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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.
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.
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.
Whenever eye movements are measured, a central part of the analysis has to do with where subjects fixate and why they fixated where they fixated. To a first approximation, a set of fixations can be viewed as a set of points in space; this implies that fixations are spatial data and that the analysis of fixation locations can be beneficially thought of as a spatial statistics problem. We argue that thinking of fixation locations as arising from point processes is a very fruitful framework for eye-movement data, helping turn qualitative questions into quantitative ones. We provide a tutorial introduction to some of the main ideas of the field of spatial statistics, focusing especially on spatial Poisson processes. We show how point processes help relate image properties to fixation locations. In particular we show how point processes naturally express the idea that image features' predictability for fixations may vary from one image to another. We review other methods of analysis used in the literature, show how they relate to point process theory, and argue that thinking in terms of point processes substantially extends the range of analyses that can be performed and clarify their interpretation.
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.
In humans and in foveated animals visual acuity is highly concentrated at the center of gaze, so that choosing where to look next is an important example of online, rapid decision-making. Computational neuroscientists have developed biologically-inspired models of visual attention, termed saliency maps, which successfully predict where people fixate on average. Using point process theory for spatial statistics, we show that scanpaths contain, however, important statistical structure, such as spatial clustering on top of distributions of gaze positions. Here, we develop a dynamical model of saccadic selection that accurately predicts the distribution of gaze positions as well as spatial clustering along individual scanpaths. Our model relies on activation dynamics via spatially-limited (foveated) access to saliency information, and, second, a leaky memory process controlling the re-inspection of target regions. This theoretical framework models a form of context-dependent decision-making, linking neural dynamics of attention to behavioral gaze data.
When watching the image of a natural scene on a computer screen, observers initially move their eyes toward the center of the image—a reliable experimental finding termed central fixation bias. This systematic tendency in eye guidance likely masks attentional selection driven by image properties and top-down cognitive processes. Here, we show that the central fixation bias can be reduced by delaying the initial saccade relative to image onset. In four scene-viewing experiments we manipulated observers' initial gaze position and delayed their first saccade by a specific time interval relative to the onset of an image. We analyzed the distance to image center over time and show that the central fixation bias of initial fixations was significantly reduced after delayed saccade onsets. We additionally show that selection of the initial saccade target strongly depended on the first saccade latency. A previously published model of saccade generation was extended with a central activation map on the initial fixation whose influence declined with increasing saccade latency. This extension was sufficient to replicate the central fixation bias from our experiments. Our results suggest that the central fixation bias is generated by default activation as a response to the sudden image onset and that this default activation pattern decreases over time. Thus, it may often be preferable to use a modified version of the scene viewing paradigm that decouples image onset from the start signal for scene exploration to explicitly reduce the central fixation bias.
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.