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Bruno Nicenboim
Researcher at University of Potsdam
Publications - 30
Citations - 707
Bruno Nicenboim is an academic researcher from University of Potsdam. The author has contributed to research in topics: Bayesian probability & Computer science. The author has an hindex of 11, co-authored 24 publications receiving 465 citations. Previous affiliations of Bruno Nicenboim include Tilburg University.
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Statistical methods for linguistic research: Foundational Ideas—Part II
TL;DR: An informal introduction to the foundational ideas behind Bayesian data analysis, using a linear mixed models analysis of data from a typical psycholinguistics experiment, and some examples illustrating the flexibility of model specification in the Bayesian framework.
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Bayesian data analysis in the phonetic sciences: A tutorial introduction
TL;DR: 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.
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Are words pre-activated probabilistically during sentence comprehension? Evidence from new data and a Bayesian random-effects meta-analysis using publicly available data
TL;DR: This 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.
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Exploratory and Confirmatory Analyses in Sentence Processing: A Case Study of Number Interference in German
TL;DR: By implementing three changes in current practice that have the potential to deliver more realistic and robust claims, suggestive evidence emerges that is consistent with the predicted number interference effects in German.
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Working memory differences in long-distance dependency resolution
Bruno Nicenboim,Shravan Vasishth,Carolina Andrea Gattei,Mariano Sigman,Mariano Sigman,Reinhold Kliegl +5 more
TL;DR: 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.