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Modelling the N400 brain potential as change in a probabilistic representation of meaning

Abstract

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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Fig. 1: The sentence gestalt (SG) model architecture, processing a sentence with a high- or low-cloze probability ending, and the model’s N400 correlate.
Fig. 2: Simulation results for the basic effects.
Fig. 3: Simulation results concerning the specificity of the N400 effect.
Fig. 4: Development across training.
Fig. 5: Comprehension performance and semantic update effects at a very early stage in training.
Fig. 6: Simulation of the interaction between delayed repetition and semantic incongruity.
Fig. 7: Simulation results from a simple recurrent network (SRN) model trained to predict the next word based on preceding context.

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Acknowledgements

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.

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M.R. developed the idea for the project, including the idea of linking the N400 to the updating of SG layer activation in the model. S.S.H. re-implemented the model for the current simulations. M.R. and J.L.M. formulated the training environment. J.L.M. formulated the new learning rule and developed the probabilistic formulation of the model with input from M.R. M.R. adjusted the model implementation, implemented the training environment, formulated and implemented the simulations, trained the networks and conducted the simulations, and performed the analyses with input from J.L.M. J.L.M. and M.R. discussed the results and wrote the manuscript.

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Correspondence to Milena Rabovsky or James L. McClelland.

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Supplementary Figures 1–11, Supplementary Table 1, Supplementary Notes 1–7, Supplementary Methods 1–4, Supplementary Discussion, Supplementary References

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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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