Published or accepted in peer-reviewed journals

Schad, D. J., B. Nicenboim, and S. Vasishth (2024). “Data aggregation can lead to biased inferences in Bayesian linear mixed models and Bayesian analysis of variance.” In: Psychological Methods. ISSN: 1082-989X. DOI: 10.1037/met0000621.

Doorn, J. van, J. M. Haaf, A. M. Stefan, E. Wagenmakers, G. E. Cox, C. P. Davis-Stober, A. Heathcote, D. W. Heck, M. Kalish, D. Kellen, et al. (2023). “Bayes Factors for Mixed Models: a Discussion”. In: Computational Brain & Behavior 6.1, pp. 140-158. ISSN: 2522-087X. DOI: 10.1007/s42113-022-00160-3.

Vasishth, S., H. Yadav, D. J. Schad, and B. Nicenboim (2023). “Sample Size Determination for Bayesian Hierarchical Models Commonly Used in Psycholinguistics”. In: Computational Brain & Behavior 6.1, pp. 102-126. ISSN: 2522-087X. DOI: 10.1007/s42113-021-00125-y.

Ben-Artzi, I., Y. Kessler, B. Nicenboim, and N. Shahar (2023). “Computational mechanisms underlying latent inverse value updating of unchosen actions”. In: Science Advances 9.42, p. eadi2704. DOI: 10.1126/sciadv.adi2704.

Stone, K., B. Nicenboim, S. Vasishth, and F. Rösler (2022). “Understanding the effects of constraint and predictability in ERP”. In: Neurobiology of Language, pp. 1-71. ISSN: 2641-4368. DOI: 10.1162/nol_a_00094.

Patterson, C., P. B. Schumacher, B. Nicenboim, J. Hagen, and A. Kehler (2022). “A Bayesian approach to German personal and demonstrative pronouns”. In: Frontiers in Psychology, Language Sciences 12. ISSN: 1664-1078. DOI: 10.3389/fpsyg.2021.672927. eprint: https://www.frontiersin.org/articles/10.3389/fpsyg.2021.672927/abstract.

Schad, D. J., B. Nicenboim, P. Bürkner, M. Betancourt, and S. Vasishth (2022). “Workflow Techniques for the Robust Use of Bayes Factors”. In: Psychological methods. ISSN: 1082-989X. DOI: 10.1037/met0000472.

Albert, A. and B. Nicenboim (2022). “Modeling Sonority in Terms of Pitch Intelligibility With the Nucleus Attraction Principle”. In: Cognitive Science 46.7, p. e13161. DOI: doi.or1/cogs.13161.

Lisson, P., D. Pregla, B. Nicenboim, D. Paape, M. L. van het Nederend, F. Burchert, N. Stadie, D. Caplan, and S. Vasishth (2021). “A computational evaluation of two models of retrieval processes in sentence processing in aphasia”. In: Cognitive Science 45.4. DOI: 10.1111/cogs.12956.

Nicenboim, B., S. Vasishth, and F. Rösler (2020). “Are words pre-activated probabilistically during sentence comprehension? Evidence from new data and a Bayesian random-effects meta-analysis using publicly available data”. In: Neuropsychologia 142, p. 107427. ISSN: 0028-3932. DOI: 10.1016/j.neuropsychologia.2020.107427.

Vasishth, S., B. Nicenboim, F. Engelmann, and F. Burchert (2019). “Computational models of retrieval processes in sentence processing”. In: Trends in Cognitive Sciences 23.11, pp. 968 - 982. ISSN: 1364-6613. DOI: 10.1016/j.tics.2019.09.003.

Nicenboim, B., S. Vasishth, F. Engelmann, and K. Suckow (2018). “Exploratory and confirmatory analyses in sentence processing: A case study of number interference in German”. In: Cognitive Science 42.S4, pp. 1075–1100. DOI: 10.1111/cogs.12589.

Nicenboim, B., T. B. Roettger, and S. Vasishth (2018). “Using meta-analysis for evidence synthesis: The case of incomplete neutralization in German”. In: Journal of Phonetics 70, pp. 39–55. DOI: 10.1016/j.wocn.2018.06.001.

Vasishth, S., B. Nicenboim, M. E. Beckman, F. Li, and E. Kong (2018). “Bayesian data analysis in the phonetic sciences: A tutorial introduction”. In: Journal of Phonetics 71, pp. 147–161. DOI: 10.1016/j.wocn.2018.07.008.

Nicenboim, B. and S. Vasishth (2018). “Models of Retrieval in Sentence Comprehension: A computational evaluation using Bayesian hierarchical modeling”. In: Journal of Memory and Language 99, pp. 1 –34. ISSN: 0749-596X. DOI: 10.1016/j.jml.2017.08.004.

Paape, D., B. Nicenboim, and S. Vasishth (2017). “Does antecedent complexity affect ellipsis processing? An empirical investigation”. In: Glossa: A journal of general linguistics. 2.1, p. 71. DOI: 10.5334/gjgl.290.

Nicenboim, B., P. Logačev, C. Gattei, and S. Vasishth (2016). “When high-capacity readers slow down and low-capacity readers speed up: Working memory and locality effects”. In: Frontiers in Psychology 7.280. ISSN: 1664-1078. DOI: 10.3389/fpsyg.2016.00280 . eprint: https://www.frontiersin.org/articles/10.3389/fpsyg.2016.00280/full.

Nicenboim, B. and S. Vasishth (2016). “Statistical methods for linguistic research: Foundational Ideas - Part II”. In: Language and Linguistics Compass 10.11, pp. 591–613. ISSN: 1749-818X. DOI: 10.1111/lnc3.12207.

Vasishth, S. and B. Nicenboim (2016). “Statistical Methods for Linguistic Research: Foundational Ideas - Part I”. In: Language and Linguistics Compass 10.8, pp. 349–369. ISSN: 1749-818X. DOI: 10.1111/lnc3.12201.

Nicenboim, B., S. Vasishth, C. Gattei, M. Sigman, and R. Kliegl (2015). “Working memory differences in long-distance dependency resolution”. In: Frontiers in Psychology 6.312. ISSN: 1664-1078. DOI: 10.3389/fpsyg.2015.00312.

Unpublished manuscripts

Boelders, S. M., B. Nicenboim, E. Butterbrod, W. de Baene, E. Postma, G. Rutten, L. Ong, and K. Gehring (2024). “Predicting cognitive function three months after surgery in patients with a glioma”. under review. DOI: 10.1101/2024.10.08.24315076.

Boelders, S., B. Nicenboim, E. Postma, G. Rutten, K. Gehring, and L. Ong (2024). “Modeling uncertainty in individual predictions of cognitive functioning for untreated glioma patients using Bayesian regression”. under review. DOI: 10.1101/2024.10.08.24315003.

Dubova, M., S. Chandramouli, G. Gigerenzer, P. Grünwald, W. Holmes, T. Lombrozo, M. Marelli, S. Musslick, B. Nicenboim, L. Ross, et al. (2024). “Is Occam’s razor losing its edge? New perspectives on the principle of model parsimony”. under review. DOI: 10.31222/osf.io/bs5xe.

Vasishth, S., B. Nicenboim, N. Chopin, and R. Ryder (2017). “Bayesian hierarchical finite mixture models of reading times: A case study”. unpublished. DOI: 10.17605/OSF.IO/FWX3S.

Short peer-reviewed papers in conferences

Nicenboim, B. (2023). “The CoFI Reader: A Continuous Flow of Information approach to modeling reading”. In: MathPsych/ICCM/EMPG. University of Amsterdam, the Netherlands.

Nicenboim, B. (2018). “The implementation of a model of choice: The (truncated) linear ballistic accumulator”. In: StanCon. Aalto University, Helsinki, Finland. DOI: 10.5281/zenodo.1465990.

Vasishth, S., N. Chopin, R. Ryder, and B. Nicenboim (2017). “Modelling dependency completion in sentence comprehension as a Bayesian hierarchical mixture process: A case study involving Chinese relative clauses”. In: Proceedings of Cognitive Science Conference. London, UK.

Vasishth, S., L. Jaeger, and B. Nicenboim (2017). “Feature overwriting as a finite mixture process: Evidence from comprehension data”. In: Proceedings of MathPsych/ICCM Conference. Warwick, UK.

Nicenboim, B. and S. Vasishth (2017). “Models of Retrieval in Sentence Comprehension”. In: StanCon. (superseeded by 10.1016/j.jml.2017.08.004). Columbia University New York, NY.

Contributions to open software

• eeguana (https://bruno.nicenboim.me/eeguana/) An R package for tidy-ish manipulation of EEG data. Author
• pangoling (https://bruno.nicenboim.me/pangoling/) An R package for estimating the log-probabilities of words in a given context using transformer models. Author
• loo (https://cran.r-project.org/web/packages/loo/). Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models. Contributor
• MNE (https://mne.tools/). Open-source Python package for exploring, visualizing, and analyzing human neurophysiological data. Bug fixing
• Bayesplot (https://github.com/stan-dev) R package providing an extensive library of plotting functions for use after fitting Bayesian models. Bug fixing

2022

Stone, K., B. Nicenboim, S. Vasishth, and F. Rösler (2022). “Dissociating constraint and predictability in ERP: Evidence from German”. In: Proceedings of the 35th annual conference on Human Sentence Processing (HSP). Santa Cruz, USA.

2021

Stone, K., S. Vasishth, F. Rösler, and B. Nicenboim (2021). “Maximising ERP Resources Using a Sequential Bayes Factor Approach to Sample Size”. In: Architectures and Mechanisms for Language Processing (AMLaP). Université de Paris, France.

Patterson, C., P. B. Schumacher, B. Nicenboim, J. Hagen, and A. Kehler (2021). “German pronoun use follows Bayesian principles”. In: Proceedings of Annual CUNY Sentence Processing Conference, University of Pennsylvania.

2020

Lisson, P., D. Pregla, D. Paape, B. Nicenboim, N. Stadie, F. Burchert, and S. Vasishth (2020). “Computational models of retrieval processes in sentence comprehension in aphasia”. In: Architectures and Mechanisms for Language Processing (AMLaP). University of Potsdam, Germany.

Albert, A. and B. Nicenboim (2020). “Incorporating continuity in phonological models of the syllable using NAP”. In: LabPhon17.

2019

Nicenboim, B. (2019). “Bayesian inference: Obstacles and opportunities”. In: 8th Biennial International Conference on the Linguistics of Contemporary English (BICLCE), Bamberg, Germany.

Lisson, P., B. Nicenboim, S. Vasishth, and D. Paape (2019). “Models of retrieval in sentence comprehension in aphasia”. In: StanCon. Cambridge, UK.

Lisson, P., M. van het Nederend, D. Pregla, S. Vasishth, B. Nicenboim, and D. Paape (2019). “Competing models of retrieval in sentence processing: The case of aphasia”. In: Architectures and Mechanisms for Language Processing (AMLaP). Institute of Cognitive Neuroscience and Centre for Language and Brain, Higher School of Economics, Russia.

2018

Guerra, E., B. Nicenboim, and A. V. Helo (2018). “A crack in the crystall ball: Evidence against pre-activation of gender features in sentence comprehension”. In: Architectures and Mechanisms for Language Processing (AMLaP). Titanic Hotel Chaussee Berlin, Germany.

Nicenboim, B. and S. Vasishth (2018). “Bayesian cognitive models of memory retrieval processes: A case study ”. In: 51. Kongress der Deutschen Gesellschaft für Psychologie. Bayesian statistics as a coherent approach to psychologists’ statistical and methodological problems. Goethe-Universität Frankfurt, Germany.

2017

Albert, A. and B. Nicenboim (2017). “Linking sonority with periodic energy: Preliminary findings from production and perception”. In: 3rd International Workshop on Dynamic Modeling, Cologne, Germany.

Nicenboim, B. and S. Vasishth (2017). “Models of retrieval in sentence comprehension: A computational evaluation using Bayesian hierarchical modeling”. In: Proceedings of the Annual CUNY Sentence Processing Conference. Massachusetts Institute of Technology, Boston, MA, USA.

2016

Nicenboim, B., F. Engelmann, K. Suckow, and S. Vasishth (2016). “Number interference as predicted by cue-based retrieval”. In: Architectures and Mechanisms for Language Processing (AMLaP). Bilbao, Spain.

2015

Nicenboim, B., F. Engelmann, K. Suckow, and S. Vasishth (2015). “Fail fast or succeed slowly: Good-enough processing can mask interference effects”. In: International Conference on Cognitive Modeling (ICCM).

Nicenboim, B., P. Logacev, C. Gattei, and S. Vasishth (2015). “When high-capacity readers slow down and low-capacity readers speed up: Working memory differences in unbounded dependencies”. In: Proceedings of Annual CUNY Sentence Processing Conference. University of Southern California, Los Angeles, CA.

Nicenboim, B., K. Suckow, and S. Vasishth (2015). “Good-enough processing can mask interference effects”. In: Proceedings of Annual CUNY Sentence Processing Conference. University of Southern California, Los Angeles, CA.

2014

Nicenboim, B., P. Logacev, C. Gattei, and S. Vasishth (2014). “When high-capacity readers slow down and low-capacity readers speed up: Working memory differences in unbounded dependencies for German and Spanish readers”. In: Mental Architecture for Processing and Learning of Language (MAPLL). Tokyo, Japan.

Nicenboim, B., S. Vasishth, and R. Kliegl (2014). “Readers with less cognitive control are more affected by surprising content: Evidence from a self-paced reading experiment in German”. In: Mental Architecture for Processing and Learning of Language (MAPLL). Tokyo, Japan.

Nicenboim, B., S. Vasishth, and R. Kliegl (2014). “Readers with less cognitive control are more affected by surprising content”. In: Architectures and Mechanisms for Language Processing (AMLaP). Edinburgh, UK.

Nicenboim, B., S. Vasishth, R. Kliegl, C. Gattei, and M. Sigman (2014). “Working-memory capacity modulates antilocality effects in syntactic dependencies”. In: Proceedings of Annual CUNY Sentence Processing Conference. The Ohio State University, Columbus, OH.

2013

Nicenboim, B., S. Vasishth, C. Gattei, P. Logačev, and M. Sigman (2013). “The effect of distance on unbounded dependencies: An individual differences perspective”. In: Architectures and Mechanisms for Language Processing (AMLaP). Aix-Marseille Université, Marseille, France.

2012

Nicenboim, B. (2012). “Processing of filler-gap dependencies in Complex NP islands: Evidence from Hebrew”. In: Proceedings of Annual CUNY Sentence Processing Conference, New York, NY.

Nicenboim, B. (2012). “Processing Complex NP islands in Hebrew”. In: Proceedings of Generative Linguistics in the Old World Conference (GLOW), Potsdam, Germany.

2024

Bayesian Inference for Complex Hierarchical Models – Methods, Applications, and Challenges (2024). Department of Psychology, Cognitive Psychology, University of Zurich, Switzerland. URL: https://www.psychology.uzh.ch/en/areas/nec/allgpsy/events.html.

The CoFI Reader: A Continuous Flow of Information approach to modeling reading (2024). Guest lecture for Advanced Neural and Cognitive Modeling, Brain and Cognitive Science research master’s, at University of Amsterdam (UvA), the Netherlands.

2022

Revising the linking hypothesis between reading times and processing difficulty using a computational model of reading (2022). Department of Linguistics, Tel Aviv University.

¿Qué puede aportar el modelado computacional de la lectura a la investigación de la comprensión del lenguaje? (2022). ¿Qué puede aportar el modelado computacional de la lectura a la investigación de la comprensión del lenguaje? Centro de Investigaciones en Neurociencias, San José, Costa Rica.

2019

Bayesian models of memory retrieval in sentence comprehension (2019). Institute of Cognitive Science, University of Osnabrueck, Germany.

2018

Memory in sentence comprehension (2018). Job Talk at the Department of Cognitive and Brain Sciences, University of Ben Gurion, Israel.

A comparison of race and mixture models for explaining memory processes in sentence comprehension (2018). Stan user group in Berlin, Germany.

2017

Bayesian models in psycholinguistics (2017). Institut für Linguistik - Phonetik, University of Cologne, Germany.

Models of retrieval in sentence comprehension: A computational evaluation using Bayesian hierarchical modeling (2017). Colloquium of the HLP Lab at University of Rochester, NY, USA.

2015

Fail fast or succeed slowly: Good-enough processing can mask interference effects (2015). Center for Research in Language at University of San Diego, California, USA.

Hierarchical multinomial processing tree models (2015). Research Seminar in Cognitive Psychology, University of Potsdam, Germany.

2013

Speed-up does not necessarily mean facilitation: Working memory differences in long-distance dependency resolution (2013). Research Seminar in Cognitive Psychology, University of Potsdam, Germany.

The Effect of Distance on Long-Distance Dependencies. An Individual Differences Perspective (2013). Research Seminar in Cognitive Psychology, University of Potsdam, Germany.

The Effect of Distance on Long-Distance Dependencies. An Individual Differences Perspective (2013). Interdisciplinary Colloquium of the Linguistic Department, Tel Aviv University, Israel.

2012

The Effect of Distance on Unbounded Dependencies. An Individual Differences Perspective (2012). Psycholinguistic Seminar, Institute of Linguistics, University of Buenos Aires, Argentina.

The Effect of Distance on Unbounded (Linguistic) Dependencies. An Individual Differences Perspective (2012). Seminar, Laboratory of Integrative Neuroscience, University of Buenos Aires, Argentina.

Workshops and summer schools

2017-2024	“Advanced Bayesian methods”,
	Yearly Summer School on Statistical Methods for Linguistics and Psychology (SMLP),
	University of Potsdam, Germany
2020-2021	“Introduction to computational Bayesian methods using Stan”, Physalia courses,
	Berlin, Germany
2020	“Methods in Advanced Statistics”, with Shravan Vasishth
	2020 Winter School organized by Netherlands Graduate School in Linguistics (LOT),
	Tilburg, Netherlands
2019	“Introduction to Bayesian statistics using brms”,
	University of Cologne, Germany
2019	“Introduction to Bayesian statistics using brms”,
	University of Edinburgh, UK
2018	Talk:“Cognitive models of memory processes in sentence comprehension: A case study using Bayesian hierarchical modeling”
	Masterclass in Bayesian Statistics, Research school,
	CIRM (Marseille Luminy, France)
2017	“Introduction to Bayesian Modeling using Stan”,
	13. Tagung der Fachgruppe Methoden und Evaluation der Deutschen Gesellschaft für Psychologie,
	Tübingen, Germany

Courses

2022-2024	Lecturer in “Bayesian Multilevel Models”, Master in Data Science & Society. Tilburg University
2021-2024	Lecturer in “Bayesian Models of Cognitive Processes”, Master in Cognitive Science & AI. Tilburg University
2020-2021	Lecturer in “Research skill: Programing with R”, Data Science & Society. Tilburg University
2020-2021	Lecturer in “Methodology for Premasters Data Science & Society”. Tilburg University
2015–2017 (Winter)	Lecturer in “Advanced Data Analysis”. University of Potsdam
2016 (Summer)	Lecturer in “Predictions in Language Processing”. University of Potsdam
2015–2016 (Winter)	Lecturer in “Individual Differences in Sentence Processing”. University of Potsdam
2015 (Summer)	Lecturer in “Predictions in Language Processing”. University of Potsdam
2008–2010	Teaching assistant in “Syntax Beginners” and “Foundations of Theoretical Linguistics” Tel Aviv University