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Kelly Chang, Yiqin Shen, Jason Webster, Geoffrey Boynton, Yuichi Shoda, Ione Fine; Population receptive field tuning in the human Fusiform Face area. Journal of Vision 2017;17(10):255. doi: 10.1167/17.10.255.
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Introduction. Population receptive field modeling (pRF; Dumoulin & Wandell, 2008) provides a powerful way of estimating the cumulative tuning of the population of cells in a single voxel. PRF models have been used to estimate spatial tuning properties in multiple cortical and subcortical visual areas, auditory frequency tuning, attentional effects, and topographic organization for size/numerosity. Here we examine whether pRF modeling would reveal systematic voxel-wise tuning preferences within the Fusiform Face area (FFA). The FFA is selectively responsive to face vs. non-face stimuli, and individual voxels show preferences for individual faces. However, it is still debated whether neurons with similar tuning preferences for identity are scattered or clustered (Dubois et al., 2015). Methods. Stimuli consisted of 19 distinct pairs of stereotypically Caucasian and African American male faces. Each pair was morphed into 7 equal steps, creating a total of 133 unique faces. Stimuli were histogram equalized for luminance. Using fMRI, we presented subjects with the full stimulus set in each run, and we collected at least 6 runs from each subject (n = 3). For each subject, we used a modified pRF model to estimate each voxel's tuning preference along our morph sequence within functionally defined FFA. Results. All voxels in the functionally defined FFA showed a strong baseline response to all faces. However, for all subjects and hemispheres, voxels also showed replicable (across sessions) tuning along the dimension of race. At a threshold of r > 0.2 (corresponding to a 1.25% false discovery rate) 58% (averaged across subjects) of voxels in the left FFA and 62% of voxels in the right FFA showed featural selectivity. Tuning preferences varied smoothly across the cortical surface of the FFA. It remains to be seen whether these topologies show inter-subject consistency or are unique across individuals.
Meeting abstract presented at VSS 2017
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