| Email: | [initial].[lastname] [at] tilburguniversity.edu |
| Office: | D108, Dante Building, Tilburg University |
| Address: | Department of Cognitive Science and Artificial Intelligence |
| Tilburg University | |
| PO Box 90153 | |
| 5000 LE Tilburg | |
| The Netherlands |
About me
I’m an assistant professor at the department of Cognitive Science and AI of Tilburg University, PI of the Computational Psycholinguistics Lab. Before that I did my PhD and Postdoc in Shravan Vasishth’s lab, at the Department of Linguistics of University of Potsdam, Germany.
[Mastodon]
[Twitter]
[Github]
[ORCID]
[OSF]
My main interests
Computational (cognitive) modeling in a Bayesian framework (using Stan)
Some representative examples:
[1] B. Nicenboim. “The CoFI Reader: A Continuous Flow of Information approach to modeling reading”. In: MathPsych/ICCM/EMPG. University of Amsterdam, the Netherlands, jul. 2023.
[2] B. Nicenboim and S. Vasishth. “Models of Retrieval in Sentence Comprehension: A computational evaluation using Bayesian hierarchical modeling”. In: Journal of Memory and Language 99 (2018), pp. 1 -34. DOI: 10.1016/j.jml.2017.08.004.
EEG
Some representative examples:
[1] B. Nicenboim, S. Vasishth, and F. Rösler. “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 (2020), p. 107427. DOI: 10.1016/j.neuropsychologia.2020.107427.
[2] K. Stone, B. Nicenboim, S. Vasishth, et al. “Understanding the effects of constraint and predictability in ERP”. In: Neurobiology of Language (dec. 2022), pp. 1-71. DOI: 10.1162/nol_a_00094.
[3] An R package for the manipulation of EEG data: https://bnicenboim.github.io/eeguana/.
Bayesian statistics
Some representative examples:
[1] B. Nicenboim and S. Vasishth. “Statistical methods for linguistic research: Foundational Ideas - Part II”. In: Language and Linguistics Compass 10.11 (2016), pp. 591-613. DOI: 10.1111/lnc3.12207.
[2] D. J. Schad, B. Nicenboim, P. Bürkner, et al. “Workflow Techniques for the Robust Use of Bayes Factors”. In: Psychological methods (2022). DOI: 10.1037/met0000472.
[3] Bayesian statistics for cognitive scientists textbook (in progress) together with Shravan Vasishth and Daniel Schad.
[4] Stan for cognitive science website with resources for Bayesian modeling with Stan.
Data and code
Data and code for my published papers is mostly in the OSF website (with some exceptions in my github repo). And I’m also contributing to the list of publicly available psycholinguistics datasets.
News
I obtained a NWO Open Competition SGW XS grant The project’s aim is to gather self-paced EEG data for benchmarks and computational modeling.