|Email:||[initial].[lastname] [at] tilburguniversity.edu|
|Office:||D108, Dante Building, Tilburg University|
|Address:||Department of Cognitive Science and Artificial Intelligence|
|PO Box 90153|
|5000 LE Tilburg|
I’m an assistant professor at the department of Cognitive Science and AI of Tilburg University and a guest researcher at the department of Linguistics the University of Potsdam, Germany. Before that I did my PhD and Postdoc in Shravan Vasishth’s lab, at the Department of Linguistics of University of Potsdam, Germany.
My main interests
Computational (cognitive) modeling
Projects involving of computational (cognitive) models in a Bayesian framework (using Stan/brms). Recently, in a paper led by Clare Patterson about German personal and demonstrative pronouns, I contributed with the Bayesian implementation of the models and model comparison. Another paper where my contribution was to help with the Bayesian implementation is this one about the update of unchosen actions in reinforcement learning.
Computational cognitive models that link memory processes with sentence comprehension, and individual differences in sentence processing. The most important output of my PhD was this paper about models of retrieval; DOI: 10.1016/j.jml.2017.08.004. The paper shows the computational implementation of two different sentence processing theories (a verbal model and an ACT-R model) on the same framework using hierarchical Bayesian modeling.
Decision making models. See here my attempt of a fully hierarchical linear ballistic accumulator in Stan (Stancon submission).
An R package for the manipulation of EEG data: https://bnicenboim.github.io/eeguana/. It is fully functional, but it’s only able to do basic preprocessing for EEG data. Feedback and comments (and github issues) are welcome.
Predictions in language using EEG. Recently, we have tried to figure out the (elusive) effects of contextual constraint in this paper. Kate Stone (first author) summarizes it nicely here. Before that, in my post-doc (in this paper), we used novel EEG data, together with a meta-analysis of available data, and we showed that the N400 effect is, at least in part, caused by linguistic preactivation that occurs prior to the predicted target word, as opposed to semantic integration that occurs after the target word has been read. While this idea has been present in the literature for more than 10 years, experimental evidence has been so far controversial and included several failed replications.
Bayesian statistics for cognitive scientists textbook (in progress) together with Shravan Vasishth and Daniel Schad. I have also been collaborating with Shravan Vasishth, Daniel Schad, and others (in different orders) in papers that deal with sample size determination for Bayesian models, the robust use of Bayes factor, and data aggregation in mixed models.
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.