2020
EMNLP
EMNLP 2020
How well does surprisal explain N400 amplitude under different experimental conditions?
Abstract
AbstractWe investigate the extent to which word surprisal can be used to predict a neural measure of human language processing difficulty—the N400. To do this, we use recurrent neural networks to calculate the surprisal of stimuli from previously published neurolinguistic studies of the N400. We find that surprisal can predict N400 amplitude in a wide range of cases, and the cases where it cannot do so provide valuable insight into the neurocognitive processes underlying the response.
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The Questioner
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Interdisciplinary and Machine Learning
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Keyword Pioneer
— neural prediction
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Interpretability
Machine Learning > Core Methods > Representation Learning
Deep Learning > Architectures > Neural Networks
Machine Learning > Learning Types > Representation Learning
Deep Learning > Architectures > Recurrent Neural Networks
Interdisciplinary > Science > Neuroscience