, 1996. 9