Lexical prediction does not rationally adapt to prediction error: ERP evidence from pre-nominal articles

People sometimes predict upcoming words during language comprehension, but debate remains on when and to what extent such predictions indeed occur. The rational adaptation hypothesis holds that predictions develop with expected utility: people predict more strongly when predictions are frequently confirmed (low prediction error) rather than disconfirmed. However, supporting evidence is mixed thus far and has only involved measuring responses to supposedly predicted nouns, not to preceding articles that may also be predicted. The current, large-sample (N = 200) ERP study on written discourse comprehension in Dutch therefore employs the well-known ‘pre-nominal prediction effect’: enhanced N400-like ERPs for articles that are unexpected given a likely upcoming noun’s gender (i.e., the neuter gender article ‘het’ when people expect the common gender noun phrase ‘de krant’, the newspaper) compared to expected articles. We investigated whether the pre-nominal prediction effect is larger when most of the presented stories contain predictable article-noun combinations (75% predictable, 25% unpredictable) compared to when most stories contain unpredictable combinations (25% predictable, 75% unpredictable). Our results show the pre-nominal prediction effect in both contexts, with little evidence to suggest that this effect depended on the percentage of predictable combinations. Moreover, the little evidence suggesting such a dependence was primarily observed for unexpected, neuter-gender articles (‘het’), which is inconsistent with the rational adaptation hypothesis. In line with recent demonstrations (Nieuwland, 2021a,b), our results suggest that linguistic prediction is less ‘rational’ or Bayes optimal than is often suggested.