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Institut
Current models of eye movement control are derived from theories assuming serial processing of single items or from theories based on parallel processing of multiple items at a time. This issue has persisted because most investigated paradigms generated data compatible with both serial and parallel models. Here, we study eye movements in a sequential scanning task, where stimulus n indicates the position of the next stimulus n + 1. We investigate whether eye movements are controlled by sequential attention shifts when the task requires serial order of processing. Our measures of distributed processing in the form of parafoveal-on-foveal effects, long-range modulations of target selection, and skipping saccades provide evidence against models strictly based on serial attention shifts. We conclude that our results lend support to parallel processing as a strategy for eye movement control.
Using a serial search paradigm, we observed several effects of within-object fixation position on spatial and temporal control of eye movements: the preferred viewing location, launch site effect, the optimal viewing position, and the inverted optimal viewing position of fixation duration. While these effects were first identified by eye-movement studies in reading, our approach permits an analysis of the functional relationships between the effects in a different paradigm. Our results demonstrate that the fixation position is an important predictor of the subsequent saccade by influencing both fixation duration and the selection of the next saccade target.
Eye movements depend on cognitive processes related to visual information processing. Much has been learned about the spatial selection of fixation locations, while the principles governing the temporal control (fixation durations) are less clear. Here, we review current theories for the control of fixation durations in tasks like visual search, scanning, scene perception, and reading and propose a new model for the control of fixation durations. We distinguish two local principles from one global principle of control. First, an autonomous saccade timer initiates saccades after random time intervals (local-I). Second, foveal inhibition permits immediate prolongation of fixation durations by ongoing processing (local-II). Third, saccade timing is adaptive, so that the mean timer value depends on task requirements and fixation history (Global). We demonstrate by numerical simulations that our model qualitatively reproduces patterns of mean fixation durations and fixation duration distributions observed in typical experiments. When combined with assumptions of saccade target selection and oculomotor control, the model accounts for both temporal and spatial aspects of eye movement control in two versions of a visual search task. We conclude that the model provides a promising framework for the control of fixation durations in saccadic tasks.
Scene viewing is used to study attentional selection in complex but still controlled environments. One of the main observations on eye movements during scene viewing is the inhomogeneous distribution of fixation locations: While some parts of an image are fixated by almost all observers and are inspected repeatedly by the same observer, other image parts remain unfixated by observers even after long exploration intervals. Here, we apply spatial point process methods to investigate the relationship between pairs of fixations. More precisely, we use the pair correlation function, a powerful statistical tool, to evaluate dependencies between fixation locations along individual scanpaths. We demonstrate that aggregation of fixation locations within 4 degrees is stronger than expected from chance. Furthermore, the pair correlation function reveals stronger aggregation of fixations when the same image is presented a second time. We use simulations of a dynamical model to show that a narrower spatial attentional span may explain differences in pair correlations between the first and the second inspection of the same image.
Eye movements are a powerful tool to examine cognitive processes. However, in most paradigms little is known about the dynamics present in sequences of saccades and fixations. In particular, the control of fixation durations has been widely neglected in most tasks. As a notable exception, both spatial and temporal aspects of eye-movement control have been thoroughly investigated during reading. There, the scientific discourse was dominated by three controversies, (i), the role of oculomotor vs. cognitive processing on eye-movement control, (ii) the serial vs. parallel processing of words, and, (iii), the control of fixation durations. The main purpose of this thesis was to investigate eye movements in tasks that require sequences of fixations and saccades. While reading phenomena served as a starting point, we examined eye guidance in non-reading tasks with the aim to identify general principles of eye-movement control. In addition, the investigation of eye movements in non-reading tasks helped refine our knowledge about eye-movement control during reading. Our approach included the investigation of eye movements in non-reading experiments as well as the evaluation and development of computational models. I present three main results : First, oculomotor phenomena during reading can also be observed in non-reading tasks (Chapter 2 & 4). Oculomotor processes determine the fixation position within an object. The fixation position, in turn, modulates both the next saccade target and the current fixation duration. Second, predicitions of eye-movement models based on sequential attention shifts were falsified (Chapter 3). In fact, our results suggest that distributed processing of multiple objects forms the basis of eye-movement control. Third, fixation durations are under asymmetric control (Chapter 4). While increasing processing demands immediately prolong fixation durations, decreasing processing demands reduce fixation durations only with a temporal delay. We propose a computational model ICAT to account for asymmetric control. In this model, an autonomous timer initiates saccades after random time intervals independent of ongoing processing. However, processing demands that are higher than expected inhibit the execution of the next saccade and, thereby, prolong the current fixation. On the other hand, lower processing demands will not affect the duration before the next saccade is executed. Since the autonomous timer adjusts to expected processing demands from fixation to fixation, a decrease in processing demands may lead to a temporally delayed reduction of fixation durations. In an extended version of ICAT, we evaluated its performance while simulating both temporal and spatial aspects of eye-movement control. The eye-movement phenomena investigated in this thesis have now been observed in a number of different tasks, which suggests that they represent general principles of eye guidance. I propose that distributed processing of the visual input forms the basis of eye-movement control, while fixation durations are controlled by the principles outlined in ICAT. In addition, oculomotor control contributes considerably to the variability observed in eye movements. Interpretations for the relation between eye movements and cognition strongly benefit from a precise understanding of this interplay.
Dynamical models of cognition play an increasingly important role in driving theoretical and experimental research in psychology. Therefore, parameter estimation, model analysis and comparison of dynamical models are of essential importance. In this article, we propose a maximum likelihood approach for model analysis in a fully dynamical framework that includes time-ordered experimental data. Our methods can be applied to dynamical models for the prediction of discrete behavior (e.g., movement onsets); in particular, we use a dynamical model of saccade generation in scene viewing as a case study for our approach. For this model, the likelihood function can be computed directly by numerical simulation, which enables more efficient parameter estimation including Bayesian inference to obtain reliable estimates and corresponding credible intervals. Using hierarchical models inference is even possible for individual observers. Furthermore, our likelihood approach can be used to compare different models. In our example, the dynamical framework is shown to outperform nondynamical statistical models. Additionally, the likelihood based evaluation differentiates model variants, which produced indistinguishable predictions on hitherto used statistics. Our results indicate that the likelihood approach is a promising framework for dynamical cognitive models.
Bottom-up and top-down as well as low-level and high-level factors influence where we fixate when viewing natural scenes. However, the importance of each of these factors and how they interact remains a matter of debate. Here, we disentangle these factors by analyzing their influence over time. For this purpose, we develop a saliency model that is based on the internal representation of a recent early spatial vision model to measure the low-level, bottom-up factor. To measure the influence of high-level, bottom-up features, we use a recent deep neural network-based saliency model. To account for top-down influences, we evaluate the models on two large data sets with different tasks: first, a memorization task and, second, a search task. Our results lend support to a separation of visual scene exploration into three phases: the first saccade, an initial guided exploration characterized by a gradual broadening of the fixation density, and a steady state that is reached after roughly 10 fixations. Saccade-target selection during the initial exploration and in the steady state is related to similar areas of interest, which are better predicted when including high-level features. In the search data set, fixation locations are determined predominantly by top-down processes. In contrast, the first fixation follows a different fixation density and contains a strong central fixation bias. Nonetheless, first fixations are guided strongly by image properties, and as early as 200 ms after image onset, fixations are better predicted by high-level information. We conclude that any low-level, bottom-up factors are mainly limited to the generation of the first saccade. All saccades are better explained when high-level features are considered, and later, this high-level, bottom-up control can be overruled by top-down influences.
Bottom-up saliency is often cited as a factor driving the choice of fixation locations of human observers, based on the (partial) success of saliency models to predict fixation densities in free viewing. However, these observations are only weak evidence for a causal role of bottom-up saliency in natural viewing behaviour. To test bottom-up saliency more directly, we analyse the performance of a number of saliency models---including our own saliency model based on our recently published model of early visual processing (Schütt & Wichmann, 2017, JoV)---as well as the theoretical limits for predictions over time. On free viewing data our model performs better than classical bottom-up saliency models, but worse than the current deep learning based saliency models incorporating higher-level information like knowledge about objects. However, on search data all saliency models perform worse than the optimal image independent prediction. We observe that the fixation density in free viewing is not stationary over time, but changes over the course of a trial. It starts with a pronounced central fixation bias on the first chosen fixation, which is nonetheless influenced by image content. Starting with the 2nd to 3rd fixation, the fixation density is already well predicted by later densities, but more concentrated. From there the fixation distribution broadens until it reaches a stationary distribution around the 10th fixation. Taken together these observations argue against bottom-up saliency as a mechanistic explanation for eye movement control after the initial orienting reaction in the first one to two saccades, although we confirm the predictive value of early visual representations for fixation locations. The fixation distribution is, first, not well described by any stationary density, second, is predicted better when including object information and, third, is badly predicted by any saliency model in a search task.