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Exploring the Representations of Individual Entities in the Brain Combining EEG and Distributional Semantics
Semantic knowledge about individual entities (i.e., the referents of proper names such as Jacinta Ardern) is fine-grained, episodic, and strongly social in nature, when compared with knowledge about generic entities (the referents of common nouns such as politician). We investigate the semantic repr...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8905499/ https://www.ncbi.nlm.nih.gov/pubmed/35280237 http://dx.doi.org/10.3389/frai.2022.796793 |
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author | Bruera, Andrea Poesio, Massimo |
author_facet | Bruera, Andrea Poesio, Massimo |
author_sort | Bruera, Andrea |
collection | PubMed |
description | Semantic knowledge about individual entities (i.e., the referents of proper names such as Jacinta Ardern) is fine-grained, episodic, and strongly social in nature, when compared with knowledge about generic entities (the referents of common nouns such as politician). We investigate the semantic representations of individual entities in the brain; and for the first time we approach this question using both neural data, in the form of newly-acquired EEG data, and distributional models of word meaning, employing them to isolate semantic information regarding individual entities in the brain. We ran two sets of analyses. The first set of analyses is only concerned with the evoked responses to individual entities and their categories. We find that it is possible to classify them according to both their coarse and their fine-grained category at appropriate timepoints, but that it is hard to map representational information learned from individuals to their categories. In the second set of analyses, we learn to decode from evoked responses to distributional word vectors. These results indicate that such a mapping can be learnt successfully: this counts not only as a demonstration that representations of individuals can be discriminated in EEG responses, but also as a first brain-based validation of distributional semantic models as representations of individual entities. Finally, in-depth analyses of the decoder performance provide additional evidence that the referents of proper names and categories have little in common when it comes to their representation in the brain. |
format | Online Article Text |
id | pubmed-8905499 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89054992022-03-10 Exploring the Representations of Individual Entities in the Brain Combining EEG and Distributional Semantics Bruera, Andrea Poesio, Massimo Front Artif Intell Artificial Intelligence Semantic knowledge about individual entities (i.e., the referents of proper names such as Jacinta Ardern) is fine-grained, episodic, and strongly social in nature, when compared with knowledge about generic entities (the referents of common nouns such as politician). We investigate the semantic representations of individual entities in the brain; and for the first time we approach this question using both neural data, in the form of newly-acquired EEG data, and distributional models of word meaning, employing them to isolate semantic information regarding individual entities in the brain. We ran two sets of analyses. The first set of analyses is only concerned with the evoked responses to individual entities and their categories. We find that it is possible to classify them according to both their coarse and their fine-grained category at appropriate timepoints, but that it is hard to map representational information learned from individuals to their categories. In the second set of analyses, we learn to decode from evoked responses to distributional word vectors. These results indicate that such a mapping can be learnt successfully: this counts not only as a demonstration that representations of individuals can be discriminated in EEG responses, but also as a first brain-based validation of distributional semantic models as representations of individual entities. Finally, in-depth analyses of the decoder performance provide additional evidence that the referents of proper names and categories have little in common when it comes to their representation in the brain. Frontiers Media S.A. 2022-02-23 /pmc/articles/PMC8905499/ /pubmed/35280237 http://dx.doi.org/10.3389/frai.2022.796793 Text en Copyright © 2022 Bruera and Poesio. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Artificial Intelligence Bruera, Andrea Poesio, Massimo Exploring the Representations of Individual Entities in the Brain Combining EEG and Distributional Semantics |
title | Exploring the Representations of Individual Entities in the Brain Combining EEG and Distributional Semantics |
title_full | Exploring the Representations of Individual Entities in the Brain Combining EEG and Distributional Semantics |
title_fullStr | Exploring the Representations of Individual Entities in the Brain Combining EEG and Distributional Semantics |
title_full_unstemmed | Exploring the Representations of Individual Entities in the Brain Combining EEG and Distributional Semantics |
title_short | Exploring the Representations of Individual Entities in the Brain Combining EEG and Distributional Semantics |
title_sort | exploring the representations of individual entities in the brain combining eeg and distributional semantics |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8905499/ https://www.ncbi.nlm.nih.gov/pubmed/35280237 http://dx.doi.org/10.3389/frai.2022.796793 |
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