The brain’s remarkable capacity to process spoken language virtually
in real time requires fast and efficient information processing machinery.
In this study, we investigated how frequency-specific brain dynamics relate
to models of probabilistic language prediction during auditory narrative
comprehension. We recorded MEG activity while participants were listening to auditory stories in Dutch. Using trigram statistical language
models, we estimated for every word in a story its conditional probability of occurrence. On the basis of word probabilities, we computed how
unexpected the current word is given its context (word perplexity) and
how (un)predictable the current linguistic context is (word entropy). We
then evaluated whether source-reconstructed MEG oscillations at different
frequency bands are modulated as a function of these language processing
metrics. We show that theta-band source dynamics are higher in high relative to low entropy states, likely reflecting lexical computations. Beta-band dynamics are increased in situations of low word entropy and perplexity
possibly reflecting maintenance of ongoing cognitive context. These findings lend support to the idea that the brain engages in the active generation
and evaluation of predicted language based on the statistical properties of
the input signal.
Original languageEnglish
Pages (from-to)283-295
JournalNeuroImage
Volume198
DOI
Publication statusPublished - Sep 2019

    Research areas

  • MEG, Language model, Surprisal, Entropy, Neural oscillations

ID: 11696365