Figure 2.

Construction of a position discrimination plot. To measure position selectivity within an ROI, we created a position discrimination plot, in which the similarity between the patterns of activity produced by any two of the five stimulus conditions is plotted against the spatial separation between the stimuli presented in those conditions. We used the correlation (Pearson's r) between two patterns of BOLD response as the measure of their similarity. To compute the correlation between a given pair of activity maps, we plotted the intensity values (t units) from one map against those from the other map on a voxel-by-voxel basis and fit a linear regression to the plot. We transformed the resulting r value to a Fisher z to allow for a linear comparison among multiple correlations measured in this way. Two such correlations computed within V1 for an example subject are shown in panels A and B. The plot in panel A corresponds to two adjacent stimulus conditions, whereas the plot in panel B corresponds to the two furthest separated stimulus eccentricities. Note that the correlation in panel A is substantially stronger than that in panel B. The fact that retinotopically proximal stimuli produce similar patterns of BOLD response whereas more distant stimuli produce less similar patterns of BOLD is an indication that activity in the ROI is position selective. To evaluate the precision of position selectivity in the BOLD response, we plotted each of the 10 correlations against the distance between the stimuli that produced it and fit a linear regression to the resulting plot (C). Panel C shows the correlations from a single run; we computed the correlations on a run-by-run basis, so a full position discrimination plot for one subject had fifty total points (see Methods). A significant negative trend in this position discrimination plot indicates that activity in theROI is sensitive to the parametric position manipulation (Fischer & Whitney, 2009a, 2009b; Bressler et al., 2007).