The visual responsiveness of each neuron was firstly evaluated moving a white luminous bar (3 cd/m) on the screen; this also allowed mapping the visual receptive field. Then, the neuronal activity was recorded with the optic flow paradigm described below.
It could be proposed that the phasic pattern would arise from the functional characteristics of the visual receptive fields. As already reported, the majority of PEc visual neurons have very large receptive fields usually extending over 30[degrees] with a broad directional selectivity [4, 27], with some of them showing a foveal-sparing receptive field with a robust response to both inward and outward directions .
This may lead to a more reliable selection regarding available parameters dedicated to the visual receptive field, with the primary goal of estimating a user's interest.
In order to analyze the perceiving mechanism, visual selective attention must be taken into consideration since a visual receptive field is comprised of different visual features that are added together and subsequently used for selection of objects.
The visual receptive field has originally been computed by a machine learning network, called the redial basis function (RBF) to estimate each parameter, which approximated a visual receptive field (e.g.
In order to compute eye gaze patterns related to a user's interest, a statistical learning algorithm has been incorporated to compute a visual receptive field so as to estimate interest .
The general setting of the problem of statistical learning is to estimate some function, which depends on an unknown distribution in a (probabilistic) visual receptive field, as previously explained.