Comparison of methods. A, Comparison between decoding accuracies obtained with the full data (dimensionality = number of voxels) and accuracies obtained with the data after reducing it by means of PCA (dimensionality = number of components needed to explain 68% of the variance). Each point represents average decoding accuracy for one observer and ROI. Dark symbols, Maximum likelihood classifier. Light symbols, Forward model. Correlation coefficients (r values), Correlation in decoding accuracy between the full data and the reduced data for each decoder. B, Decoding accuracies obtained by combining data across sessions. Increasing the number of sessions dramatically increased V1 accuracies but only slightly increased MT+ accuracies. Replacing the data with white noise yielded chance performance. Error bars indicate SDs across runs (with 1 run at a time left out of training and used for testing accuracy). Dark curves and symbols, Maximum likelihood classifier. Light curves and symbols, Forward model classifier. C, Correlation between maximum likelihood classification and forward modeling classification accuracies. Each point corresponds to the mean decoding accuracy for one observer and ROI. Correlation coefficient (r value), Correlation in decoding accuracy between maximum likelihood classifier and forward model.