Tiina Lindh‐Knuutila

The paper investigates the relationship between N400 responses and semantic decoding performance in MEG data, finding that unrelated primes yield better decoding and suggest semantic information reshaping by context.

This study examines how the brain processes misspelled words, identifying three brain regions and showing late, sustained neural responses to varying degrees of misspelling.

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.

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.