SCIENCE ADVANCES RESEARCH ARTICLE A 220264 Convolution+ReLU Max pooling ,202128 Fully connected+ReLU 622 828512 4012 Softmax Data ■Shuffled 0.0 Fig-1.Exp d in the ined VGG-Face.(A)The architecture of the VGG-Face.An le fare i jing No l University.ReLU,re tification linear unit.(B)The t SE."Ps0.001 ned VGG-Fa Predicted expression Participants'response Fg.2.Human selective units in the pretrained VGG-Facefor stimulus set (CHuman confusion matrix for stimulus set2. the results suggested a similar expression confusion effect be-and thus had limited ecological validity.If the expression-selective s with of expression selectivity emerged in the pr se(34) e collected from the same identities in the laboratory-controlled environment across expressions are different.By using the same SVC model trained Zhou etal.Sci.Ady.8.eabi4383 (2022)23 March 2022 3of11 Zhou et al., Sci. Adv. 8, eabj4383 (2022) 23 March 2022 SCIENCE ADVANCES | RESEARCH ARTICLE 3 of 11 the results suggested a similar expression confusion effect be￾tween the expression-selective units in the pretrained VGG-Face and humans. Ecological validity of expression selectivity emerged in the pretrained VGG-Face The facial expressions in stimulus set 1 and stimulus set 2 were collected from the same identities in the laboratory-controlled environment and thus had limited ecological validity. If the expression-selective units can recognize expressions, they should also be able to recog￾nize the real-life facial expressions with ecological validity. To verify this, we generated stimulus set 3 by selecting 4800 images with manually annotated expressions from the AffectNet database—a large real-world facial expression database (34). Each basic expression included 800 images. Note that, in stimulus set 3, the face identities across expressions are different. By using the same SVC model trained Fig. 1. Expression-selective units emerged in the pretrained VGG-Face. (A) The architecture of the VGG-Face. An example face image (for demonstration purposes only) is shown. Photo credit: Liqin Zhou, Beijing Normal University. ReLU, rectification linear unit. (B) The tuning value map of the expression-selective units in the pre￾trained VGG-Face. (C) The expression classification performance of the expression-selective units. The black dashed line represents the chance level. Error bars indicate SE. ***P ≤ 0.001. Fig. 2. Human-like expression confusion effect of the expression-selective units in the pretrained VGG-Face for stimulus set 2. (A) The expression discriminability of the expression-selective units emerged in the pretrained VGG-Face. The black dashed line represents the chance level. (B) The confusion matrix of the expression￾selective units in the pretrained VGG-Face for stimulus set 2. (C) Human confusion matrix for stimulus set 2. Downloaded from https://www.science.org at Southern Medical University on April 22, 2023