Neurobiology of Language and Bilingualism

This study uses MEG to examine cortico-cortical and cortico-subcortical connectivity during speech production and listening, finding frequency-specific interactions.

This study investigates the role of the left posterior angular gyrus in autobiographical recall versus semantic memory using meta-analysis and fMRI, finding it specifically engaged in autobiographical tasks.

This study uses fMRI to examine how polyglots and hyperpolyglots process their languages, finding that response magnitude scales with linguistic computation engagement.

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 study identifies white matter changes and neurocognitive transitions in middle-aged adults associated with lexical production decline, highlighting age-related shifts in compensatory strategies.

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.

This study dissociates neural tracking of prosodic pauses from multi-word chunk processing using MEG, showing distinct low-frequency brain activity patterns for each.

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 investigates how the prosodic hierarchy is represented in the brain using EEG, finding that neural entrainment and phase-amplitude coupling reflect the hierarchical structure of speech.

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.

The paper identifies modular organization in human superior temporal gyrus for phoneme processing using high-density micro-arrays.

This paper introduces BRyBI, a brain-rhythm-based model that integrates rhythmic processes to simulate speech comprehension, showing it can handle speech variations and aligns with human experiments.

This study investigates whether the multiple demand network is involved in language processing in chronic post-stroke aphasia using individualized functional localization.

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.