@article{GafosLieshout2020,
  author    = {Gafos, Adamantios I. and Lieshout, Pascal H. H. M. van},
  title     = {Models and theories of speech production},
  series = {Frontiers in psychology},
  volume    = {11},
  journal   = {Frontiers in psychology},
  publisher = {Frontiers Research Foundation},
  address   = {Lausanne},
  issn      = {1664-1078},
  doi       = {10.3389/fpsyg.2020.01238},
  pages     = {4},
  year      = {2020},
  language  = {en}
}
@phdthesis{Schwetlick2023,
  author    = {Schwetlick, Lisa},
  title     = {Data assimilation for neurocognitive models of eye movement},
  doi       = {10.25932/publishup-59828},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-598280},
  school      = {Universit{\"a}t Potsdam},
  pages     = {x, 209},
  year      = {2023},
  abstract  = {Visual perception is a complex and dynamic process that plays a crucial role in how we perceive and interact with the world. The eyes move in a sequence of saccades and fixations, actively modulating perception by moving different parts of the visual world into focus. Eye movement behavior can therefore offer rich insights into the underlying cognitive mechanisms and decision processes. Computational models in combination with a rigorous statistical framework are critical for advancing our understanding in this field, facilitating the testing of theory-driven predictions and accounting for observed data. In this thesis, I investigate eye movement behavior through the development of two mechanistic, generative, and theory-driven models. The first model is based on experimental research regarding the distribution of attention, particularly around the time of a saccade, and explains statistical characteristics of scan paths. The second model implements a self-avoiding random walk within a confining potential to represent the microscopic fixational drift, which is present even while the eye is at rest, and investigates the relationship to microsaccades. Both models are implemented in a likelihood-based framework, which supports the use of data assimilation methods to perform Bayesian parameter inference at the level of individual participants, analyses of the marginal posteriors of the interpretable parameters, model comparisons, and posterior predictive checks. The application of these methods enables a thorough investigation of individual variability in the space of parameters. Results show that dynamical modeling and the data assimilation framework are highly suitable for eye movement research and, more generally, for cognitive modeling.},
  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{RabeChandraKruegeletal.2021,
  author    = {Rabe, Maximilian Michael and Chandra, Johan and Kr{\"u}gel, Andr{\´e} and Seelig, Stefan A. and Vasishth, Shravan and Engbert, Ralf},
  title     = {A bayesian approach to dynamical modeling of eye-movement control in reading of normal, mirrored, and scrambled texts},
  series = {Psychological Review},
  volume    = {128},
  journal   = {Psychological Review},
  number    = {5},
  publisher = {American Psychological Association},
  address   = {Washington},
  issn      = {0033-295X},
  doi       = {10.1037/rev0000268},
  pages     = {803 -- 823},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}