Matsuka, T., Corter, J.E. & Markman, A. B. (2010). Some Attention Learning “Biases” in Adaptive Network Models of Categorization In The Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. xx-xx). Austin, TX: Cognitive Science Society. [PDF]

Honda, H., & Matsuka, T. (2010). Speaker's choise of frame based on rarity information. In The Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. xx-xx). Austin, TX: Cognitive Science Society. [PDF]

本田秀仁・松香敏彦（2010）. 適応的推論を生み出す知識の形成：個人学習・コ ミュニケーションが果たす役割. 人工知能学会研究会資料, SIG-KBS-A904, 61-68. [PDF]

Matsuka, T. & Honda, T. (2010). Kansei-driven algorithm as a descriptive model of human learning. The 3rd International Workshop on Kansei. [PDF]

Mutou, S., Honda, H. and Matsuka, T. (2010). Explaining context dependency in categorization using a Kansei-driven cognitive model. The 3rd International Workshop on Kansei. [PDF]

松香　本田　吉川　(2010). プロトタイプ理論再考．認知科学 17 [PDF]
Matsuka, T., Honda, H., & Yoshikawa, S. (2010). Rethinking Prototype Theory of Category Learning. Cognitive Studies, 17 XX-XX.

Hatagami, Y. & Matsuka, T. (2009). Text Mining with an Augmented Version of the Bisecting K-means Algorithm. ICONIP 200In Artificial Neural Networks, LNCS Vol.5864, (pp.352 -359). Berlin: Springer-Verlag [PDF].

Matsuka, T. Honda, H., Kiyokawa, S., & Chouchourelou, A.. (2009). On the Knowledge Organization in Concept Formation: An Exploratory Cognitive Modeling Study. In Artificial Neural Networks, LNCS Vol.5768, (pp. 678 -687). Berlin: Springer-Verlag [PDF]

Honda, H., & Matsuka, T. (2009). The use of familiarity in inferences: An experimental study. In N. A. Taatgen & H. van Rijn (Eds.), The Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 2353-2358). Austin, TX: Cognitive Science Society. [PDF]

Matsuka, T. Honda, H., Kiyokawa, S., & Chouchourelou, A.. (2009). Modeling high-order human intelligence with intelligence of swarm .In Proc. IJCNN2009. IEEE. [PDF]

Matsuka, T. Sakamoto, Y, Chouchourelou, A. & Nickerson, J. V. (2008). Toward a descriptive cognitive model of human learning. Neurocomputing, 71, 2446-2455. [PDF]

Matsuka, T & Corter, J. E. (2008). Observed attention allocation processes in category learning. Quarterly Journal of Experimental Psychology, 61, 1067-1097. [PDF]

Matsuka, T. Sakamoto, Y, & Chouchourelou, A. (2008). Modeling a flexible representation machinery of human concept learning. Neural Networks, 21, 289-302. [PDF]

Hanson, S. J., Hanson, C., Halchenko, Y., Matsuka, T., & Zaimi, A. (2007). Bottom-up and top-down brain functional connectivity underlyings comprehension of every day visual action. Brain Structure and Functions, 212, 231-244. [PDF]

Matsuka, T. & Sakamoto, Y. (2007). A Cognitive Model That Describes the Influence of Prior Knowledge on Concept Learning. In Artificial Neural Networks, Lecture Notes on Computer Science (LNCS) Vol. 4668, (pp. 912 -921). Berlin: Springer-Verlag. [PDF]

Sakamoto, Y. & Matsuka T. (2007). A weight decay model of human category learning. In Proc. IJCNN2007. IEEE [PDF]

Matsuka, T. & Sakamoto, Y. (2007). Integrating a flexible representation machinery in a model of human concept learning. In Proc. IJCNN2007. IEEE [PDF]

Matsuka, T. & Sakamoto, Y. (2007). A Model of Concept Formation with a Flexible Representation System. In Advances in Neural Networks, Lecture Notes on Computer Science (LNCS) Vol. 4491 Part I., (pp. 1139 -1147). Berlin: Springer-Verlag. [PDF]

Matsuka, T., Sakamoto, Y., Nickerson, J. V., & Chouchourelou, A. (2006). A Cognitive Model of Multi-Objective Multi-Concept Formation. In Artificial Neural Networks, Lecture Notes on Computer Science (LNCS) Vol. 4131, (pp. 563 - 572). Berlin: Springer-Verlag. [PDF]

Jian, J-Y., Matsuka, T., & Nickerson, J. V. (2006). Towards Deceptive Intention: Finding Trajectories and its Analysis. HFES

Jian, J-Y., Matsuka, T., & Nickerson, J. V. (2006) Recognizing Deception in Trajectories. In Proc. CogSci06. [PDF]

Matsuka, T., Nickerson, J. V., & Jian, J. (2006). A prototype model that learns and generalize Medin, Alton, Edelson, & Frecko (1982) XOR category structure as humans do. In Proc. CogSci06 [PDF]

Matsuka, T. & Chouchourelou, A. (2006). A model of human category learning with dynamic multi-objective hypotheses testing with retrospective verification. In Proc. WCCI06 -IJCNN2006. [PDF]

Matsuka, T., & Nickerson, J. V. (2006). Modeling human hypothesis testing behaviors with simulated evolutionary processes. In Proc. WCCI06 - CEC2006. [PDF]

Chouchourelou, A., Matsuka, T, Harber, K., & Shiffrar, M. (2006). The visual analysis of Emotional Actions. Social Neuroscience, 1, 63-74. [detail]

Matsuka, T. (2006). A Model of Category Learning with Attention Augmented Simplistic Prototype Representation. In Advances in Neural Networks, Lecture Notes in Computer Science (LNCS) Vol.3971, (pp. 34 - 40). Berlin: Springer-Verlag. [PDF]

Matsuka, T. & Chouchourelou, A. (2006). On the learning algorithms of descriptive models of high-order human cognition. In Advances in Neural Networks, Lecture Notes in Computer Science (LNCS) Vol.3971, (pp. 41 - 49). Berlin: Springer-Verlag. [PDF]

Matsuka, T. (2005). Modeling human learning as context dependent knowledge utility optimization. In L. Wang, K. Chen, Y. S. Ong (Eds.) Advances in Natural Computation, Lecture Notes in Computer Science (LNCS) Vol. 3601, (pp. 933-946). Berlin: Springer-Verlag. [PDF]

Matsuka, T. , Yamauchi, T., Hanson, C., Hanson, & S. J. (2005). Representing categorical knowledge: An fMRI Study. Proceedings of the 27 th Annual Meeting of the Cognitive Science Society . (pp. xxx-xxx). Mahwah, NJ. Lawrence Erlbaum Associates. [PDF]

Matsuka, T. (2005). Simple, individually unique, and context-dependent learning methods for models of human category learning. Behavior Research Methods, 37, 240–255. [PDF]

Matsuka, T. & Corter, J. E. (2004). Stochastic learning algorithm for modeling human category learning. International Journal of Computational Intelligence , 1 , 40-48. [PDF]

Matsuka, T. (2004). Comparisons of prototype- and exemplar-based neural network models of categorization using the GECLE framework. In Proceedings of the 26 th Annual Meeting of the Cognitive Science Society . (pp. 909-914). Mahwah, NJ. Lawrence Erlbaum Associates. [PDF]

Matsuka, T. (2004). Biased stochastic learning in computational model of category learning. In Proceedings of the 26 th Annual Meeting of the Cognitive Science Society . (pp. 915-920). Mahwah, NJ. Lawrence Erlbaum Associates. [PDF]

Hanson, S. J., Matsuka, T. , & Haxby, J. V. (2004). Combinatoric codes in ventral medial temporal lobes for objects: Haxby revisited: Is there a "face" area? NeuroImage, 23 . 156-166. [PDF]

Matsuka, T. (2004). Generalized exploratory model of human category learning. International Journal of Computational Intelligence, 1. 7-15 . [PDF]

Sakamoto, Y., Matsuka, T. & Love, B.C. (2004). Dimension-wide vs. exemplar-specific attention in category learning and recognition. In Proceedings of the 6 th International Meeting of Cognitive Modelling . (pp. 261-266). Mahwah, NJ. Lawrence Erlbaum Associates. [PDF]

Matsuka, T. , Corter, J. E.& Hanson, S. J. (2004) Irresistibly attractive fruitless feature dimensions. In Proceedings of the 6 th International Meeting of Cognitive Modelling . (pp. 370-371). Mahwah, NJ. Lawrence Erlbaum Associates. [PDF]

Matsuka, T. & Corter, J. E. (2004). Modeling human category learning with stochastic optimization methods. In Proceedings of the 6 th International Meeting of Cognitive Modelling . (pp. 196-201). Mahwah, NJ. Lawrence Erlbaum Associates. [PDF]

Matsuka, T. (2004). Exploratory approach for modeling human category learning. In Proceedings of the 6 th International Meeting of Cognitive Modelling . (pp. 190-195). Mahwah, NJ. Lawrence Erlbaum Associates. [PDF]

Matsuka, T. & Corter, J. E. (2003). Stochastic learning in neural network models of categorization. In Proceedings of the 25 th Annual Meeting of the Cognitive Science Society.

Matsuka, T. , Corter, J.E. & Markman, A.B. (2002). Allocation of attention in neural network models of categorization. In Proceedings of the 24 th Annual Meeting of the Cognitive Science Society.