  The advancements in artificial intelligence over the last decade have opened
a multitude of avenues for interdisciplinary research. Since the idea of
artificial intelligence was inspired by the working of neurons in the brain, it
seems pretty practical to combine the two fields and take the help of cognitive
data to train AI models. Not only it will help to get a deeper understanding of
the technology, but of the brain as well. In this thesis, I conduct novel
experiments to integrate cognitive features from the Zurich Cognitive Corpus
(ZuCo) (Hollenstein et al., 2018) with a transformer-based encoder model called
BERT. I show how EEG and eye-tracking features from ZuCo can help to increase
the performance of the NLP model. I confirm the performance increase with the
help of a robustness-checking pipeline and derive a word-EEG lexicon to use in
benchmarking on an external dataset that does not have any cognitive features
associated with it. Further, I analyze the internal working mechanism of BERT
and explore a potential method for model explainability by correlating it with
a popular model-agnostic explainability framework called LIME (Ribeiro et al.,
2016). Finally, I discuss the possible directions to take this research
forward.
