@misc{BostonHaleVasishthetal.2011,
  author    = {Boston, Marisa Ferrara and Hale, John T. and Vasishth, Shravan and Kliegl, Reinhold},
  title     = {Parallel processing and sentence comprehension difficulty},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-57159},
  year      = {2011},
  abstract  = {Eye fixation durations during normal reading correlate with processing difficulty but the specific cognitive mechanisms reflected in these measures are not well understood. This study finds support in German readers' eyefixations for two distinct difficulty metrics: surprisal, which reflects the change in probabilities across syntactic analyses as new words are integrated, and retrieval, which quantifies comprehension difficulty in terms of working memory constraints. We examine the predictions of both metrics using a family of dependency parsers indexed by an upper limit on the number of candidate syntactic analyses they retain at successive words. Surprisal models all fixation measures and regression probability. By contrast, retrieval does not model any measure in serial processing. As more candidate analyses are considered in parallel at each word, retrieval can account for the same measures as surprisal. This pattern suggests an important role for ranked parallelism in theories of sentence comprehension.},
  language  = {en}
}
@misc{NicenboimVasishthGatteietal.2015,
  author    = {Nicenboim, Bruno and Vasishth, Shravan and Gattei, Carolina and Sigman, Mariano and Kliegl, Reinhold},
  title     = {Working memory differences in long-distance dependency resolution},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-75694},
  pages     = {16},
  year      = {2015},
  abstract  = {There is a wealth of evidence showing that increasing the distance between an argument and its head leads to more processing effort, namely, locality effects; these are usually associated with constraints in working memory (DLT: Gibson, 2000; activation-based model: Lewis and Vasishth, 2005). In SOV languages, however, the opposite effect has been found: antilocality (see discussion in Levy et al., 2013). Antilocality effects can be explained by the expectation-based approach as proposed by Levy (2008) or by the activation-based model of sentence processing as proposed by Lewis and Vasishth (2005). We report an eye-tracking and a self-paced reading study with sentences in Spanish together with measures of individual differences to examine the distinction between expectation- and memory-based accounts, and within memory-based accounts the further distinction between DLT and the activation-based model. The experiments show that (i) antilocality effects as predicted by the expectation account appear only for high-capacity readers; (ii) increasing dependency length by interposing material that modifies the head of the dependency (the verb) produces stronger facilitation than increasing dependency length with material that does not modify the head; this is in agreement with the activation-based model but not with the expectation account; and (iii) a possible outcome of memory load on low-capacity readers is the increase in regressive saccades (locality effects as predicted by memory-based accounts) or, surprisingly, a speedup in the self-paced reading task; the latter consistent with good-enough parsing (Ferreira et al., 2002). In sum, the study suggests that individual differences in working memory capacity play a role in dependency resolution, and that some of the aspects of dependency resolution can be best explained with the activation-based model together with a prediction component.},
  language  = {en}
}
@article{BostonHalbeVasishthetal.2011,
  author    = {Boston, Marisa Ferrara and Halbe, John T. and Vasishth, Shravan and Kliegl, Reinhold},
  title     = {Parallel processing and entence comprehension difficulty},
  doi       = {10.1080/01690965.2010.492228},
  year      = {2011},
  abstract  = {Eye fixation durations during normal reading correlate with processing difficulty, but the specific cognitive mechanisms reflected in these measures are not well understood. This study finds support in German readers' eye fixations for two distinct difficulty metrics: surprisal, which reflects the change in probabilities across syntactic analyses as new words are integrated; and retrieval, which quantifies comprehension difficulty in terms of working memory constraints. We examine the predictions of both metrics using a family of dependency parsers indexed by an upper limit on the number of candidate syntactic analyses they retain at successive words. Surprisal models all fixation measures and regression probability. By contrast, retrieval does not model any measure in serial processing. As more candidate analyses are considered in parallel at each word, retrieval can account for the same measures as surprisal. This pattern suggests an important role for ranked parallelism in theories of sentence comprehension.},
  language  = {en}
}
@article{EngelmannVasishthEngbertetal.2013,
  author    = {Engelmann, Felix and Vasishth, Shravan and Engbert, Ralf and Kliegl, Reinhold},
  title     = {A framework for modeling the interaction of syntactic processing and eye movement control},
  series = {Topics in cognitive science},
  volume    = {5},
  journal   = {Topics in cognitive science},
  number    = {3},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken},
  issn      = {1756-8757},
  doi       = {10.1111/tops.12026},
  pages     = {452 -- 474},
  year      = {2013},
  abstract  = {We explore the interaction between oculomotor control and language comprehension on the sentence level using two well-tested computational accounts of parsing difficulty. Previous work (Boston, Hale, Vasishth, \& Kliegl, 2011) has shown that surprisal (Hale, 2001; Levy, 2008) and cue-based memory retrieval (Lewis \& Vasishth, 2005) are significant and complementary predictors of reading time in an eyetracking corpus. It remains an open question how the sentence processor interacts with oculomotor control. Using a simple linking hypothesis proposed in Reichle, Warren, and McConnell (2009), we integrated both measures with the eye movement model EMMA (Salvucci, 2001) inside the cognitive architecture ACT-R (Anderson et al., 2004). We built a reading model that could initiate short Time Out regressions (Mitchell, Shen, Green, \& Hodgson, 2008) that compensate for slow postlexical processing. This simple interaction enabled the model to predict the re-reading of words based on parsing difficulty. The model was evaluated in different configurations on the prediction of frequency effects on the Potsdam Sentence Corpus. The extension of EMMA with postlexical processing improved its predictions and reproduced re-reading rates and durations with a reasonable fit to the data. This demonstration, based on simple and independently motivated assumptions, serves as a foundational step toward a precise investigation of the interaction between high-level language processing and eye movement control.},
  language  = {en}
}
@article{BostonHaleVasishthetal.2011,
  author    = {Boston, Marisa Ferrara and Hale, John T. and Vasishth, Shravan and Kliegl, Reinhold},
  title     = {Parallel processing and sentence comprehension difficulty},
  series = {Language and cognitive processes},
  volume    = {26},
  journal   = {Language and cognitive processes},
  number    = {3},
  publisher = {Wiley},
  address   = {Hove},
  issn      = {0169-0965},
  doi       = {10.1080/01690965.2010.492228},
  pages     = {301 -- 349},
  year      = {2011},
  abstract  = {Eye fixation durations during normal reading correlate with processing difficulty, but the specific cognitive mechanisms reflected in these measures are not well understood. This study finds support in German readers' eye fixations for two distinct difficulty metrics: surprisal, which reflects the change in probabilities across syntactic analyses as new words are integrated; and retrieval, which quantifies comprehension difficulty in terms of working memory constraints. We examine the predictions of both metrics using a family of dependency parsers indexed by an upper limit on the number of candidate syntactic analyses they retain at successive words. Surprisal models all fixation measures and regression probability. By contrast, retrieval does not model any measure in serial processing. As more candidate analyses are considered in parallel at each word, retrieval can account for the same measures as surprisal. This pattern suggests an important role for ranked parallelism in theories of sentence comprehension.},
  language  = {en}
}