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. Author manuscript; available in PMC: 2022 Jul 12.
Published in final edited form as: Curr Biol. 2021 May 4;31(13):2785–2795.e4. doi: 10.1016/j.cub.2021.04.014

Figure 4. Measuring neural variance uniquely explained by the 2D Morphable Model and other models.

Figure 4.

Fifty model features from the 2D Morphable Model and a different model were concatenated, and neural responses were fit using all 100 features as regressors. The explained variance was then subtracted by the contribution from each individual model, to quantify the efficacy of the non-overlapping components of the two models in predicting neural responses.

A, Percentage of neural variance uniquely explained by various models compared to the 2D Morphable Model, for images after background removal (cf. Figure 2B). B, Percentage of neural variance uniquely explained by the 2D Morphable Model compared to other models. C and D, same as A and B, but for images fit by the 3D Morphable Model (cf. Figure 2C). Error-bars represent s.e.m. for 148 cells. See Figure S3G-H for a layer-wise analysis of neural variance uniquely explained by AlexNet and CORnets compared to the 2D Morphable Model and vice versa. Also see Figure S4.