  Warmth and competence represent the fundamental traits in social judgment
that determine emotional reactions and behavioral intentions towards social
targets. This research investigates whether an algorithm can learn visual
representations of social categorization and accurately predict human
perceivers' impressions of warmth and competence in face images. In addition,
this research unravels which areas of a face are important for the
classification of warmth and competence. We use Deep Convolutional Neural
Networks to extract features from face images and the Gradient-weighted Class
Activation Mapping (Grad CAM) method to understand the importance of face
regions for the classification. Given a single face image the trained algorithm
could correctly predict warmth impressions with an accuracy of about 90% and
competence impressions with an accuracy of about 80%. The findings have
implications for the automated processing of faces and the design of artificial
characters.
