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 - 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 - 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 - 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 -