Comprehenders Rationally Adapt Semantic Predictions to the Statistics of the Local Environment: a Bayesian Model of Trial-by-Trial N400 Amplitudes

When semantic information is activated by a context prior to new bottom-up input (i.e. when a word is predicted), semantic processing of that incoming word is typically facilitated, attenuating the amplitude of the N400 event related potential (ERP) – a direct neural measure of semantic processing. This N400 modulation is observed even when the context is just a single semantically related “prime” word. This so-called “N400 semantic priming effect” is sensitive to the probability of seeing a related prime-target pair within experimental blocks, suggesting that participants may be adapting the strength of their predictions to the predictive validity of their broader experimental environment. We formalize this adaptation using an optimal Bayesian learner, and link this model to N400 amplitudes using an information-theoretic measure, surprisal. We found that this model could account for the N400 amplitudes evoked by words (whether related or unrelated) as adaptation unfolds across individual trials. These findings suggest that comprehenders may rationally adapt their semantic predictions to the statistical structure of their broader environment, with implications for the functional significance of the N400 component and the predictive nature of language processing.