  Making accurate inferences about other individuals' locus of attention is
essential for human social interactions and will be important for AI to
effectively interact with humans. In this study, we compare how a CNN
(convolutional neural network) based model of gaze and humans infer the locus
of attention in images of real-world scenes with a number of individuals
looking at a common location. We show that compared to the model, humans'
estimates of the locus of attention are more influenced by the context of the
scene, such as the presence of the attended target and the number of
individuals in the image.
