Malte R. Henningsen (Schomers), Ph.D.
I studied Natural Sciences (with Computer Science and Philosophy of Science as minor subjects) at the University of Cambridge, UK. In my final year, I specialised in Psychology and Cognitive Neuroscience and subsequently moved to Freie Universität Berlin for an M.Sc. in Social, Cognitive and Affective Neuroscience. I then stayed on at FU Berlin to complete my PhD here in the Brain Language Lab. My PhD was funded by the Berlin School of Mind and Brain, where I also completed the PhD curriculum program.
My research interests are in the brain mechanisms of language comprehension, ranging from perception of speech sounds to comprehension of whole words and sentences. I am also interested in how concrete and abstracts concepts are represented in the brain. I employ neurophysiological techniques (EEG/MEG) as well as neurostimulation methods such as transcranial magnetic stimulation (TMS).
In addition to this, most of my current projects focus on using neuroanatomically grounded neural network models (in collaboration with Dr. Max Garagnani at Goldsmiths, University of London). To this end, we use deep unsupervised neural networks to simulate fundamental processes related to language processing, in particular in the area of phonology and semantics. Employing the "explainable AI" approach, we then analyze the emerging representations in the networks' hidden layers. Finally, we also try to relate our observations from neural networks to human neurophysiological data using methods such as representational similarity analysis (RSA).
Since 2019, I have been an Associated Investigator in the DFG-funded project "Matters of Activity. Image Space Material" (Cluster of Excellence at Humboldt-Universität zu Berlin).
- Dr. Max Garagnani, Dept. of Computer Science - Goldsmiths, University of London
- Prof. Yury Shtyrov, Center for Functionally Integrative Neuroscience (CFIN) - Aarhus University, Denmark
Henningsen-Schomers MR, Pulvermüller F (under review). Modelling concrete and abstract concepts using brain-constrained deep neural networks. [OSF project]
Henningsen-Schomers MR, Garagnani M, Pulvermüller F (in prep). Influence of verbal labels on concept formation and perception in a deep unsupervised neural network model.
Pulvermüller F, Tomasello R, Henningsen-Schomers MR, Wennekers T (2021). Biological constraints on neural network models of cognitive function. Nature Reviews Neuroscience, doi: 10.1038/s41583-021-00473-5
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]
Please also see my ResearchGate profile for latest publications.