Malte R. Henningsen (Schomers), Ph.D.
Malte studied Natural Sciences (with Computer Science and Philosophy of Science as minor subjects) at the University of Cambridge, UK. In his final year, he specialised in Psychology and Cognitive Neuroscience and subsequently moved to Freie Universität Berlin for an M.Sc. in Social, Cognitive and Affective Neuroscience and a PhD with Friedemann Pulvermüller in the Brain Language Lab. His PhD was funded by the Berlin School of Mind and Brain, where he also completed the PhD curriculum program. From 2017 until 2022 he was a Post-Doctoral Researcher and Lecturer in Linguistics at the Brain Language Lab, supporting the ERC-funded project Material Constraints enabling Human Cognition ("MatCo") from 2021 on. Additionally, he was Associated Investigator in the DFG-funded project "Matters of Activity. Image Space Material" (Humboldt-Universität zu Berlin) from 2019 on.
His general research interests are in the brain mechanisms of language comprehension, in particular how concrete and abstracts concepts are represented in the brain. Most of his current projects focus on using neuroanatomically grounded neural network models (in collaboration with Dr. Max Garagnani at the Dept. of Computer Science, Goldsmiths, University of London). To this end, deep unsupervised neural networks to simulate fundamental processes related to language processing, in particular in the area of phonology and semantics, are used. Employing the "explainable AI" approach, the emerging representations in the networks' hidden layers are analyzed. Another research line is trying to relate observations from neural networks to human neurophysiological data using methods such as representational similarity analysis (RSA).
Henningsen-Schomers MR, Garagnani M, Pulvermüller F (2023). Influence of language on perception and concept formation in a brain-constrained deep neural network model. Philosophical Transactions of the Royal Society B 378, doi: 10.1098/rstb.2021.0373 [PDF] [OSF project] [interactive data visualization]
Henningsen-Schomers MR, Pulvermüller F (2022). Modelling concrete and abstract concepts using brain-constrained deep neural networks. Psychological Research 86(8), 2533-2559 [PDF] [OSF project] [interactive data visualization]
Pulvermüller F, Tomasello R, Henningsen-Schomers MR, Wennekers T (2021). Biological constraints on neural network models of cognitive function. Nature Reviews Neuroscience 22, 488–502, doi: 10.1038/s41583-021-00473-5 [PDF - author manuscript]
Schilling A, Tomasello R, Henningsen-Schomers MR, Zankl A, Surendra K, Haller M, Karl V, Uhrig P, Maier A, Krauss P (2021). Analysis of continuous neuronal activity evoked by natural speech with computational corpus linguistics methods. Lang. Cogn. Neurosci. 36(2), 167–186. doi: 10.1080/23273798.2020.1803375 [PDF]
Schomers MR, Garagnani M, Pulvermüller F (2017). Neurocomputational consequences of evolutionary connectivity changes in perisylvian language cortex. Journal of Neuroscience 37(11), 3045–3055, doi: 10.1523/jneurosci.2693-16.2017 [PDF]
Schomers MR, Pulvermüller F (2016). Is the sensorimotor cortex relevant for speech perception and understanding? An integrative review. Frontiers in Human Neuroscience 10, 435, doi: 10.3389/fnhum.2016.00435 [PDF]
Schomers MR, Kirilina E, Weigand A, Bajbouj M, Pulvermüller F (2015). Causal influence of articulatory motor cortex on comprehending single spoken words: TMS evidence. Cerebral Cortex 25(10), 3894–3902, doi: 10.1093/cercor/bhu274 [PDF]
Welchman AE, Stanley J, Schomers MR, Miall RC, Bülthoff HH (2010). The quick and the dead: when reaction beats intention. Proceedings of the Royal Society B: Biological Sciences 277(1688), 1667–1674 [PDF]