@phdthesis{Chandra2020,
  author    = {Chandra, Johan},
  title     = {The role of the oculomotor control in eye movements during reading},
  doi       = {10.25932/publishup-47593},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-475930},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xiii, 115},
  year      = {2020},
  abstract  = {Most reading theories assume that readers aim at word centers for optimal information processing. During reading, saccade targeting turns out to be imprecise: Saccades' initial landing positions often miss the word centers and have high variance, with an additional systematic error that is modulated by the distance from the launch site to the center of the target word. The performance of the oculomotor system, as reflected in the statistics of within-word landing positions, turns out to be very robust and mostly affected by the spatial information during reading. Hence, it is assumed that the saccade generation is highly automated. The main goal of this thesis is to explore the performance of the oculomotor system under various reading conditions where orthographic information and the reading direction were manipulated. Additionally, the challenges in understanding the eye movement data to represent the oculomotor process during reading are addressed. Two experimental studies and one simulation study were conducted for this thesis, which resulted in the following main findings: (i) Reading texts with orthographic manipulations leads to specific changes in the eye movement patterns, both in temporal and spatial measures. The findings indicate that the oculomotor control of eye movements during reading is dependent on reading conditions (Chapter 2 \& 3). (ii) Saccades' accuracy and precision can be simultaneously modulated under reversed reading condition, supporting the assumption that the random and systematic oculomotor errors are not independent. By assuming that readers increase the precision of sensory observation while maintaining the learned prior knowledge when reading direction was reversed, a process-oriented Bayesian model for saccade targeting can account for the simultaneous reduction of oculomotor errors (Chapter 2). (iii) Plausible parameter values serving as proxies for the intended within-word landing positions can be estimated by using the maximum a posteriori estimator from Bayesian inference. Using the mean value of all observations as proxies is insufficient for studies focusing on the launch-site effect because the method exhibits the strongest bias when estimating the size of the effect. Mislocated fixations remain a challenge for the currently known estimation methods, especially when the systematic oculomotor error is large (Chapter 4). The results reported in this thesis highlight the role of the oculomotor system, together with underlying cognitive processes, in eye movements during reading. The modulation of oculomotor control can be captured through a precise analysis of landing positions.},
  language  = {en}
}
@phdthesis{Rabe2024,
  author    = {Rabe, Maximilian Michael},
  title     = {Modeling the interaction of sentence processing and eye-movement control in reading},
  doi       = {10.25932/publishup-62279},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-622792},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xiii, 171},
  year      = {2024},
  abstract  = {The evaluation of process-oriented cognitive theories through time-ordered observations is crucial for the advancement of cognitive science. The findings presented herein integrate insights from research on eye-movement control and sentence comprehension during reading, addressing challenges in modeling time-ordered data, statistical inference, and interindividual variability. Using kernel density estimation and a pseudo-marginal likelihood for fixation durations and locations, a likelihood implementation of the SWIFT model of eye-movement control during reading (Engbert et al., Psychological Review, 112, 2005, pp. 777-813) is proposed. Within the broader framework of data assimilation, Bayesian parameter inference with adaptive Markov Chain Monte Carlo techniques is facilitated for reliable model fitting. Across the different studies, this framework has shown to enable reliable parameter recovery from simulated data and prediction of experimental summary statistics. Despite its complexity, SWIFT can be fitted within a principled Bayesian workflow, capturing interindividual differences and modeling experimental effects on reading across different geometrical alterations of text. Based on these advancements, the integrated dynamical model SEAM is proposed, which combines eye-movement control, a traditionally psychological research area, and post-lexical language processing in the form of cue-based memory retrieval (Lewis \& Vasishth, Cognitive Science, 29, 2005, pp. 375-419), typically the purview of psycholinguistics. This proof-of-concept integration marks a significant step forward in natural language comprehension during reading and suggests that the presented methodology can be useful to develop complex cognitive dynamical models that integrate processes at levels of perception, higher cognition, and (oculo-)motor control. These findings collectively advance process-oriented cognitive modeling and highlight the importance of Bayesian inference, individual differences, and interdisciplinary integration for a holistic understanding of reading processes. Implications for theory and methodology, including proposals for model comparison and hierarchical parameter inference, are briefly discussed.},
  language  = {en}
}