@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}
}
@misc{OngKliegl2008,
  author    = {Ong, James Kwan Yau and Kliegl, Reinhold},
  title     = {Conditional co-occurrence probability acts like frequency in predicting fixation durations},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-56771},
  year      = {2008},
  abstract  = {The predictability of an upcoming word has been found to be a useful predictor in eye movement research, but is expensive to collect and subjective in nature. It would be desirable to have other predictors that are easier to collect and objective in nature if these predictors were capable of capturing the information stored in predictability. This paper contributes to this discussion by testing a possible predictor: conditional co-occurrence probability. This measure is a simple statistical representation of the relatedness of the current word to its context, based only on word co-occurrence patterns in data taken from the Internet. In the regression analyses, conditional co-occurrence probability acts like lexical frequency in predicting fixation durations, and its addition does not greatly improve the model fits. We conclude that readers do not seem to use the information contained within conditional co-occurrence probability during reading for meaning, and that similar simple measures of semantic relatedness are unlikely to be able to replace predictability as a predictor for fixation durations. Keywords: Co-occurrence probability, Cloze predictability, frequency, eye movement, fixation duration.},
  language  = {en}
}