Probing Brain Context-Sensitivity with Masked-Attention Generation
This work addresses fundamental questions in neurolinguistics about brain context-sensitivity, providing incremental insights by quantifying integration windows per voxel.
The study tackled the problem of identifying brain regions sensitive to contextual information and their integration window size by using GPT-2 transformers to generate word embeddings and predict fMRI activity during naturalistic text listening. The results showed that most of the language network cortex is context-sensitive, with the right hemisphere more responsive to longer contexts than the left.
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.