Word surprisal is a well-established computational predictor of human neural responses during language comprehension, but it remains less clear whether local semantic fit explains neural response variation beyond lexical expectation during naturalistic reading. Using the Dublin EEG-based Reading Experiment Corpus (DERCo), this study examined whether contextual semantic relevance predicts word-locked EEG activity in the N400 and P600 windows. Contextual semantic relevance was computed as an attention-aware measure of how strongly a target word is semantically connected to its recent discourse context, and it was compared with GPT-based word surprisal. Across 22 participants and 32 EEG channels, we tested both predictors using regression-based ERP analyses and generalized additive mixed models while controlling for lexical variables and repeated observations. Both predictors were reliably associated with EEG responses, but they showed partly different temporal and scalp-level patterns. Surprisal captured expectancy-related variation, whereas contextual semantic relevance showed robust effects across N400- and P600-window mean voltages, with particularly strong explanatory support in the P600 window. Model comparisons indicated that contextual semantic relevance contributed explanatory value beyond lexical controls and surprisal. These findings suggest that naturalistic reading depends on both lexical expectation and local semantic integration, and that contextual semantic relevance offers an interpretable computational link between discourse semantic fit and ERP dynamics.
Since the 1990s, the fields of numerical cognition and psycholinguistics have evolved largely independently. However, the research since then demonstrates that language and numbers are intrinsically related, particularly in multilingual contexts. Language characteristics (e.g., morpho-syntactic properties) and language status (e.g., monolingual vs. multilingual) influence numerical processing at multiple levels, from number identification and retrieval to advanced mathematical skills. This article builds on the contribution of Frenck-Mestre and Vaid (1992), "Language as a Factor in the Identification of Ordinary Words and Number Words", to further elaborate how language contributes to the learning of numerical concepts, especially the acquisition and representation of number words across development. While their findings provided initial important insights into bilingual number processing, subsequent research, which we discuss in the current article, further shows that language and multilingual experiences can shape lexical, semantic, and lexico-semantic access to numbers. We finish by outlining some future research directions for the study of multilingual number representations.
Previous evidence has shown an important role for semantic diversity in visual word recognition. These results are theoretically relevant, as they suggest that it is not only the mere repetition of word reading (lexical frequency) but also the semantic context in which words are encountered that affects lexical representation of words. In this study, we assess the role of semantic diversity by applying latent semantic analysis to a Spanish database, deriving word values for semantic diversity. Children and adults performed a lexical decision task to 300 words with different values of this variable while controlling the frequency, age of acquisition, and letter length. The results indicate that adults and children respond faster to words with high semantic diversity than those with lower semantic diversity. These results obtained for the first time in Spanish are consistent with previous findings and support the view that the semantic contexts in which words appear contribute to their lexical representations. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Abstract This data-driven study investigates the licensing factors of object omission in English transitive verbs (e.g., I’m eating Ø vs. *I’m devouring Ø). Using corpus data from the British component of the International Corpus of English and psycholinguistic measurements from the South Carolina Psycholinguistic Metabase (SCOPE), generalised additive models were trained on distributional and semantic features of 972 verbs to examine linear and non-linear effects on omission availability. In a complementary analysis, linear discriminant analysis was applied to fastText word embeddings to assess the predictive value of distributional co-occurrence information. Discriminant scores emerged as the strongest predictor, followed by frequency and dispersion, while purely semantic predictors contributed comparatively little once distributional measures were controlled for. Object-omitting verbs cluster towards the stative and communicative end of the verb space. The results challenge earlier monofactorial accounts and support usage-based explanations of argument realisation, though the overlap between distributional and semantic measures suggests that future research should address potential confounds and consider semantic frames as a key missing predictor.