  Humans constantly move their eyes, even during visual fixations, where
miniature (or fixational) eye movements are produced involuntarily. Fixational
eye movements are composed of slow components (physiological drift and tremor)
and fast microsaccades. The complex dynamics of physiological drift can be
modeled qualitatively as a statistically self-avoiding random walk (SAW model,
see Engbert et al., 2011). In this study, we implement a data assimilation
approach for the SAW model to explain quantitative differences in experimental
data obtained from high-resolution, video-based eye tracking. We present a
likelihood function for the SAW model which allows us apply Bayesian parameter
estimation at the level of individual human participants. Based on the model
fits we find a relationship between the activation predicted by the SAW model
and the occurrence of microsaccades. The latent model activation relative to
microsaccade onsets and offsets using experimental data reveals evidence for a
triggering mechanism for microsaccades. These findings suggest that the SAW
model is capable of capturing individual differences and can serve as a tool
for exploring the relationship between physiological drift and microsaccades as
the two most important components of fixational eye movements. Our results
contribute to the understanding of individual variability in microsaccade
behaviors and the role of fixational eye movements in visual information
processing.
