Individual fixation tendencies in person viewing generalize from images to videos

Fixation behavior towards persons in static scenes varies considerably between individuals. However, it is unclear whether these differences generalize to dynamic stimuli. Here, we examined individual differences in the distribution of gaze across seven person features (i.e. body and face parts) in static and dynamic scenes. 44 participants freely viewed 700 complex static scenes followed by eight director-cut videos (28925 frames). We determined the presence of person features using hand-delineated pixel masks (images) and Deep Neural Networks (videos). Results replicated highly consistent individual differences in fixation tendencies for all person features in static scenes and revealed that these tendencies generalize to videos. Individual fixation behavior for both, images and videos, fell into two anticorrelated clusters representing the tendency to fixate faces vs. bodies. These results corroborate a low-dimensional space for individual gaze biases towards persons and show they generalize from images to videos.