Evelina G. Fedorenko

The paper shows that the cerebellum can develop language sensitivity without contralateral neocortical inputs, using cases with atypical brain development.

The study shows that language brain areas remain selective for language over music in children and musicians, regardless of music experience.

The paper compares behavioral and neural responses to language, finding that neural data capture more detailed linguistic information than behavioral measures.

The paper applies individualized functional connectomics to over 1,000 human brains performing diverse tasks, revealing a stable left-hemisphere language network.

The paper investigates the relationship between logical reasoning and language by examining neural activity and patient performance, finding that logical reasoning does not rely on the language brain network.

The paper identifies 17 language-selective brain areas outside the core fronto-temporal network using fMRI data from 772 participants, showing they respond to both auditory and written language and are distinct from general task demands.

The paper maps human cortical connectivity using es-fMRI and finds that functional similarity, not distance, primarily determines anatomical connections.

The paper uses fMRI to show that language network lateralization is adult-like by age 4, despite continued development of other language network features.

The paper investigates the stability of language network responses across different tasks and compares it to the multiple demand network, finding robust language processing in the language network and task-dependent responses in the MD network.

This study uses fMRI to show that toddlers as young as 19-36 months have left frontal and temporal cortex responses to comprehensible language, similar to adults, suggesting early language system development.

This study identifies four cerebellar regions involved in language processing using fMRI, with one region showing selectivity for language and functional connections to the neocortical language system.

This paper introduces a faster fMRI language localizer that identifies language-selective areas in 3.5 minutes, outperforming the standard 450ms per item version in 24 participants.

The paper identifies distinct brain areas that process prosody in speech across multiple experiments and participants.

The paper identifies brain regions outside the language network involved in semantic processing of both linguistic and pictorial stimuli using fMRI experiments.

The paper identifies a human brain network specialized for abstract formal reasoning using fMRI and distinguishes it from other cognitive networks.

The paper identifies two interpretable dimensions of linguistic processing using fMRI data, showing they generalize across individuals and relate to processing difficulty and meaning abstractness.

This study uses fMRI to show that physical reasoning involves a distinct brain network overlapping with the MD system but not the language system, suggesting it is not mediated by linguistic or exclusively domain-general abstract representations.

The paper shows that constructed languages activate the same brain regions as natural languages, suggesting shared cognitive mechanisms regardless of their creation or usage characteristics.

This study evaluates how multilingual computational models capture shared meaning components in brain responses across 21 languages using fMRI data.

This study shows that high-performing ANNs and brains converge on similar representations, and identifies stimuli that modulate model-to-brain alignment.

The paper examines age-related changes in the language and executive function networks using fMRI, finding preserved language network function in older adults compared to decline in the executive network.

The paper identifies three distinct temporal receptive window sizes in neural populations of the language network using intracranial recordings.

The paper tests whether syntactic parsing is necessary for language comprehension by analyzing brain responses to syntactically ill-formed but semantically plausible and grammatically correct but semantically implausible sentences.

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

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

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

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 transformer models straighten neural sentence trajectories to improve next-word prediction, showing curvature decreases with model performance and linguistic context.

The paper tests the hypothesis that language aids in feature-based categorization but finds no evidence supporting this role in individuals with aphasia or healthy adults using fMRI.