Toward a universal decoder of linguistic meaning from brain activation
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s41467-018-03068-4.pdf
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Checksum (MD5)
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Author(s)
Pereira, Francisco
Lou, Bin
Ritter, Samuel
Gershman, Samuel J.
Botvinick, Matthew
Pritchett, Brianna L
Kanwisher, Nancy
Fedorenko, Evelina G
Date Issued
March 2018
Journal
Nature Communications
Publisher
Nature Publishing Group
Citation
Pereira, Francisco et al.“Toward a Universal Decoder of Linguistic Meaning from Brain Activation.” Nature Communications 9, 1 (March 2018): 963 © 2018 The Author(s)
Version
Final published version
Abstract
Prior work decoding linguistic meaning from imaging data has been largely limited to concrete nouns, using similar stimuli for training and testing, from a relatively small number of semantic categories. Here we present a new approach for building a brain decoding system in which words and sentences are represented as vectors in a semantic space constructed from massive text corpora. By efficiently sampling this space to select training stimuli shown to subjects, we maximize the ability to generalize to new meanings from limited imaging data. To validate this approach, we train the system on imaging data of individual concepts, and show it can decode semantic vector representations from imaging data of sentences about a wide variety of both concrete and abstract topics from two separate datasets. These decoded representations are sufficiently detailed to distinguish even semantically similar sentences, and to capture the similarity structure of meaning relationships between sentences.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
McGovern Institute for Brain Research at MIT
Terms of Use
Attribution 4.0 International (CC BY 4.0)
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DOI of Published Version
https://doi.org/10.1038/s41467-018-03068-4
