The N400 in silico: A review of computational models

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 Psychology of Learning and Motivation, Volume 76 Copyright # 2022 Elsevier Inc. ISSN 0079-7421 All rights reserved. https://doi.org/10.1016/bs.plm. 2022.03. 005 123 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 primingmanipulations, 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. 1. General introduction Whether we are listening to a podcast, reading a novel, or skimming a scientific article, each word that we encounter carries a unique meaning that needs to be activated in semantic memory. We also know from a large body of research using event-related potentials (ERPs) that each of these incoming words triggers a characteristic neural response—the N400, which has provided language researchers with an important window into how the brain processes language. In this review, we explore several computational models of the N400, and their contributions in characterizing this important neural phenomenon. During language comprehension, the N400 can be detected at the scalp surface as a negative-going waveform with a central-posterior scalp distribution, observed between 300–500ms following word onset (Kutas & Hillyard, 1980, 1984). This component is often interpreted as a basic component of semantic processing, as it is known to be elicited by any meaningful stimulus, including not only words, but also images, videos, and environmental sounds (see Kutas & Federmeier, 2011 for a review). Historically, the N400 was first described by Kutas and Hillyard (1980), who showed that semantically anomalous sentence completions produce a larger N400 than congruous completions (He spread the warm bread with *socks vs butter). However, later studies of sentence comprehension showed that large N400s are also evoked by plausible words, as long as they are unexpected in relation to their preceding contexts. Indeed, one of the strongest 124 Samer Nour Eddine et al.