A Simple Neural Network Models Categorical Perception of Facial Expressions
The performance of a neural network that categorizes facial expressions is compared with human subjects over a set of experiments using interpolated imagery. The experiments for both the human subjects and neural networks make use of interpolations of facial expressions from the Pictures of Facial Affect Database [Ekman and Friesen, 1976]. The only difference in materials between those used in the human subjects experiments [Young et al., 1997] and our materials are the manner in which the interpolated images are constructed – image-quality morphs versus pixel averages. Nevertheless, the neural network accurately captures the categorical nature of the human responses, showing sharp transitions in labeling of images along the interpolated sequence. Crucially for a demonstration of categorical perception [Harnad, 1987], the model shows the highest discrimination between transition images at the crossover point. The model also captures the shape of the reaction time curves of the human subjects along the sequences. Finally, the network matches human subjects’ judgements of which expressions are being mixed in the images. The main failing of the model is that there are intrusions of “neutral” responses in some transitions, which are not seen in the human subjects. We attribute this difference to the difference between the pixel average stimuli and the image quality morph stimuli. These results show that a simple neural network classifier, with no access to the biological constraints that are presumably imposed on the human emotion processor, and whose only access to the surrounding culture is the category labels placed by American subjects on the facial expressions, can nevertheless simulate fairly well the human responses to emotional expres-