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.

The N400 event-related brain potential is elicited by each word in a sentence and offers an important window into the mechanisms of real-time language comprehension. Since the 1980s, studies investigating the N400 have expanded our understanding of how bottom-up linguistic inputs interact with top-down contextual constraints. More recently, a growing body of computational modeling research has aimed to formalize theoretical accounts of the N400 to better understand the neural and functional basis of this component. Here, we provide a comprehensive review of this literature. We discuss “word-level” models that focus on the N400’s sensitivity to lexical factors and simple priming manipulations, as well as more recent sentence-level models that explain its sensitivity to broader context. We discuss each model’s insights and limitations in relation to a set of cognitive and biological constraints that have informed our understanding of language comprehension and the N400 over the past few decades. We then review a novel computational model of the N400 that is based on the principles of predictive coding, which can accurately simulate both word-level and sentence-level phenomena. In this predictive coding account, the N400 is conceptualized as the magnitude of lexico-semantic prediction error produced by incoming words during the process of inferring their meaning. Finally, we highlight important directions for future research, including a discussion of how these computational models can be expanded to explain language-related ERP effects outside the N400 time window, and variation in N400 modulation across different populations.

When semantic information is activated by a context prior to new bottom-up input (i.e. when a word is predicted), semantic processing of that incoming word is typically facilitated, attenuating the amplitude of the N400 event related potential (ERP) – a direct neural measure of semantic processing. N400 modulation is observed even when the context is a single semantically related “prime” word. This so-called “N400 semantic priming effect” is sensitive to the probability of encountering a related prime-target pair within an experimental block, suggesting that participants may be adapting the strength of their predictions to the predictive validity of their broader experimental environment. We formalize this adaptation using a Bayesian learning model that estimates and updates the probability of encountering a related versus an unrelated prime-target pair on each successive trial. We found that our model’s trial-by-trial estimates of target word probability accounted for significant variance in the amplitude of the N400 evoked by target words. These findings suggest that Bayesian principles contribute to how comprehenders adapt their semantic predictions to the statistical structure of their broader environment, with implications for the functional significance of the N400 component and the predictive nature of language processing.