Traces of meaning itself: encoding distributional word vectors in brain activity

  • How is semantic information stored in the human mind and brain? Some philosophers and cognitive scientists argue for vectorial representations of concepts, where the meaning of a word is represented as its position in a high-dimensional neural state space. At the intersection of natural language processing and artificial intelligence, a class of very successful distributional word vector models has developed that can account for classic EEG findings of language, that is, the ease versus difficulty of integrating a word with its sentence context. However, models of semantics have to account not only for context-based word processing, but should also describe how word meaning is represented. Here, we investigate whether distributional vector representations of word meaning can model brain activity induced by words presented without context. Using EEG activity (event-related brain potentials) collected while participants in two experiments (English and German) read isolated words, we encoded and decoded word vectors taken from the family of prediction-based Word2vec algorithms. We found that, first, the position of a word in vector space allows the prediction of the pattern of corresponding neural activity over time, in particular during a time window of 300 to 500 ms after word onset. Second, distributional models perform better than a human-created taxonomic baseline model (WordNet), and this holds for several distinct vector-based models. Third, multiple latent semantic dimensions of word meaning can be decoded from brain activity. Combined, these results suggest that empiricist, prediction-based vectorial representations of meaning are a viable candidate for the representational architecture of human semantic knowledge.

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Author:Jona SassenhagenORCiDGND, Christian FiebachORCiDGND
Parent Title (English):Neurobiology of language
Publisher:MIT Press
Place of publication:Cambridge, MA
Document Type:Article
Date of Publication (online):2020/04/06
Date of first Publication:2020/04/06
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2020/05/16
Tag:EEG; MVPA; N400; Word2vec; encoding/decoding; semantics
Page Number:23
First Page:54
Last Page:76
© 2020 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Institutes:Psychologie und Sportwissenschaften / Psychologie
Angeschlossene und kooperierende Institutionen / MPI für Hirnforschung
Dewey Decimal Classification:1 Philosophie und Psychologie / 15 Psychologie / 150 Psychologie
Open-Access-Publikationsfonds:Psychologie und Sportwissenschaften
Licence (German):License LogoCreative Commons - Namensnennung 4.0