The dynamics of invariant object recognition in the human visual system
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Isik_Meyers_Leibo_Poggio_dspace[1].pdf
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4.48 MB
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Author(s)
Isik, Leyla
Meyers, Ethan M.
Leibo, Joel Z.
Poggio, Tomaso A.
Date Issued
October 2013
Journal
Journal of Neurophysiology
Publisher
American Physiological Society
Citation
Isik, L., E. M. Meyers, J. Z. Leibo, and T. Poggio. “The Dynamics of Invariant Object Recognition in the Human Visual System.” Journal of Neurophysiology 111, no. 1 (October 2, 2013): 91–102.
Version
Author's final manuscript
Abstract
The human visual system can rapidly recognize objects despite transformations that alter their appearance. The precise timing of when the brain computes neural representations that are invariant to particular transformations, however, has not been mapped in humans. Here we employ magnetoencephalography decoding analysis to measure the dynamics of size- and position-invariant visual information development in the ventral visual stream. With this method we can read out the identity of objects beginning as early as 60 ms. Size- and position-invariant visual information appear around 125 ms and 150 ms, respectively, and both develop in stages, with invariance to smaller transformations arising before invariance to larger transformations. Additionally, the magnetoencephalography sensor activity localizes to neural sources that are in the most posterior occipital regions at the early decoding times and then move temporally as invariant information develops. These results provide previously unknown latencies for key stages of human-invariant object recognition, as well as new and compelling evidence for a feed-forward hierarchical model of invariant object recognition where invariance increases at each successive visual area along the ventral stream.
MIT Department
Massachusetts Institute of Technology. Center for Biological & Computational Learning
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
McGovern Institute for Brain Research at MIT
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1152/jn.00394.2013
