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@rubic.rutgers.edu
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.
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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.
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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
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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.
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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
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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.
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Hanson, S. J., Rebecchi, D., Halchenko,Y. & Hanson, C. Dense Mode Clustering in Brain Maps, (2007) Magn. Res. Imaging.
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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.
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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).
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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.
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One of the precursor's to the Drop-Out algorithm for Deep-Learning
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.
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hiresPDF
loresPDF
MOVIES
Steve gives a tutorial on fMRI at Google Dec 20th 2006
Catherine's talk at CMU 10/11/2013