@article{(Open Science Index):https://publications.waset.org/pdf/1202,
	  title     = {Stochastic Learning Algorithms for Modeling Human Category Learning},
	  author    = {Toshihiko Matsuka and  James E. Corter},
	  country	= {},
	  institution	= {},
	  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. },
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {1},
	  number    = {4},
	  year      = {2007},
	  pages     = {1170 - 1178},
	  ee        = {https://publications.waset.org/pdf/1202},
	  url   	= {https://publications.waset.org/vol/4},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 4, 2007},
	}