Semantic surprise predicts the N400 brain potential
Language is central to human life; however, how our brains derive meaning from language is still not well understood. A commonly studied electrophysiological measure of on-line meaning related processing is the N400 component, the computational basis of which is still actively debated. Here, we test...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Neuroimage: Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666956023000065 |
| _version_ | 1827989988565843968 |
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| author | Alma Lindborg Lea Musiolek Dirk Ostwald Milena Rabovsky |
| author_facet | Alma Lindborg Lea Musiolek Dirk Ostwald Milena Rabovsky |
| author_sort | Alma Lindborg |
| collection | DOAJ |
| description | Language is central to human life; however, how our brains derive meaning from language is still not well understood. A commonly studied electrophysiological measure of on-line meaning related processing is the N400 component, the computational basis of which is still actively debated. Here, we test one of the recently proposed, computationally explicit hypotheses on the N400 – namely, that it reflects surprise with respect to a probabilistic representation of the semantic features of the current stimulus in a given context. We devise a Bayesian sequential learner model to derive trial-by-trial semantic surprise in a semantic oddball like roving paradigm experiment, where single nouns from different semantic categories are presented in sequences. Using experimental data from 40 subjects, we show that model-derived semantic surprise significantly predicts the N400 amplitude, substantially outperforming a non-probabilistic baseline model. Investigating the temporal signature of the effect, we find that the effect of semantic surprise on the EEG is restricted to the time window of the N400. Moreover, comparing the topography of the semantic surprise effect to a conventional ERP analysis of predicted vs. unpredicted words, we find that the semantic surprise closely replicates the N400 topography. Our results make a strong case for the role of probabilistic semantic representations in eliciting the N400, and in language comprehension in general. |
| first_indexed | 2024-04-10T00:30:02Z |
| format | Article |
| id | doaj.art-9bf0ab2f50e849a2bcabc690c16af74d |
| institution | Directory Open Access Journal |
| issn | 2666-9560 |
| language | English |
| last_indexed | 2024-04-10T00:30:02Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Neuroimage: Reports |
| spelling | doaj.art-9bf0ab2f50e849a2bcabc690c16af74d2023-03-15T04:29:13ZengElsevierNeuroimage: Reports2666-95602023-03-0131100161Semantic surprise predicts the N400 brain potentialAlma Lindborg0Lea Musiolek1Dirk Ostwald2Milena Rabovsky3Department of Psychology, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476, Potsdam, GermanyAdaptive Systems Group, Department of Computer Science, Humboldt-Universitaet zu Berlin, Unter den Linden 6, 10099, Berlin, GermanyInstitute of Psychology, Otto von Guericke Universitaet Magdeburg, Universitaetsplatz 2, 39106, Magdeburg, GermanyDepartment of Psychology, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476, Potsdam, Germany; Corresponding author.Language is central to human life; however, how our brains derive meaning from language is still not well understood. A commonly studied electrophysiological measure of on-line meaning related processing is the N400 component, the computational basis of which is still actively debated. Here, we test one of the recently proposed, computationally explicit hypotheses on the N400 – namely, that it reflects surprise with respect to a probabilistic representation of the semantic features of the current stimulus in a given context. We devise a Bayesian sequential learner model to derive trial-by-trial semantic surprise in a semantic oddball like roving paradigm experiment, where single nouns from different semantic categories are presented in sequences. Using experimental data from 40 subjects, we show that model-derived semantic surprise significantly predicts the N400 amplitude, substantially outperforming a non-probabilistic baseline model. Investigating the temporal signature of the effect, we find that the effect of semantic surprise on the EEG is restricted to the time window of the N400. Moreover, comparing the topography of the semantic surprise effect to a conventional ERP analysis of predicted vs. unpredicted words, we find that the semantic surprise closely replicates the N400 topography. Our results make a strong case for the role of probabilistic semantic representations in eliciting the N400, and in language comprehension in general.http://www.sciencedirect.com/science/article/pii/S2666956023000065LanguageElectrophysiologyBayesian modellingN400Semantics |
| spellingShingle | Alma Lindborg Lea Musiolek Dirk Ostwald Milena Rabovsky Semantic surprise predicts the N400 brain potential Neuroimage: Reports Language Electrophysiology Bayesian modelling N400 Semantics |
| title | Semantic surprise predicts the N400 brain potential |
| title_full | Semantic surprise predicts the N400 brain potential |
| title_fullStr | Semantic surprise predicts the N400 brain potential |
| title_full_unstemmed | Semantic surprise predicts the N400 brain potential |
| title_short | Semantic surprise predicts the N400 brain potential |
| title_sort | semantic surprise predicts the n400 brain potential |
| topic | Language Electrophysiology Bayesian modelling N400 Semantics |
| url | http://www.sciencedirect.com/science/article/pii/S2666956023000065 |
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