The N400 component of the event-related brain potential has aroused much interest because it is thought to provide an online measure of meaning processing in the brain. However, the underlying process remains incompletely understood and actively debated. Here we present a computationally explicit account of this process and the emerging representation of sentence meaning. We simulate N400 amplitudes as the change induced by an incoming stimulus in an implicit and probabilistic representation of meaning captured by the hidden unit activation pattern in a neural network model of sentence comprehension, and we propose that the process underlying the N400 also drives implicit learning in the network. The model provides a unified account of 16 distinct findings from the N400 literature and connects human language comprehension with recent deep learning approaches to language processing.
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 658999 to M.R. We thank R. Levy, S. Frank and the members of the PDP lab at Stanford University for helpful discussion. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The authors declare no competing interests.
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Rabovsky, M., Hansen, S.S. & McClelland, J.L. Modelling the N400 brain potential as change in a probabilistic representation of meaning. Nat Hum Behav 2, 693–705 (2018). https://doi.org/10.1038/s41562-018-0406-4
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