Stephen José Hanson 
Full Professor
 

Rutgers University, RUBIC, Psychology Dept.
101 Warren Street, Newark, NJ 07102
Smith Hall Room 324
phone: (973) 353-5440 x3952
fax:     (973) 353-1171
email:  jose@psychology.rutgers.edu

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Research Interests Cognitive Science, Cognitive Neuroscience, and Computational Neuroimaging. My research focus is on memory & learning, categorization, connectionist models, neural networks, and more generally cognitive and perceptual modeling.
 

Selected Publications:
Hanson, Stephen José and Schmidt, Arielle High-resolution imaging of the fusiform face area (FFA) using multivariate non-linear classifiers shows diagnosticity for non-face categories. NeuroImage, Vol 54(2), Jan 15, 2011, 1715-1734. PDF
Foundational Issues in Human Brain Mapping, SJ Hanson & M. Bunzl, MIT Press, 2010. get it here!
Ramsey JD, Hanson SJ, Hanson C, Halchenko YO, Poldrack RA, Glymour C (2010). Six problems for causal inference from fMRI. Neuroimage, 49, 1545-1558. PDF
Poldrack, R.A., Halchenko, Y., & Hanson, S.J., (2010). Decoding the large-scale structure of brain function by classifying mental states across individuals. Psychological Science, 20, 1364-1372 PDF
Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V. & Pollmann, S. (2009). PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7, 37-53. PDF
Hanson, S. J. & Halchenko, Y. O. (2008). Brain reading using full brain support vector machines for object recognition: there is no face-identification area. Neural Computation, 20, 486-503 PDF
Hanson,S.J., Hanson, C., Halchenko, Y.,Matsuka, T. & Zaimi, A. Bottom-Up and Top-Down Brain Functional Connectivity Underlying Comprehension of Everyday Visual Action (2007) Brain Structure and Function. PDF
Hanson, S. J., Rebecchi, D., Halchenko,Y. & Hanson, C. Dense Mode Clustering in Brain Maps, (2007) Magn. Res. Imaging. PDF
Hanson, S.J, Matsuka, T., and Haxby, J.V. (2004). Combinatoric Codes in Ventral Medial Temporal Lobes for Objects: Is There a Face Area?. Neuroimage. PDF
Halchenko, Y.O., Hanson, S.J., and Pearlmutter, B.A. (2004). Fusion of Functional Brain Imaging Modalities using L-Norms Signal Reconstruction. Annual Meeting of the Cognitive Neuroscience Society, San Francisco, CA
Hanson, C., Hanson, S.J., and Schweighardt, T. (2004). Neural Correlates of Integral and Separable Processing During Category Learning. Annual Meeting of the Cognitive Neuroscience Society, San Francisco, CA
The Distribution of BOLD Susceptibility effects in the Brain is Non-Gaussian. Hanson, S.J. & Martin Bly, B. Neuroreport (2001). PDF
Hanson, S. J., Petsche, T., Kearns, M. & Rivest, R. (1994), Computational Learning Theory and Natural Learning Systems, Vol 2, MIT Press, Bradford, 447pp.
Hanson S. J., Olson, C., (1990), Connectionist Modeling and Bran Function: The Developing Interface. MIT Press/Bradford, 396pp.
Hanson, S. J. (1999), Arbib's The Metaphorical Brain-2: The Sequel?, Artificial Intelligence Journal.
Hanson, S. J.(1999), Connectionist Neuroscience. In Rutgers Invitation to Cognitive Science, E. Lepore, Z. Pylyshyn, BlackWells.
Hanson, S. J., (1995), Some comments and variations on Back-propagation. In The Handbook of Back-propagation, Y. Chauvin & D. Rummelhart (Eds.), New Jersey: Erlbaum, pp. 292-323.
Hanson, C. & Hanson S. J. (1996), Development of Schemata During Event Parsing: Neisser's Perceptual Cycle as a Recurrent Connectionist Network, Journal of Cognitive Neuroscience.
Hanson, S. J.(1990), A Stochastic Version of the Delta Rule, PHYSICA D,42, 265-272.
Hanson S. J. & Burr, D. J., (1990), What Connectionist Models Learn: Toward a theory of representation in Connectionist Networks, Behavioral and Brain Sciences, 13, 471-518. Article-djvu
Article-PDF
commentary-part1-PDF
commentary-part2-PDF
Hanson, S. J., & Timberlake, W., (1983), Regulation during challenge: a general model of learned performance under environmental constraint. Psychological Review, 90,3, 261-282. djvu hiresPDF loresPDF
 

Steve gives a tutorial on fMRI at Google Dec 20th 2006