We analyzed a publicly available MEG dataset [39], with source-reconstructed data available at [https://doi.org/ 10.6084/m9.figshare.12770366]. Full details (including details of approval from responsible ethical review board) are reported in the original publication and are briefly summarized here. The MEG data were recorded from 15 participants performing a visual contrast comparison decision-making task. Each participant' performed four experimental sessions with on average 429 trials per session. At the beginning of each trial, a reference stimulus was shown. The reference stimulus was a circular, expanding or contracting (randomized across trials) grating with a fixed contrast of  $50\%$  and a duration of  $400~\mathrm{ms}$ . The reference was followed by a test sequence made up of ten sample gratings (100ms duration each). The contrast of each sample in the sequence was drawn from a Gaussian distribution, the mean of which was either larger or smaller than  $50\%$  (randomly selected per trial). At the end of each trial, participants reported whether the average contrast of the sample gratings was higher or lower than the reference contrast. A staircase procedure was used to adjust the mean of the Gaussian distribution setting the average contrast of the 10 samples in each trial, by making decisions harder (mean of the Gaussian closer to  $50\%$ ) or easier (mean of the Gaussian further from  $50\%$ ) depending on the behavioral performance of the participant until that moment in the experimental session. The staircase was set to obtain a behavioral performance of approximately  $75\%$  correct on each session. The Regions Of Interest (ROIs) in MEG source space used to identify signal from the considered brain areas were defined based on the atlas from Glasser and colleagues[13]. All ROIs were co-registered to individual structural Magnetic Resonance Imaging (MRI) data. Source reconstruction was performed using linearly constrained minimum variance (LCMV) beamforming, by combining leadfield matrices and data covariance matrices (CMs) into a spatial filter for each source position (i.e., vertex) that was applied to the sensor-level data to compute the source estimate. To this end, the leadfield matrices were computed from 3-layer boundary element head-model (conductivity 0.3, 0.3, 0.006  $S / m$  for scalp, brain, skull respectively) based on the individual structural MRIs. CMs were computed for the interval 0s to 1.35s from stimulus onset from the pre-processes and artifact-cleaned [39] broadband MEG data  $(275\times 275$  sensors) with a regularization of  $5\%$  of CM. The source space was constrained to the cortical sheet (4096 vertices per hemisphere), and source orientations were chosen to maximize the power at each vertex. To illustrate the spatial resolution of source reconstruction, in Fig. S9A we plotted the correlation between LCMV spatial filters of neighboring sources vs their distance, finding a very small correlation  $(< 0.02)$  at distances larger than  $2.5\mathrm{cm}$  (as expected from theoretical considerations [15]). To compute FIT and TE, the time-frequency representation of sensor data was projected into source space and averaged over vertices within the ROI (80,20,10 vertices for V1, V3A, LO3, respectively). All FIT analyses