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"Language transformers  like GPT-2  have demonstrated remarkable abilities to process text  and now constitute the backbone of deep translation  summarization and dialogue algorithms  However  whether these models encode information that relates to human comprehension remains controversial  Here  we show that the representations of GPT-2 not only map onto the brain responses to spoken stories  but also predict the extent to which subjects understand narratives  To this end  we analyze 101 subjects recorded with functional Magnetic Resonance Imaging while listening to 70 min of short stories  We then fit a linear model to predict brain activity from GPT-2 s activations  and correlate this mapping with subjects  comprehension scores as assessed for each story  The results show that GPT-2 s brain predictions significantly correlate with semantic comprehension  These effects are bilaterally distributed in the language network and peak with a correlation of R 0 50 in the angular gyrus  Overall  this study paves the way to model narrative comprehension in the brain through the lens of modern language algorithms "
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