  The representations generated by many models of language (word embeddings,
recurrent neural networks and transformers) correlate to brain activity
recorded while people read. However, these decoding results are usually based
on the brain's reaction to syntactically and semantically sound language
stimuli. In this study, we asked: how does an LSTM (long short term memory)
language model, trained (by and large) on semantically and syntactically intact
language, represent a language sample with degraded semantic or syntactic
information? Does the LSTM representation still resemble the brain's reaction?
We found that, even for some kinds of nonsensical language, there is a
statistically significant relationship between the brain's activity and the
representations of an LSTM. This indicates that, at least in some instances,
LSTMs and the human brain handle nonsensical data similarly.
