Fig. 2. Normalizing response amplitude, but not variance, removes drift.

A Cross-session generalization after normalizing each session’s variance. Left, goodness-of-fit matrix after normalizing each voxel’s response variance within each session. Center, Mean cvR² as function of number of intervening sessions between train and test sessions. Model predictive power decreases with time, indicating representational drift (r = −0.25, p < 0.001). Gray lines, individual subjects; thick black line, mean across subjects. Right, Black vertical line, empirical correlation between goodness-of-fit and number of intervening sessions. Gray histogram, null distribution of correlation values. B Cross-session generalization after subtracting each session’s mean response amplitude (i.e. mean beta). Left, goodness-of-fit matrix after subtracting each voxel’s mean response within each session. Center, Mean cvR² as function of number of intervening sessions between train and test sessions. After subtracting each voxel’s mean, predictive power of V1 models no longer decrease with time (r = −0.02, p = 0.287). Source data are provided as a Source Data file.