@article{LaurinavichyuteSekerinaAlexeevaetal.2019,
  author    = {Laurinavichyute, Anna and Sekerina, Irina A. and Alexeeva, Svetlana and Bagdasaryan, Kristine and Kliegl, Reinhold},
  title     = {Russian Sentence Corpus: Benchmark measures of eye movements in reading in Russian},
  series = {Behavior research methods : a journal of the Psychonomic Society},
  volume    = {51},
  journal   = {Behavior research methods : a journal of the Psychonomic Society},
  number    = {3},
  publisher = {Springer},
  address   = {New York},
  issn      = {1554-351X},
  doi       = {10.3758/s13428-018-1051-6},
  pages     = {1161 -- 1178},
  year      = {2019},
  abstract  = {This article introduces a new corpus of eye movements in silent readingthe Russian Sentence Corpus (RSC). Russian uses the Cyrillic script, which has not yet been investigated in cross-linguistic eye movement research. As in every language studied so far, we confirmed the expected effects of low-level parameters, such as word length, frequency, and predictability, on the eye movements of skilled Russian readers. These findings allow us to add Slavic languages using Cyrillic script (exemplified by Russian) to the growing number of languages with different orthographies, ranging from the Roman-based European languages to logographic Asian ones, whose basic eye movement benchmarks conform to the universal comparative science of reading (Share, 2008). We additionally report basic descriptive corpus statistics and three exploratory investigations of the effects of Russian morphology on the basic eye movement measures, which illustrate the kinds of questions that researchers can answer using the RSC. The annotated corpus is freely available from its project page at the Open Science Framework: https://osf.io/x5q2r/.},
  language  = {en}
}
@article{LogacevVasishth2016,
  author    = {Logacev, Pavel and Vasishth, Shravan},
  title     = {A Multiple-Channel Model of Task-Dependent Ambiguity Resolution in Sentence Comprehension},
  series = {Cognitive science : a multidisciplinary journal of anthropology, artificial intelligence, education, linguistics, neuroscience, philosophy, psychology ; journal of the Cognitive Science Society},
  volume    = {40},
  journal   = {Cognitive science : a multidisciplinary journal of anthropology, artificial intelligence, education, linguistics, neuroscience, philosophy, psychology ; journal of the Cognitive Science Society},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken},
  issn      = {0364-0213},
  doi       = {10.1111/cogs.12228},
  pages     = {266 -- 298},
  year      = {2016},
  abstract  = {Traxler, Pickering, and Clifton (1998) found that ambiguous sentences are read faster than their unambiguous counterparts. This so-called ambiguity advantage has presented a major challenge to classical theories of human sentence comprehension (parsing) because its most prominent explanation, in the form of the unrestricted race model (URM), assumes that parsing is non-deterministic. Recently, Swets, Desmet, Clifton, and Ferreira (2008) have challenged the URM. They argue that readers strategically underspecify the representation of ambiguous sentences to save time, unless disambiguation is required by task demands. When disambiguation is required, however, readers assign sentences full structure—and Swets et al. provide experimental evidence to this end. On the basis of their findings, they argue against the URM and in favor of a model of task-dependent sentence comprehension. We show through simulations that the Swets et al. data do not constitute evidence for task-dependent parsing because they can be explained by the URM. However, we provide decisive evidence from a German self-paced reading study consistent with Swets et al.'s general claim about task-dependent parsing. Specifically, we show that under certain conditions, ambiguous sentences can be read more slowly than their unambiguous counterparts, suggesting that the parser may create several parses, when required. Finally, we present the first quantitative model of task-driven disambiguation that subsumes the URM, and we show that it can explain both Swets et al.'s results and our findings.},
  language  = {en}
}