James V. Haxby

Evans Family Distinguished Professor
Director, Center for Cognitive Neuroscience

My current research focuses on the development of computational methods for building models of representational spaces. We assume that distributed population responses encode information. Within a cortical field, a broad range of stimuli or cognitive states can be represented as different patterns of response. We use fMRI to measure these patterns of response and multivariate pattern (MVP) analysis to decode their meaning. We are currently developing methods that make it possible to decode an individual’s brain data using MVP classifiers that are based on other subjects’ data. We use a complex, natural stimulus to sample a broad range of brain representational states as a basis for building high-dimensional models of representational spaces within cortical fields. These models are based on response tuning functions that are common across subjects. Initially, we demonstrated the validity of such a model in ventral temporal cortex. We are working on building similar models in other visual areas and in auditory areas. We also plan to investigate representation of social cognition using this same conceptual framework.

447 Moore
HB 6207
Center for Social Brain Sciences
Cognitive Science
Psychological and Brain Sciences
B.A. Carleton College
Ph.D. University of Minnesota

Selected Publications

Haxby, J.V., Connolly, A.C., Guntupalli, J.S. (2014). Decoding neural representational spaces using multivariate pattern analysis. Annual Review of Neuroscience, 37, 435-456.

Conroy, B. R., Singer, B. D., Guntupalli, J. S. , Ramadge, P. J. and Haxby, J. V. (2013). Inter-subject alignment of human cortical anatomy using functional connectivity. NeuroImage. [PDF] [URL] DOI: 10.1016/j.neuroimage.2013.05.009

Kohler, P. J., et al. (2013). Pattern classification precedes region-average hemodynamic response in early visual cortex.. Neuroimage, 78C, 249–260. [PDF] DOI: 10.1016/j.neuroimage.2013.04.019

Abdi, H., Williams, L. J., Connolly, A. C. , Gobbini, M. I. , Dunlop, J. P. and Haxby, J. V. (2012). Multiple Subject Barycentric Discriminant Analysis (MUSUBADA): How to assign scans to categories without using spatial normalization. Computational and Mathematical Methods in Medicine, 2012. [PDF] DOI: 10.1155/2012, cited by: 4

Connolly, A. C. , Guntupalli, J. S. , Gors, J. , Hanke, M. , Halchenko, Y. O., Wu, Y. , Abdi, H. and Haxby, J. V. (2012). Representation of biological classes in the human brain. Journal of Neuroscience, 32, 2608–2618. [PDF] DOI: 10.1523/JNEUROSCI.5547-11.2012, cited by: 16

Haxby, J. V. (2012). Multivariate pattern analysis of fMRI: The early beginnings. NeuroImage, In press. DOI: 10.1016/j.neuroimage.2012.03.016, cited by: 3

Wagner, D. D. , Haxby, J. V. and Heatherton, T. F. (2012). The Representation of Self and Person Knowledge in the Medial Prefrontal Cortex. Wiley Interdisciplinary Reviews:Cognitive Science, 3, 451–470. cited by: 3

Connolly, A. C. , Gobbini, M. I. and Haxby, J. V. (2012). Three virtues of similarity-based multivariate pattern analysis: An example from the human object vision pathway. In N. Kriegeskorte and G. Kreiman (Eds.) Understanding visual population codes: Toward a common multivariate framework for cell recording and functional imaging (pp. ): MIT Press. cited by: 3

Connolly, A. C. , Gobbini, M. I. and Haxby, J. V. (2012). Three virtues of similarity-based multivariate pattern analysis: An example from the human object vision pathway. In N. Kriegeskorte and G. Kreiman (Eds.) Understanding visual population codes: Toward a common multivariate framework for cell recording and functional imaging (pp. ): MIT Press. cited by: 3

Haxby, J. V. , Guntupalli, J. S. , Connolly, A. C. , Halchenko, Y. O. , Conroy, B. R., Gobbini, M. I. , Hanke, M. and Ramadge, P. J. (2011). A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal Cortex. Neuron, 72, 404–416. [PDF] [PDF:Supp] DOI: 10.1016/j.neuron.2011.08.026, cited by: 21

Engell, A. D., Nummenmaa, L., Oosterhof, N. N. , Henson, R. N., Haxby, J. V. and Calder, A. J. (2010). Differential activation of frontoparietal attention networks by social and symbolic spatial cues. Social cognitive and affective neuroscience, 5, 432–440. cited by: 16

Sabuncu, M. R., Singer, B. D., Conroy, B., Bryan, R. E., Ramadge, P. J. and Haxby, J. V. (2010). Function-based intersubject alignment of human cortical anatomy. Cereb Cortex, 20, 130–40. [PDF] DOI: 10.1093/cercor cited by: 38

Said, C. P., Moore, C. D., Engell, A. D., Todorov, A. and Haxby, J. V. (2010). Distributed representations of dynamic facial expressions in the superior temporal sulcus. J Vis, 10. [PDF] DOI: 10.1167/10.5.11

Hanke, M. , Halchenko, Y. O. , Haxby, J. V. and Pollmann, S. (2010). Statistical learning analysis in neuroscience: aiming for transparency. Frontiers in Neuroscience, 4, 38–43. [PDF] DOI: 10.3389/neuro.01.007.2010

Hanke, M. , Halchenko, Y. O. , Sederberg, P. B., Hanson, S. J., Haxby, J. V. and Pollmann, S. (2009). PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7, 37–53. [PDF] DOI: 10.1007/s12021-008-9041-y, cited by: 70

Hanke, M. , et al. (2009). PyMVPA: A Unifying Approach to the Analysis of Neuroscientific Data. Frontiers in Neuroinformatics, 3, 3. [PDF] DOI: 10.3389/neuro.11.003.2009, cited by: 36

Montgomery, K. J., Seeherman, K. R. and Haxby, J. V. (2009). The well-tempered social brain. Psychol Sci, 20, 1211–3. [PDF] DOI: 10.1111/j.1467-9280.2009.02428.x

Conroy, B., Singer, B. D., Haxby, J. V. and Ramadge, P. J. (2009). fMRI-Based Inter-Subject Cortical Alignment Using Functional Connectivity. In Y. Bengio and D. Schuurmans and J. Lafferty and C. K. I. Williams and A. Culotta (Eds.) Advances in Neural Information Processing Systems 22 (pp. 378–386). [PDF]

Furey, M L, P Pietrini, J V Haxby, and W C Drevets, “Selective Effects of Cholinergic Modulation on Task Performance during Selective Attention,” Neuropsychopharmacology , 33:4 (2008) 913-923.

Todorov, A, M I Gobbini, K K Evans, and J V Haxby, “Spontaneous Retrieval of Affective Person Knowledge in Face Perception,” Neuropsychologia , 45:1 (2007) 163-173.

Ricciardi, E, N Vanello, L Sani, C Gentili, E Pasquale Scilingo, L Landini, M Guazzelli, A Bicchi, J V Haxby, and P Pietrini, “The Effect of Visual Experience on the Development of Functional Architecture in hMT,” Cerebral Cortex , 17:12 (2007) 2933-2939.

Montgomery, K J, N Isenberg, and J V Haxby, “Communicative Hand Gestures and Object-directed Hand Movements Activated the Mirror Neuron System,” Social Cognitive and Affective Neuroscience , 2:2 (2007) 114-122.

Haxby, J V, and M I Gobbini, “The Perception of Emotion and Social Cues in Faces,” Neuropsychologia , 45:1 (2007) 1.

Gobbini, M. I. and Haxby, J. V. (2007). Neural systems for recognition of familiar faces.. Neuropsychologia, 45, 32–41. [PDF] DOI: 10.1016/j.neuropsychologia.2006.04.015, cited by: 282

Gobbini, M. I. , Koralek, A. C., Bryan, R. E., Montgomery, K. J. and Haxby, J. V. (2007). Two takes on the social brain: a comparison of theory of mind tasks.. J Cogn Neurosci, 19, 1803–14. [PDF] DOI: 10.1162/jocn.2007.19.11.1803, cited by: 156

Gobbini, M. I. and Haxby, J. V. (2006). Neural response to the visual familiarity of faces. Brain Res Bull, 71, 76–82. [PDF] DOI: 10.1016/j.brainresbull.2006.08.003, cited by: 57

Haxby, J. V. (2006). Fine structure in representations of faces and objects. Nature Neuroscience, 9, 1084–6.

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