Neurobiology of Language and Bilingualism

This study investigates auditory suppression during speech production in humans and finds a correlation between suppression and sensitivity to auditory feedback, suggesting suppression aids speech monitoring.

This paper decodes task and stimulus information from EEG/MEG source space data, showing task effects are distributed while stimulus effects are focal.

The study shows that word representations and contextual properties dissociate along a cortical gradient during language processing, with sensory features at one end and contextual features at the other.

This paper develops phase-encoded fMRI techniques to track neural information flows during language processing with sub-second precision, revealing brain activity patterns as traveling waves.

The paper uses representational similarity learning to decode a graded multidimensional semantic space in the human anterior temporal cortex from ECoG data.

This study uses ERP to investigate how events and their components are represented in working memory, finding distinct neural patterns for event relations and roles.

The paper uses fMRI to study brain regions involved in sentence comprehension difficulty, finding distinct roles for temporal and frontal cortices in handling different aspects of comprehension.

This study examines changes in white matter connectivity during adult second language learning, finding increased intra-hemispheric connectivity and decreased interhemispheric connectivity, correlated with language performance.

The paper uses a GPT-based model to predict and control brain responses to language in humans using fMRI data.

This study uses fMRI to compare brain activity during L1 and L2 speech production, finding increased domain-general MD network activity and some language-specific left IFG activation for L2.

This study uses Bayesian modeling to analyze stroke patient data and identifies distinct brain regions associated with language and executive control impairments.

This study examines the role of the pars opercularis in auditory feedback processing and speech correction in individuals with aphasia following left-hemisphere stroke.

This study maps speech error timing and location during cortical stimulation, finding distinct latency patterns that suggest functional roles in speech planning and motor execution.

The paper models N400 amplitudes using a Sentence Gestalt model and compares it to surprisal from a language model, finding both predict aspects of N400 amplitudes.

This study uses personalized narratives in fMRI to show that children's interests enhance language processing in brain regions associated with reward and self-reference.

The paper investigates how repetition and sentence context affect implicit language learning through EEG, finding that sentence-based predictions improve with repetition regardless of constraint.

The paper shows that language models trained on developmentally realistic data can predict human brain responses to language, achieving near-maximal performance with 100 million words of training.

The paper uses cross-decoding of MEG data to show semantic activation for pictures occurs around 150 ms and for words around 230 ms.

This study investigates whether neural signatures of semantic composition in a controlled two-word paradigm extend to naturalistic story listening using MEG, finding consistent patterns with differences in latency that disappear when accounting for contextual factors.

This study uses large language models to predict human brain activity during movie watching, showing better accuracy for complex stories and identifying brain regions for multi-modal semantic representations.

The paper uses fMRI to show that language processing involves a distributed network sensitive to both structure and meaning, challenging theories of distinct brain areas for each.

The study reanalyzes language comprehension data to show that predictive processing in language depends on prior context and individual neural factors like alpha frequency.

This study identifies brain networks involved in implicit statistical learning of grammar using meta-analyses and functional connectivity, finding three subnetworks related to grammatical processing and cognitive control.

This study shows that the language network tracks semantically rich non-verbal stimuli more than semantically poor ones but less than linguistic stimuli.

The paper investigates how the ventral anterior temporal lobe and angular gyrus represent object and event concepts using fMRI and RSA, finding AG specializes in event semantics while vATL handles both.

The paper uses magnetoencephalography and machine learning to track semantic processing dynamics, showing gradual accumulation of semantic information leading to a variable plateau across individuals.

This paper investigates how speech intelligibility affects neural responses using MEG, finding that top-down mechanisms improve word processing in prefrontal cortex while auditory responses remain acoustically driven.

This study uses fMRI to investigate the neural mechanisms of bilingual language control by examining the L2 after-effect in Polish-English bilinguals.

The paper uses dynamic causal modelling to study effective connectivity during auditory repetition, finding bidirectional and inhibitory interactions between Wernickes and Brocas areas with individual variability.

The paper investigates how sentence context constraints and predictive validity affect neural responses during reading, finding that N400 amplitude decreases with higher context constraints, while alpha power shows a non-monotonic pattern.