Andrea E. Martin

The paper investigates how syntax and prosody interact in spoken language comprehension using MEG data, finding context-dependent neural interactions between the two.

This study shows that both linguistic structure and statistical properties shape neural dynamics during spoken language comprehension through cross-frequency coupling.

This study investigates whether neural tracking at the phrase rate in speech is driven by syntactic structure or sequential repetition, finding both factors influence tracking.

The paper shows that neural oscillation phases influence word perception by biasing recognition towards frequent stimuli based on region-specific sensitivity.

The paper tests models of syntactic structure processing during Dutch story listening using neural activity and finds that a predictive top-down parser best matches brain dynamics.

This study investigates how acoustic and linguistic features are encoded during speech comprehension, finding that phoneme-level features are tracked more strongly in a comprehended language and that word entropy influences this encoding.

The paper shows that delta-band neural activity is reinstated during pronoun resolution, supporting the idea that referential meaning is represented through dynamic neural patterns.

This study investigates the role of alpha and beta oscillations in naturalistic language comprehension, finding they support syntactic structure building and semantic composition through inhibition and reactivation processes.

The paper investigates how the brain encodes syntactic structure in speech using EEG, finding distinct neural responses in theta and alpha bands that correlate with linguistic structure.

The paper investigates how the brain tracks linguistic structure at different timescales during passive listening and task performance, finding automatic phrasal-rate tracking and task-specific effects in the inferior frontal gyrus.

This study shows that neuronal oscillations track pseudo-rhythmic speech by interacting with internal language predictions, generating temporal phase codes that explain speech illusions.

The paper investigates how neural oscillations reflect linguistic content beyond acoustic and rhythmic features during speech comprehension.

The paper proposes a theory and computational framework for learning structured relational representations from unstructured inputs, demonstrating how such representations can be acquired from experience.