A predictive coding model of the N400.

Publisher: Elsevier Country of Publication: Netherlands NLM ID: 0367541 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-7838 (Electronic) Linking ISSN: 00100277 NLM ISO Abbreviation: Cognition Subsets: MEDLINE

Publication: Amsterdam : Elsevier
Original Publication: Hague, Mouton.

Electroencephalography* ; Evoked Potentials*; Humans ; Male ; Female ; Bayes Theorem ; Semantics ; Brain ; Comprehension

The N400 event-related component has been widely used to investigate the neural mechanisms underlying real-time language comprehension. However, despite decades of research, there is still no unifying theory that can explain both its temporal dynamics and functional properties. In this work, we show that predictive coding - a biologically plausible algorithm for approximating Bayesian inference - offers a promising framework for characterizing the N400. Using an implemented predictive coding computational model, we demonstrate how the N400 can be formalized as the lexico-semantic prediction error produced as the brain infers meaning from the linguistic form of incoming words. We show that the magnitude of lexico-semantic prediction error mirrors the functional sensitivity of the N400 to various lexical variables, priming, contextual effects, as well as their higher-order interactions. We further show that the dynamics of the predictive coding algorithm provides a natural explanation for the temporal dynamics of the N400, and a biologically plausible link to neural activity. Together, these findings directly situate the N400 within the broader context of predictive coding research. More generally, they raise the possibility that the brain may use the same computational mechanism for inference across linguistic and non-linguistic domains.
Competing Interests: Declaration of competing interest The authors declare no conflict of interest.
(Copyright © 2024 Elsevier B.V. All rights reserved.)

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R01 HD082527 United States HD NICHD NIH HHS

Keywords: Bayesian inference; Language comprehension; Orthographic; Prediction; Prediction error; Semantic

Date Created: 20240301 Date Completed: 20240325 Latest Revision: 20240514

20240515

PMC10984641

10.1016/j.cognition.2024.105755

38428168