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. 2021 Dec 9;19(12):e3001418. doi: 10.1371/journal.pbio.3001418

Fig 7. Noise-trained VGG-19 provides a better model of human cortical responses to objects in noise.

Fig 7

(a) Classification accuracy for fMRI responses in individual visual areas for clean objects (black filled circles), objects in pixelated Gaussian noise (gray filled circles) and Fourier phase-scrambled noise (gray open circles). Error bars indicate ± 1 standard error of the mean (n = 8). Chance-level performance is 12.5%. (b) Correlational similarity of object representations obtained from human visual areas and individual layers of DNNs when comparing standard versus noise-trained networks (red versus blue, respectively). Color-coded horizontal lines at the top of each plot indicate a statistically significant advantage (p < 0.01 uncorrected) for a given DNN at predicting human neural representations of the object images. Data are available at https://osf.io/bxr2v/. DNN, deep neural network; FFA, fusiform face area; fMRI, functional magnetic resonance imaging; LOC, lateral occipital cortex; PPA, parahippocampal place area; SVM, support vector machine.