@misc{VasishthNicenboimEngelmannetal.2019,
  author    = {Vasishth, Shravan and Nicenboim, Bruno and Engelmann, Felix and Burchert, Frank},
  title     = {Computational Models of Retrieval Processes in Sentence Processing},
  series = {Trends in Cognitive Sciences},
  volume    = {23},
  journal   = {Trends in Cognitive Sciences},
  number    = {11},
  publisher = {Elsevier},
  address   = {London},
  issn      = {1364-6613},
  doi       = {10.1016/j.tics.2019.09.003},
  pages     = {968 -- 982},
  year      = {2019},
  abstract  = {Sentence comprehension requires that the comprehender work out who did what to whom. This process has been characterized as retrieval from memory. This review summarizes the quantitative predictions and empirical coverage of the two existing computational models of retrieval and shows how the predictive performance of these two competing models can be tested against a benchmark data-set. We also show how computational modeling can help us better understand sources of variability in both unimpaired and impaired sentence comprehension.},
  language  = {en}
}
@article{VasishthNicenboimBeckmanetal.2018,
  author    = {Vasishth, Shravan and Nicenboim, Bruno and Beckman, Mary E. and Li, Fangfang and Kong, Eun Jong},
  title     = {Bayesian data analysis in the phonetic sciences},
  series = {Journal of phonetics},
  volume    = {71},
  journal   = {Journal of phonetics},
  publisher = {Elsevier},
  address   = {London},
  issn      = {0095-4470},
  doi       = {10.1016/j.wocn.2018.07.008},
  pages     = {147 -- 161},
  year      = {2018},
  abstract  = {This tutorial analyzes voice onset time (VOT) data from Dongbei (Northeastern) Mandarin Chinese and North American English to demonstrate how Bayesian linear mixed models can be fit using the programming language Stan via the R package brms. Through this case study, we demonstrate some of the advantages of the Bayesian framework: researchers can (i) flexibly define the underlying process that they believe to have generated the data; (ii) obtain direct information regarding the uncertainty about the parameter that relates the data to the theoretical question being studied; and (iii) incorporate prior knowledge into the analysis. Getting started with Bayesian modeling can be challenging, especially when one is trying to model one's own (often unique) data. It is difficult to see how one can apply general principles described in textbooks to one's own specific research problem. We address this barrier to using Bayesian methods by providing three detailed examples, with source code to allow easy reproducibility. The examples presented are intended to give the reader a flavor of the process of model-fitting; suggestions for further study are also provided. All data and code are available from: https://osf.io/g4zpv.},
  language  = {en}
}
@misc{StoneNicenboimVasishthetal.2022,
  author    = {Stone, Kate and Nicenboim, Bruno and Vasishth, Shravan and R{\"o}sler, Frank},
  title     = {Understanding the effects of constraint and predictability in ERP},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe},
  number    = {829},
  issn      = {1866-8364},
  doi       = {10.25932/publishup-58759},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-587594},
  pages     = {71},
  year      = {2022},
  abstract  = {Intuitively, strongly constraining contexts should lead to stronger probabilistic representations of sentences in memory. Encountering unexpected words could therefore be expected to trigger costlier shifts in these representations than expected words. However, psycholinguistic measures commonly used to study probabilistic processing, such as the N400 event-related potential (ERP) component, are sensitive to word predictability but not to contextual constraint. Some research suggests that constraint-related processing cost may be measurable via an ERP positivity following the N400, known as the anterior post-N400 positivity (PNP). The PNP is argued to reflect update of a sentence representation and to be distinct from the posterior P600, which reflects conflict detection and reanalysis. However, constraint-related PNP findings are inconsistent. We sought to conceptually replicate Federmeier et al. (2007) and Kuperberg et al. (2020), who observed that the PNP, but not the N400 or the P600, was affected by constraint at unexpected but plausible words. Using a pre-registered design and statistical approach maximising power, we demonstrated a dissociated effect of predictability and constraint: strong evidence for predictability but not constraint in the N400 window, and strong evidence for constraint but not predictability in the later window. However, the constraint effect was consistent with a P600 and not a PNP, suggesting increased conflict between a strong representation and unexpected input rather than greater update of the representation. We conclude that either a simple strong/weak constraint design is not always sufficient to elicit the PNP, or that previous PNP constraint findings could be an artifact of smaller sample size.},
  language  = {en}
}
@article{StoneNicenboimVasishthetal.2023,
  author    = {Stone, Kate and Nicenboim, Bruno and Vasishth, Shravan and R{\"o}sler, Frank},
  title     = {Understanding the effects of constraint and predictability in ERP},
  series = {Neurobiology of language},
  volume    = {4},
  journal   = {Neurobiology of language},
  number    = {2},
  publisher = {MIT Press},
  address   = {Cambridge, MA, USA},
  issn      = {2641-4368},
  doi       = {10.1162/nol_a_00094},
  pages     = {221 -- 256},
  year      = {2023},
  abstract  = {Intuitively, strongly constraining contexts should lead to stronger probabilistic representations of sentences in memory. Encountering unexpected words could therefore be expected to trigger costlier shifts in these representations than expected words. However, psycholinguistic measures commonly used to study probabilistic processing, such as the N400 event-related potential (ERP) component, are sensitive to word predictability but not to contextual constraint. Some research suggests that constraint-related processing cost may be measurable via an ERP positivity following the N400, known as the anterior post-N400 positivity (PNP). The PNP is argued to reflect update of a sentence representation and to be distinct from the posterior P600, which reflects conflict detection and reanalysis. However, constraint-related PNP findings are inconsistent. We sought to conceptually replicate Federmeier et al. (2007) and Kuperberg et al. (2020), who observed that the PNP, but not the N400 or the P600, was affected by constraint at unexpected but plausible words. Using a pre-registered design and statistical approach maximising power, we demonstrated a dissociated effect of predictability and constraint: strong evidence for predictability but not constraint in the N400 window, and strong evidence for constraint but not predictability in the later window. However, the constraint effect was consistent with a P600 and not a PNP, suggesting increased conflict between a strong representation and unexpected input rather than greater update of the representation. We conclude that either a simple strong/weak constraint design is not always sufficient to elicit the PNP, or that previous PNP constraint findings could be an artifact of smaller sample size.},
  language  = {en}
}
@article{SchadNicenboimBuerkneretal.2022,
  author    = {Schad, Daniel and Nicenboim, Bruno and B{\"u}rkner, Paul-Christian and Betancourt, Michael and Vasishth, Shravan},
  title     = {Workflow techniques for the robust use of bayes factors},
  series = {Psychological methods},
  volume    = {28},
  journal   = {Psychological methods},
  number    = {6},
  publisher = {American Psychological Association},
  address   = {Washington},
  issn      = {1082-989X},
  doi       = {10.1037/met0000472},
  pages     = {1404 -- 1426},
  year      = {2022},
  abstract  = {Inferences about hypotheses are ubiquitous in the cognitive sciences. Bayes factors provide one general way to compare different hypotheses by their compatibility with the observed data. Those quantifications can then also be used to choose between hypotheses. While Bayes factors provide an immediate approach to hypothesis testing, they are highly sensitive to details of the data/model assumptions and it's unclear whether the details of the computational implementation (such as bridge sampling) are unbiased for complex analyses. Hem, we study how Bayes factors misbehave under different conditions. This includes a study of errors in the estimation of Bayes factors; the first-ever use of simulation-based calibration to test the accuracy and bias of Bayes factor estimates using bridge sampling; a study of the stability of Bayes factors against different MCMC draws and sampling variation in the data; and a look at the variability of decisions based on Bayes factors using a utility function. We outline a Bayes factor workflow that researchers can use to study whether Bayes factors are robust for their individual analysis. Reproducible code is available from haps://osf.io/y354c/. <br /> Translational Abstract <br /> In psychology and related areas, scientific hypotheses are commonly tested by asking questions like "is [some] effect present or absent." Such hypothesis testing is most often carried out using frequentist null hypothesis significance testing (NIIST). The NHST procedure is very simple: It usually returns a p-value, which is then used to make binary decisions like "the effect is present/abscnt." For example, it is common to see studies in the media that draw simplistic conclusions like "coffee causes cancer," or "coffee reduces the chances of geuing cancer." However, a powerful and more nuanced alternative approach exists: Bayes factors. Bayes factors have many advantages over NHST. However, for the complex statistical models that arc commonly used for data analysis today, computing Bayes factors is not at all a simple matter. In this article, we discuss the main complexities associated with computing Bayes factors. This is the first article to provide a detailed workflow for understanding and computing Bayes factors in complex statistical models. The article provides a statistically more nuanced way to think about hypothesis testing than the overly simplistic tendency to declare effects as being "present" or "absent".},
  language  = {en}
}
@misc{PaapeNicenboimVasishth2017,
  author    = {Paape, Dario L. J. F. and Nicenboim, Bruno and Vasishth, Shravan},
  title     = {Does antecedent complexity affect ellipsis processing?},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-403373},
  pages     = {29},
  year      = {2017},
  abstract  = {In two self-paced reading experiments, we investigated the effect of changes in antecedent complexity on processing times for ellipsis. Pointer- or "sharing"-based approaches to ellipsis processing (Frazier \& Clifton 2001, 2005; Martin \& McElree 2008) predict no effect of antecedent complexity on reading times at the ellipsis site while other accounts predict increased antecedent complexity to either slow down processing (Murphy 1985) or to speed it up (Hofmeister 2011). Experiment 1 manipulated antecedent complexity and elision, yielding evidence against a speedup at the ellipsis site and in favor of a null effect. In order to investigate possible superficial processing on part of participants, Experiment 2 manipulated the amount of attention required to correctly respond to end-of-sentence comprehension probes, yielding evidence against a complexity-induced slowdown at the ellipsis site. Overall, our results are compatible with pointer-based approaches while casting doubt on the notion that changes antecedent complexity lead to measurable differences in ellipsis processing speed.},
  language  = {en}
}
@article{PaapeNicenboimVasishth2017,
  author    = {Paape, Dario L. J. F. and Nicenboim, Bruno and Vasishth, Shravan},
  title     = {Does antecedent complexity affect ellipsis processing?},
  series = {Glossa : a journal of general linguistics},
  volume    = {2},
  journal   = {Glossa : a journal of general linguistics},
  number    = {1},
  publisher = {Ubiquity Press},
  address   = {London},
  issn      = {2397-1835},
  doi       = {10.5334/gjgl.290},
  pages     = {1 -- 29},
  year      = {2017},
  abstract  = {In two self-paced reading experiments, we investigated the effect of changes in antecedent complexity on processing times for ellipsis. Pointer- or "sharing"-based approaches to ellipsis processing (Frazier \& Clifton 2001, 2005; Martin \& McElree 2008) predict no effect of antecedent complexity on reading times at the ellipsis site while other accounts predict increased antecedent complexity to either slow down processing (Murphy 1985) or to speed it up (Hofmeister 2011). Experiment 1 manipulated antecedent complexity and elision, yielding evidence against a speedup at the ellipsis site and in favor of a null effect. In order to investigate possible superficial processing on part of participants, Experiment 2 manipulated the amount of attention required to correctly respond to end-of-sentence comprehension probes, yielding evidence against a complexity-induced slowdown at the ellipsis site. Overall, our results are compatible with pointer-based approaches while casting doubt on the notion that changes antecedent complexity lead to measurable differences in ellipsis processing speed.},
  language  = {en}
}
@article{NicenboimVasishthRoesler2020,
  author    = {Nicenboim, Bruno and Vasishth, Shravan and R{\"o}sler, Frank},
  title     = {Are words pre-activated probabilistically during sentence comprehension?},
  series = {Neuropsychologia : an international journal in behavioural and cognitive neuroscience},
  volume    = {142},
  journal   = {Neuropsychologia : an international journal in behavioural and cognitive neuroscience},
  publisher = {Elsevier Science},
  address   = {Oxford},
  issn      = {0028-3932},
  doi       = {10.1016/j.neuropsychologia.2020.107427},
  pages     = {27},
  year      = {2020},
  abstract  = {Several studies (e.g., Wicha et al., 2003b; DeLong et al., 2005) have shown that readers use information from the sentential context to predict nouns (or some of their features), and that predictability effects can be inferred from the EEG signal in determiners or adjectives appearing before the predicted noun. While these findings provide evidence for the pre-activation proposal, recent replication attempts together with inconsistencies in the results from the literature cast doubt on the robustness of this phenomenon. Our study presents the first attempt to use the effect of gender on predictability in German to study the pre-activation hypothesis, capitalizing on the fact that all German nouns have a gender and that their preceding determiners can show an unambiguous gender marking when the noun phrase has accusative case. Despite having a relatively large sample size (of 120 subjects), both our preregistered and exploratory analyses failed to yield conclusive evidence for or against an effect of pre-activation. The sign of the effect is, however, in the expected direction: the more unexpected the gender of the determiner, the larger the negativity. The recent, inconclusive replication attempts by Nieuwland et al. (2018) and others also show effects with signs in the expected direction. We conducted a Bayesian random-ef-fects meta-analysis using our data and the publicly available data from these recent replication attempts. Our meta-analysis shows a relatively clear but very small effect that is consistent with the pre-activation account and demonstrates a very important advantage of the Bayesian data analysis methodology: we can incrementally accumulate evidence to obtain increasingly precise estimates of the effect of interest.},
  language  = {en}
}
@article{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},
  series = {Frontiers in psychology},
  volume    = {6},
  journal   = {Frontiers in psychology},
  publisher = {Frontiers Research Foundation},
  address   = {Lausanne},
  issn      = {1664-1078},
  doi       = {10.3389/fpsyg.2015.00312},
  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{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},
  series = {Frontiers in psychology},
  volume    = {6},
  journal   = {Frontiers in psychology},
  number    = {312},
  publisher = {Frontiers Research Foundation},
  address   = {Lausanne},
  issn      = {1664-1078},
  doi       = {10.3389/fpsyg.2015.00312},
  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}
}