Abstract
Speech is often structurally and semantically ambiguous. Here we study how the human brain uses sentence context to resolve lexical ambiguity. Twenty-one participants listened to spoken narratives while magneto-encephalography (MEG) was recorded. Stories were annotated for grammatical word class (noun, verb, adjective) under two hypothesised sources of information: “bottom-up”: the most common word class given the word’s phonology; “top-down”: the correct word class given the context. We trained a classifier on trials where the hypotheses matched (about 90%) and tested the classifier on trials where they mismatched. The classifier predicted top-down word class labels, and anti-correlated with bottom-up labels. Effects peaked ∼100 ms after word onset over mid-frontal MEG sensors. Phonetic information was encoded in parallel, though peaking later (∼200 ms). Our results support that lexical representations are built in a context-sensitive manner, which precedes sensory phonetic processing. We showcase multivariate analyses for teasing apart subtle representational distinctions from neural time series.
Original language | English (US) |
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Pages (from-to) | 1045-1058 |
Number of pages | 14 |
Journal | Language, Cognition and Neuroscience |
Volume | 39 |
Issue number | 8 |
DOIs | |
State | Published - 2024 |
Keywords
- MEG
- decoding
- neural processing
- part of speech
- word class
ASJC Scopus subject areas
- Language and Linguistics
- Experimental and Cognitive Psychology
- Linguistics and Language
- Cognitive Neuroscience