Figure 3. Comparing how well AM neuronal responses to facial images can explain different models of face coding.

A, For each model, 50 features were extracted using PCA and responses of AM neurons were used to predict the model features. Decoding errors are plotted for each model. Error-bars represent s.e.m for 2100 target faces (i.e., error was computed for each target face when comparing to 2099 distractors, and s.e.m was computed for the 2100 errors). CORnet-Z performed significantly better than the other models (p<0.001) except from the 2D Morphable Model (p=0.08, Wilcoxon signed-rank test), and the 2D Morphable Model performed significantly better than the remaining models (p<0.01). B, To remove differences between models arising from differential encoding of image background, face images with uniform background were presented to different models (see STAR Methods). CORnet-Z and 2D Morphable Model performed significantly better than the other models (p<0.001), with only a small difference between the two models (p=0.03). C, To create facial images without hair, each facial image in the database was fit using a 3D Morphable Model (left). The fits were used as inputs to each model. For example, a new 2-D Morphable Model was constructed by morphing the fitted images to an average shape. 50 features were extracted from each of the models using PCA for comparison. D, Same as C, but for 110 features. For 50 features, the 2D Morphable Model and 3D Morphable model performed significantly better than the other models (p<0.001), with no significant difference between the two models (p=0.42). For 110 features, the 3D Morphable Model outperformed all other models (p<0.001). Also see Figures S1, S2, and S3.