Through linguistic abilities, our brain can comprehend and produce an infinite number of new sentences constructed from a finite set of words. Although recent research has uncovered the neural representation of semantics during comprehension of isolated words or adjective-noun phrases, the neural representation of the words during utterance planning is less understood. We apply existing machine learning methods to Magnetoencephalography (MEG) data recorded during a picture naming
experiment, and predict the semantic properties of uttered words before they are
said. We explore the representation of concepts over time, under controlled tasks,
with varying compositional requirements. Our results imply that there is enough
information in brain activity recorded by MEG to decode the semantic properties of
the words during utterance planning. Also, we observe a gradual improvement in
the semantic decoding of the first uttered word, as the participant is about to say it.
Finally, we show that, compared to non-compositional tasks, planning to compose an
adjective-noun phrase is associated with an enhanced and sustained representation
of the noun. Our results on the neural mechanisms of basic compositional structures
are a small step towards the theory of language in the brain. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/10756 |
| Date | 25 April 2019 |
| Creators | Honari Jahromi, Maryam |
| Contributors | Fyshe, Alona |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
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