@inproceedings{yan-white-2019-framework,
    title = "A Framework for Decoding Event-Related Potentials from Text",
    author = "Yan, Shaorong  and
      White, Aaron Steven",
    editor = "Chersoni, Emmanuele  and
      Jacobs, Cassandra  and
      Lenci, Alessandro  and
      Linzen, Tal  and
      Pr{\'e}vot, Laurent  and
      Santus, Enrico",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W19-2910/",
    doi = "10.18653/v1/W19-2910",
    pages = "86--92",
    abstract = "We propose a novel framework for modeling event-related potentials (ERPs) collected during reading that couples pre-trained convolutional decoders with a language model. Using this framework, we compare the abilities of a variety of existing and novel sentence processing models to reconstruct ERPs. We find that modern contextual word embeddings underperform surprisal-based models but that, combined, the two outperform either on its own."
}