Ple use of the same single-image activation estimate in multiple pairs and for the multiple comparisons along the horizontal axis. Results provide evidence for nonface face and nonplace place pairs in EVC, but no evidence for nonface face pairs in FFA and no evidence for nonplace place pairs in PPA. Activation profiles were first averaged across subjects; a modified version of this analysis that is sensitive to subject-unique activation profiles revealed some evidence for preference inversions in left FFA.Mur et al. ?Single-Image Activation of get CBR-5884 category RegionsJ. Neurosci., June 20, 2012 ?32(25):8649 ?8662 ?Table 1. Discriminability (AUC) for faces and places ROI size (voxels) Faces versus nonfaces Left FFA Right FFA Left PPA Right PPA hIT EVC Places versus nonplaces Left FFA Right FFA left PPA Right PPA hIT EVC 10 (20) 0.96*** 0.98*** 0.25** 0.27** 0.71** 0.62 0.27* 0.22** 0.97*** 1.00*** 0.56 0.71 23 (46) 0.96*** 0.99*** 0.22*** 0.28** 0.72** 0.63 0.20** 0.24* 0.99*** 1*** 0.5 0.71 55 (110) 0.94*** 0.99*** 0.18*** 0.22*** 0.72** 0.62 0.18*** 0.25* 1.00*** 1.00*** 0.5 0.72 128 (256) 0.82*** 0.94*** 0.16*** 0.22*** 0.65 0.58 0.23* 0.25* 1.00*** 1*** 0.53 0.74* 300 (600) 0.75** 0.91*** 0.16*** 0.21*** 0.58 0.56 0.32 0.22* 1.00*** 1*** 0.54 0.p values were computed using a two-sided label-randomization test and were corrected for multiple (five) comparisons using Bonferroni correction. Voxel numbers in between parentheses describe ROI sizes for bilateral hIT and EVC. ***p 0.001, **p 0.01, *p 0.05 (corrected).tion did not explain the overall organization of response patterns in PPA (Kravitz et al., 2011). The main organizational principle of PPA seemed to be spatial expanse (open vs closed places) (Kravitz et al., 2011). Since our stimulus set contained open places only, it is unlikely that the open/closed distinction can explain our within-place activation differences. Evidence for category step and within-category gradedness in PPA and FFA The gradedness of within-category activation profiles raises the question of whether the category boundary has a special status at all: Does activation drop in a step-like fashion at the boundary or does it continuously fall off across the boundary? Note that inspecting the noisy activation profile after ranking according to the same profile (Figs. 1, 2) cannot address either the question of gradedness or the question of a category step (see Materials and Methods). Testing for a drop-off of activation at the category boundary requires joint modeling of the category step and the gradedness within and outside the preferred category. This test was implemented by our category-step-and-gradedness analysis (Fig. 6; see Materials and Methods), which uses one session to derive a ranking hypothesis from the data and the other to test a piecewise linear falloff model including predictors for category step and gradedness. Statistical inference was performed by bootstrap resampling of the image set. Figure 6 B displays the results of our category-step-andgradedness analysis (PP58 price smallest two ROI sizes not shown). Right FFA and right and left PPA showed a significant drop-off of activation at the category boundary at all ROI sizes ( p 0.0025 for PPA; p 0.05, with p 0.0025 in several cases, for right FFA). Left FFA did not show a significant drop-off of activation at the category boundary, except at 55 voxels ( p 0.05). hIT and EVC both did not show a significant drop-off of activation at the category boundary for either.Ple use of the same single-image activation estimate in multiple pairs and for the multiple comparisons along the horizontal axis. Results provide evidence for nonface face and nonplace place pairs in EVC, but no evidence for nonface face pairs in FFA and no evidence for nonplace place pairs in PPA. Activation profiles were first averaged across subjects; a modified version of this analysis that is sensitive to subject-unique activation profiles revealed some evidence for preference inversions in left FFA.Mur et al. ?Single-Image Activation of Category RegionsJ. Neurosci., June 20, 2012 ?32(25):8649 ?8662 ?Table 1. Discriminability (AUC) for faces and places ROI size (voxels) Faces versus nonfaces Left FFA Right FFA Left PPA Right PPA hIT EVC Places versus nonplaces Left FFA Right FFA left PPA Right PPA hIT EVC 10 (20) 0.96*** 0.98*** 0.25** 0.27** 0.71** 0.62 0.27* 0.22** 0.97*** 1.00*** 0.56 0.71 23 (46) 0.96*** 0.99*** 0.22*** 0.28** 0.72** 0.63 0.20** 0.24* 0.99*** 1*** 0.5 0.71 55 (110) 0.94*** 0.99*** 0.18*** 0.22*** 0.72** 0.62 0.18*** 0.25* 1.00*** 1.00*** 0.5 0.72 128 (256) 0.82*** 0.94*** 0.16*** 0.22*** 0.65 0.58 0.23* 0.25* 1.00*** 1*** 0.53 0.74* 300 (600) 0.75** 0.91*** 0.16*** 0.21*** 0.58 0.56 0.32 0.22* 1.00*** 1*** 0.54 0.p values were computed using a two-sided label-randomization test and were corrected for multiple (five) comparisons using Bonferroni correction. Voxel numbers in between parentheses describe ROI sizes for bilateral hIT and EVC. ***p 0.001, **p 0.01, *p 0.05 (corrected).tion did not explain the overall organization of response patterns in PPA (Kravitz et al., 2011). The main organizational principle of PPA seemed to be spatial expanse (open vs closed places) (Kravitz et al., 2011). Since our stimulus set contained open places only, it is unlikely that the open/closed distinction can explain our within-place activation differences. Evidence for category step and within-category gradedness in PPA and FFA The gradedness of within-category activation profiles raises the question of whether the category boundary has a special status at all: Does activation drop in a step-like fashion at the boundary or does it continuously fall off across the boundary? Note that inspecting the noisy activation profile after ranking according to the same profile (Figs. 1, 2) cannot address either the question of gradedness or the question of a category step (see Materials and Methods). Testing for a drop-off of activation at the category boundary requires joint modeling of the category step and the gradedness within and outside the preferred category. This test was implemented by our category-step-and-gradedness analysis (Fig. 6; see Materials and Methods), which uses one session to derive a ranking hypothesis from the data and the other to test a piecewise linear falloff model including predictors for category step and gradedness. Statistical inference was performed by bootstrap resampling of the image set. Figure 6 B displays the results of our category-step-andgradedness analysis (smallest two ROI sizes not shown). Right FFA and right and left PPA showed a significant drop-off of activation at the category boundary at all ROI sizes ( p 0.0025 for PPA; p 0.05, with p 0.0025 in several cases, for right FFA). Left FFA did not show a significant drop-off of activation at the category boundary, except at 55 voxels ( p 0.05). hIT and EVC both did not show a significant drop-off of activation at the category boundary for either.
