Fig. 1. Changes in cross-session generalization indicate representational drift.

A Model-fitting pipeline for a single fMRI voxel timeseries measurement. Input consisted of images viewed by a particular subject in a particular session. Filter outputs were sampled by the pRF. The model assigns weights for each orientation and spatial frequency filter by multiple linear regression, using model outputs to predict response amplitudes. Example images shown here were created by the authors for illustration only and were not used in the study. B The model is trained independently on each session, and then tested on each other session. C Goodness-of-fit matrix, quantified by cross-validated R² (cvR²), testing how well the model trained on each session predicted responses in all other sessions. Different diagonals of the matrix correspond to different numbers of intervening sessions between training and testing. Goodness-of-fit matrices were computed for each subject as the median of all V1 voxels, and averaged across subjects. D Representations drift across sessions when quantified by cvR². Left, Schematic illustrating representational drift (solid line, cvR² decreases systematically with number of intervening sessions), and representational stability (dotted line, cvR² remains constant). Middle, Mean cvR² as function of number of intervening sessions between train and test sessions. Colored lines, individual subjects; black line, mean across subjects. Predictive power of models decreases with number of intervening sessions, indicating representational drift (r = −0.17, p < 0.001). Drift was significant (p < 0.05) for all 8 individual subjects. Right, Black vertical line, empirical correlation between goodness-of-fit and number of intervening sessions. Gray histogram, null distribution of correlation values computed by randomizing the order of sessions 1000 times. Correlation was computed using all off-diagonal matrix values for each subject, and averaged across subjects. E Signal-to-noise ratio does not consistently decrease (or increase) across sessions. Left, Diagonals of the goodness-of-fit matrix corresponding to training and testing on adjacent sessions. Middle, Performance of model trained and tested on adjacent sessions, as function of earlier session of the two. Model performance does not systematically decrease across sessions (r = −0.11, p = 0.086). Right, Black vertical line, empirical correlation between adjacent-session performance and number of intervening sessions. Gray histogram, null distribution of correlation values. F Same as B, quantifying goodness-of-fit with Pearson’s correlation instead of cvR² values. With this measure predictive power does not decrease with time (r = 0.04, p = 0.774). Source data are provided as a Source Data file.