James V. Haxby
James Van Loan Haxby is an American neuroscientist. He currently is a professor in the Department of Psychological and Brain Sciences at Dartmouth College and the Director for the Dartmouth Center for Cognitive Neuroscience. He is best known for his work on face perception and applications of machine learning in functional neuroimaging.
James V. Haxby | |
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Born | James Van Loan Haxby May 20, 1951 |
Nationality | American |
Known for | Neural decoding, face perception |
Awards | Fulbright Scholarship, NIH Director's Award, Cognitive Neuroscience Society Fred Kavli Distinguished Career Contributions Award |
Scientific career | |
Fields | Cognitive neuroscience |
Institutions | Dartmouth College |
Website | haxbylab |
Education
Haxby received a BA from Carleton College in 1973 and completed a Fulbright Scholarship at the University of Bonn in 1974. He obtained a PhD in clinical psychology at the University of Minnesota in 1981.[1]
Career
After receiving his PhD, Haxby held several clinical psychology positions at the Minneapolis VA Medical Center. Starting in 1982, Haxby began a two-decade tenure at the National Institutes of Health, working as a research psychologist at the National Institute on Aging and later as chief of the Section on Functional Brain Imaging at the National Institute of Mental Health. In 2002, Haxby began a professorship in the Department of Psychology at Princeton University, and in 2008 became the Evans Family Distinguished Professor of Psychological and Brain Sciences at Dartmouth College. He is currently the director of the Dartmouth Brain Imaging Center[2] and the Center for Cognitive Neuroscience at Dartmouth,[3][1]
Haxby's scientific contributions span several topics in cognitive neuroscience. He has published numerous papers using functional neuroimaging to investigate the cortical organization underlying visual perception and semantic memory.[4][5] He has also proposed an influential model of face perception where certain brain areas process invariant face properties such identity, while others process dynamic features critical for social interaction, such as emotional expressions and eye gaze.[6] Haxby has played a critical role in introducing machine learning methods to functional magnetic resonance imaging (fMRI) data analysis.[7][8][9] This approach was popularized by a paper demonstrating that neural representations of faces and object categories are encoded in a distributed fashion in human ventral temporal cortex,[10] a position that is typically contrasted with more modular accounts of the functional neuroanatomy of face processing.[11] More recently, Haxby's research has focused on the cortical topographies mediating fine-grained semantic representation,[12] methods for functional brain alignment,[13] and using naturalistic stimuli (e.g., movies) to build computational models of neural representation that are common across individuals.[14][15] He is a vocal proponent of open science.[16]
References
- "James V. Haxby - CV" (PDF).
- "Dartmouth Brain Imaging Center".
- "Center for Cognitive Neuroscience at Dartmouth".
- Ungerleider, L. G. & Haxby, J. V. (1994). "'What' and 'where' in the human brain". Current Opinion in Neurobiology. 4 (2): 157–165. doi:10.1016/0959-4388(94)90066-3. PMID 8038571. S2CID 205127941.
- Martin, A.; Wiggs, C. L.; Ungerleider, L. G. & Haxby, J. V. (1996). "Neural correlates of category-specific knowledge". Nature. 379 (6566): 649–652. Bibcode:1996Natur.379..649M. doi:10.1038/379649a0. PMID 8628399. S2CID 4310817.
- Haxby, J. V.; Hoffman, E. A. & Gobbini, M. I. (2000). "The distributed human neural system for face perception". Trends in Cognitive Sciences. 4 (6): 223–233. doi:10.1016/S1364-6613(00)01482-0. PMID 10827445. S2CID 17047447.
- Norman, K. A.; Polyn, S. M.; Detre, G. J. & Haxby, J. V. (2006). "Beyond mind-reading: multi-voxel pattern analysis of fMRI data". Trends in Cognitive Sciences. 10 (9): 424–430. doi:10.1016/j.tics.2006.07.005. PMID 16899397. S2CID 704855.
- Haxby, J. V. (2011). "Multivariate pattern analysis of fMRI: The early beginnings". NeuroImage. 62 (2): 852–855. doi:10.1016/j.neuroimage.2012.03.016. PMC 3389290. PMID 22425670.
- 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. doi:10.1146/annurev-neuro-062012-170325. PMID 25002277. S2CID 6794418.
- Haxby, J. V.; Gobbini, M. I.; Furey, M. I.; Ishai, A.; Schouten, J. L. & Pietrini, P. (2001). "Distributed and overlapping representations of faces and objects in ventral temporal cortex" (PDF). Science. 293 (5539): 2425–2430. Bibcode:2001Sci...293.2425H. doi:10.1126/science.1063736. PMID 11577229. S2CID 6403660.
- Kanwisher, N.; McDermott, J. & Chun, M. M. (1997). "The fusiform face area: a module in human extrastriate cortex specialized for face perception". Journal of Neuroscience. 17 (11): 4302–4311. doi:10.1523/JNEUROSCI.17-11-04302.1997. PMC 6573547. PMID 9151747.
- Connolly, A. C.; Guntupalli, J. S.; Gors, J.; Hanke, M.; Halchenko, Y. O.; Yu, Y.-C.; Abdi, H. & Haxby, J. V. (2012). "The representation of biological classes in the human brain". Journal of Neuroscience. 32 (8): 2508–2618. doi:10.1523/JNEUROSCI.5547-11.2012. PMC 3532035. PMID 22357845.
- Sabuncu, M. R.; Singer, B. D.; Conroy, B.; Bryan, R. E.; Ramadge, P. J. & Haxby, J. V. (2010). "Function-based intersubject alignment of cortical anatomy". Cerebral Cortex. 20 (1): 130–140. doi:10.1093/cercor/bhp085. PMC 2792192. PMID 19420007.
- Haxby, J. V.; Guntupalli, J. S.; Connolly, A. C.; Halchenko, Y. O.; Conroy, B.; Gobbini, M. I.; Hanke, M. & Ramadge, P. J. (2011). "A common, high-dimensional model of the representational space in human ventral temporal cortex". Neuron. 72 (2): 404–416. doi:10.1016/j.neuron.2011.08.026. PMC 3201764. PMID 22017997.
- Guntupalli, J. S.; Hanke, M.; Halchenko, Y. O.; Connolly, A. C.; Ramadge, P. J. & Haxby, J. V. (2016). "A model of representational spaces in human cortex". Cerebral Cortex. 26 (6): 2919–2934. doi:10.1093/cercor/bhw068. PMC 4869822. PMID 26980615.
- "Center for Open Neuroscience".