Ates across subjects. This analysis is sensitive to subject-unique activation profiles

Ates across subjects. This analysis is sensitive to subject-unique activation profiles (where different particular images may evoke higher activation in each subject).ResultsGood discriminability of object category at the single-image level To visualize the degree of category selectivity for single images, we ranked the 96 object images by the activation they elicited in each ROI (Figs. 1, 2). Visual inspection of the ranking results indicates that category-selective regions FFA and PPA show a clear preference for images of their preferred category: activation of PPA ranks (almost) all places before all nonplaces and activation of FFA ranks most faces before most nonfaces (Fig. 1). Control regions hIT and EVC do not show a clear category preference at first inspection (Fig. 2). To quantify these results, we computed ROCs and AUCs for each ROI. Consistent with visual inspection, single-image activation of FFA showed very good discrimination of faces from nonfaces, with right FFA (AUC 0.94) showing better performance than left FFA (AUC 0.82). Single-image activation of PPA showed (near) perfect discrimination of places from nonplaces (AUC 1). A two-sided condition-label randomization test on the AUCs indicated that discrimination performance of FFA and PPA for their preferred category was significantly above chance (Fig. 1; p 0.001 for each region). In addition, discrimination performance of FFA and PPA for the “opposite”, nonpreferred, category (i.e., places for FFA and faces for PPA) was significantly below chance (Fig. 1; p 0.05 for FFA, p 0.001 for PPA). In other words, activation of FFA ranked most nonplaces before most places and activation of PPA ranked most nonfaces before most faces. Could this finding simply be due to FFA’s strong activation to faces (which were among the nonplaces) and PPA’s strong activation to places (which were among the nonfaces)? If so, removing the faces from the nonplaces (FFA) and the places from the nonfaces (PPA) should abolish the effect. This was indeed the case for FFA, but not for PPA, indicating that PPA responds more weakly to faces than toMur et al. ?Single-Image Activation of Category RegionsJ. Neurosci., June 20, 2012 ?32(25):8649 ?8662 ?nonplace images in terms of their lower level visual properties (Rajimehr et al., 2011). No evidence for preference inversions in PPA and right FFA Figure 1 indicates that, despite the clear preference of FFA and PPA for images of their preferred category, some nonpreferred images appear before some preferred images in this descriptive analysis. This can be seen most clearly for FFA: some nonface images activated FFA more strongly than some face images. To test buy BMS-5 whether high-ranked nonpreferred images consistently activated the categoryselective regions more strongly than lower-ranked preferred images, we computed the PRIP (Fig. 3; see Materials and Methods). The PRIP gives an indication of the rate at which inverted pairs (i.e., nonpreferred image ranked before preferred image) replicate from one session to the next. Statistical inference was performed using a two-sided AZD0865MedChemExpress AZD0865 labelrandomization test on the PRIP. We would expect to find a PRIP of 0.5 under the null hypothesis that the apparently inverted pairs actually have equal activation. Results show that the PRIP for both FFA and PPA was significantly 0.5 for almost all ROI sizes (Fig. 3B), indicating that inverted pairs had a significant tendency to revert to category-preferential order from one session to the.Ates across subjects. This analysis is sensitive to subject-unique activation profiles (where different particular images may evoke higher activation in each subject).ResultsGood discriminability of object category at the single-image level To visualize the degree of category selectivity for single images, we ranked the 96 object images by the activation they elicited in each ROI (Figs. 1, 2). Visual inspection of the ranking results indicates that category-selective regions FFA and PPA show a clear preference for images of their preferred category: activation of PPA ranks (almost) all places before all nonplaces and activation of FFA ranks most faces before most nonfaces (Fig. 1). Control regions hIT and EVC do not show a clear category preference at first inspection (Fig. 2). To quantify these results, we computed ROCs and AUCs for each ROI. Consistent with visual inspection, single-image activation of FFA showed very good discrimination of faces from nonfaces, with right FFA (AUC 0.94) showing better performance than left FFA (AUC 0.82). Single-image activation of PPA showed (near) perfect discrimination of places from nonplaces (AUC 1). A two-sided condition-label randomization test on the AUCs indicated that discrimination performance of FFA and PPA for their preferred category was significantly above chance (Fig. 1; p 0.001 for each region). In addition, discrimination performance of FFA and PPA for the “opposite”, nonpreferred, category (i.e., places for FFA and faces for PPA) was significantly below chance (Fig. 1; p 0.05 for FFA, p 0.001 for PPA). In other words, activation of FFA ranked most nonplaces before most places and activation of PPA ranked most nonfaces before most faces. Could this finding simply be due to FFA’s strong activation to faces (which were among the nonplaces) and PPA’s strong activation to places (which were among the nonfaces)? If so, removing the faces from the nonplaces (FFA) and the places from the nonfaces (PPA) should abolish the effect. This was indeed the case for FFA, but not for PPA, indicating that PPA responds more weakly to faces than toMur et al. ?Single-Image Activation of Category RegionsJ. Neurosci., June 20, 2012 ?32(25):8649 ?8662 ?nonplace images in terms of their lower level visual properties (Rajimehr et al., 2011). No evidence for preference inversions in PPA and right FFA Figure 1 indicates that, despite the clear preference of FFA and PPA for images of their preferred category, some nonpreferred images appear before some preferred images in this descriptive analysis. This can be seen most clearly for FFA: some nonface images activated FFA more strongly than some face images. To test whether high-ranked nonpreferred images consistently activated the categoryselective regions more strongly than lower-ranked preferred images, we computed the PRIP (Fig. 3; see Materials and Methods). The PRIP gives an indication of the rate at which inverted pairs (i.e., nonpreferred image ranked before preferred image) replicate from one session to the next. Statistical inference was performed using a two-sided labelrandomization test on the PRIP. We would expect to find a PRIP of 0.5 under the null hypothesis that the apparently inverted pairs actually have equal activation. Results show that the PRIP for both FFA and PPA was significantly 0.5 for almost all ROI sizes (Fig. 3B), indicating that inverted pairs had a significant tendency to revert to category-preferential order from one session to the.