Subword Representations Successfully Decode Brain Responses to Morphologically Complex Written Words.

ABSTRACT: This study extends the idea of decoding word-evoked brain activations using a corpus-semantic vector space to multimorphemic words in the agglutinative Finnish language. The corpus-semantic models are trained on word segments, and decoding is carried out with word vectors that are composed of these segments. We tested several alternative vector-space models using different segmentations: no segmentation (whole word), linguistic morphemes, statistical morphemes, random segmentation, and character-level 1-, 2- and 3-grams, and paired them with recorded MEG responses to multimorphemic words in a visual word recognition task. For all variants, the decoding accuracy exceeded the standard word-label permutation-based significance thresholds at 350-500 ms after stimulus onset. However, the critic

SUBMITTER: Hakala T 

PROVIDER: S-EPMC11410357 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature