The N400 ERP component reflects an error-based implicit learning signal during language comprehension.

The functional significance of the N400 evoked-response component is still actively debated. An increasing amount of theoretical and computational modelling work is built on the interpretation of the N400 as a prediction error. In neural network modelling work, it was proposed that the N400 component can be interpreted as the change in a probabilistic representation of meaning that drives the continuous adaptation of an internal model of the statistics of the environment. These results imply that increased N400 amplitudes should correspond to greater adaptation, which can be measured via implicit memory. To investigate this model derived hypothesis, the current study manipulated expectancy in a sentence reading task to influence N400 amplitudes and subsequently presented the previously expected vs. unexpected words in a perceptual identification task to measure implicit memory. As predicted, reaction times in the perceptual identification task were significantly faster for previously unexpected words that induced larger N400 amplitudes in the previous sentence reading task. Additionally, it could be demonstrated that this adaptation seems to specifically depend on the process underlying N400 amplitudes, as participants with larger N400 differences during sentence reading also exhibited a larger implicit memory benefit in the perceptual identification task. These findings support the interpretation of the N400 as an implicit learning signal driving adaptation in language processing.