Articles in journal or book chapters

1. B. M. Bly, D. Rebbechi, S. J. Hanson, and G. Grasso. The RUMBA software: tools for neuroimaging data analysis. Neuroinformatics, 2(1):71-100, 2004. Keyword(s): *Algorithms, Animals, Brain Mapping/*methods, Human, Image Processing, Computer-Assisted/*methods, Magnetic Resonance Imaging/*methods, Programming Languages, Reproducibility of Results, Signal Processing, Computer-Assisted, Software/*standards/trends, Software Validation, Support, Non-U.S. Gov't, User-Computer Interface. [Abstract] [bibtex-entry]



2. Y. O. Halchenko, B. A. Pearlmutter, S. J. Hanson, and A. Zaimi. Fusion of functional brain imaging modalities via linear programming. Biomedizinische Technik (Biomedical Engineering), 48(2):102-104, 2004. Keyword(s): EEG, fMRI, fusion. [bibtex-entry]



3. S. J. Hanson, T. Matsuka, and J. V. Haxby. Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a face area?. Neuroimage, 23(1):156-66, 2004. Keyword(s): Adult, Brain Mapping, Discrimination Learning/physiology, Humans, *Image Processing, Computer-Assisted/statistics & numerical data, *Magnetic Resonance Imaging/statistics & numerical data, Mathematical Computing, Nerve Net/physiology, Neural Networks (Computer), Occipital Lobe/physiology, Orientation/physiology, Oxygen Consumption/*physiology, Parahippocampal Gyrus/physiology, Pattern Recognition, Visual/*physiology, Reference Values, Temporal Lobe/*physiology. [Abstract] [bibtex-entry]



4. T. Matsuka. Generalized Exploratory Model of Category Learning. International Journal of Computational Intelligence, 1:7-15, 2004. [bibtex-entry]



5. T. Matsuka and J. E. Corter. Stochastic learning algorithms for modeling human category learning. International Journal of Computational Intelligence, 1:40-48, 2004. [bibtex-entry]

Conference articles

1. B. M. Bly, F. G. Hillary, D. Rebbechi, R. Preciado, H. Giangrante, B. Rypma, and J. DeLuca. Analyzing fMRI data in patients with diffuse brain abnormalities. In Proceedings of the Annual Meeting of the Cognitive Neuroscience Society, San Francisco, CA, 2004. [bibtex-entry]



2. J. E. Corter and T. Matsuka. Empirical measures of attention allocation in classification learning: A replication of Medin & Schaffer (1978).. In 45th Annural Meeting of Psychonomics Society, Minneapolis, MN, 2004. [bibtex-entry]



3. Y. O. Halchenko, S. J. Hanson, and B. A. Pearlmutter. Fusion of Functional Brain Imaging Modalities using L-Norms Signal Reconstruction. In Proceedings of the Annual Meeting of the Cognitive Neuroscience Society, San Francisco, CA, 2004. Keyword(s): EEG, fMRI, fusion. [bibtex-entry]



4. C. Hanson, S. J. Hanson, and T. Schweighardt. Neural Correlates of Integral and Separable Processing During Category Learning. In Proceedings of the Annual Meeting of the Cognitive Neuroscience Society, San Francisco, CA, 2004. [bibtex-entry]



5. S. J. Hanson, T. Matsuka, C. Hanson, D. Rebbechi, Y. O. Halchenko, A. Zaimi, and B. A. Pearlmutter. Structural Equation Modeling of Neuroimaging Data: Exhaustive Search and Markov Chain Monte Carlo. In Human Brain Mapping, 2004. Keyword(s): Modeling, MCMC. [bibtex-entry]



6. S. J. Hanson, T. Matsuka, and J. V. Haxby. Combinatoric Codes in Ventral Medial Temporal Lobes for Objects: Haxby 2001 revisited. In Proceedings of the Annual Meeting of the Cognitive Neuroscience Society, San Francisco, CA, 2004. [bibtex-entry]



7. F. G. Hillary, B. M. Bly, R. Preciado, D. Rebbechi, H. M. Genova, B. Rypma, and J. DeLuca. Combining fMRI data in cases of diffuse and focal brain abnormality. In Proceedings of the Annual Meeting of the Cognitive Neuroscience Society, San Francisco, CA, 2004. [bibtex-entry]



8. T. Matsuka. Biased stochastic learning in computational model of category learning. In Proceedings of the Annual Meeting of the Cognitive Science Society, pages 915-920, 2004. [bibtex-entry]



9. T. Matsuka. Comparisons of prototype- and exemplar-based neural network models of categorization using the GECEL framework. In Proceedings of the Annual Meeting of the Cognitive Science Society, pages 909-914, 2004. [bibtex-entry]



10. T. Matsuka. Exploratory approach for modeling human category learning. In Proceedings of the 6th International Conference on Cognitive Modelling, pages 190-195, 2004. [bibtex-entry]



11. T. Matsuka. Interactions between representation and attention processes in category learning. In Annual Meeting of the Cognitive Neuroscience Society, San Francisco, CA, 2004. [bibtex-entry]



12. T. Matsuka and A. Chouchourelou. Survival of the fittest hypothesis: Computational model of category learning based on evolving category concepts by hypothesis testing. In 34th Annual meeting of Society of Computers in Psychology, 2004. [bibtex-entry]



13. T. Matsuka and J. E. Corter. Modeling category learning with stochastic processes. In Proceedings of the 6th International Conference on Cognitive Modelling, pages 196-201, 2004. Lawrence Erlbaum Associates. [bibtex-entry]



14. T. Matsuka, J. E. Corter, and S. J. Hanson. Irresistibly attractive fruitless feature dimensions. In Proceedings of the 6th International Conference on Cognitive Modelling, pages 370-371, 2004. Lawrence Erlbaum Associates. [bibtex-entry]



15. Y. Sakamoto, T. Matsuka, and B. C. Love. Dimension-wide and exempler-speficic attention in category learning and recognition. In Proceedings of the 6th International Conference on Cognitive Modelling, pages 261-266, 2004. [bibtex-entry]



16. A. Zaimi, C. Hanson, and S. J. Hanson. Event Perception of Schema-Rich and Schema-Poor Video Sequences During fMRI Scanning: Top Down Versus Bottom Up Processing. In Proceedings of the Annual Meeting of the Cognitive Neuroscience Society, San Francisco, CA, 2004. [bibtex-entry]

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