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Multilingual Computational Models Reveal Shared Brain Responses to 21 Languages
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Description: In this article, we fit encoding models based on multilingual neural language models to predict fMRI responses to 21 languages. Critically, we show that encoding models can be transferred zero-shot across languages, so that a model trained to predict brain activity in a set of languages can account for brain responses in a held-out language, even across language families. These results imply a shared component in the processing of different languages, plausibly related to a shared meaning space.
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