• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

Article

A Generative Model of Cognitive State from Task and Eye Movements

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.