A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Subword Representations Successfully Decode Brain Responses to Morphologically Complex Written Words
Tekijät: Hakala, Tero; Lindh-Knuutila, Tiina; Hulten, Annika; Lehtonen, Minna; Salmelin, Riitta
Kustantaja: MIT PRESS
Kustannuspaikka: CAMBRIDGE
Julkaisuvuosi: 2024
Lehti: Neurobiology of language
Tietokannassa oleva lehden nimi: NEUROBIOLOGY OF LANGUAGE
Lehden akronyymi: NEUROBIOL LANG
Vuosikerta: 5
Numero: 4
Aloitussivu: 844
Lopetussivu: 863
Sivujen määrä: 20
eISSN: 2641-4368
DOI: https://doi.org/10.1162/nol_a_00149
Julkaisun avoimuus kirjaamishetkellä: Avoimesti saatavilla
Julkaisukanavan avoimuus : Kokonaan avoin julkaisukanava
Verkko-osoite: https://doi.org/10.1162/nol_a_00149
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/458252538
Rinnakkaistallenteen lisenssi: CC BY
Rinnakkaistallennetun julkaisun versio: Kustantajan versio
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 critical segment-label permutation test revealed that only those segmentations that were morphologically aware reached significance in the brain decoding task. The results suggest that both whole-word forms and morphemes are represented in the brain and show that neural decoding using corpus-semantic word representations derived from compositional subword segments is applicable also for multimorphemic word forms. This is especially relevant for languages with complex morphology, because a large proportion of word forms are rare and it can be difficult to find statistically reliable surface representations for them in any large corpus.
Avainsanat:
decoding, MEG, multimorphemic words, statistical morphemes, Word2vec
Ladattava julkaisu This is an electronic reprint of the original article. |  |
Julkaisussa olevat rahoitustiedot:
Riitta Salmelin, Academy of Finland (https://dx.doi.org/10.13039/501100002341), Award ID: LASTU, 256887. Riitta Salmelin, Academy of Finland (https://dx.doi.org/10.13039 /501100002341), Award ID: 255349. Riitta Salmelin, Academy of Finland (https://dx.doi.org /10.13039/501100002341), Award ID: 315553. Minna Lehtonen, Academy of Finland (https:// dx.doi.org/10.13039/501100002341), Award ID: 288880. Annika Hultén, Academy of Finland (https://dx.doi.org/10.13039/501100002341), Award ID: 287474. Tiina Lindh-Knuutila, Aalto Brain Center. Riitta Salmelin, Sigrid Juséliuksen Säätiö (https://dx.doi.org/10.13039 /501100006306). Riitta Salmelin, Academy of Finland, Award ID: 355407.
