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Ahead of time: Early sentence slow cortical modulations associated to semantic prediction
Ahead of time: Early sentence slow cortical modulations associated to semantic prediction
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Ahead of time: Early sentence slow cortical modulations associated to semantic prediction
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Ahead of time: Early sentence slow cortical modulations associated to semantic prediction
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Ahead of time: Early sentence slow cortical modulations associated to semantic prediction
Ahead of time: Early sentence slow cortical modulations associated to semantic prediction
Journal Article

Ahead of time: Early sentence slow cortical modulations associated to semantic prediction

2019
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Overview
According to prediction-based accounts of language comprehension, incoming contextual information is constantly used to guide the pre-activation of the most probable continuations to the unfolding sentences. However, there is still scarce evidence of the build-up of these predictions during sentence comprehension. Using event-related brain potentials, we investigated sustained processes associated to semantic prediction during online sentence comprehension. To address this, participants read sentences with varying levels of contextual constraint one word at a time. A 1000 ms interval preceded the final word, which could be congruent or incongruent. A slow sustained negativity developed gradually over the course of sentences, showing differences across conditions, with increasingly larger amplitudes for high than low levels of constraint. The effect was maximal in the interval preceding the closing word. This interval elicited a left-dominant slow negative potential with a graded amplitude modulation to contextual constraint, replicating previous results in speech comprehension. We argue that these slow potentials index the engagement of cognitive operations associated to semantic prediction. In addition, we replicated the finding of an earlier onset of the N400 effect (incongruent minus congruent) for high relative to low contextual constraint, suggesting facilitated processing for contextually-supported and highly expected words. Altogether, these results are consistent with prediction-based models of language comprehension and they also strengthen the value of investigating slow components as potential indices of mechanisms linked to language prediction.