Stochastic Learning Algorithms for Modeling Human Category Learning

Authors: Toshihiko Matsuka, James E. Corter

Abstract:

Most neural network (NN) models of human category learning use a gradient-based learning method, which assumes that locally-optimal changes are made to model parameters on each learning trial. This method tends to under predict variability in individual-level cognitive processes. In addition many recent models of human category learning have been criticized for not being able to replicate rapid changes in categorization accuracy and attention processes observed in empirical studies. In this paper we introduce stochastic learning algorithms for NN models of human category learning and show that use of the algorithms can result in (a) rapid changes in accuracy and attention allocation, and (b) different learning trajectories and more realistic variability at the individual-level.

Keywords: category learning, cognitive modeling, radial basis function, stochastic optimization.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1329843

References:

[1] Kruschke, J. K. (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] Matsuka, T. (2002). Attention processes in computational models of category learning. Unpublished doctoral dissertation. Columbia University, New York, NY.
[5] 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.
[6] Matsuka, T., Corter, J. E. & Markman, A. B. (2003). Allocation of attention in neural network models of categorization. Under review
[7] Bower, G. H. & Trabasso, T. R. (1963). Reversals prior to solution in concept identification. Journal of Experimental Psychology, 66, 409-418.
[8] Rehder, B. & Hoffman, A. B. (2003). Eyetracking and selective attention in category learning
[CD-ROM]. Proceedings of the 25th Annual Meeting of the Cognitive Science Society, Boston, 2003.
[9] Macho, S. (1997). Effect of relevance shifts in category acquisition: A test of neural networks. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 30-53.
[10] Ingber, L. (1998). Very fast simulated annealing. Journal of Mathematical Modelling, 12: 967-973.
[11] Nosofsky, R. M. (1986). Attention, similarity and the identification -categorization relationship. Journal of Experimental Psychology: General, 115, 39-57
[12] Nosofsky, R. M., Palmeri, T. J., McKinley, S. C. (1994). Rule-plus-exception model of classification learning. Psychological Review, 101, 53-79
[13] Shepard, R. N., Hovland, C. L., & Jenkins, H. M. (1961). Learning and memorization of classification. Psychological Monograph, 75 (13).
[14] Bettman, J. R., Johnson, E. J., Luce, M. F., Payne, J. W. (1993). Correlation, conflict, and Choice. Journal of Experimental Psychology: Learning, Memory, and Cognition, 19, 931-951.
[15] 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.
[16] Matsuka, T. (In press). Generalized exploratory model of human category learning. International Journal of Computational Intelligence.