  Understanding the decision process underlying gaze control is an important
question in cognitive neuroscience with applications in diverse fields ranging
from psychology to computer vision. The decision for choosing an upcoming
saccade target can be framed as a selection process between two states: Should
the observer further inspect the information near the current gaze position
(local attention) or continue with exploration of other patches of the given
scene (global attention)? Here we propose and investigate a mathematical model
motivated by switching between these two attentional states during scene
viewing. The model is derived from a minimal set of assumptions that generates
realistic eye movement behavior. We implemented a Bayesian approach for model
parameter inference based on the model's likelihood function. In order to
simplify the inference, we applied data augmentation methods that allowed the
use of conjugate priors and the construction of an efficient Gibbs sampler.
This approach turned out to be numerically efficient and permitted fitting
interindividual differences in saccade statistics. Thus, the main contribution
of our modeling approach is two--fold; first, we propose a new model for
saccade generation in scene viewing. Second, we demonstrate the use of novel
methods from Bayesian inference in the field of scan path modeling.
