M.A. Shurupova - Post-graduate Student, Department of Higher Nervous Activity, Biological Faculty, Lomonosov Moscow State University. E-mail: shurupova.marina.msu@gmail.com V.N. Anisimov - Ph.D. (Biol.), Leading Research Scientist, Department of Higher Nervous Activity, Biological Faculty, Lomonosov Moscow State University. E-mail: victor_anisimov@neurobiology.ru A.V. Krasnoperov - Ph. D. (Phys.-Math.), Senior Research Scientist, Laboratory of Nuclear Issues, Joint Institute for Nuclear Research (Dubna, Moscow Region). E-mail: alexei.krasnoperov@jinr.ru A.V. Latanov - Dr.Sc. (Biol.), Professor, Head of Higher Nervous Activity Department of Biological Faculty, Lomonosov Moscow State University. E-mail: latanov@neurobiology.ru

Today there is wide interest for building models of eye movements. It could be based as on virtual solutions [11] so as on creat-ing robot-technical systems [2]. At the same time many of those solutions are the simplifications of real eye movements, have very sophisticated organization. In those cases developers miss a lot of eye movement details that our eyes realize every second during scanning the environment around us. Some of those details could not be analyzed by expert estimation during eye movement observing and could be revealed only by calculating statistics from big data matrices of gaze coordinates. Eye movements, observed in real time, reflect the processes of visual attention and mental activity. For accurate visual processing our eyes execute about five (and even more) movements per second, continuously follow the spot of interest. It is needed for projecting the image to the fovea. That is why our eyes execute saccades and fixations. During fixations brain ana-lyzes details and encodes image in the memory [6]. Some researches show, that there are relations between fixation durations and saccade amplitudes [5, 21, 23]. They revealed the definite function between parameters of fixations and saccades: after short fixation long saccade occurs and vice versa - af-ter long fixation the eye executes low amplitude saccade [20]. First case is the example of ambient (dynamic) type of vision and the second one is the focal (static) type of vision. In our work we discuss the results of analysis eye movements patterns during viewing static and dynamic scenes. We revealed, that there is relation between eye movement parameters and visual mode, we also show the function between fixations and sac-cades depending of cognitive task. Details of eye movement parameters relate to strongly expressed time and space features. For static and dynamic scenes fixation durations decrease comparing with free viewing of the same scene. We revealed, that fix-ation durations and also saccade amplitudes are higher for dynamic scenes. We either averaged data in both viewing for all scenes separately for statics and dynamics and built functions of amplitude of the next saccade from current fixation duration. When participants got the task for current scene and started to examine it, parameters of eye movements varied. We revealed separation to ambient and focal modes at 160 ms for both static and dynamic viewing. That separation reflects crucial mechan-isms of eye movement strategy for human eye movements. That changes obviously show the differences in viewing strategy between the early and the later period of visual scene viewing. The results also reveal competition between two types of visual attention in scene viewing. That are top-down and bottom-up types of visual attention. We could establish, that eye movements in humans are executed in strict relations between low-level oculomotor factors and high-level cognitive aspects of mental activity. This is kind of involuntary interaction, that is not well ob-served from expert estimation, but that exactly takes place in scene viewing in routine eye movement activity. In this way, in viewing static and dynamic scenes it is possible to obtain cluster of saccades and fixations, which are related to that saccades, and that cluster characterizes processes of global visual attention. The results and their interpretations are crucial as from neurophysiologic point of view, as for building eye movement models and creating systems of artificial vision.

 

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