People recognize faces of their own race more accurately than faces of other races. The “contact” hypothesis suggests that this “other‐race effect” occurs as a result of the greater experience we have with own‐ versus other‐race faces. The computational mechanisms that may underlie different versions of the contact hypothesis were explored in this study. We replicated the other‐race effect with human participants and evaluated four classes of computational face recognition algorithms for the presence of an other‐race effect. Consistent with the (...) predictions of a developmental contact hypothesis, “experience‐based models” demonstrated an other‐race effect only when the representational system was developed through experience that warped the perceptual space in a way that was sensitive to the overall structure of the model's experience with faces of different races. When the model's representation relied on a feature set optimized to encode the information in the learned faces, experience‐based algorithms recognized minority‐race faces more accurately than majority‐race faces. The results suggest a developmental learning process that warps the perceptual space to enhance the encoding of distinctions relevant for own‐race faces. This feature space limits the quality of face representations for other‐race faces. (shrink)

The faces of both adults and children can be classified accurately by sex, even in the absence of sex‐stereotyped social cues such as hair and clothing (Wild et al., 2000). Although much is known from psychological and computational studies about the information that supports sex classification for adults' faces, children's faces have been much less studied. The purpose of the present study was to quantify and compare the information available in adults' versus children's faces for sex classification and to test (...) alternative theories of how human observers distinguish male and female faces for these different age groups. We implemented four computational/neural network models of this task that differed in terms of the age categories from which the sex classification features were derived. Two of the four strategies replicated the advantage for classifying adults' faces found in previous work. To determine which of these strategies was a better model of human performance, we compared the performance of the two models with that of human subjects at the level of individual faces. The results suggest that humans judge the sex of adults' and children's faces using feature sets derived from the appropriate face age category, rather than applying features derived from another age category or from a combination of age categories. (shrink)

The faces of both adults and children can be classified accurately by sex, even in the absence of sex‐stereotyped social cues such as hair and clothing (Wild et al., 2000). Although much is known from psychological and computational studies about the information that supports sex classification for adults' faces, children's faces have been much less studied. The purpose of the present study was to quantify and compare the information available in adults' versus children's faces for sex classification and to test (...) alternative theories of how human observers distinguish male and female faces for these different age groups. We implemented four computational/neural network models of this task that differed in terms of the age categories from which the sex classification features were derived. Two of the four strategies replicated the advantage for classifying adults' faces found in previous work. To determine which of these strategies was a better model of human performance, we compared the performance of the two models with that of human subjects at the level of individual faces. The results suggest that humans judge the sex of adults' and children's faces using feature sets derived from the appropriate face age category, rather than applying features derived from another age category or from a combination of age categories. (shrink)

Faces provide information about a person's identity, as well as their sex, age, and ethnicity. People also infer social and personality traits from the face — judgments that can have important societal and personal consequences. In recent years, deep convolutional neural networks (DCNNs) have proven adept at representing the identity of a face from images that vary widely in viewpoint, illumination, expression, and appearance. These algorithms are modeled on the primate visual cortex and consist of multiple processing layers of simulated (...) neurons. Here, we examined whether a DCNN trained for face identification also retains a representation of the information in faces that supports social‐trait inferences. Participants rated male and female faces on a diverse set of 18 personality traits. Linear classifiers were trained with cross validation to predict human‐assigned trait ratings from the 512 dimensional representations of faces that emerged at the top‐layer of a DCNN trained for face identification. The network was trained with 494,414 images of 10,575 identities and consisted of seven layers and 19.8 million parameters. The top‐level DCNN features produced by the network predicted the human‐assigned social trait profiles with good accuracy. Human‐assigned ratings for the individual traits were also predicted accurately. We conclude that the face representations that emerge from DCNNs retain facial information that goes beyond the strict limits of their training. (shrink)