Word classes such as nouns, verbs, and adjectives are fundamental units of language, but their neural encoding remains unclear. Here, we investigate whether word classes are processed as invariant, context-independent lexical categories or depend on sentence context during language processing. We analyse source-localized magnetoencephalography (MEG) data from 200 native Dutch participants who read or listened to sentences and their scrambled counterparts (word lists) from the MOUS dataset. Time-resolved encoding models are used to predict neural responses from major word classes (noun, verb, adjective) and other linguistic variables including word frequency, surprisal, entropy, word length, and ordinal position. Across modalities, we observe a significant interaction between word class and context (sentences vs. word lists), manifest as dynamic modulation of neural responses in a widespread cortical network including bilateral perisylvian, frontal, and midline regions previously implicated in lexicosemantic, structural, and pragmatic processing. This interaction emerges early and reappears later in processing, with distinct temporal profiles for reading and listening. Within-condition effects reveal that word class contributes to neural responses at the level of individual words, but this contribution is context-dependent: it is robust in sentences across modalities and in word-list reading, but absent in auditory word lists. These results indicate that word-class encoding is shaped by the interaction between word-level properties and sentence context during real-time language processing.
Although we increasingly understand how the brain processes phrase-level meaning, the role of semantic content remains underexplored. Recent models grounded in componential experiential features offer a principled framework for linking conceptual representation to the neurobiology of semantic composition. Here we used MEG to test whether experiential semantic features are retrieved and composed during two-word phrase comprehension, and whether lexical and phrase-level representations show temporally overlapping or strictly serial activation. Participants (16 women, 12 men) read two-word phrases and their component words in a rapid parallel visual presentation (RPVP) paradigm, equating the temporal dimension of the two types of stimuli. The phrases spanned six linguistic relations. All words and phrases were independently rated on 65 experiential features across 14 experiential domains and representational similarity analysis (RSA) was used to relate neural and semantic structure. Rather than emerging after or in parallel with single-word meanings, phrase-level meanings arose earlier, outpacing single words in isolation. Phrasal contexts also elicited earlier activation of word-level meaning: a word such as 'cake' showed semantic activation at ∼312 ms in isolation, but at ∼236 ms in 'green cake.' Experiential feature representations of both component words were detectable during phrase processing, with partially overlapping temporal windows. The magnitude of composition-related effects varied systematically across phrasal relation types and experiential domains. Together, these findings show that experiential semantic features are integrated during composition in ways that vary across phrasal relations and that combining words into phrases speeds semantic access, accelerating both phrase- and word-level meaning relative to isolated words.Significance Statement This study provides temporally resolved evidence that the brain simultaneously represents multiple levels of meaning during language comprehension. Using MEG and representational similarity analysis grounded in experiential semantic features, we show that compared to single words, two-word phrases elicit earlier and more sustained alignment between neural activity patterns and semantic feature representations. Further, the meanings of both constituent words remain detectable in overlapping time windows during phrase processing. Exploratory analyses further suggest that residual phrase-level semantic alignment varies with phrasal relations and semantic content. This work advances understanding of how distributed experiential knowledge is recruited during real-time language processing and how relational structure and semantic content jointly shape meaning composition.
Shao-Nan Wang, Songhee Kim, J. Binder et al.· Journal of Neuroscience· 0 citations
Understanding speech requires listeners to integrate incoming input with prior linguistic and thematic knowledge to access meaning, a task greatly aided by prediction. Surprisal and related phenomena (e.g., next word prediction) tend to be associated with broad activation of language regions during listening. A major challenge for interpreting the neural correlates of surprisal is that lexical expectancies are constrained both by syntactic and semantic information. In contrast, semantic distance yields more of a pure metric of relatedness between one language constituent (e.g., word, phrase, n-gram) and another within a high-dimensional semantic space. We contrasted surprisal and semantic distance in running discourse during naturalistic audiovisual language comprehension. We analyzed fMRI scans from 20 English-speaking adults who viewed the full-length film 500 Days of Summer. Our focus was on brain sensitivity to either surprisal or semantic distance as language unfolded word-by-word. To this end, we implemented a series of hierarchical voxelwise encoding models. After accounting for low-level auditory and visual features, the addition of either surprisal or semantic distance improved prediction in bilateral superior temporal gyrus (STG), left inferior frontal gyrus, and right cerebellum. Critically, both semantic distance and surprisal explained unique variation in bilateral temporal cortex after accounting for the other. These results support the notion that the brain tracks both broad probabilistic prediction as well as semantic integration. More generally, these findings extend previous work on auditory-only speech by demonstrating that surprisal and semantic distance predict meaningful neural variation even within a rich audiovisual context.
Ryan M. O'Leary, Hailey C. Smith, Emily B. Myers et al.· bioRxiv· 0 citations
Language comprehension involves the integration of single words (lexical units) into phrases and sentences (multi-word structures). Previous frequency-tagging studies have found that low-frequency neural responses synchronize to the frequency of multi-word structures. However, it is currently unclear how exactly structural and lexical processes jointly impact these synchronization findings. The present magnetoencephalography experiment implemented the frequency-tagging paradigm in the visual modality with written words to investigate neural synchronization to multi-word sentences varying in internal structure (reversed word orders between verb-initial Spanish and verb-final Basque sentences) and in lexical content (real words and pseudo words). We find converging evidence that neural responses largely synchronize to structural rather than lexical features. This was observed as robust phase synchronization strength to the frequency of sentences containing reversed structures, with certain lexical modulations depending on language-specific structural features. Crucially, we also found shifted phase angle dynamics between the reversed structures of Spanish and Basque sentences independently of word-level lexical characteristics. Together, these findings suggest that neural synchronization to multi-word structures is largely driven by distinct structural features operating via two segregated neural dimensions: frequency coding for the coarser aspects (i.e., timescale/duration) and phase representing the finer-grained aspects (i.e., internal structure) of multi-word structures. Our findings thus advance key insights into the core components of the neural mechanisms supporting language comprehension. Highlights Neural synchronization to sentences is driven by structural (not lexical) features. Robust sentence-frequency synchronization across languages varying in structure. Phase angle is selectively sensitive to cross-linguistic structural differences. Lexical modulations depend on language-specific structure. Structure synchronization segregates into two dimensions: frequency and phase.
J. Martorell, S. Mancini, P. Paz-Alonso et al.· bioRxiv· 0 citations
Processing emotionally negative words in a second language (L2) feels less vivid and more cognitively demanding than in the first language (L1), yet the neural mechanisms underlying this difference remain poorly understood. Using single-trial modelling of event-related potentials (ERPs) and event-related spectral perturbations (ERSPs), we examined attention allocation to negative versus neutral words in highly proficient late Polish–English bilinguals performing a semantic categorisation task in a modified oddball paradigm. Valence and language modulated early posterior negativity (EPN) and P300, but not the N400. Mass-univariate analysis detected valence modulations in the N400 range, highlighting limitations of classic ERP approaches. Time–frequency analysis further revealed Language × Valence interactions: negative L2 words selectively elicited a cascade of delta, theta and alpha synchronisation from posterior to frontal regions across 59–641 ms, consistent with successive perceptual, lexical-semantic and regulatory processing stages. Emotional processing in L2 is thus qualitatively distinct, more effortful and regulated rather than simply being less efficient.
Iga Krzysik, Marcin Naranowicz, Olivia Molina-Nieto et al.· Bilingualism: Language and C...· 0 citations
Human language comprehension requires transforming linear word sequences into hierarchical structure. Using Mandarin as a case study, we investigated how the brain builds phrase-level structure from two sequentially presented words. Participants processed the same phrases in two tasks emphasizing either the semantic meaning of the phrase or the composition relation between the two words that form the phrase. We focused on three components of phrase building–the syntactic category of individual words, the syntactic category of the resulting constituent, and the composition relation between the two words–and examined them using representational similarity analysis, Bayesian hierarchical modeling, and variance decomposition with semantic controls. Word-category representations emerged during second-word processing and were observed mainly in left middle temporal and inferior frontal regions. Phrasal constituent category representations were found in left posterior temporal, left supramarginal, and right inferior frontal regions. Composition-relation representations were detected in left angular and left frontal regions and remained after controlling for constituent category, indicating that word-to-word relations and resulting constituent categories capture distinct aspects of composition. Variance decomposition showed that structural effects in several regions could not be fully reduced to embedding-derived semantic similarity. Task demands further modulated these representations differently: word category effects were largely task-independent, whereas constituent category effects showed frontal modulation and composition relation effects were strongest when the task explicitly required relation judgments. Together, these findings show that different aspects of phrase structure are represented in different brain regions and help explain how the brain combines words into phrases and builds more complex linguistic structures.
Fei-Zhen Cao, M. Xiang, Shu-Guang Yang et al.· Neurobiology of Language· 0 citations
Human language processing can be studied through both behavior and brain activity, yet it remains unclear whether these two data types reflect sensitivity to the same information. One influential view holds that both behavioral and neural responses are largely determined by processing effort, often estimated by word surprisal together with the context-independent properties of word frequency and length. At the same time, neural responses have been shown to encode richer aspects of linguistic content, including meaning. Here, we use neural network language models to operationalize these alternatives and systematically compare, within the same analytic computational framework, the predictive power of low-dimensional effort-based predictors and high-dimensional embedding representations that encode contextualized linguistic content, including meaning. Across 8 behavioral datasets and 5 neural datasets (4 fMRI and 1 ERP), we find that processing effort captures substantial variance in both behavioral and neural measures of language processing, in line with much previous work. However, for brain responses—but not for behavioral measures—embedding representations carry substantial predictive power beyond the estimates of processing effort. These results therefore suggest that neural data provide access to rich, high-dimensional dynamics of language comprehension, whereas behavioral data reflect a bottlenecking of these dynamics into a small set of theoretically motivated properties of contextualized linguistic input. Significance Statement Two research communities study language comprehension as it unfolds in real time: psycholinguists use behavioral measures, such as eye movements during reading, and neuroscientists measure brain activity. The two are rarely studied together, but evidence from both must be integrated into a unified theory of language processing. Here we analyze both brain and behavioral responses within a single framework based on language models, comparing two long-standing accounts of what drives responses to language: processing effort versus meaning and other features not reducible to effort. We find that behavior is dominated by effort, whereas brain responses also reflect meaning. Developing a unified theory requires both kinds of data, but with a clear understanding of which levels of representation each measure reflects.
Andrea Gregor de Varda, Yevgeni Berzak, Evelina Fedorenko et al.· bioRxiv· 0 citations
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