Milena Rabovsky

This study shows semantic update predicts reading time better than lexical surprisal, especially for content words, highlighting its role in reading behavior.

This study tests if transcutaneous auricular vagus nerve stimulation affects ERP components P3 and P600 or NE markers, but finds no significant effects.

The paper investigates how individuals differ in predicting sentence meaning versus upcoming words using neural network models to explain N400 amplitudes.

This paper reanalyzes N400 data using semantic cloze instead of lexical cloze, finding semantic cloze better predicts N400 amplitude than lexical cloze or LLM-derived probabilities.

The paper investigates whether the P600 ERP component during sentence reading predicts behavioral and neural markers of recognition memory.

The paper uses Bayesian models to predict N400 and P600 brain signals by analyzing semantic regularities in sequences of nouns.

This study links the P600 and P3 ERP components to task-evoked pupillary responses, suggesting they share a common neural generator related to norepinephrine release.

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.

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.

This study investigates how affective knowledge and facial appearance influence visual awareness of faces using EEG, finding enhanced awareness for faces linked to negative social information but not facial trustworthiness.

The paper tests a probabilistic hypothesis of the N400 brain potential by showing semantic surprise predicts its amplitude in a noun oddball paradigm.

This study investigates how sustained attention affects N400 and P600 ERP components during sentence processing, finding that P600 amplitudes correlate with attention levels while N400 does not.

The paper investigates the N400 ERP component as a learning signal during language comprehension by linking it to implicit memory adaptation.

This study uses a Sentence Gestalt model trained on a large corpus to predict N400 ERP amplitudes during naturalistic sentence processing, supporting the semantic prediction error hypothesis.

This study investigates whether visual consciousness during the attentional blink is graded or all-or-none, finding graded patterns in behavioral and ERP measures.

The paper investigates how semantic richness and density affect language production through behavioral and electrophysiological measures, finding facilitatory and inhibitory effects respectively.

The paper uses a neural network model to simulate N400 effects on articles, attributing them to implicit semantic prediction errors rather than pre-activation of word forms.

The paper investigates whether word reading is automatic by examining N400 amplitudes in a PRP paradigm, finding attentional modulation of orthographic neighborhood effects.

The paper models the N400 brain response as a change in an implicit probabilistic representation of meaning using a neural network, linking it to implicit learning and language processing.