The grip of grammar on meaning uncertainty: cross-linguistic evidence, neural correlates, and clinical relevance

Naturalistic language comprehension requires listeners to process both local probabilistic expectations and contextual semantic relations. This study tested whether contextual semantic relevance, measuring how strongly a target word relates to its recent semantic context, is associated with fMRI BOLD responses independently of word surprisal and lexical, timing, acoustic, and prosodic controls. We analyzed two public datasets: Alice (23 participants, one narrative) and Narratives (47 participants, 185 runs, four stories) using FIR/deconvolution and generalized additive mixed models. In Alice, semantic relevance was significant across all ROIs in FIR analyses, whereas surprisal was not. In GAMMs, both predictors showed broad significance. In Narratives, both predictors showed comparable spatial prevalence across ROIs. Semantic relevance showed robust BOLD associations across both datasets, with a particularly strong advantage over surprisal in the timing-sensitive Alice FIR analysis. The regionally heterogeneous direction of semantic relevance effects, with negative effects in posterior semantic regions and positive effects in frontal integration regions, suggests involvement of functionally distinct neural processes rather than a single uniform mechanism. These findings indicate that contextual semantic fit and local probabilistic expectation make partially distinct, dataset-dependent contributions to hemodynamic responses during naturalistic listening.

Large language models (LLMs) with human-like performance on linguistic tasks have transformed the study of language in neurodiverse conditions. LLMs provide representations of linguistic input in the form of high-dimensional vectors (embeddings), and next-token predictions computed from these embeddings. Previous crosslinguistic evidence suggests a complexity reduction in the form of both lower intrinsic dimensionality (ID) of LLM representations and higher mean surprisal (prediction error) in psychosis. We hypothesized that these metrics reflect a general deficit of information compression in psychosis, linked to grammatical organization as what enables predictions in language.We operationalized surprisal difference as the difference between surprisal as estimated from word frequency and surprisal as based on a contextual LM, which is sensitive to grammatical organization over and above lexical concepts. Using a dataset of 144 Turkish speakers, including 106 patients with schizophrenia-spectrum disorders (SSD) - 56 with chronic schizophrenia (SZH), 33 with first-episode psychosis (FEP), and 17 with schizoaffective disorder (SZA) - and 38 healthy controls. We report: (1) Surprisal difference is attenuated in all clinical groups relative to controls, independently of word count; (2) Compressibility (intrinsic dimension) is reduced in SZH and FEP; (3) Syntactic complexity and compressibility both predict surprisal difference. These results, further refining an alteration in the geometry of the semantic space in psychosis as previously attested, suggest a broader deficit in information compression in this disorder, with a mechanistic underpinning in the operations of grammar.

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.