At the Cognitive Neuroscience Lab at the University of Potsdam (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 and via projects within the Collaborative Research Centers Limits of Variability in Language (SFB1287) (project B03) and Data Assimilation (SFB 1294) (project B09) at the University of Potsdam.
Two papers accepted for the Annual Meeting of the Cognitive Science Society:
Lopopolo, A. & Rabovsky, M. (accepted). Predicting the N400 ERP component using the Sentence Gestalt model trained on a large scale corpus. Preprint: https://doi.org/10.1101/2021.05.12.443787
Lindborg, A. & Rabovsky, M. (accepted). Meaning in brains and machines: Internal activation update in large scale language model partially reflects the N400 brain potential.
Hodapp, A. & Rabovsky, M. (2021). The N400 ERP component reflects a learning signal during language comprehension. https://doi.org/10.1101/2021.03.25.436922