I'm a 5th year PhD student in Ken Norman's Computational Memory lab at Princeton.
I've been heavily involved in efforts to analyze fMRI neuroimaging data multivariately. The basic idea is that we should be able to use machine learning tools to learn how the patterns in your brain activity indicate what you're thinking.
I've been coordinating development on the Princeton MVPA toolbox for Matlab, an open source package to facilitate these kinds of analyses. See our TiCS review for more information.
For my PhD thesis, I'm working on series of
behavioral and fMRI memory experiments that investigate what
happens to a memory each time you
only
With Ken Norman and Ehren Newman, I worked on a neural network learning algorithm that works very well at learning to pick apart similar patterns from one another, and captures a series of fiendish and counter-intuitive behavioural findings that I helped simulate.
As part of my master's thesis, I worked (with Ken Norman) on a novel multivariate method for exploratory fMRI analysis. It calculates the isomorphism between the pattern of activity in a brain region and the patterns predicted by rich psychological models. In my thesis, I show that we can better predict which location someone is covertly attending to with this multivariate 'similarity structure' method than with standard or univariate measures on a spatial working memory dataset. I also attempt to relate this to my work on temporal context and memory.
With Agatha Lenartowicz and others, I worked on using multi-voxel pattern analyses of fMRI to investigate theories of task-switching.
[See also: the Norman lab publications page]
Norman, K. A., Detre, G. J. & Polyn, S. M. (2008). Computational models of episodic memory. In R. Sun (ed), The Cambridge Handbook of Computational Cognitive Modeling. PDF
Schneider, A.S., Oppenheimer, D.M. & Detre, G.J. (submitted). VAMP (Voting Agent Model of Preferences): A Computational Model of Individual Multi-attribute Choice. Not available online yet.
Norman, K.A., Newman, E.L. & Detre, G.J. (2007). A neural network model of retrieval-induced forgetting. Psychological Review. PDF
Norman, K. A., Newman, E. L., Detre, G. J., & Polyn, S. M. (2006). How inhibitory oscillations can train neural networks and punish competitors. Neural Computation, 18:1577-1610 PDF
Norman, K.A., Polyn, S.M., Detre, G.J. and Haxby, J.V. (2006). Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10(3). PDF
Detre, G.J., Polyn, S.M., Bannert, M.M., & Norman, K.A. (2007). Context in free recall - multi-voxel pattern analysis of fMRI. Poster presented at the Annual Meeting of the Society for Neuroscience, San Diego, CA (Nov, 2007). PDF
Schneider, A., Oppenheimer, D. Detre, G. (2007). Application of Voting Geometry to Multialternative Choice. Annual Meeting of the Cognitive Science Society, Nashville, Tennessee (2007).
Detre, G., Polyn, S.M., Moore, C.D., Natu, V.S., Singer, B.D., Cohen, J.D., Haxby, J.V., Norman, K.A. (2006). The Multi-Voxel Pattern Analysis (MVPA) toolbox. Poster presented at the Annual Meeting of the Organization for Human Brain Mapping (Jun 2006) PDF
Detre, G., Natu, V.S., Schneider, K., DeSimone, K., Kastner, S, Norman, K.A. (2005). Reading out the location being stored in spatial working memory with fMRI. Poster presented at the Annual meeting of the Society for Neuroscience, Washington, DC (Nov 2005). PDF
Polyn, S., Detre, G., Takerkart, S., Natu, V., Benharrosh, M., Singer, B., Cohen, J., Haxby, J., Norman, K., (2005). A Matlab-based toolbox to facilitate multi-voxel pattern classification of fMRI data. Poster presented at the Annual Meeting of the Organization of Human Brain Mapping, Toronto, Canada, (June 2005). PDF
Lenartowicz, A., Detre, G., Polyn, S., Chein, J., Yeung, N., Nystrom, L., Norman, K. A., Cohen, J. D. (2005). Characterization of brain states during task-switching using a neural network classifier. Poster presented at the Cognitive Neuroscience Society meeting, New York, NY. PDF
Norman, K., Newman, E., & Detre, G. (2004, November). Further predictions from a neural network model of retrieval induced forgetting. Poster presented at the 45th Annual Meeting of the Psychonomic Society. Minneapolis, MN. PDF
Norman, K. A., Newman, E. L., Detre, G. J., & Polyn, S.M. (2004). How theta oscillations can train neural networks and punish competitors. Cognitive Neuroscience, San Francisco, CA. PDF
Norman, K. A., Newman, E. L., Detre, G. J., & Polyn, S.M. (2004). How inhibitory oscillations can train neural networks and punish competitors. Computational and Systems Neuroscience, Cold Spring Harbor. PDF
I've worked as the teaching assistant for: