  Primates constantly explore their surroundings via saccadic eye movements
that bring different parts of an image into high resolution. In addition to
exploring new regions in the visual field, primates also make frequent return
fixations, revisiting previously foveated locations. We systematically studied
a total of 44,328 return fixations out of 217,440 fixations. Return fixations
were ubiquitous across different behavioral tasks, in monkeys and humans, both
when subjects viewed static images and when subjects performed natural
behaviors. Return fixations locations were consistent across subjects, tended
to occur within short temporal offsets, and typically followed a 180-degree
turn in saccadic direction. To understand the origin of return fixations, we
propose a proof-of-principle, biologically-inspired and image-computable neural
network model. The model combines five key modules: an image feature extractor,
bottom-up saliency cues, task-relevant visual features, finite
inhibition-of-return, and saccade size constraints. Even though there are no
free parameters that are fine-tuned for each specific task, species, or
condition, the model produces fixation sequences resembling the universal
properties of return fixations. These results provide initial steps towards a
mechanistic understanding of the trade-off between rapid foveal recognition and
the need to scrutinize previous fixation locations.
