A 10-hour within-participant magnetoencephalography narrative dataset to test models of language comprehension.

ABSTRACT: 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 minimiz

SUBMITTER: Armeni K 

PROVIDER: S-EPMC9177538 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature