August 2009
Volume 9, Issue 8
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2009
Decoding distinct modes of face categorization in the cortical face network
Author Affiliations
  • Yu-Chin Chiu
    Department of Psychological and Brain Sciences, Johns Hopkins University
  • Michael Esterman
    Department of Psychological and Brain Sciences, Johns Hopkins University
  • Heather Rosen
    Department of Psychological and Brain Sciences, Johns Hopkins University
  • Steven Yantis
    Department of Psychological and Brain Sciences, Johns Hopkins University
Journal of Vision August 2009, Vol.9, 467. doi:10.1167/9.8.467
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      Yu-Chin Chiu, Michael Esterman, Heather Rosen, Steven Yantis; Decoding distinct modes of face categorization in the cortical face network. Journal of Vision 2009;9(8):467. doi: 10.1167/9.8.467.

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Abstract

The fusiform face area (FFA) has been associated with stimulus (i.e., face)-specific coding rather than with a specific mode of processing (e.g., featural or configural) (Yovel & Kanwisher, 2004) using standard univariate fMRI analysis. Here, we used multivoxel pattern analysis (MVPA) to investigate whether a distributed pattern of activity in cortical face-selective areas reflects distinct modes of face categorization.

Subjects were trained to carry out one of two face-categorization tasks: gender (male vs. female) or race (Caucasian vs. Asian), in alternating blocks of trials. The difficulty of the face categorizations was manipulated by using stimuli consisting of a morphed mixture of Asian males with Caucasian females and Asian females with Caucasian males. MVPA was performed on a set of regions of interest defined by a functional localizer (faces selective regions: fusiform gyrus, inferior occipital cortex and superior temporal sulcus, and a house selective region: parahippocampal gyrus) and by retinotopic mapping (e.g., V1).

A linear classifier (support vector machine) was trained to discriminate the gender or race categorization task using leave-one-run-out cross validation. The average classification accuracy was significantly greater than chance (77%) in the cortical face network whereas that in the PPA is not different from chance (55%). The average classification accuracy in V1 was also greater than chance (65%).

These results suggest that the pattern of activity in face-selective visual processing areas contains information about the modes of processing associated with different categorization tasks. These patterns could reflect top-down attentional biases on sub-population of neurons in these areas that process task-diagnostic facial features or spatial regions of the face.

Chiu, Y.-C. Esterman, M. Rosen, H. Yantis, S. (2009). Decoding distinct modes of face categorization in the cortical face network [Abstract]. Journal of Vision, 9(8):467, 467a, http://journalofvision.org/9/8/467/, doi:10.1167/9.8.467. [CrossRef]
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