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Decoding of generic mental representations from functional MRI data using word embeddings

Francisco Pereira, Bin Lou, Brianna Pritchett, Nancy Kanwisher, Matthew Botvinick, Evelina Fedorenko
doi: https://doi.org/10.1101/057216
Francisco Pereira
Medical Imaging Technologies Siemens Healthcare
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Bin Lou
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Brianna Pritchett
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Nancy Kanwisher
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Matthew Botvinick
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Evelina Fedorenko
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Abstract

Several different groups have demonstrated the feasibility of building forward models of functional MRI data in response to concrete stimuli such as pictures or video, and of using these models to decode or reconstruct stimuli shown while acquiring test fMRI data. In this paper, we introduce an approach for building forward models of conceptual stimuli, concrete or abstract, and for using these models to carry out decoding of semantic information from new imaging data. We show that this approach generalizes to topics not seen in training, and provides a straightforward path to decoding from more complex stimuli such as sentences or paragraphs.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted June 07, 2016.
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Decoding of generic mental representations from functional MRI data using word embeddings
Francisco Pereira, Bin Lou, Brianna Pritchett, Nancy Kanwisher, Matthew Botvinick, Evelina Fedorenko
bioRxiv 057216; doi: https://doi.org/10.1101/057216
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Decoding of generic mental representations from functional MRI data using word embeddings
Francisco Pereira, Bin Lou, Brianna Pritchett, Nancy Kanwisher, Matthew Botvinick, Evelina Fedorenko
bioRxiv 057216; doi: https://doi.org/10.1101/057216

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