@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}
}
@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}
}
@phdthesis{Nicenboim2016,
  author    = {Nicenboim, Bruno},
  title     = {Dependency resolution as a retrieval process},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xiv, 209},
  year      = {2016},
  abstract  = {My thesis focused on the predictions of the activation-based model of Lewis and Vasishth (2005) to investigate the evidence for the use of the memory system in the formation of non-local dependencies in sentence comprehension. The activation-based model, which follows the Adaptive Control of Thought-Rational framework (ACT-R; Anderson et al., 2004), has been used to explain locality effects and similarity-based interference by assuming that dependencies are resolved by a cue-based retrieval mechanism, and that the retrieval mechanism is affected by decay and interference. Both locality effects and (inhibitory) similarity-based interference cause increased difficulty (e.g., longer reading times) at the site of the dependency completion where a retrieval is assumed: (I) Locality effects are attributed to the increased difficulty in the retrieval of a dependent when the distance from its retrieval site is increased. (II) Similarity-based interference is attributed to the retrieval being affected by the presence of items which have similar features as the dependent that needs to be retrieved. In this dissertation, I investigated some findings problematic to the activation-based model, namely, facilitation where locality effects are expected (e.g., Levy, 2008), and the lack of similarity-based interference from the number feature in grammatical sentences (e.g., Wagers et al., 2009). In addition, I used individual differences in working memory capacity and reading fluency as a way to validate the theories investigated (Underwood, 1975), and computational modeling to achieve a more precise account of the phenomena. Regarding locality effects, by using self-paced reading and eye-tracking-while reading methods with Spanish and German data, this dissertation yielded two main findings: (I) Locality effects seem to be modulated by working memory capacity, with high-capacity participants showing expectation-driven facilitation. (II) Once expectations and other potential confounds are controlled using baselines, with increased distance, high-capacity readers can show a slow-down (i.e., locality effects) and low-capacity readers can show a speedup. While the locality effects are compatible with the activation-based model, simulations show that the speedup of low-capacity readers can only be accounted for by changing some of the assumptions of the activation-based model. Regarding similarity-based interference, two relatively high-powered self-paced reading experiments in German using grammatical sentences yielded a slowdown at the verb as predicted by the activation-based model. This provides evidence in favor of dependency creation via cue-based retrieval, and in contrast with the view that cue-based retrieval is a reanalysis mechanism (Wagers et al., 2009). Finally, the same experimental results that showed inhibitory interference from the number feature are used for a finer grain evaluation of the retrieval process. Besides Lewis and Vasishth's (2005) activation-based model, also McElree's (2000) direct-access model can account for inhibitory interference. These two models assume a cue-based retrieval mechanism to build dependencies, but they are based on different assumptions. I present a computational evaluation of the predictions of these two theories of retrieval. The models were compared by implementing them in a Bayesian hierarchical framework. The evaluation of the models reveals that some aspects of the data fit better under the direct access model than under the activation-based model. However, a simple extension of the activation-based model provides a comparable fit to the direct access model. This serves as a proof of concept showing potential ways to improve the original activation-based model. In conclusion, this thesis adds to the body of evidence that argues for the use of the general memory system in dependency resolution, and in particular for a cue-based retrieval mechanism. However, it also shows that some of the default assumptions inherited from ACT-R in the activation-based model need to be revised.},
  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}
}
@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}
}