Probing Brain Context-Sensitivity with Masked-Attention Generation
Alexandre Pasquiou Yair Lakretz Bertrand Thirion Christophe Pallier
Two fundamental questions in neurolinguistics concerns the brain regions that
integrate information beyond the lexical level, and the size of their window of
integration. To address these questions we introduce a new approach named
masked-attention generation. It uses GPT-2 transformers to generate word
embeddings that capture a fixed amount of contextual information. We then
tested whether these embeddings could predict fMRI brain activity in humans
listening to naturalistic text. The results showed that most of the cortex
within the language network is sensitive to contextual information, and that
the right hemisphere is more sensitive to longer contexts than the left.
Masked-attention generation supports previous analyses of context-sensitivity
in the brain, and complements them by quantifying the window size of context
integration per voxel.