Annika Hultén

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 study examines how syntactic complexity affects sentence processing in the human brain across speech and reading, identifying supramodal and modality-specific neural responses in over 200 participants.

This study shows that family cultural background influences brain processing of a spoken narrative, with shared background increasing neural similarity across multiple levels of language processing.

The paper identifies a brain network for language processing that is independent of sensory modality by analyzing neuromagnetic signals from reading and listening tasks.

The paper investigates how the brain combines fragmented semantic features into complete object representations using machine-learning-based neural decoding.

The paper models semantic spaces using word co-occurrence statistics to decode brain activity related to abstract words, finding that abstractness emerges from language patterns and correlates with specific brain regions.

The paper identifies frequency-specific directed interactions in the human brain network for language using magnetoencephalography and Granger causality analysis.