Refine
Year of publication
Document Type
- Article (114)
- Postprint (16)
- Other (5)
- Conference Proceeding (4)
- Preprint (2)
- Monograph/Edited Volume (1)
- Doctoral Thesis (1)
- Review (1)
Keywords
- eye movements (14)
- Eye movements (12)
- scene viewing (8)
- attention (6)
- spatial frequencies (6)
- Reading (5)
- saccades (5)
- eye-movement control (4)
- Computational modelling (3)
- color (3)
Institute
- Department Psychologie (118)
- Institut für Physik und Astronomie (15)
- Strukturbereich Kognitionswissenschaften (6)
- Institut für Mathematik (5)
- Department Linguistik (2)
- Humanwissenschaftliche Fakultät (2)
- Interdisziplinäres Zentrum für Dynamik komplexer Systeme (2)
- Referat für Presse- und Öffentlichkeitsarbeit (2)
- Extern (1)
- Mathematisch-Naturwissenschaftliche Fakultät (1)
In eye-movement control during reading, advanced process-oriented models have been developed to reproduce behavioral data. So far, model complexity and large numbers of model parameters prevented rigorous statistical inference and modeling of interindividual differences. Here we propose a Bayesian approach to both problems for one representative computational model of sentence reading (SWIFT; Engbert et al., Psychological Review, 112, 2005, pp. 777-813). We used experimental data from 36 subjects who read the text in a normal and one of four manipulated text layouts (e.g., mirrored and scrambled letters). The SWIFT model was fitted to subjects and experimental conditions individually to investigate between- subject variability. Based on posterior distributions of model parameters, fixation probabilities and durations are reliably recovered from simulated data and reproduced for withheld empirical data, at both the experimental condition and subject levels. A subsequent statistical analysis of model parameters across reading conditions generates model-driven explanations for observable effects between conditions.
A dynamical model of saccade generation in reading based on spatially distributed lexical processing
(2002)
We explore the interaction between oculomotor control and language comprehension on the sentence level using two well-tested computational accounts of parsing difficulty. Previous work (Boston, Hale, Vasishth, & Kliegl, 2011) has shown that surprisal (Hale, 2001; Levy, 2008) and cue-based memory retrieval (Lewis & Vasishth, 2005) are significant and complementary predictors of reading time in an eyetracking corpus. It remains an open question how the sentence processor interacts with oculomotor control. Using a simple linking hypothesis proposed in Reichle, Warren, and McConnell (2009), we integrated both measures with the eye movement model EMMA (Salvucci, 2001) inside the cognitive architecture ACT-R (Anderson et al., 2004). We built a reading model that could initiate short Time Out regressions (Mitchell, Shen, Green, & Hodgson, 2008) that compensate for slow postlexical processing. This simple interaction enabled the model to predict the re-reading of words based on parsing difficulty. The model was evaluated in different configurations on the prediction of frequency effects on the Potsdam Sentence Corpus. The extension of EMMA with postlexical processing improved its predictions and reproduced re-reading rates and durations with a reasonable fit to the data. This demonstration, based on simple and independently motivated assumptions, serves as a foundational step toward a precise investigation of the interaction between high-level language processing and eye movement control.
Author summary <br /> Switching between local and global attention is a general strategy in human information processing. We investigate whether this strategy is a viable approach to model sequences of fixations generated by a human observer in a free viewing task with natural scenes. Variants of the basic model are used to predict the experimental data based on Bayesian inference. Results indicate a high predictive power for both aggregated data and individual differences across observers. The combination of a novel model with state-of-the-art Bayesian methods lends support to our two-state model using local and global internal attention states for controlling eye movements. <br /> 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.
Fixational eye movements show scaling behaviour of the positional mean-squared displacement with a characteristic transition from persistence to antipersistence for increasing time-lag. These statistical patterns were found to be mainly shaped by microsaccades (fast, small-amplitude movements). However, our re-analysis of fixational eye-movement data provides evidence that the slow component (physiological drift) of the eyes exhibits scaling behaviour of the mean-squared displacement that varies across human participants. These results suggest that drift is a correlated movement that interacts with microsaccades. Moreover, on the long time scale, the mean-squared displacement of the drift shows oscillations, which is also present in the displacement auto-correlation function. This finding lends support to the presence of time-delayed feedback in the control of drift movements. Based on an earlier non-linear delayed feedback model of fixational eye movements, we propose and discuss different versions of a new model that combines a self-avoiding walk with time delay. As a result, we identify a model that reproduces oscillatory correlation functions, the transition from persistence to antipersistence, and microsaccades.
Eye-movement experiments suggest that the perceptual span during reading is larger than the fixated word, asymmetric around the fixation position, and shrinks in size contingent on the foveal processing load. We used the SWIFT model of eye-movement control during reading to test these hypotheses and their implications under the assumption of graded parallel processing of all words inside the perceptual span. Specifically, we simulated reading in the boundary paradigm and analysed the effects of denying the model to have valid preview of a parafoveal word n + 2 two words to the right of fixation. Optimizing the model parameters for the valid preview condition only, we obtained span parameters with remarkably realistic estimates conforming to the empirical findings on the size of the perceptual span. More importantly, the SWIFT model generated parafoveal processing up to word n + 2 without fitting the model to such preview effects. Our results suggest that asymmetry and dynamic modulation are plausible properties of the perceptual span in a parallel word-processing model such as SWIFT. Moreover, they seem to guide the flexible distribution of processing resources during reading between foveal and parafoveal words.
When we fixate a stationary target, our eyes generate miniature (or fixational) eye movements involuntarily. These fixational eye movements are classified as slow components (physiological drift, tremor) and microsaccades, which represent rapid, small-amplitude movements. Here we propose an integrated mathematical model for the generation of slow fixational eye movements and microsaccades. The model is based on the concept of self-avoiding random walks in a potential, a process driven by a self-generated activation field. The self-avoiding walk generates persistent movements on a short timescale, whereas, on a longer timescale, the potential produces antipersistent motions that keep the eye close to an intended fixation position. We introduce microsaccades as fast movements triggered by critical activation values. As a consequence, both slow movements and microsaccades follow the same law of motion; i.e., movements are driven by the self-generated activation field. Thus, the model contributes a unified explanation of why it has been a long-standing problem to separate slow movements and microsaccades with respect to their motion-generating principles. We conclude that the concept of a self-avoiding random walk captures fundamental properties of fixational eye movements and provides a coherent theoretical framework for two physiologically distinct movement types.
An lterative algorithm for the estimation of the distribution of mislocated fixations during reading
(2007)
Background
Body image distortion is highly prevalent among overweight individuals. Whilst there is evidence that body-dissatisfied women and those suffering from disordered eating show a negative attentional bias towards their own unattractive body parts and others’ attractive body parts, little is known about visual attention patterns in the area of obesity and with respect to males. Since eating disorders and obesity share common features in terms of distorted body image and body dissatisfaction, the aim of this study was to examine whether overweight men and women show a similar attentional bias.
Methods/Design
We analyzed eye movements in 30 overweight individuals (18 females) and 28 normalweight individuals (16 females) with respect to the participants’ own pictures as well as gender-
and BMI-matched control pictures (front and back view). Additionally, we assessed body image and disordered eating using validated questionnaires.
Discussion
The overweight sample rated their own body as less attractive and showed a more disturbed body image. Contrary to our assumptions, they focused significantly longer on attractive
compared to unattractive regions of both their own and the control body. For one’s own body, this was more pronounced for women. A higher weight status and more frequent body checking predicted attentional bias towards attractive body parts. We found that overweight adults exhibit an unexpected and stable pattern of selective attention, with a distinctive focus on their own attractive body regions despite higher levels of body dissatisfaction. This positive attentional bias may either be an indicator of a more pronounced pattern of attentional avoidance or a self-enhancing strategy. Further research is warranted to clarify these results.
Background
Body image distortion is highly prevalent among overweight individuals. Whilst there is evidence that body-dissatisfied women and those suffering from disordered eating show a negative attentional bias towards their own unattractive body parts and others’ attractive body parts, little is known about visual attention patterns in the area of obesity and with respect to males. Since eating disorders and obesity share common features in terms of distorted body image and body dissatisfaction, the aim of this study was to examine whether overweight men and women show a similar attentional bias.
Methods/Design
We analyzed eye movements in 30 overweight individuals (18 females) and 28 normalweight individuals (16 females) with respect to the participants’ own pictures as well as gender-
and BMI-matched control pictures (front and back view). Additionally, we assessed body image and disordered eating using validated questionnaires.
Discussion
The overweight sample rated their own body as less attractive and showed a more disturbed body image. Contrary to our assumptions, they focused significantly longer on attractive
compared to unattractive regions of both their own and the control body. For one’s own body, this was more pronounced for women. A higher weight status and more frequent body checking predicted attentional bias towards attractive body parts. We found that overweight adults exhibit an unexpected and stable pattern of selective attention, with a distinctive focus on their own attractive body regions despite higher levels of body dissatisfaction. This positive attentional bias may either be an indicator of a more pronounced pattern of attentional avoidance or a self-enhancing strategy. Further research is warranted to clarify these results.
Background
Body image distortion is highly prevalent among overweight individuals. Whilst there is evidence that body-dissatisfied women and those suffering from disordered eating show a negative attentional bias towards their own unattractive body parts and others' attractive body parts, little is known about visual attention patterns in the area of obesity and with respect to males. Since eating disorders and obesity share common features in terms of distorted body image and body dissatisfaction, the aim of this study was to examine whether overweight men and women show a similar attentional bias.
Methods/Design
We analyzed eye movements in 30 overweight individuals (18 females) and 28 normal-weight individuals (16 females) with respect to the participants' own pictures as well as gender- and BMI-matched control pictures (front and back view). Additionally, we assessed body image and disordered eating using validated questionnaires.
Discussion
The overweight sample rated their own body as less attractive and showed a more disturbed body image. Contrary to our assumptions, they focused significantly longer on attractive compared to unattractive regions of both their own and the control body. For one's own body, this was more pronounced for women. A higher weight status and more frequent body checking predicted attentional bias towards attractive body parts. We found that overweight adults exhibit an unexpected and stable pattern of selective attention, with a distinctive focus on their own attractive body regions despite higher levels of body dissatisfaction. This positive attentional bias may either be an indicator of a more pronounced pattern of attentional avoidance or a self-enhancing strategy. Further research is warranted to clarify these results.
In this study we re-evaluate the estimation of the self-similarity exponent of fixational eye movements using Bayesian theory. Our analysis is based on a subsampling decomposition, which permits an analysis of the signal up to some scale factor. We demonstrate that our approach can be applied to simulated data from mathematical models of fixational eye movements to distinguish the models' properties reliably.
Process-oriented theories of cognition must be evaluated against time-ordered observations. Here we present a representative example for data assimilation of the SWIFT model, a dynamical model of the control of fixation positions and fixation durations during natural reading of single sentences. First, we develop and test an approximate likelihood function of the model, which is a combination of a spatial, pseudo-marginal likelihood and a temporal likelihood obtained by probability density approximation Second, we implement a Bayesian approach to parameter inference using an adaptive Markov chain Monte Carlo procedure. Our results indicate that model parameters can be estimated reliably for individual subjects. We conclude that approximative Bayesian inference represents a considerable step forward for computational models of eye-movement control, where modeling of individual data on the basis of process-based dynamic models has not been possible so far.
Bayesian selection of Markov Models for symbol sequences application to microsaccadic eye movements
(2012)
Complex biological dynamics often generate sequences of discrete events which can be described as a Markov process. The order of the underlying Markovian stochastic process is fundamental for characterizing statistical dependencies within sequences. As an example for this class of biological systems, we investigate the Markov order of sequences of microsaccadic eye movements from human observers. We calculate the integrated likelihood of a given sequence for various orders of the Markov process and use this in a Bayesian framework for statistical inference on the Markov order. Our analysis shows that data from most participants are best explained by a first-order Markov process. This is compatible with recent findings of a statistical coupling of subsequent microsaccade orientations. Our method might prove to be useful for a broad class of biological systems.
Sudden visual changes attract our gaze, and related eye movement control requires attentional resources. Attention is a limited resource that is also involved in working memory-for instance, memory encoding. As a consequence, theory suggests that gaze capture could impair the buildup of memory respresentations due to an attentional resource bottleneck. Here we developed an experimental design combining a serial memory task (verbal or spatial) and concurrent gaze capture by a distractor (of high or low similarity to the relevant item). The results cannot be explained by a general resource bottleneck. Specifically, we observed that capture by the low-similar distractor resulted in delayed and reduced saccade rates to relevant items in both memory tasks. However, while spatial memory performance decreased, verbal memory remained unaffected. In contrast, the high-similar distractor led to capture and memory loss for both tasks. Our results lend support to the view that gaze capture leads to activation of irrelevant representations in working memory that compete for selection at recall. Activation of irrelevant spatial representations distracts spatial recall, whereas activation of irrelevant verbal features impairs verbal memory performance.
The complex behaviour of cardiorespiratory dynamics is shown to be related to the interaction between several physiological oscillators. This study is based on electrocardiogram and respiratory flow data obtained from 3 different subjects during paced breathing at 10 different pacing cycle lengths ranging from 5 s to 12 s. Two different methods ideally suited for the analysis of synchronization pattern of coupled oscillators are applied: 1. Symbolic dynamics based on symbol coding adapted for the detection of respiratory modulation of cardiac parasympathetic activity discloses two regimes of different synchronization behaviour within the frequency area corresponding to the Arnold tongue of 1:1 frequency-locking between respiratory flow and respiratory heartbeat variation (respiratory sinus arrhythmia). 2. The analysis of the phase shift between respiratory flow and respiratory sinus arrhythmia indicates that synchronization is not a static but a dynamic phenomenon. The observed dependence of the phase shift on respiratory cycle length shows large inter-individual variation. These findings turn out to be further hints for the existence of an additional central oscillator in the frequency range of respiration interacting with the central respiratory oscillator driving mechanical respiration.