Institut für Physik und Astronomie
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Institute
In normal everyday viewing, we perform large eye movements (saccades) and miniature or fixational eye movements. Most of our visual perception occurs while we are fixating. However, our eyes are perpetually in motion. Properties of these fixational eye movements, which are partly controlled by the brainstem, change depending on the task and the visual conditions. Currently, fixational eye movements are poorly understood because they serve the two contradictory functions of gaze stabilization and counteraction of retinal fatigue. In this dissertation, we investigate the spatial and temporal properties of time series of eye position acquired from participants staring at a tiny fixation dot or at a completely dark screen (with the instruction to fixate a remembered stimulus); these time series were acquired with high spatial and temporal resolution. First, we suggest an advanced algorithm to separate the slow phases (named drift) and fast phases (named microsaccades) of these movements, which are considered to play different roles in perception. On the basis of this identification, we investigate and compare the temporal scaling properties of the complete time series and those time series where the microsaccades are removed. For the time series obtained during fixations on a stimulus, we were able to show that they deviate from Brownian motion. On short time scales, eye movements are governed by persistent behavior and on a longer time scales, by anti-persistent behavior. The crossover point between these two regimes remains unchanged by the removal of microsaccades but is different in the horizontal and the vertical components of the eyes. Other analyses target the properties of the microsaccades, e.g., the rate and amplitude distributions, and we investigate, whether microsaccades are triggered dynamically, as a result of earlier events in the drift, or completely randomly. The results obtained from using a simple box-count measure contradict the hypothesis of a purely random generation of microsaccades (Poisson process). Second, we set up a model for the slow part of the fixational eye movements. The model is based on a delayed random walk approach within the velocity related equation, which allows us to use the data to determine control loop durations; these durations appear to be different for the vertical and horizontal components of the eye movements. The model is also motivated by the known physiological representation of saccade generation; the difference between horizontal and vertical components concurs with the spatially separated representation of saccade generating regions. Furthermore, the control loop durations in the model suggest an external feedback loop for the horizontal but not for the vertical component, which is consistent with the fact that an internal feedback loop in the neurophysiology has only been identified for the vertical component. Finally, we confirmed the scaling properties of the model by semi-analytical calculations. In conclusion, we were able to identify several properties of the different parts of fixational eye movements and propose a model approach that is in accordance with the described neurophysiology and described limitations of fixational eye movement control.
The predictability problem
(2007)
We try to determine whether it is possible to approximate the subjective Cloze predictability measure with two types of objective measures, semantic and word n-gram measures, based on the statistical properties of text corpora. The semantic measures are constructed either by querying Internet search engines or by applying Latent Semantic Analysis, while the word n-gram measures solely depend on the results of Internet search engines. We also analyse the role of Cloze predictability in the SWIFT eye movement model, and evaluate whether other parameters might be able to take the place of predictability. Our results suggest that a computational model that generates predictability values not only needs to use measures that can determine the relatedness of a word to its context; the presence of measures that assert unrelatedness is just as important. In spite of the fact, however, that we only have similarity measures, we predict that SWIFT should perform just as well when we replace Cloze predictability with our measures.