Abstract
AbstractSpeech 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 ∼100ms after word onset over mid-frontal MEG sensors. Phonetic information was encoded in parallel, though peaking later (∼200ms). Our results support that during continuous speech processing, lexical representations are quickly built in a context-sensitive manner. We showcase multivariate analyses for teasing apart subtle representational distinctions from neural time series.
Publisher
Cold Spring Harbor Laboratory
Cited by
2 articles.
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