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MacInnes W., Hunt A., Clarke A. et al. Cognitive Computation. 2018. Vol. 10. No. 5. P. 703-717.

Background Used / introduction  of The early eye tracking studies of descriptive Yarbus Provided Evidence That an observer's task Influences patterns of eye Movements, a leading to the a tantalizing prospect That an observer's Intentions Could the BE inferred from Their saccade behavior. This is a dynamic and dynamic cognitive companion using a Dynamic Bayesian Network (DBN). Understanding how it comes to human cognitive goals. This model provides a Bayesian, cognitive approach to pre-frontal areas with the colliculus.  This method  has been previously shown.  This is an analysis of the observer’s task. Secondly, it is a state cognitive state . Finally, we ’ve been able to make a difference .  Results  This is the only factor that influences observers' saccadic behavior. It has been shown that it has been shown that it has been selected for the paradigms.  Conclusions  Given the generative nature of this model in real time. We have shown that it has been closely coordinated with those of human observers. Many current models of vision The area of ​​interest is within the visual scene. There are three ways to add top-down knowledge and knowledge . First of all, it is given the information available to the visual system. Matches influential theories This of bias signals by Miller & Cohen (2001), and implements selection of state without simply shifting the decision to an external homunculus. Second, our model is a generative and capable of those found in visual search. Third, our model generates relative saccadic vector information as opposed to absolute spatial coordinates. This match is more closely associated with the colliculus.   

Added: May 8, 2018