Fig. 4. Factors that impact identification performance.

a, Summary of identification performance. The bars indicate empirical performance for a set size of 120 images, the marker above each bar indicates the estimated noise ceiling (i.e. the theoretical maximum performance given the level of noise in the data), and the dashed green line indicates chance performance. The noise ceiling estimates suggest that the difference in performance across subjects is due to intrinsic differences in the level of noise. b, Scaling of identification performance with set size. The x-axis indicates set size, the y-axis indicates identification performance, and the number to the right of each line gives the estimated set size at which performance declines to 10% correct. In all cases performance scaled very well with set size. c, Retinotopy-only model versus Gabor wavelet pyramid model. Identification was attempted using an alternative retinotopy-only model that captures only the location and size of each voxel’s receptive field. This model performed substantially worse than the Gabor wavelet pyramid model, indicating that spatial tuning alone is insufficient to achieve optimal identification performance. (Results reflect repeated-trial performance averaged across subjects; see Supplementary Fig. 5 for detailed results.)