@phdthesis{Bettenbuehl2015,
  author    = {Bettenb{\"u}hl, Mario},
  title     = {Microsaccades},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  isbn      = {978-3-86956-122-6},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-72622},
  school      = {Universit{\"a}t Potsdam},
  pages     = {iv, 126},
  year      = {2015},
  abstract  = {The first thing we do upon waking is open our eyes. Rotating them in our eye sockets, we scan our surroundings and collect the information into a picture in our head. Eye movements can be split into saccades and fixational eye movements, which occur when we attempt to fixate our gaze. The latter consists of microsaccades, drift and tremor. Before we even lift our eye lids, eye movements - such as saccades and microsaccades that let the eyes jump from one to another position - have partially been prepared in the brain stem. Saccades and microsaccades are often assumed to be generated by the same mechanisms. But how saccades and microsaccades can be classified according to shape has not yet been reported in a statistical manner. Research has put more effort into the investigations of microsaccades' properties and generation only since the last decade. Consequently, we are only beginning to understand the dynamic processes governing microsaccadic eye movements. Within this thesis, the dynamics governing the generation of microsaccades is assessed and the development of a model for the underlying processes. Eye movement trajectories from different experiments are used, recorded with a video-based eye tracking technique, and a novel method is proposed for the scale-invariant detection of saccades (events of large amplitude) and microsaccades (events of small amplitude). Using a time-frequency approach, the method is examined with different experiments and validated against simulated data. A shape model is suggested that allows for a simple estimation of saccade- and microsaccade related properties. For sequences of microsaccades, in this thesis a time-dynamic Markov model is proposed, with a memory horizon that changes over time and which can best describe sequences of microsaccades.},
  language  = {en}
}
@phdthesis{Gerth2015,
  author    = {Gerth, Sabrina},
  title     = {Memory limitations in sentence comprehension},
  isbn      = {978-3-86956-321-3},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-71554},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xviii, 157},
  year      = {2015},
  abstract  = {This dissertation addresses the question of how linguistic structures can be represented in working memory. We propose a memory-based computational model that derives offline and online complexity profiles in terms of a top-down parser for minimalist grammars (Stabler, 2011). The complexity metric reflects the amount of time an item is stored in memory. The presented architecture links grammatical representations stored in memory directly to the cognitive behavior by deriving predictions about sentence processing difficulty. Results from five different sentence comprehension experiments were used to evaluate the model's assumptions about memory limitations. The predictions of the complexity metric were compared to the locality (integration and storage) cost metric of Dependency Locality Theory (Gibson, 2000). Both metrics make comparable offline and online predictions for four of the five phenomena. The key difference between the two metrics is that the proposed complexity metric accounts for the structural complexity of intervening material. In contrast, DLT's integration cost metric considers the number of discourse referents, not the syntactic structural complexity. We conclude that the syntactic analysis plays a significant role in memory requirements of parsing. An incremental top-down parser based on a grammar formalism easily computes offline and online complexity profiles, which can be used to derive predictions about sentence processing difficulty.},
  language  = {en}
}