Abstract
Language understanding requires the integration of the input with preceding context. Event-related potentials (ERPs) have contributed significantly to our understanding of what contextual information is accessed and when. Much of this research has, however, been limited to experimenter-designed stimuli with highly atypical lexical and context statistics. This raises questions about the extent to which previous findings generalize to everyday language processing of natural stimuli with typical linguistic statistics. We ask whether context can affect ERPs over natural stimuli early, before the N400 time window. We re-analyzed a data set of ERPs over ~700 visually presented content words in sentences from English novels. To increase power, we employed linear mixed effects regression simultaneously modeling random variance by subject and by item. To reduce concerns about Type I error inflation common to any type of time series analysis, we introduced a simple approach to model and discount auto-correlations at multiple, empirically determined, time lags. We compared this approach to Bonferroni correction. Planned follow-up analyses used Generalized Additive Mixture Models to assess the linearity of contextual effects, including lexical surprisal, found within the N400 time window. We found that contextual information affects ERPs in both early (~200ms after word onset) and late (N400) time windows, supporting a cascading, interactive account of lexical access.