Language ultimately aims to convey meaning. Yet, the processing of meaning remains elusive and much less understood as compared to other aspects of language such as the processing of syntax, orthography, or phonology. The goal of my research is to elucidate the processing of meaning in language, relying on the following guiding principles: First, to understand human language comprehension, we need to take the evidence provided by neuroscientific data – such as event-related brain potentials (ERPs) providing direct online indicators of electrical brain activity during comprehension – seriously, even at the cost of trading longheld beliefs about how language comprehension should work in principle. Second, it is crucial to precisely understand what these brain signals mean in terms of underlying processes. A principled way to understand a process is to rebuild it and therefore an important complementary approach I use, besides empirical EEG experiments (Rabovsky et al., 2008, 2012a, 2012b, 2012c, 2019), is the simulation of language-related brain signals with computationally explicit and theoretically precise implemented neural network models.
Specifically, the most widely used ERP component in research on language and meaning is the N400 component. Since the first report of larger N400 amplitudes in sentences with semantic incongruities such as “I take my coffee with cream and dog.”, N400 amplitudes have been found to be modulated by numerous lexical and semantic variables. However, despite more than 30 years of research and over 1000 empirical studies, the functional basis of N400 amplitudes remains unclear and actively debated (Kutas & Federmeier, 2011). To better understand the functional basis of N400 amplitudes, we simulated a broad range of empirically observed N400 effects with implemented neural network models. Results suggest that N400 amplitudes reflect the stimulus-induced change in an implicit and probabilistic representation of meaning, and that this change at the same time corresponds to the implicit prediction error contained in the previous representation (Rabovsky, Hansen, & McClelland, 2018; Rabovsky & McRae, 2014). It is often assumed that prediction errors drive learning so that our simulations suggest that larger N400 amplitudes should trigger enhanced adaptation. This model-derived prediction is currently tested in empirical EEG experiments.
Weller, P., Rabovsky, M., & Abdel Rahman, R. (2019). Semantic knowledge promotes conscious awareness of visual objects. Journal of Cognitive Neuroscience, 31, 1216-1226.
Rabovsky, M., Conrad, M., Álvarez, C.J., Paschke-Goldt, J., & Sommer, W. (2019). Attentional modulation of orthographic neighborhood effects during reading: Evidence from event-related brain potentials in a psychological refractory period paradigm. PLOS ONE, 14(1): e0199084. https://doi.org/10.1371/journal.pone.0199084
Baum, J.A.*, Rabovsky, M.*, Rose, S.B., & Abdel Rahman, R. (2018). Clear judgments based on unclear evidence: Person evaluation is strongly influenced by untrustworthy gossip. Emotion. doi: 10.1037/emo0000545
Rabovsky, M., Hansen, S.S., & McClelland, J.L. (2018). Modelling the N400 brain potential as change in a probabilistic representation of meaning. Nature Human Behaviour, 2, 693-705. Sharing link: https://rdcu.be/6AM9
Rabovsky, M., Hansen, S.S., & McClelland, J.L. (2016). N400 amplitudes reflect change in a probabilistic representation of meaning: Evidence from a connectionist model. Proceedings of the 38th Annual Meeting of the Cognitive Science Society, pp 2045-2050. Cognitive Science Society: Austin: TX.
Rabovsky, M., Schad, D.J., & Abdel Rahman, R. (2016). Language production is facilitated by semantic richness, but inhibited by semantic density: Evidence from picture naming. Cognition, 146, 240-244.
Rabovsky, M., Stein, T., & Abdel Rahman, R. (2016). Access to awareness for faces during continuous flash suppression is not modulated by affective knowledge. PLOS ONE, 11(4):e0150931.
Rabovsky, M. & McRae, K. (2014). Simulating the N400 ERP component as semantic network error: Insights from a feature-based connectionist attractor model of word meaning. Cognition, 132(1), 68-89.
Suess, F., Rabovsky, M., & Abdel Rahman, R. (2014). Perceiving emotions in neutral faces: expression processing is biased by affective person knowledge. Social Cognitive and Affective Neuroscience, 1-6.
Rabovsky, M., Sommer, W. & Abdel Rahman, R. (2012). Implicit word learning benefits from semantic richness: Electrophysiological and behavioral evidence. Journal of Experimental Psychology: Learning, Memory, & Cognition, 38(4), 1076-1083.
Rabovsky, M., Sommer, W. & Abdel Rahman, R. (2012). Depth of conceptual knowledge modulates visual processes during word reading. Journal of Cognitive Neuroscience, 24(4), 990-1005.
Rabovsky, M., Sommer, W. & Abdel Rahman, R. (2012). Time course of semantic richness effects in visual word recognition. Frontiers in Human Neuroscience, 6:11.
Rabovsky, M., Álvarez, C. J., Hohlfeld, A. & Sommer, W. (2008). Is lexical access autonomous? Evidence from combining overlapping tasks with recording event-related brain potentials. Brain Research, 1222, 156-165.