nips nips2009 nips2009-109 nips2009-109-reference knowledge-graph by maker-knowledge-mining

109 nips-2009-Hierarchical Learning of Dimensional Biases in Human Categorization


Source: pdf

Author: Adam Sanborn, Nick Chater, Katherine A. Heller

Abstract: Existing models of categorization typically represent to-be-classified items as points in a multidimensional space. While from a mathematical point of view, an infinite number of basis sets can be used to represent points in this space, the choice of basis set is psychologically crucial. People generally choose the same basis dimensions – and have a strong preference to generalize along the axes of these dimensions, but not “diagonally”. What makes some choices of dimension special? We explore the idea that the dimensions used by people echo the natural variation in the environment. Specifically, we present a rational model that does not assume dimensions, but learns the same type of dimensional generalizations that people display. This bias is shaped by exposing the model to many categories with a structure hypothesized to be like those which children encounter. The learning behaviour of the model captures the developmental shift from roughly “isotropic” for children to the axis-aligned generalization that adults show. 1


reference text

[1] E. Rosch, C. B. Mervis, W. D. Gray, D. M. Johnson, and P. Boyes-Braem. Basic objects in natural categories. Cognitive Psychology, 8:382–439, 1976.

[2] R. N. Shepard. Toward a universal law of generalization for psychological science. Science, 237:1317– 1323, 1987.

[3] W. R. Garner. The processing of information and structure. Erlbaum, Hillsdale, NJ, 1974.

[4] J. K. Kruschke. Human category learning: implications for backpropagation models. Connection Science, 5:3–36, 1993.

[5] A. Tversky and I. Gati. Similarity, separability and the triangular inequality. Psychological Review, 93:3–22, 1982.

[6] L. B. Smith and Kemler D. G. Developmental trends in free classification: Evidence for a new conceptualization of perceptual development. Journal of Experimental Child Psychology, 24:279–298, 1977.

[7] L. B. Smith. A model of perceptual classification in children and adults. Psychological Review, 96:125– 144, 1989.

[8] S. K. Reed. Pattern recognition and categorization. Cognitive Psychology, 3:393–407, 1972.

[9] D. L. Medin and M. M. Schaffer. Context theory of classification learning. Psychological Review, 85:207– 238, 1978.

[10] R. M. Nosofsky. Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115:39–57, 1986.

[11] J. R. Anderson. The adaptive nature of human categorization. Psychological Review, 98(3):409–429, 1991.

[12] D. J. Navarro. From natural kinds to complex categories. In Proceedings of CogSci, pages 621–626, Mahwah, NJ, 2006. Lawrence Erlbaum.

[13] J. B. Tenenbaum and T. L. Griffiths. Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24:629–641, 2001.

[14] M. K. Johansen and T. J. Palmeri. Are there representational shifts in category learning? Psychology, 45:482–553, 2002. Cognitive

[15] John K. Kruschke. Alcove: An exemplar-based connectionist model of category learning. Psychological Review, 99:22–44, 1992.

[16] B. C. Love, D. L. Medin, and T. M. Gureckis. SUSTAIN: A network model of category learning. Psychological Review, 111:309–332, 2004.

[17] T. L. Griffiths, K. R. Canini, A. N. Sanborn, and D. J. Navarro. Unifying rational models of categorization via the hierarchical dirichlet process. In R. Sun and N. Miyake, editors, Proceedings CogSci, 2007.

[18] D. Blackwell and J. MacQueen. Ferguson distributions via Polya urn schemes. The Annals of Statistics, 1:353–355, 1973. ´

[19] D. Aldous. Exchangeability and related topics. In Ecole d’´ t´ de probabilit´ s de Saint-Flour, XIII—1983, ee e pages 1–198. Springer, Berlin, 1985.

[20] T. L. Griffiths, M. Steyvers, and J. B. Tenenbaum. Topics in semantic representation. Psychological Review, 114:211–244, 2007.

[21] R. M. Nosofsky and S. R. Zaki. Exemplar and prototype models revisted: response strategies, selective attention, and stimulus generalization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28:924–940, 2002.

[22] A. Perfors and J.B. Tenenbaum. Learning to learn categories. In Proceedings of CogSci, 2009.

[23] E. Colunga and L. B. Smith. From the lexicon to expectations about kinds: a role for associative learning. Psychological Review, 112, 2005.

[24] C. Kemp, A. Perfors, and J. B. Tenenbaum. Learning overhypotheses with hierarchical Bayesian models. Developmental Science, 10:307–321, 2007.

[25] N. D. Goodman, J. B. Tenenbaum, J. Feldman, and T. L. Griffiths. A rational analysis of rule-based concept learning. Cognitive Science, 32:108–154, 2008.

[26] E. Sudderth, A. Torralba, W. Freeman, and A. Willsky. Describing visual scenes using transformed dirichlet processes. In Neural Information Processing Systems NIPS, 2005.

[27] R. M. Nosofsky and T. J. Palmeri. A rule-plus-exception model for classifying objects in continuousdimension spaces. Psychonomic Bulletin & Review, 5:345–369, 1998. 9