  Neural Language Models (NLMs) have made tremendous advances during the last
years, achieving impressive performance on various linguistic tasks.
Capitalizing on this, studies in neuroscience have started to use NLMs to study
neural activity in the human brain during language processing. However, many
questions remain unanswered regarding which factors determine the ability of a
neural language model to capture brain activity (aka its 'brain score'). Here,
we make first steps in this direction and examine the impact of test loss,
training corpus and model architecture (comparing GloVe, LSTM, GPT-2 and BERT),
on the prediction of functional Magnetic Resonance Imaging timecourses of
participants listening to an audiobook. We find that (1) untrained versions of
each model already explain significant amount of signal in the brain by
capturing similarity in brain responses across identical words, with the
untrained LSTM outperforming the transformerbased models, being less impacted
by the effect of context; (2) that training NLP models improves brain scores in
the same brain regions irrespective of the model's architecture; (3) that
Perplexity (test loss) is not a good predictor of brain score; (4) that
training data have a strong influence on the outcome and, notably, that
off-the-shelf models may lack statistical power to detect brain activations.
Overall, we outline the impact of modeltraining choices, and suggest good
practices for future studies aiming at explaining the human language system
using neural language models.
