Predicting the N400 ERP component using the Sentence Gestalt model trained on a large scale corpus

Lopopolo, A.; Rabovsky, M.

doi:10.1101/2021.05.12.443787

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bioRxiv · 10.1101/2021.05.12.443787

Predicting the N400 ERP component using the Sentence Gestalt model trained on a large scale corpus

Lopopolo, A.·Rabovsky, M.

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Abstract

The N400 component of the event related brain potential is widely used to investigate language and meaning processing. However, despite much research the components functional basis remains actively debated. Recent work showed that the update of the predictive representation of sentence meaning (semantic update, or SU) generated by the Sentence Gestalt model (McClelland, St. John, & Taraban, 1989) consistently displayed a similar pattern to the N400 amplitude in a series of conditions known to modulate this event-related potential. These results led Rabovsky, Hansen, and McClelland (2018) to suggest that the N400 might reflect change in a probabilistic representation of meaning corresponding to an implicit semantic prediction error. However, a limitation of this work is that the model was trained on a small artificial training corpus and thus could not be presented with the same naturalistic stimuli presented in empirical experiments. In the present study, we overcome this limitation and directly model the amplitude of the N400 elicited during naturalistic sentence processing by using as predictor the SU generated by a Sentence Gestalt model trained on a large corpus of texts. The results reported in this paper corroborate the hypothesis that the N400 component reflects the change in a probabilistic representation of meaning after every word presentation. Further analyses demonstrate that the SU of the Sentence Gestalt model and the amplitude of the N400 are influenced similarly by the stochastic and positional properties of the linguistic input.

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Lopopolo, A., Rabovsky, M.. 2021-05-13. Predicting the N400 ERP component using the Sentence Gestalt model trained on a large scale corpus. https://doi.org/10.1101/2021.05.12.443787

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