Efficient visual coding and the predictability of eye movements on natural movies.
We deal with the analysis of eye movements made on natural movies in free-viewing conditions. Saccades are detected and used to label two classes of movie patches as attended and non-attended. Machine learning techniques are then used to determine how well the two classes can be separated, i.e., how predictable saccade targets are. Although very simple saliency measures are used and then averaged to obtain just one average value per scale, the two classes can be separated with an ROC score of around 0.7, which is higher than previously reported results. Moreover, predictability is analysed for different representations to obtain indirect evidence for the likelihood of a particular representation. It is shown that the predictability correlates with the local intrinsic dimension in a movie.
Vig
Eleonora
E
Institute for Neuro- and Bioinformatics, University of Lübeck, Germany.
Dorr
Michael
M
Barth
Erhardt
E
D001185:Artificial Intelligence; D001288:Attention / Q000502:physiology; D005133:Eye Movements / Q000502:physiology*; D005556:Form Perception / Q000502:physiology*; D006801:Humans; D009039:Motion Perception / Q000502:physiology*