Generalized Exploratory Model of Human Category Learning
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:60411", author = "Toshihiko Matsuka", title = "Generalized Exploratory Model of Human Category Learning ", 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.", volume = "1", number = "4", pages = "1090-9", }
