Eigenfeatures are created by the principal component approach (PCA) used on objects described by a low-level code (i.e., pixels, Gabor jets). We suggest that eigenfeatures act like the flexible features described by Schyns et al. They are particularly suited for face processing and give rise to class-specific effects such as the other-race effect. The PCA approach can be modified to accommodate top-down constraints.

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)

Un expert en vin perçoit-il et décrit-il l’odeur d’un vin de façon plus analytique qu’un novice? Pour répondre à cette question, nous avons demandé à un groupe d’experts et un groupe de novices de décrire des vins à l’aide d’une liste de termes organisée en trois niveaux de catégorisation allant du plus général au plus spécifique. Nous avons ensuite demandé aux mêmes sujets d’apparier les descriptions réalisées par une autre personne avec les vins correspondants. Les résultats montrent que les descriptions (...) effectuées par les experts sont plus précises et conduisent à de meilleures performances d’appariement que celles effectuées par les novices. De plus, la distance entre les descriptions d’experts et celles de novices augmente avec le niveau de précision des termes. (shrink)

Un expert en vin perçoit-il et décrit-il l’odeur d’un vin de façon plus analytique qu’un novice ? Pour répondre à cette question, nous avons demandé à un groupe d’experts et un groupe de novices de décrire des vins à l’aide d’une liste de termes organisée en trois niveaux de catégorisation allant du plus général au plus spécifique. Nous avons ensuite demandé aux mêmes sujets d’apparier les descriptions réalisées par une autre personne avec les vins correspondants. Les résultats montrent que les (...) descriptions effectuées par les experts sont plus précises et conduisent à de meilleures performances d’appariement que celles effectuées par les novices. De plus, la distance entre les descriptions d’experts et celles de novices augmente avec le niveau de précision des termes. (shrink)