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

Tero Hakala1,2, Tiina Lindh-Knuutila1, Annika Hultén1

  • 1Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.

Neurobiology of Language (Cambridge, Mass.)
|September 20, 2024
PubMed
Summary
This summary is machine-generated.

Brain decoding of Finnish words shows that both whole words and their meaningful parts (morphemes) are represented in the brain. Morphologically informed word segmentation is key for accurate neural decoding.

Keywords:
MEGdecodingmultimorphemic wordsstatistical morphemesword2vec

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Area of Science:

  • Cognitive Neuroscience
  • Computational Linguistics
  • Neuroimaging

Background:

  • Decoding word-evoked brain activity using corpus-semantic vector spaces has been established for simpler languages.
  • Agglutinative languages like Finnish present unique challenges due to complex morphology and a high proportion of rare word forms.

Purpose of the Study:

  • To investigate the neural representation of multimorphemic words in Finnish.
  • To evaluate the effectiveness of different word segmentation strategies for neural decoding using corpus-semantic models.

Main Methods:

  • Trained corpus-semantic models on various word segmentations (whole word, linguistic morphemes, statistical morphemes, character n-grams) of Finnish words.
  • Performed neural decoding of magnetoencephalography (MEG) data recorded during a visual word recognition task.
  • Utilized permutation testing to assess decoding accuracy and the significance of different segmentation methods.

Main Results:

  • All tested segmentation methods achieved decoding accuracy above chance levels between 350-500 ms post-stimulus onset.
  • Only morphologically aware segmentation strategies (linguistic and statistical morphemes) reached statistical significance in brain decoding.
  • Results indicate that both whole word forms and morphemes are neurally represented.

Conclusions:

  • Neural decoding using compositional subword segments is effective for complex, morphologically rich languages like Finnish.
  • Morphological awareness in word segmentation is crucial for successful neural decoding of brain activity related to word recognition.
  • This approach offers a viable method for studying word processing in languages with extensive morphology.