  Electroencephalography (EEG) recordings of brain activity taken while
participants read or listen to language are widely used within the cognitive
neuroscience and psycholinguistics communities as a tool to study language
comprehension. Several time-locked stereotyped EEG responses to
word-presentations -- known collectively as event-related potentials (ERPs) --
are thought to be markers for semantic or syntactic processes that take place
during comprehension. However, the characterization of each individual ERP in
terms of what features of a stream of language trigger the response remains
controversial. Improving this characterization would make ERPs a more useful
tool for studying language comprehension. We take a step towards better
understanding the ERPs by fine-tuning a language model to predict them. This
new approach to analysis shows for the first time that all of the ERPs are
predictable from embeddings of a stream of language. Prior work has only found
two of the ERPs to be predictable. In addition to this analysis, we examine
which ERPs benefit from sharing parameters during joint training. We find that
two pairs of ERPs previously identified in the literature as being related to
each other benefit from joint training, while several other pairs of ERPs that
benefit from joint training are suggestive of potential relationships.
Extensions of this analysis that further examine what kinds of information in
the model embeddings relate to each ERP have the potential to elucidate the
processes involved in human language comprehension.
