@article{SchreiberOneaGaspar2021,
  author    = {Schreiber, Alexander and Onea G{\´a}sp{\´a}r, Edgar},
  title     = {Are narrow focus exhaustivity inferences Bayesian inferences?},
  series = {Frontiers in psychology / Frontiers Research Foundation},
  volume    = {12},
  journal   = {Frontiers in psychology / Frontiers Research Foundation},
  publisher = {Frontiers Research Foundation},
  address   = {Lausanne},
  issn      = {1664-1078},
  doi       = {10.3389/fpsyg.2021.677223},
  pages     = {19},
  year      = {2021},
  abstract  = {In successful communication, the literal meaning of linguistic utterances is often enriched by pragmatic inferences. Part of the pragmatic reasoning underlying such inferences has been successfully modeled as Bayesian goal recognition in the Rational Speech Act (RSA) framework. In this paper, we try to model the interpretation of question-answer sequences with narrow focus in the answer in the RSA framework, thereby exploring the effects of domain size and prior probabilities on interpretation. Should narrow focus exhaustivity inferences be actually based on Bayesian inference involving prior probabilities of states, RSA models should predict a dependency of exhaustivity on these factors. We present experimental data that suggest that interlocutors do not act according to the predictions of the RSA model and that exhaustivity is in fact approximately constant across different domain sizes and priors. The results constitute a conceptual challenge for Bayesian accounts of the underlying pragmatic inferences.},
  language  = {en}
}
@article{KorochkinaBuerkiFoschiniNickels2021,
  author    = {Korochkina, Maria and B{\"u}rki-Foschini, Audrey Damaris and Nickels, Lyndsey},
  title     = {Apples and oranges},
  series = {Journal of memory and language : JML},
  volume    = {120},
  journal   = {Journal of memory and language : JML},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0749-596X},
  doi       = {10.1016/j.jml.2021.104246},
  pages     = {17},
  year      = {2021},
  abstract  = {Despite scarce empirical evidence, introducing new vocabulary in semantic categories has long been standard in second language teaching. We examined the effect of learning context on encoding, immediate recall and integration of new vocabulary into semantic memory by contrasting categorically related (novel names for familiar concepts blocked by semantic category) and unrelated (mixed semantic categories) learning contexts. Two learning sessions were conducted 24 hours apart, with each participant exposed to both contexts. Subsequently, a test phase examined picture naming, translation and picture-word interference tasks. Compared to the unrelated context, the categorically related context resulted in poorer naming accuracy in the learning phase, slower response latencies at the immediate recall tasks and greater semantic interference in the picture-word interference task (picture naming in L1 with semantically related novel word distractors). We develop a theoretical account of word learning that attributes observed differences to episodic rather than semantic memory.},
  language  = {en}
}
@article{PueblaGarcia2021,
  author    = {Puebla, Cecilia and Garcia, Juan},
  title     = {Advocating the inclusion of older adults in digital language learning technology and research},
  series = {Bilingualism : language and cognition},
  volume    = {25},
  journal   = {Bilingualism : language and cognition},
  number    = {3},
  publisher = {Cambridge Univ. Press},
  address   = {New York},
  issn      = {1366-7289},
  doi       = {10.1017/S1366728921000742},
  pages     = {398 -- 399},
  year      = {2021},
  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}
}