We attempt to shed light on the algorithms humans use to classify images of human faces according to their gender. For this, a novel methodology combining human psychophysics and machine learning is introduced. We proceed as follows. First, we apply principal component analysis (PCA) on the pixel information of the face stimuli. We then obtain a data set composed of these PCA eigenvectors combined with the subjects' gender estimates of the corresponding stimuli. Second, we model the gender classification process on this data set using a separating hyperplane (SH) between both classes. This SH is computed using algorithms from machine learning: the support vector machine (SVM), the relevance vector machine, the prototype classifier, and the K-means classifier. The classification behavior of humans and machines is then analyzed in three steps. First, the classification errors of humans and machines are compared for the various classifiers, and we also assess how well machines can recreate the subjects' internal decision boundary by studying the training errors of the machines. Second, we study the correlations between the rank-order of the subjects' responses to each stimulus—the gender estimate with its reaction time and confidence rating—and the rank-order of the distance of these stimuli to the SH. Finally, we attempt to compare the metric of the representations used by humans and machines for classification by relating the subjects' gender estimate of each stimulus and the distance of this stimulus to the SH. While we show that the classification error alone is not a sufficient selection criterion between the different algorithms humans might use to classify face stimuli, the distance of these stimuli to the SH is shown to capture essentials of the internal decision space of humans.Furthermore, algorithms such as the prototype classifier using stimuli in the center of the classes are shown to be less adapted to model human classification behavior than algorithms such as the SVM based on stimuli close to the boundary between the classes.

It is well known that the human visual system can reconstruct depth from simple random-dot displays given binocular disparity or motion information. This fact has lent support to the notion that stereo and structure from motion systems rely on low-level primitives derived from image intensities. In contrast, the judgment of surface transparency is often considered to be a higher-level visual process that, in addition to pictorial cues, utilizes stereo and motion information to separate the transparent from the opaque parts. We describe a new illusion and present psychophysical results that question this sequential view by showing that depth from transparency and opacity can override the bias to see rigid motion. The brain's computation of transparency may involve a two-way interaction with the computation of structure from motion.