July 2013
Volume 13, Issue 9
Vision Sciences Society Annual Meeting Abstract  |   July 2013
Exploring the scale of common dimensions of information coding in ventral temporal cortex
Author Affiliations
  • J. Swaroop Guntupalli
    Department of Psychological and Brain Sciences, Dartmouth College
  • Andrew C. Connolly
    Department of Psychological and Brain Sciences, Dartmouth College
  • James V. Haxby
    Department of Psychological and Brain Sciences, Dartmouth College, Center for Mind/Brain Sciences, Universita degli studi di Trento
Journal of Vision July 2013, Vol.13, 1390. doi:https://doi.org/10.1167/13.9.1390
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      J. Swaroop Guntupalli, Andrew C. Connolly, James V. Haxby; Exploring the scale of common dimensions of information coding in ventral temporal cortex. Journal of Vision 2013;13(9):1390. doi: https://doi.org/10.1167/13.9.1390.

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      © ARVO (1962-2015); The Authors (2016-present)

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Scale of representation can refer to either categorical (super-ordinate vs sub-ordinate) or spatial (coarse-scale topographies, fine-scale topographies). Typically, we assume that they map one-to-one – coarse-scale categorical distinctions (animate vs inanimate) are reflected in coarse-scale topographies (medial vs lateral ventral temporal cortex (VT)). This is reflected in the fact that we can use a common decoding model to classify super-ordinate categories (houses vs faces) across-subjects, but fine-scale categorical distinctions (human faces vs animal faces) require individually tailored decoding models. We proposed a method that aligns representations, even at fine-scale, across subjects into common dimensions of encoding in VT. We showed that about 35 orthogonal dimensions are required to decode movie scenes, faces and objects, and 6 animal species from VT. This suggests that category decoding models reveal at least more than two dozen categorical dimensions in VT. Now the question remains: are these common categorical dimensions represented in large-scale cortical topographies? Decoding super-ordinate & sub-ordinate categorical information from a localized cortical patch after removing the low-frequency information can elucidate the spatial scale of representation of both coarse & fine- scale categorical information. We use PCA or MDS to identify common coarse-scale categorical dimensions and decode fine-scale categorical information both on those coarse dimensions and from the space orthogonal to those dimensions across different spatial frequency bands. This can elucidate the scale of representation of fine-scale information both categorically & spatially. We present results of both these analyses in VT on our studies using movies, faces and objects, and animal species.

Meeting abstract presented at VSS 2013


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