Refine
Year of publication
Document Type
- Article (26)
- Doctoral Thesis (13)
- Postprint (9)
Language
- English (48) (remove)
Is part of the Bibliography
- yes (48) (remove)
Keywords
- eye movements (48) (remove)
Institute
- Department Psychologie (27)
- Strukturbereich Kognitionswissenschaften (7)
- Humanwissenschaftliche Fakultät (4)
- Department Linguistik (2)
- Institut für Physik und Astronomie (2)
- Potsdam Research Institute for Multilingualism (PRIM) (2)
- Extern (1)
- Institut für Informatik und Computational Science (1)
- Institut für Mathematik (1)
- Mathematisch-Naturwissenschaftliche Fakultät (1)
When infants observe a human grasping action, experience-based accounts predict that all infants familiar with grasping actions should be able to predict the goal regardless of additional agency cues such as an action effect. Cue-based accounts, however, suggest that infants use agency cues to identify and predict action goals when the action or the agent is not familiar. From these accounts, we hypothesized that younger infants would need additional agency cues such as a salient action effect to predict the goal of a human grasping action, whereas older infants should be able to predict the goal regardless of agency cues. In three experiments, we presented 6-, 7-, and 11-month-olds with videos of a manual grasping action presented either with or without an additional salient action effect (Exp. 1 and 2), or we presented 7-month-olds with videos of a mechanical claw performing a grasping action presented with a salient action effect (Exp. 3). The 6-month-olds showed tracking gaze behavior, and the 11-month-olds showed predictive gaze behavior, regardless of the action effect. However, the 7-month-olds showed predictive gaze behavior in the action-effect condition, but tracking gaze behavior in the no-action-effect condition and in the action-effect condition with a mechanical claw. The results therefore support the idea that salient action effects are especially important for infants' goal predictions from 7 months on, and that this facilitating influence of action effects is selective for the observation of human hands.
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.
Learning to read in German
(2021)
In the present dissertation, the development of eye movement behavior and the perceptual span of German beginning readers was investigated in Grades 1 to 3 (Study 1) and longitudinally within a one-year time interval (Study 2), as well as in relation to intrinsic and extrinsic reading motivation (Study 3). The presented results are intended to fill the gap of only sparse information on young readers’ eye movements and completely missing information on German young readers’ perceptual span and its development. On the other hand, reading motivation data have been scrutinized with respect to reciprocal effects on reading comprehension but not with respect to more immediate, basic cognitive processing (e.g., word decoding) that is indicated by different eye movement measures. Based on a longitudinal study design, children in Grades 1–3 participated in a moving window reading experiment with eye movement recordings in two successive years. All children were participants of a larger longitudinal study on intrapersonal developmental risk factors in childhood and adolescence (PIER study). Motivation data and other psychometric reading data were collected during individual inquiries and tests at school. Data analyses were realized in three separate studies that focused on different but related aspects of reading and perceptual span development. Study 1 presents the first cross-sectional report on the perceptual span of beginning German readers. The focus was on reading rate changes in Grades 1 to 3 and on the issue of the onset of the perceptual span development and its dependence on basic foveal reading processes. Study 2 presents a successor of Study 1 providing first longitudinal data of the perceptual span in elementary school children. It also includes information on the stability of observed and predicted reading rates and perceptual span sizes and introduces a new measure of the perceptual span based on nonlinear mixed-effects models. Another issue addressed in this study is the longitudinal between-group comparison of slower and faster readers which refers to the detection of developmental patterns. Study 3 includes longitudinal reading motivation data and investigates the relation between different eye movement measures including perceptual span and intrinsic as well as extrinsic reading motivation. In Study 1, a decelerated increase in reading rate was observed between Grades 1 to 3. Grade effects were also reported for saccade length, refixation probability, and different fixation duration measures. With higher grade, mean saccade length increased, whereas refixation probability, first-fixation duration, gaze duration, and total reading time decreased. Perceptual span development was indicated by an increase in window size effects with grade level. Grade level differences with respect to window size effects were stronger between Grades 2 and 3 than between Grades 1 and 2. These results were replicated longitudinally in Study 2. Again, perceptual span size significantly changed between Grades 2 and 3, but not between Grades 1 and 2 or Grades 3 and 4. Observed and predicted reading rates were found to be highly stable after first grade, whereas stability of perceptual span was only moderate for all grade levels. Group differences between slower and faster readers in Year 1 remained observable in Year 2 showing a pattern of stable achievement differences rather than a compensatory pattern. Between Grades 2 and 3, between-group differences in reading rate even increased resulting in a Matthew effect. A similar effect was observed for perceptual span development between Grades 3 and 4. Finally, in Study 3, significant relations between beginning readers’ eye movements and their reading motivation were observed. In both years of measurement, higher intrinsic reading motivation was related to more skilled eye movement patterns as indicated by short fixations, longer saccades, and higher reading rates. In Year 2, intrinsic reading motivation was also significantly and negatively correlated with refixation probability. These correlational patterns were confirmed in cross-sectional linear models controlling for grade level and reading amount and including both reading motivation measures, extrinsic and intrinsic motivation. While there were significant positive relations between intrinsic reading motivation and word decoding as indicated by the above stated eye movement measures, extrinsic reading motivation only predicted variance in eye movements in Year 2 (significant for fixation durations and reading rate), with a consistently opposite pattern of effects as compared to intrinsic reading motivation. Finally, longitudinal effects of Year 1 intrinsic reading motivation on Year 2 word decoding were observed for gaze duration, total reading time, refixation probability, and perceptual span within cross-lagged panel models. These effects were reciprocal because all eye movement measures significantly predicted variance in intrinsic reading motivation. Extrinsic reading motivation in Year 1 did not affect any eye movement measure in Year 2, and vice versa, except for a significant, negative relation with perceptual span. Concluding, the present dissertation demonstrates that largest gains in reading development in terms of eye movement changes are observable between Grades 1 and 2. Together with the observed pattern of stable differences between slower and faster readers and a widening achievement gap between Grades 2 and 3 for reading rate, these results underline the importance of the first year(s) of formal reading instruction. The development of the perceptual span lags behind as it is most apparent between Grades 2 and 3. This suggests that efficient parafoveal processing presupposes a certain degree of foveal reading proficiency (e.g., word decoding). Finally, this dissertation demonstrates that intrinsic reading motivation—but not extrinsic motivation—effectively supports the development of skilled reading.
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.
Recent studies using the gaze-contingent boundary paradigm reported a reversed preview benefit- shorter fixations on a target word when an unrelated preview was easier to process than the fixated target (Schotter & Leinenger, 2016). This is explained viaforeedfixatiotzs-short fixations on words that would ideally be skipped (because lexical processing has progressed enough) but could not be because saccade planning reached a point of no return. This contrasts with accounts of preview effects via trans-saccadic integration-shorter fixations on a target word when the preview is more similar to it (see Cutter. Drieghe, & Liversedge, 2015). In addition, if the previewed word-not the fixated target-determines subsequent eye movements, is it also this word that enters the linguistic processing stream? We tested these accounts by having 24 subjects read 150 sentences in the boundary paradigm in which both the preview and target were initially plausible but later one, both, or neither became implausible, providing an opportunity to probe which one was linguistically encoded. In an intervening buffer region, both words were plausible, providing an opportunity to investigate trans-saccadic integration. The frequency of the previewed word affected progressive saccades (i.e.. forced fixations) as well as when transsaccadic integration failure increased regressions, but, only the implausibility of the target word affected semantic encoding. These data support a hybrid account of saccadic control (Reingold, Reichle. Glaholt, & Sheridan, 2012) driven by incomplete (often parafoveal) word recognition, which occurs prior to complete (often foveal) word recognition.
Moving arms
(2018)
Embodied cognition postulates a bi-directional link between the human body and its cognitive functions. Whether this holds for higher cognitive functions such as problem solving is unknown. We predicted that arm movement manipulations performed by the participants could affect the problem-solving solutions. We tested this prediction in quantitative reasoning tasks that allowed two solutions to each problem (addition or subtraction). In two studies with healthy adults (N=53 and N=50), we found an effect of problem-congruent movements on problem solutions. Consistent with embodied cognition, sensorimotor information gained via right or left arm movements affects the solution in different types of problem-solving tasks.
A central insight from psychological studies on human eye movements is that eye movement patterns are highly individually characteristic. They can, therefore, be used as a biometric feature, that is, subjects can be identified based on their eye movements. This thesis introduces new machine learning methods to identify subjects based on their eye movements while viewing arbitrary content. The thesis focuses on probabilistic modeling of the problem, which has yielded the best results in the most recent literature. The thesis studies the problem in three phases by proposing a purely probabilistic, probabilistic deep learning, and probabilistic deep metric learning approach. In the first phase, the thesis studies models that rely on psychological concepts about eye movements. Recent literature illustrates that individual-specific distributions of gaze patterns can be used to accurately identify individuals. In these studies, models were based on a simple parametric family of distributions. Such simple parametric models can be robustly estimated from sparse data, but have limited flexibility to capture the differences between individuals. Therefore, this thesis proposes a semiparametric model of gaze patterns that is flexible yet robust for individual identification. These patterns can be understood as domain knowledge derived from psychological literature. Fixations and saccades are examples of simple gaze patterns. The proposed semiparametric densities are drawn under a Gaussian process prior centered at a simple parametric distribution. Thus, the model will stay close to the parametric class of densities if little data is available, but it can also deviate from this class if enough data is available, increasing the flexibility of the model. The proposed method is evaluated on a large-scale dataset, showing significant improvements over the state-of-the-art. Later, the thesis replaces the model based on gaze patterns derived from psychological concepts with a deep neural network that can learn more informative and complex patterns from raw eye movement data. As previous work has shown that the distribution of these patterns across a sequence is informative, a novel statistical aggregation layer called the quantile layer is introduced. It explicitly fits the distribution of deep patterns learned directly from the raw eye movement data. The proposed deep learning approach is end-to-end learnable, such that the deep model learns to extract informative, short local patterns while the quantile layer learns to approximate the distributions of these patterns. Quantile layers are a generic approach that can converge to standard pooling layers or have a more detailed description of the features being pooled, depending on the problem. The proposed model is evaluated in a large-scale study using the eye movements of subjects viewing arbitrary visual input. The model improves upon the standard pooling layers and other statistical aggregation layers proposed in the literature. It also improves upon the state-of-the-art eye movement biometrics by a wide margin. Finally, for the model to identify any subject — not just the set of subjects it is trained on — a metric learning approach is developed. Metric learning learns a distance function over instances. The metric learning model maps the instances into a metric space, where sequences of the same individual are close, and sequences of different individuals are further apart. This thesis introduces a deep metric learning approach with distributional embeddings. The approach represents sequences as a set of continuous distributions in a metric space; to achieve this, a new loss function based on Wasserstein distances is introduced. The proposed method is evaluated on multiple domains besides eye movement biometrics. This approach outperforms the state of the art in deep metric learning in several domains while also outperforming the state of the art in eye movement biometrics.
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.