At the Division of Cognitive Neuroscience (Rabovsky Lab), we combine explicit computational models (specifically, artificial neural network models, aka deep learning models) and neuroscientific evidence (mostly event-related brain potentials, ERPs) in order to understand the neurocognition of language and meaning.
We receive funding from the German Research Foundation (DFG) via an Emmy Noether grant. We are associated with the Collaborative Research Centers Limits of Variability in Language (SFB1287) and Data Assimilation (SFB 1294) at the University of Potsdam.
Alice Hodapp joined our lab as a PhD student and will focus on investigating the idea that N400 amplitudes may reflect a learning signal driving adaptation. Antonia Heinrich started as a student assistant. Welcome Alice and Antonia!
Sophie Arana from the MPI for Psycholinguistics in Nijmegen completed the first of three research stays in our lab aimed at developing an MEG study probing the temporal evolvement of representations formed during sentence comprehension. Many thanks for visiting and we are looking forward to seeing you again!
Rabovsky, M., & McClelland, J. L. (2020). Quasi-compositional mapping from form to meaning: a neural-network based approach to capturing neural responses during language comprehension. Philosophical Transactions of the Royal Society B. 375: 20190313.