Eye movements and gaze are one of the most fundamental way to actively and attentively interact with both other humans and the surrounding world.
Gaze-shift mechanisms can be conceived as a motor program implementation of an active random sampling strategy that the Human Visual System has evolved in order to efficiently and effectively infer properties of the environment. However, one point that is not addressed by the great majority of computational models is the "noisy", idiosyncratic variation of the random exploration exhibited by different observers when viewing the same scene, or even by the same subject along different trials. Such variations speak of stochastic nature of scanpaths - the succession of gaze-shifts - , which is particularly evident for those resulting from saccadic eye movements.
If observed gaze-shifts are generated by an underlying stochastic process the distribution functions and the temporal dynamics of eye movements should be completely specified by the stochastic process. By analysing the long-tail distributions resulting from the analysis of eye-tracking data, we have proposed the foraging hypothesis as an explanation.
Up to now the foraging approach has been useful to provide generative models of observers’ attentive behaviour (either human or artificial, e.g., robots) while viewing images, videos or interacting with the world as well as a novel statistical analysis tool for either video retrieval and coding, attentive monitoring of multiple cameras, expertise understanding and clinical assessment.
This research work was initially inspired by seminal work of Prof. Larry Stark, University of California, Berkeley. Most of it has been carried on with Prof. Mario Ferraro (University of Torino)