@article{EngbertRabeKliegletal.2021,
author = {Engbert, Ralf and Rabe, Maximilian Michael and Kliegl, Reinhold and Reich, Sebastian},
title = {Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics},
series = {Bulletin of mathematical biology : official journal of the Society for Mathematical Biology},
volume = {83},
journal = {Bulletin of mathematical biology : official journal of the Society for Mathematical Biology},
number = {1},
publisher = {Springer},
address = {New York},
issn = {0092-8240},
doi = {10.1007/s11538-020-00834-8},
pages = {16},
year = {2021},
abstract = {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.},
language = {en}
}
@article{SeeligRabeMalemShinitskietal.2020,
author = {Seelig, Stefan A. and Rabe, Maximilian Michael and Malem-Shinitski, Noa and Risse, Sarah and Reich, Sebastian and Engbert, Ralf},
title = {Bayesian parameter estimation for the SWIFT model of eye-movement control during reading},
series = {Journal of mathematical psychology},
volume = {95},
journal = {Journal of mathematical psychology},
publisher = {Elsevier},
address = {San Diego},
issn = {0022-2496},
doi = {10.1016/j.jmp.2019.102313},
pages = {32},
year = {2020},
abstract = {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.},
language = {en}
}
@article{KwarikundaSchiefeleSsenyongaetal.2021,
author = {Kwarikunda, Diana and Schiefele, Ulrich and Ssenyonga, Joseph and Muwonge, Charles Magoba},
title = {Secondary school students' motivation profiles for physics learning},
series = {African journal of research in mathematics, science and technology education : official journal of the Southern African Association for Research in Mathematics, Science and Technology Education},
volume = {25},
journal = {African journal of research in mathematics, science and technology education : official journal of the Southern African Association for Research in Mathematics, Science and Technology Education},
number = {2},
publisher = {Routledge, Taylor \& Francis Group},
address = {Abingdon},
issn = {1028-8457},
doi = {10.1080/18117295.2021.1956720},
pages = {197 -- 210},
year = {2021},
abstract = {For efficient and effective pedagogical interventions to address Uganda's alarmingly poor performance in Physics, it is vital to understand students' motivation patterns for Physics learning. Latent profile analysis (LPA)-a person-centred approach-can be used to investigate these motivation patterns. Using a three-step approach to LPA, we sought to answer the following research questions: RQ1, which profiles of secondary school students exist with regards to their motivation for Physics learning; RQ2, are there differences in students' cognitive learning strategies in the identified profiles; and RQ3, does students' gender, attitudes, and individual interest predict membership in these profiles? The sample comprised 934 Grade 9 students from eight secondary schools in Uganda. Data were collected using standardised questionnaires. Six motivational profiles were identified: (i) low-quantity motivation profile (101 students; 10.8\%); (ii) moderate-quantity motivation profile (246 students; 26.3\%); (iii) high-quantity motivation profile (365 students; 39.1\%); (iv) primarily intrinsically motivated profile (60 students, 6.4\%); (v) mostly extrinsically motivated profile (88 students, 9.4\%); and (vi) grade-introjected profile (74 students, 7.9\%). Low-quantity and grade-introjected motivated students mostly used surface learning strategies whilst the high-quantity and primarily intrinsically motivated students used deep learning strategies. Lastly, unlike gender, individual interest and students' attitudes towards Physics learning predicted profile membership. Teachers should provide an interesting autonomous Physics classroom climate and give students clear instructions in self-reliant behaviours that promote intrinsic motivation.},
language = {en}
}
@article{MalemShinitskiOpperReichetal.2020,
author = {Malem-Shinitski, Noa and Opper, Manfred and Reich, Sebastian and Schwetlick, Lisa and Seelig, Stefan A. and Engbert, Ralf},
title = {A mathematical model of local and global attention in natural scene viewing},
series = {PLoS Computational Biology : a new community journal},
volume = {16},
journal = {PLoS Computational Biology : a new community journal},
number = {12},
publisher = {PLoS},
address = {San Fransisco},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1007880},
pages = {21},
year = {2020},
abstract = {Author summary
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.
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.},
language = {en}
}