Bayesian semantic surprise based on different types of regularities predicts the N400 and P600 brain signals

Abstract

The brains remarkable ability to extract patterns from sequences of events has been demonstrated across cognitive domains and is a central assumption of predictive processing theories. While predictions shape language processing at the level of meaning, little is known about the underlying learning mechanism. Here, we investigated how continuous statistical inference in a semantic sequence influences the neural response. 60 participants were presented with a semantic oddball-like roving paradigm, consisting of sequences of nouns from different semantic categories. Unknown to the participants, the overall sequence contained an additional manipulation of transition probability between categories. Two Bayesian sequential learner models that captured different aspects of probabilistic learning were used to derive theoretical surprise levels for each trial and investigate online probabilistic semantic learning. The N400 ERP component was primarily modulated by increased probability with repeated exposure to the categories throughout the experiment, which essentially represents repetition suppression. This N400 repetition suppression likely prevented sizeable influences of more complex predictions such as those based on transition probability, as any incoming information was already continuously active in semantic memory. In contrast, the P600 was associated with semantic surprise in a transition probability model over recent observations, possibly indicating a working memory update in response to violations of these conditional dependencies. The results support probabilistic predictive processing of semantic information and demonstrate that continuous update of distinct statistics differentially influences language related ERPs.