摘要：Recently, cognitive neuroscientists have increasingly studied the brain responses to narratives . At the same time, we are witnessing exciting developments in natural language processing where large-scale neural network models can be used to instantiate cognitive hypotheses in narrative processing . Yet, they learn from text alone and we lack ways of incorporating biological constraints during training . To mitigate this gap, we provide a narrative comprehension magnetoencephalography (MEG) data resource that can be used to train neural network models directly on brain data . We recorded from 3 participants, 10 separate recording hour-long sessions each, while they listened to audiobooks in English . After story listening, participants answered short questions about their experience . To minimize head movement, the participants wore MEG-compatible head casts, which immobilized their head position during recording . We report a basic evoked-response analysis showing that the responses accurately localize to primary auditory areas . The responses are robust and conserved across 10 sessions for every participant . We also provide usage notes and briefy outline possible future uses of the resource .
