Bayesian Dynamical Modeling of Fixational Eye Movements

TL;DR

This paper applies Bayesian data assimilation to a self-avoiding random walk model of fixational eye movements, revealing individual differences and potential microsaccade triggering mechanisms.

Contribution

It introduces a Bayesian parameter estimation method for the SAW model, linking physiological drift to microsaccades in high-resolution eye-tracking data.

Findings

SAW model fits individual eye movement data

Identifies a relationship between model activation and microsaccades

Supports a triggering mechanism for microsaccades

Abstract

Humans constantly move their eyes, even during visual fixations, where miniature (or fixational) eye movements occur involuntarily. Fixational eye movements comprise slow components (physiological drift and tremor) and fast components (microsaccades). The complex dynamics of physiological drift can be modeled qualitatively as a statistically self-avoiding random walk (SAW model, Engbert, Mergenthaler, Sinn, & Pikovsky, 2011). In this study, we implement a data assimilation approach for the SAW model to explain statistics of fixational eye movements and microsaccades in experimental data obtained from high-resolution eye-tracking. We discuss and analyze the likelihood function for the SAW model, which allows us to apply Bayesian parameter estimation at the level of individual human observers. Based on model fitting, we find a relationship between the activation predicted by the SAW model and the occurrence of microsaccades. The model's latent activation relative to microsaccade onsets and offsets using experimental data lends support to the existence of a triggering mechanism for microsaccades. Our findings suggest that the SAW model can capture individual differences and serve as a tool for exploring the relationship between physiological drift and microsaccades as the two most essential components of fixational eye movements. Our results contribute to understanding individual variability in microsaccade behaviors and the role of fixational eye movements in visual information processing.