Generalized Exploratory Model of Human Category Learning

Authors: Toshihiko Matsuka

Abstract:

One problem in evaluating recent computational models of human category learning is that there is no standardized method for systematically comparing the models' assumptions or hypotheses. In the present study, a flexible general model (called GECLE) is introduced that can be used as a framework to systematically manipulate and compare the effects and descriptive validities of a limited number of assumptions at a time. Two example simulation studies are presented to show how the GECLE framework can be useful in the field of human high-order cognition research.

Keywords: artificial intelligence, category learning, cognitive modeling, radial basis functions.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1077161

References:

[1] Kruschke, J. E. (1992). ALCOVE: An exemplar-based connectionist model of category learning, Psychological Review, 99. 22-44.
[2] Kruschke, J.K., & Johansen, M. K. (1999). A model of probabilistic category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25, 1083-1119.
[3] Love, B.C. & Medin, D.L. (1998). SUSTAIN: A model of human category learning. Proceeding of the Fifteenth National Conference on AI (AAAI-98), 671-676.
[4] Poggio, T. & Girosi, F. (1989) A Theory of Networks for Approximation and Learning). AI Memo 1140/CBIP Paper 31, Massachusetts Institute of Technology, Cambridge, MA.
[5] Poggio, T. & Girosi, F. (1990). Regularization algorithms for learning that are equivalent to multilayer networks. Science, 247, 978-982.
[6] Kruschke, J. E. (1993). Three principles for models of category learning. In G. V. Nakamura, R. Taraban, & D. L. Medin (Eds.), Categorization by human and machines: The psychology of learning and motivation (Vol. 29, pp. 57-90). San Diego, CA: Academic Press.
[7] Matsuka, T. & Corter, J. E. (2003). Neural network modeling of category learning using Generalized Radial Basis Functions. Paper presented at 36th Annual Meeting of the Society of Mathematical Psychology. Ogden, UT.
[8] Rosseel, Y. (1996). Connectionist models of categorization: A statistical interpretation. Psychologica Belgica, 36, 93-112
[9] Matsuka, T., Corter, J. E. & Markman, A. B. (2003). Allocation of attention in neural network models of categorization. Under review.
[10] Nosofsky, R.M., Gluck, M.A., Palmeri, T.J., McKinley, S.C., & Glauthier, P. (1994). Comparing models of rule-based classification learning: A replication and extension of Shepard, Hovland, and Jenkins (1961). Memory and Cognition, 22, 352-369.
[11] Haykin, S. (1999). Neural Networks: A Comprehensive Foundation (2nd ed.). Upper Saddle River, NJ: Prentice Hall.
[12] Matsuka, T & Corter, J. E. (2003). Stochastic learning in neural network models of category learning. In Proceedings of the 44th Annual Meeting of the Cognitive Science Society. Boston, MA
[13] Matsuka, T. & Corter, J.E (2004). Stochastic learning algorithm for modeling human category learning. International Journal of Computational Intelligence. Accepted for publication.
[14] Hanson, S. J., & Gluck, M. A. (1991). Spherical units as dynamic consequential regions: Implications for attention and cue-competition in categorization. Advances in Neural Information Processing Systems #3. San Mateo, CA: Morgan Kaufman, 656-665.
[15] Medin, D.L. & Schaffer, M.M. (1978). Context theory of classification learning, Psychological Review, 85, 207-238.
[16] Matsuka, T (2002). Attention processes in computational models of categorization. Unpublished Doctoral Dissertation. Columbia University, NY.
[17] Matsuka, T. & Corter, J. E. (2003). Empirical studies on attention processes in category learning. Poster presented at 44th Annual Meeting of the Psychonomic Society. Vancouver, BC, Canada.
[18] Hardle, W., Hall, P., & Marron, J. S. (1988). How far are automatically chosen regression smoothing parameters from their optimum? Journal of American Statistical Association, 83, 86-05.
[19] Cherkassky, V. & Mulier, F. (1997). Learning from data: Concepts, Theory, and Methods. New York: Wiley
[20] Minda, J.P. & Smith, J.D. (2002). Comparing prototype-based and exemplar-based accounts of category learning and attentional allocation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 275-292.
[21] Nosofsky, R.M. & Zaki, S. R. (2002). Exemplar and prototype models revisited: Response strategies, selective attention, and stimulus generalization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 924-940.
[22] Shanks, D.R. (1991). Categorization by a connectionist Network. Journal of Experimental Psychology: Learning, Memory, and Cognition, 17, 433-443.
[23] Lassaline, M.E. (1990). The basic level in hierarchical classification. Unpublished master-s thesis. University of Illinois, Champaign.
[24] Shepard, R.N., Hovland, C.L., & Jenkins, H.M. (1961). Learning and memorization of classification. Psychological Monograph, 75(13).
[25] Gottwald, R. L. & Garner, W. R. (1975). Filtering and condensation tasks with integral and separable dimensions. Perception & Psychophysics, 2, 50-55.
[26] Ingber, L. (1989). Very fast simulated annealing. Journal of Mathematical Modelling, 12: 967-973.