Predicting Known Sentences: Neural Basis of Proverb Reading Using Non-parametric Statistical Testing and Mixed-Effects Models

Abstract

Predictions of future events play an important role in daily activities, such as visual search, listening, or reading. They allow us to plan future actions and to anticipate their outcomes. Reading, a natural, commonly studied behavior, could shed light over the brain processes that underlie those prediction mechanisms. We hypothesized that different mechanisms must lead predictions along common sentences and proverbs. The former ones are more based on semantic and syntactic cues, and the last ones are almost purely based on long-term memory. Here we show that the modulation of the N400 by Cloze-Task Predictability is strongly present in common sentences, but not in proverbs. Moreover, we present a novel combination of linear mixed models to account for multiple variables, and a cluster-based permutation procedure to control for multiple comparisons. Our results suggest that different prediction mechanisms are present during reading.