Fig. 8: Source localization of regions defined using fMRI.

a Functional anatomy from one participant, on the inflated cortical surface (left panel, side view, anterior to left; right panel, ventral view, anterior at top; similar results were obtained in 14 participants). Regions of interest were defined using fMRI responses to movie clips, in color and black-and-white, of faces, bodies, objects, scrambled objects⁷⁶. The activation map (yellow-orange) shows voxels with higher responses to color clips compared to black-and-white clips. Contours show ROIs for faces > objects (including the fusiform face area, FFA), intact shapes > scrambled shapes (lateral object area, LO), and places > objects (including parahippocampal place area, PPA). Regions of interest for V1, V2, MT (middle temporal area), frontal, and precentral ROIs were defined using an anatomical atlas. b Source localization estimates of the current source density (CSD) of magnetoencephalography (MEG) responses to color arising from each functional ROI, averaged across participants and calculated with dynamical Statistical Parametric Mapping (dSPM; see “Methods” section). 0 on the x-axis is stimulus onset. Values on the y-axis are unitless. Transparent shading shows SEM. c Maximum amplitude of CSD in each ROI of the ventral visual pathway, calculated as distance from peak to trough of the time course in b. Error bars are SEM (n = 14). There was a significant effect of ROI on response magnitude (repeated measures one-way ANOVA, p = 0.002). Responses source-localized to the color-biased regions were different from those of LO (two-sided paired t-test, p = 0.01) and place-biased regions (p = 0.02), but not face-biased ROIs (p = 0.12). d Average classifier performance on decoding luminance polarity generalizing-across-hue (12 problems averaged together; see Fig. 1a) trained using only those MEG data localized to the MRI-defined ROIs (N = 14 participants). Each line shows the average accuracy of one ROI-restricted classifier averaged across participants (color key in a). e Average classifier performance on decoding hue generalizing-across-luminance-polarity (12 problems averaged together; see Fig. 1b). f The distribution of sensors used as features for the classifiers across participants (N = 18). Color bar shows the percent likelihood that any given sensor was selected as a feature. g As in f, but for decoding hue generalizing across luminance contrast.