TY - JOUR A1 - Rabe, Maximilian Michael A1 - Chandra, Johan A1 - Krügel, André A1 - Seelig, Stefan A. A1 - Vasishth, Shravan A1 - Engbert, Ralf T1 - A bayesian approach to dynamical modeling of eye-movement control in reading of normal, mirrored, and scrambled texts JF - Psychological Review N2 - 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. KW - reading eye movements KW - dynamical models KW - Bayesian inference KW - oculomotor KW - control KW - individual differences Y1 - 2021 U6 - https://doi.org/10.1037/rev0000268 SN - 0033-295X SN - 1939-1471 VL - 128 IS - 5 SP - 803 EP - 823 PB - American Psychological Association CY - Washington ER - TY - JOUR A1 - Seelig, Stefan A. A1 - Rabe, Maximilian Michael A1 - Malem-Shinitski, Noa A1 - Risse, Sarah A1 - Reich, Sebastian A1 - Engbert, Ralf T1 - Bayesian parameter estimation for the SWIFT model of eye-movement control during reading JF - Journal of mathematical psychology N2 - 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. KW - dynamical models KW - reading KW - eye movements KW - saccades KW - likelihood function KW - Bayesian inference KW - MCMC KW - interindividual differences Y1 - 2020 U6 - https://doi.org/10.1016/j.jmp.2019.102313 SN - 0022-2496 SN - 1096-0880 VL - 95 PB - Elsevier CY - San Diego ER - TY - JOUR A1 - Engbert, Ralf A1 - Rabe, Maximilian Michael A1 - Schwetlick, Lisa A1 - Seelig, Stefan A. A1 - Reich, Sebastian A1 - Vasishth, Shravan T1 - Data assimilation in dynamical cognitive science JF - Trends in cognitive sciences N2 - Dynamical models make specific assumptions about cognitive processes that generate human behavior. In data assimilation, these models are tested against timeordered data. Recent progress on Bayesian data assimilation demonstrates that this approach combines the strengths of statistical modeling of individual differences with the those of dynamical cognitive models. Y1 - 2022 U6 - https://doi.org/10.1016/j.tics.2021.11.006 SN - 1364-6613 SN - 1879-307X VL - 26 IS - 2 SP - 99 EP - 102 PB - Elsevier CY - Amsterdam ER - TY - THES A1 - Rabe, Maximilian Michael T1 - Mixed model analysis of trial history in naming experiments T1 - Experimentalverlaufsanalyse mit gemischten Modellen in Naming-Experimenten N2 - Several authors highlighted that the time course of an experiment itself could have a substantial influence on the interpretability of experimental effects. Since mixed effects modeling had enabled researchers to investigate more complex problems with more precision than before, two naming experiments were conducted with college students, with and without non-words intermixed, and analyzed with regard to frequency, quality, interactive and trial-history effects. The present analyses build on and extend the Bates, Kliegl, Vasishth, and Baayen (2015) approach in order to converge on a parsimonious model that accounts for autocorrelated errors caused by trial history. For three of four cases, a history-sensitive model improved the model fit over a history-naïve model and explained more deviance. In one of these cases, the herein presented approach helped reveal an interaction between stimulus frequency and quality that was not significant without a trial history account. Main and joint effects, limitations, as well as directions for further research, are briefly discussed. N2 - Verschiedene Autoren haben darauf aufmerksam gemacht, dass bereits der zeitliche Verlauf eines Experiments einen wesentlichen Einfluss auf die Interpretierbarkeit experimenteller Effekte haben kann. Nachdem gemischte Modelle der Wissenschaft ermöglichten, komplexere Fragestellungen mit höherer Präzision als zuvor zu untersuchen, wurden zwei Naming-Experimente mit Collegestudenten durchgeführt, je mit und ohne Pseudowörter, sowie hinsichtlich ihrer Auftrittshäufigkeits-, Stimulusqualitäts-, Interaktions- und Experimentalverlaufseffekte untersucht. Die vorliegenden Analysen beruhen auf dem Ansatz von Bates, Kliegl, Vasishth und Baayen (2015) und erweitern diesen, um ein Parsimonious Model zu bestimmen, welches durch den Experimentalverlauf hervorgerufene Autokorrelationen berücksichtigt. In drei von vier Fällen verbesserte die verlaufsabhängige Analyse die Modellanpassung gegenüber der gewöhnlichen verlaufsunabhängigen Variante und klärte somit mehr Abweichung auf. In einem dieser Fälle half der Ansatz, eine Interaktion zwischen Auftrittshäufigkeit und Stimulusqualität aufzudecken, die ohne Berücksichtigung des Experimentalverlaufs nicht signifikant gewesen war. Haupt- und Interaktionseffekte, Einschränkungen sowie Anregungen für weiterführende Forschung werden kurz erörtert. KW - autocorrelations KW - mixed effects modeling KW - trial history KW - reading aloud KW - Autokorrelationen KW - gemischte Modelle KW - Experimentalverlauf Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-82735 ER - TY - THES A1 - Rabe, Maximilian Michael T1 - Modeling the interaction of sentence processing and eye-movement control in reading T1 - Modellierung der Interaktion von Satzverarbeitung und Blickbewegungskontrolle beim Lesen N2 - 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. N2 - Die Evaluierung prozessorientierter kognitiver Theorien durch zeitlich geordnete Beobachtungen ist ein zentraler Baustein für die Weiterentwicklung der Kognitionswissenschaft. Die hier präsentierten Ergebnisse integrieren Erkenntnisse aus der Forschung zur Blickbewegungskontrolle und zur Satzverarbeitung beim Lesen und gehen dabei auf Herausforderungen bei der Modellierung von zeitlich geordneten Daten, statistischer Inferenz und interindividueller Variabilität ein. Unter Verwendung von Kerndichteschätzung und einer pseudo-marginalen Wahrscheinlichkeitverteilung für Fixationsdauern und -orte wird eine Implementation für die Likelihood des SWIFT-Modells zur Blickbewegungskontrolle beim Lesen (Engbert et al., Psychological Review, 112, 2005, S. 777–813) eingeführt. Im breiteren Kontext der Datenassimilation wird Bayes'sche Parameterinferenz mit adaptiven Markov-Chain-Monte-Carlo-Techniken verwendet, um eine zuverlässige Modellanpassung zu ermöglichen. In verschiedenen Studien hat sich dieser methodische Rahmen als geeignet erwiesen, um zuverlässige Parameterrückgewinnung aus simulierten Daten und Vorhersage experimenteller Zusammenfassungsstatistiken zu ermöglichen. Trotz dessen Komplexität kann SWIFT innerhalb eines fundierten Bayes'schen Workflows angepasst werden und macht daraufhin zuverlässige Vorhersagen für interindividuelle Unterschiede sowie die Modellierung experimenteller Effekte bei verschiedenen geometrischen Änderungen von Text. Basierend auf diesen Fortschritten wird das integrierte dynamische Modell SEAM eingeführt. Dieses kombiniert die Forschungsgebiete der traditionell psychologisch geprägten Blickbewegungskontrolle und der traditionell psycholinguistisch geprägten postlexikalischen Sprachverarbeitung in Form von cue-basiertem Gedächtnisabruf (Lewis & Vasishth, Cognitive Science, 29, 2005, S. 375–419). Der Nachweis der Durchführbarkeit solcher integrativer Modelle stellt einen bedeutenden Fortschritt bei der natürlichen Sprachverarbeitung beim Lesen dar und legt nahe, dass die vorgestellte Methodik nützlich sein kann, um komplexe kognitive dynamische Modelle zu entwickeln, die Prozesse auf den Ebenen der Wahrnehmung, höheren Kognition, und (okulo-)motorischen Kontrolle integrieren. Diese Erkenntnisse fördern insgesamt die prozessorientierte kognitive Modellierung und betonen die Bedeutung der Bayes'schen Inferenz, individueller Unterschiede und interdisziplinärer Integration für ein ganzheitliches Verständnis von Leseprozessen. Implikationen für Theorie und Methodologie, einschließlich Vorschlägen für Modellvergleich und hierarchische Parameterinferenz, werden kurz diskutiert. KW - eye movements KW - dynamical cognitive modeling KW - sequential likelihood KW - psycholinguistics KW - cognitive psychology KW - sentence processing KW - reading KW - Blickbewegungen KW - Dynamische kognitive Modellierung KW - Sequenzielle Likelihood KW - Psycholinguistik KW - Kognitionspsychologie KW - Satzverarbeitung KW - Lesen Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-622792 ER - TY - JOUR A1 - Engbert, Ralf A1 - Rabe, Maximilian Michael A1 - Kliegl, Reinhold A1 - Reich, Sebastian T1 - Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics JF - Bulletin of mathematical biology : official journal of the Society for Mathematical Biology N2 - Newly emerging pandemics like COVID-19 call for predictive models to implement precisely tuned responses to limit their deep impact on society. Standard epidemic models provide a theoretically well-founded dynamical description of disease incidence. For COVID-19 with infectiousness peaking before and at symptom onset, the SEIR model explains the hidden build-up of exposed individuals which creates challenges for containment strategies. However, spatial heterogeneity raises questions about the adequacy of modeling epidemic outbreaks on the level of a whole country. Here, we show that by applying sequential data assimilation to the stochastic SEIR epidemic model, we can capture the dynamic behavior of outbreaks on a regional level. Regional modeling, with relatively low numbers of infected and demographic noise, accounts for both spatial heterogeneity and stochasticity. Based on adapted models, short-term predictions can be achieved. Thus, with the help of these sequential data assimilation methods, more realistic epidemic models are within reach. KW - Stochastic epidemic model KW - Sequential data assimilation KW - Ensemble Kalman KW - filter KW - COVID-19 Y1 - 2020 U6 - https://doi.org/10.1007/s11538-020-00834-8 SN - 0092-8240 SN - 1522-9602 VL - 83 IS - 1 PB - Springer CY - New York ER -