arXiv subjects

Lisa Schwetlick

Publications and source records attributed to Lisa Schwetlick.

5 recordsLinked to original sources

arXiv2025

Bayesian Dynamical Modeling of Fixational Eye Movements

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.

stat.AP↗

arXiv2025

Distinctive pupil and microsaccade-rate signatures in self-recognition

Pupil dynamics and fixational eye movements are primarily involuntary processes that actively support visual perception during fixations. Both measures are known to be sensitive to ongoing cognitive and affective processing. In a visual fixation experiment (N=116) we demonstrate that self-recognition, familiar faces, and unfamiliar faces elicit specific responses in pupil dynamics and microsaccade rate. First, the pupil response comprises an immediate pupil constriction followed by a dilation in response to stimulus onsets. We observe attenuated constriction and greater dilation when faces are recognized compared to unknown faces. This effect is strongest for one's own face. Second, microsaccade rates, which generally show inhibitory responses to incoming stimuli, generate stronger inhibition for familiar faces compared to unknown faces. Again, the strongest inhibition is observed in response to one's own face. Our results imply that eye-related physiological measures expose hidden knowledge in face memory and could contribute to biometric authentication and identity validation procedures.

q-bio.NC↗

arXiv2022

A dynamical scan path model for task-dependence during scene viewing

In real-world scene perception human observers generate sequences of fixations to move image patches into the high-acuity center of the visual field. Models of visual attention developed over the last 25 years aim to predict two-dimensional probabilities of gaze positions for a given image via saliency maps. Recently, progress has been made on models for the generation of scan paths under the constraints of saliency as well as attentional and oculomotor restrictions. Experimental research demonstrated that task constraints can have a strong impact on viewing behavior. Here we propose a scan path model for both fixation positions and fixation durations, which includes influences of task instructions and interindividual differences. Based on an eye-movement experiment with four different task conditions, we estimated model parameters for each individual observer and task condition using a fully Bayesian dynamical modeling framework using a joint spatial-temporal likelihood approach with sequential estimation. Resulting parameter values demonstrate that model properties such as the attentional span are adjusted to task requirements. Posterior predictive checks indicate that our dynamical model can reproduce task differences in scan path statistics across individual observers.

q-bio.NC↗

arXiv2020

A Mathematical Model of Local and Global Attention in Natural Scene Viewing

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.

q-bio.NC↗

arXiv2020

Discriminative Viewer Identification using Generative Models of Eye Gaze

We study the problem of identifying viewers of arbitrary images based on their eye gaze. Psychological research has derived generative stochastic models of eye movements. In order to exploit this background knowledge within a discriminatively trained classification model, we derive Fisher kernels from different generative models of eye gaze. Experimentally, we find that the performance of the classifier strongly depends on the underlying generative model. Using an SVM with Fisher kernel improves the classification performance over the underlying generative model.

cs.LG↗