SCIENCE ADVANCES RESEARCH ARTICLE .0 95 0.85 of facial e of the exp sin thep dVGG-Fac e(A)Example facial stimuiu the seven c s refer to the id cation f ofthe a the x xis indic e the ge of this。 peforeachilstinmuig cate fit (DI ogistic regression was much high r than the other two regressions.Error bars indicate SE."Ps0.05 and "Ps 0.01 The e ssion-selective units in ther othesized that if the select facial expressions tthe similarity in the es of th selective units in the pr retrained vGG-face were s-shanee expressions in a human-like way.It might result from the ().Toquantify this f we fitted linear,quadratic (Poly2) in phy to e fit(R)of the logistic function to the curves should be the best in images.Asillustrated in Fig.4(Cand D).we found that all seven using morphed the cate cal ) happiness-anger,happiness-fear,angerdisgust,happinc -sadness anger-fear. and disgust The hun perc ptio n imly spo ntan pretaimedVecefaceLbtoeinhewMhd8aingeneral visual experience(-16)or without any visual experiene ns were sel f the ated that the hum ,and then the traind SVCmodel was ap etrained fo identify expressions the morphed images.At cach morph Ho rence of the human-like Zhou etal.Sci.Ady.8.eabi4383 (2022)23 March 2022 5of11 Zhou et al., Sci. Adv. 8, eabj4383 (2022) 23 March 2022 SCIENCE ADVANCES | RESEARCH ARTICLE 5 of 11 The expression-selective units in the pretrained VGG-Face showed human-like categorical perception for morphed facial expressions One may argue that the similarity in the expression confusion effect does not necessarily mean that expression-selective units perceive expressions in a human-like way. It might result from the similarities in physical properties of the expression images since the image￾based PCA (i.e., PCs based on pixel intensities and shapes) could also yield a confusion matrix similar to that of humans (35). Therefore, to further confirm whether these units could exhibit a human-like psychophysical response to facial expressions, we tested whether their responses showed a categorical perception of facial expressions by using morphed expression continua. Considering the generality of the categorical emotion perception in humans, we systematically tested the categorical effect in seven expression continua including happiness-anger, happiness-fear, anger-disgust, happiness-sadness, anger-fear, disgust-fear, and disgust-sadness. All of them have been tested in humans (36–40). In detail, we designed a morphed expres￾sion discrimination task (Fig. 4A) that resembled the ABX dis￾crimination task designed for humans (36, 39, 40). The prototypic expressions were selected from stimulus set 1. For each expression continuum, images of the two prototypic expressions were used to train an SVC model, and then the trained SVC model was applied to identify expressions of the morphed images. At each morph level of the continuum, the identification frequency of one of the two ex￾pressions was defined as the units’ identification rate at the current morph level. We hypothesized that if the selective units perceived expressions like humans, i.e., showing categorical effect, then the identification curves should be S-shaped. As predicted, for all continua, the identification curves of the expression-selective units in the pretrained VGG-Face were S-shaped (Fig. 4B). To quantify this effect, we fitted linear, quadratic (Poly2), and logistic functions to each identification curve, respectively. If the units exhibited a human-like categorical effect, the goodness of fit (R2 ) of the logistic function to the curves should be the best. Otherwise, the goodness of fit of the linear function to the curves should be the best if the units’ response followed the physical changes in images. As illustrated in Fig. 4 (C and D), we found that all seven identification curves showed typical S-like patterns (logistic versus linear: P = 0.002 and logistic versus Poly2: P = 0.002, Mann-Whitney U test). The human-like expression perception only spontaneously emerged in theDCNN with domain-specific experience (pretrained VGG-Face), but not in those with domain-general visual experience (VGG-16) or without any visual experience (untrained VGG-Face) So far, we had demonstrated that the human-like perception of ex￾pression could spontaneously emerge in the DCNN pretrained for face identity recognition. However, how did these expression-selective units achieve human-like expression perception? Specifically, it was still unknown whether the spontaneous emergence of the human-like Fig. 4. Categorical perception of facial expressions of the expression-selective units in the pretrained VGG-Face. (A) Example facial stimuli used in a morph continuum (happiness-anger). An example face image (for demonstration purposes only) is shown. Photo credit: Liqin Zhou, Beijing Normal University. (B) The identification rates for the seven continua. The identification rates refer to the identification frequency of one of the two expressions. Labels along the x axis indicate the percentage of this ex￾pression in facial stimuli. Black dots represent true identification rates. Blue solid lines indicate fitting for the logistic function. (C) Goodness of fit (R2 ) of each regression type for each expression continuum. The black dashed lines represent R2 at 0.95 and 1.00, separately. (D) Mean goodness of fit (R2 ) among expression continua. The R2 in the logistic regression was much higher than the other two regressions. Error bars indicate SE. *P ≤ 0.05 and **P ≤ 0.01. Downloaded from https://www.science.org at Southern Medical University on April 22, 2023